NorFor - The Nordic Feed Evaluation System (Eaap Publication)

NorFor – The Nordic feed evaluation system

EAAP – European Federation of Animal Science

NorFor Nordic feed evaluation system

The European Association for Animal Production wishes to express its appreciation to the Ministero per le Politiche Agricole e Forestali and the Associazione Italiana Allevatori for their valuable support of its activities

NorFor – The Nordic feed evaluation system

EAAP publication No. 130

edited by: Harald Volden

Wageningen Academic  P u b l i s h e r s

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned. Nothing from this publication may be translated, reproduced, stored in a computerised system or published in any form or in any manner, including electronic, mechanical, reprographic or photographic, without prior written permission from the publisher: Wageningen Academic Publishers P.O. Box 220 6700 AE Wageningen The Netherlands www.WageningenAcademic.com [email protected]

ISBN: 978-90-8686-162-0 e-ISBN: 978-90-8686-718-9 DOI: 10.3920/978-90-8686-718-9 ISSN 0071-2477

Photographer: Jens Tønnesen from Dansk Landbrugs Medier

First published, 2011

©Wageningen Academic Publishers The Netherlands, 2011

The individual contributions in this publication and any liabilities arising from them remain the responsibility of the authors. The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the European Association for Animal Production concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The publisher is not responsible for possible damages, which could be a result of content derived from this publication.

Table of contents Preface Anders H. Gustafsson

9

Acknowledgements

11

Glossary

13

1. Introduction H. Volden and A.H. Gustafsson

21

2. Overall model description H. Volden

23

3. Animal input characteristics. M. Åkerlind, N.I. Nielsen and H. Volden

27

4. Feed fraction characteristics H. Volden

33

5. Feed analyses and digestion methods M. Åkerlind, M. Weisbjerg, T. Eriksson, R. Tøgersen, P. Udén, B.L. Ólafsson, O.M. Harstad and H. Volden

41

6. Feed calculations in NorFor H. Volden

55

7. Digestion and metabolism in the gastrointestinal tract H. Volden and M. Larsen

59

8. Energy and metabolizable protein supply H. Volden and N.I. Nielsen

81

9. Animal requirements and recommendations N.I. Nielsen and H. Volden

85

10. Prediction of voluntary feed intake H. Volden, N.I Nielsen, M. Åkerlind, M. Larsen, Ø. Havrevoll and A.J. Rygh

113

11. Chewing index system for predicting physical structure of the diet P. Nørgaard, E. Nadeau Å. Randby and H. Volden

127

12. Prediction of milk yield, weight gain and utilisation of N, P and K M. Åkerlind and H. Volden

133

13. Standard feed value M. Åkerlind and H. Volden

137

NorFor – The Nordic feed evaluation system

7

14. System evaluation H. Volden, N.I. Nielsen, M. Åkerlind and A.J. Rygh

141

15. The NorFor IT system, ration formulation and optimisation H. Volden, H.D. Rokkjær, A. Göran and M. Åkerlind

167

Keyword index

179

8

NorFor – The Nordic feed evaluation system

Preface The present volume is the first comprehensive, published description of the Nordic Feed Evaluation System, NorFor. It includes the results of extensive work initiated as a project in 2001 by the farmers’ dairy cooperatives in Denmark, Norway, Iceland and Sweden. The overall aim was to create an identical, common feed evaluation system for all four countries to facilitate communication between farmers, consultants and feed industry representatives. Additional goals were to make dissemination of relevant research findings faster and more efficient, both within the four countries and internationally. Towards that end, a group of experts in ruminant nutrition was commissioned to develop such a system. Since we regarded this as a request to overhaul the current national systems comprehensively, we concluded that it provided an opportunity to develop a completely new feed evaluation system based robustly on current, published knowledge. NorFor was a development project that ran from 2002 to 2006 and included comparisons of various feed evaluation systems applied in western countries. The development also included validation of the models based on available research data from the Nordic countries. Significant parts of the development work involved close cooperation with scientists at the agricultural universities and research institutes in the Nordic countries. In addition, we have shared information and have had constructive discussions with experts in the feed industry and feed laboratories. We greatly appreciate the past and ongoing cooperation. From 2007 onwards, NorFor has been jointly funded and managed by the four organizations mentioned above through a cooperative organization (NorFor F.M.B.A.). The present description of NorFor includes all parts of the model. References to the most important sources of information are also given. We thereby provide a description of the biological basis for NorFor. The NorFor project group hopes this volume will be useful for dairy farmers, advisors, scientists and all other actors in the dairy industry in the four NorFor countries. We also recommend the reader to use the IT tools, since many of the nutritional interactions in the model are easier to use and understand if they are applied than if the texts, tables and graphs are read. NorFor Anders H. Gustafsson Leader of NorFor 2002-2009

NorFor – The Nordic feed evaluation system

9

Acknowledgements This publication summarizes the entire work of NorFor, including development and documentation. Numerous people have contributed to the compilation of this written document. NorFor has been developed over several years, thus the nature of their contributions has differed greatly. However, despite these differences, every contribution was equally appreciated. Thus, NorFor F.M.B.A. and the authors would like to sincerely thank everyone who has contributed to the project, and to this first version of the NorFor scientific publication. The main author of the present publication was Harald Volden, of TINE and the Norwegian University of Life Sciences, who has been the leader of the NorFor scientific development group. In addition, various people have played important roles in preparing the publication, including: Nicolaj I. Nielsen of AgroTech and the Danish Cattle Association; Maria Åkerlind of the Swedish Dairy Association; and Peder Nørgaard of the University of Copenhagen. Other persons, listed in the relevant chapters in this publication, have also contributed as authors. In addition, the following persons (in alphabetical order of family names) have contributed in different working groups during the development and/or documentation of NorFor: • Feed evaluation and feeding standards: Ole Aaes, Jan Bertilsson, Anders H. Gustafsson, Øystein Havrevoll, Mårten Hetta, Mogens Larsen, Maria Mehlqvist, Elisabeth Nadeau, Nicolaj I. Nielsen, Peder Nørgaard, Åshild Randby, Arnt Johan Rygh, Ingunn Schei, Harald Volden and Maria Åkerlind • Feed analysis and tables: Lars Bævre, Julia Beck, Margareta Emanuelson, Torsten Eriksson, Odd Magne Harstad, Erica Lindberg, Maria Mehlqvist, Jens Møller, Bragi Lindal Olafsson, Rudolf Thøgersen, Peter Udén, Harald Volden, Martin Riis Weisbjerg and Maria Åkerlind • IT-system: Johannes Frandsen, Henrik D. Rokkjær, Anders Göran (Tomlab), Niels Jafner, Ågot Ligaarden, Aud Elin Rivedal and Harald Volden • Communication: Marie Liljeholm, Maria Mehlqvist and Arnt Johan Rygh • National advisory tools: Åse Marit Flittie Anderssen, Henrik Martinussen, Patrik Nordgren, Berglind Ósk Óðinsdóttir and Maria Åkerlind. We would also like to thank all scientists who have contributed to fruitful discussions and made valuable comments on the model and documents. The present documentation was reviewed by Torben Hvelplund, University of Aarhus, Denmark, and Peter Udén, Swedish University of Agricultural Sciences, Sweden and they are greatly acknowledged for their work. Responsibility for the final content of the model and this present EAAP publication of the NorFor systems rests entirely with the authoring group and NorFor F.M.B.A.

NorFor – The Nordic feed evaluation system

11

Glossary Abbreviation AA AA-N AAj AAj_AATN AATN AATN_bal AATN_dep AATN_Eff AATN_gain AATN_gest AATN_maint AATN_milk AATN_mob AATN_NEG AATN_NEG_Min AATN_NEL AATN20 AATN8 ACF ADG ADG_calc Age Age_end Age_start ALF APL Avail_AAT b_car BCS BCS_change BCS_kg BUF BW BW_birth BW_calc BW_end BW_mat BW_start C12:0 C14:0 C16:0 C18:0 C18:1 C18:2 C18:3 C20:5

Unit g N/100 g N g/d % g/d % g/d g/d g/d g/d g/d g/MJ g/MJ g/MJ g/kg DM g/kg DM g/kg DM g/d g/d days days days g/kg DM g/d mg/kg DM BCS/d kg/BCS g/kg DM kg kg kg kg kg kg g/100 g FA g/100 g FA g/100 g FA g/100 g FA g/100 g FA g/100 g FA g/100 g FA g/100 g FA

Parameter name Amino acids Amino acid nitrogen in feedstuff Individual amino acid Individual amino acid absorbed in small intestine Amino acids absorbed in the small intestine AAT balance AAT for deposition in cows AAT efficiency of utilisation AAT requirement for weight gain in primiparous cows and growing cattle AAT requirement for gestation in cows and heifers AAT requirement for maintenance AAT requirement for milk production AAT for mobilisation in cows Available AAT to energy ratio in growing cattle AAT/NEG minimum recommendation Available AAT to energy ratio in cows Standard feed value for AAT at 20 kg DMI Standard feed value for AAT at 8 kg DMI Acetic acid in feedstuff Average daily body weight gain Estimated daily weight gain Current age Age at calving or sale Age at the start of a feeding period Alcohol in feedstuff Animal production level Available AAT for milk production Beta-carotene in feedstuff Body condition score, from 1 to 5 Daily change in body condition score Weight per unit body condition score Butyric acid in feedstuff Current body weight Body weight at birth Calculated body weight at a given age Body weight at calving or sale Body weight for a mature animal Body weight at the start of a feeding period Lauric acid in feedstuff Myristic acid in feedstuff Palmitic acid in feedstuff Stearic acid in feedstuff Oleic acid in feedstuff Linoleic acid in feedstuff Linolenic acid in feedstuff EPA, Eicosapentaenoic acid in feedstuff

NorFor – The Nordic feed evaluation system

13

Abbreviation

Unit

Parameter name

C22:6 Ca Ca_gain

g/100 g FA g/kg DM g/d

Ca_gest Ca_intake_Min Ca_main Ca_milk CAD CFat CFatD CHO CHOD CI Cl Cl_gain

g/d g/d g/d g/d mEq/kg DM g/kg DM %

Cl_gest Cl_intake_Min Cl_main Cl_milk Co conc_share corrNDF_fac CP CP_intake CPcorr CPD CTo Cu DIM DH DM DM1 DM2 DMcorr DMuncorr DMI DMIc DMIstd

g/d g/d g/d g/d mg/kg DM % of DM

EB EBW EBWG ECM ECM_response ECMherd e_Comp eCP ED

% kg g/d kg/d kg/d kg/d g/100g g/d

DHA, Docosahexaenoic acid in feedstuff Calcium in feedstuff Calcium requirement for weight gain in growing cattle and primiparous cows Calcium requirement for gestation Calcium minimum recommendation Calcium requirement for maintenance Calcium requirement for milk production Cation anion difference in feedstuff Crude fat in feedstuff Apparent total digestibility of crude fat Carbohydrates Apparent total digestibility of carbohydrates Chewing index of feedstuff Chloride in feedstuff Chloride requirement for weight gain in growing cattle and primiparous cows Chloride requirement for gestation Chloride minimum recommendation Chloride requirement for maintenance Chloride requirement for milk production Cobalt in feedstuff Concentrate share in ration Correction factor for kdNDF Crude protein in feedstuff Daily intake of crude protein Crude protein corrected in feedstuff Apparent total digestibility of crude protein Observed chewing time Copper in feedstuff Days in milk Danish Holstein Dry matter in feedstuff Dry matter, first step Dry matter, second step Dry matter, corrected for volatile losses Dry matter, not corrected for volatile losses Dry matter intake Dry matter intake of concentrate Dry matter intake when calculating standard feed values Energy balance Empty body weight Empty body weight gain Energy corrected milk Predicted ECM response Average daily ECM yield per cow in the herd Amino acid composition in endogenous amino acids Endogenous crude protein Efficient degradability

14 

% min/kg DM g/kg DM g/d

g/kg DM g/d g/kg DM % min/kg NDF mg/kg DM days g/kg g/kg g/kg DM1 g/kg g/kg kg DM/d kg DM/d 8 or 20 kg DMI

NorFor – The Nordic feed evaluation system

Abbreviation

Unit

Parameter name

EFOS

% of OM

EI Ep erd erd_CP erd_NDF erd_RestCHO erd_ST ETo f_milk FA FA
min/kg DM MJ/MJ

Organic matter digestibility of feedstuff (EFOS method) Eating index Energy retained as protein Effective rumen degradability Effective rumen degradation of crude protein Effective rumen degradation of NDF Effective rumen degradation of residual fraction Effective rumen degradation of starch Observed eating time Observed or expected fat content in milk Fatty acids in feedstuff Sum of fatty acids with less than 12 carbons Feed analysis system Fat mass Iron in feedstuff Fat free mass Formic acid in feedstuff Fermentation products in feedstuff Feed ration calculator Fill value of feedstuff Intake of fill value Fill value metabolic regulation factor Fill value roughage Fill value substitution rate factor Fill value corrected for ammonia and acids Daily fat gain Daily protein gain Predicted weight gain from avaliable AATN Predicted weight gain from avaliable energy Gross energy Days of gestation Iodine in feedstuff Icelandic breed Indigestible crude protein in feedstuff Intake capacity Growing bulls intake capacity Intake capacity of lactating cows Intake capacity of dry cows Effect of exercise on instake capacity Effect of gestation on intake capacity Growing heifers intake capacity Intake capacity of growing cattle of Jersey breed Indigestible NDF in feedstuff Indigestible starch in feedstuff Organic matter digestibility of feedstuff (IVOS method) Jersey Potassium in feedstuff Potassium excreted in faeces and urine

JER K K_excreted

% % % % min/kg NDF g/kg milk g/kg CFat g/kg CFat kg mg/kg DM kg g/kg DM g/kg DM none FV/kg DM FV/d none FV/kg DM none g/d g/d g/d g/d MJ/d days mg/kg DM g/kg CP FV/d FV/d FV/d FV/d FV/d FV/d g/kg NDF g/kg ST % of OM g/kg DM g/d

NorFor – The Nordic feed evaluation system

15

Abbreviation

Unit

Parameter name

K_gain

g/d

K_gest K_intake_Min K_main K_milk K_u K_u_pct kd kdCP

g/d g/d g/d g/d g/d % %/h %/h

kdNDF

%/h

kdRestCHO kdST

%/h %/h

kg kg_corr km kmg

MJ/MJ MJ/MJ MJ/MJ MJ/MJ

kp l_milk LAF li_mCFat li_mCP lid_NDF lid_ST m_Comp

%/h g/kg milk g/kg DM g/d g/d g/d g/d g/100 g

MBT mCFat mCP ME Mg Mg_gain

none g/d g/d MJ/d g/kg DM g/d

Mg_gest Mg_intake_Min Mg_main Mg_milk Mn Mo MP MPY MRT MR mST MY N

g/d g/d g/d g/d mg/kg DM mg/kg DM g/d g/d H

Potassium requirement for weight gain in growing cattle and primiparous cows Potassium requirement for gestation Potassium minimum recommendation Potassium requirement for maintenance Potassium requirement for milk production Potassium utilization total Potassium utilization Fractional degradation rate Degradation rate of potentially degradable crude protein in feedstuff Degradation rate of potentially degradable NDF in feedstuff Degradation rate of rest fraction in feedstuff Degradation rate of potentially degradable starch in feedstuff Utilisation of ME to NE for growth Utilisation of ME to NE for growth Utilisation of ME to NE for maintenance Utilisation of ME to NE for growth and maintenance Fractional passage rate Observed or expected lactose content in milk Lactic acid in feedstuff Microbial crude fat synthesis in the large intestine Microbial protein synthesis in the large intestine NDF digested in the large intestine Starch from feed digested in the large intestine Individual amino acid composition of microbial amino acids Mobile bag technique Microbial crude fat Microbial crude protein Metabolizable energy Magnesium in feedstuff Magnesium requirement for weight gain in growing cattle and primiparous cows Magnesium requirement for gestation Magnesium minimum recommendation Magnesium requirement for maintenance Magnesium requirement for milk production Manganese in feedstuff Molybdenum in feedstuff Metabolizable protein Milk protein yield Mean retention time in the rumen Effect of metabolic regulation on intake capacity Microbial starch in the rumen Milk yield Nitrogen

16 

g/d kg/d

NorFor – The Nordic feed evaluation system

Abbreviation

Unit

Parameter name

N_excreted N_faeces N_gain

g/d g/d g/d

N_gest N_milk N_u N_u_pct N_urine N_urine_pct Na Na_gain

g/d g/d g/d % g/d % g/kg DM g/d

Na_gest Na_intake_Min Na_main Na_milk NDF NDFD NDFIr NDFr NDS NDSD NE NE_gest NE_maint NEG NEG_bal NEG_DM NEG_gain NEL NEL20 NEL8 NEL_bal NEL_dep NEL_DM NEL_gain

g/d g/d g/d g/d g/kg DM % kg/d g/kg DM g/kg DM g/g none MJ/d MJ/d MJ/d % MJ/kg DM MJ/d MJ/d MJ/kg DM MJ/kg DM % MJ/d MJ/kg DM MJ/d

NEL_milk NEL_mob NEL_variable

MJ/d MJ/d %

NH3N NIRS NR OM OMD P P_excreted

g N/ kg N none

Nitrogen excreted in faeces and urine Nitrogen excreted in faeces Nitrogen utilization for weight gain in primiparous cows and growing cattle Nitrogen utilization for gestation Nitrogen utilization for milk Nitrogen utilization total Nitrogen utilization Nitrogen excreted in urine Nitrogen excreted in urine (percentage) Sodium in feedstuff Sodium requirement for weight gain in growing cattle and primiparous cows Sodium requirement for gestation Sodium minimum recommendation Sodium requirement for maintenance Sodium requirement for milk production Neutral detergent fibre in feedstuff Apparent total digestibility of NDF Intake of roughage NDF NDF in roughage Neutral detergent solubles Digested neutral detergent solubles Net energy Net energy requirement for gestation Net energy requirement for maintenance Net energy growth Energy balance for growing cattle Net energy growth Energy requirement for growth of growing cattle Net energy lactation Standard feed value for NEL at 20 kg DMI Standard feed value for NEL at 8 kg DMI Energy balance for cows Energy requirement for deposition in cows Net energy for lactation in ration Energy requirement for weight gain in primiparous cows Energy requirement for milk production Energy supply from mobilisation in cows Energy balance variable, depending on mobilisation and deposition Ammonia nitrogen in feedstuff Near infrared spectroscopy Norwegian Red Organic matter in feedstuff Apparent total digestibility of organic matter Phosphorus in feedstuff Phosphorus excreted in faeces and urine

g/kg DM % g/kg DM g/d

NorFor – The Nordic feed evaluation system

17

Abbreviation

Unit

Parameter name

P_gain

g/d

P_gest P_intake_Min P_main p_milk P_milk P_u P_u_pct PBVN_DM_Min PBVN PBVN20 PBVN8 pdCP pdNDF pdST PL PRF Protein_mass Protein_respons q r_emCP

g/d g/d g/d g/kg milk g/d g/d % g/kg DM g/d g/kg DM g/kg DM g/kg CP g/kg NDF g/kg ST mm g/kg DM kg g/d

r_kp1

%/h

r_kp2

%/h

r_kpc

%/h

r_kpl r_kpNDFc r_kpNDFr r_kpr

%/h %/h %/h %/h

r_mAA r_mCFat r_mCP r_mOM r_mST r_outOM RD rd_CFat rd_CP rd_CPcorr rd_FPF

g/d g/d g/d g/d g/d g/d

rd_NDF rd_NH3N_CP rd_OM

g/d g/d g/d

Phosphorus requirement for weight gain in growing cattle and primiparous cows Phosphorus requirement for gestation Phosphorus minimum recommendation Phosphorus requirement for maintenance Observed or expected protein content in milk Phosphorus requirement for milk production Phosphorus utilization, total Phosphorus utilization Minimum recommendation of PBVN Protein balance in rumen, total Standard feed value for PBV at 20 kg DMI Standard feed value for PBV at 8 kg DMI Potentially degradable crude protein in feedstuff Potentially degradable NDF in feedstuff Potentially degradable starch in feedstuff Most frequent particle length of feedstuff Propionic acid in feedstuff Protein mass Predicted milk protein yield Ratio between ME and GE Efficiency of microbial protein synthesis in the rumen Rumen passage rate of NDF particles>6mm from pool 1 to 2 Rumen passage rate of NDF particles>6mm from pool 2 Rumen passage rate of protein and starch particles<=6mm Rumen passage rate of liquid Rumen passage rate of NDF particles<=6mm Rumen passage rate NDF particles>6mm Rumen passage rate of protein and starch particles>6mm Microbial synthesis of amino acids in the rumen Microbial crude fat synthesis in the rumen Microbial protein synthesis in the rumen Microbial synthesis of organic matter in the rumen Microbial starch synthesis in the rumen Passage of organic matter to small intestine Red Danish Crude fat degraded in the rumen Crude protein degraded in the rumen Corrected crude protein degraded in the rumen Fermentation products from feed degraded in the rumen NDF degraded in the rumen NH3-crude protein degraded in the rumen Rumen degraded organic matter

18 

g/kg rd OM

g/d g/d g/d g/d

NorFor – The Nordic feed evaluation system

Abbreviation

Unit

Parameter name

rd_pdCP

g/d

rd_pdNDFc

g/d

rd_pdNDFr

g/d

rd_pdST rd_RestCHO rd_sCP rd_sST rd_ST RestCHO RestCHOcorr RFA RI RLI

g/d g/d g/d g/d g/d g/kg DM g/kg DM g/kg CFat min/kg DM g/g NDF

Rough_share roughage_appetite RTo RUP S s+pdCP sCP Se si_outOM sid_AA

% of DM % min/kg NDF

Potentially degradable crude protein degraded in the rumen Potentially degradable NDF in concentrate degraded in the rumen Potentially degradable NDF in roughage degraded in the rumen Potentially degradable starch degraded in the rumen Rest CHO fraction degraded in the rumen Soluble crude protein degraded in the rumen Soluble starch degraded in the rumen Total starch degraded in the rumen Rest fraction in feedstuff Rest fraction corrected in feedstuff Residual fatty acids Rumination index Rumen load index, rumen degraded starch and rest fraction per unit of NDF Roughage percentage in the diet Roughage appetite proportion Observed ruminating time Rumen undegradable protein Sulphur in feedstuff Sum of sCP and pdCP in feedstuff Soluble crude protein in feedstuff Selenium in feedstuff Passage of organic matter to the large intestine Amino acids from feed digested in the small intestine Individual amino acids from feed digested in the small intestine Crude fat from feed digested in the small intestine Crude protein from feed digested in the small intestine Endogenous amino acids digested in the small intestine Microbial amino acids digested in the small intestine Microbial crude protein digested in the small intestine Microbial fatty acids digested in the small intestine Microbial starch digested in the small intestine Starch from feed digested in the small intestine Swedish Holstein Swedish Red Soluble starch in feedstuff Starch in feedstuff Content of starch and sugar in the diet Total intake of starch and sugar Apparent total digestibility of starch Sugar in feedstuff

g/kg DM g/kg CP g/kg CP mg/kg DM g/d g/d

sid_AAj sid_CFat sid_CP

g/d g/d

sid_eAA

g/d

sid_mAA sid_mCP

g/d g/d

sid_mFA sid_mST sid_ST SH SR sST ST ST_SU_DM ST_SU_intake STD SU

g/d g/d g/d g/kg ST g/kg DM g/kg DM g/d % g/kg DM

NorFor – The Nordic feed evaluation system

19

Abbreviation

Unit

Parameter name

SubR TAF TCL td_CFat td_CHO td_CP td_CPcorr td_OM TMR uNDF uNDS uOM Urea N VFA VitA VitD VitE VOS

none g/kg DM mm g/d g/d g/d g/d g/d g/kg DM g/kg DM g/kg DM g/g

YHerd Zn WPC

kg ECM/year mg/kg DM weeks

Substitution rate Total acids in feedstuff Theoretical cutting length Total apparently digested crude fat Total apparently digested carbohydrates Total apparently digested crude protein Total apparently digested crude protein corrected Total apparently digested organic matter Total mixed ration Undegraded NDF Undegraded neutral detergent solids Undegraded organic matter Urea nitrogen Volatile fatty acids Vitamin A in feedstuff Vitamin D in feedstuff Vitamin E in feedstuff Organic matter digestibility of feedstuff in vitro (VOS method) Yield level in the herd Zinc in feedstuff Weeks post calving

20 

1000 IU/kg DM 1000 IU/kg DM IU/kg DM % of OM

NorFor – The Nordic feed evaluation system

1. Introduction H. Volden and A.H. Gustafsson Feed is one of the major expenses in modern cattle production. In addition to feed prices, its overall costs are affected by the efficiency of feed utilization and the output of animal products to be marketed. Hence, there is a clear need to evaluate feed quality in order to maximise profitability. This requires information on both animal requirements and nutrient supply, since formulation of an appropriate ration involves balancing available feeds in proportions that match the amounts of nutrients supplied to the animals’ nutrient requirements as closely as possible. There are two principal methods used to describe animal nutrition: those based on mechanistic approaches, which describe responses to nutrients from chemical and physiological processes in the gastrointestinal tract and intermediary, and empirical approaches describing simple relationships between nutrient intakes and production responses. The challenge in the development of new feed evaluation systems is to accurately predict responses to nutrients so that any difference in product income and feed costs can be maximized, while improving overall feed efficiency. Feed efficiency is also of great importance due to its impact on enteric methane emission to the atmosphere and nitrogen and phosphorus passing into the environment. Feed evaluation for cattle has long traditions. Important milestones are introductions of the Weende analysis by Henneberg and Stohmann (1864), and the starch equivalent system by Kellner (1912). In the Nordic countries the introduction of the milk production value by Hansson (1913) and the Scandinavian feed unit by Møllgaard (1929) were of great magnitude for modern cattle production. Extensive research in the 1950’s and 60’s further improved our knowledge in feed evaluation, and in the period from 1970 to 1990 most countries introduced new systems for energy based either on metabolizable energy (ME) or net energy for lactation (NEL) or growth (NEG) (Van der Honing and Alderman, 1988). Also the knowledge of protein evaluation for cattle have increased considerably during the last 40 years, from use of total or digestible crude protein to the protein evaluation systems that predict the host animal amino acid (AA) supply from dietary protein escaped ruminal degradation and from microbial protein synthesized in the rumen (Madsen, 1985; NRC, 1985; Vérité et al., 1987; AFRC, 1992; Tamminga et al., 1994). Traditional feed evaluation systems are additive and generally do not take into account interactions in digestion and nutrient metabolism. When interactions and non-linear relationships are considered, in attempts to describe feed metabolism, individual feeds will no longer have fixed values. Hence, the value of a given feedstuff will depend on how the feed is used. This means that the feed value cannot be determined until we know which feed ration will be used and the production situation in which it will be applied. Therefore, the development of a new feed evaluation system must consist of a ration evaluation system which can be used to optimize nutrient supply and production responses, rather than a system that can predict individual feed values. Development of new feed evaluation systems (Russell et al., 1992; Sniffen et al., 1992; Fox et al., 1992; NRC, 2001) and mechanistic models (Baldwin, 1995; Danfær et al., 2006), which describe nutrient supply and the nutritional requirements of cattle, are important for understanding ruminant nutrition and constitute an important step towards implementing more sophisticated nutritional strategies to optimize production responses in cattle. The use of non-linear semi-mechanistic feed evaluation systems for ration optimization requires powerful computing tools. Recent improvements in non-linear optimization tools and algorithms enable the development of more complex feed evaluation systems that can also be used in practice. The aim of this book is to provide a detailed description of the semi-mechanistic feed evaluation system NorFor, which has already been implemented by advisory services in four Nordic countries, i.e. Denmark, Iceland, Norway and Sweden.

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_1, © Wageningen Academic Publishers 2011

21

References AFRC (Agricultural and Food Research Council), 1992. Nutritive requirements of ruminant animals: protein. AFRC Technical Committee on Response to Nutrients. Report No. 9. Nutrition Abstracts and Reviews: Series B 62: 787-835. Baldwin, R.L., 1995. Modelling Ruminant Digestion and Metabolism. Chapman and Hall, London, UK. 592 pp. Danfær, A., P. Huhtanen, P. Udén, J. Sveinbjörnsson and H. Volden, 2006. The Nordic dairy cow model, Karoline – Description. In: Kebreab, E., J. Dikstra, A. Bannink, J. Gerrits and J. France (eds.). Nutrient Digestion and Utilisation in Farm Animals: Modelling Approaches. CAB International, Wallingford, UK, pp. 383-406. Fox, D.G., C.J. Sniffen, J.D. O’Connor, J.B. Russell and P.J. Van Soest, 1992. A net carbohydrate and protein system for evaluating cattle diets. III. Cattle requirements and diet adequacy. Journal of Animal Science 70: 3578-3596. Hansson, N., 1913. En ny metod för beräkning av fodermedlens produktionsvärde vid utfodring af mjölkkor. Meddelande Nr. 85 från centralanstalten för försöksväsendet på jordbruksområdet. Ivar Hæggströms Boktryckeri Aktiebolag, Stockholm, Sweden. (In Swedish). Henneberg, W. and F. Stohmann, 1864. Begründung einer rationellen fütterung der wiederkauer. Schwetske and Söhne, Braunschweig, Germany. (In German). Kellner, O., 1912. Die Ernährung der Landwirtschaftlichen nutztiere. Raul Parey, Berlin, Germany. (In German). Madsen, J., 1985. The basis for the proposed Nordic protein evaluation system for ruminants. The AAT-PBV system. Acta Agriculturae Scandinavica Supplement 25: 9-20. Møllgaard, H., 1929. Fütterungslehre des milchviehs. Verlag von M. and H. Schalper, Hannover, Germany. (In German). NRC (National Research Council), 1985. Ruminal nitrogen usage. National Academy Press, Washington, DC, USA. 138 pp. NRC, 2001. Nutrient requirements of dairy cattle. Seventh revised edition, National Academy Press, Washington, DC, USA. 408 pp. Russell, J.B., J.D. O’Connor, D.G. Fox, P.J. Van Soest and C.J. Sniffen, 1992. A net carbohydrate and protein system for evaluating cattle diets. I. Ruminal fermentation. Journal of Animal Science 70: 3551-3561. Sniffen, C.J., J.D. O’Connor, P.J. Van Soest, D.G. Fox and J.B. Russell, 1992. A net carbohydrate and protein system for evaluating cattle diets. II. Carbohydrate and protein availability. Journal of Animal Science 70: 3562-3577. Tamminga, S., W.M. Van Straalen, A.P.J. Subnel, R.G.M. Meijer, A. Steg, C.J.G. Wever and M.C. Block, 1994. The Dutch protein evaluation system: the DVE/OEB-system. Livestock Production Science 40: 139-155. Van der Honing, Y. and G. Alderman, 1988. Feed evaluation and nutritional requirements. III. Ruminants. Livestock Production Science 19: 217-278. Vérité, R., P. Chapoutot, B. Michalet-Doreau, J.L. Peyraud and C. Poncet, 1987. Révision du système des protéines digestibles dans l’intestin (PDI). Bulletin Technique C.R.Z.V. Thei1, INRA 70: 19-34.

22 

NorFor – The Nordic feed evaluation system

2. Overall model description H. Volden The NorFor system is a semi-mechanistic, static and science-based model, which predicts nutrient supply and requirements for maintenance, milk production, growth and pregnancy in cattle. The model can be divided into five parts: (1) an input section describing characteristics of the animal and feeds available; (2) a module simulating processes in the digestive tract and the intermediary metabolism, termed the feed ration calculator (FRC); (3) a module predicting feed intake; (4) a module predicting the physical structure of the diet; and (5) an output section describing nutrient supply, nutrient balances and production responses (Figure 2.1). The input variables for the model are animal and feed characteristics. For dairy cows, the main input variables are body weight (BW), stage of lactation, pregnancy day and planned or potential daily milk production. For growing animals (bulls, steers and heifers) input variables are BW and average daily weight gain (ADG). The feed dry matter (DM) is separated into ash, crude protein (CP), crude fat (CFat), neutral detergent fibre (NDF), starch (ST), sugar (SU), fermentations products (FPF) such as organic acids and alcohols, and a residual fraction (RestCHO). The CP is divided into soluble (sCP), potentially degradable (pdCP), indigestible (iCP) and ammonia (NH3N). The NDF is divided into a total indigestible (iNDF) and a potentially degradable (pdNDF) fraction. The ST is divided into soluble (sST), potentially degradable (pdST) and indigestible (iST) fractions. The FPF are separated into lactic acid (LAF), volatile fatty acids (VFA) and alcohols. Fractional degradation rates (kd) of the soluble and potentially degradable feed fractions are also required for the model. The FRC consists of four sections: (1) the rumen, (2) the small intestine, (3) the large intestine and (4) metabolism (Figure 2.2). Feed organic matter (OM) entering the rumen is either fermented and used for microbial production, or it escapes from the rumen for further digestion in the lower digestive tract. Ruminal degradation of CP, ST and NDF in concentrate feeds are assumed to follow first-order single-compartment kinetics, while degradation of NDF in roughage is modelled as a Input Feed characteristics (chemical composition and particle length) Animal characteristics (body weight, breed, stage of lactation)

Physical structure (chewing time)

Gastro-intestinal tract and intermediary metabolism Nutrient calculator Nutrient digestion and metabolism

Feed intake

Output Nutrient supply Ration energy and protein value. Predicted milk yield, protein production, Nutrient balances in the rumen, etc.

Figure 2.1. Overview of the NorFor model. H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_2, © Wageningen Academic Publishers 2011

23

FEED

24 

RUMEN

SMALL INTESTINE

HIND GUT

iNDF

pdNDF

pdNDF

pdNDF

F ib r e

iNDF

iST

iST

iST

pdST

pdST

pdST

St a r ch

sST

sST

Rest CHO

Rest CHO

R est FPF

FA

FA

iCP

Glycerol , galactose

CFat

Lipids iCP

sCP

sCP

mCFat

mCP

Microbial growth

Energy and nitrogen for microbial growth

pdCP

pdCP

P r ot ein

Energy and nitrogen for microbial growth

mST

mST

mCFat

mCP

mCP

Microbial growth

NH 3

eCP

eCP

PBV N

Net energy

Energy Energy

Energy

of CP_intake

UREA 4.6%

Milk or gain

AA in milk or gain

AAT N to milk or gain Utilization

AAT N

dep. / mob.

AAT N for hair and skin

Endogenous N

Endogenous urinary-N

INTERMEDIARY METABOLISM

Figure 2.2. Flow diagram of the gastrointestinal tract and intermediary compartments of the NorFor feed evaluation system. For all abbreviations check the abbreviation list.

FAECES

NorFor – The Nordic feed evaluation system

two-compartment system, with a non-escapable and an escapable pool. The nutrients available for microbial growth come from ruminally degraded NDF, ST, RestCHO, glycerol, CP and LAF. The efficiency of microbial synthesis depends on the level of feed intake and diet composition. The input to the small intestine consists of OM from microbes, unfermented feed fractions escaping from the rumen and endogenous secretions. These components are partly digested in the small intestine and are either metabolised or enter the large intestine. The OM passing into the large intestine is subjected to microbial fermentation, and the digested OM not used for microbial synthesis is absorbed and metabolised. Faecal excretion consists of OM from microbes synthesised in the large intestine, feed that has escaped previous digestion and undigested rumen microbial material. The intermediary metabolism section yields ME calculated from total tract digestible OM. Net energies for maintenance, lactation, growth and pregnancy are predicted from the ME. Different coefficients are used to calculate NE for maintenance, lactation, and growth. Net energy for lactation is used for dairy cows, while NEG is used as the energy measurement for growing cattle. The nitrogen (N) fractions entering the intermediary metabolism consist of NH3N absorbed from the rumen, dietary, microbial and endogenous amino acids (AA) absorbed from the small intestine, and NH3N from the large intestine. The absorbed AAs are utilized for maintenance, growth, pregnancy and milk production. The efficiency of AA utilization is specific for each production/process. The metabolizable protein available for animal production is assigned as amino acids absorbed from the small intestine (AATN). The N which is not used for maintenance or production is excreted in the urine. Predicting nutritive value is only one part of ration formulation as formulation involves both the selection of feed ingredients and the prediction of feed intake. Therefore, the NorFor system contains a module to predict the intake of feeds. For prediction of feed intake dietary fill values (FV) and animal intake capacity (IC) are applied. In roughages, FV is calculated from OM digestibility (OMD) and NDF content, and in ensiled forages the basic FV is also corrected for content of VFA, LAF and NH3N. Animal IC is dependent on BW, milk yield, stage of lactation, lactation number, ADG and physical activity. The model uses a combination of dietary physical effects and metabolic factors to impact feed intake, and the effect of easily fermentable carbohydrates on roughage intake is accounted for by using a substitution rate factor (SubR). A minimum amount of large particles is essential for optimal rumen function. Hence, a module to evaluate the physical structure of the diet is included in NorFor. The dietary physical effect is described by a total chewing index (CI), which is calculated as the sum of an eating (EI) and ruminating (RI) index for each individual feed. The EI value reflects the associated chewing activity as feed is consumed and is calculated from the particle length and NDF content of the feed. The RI value is calculated from particle length, NDF content and a hardness factor, which is dependent on the iNDF content of the feed. The hardness factor reflects the lignification of the structural fibre of the feed and the associated physical force required for the comminution of large particles. The output from a model calculation in NorFor describes the intake of the individual feeds in the total ration. It consists of variables describing efficiencies of digestion and nutrient utilization, production (milk, ADG), N excretion, nutrient balances, energy (NEL or NEG) and protein (AATN) values of the ration.

NorFor – The Nordic feed evaluation system

25

3. Animal input characteristics. M. Åkerlind, N.I. Nielsen and H. Volden Animal characteristics are needed for calculation of nutrient requirements and IC for both cows and growing cattle. Information on animal parameters, such as BW, is also needed when calculating ruminal variables, such as passage rates of feed fractions out of the rumen and efficiency of microbial protein synthesis. Moreover, the physical activity of the animal (simply classified according to whether it is loose or tied up) will affect the IC and energy requirements.

3.1 Input data for cows All required input data for cows are listed in Table 3.1 and default values for different breeds are compiled in Table 3.2. The information needed for cows in the system is breed, lactation number, BW, stage of lactation (days in milk) and milk production. Pregnancy and whether the cow is dry or lactating are also factors required for determining IC and requirements for gestation. When calculating energy and protein requirements for gestation, information on mature body weight (BW_mat) is needed since this variable is breed-specific (see Table 3.2 and Sections 9.1.3 and 9.2.5). BW_mat is also needed to determine protein requirements for growth in primiparous cows (Section 9.2.3). Animal weight changes can be estimated as changes in body condition score (BCS), where the BW per condition scores depends on the breed. The BCS in NorFor uses a scale from 1 to 5, where 1 is thin and 5 is fat (Gillund et al., 1999). One unit of BCS corresponds to 60 kg BW except for Jersey which is set to 45 kg BW; these values are based on data from several previous studies (e.g. Enevoldsen and Kristensen,1997; Gillund et al., 1999; Nielsen et al., 2003; Bossen, 2008). Table 3.1 Input data for cows in NorFor. Input data

Unit

Dry cow Lactation number Days in milk Daily milk yield Protein content in milk Fat content in milk Lactose content in milk Yield level of the herd Body weight, current Body weight, mature2 Weight gain in primiparous cows Days of gestation Daily change in body condition score Weight per unit body condition score2 Activity Breed3

none1 No. days kg/day g/kg g/kg g/kg kg ECM kg kg kg/day days BCS/day kg/BCS none1 none1

1

No unit. Default values can be taken from Table 3.2. 3 Abbreviations for breeds are explained in Table 3.2. 2

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_3, © Wageningen Academic Publishers 2011

27

429 428 430 429 431 430 432 431 433 432 434 433 435 434 435 436 436 437

compiled in Table 3.2. The information needed for cows in default the system is breed, lactation number, All required input data for cows are listed in Table 3.1 and values for different breeds are BW, stageinofTable lactation in milk) andneeded milk production. whether the cow is dry compiled 3.2. (days The information for cows inPregnancy the systemand is breed, lactation number, or lactating arelactation also factors IC andPregnancy requirements gestation. When BW, stage of (daysrequired in milk)for anddetermining milk production. andfor whether the cow is dry calculating protein requirements for gestation, information on mature body weight or lactatingenergy are alsoand factors required for determining IC and requirements for gestation. When (BW_mat) needed since this requirements variable is breed-specific Table 3.2on andmature Sections 9.1.3 and calculating isenergy and protein for gestation,(see information body weight 9.2.5). BW_mat is also needed to determine protein requirements for3.2 growth in primiparous cows (BW_mat) is needed since this variable is breed-specific (see Table and Sections 9.1.3 and Table 3.2. Default values for mature body weight (BW_mat) and the weights corresponding to a (Section 9.2.3). is also needed to determine protein requirements for growth in primiparous cows 9.2.5). BW_mat body condition score unit (BCS_kg) for dairy cows of different breeds. (Section 9.2.3). Table Abbr.3.1 3.1 Table 3.2

Dairy breed

BW_mat kg

BCS_kg kg/BCS

437 438 439 438 439 440 441 440 442 441 443 442 444 443 445 444 445 446

DH 3.2 Danish Holstein 640 60 Table IB Icelandic breed 470 60 where the Animal weight changes can be estimated as changes in body condition score (BCS), JER Jersey 440 45 BW per weight condition scorescan depends on the breed. The BCS in NorFor usesscore a scale fromwhere 1 to 5,the Animal changes be estimated as changes in body condition (BCS), NR Norwegian Red 600 where 1 is thin and 5 is fat (Gillund et al., 1999). One unit of BCS corresponds to 60 kg BW BW per condition scores depends on the breed. The BCS in NorFor uses a scale60 from 1 to 5, RD Danish Red 660 60 except for Jersey which is set to 45 kg BW; these values are based on data from several previous where 1 is thin and 5 is fat (Gillund et al., 1999). One unit of BCS corresponds to 60 kg BW studies (e.g.Jersey Enevoldsen and Kristensen,1997; Gillund et are al., based 1999; on Nielsen et al., 2003; previous Bossen, SH for Swedish 640 60 except which is setHolstein to 45 kg BW; these values data from several 2008). SR Swedish Red 620 60 studies (e.g. Enevoldsen and Kristensen,1997; Gillund et al., 1999; Nielsen et al., 2003; Bossen, 2008).

446 447 448 447 449 448 449 450

The energy requirement for milk production is based on the production of energy corrected milk The energy requirement for milk production is equations based on (Sjaunja the production1990) of energy corrected (ECM), which can be calculated either of depending on milk The energy requirement for milkfrom production is two based on the productionetofal., energy corrected milk (ECM), which can be calculated from either of two equations (Sjaunja et al., 1990) depending on whether information milk lactose is available: (ECM), which can beon calculated fromcontent either of two equations (Sjaunja et al., 1990) depending on whether information on milk lactose content is available: whether information on fmilk lactose content _ milk p _ milkis available: l _ milk ⎞ ⎛ [Eq. 3.1]

450 451 451 452 453 452 454 453 454

ECM = MY ⋅ ⎜ 0.01 + 0 .122 ⋅ + 0 .077 ⋅ + 0.053 ⋅ ⎟ ⎝⎛ f _10 milk p _10 milk l _10 milk ⎠⎞ ECM = MY ⋅ ⎜ 0.01 + 0 .122 ⋅ + 0 .077 ⋅ + 0.053 ⋅ ⎟ 10 10 10 ⎠ ⎝

f _ milk p _ milk ⎞ ⎛ ECM = MY ⋅ ⎜ 0 .25 + 0 .122 ⋅ + 0 .077 ⋅ ⎟ ⎝⎛ f _10 milk p _10 milk ⎠⎞ ECM = MY ⋅ ⎜ 0 .25 + 0 .122 ⋅ + 0 .077 ⋅ ⎟ 10 ⎠ where ECM⎝ is the energy corrected milk,10 kg/day;



[Eq. 3.1]

3.1



[Eq. 3.2] [Eq. 3.2]

3.2

MY is the daily milk yield, kg/day; and f_milk,

where the energy corrected kg/day; MYand is the daily milk yield, in kg/day; f_milk, g/kg. p_milkECM and isl_milk are the contentsmilk, of fat, protein anhydrous lactose milk, and respectively, p_milk and l_milk are the corrected contents ofmilk, fat, protein in milk, respectively, where ECM is the energy kg/day; and MYanhydrous is the dailylactose milk yield, kg/day; and f_milk, g/kg. p_milk and l_milk are therations contents fat, protein and the anhydrous lactose in milk, respectively, When formulating feed forofgroups of cows, daily ECM yield (ECMherd) can be estimated g/kg. from breed-specific lactation curves: 23 ECMherd = a + b ∙ YHerd – c ∙ DIM + ln(DIM) ∙ d

23

3.3

where ECMherd is the daily estimated ECM yield, kg/d; YHerd is the herd’s average ECM yield per cow, kg/305 d; DIM is days in milk; a, b, c and d are regression coefficients presented in Table 3.3 for primiparous and multiparous cows of different breeds. Standard lactation curves are available for different dairy breeds and lactation numbers. The ECMHerd value refers to the herd’s 305-d lactation yield, which is based on national herd recording data. Danish cow recording data (Danish Cattle Association) have been used to parameterize the standard lactation curves for the dairy breeds Jersey, Danish Holstein and Danish Red. Icelandic, Norwegian and Swedish national herd recording data are the basis for the standard lactation curves for the Icelandic breed, Norwegian Red, Swedish Holstein and Swedish Red cattle, respectively (Farmers Association of Iceland; Tine Dairies in Norway; Swedish Dairy Association). Daily milk protein yield is calculated from milk yield and milk protein content according to Equation 3.4. Protein production is essential for calculating the AA requirement for milk production: MPY = MY ∙ p_milk

3.4

where MPY is the milk protein yield, g/day; MY is the milk yield, kg/day; and p_milk is the milk protein content, g/kg.

28 

NorFor – The Nordic feed evaluation system

Table 3.3. The multiple regression coefficients a, b, c and d is used to predict daily ECM yield from standardised lactations curve in Equation 3.3. Breed1

Lactation

a

b

c

d

DH DH IB IB JER JER NR NR RD RD SH SH SR SR

primiparous multiparous primiparous multiparous primiparous multiparous primiparous multiparous primiparous multiparous primiparous multiparous primiparous multiparous

-3.10 3.17 0.88 6.34 -8.15 -2.98 -7.09 -0.59 -6.20 1.45 -4.05 2.93 -4.46 -0.21

0.00325 0.00338 0.00288 0.00269 0.00324 0.00335 0.00314 0.00310 0.00335 0.00330 0.00299 0.00299 0.00304 0.00317

0.01685 0.06040 0.04009 0.06234 0.02810 0.05630 0.06440 0.09100 0.02138 0.06973 0.03560 0.06100 0.03970 0.06920

1.140 1.025 1.627 1.569 2.654 2.305 3.741 3.378 1.750 1.844 2.591 1.997 2.677 2.520

1 The

abbreviations of the different breeds are shown in Table 3.2.

3.2 Input data for growing cattle When calculating nutrient requirements and the IC for growing cattle, information on BW, ADG, sex, breed and activity are needed, and for heifers the days of gestation are also required (Table 3.4). Table 3.4. Input data for growing cattle. Parameter

Unit

Sex Breed2 Activity Body weight3 Average daily gain3 Days of gestation (heifers) Body weight, birth4 Body weight, start4 Body weight, end4 Body weight, mature4 Age, start4 Age, current4 Age, end4

none1 none1 none1 kg g/day days kg kg kg kg days days days

1

No unit. classifications of different breeds are shown in Table 3.5. 3 Body weight and daily gain are required parameters and can be estimated from the parameters that are marked by 4. 4 These parameters are required if current body weight and average daily gain data are not available. 2 The

NorFor – The Nordic feed evaluation system

29

485 486

sex, breed and activity are needed, and for heifers the days of gestation are also required (Table 3.4).

487 488 489

Table 3.4

490

3.2.1 Estimation of body weight and daily weight gain from a growth function

491 492 493 494 497 495 498 496 499 497 500 498 501 499 502 500 503 501 504 502 505 503 506 504 507 505 508 506 509 510 507 508 509 510 511

Whenplanning planningfeed feedrations rations animals of different ages, of BW_mat, ageBW andatBW When forfor animals of different ages, datadata of BW_mat, age and the at the start of the period andand planned ageage and BW at atthetheend start ofrearing the rearing period planned and BW endofofthe therearing rearingperiod period are are needed. needed. Expected Expected BW and can be estimated from the following logistic growth equation based BW and ADG canADG be estimated from the following logistic growth equation based on: on:

512 511 512 513 514 513 515 516 514 517 515 518 516 519 517 520 518 521 519 522 520 523 521 524 522 525 523 526 524 527 525 528 526 529 527 530 528 531 529 532 530 533 531 534 532 533 535 534 535

3.2.1 Estimation of body weight and daily weight gain from a growth function

(Age−for ))))  age, kg; BW_start is the BW[Eq. ( A⋅(1−e( −B⋅BW Agethe _ start where BW_calc the⋅ eestimated current at the start, kg; 3.5] BW _ calc = BW _is start 3.5 A is described in Equation 3.6; B is described in Equation 3.8; Age is the current age, days; Age_start is the age at start, days. IfBW Age_start iscurrent 0 the BW_start the same is asthe BW_birth. whereBW_calc BW_calc theestimated estimated kg; isBW_start the kg; start, kg; A where isisthe BW forfor thethe current age,age, kg; BW_start is the BW atBW the at start,

is is described 3.7; in1)))) Equation3.8; 3.8;Age Age the current age, days; Age_start A describedininEquation Equation 3.6; B B is described described in Equation isisthe current age, days; ⋅(1−e(−B⋅(Age−Age _ start+24 A⋅(1−e(−B⋅(Age−Age _ start)))) ⋅1000 ADG calcis =atthe BW _ start e (AIf − BW _ is start ⋅ e (is Age_start age at ⋅start, days. If Age_start 0 the BW_start the same as BW_birth. is the_ age start, days. Age_start is 0 theisBW_start the same as BW_birth. [Eq. 3.6]

) ( ADG _ calc = (BW _ start ⋅ e (A⋅(1−e(−B⋅(Age−Age _ start+1)))) − BW _ start ⋅ e (A⋅(1−e(−B⋅(Age−Age _ start)))) )⋅1000  where ADG_calc is the estimated average daily gain for the current age, g/day; BW_start is the

3.6

[Eq. 3.6] BW at the start, kg; A is described in equation 3.7; B is described in equation 3.8; Age is the where ADG_calc is the estimated average daily gain for the current age, g/day; BW_start is the BW current age, days; Age_start is the age at start, days. If Age_start is 0 the BW_start is the same as where ADG_calc is is thedescribed estimatedin average daily3.7; gainBfor the current in age, g/day; BW_start at the start, kg; A Equation is described Equation 3.8; Ageisisthe the current BW_birth. BW at the start, kg; A is described in equation 3.7; B is described in equation 3.8; Age is the

age, days; Age_start is the age at start, days. If Age_start is 0 the BW_start is the same as BW_birth.

current age, days; Age_start is the age at start, days. If Age_start is 0 the BW_start is the same as Factor A and B in Equations 3.5 and 3.6 are calculated as: BW_birth. Factor A and B in Equations 3.5 and 3.6 are calculated as: _ mat .1 ⎞ Factor⎛ABW and B in⋅ 1Equations 3.5 and 3.6 are calculated as:

A = ln⎜⎜ [Eq. 3.7] 3.7 ⎟⎟  ⎝ BW _ start ⎠ ⎛ BW _ mat ⋅ 1.1 ⎞ ⎟ A = ln⎜⎜ [Eq. 3.7] BW__start mat ⋅ 1⎟⎠.1 / BW _ start ) ⎞ ⎛⎝ ln(BW ⎟⎟ ln ⎜⎜ ln( BW _ mat ⋅ 1.1 / BW _ end ) ⎠ [Eq. 3.8]  3.8 B= ⎝ _ start ⎛ ln(Age BW __ end mat −⋅ 1Age .1 / BW _ start ) ⎞ ⎟⎟ ln ⎜⎜ ln(and BW _Bmat .1 / BW _used end ) ⎠in Equations 3.5 and 3.6; BW_mat is the mature body weight, kg; where A are⋅ 1factors [Eq. 3.8] B = ⎝A where and B are −factors used in Equations 3.5 and 3.6; BW_mat is the mature body weight, kg; end Ageweight _ start BW_startAge is _the body at start or at birth, kg; BW_end is the body weight at the end of the

BW_start is the body weight at start or at birth, kg; BW_end is the body weight at the end of the feedingperiod; period;Age_start Age_start at start, days; Age_end theatage theofend feeding is is thethe ageage at start, days; and and Age_end is theisage theat end the of the feeding where and BIfare factors used Equations 3.5 and 3.6; BW_mat the mature body weight, kg; period,Aperiod, days. Age_start is 0in days, the BW_start is the same asis BW_birth. feeding days. BW_start is the body weight at start or at birth, kg; BW_end is the body weight at the end of the

feeding period; Age_start is the age at start, days; and Age_end is the age at the end of the Anexample exampleofofthe thelogistic logistic growth function of ADG during the rearing is shown in An growth function of ADG and and BW BW during the rearing periodperiod is shown feeding period, days. in Figure3.1. 3.1. Figure An example of the logistic growth function of ADG and BW during the rearing period is shown Figure 3.1of the feeding period depends on whether the animal will be sold, slaughtered or used for The end in Figure 3.1.

replacement. Default values for birth weights (BW_birth) and BW_mat that can be used for estimating

The of the feeding period depends on whether the animal will be sold, slaughtered or used for BWend and Figure 3.1ADG for different breeds and gender are shown in Table 3.5. BW_birth values for beef replacement. Default values for birth weights (BW_birth) and BW_mat that can be used for breeds were collected from Danish, Norwegian and Swedish national recording data compiled by estimating BW and ADG for different breeds and gender are shown in table 3.5. BW_birth values The end of the feeding period depends on whether animal will Research be sold, slaughtered used for thebeef Danish Cattle Norwegian Meattheand Poultry andordata Cattle statistics for breeds wereAssociation, collected from Danish, Norwegian and Swedish nationalCentre recording replacement. Default values for birth weights (BW_birth) andDanish, BW_mat that can beand used for (2007), respectively. Values for BW_mat were taken from Norwegian Swedish slaughter compiled by the Danish Cattle Association, Norwegian Meat and Poultry Research Centre and estimating BW and ADG for different breeds and gender are supplied shown inby table 3.5. BW_birth data for animals over 4respectively. years of ageValues over the 10 years the Danish Cattlevalues Association; Cattle statistics (2007), forlast BW_mat were taken from Danish, Norwegian for beef breeds were collected from Danish, Norwegian and Swedish national recording data Norwegian and data Poultry Research Centre and Dairy respectively. and Swedish Meat slaughter for animals over 4 years of Swedish age over the lastAssociation, 10 years supplied by the compiled by the Danish Cattle Association, Norwegian Meat and Poultry Research Centre and Danish Cattle Association; Norwegian Meat and Poultry Research Centre and Swedish Dairy Cattle statistics (2007), respectively. Values for BW_mat were taken from Danish, Norwegian Association, respectively. and Swedish slaughter data for animals over 4 years of age over the last 10 years supplied by the Danish Cattle Association; Norwegian Meat and Poultry Research Centre and Swedish Dairy Association, Table 3.5 respectively. Table 3.5 25 25

30 

NorFor – The Nordic feed evaluation system

ADG_calc, g/day BW_calc, kg

1000 900 800 700 600 500 400 300 200 100 0

ADG, 24mo ADG, 27mo BW, 24mo BW, 27mo

0

5

10 15 Age, months

20

25

30

Figure 3.1. A logistic growth function predicting BW (BW_calc) and estimated weight gain (ADG_ calc) during the rearing of a heifer which is scheduled to calve at a live weight of 560 kg at either 24 or 27 months of age. To achieve a final weight of 560 kg, the weight gain is faster for a heifer finished at 24 months of age (ADG, 24mo; BW, 24mo) than for a heifer finished at 27 months (ADG, 27mo; BW, 27mo). Table 3.5. Default values for birth weight (BW_birth) and mature body weight (BW_mat) for heifers, bulls and steers of different breeds. Breeds Early maturing dairy breeds Danish Holstein Danish Red Icelandic breed Jersey Norwegian Red Swedish Holstein Swedish Red Early maturing beef breeds Aberdeen Angus Dexter Galloway Hereford Highland cattle Tiroler Grauvieh Late maturing beef breeds Belgian Blue Blonde d’Aquitaine Charolais Chianina Limosin Piemontese Saler Simmenthal Early·late maturing breed Crossbred1 1

BW_birth BW_birth BW_mat BW_mat BW_mat for heifer kg for bulls kg for heifer kg for bulls kg for steers kg 40 40 33 28 39 39 39

41 41 33 30 41 41 41

640 660 470 440 600 640 620

950 950 800 650 950 950 950

750 750 700 550 750 750 750

36 21 34 40 29 39

38 24 35 42 30 42

700 340 550 700 500 700

950 450 850 950 700 950

750 400 750 750 600 750

44 44 46 50 41 41 39 44

47 47 49 55 43 43 41 46

850 850 750 850 750 750 750 750

1,200 1,200 1,200 1,200 1,200 1,200 1,200 1,200

1,050 1,050 1,050 1,050 1,050 1,050 1,050 1,050

42

44

750

1,050

950

Crossbreeds of early and a late maturing breeds are assigned specific factors in some of the equations in Chapter 9.

NorFor – The Nordic feed evaluation system

31

References Bossen, D., 2008. Feeding strategies for dairy cows. Individual feed allocation using automatic live weight registrations as management parameter. PhD thesis. University of Copenhagen, Faculty of Life Sciences, Denmark. 159 pp. Enevoldsen, C. and T. Kristensen, 1997. Estimation of body weight from body size measurements and body condition scores in dairy cows. Journal of Dairy Science 80: 1988-1995. Gillund, P., O. Reksen, K. Karlberg, Å.T. Randby, I. Engeland and B. Lutnaes, 1999. Testing of a body condition score method in Norwegian cattle. Norsk Veterinærtidsskrift 111: 623-632. (In Norwegian with English summary). Nielsen, H.M., N.C. Friggens, P. Løvendahl, J. Jensen and K.L. Ingvartsen, 2003. Influence of breed, parity, and stage of lactation on lactational performance and relationship between body fatness and live weight. Livestock Production Science 79: 119-133. Sjaunja, L.-O., L. Bævre, I. Junkkarainen, J. Pedersen and J. Setälä, 1990. A Nordic proposition for an energy corrected milk (ECM) formula. 26th session of the International Committee for recording the productivity of Milk Animals (ICRPMA).

32 

NorFor – The Nordic feed evaluation system

4. Feed fraction characteristics H. Volden Absorbed nutrients are provided from fermentation and digestion in the gastrointestinal tract, and the predictions are sensitive to the nutrient profiles of the feed. NorFor has an extensive feed table (www.norfor.info) that lists chemical composition and digestion characteristics of typical Nordic feedstuffs. The feed table values are continuously updated as new information becomes available.

4.1 Definition of roughage and concentrates Roughage and concentrates are feedstuff classes that are generally used and several criteria characterize both groups of feedstuffs, e.g. fibre content, energy density, moisture content and particle length. In NorFor it is necessary to identify a feedstuff either as ‘roughage’ or ‘concentrate’. This is decided from information on particle length; feedstuffs with particle lengths greater or less than 6 mm are characterized as roughages and concentrates, respectively. The identification of feedstuff class is necessary when describing the feed FV and in the calculation of the CI. This information is also necessary to predict the fractional passage rate of a feed fraction out of the rumen. Feedstuffs are further classified as liquid, fine, coarse, rolled, chopped, and unchopped, since this is used for identification of most frequent particle length (PL) which affects the calculated CI values.

4.2 Division of organic matter The DM is separated into OM and ash. The OM is further divided into the following components: CP, NDF, ST, CFat, LAF, VFA, alcohols and a calculated RestCHO fraction including SU. The main chemical components are further divided into sub-fractions (Figure 4.1) that have uniform rates of kd in the rumen.

Soluble Potentially degradable Total indigestible

Protein NDF

‘Soluble’ Potentially degradable Total indigestible

Amino acids Potentially degradable Total indigestible

Starch

Fermentation pr. Sugar Residual CHO Crude fat

Fatty acids

Ash

Figure 4.1. The feed fractionation scheme in NorFor.

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_4, © Wageningen Academic Publishers 2011

33

4.3 Crude protein fractions and amino acids The composition and properties of dietary proteins strongly influence protein metabolism in the gastrointestinal tract of ruminants, hence information on these variables is essential for robust feed evaluation. Feed CP (N·6.25) consists primarily of AA and smaller amounts of non-amino nitrogen, which is composed of NH3N, urea and nucleic acids. In NorFor CP is partitioned into three fractions: (1) soluble (sCP), (2) potentially degradable in the rumen (pdCP) and (3) total tract-indigestible (iCP). As example, Table 4.1 presents average CP fractions for a limited number of common feedstuffs. The sCP fraction contains soluble proteins, peptides, free AA, and non-amino nitrogen. When calculating ruminal degradation of the sCP fraction instantaneous degradation of NH3N and urea is assumed and a fractional degradation rate of 150%/h is used for the soluble AA fractions (Russel et al., 1992; Volden et al., 1998, 2002). The pdCP fraction is assumed to be insoluble, but degradable by microbes in the rumen. Individual rates of degradation (kdCP) for the pdCP fraction are estimated from ruminal degradation profiles using the in sacco technique (see Section 5.2.2 for a description of the method), assuming that the degradation profile follows first-order kinetics. Intestinal digestibility of the undegraded rumen feed protein (RUP) may vary considerably between feeds (Hvelplund, 1985; Van Straalen and Tamminga, 1990; Volden and Harstad, 1995). The in sacco mobile bag technique (MBT) provides an easy and fast method to determine intestinal digestibility of protein (Hvelplund et al., 1992, 1994) (see Section 5.2.4 for a description of the method). In NorFor, the MBT technique is used to determine feed iCP. The iCP represents a protein fraction that is completely resistant to both microbial degradation in the rumen and digestion in the small intestine. However, ruminal pre-incubation has been shown to have important effects on the intestinal digestion of protein in several feeds (Volden and Harstad, 1995;Vanhatalo et al., 1996). Consequently, iCP is determined in feed residues after 16 and 24 h of ruminal incubation for concentrates and roughages, respectively. Volden and Harstad, 1995 observed that pre-incubation in the rumen increased intestinal digestion and thus reduced the iCP. This implies that the feed protein contains a fraction that is not degradable Table 4.1. Crude protein (CP) fractions in selected feeds and the corresponding fractional degradation rate of the potential degradable protein fraction. Feedstuff

Barley Wheat Maize Peas Rape seed Rape seed meal Soybean meal Maize silage, high digestibility Maize silage, low digestibility Grass silage, high digestibility Grass silage, low digestibility

NorFor feed code

CP g/kg DM

Protein fractions, g/kg CP sCP1

pdCP2

iCP3

001-0016 001-0020 001-0014 003-0006 002-0007 002-0042 002-0053 006-0307 006-0309 006-0461 006-0463

113 131 96 239 218 388 516 78 75 173 144

290 220 114 711 280 216 160 464 437 668 628

670 750 886 289 660 734 840 432 459 267 228

36 29 51 28 85 58 11 140 140 38 49

kdCP4 %/h 11.3 14.3 2.5 9.1 9.5 9.5 7.9 4.6 4.6 12.1 8.0

1

sCP=soluble crude protein fraction. pdCP=potentially degradable crude protein. 3 iCP=total indigestible crude protein. 4 kdCP=fractional degradation rate of pdCP. 2

34 

NorFor – The Nordic feed evaluation system

in the rumen, but is digestible in the small intestine, explaining why the sum of sCP + pdCP + iCP in most feedstuffs is not 1000 g/kg CP. The remaining CP fraction is added to the protein fraction digested in the small intestine. The amount of RUP is one of the primary factors determining the amount of dietary AAs entering the small intestine. NorFor predicts individual AAs absorbed from the small intestine. Thus, information regarding the AA profile of the RUP is required. A variable fraction of the feed protein is degraded in the rumen and the AA profile of the RUP may differ from the AA profile of the intact feed protein. Several authors have compared the AA profiles of intact feed protein and in sacco residues after ruminal incubation (Skórko-Sajko et al., 1994; Skiba et al., 1996; Weisbjerg et al., 1996; Zebrowska et al., 1997; Prestløkken, 1999; Harstad and Prestløkken, 2000; Harstad and Prestløkken, 2001). Although a limited number of feeds were evaluated, the acquired data suggest that there are only minor differences between the AA profiles of intact feed protein and RUP. Therefore, the AA profile and the sum of AA nitrogen (AA-N) in RUP are assumed to be the same as those of the original feedstuff. The NorFor feedstuff table (www.norfor.info) has information on the AA profiles of individual feeds.

4.4 Carbohydrate fractions Carbohydrates (CHO) are heterogeneous feed constituents that collectively comprise the largest component of cattle diets, and make the major contribution to supporting ruminal microbial growth. CHO digestion in the gastrointestinal tract varies considerably and the end-products from ruminal fermentation and intestinal digestion are the major nutrient supply for animal production. Moreover, the rate of CHO degradation in the rumen is a major factor affecting ruminal pH and thus the rumen environment and feed utilization. A good balance between the different CHO fractions is essential to maintain normal rumen function and optimize rumen fermentation. Carbohydrates can be divided into two main fractions, structural and non-structural The structural CHO originates from plant cell wall material (consisting of cellulose, hemicellulose and lignin), which in NorFor is defined as NDF (Van Soest, 1994). The NDF fraction is further divided into a potentially degradable fraction (pdNDF) and a total indigestible fraction (iNDF). The iNDF is determined from ruminal in sacco incubation for 288 h (see Section 5.2.3 for a description of the method), and the pdNDF fraction is estimated as total NDF minus iNDF. In concentrates, the rate of pdNDF degradation (kdNDF) is estimated by in sacco degradation, assuming first-order degradation kinetics (see Section 5.2.2 for a description of the method). In roughage, kdNDF is predicted from the combination of in vivo OMD and iNDF and using the Lucas principle applied to the excretion of non-fibre matter (Weisbjerg et al., 2004a, b). A complete description of the method and calculations is presented in Section 6.1. Examples of NDF content and degradation characteristics in commonly used feedstuffs are presented in Table 4.2. The non-structural carbohydrates consist of carbohydrate-based cell contents, including ST, SU, β-glucans and some of the pectins. In NorFor ST and SU are determined analytically, while β-glucans and pectins are part of the RestCHO fraction. Starch is an important feed constituent for high-yielding cattle due to its high digestibility and importance as a source of substrates for propionic acid fermentation in the rumen. Inadequate ST intake may depress microbial activity and thus reduce microbial protein synthesis. However, excessive levels of ST may depress fibre digestibility and roughage utilization, as well as causing ruminal acidosis and abnormalities in the rumen tissue (Owens et al., 1998). Starch digestibility in the rumen depends on grain type, grain processing and particle length (Mills et al., 1999; Offner and Sauvant, 2004; Larsen et al., 2009). In NorFor, ST is partitioned into three fractions (Table 4.3): ‘soluble’ (sST), potentially degradable (pdST) and indigestible (iST). The ST fractions are calculated from in sacco degradation profiles (see Section 5.2.2). The sST fraction represents small starch particles NorFor – The Nordic feed evaluation system

35

Table 4.2. NDF fractions in selected feeds and the corresponding fractional degradation rate of the potential degradable NDF fraction. Feedstuff

Barley Wheat Maize Peas Rape seed Rape seed meal Soybean meal Maize silage, high digestibility Maize silage, low digestibility Grass silage, high digestibility Grass silage, low digestibility

NorFor feed code

NDF g/kg DM

NDF fractions, g/kg NDF pdNDF1

iNDF2

001-0016 001-0020 001-0014 003-0006 002-0007 002-0042 002-0053 006-0307 006-0309 006-0461 006-0463

198 127 111 126 188 290 133 338 397 501 597

836 831 913 984 686 500 939 828 803 888 793

164 169 87 16 314 500 61 172 197 112 207

kdNDF3 %/h 3.2 3.5 3.0 10.4 13.3 6.7 5.0 3.4 3.1 5.0 3.7

1

pdNDF=potentially degradable NDF. iNDF=total indigestible NDF. 3 kdNDF=fractional degradation rate of pdNDF. 2

Table 4.3. Starch fractions in selected feeds and the corresponding fractional degradation rate of the potential degradable starch fraction. Feedstuff

Barley Wheat Maize Peas Maize silage, high digestibility Maize silage, low digestibility

NorFor feed code

Starch g/kg DM

Starch fractions, g/kg ST sST1

pdST2

iST3

001-0016 001-0020 001-0014 003-0006 006-0307 006-0309

615 667 712 511 347 299

350 399 230 230 500 500

650 601 770 770 500 500

3 5 30 30 10 10

kdST4 %/h 39.2 59.5 9.0 9.0 40.0 40.0

1

sST=small starch granules (‘water soluble starch fraction’). pdST=potentially degradable starch. 3 iST=total indigestible starch. 4 kdST=fractional degradation rate of pdST. 2

that are lost from nylon bags washed in cold water. It is assumed that the sST fraction follows the ruminal liquid pool and the fractional degradation rate is set to 150%/h, irrespective of feed source. The pdST fraction is calculated as the difference between the asymptotic value obtained from in sacco incubation and the sST value. The iST fraction is assumed to be completely resistant to digestion in the small intestine and is determined by the MBT (Norberg et al., 2007). The fractional degradation rates (kdST) of pdST are feed-specific and highly variable, ranging from 9%/h (maize) to 79%/h (oats) (Table 4.3). The kdST is estimated from in sacco degradation profiles.

36 

NorFor – The Nordic feed evaluation system

704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 720 720 719 720 721 721 721 722 722 722 723 723 723 724 724 724 725 725 725 726 726 726 727 727 727 728 728 728 729 729 729 730 730 730 731 731 731 732 732 732 733 733 733 734 734 734 735 735 735 736 736 736 737 737 737 738 738 738 739 739 739 740 740 740 741 741 741 742 742 742 743 743 743 744 744 744 745 745 745 746 746 746 747 747 747 748 748 748 749 749 749 750 750 750 751 751 751 752 752 752 753 753 753 754 754 754 755 755 755 756 756 756 757 757 757 758 758 758 759 759 759

MBT (Norberg et al., 2007). The fractional degradation rates (kdST) of pdST are feed-specific and highly variable, ranging from 9%/h (maize) to 79%/h (oats) (Table 4.3). The kdST is estimated from in sacco degradation profiles. Table 4.3

The RestCHO fraction includes β-glucans, pectic substances and SU. Although the SU fraction

The RestCHO fraction includes β-glucans, pectic substances and SU. Although the SU fraction maybe beanalytically analyticallydetermined, determined, it categorized is categorized a part of RestCHO the RestCHO fraction. This is because may it is as aaspart of the fraction. This is SU is not analyzed in all in samples and tabulated values areare normally used. The because SUroutinely is not routinely analyzed all samples and tabulated values normally used. TheRestCHO is estimated by: RestCHO is estimated by: Re stCHO = 1000 − Ash − CP − CFat − NDF − ST − FPF 

[Eq. 4.1]

4.1

where residual carbohydrate fraction in the g/kg DM; the ash content whereRestCHO RestCHOisisthethe residual carbohydrate fraction infeed, the feed, g/kg Ash DM;isAsh is the ash content in the feed, g/kg DM; CPCP is the crude protein content in theinfeed, g/kg DM; CFat is CFat the crude fatcrude fat in the feed, g/kg DM; is the crude protein content the feed, g/kg DM; is the content in the feed, g/kg DM; and is sum of products in content in in the the feed, feed, g/kg g/kg DM; DM; NDF and FPF FPF is the the sum of the theinfermentation fermentation products in isthe thethefeed, feed, content is the NDF content the feed, g/kg DM; ST starch content inthe the feed, g/kg DM; NDF is the NDF content in the feed, g/kg DM; ST is the starch content content Equation Equationin4.7. 4.7. feed, g/kg DM; and FPF is the sum of the fermentation products in the feed, in the feed, the fermentation products in the feed, Equation 4.7. Equation 4.7.g/kg DM; and FPF is the sum of 34 If N, the the RestCHO RestCHO If the the feedstuff feedstuff contains contains low low molecular molecular weight weight N N fractions, fractions, such such as as urea urea and and NH NH33N, the3RestCHO If contains low molecular weight Nformulas: fractions, such such as urea If the thefeedstuff feedstuff contains low molecular weight N fractions, asand ureaNH and N, the RestCHO is corrected (RestCHOcorr) using the 3N,NH is corrected (RestCHOcorr) using the following following formulas: is using thethe following formulas: is corrected corrected(RestCHOcorr) (RestCHOcorr) using following formulas: For For urea urea correction: correction: For For urea ureacorrection: correction: UreaN UreaN CFat − NDF − ST − FPF Re Re stCHOcorr stCHOcorr = = 1000 1000 − − Ash Ash − − CPcorr CPcorr − − CP CP ⋅⋅ UreaN − − CFat − NDF − ST − FPF 2915 − CFat − NDF − ST − FPF  Re stCHOcorr = 1000 − Ash − CPcorr − CP ⋅ 2915 2915

[Eq. [Eq. 4.2] 4.2] [Eq. 4.2]

CP NH 3 N CFat − NDF − ST − FPF Re Re stCHOcorr stCHOcorr = = 1000 1000 − − Ash Ash − − CPcorr CPcorr − − CP ⋅⋅ NH 33 N − − CFat − NDF − ST − FPF  6 1000 − CFat − NDF − ST − FPF Re stCHOcorr = 1000 − Ash − CPcorr − .25 ⋅ 1000 6.25 1000

[Eq. [Eq. 4.3] 4.3] [Eq. 4.3]

⎛⎛ NH NH3N N ⎞⎞ CPcorr CPcorr = CP  = ⎜⎛⎜11 − − NH33N ⎟⎟⎞ ⋅⋅ CP 1000 ⎠⎟⎠ ⋅ CP CPcorr = ⎝⎜⎝1 − 1000 1000 ⎠ ⎝

[Eq. [Eq. 4.4] 4.4] [Eq. 4.4]

4.2

For ammonia correction: For For ammonia ammoniacorrection: correction: For ammonia correction:

4.3

whereRestCHOcorr RestCHOcorr is the residual carbohydrate fraction corrected for low molecular where is residual carbohydrate fraction corrected for nitrogen where RestCHOcorr is the the residual carbohydrate fraction corrected for low low molecular molecular nitrogen nitrogen fractions in the feed, g/kg DM; Ash is the ash content in the feed, g/kg DM; CP is the crude protein where RestCHOcorr is the residual carbohydrate fraction corrected for low molecular nitrogen fractions in the feed, g/kg DM; Ash is the ash content in the feed, g/kg DM; CP is the crude fractions in the feed, g/kg DM; Ash is the ash content in the feed, g/kg DM; CP is the crude fractions the feed, feed, the content infat thecontent feed, g/kg DM; CPg/kg is the crude contentcontent ininthe DM; CFat theash fat content in the g/kg DM; NDF is the protein in feed, g/kg DM; CFat is the crude in the feed, DM; NDF is protein content in the theg/kg feed,DM; g/kgAsh DM;isis CFat iscrude the crude fat content in feed, the feed, g/kg DM; NDF is neutral protein content incontent thefibre feed, g/kg DM; CFat isDM; the crude incontent the content feed, DM;feed, NDF isDM; FPF the neutral detergent in feed, g/kg DM; ST is the in the g/kg detergent fibre incontent the feed, g/kg ST isfat the starch ing/kg the g/kg the neutral detergent fibre content in the the feed, g/kg DM; STcontent is the starch starch content in feed, the feed, g/kg the neutral fibre content in products theinfeed, g/kg ST is the starch content inCPcorr the feed, g/kg protein DM; FPF the sum fermentation the feed, Equation 4.7, DM; is the sumis of products the in feed, Equation 4.7, g/kgg/kg DM; CPcorr isis DM; FPF isdetergent thefermentation sum of of fermentation products in theDM; feed, Equation 4.7, g/kg DM; CPcorr iscrude DM; FPF the sum of products themolecular feed, Equation 4.7, g/kg CPcorr crude protein content in the corrected for low N g/kg DM; NH the Nisis isammonia the crude protein content infermentation the feed feedfor corrected for in low molecular N fractions, fractions, g/kgDM; DM; NH3N content inisthe feed corrected low molecular N fractions, g/kg DM; NH N 3N is 3the crude protein content in the feed corrected for low molecular N fractions, g/kg DM; NH ammonia N content in feed, g/kg N; UreaN is the urea N content in feed, g/kg N; 2915 3N is the ammonia N content in feed, g/kg N; UreaN is the urea N content in feed, g/kg N; 2915 content in feed, g/kg N; UreaN is the urea N content in feed, g/kg N; 2915 corresponds to the CP ammonia N content in content feed, g/kg N; UreaN is the urea N content in feed, g/kg N; 2915 corresponds to of kg corresponds to the the CP content of one one kg of of urea. urea. content of one kgCP of urea. corresponds to the CP content of one kg of urea. The CPcorr is calculated as: The calculated The CPcorr CPcorris calculatedas: The CPcorr isiscalculated as:as: 4.4

where CPcorr is the crude protein content in the feed corrected for low molecular weight N fractions,

where the crude N where CPcorr CPcorr is crude protein protein content content in in the the feed feed corrected corrected for for low low molecular molecular weight N g/kg DM; CPisisthe theCP crude protein content in the g/kg DM; and NH N isweight the or urea where CPcorr the crude protein content the feedfeed, molecular fractions, g/kg is crude protein in the g/kg DM; and N is ammonia theN 3 fractions, g/kgisDM; DM; CP is the the crude proteinincontent content incorrected the feed, feed, for g/kglow DM; and3NH NHweight 3N is the N content in the feed, g/kg N. fractions, g/kg DM; CP is the crude protein content in the feed, g/kg DM; and NH N is the ammonia or urea N content in the feed, g/kg N. 3 ammonia or urea N content in the feed, g/kg N. ammonia or urea N content in the feed, g/kg N. The RestCHO RestCHOfraction fractionrepresents representsthe therapidly rapidlyfermented fermentedwater-soluble water-soluble CHO fractions; The CHO fractions; aa a heterogeneous The RestCHO fraction represents the rapidly fermented water-soluble CHO fractions; The RestCHO fraction rapidly CHO fractions; arate (Weisbjerg et heterogeneous group carbohydrates, ranging from SU, has ruminal degradation group of carbohydrates, rangingthe from SU, fermented which awhich high ruminal degradation heterogeneous group of ofrepresents carbohydrates, ranging from has SU,water-soluble which has aa high high ruminal degradation heterogeneous group of carbohydrates, ranging from SU, which has a high ruminal degradation rate (Weisbjerg et al., 1998) to soluble fibre fractions, which have moderate degradation al., 1998) to soluble fibre fractions, which have moderate degradation rates (Sniffen et al., 1992; rate (Weisbjerg et al., 1998) to soluble fibre fractions, which have moderate degradation rates rates rate (Weisbjerg et al.,Lanzas 1998) to fibre which have rates (Sniffen al., 1992; et al., In NorFor the fractional degradation of (Sniffen etal., al.,2007). 1992; Lanzas et soluble al., 2007). In fractions, NorFor the fractional degradation rate of RestCHO RestCHO Lanzas etet In NorFor the2007). fractional degradation rate ofmoderate RestCHOdegradation inrate roughage (kdRestCHO) (Sniffen et al., 1992; Lanzas al., 2007). in (kdRestCHO) is calculated as: in roughage (kdRestCHO) is et calculated as:In NorFor the fractional degradation rate of RestCHO is roughage calculated as: in roughage (kdRestCHO) is calculated as: SU ⎛⎛ SU ⎞⎞⎟ ⋅ 150 SU ⎞⎞⎟ ⋅ 10 + ⎛⎛⎜ SU kd = ⎜⎜⎛11 − − kd Re Re stCHO stCHO = SU ⎟⎠⎞ ⋅ 150 SU ⎟⎠⎞ ⋅ 10 + ⎜⎝⎛ Re stCHO Re stCHO ⎝ Re stCHO Re stCHO ⎠⎟ ⋅ 10 + ⎝⎜ ⎠⎟ ⋅ 150 kd Re stCHO = ⎝⎜1 − ⎝ Re stCHO ⎠ ⎝ Re stCHO ⎠



[Eq. [Eq. 4.5] 4.5] [Eq. 4.5]

4.5

where kdRestCHO is the degradation rate of the rest carbohydrate fraction, %/h; SU is the sugar

where kdRestCHO is the rate of fraction, %/h; SU is where kdRestCHO is the degradation degradation ratecarbohydrate of the the rest rest carbohydrate carbohydrate fraction,4.1; %/h; is the the sugar sugar content, g/kg DM;RestCHO RestCHO is therest rest fraction, Equation theSU fractional degradation where kdRestCHO is the degradation of the rest carbohydrate fraction, SU is the sugar content, g/kg is carbohydrate fraction, 4.1; the content, g/kg DM; DM; RestCHO is the the restrate carbohydrate fraction, Equation Equation 4.1;%/h; the fractional fractional rate of SU is 150%/h; and the fractional degradation rate of the non-sugar part of the RestCHO content, g/kg DM; RestCHO is the rest carbohydrate fraction, Equation 4.1; the fractional

fraction is 10%/h. In concentrate feeds the kdRestCHO is set to 150%/h. 35 35 35

NorFor – The Nordic feed evaluation system

37

760 761 762 763

degradation ratefat of SU is 150 %/h;acids and the fractional degradation rate of the non-sugar part of the 4.5 Crude and fatty RestCHO fraction is 10 %/h.

764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781

richCrude than CPfatorand CHO, although 4.5 fatty acids the energy value is dependent on the proportion of fatty acids (FA)

782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808

Fatconcentrate normally feeds constitutes a low proportion of cattle diets. However, fat is often used as an energy In the kdRestCHO is set to 150%/h. source for high productivity cattle. On a weight basis, fat is approximately 2.2 times more energy-

in the CFat. constitutes However, arumen microorganisms cannot toleratefat high levels of as fatan(Jenkins, Fat normally low proportion of cattle diets. However, is often used energy 1993). Therefore, bothproductivity the level and type FA are basis, important to consider when optimizing cattle source for high cattle. Onofa weight fat is factors approximately 2.2 times more diets. The NorFor (www.norfor.info) includes information on FA profiles of individual energy-rich than CPfeedstuff or CHO,table although the energy value is dependent on the proportion of fatty feedstuffs. FA metabolism in the cannot gastro tolerate intestinal tract is not and is, acids (FA) inHowever, the CFat.individual However, rumen microorganisms high levels of expressed fat (Jenkins, Therefore, both and typemodule. of FA areConsequently, important factors consider when therefore,1993). not implemented in the the level metabolism onlytoinformation on dietary includes onlevel FA of the optimizing cattle diets. NorFor feedstuff (www.norfor.info) FA composition can beThe evaluated, which istable important when focusing on theinformation optimal FA profiles individual feedstuffs. However, individual FA metabolism intestinal tract feeds. diet, on of milk fat quality and milk off-flavours. The proportion of FAininthe thegastro CFat varies between is notmain expressed andlipids is, therefore, implemented in are the metabolism module. Consequently, The storage in plantnot seeds and grains triacylglycerols, which consist of three fatty only information on dietary FA composition can be evaluated, which is important acids attached to glycerol. In these feeds the proportion of FA in CFat is 80 towhen 85%focusing (Danfær et al., on the optimal FA level of the diet, on milk fat quality and milk off-flavours. The proportion of 2006). In contrast, forage lipids are mainly in the form of galactolipids, which contain galactose in FA in the CFat varies between feeds. The main storage lipids in plant seeds and grains are addition to glycerol and unsaturated Thus, their In FAthese proportions triacylglycerols, which consist of threefatty fatty acid acidsmoieties. attached to glycerol. feeds theare substantially lower: approximately 45% (Danfær et al., 2006). When the rumen OM fermentation is predicted in proportion of FA in CFat is 80 to 85% (Danfær et al., 2006). In contrast, forage lipids are mainly NorFor, theofglycerol and galactose are added to the feed fractions, and serve as energy in the form galactolipids, which contain galactose in fermentable addition to glycerol and unsaturated fatty sources for microbial growth. acid moieties. Thus, their FA proportions are substantially lower: approximately 45% (Danfær et al., 2006). When the rumen OM fermentation is predicted in NorFor, the glycerol and galactose are to the fermentable feed fractions, and serve as energy sources for microbial growth. 4.6added Fermentation products

4.6 Fermentation products Ensiled forages dominate as winter roughages in the Nordic countries. The feedstuffs have highly

Ensiled dominate as winter in the Nordic The feedstuffs highly acids) variableforages FPF (i.e. VFA, LAF and roughages alcohols) contents. Thecountries. VFA (acetic, propionic have and butyric variable FPF (i.e. account VFA, LAF alcohols) contents. Themajor VFA (acetic, can collectively forand up to 70 g, and LAF (the organicpropionic acid) for and 40 tobutyric 150 g per kg DM acids) collectively account to 70 g, and lactic acid (the major organic for 40 tosources for of the can silage. The VFA, whichfor areupend-products of ruminal fermentation, areacid) not energy 150 g per kg DM of the silage. The VFA, which are end-products rumen microorganisms. Lactic acid represents a minor sourceofofruminal energy fermentation, for microbialare growth, and not sources forlactic rumenacid microorganisms. a minor energy the energy ATP yield from metabolism isLactic only acid 25%represents of the yield fromsource CHOof fermentation (Van for microbial growth, and the ATP yield from lactic acid metabolism is only 25% of the yield Soest, 1994). A high content of fermentation products in silages has a negative effect on forage intake from CHO fermentation (Van Soest, 1994). A high content of fermentation products in silages (Huhtanen et effect al., 2002, 2007). Therefore, NorFor relative NorFor silage index (Huhtanen has a negative on forage intake (Huhtanen et al.,incorporates 2002; 2007).the Therefore, et al., 2002)the when calculating feed(Huhtanen intake (seeet Section 6.2). The silage index information incorporates relative silage index al., 2002) when calculating feed requires intake (see on fermentation acids (TAF), calculated as: section 6.2). The silage index requires information on fermentation acids (TAF), calculated as: TAF = LAF + ACF + PRF + BUF 

[Eq. 4.6]

4.6

where of of total fermentation acidsacids in theinfeed, g/kg DM; the content whereTAF TAFisisthe thecontent content total fermentation the feed, g/kgLAF DM;isLAF is the of content of lactic g/kg DM; ACF is the acid content in the in feed, PRF is the lacticacid acidininthe thefeed, feed, g/kg DM; ACF is acetic the acetic acid content theg/kg feed,DM; g/kg DM; PRF is the propionic feed, g/kg DM; BUF is the acid acid content in thein feed, propionicacid acidcontent contentininthethe feed, g/kg DM; BUF is butyric the butyric content the g/kg feed, g/kg DM. DM

Ensiled forage also contains variable amounts of alcohol (Randby et al., 1999). This is taken into

Ensiled forage also contains variable amounts of alcohol (Randby et al., 1999). This is taken into accountwhen whenFPF FPFisiscalculated: calculated: account FPF = TAF + FOF + ALF



[Eq. 4.7]

4.7

36 where content of fermentation products in thein feed, TAF is the content where FPF FPFisisthethe content of fermentation products theg/kg feed,DM; g/kg DM; TAF is theofcontent of fermentation acids in the feed, Equation 4.6, g/kg DM; FOF is the content of formic acid in the fermentation acids in the feed, Equation 4.6, g/kg DM; FOF is the content of formic acid in the feed, feed, g/kg DM; the content of alcohol feed, g/kgDM. DM. g/kg DM; ALFALF is theis content of alcohol in in thethe feed, g/kg

809 810 811 812 813 814 815 816

4.7 Minerals

817

4.8 Vitamins

4.7 Minerals

Mineral elements may adversely affect production, animal health and reproduction (NRC, 2001). Several elements are also important for maintaining normal rumen function. NorFor predicts Mineral elements may adversely affect production, animal health and reproduction (NRC, 2001). mineral supply and incorporates requirements for the following elements: Calcium (Ca), Several elements are also important for maintaining normal rumen function. NorFor(S), predicts Phosphorus (P), Magnesium (Mg), Potassium (K), Sodium (Na), Chlorine (Cl), Sulfur Iron mineral (Fe), Manganese (Mn), Zinc (Zn), Copper (Cu), Cobalt (Co), Selenium (Se), Molybdenum (Mo) and Iodine (I). Animal mineral requirements are described in Chapter 10. The NorFor feedstuff 38  (www.norfor.info) includes information on the mineral NorForcontent – The of Nordic feedfeedstuffs. evaluation system individual table

supply and incorporates requirements for the following elements: Calcium (Ca), Phosphorus (P), Magnesium (Mg), Potassium (K), Sodium (Na), Chlorine (Cl), Sulfur (S), Iron (Fe), Manganese (Mn), Zinc (Zn), Copper (Cu), Cobalt (Co), Selenium (Se), Molybdenum (Mo) and Iodine (I). Animal mineral requirements are described in Chapter 9. The NorFor feedstuff table (www.norfor. info) includes information on the mineral content of individual feedstuffs.

4.8 Vitamins Rumen microbes synthesize adequate amounts of vitamin K to meet the requirements of most cattle, except in young calves. However, cattle require a dietary source of vitamins A and E. Vitamin D is also normally included in the diet. NorFor incorporates requirements for vitamins A, E and D (see Chapter 9) and the NorFor feedstuff table (www.norfor.info) provides information on the vitamin A, E and D contents of individual feedstuffs.

References Danfær, A., P. Huhtanen, P. Udén, J. Sveinbjörnsson and H. Volden, 2006. The Nordic dairy cow model, Karoline – Description. In: Kebreab, E., J. Dijkstra, A. Bannink, J. Gerrits and J. France (eds.). Nutrient digestion and utilisation in farm animals: Modelling approaches. CAB International, Wallingford, UK, pp. 383-406. Harstad, O.M. and E. Prestløkken, 2000. Effective degradability and intestinal digestibility of individual amino acids in solvent-extracted soybean meal (SBM) and xylose-treated SBM (SoyPass®) determined in sacco. Animal Feed Science and Technology 83: 31-47. Harstad, O.M. and E. Prestløkken, 2001. Rumen degradability and intestinal digestibility of individual amino acids in corn gluten meal, canola meal and fish meal determined in sacco. Animal Feed Science and Technology 94: 127-135. Huhtanen, P., H. Khalili, J.I. Nousiainen, M. Rinne, S. Jaakkola, T. Heikkilä and J. Nousiainen, 2002. Prediction of the relative intake potential of grass silage by dairy cows. Livestock Production Science 73: 111-130. Huhtanen, P., M. Rinne and J. Nousiainen, 2007. Evaluation of the factors affecting silage intake of dairy cows: a revision of the relative silage dry-matter index. Animal 1: 758-770. Hvelplund, T., 1985. Digestibility of rumen microbial protein and undegraded dietary protein estimated in the small intestine of sheep and by the in sacco procedure. Acta Agriculturae Scandinavica Supplement 25: 132-144. Hvelplund, T., M.R. Weisbjerg and L.S. Andersen, 1992. Estimation of the true digestibility of rumen undegraded protein in the small intestine of ruminants by the mobile bag technique. Acta Agriculturae Scandinavica, section A: Animal Science 42: 34-39. Hvelplund, T., F.D. DeB. Hovell, E.R. Ørskov and D.J. Kyle, 1994. True intestinal digestibility of protein estimated with sheep on intragastric infusion and with the mobile nylon bag technique. Proceedings of the Society of Nutrition Physiology, 3. pp. 64. Jenkins, T.C., 1993. Lipid metabolism in the rumen. Journal of Dairy Science 76: 3851-3863. Lanzas, C., C.J. Sniffen, S. Seo, L.O. Tedeschi and D.G. Fox, 2007. A revised CNCPS feed carbohydrate fraction scheme for formulating rations for ruminants. Animal Feed Science and Technology 136: 167-190. Larsen, M., P. Lund, M.R. Weisbjerg and T. Hvelplund, 2009. Digestion site of starch from cereals and legumes in lactating dairy cows. Animal Feed Science and Technology 153: 236-248. Mills, J.A.N., J. France and J. Dijkstra, 1999. A review of starch digestion in the lactating dairy cow and proposals for a mechanistic model: 1. Dietary starch characterization and ruminal starch digestion. Journal of Animal and Feed Sciences 8: 291-340. Norberg, E., O.M. Harstad and H. Volden, 2007. Assessment of recovery site of mobile nylon bags for measuring ileal digestibility in dairy cows. Journal of Dairy Science 90: 418-421. NRC (National Research Council), 2001. Nutrient requirements of dairy cattle. Seventh revised edition, National Academy press, Washington, DC, USA. Offner, A. and D. Sauvant, 2004. Prediction of in vivo starch digestion in cattle from in sacco data. Animal Feed Science and Technology 111: 41-56. Owens, F.N., D.S. Secrist, W.J. Hill and D.R. Gill, 1998. Acidosis in cattle – a review. Journal of Animal Science 76: 1275-1286.

NorFor – The Nordic feed evaluation system

39

Prestløkken, E., 1999. Protein value of expander-treated barley and oats for ruminants. Ph.D. Dissertation No. 5. Department of Animal Science, Agricultural University of Norway, Aas, Norway. Randby, Å.T., I. Selmer-Olsen and L. Bævre, 1999. Effect of ethanol in feed on milk flavour and chemical composition. Journal of Dairy Science 82: 420-428. Russell, J.B., J.D. O’Connor, D.G. Fox, P.J. Van Soest and C.J. Sniffen, 1992. A net carbohydrate and protein system for evaluating cattle diets. I. Ruminal fermentation. Journal of Animal Science 70: 3551-3561. Skórko-Sajko, H., T. Hvelplund and M.R. Weisbjerg, 1994. Rumen degradation and intestinal digestibility of amino acids in different roughages estimated by nylon bag techniques. Journal of Animal and Feed Sciences 3: 1-10. Skiba, B., M.R. Weisbjerg and T. Hvelplund, 1996. Effective degradability in the rumen and total tract digestibility of amino acids in different roughages determined in sacco. Journal of Animal and Feed Sciences 5: 347-363. Sniffen, C.J., J.D. O’Connor, P.J. Van Soest, D.G. Fox and J.B. Russell, 1992. A net carbohydrate and protein system for evaluating cattle diets. II. Carbohydrate and protein availability. Journal of Animal Science 70: 3562-3577. Vanhatalo, A., P. Dakowski and P. Huhtanen, 1996. Effects of stage of growth and duration of rumen incubation time on intestinal digestibility of rumen-undegradable nitrogen of grass by mobile-bag method in cows. Acta Agriculturae Scandinavica, Section A - Animal Science 46: 1-10. Van Soest, P.J, 1994. Nutritional Ecology of the Ruminant. (2nd ed.) Cornell University press, Ithaca, NY, USA. 476 pp. Van Straalen, W.M. and S. Tamminga, 1990. Protein degradation of ruminant diets. In: Wiseman, J. and D.J.A. Cole (eds.). Feedstuff Evaluation. Butterworths, London, pp. 55-72. Volden, H. and O.M. Harstad, 1995. Effect of rumen incubation on the true indigestibility of feed protein in the digestive tract determined by the nylon bag techniques. Acta Agriculturae Scandinavica, Section A - Animal Science 45: 106-115. Volden, H., W. Velle, O.M. Harstad, A. Aulie and Ø. Sjaastad, 1998. Apparent ruminal degradation and rumen escape of lysine, methionine and threonine - administered intraruminally in mixtures to high yielding cows. Journal of Animal Science 76: 1232-1240. Volden, H.L., T. Mydland, V. Olaisen, 2002. Apparent ruminal degradation and rumen escape of soluble nitrogen fractions in grass, and grass silage administered intraruminally to lactating dairy cows. Journal of Animal Science 80: 2704-2716. Weisbjerg, M.R., T. Hvelplund, S. Hellberg, S. Olsson and S. Sanne, 1996. Effective rumen degradability and intestinal digestibility of individual amino acids in different concentrates determined in sacco. Animal Feed Science and Technology 62: 179-188. Weisbjerg, M.R., T. Hvelplund and B.M. Bibby, 1998. Hydrolysis and fermentation rate of glucose, sucrose and lactose in the rumen. Acta Agriculturae Scandinavica, Section A - Animal Science 48: 12-18. Weisbjerg, M.R., T. Hvelplund and K. Søegaard, 2004a. Prediction of digestibility of neutral detergent solubles using the Lucas principle. Journal of Animal and Feed Sciences 13, Supplement 1: 239-242. Weisbjerg, M.R., T. Hvelplund and K. Søegaard, 2004b. Prediction of NDF digestibility based on assumptions about true digestibility and endogenous loss of NDS. Journal of Animal and Feed Sciences, 13, Supplement 1: 243-246. Zebrowska, T., Z. Dlugolecka, J.J. Pajak and W. Korczynski, 1997. Rumen degradability of concentrate protein, amino acids and starch, and their digestibility in the small intestine of cows. Journal of Animal and Feed Sciences 6: 451-470.

40 

NorFor – The Nordic feed evaluation system

5. Feed analyses and digestion methods M. Åkerlind, M. Weisbjerg, T. Eriksson, R. Tøgersen, P. Udén, B.L. Ólafsson, O.M. Harstad and H. Volden Feed characteristics are determined via chemical analyses and digestion methods. Specific NorFor methods for determining parameters such as DM, sCP, iNDF and the in sacco methods are fully described in this chapter. Tables 5.1 and 5.2 present an overview of the feed analysis and digestion methods, respectively. Table 5.1. Recommended NorFor feed analysis methods. Parameter

Abbrev.

Unit

Reference method

Dry matter in concentrate Dry matter in roughage Ash Crude protein

DM

g/kg

EC No. 152/2009

DM

g/kg

Ash CP

g/kg DM g/kg DM

Soluble CP

sCP

g/kg CP

Ammonia nitrogen

NH3N

g N/kg N

Individual AA1 Crude fat Individual FA2

AAj CFat FAj

Neutral detergent fibre Starch

NDF

Free choice of MgO-method or Autoanalyzer (Broderick and Kang, 1980) g/100g CP EC No. 152/2009 g/kg DM EC No. 152/2009 g/100 g FA CEN ISO/TS 17764-1:2007 CEN ISO/TS 17764-2:2007 g/kg DM ISO 16472:2006 IDT

ST

g/kg DM

Lactic, propionic, butyric, formic acids and alcohol (ethanol) Sugar Calcium Phosphorus Magnesium Potassium Sodium Chloride Sulphur

LAF, PRF, g/kg DM BUF, FOF, ALF SU Ca P Mg K Na Cl S

g/kg DM g/kg DM g/kg DM g/kg DM g/kg DM g/kg DM g/kg DM g/kg DM

EC No. 152/2009 EC No. 152/2009 or Dumas

NorFor method

See Section 5.1.1 See Section 5.1.2 See Section 5.1.3

Spectrophotometric method or the plate count method described by Bach Knudsen (1997) and Bach Knudsen et al. (1987) HPLC or GC

EC No. 152/2009 ICP or free choice of method EC No. 152/2009 or ICP ICP or free choice of method ICP or free choice of method ICP or free choice of method EC No. 152/2009 or ICP ICP or free choice of method

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_5, © Wageningen Academic Publishers 2011

41

Table 5.1. Continued. Parameter

Abbrev.

Unit

Reference method

Iron, Copper, Manganese and Zinc Other micro minerals Vitamin A β-carotene Vitamin D Vitamin E

Fe, Cu, Mn, Zn

g/kg DM

EC No. 152/2009 or ICP

g/kg DM IU/kg DM IU/kg DM IU/kg DM IU/kg DM

Free choice of method EC No. 152/2009 or Jensen et al. (1998) EC No. 152/2009 or Jensen et al. (1998) Any appropriate method is acceptable EC No. 152/2009 or Jensen et al. (1999)

VitA b-car VitD VitE

NorFor method

1 The amino acids that can be reported in the NorFor feed tables are: alanine, arginine, asparagine, cysteine, glutamine,

glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine. 2 The fatty acids that can be reported in the NorFor feed tables are FA
Table 5.2. Recommended NorFor digestion methods. Parameter

Abbrev.

Unit

Organic matter digestibility

OMD

%

Potential degradable CP Indigestible CP Degradation rate of CP Potential degradable NDF Indigestible NDF Degradation rate of NDF in concentrates Soluble ST Potential degradable ST Indigestible ST Degradation rate of ST

Type of method

Method description See Section 5.2.1

pdCP iCP kdCP pdNDF iNDF kdNDF

In vivo and in vitro methods g/kg CP In sacco method g/kg CP Mobile bag technique %/h In sacco method g/kg NDF In sacco method g/kg NDF In sacco method %/h In sacco method

sST pdST iST kdST

g/kg ST g/kg ST g/kg ST %/h

In sacco method In sacco method Mobile bag technique In sacco method

See Section 5.2.3 See Section 5.2.3 See Section 5.2.4 See Section 5.2.3

See Section 5.2.2 See Section 5.2.4 See Section 5.2.2 See Section 5.2.3 See Section 5.2.3 See Section 5.2.2

5.1 Feed analyses 5.1.1 Dry matter in roughage Dry matter is defined as the proportion of the sample remaining after drying to a constant weight at a defined temperature, and after compensation for the loss of volatile compounds in some feeds (e.g. silage). The DM value can be determined either by a single- or two-step method. The singlestep method is used when only DM is required, while the two-step method is recommended when the sample is used for further chemical analyses. The drying temperature of 60 °C is chosen to be consistent with the NDF method, described by Mertens (ISO 16472:2006 IDT), and is also used for sample preparation before other chemical, in vitro or in sacco analyses. Note that in concentrates

42 

NorFor – The Nordic feed evaluation system

987 988 989 990 991 992 993 994 995 996 997

step method is used when only DM is required, while the two-step method is recommended when the sample is used for further chemical analyses. The drying temperature of 60ºC is chosen to be consistent with the NDF method, described by Mertens (ISO 16472:2006 IDT), and is also used for sample preparation before other chemical, in vitro or in sacco analyses. Note that in concentrates DM is determined at a temperature of 103ºC, as described in European Commission DM is determined at a temperature of 103 °C, as described in European Commission Regulation Regulation EC No. 152/2009.

998

DM DM uncorrSing lestep = uncorrSinglestep

999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1010 1010 1009 1011 1011 1012 1012 1013 1013 1014 1014 1015 1015 1016 1016 1017 1017 1018 1018 1019 1019 1020 1020 1021 1021 1022 1022 1023 1023 1024 1024 1025 1025 1026 1026 1027 1027 1028 1028 1029 1029 1030 1030 1031 1031 1032 1032 1033 1033 1034 1034 1035 1035 1036 1036 1037 1037 1038 1038 1039 1039 1040 1040 1041 1041 1042 1042

EC No. 152/2009.

In the single-step procedure, roughage samples should be dried to constant weight at 60ºC and thereafter weighed hotprocedure, or kept in a roughage desiccator samples until weighing. DM is calculated In the single-step shouldThe be uncorrected dried to constant weight at 60 °C and as:thereafter weighed hot or kept in a desiccator until weighing. The uncorrected DM is calculated as:

where DM

Dry _ weight ⋅ 1000 Fresh _ weight

[Eq. 5.1]



5.1

is the uncorrected dry matter before compensation for volatiles, g/kg;

uncorrSinglestep is the uncorrected dry matter before compensation for volatiles, g/kg; where DMuncorrSinglestep Dry_weight is the sample weight after drying, g; and Fresh_weight is the sample weight before Dry_weight is the sample weight after drying, g; and Fresh_weight is the sample weight before drying, drying, g. g.

Volatiles during drying are added the uncorrected as described in Equation 5.5 to 5.7. Volatiles lostlost during drying are added to thetouncorrected DM asDM described in Equation 5.5 to 5.7. In In thethe two-step procedure, the dried sample after the first step (DM1) should should be two-step procedure, the dried sample after thedrying first drying step (DM1) be equilibrated equilibrated in room temperature for a minimum four and hoursweighed, and weighed, so DM1 cancalculated be in room temperature for a minimum of fourofhours so DM1 can be as: calculated as:Equilibrated _ weight [Eq. 5.2] Equilibrat ed _ weight DM1Twostep = ⋅1000 Equilibrated_ [Eq. DM11Twostep == Fresh _ weight weight⋅1000 [Eq. 5.2] 5.2] 5.2 ⋅1000  DM Twostep Fresh_ weight Fresh_ weight

DM1 obtained from thefrom two-step procedure,procedure, g/kg; Equilibrated_weight is where DM1 Twostep is theis where DM1 DM1 obtained the two-step g/kg; Equilibrated_weight where DM1 is thethe DM1 obtained from the two-step procedure, g/kg; Equilibrated_weight is Twostep Twostep theisdry samplesample weight after 4 hafter equilibration in air, g; and Fresh_weight is the sampleisweight weight 4 h equilibration air, g; and Fresh_weight sample weight 45air, thethe drydry sample weight after 4 h equilibration in g;inand Fresh_weight is the samplethe weight before drying, g. beforedrying, drying,g.g. before After DM1 determination the sample can be ground. The second DM (DM2) determination in the After DM1 thethe sample cancan be ground. The The second DM (DM2) determination in the in the Aftersample DM1determination determination sample (DM2) determination ground should also be performed at 60ºCbeforground. 16 h when itsecond is used DM to prepare samples for ground sample should also be be performed at 60ºC for°C 16for h when itwhen is used to used prepare samples for ground sample should also performed at 60 16 h it is to prepare chemical analyses. The sample should then be weighed while still warm or kept in a desiccator samples for chemical analyses. The sample should then be weighed while still warm or kept in a desiccator chemical analyses. The sample should then be weighed while still warm or kept in a desiccator until until weighing (DM2), then until weighing (DM2), then weighing (DM2), then: Dry _ weight Dry _ weight ⋅1000 DM2Twostep = DM2Twostepweight = _ before_ drying ⋅1000 weight_ before_ drying

[Eq. 5.3] [Eq. 5.3]



5.3

where DM2 theobtained DM2 obtained the procedure, two-step procedure, g/kg; isDry_weight is the is the is DM2 from the from two-step g/kg; Dry_weight the where DM2 Twostep Twostep where DM2 Twostep is the DM2 obtained from the two-step procedure, g/kg; Dry_weight is the sample weight afterafter the second drying, g; and g; weight_before_drying is the sample weight before sample weight the second drying, and weight_before_drying is the sample weight before sample weight after the second drying, g; and weight_before_drying is the sample weight before thethe second drying, g. g. second drying, the second drying, g. Uncorrected DMDM in the two-step procedure is calculated as: Uncorrected thetwo-step two-step procedure is calculated Uncorrected DM ininthe procedure is calculated as: as: DM1 ⋅ DM 2 DM1 ⋅ DM2 DM DM uncorrTwos tep = uncorrTwostep DM uncorrTwostep = 1000 1000

[Eq. 5.4] [Eq. 5.4]

5.4

where the uncorrected DMcompensation before compensation for g/kg; volatiles, uncorrTwostep where DMDM is the is uncorrected DM before for volatiles, DM1g/kg; is theDM1 is the uncorrTwostep where DM is the uncorrected DM before compensation for volatiles, g/kg; DM1 is the uncorrTwostep DM in first step, Equation 5.2; and DM2 is the DM in the second step, Equation 5.3. DM in first step, Equation 5.2; and DM2 is the DM in the second step, Equations 5.3. DM in first step, Equation 5.2; and DM2 is the DM in the second step, Equations 5.3. Since is intended the water free proportion of the feed,compounds volatile compounds Since DMDM is intended to be to thebewater free proportion of the feed, volatile lost duringlost during Since DM is intended to be the water free proportion of the feed, volatile compounds lost during drying should be added the uncorrected DM. These compounds include lactic acid, VFA, lower drying should be added to thetouncorrected DM. These compounds include lactic acid, VFA, drying should be added to the uncorrected DM. These compounds include lactic acid, VFA, lower alcohols and ammonia.InIndrying dryingatat60 60ºC, lower alcohols are assumed to to be be completely alcohols and ammonia. °C, lower alcohols are assumed completely lost, large lower alcohols and ammonia. In drying at 60ºC, lower alcohols are assumed to be completely lost, large proportions of ammonia and VFA are but lost,only but only a small amount of lactic acid is lost proportions of ammonia and VFA are lost, a small amount of lactic acid is lost (Porter and lost, large proportions of ammonia and VFA are lost, but only a small amount of lactic acid is lost (Porter and Murray, 2001). Theof losses ofand VFA andacid lacticincrease acid increase with decreasing pH. Table Murray, 2001). The losses lactic with decreasing pH. Table shows the (Porter and Murray, 2001). TheVFA losses of VFA and lactic acid increase with decreasing pH.5.3 Table 5.3correction shows the correction for thewhich losses,result whichinresult in the equations factors forfactors the losses, equations below.below. 5.3 shows the correction factors for the losses, whichthe result in the equations below. Final DM for silage with a pH lower than 5 is calculated as (Porter and Murray, 2001): Final a pH lower than 5 is5calculated as (Porter and Murray, 2001):2001): FinalDM DMfor forsilage silagewith with a pH lower than is calculated as (Porter and Murray, ⋅ (0.45( − 0.09 ⋅ pH) + ACF ⋅ (1.5 −( 0.223 ⋅ pH) ⎞ ) ⎛ LAF LAF .45−−00.09 .09⋅ pH ACF .223 ⋅ pH) +) +ACF ⋅ pH ⋅ (⋅00.45 ⋅ (⋅1.15.5−−0.0223 ⋅ pH ⎜ ⎛⎛LAF ⎟) ⎞ ⎞

1043 ⎜⎜ ⋅ (1.4 − 0.182 ⋅ pH) + BUF ⋅ (1.4 − 0.182 ⋅ pH)⎟ ⎟ ⎟ PRF corr = DM uncorr + ⎜ + 1043 DMDM )⎟)⎟  = DM ⎜⎜++PRF DMcorr PRF⋅ (⋅1(1.4.4−−00.182 .182⋅ pH ⋅ pH) +) +BUF BUF⋅ (⋅1(.14.9−−0.0182 .272⋅ pH ⋅⎟pH ⎜ +++ALF corr = DMuncorr uncorr + NH N ⋅ 0.6 3 ⎝ ⎜⎜+ ALF + NH ⎝⎜ + ALF + NH3 NN⋅ 0⋅ 0.6.6 3 ⎝

⎠ ⎟⎟ ⎠⎟ ⎠

1044 1044 1045 Final DM for silage with a pH higher than 5 is calculated as: 1045 Final DM–for withfeed a pHevaluation higher than 5system is calculated as: NorFor Thesilage Nordic 1046 1046 ⎛ ACF ⋅ (1.5 − 0.223 ⋅ pH ) + PRF ⋅ (1.4 − 0.182 ⋅ pH )⎞ 1047 ⎟ )⎞ DM corr = DM uncorr + ⎜⎜ ⎛ ACF ⋅ (1.5 − 0.223 ⋅ pH ) + PRF ⋅ (1.4 − 0.182 ⋅ pH − 03.182 ACF ⋅⋅ (11..54 −− 00..223 1.4NH 1047 DM corr = DM uncorr⎝ +⎛⎜⎜BUF 182⋅⋅pH pH))++PRF ALF⋅ (+ N ⋅ 0⋅.pH 6 )⎟⎠⎞ ⎟⎟

[Eq. 5.5] [Eq. 5.5]5.5] [Eq.

5.5 43

[Eq. 5.6] [Eq. 5.6]

Table 5.3. Correction of DM for loss of volatiles, g/kg. Volatile compound

pH

Factor for equations

Lactic acid Acetic acid Propionic acid Butyric acid Lower alcohols Ammonia nitrogen

Only if pH<5 For all pH For all pH For all pH For all pH For all pH

0.45-0.09·pH 1.5-0.223·pH 1.4-0.182·pH 1.9-0.272·pH 1 0.6

Final DM for silage with a pH higher than 5 is calculated as: 1049 where DMcorr is the .5 − 0.223 ⋅and pH ) final 4 − 0.182g/kg; ⋅ (1corrected + PRFdry ⋅ (1.matter, ⋅ pH )DM ⎛ ACF ⎞ uncorr corr uncorr, Equations 5.1 and 5.4, g/kg; ⎜ is thelactic ⎟  uncorr DM DM uncorr 5.6 1049 finalg/kg dry matter, g/kg;DM; DM 5.1 and 5.4, g/kg; 1050 LAF isDM the+corr amount acid and in feed, uncorrected ACF, Equations is the amount of acetic acid in corr = where ⎜ + BUF ⋅corrected ⎟ (1.9 − acid ) + ALF +uncorrected 0.272in⋅ pH NH 3 N ⋅ 0.6 DM; ⎝ ⎠ acid 1050 theuncorrected amount lactic feed, ACFinisfeed, the amount of acetic acid 1051 LAF feed, is g/kg DM; PRF is theg/kg amount of propionic g/kg uncorrected DM;in 1051 feed, g/kg DM; PRF isinthe amount of propionic aciduncorr in feed, g/kgamount uncorrected DM; whereis DM and final dry matter, g/kg; DM , Equations 5.1 of and 5.4, g/kg; ACF 1052 BUF theuncorrected amount ofcorrected butyric acid feed, g/kg uncorrected DM; ALF is the lower corr is the 1052 alcohols BUF the ofg/kg butyric acid in feed, g/kg uncorrected DM; ALF theamount amount ofpropionic lower 33N is ammonia in amount the feed, uncorrected DM NH 1053 nitrogen, g/kg uncorrected is theisamount of acetic acid in feed, g/kgand uncorrected DM; PRF is isthe of acid in 1053 N is ammonia alcohols in uncorrected the feed, g/kgDM; uncorrected and NH3of 1054 DM. feed, g/kg BUF isDM the amount butyric acidnitrogen, in feed, g/kg g/kguncorrected uncorrected DM; ALF 1054 1055 DM. is the amount of lower alcohols in the feed, g/kg uncorrected DM and NH3N is ammonia nitrogen, 1055 1056 Table 5.3 g/kg uncorrected DM. 1056 1057 Table 5.3 1057 1058 We have also developed a simple equation, which corrects for losses of volatiles when not We have have also alsoindeveloped developed simple equation, which corrects for losses volatiles when 1058 aasimple equation, corrects for losses of of volatiles when notanalyzed for in 1059 We analyzed for silage samples. The equationwhich was developed from Norwegian grass silage silage samples. The equation was developed from Norwegian grass silage samples that were analyzed 1059 for were in silage samples. equation products was developed from Norwegian silage for The 1060 analyzed samples that analyzed fermentation and estimated losses of grass volatiles. When for fermentation products and estimated losses of corrected volatiles. When DM<700 g/kg, uncorrected DM 1060 that wereuncorrected analyzed for fermentation products and estimated losses of volatiles. When 1061 samples DM < 700 g/kg, DM of silage should be for loss of volatiles by the of silage be corrected forofloss of should volatiles the equation: 1061 < 700should g/kg, uncorrected DM silage be by corrected for loss of volatiles by the 1062 DM equation: 1062 1063 equation: 1063 5.7 1064 DM corr [Eq. 5.7] corr = 0.99 ⋅ DM uncorr uncorr + 10  1064 DM corr = 0.99 ⋅ DM uncorr + 10 [Eq. 5.7] 1065 1065 1066 where is the corrected and final DM compensated for losses of volatiles, g/kg; and whereDM DMcorr corr corr is the corrected and final DM compensated for losses of volatiles, g/kg; and DMuncorr 1066 where the DM, corrected andEquations final compensated DM uncorr DM 1067 is corr theisuncorrected DM, 5.1 and 5.4. for losses of volatiles, g/kg; and is the uncorrected Equations 5.1DM and 5.4. uncorr DMuncorr is the uncorrected DM, Equations 5.1 and 5.4. 1067 1068 1068 1069 The thethe chemical composition of a of sample on a on corrected DM DM basis The simple simpleprocedure proceduretotorecalculate recalculate chemical composition a sample a corrected 1069 to recalculate the chemical composition 1070 The basissimple for theprocedure two-step procedure is illustrated in Equation 5.8: of a sample on a corrected DM for the two-step procedure is illustrated in Equation 5.8: 1070 1071 basis for the two-step procedure is illustrated in Equation 5.8: 1071 DMuncorr uncorr 1072  [Eq. 5.8] 5.8 X corr corr = X uncorr uncorr ⋅ 1000 ⋅ DM DM 2 ⋅ uncorr DMcorr corr 1072 [Eq. 5.8] X corr = X uncorr ⋅ 1000 ⋅ DM2 ⋅ DMcorr 1073 where Xcorr is the chemically analyzed parameter, i.e. NDF, CP, ST, etc., g/kg corrected DM; Xuncorr 1073 where corr is the chemically analyzed parameter, i.e. NDF, CP, ST etc., g/kg corrected DM; 1074 Xcorr is the X analyzed per kg kg prepared g/kg DM1; DMg/kg is the uncorrected DM, uncorr 1074 the parameter chemically analyzed parameter, i.e. NDF, CP, ST etc., corrected DM; where 1075 X theisanalyzed parameter per preparedsample, sample, g/kg DM1; DM uncorr uncorr uncorr is corr uncorr is the uncorrected Equations 5.1 and 5.4; DM2 is the DM measured in the second step, Equation 5.3; and DM 1075 is the analyzed kg prepared sample, DM1; step, DMuncorr is the 5.3; uncorrected corr is uncorr 1076 X DM, Equations 5.1 andparameter 5.4; DM2per is the DM measured in g/kg the second Equation and the corr DM corrected for volatile loss, Equation 5.5, 5.6 or 5.7. 1076 DM, Equations 5.1 and 5.4; DM2 is the DM measured in the second step, Equation 5.3; and 1077 is the DM corrected for volatile loss, Equation 5.5 to 5.7. DM corr 1077 DMcorr is the DM corrected for volatile loss, Equation 5.5 to 5.7. Crudeprotein protein 1078 5.1.2 Crude 1078 protein in addition to nitrogen content analyzed by Kjeldahl or Dumas × 6.25 as 1079 5.1.2 For CPCrude determination, 1079 ininaddition nitrogen content analyzed bytaken Kjeldahl Dumas 6.25 as as shown For CP CPindetermination, determination, addition nitrogen content analyzed by Kjeldahl or Dumas·6.25 1080 For shown table 5.1, ammonia lossestoto during drying should also be into or account. If×ammonia 1080 in table 5.1, ammonia losses drying should also be taken into account. IfIfammonia 1081 shown is analysed in fresh samples, it isduring assumed that 60% of thebe ammonia is emitted during drying is not innot Table 5.1, ammonia losses during drying should also taken account. ammonia 1081 notsection analysed in fresh samples, is assumedthat that60% 60% of of the the ammonia during drying 1082 is (see analysed in5.1.1): fresh samples, it isit assumed ammoniaisisemitted emitted during drying (see 1082 section 5.1.1): 1083 (see Section 5.1.1): 1083 ⎛ N ⎞ DM uncorr 1084 [Eq. 5.9] CP = ⎜⎛ N + NH 33 N ⋅ 0.6 ⎟⎞ ⋅ DM uncorr ⋅ 6.25 corr ⋅ 6.25  1084 [Eq. 5.9] 5.9 CP = ⎝⎜ DM 2 + NH 3 N ⋅ 0.6 ⎠⎟ ⋅ DMuncorr corr ⎝ DM 2

1085 1085 1086 1086

⎠

DM corr

where CP is the crude protein, g/kg corrected DM; N is the amount of nitrogen analysed, g/kg where CP 33isNthe crude protein,analysed g/kg corrected DM;sample, N is theg/kg amount of nitrogen analysed, g/kg DM1; NH is the ammonia in the fresh uncorrected DM. DM1; NH3N is the ammonia analysed in the fresh sample, g/kg uncorrected DM.

44 

NorFor – The Nordic feed evaluation system

where CP is the crude protein, g/kg corrected DM; N is the amount of nitrogen analysed, g/kg DM1; NH3N is the ammonia analysed in the fresh sample, g/kg uncorrected DM; DM2 is the DM measured in the second step, Equation 5.3; DMuncorr is the uncorrected DM, Equation 5.1 or 5.4; DMcorr is the DM corrected for volatiles, Equations 5.5, 5.6 or 5.7. 5.1.3 Soluble crude protein 1087 1087 1088 1087 1088 1089 1089 1088 1090 1090 1089 1091 1091 1090 1092 1092 1091 1093 1093 1092 1094 1094 1093 1094 1095 1095 1095 1096 1096 1097 1096 1097 1098 1098 1097 1099 1099 1098 1100 1100 1099 1101 1101 1100 1102 1102 1101 1103 1103 1102 1104 1104 1103 1105 1105 1104 1106 1106 1105 1107 1107 1106 1108 1108 1107 1109 1109 1108 1110 1110 1109 1111 1111 1110 1112 1112 1111 1113 1113 1112 1114 1114 1113 1115 1115 1114 1116 1116 1115 1117 1117 1116 1118 1118 1117 1119 1119 1118 1120 1120 1119 1121 1121 1120 1122 1122 1121 1123 1123 1122 1124 1124 1123 1125 1125 1124 1126 1126 1125 1127 1126 1127 1128 1128 1127 1129 1129 1128 1130 1130 1129 1130

5.1.3 Soluble protein The procedure to determine 5.1.3 Soluble crude crude protein sCP in all types of animal feeds is described in Table 5.4. A dried

The to sCP of is in 5.4. dried and procedure milled sample isprotein extracted borate-phosphate (pH 6.75) at 39 1 and hour. After 5.1.3 Soluble crude The procedure to determine determine sCP in ininall allatypes types of animal animal feeds feedsbuffer is described described in table table 5.4.°CA A for dried and milled sample is extracted in a borate-phosphate buffer (pH 6.75) at 39ºC for 1 hour. After centrifugation, sCP ininsCP the supernatant determined Kjeldahl or some other milled sample is extracted a borate-phosphate bufferfeeds (pH 6.75) atthe 39ºC 1 hour. After The procedure tothe determine in all types ofisanimal isusing described infortable 5.4. A dried andsuitable centrifugation, thenitrogen sCP supernatant is determined using the or some other suitable centrifugation, sCP in in the the isFor determined using the Kjeldahl or 1NH some methodsample for total determination. silage theKjeldahl of should be corrected milled isthe extracted in asupernatant borate-phosphate buffersamples (pH 6.75) atcontent 39ºC for hour. Aftersuitable 3Nother N should be method for total nitrogen determination. For silage samples the content of NH 3 method for during totalthe nitrogen For silageof samples thethe content of NH for losses drying, andsupernatant in the calculation sCP. 3N should centrifugation, sCP in determination. the is determined using Kjeldahl or some otherbe suitable corrected for losses during drying, the calculation of corrected losses duringdetermination. drying, and and in inFor the silage calculation of sCP. sCP. method forfortotal nitrogen samples the content of NH3N should be corrected for losses during drying, and in the calculation of sCP. 5.2 Digestion methods used to predict digestion of nutrients Table 5.4 5.4 Table Table 5.2.1 5.4 Organic matter digestibility 5.2 5.2 Digestion Digestion methods methods used used to to predict predict digestion digestion of of nutrients nutrients 5.2 Digestion methods used to predict digestion of nutrients 5.2.1 Organic matter digestibility The reference method for OMD is based on sheep fed at maintenance (EAAP, 1969). In roughage

5.2.1 Organic matter digestibility

OMD can also be determined from different in vitro analysis, which has been calibrated against the The reference method for OMD 5.2.1 Organic matter digestibility The reference method for OMD is is based based on on sheep sheep fed fed at at maintenance maintenance (EAAP, (EAAP, 1969). 1969). In In roughage roughage OMD can also be determined from different in vitro analysis, which has been calibrated against in vivo method. In NorFor three in vitro methods are available, i.e. VOS (rumen digestible OMD can alsomethod be determined from different in vitro which has been calibrated against organic The reference for OMD is based on sheep fedanalysis, at maintenance (EAAP, 1969). In roughage the in vivo method. In NorFor three in vitro methods are available, i.e. VOS (rumen digestible matter), IVOS (in vitro organic matter digestibility) and EFOS (enzyme digestible organic the in vivo method. In NorFor from threedifferent in vitro methods are available, VOS digestible OMD can also be determined in vitro analysis, whichi.e. has been(rumen calibrated against matter). organic matter), IVOS (in vitro organic matter digestibility) and EFOS (enzyme digestible These methods are briefly described below. organic matter), IVOS (in vitro organic matter digestibility) and EFOS (enzyme digestible the in vivo method. In NorFor three in vitro methods are available, i.e. VOS (rumen digestible organic matter). These methods are described below. matter). IVOS These (in methods are briefly briefly described below. and EFOS (enzyme digestible organic matter), vitro organic matter digestibility) The VOS method was developed in Sweden and described organic matter). These methods are briefly described below. by Lindgren 1979, 1983, 1988. A 0.5 g The VOS method was in Sweden and described by Lindgren 1979, 1983, 1988. A The method was developed developed byformed Lindgren 1983, A 0.5 0.5and 1 ml driedVOS sample is incubated at 38in°CSweden for 96and h indescribed a solution by1979, mixing 491988. ml buffer gg dried sample is incubated at 38ºC for 96 h in aa solution formed by mixing 49 ml buffer and 11 dried sample is incubated at 38ºC for 96 h in solution formed by mixing 49 ml buffer and The VOS method was developed inare Sweden and described by Lindgrenthe 1979, 1983, 1988. A 0.5 rumen fluid. Incubation residues then combusted to determine VOS digestibility coefficient ml rumen fluid. Incubation residues are then combusted to determine the VOS digestibility rumen fluid. residues are96 then combusted toformed determine the VOS digestibility gml sample isIncubation incubated atis38ºC for h in a the solution by 49with ml buffer and 150% grass ofdried OM. The OMD in vivo calculated from VOS value. Formixing forage more than coefficient of OM. The OMD in vivo is calculated from the VOS value. For forage with more coefficient of OM. OMD in vivoare isand calculated fromthan the VOS value. forage with ml fluid. Incubation residues thenhence combusted to determine theFor VOS digestibility or arumen whole crop ofThe maize or cereals, less 50% leguminous plants onmore aplants DM basis, the than 50% or whole crop of maize or and hence less than 50% leguminous than 50% grass grass or aaThe whole crop of maize or cereals, cereals,from and the hence less than For 50%forage leguminous plants coefficient of OM. OMD in vivo is calculated VOS value. with more OMD is basis, calculated as follows (Lindgren, 1983): on a DM the OMD is calculated as follows (Lindgren, 1983): on calculated as or follows (Lindgren, 1983): thana DM 50%basis, grass the or aOMD wholeiscrop of maize cereals, and hence less than 50% leguminous plants on a DM basis, the OMD is calculated as follows (Lindgren, 1983): OMD = − 2 . 0 + 0 . 90 ⋅ VOS [Eq. 5.12] 5.12]  5.12 OMD = −2.0 + 0.90 ⋅ VOS [Eq. OMD = −2.0 + 0.90 ⋅ VOS [Eq. 5.12] For forage samples containing more than 50%50% leguminous plants (Lindgren, 1983):1983): Forforage foragesamples samplescontaining containing more than leguminous plants (Lindgren, For more than 50% leguminous plants (Lindgren, 1983): For forage samples containing more than 50% leguminous plants (Lindgren, 1983): OMD [Eq. OMD = = 23 23..00 + + 00..62 62 ⋅⋅ VOS VOS  [Eq. 5.13] 5.13] 5.13 OMD = 23.0 + 0.62 ⋅ VOS [Eq. 5.13] where OMD is the calculated in digestibility of matter, % and VOS is where is is thethe calculated in vivo vivo digestibility of organic organic matter, matter, % of of OM; OM; VOS and is the theVOS is the whereOMD OMD calculated in vivo digestibility of organic % and of OM; digestibility of organic matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. where OMD is the calculated in vivo digestibility of organic matter, % of OM; and VOS is the digestibility of organic matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. The method on the method by & The IVOS method is is based method presented by Tilley & Terry Terry (1963). (1963). Samples Samples are are The IVOS IVOSfor method isbased basedon onthe thefollowed methodpresented presented byTilley Tilley and Terry (1963). Samples are incubated incubated 48 h in rumen fluid, by 48 h digestion by pepsin and HCl), the major incubated for 48 h in rumenon fluid, followed by 48 h digestion by pepsin and HCl), the only only major The IVOS method is based the method presented by Tilley & Terry (1963). Samples are for 48 h in rumen fluid, followed by 48 h digestion by pepsin and HCl, the only major modification modification being that residues are combusted to determine OM digestibility. In vivo OMD can modification residues combusted to hdetermine digestibility. In vivo OMDmajor can incubated for being 48 h inthat rumen fluid,are followed by 48 digestionOM by pepsin and HCl), the only be calculated from IVOS using the equations of Møller et al., 1989. being that residues are combusted to determine OM digestibility. In vivo OMD can be calculated be calculated being from IVOS using the of to Møller et al.,OM 1989. modification that residues areequations combusted determine digestibility. In vivo OMD can from IVOS using the equations of Møller et al. (1989). be calculated from IVOS using the equations of Møller et al., 1989. For For grass, grass, clover clover grass, grass, legumes legumes and and silages silages of of grass, grass, clover clover grass, grass, legumes legumes and and small small grain grain whole whole crops OMD is calculated from: For grass, clover grass, legumes and silages of grass, clover grass, legumes and smallwhole grain whole crops OMD is calculated from: For grass, clover grass, legumes and silages of grass, clover grass, legumes and small grain cropsOMD OMDisiscalculated calculated from: crops from: [Eq. OMD [Eq. 5.14] 5.14] OMD = = 44..10 10 + + 00..959 959⋅⋅ IVOS IVOS [Eq. 5.14] OMD = 4.10 + 0.959⋅ IVOS  5.14 For For maize maize whole whole crop crop silage silage the the equation equation used used to to calculate calculate OMD OMD is is (Søegaard (Søegaard et et al., al., 2001): 2001): For maize whole crop silage the equation used to calculate OMD is (Søegaard et al., 2001): 48 48 48

NorFor – The Nordic feed evaluation system

45

Table 5.4. Description of the method to determine the soluble CP (sCP) (modified from Hedqvist and Udén, 2006). Item

Procedure

Sample • The samples are dried as specified for the NorFor samples and ground by a hammer preparation mill to pass a 1 mm sieve. Avoid heating during grinding. See note 1. Reagents • Only use recognized analytical grade reagents. • Water: distilled or deionised water. • Mono-sodium dihydrogen phosphate monohydrate (NaH2PO4·H2O) (CAS-No 10049-21-5). • Di-sodium tetraborate decahydrate (Na2B4O7·10 H2O) (CAS-No. 17.48-96-4). • Borate-phosphate buffer (modified from Licitra et al., 1996), pH 6.75±0.05. Dissolve 12.2 g of sodium dihydrogen phosphate and 8.91 g of sodium tetraborate in 900 ml of water. Check the pH with a pH-meter and if necessary adjust the pH. Dilute with water in a 1000 ml volumetric flask. Prepare fresh buffer solution daily. • Sulphuric acid, ρ20 1.84 g/ml. • Catalyst: Kjeltabs CF 5 g (CuSO4·5H2O: approximately 0.10 g Cu per tablet, Thompson and Capper Ltd.) or equivalent. • Titrate for the auto-burette in the Kjeltech apparatus, with for example 0.1 M HCl. Equipment • Analytical balance (capable of weighing to the nearest 1 mg). • Centrifuge test tubes, 50 ml, with lids. • Dispenser or pipette 50±0.5 ml. • Water bath, thermostatted at 39±0.5 °C (or incubating chamber, 39±0.5 °C). • Glass rods. • Centrifuge suitable for the centrifuge tubes and capable of spinning at 3,000 g (given values of g are for the bottom of the test tubes). • Pipette 20±0.2 ml. • Kjeldahl equipment or other equipment for total nitrogen determination in liquids. • Heating block suitable for digesting the samples. • pH-meter, calibrated and capable of measuring pH to the nearest 0.01 pH unit. Procedure • Weigh approximately 1.5 g of the test sample to the nearest 1 mg in a centrifuge tube (see note 2). • Add 50±0.5 ml borate-phosphate buffer, pre-heated to 39 °C, to the samples (see note 3). • A blank sample of 50 ml borate-phosphate buffer should be included in each series of samples. • To hydrate the sample, mix it gently (e.g. with a glass rod) then place the lid on the tube and shake the sample thoroughly. • Incubate in a water bath or an incubating chamber at 39±0.5 °C for 1 h±5 minutes, and shake the tubes manually every 15 minutes. • Centrifuge the tubes at 3,000·g for 10 min (see note 4). • Pipette 20±0.2 ml of the supernatant and transfer to Kjeldahl tubes. • Add salt/catalyst and the appropriate volume of sulphuric acid to the tubes according to the standard procedure in the lab. Some feed samples foam extensively when the acid is added. Foaming during digestion in the Kjeldahl analysis can be reduced if the acidified samples are allowed to stand at room temperature for 1-2 hours or overnight. • Increase the temperature of the digestor stepwise, to prevent foaming of the samples. Do not include the time it takes to reach working temperature in the total digestion time. • Analyse the nitrogen content by Kjeldahl distillation. • Calculate the content of soluble crude protein. 46 

NorFor – The Nordic feed evaluation system

3. 3. 4. 4.

Analytical steps involving sulphuric acid addition should be performed Analytical involving sulphuric acid addition should be performed in sequencesteps without any interruption. in sequence without any interruption. Some insoluble particles (containing trapped air), particularly from Some (containing trapped air), particularly forageinsoluble may floatparticles on the surface after centrifugation, but if the from forage may float on the pipetted surface after centrifugation, the cause supernatant is carefully the insoluble matterbut willifnot supernatant is carefully pipetted notorcause contamination. The particles maythe beinsoluble removed matter with a will spoon a paper Table 5.4. Continued. contamination. Thecause particles may be withcan a spoon or a paper tissue. If they still problems theremoved supernatant be carefully tissue. they still cause problems the supernatant be carefully pouredIfinto a beaker through a tea-strainer and thencan pipetted into acontaining beaker through a tea-strainer and pipettedit is Item Procedure 5. poured For samples measurable amounts of then ammonium 5. For samples ammonium it is drying. necessary to containing correct the measurable sCP for lossamounts of sCP asofammonia during correct the sCP forallloss of sCPinaswhich ammonia during drying. This lossofistosCP currently set to for 60%. Calculations The necessary content per kg CP, samples the ammonium nitrogen content This lossof currently 60%. Calculations The content per kgset CP, all samples in which the ammonium is zero, can beissCP calculated as:tofor Calculations nitrogen The content of sCP per kg CP, all samples content is zero, can befor calculated as: in which the ammonium (V1 content − V0 ) ⋅ c ⋅ 14 ⋅ 6.can 25 ⋅ Vbe nitrogen is.007 zero, calculated as: 2 sCP = (V − V ) ⋅ c ⋅ 14.007 ⋅ 6.25 ⋅ V2 ⋅⋅ 1000 sCP = 1 0 m ⋅ CP ⋅ V3 1000 m ⋅ CP ⋅ V3



5.10

[Eq. 5.10] [Eq. 5.10] For corrected forfor a 60% lossloss of ammonium For silage silagesamples, samples,which whichhave havetotobebe corrected a 60% of ammonium nitrogen For silageduring samples, which corrected for a 60% loss of ammonium nitrogen drying, thehave equation during drying, the equation is:to beis: nitrogen equation is:⋅ NH3 N ⋅1000 ⋅ 6.25 (V1during − V0 ) ⋅ cdrying, ⋅ 14.007the ⋅ 6.25 ⋅ V2 0.60

(V1 − V0 ) ⋅ c m ⋅14⋅ V .007 ⋅ 6.25 ⋅ V2 + 0.60 ⋅ NH3DM N ⋅1000 ⋅ 6.25 1 3 +

⋅1000 m ⋅ V3 0.60 ⋅ NH N ⋅1000 ⋅ 6DM .25 1 ⋅1000  CPuncorr + 0.60 ⋅ NH3 N ⋅1000 ⋅ 6.25 3DM1 [Eq. 5.11] CPuncorr + DM1 g/kg CP; V0 = volume of HCl [Eq.used 5.11] where sCP is the soluble crude protein sCP = sCP =

5.11

= volume of titration HCl usedof where sCP soluble crude protein CP;CP; V0HCl volume of used for for titration a blank sample, ml; V1 =g/kg where sCPisof isthe the soluble crude protein g/kg V of HCl used for titration 0=volume used for titration of for titration of a blank sample, ml; V1 = volume of HCl of a blank sample, ml; V1=volume of HCl used for titration of sample, ml; V2=volume 58 of added buffer, ml; V3 is the volume of pipetted extract, ml; c is the concentration 58

of titrant (mol/l); m is the sample size, g; CPuncorr is the CP in pre-dried sample, g/kg DM1; 14.007 is the molar weight of nitrogen, g/mol; 6.25=Factor for converting nitrogen content to crude protein; DM1is the DM in first step, Equation 5.2; and NH3N is ammonia nitrogen g/kg fresh sample (see note 5).

1 The particle length of the ground material should be verified regularly according to EU regulations

for animal feed analysis (EC No. 152/2009). All the material should be able to pass through a sieve with a quadratic square mesh of 1·1 mm. Heating of samples during grinding should be avoided. 2 1.5 g sample and 50 ml buffer are recommended. If using the common 50 ml centrifuge tubes from Falcon, NUNC, Greiner etc. the tubes will be very full when using 50 ml of buffer and the shaking might cause problems. In these circumstances we recommend using 1.2 g sample and 40 ml of buffer. Depending on the facilities in the laboratory other multiples with this sample:buffer ratio could be used, e.g. 3 g of sample and 100 ml buffer. 3 Analytical steps involving sulphuric acid addition should be performed in sequence without any interruption. 4 Some insoluble particles (containing trapped air), particularly from forage may float on the surface after centrifugation, but if the supernatant is carefully pipetted the insoluble matter will not cause contamination. The particles may be removed with a spoon or a paper tissue. If they still cause problems the supernatant can be carefully poured into a beaker through a tea-strainer and then pipetted. 5 For samples containing measurable amounts of ammonium it is necessary to correct the sCP for loss of sCP as ammonia during drying. This loss is currently set to 60%. For maize whole crop silage the equation used to calculate OMD is (Søegaard et al., 2001): 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144

OMD = 6.73 + 0.950⋅ IVOS



[Eq. 5.15]

5.15

where calculated in vivo digestibility of organic matter,matter, % of OM; andOM; IVOS is the whereOMD OMDisisthethe calculated in vivo digestibility of organic % of and IVOS is the digestibility in in vitro, % of digestibilityofoforganic organicmatter matter vitro, % OM. of OM. The digestibility of fresh whole crop cereals (barley, wheat, maize), straw and concentrates can The digestibility of fresh whole crop cereals (barley, wheat, maize), straw and concentrates can be be determined using the EFOS method (Weisbjerg and Hvelplund, 1993). The EFOS method determined using the EFOS method (Weisbjerg and Hvelplund, 1993). The EFOS method begins begins with a 24 h pepsin-HCl treatment of the sample, after which the sampled is heated to 80ºC for 45 min, treated for 24 h with an enzyme mixture at 40ºC, and then incubated for a further 19 h at 60ºC. For fresh whole crops of wheat, barley and maize the equation used to calculate OMD in vivo is NorFor – et The 47 (Søegaard al.,Nordic 2001): feed evaluation system OMD = 20.4 + 0.727 ⋅ EFOS

[Eq. 5.16]

1132 1132 1132 1133 1133 1133 1134 1134 1134 1135 1135 1135 1136 1136 1136 1137 1137 1137 1138 1138 1138 1139 1139 1139 1140 1140 1140 1141 1141 1141 1142 1142 1142 1143 1143 1143 1144 1144 1144 1145 1145 1145 1146 1146 1146 1147 1147 1147 1148 1148 1148 1149 1149 1149 1150 1150 1150 1151 1151 1151 1152 1152 1152 1153 1153 1153 1154 1154 1154 1155 1155 1155 1156 1156 1156 1157 1157 1157 1158 1158 1158 1159 1159 1159 1160 1160 1160 1161 1161 1161 1162 1162 1162 1163 1163 1163 1164 1164 1164 1165 1165 1165 1166 1166 1166 1167 1167 1167 1168 1168 1168 1169 1169 1169 1170 1170 1170 1171 1171 1171 1172 1172 1172 1173 1173 1173

where OMD is the calculated in vivo digestibility of organic matter, % of OM; and IVOS is the where OMD is the in vivo digestibility of where OMDof is organic the calculated calculated in vivo digestibility of organic organic matter, matter, % % of of OM; OM; and and IVOS IVOS is is the the digestibility matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. The digestibility of fresh whole crop cereals (barley, wheat, maize), straw and concentrates can The digestibility of whole crop (barley, wheat, maize), straw and can The digestibility of fresh fresh wholemethod crop cereals cereals (barley, wheat, maize),1993). straw The and concentrates concentrates can be determined using the EFOS (Weisbjerg and Hvelplund, EFOS method be determined using the EFOS method (Weisbjerg and Hvelplund, 1993). The EFOS method be determined using the EFOS method (Weisbjerg and Hvelplund, 1993). The EFOS method with a 24 h pepsin-HCl treatment of the sample, after which the sampled is heated to 80 °C for 45 begins with aa 24 h pepsin-HCl treatment of the sample, after which the sampled is heated to 80ºC begins with 24 h treatment of sample, after which the sampled is heated to 80ºC begins with treated a for 24 24 h pepsin-HCl pepsin-HCl treatment of the the sample, after which theincubated sampled is heated to19 80ºC min, treated h with an enzyme mixture at 40 °C, and then incubated for a further h at for 45 min, for 24 h with an enzyme mixture at 40ºC, and then for a further 19 h 60 °C. for 45 for 45 min, min, treated treated for for 24 24 h h with with an an enzyme enzyme mixture mixture at at 40ºC, 40ºC, and and then then incubated incubated for for aa further further 19 19 hh at 60ºC. at 60ºC. at 60ºC. For fresh of of wheat, barley and and maize the equation used to calculate OMD in vivo is For freshwhole wholecrops crops wheat, barley maize the equation used to calculate OMD in vivo is For fresh crops For fresh whole whole crops of of wheat, wheat, barley barley and and maize maize the the equation equation used used to to calculate calculate OMD OMD in in vivo vivo is is (Søegaard (Søegaardet etal., al.,2001): 2001): (Søegaard et al., 2001): (Søegaard et al., 2001): OMD = .4 + .727 ⋅⋅ EFOS OMD = 20 20 +0 0 OMD = 20..4 4+ 0..727 727 ⋅ EFOS EFOS



[Eq. 5.16] [Eq. [Eq. 5.16] 5.16]

5.16

[Eq. [Eq. 5.17] 5.17] [Eq. 5.17]

5.17

For equation should be used (Hvelplund et al., 1999): For straw the following equation should be (Hvelplund et Forstraw strawthe thefollowing following equation should be used (Hvelplund al., 1999): For straw the following equation should be used used (Hvelplund et al., al.,et1999): 1999): OMD 22 0.752 OMD = = 22...000 + + 752 ⋅⋅⋅ EFOS EFOS OMD = 22 + 00..752 EFOS



For concentrate Weisbjerg and Hvelplund (1993) should be For concentrate mixtures, mixtures, the the equation equation presented presented by by Weisbjerg and Hvelplund (1993) should be For byby Weisbjerg andand Hvelplund (1993) should be be used: For concentrate concentratemixtures, mixtures,the theequation equationpresented presented Weisbjerg Hvelplund (1993) should used: used: used: OMD = 55..38 + 00..867 ⋅ EFOS OMD OMD = = 5.38 38 + + 0.867 867 ⋅⋅ EFOS EFOS



[Eq. 5.18] [Eq. [Eq. 5.18] 5.18]

5.18

where calculated in vivo digestibility of organic matter,matter, % of OM; andOM; EFOS the whereOMD OMDis isthe the calculated in vivo digestibility of organic % of andis EFOS is the where OMD is the in vivo digestibility of where OMDof is organic the calculated calculated in vivo digestibility of organic organic matter, matter, % % of of OM; OM; and and EFOS EFOS is is the the digestibility matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM. digestibility of organic matter in vitro, % of OM.

5.2.2 In sacco rumen degradation of crude protein, NDF and starch 5.2.2 of crude protein, NDF and starch 5.2.2 In In sacco sacco rumen rumendegradation degradation crude protein, NDF starch 5.2.2 In sacco rumen degradation ofof crude protein, NDF andand starch

The in sacco technique is used for determining kdCP in roughage and concentrate, kdNDF in The in technique is used kdCP roughage and concentrate, kdNDF in The in sacco saccoand technique isroughage used for for determining determining kdCPIt in in roughage and concentrate, kdNDF in concentrate kdST in and concentrate. is also used to determine pdCP, sST, pdST concentrate and kdST in roughage and concentrate. It is also used to determine pdCP, sST, pdST The in sacco technique is usedand forconcentrate. determiningIt is kdCP in roughage and pdCP, concentrate, kdNDF in concentrate and kdST in roughage also used to determine sST, pdST and iNDF. The in sacco method has several weaknesses, e.g., particle losses, microbial and iNDF. The in sacco method has several weaknesses, e.g., particle losses, microbial concentrate and kdST in roughage and concentrate. It is also used to determine pdCP, sST, pdST and and iNDF. The in sacco method has several weaknesses, e.g., particle losses, microbial contamination of feed residues, different ruminal environment outside vs. inside the bag and precontamination of feed residues, different ruminal environment outside vs. inside the bag and preiNDF. The in sacco method has several weaknesses, e.g. particle losses, microbial contamination contamination of feed residues, different ruminal environment outside vs. inside the bag and pretreatment of feed samples (Nozière and Michalet-Doreau 2000). Therefore, an important task for treatment of feed samples (Nozière and Michalet-Doreau Therefore, an important task treatment offeed feedtable samples (Nozière and Michalet-Doreau 2000). Therefore, an important task for for of feed of feed residues, different ruminal environment outside2000). vs. inside the of bag and pre-treatment the NorFor and analysis group was to standardize critical parts the in sacco the NorFor feed table and analysis group was to standardize critical parts of the in sacco the NorFor feed table and analysis group was to standardize critical parts ofstandard thetask in sacco samples (Nozière and Michalet-Doreau, 2000). Therefore, an important for the NorFor feed procedure to minimize between-laboratory variation. The NorFor in sacco protocol, procedure to minimize between-laboratory variation. The NorFor in sacco standard protocol, procedure to minimize between-laboratory variation. The parts NorFor sacco standard protocol,to minimize table on andthe analysis group was to (1995), standardize critical of in the in sacco procedure based work of Madsen et al. are presented in Table 5.5. based on the work of Madsen et al. (1995), are presented in Table 5.5. based on the work of Madsen et al. (1995), are presented in Table 5.5.

between-laboratory variation. The NorFor in sacco standard protocol, based on the work of Madsen

Table 5.5 et al. (1995), are presented in Table 5.5. Table Table 5.5 5.5

5.2.3 Indigestible NDF 5.2.3 5.2.3 Indigestible Indigestible NDF NDF The method for determining involves samples in sacco for 288 hh in the Table 5.5. Standard in saccoiNDF procedure in incubating NorFor forfeed determining feed degradation characteristics, The method for determining iNDF involves incubating feed samples in sacco for the The method for determining iNDF involves incubating feed samples in sacco for 288 288in h in in the rumen, and essentially follows the method described in section 5.2.2 for determining sacco rumen, and essentially follows the method described in section 5.2.2 for determining in sacco modified from Madsen et al. (1995). rumen, and essentially follows the method described in section 5.2.2 for determining in sacco

NDF degradation. 10-15 μm pores and 100-200 NDF degradation. Feed Feed samples samples of of 2 2g g are are incubated incubated in in bags bags with with 10-15 μm and 22 degradation. Feed samples of 2 g are incubated in bags with 2 NDF 10-15iNDF μm pores pores and 100-200 100-200 2. For cm surface area, equivalent to 10-20 mg sample/cm determination the 2 effective 2. For the cm effective surface area, equivalent to 10-20 mg sample/cm the iNDF determination the Item Procedure cm effective surface area, equivalent to 10-20 mg sample/cm . For Como, the iNDF determination the polyester cloth Saatifil PES 12/6 (Saatitech S.p.A., 22070 Veniano, Italy) with pore size polyester cloth Saatifil PES 12/6 (Saatitech S.p.A., 22070 Veniano, Como, Italy) with pore size polyester cloth Saatifil PES 12/6 (Saatitech S.p.A., 22070 Veniano, Como, Italy) with pore size

Animals and Non-lactating dairy cows of the Nordic dairy cow population. Cows are fed at diet maintenance level and the diet consists of hay, straw and concentrate. The hay and 49 49 The CP content of the diet should be higher than straw to concentrate ratio is 67:33. 49 120 g/kg DM. The concentrate should contain a minimum of three sources of protein. Daily ration is divided into two or more meals of equal size with an adaptation period of 14 days. If animals have been on pasture or fed diets or feeding levels totally different from the standard, the minimum adaptation period is 21 days. Replication Three cows are required for the determination of each feed parameter, except for iNDF determinations, where two cows are needed. The number of bags per animal is not specified. There is no need to replicate the number of days. Sample Preferably the samples should be freeze-dried, but oven drying at 45 °C is also preparation acceptable. For NDF determination, a drying temperature of 60 °C is recommended. The samples should be ground in a mill with a screen size of 1.5 mm. Cutter mill is preferable but a hammer mill is also acceptable. Sample size should be 1.0-2.0 g of dried sample depending on the bag surface area.

48 

NorFor – The Nordic feed evaluation system

Table 5.5. Continued. Item

Procedure

Bags

Bag size refers to internal dimensions when the bag is sealed and mounted on the carrying device to be used. There should be 10 mg sample/cm2 when samples of the required size are placed in the bag. The internal length:internal width ratio should preferably be 1:1.3 (and thus 8.1·6.2 cm for 1.0 g samples and 11.4·8.8 cm for a 2.0 g sample). Bags should have round corners. The pore size should be 38 μm. Recommended bag material is polyester of the model Saatifil PES 38/31 manufactured by Saatitech S.p.A (22070 Veniano, Como, Italy). Any method can be used to seal the filled bags, but the standardized internal bag length must not be altered. Currently, the bags are mounted on a rubber stopper in Denmark and Iceland. In Norway the bags are closed with a rubber band, while in Sweden the open end of the bag is inserted through a slit and strapped. The bags may be re-used for incubations for a maximum of 20 runs. When determining kdCP, the incubation times should be 0, 2, 4, 8, 16, 24 and 48 h, while for roughage and concentrates with low degradation rates the time should be extended up to 96 h. As a rule, concentrates in which less than 80% of total N has disappeared after 48 h should be incubated for 96 h. When determining kdNDF the incubation times should be 2, 4, 8, 16, 24, 48 and 96 h. The 0 h is omitted from the calculation. When determining the starch degradation rate the incubation times should be 0, 2, 4, 8, 16, 24, 48 and 72 h.

Incubation interval

Incubation conditions

The bags should be pre-soaked prior to incubation (including 0 hr bags) for 20 minutes at 39 °C in tap water without agitation. The bags that will be incubated for 2, 4 and 8 h should be inserted simultaneously 15-30 minutes prior to morning feeding. For bags that will be incubated for longer times the insertion time is not specified. The inserted bags should be placed in the ventral rumen. The bag attachment device should allow the bags to be squeezed by rumen contractions. Rinsing Bags removed from the rumen, and 0 h bags, must first be rinsed in cold tap water with no squeezing or manipulation before machine washing. Bags may be machine-washed immediately or freeze-stored after the cold tap water rinsing and thawed before machine washing. Bags incubated for all times, 0 to 96 h, should be washed. It is preferable to use identical washing machines and identical washing programs. Use a washing program without spinning and a water temperature of 25 °C. At present, stomacher treatment is allowed after the machine-washing to reduce microbial contamination of roughage samples; this procedure is used in Denmark. After rinsing the residues are quantitatively removed from the bags and analyzed for chemical constituents. Alternatively, bags including the residues are dried in an oven at 45 °C for 48 h and then weighted after equilibrated in room temperature. Residues are analyzed for chemical constituents. Residue Analyze the remaining samples for nitrogen, NDF or starch. There is no specified analysis quantitative analysis of the residues from each cow or analysis of pooled residues from emptied bags. Calculations Calculations should be conducted by non-linear curve fitting, by applying the least squares method to untransformed values. Degradation profiles should currently be fitted without a lag phase. Calculation The curve fitting function for NDF degradation is: of kdNDF NDF − NDF t  5.19 NDFD t = NDF

[5.19]

where NDFDt is NDF degraded at time t, g/g; NDFt is the remaining amount of NDF at time t; NDF is the amount of NDF in the bags prior to ruminal incubation. If the CPDt =

CP − CPt CP

NorFor – The Nordic feed evaluation system ST− ST

[5.22]

49

analysis

specified quantitative analysis of the residues from each cow or analysis of pooled residues from emptied bags. Calculations Calculations should be conducted by non-linear curve fitting, by applying the least squares method to untransformed values. Degradation profiles should currently be fitted without a lag phase. Calculation The curve fitting function for NDF degradation is: Table 5.5. Continued. NDF t of kdNDF [Eq. 5.19]

Item

Calculation of kdNDF (continued)

NDFD t =

ProcedureNDF

where NDFDt is NDF degraded at time t, g/g; NDFt is the remaining amount of NDF at timeresidue t; NDF is amount of NDF theanalysis, bags priorthen to ruminal incubation atthe time t is too smallinfor the NDF content from the incubation. If the incubation residue at time t is too small for analysis, then the previous time should be used. Fit the equation according to Ørskov and McDonald NDF content from the previous time should be used. Fit the equation according (1979): to Ørskov and McDonald (1979): 96 ⋅t NDFD NDFDcurvefit e-kdNDF NDFDtt = = NDFD ⋅ (1∙ −(1e –− kdNDF ) 96 ∙ t) curvefit

5.20 [Eq. 5.20] , where NDFDt is the degraded NDF fraction at time t; NDFDcurvefit is the where NDFD degraded NDF fraction at time t; NDFD is the the asymptotic asymptotic value pdNDF obtained from the curve fitting and kdNDF 96 is t isofthe curvefit value of pdNDF fromisthe curve fitting kdNDF is the degradation rate of should be ≤1. degradation rate ofobtained NDF. There a restriction that and NDFD 96 96 When h (section that 5.2.3) is available, report NDFD at 288 h as NDF. iNDF Thereatis288 a restriction NDFD should be ≤1. When iNDF at 288 h (Section 96 pdNDF correct kdNDF according to: h as pdNDF and correct kdNDF according to: 5.2.3) isand available, report NDFD at 288 kdNDF =

NDFD curvefit ⋅ kdNDF 96 pdNDF



[Eq. 5.21]

5.21

where kdNDF is the corrected degradation rate for NDF, NDFDcurvefit is the asymptotic

where is the NDF corrected degradation ratecurve for NDF, NDFD is theis the potentially value kdNDF of degraded obtained from the fitting andcurvefit pdNDF asymptotic degradeddetermined NDF obtained from the curve fittingfrom and pdNDF degradablevalue NDFoffraction from iNDF estimated the 288 h ruminal is the potentially degradable NDF fraction determined from iNDF estimated incubation. from the 288 h ruminal incubation. − NDF NDF Calculation The curve fitting fort crude protein degradation is applied to data on degraded NDFD t = fitting for crude protein degradation is applied to data on degraded The curve NDF 0, 2, 4, 8, 16, 24, and 48 h, and if available 96 h. If the [5.19] fractions at times incubation fractions at times 0, 2, 4, 8, 16, 24, and 48 h, and if available 96 h. If the The curve fitting for crude protein degradation isCP applied to data onthe degraded residue at time t is too small for the analysis, content from previous time incubation residue at time t is too61small for the analysis, CP content from the fractions at used: times 0, 2, 4, 8, 16, 24, and 48 h, and if available 96 h. If the should be previous time should be used:

Calculation of kdCP and of kdCP and Calculation pdCP pdCP of kdCP and pdCP

Calculation of kdST, Calculation sST and of kdST, pdST sST and pdST

50 

incubation residue at time t is too small for the analysis, CP content from the CP CPt− CPshould previous be used: t 5.22 CPDtt = time [Eq. 5.22] CPD CP CP [5.22] CPt [Eq. 5.22] CPDt = where CPD is the CP degraded at time t, g/g; CP is the remaining amount of CP at CP is where CPD t t the CP degraded at time t, g/g; CPt is tthe remaining amount of time and CP theisamount of crude protein in the bags prior to to ruminal CP at t;time andisCP the amount of crude protein in the bags prior ruminalincubation. ST −t;tST isdegradation at time t, g/g; CPt is the remaining amount of where CPD t the CP degraded Ruminal CP is fitted to the following equation: STD = incubation. Ruminal CP degradation is fitted to the following equation: t CP at timeST t; and CP is the amount of crude protein in the bags prior to ruminal [5.25] incubation. Ruminal CP degradation is fitted to the following equation: [Eq. 5.23] CPD t = CPD 0 + CPD curvefit ⋅ (1 − e − kdCP ⋅t )  5.23

− kdCP ⋅ t where CPD the CP degraded at time estimate [Eq. 5.23] t is CPD t = CPD ) t, CPD0 is the intercept or an 0 + CPD curvefit ⋅ (1 − e where CPDof theCP CPatdegraded time t, CPD0 is the intercept of solubility time 0 h, at CPD asymptotic valueorofantheestimate of t isthe curvefit is the where CPDof theCP CPatdegraded at CPD time t, CPDis theasymptotic intercept orvalue an estimate t isthe 0 is solubility time 0 h, the the insoluble insoluble but degradable proportion of crude protein obtained from the of curve of solubility of the CP at time 0 h, CPDcurvefit curvefit is the asymptotic value of the but degradable of cruderate protein from the curvearefitting, and kdCP fitting, and kdCPproportion is the degradation of CP.obtained Restriction conditions insoluble but degradable proportion of crude protein obtained from the curve ≤1 of andCP. 0≤(CPD 0≤CPD is the degradation rate Restriction are 0≤CPD0≤1, 0≤CPD96≤1 and 0≤1, 0≤CPD96 0+CPDconditions 96)≤1. fitting, and kdCP is the degradation rate of CP. Restriction conditions are CP particle+CPD losses should be corrected for according to Weisbjerg et al. (1990): 0≤(CPD )≤1. 0 0≤CPD 96 96≤1 and 0≤(CPD0+CPD96)≤1. 0≤CPD0≤1, ⎛ losses ⎞according CPDfor CPparticle particle lossesshould shouldbebecorrected corrected for to Weisbjerg al. (1990): curvefit CP according al.et5.24] (1990): [Eq. ⎟⎟ ⋅1000 to Weisbjerg et = ⎜⎜ CPD pdCP curvefit + (CPD 0 − sCP ) ⋅ − 1 CPD 0 ⎝⎛ ⎠ CPD curvefit ⎞ 5.24] ⎜⎜ CPD curvefit + (CPD 0 − sCP ) ⋅ ⎟⎟ ⋅1000  pdCP =pdCP 5.24 where is the potential degradable protein fraction, (g/kg CP);[Eq. CPD curvefit 1 − CPD 0 ⎠ ⎝ is the asymptotic value of the potentially degradable fraction obtained from the where pdCP is the potential degradable protein fraction, (g/kg CP); CPDcurvefit wherefitting, pdCPCPD is the0 ispotential degradable CP); CPDcurvefit the in sacco soluble protein fraction fraction, at time 0 (g/kg and sCP is the curve is the asymptotic value of the potentially degradable fraction obtained from the soluble crude protein, analysed according todegradable section 5.1.3. There is no is the asymptotic value of the potentially fraction obtained from the curve fitting, CPD0 is the in sacco soluble fraction at time 0 and sCP is the correction for microbial contamination, exceptfraction for the at possible curve fitting, CPD0 isanalysed the in sacco soluble time 0stomacher and sCP is the buffer soluble crude protein, according to section 5.1.3. There is no treatment. soluble crude protein, analysed according tofor Section 5.1.3. There is no correction for correction for microbial contamination, except the possible stomacher The curve fitting for starch degradation is applied data on degraded fractions microbial contamination, except for the possibletostomacher treatment. treatment. at times 0, 2, 4, 8, 16, 24, 48 and 72 h. If the incubation residue at time t is too The curve fitting for starch degradation is applied to data on degraded fractions small for analysis, the ST content from the previous time should be used: at times 0, 2, 4, 8, 16, 24, 48 and 72 h. If the incubation residue at time t is too small for analysis, the ST content from the previous time should be used: ST STDt = t ST [Eq. 5.25] ST t STDt =STD where t, g/g; STt is the remaining amount of ST t is the ST degraded at time NorFor – The Nordic feed evaluation system [Eq. 5.25] ST at time t, ST is the amount of ST in the bags prior to ruminal incubation. ST where STDt is the ST degraded at time t, g/g; STt is the remaining amount of degradation is fitted with the following equation: ST at time t, ST is the amount of ST in the bags prior to ruminal incubation. ST

0

96

0

96

CP particle losses should be corrected for according to Weisbjerg et al. (1990): ⎛ CPD curvefit ⎞ [Eq. 5.24] ⎟ ⋅1000 pdCP = ⎜⎜ CPD curvefit + (CPD 0 − sCP ) ⋅ 1 − CPD 0 ⎟⎠ ⎝ NDF NDF t where is −the potential degradable protein fraction, (g/kg CP); CPDcurvefit NDFD tpdCP = NDF [5.19] is the asymptotic value of the potentially degradable fraction obtained from the Table 5.5. Continued. curve fitting, CPD0 is the in sacco soluble fraction at time 0 and sCP is the soluble crude protein, analysed according to section 5.1.3. There is no Item Procedure correction for microbial contamination, except for the possible stomacher CP − CPt treatment. CPDtcurve = Calculation The fitting for starch degradation is applied to data on degraded fractions at Calculation The curve fitting for starch degradation is applied to data on degraded fractions CP of kdST, times 0, 2, incubationresidue residueatattime timet ist[5.22] istoo too small of kdST, at times 0, 2,4,4,8,8,16, 16,24, 24,48 48and and 72 72 h. h. If If the the incubation sST and for analysis, the ST content from the previous time should be used: sST and small for analysis, the ST content from the previous time should be used: pdST pdST ST − STt STDt = STt  5.25 STDt = ST [5.25] ST [Eq. 5.25] whereSTD STDt tisisthe theST STdegraded degraded time t, g/g; is the remaining amount of where at at time t, g/g; STST t is tthe remaining amount of ST at at time timet,t,ST STisisthe theamount amountofof bags prior to ruminal incubation. ST STST in in thethe bags prior to ruminal incubation. ST ST degradationisisfitted fittedwith withthe thefollowing following equation: degradation equation: [Eq. 5.26] STD t = STD 0 + STD curvefit ⋅ (1 − e − kdST ⋅ t )  5.26 where STDt is the ST degraded at time t, STD0 is the intercept or an estimate of where STD is the ST degraded at time is the t, STD asymptotic intercept of theorinsoluble, an estimate of solubility of tST at time 0 h, STDcurvefit 0 is thevalue but degradable proportion obtained from the curve fitting, and kdST is solubility of ST at time 0ofh,ST STD curvefit is the asymptotic value of the insoluble, the constantofofST ST.obtained Restriction conditions arefitting, 0≤STD 0≤1, butdegradation degradable rate proportion from the curve and kdST is the 0≤STD 72≤1 and 0+STD 72)≤1. degradation rate0≤(STD constant of ST. Restriction conditions are 0≤STD0≤1, 0≤STD72≤1 STD and STD+STD Equation 5.26 are the same as soluble starch and curvefit in )≤1. and 00≤(STD 0

72

STD0 and STDcurvefit in Equation 5.26 are the same as soluble starch and potentially degradable starch (g/kg ST). 62

5.2.3 Indigestible NDF

1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197

The method for determining iNDF involves incubating feed samples in sacco for 288 h in the rumen, and essentially follows the method described in Section 5.2.2 for determining in sacco NDF degradation. Feed samples of 2 g are incubated in bags with 10-15 µm pores and 100-200 cm2 effective surface area, equivalent to 10-20 mg sample/cm2. For the iNDF determination the polyester cloth Saatifil PES 12/6 (Saatitech S.p.A., 22070 Veniano, Como, Italy) with pore size 12 12 opensurface surfacearea areaof of 6% 6% is recommended. should be performed on on at µmμm andand open recommended.Each Eachdetermination determination should be performed at least twoanimals. animals. least two The as:as: The iNDF iNDFcontent contentisiscalculated calculated ⎛ NDF 288 ⎞ iNDF = ⎜ ⎟ ⋅ 1000 ⎝ NDF ⎠



[Eq. 5.27]

where iNDF is the total indigestible NDF fraction in the feed, g/kg NDF; NDF

5.27

is the amount of

288 amount of where iNDF is the total indigestible NDF fraction in the feed, g/kg NDF; NDF288 is the NDFininthe thebag bagremaining remaining after of ruminal incubation, mg;NDF and is NDF is the amount NDF after 288288 h ofh ruminal incubation, mg; and the amount of NDFof NDF in the thebag bagbefore beforeruminal ruminal incubation, in incubation, mg.mg.

For measuring measuringiNDF iNDFininroughage roughageinincommercial commercial feed laboratories near infrared spectroscopy (NIRS) For feed laboratories near infrared spectroscopy calibrations have been (Nordheim et al., (NIRS) calibrations havedeveloped been developed (Nordheim et 2007). al., 2007). 5.2.4 indigestible starch 5.2.4 Indigestible Indigestiblecrude crudeprotein proteinand and indigestible starch

The reference method for determining iCP and iST is the MBT technique. The procedure, based on work of Madsen 1995), is described Table 5.6.MBT technique. The procedure, based on Thethereference method et foral.determining iCP andiniST is the

the work of Madsen et al. 1995), is described in Table 5.6.

Table 5.6

5.3 References Bach Knudsen, K.E., 1997. Carbohydrate and lignin contents of plant material used in animal feeding. Animal Feed Science and Technology 67: 319-338.

NorFor – The Nordic feed evaluation system

Bach Knudsen, K.E., P. Aman, and B.O. Eggum, 1987. Nutritive value of Danish-grown barley varieties. I: Carbohydrates and other major constituents Journal of Cereal Science 6: 173-186.

51

1307 1307 1308 1308 1309 1309

Table 5.6. The standard mobile nylon bag procedure for determining intestinal digestibility of rumen undegraded protein and starch, modified from Madsen et al. (1995). Table 5.6 5.6 The standard mobile mobile nylon nylon bag bag procedure procedure for for determining determining intestinal intestinal digestibility digestibility of of Table Item The standard Procedurestarch, rumen rumen undegraded undegraded protein protein and and starch, modified modified from from Madsen Madsen et et al. al. (1995). (1995). Item Procedure Item Animals and Procedure Duodenalfistulated fistulatedcows cowsfed fedatatmaintenance maintenance level orproduction at production Animals and diet diet Duodenal level or at at levellevel (Michalak Animals and Duodenal fistulated cows fed at maintenance level or production level diet et al., 2003). When cows are cows fed atare maintenance level thelevel dietthe is the same as for the (Michalak et al., 2003). When fed at maintenance (Michalak et al., 2003). When cows are fed at maintenance level the diet diet ruminal in sacco procedure (Table 5.5). is the the same same as as for for the the ruminal ruminal in in sacco sacco procedure. procedure. is Replications Minimum Minimumtwo tworeplicates replicatesper percow. cow. Replications Replications Minimum two replicates per cow. Nylon bags bags The bag’s pore size should bebe11 1111-15 – 15 15 µm. µm. The bag surface area should Nylon The bag’s bag’spore poresize sizeshould shouldbe µm.The Thebag bagsurface surfacearea areashould should be 6·6 cm. Nylon bags The – be 6×6 Samples Concentrate be 6×6 cm. cm. sample size is 10 to 15 mg per cm22 of the bag’s surface area, approximately of2the the bag’s surface surface 0.5 area,g. The samples Samples Concentrate sample size size is 10 10isto to515 15 mg perper cm2cm 1 g. Roughage sample to mg 7 mg , approximately of bag’s area, Samples Concentrate sample size is per cm 2 approximately 1 g. Roughage sample size is 5 to cm approximately 1 g. Roughage sample sizeConcentrate is 5 to 77 mg mg per per cm2,, should be incubated in should be pre-incubated in the rumen. samples approximately 0.5 g. The Theand samples shouldfor be24 pre-incubated in the the rumen. rumen. approximately g. samples should be pre-incubated in the rumen for 0.5 16 hours roughage hours. Concentrate samples should be incubated in the for hours Concentrate should bein incubated the rumen rumen forat16 16pH=2.4 hours and and PreStep 1. Placesamples the sample bag 0.004 MinHCl solution for 1 h. roughage for for 24 24 hours. hours. roughage incubation Step 2. Place the sample bag in a pepsin/HCl solution (100 mg pepsin per litre of Pre-incubation Step Pre-incubation Step 1. 1. Place Place the the sample sample bag bag in in 0.004 0.004 M M HCl HCl solution solution at at pH pH = = 2.4 2.4 for for 11 h. h. 0.004 M HCl solution) for 2 h at 40 °C in a shaking water bath. Step 2. 2. Place Place the the sample sample bag bag in in aa pepsin/HCl pepsin/HCl solution solution (100 (100 mg mg pepsin pepsin per per Step Incubation in litre After pre-incubation the bags are introduced to the duodenum through a duodenal litre of of 0.004 0.004 M M HCl HCl solution) solution) for for 2 2h h at at 40ºC 40ºC in in aa shaking shaking water water bath. bath. the duodenum cannula. When determining the introduced iCP fraction the duodenum bags are collected Incubation in the the After After pre-incubation the bags bags are are introduced to the the duodenum through aafrom the faeces, Incubation in pre-incubation the to through and collection while when determining the AA profile of digestible the bags are collected duodenum and duodenal cannula. When the fraction bags duodenum and duodenal cannula. When determining determining the iCP iCP fraction the theprotein bags are are collection from collected fromcannula. the faeces, faeces, while while when when determining determining the the AA AA profile profile of of from faeces. from an ileal collection from collected from the faeces. digestible the bags are from an ileal Washing Rinse the protein bags with Then wash in a sieve basket in cold running faeces. digestible protein the tap bagswater. are collected collected fromthe an bags ileal cannula. cannula. Washing Rinse the bags with tap tap water. Washbyaa sieve sieve basketetin inal. cold running water water the for bags two hours aswater. described Hvelplund (1992). Alternatively the bags Washing Rinse with Wash basket cold running water for as by al. Alternatively for two hours as described described by Hvelplund Hvelplund et al. (1992). (1992). Alternatively the cantwo be hours washed in a washing machine et using the same procedurethe as for the rumen bags be bags can be washed washed in aa washing washing machine machine using using the the same same procedure procedure as as bags can (Table 5.5). in for the rumen bags. for the rumen bags. Calculation Indigestible crude protein is calculated as: Calculation Indigestible Calculation Indigestible crude crude protein protein is is calculated calculated as: as: ⎛N MBT = ⎜⎛⎜ N MBT iCP iCP = N ⎝⎝ N

⎞⎞ ⎟⎟ ⋅⋅ 1000 1000  ⎠⎠

[Eq. [Eq. 5.28] 5.28]

5.28

where iCP is the indigestible crude protein in the feed, g/kg CP; N

is the nitrogen

MBT where iCP is the indigestible crude protein in the g/kg CP; N is where iCP is thebag indigestible crude protein inthrough the feed, feed, g/kg CP;intestine, NMBT MBT is g; and N is the content in the residue that has passed the small the nitrogen nitrogen content content in in the the bag bag residue residue that that has has passed passed through through the the small small the amount of nitrogen in the weighednitrogen sample, g.the weighed sample, g. intestine, intestine, g; g; and and N N is is the the amount amount of of nitrogen in in the weighed sample, g. Indigestible starch is calculated as: Indigestible Indigestible starch starch is is calculated calculated as: as:

⎛ ST MBT iST = = ⎛⎜⎜ STMBT iST ST ⎝⎝ ST

⎞⎞⎟ ⋅1000 ⎟ ⋅1000  ⎠⎠

[Eq. 5.29] 5.29] [Eq.

5.29

where the indigestible feed, is the where iST iST is indigestible starch starch in in the feed, g/kg g/kg ST; ST; ST STMBT MBT is the wherecontent iSTisisthe the indigestible starch inthethe feed, g/kg ST is the starch content starch in the the residue bag bag that passed passed through theST; small intestine, MBT starch content in residue that through the small intestine, in the residue bag that passed through the small intestine, g; and ST is the amount of g; and ST is the amount of starch in the weighed sample, g. g; and ST is the amount of starch in the weighed sample, g.

starch in the weighed sample, g.

64 64

52 

NorFor – The Nordic feed evaluation system

References Bach Knudsen, K.E., 1997. Carbohydrate and lignin contents of plant material used in animal feeding. Animal Feed Science and Technology 67: 319-338. Bach Knudsen, K.E., P. Aman and B.O. Eggum, 1987. Nutritive value of Danish-grown barley varieties. I: Carbohydrates and other major constituents Journal of Cereal Science 6: 173-186. Broderick, G.A. and J.H. Kang, 1980. Automated simultaneous determination of ammonia and total amino acids in ruminal fluid and In vitro Media. Journal of Dairy Science 64: 67-75. EAAP (European Association of Animal Production), 1969. Methods for determination of digestibility coefficients of feeds for ruminants. Report No. 1 from the Study Commission on Animal Nutrition. Mariendals Boktrykkeri A/S, Gjøvik, Norway. EC No 152/2009. Commission regulation laying down the methods of sampling and analysis for the official control of feed. Official Journal of the European Union L54/1:1-130. Available at: http://eurlex.europa.eu/LexUriServ/ LexUriServ.do?uri=OJ:L:2009:054:0001:0130:EN:PDF. Accessed 14 October 2009. Hedqvist, H. and P. Udén, 2006. Measurement of soluble protein degradation in the rumen. Animal Feed Science and Technology 126: 1-21. Hvelplund, T., M.R. Weisbjerg and L.S. Andersen, 1992. Estimation of the true digestibility of rumen undegraded protein in the small intestine of ruminants by the mobile bag technique. Acta Agriculturae Scandinavica, Section A - Animal Science 42: 34-39. Hvelplund, T., M.R. Weisbjerg and K. Søegaard, 1999. Use of in vitro digestibility methods to estimate in vivo digestibility of straws. Tanzania Society of Animal Production Conference, Arusha, Tanzania, 3-6 August. TSAP proceedings 26, pp. 70-78. ISO 16472:2006 IDT. Animal feeding stuffs - Determination of amylase-treated neutral detergent fibre content (aNDF). Jensen, S.K., C. Jensen, K. Jakobsen, R.M. Engberg, J.O. Andersen, C. Lauridsen, P. Sørensen, P. Henckel, L.H. Skibsted and G. Bertelsen, 1998. Supplementation of broiler diets with retinol acetate, β-carotene or canthaxanthin: Effect on vitamin and oxidative status of broilers in vivo and meat stability. Acta Agriculturae Scandinavica. Section A - Animal Science 48: 28-37. Jensen, S.K., R.M. Engberg and M.S. Hedemann, 1999. All-rac-α-tocopherol acetate is a better vitamin E source than all-rac-α-tocopherol succinate for broilers. Journal of Nutrition 129: 1355-1360. Licitra, G., T.M. Hernandez, P.J. Van Soest, 1996. Standardization of procedures for nitrogen fractionation of ruminant feeds. Animal Feed Science and Technology 51: 347-358. Lindgren, E., 1979. Vallfodrets näringsvärde bestämt in vivo och med olika laboratoriemetoder. Rapport 45. Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden. 66 pp. (In Swedish with English summary). Lindgren, E., 1983. Nykalibrering av VOS-metoden för bestämning av energivärde hos vallfoder. Working paper. Department of Animal Nutrition and Management, University of Agricultural Sciences, Uppsala, Sweden. 4 pp. (In Swedish). Lindgren, E., 1988. Fodrets energivärde. Course paper Feed Science HNU 3. Department of Animal Nutrition and Management, University of Agricultural Sciences, Uppsala, Sweden. 49 pp. (In Swedish). Madsen, J., T. Hvelplund, M.R. Weisbjerg, J. Bertilsson, I. Olsson, R. Spörndly, O.M. Harstad, H. Volden, M. Tuori, T. Varvikko, P. Huhtanen and B.L. Olafsson, 1995. The AAT/PBV protein evaluation system for ruminants. A revision. Norwegian Journal of Agricultural Science, Supplement No. 19. 37 pp. Michalak, S., M.R. Weisbjerg and T. Hvelplund, 2003. Effect of ration composition fed to cows on the mobile bag digestibility. Journal of Animal and Feed Sciences 12: 23-32. Møller, E, P.E. Andersen and N. Witt, 1989. En sammenligning af in vitro opløselighed in vivo fordøjelighed af organisk stof i grovfoder. 13. Beretning fra Fællesudvalget for Statens Planteavls-og Husdyrbrugsforsøg, Danmark. 23 pp. (In Danish). Nordheim, H., H. Volden, G. Fystro and T. Lunnan, 2007. Prediction of in sacco degradation characteristics of neutral detergent fibre (NDF) in temperate grasses and red clover (Trifolium pratense R.) using Near-Infrared Reflectance Spectroscopy (NIRS). Animal Feed Science and Technology 139: 92-108. Nozière, P. and B. Michalet-Doreau, 2000. In sacco methods. In: D’Mello J.P.F. (ed.). Farm animal metabolism and nutrition. CABI Publishing, Wallingford, UK, pp. 233-253.

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53

Ørskov, E.R. and I. McDonald, 1979. The estimation of protein degradability in the rumen from measurements adjusted for rate of passage. Journal of Agricultural Science 92: 499. Porter, M.G. and R.S. Murray, 2001. The volatility of components of grass silage on oven drying and the inter-relationship between dry-matter content estimated by different analytical methods. Grass and Forage Science 56: 405-411. Søegaard, K., M.R. Weisbjerg, R. Thøgersen and M. Mikkelsen, 2001. Laboratoriemetoder til bestemmelse af fordøjlighed i grovfoder til kvæg med særlig vægt på stivelseholdige helsædesgrøder. DJF Rapport nr 34. Husdyrbrug, Danmark 28 pp. (In Danish). Tilley J.M.A. and R.A. Terry, 1963. A two-stage technique for the in vitro digestion of forage crops. Journal of British Grassland Society 18: 104-111. Weisbjerg, M.R., P.K. Bhargava, T. Hvelplund and J. Madsen, 1990. Anvendelse af nedbrydningsprofiler fodermiddelvurderingen. Beretning no. 679. Statens Husdyrbrugsforsøg, Foulum, Denmark. (In Danish). Weisbjerg, M.R. and T. Hvelplund, 1993. Bestemmelse af nettoenergiindhold (FEK) i råvarer og kraftfoderblandinger. Forskningsrapport nr. 3. Statens Husdyrbrugsforsøg, Denmark. 39 pp. (In Danish).

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6. Feed calculations in NorFor 1310 1310 1311 1310 1311 1312 1310 1311 1312 1310 1313 1312 1311 1310 1313 1314 1312 1311 1313 1314 1311 1315 1312 1313 1314 1315 1312 1316 1313 1314 1315 1316 1313 1317 1314 1315 1316 1317 1314 1315 1316 1317 1315 1318 1316 1317 1318 1316 1319 1317 1318 1317 1319 1320 1318 1319 1320 1321 1318 1319 1320 1318 1321 1322 1319 1320 1321 1322 1319 1323 1320 1321 1322 1323 1320 1324 1321 1322 1323 1324 1321 1325 1322 1323 1324 1325 1322 1326 1323 1324 1325 1326 1323 1327 1324 1325 1326 1327 1324 1328 1325 1326 1327 1328 1325 1329 1326 1327 1328 1329 1326 1330 1327 1328 1329 1330 1327 1331 1328 1329 1330 1331 1328 1332 1329 1330 1331 1332 1329 1333 1330 1331 1332 1333 1330 1334 1331 1332 1333 1334 1331 1335 1332 1333 1334 1335 1332 1336 1333 1334 1335 1336 1333 1337 1334 1335 1336 1337 1334 1335 1336 1337 1338 1335 1338 1336 1337 1338 1336 1339 1337 1338 1339 1337 1340 1339 1338 1340 1339 1338 1341 1340 1341 1339 1340 1341 1342 1339 1340 1342 1341 1340 1342 1341 1342 1341 1342 1343 1342 1343 1343 1344 1343 1344 1343 1344 1343 1344 1344 1344

H. Volden

6.0 Feed calculations in NorFor 6.0 Feed calculations in NorFor program. In addition to the feed constituents described NorFor has an extensive feed in characterization H. Volden 6.0 Feed calculations NorFor H. Volden NorFor has 4, ancalculations extensive feed characterization program. Infrom addition to the feed constituents 6.0 Feed in NorFor in Chapter several feed characteristics are calculated information based on chemical fractions, H. Volden NorFor hasinan extensive feed characterization program. In additionfrom to the feed constituents 6.0 described Feed Chapter calculations 4, and several in feedNorFor characteristics are calculated information on evaluation andVolden their degradation digestion characteristics. Nevertheless, efficient use based of a feed NorFor H. has an extensive feed characterization program. In addition to the feed constituents 6.0 Feed calculations in described in Chapter 4, several feedNorFor characteristics are calculated from Nevertheless, information based on use chemical fractions, and their degradation and digestion characteristics. efficient

system infractions, requires commercial feed analyses that are reliable and can be performed at a H. NorFor Volden has an extensive feeddegradation characterization program. In addition to the feed constituents described inpractice Chapter 4, several feed characteristics are calculated from information based on use chemical and their and digestion characteristics. Nevertheless, efficient H. of aVolden feed evaluation system indegradation practice requires commercial feed analyses that in are reliable anduse NorFor has an extensive feed characterization program. In addition to the feed constituents described in Chapter 4, several feed characteristics calculated from information based on chemical fractions, and their and digestion characteristics. Nevertheless, efficient low cost. Therefore, several feed characteristics are predicted using either vitro methods or NIRS. of feed evaluation incharacterization practice requires commercial feed analyses that are reliable and NorFor has extensive feed program. In addition to the feed constituents canaa be performed at system a4,low cost. Therefore, several feed characteristics are predicted using either described inan Chapter several feed characteristics are calculated from information based on chemical fractions, their and digestion characteristics. Nevertheless, efficient use of feed evaluation system indegradation practice requires commercial feed analyses that are reliable and can be performed atand a4,low cost. Therefore, several feed characteristics are predicted using either described in Chapter several feed characteristics are calculated from information based on in vitro methods or NIRS. chemical fractions, and their degradation and digestion characteristics. Nevertheless, efficient use of a feed evaluation system in practice requires commercial feed analyses that are reliable and can be performed at a low cost. Therefore, several feed characteristics are predicted using either 6.1 Calculation of kdNDF in roughage in vitro methods or and NIRS. chemical fractions, their and digestion Nevertheless, efficient of a be feed evaluation system indegradation practice requires commercial feed analyses that are reliable and use can performed a low cost. Therefore, several feed characteristics. characteristics are predicted using either in vitro methods oratNIRS. of a feed evaluation system in practice requires commercial feed analyses are reliable and 6.1vitro Calculation in roughage can a kdNDF low cost. Therefore, feed characteristics are that predicted using either in methods oratof 6.1 Calculation ofNIRS. kdNDF in roughage Thebe inperformed sacco method wascost. originally usedseveral to predict the kdNDF in roughage. However, the fractional can be awas low Therefore, characteristics are predicted using The in performed sacco method originally used toseveral predictfeed the kdNDF in roughage. However, theeither in vitro methods oratof NIRS. 6.1 Calculation kdNDF in roughage kd is a non-linear parameter and Nordheim et al. 2007 showed that the NIRS method was not suitable in vitro methods or NIRS. The in sacco method was originally used to predict the kdNDF in roughage. However, the fractional kd method is a non-linear parameter andtoNordheim etkdNDF al. 2007inshowed thatHowever, the NIRSthe method 6.1 Calculation of was kdNDF in roughage The in sacco originally used predict method theet roughage. for predicting kdNDF. Therefore, an alternative was introduced that can bemethod used to estimate fractional kd is a for non-linear parameter and Nordheim al. 2007 showed that the NIRS was not suitable predicting kdNDF. Therefore, an alternative method was introduced that can 6.1 Calculation of kdNDF in roughage The in sacco method was originally used toNordheim predictan theet kdNDF roughage. fractional kd is afor non-linear parameter and al. 2007inmethod showed thatHowever, the NIRSthe method 6.1 Calculation of kdNDF in roughage was not suitable predicting kdNDF. Therefore, alternative was introduced that can kdNDF from the combination of OMD and NDF digestibility (Weisbjerg et al., 2004a,b, be used tokd estimate kdNDF from theused combination OMD and in NDF digestibility (Weisbjerg et 2007), The in sacco method was originally toNordheim predictof the kdNDF roughage. the fractional is afor non-linear parameter and et al. and 2007 showed thatHowever, the NIRS method was not suitable predicting kdNDF. Therefore, an alternative method was introduced that can be used to estimate kdNDF from theWeisbjerg combination of OMD NDF digestibility (Weisbjerg et in which can be predicted from either in vitro or NIRS data calibrated to in vivo OMD determined at The in sacco method was originally used to predict the kdNDF in roughage. However, the al., 2004a; Weisbjerg et al., 2004b; et al., 2007), which can be predicted from either fractional kd is afor non-linear parameter and Nordheim et al. and 2007 showed that the NIRS method was not to suitable predicting kdNDF. Therefore, anOMD alternative method introduced that can be used estimate kdNDF theWeisbjerg combination of NDF digestibility (Weisbjerg et in al., 2004a; Weisbjerg et al.,from 2004b; etbeal., 2007), which can bewas predicted from either fractional kd is a non-linear parameter and Nordheim et al. 2007 showed that the NIRS method maintenance level. Digestibility of NDF can determined from OMD and feed NDF concentration vitro or NIRS data calibrated to in vivo OMD determined at maintenance level. Digestibility of was not to suitable for kdNDF predicting kdNDF. Therefore, anOMD alternative method introduced can be used estimate combination of NDF (Weisbjerg et in al., 2004a; Weisbjerg et al.,from 2004b; Weisbjerg et al., 2007), which candigestibility bewas predicted from that either vitro or data calibrated to inthe vivo OMD determined atand maintenance level. Digestibility of was not suitable for predicting kdNDF. Therefore, an alternative method introduced can NDF canNIRS be determined from OMD and feed NDF concentration combined with information onin combined with information on the digestibility ofOMD NDS (neutral detergent solubles) (Weisbjerg et be used to estimate kdNDF from combination of NDF (Weisbjerg et al., 2004a; Weisbjerg et from al., 2004b; Weisbjerg et al., 2007), which candigestibility bewas predicted from that either vitro or NIRS data calibrated to inthe vivo OMD determined atand maintenance level. Digestibility of NDF can be determined OMD and feed NDF concentration combined with information on be used to estimate kdNDF from the combination of OMD and NDF digestibility (Weisbjerg et the digestibility of NDS (neutral detergent solubles) (Weisbjerg et al., 2004a). This method al., 2004a). This method assumes that the sum of excreted NDF and NDS accounts for the total al., 2004a; et from al., 2004b; Weisbjerg et al.,concentration 2007), can 2004a). be level. predicted from either vitrodigestibility or data calibrated to indetergent vivo determined at which maintenance Digestibility of NDF canNIRS beWeisbjerg determined OMD andOMD feed NDF combined with information onin the ofsum NDS (neutral solubles) (Weisbjerg al., This method al., 2004a; et from al., Weisbjerg et al., 2007), which can be level. predicted from either in assumes that the of excreted NDF and NDS accounts for theet total undigested OM. Using the undigested OM. Using the2004b; Lucas principle (Lucas et al., 1964), Weisbjerg etinformation al. (2004a) estimated vitro or data calibrated to indetergent vivo OMD determined atfor maintenance Digestibility of NDF canNIRS beWeisbjerg determined OMD and feed NDF concentration with on the digestibility of NDS (neutral solubles) (Weisbjerg etcombined al., 2004a). This method assumes that the sum of excreted NDF and NDS accounts the total undigested OM. Using the vitro or NIRS data calibrated to in vivo OMD determined at maintenance level. Digestibility of Lucas principle (Lucas et al., 1964), Weisbjerg et al. (2004a) estimated a true NDS digestibility NDF can be determined from OMD and feed NDF concentration with information the digestibility of NDS (neutral detergent solubles) (Weisbjerg etcombined al., 2004a). This method assumes that the(Lucas sum ofetexcreted NDF and NDS for the total undigested OM. Usingon theDM (see a true NDS digestibility of 101.3% and an endogenous lossestimated of 90.2 ga NDS per kg ingested Lucas principle al., 1964), Weisbjerg etaccounts al. (2004a) true NDS digestibility NDF can beand determined from OMD feed NDF concentration with information onin of 101.3% an endogenous loss ofand 90.2 gNDS NDS per kgEquation ingested DM (see equation 6.2). The the digestibility of NDS (neutral detergent (Weisbjerg al., 2004a). This method assumes that thean sum NDF and for estimated theetcombined total undigested OM. Using Lucas principle (Lucas etexcreted al.,digestibility 1964), Weisbjerg etaccounts al. (2004a) a true NDS digestibility Equation 6.2). The inofvivo ofsolubles) pdNDF (D; 6.8) can be determined by the combining of 101.3% and endogenous loss of 90.2 g NDS per kg ingested DM (see equation 6.2). The in the digestibility of NDS (neutral detergent solubles) etby al., 2004a). This method vivo digestibility of pdNDF (D; Equation 6.8) can be(Weisbjerg determined combining NDF digestibility assumes that the sum of excreted NDF and NDS accounts for the total undigested OM. Using the Lucas principle (Lucas et al., 1964), Weisbjerg et al. (2004a) estimated a true NDS digestibility of 101.3% and an endogenous loss of 90.2 g NDS per kg ingested DM (see equation 6.2). The in be equal NDF digestibility determined at maintenance and pdNDF. When D is known and assumed to vivo digestibility of pdNDF (D; pdNDF. Equation 6.8) can beknown determined by combining NDF digestibility assumes that the sum of excreted NDF and NDS accounts for the total undigested OM. Using the determined at maintenance and When D is and assumed to be equal to rumen Lucas principle (Lucas etthen al.,(D; 1964), et al. (2004a) estimated a true NDS of 101.3% and an loss ofWeisbjerg 90.2 gcan NDS per kg ingested DM (see equation 6.2). The in model vivo digestibility ofendogenous pdNDF Equation 6.8) can be determined by combining NDFdigestibility digestibility to rumen digestibility, the kdNDF be calculated. NorFor uses a two-compartment determined at maintenance and pdNDF. When D is known and assumed to be equal to rumen Lucas principle (Lucas et al., 1964), al. (2004a) atotrue NDS digestibility digestibility, then the kdNDF can be Weisbjerg calculated. NorFor uses aestimated two-compartment model to of and an endogenous loss 90.2 g NDS per kguses ingested DM (see equation 6.2). in vivo digestibility of pdNDF (D; Equation 6.8) can be determined by combining NDF digestibility determined ateffective maintenance and pdNDF. When DetNorFor is known and assumed be equal to rumen to 101.3% estimate rumen NDF (Allen and 1988), and from this model the digestibility, then the kdNDF can beof calculated. aMertens, two-compartment model toThe of 101.3% and anof endogenous loss ofdegradation 90.2 g(Allen NDS per kg ingested DM (see equation 6.2). The estimate effective rumen NDF degradation and Mertens, 1988), and from this model thein vivo digestibility pdNDF (D; Equation 6.8) can be determined by combining NDF digestibility determined at maintenance and pdNDF. When D is known and assumed to be equal to rumen digestibility, then the kdNDF can be calculated. NorFor uses a two-compartment model to estimate effective rumen NDF degradation (Allen and Mertens, 1988), and from this model the fractional degradation rate can be solved (Huhtanen et al., 2006). In NorFor the kdNDF in roughage vivo digestibility of pdNDF (D; Equation 6.8) can be determined by combining NDF digestibility fractional degradation rate can be solved (Huhtanen et al., 2006). In NorFor the kdNDF in determined atthen maintenance andcan pdNDF. When DNorFor isand known and assumed to be equal tomodel rumen digestibility, the kdNDF be calculated. uses a two-compartment model to the estimate effective rumen NDF degradation (Allen Mertens, 1988), and from this fractional degradation rate can be solved (Huhtanen et al., 2006). In NorFor the kdNDF in is estimated using the following formulas: determined at maintenance and pdNDF. When D is known and assumed to be equal to rumen roughageeffective is estimated using the following formulas: digestibility, then the kdNDF can be calculated. NorFor uses a two-compartment model to the estimate rumen degradation (Allen and 1988), and from this model fractional degradation rateNDF can be solved (Huhtanen et Mertens, al., 2006). In NorFor the kdNDF in roughage is estimated using the following formulas: digestibility, then the kdNDF can be calculated. NorFor uses a two-compartment model to the estimate rumen degradation (Allen and 1988), and from this model fractional degradation rateNDF can be solved (Huhtanen et Mertens, al., 2006). In NorFor the kdNDF in roughageeffective is estimated using the following formulas: estimate effective NDF degradation (Allen and Mertens, 1988), and from this the NDS = 1000 −estimated Ash −rumen NDF [Eq.model 6.1]  can 6.1 fractional degradation rate be solved (Huhtanen et al., 2006). In NorFor the kdNDF in roughage is using the following formulas: NDS = 1000 − Ash − NDF [Eq. 6.1] fractional degradation rate can be solved (Huhtanen et al., 2006). In NorFor the kdNDF in 90 . 2 roughage is1−.estimated using the following formulas: NDS = 1000 Ash−− NDF [Eq. 6.1] [Eq. 6.2] NDSD = 013 90 .using 2 roughage the following formulas: NDS NDSD = is 1 .−estimated 013 6.2] 6.2 NDS = 1000 Ash−− NDF [Eq. 6.1] 90 . 2  [Eq. 6.2] NDSD = 1 . 013 − NDS ( ) uOM==1000 OM −⋅ 1Ash − OMD 6.3] NDS − NDS NDF [Eq. 6.1] 90 . 2 NDSD = 1 .⋅−013 −− NDF 6.2] (1⋅Ash ) uOM OM − OMD 6.3] NDS ==1000 [Eq. 90 ). 2 )  uNDS = NDS NDSD [Eq. 6.1] 6.4] 6.3 NDS (1(−1 −OMD uOM 6.3] [Eq. 6.2] NDSD==OM = 1 .⋅013 −NDSD 90 . 2 ) uNDS NDS ⋅ ( 1 − [Eq. 6.4] ( NDS ) uOM = OM ⋅ 1 − OMD 6.3] NDSD = 1 . 013 − [Eq. 6.2] uNDF = uOM − uNDS 6.5] 6.4] uNDS = NDS ⋅ (1 − NDSD) NDS uNDF uOM uNDS [Eq. 6.5] ) ))  uOM === OM ⋅ (⋅1− [Eq. (NDF uNDF 6.4 uNDS NDS (1−−−OMD NDSD [Eq. 6.3] 6.4] uNDF [Eq. 6.5] NDFD==OM =uOM 6.6] ) uOM ⋅ (1−−−uNDS OMD [Eq. 6.3] ( NDF uNDF ) [Eq. 6.5] 6.4] uNDS ⋅− (1NDF −uNDF NDSD)) 6.6] NDFD===NDS uNDF uOM [Eq. ( NDF −uNDS [Eq. 6.6] 6.4] uNDS 1 − NDSD) NDFD==NDS ⋅ (NDF uNDF = uOM [Eq. 6.5]  .1 − 5 .072 ⋅ OMD ⋅ 100 ) 6.5 ⎞ (NDF− −uNDS uNDF ⎛) ( 448 6.6] NDFD==uOM −NDF ⎜ ( 448 . 1 − 5 . 072 ⋅ OMD ⋅ 100 ) + iNDF ⎞⎟ uNDF uNDS [Eq. 6.5] ⎛ (NDFNDF − uNDF⎛⎜⎝) ( 448 . 1 −NDF ⋅ 0⋅ .OMD 001 ⋅ 100 ) + iNDF ⎞⎟⎠ 5 . 072 NDFD = [Eq. 6.6] pdNDF (NDF = 1000 − ⎜) 6.7] − uNDF + iNDF ⎟⎠ ⋅ 0 . 001 6.6] NDFD =corr =NDF  .1 −NDF 6.6 2 5 . 072⋅ 0⋅ .OMD ⋅ 100 ) pdNDF 1000 − ⎝⎛⎝ ( 448 ⎞ [Eq. 6.7] NDF 001 + iNDF ⎠⎟ NDF ⎜ ( 448 . 1 − 5 . 072 ⋅ OMD pdNDF corr = 1000 − [Eq. 6.7] 2 corr ⋅ 100 ) NDFD ⎛ ⎞ NDF ⋅ 0 . 001 ⎝ ⎠ 2 D= 6.8] pdNDF − ⎜⎛ ( 448 . 1 − 5 . 072 ⋅ OMD ⋅ 100 ) + iNDF ⎟⎞ [Eq. 6.7] NDFD corr = 1000 NDF ⋅ 0 . 001 ⎞ + iNDF ⎟⎠ D = ⎛ pdNDF ⎜⎝ 2 [Eq. 6.7] 6.8] NDFD corr pdNDF = 1000 [Eq. ⎟⎞ − ⎝ D = ⎛⎜ pdNDF NDF ⋅ 0 . 001 corr corr [Eq. 6.8] ⎠  6.7 2 pdNDF NDFD [Eq. 6.7] 1000= 1000 ⎞⎟⎠ − corr D = ⎛⎜⎜⎝⎝ pdNDF [Eq. 6.8] 2 1000corr ⎟⎠ NDFD pdNDF 1000corr ⎞⎠⎟ D = ⎛⎝⎜ NDFD [Eq. 6.8]  6.8 D = ⎛⎝ pdNDF [Eq. 6.8] 1000corr ⎞⎠ ⎜⎛ pdNDFcorr ⎟⎞ ⎜⎝ 1000 ⎟⎠ ⎝ 1000 ⎠

46.37 ⋅ D 48.30 + 1− D 2

65 65 65 65  65 65

6.9

1345

kdNDF = −3.475 +

1346 1347 1348 1349

where NDS is the content of neutral detergent solubles, g/kg DM; NDSD is the digestibility of NDS,g/g; g/g;OM OMis is organic matter content the g/kg feed,DM; g/kguOM DM;is uOM is the undigested OM, NDS, thethe organic matter content of theoffeed, the undigested OM,

[Eq. 6.9]

where NDS is the content of neutral detergent solubles, g/kg DM; NDSD is the digestibility of

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_6, © Wageningen Academic Publishers 2011

55

1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361

kdNDF = −3.475 +

46.37 ⋅ D 1− D 2

48.30 +

[Eq. 6.9]

where NDS is the content of neutral detergent solubles, g/kg DM; NDSD is the digestibility of g/kg DM; OMD is organic uNDS the undigested NDS, g/kgOM, DM; uNDF is NDS, g/g; OM is the organicmatter matter digestibility, content of the%; feed, g/kgisDM; uOM is the undigested g/kg DM; OMD is g/kg organic matter digestibility, %; uNDS is undigested g/kg DM; of pdNDF, undigested NDF, DM; NDFD is the digestibility ofthe NDF, g/g; D isNDS, the digestibility uNDF is undigested NDF, g/kg DM; NDFD is the digestibility of NDF, g/g; D is the digestibility g/g; pdNDF corr is the corrected potentially degradable NDF, g/kg NDF; iNDF is the total indigestible of pdNDF, pdNDF is the is corrected potentially degradable NDF, g/kg NDF; iNDF is the NDF, g/kgg/g; NDF; and corr kdNDF the fractional degradation rate of pdNDF, %/h. total indigestible NDF, g/kg; and kdNDF is the fractional degradation rate of pdNDF, %/h.

In the calculation of kdNDF, a rumen retention time of pdNDF of 60 h is assumed, this represents the

In the calculation of kdNDF, a rumen retention time of pdNDF of 60 h is assumed, this represents mean rumen retention time of sheep fed at maintenance and provides the basis for the determination the mean rumen retention time of sheep fed at maintenance and provides the basis for the of OMD. Evaluation the method showed calculated kdNDF kdNDF values were determination of OMD.of Evaluation of the methodthat showed that calculated valuesvery weresensitive to pdNDF. Therefore, an adjusted pdNDF (pdNDF ) parameter was introduced, which yields a more corr (pdNDFcorr) parameter was introduced, very sensitive to pdNDF. Therefore, an adjusted pdNDF robustyields relationship NDF and OM digestibility, the calculation less which a more between robust relationship between NDF andand OMmakes digestibility, and makes thesensitive when values of pdNDF and OMD extreme. The and regression formula in The Equation 6.7, which calculation less sensitive when are values of pdNDF OMD are extreme. regression formulais used to predict iNDF OMD, based on iNDF data acquired from 20,190onforage samplesfrom obtained in equation 6.7,from which is usedisto predict from OMD, is based data acquired 20190from the forage obtaineddatabase. from the NorFor feed analysis database. NorForsamples feed analysis

1362 1363 1364 1365 1366 1367

6.2 of theof fillthe value roughage 6.2Calculation Calculation fillinvalue in

1368

FV =

1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384

roughage

Feed intake is calculated from information regarding animal IC and feed FV. Concentrate feedstuffs have a fixed FV from of 0.22 FV/kg DMregarding (Kristensen, 1983)IC whereas a variable FV is usedfeedstuffs Feed intake is calculated information animal and feed FV. Concentrate for roughages (Table 6.1) and is calculated from the OMD and the NDF content. The roughage have a fixed FV of 0.22 FV/kg DM (Kristensen, 1983) whereas a variable FV is used for roughages FV is estimated (Table 6.1) andas: is calculated from the OMD and the NDF content. The roughage FV is estimated as: 0.86 − OMD ⋅ 0.005 ⎛ NDF ⎞ − 0.000029⋅⎜ ⎟ ⎝ 10 ⎠ 0.94 + 0.56 ⋅ e

2.9



6.10

[Eq. 6.10]

where value, kg kg DM; OMD is organic matter digestibility, %; and whereFV FVisisroughage roughagefillfill value, DM; OMD is organic matter digestibility, %;NDF andisNDF is feed feed NDF content, g/kg DM.

NDF content, g/kg DM.

The numerator in equation 6.10 was originates from the Danish fill unit system (Kristensen, The numerator in Equation 6.10 was originatesforfrom the Danish fill unit (Kristensen, 1983) 1983) and the denominator provides a correction roughage type, which cansystem be explained by and the denominator provides a correction for roughage type, which can be explained by differences differences in NDF content (e.g. between grasses and maize).

in NDF content (e.g. between grasses and maize).

Table 6.1

The intake of grass silage is dependent on fermentation quality, i.e. the content of fermentation acids

The silage is dependent quality, i.e., theetcontent of fermentation and intake NH3Nofingrass the ensiled feed. Based on afermentation meta-analysis, Huhtanen al. (2002) developed a relative acids NH3N in thecorrects ensiled feed. onbased a meta-analysis, Huhtanen et al.acids (2002) developed silageand index, which silageBased intake on information on total (TAF; Equation 4.6) aand relative silage which corrects intake based on total acids (TAF; NH3N. Theindex, relative silage indexsilage is in NorFor usedon to information correct the FV according to the following equation formula:4.6) and NH3N. The relative silage index is in NorFor used to correct the FV according to the following formula:

Table 6.1. Fill value (FV) in selected roughage feeds and their corresponding organic matter 66 digestibility (OMD) and NDF content. Feedstuff

NorFor feed code

FV, FV/kg DM

OMD, %

NDF, g/kg DM

Clover grass, 6-8 cm, 20% clover Maize silage, high digestibility Maize silage, low digestibility Grass silage, high digestibility Grass silage, low digestibility Wheat straw, untreated

006-0059 006-0307 006-0309 006-0461 006-0463 006-0413

0.41 0.39 0.44 0.50 0.58 0.68

79.0 78.7 74.6 76.5 66.5 44.0

380 338 397 501 597 820

56 

NorFor – The Nordic feed evaluation system

1385 1385 1386 1387 1386 1388 1387 1389 1388 1390 1389 1391 1390 1392 1391 1393 1392 1394 1393 1395 1394 1396 1395 1396 1397 1398 1397 1399 1398 1400 1399 1401 1400 1402 1401 1403 1402 1404 1403 1405 1404 1405 1406 1407 1406 1408 1407 1409 1408 1410 1409 1411 1410 1412 1411 1413 1412 1414 1413 1414

1415 1416 1415 1417 1416 1418 1417 1419 1418 1419

= FVcorr FV corr =

86 − OMD ⋅⋅ 00..005 005 − OMD 00..86

2,9

⎛ NDF ⎞2,9 − ⋅⋅⎛⎜⎜ NDF ⎞⎟⎟ ⋅ 0.005 0.86 − OMD 000029 −00,,000029 10 ⎠⎠2,9 FVcorr = 0.94 + 0.56 ⋅ e ⎝⎝ 10 0.94 + 0.56 ⋅ e ⎛ NDF ⎞ −0,000029⋅⎜ ⎟ ⎝ 10 ⎠ 0.94 + 0.56 ⋅ e

( (

) )

⎞ ⎛⎛⎜ ⎛⎛ − 0.000531⋅ (TAF)22 − 6400 − − 44..765 − ln( 765 ⋅⋅ (ln( ln(NH NH33 N N)) − ln(50 50))) ⎞⎞⎟⎟ ⎞⎟⎟ − ⎜⎜ − 0.000531⋅ TAF − 6400 +  ⋅⋅ ⎜⎜11 − + ⎟ ⎜⎛ ⎜⎜⎛ ⎟⎟ 100 100 ⎟ 100 ⎝⎜⎝ ⎝⎝⎜ − 0.000531⋅ 100 ( TAF)2 − 6400 − 4.765 ⋅ (ln(NH3 N) − ln(50)) ⎠⎞⎠⎟ ⎞⎠⎠⎟ ⋅ 1− + ⎜ ⎜ ⎟⎟ 100 100 ⎝ ⎝ [Eq. 6.11] ⎠ ⎠

6.11

[Eq. 6.11]

where FVcorr is the silage fill value corrected for silage fermentation products, [Eq. FV/kg 6.11] DM; OMD where FVcorr ismatter the silage fill value corrected for silage fermentation products,isFV/kg DM;NDF content, is the is organic digestibility, % as in in Section the feed feed OMD the organic matter digestibility, % described as described section5.2.1; 5.2.1; NDF NDF is the NDF where FVcorr is is thethe silage fill value corrected for silage fermentation products, DM;Equation 4.6; g/kg DM; TAF content of total fermentation acids in the ensiled feed, FV/kg g/kg DM, content, g/kg DM; TAF is the content of total fermentation acids in the ensiled feed, g/kg DM, OMD is the organic matter of digestibility, % in as the described section 5.2.1; and NH is the NH content ammonia N ensiledinfeed, g/kg N. NDF is the feed NDF 3N Equation 4.6; and 33N is the content of ammonia N in the ensiled feed, g/kg N. content, g/kg DM; TAF is the content of total fermentation acids in the ensiled feed, g/kg DM, is the contentforage of ammonia N the ensiled feed, g/kg N. FV at a TAF value of Equation 4.6; NH3N Equation6.11 6.11and shows that in ensiled there is in a break in calculated Equation show that in ensiled forage there is a break pointpoint in calculated FV at a TAF value of g/kg DM anda aNH3N NH3Ncontent contentof of50 50 g/kg g/kg N. Higher values results in a ahigher and vice 8080 g/kg DM and Higher values results in higherFV FV and vice versa. N. Equation 6.11 show that in ensiled forage there is a break point in calculated FV at a TAF value UsingUsing this approach, NorFor is able to to take into of silage silagefermentation fermentation quality versa. this approach, NorFor is able take intoaccount accountthe the effect effect of of 80 g/kg DM and a NH3N content of 50 g/kg N. Higher values results in a higher FV and vice quality on forage intake. on forage intake. versa. Using this approach, NorFor is able to take into account the effect of silage fermentation quality on forage intake. 6.3 Calculation of fractional degradation rate (kd) in feed mixtures

6.3 of fractional degradation rate (kd) rate in feed(kd) mixtures 6.3Calculation Calculation of fractional degradation in feed mixtures FeedCalculation mixtures (e.g.ofcompound feeds from the feed industry) arefeed commonly used as inputs to the 6.3 fractional degradation rate (kd) in mixtures

NorFor system. Feed characteristics (based on the composition) are often from tabulated to the Feed mixtures mixtures (e.g.compound compound feeds from industry) are calculated commonly used Feed (e.g. from the feed feed industry) are commonly used asparameter, inputsastoinputs the values. However, the kd of the feeds potentially degradable feed fraction is a non-linear NorFor system. Feed characteristics (based on composition) are often calculated from tabulated NorFor system. Feed characteristics (based on composition) are often calculated from tabulated hence simple weighted mean ofpotentially kd in a feeddegradable mixture cannot be calculated (Danfær et al., 2006). hence values.aHowever, However, thekdkdof ofthe the fraction iseffective a non-linear parameter, values. the potentially degradable feedfeed fraction is a non-linear parameter, In a simple one-compartment model for rumen degradability, the rumen degradability hence a simple weighted mean of kd a feed mixturecannot cannot be be calculated calculated (Danfær 2006). a simple weighted mean of kd in ainfeed mixture (Danfæretetal.,al., 2006). In a (ED) can be estimated according to the general formula: In a simple one-compartment model rumen degradability,the therumen rumeneffective effectivedegradability degradability (ED) can simple one-compartment model forfor rumen degradability, (ED) can according to the general be estimated according formula: kd a kd be estimated kdto the general kd formula: kd kd kd kd n 2 1 ⋅ pdXX + a n 2 1 pdXX 22 + pdXX nn = pdXX11 + + pdXX pdXX 22 + + ⋅⋅ ⋅⋅ pdXX pdXX nn )) ⋅ pdXX11 + ⋅⋅ pdXX + ⋅⋅ ⋅⋅ ⋅⋅ pdXX = ⋅⋅ ((pdXX kd kd kd kd kd11 + + kp kp kd 22 + + kp kp kd nn + + kp kp kd aa + + kp kp kd a kd1 kd 2 kd n ED = ⋅ pdXX1 + ⋅ pdXX 2 + ⋅ ⋅ ⋅ pdXX n = ⋅ (pdXX1[Eq. + pdXX 6.12] 2 + ⋅ ⋅ pdXX n ) kd1 + kp kd 2 + kp kd n + kp kd a + kp

ED = = ED



6.12

6.12] are the are the fractional degradation rates of feeds 1, 2…,n, , %/h;[Eq. pdXX where 1,2,···,n wherekd kd1,2,···,n pdXX1, 2, …, n are the 1,2,···,n 1,2,···,n 1, 2,…,n are the fractional degradation rates of feeds 1, 2, …, n, , %/h; aggregated kd of feeds potentially degradable feed fraction of feeds 1,2,···,n, g/kg; kdg/kg; potentially degradable feed fraction of feeds 1, 2, …, n, kd is the aggregated kd of feeds 1, aa is the a where kdand fractional degradation of feeds 1, 2…,n, , %/h; pdXX1,2,···,n are the 1,2,···,n 1,2,···,n; kpare is the the fractional passage passage rate rates of pdXX, %/h. 2, …, n, %/h; and kp is the fractional rate of pdXX, %/h. potentially degradable feed fraction of feeds 1,2,···,n, g/kg; kda is the aggregated kd of feeds 1,2,···,n; and kp iscan the be fractional passage rate to of kd pdXX, %/h. : The ED equation solved with respect The ED equation can be solved with respect toaa kd : The ED equation can be solved kp with respect to kda: kp ⎛⎛⎜ ⎞⎞⎟ pdXX ⎞ ⎛ kp ⎛ ⎟ Shareii ⋅⋅ XX XX ii ⋅⋅ pdXXii ⎞⎟⎟ ∑ ⎜⎜ Share kd a = ⎜⎜ ∑ ⎜⎛ ⎟⎟⎞ 1000 ⎠⎠ 1000 ii ⎝⎝⎛ − 11 pdXX ⎞ ⎜⎜ ⎟⎟ − i Share XX ⋅ ⋅ ∑ ⎜ ⎟ ⎛ ⎞ pdXXi ii ⎞⎞ 1000 kp ⎞⎞ ⎞⎟ ⎟⎟ ⎛⎛⎜1 + ⎠ kp ⎜⎜ ∑ ⎛⎜⎜ ⎛⎛⎜ Share ⎝⋅ XX ⋅i pdXX ⎟ ⎟ ⎟ i ⎜ ⎟ + ⋅ ⋅ Share XX 1 i i ∑ ⎜ ⎟ ⎜ ⎟ ⎟ i i 1000 ⎜⎜ ⎜⎜ ⎝ 1000 ⎠⎠ ⎜⎝⎝⎛ kdXX kdXXii ⎟⎠⎠ ⎟⎟⎠ ⎟⎠ − 1 ⎝⎝⎜ ii ⎝⎛⎝ ⎝⎛ pdXX i ⎞⎟ ⎜1 + kp ⎞⎟ ⎠⎞⎟ ⎠⎟ ⎜ Share XX ⋅ ⋅ ∑ ⎜ i i ⎜ ⎜ 1000 ⎠ ⎜⎝ kdXXi ⎟⎠ ⎟⎠ ⎟⎠ ⎝ i ⎝⎝ kd = kd aa =

a



[Eq. 6.13]

6.13

[Eq. 6.13]

rate of of thethe feed mixture, %/h; pdXX where where kd kdaaaisisthe theaggregated aggregatedfractional fractionaldegradation degradation rate feed mixture, %/h; pdXX ii is i is potentially degradabledegradable CP, ST orCP, NDF the i=1…n’th g/kg; Share the proportion of theoffeed in the potentially STfor or NDF for the i =feed, 1…n’th feed, g/kg;isShare is the proportion themixture, aggregated fractional degradation of the feed mixture,%/h. %/h; pdXXi is where a is the feedkdin the %; and kp is the fractional rate passage rate of pdXX, mixture, %; and kp isCP, the fractional of pdXX, potentially degradable ST or NDF passage for the i rate = 1…n’th feed, %/h. g/kg; Share is the proportion of the feed in the mixture, %; and kp is the fractional passage rate of pdXX, %/h.

When kda is calculated for pdCP and pdST a kp of 3.337%/h is assumed and for pdNDF a kp of 2.8705%/h is used. These fractional kp values represent those for cows fed at maintenance level, corresponding to the feeding level at which the 67 in sacco kd is determined.

6.4 Cation-anion difference

67

The dietary balance of acids and bases influences many physiological variables and metabolic functions, including growth rate, appetite, amino acid and energy metabolism, calcium utilization, vitamin metabolism, intestinal absorption and kidney function (Underwood and Suttle, 1999). Reducing the dietary cation-anion difference (CAD) in the dry period feed is often recommended to NorFor – The Nordic feed evaluation system

57

1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461

The dietary balance of acids and bases influences many physiological variables and metabolic functions, including growth rate, appetite, amino acid and energy metabolism, calcium utilization, vitamin metabolism, intestinal absorption and kidney function (Underwood and Suttle, 1999). Reducing the dietary cation-anion difference (CAD) in the dry period feed is often recommended to prevent milk fever (NRC, 2001). The CAD is expressed in milliequivalents (meq) permilk kg DM or (NRC, in the total ration meq per cow and day. In NorFor, the following prevent fever 2001). TheasCAD is expressed in milliequivalents (mEq) per kg DM or in equation is used to calculate CAD:

the total ration as mEq per cow and day. In NorFor, the following equation is used to calculate CAD:

⎛⎛ K Na ⎞ ⎛ Cl S ⋅ 2 ⎞ ⎞ + + CAD = ⎜⎜ ⎜ ⎟−⎜ ⎟ ⎟⎟ ⋅ 1000  ⎝ ⎝ 39.1 23.0 ⎠ ⎝ 35.5 32.0 ⎠ ⎠

[Eq. 6.14]

6.14

where CAD is the dietary cation-anion difference, mEq/kg DM; K is the potassium content in feed,

where CAD is the dietary cation-anion difference, meq/kg DM; K is the potassium content in g/kg DM; Na is the sodium content in feed, g/kg DM; Cl is the chloride content in feed, g/kg DM; feed, g/kg DM; Na is the sodium content in feed, g/kg DM; Cl is the chloride content in feed, S is DM; the sulphur feed,ing/kg 35.5 and is theis molecular weight of K, g/kg S is thecontent sulphur in content feed,DM; g/kg39.1, DM; 23.0, 39.1, 23.0, 35.532.0 and 32.0 the molecular Na, Cl and S, respectively. weight of K, Na, Cl and S, respectively.

References 6.5 References Allen, M.S. Mertens, 1988. Evaluating constraints on fibreon digestion by rumen microbes. Allen, M.S.and andD.R. D.R. Mertens, 1988. Evaluating constraints fibre digestion by rumenJournal of Nutrition 118: 261-270. microbes. Journal of Nutrition 118: 261-270.

Danfær, A., P. Huhtanen, P. Udén, J. Sveinbjörnsson and H. Volden, 2006. The Nordic dairy cow model, Karoline –

Danfær, A., P. Huhtanen, J. Sveinbjörnsson and H. Volden, Nordic dairy and cowutilisation Description. In: Kebreab,P.E.,Udén, J. Dikstra, A. Bannink, J. Gerrits and J. France2006. (eds.).The Nutrient digestion model, Karoline – Description. In: Kebreab, J. Dikstra, Wallingford, A. Bannink,UK, J. Gerrits and J. France in farm animals: Modelling approaches. CABE., International, pp. 383-406. (eds.) Nutrient utilisation farmS.animals: CAB2002. Prediction of the Huhtanen, P., H. digestion Khalili, J.I.and Nousiainen, M.in Rinne, Jaakkola,Modelling T. Heikkiläapproaches. and J. Nousiainen, International, pp. 383-406. relative intake potential of grass silage by dairy cows. Livestock Production Science 73: 111-130. Huhtanen, P., S. Ahvenjärvi, M.R. Weisbjerg and P. Nørgaard, 2006. Digestion and passage of fibre in ruminants.

Huhtanen, P., H. Khalili, J.I. Nousiainen, M. Rinne, S. Jaakkola, T. Heikkilä and J. Nousiainen, In: Sejrsen, K., T. Hvelplund, M.O. Nielsen (eds.). Ruminant physiology, digestion, metabolism and impact on 2002. Prediction of the relative intake potential of grass silage by dairy cows. Livestock nutritionScience of gene expression, immunology and stress. Proceedings of Xth ISRP. Wageningen Academic Publishers, Production 73: 111-130.

Wageningen, the Netherlands, pp. 87-135. Kristensen, V.F., 1983. StyringM.R. av foderopptagelsen hjælp af2006. foderrasjonens Huhtanen, P., S. Ahvenjärvi, Weisbjerg and P.ved Nørgaard, Digestionsammensætning and passage of og valg af In:Sejrsen, V. Østergaard A. Neimann-Sørensen foderrationer fibrefodringsprincip. in ruminants. In: K., T. and Hvelplund, M. O. Nielsen(eds.). (eds.).Optimale Ruminant physiology,til malkekoen. digestion, metabolism and impact on nutrition of geneRapport expression, immunology stress. Foderværdi, foderopptagelse, omsætning og produktion nr. 551. Beretning fraand Statens Husdyrbrugsforsøg, Wageningen Academic Publishers, The Netherlands, pp 87-135. Proceedings of Xth ISRP. Foulum, Denmark, pp. 7.1-7.35. (In Danish). Licitra, G., T.M. Hernandez and P.J. Van Soest, 1996. Standardization of procedures for nitrogen fractionation of Kristensen, Styring av foderopptagelsen af foderrasjonens sammensætning ruminantV.F., feeds.1983. Animal Feed Science and Technologyved 51: hjælp 347-358. og valg af fodringsprincip. In V. Østergaard and A. Neimann-Sørensen (eds.) OptimaleIn: Gurland J. (ed.). Lucas, H.L., 1964. Stochastic elements in biological models; their sources and significance. foderrationer til malkekoen. Foderværdi, foderopptagelse, omsætning og produktion Rapport nr. Stochastic models in medicine and biology. University Wisconsin Press, Madison, WI, USA. 355 pp. 551. Beretning fra Statens Husdyrbrugsforsøg, Foulum, Denmark pp. 7.1-7.35. (in Danish). Nordheim, H., H. Volden, G. Fystro and T. Lunnan, 2007. Prediction of in situ degradation characteristics of neutral detergent fibre (aNDF) in temperate grasses and red clover using near-infrared reflectance spectroscopy (NIRS). Animal Feed Science and Technology 139: 92-108. 68 revised edition, National Academy press, Washington, DC, Nutrient Requirements of Dairy Cattle, 2001. Seventh USA. 381 pp. Underwood, E.J. and N.F. Suttle, 1999. The mineral nutrition of livestock. CABI Publishing, Wallingford, Oxon, UK. 614 pp. Van Soest, P.J., 1994. Nutritional ecology of the ruminant. (2nd edition) Cornell University press, Ithaca, NY, USA. 476 pp. Weisbjerg, M.R., T. Hvelplund and K. Søegaard, 2004a. Prediction of digestibility of neutral detergent solubles using the Lucas principle. Journal of Animal and Feed Science 13, Supplement 1: 239-242. Weisbjerg, M.R., T. Hvelplund and K. Søegaard, 2004b. Prediction of NDF digestibility based on assumptions about true digestibility and endogenous loss of NDS. Journal of Animal and Feed Science 13, Supplement 1: 243-246. Weisbjerg, M.R., K. Søegaard and P. Lund, 2007. Estimating fractional rate of NDF degradation from in vivo digestibility. Journal of Animal and Feed Science 16, Supplement 2: 145-150.

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1496 1496 1497 1497 1498 1498 1499 1499 1500 1500 1501 1501 1502 1502 1503 1503 1504 1504 1505 1505 1506 1506 1507 1507 1508 1508 1509 1509 1510 1510 1511 1511 1512 1512 1513 1513 1514 1514 1515 1515 1516 1516 1517 1517 1518 1518 1519 1519 1520 1520 1521 1521 1522 1522 1523 1523 1524 1524 1525 1525 1526 1526 1527 1527 1528 1528 1529 1529 1530 1530 1531 1531 1532 1532 1533 1533 1534 1535 1536

7. Digestion and metabolism in the gastrointestinal tract H. Volden and M. Larsen

7.0 Digestion and metabolism in the gastrointestinal tract 7.0 Digestion and metabolism in the gastrointestinal tract In Volden NorFor theM. digestion H. and Larsen and metabolism are simulated in three compartments: (1) the rumen, (2)

H. and M. Larsen theVolden small intestine and (3) the large intestine. This chapter describes the modelling of the digestion processesand in the three compartments and microbial OM synthesis in the rumen and2)large Digestion metabolism are simulated inthe three compartments in NorFor: 1) the rumen, the intestine. Digestion andequations metabolism are in three compartments in 1) the rumen, 2) small and 3) theinlarge intestine. This chapter describes theNorFor: modelling of the digestion Most intestine of the thissimulated chapter could have been presented in a simpler form, butthewe present small and 3) the largeare intestine. chapter theprogram modelling of the digestion processes three compartments and This the microbial OM synthesis in the rumen and large this makes them intestine hereininthe the format they implemented in thedescribes computer since we believe processes in the three compartments and the microbial OM synthesis in the rumen and large but intestine. Most of the equations in this chapter could have been presented in a simpler form, it easier to follow their biological rationale. intestine. the equations in this could havein been a simpler form, we presentMost themofhere in the format theychapter are implemented the presented computer in program since we but we present them here in thetoformat they arebiological implemented in the computer program since we believe this makes it easier follow their rationale. 7.1 Rumen believe this makes it easier to follow their biological rationale.

7.1 Rumen Fractional 7.1 Rumen rates of degradation and passage are used to calculate the ruminal digestion and Fractional rates of degradation and passage are used to calculate the ruminal digestion and

fermentation ofofnutrients. Reliable estimates of digestion are the highly dependent onand correct estimates Fractional rates degradation and estimates passage are to calculate ruminal digestion fermentation of nutrients. Reliable of used digestion are highly dependent on correct of feed passage rates (Allen and Mertens, 1988) and both extrinsic and intrinsic feed have to fermentation of nutrients. Reliable estimates of digestion dependent onintrinsic correct factors estimates of feed passage rates (Allen and Mertens, 1988)are andhighly both extrinsic and feed be considered (Robinson et al., 1987b; Lechener-Doll et al., 1991; Huhtanen and Kukkonen, estimates of feed passage rates (Allen and Mertens, 1988) and both extrinsic and intrinsic feed factors have to be considered (Robinson et al., 1987b; Lechener-Doll et al., 1991; Huhtanen and 1995). Fibre degradation rate is dependent notetisonly by intrinsic attributes fibreHuhtanen fraction, but also by factors have1995). to be considered (Robinson al., 1987b; Lechener-Doll etof al.,the 1991; and Kukkonen, Fibre degradation rate dependent not only by intrinsic attributes of the fibre Kukkonen, factors influencing rumen degradation environment raterumen is(Hoover, dependent 1986; not only Van by Soest, intrinsic 1994) attributes andSoest, NorFor of 1994) thetakes fibre fraction, but1995). also byFibre factors influencing environment (Hoover, 1986; Van andthis into fraction, but also by factors influencing rumen environment 1986; Van Soest, 1994) on andnutrient accounttakes when estimating ruminal digestion. Moreover, microbial activity depends NorFor this into account whenfibre estimating ruminal fibre(Hoover, digestion. Moreover, microbial NorFor takes thison into account when estimating ruminal fibre digestion. Moreover, microbial degradation and passage rates, and the amount of bacterial OM in the rumen activity depends nutrient degradation and passage rates, and thesynthesized amount of bacterial OM is calculated activity nutrient degradation andglycerol passage rates, and acid the amount of bacterial OM synthesized in of theon rumen is calculated from the sum of carbohydrates, proteins, glycerol and of rumen from thedepends sum carbohydrates, proteins, and lactic fermented. The efficiency synthesized in the rumen calculated the sum of and carbohydrates, proteins, glycerol lactic acid fermented. Theisis efficiency offrom rumen microbial OM synthesis is highly variable microbial OM synthesis highly variable (Hespell Bryant, 1979; Volden, 1999)and and in NorFor, lactic acidand fermented. The efficiency of rumen microbial synthesis is highly variable (Hespell 1979; 1999) and level in NorFor, efficiency is dependent on both feed efficiency isBryant, dependent onVolden, both feed intake and OM diet composition. (Hespell andand Bryant, 1979; Volden, 1999) and in NorFor, efficiency is dependent on both feed intake level diet composition. intake level and diet composition.

7.1.1 Rumen fractional passage rates 7.1.1 Rumen fractional passage rates 7.1.1 Rumenruminal fractional Undegraded feed passage fractionsrates are assumed to have passage rates, corresponding to one of Undegradedruminal ruminal feed fractions are assumed to passage have passage rates, corresponding Undegraded feed areliquid, assumed to have corresponding to one of to one of the following four ruminalfractions phases: 1) 2) roughage CP andrates, ST particles, 3) concentrate the following four ruminal phases: (1) liquid, (2) roughage CP and ST particles, (3) concentrate the following phases: 1) liquid, roughageproposed CP and ST 3) al. concentrate particles or 4) four NDFruminal in roughage particles. The2)equations byparticles, Sauvant et (1995) are particles or (4) NDF in roughage particles. The equations proposed by Sauvant et al. particles or 4) NDF in roughage particles. The equations proposed by Sauvant et al. (1995) are(1995) are used to calculate the fractional passage rates of liquid (r_kpl), and the CP and ST in roughage used to tocalculate calculate thefractional fractional passage rates of liquid (r_kpl), andCP theand pdCP and pdST in roughage used passage rates of liquid (r_kpl), and the ST in roughage particles (r_kpr): the particles particles(r_kpr): (r_kpr): ∑ DMI i ⋅ 1000 r _ kpl = 2.45 + 0.055 ⋅ ∑i DMI i ⋅ 1000 + 0.0004 ⋅ rough _ share 22 [Eq. 7.1] r _ kpl = 2.45 + 0.055 ⋅ i BW 0.75 + 0.0004 ⋅ rough _ share  7.1 [Eq. 7.1] BW 0.75 ∑ DMI i ⋅ 1000 r _ kpr = 0.35 + 0.022 ⋅ ∑i DMI i ⋅ 1000 + 0.0002 ⋅ rough _ share 22  [Eq. 7.2] 7.2 r _ kpr = 0.35 + 0.022 ⋅ i BW 0.75 + 0.0002 ⋅ rough _ share [Eq. 7.2] 0 . 75 BW where r_kpl is the fractional passage rate of liquid out of the rumen, %/h; r_kpr is the fractional where r_kpl is the fractional passage rate of liquid out of the rumen, %/h; r_kpr is the fractional passage rate ofthe roughage particles out of rumen,of%/h; DMIi is%/h; the dry matter of the of the where r_kpl fractional passage ofthe liquid rumen, r_kpr is the intake fractional passage rate is of roughage particles outrate of the rumen,out %/h; the DMI i is the dry matter intake of the of i=1...n’th feedstuff, kg/d; BW is the animal body weight, kg; and rough_share is the proportion of passage rate of roughage particles out of the rumen, %/h; DMI is the dry matter intake of the of i and rough_share is the the i=1...n'th feedstuff, kg/d; BW is the animal body weight, kg; roughage in the diet, % the i=1...n'th kg/d; BW proportion offeedstuff, roughage inof theDM. diet,is%the ofanimal DM. body weight, kg; and rough_share is the proportion of roughage in the diet, % of DM. The passage passagerate rateof ofpdCP pdCPand andpdST pdSTininconcentrate concentratefeed feedparticles particles(r_kpc) (r_kpc) calculated from The is is calculated from a a modified 71 NRC equation (NRC, 2001): modified NRC equation (NRC, 2001): 71 ∑ DMIi ⋅1000

1537

r _ kpc = 2.504 + 0.1375 ⋅ i

1538 1539 1540 1541 1542

where r_kpc is the fractional passage rate of crude protein and starch in concentrate feed particles out of the rumen, %/h; DMIi is the dry matter intake of the i=1...n'th feedstuff, kg/d; BW is the animal body weight, kg; and conc_share is the proportion of concentrate in the diet, % of DM.

BW

− 0.02 ⋅ conc _ share



H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_7, © Wageningen Academic Publishers 2011

[Eq. 7.3]

7.3

59

1535 1536

modified NRC equation (NRC, 2001):

1537

r _ kpc = 2.504 + 0.1375 ⋅ i

1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575

∑ DMI i ⋅1000

BW

[Eq. 7.3]

− 0.02 ⋅ conc _ share

where r_kpc is the fractional passage rate of crude protein and starch in concentrate feed particles out

where is %/h; the fractional of crude starch in concentrate feed particles of the r_kpc rumen, DMIi is passage the dry rate matter intakeprotein of theand i=1...n’th feedstuff, kg/d; BW is the animal out of the rumen, %/h; DMI i is the dry matter intake of the i=1...n'th feedstuff, kg/d; BW is the body weight, kg; and conc_share is the proportion of concentrate in the diet, % of DM. animal body weight, kg; and conc_share is the proportion of concentrate in the diet, % of DM.

A sensitivity sensitivitytest test pre-evaluation the model that therate passage rate inof pdNDF in A andand pre-evaluation of theofmodel showedshowed that the passage of pdNDF concentrateparticles particleshad had a significant effect on estimated the estimated digestibility in the and rumen and concentrate a significant effect on the NDF NDF digestibility in the rumen that Equation Equation7.3 7.3underestimated underestimateddigestibility digestibility pdNDF concentrates. Based on these evaluations, that ofof pdNDF in in concentrates. Based on these the following rate equation formulated: evaluations, themodified followingpassage modified passage rate was equation was formulated: ∑ DMI i ⋅1000 ⎞ ⎛ ⎟ ⎜ − 0.02 ⋅ conc _ share ⎟ ⋅ 0.43 r _ kpNDFc = ⎜ 2.504 + 0.1375 ⋅ i BW ⎟ ⎜ ⎠ ⎝



[Eq. 7.4]

7.4

wherer_kpNDFc r_kpNDFcisisthe thefractional fractional passage of NDF the rumen in concentrate particles, %/h; where passage raterate of NDF out out the rumen in concentrate particles, %/h; the dry matter intake i=1...n'thfeedstuff, feedstuff, kg/d; body weight, DMIDMI matter intake of of thethei=1...n’th kg/d; BW BWisisthe theanimal animal body weight, kg; i is dry i is the kg; conc_share is the proportionofofconcentrate concentrate in in the the diet, andand conc_share is the proportion diet, %/DM. %/DM. The hashas been used as the reference method for estimating the the ruminal The rumen rumenevacuation evacuationtechnique technique been used as the reference method for estimating ruminal passage rate of NDF particles in roughage, since it has been shown to be a robust and passage rate of NDF particles in roughage, since it has been shown to be a robust and reliable method reliable method for estimating passage kinetics of NDF fractions (Robinson et al., 1987b; for estimating passage kinetics of NDF fractions (Robinson et al., 1987b; Huhtanen and Kukkonen, Huhtanen and Kukkonen, 1995; Stensig et al., 1998). A dataset based on the rumen evacuation 1995; Stensig et al., 1998). A dataset based onanthe rumen to evacuation compiled technique was compiled (Table 7.1) to develop equation predict thetechnique fractionalwas passage rate (Table 7.1) to develop equation)to the fractional passageflow rate data of roughage NDFin(r_kpNDFr ) of roughage NDFan (r_kpNDFr outpredict of the rumen. When duodenal were missing the out of the rumen. When duodenal flowreplaced data were missing in theassuming dataset,that measurements dataset, measurements of faecal excretion ruminal outflow, digestion in of faecal excretion replaced ruminalforoutflow, that NDF digestion in theMoreover, large intestine for 10% the large intestine accounts 10 % ofassuming the total tract digestion. if dataaccounts on pdNDF passage notdigestion. available, the kp of pdNDF be 0.78was of the for total of the total tractwas NDF Moreover, if datawas onassumed pdNDF to passage notvalue available, the kp of NDF (Minde Rygh,to 1997; Stensig et al., 1998; 2002).(Minde In the experiments used for pdNDF was and assumed be 0.78 of the value forLund, total NDF and Rygh, 1997; Stensig et al., parameterization, several different markerused methods were used to estimate intestinal flowmarker and alsomethods 1998; Lund, 2002). In the experiments for parameterization, several different different methods for analyzing NDF feed and digesta. The Proc Mixed procedure in SAS were used to estimate intestinaltotal flow andinalso different methods for analyzing total NDF in feed and software was therefore used to adjust the r_kpNDFr to account for differences in the proportion of concentrate in the diet and for methodological differences. A decrease in kp with increasing proportion of concentrate in the diet has been observed in several cases (Sauvant et al., 1995; Table 7.1. Descriptive statistics of data used to develop the equation for predicting the passage rate NRC, 2001).

of NDF in roughage.

Table 7.1

Reference

Diets,

Eriksen and Ness, 2007 Minde and Rygh, 1997 Mydland, 2005 Prestløkken, 1999 Robinson et al., 1987a,b,c Stensig and Robinson, 1997 Stensig et al., 1998 Volden, 1999 Volden, unpublished data Volden, unpublished data Volden, unpublished data Volden, unpublished data Volden, unpublished data Volden, unpublished data Volden, unpublished data

4 4 3 3 8 4 4 6 2 3 3 3 3 3 4

DMI,

Concentrate

33.3 25.5 31,4 30.5 8.7-37.7 35.0 26.4 32.2 and 16.1 11.2 31.3 29.5 24.6 19.3 16.8 29.9

28.2 50.7 31.3 59.9 61.3 37.6 43.1 60.0 40.1 24.3 48.6 51.0 35.0 0 27.3

n is used as input variable g/kg proportion, In NorFor, total NDF intake per kg BW forBW predicting r_kpNDFr. Since % the NDF content is generally lower in concentrates than in roughage, the negative effect of a higher of concentrate on kp8 is indirectly accounted 21.5-31.4 for in the r_kpNDFr equation. As Bosch,proportion 1991 6.0-38.1

60 

72

NorFor – The Nordic feed evaluation system

digesta. The Proc Mixed procedure in SAS software was therefore used to adjust the r_kpNDFr to account for differences in the proportion of concentrate in the diet and for methodological differences. A decrease in kp with increasing proportion of concentrate in the diet has been observed in several cases (Sauvant et al., 1995; NRC, 2001). In NorFor, total NDF intake per kg BW is used as input variable for predicting r_kpNDFr. Since the NDF content is generally lower in concentrates than in roughage, the negative effect of a higher proportion of concentrate on kp is indirectly accounted for in the r_kpNDFr equation. As the substitution of roughage with concentrates generally reduces the NDF/BW ratio, the predicted value of r_kpNDFr will be lower. The r_kpNDFr is calculated from the following equation: 1.5106

r_kpNDFr = 0.480 + 1+

∑DMIi ∙ NDFi i

-3.198



7.5

BW ∙ 7.484

where r_kpNDFr is the fractional passage rate of pdNDF in roughage particles, %/h; NDFi is the NDF content in the i=1...n’th feedstuff, g/kg DM; DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; and BW is the animal body weight, kg. An evaluation of the Equation 7.5 for use with growing cattle gave unrealistically low passage rates for rations with a low NDF intake (e.g. diets with a concentrate proportion>0.8). Thus, to obtain reliable estimates of NDF digestion in the rumen in these diets, the equation was forced through 0.5%/h as an intercept. Figure 7.1 shows how the passage rate of different feed fractions changes with level of feed intake (DMI/kg BW), assuming a fixed ratio between roughage and concentrate in the diet (50:50) and an NDF concentration of 400 g/kg DM. There are large differences in passage rates between the 16.0

r_kpl r_kpr r_kpc r_kpNDFc r_kpNDFr

14.0

Passage rate (%/h)

12.0 10.0 8.0 6.0 4.0 2.0 0.0 8

12

16

20 24 28 Dry matter intake (g/kg BW)

32

36

40

Figure 7.1. Passage rates (%/h) of liquids, r_kpl; protein and starch in roughage, r_kpr; protein and starch in concentrate, r_kpc; NDF in concentrate, r_kpNDFc; and NDF in roughage, r_kpNDFr at different dry matter intake (g/kg BW). NorFor – The Nordic feed evaluation system

61

1592 1593 1594 1595

1592 (50:50)(50:50) and an and NDFanconcentration NDF concentration of 400 g/kg of 400 DM. g/kgThere DM.are There large aredifferences large differences in passage in passage rates rates 1593 between between the fractions. the fractions. For example, For example, at a DMI at aofDMI 25 g/kg of 25 BW, g/kgtheBW, mean therumen mean retention rumen retention time time 1594 (MRT)(MRT) of CP in ofconcentrate CP in concentrate particles particles is estimated is estimated to be 20tohbe (r_kpc 20 h (r_kpc = 5.0%/h), = 5.0%/h), while atwhile the same at the same 1595 intake level, intakethe level, MRT theofMRT NDFofinNDF roughage in roughage is 64 h is (r_kpNDFr 64 h (r_kpNDFr = 1.56%/h.). = 1.56%/h.).

1596 1596 fractions. For example, at a DMI of 25 g/kg BW, the mean rumen retention time (MRT) of CP in 1597 1597 Figure Figure 7.1 7.1

concentrate particles is estimated to be 20 h (r_kpc=5.0%/h), while at the same intake level, the

MRT of NDF in roughage is 64 h (r_kpNDFr=1.56%/h.). 1598 1598 1599 1600 1601 1602 1603 1604 1605

7.1.2 Rumen Rumen degradation ofofcrude 1599 7.1.2 Rumen degradation degradation crude of protein crude protein protein Ruminal degradation and theand escape of dietary proteinprotein (the sum of sum sCP of andsCP pdCP) described 1600 Ruminal degradation the escape of dietary (the and are pdCP) are described Ruminal and thedegradation escape of dietary protein (theMcDonald, sum1979). of sCP1979). andsCP pdCP) arefraction described by by a one-compartment degradation model (Ørskov and McDonald, The fraction 1601 by degradation a one-compartment model (Ørskov and The sCP consists of bothof non-AA solublesoluble N,model suchN, as NH3as 1602 N and N and urea-N, which1979). are which assumed are sCP assumed to fraction be to beconsists of consists both non-AA such NH a one-compartment degradation (Ørskov and3urea-N, McDonald, The 1603 completely completely degraded degraded inN, thesuch rumen, in the and soluble and soluble AA-N which AA-N is which only is partly onlydegraded partly degraded in the in the both non-AA soluble as rumen, NH 3N and urea-N, which are assumed to be completely degraded 1604 rumen: rumen: and soluble AA-N which is only partly degraded in the rumen: in the rumen, 1605 ⎛ ⎛ NH 3 N i ⎞NH 3 N i sCPi sCP 150 150 i ⋅ rd _ sCP rd DMI=i ⋅∑CP =∑ + ⎜ ∑ DMI+i⎜⋅∑CP DMI _ sCP DMI i ⋅ i ⋅ CP⋅ i ⋅ i ⋅ i ⋅ CPi ⋅ ⎟⎟ 1000 150 kpl + r⎜⎝ _i kpl ⎜⎝ i 1000 ⎠ 1000 + r _150 1000 i i

1606 1606 1607 1608 1609 1610 1611 1611 1612 1612 1613 1613 1614 1614 1615 1615 1616 1616 1617 1617 1618 1618 1619 1619 1620 1620 1621 1621 1622 1622 1623 1623 1624 1624 1625 1625 1626 1626 1627 1627 1628 1628 1629 1629 1630 1630 1631 1631 1632 1632 1633 1633 1634 1634 1635 1635 1636 1636 1637 1637 1638 1638 1639 1639 1640 1640 1641 1641 1642 1642 1643 1643 1644 1644 1645 1645 1646 1646 1647 1647 1648 1648 1649 1649



⎛ ⎛ NH 3 N i ⎞NH 3 N150 150 i ⎞⎟ ⋅ − ⎜⎜ ∑ DMI−i⎜⎜⋅∑CP DMI i ⋅ i ⋅ CPi ⋅ ⎟⎟ ⋅ 1000 ⎠ 1000 150 + r⎟⎠ _150 kpl + r _ kpl ⎝i ⎝i

⎞ ⎟⎟ ⎠

[Eq. 7.6] [Eq. 7.6]

7.6

1607 where rd_ sCP is the soluble crude protein degraded in the rumen, g/d; DMIi is the dry matter intake the dry matter where rd_ sCPrd_ is the crude protein degraded in the rumen, g/d; DMI 1608 dry matter where sCPsoluble is the soluble crude protein degraded in the rumen, g/d; i isDMI i is the of the i=1...n’th feedstuff, kg/d; CPi is theiscrude protein content in the i=1...n’th feedstuff, g/kg DM; intake of the i=1...n'th feedstuff, kg/d; CP 1609 the crude protein contentcontent in the i=1...n'th in the i=1...n'th feedstuff, feedstuff, intake of the i=1...n'th feedstuff, kg/d; CPcrude i i is theprotein sCP is the soluble crude protein content in the i=1...n’th feedstuff, g/kg CP; NH N is the i 3 i g/kg DM; g/kgsCP DM; 1610 the soluble crude protein crude protein contentcontent in the i=1...n'th in the i=1...n'th feedstuff, feedstuff, g/kg CP; g/kg NHCP; Ni NH isammonia i is sCP i is the soluble 3Ni is the and urea content in the i=1...n'thg/kg feedstuff, g/kgr_kpl CP; and r_kpl is the liquid3rate, andammonia urea N content inNthe i=1...n’th feedstuff, CP; and is the liquid passage Equation the ammonia and urea N content in the i=1...n'th feedstuff, g/kg CP; and r_kpl is the liquid passage rate, Equation 7.1. The fractional degradation rate of AA-N is fixed at 150 %/h. 73at 150%/h. 7.1. The fractional degradation rate of AA-N73 is fixed passage rate, Equation 7.1. The fractional degradation rate of AA-N is fixed at 150 %/h. The degradation of pdCP in the rumen is calculated as: The degradation degradationofofpdCP pdCPinin rumen is calculated The thethe rumen is calculated as: as: pdCP rd _ pdCP = ∑ DMIi ⋅ CPi ⋅ pdCPii rd _ pdCP = ∑i DMIii ⋅ CPii ⋅ 1000i 1000 ii

kdCPi ⋅  kdCPi ⋅ kdCPi + r _i kp kdCPii + r _ kp

[Eq. 7.7] [Eq. 7.7]

7.7

where rd_pdCP is the degradation of potentially degradable crude protein in the rumen, g/d; DMIi is

where is the degradation of potentially crude proteinprotein in the rumen, DMI i the dryrd_pdCP matter intake of the i=1...n’th feedstuff,degradable kg/d; CPi is the crude contentg/d; in the i=1...n’th where rd_pdCP isintake the degradation of potentially proteinprotein in the content rumen, g/d; DMI ii is the crude in the is the dry matter of the i=1...n'th feedstuff degradable , kg/d; CPi crude feedstuff, g/kg DM; pdCP is the potentially degradable crude protein content in the i=1...n’th crudeprotein proteincontent contentin inthe the is the dry feedstuff, matter intake thei i=1...n'th feedstuff , kg/d; CPii is thecrude i=1...n'th g/kgofDM; pdCPi is the potentially degradable feedstuff, g/kg CP;g/kg kdCPi is pdCP the fractional degradation rate of crude pdCPprotein in the i=1...n’th %/h; i=1...n'th thefractional potentially degradable in feedstuff, the i=1...n'th feedstuff, feedstuff, g/kg DM; CP; kdCPi iiisisthe degradation rate of pdCP incontent the i=1...n'th and r_kp is the fractional passage rate, %/h. If the feed used is concentrate then r_kp=r_kpc, Equation i i=1...n'th feedstuff, g/kg CP; kdCP is the fractional degradation rate of pdCP in the i=1...n'th i feedstuff, %/h; and r_kp is the fractional passage rate, %/h. If the feed used is concentrate then feedstuff, and is and the fractional rate, r_kp=r_kpr, %/h. If the feed used is7.2. concentrate then 7.3, and if%/h; the feedr_kp is7.3, roughage r_kp=r_kpr, Equation 7.2. r_kp=r_kpc, Equation if the feedpassage is roughage Equation r_kp=r_kpc, Equation 7.3, and if the feed is roughage r_kp=r_kpr, Equation 7.2.

The total totaldegradation degradationofofdietary dietary in the rumen (g/d) is calculated The CPCP in the rumen (g/d) is calculated as: as: The total degradation of dietary CP in the rumen (g/d) is calculated as: rd _ CP = rd _ sCP + rd _ pdCP  rd _ _ CP CP = = rd rd _ sCP + + rd rd __ pdCP pdCP rd _ sCP

[Eq. 7.8] [Eq. 7.8]

7.8

where in in thethe rumen, g/d; rd_sCP is is degradation where rd_CP rd_CPisisthe thetotal totaldegradation degradationofofdietary dietarycrude crudeprotein protein rumen, g/d; rd_sCP where rd_CPofissoluble the totalprotein degradation of dietary crude protein in the rumen, g/d;degradation rd_sCP is of degradation inEquation the rumen, Equation 7.6; and is the of soluble protein in the rumen, 7.6; and rd_pdCP is rd_pdCP the degradation of potentially degradable degradation of soluble protein protein in in the the rumen, rumen, Equation Equation 7.7. 7.6; and rd_pdCP is the degradation of potentially degradable protein in the rumen, protein Equation 7.7.rumen, potentially degradable in the Equation 7.7. The energy for microbial growth that comes from the protein degraded in the rumen is corrected The energy energyfor formicrobial microbialgrowth growth that comes from the protein degraded in the rumen is corrected for The comes from for dietary ammonia or urea and isthat calculated from:the protein degraded in the rumen is corrected dietary ammonia oror urea for dietary ammonia ureaand andisiscalculated calculated from: from: rd _ CPcorr rd __ CPcorr CPcorr rd

= = =

rd _ CP rd rd _ _ CP CP

− rd _ NH 3 N _ CP − rd − rd __ NH NH 33 N N __ CP CP

[Eq. 7.9] [Eq. 7.9]



7.9

where rd_CPcorr is the ammonia- or urea-corrected dietary crude protein degraded in the rumen, where rd_CPcorr ammoniaor urea-corrected dietary degraded the rumen, where rd_CPcorr isisthethe ammoniaor dietary crude proteinprotein degraded in3_CP the rumen, g/d; rd_CP is the total degradation of urea-corrected dietary crude protein in the crude rumen, g/d; rd_NH isinthe is the g/d; isisthe degradation of in dietary crudecrude protein in7.10. the in rumen, g/d; rd_NH g/d; rd_CP rd_CP thetotal degradation ofthedietary protein the rumen, g/d;33_CP rd_NH dietary ammonia ortotal urea N available rumen, Equation 3_CP is the dietary available in the rumen, Equation 7.10.7.10. dietaryammonia ammoniaororurea ureaN N available in the rumen, Equation The variable rd_NH3N_CP in Equation 7.9 is: The variable rd_NH33N_CP in Equation 7.9 is:

The variable rd_NH3N_CP in Equation 7.9 is: NH 3 N i rd _ NH 3 N _ CP = ∑ DMI i ⋅ CPi ⋅ NH N ii 3N 3 1000 rd _ _ NH NH 3 3N N_ _ CP CP = DMI ii ⋅⋅ CP CPii ⋅⋅ NH =∑ i DMI rd ∑ 1000 1000 ii



[Eq. 7.10] [Eq. 7.10]

7.10

where rd_NH3N_CP is the dietary ammonia or urea N available in the rumen, g/d; DMIi is the where rd_NH3N_CP is the dietaryfeedstuff, ammoniakg/d; or urea the rumen, g/d;inDMI ii is the the crudeinprotein content the i=1...n'th dry intake of the i=1...n'th CPiNisavailable 62 matter NorFor – The Nordic feed evaluation system ii is N thecontent crude protein content in the i=1...n'th dry matterg/kg intake of the kg/d;orCP the ammonia urea in the i=1...n'th feedstuff, g/kg feedstuff, DM; andi=1...n'th NH3Ni isfeedstuff, feedstuff, g/kg DM; and NH33Nii is the ammonia or urea N content in the i=1...n'th feedstuff, g/kg CP. CP.

where rd_NH3N_CP is the dietary ammonia or urea N available in the rumen, g/d; DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; CPi is the crude protein content in the i=1...n’th feedstuff, g/kg DM; and NH3Ni is the ammonia or urea N content in the i=1...n’th feedstuff, g/kg CP. 7.1.3 Rumen degradation of carbohydrates 1653 1653 1654 1654 1653 1655 1655 1654 1653 1656 1656 1655 1654 1657 1656 1657 1655 1656 1657 1658 1658 1657 1659 1659 1658 1660 1660 1659 1658 1661 1661 1660 1659 1662 1662 1661 1660 1663 1663 1662 1661 1664 1664 1663 1662 1665 1665 1664 1663 1666 1666 1665 1664 1667 1667 1666 1665 1668 1667 1668 1666 1667 1668 1669 1669 1668 1670 1670 1669 1671 1671 1670 1669 1672 1672 1671 1670 1673 1673 1672 1671 1674 1674 1673 1672 1675 1675 1674 1673 1676 1676 1675 1674 1677 1677 1676 1675 1677 1678 1676 1678 1677 1678 1679 1679 1678 1680 1680 1679 1681 1681 1680 1679 1682 1682 1681 1680 1683 1683 1682 1681 1684 1684 1683 1682 1685 1685 1684 1683 1686 1686 1685 1684 1687 1687 1686 1685 1688 1688 1687 1686 1689 1689 1688 1687 1690 1690 1689 1688 1691 1691 1690 1689 1692 1692 1691 1690 1693 1693 1692 1691 1694 1694 1693 1692 1694 1693 1694

ruminal degradation of RestCHO in and roughage, and in ruminal degradation of ST ST and and in concentrate concentrate anddepends roughage,primarily and NDF NDFupon in concentrate. concentrate. The amount of microbial OMRestCHO synthesized in the rumen the fermentability Small starch granules disappearing from ruminal in sacco bags (30-40 µm) are assumed to flow flow Small starch granules disappearing from ruminal in sacco bags (30-40 µm) are assumed to ruminal degradation of A STone-compartment and RestCHO in concentrate and roughage, and NDF in concentrate. of the carbohydrates. digestion model is used to calculate ruminal degradation with the liquid fraction, and their degradation is calculated as with the liquid fraction, and their degradation is calculated as Small starch granules disappearing from ruminal in sacco bags (30-40 µm) are assumed to flow granules ruminal degradation and RestCHO concentrateand andNDF roughage, and NDF inSmall concentrate. of ST and RestCHOofinSTconcentrate andinroughage, in concentrate. starch with the liquid fraction, and theirsacco degradation is calculated as assumed Small starch granules disappearing from ruminal in sacco bags (30-40 µm) are assumed flow fraction, disappearing from ruminal bags (30-40 µm) are to flow with thetoliquid sSTi in 150 150 i ⋅⋅ their [Eq. rd DMI ⋅ ST ⋅ sST ∑ [Eq. 7.11] 7.11] rd _ _ sST sST DMIfraction, with the==liquid and degradation is calculated as ∑ ii ⋅ STii ⋅ 1000 and their degradation is calculated as: 150 + + _ kpl kpl ii 1000 rr _ sSTi 150 150 [Eq. 7.11] rd _ sST = ∑ DMI i ⋅ STi ⋅ ⋅ 1000 150 + r _ i sSTi 150 kpl [Eq.matter 7.11] intake rd _ sST DMI ST = ⋅ ⋅ ⋅  ∑ is the dry where rd_sST is the degradation of soluble starch in the rumen, g/d; DMI i i i matter intake 7.11 where rd_sST is the degradation starch in the rumen, g/d; DMIi is the dry 1000 150 +of r _soluble kpl i of the i=1...n'th feedstuff, kg/d; ST is the starch content in the i=1...n'th feedstuff, g/kg DM; sSTii i of the i=1...n'th feedstuff, kg/d; ST is the starch content in the i=1...n'th feedstuff, g/kg DM; sST i where rd_sST is the degradation of soluble starch in the rumen, g/d; DMIi is the dry matter intake is the soluble in the i=1...n'th feedstuff, ST; and r_kpl is the liquid where rd_sSTstarch isfeedstuff, thecontent degradation ofis soluble starch ing/kg the rumen, g/d; DMI isruminal the dryDM; matter of is the soluble starch content in the i=1...n'th feedstuff, g/kg ST; and r_kpl is the ruminal liquid the starch content in the i=1...n'th feedstuff, g/kg sSTintake of the i=1...n'th kg/d; ST i i i drytomatter intake where rd_sST is the degradation of soluble starch in the rumen, g/d; DMI i is the passage rate, Equation 7.1.The fractional degradation rate of the sST fraction is set 150 %/h for the i=1...n’th feedstuff, kg/d; ST is the starch content in the i=1...n’th feedstuff, g/kg DM; sST is the passage rate, Equation 7.1.The fractional degradation rate of the sST fraction is set to 150 %/h for is soluble starch content in the feedstuff, g/kg ST; and r_kpl is the ruminal liquid of the the i=1...n'th feedstuff, kg/d; STi i=1...n'th i is the starch content in the i=1...n'th feedstuff, g/kg DM; sSTi i all feedstuffs. all feedstuffs. soluble starch content in theini=1...n’th feedstuff, g/kgrate ST;ofST; and r_kpl is the ruminal passage passage rate, Equation 7.1.The fractional degradation theand sSTr_kpl fraction is ruminal set toliquid 150 %/h for rate, is the soluble starch content the i=1...n'th feedstuff, g/kg is the liquid Equation 7.1.The fractional degradation rate of therate sSToffraction is set to is 150%/h for %/h all feedstuffs. all feedstuffs. passage rate, Equation 7.1.The fractional degradation the sST fraction set to 150 for The same The same type type of of equation equation is is also also used used to to calculate calculate the the degradation degradation of of the the RestCHO RestCHO fraction fraction in in all feedstuffs. the rumen: rumen:type of equation is also used to calculate the degradation of the RestCHO fraction in the The The same same type of equation is also used to calculate the degradation of the RestCHO fraction in the the rumen: The same type of equation is also used to kd calculate the degradation of the RestCHO fraction in rumen: Re stCHO stCHO kd Re [Eq. 7.12] 7.12] rd _ _rumen: Re stCHO stCHO = DMI i ⋅⋅ Re Re stCHOcorr stCHOcorr i ⋅⋅ =∑ ∑ DMI [Eq. rd Re the ii

i

i kd Re stCHO + r _ kpl kd Re + r _ kpl kdstCHO Re stCHO

rd _ Re stCHO = ∑ DMI i ⋅ Re stCHOcorr i ⋅



[Eq. 7.12]

7.12

kd Re + r _ kpl i kdstCHO Re stCHO rd _ Re stCHO Re stCHOcorr = ∑ DMIis is the the[Eq. dry 7.12] matter where rd_RestCHO isi ⋅the the degradation of RestCHO in in the the rumen, rumen, g/d; g/d; DMI DMIii is i ⋅ of dry matter where rd_RestCHO degradation kd ReRestCHO stCHO + r _ kpl i where rd_RestCHO is the degradation of RestCHO in the rumen, g/d; DMI is the dry matter intake intake of the i=1...n'th feedstuff, kg/d; RestCHOcorr is the ammoniaor urea-N corrected icorrected intake of the i=1...n'th kg/d; RestCHOcorr therumen, ammoniaurea-N ii isthe dry matter where rd_RestCHO is feedstuff, the degradation of RestCHO in g/d; or DMI i is the of the i=1...n’th feedstuff, kg/d; RestCHOcorr ammoniaor urea-N corrected RestCHO content RestCHO content (Equations 4.2,kg/d; 4.3) RestCHOcorr in the the i=1...n'th i=1...n'th feedstuff, g/kg DM; kdRestCHO is the the RestCHO content (Equations 4.2, 4.3) in feedstuff, g/kg kdRestCHO is i is the therumen, ammoniaor urea-N intake of the i=1...n'th i isthe is thecorrected dry matter where rd_RestCHO is feedstuff, the degradation of RestCHO in g/d;DM; DMI i fractional rate 4.5; and r_kpl is ruminal liquid passage (Equationsdegradation 4.2 and(Equations 4.3) inof theRestCHO, i=1...n’th feedstuff, g/kg kdRestCHO is the fractional fractional degradation rate of RestCHO, Equation 4.5; andDM; r_kpl is the theDM; ruminal liquid passage RestCHO content 4.2, 4.3) RestCHOcorr inEquation the i=1...n'th feedstuff, g/kg kdRestCHO is thedegradation intake of the i=1...n'th feedstuff, kg/d; i is the ammonia- or urea-N corrected rate, Equation 7.1. rate of RestCHO, Equation 4.5; and r_kpl is the ruminal liquid passage rate, Equation 7.1. rate, Equation 7.1. fractional degradation rate of RestCHO, 4.5; feedstuff, and r_kpl g/kg is theDM; ruminal liquid passage RestCHO content (Equations 4.2, 4.3) inEquation the i=1...n'th kdRestCHO is the rate, Equation 7.1. fractional degradation rateofofpdST RestCHO, Equation 4.5;orand r_kpl is the liquid The ruminal degradation forfor either roughage concentrate is ruminal estimated as: passage The ofof pdST for either roughage or concentrate is estimated as: The ruminal ruminal degradation pdST either roughage or concentrate is estimated as: rate, Equationdegradation 7.1. The ruminal degradation of pdST for either roughage or concentrate is estimated as: pdSTi kdSTi kdST i ⋅⋅ i rd __ pdST pdST DMIi ⋅⋅ ST STi ⋅⋅ pdST =  7.13 The ruminal for either roughage or concentrate is estimated [Eq. as: 7.13] [Eq. 7.13] rd =∑ DMI ∑ degradation i i of pdST

1000 kdST kdST + rr _ _ kp 1000 ii + pdST kdST i⋅ i kp [Eq. 7.13] 1000 kdST + r kp pdST kdST i ofi_potentially i where rd_pdST is⋅ ST the⋅ degradation degradable starch in the rumen, g/d; DMIi is the i⋅ rd _ pdST = DMI [Eq.DMI 7.13] ∑ i i is the where rd_pdST is the degradation of potentially degradable starch in the rumen, g/d; where rd_pdST is the degradation offeedstuff, potentially degradable starch in the rumen, g/d; DMI ii is the 1000 kdST dry matter intake of the i=1...n’th feedstuff, i + r _ kp kg/d; STi is the starch content in the i=1...n’th i i

rd _ pdST = ∑i DMIi ⋅ STi ⋅

is the the starch starch content in the the i=1...n'th i=1...n'th dry matter matter intakeisof of the i=1...n'th feedstuff, feedstuff, kg/d;degradable STii is dry intake i=1...n'th kg/d; ST in where rd_pdST the degradation of potentially starchcontent in rumen, g/d; feedstuff, DMI i is theg/kg ST; g/kg DM; pdSTi isthe the potentially degradable starch content in the the i=1...n’th feedstuff, g/kg DM; pdSTi is the potentially degradable starch content in the i=1...n'th feedstuff, feedstuff, g/kg DM; pdSTi is the potentially degradable starch content in the i=1...n'th feedstuff, is the starch content in the i=1...n'th dry matter intake of the i=1...n'th feedstuff, kg/d; ST i the where rd_pdST is the degradation of potentially degradable starch in the rumen, g/d; DMI kdST is the fractional degradation rate of pdST in the i=1...n’th feedstuff, %/h; and r_kp is the i is r_kp i kdST g/kg ST; kdST is the fractional degradation rate of pdST in the i=1...n'th feedstuff, %/h; and i is the fractional degradation rate of pdST in the i=1...n'th feedstuff, %/h; and r_kp g/kg ST; i DM; pdSTi is the potentially degradable starch content in the i=1...n'th feedstuff, feedstuff, g/kg is the starch content in the i=1...n'th dry matter intake of the i=1...n'th feedstuff, kg/d; ST i fractional passage rate, %/h. If the feed is concentrate then r_kp=r_kpc, Equation 7.3, and if the is the fractional passage rate, %/h. If the feed is concentrate then r_kp=r_kpc, Equation 7.3, and if is theST; fractional passage rate, %/h. If the feedrate is concentrate then r_kp=r_kpc, Equation 7.3, if g/kg kdST is thepdSTi fractional degradation of pdSTstarch in the i=1...n'th feedstuff, %/h; andand r_kp i DM; feedstuff, g/kg is the potentially degradable content in the i=1...n'th feedstuff, feedfeed is roughage r_kp=r_kpr, Equation 7.2. the feed is roughage r_kp=r_kpr, Equation 7.2. the is roughage r_kp=r_kpr, Equation 7.2. is theST; fractional passage rate, %/h. If the feedrate is concentrate theni=1...n'th r_kp=r_kpc, Equation if g/kg kdSTi is the fractional degradation of pdST in the feedstuff, %/h;7.3, andand r_kp thethe feed is roughage r_kp=r_kpr, Equation 7.2.is concentrate then r_kp=r_kpc, Equation 7.3, and if is fractional passage rate, %/h. If the feed The total degradation ofofdietary dietary starch in the the rumen is calculated calculated as: as: The total totaldegradation degradationof dietary starch in the rumen is calculated The starch in rumen is as: the feed is roughage r_kp=r_kpr, Equation 7.2. The total degradation of dietary starch in the rumen is calculated as: rd __ ST ST = rd __ sST sST + rd _ _ pdST  [Eq. 7.14] 7.14] rd rd + rd [Eq. 7.14 The total= degradation ofpdST dietary starch in the rumen is calculated as: rd _ ST = rd _ sST + rd _ pdST [Eq. 7.14] where rd_ST is total degradation dietary starch in rumen, g/d; rd_sST is the where the degradation ofof dietary starch in the the rumen, g/d;g/d; rd_sST is[Eq. the where rd_ST isthe the total degradationof dietary starch in the rumen, rd_sST is 7.14] the degradation of rd _ STrd_ST = rd _ is sST + total rd _ pdST degradation of soluble starch in the rumen, Equation 7.11; and rd_pdST is the degradation of degradable degradation of soluble starch in the rumen, Equation 7.11; and rd_pdST is the degradation of solublerd_ST starchisinthethe rumen, Equation rd_pdST the degradation where total degradation of 7.11; dietaryand starch in the is rumen, g/d; rd_sSTofispotentially the potentially degradable starch in the rumen, Equation 7.13. potentially degradable starch in the rumen, Equation 7.13. degradation of soluble 7.11; and rd_pdST is the degradation of starch in the rumen, Equation 7.13. where rd_ST is the total degradation of dietary starch in the rumen, g/d; rd_sST is the potentially degradationdegradable of soluble starch starch in in the the rumen, rumen, Equation Equation 7.13. 7.11; and rd_pdST is the degradation of Increased levels of STstarch and SU in rumen, diets have a negative potentially degradable in the Equation 7.13. effect on the digestion of fibre in the rumen (Lindberg, 1981; Mould et al., 1983; Khalili75 and 75 Huhtanen, 1991; Huhtanen and Jaakkola, 1993;

Stensig et al., 1998). The decreased degradation rate of fibre with increases in the supply of non75 structural carbohydrates has mainly been explained by the negative effect of a decreased ruminal 75

NorFor – The Nordic feed evaluation system

63

1706 1706 1707 1708 1707 1709 1708 1709 1710 1710 1711 1712 1711 1713 1712 1714 1713 1715 1714 1716 1715 1717 1716 1717 1718 1718 1719 1720 1719 1721 1720 1722 1721 1723 1722 1724 1723 1725 1724 1726 1725 1726 1727 1727 1728 1728 1729 1729 1730 1730 1731 1731 1732 1733 1732 1733 1734 1734 1735 1735

Increased levels of ST and SU in diets have a negative effect on the digestion of fibre in the rumen (Lindberg, Mould al., 1983; andeffect Huhtanen, Huhtanen and Increased levels of1981; ST and SU inetdiets have aKhalili negative on the1991; digestion of fibre in Jaakkola, the 1993; Stensig et al., 1998). The et decreased of fibre 1991; with increases the Jaakkola, supply of rumen (Lindberg, 1981; Mould al., 1983;degradation Khalili andrate Huhtanen, Huhtaneninand non-structural carbohydrates hasdecreased mainly been explainedrate by of thefibre negative of aindecreased 1993; Stensig et al., 1998). The degradation with effect increases the supply of ruminal pH on carbohydrates the activity of has cellulolytic bacteria. However, thenegative decreaseeffect in fibre non-structural mainly been explained by the of adegradation decreased has pH on the activity of cellulolytic bacteria. However, the decrease in fibre degradation has also been also beenpH attributed to substrate-specific of the non-fibre digesting bacteria at the has ruminal on the activity of cellulolyticstimulation bacteria. However, the decrease in fibre degradation attributed to substrate-specific stimulation of the bacteria atatthe of expense cellulolytic bacteria (Mould etstimulation al., 1983; Huhtanen anddigesting Jaakkola, 1993; Weisbjerg et also beenofattributed to substrate-specific of non-fibre the non-fibre digesting bacteria the expense cellulolytic bacteria al., 1983; Huhtanen and Jaakkola, Weisbjerg et al.et1999). To al. 1999).ofTo take into(Mould account the effect easily degradable CHO on 1993; ruminal NDFWeisbjerg digestion, a expense cellulolytic bacteriaet (Mould etofal., 1983; Huhtanen and Jaakkola, 1993; take into account the effect of the easily degradable on ruminal NDF a rumenin load rumen load the ratio ofeffect easily carbohydrates to ruminal totaldigestion, fibre) is included al. 1999). Toindex take (RLI; into account ofdegradable easily CHO degradable CHO on NDF digestion, a index (RLI; the ratio easily to total fibre)toistotal included NorFor to NorFor to derive a(RLI; correction factor forcarbohydrates kdNDF: rumen load indexof thedegradable ratio of easily degradable carbohydrates fibre) in is included in derive a correction factora for kdNDF:factor for kdNDF: NorFor to derive correction rd _ ST + rd _ Re stCHO − ∑ DMI i ⋅ (Re stCHOcorri − SU i ) RLI = rd _ ST + rd _ Re stCHO − ∑i DMI i ⋅ (Re stCHOcorri − SU i ) [Eq. 7.15] i i ⋅ (Re stCHOcorri − SU i ) ∑ DMI i ⋅ NDFi + ∑ DMI  7.15 RLI = [Eq. 7.15] i∑ DMI i ⋅ NDFi + ∑ i DMI i ⋅ (Re stCHOcorri − SU i ) i

i

whereRLI RLIisisthe theratio ratioofofrapidly rapidly degraded carbohydrates to slowly degradable carbohydrates in the where degraded carbohydrates to slowly degradable carbohydrates in diet, g/g; rd_ST is the starch degraded in the rumen, Equation 7.14; rd_RestCHO is the degradation the diet, g/g;is rd_ST is the starch degraded the rumen, Equation rd_RestCHO is the in where RLI the ratio of rapidly degraded in carbohydrates to slowly7.14; degradable carbohydrates of RestCHO inRestCHO theisrumen, Equation 7.12; SUrumen, sugar in the in i=1...n’th degradation the rumen, Equation 7.12; SU the diet, g/g;of rd_ST the in starch degraded in the Equation 7.14;content rd_RestCHO thefeedstuff, g/kg sugar the is i=1...n'th i is thecontent i is the DM; RestCHOcorr is the RestCHO in the i=1...n’th nitrogen-corrected isRestCHO the sugar content in thefeedstuff, i=1...n'th i=1...n'th Equations degradation of RestCHO in nitrogen-corrected the rumen, Equation 7.12; SUi content feedstuff, g/kg DM; iRestCHOcorr i is the feedstuff, Equations g/kgDMI DM;i is RestCHOcorr 4.2the anddry 4.3;matter DMI nitrogen-corrected the dry intake RestCHO offeedstuff, the i=1...n'th content feedstuff, in the kg/d; 4.2 and 4.3; intake of matter the i=1...n’th kg/d; andi=1...n'th NDF i is the i is i is the NDF isthe thei=1...n’th NDF4.2 content in the i=1...n'th feedstuff, DM. and NDFin feedstuff, and 4.3; DMI dry matter g/kg intake of the i=1...n'th feedstuff, kg/d; i Equations i is the content feedstuff, g/kg DM. and NDFi is the NDF content in the i=1...n'th feedstuff, g/kg DM. The is based on aonmodified equation derived from from the work Danfær et The kdNDF kdNDFcorrection correctionfactor factor is based a modified equation derived the of work of Danfær et al. (2006): The kdNDF correction factor is based on a modified equation derived from the work of Danfær et al. (2006): al. (2006): 0.5431 corrNDF _ fac = 0.4561 + 7.16 [Eq. 7.16] 5431− RLI ⎞ ⎛ 00..7657 corrNDF _ fac = 0.4561 + ⎜ ⎟ [Eq. 7.16] 0.7657 − RLI 1 + e ⎝⎛⎜ − 0.1589 ⎠⎞⎟ − 0.1589 ⎠ ⎝ 1 + ekdNDF where corrNDF_fac is the correction factor, 0-1; and RLI is the ratio of rapidly degraded where corrNDF_fac is the kdNDF factor,7.15. 0-1; and RLI is the ratio of rapidly degraded carbohydrates to total fibre in thecorrection diet, Equation carbohydrates to totalisfibre in the diet, Equation 7.15.0-1; and RLI is the ratio of rapidly degraded where corrNDF_fac the kdNDF correction factor, carbohydrates to total fibre in the diet, Equation 7.15. Figure 7.2 illustrates the relationship between corrNDF_fac and RLI. The kdNDF is multiplied by Figure 7.2 illustrates the relationship between corrNDF_fac and RLI. The kdNDF is multiplied thethe corrNDF_fac and with increasing of ruminal ruminal degraded starchand and sugars by corrNDF_fac and willthus thusdecrease decrease with increasing level level of degraded starch Figure 7.2 illustrates thewill relationship between corrNDF_fac and RLI. The kdNDF is multiplied in the diet. Equation 7.15 show that starch sources with a slow kdST have a less negative impact on sugars in the diet. Equation that starch sources with a slow kdST have a lessstarch negative by the corrNDF_fac and will7.15 thusshow decrease with increasing level of ruminal degraded and impact onthe ruminal NDF digestion than that rapidly degradable starch sources. RestCHO sugars in diet. Equation 7.15 show starch sources with a slow kdST have a less i-SUnegative i is an ana impact ruminalofNDF digestion rapidly sources. RestCHO indirecton estimate soluble fibre inthan the diet, anddegradable it is knownstarch that the soluble fibre fraction has i-SUi is indirect estimate of soluble fibreNDF in thedigestibility diet, and it is known that soluble fraction has a less negative impact on ruminal when there arethe high levelsfibre of easily less negative1carbohydrates impact on ruminal digestibility areFor highfurther levelsevaluation of easily of RLI, fermentable in theNDF diet (Voelker andwhen Allen,there 2003). fermentable carbohydrates in the diet (Voelker and Allen, 2003). For further evaluation of RLI, see section 14.5. see section0.9 14.5. Figure 7.2 0.8 Figure 7.2 The ruminal 0.7degradation of NDF in concentrates (rd_pdNDFc) in the rumen is estimated from a The ruminal degradation of NDF in concentrates one-compartment degradation model, as follows: (rd_pdNDFc) in the rumen is estimated from a one-compartment degradation model, as follows: 0.6 76 0.5 76 corrNDF_fac

1695 1696 1695 1697 1696 1698 1697 1699 1698 1700 1699 1701 1700 1702 1701 1703 1702 1704 1703 1705 1704 1705

0.4 0.3 0.2 0

0.1

0.2

0.3 0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

Rumen load index (RLI)

Figure 7.2. Relationship between the kdNDF correction factor (corrNDF_fac; Equation 7.16) and the rumen load index (RLI). 64 

NorFor – The Nordic feed evaluation system

ruminal NDF digestion than rapidly degradable starch sources. RestCHOi-SUi is an indirect estimate of soluble fibre in the diet, and it is known that the soluble fibre fraction has a less negative impact on ruminal NDF digestibility when there are high levels of easily fermentable carbohydrates in the diet (Voelker and Allen, 2003). For further evaluation of RLI, see Section 14.5. 1736 1736 1736 1737 1737 1737 1738 1738 1739 1738 1739 1740 1739 1740 1741 1740 1741 1742 1741 1742 1743 1742 1743 1744 1743 1744 1745 1744 1745 1746 1745 1746 1747 1746 1747 1748 1747 1748 1749 1748 1749 1750 1749 1750 1751 1750 1751 1752 1751 1752 1753 1752 1753 1754 1753 1754 1755 1754 1755 1756 1755 1756 1757 1756 1757 1758 1757 1758 1758 1759 1759 1760 1759 1760 1761 1760 1761 1762 1761 1762 1763 1762 1763 1764 1763 1764 1765 1764 1765 1766 1765 1766 1766 1767 1767 1768 1767 1768 1769 1768 1769 1770 1769 1770 1771 1770 1771 1772 1771 1772 1773 1772 1773 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782

The ruminal degradation of NDF in concentrates (rd_pdNDFc) is estimated from a one-compartment degradation model, as follows: pdNDF rd _ pdNDFc = ∑ DMI i ⋅ NDFi ⋅ pdNDFi 1000 i rd _ pdNDFc = ∑i DMIi ⋅ NDFi ⋅ pdNDF rd _ pdNDFc = ∑i DMI i ⋅ NDFi ⋅ 1000 i 1000 i

kdNDFi ⋅ corrNDF _ fac ⋅ kdNDFi ⋅ corrNDF _ fac _ fac) +_rfac _ kpNDFc  ⋅ (kdNDFkdNDF i ⋅ corrNDF i ⋅ corrNDF _ fac) + r _ kpNDFc ⋅ (kdNDFi ⋅ corrNDF (kdNDFi ⋅ corrNDF _ fac) + r _ kpNDFc

[Eq. 7.17] [Eq. 7.17] [Eq. 7.17]

7.17

where, degradation of potentially degradable NDF NDF in concentrates in the rumen, where,rd_pdNDF rd_pdNDFisisthethe degradation of potentially degradable in concentrates in the rumen, where, rd_pdNDF ismatter the degradation of potentially degradable NDF in iconcentrates in the rumen, g/d; DMI thethe drydry intake of the i=1...n'th feedstuff, kg/d; NDF is NDF content in content the i isis g/d; DMI matter intake of the i=1...n’th feedstuff, kg/d; NDF is NDF in the where, rd_pdNDF the degradation of potentially degradable NDF in iconcentrates in the iis the dryismatter g/d; DMI intake of the i=1...n'th feedstuff, kg/d; NDF iscontent NDF icontent in rumen, the ifeedstuff, i=1...n'th g/kg DM; pdNDF potentially degradable NDF in the i=1...n'th i is the i=1...n’th feedstuff, g/kg DM; pdNDF is the potentially degradable NDF content in the i=1...n’th g/d; DMI is the dry matter intake of the i=1...n'th feedstuff, kg/d; NDF is NDF content in the ifeedstuff, g/kg DM; pdNDFi is the i content in the i=1...n'th i potentially degradable NDF i=1...n'th is is thethe fractional degradation raterate of pdNDF in the feedstuff, g/kg kdNDF feedstuff,feedstuff, g/kgNDF; NDF; kdNDF degradation of pdNDF ini=1...n'th the i=1...n’th i=1...n'th g/kg DM;ii pdNDF is the potentially degradable NDF content in the i=1...n'thfeedstuff, i fractional theNDF fractional degradation rate of pdNDF the i=1...n'th feedstuff, g/kg kdNDF feedstuff, %/h; NDF; corrNDF_fac isisi the degradation rate correction factor,inEquation 7.16; and is the fractional degradation rate of pdNDF in the i=1...n'th feedstuff, g/kg NDF; kdNDF %/h; corrNDF_fac is the NDF degradation rate correction factor, Equation 7.16;7.16; and and r_kpNDFc is iis the NDF degradation rate correction factor, Equation feedstuff, %/h; corrNDF_fac r_kpNDFc is the passage rate of NDF in concentrates from the rumen, Equation 7.4. feedstuff, %/h; isofthe NDF degradation rate factor, the passage of NDFrate in concentrates from the rumen, 7.4.Equation r_kpNDFc israte thecorrNDF_fac passage NDF in concentrates fromcorrection theEquation rumen, Equation 7.4. 7.16; and r_kpNDFc is the passage rate of NDF in concentrates from the rumen, Equation 7.4. In NorFor degradation of NDF in roughage (rd_pdNDFr) is estimated from a two-compartment In degradation of NDF in roughage (rd_pdNDFr) is estimated from In NorFor NorFor ruminal of NDF in roughage (rd_pdNDFr) istwo-compartment estimatedpool. from a twodigestion model (Allendegradation and Mertens, 1988) comprising a non-escapable andaan escapable In NorFormodel degradation of NDF in roughage (rd_pdNDFr) is estimated from aantwo-compartment digestion (Allen Mertens, 1988) comprising a non-escapable andfrom pool. compartment digestion model (Allen and Mertens, 1988) comprising aescapable non-escapable The non-escapable pooland represents large particles that are unable to escape the rumen, while and an digestion model (Allen and Mertens, 1988) comprising a non-escapable and an escapable pool. The non-escapable poolnon-escapable represents particles thatpass are unable to escape rumen, escapable pool. pool represents large particles thatfrom are the unable to while escape the escapable poolThe represents smalllarge particles that can out of the rumen. This model takes into from The non-escapable pool represents large particles thatpass are unable to escape from rumen, while the escapable pool the represents small particles thatincan outand of the rumen. Thisthe model takes into account the selective retention of feed particles the rumen provides a pass more realistic the rumen, while escapable pool represents small particles that can out of the rumen. the escapable pool represents small particles thatincan outand of the rumen.a more This model takes into This account the selective retention of feed particles the pass rumen provides realistic estimate of ruminal NDF digestion than a one-compartment model (Huhtanen et al., 1995; Minde model takes into account the of selective retention of rumen feed particles in thea rumen and provides a more account the selective retention feed in the and provides more realistic estimate of1997). ruminal NDF digestion thanparticles a one-compartment model et al., 1995; Minde and Rygh, The pdNDF passage from thethan non-escapable pool(Huhtanen to themodel escapable pool, and realistic estimate of ruminal NDF digestion a one-compartment (Huhtanen et al., 1995; estimate of ruminal NDF digestion than a one-compartment model (Huhtanen et al., 1995; Minde and Rygh, 1997). The pdNDF passage from the non-escapable pool to the escapable(Equation pool, and subsequently from the escapable pool out of the rumen, is expressed by r_kpNDFr Minde and Rygh, 1997). The pdNDF passage from the non-escapable pool to the escapable pool, and Rygh, 1997). The passage from non-escapable pool to the escapable(Equation pool, and subsequently from the pdNDF escapable pool out of the thepool rumen, is expressed 7.5). The MRT of pdNDF in the non-escapable is assumed to beby 40r_kpNDFr % of the total rumen and subsequently from the escapable pool out of the rumen, is expressed by r_kpNDFr (Equation subsequently from the escapable pool out of the rumen, is expressed by r_kpNDFr (Equation 7.5). The MRTvalue of pdNDF in the non-escapable pool isprofiles assumed to be 401996; % ofMinde the total rumen MRT; a mean estimated from duodenal marker (Stensig, and Rygh, 7.5). MRT of ininthe pool isprofiles assumed to beto40 %40% of the rumen 7.5). The The MRT ofpdNDF pdNDF thenon-escapable non-escapable pool is assumed be of total theand total rumen MRT; MRT; aThe mean value estimated from duodenal marker (Stensig, 1996; Rygh, 1997). release factor (r_kp1) from the non-escapable to the escapable poolMinde is calculated as: MRT; mean value estimated from duodenal marker profiles (Stensig, 1996; Minde and Rygh, a meanaThe value estimated from duodenal profiles (Stensig, 1996; Minde and Rygh, 1997). The 1997). release factor (r_kp1) from themarker non-escapable to the escapable pool is calculated as: 1997). release factorfrom (r_kp1) from the non-escapable to the escapable pool is calculated as: releaseThe factor (r_kp1) the non-escapable to the escapable pool is calculated as: 100 [Eq. 7.18] r _ kp1 = 100 100 [Eq. 7.18] r _ kp1 = 100 ⋅ 0.4 100 [Eq. 7.18] 7.18 r _ kp1 = r _ kpNDFr ⋅ 0.4  100 r _ kpNDFr ⋅ 0.4 r _ kpNDFr

where r_kp1 is the release rate of pdNDF from the non-escapable to the escapable ruminal NDF where r_kp1 isisr_kpNDFr the rate ofpassage pdNDF from the non-escapable to the in escapable ruminal NDF where%/h; r_kp1 therelease release rate of pdNDF the from non-escapable toroughage, the escapable ruminal NDF pool, and is the rate offrom pdNDF the rumen Equation where%/h; r_kp1 is r_kpNDFr the release is rate ofpassage pdNDFrate from the non-escapable to the in escapable ruminal NDF pool, and the of pdNDF from the rumen roughage, Equation 7.5. pool, %/h; and r_kpNDFr is the passage rate of pdNDF out of the rumen in roughage, Equation 7.5. pool, 7.5. %/h; and r_kpNDFr is the passage rate of pdNDF from the rumen in roughage, Equation 7.5. The thethe escapable poolpool out out of the is: is: The passage passageofofpdNDF pdNDFfrom from escapable of rumen the rumen The passage of pdNDF from the escapable pool out of the rumen is: The passage of pdNDF from the escapable pool out of the rumen is: 100 [Eq. 7.19]  7.19 r _ kp 2 = 100 100 [Eq. 7.19] r _ kp 2 = 100 ⋅ 0 .6 100 [Eq. 7.19] r _ kp 2 = r _ kpNDFr ⋅ 0 .6 100 r _ kpNDFr ⋅ 0 .6

r _ kpNDFr where r_kp2 is the passage rate of pdNDF from the escapable pool out of the rumen, %/h; and where r_kp2 is the passage rate of pdNDF from the escapable pool out of the rumen, %/h; and r_kpNDFr is the passage rate out of rumen ofescapable NDF in roughage, where r_kp2 is passage from pool out of Equation the rumen, %/h; and r_kpNDFr is thethe passage rate outof ofpdNDF thethe rumen ofthe NDF in roughage, Equation 7.5. 7.5.

where r_kp2 passage rate fromofthe escapable pool out of the rumen, r_kpNDFr is is thethe passage rate outofofpdNDF the rumen NDF in roughage, Equation 7.5. %/h; and r_kpNDFr is the passage rate outdigestion of the rumen of NDF in roughage, Equationof7.5. From the two-compartment model the rumen degradation inisroughage is From the two-compartment digestion model the rumen degradation of pdNDF inpdNDF roughage From the two-compartment digestion model the rumen degradation of pdNDF in roughage is calculated as: calculated as: From the two-compartment digestion model the rumen degradation of pdNDF in roughage is calculated as: kdNDFi ⋅ corrNDF _ fac ⎛ ⎞ calculated as: ⋅ ⎜ ⎟ rd _ pdNDFr = ∑ DMI i ⋅ NDFi ⋅ i

pdNDFi 1000

⎜ ( kdNDFi ⋅ corrNDF _ fac) + r _ kp1 ⎟ ⋅⎜ ⎞ ⎟⎟ r _ kp1 ⎜ ⎛⎜1 + ⎟⎟ ⎜⎜ ⎟ ⎝ ⎝ ( kdNDFi ⋅ corrNDF _ fac) + r _ kp 2 ⎠ ⎠



[Eq. 7.20]

7.20

77 where rd_pdNDFr is the ruminal degradation of 77potentially degradable NDF in roughage, g/d; DMIi is where rd_pdNDFr is the ruminal degradation of potentially NDF ininroughage, g/d; feedstuff, 77 the dry matter intake of the i=1...n’th feedstuff, kg/d; NDFdegradable is NDF content the i=1...n’th DMIi is the dry matter intake of the i=1...n'th feedstuff, kg/d; iNDFi is NDF content in the i=1...n'th feedstuff, g/kg DM; pdNDFi is the potentially degradable NDF content in the i=1...n'th feedstuff, g/kg NDF; kdNDFi is the fractional degradation rate of pdNDF in the i=1...n'th NorFor –%/h; The corrNDF_fac Nordic feedisevaluation system rate correction factor, Equation 7.16; r_kp1 65 feedstuff, the NDF degradation is the release rate of pdNDF from the non-escapable to the escapable ruminal NDF pool, Equation 7.18; and r_kp2 is the passage rate of pdNDF from the escapable pool out of the rumen,

1774 1774 1775 1776 1775 1775 1777 1776 1776 1778 1777 1777 1779 1778 1778 1780 1779 1779 1781 1780 1780 1782 1781 1781 1783 1782 1782 1784 1783 1783 1785 1784 1784 1786 1785 1785 1787 1786 1786 1788 1787 1787 1789 1788 1788 1790 1789 1789 1791 1790 1790 1791 1791 1792 1793 1792 1792 1794 1793 1793 1795 1794 1794 1796 1795 1795 1797 1796 1796 1798 1797 1797 1799 1798 1798 1799 1800 1799 1801 1800 1800 1802 1801 1801 1803 1802 1802 1804 1803 1803 1805 1804 1804 1806 1805 1805 1807 1806 1806 1808 1807 1807 1809 1808 1808 1810 1809 1809 1810 1811 1810 1811 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820

⎞⎟ kp1) + r _ kp1 ⋅ corrNDFr __ fac ⎜ ⎛⎜((1kdNDF ⎟⎟ kdNDF + ii ⋅ corrNDF _ fac ) + r _ kp1 [Eq. 7.20] 7.20] ⋅⋅ ⎜⎜⎜ ⎜⎝ ( kdNDF _ fac) + r _ kp 2 ⎟⎠ ⎟⎟⎟⎠ [Eq. i ⋅ corrNDF ⎜⎝ ⎛⎛ ⎞⎞ ⎟ kp11 rr __ kp ⎜⎜ ⎜1 + ⎟ ⎟ ⎟ ⎜ ⎟ ⎜⎜⎝ ⎜⎝1 + (( kdNDF kdNDFii ⋅⋅ corrNDF corrNDF __ fac fac)) + kp 22 ⎟⎠⎠ ⎟⎠⎠ + rr __ kp ⎝⎝ where rd_pdNDFr is the ruminal degradation of potentially degradable NDF in roughage, g/d; 1000 i pdNDF pdNDFi rd rd __ pdNDFr pdNDFr = DMI ii ⋅⋅ NDF NDFii ⋅⋅ 1000 i =∑ ∑ DMI ii 1000

DMIi is the dry matter intake of the i=1...n'th feedstuff, kg/d; NDFi is NDF content in the where rd_pdNDFr is the DM; ruminal degradation of degradable NDF in g/d; where rd_pdNDFr isg/kg ruminal degradation of potentially potentially degradable NDF in roughage, roughage, g/d; i=1...n'th feedstuff, pdNDF the potentially degradable NDF content infeedstuff, the i=1...n'th i is g/kgi DM; pdNDF isthe the potentially degradable NDFkg/d; content in the i=1...n’th g/kg NDF; is the dry matter intake of the i=1...n'th feedstuff, NDF is NDF content in the DMI i i is the dry matter intake of the i=1...n'th feedstuff, kg/d; NDF is NDF content in the DMI i i is fractional degradation rate of pdNDF in the i=1...n'th feedstuff, g/kg NDF; kdNDF i kdNDF is the fractional degradation rate of pdNDF in the i=1...n’th feedstuff, %/h; corrNDF_fac is i=1...n'th feedstuff, g/kg DM; pdNDF is the potentially degradable NDF content in the i=1...n'th i potentially degradable content in the i=1...n'th i=1...n'thi feedstuff, g/kg DM;ispdNDF i is the feedstuff, %/h; corrNDF_fac the NDF degradation rate correctionNDF factor, Equation 7.16; the NDF degradation rate correction factor, Equation 7.16; r_kp1 is the release rate of r_kp1 pdNDF from is the fractional degradation rate of pdNDF in the i=1...n'th feedstuff, g/kg NDF; kdNDF ii is feedstuff, g/kg NDF; kdNDF the fractional degradation rate of pdNDF in the i=1...n'th is the release rate of pdNDF from the non-escapable to the escapable ruminal NDF pool, the non-escapable to the escapable ruminal NDF pool, Equation 7.18; and r_kp2 isthetherumen, passage rate feedstuff, %/h; corrNDF_fac is NDF degradation rate correction factor, Equation r_kp1 feedstuff, %/h; and corrNDF_fac is the the NDF degradation rate correction factor, Equation 7.16; r_kp1 Equation 7.18; r_kp2 is the passage rate of pdNDF from the escapable pool out of7.16; is the release rate of pdNDF from the non-escapable to the escapable ruminal NDF pool, of pdNDF from the escapable pool out of the rumen, Equation 7.19. is the release rate of pdNDF from the non-escapable to the escapable ruminal NDF pool, Equation 7.19. Equation Equation 7.18; 7.18; and and r_kp2 r_kp2 is is the the passage passage rate rate of of pdNDF pdNDF from from the the escapable escapable pool pool out out of of the the rumen, rumen, Equation 7.19. Equation 7.19. In a feed ration the total degradation of NDF in the rumen is calculated as: In a feed ration the total degradation of NDF in the rumen is calculated as: In a feed feed =ration ration the total total+ degradation degradation of NDF in in the the rumen rumen is is calculated calculated as: as: In the rd _a NDF rd _ pdNDFc rd _ pdNDFr of NDF 

[Eq. 7.21]

7.21

rd = + rd __ pdNDFr [Eq. 7.21] rd _ _ NDF NDF = rd rd _ _ pdNDFc pdNDFc +total rddegradation pdNDFr [Eq. 7.21] where rd_NDF isisthe of NDF in the g/d; rd_pdNDF is the degradation of where rd_NDF thetotal degradation of NDF in rumen, the rumen, g/d; rd_pdNDF is the degradation of potentially degradable NDF in concentrate, Equation 7.17;7.17; and rd_pdNDFr is the degradation of potentially degradable NDF in concentrate, Equation and rd_pdNDFr is the degradation of where rd_NDF is degradation of NDF in rumen, g/d; where rd_NDF is the the total total degradation of Equation NDF in the the rumen, g/d; rd_pdNDF rd_pdNDF is is the the degradation degradation of of potentially degradable NDF in roughage, 7.20. potentially degradable NDF roughage, Equation 7.20.and potentially degradable NDF in in concentrate, Equation 7.17; rd_pdNDFr is is the the degradation degradation of of potentially degradable NDF in concentrate, Equation 7.17; and rd_pdNDFr potentially degradable NDF in roughage, Equation 7.20. potentially degradable NDFof in crude roughage, Equation 7.20. 7.1.4 Rumen metabolism fat and fermentation products

7.1.4 Rumen metabolism of crude fat and fermentation products

The CFat has ametabolism variable FA of component (see section 4.5). In concentrates, the sole non-fatty acid 7.1.4 fermentation products 7.1.4 Rumen Rumen metabolism of crude crude fat fat and and fermentation products component (1000-FA) of triacylglycerols is assumed to consist of glycerol, while in roughage The CFat CFathas has a variable FA component (see Section 4.5). In concentrates, the non-fatty acid The aa variable FA 4.5). concentrates, acid The CFat has variable FAtocomponent component (see section 4.5). Ingalactose. concentrates, the sole non-fatty acid this component is assumed consist of (see bothsection glycerol andIn Sincethe thesole fattynon-fatty acids provide component (1000-FA) of triacylglycerols is assumed to consist of glycerol, while in roughage this component (1000-FA) of triacylglycerols is assumed to consist of glycerol, while in roughage component of triacylglycerols assumed to consist ofofglycerol, in roughage no ATP for (1000-FA) microbial growth (Van Soest, is 1994), the metabolism CFat in while the rumen is this component is consist both glycerol the fatty acids provide component is assumed toto ofof andgalactose. galactose.Since Since fatty acids provide no this component is assumed toconsist consist ofboth both glycerol glycerol and and galactose. Since thethe fatty acids provide calculated from theassumed following equation: no ATP microbial growth (Van Soest, of CFat in is ATP forfor microbial 1994),the themetabolism metabolism CFat in rumen the rumen no ATP for microbialgrowth growth(Van (Van Soest, Soest, 1994), 1994), the metabolism of of CFat in the the rumen is is calculated calculated from equation: from the following equation: calculated from the the following following equation: 1000 − FA i [Eq. 7.22] rd _ CFat = ∑ DMI i ⋅ CFat i ⋅ 1000 i 1000 − FA [Eq.  rd __ CFat CFat = =∑ DMI i ⋅⋅ CFat CFat i ⋅⋅ 1000 − FA ii [Eq. 7.22] 7.22] rd ∑ DMI i i 1000 ii 1000 of crude fat in the rumen, g/d; DMI is the dry matter intake of where rd_CFat is the metabolism i

7.22

the i=1...n'th feedstuff, kg/d; CFati isofthe crude in theg/d; i=1...n'th DM; where rd_CFat is the metabolism crude fatfatincontent the rumen, DMIi feedstuff, is the dry g/kg matter intake of the is dry intake of where is metabolism of crude fat in the rumen, g/d; DMI is the thefeedstuff, dry matter matterg/kg intake of and FA where rd_CFat is the the kg/d; metabolism of the crude fat i=1...n'th in the rumen, g/d; DMI the proportion ofCFat fattyi acids in the feedstuff, g/kgii CFat; and FArd_CFat i=1...n’th i is feedstuff, is crude fat content in the i=1...n’th DM; i the i=1...n'th feedstuff, kg/d; CFat is the crude fat content in the i=1...n'th feedstuff, g/kg DM; i thei=1...n’th crude fat feedstuff, content in the i=1...n'th feedstuff, g/kg DM; the i=1...n'th feedstuff, kg/d; CFatini isthe is the proportion of fatty acids g/kg CFat. is the proportion of fatty acids in the i=1...n'th feedstuff, g/kg CFat; and FA and theassumed proportion ofcompletely fatty acids in the i=1...n'th g/kg CFat; ii is The FA FPF are to be liberated in the feedstuff, rumen. The VFAs do not yield ATP for microbial growth, while lactic acid (the dominating acid in silage) only provides a do small amount The FPF FPFare areassumed assumed to completely be completely liberated in the rumen. Thedo VFAs not yield ATP for The be liberated in rumen. The yield for The FPF are tosynthesis be completely liberated in the the rumen. The VFAs VFAs do not notFPF yieldinATP ATP for of energy forassumed microbialto (Van Soest, 1994). The metabolism of dietary the rumen microbial growth, while lactic acid (the dominating acid in silage) only provides a small amount of microbial growth, while lactic acid (the dominating acid in silage) only provides a small amount microbial growth, lactic acid (the dominating acid in silage) only provides a small amount is calculated from while the equation: of energy microbial synthesis of FPF in energy forfor Soest,1994). 1994).The Themetabolism metabolism of dietary inrumen the rumen is of energy formicrobial microbialsynthesis synthesis (Van (Van Soest, Soest, 1994). The metabolism of dietary dietary FPFFPF in the the rumen is calculatedfrom from theequation: equation: calculated is fromithe rdcalculated _ FPF = ∑ DMI ⋅the FPFequation: [Eq. 7.23] i i

[Eq. [Eq. 7.23] 7.23]

rd rd __ FPF FPF = =∑ DMI ii ⋅⋅ FPF FPFii  ∑ DMI ii

7.23

where rd_FPF is the liberation of dietary fermentation products in the rumen, g/d; DMIi is the dry matterrd_FPF intake isofthe theliberation i=1...n’thoffeedstuff, kg/d; FPFi is the fermentation content 78and products dry in the where dietary fermentation in the rumen, g/d; product DMIi is the i=1...n’th feedstuff, Equation 4.7. kg/d; and FPFi is the fermentation product content in the matter intake of the i=1...n'th feedstuff, i=1...n'th feedstuff, Equation 4.7.

78 78

7.1.5 Effective rumen degradability 7.1.5 Effective rumen degradability Effective (erd) is used as anasestimate of ruminal digestibility. The erdThe is also Effectiverumen rumendegradability degradability (erd) is used an estimate of ruminal digestibility. erd is also used onon ruminal digestibility. TheThe erd erd values are are calculated usedto toevaluate evaluatethe theeffect effectofofdiet dietcomposition composition ruminal digestibility. values calculated for the main nutrients, as described in Equations 7.27 CP, ST,NDF and for the main nutrients, as described in Equations 7.24, 7.24, 7.25,7.25, 7.267.26 andand 7.27 forfor CP, ST, NDF and RestCHO, respectively: RestCHO, respectively: rd _ CP ⋅ 100

1821

erd _ CP =

1822 1823 1824 1825 1826

where %;%; rd_CP is is thethe degradation whereerd_CP erd_CPisisthe theeffective effectivedegradability degradabilityofofcrude crudeprotein proteinininthetherumen, rumen, rd_CP the crude protein in the feedstuff, degradation of crude protein in Equation the rumen,7.8; Equation CPi isprotein of crude protein in the rumen, CPi is 7.8; the crude content incontent the i=1...n’th the dry intake offeedstuff, the i=1...n'th feedstuff, kg/d. i=1...n'th g/kg andmatter DMIi is g/kg DM;feedstuff, and DMI is DM; the dry intake of matter the i=1...n’th kg/d.

1827 1828 1829

∑ DMI i ⋅ CPi i



[Eq. 7.24]

7.24

i

erd _ ST =

66 

rd _ ST ⋅ 100 i ⋅ STi

∑ DMI i

[Eq. 7.25]

NorFor – The Nordic feed evaluation system

where erd_ST is the effective degradability of starch in the rumen, %; rd_ST is the degradation of

1822 1823 1823 1824 1824 1825 1825 1826 1826 1827 1827 1828 1828 1829 1829 1830 1830 1831 1831 1832 1832 1833 1833 1834 1834 1835 1835 1836 1836 1837 1837 1838 1838 1839 1839 1840 1840 1841 1841 1842 1842 1843 1843 1844 1844 1845 1845 1846 1846 1847 1847 1848 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858

1859

1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876

where where erd_CP erd_CP is is the the effective effective degradability degradability of of crude crude protein protein in in the the rumen, rumen, %; %; rd_CP rd_CP is is the the degradation degradation of of crude crude protein protein in in the the rumen, rumen, Equation Equation 7.8; 7.8; CP CPii is is the the crude crude protein protein content content in in the the i=1...n'th is the the dry dry matter matter intake intake of of the the i=1...n'th i=1...n'th feedstuff, feedstuff, kg/d. kg/d. i=1...n'th feedstuff, feedstuff, g/kg g/kg DM; DM; and and DMI DMIii is rd rd __ ST ST ⋅⋅100 100 erd __ ST ST = erd =  DMI ∑ STii ∑ DMI ii ⋅⋅ ST

[Eq. [Eq. 7.25] 7.25]

7.25

i i

where degradability of in %; is of whereerd_ST erd_STis theeffective effective degradability of starch inrumen, the rumen, %; rd_ST is the degradation of where erd_ST isisthe the effective degradability of starch starch in the the rumen, %; rd_ST rd_ST is the the degradation degradation of is starch content in the i=1...n'th feedstuff, g/kg DM; starch Equation 7.14; ST starchin therumen, rumen, Equation 7.14; is the starch content in the i=1...n’th feedstuff, g/kg DM; is the the starch content in the i=1...n'th feedstuff, g/kg DM; starch ininthe the rumen, Equation 7.14; STii ST i and intake of i=1...n'th feedstuff, kg/d. and DMI isisthe the dry matter intake of the the i=1...n'th feedstuff, kg/d.kg/d. and DMI DMIii iis thedry drymatter matter intake of the i=1...n’th feedstuff, rd __ NDF rd NDF ⋅⋅100 100 erd erd __ NDF NDF = = ∑ DMI ⋅ NDF ∑ DMI ii ⋅ NDFii

[Eq. [Eq. 7.26] 7.26]



7.26

ii

where the effective degradability of the %; is whereerd_NDF erd_NDFis effective degradability of in NDF in the rumen, %; rd_NDF is the ruminal where erd_NDF is is thethe effective degradability of NDF NDF in the rumen, rumen, %; rd_NDF rd_NDF is the the ruminal ruminal is the NDF content in the i=1...n'th feedstuff, g/kg degradation of NDF, Equation 7.21; NDF i degradation of NDF, Equation 7.21; NDF is the NDF content in the i=1...n’th feedstuff, degradation of NDF, Equation 7.21; NDFi is ithe NDF content in the i=1...n'th feedstuff, g/kg g/kg DM; DM; and the matter intake i=1...n'th feedstuff, kg/d. ii is and DMI matter intake of of thethe i=1...n’th is dry the dry dry matter intake of the i=1...n'thfeedstuff, feedstuff, kg/d. kg/d. DM; and DMI DMI i is the rd rd _ _ Re Re stCHO stCHO ⋅⋅ 100 100 erd __ Re Re stCHO stCHO = erd = DMI ⋅ Re stCHOcorr ∑ i ∑ DMI i ⋅ Re stCHOcorr ii

[Eq. [Eq. 7.27] 7.27]



7.27

ii

whereerd_RestCHO erd_RestCHO effective degradability of RestCHO the rumen, %; rd_RestCHO is where is the effective degradability of in rumen, %; is where erd_RestCHO is is thethe effective degradability of RestCHO RestCHO in the the in rumen, %; rd_RestCHO rd_RestCHO is the in the rumen, Equation 7.12; RestCHOcorr corrected the degradation degradationof RestCHO rumen, Equation 7.12; RestCHOcorr the corrected RestCHO ii is the degradation ofofRestCHO RestCHO in in thethe rumen, Equation 7.12; RestCHOcorr is the the i iscorrected matter RestCHO in feedstuff, Equations 4.2 and 4.3; and ii is content incontent the i=1...n’th feedstuff, Equations 4.2 and drydry matter intake of the is the the dry matter RestCHO content in the the i=1...n'th i=1...n'th feedstuff, Equations 4.24.3; and and 4.3; DMI and DMI DMI i is the intake of i=1...n'th feedstuff, i=1...n’th feedstuff, intake of the the i=1...n'thkg/d. feedstuff, kg/d. kg/d. 7.1.6 Microbial efficiency and chemical composition 7.1.6 7.1.6 Microbial Microbialefficiency efficiencyand andchemical chemicalcomposition composition

The The equation equation presented presented by by Archimède Archimède et et al. al. (1997) (1997) was was used used as as aa basis basis to to estimate estimate efficiency efficiency of of protein synthesis in the rumen (r_emCP). However, when the equation was evaluated microbial microbial protein synthesisby in Archimède the rumen (r_emCP). However, whenas thea equation evaluated The equation presented et al. (1997) was used basis to was estimate efficiency of using Norwegian data, r_emCP overestimated at intake (Figure 7.3), using Norwegian r_emCPinwas was at high highHowever, intake levels levels (Figure 7.3), and and the the microbial proteindata, theoverestimated rumen (r_emCP). when the was evaluated dietary proportion ofsynthesis easily degradable carbohydrates affected the efficiency in aequation curvilinear using Norwegian data, r_emCP was overestimated at high intake levels (Figure 7.3), and the dietary manner. This carbohydrate effect was also discussed by Archimède et al. (1997), who classified 79 79 proportion of easily degradable carbohydrates affected the efficiency in a curvilinear manner. This the carbohydrate source into rapidly degradable starch, slowly degradable starch and digestible carbohydrate effect was also (1997), who classified the carbohydrate fibre. Thus, a new equation wasdiscussed developedbytoArchimède account for et theal.effects of carbohydrate intake on source into rapidly degradable slowly degradable starch and digestible fibre. Thus, a new the efficiency of microbial proteinstarch, synthesis.

equation was developed to account for the effects of carbohydrate intake on the efficiency of

Figure 7.3 protein synthesis. microbial

For equation is used: For dairy dairycows, cows,the thefollowing following equation is used: ⎛ 1000 ⋅ ∑ DMI i ⎜ i r _ emCP = ln ⎜ BW ⎜ ⎝

∑ DMI i ⋅ STi + ∑ DMI i ⋅ Re stCHOcorri ⎞ ⎟ i i − ⎟ ⋅ 55.6 + 0.4166 ⋅ ∑ DMI i ⎟ ⎠ i

⎛ ∑ DMI i ⋅ STi + ∑ DMI i ⋅ Re stCHOcorri ⎜ i 0.0008868 ⋅ ⎜ i ∑ DMI i ⎜ i ⎝

2

⎞ ⎟ ⎟ − 54.56 ⎟ ⎠

 [Eq. 7.28]

7.28

wherer_emCP r_emCPisisthe themicrobial microbialprotein proteinsynthesis synthesis efficiency rumen, g/kg organic matter actually where efficiency in in thethe rumen, g/kg organic matter digested in the rumen; DMI is the dry matter intake of the i=1...n’th feedstuff, kg/d; BW the animal BW actually digested in the rumen; i DMIi is the dry matter intake of the i=1...n'th feedstuff, kg/d;is body weight,body kg; ST is the starch content in the i=1...n’th feedstuff, g/kg DM; and RestCHOcorr is the animal weight, kg; ST i i is i is the starch content in the i=1...n'th feedstuff, g/kg DM; and the nitrogen-corrected RestCHO content (Equations 4.3) in the RestCHOcorr RestCHO content4.2, (Equations 4.2,i=1...n’th 4.3) in thefeedstuff, i=1...n'th g/kg DM. i is the nitrogen-corrected feedstuff, g/kg DM.

Figure 7.4 illustrates how the r_emCP changes with the ST and RestCHO content of the diet (at two

Figure 7.4feed illustrates changesBW). with There the ST is and RestCHO content diet (at levels of intake:how 18 the andr_emCP 36 g DMI/kg a curvilinear effect of of the rapidly degradable two levels of feed 18and andthe 36 efficiency g DMI/kg BW).There a curvilinear carbohydrates onintake: r_emCP is highest atis235 g/kg DMeffect of STof + rapidly RestCHO in the diet, degradable on r_emCPinand efficiency highest at 235 g/kg of ST + concentrate equivalent carbohydrates to 35-45% concentrate thethe diet, which is corresponds well to DM the optimum RestCHO in the by diet, equivalent et to al. 35-45 % concentrate in the diet, which corresponds well to the level observed Archimède (1997). optimum concentrate level observed by Archimède et al. (1997). Figure 7.4

NorFor – The Nordic feed evaluation system

Evaluation of Equation 7.28 for growing cattle showed that it yielded unrealistically low r_emCP values when proportions of ST and RestCHO in the diet were high (> 400 g/kg DM,

67

Microbial N (g/d). (NorFor)

350 300 250 200 150 100 50 0

0

50

100

150

200

250

300

350

Microbial N (g/d). (Archimède et al.)

Microbial protein efficency (g/kg RDOM)

Figure 7.3. Relationship between NorFor and the Archimède et al. (1997) estimates of microbial N supply (data from Rygh, 1997; Volden, 1999; Selemer-Olsen, 1996, Prestløkken, 1999; Mydland, 2008; Kjos, unpublished data; Volden: unpublished data). 225 18 g/kg BW 36 g/kg BW

200 175 150 125 100 75 50 25 0 0

100

200

300

400

500

ST + RestCHO (g/kg DM)

Figure 7.4. Effect of feeding level (g DMI/kg BW) and starch (ST) + residual carbohydrate (RestCHO) content of the diet on efficiency of microbial protein synthesis in the rumen (r_emCP) estimated from Equation 7.28, g/ kg rumen degraded OM (rd_OM). Evaluation of Equation 7.28 for growing cattle showed that it yielded unrealistically low r_emCP values when proportions of ST and RestCHO in the diet were high (>400 g/kg DM, corresponding

68 

NorFor – The Nordic feed evaluation system

1873 1874 1875 1876 1877 1878 1879

1872 FigureFigure 7.4 7.4 1873 1874 Evaluation of Equation 7.28 for7.28 growing cattle showed that it yielded unrealistically low r_emCP 1875 Evaluation of Equation for growing cattle showed that it yielded unrealistically low r_emCP values values when proportions of ST and RestCHO in the diet were 400(> g/kg 1876 when proportions of ST and RestCHO in the diethigh were(>high 400DM, g/kg DM, corresponding to 65-75 concentrate in the diet). avoid adjusted equation was used 1877 corresponding to % 65-75 % concentrate in theTo diet). To this, avoidanthis, an adjusted equation was used to calculate 65-75% concentrate in the diet). Tocattle: avoid this, an adjusted equation was used to calculate r_emCP to r_emCP in growing cattle: 1878 to calculate r_emCP in growing in growing cattle: 1879 2 ⎞ ⎛ ⎛ ⎛ ∑ DMI i ⎛⋅ ST i + ∑ DMI i ⋅ Re stCHOcorr i ⎞ ⎛ ∑ DMI i ⎛⋅ ST i + ∑ DMI i ⋅ Re stCHOcorr i ⎞ ⎜ ⎟ ⎞ ⎜ ⎜ ⎟ ⎜ ⎟ i ⎟ ⎜ ∑ DMI ii ⋅ ST i + ∑ DMI i ⋅ Re stCHOcorr ⎜ ∑ DMI ii ⋅ ST i + ∑ DMI i ⋅ Re stCHOcorr ⎜ ⎟ i i ⎜ i i ⎟ − 0 . 0004323 ⎜ i ⎟ ⎟ − 0 .⋅0004323 ⎜ i ⋅ 3 . 17⋅ ⎜+ 0 . 248 ⋅ ⎜ i ⎜ 3 . 17 + ⎜0 . 248 ⎟ ∑ DMI i ∑ DMI ∑ DMI i ∑ DMI ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ i i ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎜ ⎟ ⎜ ⎝ ⎠ ⎝ ⎠ ⎟ ⎝ ⎠ ⎝ r _ emCP r=_⎜ emCP = ⎜ 2 ⎟ ⎜ ⎜ ⎛ ⎞ ⎛ ⎞ STDMI ReDMI stCHOcorr STDMI ReDMI stCHOcorr ⎞ ⎜ i + ∑i DMI i ⎟ i + ∑i DMI i ⎟ ⎟ ⋅ ST i i+⋅ ∑ ⋅ ST i i+⋅ ∑ ⎜ ∑ DMI i ⎛⎜⋅ ∑ ⎜ ∑ DMI i ⎛⎜⋅ ∑ ⎜ i ⋅ Re stCHOcorr i ⎟ i ⋅ Re stCHOcorr i i i i ⎜ 1 − 0 . 001879 ⎟ i i i i ⎜ ⎟ ⎜ ⎟ ⎜ 1 − 0⋅ . 001879 ⋅ ⎜ + 0 . 000003548 ⋅ ⎟ + 0 . 000003548 ⋅⎜ ⎜ ∑ DMI i ∑ DMI ∑ DMI i ∑ DMI ⎜ ⎟ ⎜ ⎟ ⎟ ⎜ ⎜ ⎟ ⎜ i i ⎜ ⎟ ⎜ ⎟ ⎟ ⎜ ⎜ ⎟ ⎜ ⎜ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎝

1880 1880



2

2

⎞ ⎟ ⎟ ⎟ ⎟ ⎟ 2 ⎟ ⎞ i ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ ⎠ ⎞ ⎟ ⎟ ⎟ ⎟ ⎠

7.29

2 ⎛ 1000 ⋅ ∑ ⎛DMI 1000 i ⋅⎞ ∑ DMI i ⎞

⎛ 1000 ⋅ ∑ ⎛DMI 1000 i ⋅⎞ ∑ DMI i ⎞

⎜ ⎟ ⎜ ⎟ ⎟ ⎟ i⎜ i⎜ + ln ⎜ 1882 ⎟ i⋅ 33 . 695 ⎟+⋅ 033. 05575 ⎟i . 695 +⋅ 0⎜ . 05575BW ln ⎜ +BW ⋅⎜ ⎟ BW BW ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ DMI ⎠, ST , RestCHOcorr ⎝ ⎝ ⎝ ⎠ 1883 where are described in Equation 7.28. r_emCP, i i i⎠ and BW 1882 1881 [Eq. 7.29] 1881 [Eq. 7.29] 1884 where r_emCP, DMI , ST , RestCHOcorr and BW described in Equation 1883 where r_emCP, DMIi, iSTi, iRestCHOcorri and are are described in Equation 7.28. 7.28. i BW 1885 When the ST and RestCHO content in the diet is less than 300 g/kg DM, the effect of rapidly 1884 80 in Equations 80 1886 fermentable carbohydrates on r_emCP is identical 7.28DM, and 7.29. 1885 When RestCHO content in the less is than 300 g/kg the effect of rapidly Whenthe theST STand and RestCHO content indiet theisdiet less than 300 g/kg DM, the effect of rapidly 1887 1886 fermentable carbohydrates on r_emCP is identical in Equations 7.28 and 7.29. fermentable carbohydrates on r_emCP is identical in Equations 7.28 and 7.29. 1888 1887 The ATP for microbial growth per unit digested OM differs between substrates (Van Soest, 1889 1994) as the relative proportions of ATPdigested suppliedOM from protein and lactic acid are onlySoest, 0.5 and 1888 The for microbial growth per differs between substrates (Van TheofATP ATP forsupplied microbial growth perunit unit digested OM differs between substrates (Van Soest, 1994) as 1890 0.2 those from carbohydrates, respectively. This is taken into account when 1889 1994) as the relative proportions of ATP supplied from protein and lactic acid are only 0.5 and the relative proportions of ATPsupply: supplied from protein and lactic acid are only 0.5 and 0.2 of those 1891 microbial 1890 calculating 0.2 of thosethe supplied fromprotein carbohydrates, respectively. This is taken into account when 1892 supplied from carbohydrates, respectively. This is taken into account when calculating the microbial 1891 calculating the microbial protein supply: ⎛ protein supply: ⎛ (rd _ ST + rd _ NDF+ rd _ restCHO+ rd _ CFat)⎞ ⎞ 1892 ⎜ r _ emCP⋅ ⎜ ⎟⎟

1893 1893 1894 1895 1894 1896 1895 1897 1896 1898 1897 1899 1898 1900 1899 1901 1900 1902 1901 1903 1902 1904 1903 1905 1904 1906 1905 1907 1906 1908 1907 1909 1908 1910 1909 1911 1910 1912 1911 1913 1912 1914 1913

1914 1915

⎟⎟ ⎜ + rd _ CPcorr⋅ 0.5 + rd _ FPF⋅ 0.125 ⎜ ⎝ rd_ST ⎝ _ ST ++rd _ NDF _ restCHO _ CFat )⎠⎞⎠⎞⎟) ⎞⎞ rd_ NDF++rd rd_ restCHO+ +rdrd_ CFat r _ mCP= ⎛⎜⎛⎜r _ emCP⋅ ⎛⎜⎛⎜((rd ⎟⎟ ⎟ ⎟ 1000 ⎜⎜ r _ emCP⎜⋅⎜++rd ⎟ _ CPcorr .5 ++rd _ FPF .125 CPcorr⋅ 0⋅0.5 rd_ FPF⋅ ⋅00.125 ⎠ ⎠ ⎟⎠ ⎟⎠ ⎝⎝ rd_ ⎝ r _ mCP== ⎝ 1000 1000

[Eq. 7.30]

[Eq. 7.30]  7.30 where r_mCP is the rumen microbial protein flow out of the rumen, g/d; r_emCP is the efficiency of microbial CP, Equation 7.28 for cows and flow Equation 7.29 for growing cattle; rd_ST isefficiency the efficiency of where r_mCP therumen rumen microbial protein flow of the rumen, r_emCP is the where r_mCP isisthe microbial protein outout of the rumen, g/d;g/d; r_emCP is the degradation of starch in the rumen, Equation 7.14; rd_NDF is degradation NDF is in the degradation microbial CP, Equation 7.28 for and 7.29 growing cattle; rd_ST isthe the of microbial CP, Equation 7.28 forcows cows andEquation Equation 7.29 for forthe growing cattle;ofrd_ST rumen, Equation 7.21; rd_RestCHO is degradation of rest carbohydrates in the rumen, Equation of starch in of thestarch rumen, Equation 7.14; rd_NDF is the degradation of NDF theinrumen, degradation in the rumen, Equation 7.14; rd_NDF is the degradation of in NDF the Equation 7.12; rd_CFat is the rumen degradation of crude fat,ofEquation 7.22; rd_CPcorr the degradation rumen, Equation 7.21; is degradation rest carbohydrates the is rumen, Equation 7.21; rd_RestCHO is rd_RestCHO degradation of rest carbohydrates in the rumen,inEquation 7.12; rd_CFat is the of ammonia orisurea crude protein in thefat, rumen, Equation 7.9; rd_FPFisisthe thedegradation liberation 7.12; rd_CFat the corrected rumen degradation of crude Equation 7.22; rd_CPcorr rumen degradation of crude fat, Equation 7.22; rd_CPcorr is the degradation of ammonia of ammonia feed fermentation products crude in the protein rumen, in Equation 7.23.Equation 7.9; rd_FPF is the liberation or urea of or urea corrected the rumen,

corrected crude protein in the rumen, Equation 7.9; rd_FPF is the liberation of feed fermentation

of feed fermentation products in the7.23. rumen, Equation 7.23. products in isthe Equation The r_mCP therumen, estimated daily flow of microbial protein out of the rumen equivalent to the microbial protein supply. When calculating the dailyprotein r_mCP,out it is the proportion The r_mCP is the estimated daily flow of microbial of assumed the rumenthat equivalent to the of The r_mCP is the estimated daily flow of microbial protein out of the rumen equivalent lactic acidprotein in FPF is 0.5 and, thus, 0.125 is used as a coefficient forassumed rd_FPF in the Theof to the microbial supply. When calculating the daily r_mCP, it is that theequation. proportion ATP from CFat is derived from fermentation of glycerol and galactose (Equation 7.22). In microbial protein supply. When calculating the daily r_mCP, it is assumed that the proportion of lactic acid in FPF is 0.5 and, thus, 0.125 is used as a coefficient for rd_FPF in the equation. The NorFor, theCFat for calculating r_mCPisimpacts directly on microbial synthesis lacticfrom acid inequation FPF is 0.5 and, 0.125 used asalso a coefficient fortherd_FPF in the equation. ATP is derived fromthus, fermentation of glycerol and galactose (Equation 7.22). In of The ATP OM, fat. The OM composition ofof rumen microbes is based on(Equation weighted synthesis averages for fromstarch CFat is derived fermentation glycerol galactose 7.22). In of NorFor, the NorFor, the and equation forfrom calculating r_mCP impacts alsoand directly on the microbial solid-adherent bacteria, liquid-associated bacteria and liquid-associated protozoa present in the equation for calculating r_mCP impacts also directly on the microbial synthesis of OM, starch OM, starch and fat. The OM composition of rumen microbes is based on weighted averages for and fat. ratio 60:30:10, estimated from data presented Volden and Harstad (1998a) and Voldeninetthe al. bacteria, The OM composition of rumen microbes is by based weighted averages for solid-adherent solid-adherent bacteria, liquid-associated bacteria andon liquid-associated protozoa present (1999a). The chemical composition (g/kg OM) of the microbial fraction is 512 CP, 167 CFat, 51 ratio 60:30:10, estimated presented by Volden and Harstad Voldenestimated et al. liquid-associated bacteriafrom anddata liquid-associated protozoa present in (1998a) the ratioand 60:30:10, from ST and a residual fraction of 270. The amount of microbial OM supplied from the rumen is (1999a). The chemical composition (g/kg(1998a) OM) ofand the Volden microbial is 512 CP,chemical 167 CFat, 51 data presented by Volden and Harstad et fraction al. (1999a). The composition calculated from: ST and a residual fraction of 270. The amount of microbial OM supplied from the rumen is

(g/kg OM) of the microbial fraction is 512 CP, 167 CFat, 51 ST and a residual fraction of 270. The

calculated amount offrom: microbial OM supplied from the rumen is calculated from: r _ mOM =

r _ mCP 512 r 0_.mCP

[Eq. 7.31]

1915 1916 1917 1916 1918 1917 1919 1918 1920 1919 1921 1920 1922 1921 1923 1922

[Eq. 7.31] 7.31 0.512 where r_mOM is the microbial organic matter flow out of the rumen, g/d; r_mCP is the microbial protein flow out of rumen, Equation 7.30; and 0.512 is the proportion ofg/d; CP in the microbial wherer_mOM r_mOM themicrobial microbial organic matter out ofrumen, the rumen, r_mCP is the microbial where isisthe organic matter flowflow out of the g/d; r_mCP is the microbial OM. proteinflow flowout outof ofthe therumen, rumen,Equation Equation7.30; 7.30;and and 0.512 proportion of CP in the microbial OM. protein 0.512 is is thethe proportion of CP in the microbial OM. The thethe microbial CFat supply is: is: The equation equationfor forcalculating calculating microbial CFat supply

1923

r _ mCFat = r _ mCP ⋅

r _ mOM =



The equation for calculating the microbial CFat supply is: r _ mCFat = r _ mCP ⋅

167 512 167 512



[Eq. 7.32]

7.32

[Eq. 7.32] 81 81

NorFor – The Nordic feed evaluation system

69

1924 1924 1925 1925 1926 1926 1927 1927 1928 1928 1929 1929 1930 1930 1931 1931 1932 1932 1933 1933 1934 1934 1935 1935 1936 1936 1937 1937 1938 1938 1939 1939 1940 1940 1941 1941 1942 1942 1943 1943 1944 1944 1945 1945 1946 1946 1947 1947 1948 1948 1949 1949 1950 1950 1951 1951 1952 1952 1953 1953 1954 1954 1955 1955 1956 1956 1957 1957 1958 1958 1959 1959 1960 1960 1961 1961 1962 1962 1963 1963 1964 1964 1965 1965 1966 1966

where r_mCFat is the microbial crude fat flow out of the rumen, g/d; r_mCP is the microbial where crude fat7.30; flowand out 167/512 of the rumen, r_mCP is the microbial proteinr_mCFat flow out isofthe themicrobial rumen, Equation is the g/d; CFat:CP ratio in the rumen where r_mCFat is the the rumen, microbial crude 7.30; fat flow of theisrumen, g/d; r_mCP therumen microbial protein protein flow out of Equation andout 167/512 the CFat:CP ratio inisthe microbial OM. microbial OM. flow out of the rumen, Equation 7.30; and 167/512 is the CFat:CP ratio in the rumen microbial OM. The supply of microbial ST is calculated as: The is is calculated as: as: The supply supplyofofmicrobial microbialSTST calculated 51 r _ mST = r _ mCP ⋅ 51 512 r _ mST = r _ mCP ⋅ 512

[Eq. 7.33] [Eq. 7.33]



7.33

where r_mST is the microbial starch flow out of the rumen, g/d; r_mCP is the microbial protein wherer_mST r_mSTisisthethe microbial starch flow outtheofrumen, the rumen,r_mCP g/d; r_mCP is the microbial where microbial starch of therumen microbial protein protein flow out of the rumen, Equation 7.30;flow and out 51/512 is the ratiog/d; of ST:CP inisthe microbial flow out of the rumen, Equation 7.30; and 51/512 is the ratio of ST:CP in the rumen microbial OM. flow out of the rumen, Equation 7.30; and 51/512 is the ratio of ST:CP in the rumen microbial OM. OM. The proportion proportionofofAAs AAsininthe themicrobial microbialprotein proteinwas wassetsettoto0.73 0.73 (Volden and Harstad, 1998a, The (Volden and Harstad, 1998a, b; b; Volden The proportion of AAs in the microbial protein was set to 0.73 (Volden and Harstad, 1998a, b; et al., 1999a, b) and the amount of microbial AAs flowing out of the rumen is therefore: Volden et al., 1999a, b) and the amount of microbial AAs flowing out of the rumen is therefore: Volden et al., 1999a, b) and the amount of microbial AAs flowing out of the rumen is therefore: [Eq. 7.34] 7.34 r _ mAA = r _ mCP ⋅ 0 .73  [Eq. 7.34] r _ mAA = r _ mCP ⋅ 0 .73 where AA flow outout of of thethe rumen, g/d;g/d; r_mCP is theismicrobial protein wherer_mAA r_mAAisisthe themicrobial microbial AA flow rumen, r_mCP the microbial protein flow where r_mAA theEquation microbial AA flow out0.73 ofisthe rumen, g/d; r_mCP isin thethe microbial protein flow ofrumen, the isrumen, Equation 7.30; is the proportion ofAAs AAs in microbial protein. out ofout the 7.30; andand 0.73 the proportion of microbial protein. flow out of the rumen, Equation 7.30; and 0.73 is the proportion of AAs in the microbial protein. The AAs in the microbial protein (presented in Table 7.2) was estimated The composition compositionofofessential essential AAs in the microbial protein (presented in Table 7.2) was estimated The composition of essential AAs inHarstad the microbial protein (presented in(1999b), Table 7.2) wasonestimated from data presented byby Volden and (1998b) and and Volden et al.et based from data presented Volden and Harstad (1998b) Volden al. (1999b), based from data averages presentedfor bysolid-adherent Volden and Harstad (1998b) and Voldenbacteria et al. (1999b), based on on weighted weighted bacteria, liquid-associated and liquid-associated averages for solid-adherent bacteria, liquid-associated bacteria and liquid-associated weighted in averages bacteria, liquid-associated bacteria and liquid-associatedprotozoa in protozoa the ratiofor ofsolid-adherent 60:30:10. the ratio in ofthe 60:30:10. protozoa ratio of 60:30:10. Table 7.2 7.1.7 Protein balance in the rumen Table 7.2

7.1.7 Protein balance in the rumen

7.1.7 Protein balance in protein the rumen Low levels levels degradable protein in the reduce the ruminal NDF digestion and microbial Low ofofdegradable in the dietdiet willwill reduce the ruminal NDF digestion and microbial Low levels of degradable protein in the diet reduce theetStern ruminal digestion and microbial protein synthesis (Hoover, 1986; Clark et1992; al., 1992; et NDF al.,In1994). NorFor the protein protein synthesis (Hoover, 1986; Clark et al.,will Stern al., 1994). NorForIn the protein protein (Hoover, 1986; Clark et al., 1992; Stern et al., 1994). In NorFor the protein balancesynthesis therumen rumen (PBV ) is used to evaluate the adequacy of protein for microbial growth, and ) is used to evaluate the adequacy of protein for microbial growth, balance ininthe (PBV N N balance in the from rumen (PBV to evaluate the adequacy of protein for microbial growth, N) is used and is calculated from the following equation: is calculated the following equation: and is calculated from the following equation: ⎞ ⎛⎛ ⎞ PBVN = rd _ CP + ⎜ ⎜ ∑ DMI i ⋅ CPi ⎟ ⋅ 0.046 ⎟ − r _ mCP

[Eq. 7.35]

⎟⎞ ⎟⎞ composition (g/100 g of AA) of the rumen microbes entering ⎜⎛ ⎜⎛ amino acid Table [Eq. 7.35] PBVN 7.2. CP + ⎜⎝⎜ ⎜⎝⎜ ∑i DMI .046 ⎟⎠⎟ − r _ mCP = rd _Essential i ⋅ CPi ⎟⎠⎟ ⋅ 0(AA) ⎠ ⎠ AAs. ⎝ ⎝ iand of endogenous the small intestine

where PBVN is the protein balance in the rumen, g/d; rd_CP is the degradation of crude protein in where PBV balance in the matter rumen, g/d; rd_CP the degradation crudeCP protein N is the protein the rumen, Equation 7.8; DMI dry intake of theisi=1...n'th feedstuff,ofkg/d; Amino acid g/100 g i is the i is thein the rumen, Equation DMI matterg/kg intake of the kg/d;flow CPi isout theof i is the dry crude protein content7.8; in the i=1...n'th feedstuff, DM; andi=1...n'th r_mCP isfeedstuff, the microbial crude protein content7.30. in the i=1...n'th feedstuff, g/kg DM; and r_mCP is the microbial flow out of 1 2 the rumen, Equation Microbial Endogenous the rumen, Equation 7.30. The parameter 0.046 is the proportion the dietary protein (N x 6.25) 4.0 recycled back to the Arginine 5.1of The parameter 0.046 is the proportion of Danish the dietary protein (N x 6.25) recycled back to the rumen. This value is parameterized from (Weisbjerg et al., 1998) and Norwegian (Rygh, Histidine 2.4 2.8and Norwegian (Rygh, rumen. This value is1999; parameterized fromH. Danish (Weisbjerg et al.,N.1998) 1997; Prestløkken, Volden, 1999; Volden, unpublished; P. Kjos, unpublished) Isoleucine 5.9 H. Volden, unpublished; N. P.3.6 1997; Prestløkken, 1999; Volden, 1999; Kjos, unpublished) Leucine 7.9 3.8 82 82 Lysine 7.4 6.3

Methionine Phenylalanine Threonine Tryptophan Valine 1 2

2.5 5.7 5.3 1.6 5.9

1.1 3,6 5.8 1.0 4.8

Data from Volden and Harstad (1998b) and Volden et al. (1999) Data from Larsen et al. (2000).

70 

NorFor – The Nordic feed evaluation system

1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Low levels of degradable protein in the diet will reduce the ruminal NDF digestion and microbial protein synthesis (Hoover, 1986; Clark et al., 1992; Stern et al., 1994). In NorFor the protein balance in the rumen (PBVN) is used to evaluate the adequacy of protein for microbial growth, and is calculated from the following equation: ⎛⎛ ⎞ ⎞ PBVN = rd _ CP + ⎜⎜ ⎜⎜ ∑ DMI i ⋅ CPi ⎟⎟ ⋅ 0.046 ⎟⎟ − r _ mCP ⎠ ⎝⎝ i ⎠



[Eq. 7.35]

7.35

where PBV is the protein balance in the rumen, g/d; rd_CP is the degradation of crude protein in

N is the protein thedry rumen, g/d;intake rd_CPof is the of crude protein in is the where PBVNEquation the rumen, 7.8;balance DMI isinthe matter the degradation i=1...n’th feedstuff, kg/d; CP the rumen, Equation 7.8; DMIi isithe dry matter intake of the i=1...n'th feedstuff, kg/d; CPi is the i crude protein content in the i=1...n’th feedstuff, g/kg DM; and r_mCP is the microbial flow out of crude protein content in the i=1...n'th feedstuff, g/kg DM; and r_mCP is the microbial flow out of the rumen, Equation 7.30. the rumen, Equation 7.30.

The parameter parameter0.046 0.046isisthe theproportion proportionofofthe thedietary dietaryprotein protein recycledback backtotothe the rumen. This The (N(N·6.25) x 6.25) recycled value isThis parameterized from Danishfrom (Weisbjerg et al., 1998)etand (Rygh, 1997; Prestløkken, rumen. value is parameterized Danish (Weisbjerg al.,Norwegian 1998) and Norwegian (Rygh, 1999;Prestløkken, Volden, 1999; Volden, unpublished data; Kjos, unpublished data) determinations, 1997; 1999; Volden, 1999; H. Volden, unpublished; N. P. Kjos, unpublished) from a linear relationship between flowing into the duodenum (corrected for duodenum endogenous protein) and the determinations, from a total linearNrelationship between N flowing into the (corrected 82 total dietary CP intake in trials with diets having CP contents<165 g/kg DM. Thus, this factor for endogenous protein) and the dietary CP intake in trials with diets having CP contents <165 describes the proportion a net duodenal flow microbialofprotein from recycled At a dietary CP content g/kg DM. Thus,of this factor describes theof proportion a net duodenal flow of N. microbial protein of 162 g/kg DM, N intake similar to the flowthe at Ntheintake duodenum, while at lower from recycled N. Atthe a dietary CPwas content of 162 g/kgNDM, was similar to the N flowN intake at the duodenum, while at lower N intake values duodenal flow was higher the N intake values the duodenal flow was higher than the the N intake when corrected forthan endogenous excretion. when corrected for endogenous excretion.

7.2. Small intestine

7.2. Small intestine

The into thethe duodenum are are of dietary, microbial and animal origin.origin. In The OM OMcomponents componentsflowing flowing into duodenum of dietary, microbial and animal In the the small intestine, theyare aresubjected subjected to to enzymatic it isitassumed that the small intestine, they enzymaticdigestion. digestion.However, However, is assumed that the small small intestine enzymes to digest NDF microbialcell cellwalls. walls.The Theflow flow of of OM OM out intestine lackslacks enzymes to digest NDF andand microbial out of ofthe the rumen rumen is calculated as: is calculated as: ⎛ ⎞ ⎛ rd _ CPcorr + rd _ ST + rd _ NDF + rd _ NH3N _ CP ⎞ ⎟⎟ + r _ mOM r _ outOM = ⎜⎜ ∑ DMIi ⋅ OMi ⎟⎟ − ⎜⎜ ⎠ ⎝i ⎠ ⎝ + rd _ CFat + rd _ FPF + rd _ Re stCHO



7.36

7.36]matter content where r_outOM is the flow of organic material out of the rumen, g/d; OMi is the[Eq. organic in the i=1...n’th feedstuff, g/kg DM; DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; where r_outOM is the flow of organic material out of the rumen, g/d; OMi is the organic matter rd_CPcorr is ammoniaor urea-corrected in the rumen, Equation 7.9; content in the i=1...n'th feedstuff, g/kg DM; dietary DMIi iscrude the dryprotein matterdegraded intake of the i=1...n'th rd_ST is the rumen degradation of dietary starch, Equation 7.14; rd_NDF is the rumen degradation feedstuff, kg/d; rd_CPcorr is ammonia- or urea-corrected dietary crude protein degraded in the of NDF, Equation rd_NH3N_CP is the dietary ammonia or Equation urea N available in theis rumen, rumen, Equation 7.9;7.21; rd_ST is the rumen degradation of dietary starch, 7.14; rd_NDF Equation rd_CFat is the Equation rumen metabolism of crude isfat, 7.22; rd_FPF the rumen 7.10; degradation of NDF, 7.21; rd_NH3N_CP theEquation dietary ammonia or ureaisNthe rumen available rumen,fermentation Equation 7.10; rd_CFat Equation is the rumen metabolism of crude fat,rumen Equation liberationinofthedietary products, 7.23; rd_RestCHO is the degradation 7.22; rd_FPFcarbohydrates, is the rumen liberation of 7.12; dietaryr_mOM fermentation Equation of residual Equation is theproducts, microbial organic7.23; matter flowing out of rd_RestCHO is the rumen the rumen, Equation 7.31.degradation of residual carbohydrates, Equation 7.12; r_mOM is the microbial organic matter flowing out of the rumen, Equation 7.31.

7.2.1 Digestion of dietary protein, amino acids, starch and fatty acids 7.2.1 Digestion of dietary protein, amino acids, starch and fatty acids

The in in thethe small intestine is calculated fromfrom the ruminal escape The digestion digestionofofdietary dietaryprotein protein small intestine is calculated the ruminal escape fraction fraction and the iCP fraction (see section 4.3) as: and the iCP fraction (see Section 4.3) as: ⎛ ⎞ iCPi ⎞ ⎛ sid _ CP = ⎜⎜ ∑ DMIi ⋅ CPi ⎟⎟ − rd _ CP − ∑ ⎜ DMIi ⋅ CPi ⋅ ⎟  1000 ⎠ i⎝ ⎝i ⎠

[Eq. 7.37]

7.37

where sid_CP is the digestion of dietary protein in the small intestine that has escaped from the

where is theisdigestion dietary protein small intestine that kg/d; has escaped the protein rumen,sid_CP g/d; DMI the dry of matter intake of in thethe i=1...n’th feedstuff, CP is from the crude i the dry matter intake of the i=1...n'th feedstuff, kg/d; CPi is the icrude protein rumen, g/d; DMIi is content in the i=1...n’th feedstuff, g/kg DM; rd_CP is the degradation of crude protein in the rumen, content in the i=1...n'th feedstuff, g/kg DM; rd_CP is the degradation of crude protein in the Equation 7.8; and7.8; iCPand the total indigestible crude protein content in the i=1...n’th feedstuff, g/kg CP. i is iCP rumen, Equation i is the total indigestible crude protein content in the i=1...n'th feedstuff, g/kg CP.

It is assumed that the proportion of AAs in ruminal escape protein and iCP are identical to the intact

feed protein.that Digestion of dietary AAsininruminal the small intestine is and calculated It is assumed the proportion of AAs escape protein iCP are as: identical to the intact feed protein. Digestion of dietary AAs in the small intestine is calculated as:

83 NorFor – The Nordic feed evaluation system

71

2006 2006 2007 2008 2007 2009 2008 2010 2009 2011 2010 2012 2011 2013 2012 2014 2013 2015 2014 2016 2015 2017 2016 2018 2017 2019 2018 2020 2019 2021 2020 2022 2021 2023 2022 2024 2023 2025 2024 2025 2026 2026 2027 2028 2027 2029 2028 2030 2029 2031 2030 2032 2031 2033 2032 2034 2033 2035 2034 2036 2035 2037 2036 2038 2037 2039 2038 2040 2039 2041 2040 2042 2041 2043 2042 2043

AA i sid _ AA = ∑ DMI i ⋅ CPi ⋅ 100 i AA i sid _ AA = ∑ DMI i ⋅ CPi ⋅ 100 i

⎛ ⎛ sCPi NH 3 N i 150 ⎜ 1 − ⎜⎜ ⋅ + − 1000 ⎜ ⎝ 1000 150 + r _ kpl ⋅ ⎜⎛ ⎛ sCP NH N 150 3 i − i kdCPi + ⎞ iCP ⎜⎜1−−⎛⎜ ⎜⎜pdCPi ⋅⋅ i − ⎜⎜ ⎜ ⎝ 1000 150 + r _ kpl ⎟⎟ 1000 + 1000 kdCP r _ kp 1000 ⋅ ⎝⎜ ⎝ i ⎠ ⎞ iCPi kdCPi ⎜ − ⎛⎜ pdCPi ⋅ ⎟ ⎜ ⎜ 1000 kdCP + r _ kp ⎟ − 1000 i ⎠ ⎝ ⎝

NH 3 N i 1000 NH 3 N i 1000

⎞⎞ 150 ⎟⎟ 150 + r _ kpl ⎟⎠ ⎟ ⎞ ⎟⎞ 150 ⎟ ⎟⎟ ⋅ 150 + r _ kpl ⎟⎠ ⎟⎟ ⎠⎟  ⎟ [Eq. 7.38] ⎟ ⎠ ⋅

7.38

[Eq. from 7.38] where intestine that escaped thethe rumen, wheresid_AA sid_AAisisthe thedigestion digestionofoftotal totaldietary dietaryAAs AAsininthethesmall small intestine that escaped from is the dry matter intake of the i=1...n'th feedstuff, kg/d; CP is the crude protein rumen, g/d; DMI i i g/d; DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; CPi is the crude protein content in where sid_AA is the digestion of total dietary AAs in the intestine escaped from the content in the i=1...n'th g/kg AA AAsmall content in thethat i=1...n'th feedstuff, i is the the i=1...n’th feedstuff,feedstuff, g/kg DM; AADM; is the AA content in the i=1...n’th feedstuff, g/100 g CP; sCPi i rumen,g g/d; is the soluble dry matter intake of the i=1...n'th feedstuff, CPi isg/kg the crude protein g/100 CP; DMI sCPii is crude protein content in the i=1...n'thkg/d; feedstuff, CP; iCP i is is the soluble crudethe protein content the i=1...n’th feedstuff, g/kg iCPi is total indigestible crude content in CP; the content in the i=1...n'th feedstuff, g/kginDM; i is the AA the ammonia total indigestible crude protein content in theAA i=1...n'th feedstuff, g/kg CP;i=1...n'th NH3Ni isfeedstuff, protein content in the i=1...n’th feedstuff, g/kg CP; NH N is the ammonia and urea N content in the g/100 g CP; sCPi is in thethe soluble crude protein content the3 i=1...n'th feedstuff, g/kg CP; iCPi is and urea N content i=1...n'th feedstuff, g/kg CP;inr_kpl isi the liquid passage rate, Equation i=1...n’th feedstuff, g/kg CP; r_kpl is the liquid passage rate, Equation 7.1; the fractional degradation total indigestible crude protein content in the i=1...n'th feedstuff, g/kg CP; NH N is the ammonia 3 i 7.1; the fractional degradation rate of AA-N is fixed at 150 %/h; kdCPi is the fractional rateurea of AA-N atin 150%/h; kdCPfeedstuff, the fractional degradation rate ofpassage pdCP in the i=1...n’th and N content in the i=1...n'th feedstuff, CP; r_kpl the is liquid passage rate, Equation degradation rateisoffixed pdCP the i=1...n'th %/h; and is r_kp the ruminal rate, i isg/kg feedstuff, %/h; and r_kp is the ruminal passage rate, %/h. If the feed is concentrate then r_kp=r_kpc, is the fractional 7.1; the fractional degradation rate of AA-N is fixed at 150 %/h; kdCP i feed is roughage %/h. If the feed is concentrate then r_kp=r_kpc, Equation 7.3, and if the degradation rate of pdCP theisi=1...n'th feedstuff, %/h; and r_kp is 7.2. the ruminal passage rate, Equation 7.3, and if the feed roughage r_kp=r_kpr, Equation r_kp=r_kpr, Equation 7.2.in %/h. If the feed is concentrate then r_kp=r_kpc, Equation 7.3, and if the feed is roughage r_kp=r_kpr, Equation 7.2. NorFor thethe small intestine digestion of arginine, histidine, isoleucine, leucine, lysine, NorForcalculates calculates small intestine digestion of arginine, histidine, isoleucine, leucine, lysine, methionine, threonine, tryptophan and valine. When calculating individualindividual AA methionine,phenylalanine, phenylalanine, threonine, tryptophan and valine. When calculating AA NorFor calculates the small intestine digestion ofofarginine, histidine, isoleucine, leucine, lysine, digestion, ititisisassumed thatthat the the AAAA profile of the ruminally undegraded protein protein and the iCP are digestion, assumed profile the ruminally undegraded and the iCP are methionine, phenylalanine, threonine, tryptophan and valine. calculating individual AA the intact feed protein. The general equation toWhen calculate intestinal digestiondigestion of an the same sameasasininthethe intact feed protein. The general equation to calculate intestinal of an digestion, is assumed individual itdietary AA is:that the AA profile of the ruminally undegraded protein and the iCP are individual dietary AA is: the same as in the intact feed protein. The general equation to calculate intestinal digestion of an individual dietary AA is: ⎛ ⎛ sCPi ⎞⎞ NH 3 N i NH 3 N i 150 150 ⎜1 − ⎜⎜ ⎟⎟ ⋅ + − ⋅ 1000 1000 150 + r _ kpl ⎟⎠ ⎟ ⎜ ⎝ 1000 150 + r _ kpl 7.39 ⋅ H 2 O corr ⋅ ⎜⎛ ⎛ sCP ⎟⎞  NH N NH N ⎞ 150 150 3 i − 3 i ⋅ 100 ⎜⎜1 −⎛ ⎜⎜pdCPii ⋅ ⎟ ⎟⎟ kdCP i + ⎞ iCP i i ⎜ 1000 ⋅150 + r _ kpl ⎟ 1000 − AA ji 1000 150 + r _ kpl ⎟⎠ ⎟⎟ ⎟ ⎜⎜ − ⎜ ⎝1000 kdCP i + r _ kp ⎠ 1000 sid _ AA j = ∑ DMI i ⋅ CPi ⋅ ⋅ H 2 O corr ⋅ ⎜⎝ ⎝ ⎠⎟ 100 ⎞ iCPi kdCP i i ⎜ − ⎛⎜ pdCPi ⋅ [Eq. 7.39]histidine,⎟⎟isoleucine, ⎟⎟ −, g/d, j=arginine, where sid_AAj is the intestinal digestion the individual AA ⎜ ⎜ of ⎠ ⎝ ⎝ 1000 kdCP i + r _ kp ⎠ j 1000 leucine, lysine, methionine, phenylalanine, threonine, tryptophan or valine; AAji is the individual [Eq. 7.39] where sid_AA is the intestinal digestion of the individual AA , g/d, j = arginine, histidine, sid _ AA j = ∑ DMI i ⋅ CPi ⋅

AA ji

j AA content in the i=1...n’th feedstuff, g/100 g CP; H O

j

is the water correction factor for each

j 2 corr tryptophan or valine; AA ji is the isoleucine, leucine, lysine, methionine, phenylalanine, threonine, individual AAj jcontent ; DMI is the dry matterfeedstuff, intake ofg/100 the i=1...n’th feedstuff, kg/d; where sid_AA is the intestinal digestion of the individual AAHj, 2g/d, = the arginine, histidine, i i is the crude protein individual AA in the i=1...n'th g CP; O waterCP correction j corrj is content in the i=1...n’th feedstuff, g/kg DM; AAN is the amino acid content in the feedstuff, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan or valine; AA ji isCP the i intake of the i=1...n'th feedstuff,i=1...n’th factor for each individual AAj; DMIi is the dry matter kg/d; i g/100 g CP; sCP theinsoluble protein content theHi=1...n’th feedstuff, g/kgcontent CP; iCPi is the theini=1...n'th feedstuff, g/100 gin CP; Ocorr iisisthe water individual AA j content 2AAN the aminocorrection acid is the crude protein the crude i=1...n'th feedstuff, g/kg DM; i iscontent total crude protein ini dry the i=1...n’th feedstuff, g/kgcontent CP;feedstuff, NH Ni is the ammonia and factor for each individual AAj; DMI the matter intake of protein the i=1...n'th kg/d; CPi i issCP in theindigestible i=1...n'th feedstuff, g/100 gcontent CP; is the soluble crude in 3the i=1...n'th is the amino acid content is the N crude protein content in the i=1...n'th feedstuff, g/kg DM; AAN urea content in the i=1...n’th feedstuff, g/kg CP; r_kpl is the liquid passage rate, Equation 7.1; the i feedstuff, g/kg CP; iCP is the total indigestible crude protein content in the i=1...n'th feedstuff, i in theCP; i=1...n'th g/100 gand CP;urea sCP the protein content in the i=1...n'th is the ammonia Ni iscontent in the i=1...n'th feedstuff, g/kg CP; r_kpl is g/kg NH fractional degradation rate of soluble AA-N is soluble fixed atcrude 150%/h; kdCP is the fractional degradation 3Nifeedstuff, i feedstuff, g/kg iCP is the total indigestible crude protein content thepassage i=1...n'th feedstuff, iEquation the passage rate, 7.1; the fractional degradation rate ofinsoluble AA-N is fixed rateliquid of pdCP inCP; the i=1...n’th feedstuff, %/h; and r_kp is the fractional rate, %/h.atIf the feed is the the ammonia degradation and urea N content inifthe i=1...n'th feedstuff, g/kg CP; is g/kg CP; NH 3Nithen i is 150 %/h; kdCP fractional rate of the i=1...n'th feedstuff, %/h;r_kpl and is concentrate r_kp=r_kpc, Equation 7.3, andpdCP thein feed is roughage r_kp=r_kpr, Equation 7.2. the rate, Equation theIffractional ofr_kp=r_kpc, soluble AA-N is fixed7.3, at r_kpliquid is thepassage fractional passage rate,7.1; %/h. the feed isdegradation concentraterate then Equation 150 %/h; the fractional degradation rate of pdCP in the i=1...n'th feedstuff, %/h; and i isroughage and the kdCP feed is Equation 7.2. Theifindividual AA supplyr_kp=r_kpr, is calculated as water-free AA, and H O correction factors (H O ) of r_kp is the fractional passage rate, %/h. If the feed is concentrate then2r_kp=r_kpc, Equation 7.3,2 corr 0.8966, 0.8839, 0.8627, 0.8627, 0.8767, 0.8792, 0.8910, 0.9118 and 0.8462 are used for and if the feed is roughage r_kp=r_kpr, 7.2. AA, and 0.8487, The individual AA supply is calculated Equation as water-free H2O correction factors (H2Ocorr) of arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan 0.8966, 0.8839, 0.8627, 0.8627, 0.8767, 0.8792, 0.8910, 0.8487, 0.9118 and 0.8462 are used for and valine, respectively. The individual AA supply is calculated as water-free AA, and H2O correction factors (H2Ocorr) of 0.8966, 0.8839, 0.8627, 0.8627, 0.8767, 0.8792, 0.8910, 0.8487, 0.9118 and 0.8462 are used for

The digestion of ruminally undegraded starch 84in the small intestine is calculated as for protein: iSTi sid_ST = ∑(DMIi ∙STi) – rd_ST – ∑(DMIi ∙ STi ∙ 84 i i 1000

7.40

where sid_ST is the digestion of ruminally undegraded dietary starch in the intestine, g/d; DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; STi is the starch content in the i=1...n’th feedstuff, g/kg DM; rd_ST is the rumen degradation of starch, Equation 7.14; and iSTi is the indigestible starch content in the i=1...n’th feedstuff, g/kg ST.

72 

NorFor – The Nordic feed evaluation system

2050 2056 2056 2051 2057 2057 2052 2058 2058 2053

2054 2055 2059 2056 2059 2057 2058

2060 2059 2060 2061 2061 2062 2062 2060 2063 2063 2061 2064 2064 2062 2065 2063 2065 2066 2066 2064 2067 2067 2065 2068 2068 2066 2069 2069 2067 2070 2070 2068 2071 2071 2069 2072 2072 2070 2073 2073 2071 2074 2072 2074 2075 2073 2075 2074 2076 2076 2075 2077 2077 2076 2078 2078 2077 2079 2079 2078 2080 2080 2079 2081 2081 2082 2082 2083 2083 2084 2084 2085 2085 2086 2086 2087 2087 2088 2088 2089 2089 2090 2090 2091 2091 2092 2092 2093 2093 2094 2094 2095 2095 2096 2096

2097 2097 2098 2098 2099 2099 2100 2100 2101 2101 2102 2102 2103 2103 2104 2104 2105 2105 2106 2106

where sid_ST is the digestion of ruminally undegraded dietary starch in the intestine, g/d; DMIi is equation by et al. is to the of dietary fatty equation by Weisbjerg Weisbjerg al. (1992) (1992) is used used to calculate calculate the digestion digestion dietary fatty acids acids in in the the content inofthe i=1...n'th the dry matter intake of theeti=1...n'th feedstuff, kg/d; STi is the starch small as follows: small intestine, intestine, as rd_ST follows: feedstuff, g/kg DM; is the rumen degradation of starch, Equation 7.14; and iSTi is the indigestible starch content in the i=1...n'th feedstuff, g/kg ST. ⎛⎛ ⎛⎛ ⎞⎞ ⎞⎞ ⎜⎜ ∑ ⎟ ⎟⎟ ((form )) −− rd ..88 − 00..0169 DMI __and CFat ⋅⋅the ⎜⎜ 93 ii ⋅⋅ CFat iiacids, − 93 0169 DMI CFat rd CFat ∑ Dietary CFat entering the small intestine is assumed to be in of fatty the Dietary CFat⎛⎛ entering the small intestine⎞⎞is⎜⎜assumed to be ⎝⎜⎝inii the form of fatty acids, and⎟⎠⎟⎠ ⎟⎟the equation ⎜ ⎟ ( ) sid _ CFat DMI CFat rd _ CFat = ⋅ − ⋅ equation al. (1992) used⎟to fatty acids in⎟the ⎜ calculate the digestion of dietary ii et ) − rdto_isCFat ⋅ CFat sidWeisbjerg _ CFatby=Weisbjerg DMI ∑ (al. ⎜⎜ ∑ ⎟ intestine, by (1992) isiiused calculate fatty acids in the small ⎟⎠ ⋅ ⎜⎜ the digestion of dietary 100 i as follows: ⎝ et 100 ⎟⎟ small intestine, ⎠ ⎜ as follows: ⎝ i ⎜⎜ ⎟⎟ ⎝⎝ ⎠⎠ ⎛ ⎛ ⎞⎞ ⎜ 93.8 − 0.0169 ⋅ ⎜ ∑ (DMI i ⋅ CFat i ) − rd _ CFat ⎟ ⎟ ⎜ ⎟⎟ ⎛ ⎞ ⎜ ⎝i ⎠  sid _ CFat = ⎜⎜ ∑ (DMI i ⋅ CFat i ) − rd _ CFat ⎟⎟ ⋅ ⎜ ⎟ 100 ⎝i ⎠ ⎜ ⎟ ⎜ ⎟ where sid_CFat is the digestion of dietary crude fat in the small intestine, ⎝ ⎠ g/d; where sid_CFat is the digestion of dietary crude fat in the small intestine, g/d;

[Eq. 7.41 [Eq. 7.41] 7.41]

DMI is the the dry dry DMI ii is the fat in i=1...n'th matter intake kg/d; CFat where sid_CFat thei=1...n'th digestion feedstuff, of dietary crude in the intestine, g/d; DMI is the dry matter ii is is small the crude crude fat content content in ithe the i=1...n'th matter intake of ofisthe the i=1...n'th feedstuff, kg/d; fat CFat [Eq.7.22. 7.41] feedstuff, g rd_CFat is degradation of crude fat, intake of the i=1...n’thand feedstuff, kg/d; CFat content the i=1...n’th feedstuff, g kg/ feedstuff, g kg/DM; kg/DM; and rd_CFat is the the degradation crude fat,inEquation Equation 7.22. i is the crudeoffat DM; and rd_CFat is the degradation of crude fat, Equation 7.22.

where sid_CFat is the of dietary crude fat in acids, the small intestine, g/d; DMI i is the dry 7.2.2 ofdigestion microbial protein, amino 7.2.2 Digestion Digestion microbial protein, amino acids, starch starch and and fatty fatty acids acids matter intake of the of i=1...n'th feedstuff, kg/d; CFat i is the crude fat content in the i=1...n'th 7.2.2 Digestion of microbial protein,intestinal amino acids, starch and fatty acidsOM. However, multiple Few available on digestibility of feedstuff, kg/DM; and rd_CFat the degradation crude fat, Equation 7.22. OM. However, multiple Few data datagare are available on the the issmall small intestinal of digestibility of microbial microbial

regression regression analyses analyses by by Tas Tas et et al. al. (1981) (1981) and and Storm Storm et et al. al. (1983) (1983) indicating indicating that that 87 87 and and 85% 85% of of Few data are available on found the small intestinal digestibility microbial OM. However, multiple 7.2.2 Digestion of microbial protein, amino acids, starch andoffatty acids bacterial AAs (which are in both microbial cytoplasm and cell walls) are digestible in the bacterial AAs (which are found in both microbial cytoplasm and cell walls) are digestible in regression analyses by Tas et Similarly, al.intestinal (1981) Hvelplund and Storm et al. (1983) indicating that 87digestibility and 85% ofthe Few data are available on the small digestibility of microbial OM. However, multiple small intestine, respectively. (1985) found an average true of small intestine, respectively. Similarly, Hvelplund (1985) found an average true digestibility of bacterial AAs (which found in both microbial and cell walls) digestible the small regression analyses by are Tasintestines et al. (1981) and Storm etcytoplasm al. (1983) indicating that are 87Mason and 85% ofin bacterial AAs in sheep of 85%, with minor variations, while and White (1971) bacterial AAs in sheep intestines of 85%, with minor variations, while Mason and White (1971) intestine,AAs respectively. Hvelplund (1985) foundand ancell average true digestibility of bacterial bacterial (which areSimilarly, found in both microbial cytoplasm walls) are digestible in the found that AAs in cell wall mostly indigestible in the small intestine. Nucleic found that AAs in the the microbial microbial cell wall are are mostlyfound indigestible inand theWhite small(1971) intestine. Nucleic AAs in sheep intestines of Similarly, 85%, with minor variations, while that small intestine, respectively. Hvelplund (1985) anMason average true digestibility offound acids are also efficiently digested in the small intestine. NRC (1985) showed that 75 to 90% of acids are also efficiently digested in the small intestine. NRC (1985) showed that 75 to 90% bacterial AAsmicrobial in sheep intestines withindigestible minor variations, and Nucleic White (1971) AAs in the cell wall of are85%, mostly in the while smallMason intestine. acids are alsoof the nucleic acids are digested and absorbed in the small intestine. In NorFor, the following fixed the nucleic acids are digested and absorbed in the small intestine. In NorFor, the following fixed found that AAs in theinmicrobial wall areNRC mostly indigestible the75 small intestine. efficiently digested the smallcell intestine. (1985) showedinthat to 90% of theNucleic nucleic acids are digestibility coefficients for microbial CP and AAs (Storm et al., 1983; Hvelplund, 1985), digestibility coefficients forsmall microbial CPintestine. and AAs (Storm etshowed al., fixed 1983; Hvelplund, 1985), starch starch acids are also digested in the small NRC thatdigestibility 75 to 90% of digested and efficiently absorbed in the intestine. In NorFor, the(1985) following coefficients and CFat are used: the acids are digested and absorbed in the small intestine. In NorFor, and CFat are used: for nucleic microbial CP and AAs (Storm et al., 1983; Hvelplund, 1985), starchthe andfollowing CFat arefixed used: digestibility coefficients for microbial CP and AAs (Storm et al., 1983; Hvelplund, 1985), starch and [Eq. sid _CFat mCPare = rused: _ mCP ⋅ 0 .85 [Eq. 7.42] 7.42] sid _ mCP = r _ mCP ⋅ 0 .85 

7.42

[Eq. 7.42]

sid _ mCP = r _ mCP ⋅ 0 .85

sid = rr __ mAA sid __ mAA mAA = mAA ⋅⋅ 0 0..85 85 

sid ⋅ 0.⋅85 sid __mAA mST==r _r mAA _ mST 0 .9

sid _ mST = r _ mST ⋅ 0 .9



sid sid __ mFA mFA = = rr __ mCFat mCFat ⋅⋅ 0 0..85 85 ⋅⋅ 0 0..65 65 

85

85 85

[Eq. 7.43 [Eq. 7.43] 7.43]

[Eq. 7.43] [Eq. 7.44] [Eq. 7.44] 7.44

[Eq. [Eq. 7.45] 7.45]

7.45

where sid_mCP, sid_mAA, sid_mST andand sid_mFA are the amounts of microbial protein, amino acids, where where sid_mCP, sid_mCP, sid_mAA, sid_mAA, sid_mST sid_mST and sid_mFA sid_mFA are are the the amounts amounts of of microbial microbial protein, protein, amino amino starch and fatty acids digested in the small intestine, respectively, g/d; r_mCP isg/d; the microbial protein acids, starch and fatty acids digested in the small intestine, respectively, r_mCP acids, starch and fatty acids digested in the small intestine, respectively, g/d; r_mCP is is the the synthesis inprotein the rumen, Equation 7.30; r_mAA is the microbial aminoisacid synthesis inamino the rumen, microbial synthesis in Equation 7.30; r_mAA acid microbial protein synthesis in the the rumen, rumen, Equation 7.30; r_mAA is the the microbial microbial amino is acid Equation 7.34; r_mST is the microbial starch synthesis in the rumen, Equation 7.33; r_mCFat the synthesis in the rumen, Equation 7.34; r_mST is the microbial starch synthesis in the rumen, synthesis in the rumen, Equation 7.34; r_mST is the microbial starch synthesis in the rumen, microbial crude fat synthesis in the rumen, Equation 7.32; 0.85 is the intestinal digestion coefficient; Equation 7.33; r_mCFat the microbial crude fat in Equation r_mCFat is is microbial crude fat synthesis synthesis in the the rumen, rumen, Equation Equation 7.32; 7.32; 0.85 0.85 is is andintestinal 0.65 is7.33; thedigestion proportion ofthe fatty acids in0.65 the microbial crude fat. the coefficient; and is the proportion of fatty acids in the microbial the intestinal digestion coefficient; and 0.65 is the proportion of fatty acids in the microbial crude crude fat. fat. When calculating the supply of individual AAs from rumen microbes, it is assumed that the AA profile of the rumen microbes passing out of the rumen is similar to that of microbes in the rumen When calculating the supply of AAs from microbes, it is that the When of individual individual from rumen rumen microbes, is assumed assumed theofAA AA (Table calculating 7.2), and thatthe thesupply intestinal digestibilityAAs coefficient for each individualitAA is similarthat to that profile of the rumen microbes passing out of the rumen is similar to that of microbes in the profile the rumen microbes passingresidual out of the rumenconsisting is similarmostly to thatofofmicrobial microbescell in walls, the rumen rumen the totalofmicrobial AAs. The microbial fraction, (Table 7.2), that the digestibility coefficient (Table 7.2),toand and that undigested the intestinal intestinal digestibility coefficient for for each each individual individual AA AA is is similar similar to to is assumed remain in the small intestine. that of the total microbial AAs. The microbial residual fraction, consisting mostly of that of the total microbial AAs. The microbial residual fraction, consisting mostly of microbial microbial cell assumed to undigested in intestine. cell walls, is assumed to remain remain undigested in the the small intestine. 7.2.3walls, Flow is and digestion of endogenous protein insmall the small intestine

7.2.3 Flow digestion endogenous protein the small 7.2.3 Flow and and material digestion of endogenous proteininin inthe theduodenum small intestine intestine The endogenous of of animal origin emerging consists of epithelial cells

The material of emerging in consists of cells fromendogenous the rumen, the abomasum and theorigin intestinal wall, enzymes excreted from the pancreas and the The endogenous material of animal animal origin emerging in the the duodenum duodenum consists of epithelial epithelial cells from the rumen, rumen, the abomasum andfrom the intestinal intestinal wall, enzymes excreted from the the pancreas intestinal wall, andthe biliary excretions the liver. In NorFor, endogenous excretion is assumed from the abomasum and the wall, enzymes excreted from pancreastoand and the intestinal wall, and biliary excretions liver. In endogenous excretion is be only protein. Based data from Brandtfrom et al.the (1980), and McLeod (1982), Hvelplund the intestinal wall, andon biliary excretions from the liver.Ørskov In NorFor, NorFor, endogenous excretion is and Madsen (1985), Bruchem et al. andBrandt Voigt etet theØrskov averageand endogenous assumed to only protein. Based on data al. (1980), McLeod (1982), assumed to be be only Van protein. Based on (1985a) data from from Brandt etal. al.(1993), (1980), Ørskov and McLeod CP (1982), Hvelplund and Madsen (1985), Van Bruchem et al. (1985a) and Voigt et al. (1993), the average Hvelplund and Madsen (1985), Van Bruchem et al. (1985a) and Voigt et al. (1993), the average endogenous endogenous CP CP flow flow in in the the duodenum duodenum was was estimated estimated to to be be 30 30 g/kg g/kg OM OM flow. flow. The The endogenous endogenous NorFor –are Thedigested Nordic and feedreabsorbed evaluationinsystem 73 % proteins the small intestine and a digestibility coefficient proteins are digested and reabsorbed in the small intestine and a digestibility coefficient of of 60 60 % is is used used (Voigt (Voigt et et al., al., 1993, 1993, Larsen Larsen et et al., al., 2000). 2000). The The proportion proportion of of AAs AAs in in the the total total endogenous endogenous protein protein (N (N x x 6.25) 6.25) was was set set to to 0.5: 0.5:

2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122

2123

2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153

the intestinal wall, and biliary excretions from the liver. In NorFor, endogenous excretion is assumed to be only protein. Based on data from Brandt et al. (1980), Ørskov and McLeod (1982), Hvelplund and Madsen (1985), Van Bruchem et al. (1985a) and Voigt et al. (1993), the average endogenous CP flow in the duodenum was estimated to be 30 g/kg OM flow. The endogenous proteins areduodenum digested and smallOM intestine andendogenous a digestibility coefficient of 60 % flow in the was reabsorbed estimated toin bethe 30 g/kg flow. The proteins are digested is used (Voigt et al., 1993, Larsen et al., 2000). The proportion of AAs in the total endogenous and reabsorbed in the small intestine and a digestibility coefficient of 60% is used (Voigt et al., 1993, protein x 6.25) to 0.5: of AAs in the total endogenous protein (N·6.25) was set to 0.5: Larsen et(Nal., 2000).was Theset proportion sid _ eAA = r _ outOM ⋅ 0 .03 ⋅ 0 .5 ⋅ 0 .6 

[Eq. 7.46] 7.46

where sid_eAA ofof endogenous AAs digested in theinsmall intestine, g/d; and r_outOM where sid_eAAisisthe theamount amount endogenous AAs digested the small intestine, g/d; and is the flow of organic material out of the rumen, Equation 7.36. r_outOM is the flow of organic material out of the rumen, Equation 7.36. When calculating calculating the small intestine, thethe endogenous AAAA When the absorption absorptionofofindividual individualAAs AAsfrom fromthethe small intestine, endogenous profile (Table profile (Table 7.2) 7.2)isis based basedon ondata datapresented presentedby byLarsen Larsenetetal.al.(2000), (2000),and andit itisisassumed assumedthat thatthethe intestinal digestibility AAs is the same as for endogenous AAs.AAs. intestinal digestibilityofofindividual individualendogenous endogenous AAs is the same as total for total endogenous

7.3 Large Largeintestine intestine 7.3 The OMflowing flowinginto into large intestine consists of undigested feed, microbial material and The OM thethe large intestine consists of undigested feed, microbial material and endogenous endogenous material. In the large intestine, this OM is subjected to microbial fermentation. material. In the large intestine, this OM is subjected to microbial fermentation. The flow of endogenous protein into the large intestine is estimated to be three times the endogenous OM digested in the small intestine (Brandt et al., 1980; Voigttimes et al., and the The flow of endogenous protein into the large intestine is estimated to be three the1993) endogenous total flow of OM theintestine large intestine as: et al., 1993) and the total flow of OM OM digested in theinto small (Brandt is et calculated al., 1980; Voigt into the large intestine is calculated as: ⎞ ⎛ _ outOM = ⎛⎜ ∑ (DMI ⋅ NDF⎞ ) −⎛rd _ NDF ⎞⎟ + ⎛⎜ ∑ (DMI ⋅ ST ) − (rd _ ST ⎞ ⎜⎜ ∑ (DMI i ⋅ NDF si _ outOM = si ⎜ i ) − rd i_ NDF ⎟⎟i + ⎜⎜ ∑ (DMI i ⋅⎟ST⎜i ) − (rd _ iST +isid _ ST )⎟⎟ + + sid _ ST )⎟⎟ + i ⎝i ⎠ ⎝86 ⎠ ⎝i ⎠ ⎝i ⎠ ⎛ ⎞ ⎛ ⎞ ⎛ 2123 ⎞ ⎛ ⎞ 7.47 _ CP _ CP ⎟⎟⋅ CFat CFat7.46] ) ⎟⎟ +  [Eq. 7.46] ⋅ CP+i )sid − (_rdCP + ⎜⎜ ∑ ()DMI ⋅ CFat ) − ( rd _ CFat + sid _[Eq. ⎜⎜ ∑ ( DMI i ⋅ CP⎜⎜i∑) −( DMI ⎟⎟ + ⎜⎜+∑sid ( rd _i CP ( DMI i i − ( rdi _ CFati + sid _ CFat ) ⎟⎟ + i i ⎝ ⎠ ⎝ ⎠ i i ⎝ ⎠ ⎝ ⎠ r _ ⋅outOM r _( rmST r _) mCFat ⋅ 3 ⋅ 0 .4⋅ 0) .+15( r) _+ mCP ⋅ 0 .15⋅ 0) .+1)(+ ⋅ 0 .1)⋅+0 .(15 ⋅ 0 .15 ) + ( r _ outOM ⋅ 0(.03 3 ⋅ 0 .4 ) +⋅ 0(.r03 _ mCP ( r _ mST _ mCFat + ( ) r _ mCP ⋅ 0 . 53 (r _ mCP ⋅ 0.53 )

2124si_outOM is the flow of organic matter into the large intestine, g/d; DMI is the dry matter where iDMI is the dry matter the flowmatter organic matter the large 2125 i where issi_outOM the flow ofisorganic into the largeinto intestine, DMI i isg/d; the dry matter intakesi_outOM of thewhere i=1...n’th feedstuff, kg/d; of NDF NDF content in g/d; theintestine, i=1...n’th feedstuff, g/kg DM; i is the intake of the i=1...n'th feedstuff, kg/d; NDF 2126 is the NDF content in the i=1...n'th feedstuff, g/kg i intake of the i=1...n'th feedstuff, kg/d; NDF the NDFEquation content in7.21; the i=1...n'th feedstuff, g/kg i is rumen, rd_NDF is the degradation of NDF in the ST is the starch content in the i 2127 the starch content in DM; rd_NDF is the degradation of rumen, NDF inEquation the rumen, Equation 7.21; STi is content in sid_ST DM; rd_NDF is the degradation of NDF in the 7.21; ST i is the starch i=1...n’th feedstuff, g/kg DM; rd_ST is the degradation of starch in the rumen, Equation 7.14; i=1...n'th feedstuff, g/kg DM; is the degradation of starch the rumen, Equation 7.14; the 2128 i=1...n'ththe feedstuff, g/kg DM; rd_ST is therd_ST degradation of starch in the rumen,inEquation 7.14; is the digestion of starch in the small intestine, 7.40; CP is the crude protein in the is the of starch theEquation small intestine, Equation 7.40; CP 2129 is thecontent crude protein i CP sid_ST is thesid_ST digestion of digestion starch in the small in intestine, Equation 7.40; i is the crudei protein i=1...n’th feedstuff, g/kg DM; rd_CP is the degradation of crude protein in the rumen, Equation 7.8; 2130 content in the i=1...n'th feedstuff, g/kg DM; rd_CP is the degradation of crude protein in the content in the i=1...n'th feedstuff, g/kg DM; rd_CP is the degradation of crude protein in the sid_CP is the digestion of crude protein the small intestine, Equation 7.37; CFat fat 2131Equation rumen, Equation 7.8; isin the digestion of crude the small intestine, Equation rumen, 7.8; sid_CP is thesid_CP digestion of crude protein in theprotein small in intestine, Equation i is the crude content 2132 in the i=1...n’th is the feedstuff, crude fat g/kg content DM; in rd_CFat the i=1...n'th is the feedstuff, metabolism g/kg of DM; crude rd_CFat fat in is the the rumen, metabolism 7.37; CFat i 7.37; CFati is the crude fat content in the i=1...n'th feedstuff, g/kg DM; rd_CFat is the metabolism 2133 fat of crude fat inis the Equation 7.22; sid_CFat is the digestion ofinfatty acids the smallis Equation 7.22; sid_CFat therumen, digestion of fatty acids indigestion the small Equation 7.41;inr_outOM of crude in the rumen, Equation 7.22; sid_CFat is the ofintestine, fatty acids the small intestine, Equation 7.41; the flow of r_mCP organic matter out ofEquation theofrumen, Equation 7.36; the2134 flow of organic matter out of the rumen, 7.36; therumen, synthesis microbial protein intestine, Equation 7.41; r_outOM is r_outOM the flowEquation ofis organic matter out ofis the 7.36; 2135 is the synthesis of microbial the rumen, Equation 7.30; is the synthesis r_mCP is ther_mCP synthesis of7.30; microbial protein theprotein rumen, Equation 7.30; r_mST isrumen, ther_mST synthesis in the rumen, Equation r_mST is theinsynthesis ofinmicrobial starch in the Equation 7.33; 2136 isof microbial inEquation the rumen, Equation 7.33; r_mCFat is the synthesis of microbial crude fat of microbial starch in thestarch rumen, 7.33; r_mCFat is the synthesis of7.32; microbial crude fat r_mCFat the synthesis of microbial crude fat in the rumen, Equation parameters 0.03·3·0.4 2137 inEquation the rumen, Equation 7.32; 0.03·3·0.4 parametersexpress 0.03·3·0.4 express the flow ofendogenous undigested endogenous in the rumen, 7.32; parameters the flow of undigested express the flow of undigested endogenous crude protein into the large intestine; the parameter 0.15 2138 crude the large the parameter 0.15 is theofproportion ofmicrobial undigested microbial crude protein into protein the largeinto intestine; theintestine; parameter the proportion undigested is the proportion of undigested microbial protein;0.15 theofisparameter 0.1 is the proportion of undigested 2139the parameter protein; the0.1 parameter 0.1 is theofproportion undigested microbial starch; the0.5 parameter 0.5 is protein; is the proportion undigested microbial starch; the parameter is microbial starch; the parameter 0.15 is themicrobial proportion of the undigested microbial crude fat; and the 2140 the proportion of the undigested crude fat; and the parameter 0.53 is the the proportion of the undigested microbial crude fat; and the parameter 0.53 is the ratio of the ratio of the parameter 0.53 isfraction theresidual ratiotoof the microbial 2141 residual microbial fraction to cruderesidual protein. fraction to crude protein. microbial crude protein. 2142 In Equation Equation 7.47, theexpression expression r_mCP·0.53 is an estimate of the microbial residual fraction fraction flowing 2143 In Equation 7.47, ther_mCP·0.53 expression r_mCP·0.53 is of an the estimate of the microbial residual In 7.47, the is an estimate microbial residual fraction flowing the large intestinesee (0.53=270/512; seeand section 7.1.6), and as discussed earlier,that we into2144 the into large (0.53=270/512; Section 7.1.6), as discussed earlier, wewe assumed flowing theintestine largeinto intestine (0.53=270/512; see section 7.1.6), and as discussed earlier, that this is indigestible in the small intestine. Theinto components into this2145 fraction is indigestible infraction the small intestine. components flowing the large intestine arethe assumed thatassumed this fraction is indigestible in the smallThe intestine. The components flowing intoflowing the 2146 are to microbial fermentation. However, only ruminally undegraded large intestine areintestine subjected to subjected microbialHowever, fermentation. However, ruminally undegraded subjected tolarge microbial fermentation. only ruminallyonly undegraded NDF, undigested rumen 2147 NDF, undigested rumen escape starch, undigested rumen microbial starch and cell walls are NDF, undigested rumen escape starch, undigested rumen microbial starch and cell walls are escape starch, undigested rumen microbial starch and cell walls are assumed to be substrates for 2148 tofermentation. assumed to beA substrates microbial fermentation. assumed be substrates for microbialfor fermentation. A one-compartment model is model is microbial one-compartment degradation modelAisone-compartment useddegradation to calculatedegradation NDF degradation, used NDF to calculate NDF degradation, and fixed degradation rates 4 and 16.7 %/h, used2149 to calculate degradation, and fixed degradation and passage and ratespassage of 4 and 16.7of%/h, 2150 respectively, used for alldegradation feeds. The degradation of large NDF intestine in the large intestine is calculated respectively, are used for are all feeds. The of NDF in the is calculated 2151 from: from: 2152

74 

NorFor ⎞ – The pdNDFi ⎞⎞ 4 Nordic feed evaluation system ⎛ ⎛_ NDF = ⎛⎜ ∑ ⎛⎜ DMI pdNDF 4 i ⎞ −i rd [Eq. 7.48] [Eq. 7.48] i ⋅ NDF lid _2153 NDF = ⎜⎜ lid NDF ⎟⎟⎟⋅ − rd _ NDF ⎟⎟ ⋅ ∑ ⎜ DMI i ⋅ NDF ⎟ ⋅ _1000 ⎜ i⋅ 4 + 16.7 ⎝ i ⎝ 1000 ⎠ ⎝i⎝ ⎠⎠ 4 + 16.7 ⎠ 2154

2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2162 2159 2162 2163 2160 2162 2163 2162 2164 2161 2163 2164 2163 2164 2165 2164 2165 2165 2166 2165 2166 2167 2166 2167 2166 2168 2167 2168 2167 2169 2168 2169 2168 2170 2169 2170 2169 2171 2170 2171 2170 2172 2171 2172 2171 2173 2172 2173 2172 2174 2173 2174 2173 2175 2174 2175 2174 2176 2175 2176 2175 2177 2176 2176 2177 2177 2178 2177 2178 2179 2178 2179 2178 2180 2179 2180 2179 2181 2180 2181 2180 2182 2181 2182 2181 2183 2182 2183 2182 2184 2183 2184 2183 2185 2184 2185 2184 2186 2185 2186 2185 2187 2186 2187 2186 2188 2187 2188 2187 2189 2188 2189 2188 2190 2189 2190 2189 2191 2190 2191 2190 2192 2191 2192 2191 2193 2192 2193 2192 2193 2193 2194 2194 2195 2194 2195 2194 2196 2195 2196 2195 2197 2196 2197 2196 2198 2197 2198 2197 2199 2198 2199 2198 2199 2199 2200 2200 2200 2200 2201 2201 2201 2201

assumed that this fraction is indigestible in the small intestine. The components flowing into the large intestine are subjected to microbial fermentation. However, only ruminally undegraded NDF, undigested rumen escape starch, undigested rumen microbial starch and cell walls are assumed to be substrates for microbial fermentation. A one-compartment degradation model is used to calculate NDF degradation, and fixed degradation and passage rates of 4 and 16.7 %/h, respectively, are used forand all passage feeds. The degradation NDF in the large intestine is calculated and fixed degradation rates of 4 andof 16.7%/h, respectively, are used for all feeds. The from:

degradation of NDF in the large intestine is calculated from: ⎛ ⎛ ⎞ pdNDFi ⎞ 4 lid _ NDF = ⎜⎜ ∑ ⎜ DMI i ⋅ NDFi ⋅ ⎟ − rd _ NDF ⎟⎟ ⋅ 1000 ⎠ ⎝i⎝ ⎠ 4 + 16.7



[Eq. 7.48]

7.48

where lid_NDF is the degradation of NDF in the large intestine, g/d; DMI is the dry matter intake of

i where lid_NDF is the degradation of NDF in the large intestine, g/d; DMIi is the dry matter the i=1...n’th feedstuff, kg/d; NDFi is the NDF content in the i=1...n’th feedstuff, g/kg DM; pdNDFi intake of the i=1...n'th feedstuff, kg/d; NDFi is the NDF content in the i=1...n'th feedstuff, g/kg is the potentially degradable NDF content in the i=1...n’th feedstuff, g/kg NDF; rd_NDF is the DM; pdNDFi is the potentially degradable NDF content in the i=1...n'th feedstuff, g/kg NDF; degradation of NDF in the rumen, Equation 7.21; the parameter 4 is the degradation rate of NDF, rd_NDF is the degradation of NDF in the rumen, Equation 7.21; the parameter 4 is the The degradation in 16.7 the large intestine is derived from where, itout is %/h. %/h;starch and the parameter is the fractional passage ratethe offollowing NDF passage outequation, of the large intestine, degradation rate of NDF, %/h; and the parameter 16.7 is the fractional rate of NDF The starch degradation in the large intestine is derived from theisfollowing equation, where, it is of assumed that the digestibility coefficient of the starch fraction 0.9: the large intestine, %/h.in the large The starch degradation intestine is from equation, where, is assumed that the digestibility coefficient of the starch fraction isfollowing 0.9: The degradation in the large intestine is derived derived from the the following equation,equation, where, it it where, is Thestarch starch degradation in the large intestine is derived from the following it is assumed that the digestibility coefficient of the starch fraction is 0.9: assumed that the digestibility coefficient of the starch fraction is 0.9: ⎛ ⎞ assumed the digestibility coefficient of the starch fraction is 0.9: ⎟ [Eq. 7.49] ( ) lid _ ST = ⎜⎜⎛that DMI ⋅ ST − rd _ ST − sid _ ST ⋅ 0 . 9 ∑ ⎞ i i [Eq. 7.49] lid _ ST = ⎜⎛⎝⎜ ∑i (DMIi ⋅ STi ) − rd _ ST − sid _ ST ⎟⎟⎞⎠⎟ ⋅ 0.9 87 ⎛⎜⎝ ∑i (DMIi ⋅ STi ) − rd _ ST − sid _ ST ⎞⎟⎠ ⋅ 0.9 [Eq. lid _ ST = [Eq. 7.49] 7.49] lid _ ST = ⎜⎜ ∑ (DMI i ⋅ STi ) − rd _ ST − sid _ ST ⎟⎟ ⋅ 0.9  7.49 ⎝ ii ⎠ ⎝ ⎠ where lid_ST is the degradation of starch in the large intestine, g/d; DMIi is the dry matter intake matter intake where lid_ST isfeedstuff, the degradation of starch in the large intestine, g/d; DMIfeedstuff, i is the dry of the i=1...n'th kg/d; ST is starch the starch content in the i=1...n'th g/kg iof where lid_STisisfeedstuff, thedegradation degradation in the large intestine, g/d; DMI isdry the dryDM; matter intake of is the matter where lid_ST the of starch in the large intestine, g/d; DMI i of the i=1...n'th kg/d; ST is the starch content in the i=1...n'th feedstuff, g/kg DM; i i thethe dry matter intake intake where lid_ST is the degradation ofinstarch in the large intestine, g/d; DMI rd_ST is the degradation of starch Equation 7.14; and sid_ST digestion of rd_ST is i is is thethe i=1...n’th feedstuff, kg/d; ST isthe therumen, starchcontent content in thei=1...n'th i=1...n’th feedstuff, g/kg DM; of i=1...n'th feedstuff, kg/d; ST is the starch in the feedstuff, g/kg DM; rd_ST is the degradation of starch in the rumen, Equation 7.14; and sid_ST is the digestion of i of the i=1...n'th feedstuff, kg/d; STi is the starch content in the i=1...n'th feedstuff, g/kg DM; starch in the small intestine, Equation 7.40. the degradation ofintestine, starch in the in rumen, Equation 7.14;7.14; and and sid_ST is the digestion ofofstarch in the rd_ST is degradation of rumen, starch Equation 7.40. rd_ST in is the the small degradation of starch starch in the the rumen, Equation Equation 7.14; and sid_ST sid_ST is is the the digestion digestion of starch in the small intestine, Equation 7.40. small intestine, Equation 7.40. starch the small intestine, Equation 7.40.is used for the efficiency of microbial protein synthesis A fixedinvalue of 150 g/kg degraded CHO A value of 150 (Demeyer, g/kg degraded CHO used for the efficiency of microbial in fixed the large intestine 1990), andisthe composition of microbial OM in protein the largesynthesis A value of g/kg degraded CHO isthe used for the efficiency of synthesis in the large intestine (Demeyer, 1990), and composition of microbial OM in protein theislarge A fixed fixed value 150to g/kg degraded CHO is used the efficiency ofsynthesis microbial protein synthesis in A fixed value ofof150 150 g/kg degraded used forfor the efficiency of microbial microbial protein synthesis intestine is assumed be similar to CHO that inisthe rumen. Microbial protein calculated in the large intestine (Demeyer, 1990), and the composition of microbial OM in the large intestine is assumed to be similar to that in the rumen. Microbial protein synthesis is calculated the large intestine (Demeyer, 1990), and the composition of microbial OM in the in the large intestine (Demeyer, 1990), and the composition of microbial OM in the largelarge intestine is as: intestine to that in rumen. synthesis is as: intestine istoassumed assumed to be be similar tothe that in the the Microbial rumen. Microbial Microbial protein synthesis is calculated calculated assumedis be similar tosimilar that into rumen. proteinprotein synthesis is calculated as: as: as: 270 ⎞ [Eq. 7.50] li _ mCP = (lid _ NDF + lid _ ST + r _ mST ⋅ 0 .1 + r _ mCP ⋅ 270 ⋅ 0 .75 ⎟⎞ ⋅ 0 .15 [Eq. 7.50] li _ mCP = (lid _ NDF + lid _ ST + r _ mST ⋅ 0 .1 + r _ mCP ⋅ 512 ⋅ 0 .75 ⎠⎟ ⋅ 0 .15  7.50 270 ⎞ 512 ⎠ 270 [Eq. ⎞ (lid __ NDF li [Eq. 7.50] 7.50] li __ mCP mCP = = (lid NDF + + lid lid _ _ ST ST + + rr _ _ mST mST ⋅⋅ 0 0 ..1 1+ + rr _ _ mCP mCP ⋅⋅ 512 ⋅⋅ 00 ..75 75 ⎟⎟⎠ ⋅⋅ 00 ..15 15 512 ⎠ intestine, where inin the large intestine, g/d; lid_NDF is the whereli_mCP li_mCPisisthe themicrobial microbialprotein proteinsynthesis synthesis the large g/d; lid_NDF is the degradation where li_mCP is the in microbial protein synthesis in the large intestine, lid_NDFof is starch the in degradation of NDF the large intestine, Equation 7.48; lid_ST is theg/d; degradation of NDF in the large intestine, Equation 7.48; lid_ST is the degradation of starch in the large where li_mCP the microbial protein synthesis in large intestine, lid_NDF is the degradation of is NDF in the large intestine, Equation 7.48; lid_ST is theg/d; degradation of starch in intestine, where li_mCP is the microbial protein synthesis in the the large intestine, g/d; lid_NDF is 7.33; the r_mCP the large intestine, Equation 7.49; r_mST the synthesis of microbial starch in the rumen, Equation 7.49; r_mST is the synthesis ofis microbial starch in the rumen, Equation is the degradation of NDF in the large intestine, Equation 7.48; lid_ST is the degradation of starch in the large intestine, Equation 7.49; r_mST is the synthesis of microbial starch in the rumen, degradation of NDF in is thethe large intestine, Equation crude 7.48; lid_ST of starch inthe Equation 7.33; r_mCP synthesis of microbial protein isinthe thedegradation rumen, Equation 7.30; synthesis of microbial crude protein in microbial the rumen, Equation 7.30; the parameter 270/512 is the ratio the large intestine, Equation 7.49; r_mST is the synthesis of microbial starch in the rumen, Equation 7.33; r_mCP is the synthesis of crude protein in the rumen, Equation 7.30; the the large intestine, 7.49; r_mST is theinsynthesis of microbial in the rumen, parameter 270/512 Equation is the ratio of cell wall:CP the microbial fraction;starch the parameter 0.75 is the of cell wall:CP in the microbial fraction; the parameter 0.750.15 isin the proportion ofofmicrobial cell walls Equation 7.33; r_mCP is the synthesis of protein Equation 7.30; the parameter 270/512 is the ratio of cell wall:CP thecrude microbial fraction; the parameter 0.75 is the Equation 7.33; r_mCP is the synthesis of microbial microbial crude protein in the rumen, Equation 7.30; the proportion of microbial cell walls digested; andinthe parameter isthe therumen, efficiency microbial parameter 270/512 is the ratio of cell wall:CP in the microbial fraction; the parameter 0.75 is the digested; and the parameter 0.15 is the efficiency of microbial protein synthesis in the large proportion of microbial cell walls digested; and the parameter 0.15 is the efficiency of microbial parameter 270/512 thelarge ratiointestine. of cell wall:CP in the microbial fraction; the parameter 0.75 is theintestine. protein synthesis inisthe proportion of cell digested; protein synthesis in the large intestine. proportion of microbial microbial cell walls walls digested; and and the the parameter parameter 0.15 0.15 is is the the efficiency efficiency of of microbial microbial protein synthesis in the large intestine. The synthesis of microbial fat in the large intestine is calculated from li_mCP protein synthesis in the large The synthesis of microbial fatintestine. in the large intestine is calculated from li_mCP as: as: The synthesis of microbial fat in the large intestine is calculated from li_mCP as: The synthesis of microbial fat The synthesis microbial in the the large large intestine intestine is is calculated calculated from from li_mCP li_mCP as: as: [Eq. 7.51] li _ mCFat = li _ofmCP ⋅ 0.326 fat  in 7.51 li _ mCFat = li _ mCP ⋅ 0.326 [Eq. 7.51] li = ⋅⋅ 00..326 [Eq. 7.51] li _ _ mCFat mCFat = li li _ _ mCP mCP 326microbial where li_mCFat is is the microbial fat synthesis in theinlarge intestine, g/d; li_mCP is[Eq. the 7.51] microbial where li_mCFat the fat synthesis the large intestine, g/d; li_mCP is the microbial where li_mCFat is the microbial fat synthesis in the large intestine, g/d; li_mCP is theratio microbial protein synthesis ininthe large intestine, Equation 7.50; and thethe parameter 0.326 is the of of CFat:CP protein synthesis the large intestine, Equation 7.50; and parameter 0.326 is the ratio where is the fat in large g/d; the microbial proteinli_mCFat synthesis themicrobial large intestine, Equation 7.50; andintestine, the parameter 0.326 isis of where li_mCFat isin the microbial fat synthesis synthesis in the the large intestine, g/d; li_mCP li_mCP isthe theratio microbial CFat:CP in the microbial fraction. in the microbial fraction. protein in intestine, CFat:CP in the microbial fraction. protein synthesis synthesis in the the large large intestine, Equation Equation 7.50; 7.50; and and the the parameter parameter 0.326 0.326 is is the the ratio ratio of of CFat:CP the fraction. CFat:CP in tract the microbial microbial fraction. 7.4 Totalin digestion

7.4Total Totaltract tract digestion 7.4 digestion

Apparent digestibility and energy values (based on metabolizable energy, ME) of diets are 7.4 tract digestion Apparent and energy values (based on metabolizable energy, ME) of diets are Es 7.4 Total Totaldigestibility tractactual digestion calculated from feed intake and composition. The energy system developed by Van Apparent digestibility andenergy energyvalues values (based on metabolizable energy, ME) of diets are calculated Apparent digestibility and (based on metabolizable energy, ME) of diets are calculated from actual feed intake and composition. The energy system developed by Van Es Apparent digestibility andME, energy valuesestimates (based onofmetabolizable energy,ofME) diets areCFat (1978) is used to predict for which totalsystem tract digestion CP, CHO and from actual feed intake and composition. The energy developed by of Van Es (1978) is used to calculated from actual feed intake and composition. The energy system developed by Van Es (1978) is used to predict ME, for which estimates of total tract digestion of CP, CHO and CFat calculated actualtotal feedtract intake and composition. The energy developed are needed.from Apparent digestion and digestibility of CPsystem are calculated as: by Van Es predict ME, for which estimates of total tract digestion of CP, CHO and CFat are needed. Apparent (1978) is used to predict ME, for which estimates of total tract digestion of CP, CHO and CFat are needed. Apparent total tract digestion and digestibility of CP are calculated as: (1978) is used to predict ME, for which estimates of total tract digestion of CP, CHO and CFat are needed. Apparent total tract digestion of CP total tract digestion ofand CP digestibility are calculated as: are calculated as: are needed. Apparent and total⎛digestibility ⎞ as: ⎛tract digestion ⎞ and digestibility of CP are calculated ⎛ ⎞ ⎜⎛ ⎜⎛ ∑ DMI i ⋅ CPi ⎟⎞ − rd _ CP − sid _ CP + r _ mCP ⋅ 0.15 + ⎟⎞ td _ CP = ⎜⎜⎛ ∑ DMI i ⋅ CPi ⎟⎟⎞ − ⎜⎛ ⎜⎝⎜⎜ ∑i DMI i ⋅ CPi ⎟⎠⎟⎟ − rd _ CP − sid _ CP + r _ mCP ⋅ 0.15 + ⎟⎞ td _ CP = ⎛⎜⎝⎜ ∑i DMI i ⋅ CPi ⎞⎟⎠⎟ − ⎜⎛ ⎛⎜⎝⎛ ∑i DMI ⋅ CP ⎞⎟⎠⎞ − rd _ CP − sid _ CP + r _ mCP ⋅ 0.15 + ⎟⎞ ⋅ 0.ii03 + si− _sid + li⋅ _0.mCP _ outOM outOM ⎟⎟ −⋅ 3rd⋅ 0_.4CP DMI ii ⋅ CP _ CP +⋅ 0r.025 _ mCP 15 + ⎟ ⎜⎝⎛ ∑i DMI i ⋅ CPi ⎟⎟⎠⎞ − ⎝⎜⎜⎜ ⎜⎝⎜r ∑ td __ CP CP = ⎠⎠ ⋅ 3 ⋅ 0.4 + si _ outOM ⋅ 0.025 + li _ mCP ⎠⎟⎟⎟⎠  = ⎜⎜ ∑ td DMI i ⋅ CPi ⎟ − ⎜⎝ ⎝r _ii outOM ⋅ 0.03 ⎟⎟ ⎝⎝ ii ⎠⎠ ⎜⎜ ⎝⎝ rr __ outOM + si + li outOM ⋅⋅ 00..03 03 ⋅⋅ 33 ⋅⋅ 00..44 + si __ outOM outOM ⋅⋅ 0 0..025 025 + li _ _ mCP mCP ⎠⎠

88 88 88 NorFor – The Nordic feed evaluation system 88

[Eq. 7.52] [Eq. 7.52] [Eq. [Eq. 7.52] 7.52]

7.52

75

2202 2202 2202 2203 2203 2203 2204 2204 2204 2205 2205 2205 2206 2206 2206 2207 2207 2207 2208 2208 2208 2209 2209 2209 2210 2210 2210 2210 2211 2211 2211 2211 2212 2212 2212 2213 2213 2213 2214 2214 2215 2214 2215 2216 2215 2216 2217 2216 2217 2218 2217 2218 2219 2218 2219 2220 2219 2220 2220 2221 2221 2221 2222 2222 2222 2223 2222 2223 2223 2224 2223 2224 2224 2225 2224 2225 2225 2226 2225 2226 2227 2226 2226 2227 2228 2227 2227 2228 2228 2228 2229 2229 2229 2229 2230 2230 2231 2230 2230 2231 2231 2231 2232 2232 2232 2232 2233 2233 2234 2233 2234 2233 2235 2234 2235 2234 2236 2235 2236 2235 2237 2236 2237 2236 2238 2237 2238 2237 2238 2238

td td__CP CP ⋅100 CPD td__CP CP ⋅100 td CPD == DMI ⋅100 CPD == ∑ CPii ⋅100 ∑ DMIii ⋅⋅CP DMI CPii ∑ DMI ∑ i ii ⋅⋅CP



[Eq. 7.53] [Eq. 7.53] [Eq. 7.53] [Eq. 7.53]

7.53

i ii

where the total tract digestion ofofcrude protein, g/d; CPD is thethe apparent total wheretd_CP td_CPis theapparent apparent total tract digestion of crude protein, g/d; CPD is apparent the apparent where td_CP the apparent total tract digestion crude protein, g/d; CPD is total where td_CP isis apparent total tract digestion ofof crude protein, g/d; CPD is thethe apparent total td_CP isisthe the apparent total tract digestion crude protein, g/d; CPD is apparent totaltotal tract tract digestibility of crude protein, %; DMI is the dry matter intake of the i=1...n'th feedstuff, i is the dry matter intake of the i=1...n'th feedstuff, tract digestibility of crude protein, %; DMI digestibility of crude protein, %; DMI is the dry matter intake of the i=1...n’th feedstuff, i is matter of thethe i=1...n'th feedstuff, tract digestibility of protein, %; DMI digestibility ofcrude crude protein, %;in DMI thedry dry matterintake intake of i=1...n'th feedstuff, kg/d; CPi i i i=1...n'th i isthe the crude protein content the feedstuff, g/kg DM; rd_CP is is the kg/d; CP i is kg/d; CP is the crude protein content in the i=1...n'th feedstuff, g/kg DM; rd_CP the i is is the crude protein content in the i=1...n’th feedstuff, g/kg DM; rd_CP is the degradation of crude the crude protein content in the i=1...n'th feedstuff, g/kg DM; rd_CP is the kg/d; CP CP is the crude protein content in the i=1...n'th feedstuff, g/kg DM; rd_CP is the i i degradation of crude protein ininthe rumen, Equation 7.8; sid_CP is is the digestion of of crude protein degradation of crude protein the rumen, Equation 7.8; sid_CP digestion protein protein in the rumen, Equation sid_CP is synthesis the digestion crude protein incrude the small degradation of protein ininthe rumen, Equation 7.8; sid_CP isof digestion ofinof crude protein degradation of crude crude protein the7.8; rumen, Equation 7.8; sid_CP isthethe the digestion crude proteinintestine, in the small intestine, Equation 7.37; r_mCP is the of microbial protein the rumen, in the small small intestine, Equation 7.37; r_mCP the synthesis microbial protein in rumen, in the intestine, Equation 7.37; isis the ofof protein in thethe rumen, Equation 7.37; r_mCP is the flow synthesis of microbial protein in the rumen, Equation 7.30; r_outOM is small intestine, Equation 7.37; r_mCP ismatter thesynthesis synthesis ofmicrobial microbial protein in the rumen, Equation 7.30; r_outOM is the ofr_mCP organic out of the rumen, Equation 7.36; si_outOM Equation 7.30; r_outOM is the flow of organic matter out of the rumen, Equation 7.36; si_outOM Equation 7.30; r_outOM is the flow of organic matter out of the rumen, Equation 7.36; si_outOM Equation 7.30; r_outOM is the of organic matter out 7.36; of 7.47; the si_outOM rumen, Equation 7.36; si_outOM the matter outflow of the rumen, Equation is the flow of organic matter is theflow flowof oforganic organic matter into the large intestine, Equation and li_mCP is the microbial is the flow of organic matter into the large intestine, Equation 7.47; and li_mCP the microbial is the flow of into intestine, Equation 7.47; and li_mCP is is the microbial is thethe flow of organic organic matter intothe thelarge large intestine, Equation 7.47; and li_mCP is the microbial protein synthesis in thematter large intestine, Equation 7.50, parameter values 0.03, 3, 0.4 and 0.15 arein the large into large intestine, Equation 7.47; and li_mCP is the microbial protein synthesis protein synthesis in the large intestine, Equation 7.50, parameter values 0.03, 3, 0.4 and 0.15 protein synthesis in the large intestine, Equation 7.50, parameter values 0.03, 3, 0.4 and 0.15 areare protein synthesis in the large intestine, Equation 7.50, parameter values 0.03, 3, 0.4in and 0.15 are7.47; and explained in Equation 7.47, and the parameter 0.025 is the proportion of endogenous protein intestine, Equation 7.50; parameter values 0.03, 3, 0.4 and 0.15 are explained Equation explained in Equation 7.47, and the parameter 0.025 is the proportion of endogenous protein explained in Equation 7.47, and the parameter 0.025 isis the proportion ofof endogenous protein explained in Equation 7.47, and the parameter 0.025 the proportion endogenous protein excreted in the large intestine per unit of OM entering the large intestine. the parameter 0.025 is the proportion of endogenous protein excreted in the large intestine per unit excreted in the large intestineper per unit OM entering the large intestine. excreted in intestine excreted in the the large large perunit unitofof ofOM OMentering enteringthe thelarge largeintestine. intestine. of OM entering theintestine large intestine. In Equation 7.52, 25 g protein per kg OM flowing into the large intestine is used as an estimate of Equation 7.52, 25 protein per kg OM flowing into the large intestine used estimate In gg per kg OM flowing is is used as as anan estimate of of endogenous protein the large (van Bruchem et al., 1985b). Dietary In Equation Equation 7.52, 7.52, 25 25excretion gprotein proteinin per kg OMintestine flowinginto intothe thelarge largeintestine intestine is used as an estimate of endogenous protein excretion in the large intestine (van Bruchem et al., 1985b). In Equation 7.52, 25 g protein per kg the OM flowing into the intestine isDietary used an estimate of endogenous excretion large intestine (van etaffect 1985b). Dietary ammonia andprotein urea provide no in energy for animal although theylarge the apparent totalastract endogenous protein excretion inthe the large intestine (vanBruchem Bruchem etal., al., 1985b). Dietary ammonia and urea provide no energy for the animal although they affect apparent total tract ammonia and urea no animal although they thethe apparent total tract endogenous protein excretion in thefor large intestine (van Bruchem et al., 1985b). Dietary ammonia and digestibility values of CP. Thus, ammoniaand urea-corrected protein digestion values are used ammonia and ureaprovide provide noenergy energy forthe the animal although theyaffect affect the apparent total tract digestibility values CP. Thus, and digestion values are used digestibility values of CP. Thus, ammoniaand urea-corrected protein digestion values are used urea no energy theammoniaanimal although they affectprotein the apparent total tract digestibility in theprovide calculation ofof ME: digestibility values of CP.for Thus, ammoniaandurea-corrected urea-corrected protein digestion values are used values in of calculation of ME: ofthe CP.calculation Thus, ammoniain the calculation ofME: ME:and urea-corrected protein digestion values are used in the calculation of ME: ⎞ ⎛⎛ ⎞ ⎟ ⎛ ⎞ ⎜⎛ ⎜ ∑ DMI i ⋅ CPi ⎟⎟⎞ − ⎞ rd _ CP − jed _ CP + r _ mCP ⋅ 0.15 + ⎞⎟ ⎞⎞ td _ CPcorr = ⎛⎜⎜⎛∑ DMI i ⋅ CPcorr i ⎟⎞⎟ ⎞− ⎜ ⎛⎛⎜⎜⎝ ⎛⎜⎛∑i ∑DMI ⎟ ⎞ ⋅ CP rd _ CP jed _ CP r _ mCP 0 . 15 − − + ⋅ + ⎠ ⎟ DMIi i ⋅ CP − rd _ CP − jed _ CP + r _ mCP ⋅ 0 .15 + ⎟⎟ i i ⎟ ⎜ ⎜ ⎟ _ CP − jed _ CP + r _ mCP ⋅ 0 .15⎟+ td _ CPcorr CPcorr == ⎝⎜⎜⎛∑i DMI DMI ⋅ ⋅CPcorr CPcorr ⎠⎟ ⎟⎞−−⎜ ∑ DMI ⋅ CP⎠i ⎠⎟⋅ 3−⋅ rd 0 .4 + si _ outOM ⋅ 0 .025 + li _ mCP ⎟ td _ CPcorr = ⎜⎝⎜⎜ i∑ DMIi ii ⋅ CPcorri ii⎟⎠ ⎟⎟ −⎝⎜ ⎜⎜⎜⎝r ⎝⎜⎝_i iioutOMi ⋅ 0 .03 ⎝ ∑i ⎠ ⎜r _ outOM ⋅ 0 .03 ⎠⋅ 3 ⋅ 0 .4 + si _ outOM ⋅ 0 .025 + li _ mCP ⎠⎟ ⎟⎟⎟  ⎝i ⎠ ⎝ ⎝ r _ outOM ⋅ 0 .03 ⋅ 3 ⋅ 0 .4 + si _ outOM ⋅ 0 .025 + li _ mCP ⎠ ⎠ ⎝ r _ outOM ⋅ 0 .03 ⋅ 3 ⋅ 0 .4 + si _ outOM ⋅ 0 .025 + li _ mCP[Eq. ⎠

7.54

7.54] [Eq. 7.54] [Eq. [Eq. 7.54] 7.54] Equation similar to Equation 7.52,7.52, exceptexcept that ammoniaand urea-corrected CP (CPcorrCP i; (CPcorr ; Equation7.54 7.54is is similar to Equation that ammoniaand urea-corrected i Equation 7.54 is similar to Equation 7.52, except that ammoniaand urea-corrected CP (CPcorr i; i; Equation 7.54 is similar Equation 7.52, except that ammoniaand urea-corrected CP (CPcorr Equation 4.4) are used as to input. Equation 7.54 is similar to Equation 7.52, except that ammoniaand urea-corrected CP (CPcorr Equation 4.4) are used as input. i; Equation 4.4) are used as input. Equation 4.4) are used as input. Equation 4.4) are used as input. The apparent total tract digestion and digestibility of CHO are calculated from: Theapparent apparenttotal total tract digestion digestibility of CHO are calculated The tract digestion and and digestibility of CHO are calculated from: from: The The apparent apparent total total tract tract digestion digestion and and digestibility digestibility of of CHO CHO are are calculated calculated from: from: ⎛ rd _ NDF + rd _ ST + rd _ CFat + rd _ FPF + rd _ Re stCHO + sid _ ST + sid _ mST ⎞ ⎜⎛ rd _ NDF + rd _ ST + rd _ CFat + rd _ FPF + rd _ Re stCHO + sid _ ST + sid _ mST ⎟⎞ __ ST __ ST + td _ CHO = ⎜⎛ rd + 51 270 ⎞ + ⎞ + ⎛ 270 ⎟ ⎞⎟⎞ rd _ _ NDF NDF + rd ST rd __ CFat CFat + rd __ FPF FPF + rd __ Re Re stCHO stCHO sid ST sid __ mST mST +⎛ rd + rd + rd + rd + sid + sid 7.55 td _ CHO = ⎜⎜⎛⎜− r _ mCP ⋅ ⎜⎛ 270 ⎟⎞ ⋅ 0.25 + lid _ NDF + lid _ ST − li _ mCP ⋅ ⎜⎛ 270 + 51 ⎟⎞ ⎟  ⎟⎟ td _ CHO = + 270 270 51 512 512 ⎜ ⎞ ⎞ ⎛ ⎛ ⋅ + + − ⋅ − ⋅ r _ mCP 0 . 25 lid _ NDF lid _ ST li _ mCP ⎠ ⎠ ⎝ ⎝ ⎟ ⎟ ⎜ ⎜ ⎠⎟ ⎟ td _ CHO = ⎝⎜⎜ − r _ mCP ⋅ ⎛⎜ 270 ⎞⎟ ⋅ 0.25 + lid _ NDF + lid _ ST − li _ mCP ⋅ ⎛⎜ 270 + 51 ⎞⎟ 512 ⎠ ⎟ ⎟ ⎠ ⎝ ⎝⎜⎜ − r _ mCP ⋅⎝⎜⎝512 ⎠ ⋅ + + − ⋅ 0 . 25 lid _ NDF lid _ ST li _ mCP ⎟ ⎜ 512 7.55]⎠⎟ ⎝⎝ 512 ⎝⎝ 512 ⎠⎠ [Eq. ⎝ 512 ⎠⎠ [Eq. 7.55]⎠ [Eq. [Eq. 7.55] 7.55] td _ CHO CHOD = ⋅100  [Eq. 7.56] 7.56 td _ CHO CHOD = ∑ DMIi ⋅ OMi − ∑ DMIi ⋅ CPcorr td [Eq. 7.56] i − ∑ DMIi ⋅ CFati + rd _ CFat ⋅ 100 td __ CHO CHO CHOD = ⋅ 100 [Eq. 7.56] DMI ⋅ OM − DMI ⋅ CPcorr − DMI ⋅ CFat + rd _ CFat ∑ ∑ ∑ i i i i i i i i i CHOD = ∑ DMI ⋅ OM − ∑ DMI ⋅ CPcorr − ∑ DMI ⋅ CFat + rd _ CFat ⋅ 100 [Eq. 7.56] i DMIii ⋅ OMii − i∑ DMIii ⋅ CPcorrii −i ∑ DMIii ⋅ CFatii + rd _ CFat ∑ i i i i i i wheretd_CHO td_CHO apparent digestion of carbohydrates, g/d;isCHOD the total tract where is is thethe apparent totaltotal tract tract digestion of carbohydrates, g/d; CHOD the totalistract where td_CHO iscarbohydrates, the apparent %; total digestion of carbohydrates, CHOD is the total tract digestibility ofofcarbohydrates, rd_NDF is theisdegradation of NDFofg/d; inNDF the rumen, Equation digestibility %;tract rd_NDF the degradation in the rumen, Equation 7.21; where td_CHO is the apparent total tract digestion of carbohydrates, g/d; CHOD is the total digestibility of the carbohydrates, rd_NDF is the degradation of 7.14; NDF rd_CFat ing/d; the CHOD rumen, Equation whererd_ST is degradation the apparent%; total tract digestion ofEquation carbohydrates, is the total tract tract 7.21; of starch the rumen, isisthe degradation rd_STtd_CHO is theisofdegradation of starch in in the rumen, Equation 7.14; rd_CFat the degradation of crude digestibility carbohydrates, %; rd_NDF is the degradation of NDF in the rumen, Equation 7.21; rd_ST is the degradation of inrd_FPF the rumen, Equation 7.14; rd_CFat is the degradation digestibility ofthe carbohydrates, %;starch rd_NDF is the of NDF in the rumen, Equation of in rumen, Equation 7.22, isdegradation the degradation dietary fermentation fatcrude inrd_ST thefat rumen, Equation 7.22, rd_FPF is rumen, the degradation ofof fermentation products in the 7.21; the degradation of starch in Equation 7.14; rd_CFat is degradation of crude fat inis rumen, Equation 7.22, rd_FPF is the degradation ofdietary dietary fermentation 7.21; rd_ST isthe the degradation of starch in the the rumen, Equation 7.14; rd_CFat is the the degradation products in the rumen, Equation 7.23; rd_RestCHO is the degradation of RestCHO in the rumen, of crude fat in the rumen, Equation 7.22, rd_FPF is the degradation of dietary fermentation rumen, Equation 7.23; rd_RestCHO is the degradation of RestCHO in the rumen, Equation 7.12; products rumen, Equation 7.23;7.22, rd_RestCHO thedegradation degradationof ofdietary RestCHO in the rumen, of crude in fatthe in the rumen, Equation rd_FPF isisthe fermentation products the rumen, 7.23; is RestCHO in the rumen, sid_ST isin of starch inrd_RestCHO the small intestine, Equationof 7.40; r_mCP synthesis of products in the the digestion rumen, Equation Equation 7.23; rd_RestCHO is the the degradation degradation of RestCHO in is thethe rumen, 89 microbial protein in the rumen, Equation 7.30; lid_NDF is the degradation of NDF in the large 89 intestine, Equation 7.48, lid_ST is the degradation of starch in the large intestine, Equation 7.49; 89 89

DMIi is the dry matter intake of the i=1...n’th feedstuff, kg/d; OMi is the organic matter content in the i=1...n’th feedstuff, g/kg DM; CPcorri is the ammonia or urea corrected crude protein content in the i=1...n’th feedstuff, g/kg DM, Equation 4.4; CFati is the crude fat content in the i=1...n’th feedstuff, g/kg DM; and the parameters 270, 512, 0.25 and 51 are the microbial cell wall content, microbial crude protein content, coefficient of undigested microbial cell walls and the microbial starch content, respectively. Apparent total tract digestion and digestibility of CFat is calculated from the equations: 76 

NorFor – The Nordic feed evaluation system

2244 2243 2245 2244 2245 2244 2246 2245 2246 2245 2247 2246 2247 2246 2248 2247 2248 2247 2249 2248 2249 2248 2250 2249 2250 2249 2251 2250 2251 2250 2251 2252 2252 2251 2252 2253 2252 2253 2253 2253 2254 2254 2255 2254 2255 2254 2256 2255 2256 2255 2257 2256 2257 2256 2258 2257 2258 2257 2259 2258 2259 2258 2260 2259 2260 2259 2261 2260 2261 2260 2262 2261 2262 2261 2263 2262 2263 2262 2264 2263 2264 2263 2265 2264 2265 2264 2265 2266 2266 2265 2266 2267 2266 2267 2267 2267 2268 2268 2269 2268 2269 2268 2270 2269 2270 2269 2271 2270 2271 2270 2272 2271 2272 2271 2273 2272 2273 2272 2273 2273 2274 2274 2274 2275 2275 2274 2276 2275 2276 2275 2277 2276 2277 2276 2278 2277 2278 2277 2278 2278

corrected crudecontent proteinincontent in the i=1...n'th feedstuff, DM,i isEquation 4.4; CFat i is the the 270, ammonia or urea organic the i=1...n'th feedstuff, g/kg and DM;g/kg CPcorr crude fatmatter content in the i=1...n'th g/kgfeedstuff, DM; the parameters 512,CFat 0.25 and is the51 corrected crude protein content infeedstuff, the i=1...n'th g/kg DM, Equation 4.4; crude fat content in the content i=1...n'th feedstuff, g/kgfeedstuff, DM; and g/kg the parameters 270,4.4; 512,CFat 0.25ii is and the51 corrected crude protein in the i=1...n'th DM, Equation are the microbial cell wall content, microbial crude protein content, coefficient of undigested crude fat content in thewall i=1...n'th feedstuff, g/kg DM; and the parameters 270, 512, 0.25 and 51 are the microbial cell content, microbial crude protein content, coefficient of undigested crude content in the i=1...n'th feedstuff, g/kg DM;protein and the parameters 270, 512, 0.25 and 51 microbial cell walls and thecontent, microbial starch content, respectively. are thefatmicrobial cell wall microbial crude content, coefficient of undigested microbial cell walls and the microbial starch content, respectively. are the microbial celland wall microbial crude protein content, coefficient of undigested microbial cell walls thecontent, microbial starch content, respectively. microbial cell walls and the microbial starch content, Apparent total tract digestion and digestibility of CFatrespectively. is calculated from the equations: Apparent total tract digestion and digestibility of CFat is calculated from the equations: Apparent total tract digestion and digestibility of CFat is calculated from the equations: Apparent and digestibility of CFat is calculated from the equations: td _ CFat =total sid _tract CFatdigestion − li _ mCFat [Eq. 7.57] td _ CFat = sid _ CFat − li _ mCFat  [Eq. 7.57] td _ CFat = sid _ CFat − li _ mCFat [Eq. 7.57] td _ CFat = sid _ CFat − li _ mCFat [Eq. 7.57] td _ CFat td _ CFat CFatD = ⋅ 100  [Eq. 7.58] CFatD = ∑ (DMI i ⋅td ⋅ 100 [Eq. 7.58] _ CFat CFat rd _ CFat i )) − CFat CFatD = ∑i (DMI i ⋅td [Eq. 7.58] _ CFat i − rd _ CFat ⋅ 100 ( ) DMI ⋅ CFat − rd _ CFat CFatD = ∑i ⋅ 100 [Eq. 7.58] i i

7.57 7.58

∑i (DMI i ⋅ CFat i ) − rd _ CFat

i wheretd_CFat td_CFat theapparent apparenttotal total tract digestion crude CFatD is total the total where isisthe tract digestion of of crude fat,fat, g/d;g/d; CFatD is the tracttract digestibility where td_CFat is the apparent total tract digestion of crude fat, g/d; CFatD is the total tract digestibility of%; crude fat, %;issid_CFat is the digestion of crude fat inCFatD theintestine, small intestine, Equation of crude fat, sid_CFat the digestion of crude fat in the small Equation 7.41; li_mCFat where td_CFat is the apparent total tract digestion of crude fat, g/d; is the total tract digestibility of is crude fat, %; sid_CFat is digestion the digestion of crude fat inCFatD the small intestine, Equation where td_CFat apparent total tract of fat, g/d;intestine, isEquation the total tract 7.41; li_mCFat is the themicrobial synthesis of microbial crude fatcrude in the large 7.51; DMI digestibility of crude fat, %; sid_CFat is the digestion of crude fat in the small intestine, Equation is the synthesis of crude fat in the large intestine, Equation 7.51; DMI is theii dry matter 7.41; li_mCFat is the fat, synthesis of microbial crude fat in the large intestine, Equation 7.51; i DMI digestibility of crude isfeedstuff, the digestion ofCFat crude fat incrude the small intestine, Equation theintestine, fat content in the is the li_mCFat dry matter of%; thesid_CFat i=1...n'th kg/d; 7.41; isintake the synthesis of microbial crudeis fat in the large Equation 7.51; DMI i isfat i intake of the i=1...n’th feedstuff, kg/d; CFat the crude content in the i=1...n’th feedstuff, g/kg is the crude fat content in the is the dry matter intake of the i=1...n'th feedstuff, kg/d; CFat i 7.41; li_mCFat isintake the synthesis of microbial fat inCFat the large intestine, 7.51; DMIi i=1...n'th feedstuff, g/kg DM; and rd_CFat iscrude thei degradation of crude fat fat inEquation the small intestine is the crude content in the is the dry matter of the i=1...n'th feedstuff, kg/d; i DM; and rd_CFat is the degradation of crude fat in the small intestine that came from the rumen, i=1...n'th feedstuff, g/kgofDM; and rd_CFat is the degradation of crude fat in the small intestine is the crude fat content in the is the dry matter intake the i=1...n'th feedstuff, kg/d; CFat that camefeedstuff, from the rumen, Equation 7.22. is the degradationi of crude fat in the small intestine i=1...n'th g/kg DM; and rd_CFat that came from Equation 7.22. is the degradation of crude fat in the small intestine Equation 7.22.the rumen, i=1...n'th DM; and rd_CFat that camefeedstuff, from the g/kg rumen, Equation 7.22. that came from the rumen, Equation 7.22. The apparent total tract digestion and digestibility of OM are used for diet evaluations and also in The apparent total tract digestion and digestibility of OMofare used diet for evaluations and also inand also in Theapparent apparent total tract digestion and OM arefor diet evaluations testing and validating the system. They aredigestibility calculated from: The total tract digestion and digestibility of OM are used forused diet evaluations and also in testing and validating the system. and They are calculated from: The apparent total tract digestion digestibility of OM are used for diet evaluations and also in testing and validating the system. They are calculated from: testing and validating the system. They are calculated from: testing system. td _ OMand = tdvalidating _ CP + td _the CFat + td _ They CHO are calculated from: [Eq. 7.59] td _ OM = td _ CP + td _ CFat + td _ CHO [Eq. 7.59] td _ OM = td _ CP + td _ CFat + td _ CHO  [Eq. 7.59] 7.59 td _ OM = td _ CP + td _ CFat + td _ CHO [Eq. 7.59] td _ OM td _ OM OMD = ⋅ 100 [Eq. 7.60] OMD = ∑ DMI [Eq. 7.60] td _ OM ⋅ OM i ⋅ 100 ⋅ OM i ⋅ 100 OMD = ∑i DMI [Eq. 7.60] td _ iiOM OMD = ∑i DMI i ⋅ OM i ⋅ 100 [Eq. 7.60]  7.60 ∑i DMI i ⋅ OM i

i where td_OM is the apparent total tract digestion of organic matter, g/d; OMD is the total tract where td_OM is the apparent total tract digestion of organic matter, g/d; OMD is the total tract digestibility ofisorganic matter,total %; td_CP is the total digestion crude protein, where td_OM the apparent tract digestion of tract organic matter,of g/d; OMD is theEquation total tract digestibility ofisorganic matter, %; total td_CP is thedigestion total tract digestion of crude protein, Equation wheretd_CFat td_OM is the apparent tract of organic matter, g/d; is the total tract where td_OM the apparent tract digestion of organic matter, g/d; OMD isthe theOMD totaltract tract 7.52; the total tracttotal digestion ofiscrude fat, Equation 7.57;of td_CHO is total digestibility ofis organic matter, %; td_CP the total tract digestion crude protein, Equation 7.52; td_CFat is the total tract digestion of crude fat, Equation 7.57; td_CHO is the total tractEquation 7.52; digestibility of organic matter, %; td_CP is the total tract digestion of crude protein, Equation digestibility of organic matter, %; td_CP is the total tract digestion of crude protein, dry matter intake of theisi=1...n'th digestion of carbohydrates, Equation 7.55; DMIi is 7.52; td_CFat is the total tract digestion of crude fat,the Equation 7.57; td_CHO the total tract digestion of carbohydrates, Equation 7.55; DMIi fat, is the dry matter intake of theis i=1...n'th 7.52; td_CFat is and the tract digestion ofcrude crude Equation 7.57; td_CHO the total tracttract digestion td_CFat is the totaltotal tract digestion of Equation 7.57; td_CHO is the total organic matter content in thematter i=1...n'th feedstuff, g/kg DM. feedstuff, kg/d; OM is the dry intake of the i=1...n'th digestion of carbohydrates, Equation 7.55; DMI i is the i fat, is the organic matter content in the i=1...n'th feedstuff, g/kg DM. feedstuff, kg/d; and OM i digestion of carbohydrates, Equation 7.55; DMI is the dry matter intake of the i=1...n'th i organic matter content in the i=1...n'th feedstuff, g/kg DM. feedstuff, kg/d; and Equation OMi is the7.55; of carbohydrates, DMI dry matter intake of the i=1...n’th feedstuff, kg/d; and i is the content in the i=1...n'th feedstuff, g/kg DM. feedstuff, kg/d; and OMi is the organic matter 7.5 Implications

OMImplications 7.5 i is the organic matter content in the i=1...n’th feedstuff, g/kg DM.

7.5 Implications This chapter describes the NorFor modelling of digestion and microbial processes in the ThisImplications chapter describes the NorFor modelling of digestion and microbial processes in the 7.5 gastrointestinal tract. By the digestive tract intoand different compartments, is possible This chapter describes theseparating NorFor modelling of digestion microbial processes init the 7.5 Implications gastrointestinal tract. By separating the digestive tract intoand different compartments, is possible This chapter describes the NorFor modelling of digestion microbial processes init the to improve calculations and to evaluate the effects of partial digestion and metabolism gastrointestinal tract. By separating the digestive tract into different compartments, it ison possible to improve calculations and to evaluate the effectstract of partial digestion and metabolism on gastrointestinal tract. By separating the digestive into different compartments, it is possible digestion and microbial processes. This can improve our understanding of the mechanisms to improve calculations and to evaluate the effects of our partial digestion and metabolism on digestion and microbial processes. This can improve understanding of the mechanisms This chapter describes the NorFor modelling of digestion and microbial to improveand calculations to evaluate of our partial digestion and onprocesses in the digestion microbial and processes. This the caneffects improve understanding of metabolism the mechanisms gastrointestinal tract. By separating the digestive tract into different compartments, digestion and microbial processes. This can improve our understanding of the mechanisms it is possible to

improve calculations and to evaluate the effects 90 of partial digestion and metabolism on digestion and microbial processes. This can improve our 90 understanding of the mechanisms affecting predictions 90 90 of absorbed energy and AAs and, hence, facilitate optimization of the digestion efficiency and thus nutrient supply to the animal.

References Allen, M.S. and D.R. Mertens, 1988. Evaluating constraints on fibre digestion by rumen microbes. Journal of Nutrition 118: 261-270. Archiméde, H., D. Sauvant and P. Schmidely, 1997. Quantitative review of ruminal and total tract digestion of mixed diet organic matter and carbohydrates. Reproduction Nutrition Development 37: 173-189. Bosch, M.W., 1991. Influence of stage of maturity of grass silages on digestion processes in dairy cows. Thesis, Agricultural University, Wageningen, the Netherlands. 150 pp. Brandt, M., K. Rohr and P. Lebzien, 1980. Bestimmung des endogenen protein-N im duodenalchymus von milchkühen mit hilfe von15N. Zeitschrift für Tierphysiologie, Tierernärung und Futtermittelkunde 44: 26-27. Clark, J.H., T.H. Klusmeyer and M.R. Cameron, 1992. Microbial protein synthesis and flows of nitrogenous fractions to the duodenum of dairy cows. Journal Dairy Science 75: 2304-2323. Danfær, A., P. Huhtanen, P. Udén, J. Sveinbjörnsson and H. Volden, 2006. The Nordic dairy cow model, Karoline – Description. In: Kebreab, E., J. Dijkstra, A. Bannink, J. Gerrits and J. France (eds.). Nutrient Digestion and Utilisation in Farm Animals: Modelling Approaches. CAB International, Wallingford, UK, pp. 383-406.

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Demeyer, D., 1990. Quantitative aspects of microbial metabolism in rumen and hindgut. Indian Summer Course on Rumen Microbial Metabolism and Ruminant digestion. Theix, France, September 24th-October 3rd 1990. 22 pp. Hespell, R.B. and M.P. Bryant, 1979. Efficiency of rumen microbial growth: Influence of some theoretical and e1perimental factors on YATP. Journal of Animal Science 49: 1640-1659. Hoover, W.H., 1986. Chemical factors involved in ruminal fiber digestion. Journal of Dairy Science 69: 2755-2766. Huhtanen, P. and S. Jaakkola, 1993. The effect of the forage preservation method and the proportion of concentrate on digestion of cell wall carbohydrates and rumen digesta pool size in cattle. Grass and Forage Science 48: 155-165. Huhtanen, P., S. Jakkola and U. Kukkonen, 1995. Ruminal plant cell wall digestibility estimated from digestion and passage kinetics utilizing mathematical models. Animal Feed Science and Technology 52: 159-173. Huhtanen, P. and U. Kukkonen, 1995. Comparison of methods, markers, sampling sites and models for estimating digesta passage kinetics in cattle fed at two levels of intake. Animal Feed Science and Technology 52: 141-158. Hvelplund, T., 1985. Digestibility of rumen microbial protein and undegraded dietary protein estimated in the small intestine of sheep and by the in sacco procedure. Acta Agriculturae Scandinavica Supplement 25: 132-144. Hvelplund, T. and J. Madsen, 1985. Amino acid passage to the small intestine in dairy cows compared with estimates of microbial protein and undegraded protein from analysis on the feed. Acta Agriculturae Scandinavica Supplement 25: 21-36. Khalili, H. and P. Huhtanen, 1991. Sucrose supplementation in cattle given grass silage based diet. 2. Digestion of cell wall carbohydrates. Animal Feed Science and Technology 33: 262-273. Larsen, M., T.G. Madsen, M.R. Weisbjerg, T. Hvelplund and J. Madsen, 2000. Endogenous amino acid flow in the duodenum of dairy cows. Acta Agriculturae Scandinavica, section A: Animal Science 50: 161-173. Lechener-Doll, M., M. Kaske and W.V. Engelhardt, 1991. Factors affecting the mean retention time of particles in the forestomach of ruminants and camelids. In: T. Tsuda, Y. Sasaki and R. Kawashima (eds.). Physiological aspects of digestion and metabolism in ruminants. Academic Press, San Diego, CA, USA, pp. 455-482. Lindberg, J.E., 1981. The effect basal diet on the ruminal degradation of dry matter, nitrogenous compounds and cell walls in nylon bags. Swedish Journal of Agricultural Research. 11: 159-169. Lund, P, 2002. The effect of forage type on passage kinetics and digestibility of fibre in dairy cows. Ph.D. thesis, The Royal Veterinary and Agricultural University, Copenhagen, Denmark. 169 pp. Mason, V.C. and F. White, 1971. The digestion of bacterial mucopeptide constituents in sheep. I. The metabolism of 2,6 diaminopimelic acid. The Journal of Agricultural Science, Cambridge 77: 91-98. Minde, A. and A.J. Rygh, 1997. Metoder for å bestemme nedbrytingshastighet, passasjehastighet og vomfordøyelighet av NDF hos mjølkeku. Hovedoppgave ved Institutt for husdyrfag, Norges landbrukshøgskole, Norway. 118 pp. (In Norwegian). Mould, F.L., E.R. Ørskow and S.O. Mann, 1983. Associative effects of mixed feeds. I. Effects of type and level of supplementation and the influence of rumen fluid pH on cellulolysis in vivo and dry matter digestion of various roughage. Animal Feed Science and Technology 10: 15-30. Mydland, L.T, 2005. Molecular and chemical characterization of the rumen microbiota and quantification of rumen microbial protein synthesis. Ph.D. Thesis. Norwegian University of Life Sciences, Aas, Norway. 155 pp. NRC (National Research Council), 1985. Ruminal nitrogen usage. National Academy Press, Washington DC, USA. 138 pp. NRC, 2001. Nutrient requirements of dairy cattle. Seventh revised edition, National Academy press, Washington, DC, USA. 408 pp. Ørskov, E.R. and I. McDonald, 1979. The estimation of protein degradability in the rumen from measurements adjusted for rate of passage. Journal of Agricultural Science 92: 499-503. Ørskov, E.R. and N.A. MacLeod, 1982. The flow of N from the rumen of cows and steers maintained by intraruminal infusion of volatile fatty acids. Proceedings of the Nutrition Society. 41:76A. Prestløkken, E., 1999. Protein value of expander-treated barley and oats for ruminants. Ph.D. thesis, Agricultural University of Norway, Aas, Norway. 137 pp. Robinson, P.H., S. Tamminga and A.M. Van Vuuren, 1987a. Influence of the declining level of feed intake and varying the proportion of starch in the concentrate on milk production and whole tract digestibility in dairy cows. Livestock Production Science 17: 19-35. Robinson, P.H., S. Tamminga and A.M. Van Vuuren, 1987b. Influence of the declining level of feed intake and varying the proportion of starch in the concentrate on rumen digesta quantity, composition and kinetics of ingesta turnover in dairy cows. Livestock Production Science 17: 37-62.

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Robinson, P.H., S. Tamminga and A.M. Van Vuuren, 1987c. Influence of the declining level of feed intake and varying the proportion of starch in the concentrate on rumen fermentation in dairy cows. Livestock Production Science 17: 173-189. Rygh, G.K., 1997. Virkning av nitrogenkilde I kraftfôret på kjemisk sammensetting av vommikrober og syntesen av protein og aminosyrer I voma hos melkekyr. Hovedoppgave Norges landbrukshøgskole, Norway. 122 pp. (In Norwegian). Sauvant, D., J. Dijkstra and D. Mertens, 1995. Optimization of ruminal digestion: a modelling approach. In: Journet, M., E. Grenet, M.H. Farce, M. Theriez, C. Demarkquilly (eds.). Recent developments in the nutrition of herbivores. Proceedings of IVth International Symposium on the Nutrition of Herbivores, Clermont-Ferrand, France. September 11-15, 1995. INRA Paris Cedex, France. pp 143-165. Selmer-Olsen, I., 1996. Microbial protein synthesis in the rumen of cows fed extensively or restricted fermented grass silage. Proc. XIth Int. Silage Conference, Aberystwyth, UK, pp. 226-227. Stensig, T., 1996. Digestion and passage kinetics of forage fibre in dairy cows. Ph.D. thesis, The Royal Veterinary and Agricultural University, Copenhagen, Denmark. 139 pp. Stensig, T. and P. Robinson, 1997. Digestion and passage kinetics of forage fiber in dairy cows as affected by fiber-free concentrate in the diet. Journal of Dairy Science. 80: 1339-1352. Stensig, T., M.R. Weisbjerg and T. Hvelplund, 1998. Evaluation of different methods for the determination of digestion and passage rates of fibre in the rumen of dairy cows. Acta Agriculturae Scandinavica, Section A - Animal Science 48: 141-154. Stern, M.D., G.A. Varga, J.H. Clark, J.L. Firkins, J.T. Huber and D.L. Palmquist, 1994. Evaluation of chemical and physical properties of feed that affect protein metabolism in the rumen. Journal of Dairy Science 77: 2762-2786. Storm, E., D.S. Brown and E.R. Ørskov, 1983. The nutritive value of rumen micro-organisms in ruminants. 3. The digestion of microbial amino acids and nucleic acids in, and losses of endogenous nitrogen from, the small intestine of sheep. British Journal of Nutrition 50: 479-485. Tas, M.V., R.A. Evans and R.F.E. Axford, 1981. The digestibility of amino acids in the small intestine in sheep. British Journal of Nutrition 45: 167-174. Van Bruchem, J., S.M.G. Rouwers, G.A. Bangma, S.C.W. Lammers-Wienhoven and P.W.M. Van Adrichem, 1985a. Digestion of proteins f varying degradability in sheep. 2. Amount and composition of the protein entering the small intestine. Netherlands Journal of Agricultural Science 33: 273-284. Van Bruchem, J., L.J.G.M. Bongers, J.D. Van Walsem, W. Onck and P.W.M. Van Adrichem, 1985b. Digestion of proteins f varying degradability in sheep. 3. Apparent and true digestibility in the small intestine and ileal endogenous flow of N and amino acids. Netherlands Journal of Agricultural Science 33: 285-295. Van Es, A.J.H., 1978. Feed evaluation for ruminants. 1. The systems in use from May 1977 onwards in the Netherlands. Livestock Production Science 5: 331-345. Van Soest, P.J., 1994. Nutritional ecology of the ruminant (second edition). Cornell University Press, Ithaca, NY, USA. 476 pp. Voelker, J.A. and M.S. Allen, 2003. Pelleted beet pulp substituted for high-moisture corn: 2. Effects on digestion and rumen digestion kinetics in lactating dairy cows. Journal of Dairy Science 86: 3553-3561. Voigt, J., J. Van Bruchem, S.C.W. Lammers-Wienhoven, L.J.G.M. Bongers, U. Schonhusen, G.W. Valkenburg, J.J.M.H. Ketelaars and S. Tamminga, 1993. Flow of endogenous protein along the gastrointestinal tract of sheep fed on whole grass hay and hay-concentrate diets. Proceedings of the Society of Nutrition Physiology 1: 63. Volden, H., 1999. Effects of level of feeding and ruminally undegraded protein on ruminal bacterial protein synthesis, escape of dietary protein, intestinal amino acid profile, and performance of dairy cows. Journal of Animal Science 77: 1905-1918. Volden, H. and O.M. Harstad, 1998a. Chemical composition of bacteria harvested from the rumen of dairy cows fed three diets differing in protein content and rumen protein degradability at two levels of intake. Acta Agriculturae Scandinavica, Section A - Animal Science 48: 202-209. Volden, H. and O.M. Harstad, 1998b. Amino acid composition of bacteria harvested from the rumen of dairy cows fed three diets differing in protein content and rumen protein degradability at two levels of intake. Acta Agriculturae Scandinavica, Section A - Animal Science 48: 210-215. Volden, H., L.T. Mydland and O.M. Harstad, 1999a. Chemical composition of protozoal and bacterial fractions isolated from ruminal contents of dairy cows fed diets differing in dietary nitrogen supplementation. Acta Agriculturae Scandinavica, Section A - Animal Science 49: 235-244.

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Volden, H., O.M. Harstad and L.T. Mydland, 1999b. Amino acid content and profile of protozoal and bacterial fractions isolated from ruminal contents of lactating dairy cows fed diets differing in nitrogen supplementation. Acta Agriculturae Scandinavica, Section A - Animal Science 49: 245-250. Weisbjerg, M.R., T. Hvelplund and C.F. Børsting, 1992. Digestibility of fatty acids in the gastrointestinal tract of dairy cows fed with tallow or saturated fats rich in stearic acid or palmitic acid. Acta Agriculturae Scandinavica, Section A - Animal Science 42: 115-120. Weisbjerg, M., T. Hvelplund, V.F. Kristensen and T. Stensig, 1998. The requirement for rumen degradable protein and the potential for nitrogen recycling to the rumen in dairy cows. Proceedings of the 25th Scientific Conference AICC-Arusha, August 5-7. TSAP Conferences Series 25: 110-118. Weisbjerg, M.R., H. Gado, T. Hvelplund and B.B. Jensen, 1999. The effect of easily fermentable carbohydrates and pH on fibre digestibility and VFA pattern in an in vitro continuous culture system. South African Journal of Animal Science ISRP. 29: 112-113.

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2542 2542 2543 2543 2544 2544 2545 2545 2546 2546 2547 2547 2548 2548 2549 2549 2550 2550 2551 2551 2552 2552 2553 2554 2553 2554 2555

2556 2555 2557 2556 2558 2557 2559 2558 2560 2559 2561 2560 2562 2561 2563 2562 2564 2563 2565 2564 2566 2565 2566 2567 2568

2567 2569

8. Energy and metabolizable protein supply 8.0 Energy and metabolizable protein supply 8.0 Energy and metabolizable protein supply H. Volden and N. I. Nielsen

H. and N.I. N. I.Nielsen Nielsen H. Volden Volden and

Several systems are used to measure energy (Van Es, 1978; Møller et al., 1983; INRA, 1989; Several systems are used to measure energyprotein (Van Es, Es, 1978; Møller alNRC, .,1983; 1983; INRA, 1989; Several systems are1993) used and to measure energy (Van 1978; Møller etetal., INRA, 1989; NRC, 1989; AFRC, metabolizable (MP) (Madsen, 1985; 1985; Vérité et NRC, NRC, 1989; AFRC, 1993) and metabolizable protein (MP) (Madsen, 1985; NRC, 1985; et 1989; AFRC, 1993) andTamminga metabolizable (MP) (Madsen, NRC, 1985; etVérité al., 1987; al ., 1987; AFRC, 1992; et al.,protein 1994) supplies in cattle. 1985; Feed evaluation forVérité cattle are al .,continues 1987; AFRC, 1992; Tamminga et alsupplies .,systems 1994) in supplies cattle. Feed evaluation for cattle are in development and several new have beenin proposed (Sniffen et cattle al., 1992; AFRC, 1992; Tamminga et al., 1994) cattle. Feed evaluation for is in continues in continues development and several new systems systems been proposed (Sniffen et al2001; ., 1992; NRC, 2001; FiM, aim of the have new ishave to refine and more describe theThomas, development and 2004). severalThe new systems been proposed (Sniffen et al.,carefully 1992; NRC, NRC, 2001; FiM, 2004). The aim of the new systems is to refine and more carefully describe the interactions between the animal, its feed and the environment to predict performance. Also in thebetween 2004). The aim of the new systems is to refine and more carefully describe the interactions interactions between the animal, its feed and the environment to predict performance. Also in the development of NorFor an important objective is to obtain improved estimates of the animal’s the animal, its feed and the environment to predict performance. Also in the development of NorFor development NorFor objective is to obtain improved estimates thediet animal’s dietary energy of and proteinisan supplies byimproved taking intoestimates account the of feeding levelof and an important objective toimportant obtain ofeffects the animal’s dietary energy and protein composition on and bothprotein ration digestibility microbial activity.the effects of feeding level and diet dietary energy supplies byand taking into account

supplies by taking into account the effects of feeding level and diet composition on both ration composition on both ration digestibility and microbial activity. digestibility and microbial 8.1 Metabolizable and netactivity. energy supply

Metabolizable energy isand assumed to equal gross energy (GE) minus energy in faeces, urine and 8.1 net net energy supply 8.1 Metabolizable Metabolizable and energy supply methane and NEenergy is derived from estimates of gross the partial utilization efficiency (k)in offaeces, ME (Van Es, and Metabolizable is assumed to equal energy (GE) minus energy urine 1975). The NENE of diets used for maintenance, lactation and growth are calculated using methane and is derived from estimates of the partial utilization efficiency (k) of ME (Van Metabolizable assumed to In equal gross energy in faeces, urineEs, and coefficients, km, energy kl and kgis, respectively. NorFor, theenergy energy (GE) supplyminus and value for dairy cows is 1975). The NE of diets used for maintenance, lactation and growth are calculated using methane and NE is fromonestimates the partialbyutilization efficiency (k) for of ME (Van Es, expressed in terms of derived NEL, based the systemofdeveloped Van Es (1975, 1978) and coefficients, kin , NEG kldiets andbased kg, respectively. In NorFor, the and energy supply and value for dairy cows is mof 1975). The NE used foramaintenance, growth areby calculated using coefficients, growing cattle on combination oflactation the systems developed INRA (1989) and expressed in terms of NEL, In based on thethesystem developed by value Van Es (1975, 1978) and for km, kEs and k , respectively. NorFor, energy supply and for dairy cows is expressed in Van (1975, 1978). l g

growing NEGon based on a combination systems developed by INRA (1989) and terms of cattle NEL,inbased the system developed of bythe Van Es (1975, 1978) and for growing cattle in

Van Es (1975, The point for predicting of thethe NEsystems supply isdeveloped the calculation of GE. In NorFor the following NEGstarting based on 1978). a combination by INRA (1989) and Van Es (1975, 1978). equation is modified from Van Es (1978) and used to calculate GE supply:

The starting point for predicting the NE supply is the calculation of GE. In NorFor the following The starting point for predicting the NE supply is the calculation of GE. In NorFor the following equation is modified from Van Es (1978) and used to⎛ calculate GE supply: ⎛ CPi NH3Ni ⎞ ⎞ ⎞ equation modified Esi ⋅(1978) and calculate supply: ⎜⎜ DMIito 24.1 ⋅ ∑is(DMI ) + 36.6Van ⋅ ∑ (DMI CFat i ) + 18 .5 ⋅ ∑used ⋅ ⎜ OM ⋅ ⎟ ⎟⎟ ⎟⎟ i ⋅ CPcorrifrom i − CPcorrGE i − CFat i− GE =

i

i⎝

i

⎝

6.25

1000 ⎠ ⎠ ⎠

⎛ CPi NH3Ni ⎞ ⎞ ⎞ ⎛ 24.1 ⋅ ∑ (DMIi ⋅ CPcorri ) + 36.6 ⋅ ∑ (DMIi ⋅ CFat i ) + 18.5 ⋅ ∑ ⎜⎜ DMIi ⋅ ⎜ OM i − CPcorri − CFat i − ⋅ ⎟⎟⎟ [Eq. 6.25 8.1] 1000 ⎠ ⎟⎠ ⎟⎠ ⎝ i i i⎝ GE = 1000 1000



8.1

2570 2568 2571 2569 2572 2570 2573 2571 2574 2572 2575 2573 2576 2574 2577 2575 2578 2576 2579

where GE is the gross energy intake, MJ/d; DMIi is the dry matter intake of the i=1...n'th [Eq. 8.1] where GEkg/d; is the grossi is energy intake,ofMJ/d; DMIor the dry mattercrude intake of theofi=1...n’th feedstuff, feedstuff, CPcorr the content ammoniaprotein the i isurea-corrected i=1...n'th Equation 4.4; CFati is theorcrude fat content in the i=1...n'th feedstuff, g/kg feedstuff, kg/d; CPcorr content ofintake, ammoniaurea-corrected crude protein of the i=1...n’th i is the where GEfeedstuff, is the gross energy MJ/d; DMI is the dry matter intake of the i=1...n'th i DM; OMi is the organic matter content of the i=1...n'th feedstuff, g/kg DM;g/kg CPi DM; is the OM crudeis the organic Equation 4.4; crude fat content in the i=1...n’th feedstuff, i is the feedstuff, kg/d;CFat CPcorr i is the content of ammonia- or urea-corrected crude protein ofi the protein content of the i=1...n'th feedstuff, g/kg DM; andCP NHis the ammonia or urea Nof content 3Nthe i is crude matter content of the i=1...n’th feedstuff, g/kg DM; protein content theg/kg i=1...n’th i=1...n'th feedstuff, Equation 4.4; CFati is the crude fat icontent in the i=1...n'th feedstuff, of the i=1...n'th feedstuff, g/kg CP. feedstuff, g/kg DM; and NH N is the ammonia or urea N content of the i=1...n’th feedstuff, g/kg CP.

2581

⎛ ⎞ [Eq. 8.2] ⎟⎟ + 13.9is⋅ ∑the .0 ⋅ tdis_ CPcorr .7 ⋅ td _ CFat DMI + 37intake + 14.5 ⋅ ⎜⎜ td _ CHO − ∑ DMI i ⋅ SUiDMI i ⋅ SU i where18ME the daily of metabolizable energy, MJ/d; dry matter intake of the i i i ⎠ ⎝ ME = 102 i=1...n’th feedstuff, kg/d; SUi is the sugar content in the i=1...n’th feedstuff, g/kg DM; td_CPcorr 1000

2577 2578 2580 2579

2580 2581

DM; OMi is the organic matter 3 i content of the i=1...n'th feedstuff, g/kg DM; CPi is the crude protein content of the i=1...n'th feedstuff, and NH3N ammonia or urea i is the NorFor calculates ME supply from apparent g/kg total DM; tract digestion, using a modified form of anN content NorFor calculates ME supply from apparent total tract digestion, using a modified form of an equation of the i=1...n'th equation for bothfeedstuff, dairy cowsg/kg and CP. growing cattle according to Van Es (1978): for both dairy cows and growing cattle according to Van Es (1978): NorFor calculates ME supply from apparent total tract digestion, using a modified form of an ⎛ ⎞ 18.0 ⋅for td _both CPcorr + 37.7cows ⋅ td _ CFat 14.5 ⋅ ⎜⎜ td _ CHO DMIi ⋅ SUi ⎟⎟to+ 13 .9 ⋅ ∑Es DMI i ⋅ SUi equation dairy and+growing cattle− ∑according Van (1978): i i ⎝ ⎠ ME =  8.2 1000

is the total tract digestion of ammonia- or urea-corrected crude protein, Equation [Eq.7.54; 8.2] td_CFat is the total tract digestion of crude fat, Equation 7.57; and td_CHO is the total tract digestion of carbohydrates, Equation 7.55. 102

The efficiency of ME utilization differs between maintenance and different forms of production and also due to diet composition (Blaxter, 1962). Van Es (1975) proposed the use of different efficiencies for maintenance, lactation and growth to calculate relevant NE values, and also suggested that the efficiency should be calculated as a function of the ME to GE ratio (q).

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_8, © Wageningen Academic Publishers 2011

81

2588 2588 2589 2589 2590 2590 2591 2591 2592 2592 2593 2593 2594 2594 2595 2595 2596 2596 2597 2597 2598 2598 2599 2599 2600 2600 2601 2601 2602 2602 2603 2603 2604 2604 2605 2605 2606 2606 2607 2607 2608 2608 2609 2609 2610 2610

The The efficiency efficiency of of ME ME utilization utilization differs differs between between maintenance maintenance and and different different forms forms of of production production and also also due due to to diet diet composition composition (Blaxter, (Blaxter, 1962). 1962). Van Van Es Es (1975) (1975) proposed proposed the the use use of of different different and efficiencies efficiencies for for maintenance, maintenance, lactation lactation and and growth growth to to calculate calculate relevant relevant NE NE values, values, and and also also suggested that that the the efficiency efficiency should should be be calculated calculated as as aa function function of of the the ME ME to to GE GE ratio ratio (q). (q). suggested

Theslope slopeofofME MEutilization utilization versus q are almost similar for maintenance and lactation, and Van Es The versus are almost similar for maintenance maintenance and lactation, lactation, and Van Van The slope of ME utilization versus qq are almost similar for and and (1975) proposed that variation in maintenance NE, due to changes in q, could be accommodated by Es (1975) proposed that variation in maintenance NE, due to changes in q, could be Es (1975) proposed that variation in maintenance NE, due to changes in q, could be calculating it inby of NEL, adjusting difference in the intercept. Theintercept. NEL supply to accommodated byterms calculating it in inand terms of NEL, NEL,for andthe adjusting for the the difference in the the intercept. accommodated calculating it terms of and adjusting for difference in dairy cows is calculated as: is The NEL supply to The NEL supply to dairy dairy cows cows is calculated calculated as: as: NEL = 004 ⋅⋅((qq − 57)) )) ⋅⋅ ME ME  NEL = 00..66 ⋅⋅ (11 + + 00..004 − 57

8.3

[Eq. 8.3] 8.3] [Eq.

where lactation, MJ/d; qq is ratio between metabolizable energy and whereNEL NELis thenet netenergy energy lactation, MJ/d; is the between metabolizable energy where NEL isisthe the net energy lactation, MJ/d; isqthe the ratioratio between metabolizable energy and and gross gross energy, %; ME and ME ME is metabolizable metabolizable energy, Equation 8.2. energy, %; and is metabolizable energy, Equation 8.2. gross energy, %; and is energy, Equation 8.2. The calculation ofofNEG NEG is more more complex, because and differ considerably as affected affected by q. q. by q. m differ The is complex, because kkgg and considerably as by m Thecalculation calculationof NEG is more complex, because kgkkand km differ considerably as affected It aa constant adjustment to for thus, ItIt is isisnot not possible totoapply apply constant adjustment to account account for this this difference, thus, the the notpossible possibleto apply a constant adjustment to account fordifference, this difference, thus, the calculation is necessary necessary (McHardy, (McHardy, 1966; 1966; Harkins Harkins et et al al., ., 1974). 1974). To To calculation of of aa joint joint efficiency, efficiency, kkmg mg,, is calculation of a joint efficiency, kmg, is necessary (McHardy, 1966; Harkins et al., 1974). To solve this problem solve this problem McHardy (1966) introduced the concept of animal production level (APL), solve this problem McHardy (1966) introduced the concept of animal production level (APL), McHardy (1966) introduced theNE concept of animal production level (APL), which equals the ratio which equals the ratio ratio of the the total total requirement (maintenance + which equals the of NE requirement (maintenance + growth) growth) to to the the net net energy energy for for of the total NE requirement (maintenance + growth) to the net energy for maintenance. In general maintenance. In general this is calculated as: maintenance. In general this is calculated as:

this is calculated as:

(

)

0.75 ⋅ k ⋅ factor1 ⋅120 120 BW 0.75 + (55..48 + 99..39 NE + NE 48 ⋅⋅ gain gain __ prot prot + 39 ⋅⋅ gain gain __ fat fat ) ⋅ km + NE m m ⋅ factor1 ⋅ 88 m + NE gg = BW 88 = 0.75 120 NE 0.75 B NE m m ⋅ factor1 ⋅ 120 BW W ⋅⋅ kk m m ⋅ factor1 ⋅ 88 88

2611 2611

APL = APL =

2612 2612 2613 2613 2614 2614 2615 2615 2616 2616 2617 2617 2618 2618 2619 2619 2620 2620

where level, MJ/MJ; NE isnet the net requirement for whereAPL APLis theanimal animalproduction production level; NEm is requirement for maintenance; NEg m theenergy net energy energy requirement for where APL isisthe the animal production level, MJ/MJ; NEthe m is is the the net net energy energy requirement for growth; BW is the the kg; body weight, kg; k km isefficiency the maintenance; NEgg requirement m is is the net energy for requirement growth; BWfor is growth; the bodyBW weight, kmweight, is the partial of maintenance; NE is is body kg; the partial efficiency of utilization maintenance, Equation 8.6; is 90 partial efficiencyenergy of metabolizable metabolizable energy utilization for for maintenance, Equation 8.6; factor1 is and 90 steers, metabolizable utilization energy for maintenance, Equation 8.6; factor1 is 90 forfactor1 heifers for heifers andofsteers, steers, 91 for bulls bulls of dairy breeds andbeef 100breeds for bulls bulls of beef beefetbreeds breeds (Garciagain_prot et al. al.,, et for and for dairy and 100 for of (Garcia 91 heifers for bulls dairy 91 breeds andof 100 forbreeds bulls of (Garcia al., 2007); is the 2007); is gain, 9.8; is fat Equation 2007); gain_prot is the the protein protein gain, Equation Equation 9.8; gain_fat gain_fat is the the daily daily fat retention, retention, Equation 9.9. 9.9. proteingain_prot gain, Equation 9.8; gain_fat is the daily fat retention, Equation 9.9.

2623 2621 2621 2624 2625 2622 2622 2623 2626 2624 2627 2625 2628 2626 2629 2627 2628

2630 2629 2630 2631 2630 2631 2631 2632 2632

2633 2633 2634 2634 2635 2635 2636 2636 2637 2637 2638 2638 2638 2639 2639 2639 2640 2640 2641 2641 2642 2642 2643 2643 2644 2644 2645 2645 2646 2646 2647 2647 2648



[Eq. [Eq. 8.4] 8.4]

8.4

Using the APL concept, a joint efficiency of ME utilization for maintenance and growth is Using concept, utilization forfor maintenance andand growth is is calculated Usingthe theAPL APL concept,aajoint jointefficiency efficiencyofofME ME utilization maintenance growth calculated as follows: calculated as follows:

as follows:

APL partial efficiency of metabolizable energy for maintenance and growth; km where kmgkk m is⋅⋅ the joint kk gg ⋅⋅ APL m = [Eq. 8.5] 8.5]  8.5 kk mg [Eq. mg is the=partial efficiency k + k ⋅ ( APL k gg + k m −1 1) of metabolizable energy utilization for maintenance, Equation 8.6; and kg m ⋅ APL − is the partial efficiency of metabolizable energy utilization for growth, Equation 8.7. is the joint partial efficiency of metabolizable energy for maintenance and growth; km where wherekkmg km is mg m mg is the joint partial efficiency of metabolizable energy for maintenance and growth; is thepartial partialefficiency efficiency of of metabolizable forfor maintenance, Equation 8.6; and g the metabolizableenergy energyutilization utilization maintenance, Equation 8.6;kand kg is g The partial efficiencyofofmetabolizable ME utilization forutilization maintenance (Van Es, 1975)8.7. and growth (Blaxter, is thepartial partialefficiency efficiency forfor growth, Equation the of metabolizableenergy energy utilization growth, Equation 8.7. 1974) are calculated using the following equations:

103 103 The partial efficiency of ME utilization for maintenance (Van Es, 1975) and growth (Blaxter, The partial efficiency of ME utilization for maintenance (Van Es, 1975) and growth (Blaxter, 1974) 1974) are calculated the following equations: ⎛ ME ⎞usingfollowing are equations: [Eq. 8.6] k m calculated = 0.287 ⋅ ⎜ using⎟ +the 0.554 GE ⎝ ⎠ ⎛⎛ ME ⎞⎞ ME [Eq. kk m = 0.287 ⋅ ⎟⎟ + 8.6 +0 0..554 554  [Eq. 8.6] 8.6] m = 0.287 ⋅ ⎜⎜ ⎝⎝ GE GE ⎠⎠

⎛ ME ⎞ k g = 0.78 ⋅ ⎜ ⎟ + 0.006 ⎝ GE ⎛⎛ ME ⎞⎞ ⎠ ME kk gg = ⎟⎟ + = 00..78 +0 0..006 006  78 ⋅⋅ ⎜⎜ ⎝⎝ GE GE ⎠⎠

[Eq. 8.7] [Eq. 8.7]

8.7

thepartial partialefficiency efficiency of metabolizable energy utilization for maintenance; where g is the where kkm isisthe of metabolizable energy utilization for maintenance; k is thekpartial

mis the partial efficiency of metabolizable energy utilization for maintenance; k is gthe where km g partial efficiency of metabolizable energy utilization for ME growth; ME is metabolizable energy, m g efficiency of metabolizable energy utilization for growth; is metabolizable energy, Equation partial efficiency of metabolizable energy utilization for growth; ME is metabolizable energy, Equation 8.2; GE is gross energy, Equation 8.1. 8.2; GE is gross energy, Equation 8.1. 8.1. Equation 8.2; GE is gross energy, Equation

Based on a joint k , the NEG value of a feed ration for growing cattle is calculated as:

Basedon onaajoint jointkmg k ,,mg , the NEG value a feed ration for growing is calculated as: the NEG value of ration for cattle is as: Based the NEG value of aaoffeed feed ration for growing growing cattlecattle is calculated calculated as: mgmg NEG ==kkkmgmg⋅⋅ ME ⋅ ME NEG NEG = mg ME



[Eq. 8.8] [Eq. 8.8] 8.8

whereNEG NEG the energy MJ/d, k the thethe joint partial efficiency of metabolizable where netnet energy forfor growth, MJ/d, kmg partial efficiency of metabolizable where NEGisis isthe the net energy forgrowth, growth, MJ/d, kmgisjoint is joint partial efficiency of metabolizable mg ismg energy for maintenance and growth, in Equation 8.5; ME is metabolizable energy, Equation 8.2. 8.2. energy for maintenance and growth, in Equation 8.5; ME is metabolizable energy, Equation 8.2. energy for maintenance and growth, in Equation 8.5; ME is metabolizable energy, Equation The NorFor system is a feed ration evaluation system rather than a single feed evaluation system, Thethe NorFor system is a feed ration evaluation systemas: rather than a single feed evaluation system, and 82  diet NE value for lactation and growth is calculated NorFor – The Nordic feed evaluation system

and the diet NE value for lactation and growth is calculated as: NEL NEL NEL NEL __ DM DM = = NEL

[Eq. 8.9]

2640 2641 2641 2642 2642 2643 2643 2644 2644 2645 2645 2646 2646 2647 2647 2648 2648 2649 2649 2650 2650 2651 2651 2652 2652 2653 2653 2654 2654 2655 2655 2656 2656 2657 2657 2658 2658 2659 2661 2659 2660 2662 2660 2663 2664 2665 2666 2667 2668 2669

[Eq. 8.8]

NEG = k mg ⋅ ME

where NEG is the net energy for growth, MJ/d, kmg is the joint partial efficiency of metabolizable the joint partial efficiency of Equation metabolizable where NEG is the net energy for growth, MJ/d, k8.5; mg isME energy for maintenance and growth, in Equation is metabolizable energy, 8.2. energy for maintenance and growth, in Equation 8.5; ME is metabolizable energy, Equation 8.2. The NorFor system is a feed ration evaluation system rather than a single feed evaluation system, The NorFor system is is a feed ration evaluation ratherrather than athan single feed evaluation system, system, Thethe NorFor system alactation feed ration evaluation system a single feed evaluation and diet NE value for and growth system is calculated as: and the diet NE value for lactation and growth is calculated as: and the diet NE value for lactation and growth is calculated as: NEL NEL _ DM = NEL NEL _ DM = ∑ DMIi  ∑ i DMIi

[Eq. 8.9] [Eq. 8.9]

8.9

NEG NEG _ DM = NEG NEG _ DM = ∑ DMI i ∑ i DMI i

[Eq. 8.10] [Eq. 8.10]

8.10

i



i where NEL_DM is the diet net energy value for lactation; MJ/kg DM; NEG_DM is the diet net energyNEL_DM value forisgrowth, MJ/kg DM; NEL net energy forDM; lactation, Equation 8.3;net NEG is net where the diet net energy value for is lactation; MJ/kg NEG_DM is the diet where is the diet net 8.8; energy value lactation; DM;Equation NEG_DM is NEG the diet net energy value for growth, MJ/kg DM;and NEL is for net energy forMJ/kg lactation, 8.3; is net energyNEL_DM for growth, Equation DMI i is the dry matter intake of the i=1...n’th feedstuff, kg/d.

energy for growth, MJ/kg NEL is net energy for lactation, Equation 8.3; NEG is net energy value for growth, Equation 8.8;DM; and DMI i is the dry matter intake of the i=1...n'th feedstuff, energy for growth, Equation 8.8; and DMIi is the dry matter intake of the i=1...n'th feedstuff, kg/d. 8.2 Metabolizable protein supply kg/d.

8.2 protein supply AAs from the small intestine are rumen microbial protein, rumen TheMetabolizable sources of digested and absorbed 8.2 Metabolizable protein supply

The sources ofdietary digested and absorbed AAs fromprotein. the smallMetabolizable intestine are rumen microbial protein, undegraded protein and endogenous protein is expressed as AATN: The sources of digested andprotein absorbed from theprotein. small intestine are rumen microbial protein, rumen undegraded dietary andAAs endogenous Metabolizable protein is expressed as rumen AATN:undegraded dietary protein and endogenous protein. Metabolizable protein is expressed as 8.11 AATNN: = sid _ AA + sid _ mAA + sid _ eAA  [Eq. 8.11] AAT

whereAAT AATNNisisthe theAAs AAsabsorbed absorbed small intestine, sid_AA is dietary digested in the where in in thethe small intestine, g/d;g/d; sid_AA is dietary AAs AAs digested in 104 the small intestine, Equation7.38; 7.38;sid_mAA sid_mAAisismicrobial microbial AAs digested in the small intestine, small intestine, Equation digested in the small intestine, Equation 104 Equation 7.43; sid_eAA is endogenous AAs digested the small intestine, Equation 7.43; sid_eAA is endogenous AAs digested in the in small intestine, Equation 7.46.7.46. AAs is calculated in the way as forasAAT and and expressed The The supply supplyofofAAT AATNNfrom fromindividual individual AAs is calculated in same the same way for AAT AATN: expressed in % of total

in % of total AATN:

sid _ AA j + sid _ mAA ⋅ m _ Comp j + sid _ eAA ⋅ e _ Comp j

⋅ 100 

8.12

2670

AA j _ AATN =

2671 2672 2673 2674 2675 2676 2677 2678 2679

j N j where AAj_AAT N is the individual AA j absorbed in the small intestine, g/d; j = arginine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan or valine; sid_AAj is histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan or valine; the absorption of the individual AAj in the small intestine from rumen undegraded feed protein, sid_AAj is the absorption of the individual AAj in the small intestine from rumen undegraded Equation 7.39, sid_mAA the absorbed in the small microbial protein, Equation feed protein, Equation 7.39,issid_mAA is theAA absorbed AA inintestine the smallfrom intestine from microbial 7.43; m_Comp is the AA composition of sid_mAA shown in Table 7.2; sid_eAA is the absorbed AA protein, Equationj 7.43; m_Compj is the AA composition of sid_mAA shown in Table 7.2; in the small intestine from protein, Equation 7.46, g/d; e_Comp AA composition sid_eAA is the absorbed AAendogenous in the small intestine from endogenous protein, Equation g/d; j is the7.46, of sid_eAA shown in Table 7.2ofand AATNshown is the in total absorbed 8.11. AA composition sid_eAA Table 7.2 andAA, AATEquation absorbed e_Comp j is the N is the total AA, Equation 8.11.

2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700

AATN

[Eq. 8.12]

where AA _AAT is the individual AA absorbed in the small intestine, g/d; j=arginine, histidine,

8.3 Implications

8.3 Implications

NorFor approach to calculate energy and and amino acid supply to theto the animal. NorForuses usesaasemi-mechanistic semi-mechanistic approach to calculate energy amino acid supply The ruminal and intestinal metabolism digestion functions thecompeting competing processes of animal. The ruminal and intestinal metabolism and and digestion areare functions ofofthe processes of fermentation andBy passage. taking these factors into account, realistic fermentation and passage. takingBythese factors into account, more more realistic and and accurate diet accurate diet values can be predicted. NorFor does not calculate NE from the absorbed values can be predicted. NorFor does not calculate NE from the absorbed end end products of total products of total tract of energy and utilization from individual tract digestion. The digestion. predictionThe of prediction energy supply and supply utilization from individual absorbed VFAs, absorbed VFAs, long chain FA, AAs and glucose from starch requires a much more complex long chain FA, AAs and glucose from starch requires a much more complex model. Our ability to model. Our ability to use this information in an applied ration evaluation system which can use thisallinformation in anfrom applied rationinevaluation system which canthe predict all the and end products predict the end products digestion the gastrointestinal tract, and partitioning from digestion in the gastrointestinal tract, and the partitioning and efficiency of utilization in the efficiency of utilization in the metabolic compartments, are currently constrained by limitations metabolic areascurrently constrained by limitations ofthe available information. Thus, of availablecompartments, information. Thus, a first step we have chosen to calculate NE supply from as a first step we have chosen to calculate the NE supply from ME, by taking into account animal ME, by taking into account animal and feed interactions that occur in the ruminal and intestinal and feed interactions that occur in the ruminal and intestinal compartments. compartments.

8.4 References AFRC, Agricultural and Food Research Council, 1992. Technical Committee on Responses to nutrients. Nutritive requirements of ruminant animals: Protein. AFRC Technical Committee on Response to Nutrients. Report No. 9. Nutrition Abstracts and Reviews (Series B) 62: 787-835.

NorFor – The Nordic feed evaluation system

AFRC, Agricultural and Food Research Council, 1993. Energy and protein requirements of ruminants. AFRC Technical Committee on Responses to nutrients. CAB International, Wallingford, UK.

83

References AFRC (Agricultural and Food Research Council), 1992. Technical Committee on Responses to nutrients. Nutritive requirements of ruminant animals: Protein. AFRC Technical Committee on Response to Nutrients. Report No. 9. Nutrition Abstracts and Reviews (Series B) 62: 787-835. AFRC, 1993. Energy and protein requirements of ruminants. AFRC Technical Committee on Responses to nutrients. CAB International, Wallingford, UK. Blaxter, K.L., 1962. The energy metabolism of ruminants. Hutchinson, London, UK. Blaxter, K.L., 1974. Metabolizable energy and feeding systems for ruminants. In: Swan H. and D. Lewis (eds.). Proceedings Nutrition Conference Feed Manufacture, Nottingham, UK, pp. 3-25. Harkins, J., R.A. Edwards and P. McDonald, 1974. A new energy system for ruminants. Animal Production. 19: 141-148. INRA (Institute National de la Recherche Agronomique), 1989. Energy: the Feed Unit Systems. In: Jarrige, R. (ed.) Ruminant Nutrition. Recommended allowances and feed tables. John Libbey and Co Ltd, London, UK, pp. 23-32. Garcia, F., J. Agabriel and D. Micol, 2007. Alimentation des bovins en croissance et à l’engrais. In: INRA (ed.). Alimentation des bovin, ovins et caprin. Besoin des animaux - Valeurs des aliments. Edition Quæ, Versaille, France, pp. 89-120. (In French). Madsen, J., 1985. The basis for the proposed Nordic protein evaluation system for ruminants. The AAT-PBV system. Acta Agriculturae Scandinavica Supplement 25: 9-20. McHardy, F.V., 1966. Simplified ration formulation. 9th international Congress in Animal Production. Edinburg, Scientific program and abstracts, Oliver and Boyd, Edinburg, UK, pp. 25. (Abstract). Møller, P.D., P.E. Andersen, T. Hvelplund, J. Madsen and K.V. Thomsen, 1983. En ny beregningsmetode for formidlenes energiverdi til kvæg. Beretning 555, Statens Husdyrbrugsforsøg, Denmark. 60 pp. (In Danish). NRC (National Research Council), 1985. Ruminal nitrogen usage. National Academy Press, Washington, DC, USA.138 pp. NRC, 1989. Nutrient requirements for dairy cattle. Update 1989. National Academy press, Washington, DC, USA. NRC, 2001. Nutrient requirements of dairy cattle. Seventh revised edition, National Academy press, Washington, DC, USA. 408 pp. Sniffen, C.J., J.D. O`Connor, P.J. Van Soest, D.G. Fox and J.B. Russel, 1992. A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science 70: 3562-3577. Tamminga, S., W.M. Van Straalen, A.P.J. Subnel, R.G.M. Meijer, A. Steg, C.J.G. Wever and M.C. Block, 1994. The Dutch protein evaluation system: the DVE/OEB-system. Livestock Production Science 40: 139-155. Thomas C. (ed.), 2004. Feed into milk: A new applied feeding system for dairy cow. Nottingham University Press, UK. Van Es, A.J.H., 1975. Feed evaluation for dairy cows. Livestock Production Science 2: 95-107. Van Es, A.J.H., 1978. Feed evaluation for ruminants. 1. The systems in use from May 1977 onwards in the Netherlands. Livestock Production Science 5: 331-345. Vérité, R., P. Chapoutot, B. Michalet-Doreau, J.L. Peyraud and C. Poncet. 1987. Révision du système des protéines digestibles dans l’intestin (PDI) Bulletin Technique C.R.Z.V. Thei1, Institute National de la Recherche Agronomique, INRA 70: 19-34. (In French).

84 

NorFor – The Nordic feed evaluation system

9. Animal requirements and recommendations 2758

N.I. Nielsen and H. Volden

2759 2760

9.0 Animal requirements and recommendations

2761 2762 2763 2764 2765 2766 2767 2768

systems predict in energy requirements from digested nutrients. used existing One of thethat challenges developing the NorFor feed evaluation system NorFor has beenhas to combine our Dutch equations to predict energy requirements for milk production, maintenance and gestation sub-models with existing feed evaluation systems, i.e. combining NorFor’s digestion model withfor dairy cows, while Frenchenergy equations have been used to predict energy demand for growth. contrast to systems that predict requirements from digested nutrients. NorFor has used existing In Dutch equations to predict energy requirements for milk maintenance and gestation energy requirements, NorFor has developed itsproduction, own equations to determine the AATfor requirements N dairy cows,production while French equations used to predict energy demand for In are mainly for milk and growth.have Thebeen recommendations for minerals andgrowth. vitamins contrast to energy requirements, NorFor has developed its own equations to determine the AATN implemented from NRC. requirements for milk production and growth. The recommendations for minerals and vitamins are mainly implemented from NRC.

2769 2770 2771 2772 2773 2774 2775 2776

9.1 Energy

2777 2778 2779 2780 2781

9.1.1 9.1.1 Maintenance Maintenance

2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793

One of the challenges in developing the NorFor feed evaluation system has been to combine our N. I. Nielsen and H. Volden

sub-models with existing feed evaluation systems, i.e. combining NorFor’s digestion model with

9.1 Energy

The net energy (NE) requirement for maintenance and growth in growing cattle is based on the French

The net energy (NE) requirement for maintenance and growth in growing cattle is based on the system (Robelin and Daenicke, 1980; INRA, 1989; Garcia et al., 2007), while the NE requirement French system (Robelin and Daenicke, 1980; INRA, 1989; Garcia et al., 2007), while the NE for maintenance and milk production in cows is based on the Dutch system by Van Es (1978). In requirement for maintenance and milk production in cows is based on the Dutch system by Van the(1978). Dutch In system the NE requirement for lactation dependent on the production level, where the Es the Dutch system the NE requirement forislactation is dependent on the production energy requirements increases with increased milk production and this effect is a compensation for level, where the energy requirements increases with increased milk production and this effect is a reduced digestibility withdigestibility increased with feeding level. feeding However, in NorFor, effect isthis directly taken compensation for reduced increased level. However,this in NorFor, into account when calculating thewhen energy value ofthe a ration. effect is directly taken into account calculating energy value of a ration. The NE requirement for maintenance in cows is calculated according to Van Es (1978) and for growing cattle according Garcia et alin . (2007) the following equation which is illustrated in growing The NE requirement for to maintenance cows isvia calculated according to Van Es (1978) and for Table cattle 9.1. according to Garcia et al. (2007) via the following equation which is illustrated in Table 9.1. NE _ ma int = factor _ 1 ⋅ BW0.75 ⋅ NE _ exercise



[Eq. 9.1]

9.1

where daily energy requirement for maintenance, MJ NE/d; BW is BW the weight whereNE_maint NE_maintis isthethe daily energy requirement for maintenance, MJ NE/d; is the of weight of the is 1isfor tied-up animals and 1.1 or grazing animals;animals; theanimal, animal,kg; kg;NE_exercise NE_exercise 1 for tied-up animals andfor 1.1loose-housed for loose-housed or grazing and thatthat determines the maintenance requirement per kg per metabolic weight, weight, andfactor_1 factor_1isisa constant a constant determines the maintenance requirement kg metabolic

cows has a value of 0.29256, which for cows factor_1= 0.29256, for heifers and steers factor _ 1 = 90 ⋅

4.848 k mg ⋅ 1000 k m , for bulls of

4.184 k mg for heifers and steers factor_1 = 90· 4.848· k mg, factor _ 1 = 91 ⋅ ⋅ 1000 k dairy breeds or crossbreed 1000 kmm and for bulls of beef breeds 4.184 k mg for bulls of dairy breeds or crossbreed factor_1 = 91· · , k 4.848 mg 1000 k factor _ 1 = 100 ⋅ ⋅ ; the factor 4.184/1000 converts kcal to MJ; m the km is used to calculate 4.184 k 1000 k m mg and for bulls of beef breeds factor_1 = 100· · , the ME requirement, Equation 8.6; kmg is the combined coefficient for utilisation of ME to NE for 1000 k m

maintenance and growth is used to calculate the NE maintenance requirement, Equation 727; the the factor 4.184/1000 kcal MJ; the km isinused the ME requirement, coefficients 90, 91, and converts 100 are the NEtorequirements kcal to percalculate kg metabolic weight (Garcia et Equation 8.6; kmg isCompared the combined forsystem, utilisation of ME to NEof for maintenance al ., 2007). with thecoefficient INRA (1989) in which all types growing cattle are and growth,

Equation 8.5; the coefficients 90, 91, and 100 are the NE requirements in kcal per kg metabolic weight (Garcia et al., 2007). 108

Compared with the INRA (1989) system, in which all types of growing cattle are assigned the same maintenance requirement per kg metabolic weight, the revised system (Garcia et al., 2007) estimates a significantly higher maintenance requirement for bulls of beef breeds.

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_9, © Wageningen Academic Publishers 2011

85

Table 9.1. NE requirement for maintenance in dairy cows and heifers related to BW and activity1 (MJ/d).

2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833

BW (kg)

Tied up

Loose

Heifer Heifer Heifer Heifer Cow Cow Cow Cow

100 200 300 400 400 500 600 700

10 17 24 29 26 31 35 40

11 19 26 32 29 34 39 44

assigned the same maintenance requirement per kg metabolic weight, the revised system (Garcia 1 In the calculation of the maintenance requirement for heifers a km of 0.71 and a kmg of 0.62 were used. et al., 2007) estimates a significantly higher maintenance requirement for bulls of beef breeds. Table 9.1

9.1.2 Lactation 9.1.2 Lactation TheNE NErequirement requirement production kg ECM 3.14 MJ(Van NELEs,(Van Es,Sjaunja 1975;etSjaunja et The forfor thethe production of 1 of kg 1ECM is 3.14isMJ NEL 1975; al., 1990): al ., 1990): NEL _ milk = ECM ⋅ 3.14



[Eq. 9.2]

9.2

where daily energy requirement for production of ECM, MJ NEL/d; and ECM whereNEL_milk NEL_milkisisthethe daily energy requirement for production of ECM, MJ NEL/d; and ECM is is theenergy-corrected energy-corrected milk milk yield, yield, Equation the Equation3.1 3.1and and3.2. 3.2.

9.1.3 Gestation 9.1.3 Gestation Cows areare generally pregnant for 284 In the In first days gestation, is Cowsand andheifers heifers generally pregnant for days. 284 days. the150 first 150 days ofthere gestation, there is practically requirement for gestation, but thereafter the energy increases increases practicallynonoenergy energy requirement for gestation, but thereafter the requirement energy requirement exponentially (Figure 9.1). The energy requirement for gestation is calculated according to the exponentially (Figure 9.1). The energy requirement for gestation is calculated according to the equation below, which is based on tabulated values (data not shown) from Van Es (1978).

equation below, which is based on tabulated values (data not shown) from Van Es (1978). NE _ gest =

BW _ mat 0.0144 ⋅ gest _ day −1.1595 ⋅e 600

[Eq. 9.3]

20 DH where NE_gest is the daily 18 NE requirement for gestation in cows and heifers, MJ/d; BW_mat is Jersey the mature weight for the breed, kg (see Table 3.5); and gest_day is the actual gestation day. 16 14 Figure 9.1 12 10 8 9.1.4 Growth 6 4 growth and gain expresses the same physiological process and are In the following, the terms used synonymously. The2 NE requirement for growth in primiparous cows is calculated using the equation below (Berg and 0 Matre, 2001) and is illustrated in Table 9.2. NorFor does not assume 0 50 100 150 200 250 300 growth in multiparous cows. Energy requirement (MJ NE/d)

2794 2795 2796 2797

Animal type

NEL _ gain = 0.00145 ⋅ BW + 12.48 ⋅

Days in gestation ADG + 0.68 1000

[Eq. 9.4]

Figure 9.1. Estimated energy requirements (MJ NE/d) for pregnancy with respect to the day of

where NEL_gain is the BW. dailyJersey energyhas requirement growth primiparous gestation and mature a maturefor BW of 440inkg; Holstein cows, has a MJ/d; matureBW BWisof 640 kg. the weight of the cow, kg; and ADG is the daily gain of the cow, g/d. Table 9.2

86 

NorFor – The Nordic feed evaluation system

The NE requirement for growth in growing cattle is calculated according to the following INRA (1989) equation, which has been slightly modified to include a 10% increase:

2810 2808 2811 2809 2812 2810 2811 2813 2812 2813 2814 2815 2816 2814 2817 2815 2818 2816 2817 2819 2818 2820 2819 2821 2822 2820 2823 2821 2824 2822 2823 2825 2824 2826 2825 2827 2828 2826 2829 2827 2830 2828 2831 2829 2834 2832 2830 2833 2835 2831 2832 2836 2833 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871

exponentially (Figure 9.1). The pregnant energy requirement forIngestation calculated according to is the Cows and heifers are generally for 284 days. the first is150 days gestation, there equation below, whichrequirement is based onfor tabulated values not shown) fromrequirement Van Es (1978). practically no energy gestation, but (data thereafter the energy increases exponentially (Figure 9.1). The energy requirement for gestation is calculated according to the BW _which mat is ⋅ geston _ day −1.1595 values (data not shown) from Van Es[Eq. equation based tabulated (1978). 9.3] NE _ gest below, = ⋅ e 0.0144

600 BW _ mat 0.0144 ⋅ gest _ day −1.1595 [Eq.BW_mat 9.3]  NE _ gest = e where NE_gest600 is the⋅daily NE requirement for gestation in cows and heifers, MJ/d; is

9.3

the mature weight for the breed, kg (see Table 3.5); and gest_day is the actual gestation day. whereNE_gest NE_gestisisthe thedaily daily NE requirement gestation in cows heifers, BW_mat is the where NE requirement for for gestation in cows and and heifers, MJ/d;MJ/d; BW_mat is mature weight for the breed, kg (see Table 3.2); and gest_day is the actual gestation day. Figure 9.1 weight for the breed, kg (see Table 3.5); and gest_day is the actual gestation day. the mature

9.1.4 Growth Figure 9.1

9.1.4 Growth In growth andand gaingain expresses the same physiological process and areand are used Inthe thefollowing, following,the theterms terms growth expresses the same physiological process 9.1.4 Growth TheThe used synonymously. requirement growth in primiparous cowsisiscalculated calculatedusing usingthe theequation synonymously. NE NE requirement forfor growth in primiparous cows equation belowand (Berg and Matre, and is illustrated in Table 9.2. NorFor doesnot notand assume In the following, theMatre, terms growth and is gain expresses same physiological process are growth in below (Berg 2001) 2001) and illustrated intheTable 9.2. NorFor does assume growth in multiparous cows. used synonymously. NE requirement for growth in primiparous cows is calculated using the multiparous cows. The equation below (Berg and Matre, 2001) and is illustrated in Table 9.2. NorFor does not assume ADG growth in multiparous cows. [Eq. 9.4] = 0.00145 ⋅ BW + 12.48 ⋅ + 0.68  NEL _ gain

9.4

1000 ADG NEL _ gain = 0.00145 ⋅ the BWdaily + 12.48 ⋅ + 0.68 9.4] where NEL_gain energy requirement for growth in primiparous cows, MJ/d; where NEL_gain isisthe daily energy requirement for growth in primiparous cows,[Eq. MJ/d; BW isBW is the 1000

weight of of thethe cow, kg; of the the cow, cow,g/d. g/d. the weight cow, kg;and andADG ADGisisthe thedaily daily gain gain of where NEL_gain is the daily energy requirement for growth in primiparous cows, MJ/d; BW is Theweight NE forand growth cattleofisthe calculated Table 9.2 requirement the of the cow, kg; ADGinis growing the daily gain cow, g/d. according to the following INRA (1989) equation, which has been slightly modified to include a 10% increase:

The NE for growth in growing cattle is calculated according to the following INRA Table 9.2requirement ⎛ k mg ⎞ 4.184 (1989) equation, which been to ⋅include a⎟10% [Eq. 9.5] NEG _ gain _ prot 39 ⋅ gain modified _ fat ) ⋅ ⋅ 1.10increase: = ⎜ (5.48 ⋅ gainhas + 9.slightly  9.5 ⎜ 1000 k g _ corr ⎠⎟ ⎝ The NE requirement for growth in growing cattle is calculated according to the following INRA 109 to include a 10% increase: (1989) which hasdaily beenNE slightly modified where equation, NEG_gain is the requirement for gain in growing cattle, MJ NEG/d; 5.48 and 9.39 where NEG_gain is the daily NE requirement for gain in kcal/g; growinggain_prot cattle, MJisNEG/d; 5.48 and retention, are the energy content in protein and fat, respectively, the daily protein 109 9.39 are the energy content in protein and fat, respectively, kcal/g; gain_prot is the daily protein Equation 9.8; gain_fat is the daily fat retention, Equation 9.9; the factor 4.184/1000 converts kcal to retention, Equation 9.8; gain_fat is the daily fat retention , Equation 9.9; the factor 4.184/1000 MJ; kmg is the combined utilisation coefficient of ME to NE for maintenance and growth, Equation converts kcal to MJ; kmg is the combined utilisation coefficient of ME to NE for maintenance and 8.5 andEquation kg_corr is8.5 theand utilisation of ME to NE of forME growth, forcorrected the proportion of the utilisation coefficient to NEcorrected for growth, growth, kg_corr iscoefficient energy that originates from protein retention in the total daily energy retention, Equation 9.6. for the proportion of energy that originates from protein retention in the total daily energy retention, Equation 9.6.

The utilization coefficient kg_corr takes into account the dependence of the utilization of ME for

growth on thecoefficient proportionkg_corr of energy retained as the protein. Thus, of thethe ratio between and kg_corr is takes into account dependence utilization of kME The utilization mg for growth the proportion of energy retained as protein. Thus,for thevariations ratio between kmg proportion and kg_corr isof energy used toonadjust the NE requirement for gain to account in the used adjust thefrom NE requirement for gainintothe account variations the proportion of 1989). energy The 10% that to originates protein retention total for daily energy in retention (INRA, that originates from 1.1 protein retention9.5) in the daily energy retention Thecompiled 10% adjustment (factor in Equation in total NE for growth was added (INRA, because1989). a dataset from adjustment (factor Equationexperiments 9.5) in NE for growth added because a dataset(Homb, compiled 11 Norwegian and1.1 sixinSwedish with bullswas involving 120 treatments 1974; Hole, from 11 Norwegian and six Swedish experiments with bulls involving 120 treatments (Homb, 1985; Olsson and Lindberg, 1985; Olsson, 1987; Johansen, 1992; Skaara, 1994; Randby, 2000a; 1974; Hole, 1985; Olsson and Lindberg, 1985; Olsson, 1987; Johansen, 1992; Skaara, 1994; Randby, 2000b; Berg and Volden, 2004; Volden and Berg, 2005; Berg, unpublished data; Havrevoll, Randby, 2000a; Randby, 2000b; Berg and Volden, 2004; Volden and Berg, 2005; Berg, unpublishedHavrevoll, data; Olsson, unpublished data) showed an underprediction of requirements (r2=0.90 unpublished; unpublished; Olsson, unpublished) showed an underprediction of 2 see Figure 9.2). Although, this modification was made, Figure 9.2 shows that there and RMSE=4.4; requirements (r = 0.90 and RMSE = 4.4; see Figure 9.2). Although, this modification was made, is also9.2 a slope effect indicating the estimated energythat supply is too high compared Figure shows that there is alsothat a slope effect indicating the estimated energy supply to is the too energy high compared to the energy requirement in larger animals (>50 MJ NEG/d) and vice versa for small animals. However, it has not been possible to improve this relationship. Table 9.2. NE requirement (MJ/d) for growth in primiparous cows related to BW and daily gain.

Figure 9.2

BW, kg

Daily weight gain, g/d

The utilisation coefficient of ME to NE for growth, corrected for the proportion of energy that originates from protein retention, is calculated according to the INRA (1989) equation:

250

500

750

k g _ corr = 0.35 + 0.25 ⋅ (1 − Ep )2

[Eq. 9.6] 400 4.4 7.5 10.6 500 4.5 7.6 10.7 for growth, corrected for 10.9 the proportion of where 600 kg_corr is the utilisation 4.7coefficient of ME to NE7.8 energy that originates from protein retention; and 700 in the total amount of 4.8energy retained per day 7.9 11.1 Ep is the daily NE requirement for protein retention relative to the total daily NE retention, Equation 9.7.

NorFor – The Nordic feed evaluation system 5.48 ⋅ gain _ prot

Ep =

5.48 ⋅ gain _ prot + 9.39 ⋅ gain _ fat

[Eq. 9.7]

87

⎛ 80_ prot + 9.39 ⋅ gain _ fat ) ⋅ 4.184 ⋅ k mg NEG _ gain = ⎜ (5.48 ⋅ gain ⎜⎛ 41000 .184 k gk_mg corr NEG _ gain = ⎝⎜ (5.48 ⋅ gain 70_ prot + 9.39 ⋅ gain _ fat ) ⋅ 1000 ⋅ k ⎜ g _ corr ⎝

NEG requirement (MJ/d)

2834 2834 2835 2835 2836 2837 2836 2838 2837 2839 2838 2840 2839 2841 2840 2842 2841 2843 2842 2844 2843 2845 2844 2846 2845 2847 2846 2848 2847 2849 2848 2850 2849 2851 2850 2852 2851 2853 2852 2854 2853 2855 2854 2856 2855 2857 2856 2858 2857 2859 2858 2860 2859 2861 2860 2862 2861 2863 2862 2863 2864 2864 2865 2866 2865 2867 2866 2868 2867 2869 2868 2870 2869 2870 2871 2871

⎞ ⎟ ⋅ 1.10 ⎟⎞ ⎠⎟ ⋅ 1.10 ⎟ ⎠

[Eq. 9.5] [Eq. 9.5]

where NEG_gain is the60 daily NE requirement for gain in growing cattle, MJ NEG/d; 5.48 and 9.39 the energyiscontent inNE protein and fat, respectively, kcal/g;cattle, gain_prot is the daily whereare NEG_gain the50 daily requirement for gain in growing MJ NEG/d; 5.48protein and retention, Equation 9.8; gain_fat is theand daily retention , Equation 9.9; the factor 9.39 are the energy content in protein fat,fatrespectively, kcal/g; gain_prot is the 4.184/1000 daily protein converts kcal to MJ;9.8; kmg is the combined utilisation coefficient of ME to the NE factor for maintenance retention, Equation is the daily fat retention , Equation 9.9; 4.184/1000and 40gain_fat growth, Equation 8.5kand is the utilisation coefficient of ME to NE for for growth, corrected g_corr converts kcal to MJ; combined utilisation coefficient of ME to NE maintenance and mg iskthe 30 k thatisoriginates for the proportion of energy from coefficient protein retention daily energy the utilisation of MEintothe NEtotal for growth, corrected growth, Equation 8.5 and g_corr retention, Equationof9.6. for the proportion energy 20 that originates from protein retention in the total daily energy retention, Equation 9.6. 10 kg_corr takes into account the dependence of the utilization of ME for The utilization coefficient growth on the proportion of energy retained as protein. Thus, the ratio between kmg and kg_corr of ME foris The utilization coefficient 0 kg_corr takes into account the dependence of the utilization used to on adjust NE requirement for gain toasaccount for variations in the proportion of kenergy growth the the proportion of energy retained protein. Thus, the ratio between k and is mg g_corr 0 10 20 30 40 50 60 70 80 that from retention theto total daily for energy retention (INRA, 1989).ofThe 10% usedoriginates to adjust the NEprotein requirement forin gain account variations in the proportion energy adjustment (factor in Equation 9.5) fordaily growth was added because a dataset NEG supply (MJ/d) that originates from1.1 protein retention inin theNE total energy retention (INRA, 1989).compiled The 10% from 11 Norwegian andinsix Swedish9.5) experiments bulls involving 120 treatments adjustment (factor 1.1 Equation in NE for with growth was added because a dataset(Homb, compiled 1974; Hole, 1985; Olsson Lindberg, 1985; 1987; Johansen, 1992; Skaara, 1994; from 11 Norwegian and sixand Swedish experiments with bulls 120 treatments (Homb, Figure 9.2. The relationship between energyOlsson, supply andinvolving energy requirement after a modification of Randby, 2000a; Randby, 2000b; Berg aand Volden, 2004; Berg, 2005; Berg, 1974; Hole, 1985; Olsson and Lindberg, 1985; Olsson, 1987; 1992; Skaara, 1994; in order to the original French system where 10% increase inVolden NEJohansen, forand growth was implemented unpublished; Havrevoll, unpublished; unpublished) showed underprediction of line shows Y=X Randby, Randby, 2000b; Berg Olsson, and Volden, 2004;and Volden andan Berg, 2005; Berg, obtain a2000a; closer relationship between energy supply energy requirements. Solid RMSE = 4.4; see Figure 9.2). Although, this modification was made, requirements Havrevoll, (r2 = 0.90 and unpublished; unpublished; Olsson, unpublished) showed an underprediction of 2=0.90 and and dotted line shows relationship between supply and requirement (r RMSE=4.4). The 2 that there is also a slope effect indicating that the estimated energy supply Figure 9.2 shows is too = 0.90 and RMSE = 4.4; see Figure 9.2). Although, this modification was made, requirements (r data are from the trials described in Table 9.11. high compared to that the energy larger animals that (>50the MJestimated NEG/d) and vicesupply versa for Figure 9.2 shows there isrequirement also a slope in effect indicating energy is too smallcompared animals. However, it has not been possible improve(>50 this MJ relationship. high to the energy requirement in largertoanimals NEG/d) and vice versa for small animals. However, it has not been possible to improve this relationship. requirement Figure 9.2 in larger animals (>50 MJ NEG/d) and vice versa for small animals. However, we were not able Figure 9.2to improve this relationship when using the described approach. The utilisation coefficient of ME to NE for growth, corrected for the proportion of energy that originates fromcoefficient protein retention, is NE calculated according tocorrected theforINRA (1989) Theutilisation utilisation coefficient of ME to for NEgrowth, for growth, for the equation: proportion The of ME to corrected the proportion of energy of thatenergy that originates retention, is calculated according to thetoINRA (1989) equation: originatesfrom fromprotein protein retention, is calculated according the INRA (1989) equation: [Eq. 9.6] k g _ corr = 0.35 + 0.25 ⋅ (1 − Ep )2 [Eq. 9.6] k g _ corr = 0.35 + 0.25 ⋅ (1 − Ep )2  9.6 where kg_corr is the utilisation coefficient of ME to NE for growth, corrected for the proportion of energy theistotal amount of coefficient energy retained perto day originates fromcorrected protein andof wherekin kg_corr is utilisation coefficient of ME NEgrowth, for growth, for the proportion of where thethe utilisation of ME NEtothat for corrected for theretention; proportion g_corr Ep is the NE amount requirement for protein retention relative to the total daily NE retention, energy in the retained perper day that originates from protein retention; and and Ep is energy indaily thetotal total amountofofenergy energy retained day that originates from protein retention; Equation 9.7. Ep the daily NE requirement for protein retention relative thetotal totaldaily dailyNE NEretention, retention, Equation 9.7. theisdaily NE requirement for protein retention relative totothe Equation 9.7. 5.48 ⋅ gain _ prot [Eq. 9.7]  9.7 Ep = + 9_.39 ⋅ gain _ fat 5.48 ⋅ gain5._48prot ⋅ gain prot [Eq. 9.7] Ep = 5.48 ⋅ gain _ prot + 9.39 ⋅ gain _ fat

where Ep is the daily NE requirement for protein retention relative to the total daily NE retention;

2872 2873 2872 2874 2873 2875 2874 2876 2875 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886

where Ep is the daily NE requirement for protein retention relative to the total daily NE retention; 5.48 and9.39 9.39are arethethe energy content in protein andrespectively, fat, respectively, kcal/g; gain_prot is the daily 5.48 content infor protein andretention fat, gain_prot the daily whereand Ep is the daily energy NE requirement protein relative tokcal/g; the total daily NEis retention; protein retention, Equation 9.8; and gain_fat is the daily fat retention, Equation 9.9. protein and gain_fat is and the daily fat retention,kcal/g; Equation 9.9. is the daily 5.48 andretention, 9.39 are Equation the energy9.8; content in protein fat, respectively, gain_prot protein retention, Equation 9.8; and gain_fat is the daily fat retention, Equation 9.9. The following followingequations equations used to calculate the daily retention of protein and fat (Robelin and The areare used to calculate the daily retention of protein and fat (Robelin & Daenicke,1980) 1980)and and examples of and fat and protein retention in growing cattle Daenicke, examples of fat protein retention in growing cattle are givenare in given Tablesin Tables 110 9.3 9.3 and and9.4. 9.4. 110

2887 2888 2889 2890

where to factor_5 are dependent on the type of system 88  gain_fat is the daily fat retention, g/d; factor_3 NorFor – The Nordic feed evaluation growing cattle (Table 9.5); EBW is the empty body weight, Equation 9.12; Fat_mass is the fat content in the EBW, Equation 9.13 and EBWG is the empty body weight gain, Equation 9.10.

gain _ prot = factor _ 1 ⋅ 1.06 ⋅ (EBWG − gain _ fat) ⋅ FFM0.06



[Eq. 9.8]

9.8

where daily protein retention, kg/d;kg/d; factor_1 is dependent on the on typethe of type growing wheregain_prot gain_protisisthethe daily protein retention, factor_1 is dependent of growing cattle is the empty bodybody weight gain, gain, Equation 9.10; gain_fat is the daily fat daily fat cattle(Table (Table9.5); 9.5);EBWG EBWG is the empty weight Equation 9.10; gain_fat is the retention, FFM is fat freefree mass, Equation 9.11.9.11. retention,Equation Equation9.9; 9.9;and and FFM is fat mass, Equation ⎛ ⎛ ⎛ 1000 3 + 2 ⋅_factor _ 4 ⋅ ln( EBW _) )5⋅ factor _ 5 ⎞⎞ ⋅ Fat _ mass⎛ ⎞ ⎛⎜ (factor ⎞⎞ _ 3 + 2_⋅ factor 4 ⋅ ln( EBW ) ) ⋅ factor [Eq. ⎟ ⎟ ⋅ ( EBWG⎟)⎟1,⋅78 gain __fat fat==⎜ ⎛⎜⎜1000 ( EBWG )1.78 ⋅ ⎜ ⋅ Fat _ mass ⎞⎟ ⋅ ⎜ (⎟factor gain ⎟⎟ ⎜ . 1 78 ⎜ ⎝⎜ ⎝ ⎟ ⎟ EBW ⎠ ⎜ ⎠ 1,78 _ 5 EBW Factor Factor _ 5 ⎠⎠ ⎝ ⎠⎠ ⎝ ⎝⎝

9.9]

9.9

Table 9.3. Estimated fat and protein retention in heifers of dairy breeds related to BW and ADG. BW, kg

200 300 400 500

800 g ADG

600 g ADG

Fat, g/d

Protein, g/d

Fat, g/d

Protein, g/d

142 207 270 333

134 125 114 102

85 124 162 199

105 101 95 88

Table 9.4. Estimated fat and protein retention in bulls of dairy breeds related to BW and ADG. BW, kg

ADG, g/d

Fat, g/d

Protein, g/d

100 200 300 400 500 100 200 300 400 500

1,200 1,500 1,400 1,200 1,000 1,000 1,300 1,200 1000 800

86 200 297 309 290 62 175 226 223 195

200 238 211 172 139 168 211 187 151 120

2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886

The following equations are used to calculate the daily retention of protein and fat (Robelin & Table 9.5.1980) Coefficients used for fatretention and protein retention in growing Daenicke, and examples of calculating fat and protein in growing cattle are given cattle. in Tables 9.3 and 9.4.

2887 2888 2889 2890

where onon thethe type ofof growing where gain_fat gain_fatisisthe thedaily dailyfat fatretention, retention,g/d; g/d;factor_3 factor_3totofactor_5 factor_5are aredependent dependent type growing cattle9.5); (Table 9.5);isEBW is the empty body weight, Equation Fat_mass is fat the content fat cattle (Table EBW the empty body weight, Equation 9.12;9.12; Fat_mass is the in the content in the EBW, 9.13 and EBWG is body the empty body weight gain, Equation EBW, Equation 9.13Equation and EBWG is the empty weight gain, Equation 9.10. 9.10.

2891

EBWG =

2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902

Animal

Factor_1

Factor_2

Factor_3

gain _ prot = factor _ 1 ⋅ 1.06 ⋅ (EBWG − gain _ fat) ⋅ FFM0.06

Heifer or steer 0.1616

-6.311

1.811

Factor_4 0

Factor_5 0.8

Factor_6 [Eq. 9.8]

1.046

Factor_7 -0.3939

1 Bull EM -1.68retention, 0.0189 0.1609 1.0 on the 1.023 -0.2855 where gain_prot is0.1541 the daily protein kg/d; factor_1 is dependent type of growing 1 9.5); 0.1541 cattle (Table EBWG is the-5.433 empty body1.5352 weight gain, is the daily fat Bull LM 0 Equation 9.10; 1.2 gain_fat1.024 -0.2704 retention, 9.9; and FFM is fat free1.3708 mass, Equation 9.11. 1.0 Bull CB1Equation0.1541 -5.7541 0.0442 1.023 0.27795 ⎛ ⎛ 1000 ⋅ Fat _ mass ⎞ ⎛ (factor _ 3 + 2 ⋅ factor _ 4 ⋅ ln( EBW ) ) ⋅ factor _ 5 ⎞ ⎞ 1 EM: early maturing; CB: cross breed. ⎜ ⎟ ⎟ ⋅ ( EBWG )1,78 _ fat = ⎜ ⎜ maturing; LM:⎟ ⋅late gain ⎜⎝ ⎟⎟ EBW ⎠ ⎜⎝ Factor _ 51,78 ⎠⎠ ⎝

EBW ⋅ factor _ 6 ⋅ ADG  BW

[Eq. 9.9]

[Eq. 9.10]

9.10

whereEBWG EBWGisisthe thedaily daily empty body weight gain, the empty body weight, Equation where empty body weight gain, g/d; g/d; EBWEBW is theisempty body weight, 9.12; BW is the weight of the animal, kg. However, BW for primiparous cows is corrected with the Equation 9.12; BW is the weight of the animal, kg. However, BW for primiparous cows is followingwith termthe600/BW_mat; is dependent onisthe type of on growing (Table 9.5); and corrected following termfactor_6 ·600/BW_mat; factor_6 dependent the typecattle of growing ADG(Table is the average live weight, g/d.of live weight, g/d. cattle 9.5); anddaily ADGgain is theofaverage daily gain FFM = EBW − Fat _ mass



[Eq. 9.11]

where FFM is the fat free mass of the EBW, kg; EBW is the empty body weight, Equation 9.12; Fat_mass the fat content in the EBW,system Equation 9.13. and NorFor – TheisNordic feed evaluation EBW = e (factor _ 7 + factor _ 6⋅ln(BW ))

[Eq. 9.12]

9.11 89

2894 2894 2895 2895 2896 2896 2897 2897 2898 2898 2899 2899 2900 2900 2901 2901 2902 2902 2903 2903 2904 2904 2905 2905 2906 2906 2907 2907 2908 2908 2909 2909 2910 2910 2911 2911 2915 2915 2912 2912 2916 2916 2913 2915 2917 2913 2917 2916 2914 2918 2918 2914 2917 2919 2919 2918 2920 2920 2919 2921 2921 2920 2922 2922 2921 2922 2923 2923 2924 2924 2923 2925 2925 2924 2926 2926 2925 2927 2927 2926 2928 2928 2927 2929 2929 2928 2930 2930 2929 2931 2931 2930 2932 2932 2931 2933 2933 2932 2934 2934 2933 2935 2935 2934 2936 2936 2935 2937 2937 2936 2938 2938 2937 2939 2939 2938 2940 2940 2939 2941 2941 2940 2942 2942 2941 2943 2943 2942 2944 2944 2943 2945 2945 2944 2946 2946 2945 2947 2947 2946 2948 2948 2947 2949 2949 2948 2950 2950 2949 2951 2951 2950 2952 2952 2951 2953 2953 2952 2954 2954 2953 2955 2955 2954 2956 2956 2955 2957 2957 2956 2958 2958 2957 2959 2959 2958 2959

corrected corrected with with the the following following term term ·600/BW_mat; ·600/BW_mat; factor_6 factor_6 is is dependent dependent on on the the type type of of growing growing cattle cattle (Table (Table 9.5); 9.5); and and ADG ADG is is the the average average daily daily gain gain of of live live weight, weight, g/d. g/d. [Eq. [Eq. 9.11] 9.11]

FFM = EBW − Fat _ mass FFM = EBW − Fat _ mass

9.12; where kg; EBW is empty body weight, Equation whereFFM FFMis thefat fatfree freemass massof theEBW, EBW, EBW is the empty body weight, Equation where FFM isisthe the fat free mass ofofthe the EBW, kg;kg; EBW is the the empty body weight, Equation 9.12;9.12; and and Fat_mass is content the EBW, Equation 9.13 Fat_mass is the fatfat content inin the Fat_mass is the the fat content in theEBW, EBW,Equation Equation9.13. 9.13.. and _ 7 + factor _ 6⋅ ln(BW )) EBW = e ((factor EBW = e factor _ 7 + factor _ 6⋅ln(BW ))



[Eq. [Eq. 9.12] 9.12]

9.12

where body weight, kg; and are on of whereEBW EBWis theempty empty body weight, kg; factor_6 7 are dependent on the type of growing where EBW isisthe the empty body weight, kg; factor_6 factor_6 and 77and are dependent dependent on the the type type of growing growing cattle (Table 9.5); and BW is is thethe weight of the the animal, kg. However, However, BW for for primiparous cows is iscows is cattle is the weight of animal, kg. BW primiparous cows cattle(Table (Table9.5); 9.5);and andBW BW weight of the animal, kg. However, BW for primiparous corrected term ·600/BW_mat. BW_mat is BW 3.5). corrected with the following term ·600/BW_mat. BW_mat is the the maturity BW (Table (Table 3.5). 3.5). correctedwith withthe thefollowing following term 600/BW_mat. BW_mat is maturity the maturity BW (Table 2 + factor_ 3⋅ ln( EBW ) + factor _ 4 ⋅ (ln( EBW ) 22 )) Fat _ mass = ee (factor_ Fat _ mass = (factor_ 2 + factor_ 3⋅ ln( EBW ) + factor _ 4 ⋅ (ln( EBW ) )) 2



[Eq. [Eq. 9.13] 9.13]

9.13

where content in EBW, kg; EBW is body weight, Equation whereFat_mass Fat_massis thefat content in the EBW, EBW is empty the empty weight, Equation 9.12; where Fat_mass isisthe the fatfat content in the the EBW, kg; kg; EBW is the the empty bodybody weight, Equation 9.12; factor_2 to factor_4 are dependent on the type of growing cattle (see Table 9.5). 9.12; factor_2 to factor_4 are dependent on the type of growing cattle (see Table 9.5). factor_2 to factor_4 are dependent on the type of growing cattle (see Table 9.5). where Protein_mass is the protein content in the EBW, kg; factor_1 is dependent on the type where Protein_mass is the protein content in the EBW, kg; factor_1 is dependent on the type of of 9.11.. growing cattle cattle (see (see Table Table 9.5); 9.5);1.and and FFM is the fat free mass of the EBW, Equation 9.11 growing 06 FFM is the fat free mass of the EBW, Equation 1 . 06 [Eq. 9.14] Pr otein _ mass = factor _ 1 ⋅ FFM where protein content on the type of  in the EBW, kg; factor_1 is dependent [Eq. 9.14] 9.14 Pr oteinProtein_mass _ mass = factoris_the 1 ⋅ FFM growing Table 9.3 9.3cattle (see Table 9.5); and FFM is the fat free mass of the EBW, Equation 9.11. Table

where Protein_mass is the protein content in the EBW, kg; factor_1 is dependent on the type of

Table 9.3 9.4 cattle (see Table 9.5); and FFM is the fat free mass of the EBW, Equation 9.11. Table 9.4 growing Table 9.5 Table 9.4 9.5

111 111

9.1.5 Mobilization and deposition Table 9.5 9.1.5 Mobilization Mobilization and deposition deposition 9.1.5 and NorFor considers mobilization deposition BCSsection3.1) (see Section 3.1)primifor both NorFor considers considers mobilization and and deposition usingusing BCS (see (see for both both and primi- and NorFor mobilization and deposition using BCS section3.1) for primi- and 9.1.5 Mobilization and deposition multiparous cows.The The amount of NE deposited when aiscow is fed its above its requirement energy requirement multiparous cows. amount of NE deposited when a cow fed above energy multiparous cows. The amount of NE deposited when a cow is fed above its energy requirement andhas hasconsiders positive energy balance (EB>100%) is BCS calculated as: NorFor mobilization and(EB>100%) deposition using (see for both primi- and and has positive energy balance (EB>100%) is calculated calculated as:section3.1) and aaapositive energy balance is as: multiparous cows. The amount of NE deposited when a cow is fed above its energy requirement and a positive balance (EB>100%) NELhas _ dep dep = NEL NEL − −energy NE __ ma ma int − − NEL NEL gain − − NE NEis__calculated gest − − NEL NELas: _ milk milk  [Eq. NEL _ = NE int __ gain gest _ [Eq. 9.15] 9.15]

9.15

NELamount _ dep = of NELNE − NE _ ma intwhen − NEL gain − NE gestEB<100% − NELis_ calculated milk [Eq. 9.15] The aa_cow is EB<100% as: The amount mobilized when a cow is _fed is calculated The amount ofofNE NE mobilized mobilized when cow is fed fed EB<100% is calculated as: as:

The mobilized when cow__isgain fed− calculated NEL _ − ⋅⋅ ((NEL − int −EB<100% NE __ milk  NELamount _ mob mob = =of −1 1NE NEL − NE NE __ ma ma int − −aNEL NEL gain NE __ gest gest − −isNEL NEL milk)) as:

[Eq. 9.16] 9.16] [Eq.

9.16

NEL _ NEL_dep mob = −1 ⋅ (is NEL −energy NE _ ma int − NEL _MJ/d; gain −NEL_mob NE _ gest − is NEL _mobilised milk) [Eq.MJ/day; 9.16] where thethe deposition, theis energy, whereNEL_dep NEL_depisisthe energy deposition, MJ/d; NEL_mob the mobilised energy, MJ/day; NEL where energy deposition, MJ/d; NEL_mob is the mobilised energy, MJ/day; is the energy intake, Equation 8.3; NE_maint is the energy required for maintenance, NEL the energy intake, Equation8.3; 8.3;NE_maint NE_maint isisthe for for maintenance, NEL is theis energy intake, Equation theenergy energyrequired required maintenance, Equation where NEL_dep is the energy MJ/d;for NEL_mob the mobilised energy, MJ/day; Equation 9.1; NEL_gain NEL_gain is the the deposition, energy required for growth inisprimiparous primiparous cows, Equation 9.4; NE_gest is Equation 9.1; is energy required growth in cows, Equation 9.4; 9.1; NEL_gain is the energy required for growth in primiparous cows, Equation 9.4; NEL is the energy intake, Equation 8.3; NE_maint is the energy required for maintenance, NE_gest is required for gestation, Equation 9.3 and NEL_milk is energy required for NE_gest is energyfor required for gestation, Equation 9.3 and NEL_milk is energy required for milk milk energy required gestation, Equation 9.3; and NEL_milk is energy required for milk production, Equation 9.1; NEL_gain production, Equation 9.2.is the energy required for growth in primiparous cows, Equation 9.4; production, Equation 9.2. Equation 9.2. NE_gest is energy required for gestation, Equation 9.3 and NEL_milk is energy required for milk production, The BCS BCS is is Equation used as as aa 9.2. measure for for quantifying quantifying mobilization mobilization and and deposition deposition during during lactation. lactation. As As The used measure The BCS is used asone a measure forcorresponds quantifyingto mobilization and for deposition during lactation. As stated in section 3.1, unit Jersey stated in section 3.1, one unit of of BCS BCS corresponds to 60 60 kg kg BW BW except except for Jersey cows cows which which is is stated in isSection onevariations unit BCS to 60 BW except for lactation. Jersey The used as3.1, a measure forof quantifying andkg deposition during et al alcows 45 kgBCS BW. However, large variations existcorresponds on mobilization such an an estimate estimate as reported by Nielsen Nielsen et .. As which is 45 kg BW. However, large exist on such as reported by stated section 3.1, one unit ofkg BCS to 60 BW except foris which 45 kgin BW. However, variations exist onBCS. such ankgestimate as reported bycows Nielsen et isal. (2003), (2003), ranging from 20 to 110 BW per unit This large variation related to (2003), ranging from 20large to 110 kg BWcorresponds per unit BCS. This large variation isJersey related to differences differences et al. in the data 45 kg BW. However, large variations existBCS. onthe such anlarge estimate as reported byvariables Nielsen in the data that have been used forper estimating the relationship between these variables (including ranging from 20 to 110 kg BW unit This variation isthese related to differences in the data that have been used for estimating relationship between (including (2003), ranging 20 toestimating 110and kg BW unit BCS. This large these variation is related to differences variations in e.g. breed, stage of lactation). that have been used forparity, theper relationship between variables (including variations in variations in e.g.from breed, parity, and stage of lactation). in thebreed, data that haveand been usedof forlactation). estimating the relationship between these variables (including e.g. parity, stage and stageof variations in e.g. breed, The NE for the kg The NE requirement requirement forparity, the deposition deposition ofof11 lactation). kg body body tissue tissue is is 31 31 MJ. MJ. A A weight weight loss loss of of 1 1 kg kg body body tissue supplies the cow with 24.8 MJ NEL, due to 80% conversion efficiency (INRA, 1989). The tissue supplies the cow with 24.8 MJ NEL, due to 80% conversion efficiency (INRA, 1989). The NE requirement for the deposition of 1 kg body tissue is 31 MJ. A weight loss ofThe 1 kg body The NE of requirement for the depositionis ofcalculated 1 kg bodyusing tissuethe is following 31 MJ. A weight lossand of 1examples kg body amount NE deposited or mobilized equations, amount of NE deposited or mobilized is calculated using the following equations, and examples tissue supplies the cow with 24.8 MJ NEL, due to 80% conversion efficiency (INRA, 1989). The tissue supplies the cow are shown shown in Table Table 9.6. with 24.8 MJ NEL, due to 80% conversion efficiency (INRA, 1989). The are 9.6. amountofofin NEdeposited deposited or mobilized is calculated the following equations, and examples amount NE or mobilized is calculated using using the following equations, and examples are shown inTable Table 9.6.⋅ BCS _ kg ⋅ 31.0 are shown 9.6. NEL _ [Eq. NEL _ dep dep = =inBCS BCS _ _ change change ⋅ BCS _ kg ⋅ 31.0 [Eq. 9.17] 9.17] NEL _ dep = BCS _ change ⋅ BCS _ kg ⋅ 31 .0  .8) NEL NEL _ _ mob mob = =− −1 1 ⋅⋅ ((BCS BCS _ _ change change ⋅⋅ BCS BCS _ _ kg kg ⋅⋅ 24 24.8)

[Eq. 9.17] [Eq. [Eq. 9.18] 9.18]

9.17

NEL _ NEL_dep mob = −1 ⋅ (is BCS change ⋅deposition, BCS _ kg ⋅ 24 .8)  NEL_mob is the mobilised energy, [Eq.MJ/day; 9.18] 9.18 where the_energy energy MJ/d; where NEL_dep is the deposition, MJ/d; NEL_mob is the mobilised energy, MJ/day; BCS_change is the change in BCS per day, units/d; and BCS_kg is the BW per unit BCS (Table BCS_change is the change in BCS per day, units/d; and BCS_kg is the BW per unit BCS (Table where the energy MJ/d;and NEL_mob is Jersey). the mobilised energy, MJ/day; 3.2) different breeds (60 for 45 3.2) for forNEL_dep different is breeds (60 kg kgdeposition, for large large breeds breeds and 45 kg kg for for Jersey). BCS_change is the change in BCS per day, units/d; and BCS_kg is theNordic BW perfeed unit evaluation BCS (Table system 90  NorFor – The 3.2) for different breeds (60 kg for large breeds and 45 kg for Jersey). 112 112

Table 9.6. NEL requirements for deposition and mobilization in different breeds of cows. Daily change in BCS1

Weight change, kg/d

NEL requirement, MJ/d

Large breed

Jersey

Large breed

Jersey

-0.02 -0.01 0 0.01 0.02

-1.2 -0.6 0 0.6 1.2

-0.9 -0.45 0 0.45 0.9

-30 -15 0 19 37

-22 -11 0 14 28

1 One BCS = 60 kg for large breeds and 45 kg for Jersey, i.e. 0.01 BCS corresponds to 600 g BW for large breeds and

450 g BW for Jersey.

where NEL_dep is the energy deposition, MJ/d; NEL_mob is the mobilised energy, MJ/day; BCS_

2960 2961 2962 2963 2964 2965 2966 2967 2968

change Table 9.6is the change in BCS per day, units/d; and BCS_kg is the BW per unit BCS for different

2969

NEL _ var iable =

using the following empirical equation (Volden, unpublished data), which is illustrated in Figure 9.3. 100 − 35 .25 ⋅ DIM 0.5 + 6.35 ⋅ DIM − 0.602 ⋅ DIM 1.5 + 0.022336 ⋅ DIM 2 ⎛1 − 0.32745 ⋅ DIM 0.5 + 0.0556 ⋅ DIM − 0.005106 ⋅ DIM1.5 ⎞ ⎟ ⎜ ⎟ ⎜ 2 2 .5 0 . 000177 DIM 0 . 000000856 DIM + ⋅ + ⋅ ⎠ ⎝



9.19

[Eq. 9.19]

where NEL_variable is the recommended energy balance; and DIM is days in milk.

where NEL_variable is the recommended energy balance; and DIM is days in milk.

104

Figure 9.3

For DH, RD and JER 102default changes in BCS depend on the breed, parity and lactation stage. Thus, the lactation period is divided into four periods: in the first four weeks post-calving, it is assumed that 70%100 of body reserves are mobilized, while 30% is assumed to be mobilized in weeks 5 to 8 post-calving. In week 9 no mobilization or deposition is assumed. From week 10 until the end of the lactation period, a fixed energy deposition is required, which corresponds to 98 the total energy mobilized in early lactation (Table 9.7). 96 Table 9.7 94 The energy balance is calculated as energy supplied from the feed ration divided by the total NE 92 requirement 0 50 100 150 200 250 300

Energy balance (%)

2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987

breeds (60 kg for large breeds and 45 kg for Jersey).

Different approaches are used in NorFor for predicting mobilization or deposition during the lactation depending the breed. For NR, and SR the estimated changes in Differentperiod, approaches areonused in NorFor forSH predicting mobilization or deposition during the mobilization and deposition byFor a variable early lactationchanges EB is below lactation period, dependingare onmodelled the breed. NR, SHEB, andi.e. SRinthe estimated in mobilization 100% indicating are mobilization, in midEB, and late lactation the targetEB EB is is below 100%. The and deposition modelledwhereas by a variable i.e. in early lactation 100% indicating variable EB is calculated empirical equation (Volden, which is mobilization, whereas inusing mid the andfollowing late lactation the target EB is 100%. unpublished), The variable EB is calculated illustrated in Figure 9.3.

NEL ⋅ 100 Days in milk NE _ ma int + NEL _ milk + NEL _ gain + NE _ gest + NEL _ dep − NEL _ mob

2988

NEL _ bal =

2989 2990 2991

9.21]SR breeds. In Figure 9.3. Recommended energy balance (EB) during lactation for NR, [Eq. SH and early lactation EB can be lower than 100% due to mobilization of energy from body reserves. In NEG 100 mid EB should be ⋅greater than 100% in a short period due to deposition of body reserves. [Eq. 9.21] NEGlactation, _ bal =

2992 2993 2994 2995 2996 2997

where NEL_bal and NEG_bal is the energy balancies for cows and growing cattle respectively, %; NEL and NEG is the energy supply from the ration, Equation 8.1 and 8.8 respectively; NorFor – The Nordic feed evaluation system NE_maint is the energy requirement of maintenance, Equation 9.1; NEL_milk is the energy requirement of milk production, Equation 9.2; NEL gain and NEG_gain is the energy requirement for weight gain in primiparous cows and growing cattle, Equation 9.4 and 9.5,

NE _ ma int + NEG _ gain + NE _ gest

91

2973 2972 2974 2973 2975 2974 2976 2975 2977 2976 2978 2977 2979 2978 2980 2979 2981 2980 2982 2981 2983 2982 2984 2983 2985 2984 2986 2985 2987 2986 2987 2988 2988 2989 2990 2989 2990 2991 2991 2992 2993 2992 2994 2993 2995 2994 2996 2995 2997 2996 2998 2997 2999 2998 3000 2999 3000

where NEL_variable is the recommended energy balance; and DIM is days in milk. Figure 9.3 Figure 9.3 For DH, RD and JER default changes in BCS depend on the breed, parity and lactation stage. Thus, theRD lactation period is divided four periods: in the first four weeks it is For DH, and JER default changesinto in BCS depend on breed, parity andpost-calving, lactation stage. assumed 70% of bodyisreserves while is assumed to be mobilized init is Thus, thethat lactation period dividedare intomobilized, four periods: in 30% the first four weeks post-calving, For DH, RD and JER default changes in BCS depend on the breed, parity lactation weeks 5 to 8 post-calving. In week 9are nomobilized, mobilization or deposition is assumed. From weekin10stage. Thus, assumed that 70% of body reserves while 30% is assumed to beand mobilized the lactation period is divided into four periods: inorthe firstis four post-calving, it to is assumed until the end8 of the lactation fixed energy deposition required, which corresponds weeks 5 to post-calving. Inperiod, week 9ano mobilization deposition is weeks assumed. From week 10 thattotal 70% ofofbody reserves are mobilized, whiledeposition 30% is assumed to be mobilized in weeks the energy mobilized inperiod, early lactation (Table 9.7). until the end the lactation a fixed energy is required, which corresponds to 5 to 8 post-calving. week 9-13 no mobilization or deposition is assumed. From week 14 until the end the total energyInmobilized in early lactation (Table 9.7). of the9.7 lactation period, a fixed energy deposition is required, which corresponds to the total energy Table Table 9.7 in early lactation (Table 9.7). mobilized The energy balance is calculated as energy supplied from the feed ration divided by the total NE requirement The is is calculated as energy supplied from from the feed by the total NE total NE Theenergy energybalance balance calculated as energy supplied theration feed divided ration divided by the requirement requirement. NEL ⋅ 100 NEL _ bal = NEL ⋅ 100 NEL _ bal = NE _ ma int + NEL _ milk + NEL _ gain + NE _ gest + NEL _ dep − NEL _ mob  NE _ ma int + NEL _ milk + NEL _ gain + NE _ gest + NEL _ dep − NEL _ mob [Eq. 9.21]

9.20

[Eq. 9.21]

NEG ⋅ 100 NEG _ bal =  NEG ⋅_100 NE _ ma int gain + NE _ gest + NEG _ bal = NE _ ma int + NEG _ gain + NE _ gest

[Eq. 9.21] [Eq. 9.21]

9.21

where NEL_bal and NEG_bal is the energy balancies for cows and growing cattle respectively, where NEL_bal and NEG_bal is the energy balancies for cows and growing cattle respectively, %; %; NEL and NEG is NEG_bal the energyissupply frombalancies the ration,for Equation 8.1growing and 8.8 cattle respectively; where NEL_bal and the energy cows and respectively, NEL and NEG the energy supplyoffrom the ration,Equation Equation 8.1 and and8.8 8.8respectively; respectively; NE_maint is NEG the is energy maintenance, 9.1; is the energy NE_maint is %; NEL and is the requirement energy supply from the ration, Equation 8.1NEL_milk the energyisrequirement of maintenance, Equation 9.1; NEL_milk is the energy of milk requirement of production, Equation 9.2; NEL gain and NEG_gain is the energy NE_maint themilk energy requirement of maintenance, Equation 9.1; NEL_milk is therequirement energy production, Equation 9.2; NEL_gain and NEG_gain is the energy requirement for weight gain in requirement of formilk weight gain in primiparous cows andgain growing cattle, Equation 9.4 and 9.5, production, Equation 9.2; NEL and NEG_gain is the energy primiparousfor cows andisgain growing cattle, Equation 9.4in and 9.5,and respectively; NE_gest the requirement respectively; NE_gest the requirement of cows gestation cows heifers, Equation 9.3;is requirement weight in primiparous and growing cattle, Equation 9.4 and 9.5, of gestation cows and Equation 9.3; and NEL_dep NEL_mob is and the energy NEL_dep andin NEL_mob isheifers, the energy deposited mobilised, 9.17 9.18 respectively; NE_gest is the requirement of gestation in cowsand andEquation heifers, Equation 9.3; deposited and respectively, NEL_dep and NEL_mob is includedand in the equation only if BCS is reported. NEL_dep NEL_mob isand the energy deposited mobilised, 9.17ischange and 9.18 mobilised,and Equation 9.17 9.18 respectively, NEL_dep andEquation NEL_mob included in the equation respectively, NEL_mob is included in the equation only if BCS change is reported. only if BCSNEL_dep change isand reported. 113 113

Table 9.7. Estimated mobilization1 and deposition2 during lactation for the DH, RD and JER breeds. Breed

DH DH RD RD DJ DJ

Parity

1 >1 1 >1 1 >1

Mobilization1, Mobilisation, MJ NEL/d BCS/lactation

0.45 0.60 0.35 0.50 0.45 0.60

Deposition, MJ NEL/d

1-4 wpc

5-8 wpc

9-13 wpc

14 wpc→

16.7 22.3 13.0 18.6 12.6 16.7

7.2 9.6 5.6 8.0 5.4 7.2

0 0 0 0 0 0

3.4 4.5 2.6 3.7 2.5 3.4

1 Mobilization is assumed to take place during the first eight weeks post-calving (wpc): 70% in the first 4 wpc and 30%

in 5 to 8 wpc. E.g. an older DH cow is assumed to mobilize 0.60 units of BCS in total, i.e. 0.42 BCS units during the first 4 wpc and 0.18 BCS units during 5 to 8 wpc. 2 Deposition is assumed to take place from 14 wpc and throughout lactation with a fixed amount of energy per day. The sum of this energy corresponds to the energy lost in the first 8 wpc using a standard lactation cycle of 340 days.

92 

NorFor – The Nordic feed evaluation system

9.2 Protein While the energy requirements in NorFor are obtained from feed evaluation systems used in France and the Netherlands, NorFor has developed its own equations for the AATN and PBVN requirements for the production of milk and growth, based on Nordic research data. 9.2.1 Maintenance The AATN requirement for maintenance in cows and growing cattle is calculated according to NRC (1985), with modifications to account for endogenous losses, as described in Sections 7.2.3 and 7.4. The AATN requirement for maintenance is based on N secreted in urine, N used for hair and skin growth, and the secretion of endogenous N in the faeces. The AATN requirement for maintenance is illustrated in Figure 9.4 and Table 9.8, and is calculated according to Equation 9.22. The first term estimates basal AATN requirement, the second term the AATN requirement for hair and skin, and the third term estimates endogenous losses of AATN as an indirect function of DMI. Thus, the AATN requirement for maintenance increases when DMI increases.

AATN requirement (g/d)

600

Jersey DH

550 500 450 400 350 300 250

12

14

16

18

20 DMI (kg)

22

24

26

Figure 9.4. Estimated AATN requirement (g/d) for maintenance with respect to BW and DMI. Examples of a Jersey cow of 440 kg and a DH of 640 kg. The ration was a TMR with a fixed ratio between forage and concentrates. Table 9.8. AATN requirements (g/d) for maintenance depending on BW and DMI. BW (kg)

400 500 600 700

DMI (kg/d) 8 kg forage and 0 kg concentrates

8 kg forage and 8 kg concentrates

8 kg forage and 16 kg concentrates

199 203 207 210

346 339 337 336

499 490 484 478

NorFor – The Nordic feed evaluation system

93

3009 3013 3010 3014 3011 3012 3015 3013 3016 3017 3014 3018 3015 3019 3016 3020 3017 3021 3018 3022 3019 3023 3020 3024 3021 3025 3022 3026 3023 3027 3024 3028 3025 3029 3026 3030 3027 3031 3028 3029 3032 3030 3033 3031 3034 3032 3033 3035 3036 3034 3037 3038 3035 3039 3036 3040 3037 3038 3039 3040

N

N

and skin and the secretion of endogenous N in the faeces. AATN requirement for Thus, thegrowth, AATN requirement for maintenance increases when DMIThe increases. maintenance is illustrated in Figure0.9.4 and Table 9.8, and is calculated according to Equation 9.22. 0 . 5 6 r _ outOM ⋅ 0.03 ⋅ 0.5 ⋅ 3 ⋅ 0.4 2.75 ⋅ BW 0.2 ⋅ BW AATN int = estimates basal + + + si _the outOM ⋅ 0N .025 ⋅ 0. 5 the second term AAT requirement for hair The first AATN requirement, − materm 0.67 0.67 0.67 and skin, and the third term estimates endogenous losses of AATN as an indirect function of DMI. [Eq. 9.22] Thus, the AATN requirement for maintenance increases when DMI increases. .5 daily 0.6 rrequirement where AATN_maint is0the AAT g/d; BW is the weight of the ⋅ 0.5maintenance, ⋅ 3 ⋅ 0. 4 2.75 ⋅ BW 0.2 ⋅ BW N _ outOM ⋅ 0.03for  9.22 + + + si _ outOM ⋅ 0.025 ⋅ 0.5 AATN − ma int = animal, kg; r_outOM 0.67 is the amount 0.67 of OM leaving 0.67the rumen and entering the small intestine, [Eq. 9.22] Equation 7.36; si_outOM is the amount of OM leaving the small intestine and entering the large where AAT is the AATutilization for maintenance, BW is the0.03 weight of the N_maint N requirement intestine, Equation 7.47; 0.67daily is a fixed coefficient of AAT N for g/d; maintenance; animal, kg; r_outOM is the amount of OM leaving the rumen and entering the small intestine, where AAT _maint of is the daily AAT g/d;the BWsmall is theintestine weight from of the refers to theNamount endogenous CP, which is 3%for of maintenance, the OM entering N requirement Equation isinclude the amount of OMthe leaving the intestine andintestine, entering animal, kg;7.36; is the to amount of all OMendogenous leaving rumen andsmall entering the small the rumen; 3r_outOM is a si_outOM multiplier CP produced in the small intestine; 0.5 is the large intestine, Equation 7.47; a fixed utilization coefficient of forentering maintenance; 0.03 refers Equation si_outOM is0.67 the is amount OM leaving the small the AA-N7.36; proportion of endogenous CP;of0.4 is used because 60%intestine ofAAT endogenous CP is the large Nand intestine, Equation 7.47; 0.67toisthe aCP, fixed utilization ofwhich AAT maintenance; 0.03 from the reabsorbed; and of 0.025 refers amount ofisendogenous is 2.5% ofsmall the OM entering to the amount endogenous which 3%coefficient of theCP, OM entering the intestine N for refers to 3the of endogenous CP,allwhich is 3% of the entering from the large intestine from the to small intestine. rumen; isamount a multiplier include endogenous CP OM produced in the thesmall smallintestine intestine; 0.5 is the the rumen; 3 is a multiplier to include endogenous CP produced the small intestine; 0.5 is AA-N proportion of endogenous CP;all0.4 is used because 60% ofinendogenous CP is reabsorbed; and the AA-N of endogenous CP; 0.4 is used because 60%ofofthe endogenous CP isthe large intestine Figure 9.4proportion 0.025 refers to the amount of endogenous CP, which is 2.5% OM entering reabsorbed; and intestine. 0.025 refers to the amount of endogenous CP, which is 2.5% of the OM entering from the small the large Table 9.8intestine from the small intestine.

9.2.2 Lactation Figure 9.4

9.2.2 Lactation TheAAT AAT requiredforfor milk protein depends on milk protein yieldtheand the utilization of the AATN Table 9.8NNrequired milk protein depends on milk protein yield and utilization of the AAT The N available for milk protein production. The AAT required for milk production is calculated as: for milk production is calculated as: available for milk protein production. The AATN required N MPY AATN _ Eff

9.2.2 AATNLactation _ milk =

[Eq. 9.23]  9.23 protein depends on milk protein yield and the utilization of the AAT The AATN required for milk N 100 available for milk protein production. The AATN required for milk production is calculated as: where AATN_milk is the AATN required for milk production, g/d; MPY is the daily milk protein whereEquation AAT _milkMPY is the AATN required for milk g/d; MPY is the the synthesis daily milk yield, is the efficiency (%)production, at which AAT used9.23] in of protein N is [Eq. AATN _ milkN= 3.4; and AATN_Eff yield,protein, Equation 3.4; and AAT Eff AATN_Eff is the efficiency (%) at which AATN is used in the synthesis of milk Equation 9.24. N_ 100 milk protein, Equation 9.24.

where AATN_milk is the AATprotein for milkisproduction, g/d; MPY is theetdaily milk protein al., 1988; Subnel et The utilization of AA for milk production highly variable (MacRae N required is the efficiency (%) at AAT used in the synthesis yield, Equation 3.4; andfor AAT al ., 1994), and the DVE protein evaluation system is which the only system that indirectly takes of N_Eff N is The utilization ofDutch AA milk protein production is highly variable (MacRae et al., 1988; Subnel et milk protein,and Equation 9.24.DVE protein evaluation system is the only system that indirectly takes into al., 1994), the Dutch account this variability in efficiency. Subnel 114 et al. (1994) showed that the efficiency of milk protein et almilk ., 1988; Subnel et The utilization of AA for milk protein highly variablefor (MacRae production is dependent on the ratioproduction of MP andisNEL available milk and protein production. al., 1994), and the Dutch DVE protein evaluation system is the only system that indirectly takes

When developing the NorFor system we also wanted to account for variations in efficiency when calculating the milk protein requirement, in order to improve the modelling of protein utilization in 114 dairy cows (Van Straalen, 1994). Hence, a dataset compiled from 16 studies with Norwegian dairy cows including 57 treatments (Thuen, 1989; Volden, 1990; Minde and Rygh, 1997; Prestløkken, 1999; Volden, 1999; Volden and Harstad, 2002; Randby, 2003; Mydland, 2005; Schei et al., 2005;

Table 9.9. Descriptive statistics of the dataset1 used for developing the equation to predict the efficiency of milk protein production.

DMI (kg/d) Milk (kg/d) ECM (kg/d) Milk protein (g/d) Milk protein (%) PBVN (g/kg DM) AATN/NEL (g/MJ) AATN efficiency (%)

Average

Minimum

Maximum

SD

16.5 24.7 25.3 783 3.17 28 15.7 68.5

9.9 12.5 13.0 413 2.93 -3 13.5 44.7

20.1 30.7 31.5 1,031 3.54 68 24.7 80.8

2.2 3.5 3.8 120 0.12 16 1.7 6.7

1

(Thuen, 1989; Volden, 1990; Minde and Rygh, 1996; Prestløkken, 1999; Volden, 1999; Volden and Harstad, 2002; Randby, 2003; Mydland, 2005; Schei et al., 2005; Kjos, unpublished data).

94 

NorFor – The Nordic feed evaluation system

Kjos, unpublished data) was used to develop an equation to predict AATN utilization for milk protein production (Table 9.9). The efficiency of AATN (AATN_Eff) for milk protein production was calculated by dividing milk protein yield by the amount of AATN available for milk production. The AATN available for milk production was calculated as the total AATN supply minus the AATN requirement for maintenance and pregnancy ± AATN for deposition or mobilization. The ratio of AATN/NEL for milk production was calculated from the amount of AATN available for milk protein production and the NEL requirement for milk production (see Equation 9.25). The dataset showed a high correlation between AATN_Eff and AATN_NEL (r2=0.89 and RMSE=2.3) and the relationship was curvilinear, as shown in Figure 9.5. In NorFor, the following equation is used to calculate the AATN_Eff: AATN_Eff = 189.4 – 11.14 ∙ AATN_NEL + 0.2150 ∙ AATN_NEL2

9.24

where AATN_Eff is the efficiency of AATN for milk protein production, %; and AATN_NEL is the ratio between AATN and energy available for milk production, g/MJ, Equation 9.25. The ratio between MP and energy available for milk protein production in Equation 9.24 is calculated as: AATN_NEL =

Avail_AATN NEL_milk



9.25

where AATN_ NEL is the ratio between AATN and the energy available for milk production, g/MJ; NEL_milk is the energy required for milk production, Equation 9.2; and Avail_AATN is the amount of AATN available for milk production, Equation 9.26. The amount of AATN available for milk protein production is calculated as: Avail_AATN = AATN – AATN_maint – AATN_gest + AATN_mob – AATN_dep

9.26

where Avail_AATN is the amount of AATN available for milk production, g/d; AATN is the AATN supplied from the feed ration, Equation 8.11; AATN_maint is the AATN required for maintenance, Equation 9.22; AATN_gain is the AATN requirement for growth in primiparous cows, Equation 9.32; AATN_gest is the AATN requirement for gestation, Equation 9.42; AATN_mob is the AATN

AATN efficiency (%)

90 80 70 60 50 40 30 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 AATN, (g/MJ NEL)

Figure 9.5. AATN efficiency (%) in dairy cows in relation to AATN supply based on the trials described in Table 9.9 (r2=0.89 and RMSE=2.3). AATN supply is defined as AATN available for milk production per MJ NEL available for milk production. NorFor – The Nordic feed evaluation system

95

3081 3088 3086 3082 3089 3083 3087 3090 3084 3088 3091 3085 3089 3092 3086 3090 3093 3087 3091 3094 3088 3092 3095 3089 3093 3096 3094 3097 3090 3095 3098 3091 3096 3092 3099 3097 3093 3100 3098 3094 3101 3095 3099 3096 3102 3100 3097 3101 3103 3098 3104 3102 3099 3105 3103 3100 3106 3101 3104 3107 3105 3102 3106 3103 3108 3107 3104 3109 3110 3105 3108 3106 3109 3111 3107 3110 3112

where Avail_AAT N is the amount of AATN available for milk production, g/d; AATN is the AATN Figure 9.5 Equation 9.41.the feed ration, Equation 8.11; AATN_maint is the AATN required for maintenance, supplied from Equation 9.22; AATN_gain is the AATN requirement for growth in primiparous cows, Equation withrequirement increases infor AAT The in AATN_Eff N supports the commonly held assumption that 9.32;decrease AAT AAT gestation, Equation 9.42; AATN_mob is the AATN N Figure 9.5 N_gest is the returns diminish with increases in MP intake, as observed MacRae al. (1988)in and Subnel et is the AATNetdeposition body mass, from mobilized body mass, Equation 9.40 and AATN_dep by al. (1994).9.41. However, in contrast to the results of Subnel et al. (1994), neither the level of milk Equation withEquation increases in AAT the commonly helddeposition assumption The AAT N_Eff N supports production norinthe feeding level had an effect onand the AATNN_dep _Eff. The AAT was closelyinthat fromdecrease mobilized body mass, 9.40 AAT is the AAT body mass, N_Eff N returns diminish with increases in MP intake, as observed by MacRae et al . (1988) and Subnel et related to AAT _NEL, which supports the observations of Subnel et al . (1994) and Van Straalen Equation 9.41.N Figure 9.5 al . (1994). to the results of Subnel et al. the (1994), neitherofthe level of milk (1994), whoHowever, observedina contrast strong negative relationship between efficiency milk protein yield production nor the feeding levelavailable had an effect on the AATproduction. was closely N_Eff. The AAT N_Eff and the ratio of MP and energy for milk protein These results demonstrate The decrease in AATN_Eff with increases in AATN supports the commonly held assumption that related to AAT _NEL, which supports thein observations of Subnel et al. (1994) Straalen NAAT that the effect of extraNMP supply is dependent on Nthe amount of commonly energy available forVan milk _Eff with increases AAT supports the heldand that The decrease in returns diminish with increases in MP intake, as observed by MacRae etassumption al. (1988) and Subnel (1994), who observed aagrees strong negative relationship between the efficiency of milk protein yield production, which alsoincreases with Hvelplund and Madsen (1990). returns diminish with in MP intake, as observed by MacRae et al . (1988) and Subnel et of milk et al. (1994). However, in contrast to the results of Subnel et al. (1994), neither the level and the ratio of MP and energy available for milk proteinetproduction. These results demonstrate al. (1994). However, in contrast to the results of Subnel al. (1994), neither the level of milk production nor the feeding level had an effect on the AAT _Eff. The AAT _Eff was closely related that the effect extra MP supply is dependent onthe theAAT amount of available for milk N energy production norofthe feeding level had an effect on The AAT N_Eff. N_EffNwas closely Based on the AAT to AAT _NEL, which available supports for milk the production observations and of the Subnel AAT _Eff, et al. the (1994) milk protein and Van production Straalen (1994), N N production, which also agrees with Hvelplund and Madsen (1990). related toN AAT N_NEL, which supports the observations of Subnel et al. (1994) and Van Straalen can calculated as: a strong whobeobserved a strong negative relationship between the efficiency of milk protein yield and the ratio (1994), who observed negative relationship between the efficiency of milk protein yield of MP and energy available for milk protein production. These These resultsresults demonstrate that the effect 2 and the ratio of MP and energy available for milk protein production. demonstrate Pr oteinon_ the responseN=available Avail_AAT · 189.4 - 11.14·AAT _NEL + N0.2150·AAT _NEL Based forNmilk production and the AAT _Eff, thefor milk protein production Nmilk of extra MP AAT supply dependent the amount ofNamount energy available production, which also that the effect of extraisMP supply ison dependent on the of energy available for milk can be calculated as: [Eq. 9.25] agrees withwhich Hvelplund and Madsen (1990).and Madsen (1990). production, also agrees with Hvelplund Pr otein _ response = Avail_AATN · 189.4 - 11.14·AATN _NEL + 0.2150·AATN _NEL2 Based on the AAT available for milk the the AAT the milk protein production The maximum milk protein production canproduction be found byand putting first_Eff, derivative of this equation [Eq. 9.25] Based on the AATN N available for milk production and the AATN_Eff,Nthe milk protein production to 0: be calculated as: can can be calculated as:

3108 3113 3111 3112 3114 3109 3110 3113 3115 3116 3114 3111 3120 3117 3112 3115 3121 3118 3116 3122 3119 3113 3117 3123 3114 3118 3124 3119 3125 3115 3126 3116 3127 3117 3118 3128 3119 3129 3130 3131 3132

9.29

(

)

(

)

(

)

2 this The maximum milk=protein production can be found by putting the first derivative of Pr otein _ response Avail_AAT  equation N · 189.4 - 11.14·AATN _NEL + 0.2150·AATN _NEL 2 to 0: otein _ response ⎛ Avail _ AATN ⎞ Avail _ AATN d Pr 9.25] ⎟ = [Eq. = 189 .4 − 22 .93 ⋅ + 0.6642 ⋅ ⎜ 0

9.27

⎜ NEL _ milk ⎟ dAvail _ AATN NEL _ milk The maximum milk protein production can be found by putting the first derivative of this equation to 0: ⎝ ⎠

2 [Eq. 9.28] ⎛ Avail Avail AAT _ AAT d Pr maximum otein _ response The milk protein production can_be found putting the first derivative of this equation N +by N ⎟⎞ = 0 = 189 .4 − 22 .93 ⋅ 0.6642 ⋅ ⎜⎜  ⎟ to 0: dAvail _ AATN NEL _ milk ⎝ NEL _ milk ⎠ [Eq. 9.28] The obtained as: as: The maximum maximumpoint pointisisthen then obtained

2 ⎛ Avail _ AATN ⎞ _ AATN d Pr otein _ response 22.93 .93⋅ Avail ⎞ ⎛ b ⎞ .4⎛ −−22 ⎟ = [Eq. + 0.6642 ⋅ ⎜⎜ 0 9.29] = −⎜ = 17 .3  AAT N _ NEL = − ⎜ = ⎟189 ⎟ ⎟ ThedAvail maximum _ AATpoint NEL _ milk ⋅ 0 .6642 ⎠as: ⎝ NEL _ milk ⎠ N⎝ 2 ⋅ ais⎠ then⎝ 2obtained

9.28

[Eq. 9.28]

This means that⎛ the milk level of 17.3 b maximum ⎛ − 22 .93 ⎞ protein production is achieved at an AAT ⎞ N_NEL [Eq. 9.29] AAT means _ NEL 17 .3production is achieved at an AATN_NEL = −the ⎜ maximum ⎟ = − ⎜ milk ⎟ =efficiency This that protein level of 17.3 NNEL, g/MJ which corresponds to an of 61%. However, although the maximum is 17.3 g/ ⎝ 2⋅a ⎠ ⎝ 2 ⋅ 0 .6642 ⎠ g/MJ NEL,the which corresponds to an efficiency of 61%. implying However, that although the maximum is 17.3 MJ NEL, first derivative equation is curvilinear, increasing the AAT _NEL The optimal maximum pointsupply is then obtained as:is curvilinear, N to supply The AAT can be described as the levelimplying at whichthat milkincreasing protein production _NEL g/MJ NEL, the first equation the AAT Nbegins N derivative by one unit from 16.3 to 17.3 will only marginally increase milk protein production (Figure 9.6), This means thatunit thefrom maximum is achieved at AATN_NEL level of 17.3 supply. Therefore, for milk anan efficient output of milk be only marginally increased extraprotein AAT supply by one 16.3 by tomilk 17.3 will only marginally increase protein production (Figure Nproduction but if AAT _NEL below 15 g AAT /MJ NEL then the marginal response is almost linear. The b isthat 22 .an 93 −AAT ⎛ ⎞ ⎛ ⎞ N N g/MJ NEL, which corresponds to efficiency of 61%. However, although the maximum is 17.3 _NEL is not lower than 15 and not higher than 17.3 g/MJ. protein it is important the 15Ng ⎟AAT almost 9.6), but_ NEL if AAT [Eq. 9.29]linear. AAT N = 17 .N3/MJ NEL then the marginal response is = −N⎜_NEL ⎟ =is−below ⎜ optimal AAT supply can be described as the level at which milk protein production begins to be 2 a 2 0 . 6642 ⋅ ⋅ ⎝ derivative ⎠and assessments, ⎝ equation ⎠ is g/MJ NEL, theNresults first implying that increasing the AAT N_NEL Based on these thecurvilinear, NorFor system has no fixed AATN requirement for 116 onlyprotein marginally by extrawill AAT supply. Therefore, for an efficient output of milk protein supply by one unitincreased from 16.3 tooptimizing 17.3 only marginally increase milk protein production (Figure milk production. When diets for dairy cows, a minimum value of 15 g N it is means important that theisAAT _NEL iscorresponds not lower than 15 notan than 17.3 g/MJ. AAT _NEL below 15 g AAT /MJ NELto then theand marginal response isminimum almost linear.Based on 9.6), but /MJif NEL recommended, which AAT ofhigher 70%. This AAT N N N N_Eff This that is the maximum milk protein production isanachieved at AAT N N_NEL level of 17.3 theseNEL, results andiscorresponds assessments, the NorFor has no fixed AAT requirement milk protein recommendation also based on fact that itsystem is seldom economically efficient to supply for rations g/MJ which to the an efficiency of 61%. However, although the maximum is N 116cost of feed protein compared to energy. 17.3 with high protein content, due to the relatively high production. When optimizing diets for dairy cows, a minimum value of 15 g AAT _NEL g/MJ NEL, the first derivative equation is curvilinear, implying that increasing the AAT N N/MJ NEL is recommended, which to an AAT 70%. This recommendation supply by one unit from corresponds 16.3 to 17.3 will only marginally milkminimum protein production (Figure is also N_Eff ofincrease AAT isisbelow 15 geconomically AATN/MJ NEL then the marginal response is almost linear. content, 9.6), but ifthe based on fact that it seldom efficient to supply rations with high protein N_NEL Figure 9.6 due to the relatively high cost of feed protein compared to energy. 116 The AATN balance (AATN_bal) can also be used as a target variable when optimizing diets in The AAT (AATN_bal) can also be used as a target variable when optimizing diets in practice, using the equation: N balance

3133

practice, using the equation:

3134

AATN _ bal =

3135

[Eq. 9.30] where AATN_bal is the AATN balance, %; AATN is the AATN supply from the feed ration, Equation 8.11; AATN_maint is the AATN required for maintenance, Equation 9.22; AATN_gain is the AATN where AAT is the in AAT AATNEquation is the AAT the feed ration, N_bal N balance, %; N supply required for growth primiparous cows, 9.32; AATfrom N_gest is the AATN required for _maint is the AAT required for maintenance, Equation 9.22; AAT is Equation 8.11; AAT N N N_gain gestation, Equation 9.42; AATN_mob is the AATN from mobilized body mass, Equation 9.40; the AATN required for growth in primiparous cows, Equation 9.32; AATN_gest is the AATN AATN_dep is the AATN deposition in body mass, Equation 9.41; AATN_milk is the AATN required required for gestation, Equation 9.42; AATN_mob is the AATN from mobilized body mass, for milk 9.40; protein, Equation 9.23; and deposition R is the relative response _dep is the AAT in bodymarginal mass, Equation 9.41;coefficient, AAT _milkEquation is the 9.31. Equation AAT

3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147

AATN ⋅ 100 ⋅ R  AATN _ ma int+ AATN _ gain + AATN _ gest − AATN _ mob + AATN _ dep + AATN _ milk

N

N

9.30

N

AATN required for milk protein, Equation 9.23; and R is the relative marginal response coefficient, Equation 9.31.

96Equation  NorFor Theprotein Nordic feedtoevaluation system In 9.30 the R function describes the actual response in – milk relative the maximum milk protein response, which is achieved at 17.3 g AATN_NEL/MJ.

The optimal AATN supply can be described as the level at which milk protein production begins to be only marginally increased by extra AATN supply. Therefore, for an efficient output of milk protein it is important that the AATN_NEL is not lower than 15 and not higher than 17.3 g/MJ. Based on these results and assessments, the NorFor system has no fixed AATN requirement for milk protein production. When optimizing diets for dairy cows, a minimum value of 15 g 5.0 AATN/MJ NEL is recommended, which corresponds to an AATN_Eff of 70%. This minimum recommendation is also based on the fact that it is seldom economically efficient to supply rations 0.0 with high protein content, due to the relatively high cost of feed protein compared to energy.

3128 3129 3130 3131 3132

Figure 9.6

Marginale response in milk protein yield ( %)

3120 3121 3122 3123 3124 3125 3126 3127

-5.0

-10.0 The AATN balance (AATN_bal) can also be used as a target variable when optimizing diets in practice, using the equation: -15.0

3133

-20.0

AATN ⋅ 100 ⋅ R AAT-25.0 N _ ma int+ AATN _ gain + AATN _ gest − AATN _ mob + AATN _ dep + AATN _ milk

3134

AATN _ bal =

3135 3136 3137 3138 3139 3140 3141 3142 3143

[Eq. 9.30] -30.0 13 14AAT is15the AAT 16 supply17 19 where AATN_bal is the12AATN balance, %; from the18 feed ration, N N for maintenance, Equation 8.11; AATN_maint is the AATN required AAT (g/MJ NEL)Equation 9.22; AATN_gain is N the AATN required for growth in primiparous cows, Equation 9.32; AATN_gest is the AATN is theprotein AATN from mobilized mass,to AAT supply. required for gestation, Equation 9.42;response AATN_mob Figure 9.6. Estimated marginal of milk production inbody relation N Equation 9.40; is AAT N_dep is the AATN deposition in body mass, Equation 9.41; AATN_milk is the AATN supply defined as the AATN available for milk production per MJ NEL available for milk AATN required for milk protein, Equation 9.23; and R is the relative marginal response coefficient, production. The data are from the trials described in Table 9.9. Equation 9.31.

3144 3145 3146

In Equation 9.30 the R function describes the actual response in milk protein relative to the In Equation 9.30 the R function describes the actual response in milk protein relative to the maximum maximum milk protein response, which is achieved at 17.3 g AATN_NEL/MJ.

3147 3148 3149

milk protein response, which is achieved at 17.3 g AATN_NEL/MJ. R = −4.165 − 0.1402 ⋅ AATN _ NEL + 2.662 ⋅ ln(AATN _ NEL) 

[Eq. 9.31]

9.31

where R is the response factor for milk protein production, AATN_NEL is the AATN and NEL ratio

3150 3151

where R isfor themilk response factor forEquation milk protein production, AAT the AATN and NEL available production, 9.25. During the dryN_NEL periodisR=1. ratio available for milk production, Equation 9.25. During the dry period R=1.

3152 3153 3154 3155 3156 3157 3158 3159 3160

is between 95 and In theory, an AATan ofN100 The value of AAT Therecommended recommended value of AAT is between 95103%. and 103%. In theory, AAT _bal%of 100% N_bal_bal N_bal N should toto thethe maximum milkmilk protein response, i.e. 17.3 AATgNAAT /NEL and an and an AAT _bal shouldcorrespond correspond maximum protein response, i.e.g17.3 /NEL N N of 95% should correspond 15.0 g /NEL. AATN/NEL. However, thisrarely is rarely case, AAT N_bal of 95% should correspond to 15.0 to g AAT However, this is thethe case, mainly because N mainly because the AAT _bal is directly influenced by the actual protein content in milk (see N the AAT _bal is directly influenced by the actual protein content in milk (see Equation 9.30), which Equation N 9.30), which is not the case for AATN/NEL. Thus, the recommendation for AATN/NEL is is not the case for AATN/NEL. Thus, the recommendation for AATN/NEL is based on an AATN_Eff of 31.7 g/kg. This based on an AATN_Eff derived from data with an average milk protein content derived from data with an average milk protein content of 31.7 g/kg. This means that in late lactation, means that in late lactation, where the protein content in milk is high, the AAT N/NEL where the protein content in Nmilk high, thebeAAT of 15.0 g AATN/NEL /NELiscan easily achieved, butrecommendation AATN_bal will often be below recommendation of 15.0 g AAT N/NEL canlower easily(95%) be achieved, but AAT the recommended limit. N_bal will often be below the lower (95%) recommended limit.

3161 3162 3163 3164 3165 3166

9.2.3 Growth Growth 117 The AATN requirement for growth in primiparous cows is based on the requirements of growing heifers (Robelin and Daenicke, 1980). The requirement is calculated from the retention of body The AAT N requirement for growth in primiparous cows is based on the requirements of growing protein the efficiency of AATN1980). utilization growth (Volden, 2001) using thethe following heifers and (Robelin and Daenicke, Thefor requirement is calculated from retention of body equation: protein and the efficiency of AAT utilization for growth (Volden, 2001) using the following equation:

3167

AATN _ gain =

3168 3169 3170 3171 3172 3173 3174

where AATN_gain is the daily AATN requirement for growth in primiparous cows, g/d; gain_prot is the daily protein retention, Equation 9.8; BW_mat is the mature body weight, Table 3.2; and the term beginning with 104.6·e is the utilisation equation of AATN for growth in primiparous cows.

N

gain _ prot ⋅1000 600 ⎛ − 0.0019⋅BW⋅ ⎜ BW _ mat ⎜104 .6 ⋅ e ⎜⎜ ⎝

⎞ ⎟ ⎟ ⎟⎟ ⎠



[Eq. 9.32]

9.32

100

NorFor – The Nordic feed evaluation system

The AATN requirement for growth in primiparous cows is shown in Table 9.10.

97

3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179

100

where AATN_gain is the daily AATN requirement for growth in primiparous cows, g/d; gain_prot is the daily protein retention, Equation 9.8; BW_mat is the mature body weight, Table 3.2; and the term beginning with 104.6·e is the utilisation equation of AATN for growth in primiparous cows.

where AAT _gain is the daily AAT requirement for growth in primiparous cows, g/d; gain_prot is the daily protein retention, Equation 9.8; BW_mat is the mature body weight, Table 3.2; and the term beginning with 104.6·e is the utilisation equation of AATN for growth in primiparous cows. Table 9.10 The AATN requirement for growth in primiparous cows is shown in Table 9.10.

N N The AATN requirement for growth in primiparous cows is shown in Table 9.10.

The AATN required for growth in growing cattle depends on the amount of protein deposited and Theefficiency AATN required for growth in growing depends onAAT the amount offor protein deposited and the the with which the available AATN cattle is deposited. The growth is N required efficiencyusing with the which the available AATN is deposited. The AATN required for growth is calculated calculated following equation:

3180

using the following equation:

3181

AATN _ gain =

3182

gain _ prot AATN _ Eff 100

[Eq. 9.33]



9.33

where AAT _gain is the AAT requirement for growth in growing cattle, g/d; gain_ prot is the

3183 3184 3185

N where AATNN _gain the AAT N requirement for growth in growing cattle, g/d; gain_ prot is the daily retention of is protein, Equation 9.8; and AATN_Eff is the efficiency at which AATN is used for daily retention of protein, Equation 9.8; and AATN_Eff is the efficiency at which AATN is used for growth, Equation 9.34. growth, Equation 9.34.

3186 3187 3188 3189 3190 3191 3192 3193 3194

Thus, thethe AAT for growth is utilized with variable due to the due to the N available Thus,ininNorFor NorFor AAT for growth is utilized withefficiency, variable efficiency, N available i.e. the same rationale that was used in relation to diminishing returns with increases in MP intake, diminishing returns with increases in MP intake, i.e. the same rationale that was used in relation to milk production in dairy cows (see section 9.2.2). A dataset compiled from 11 Norwegian and six milk production in dairy cows (see Section 9.2.2). A dataset compiled from 11 Norwegian and six Swedish experiments with bulls involving 120 treatments (Homb, 1974; Hole, 1985; Olsson and Swedish experiments involving treatments (Homb, 1974; Hole, 2000b; 1985; Olsson and Lindberg, 1985; Olsson, with 1987;bulls Johansen, 1992; 120 Skaara, 1994; Randby, 2000a; Randby, Lindberg, 1985; Olsson, 1987; Johansen, 1992; Skaara, 1994; Randby, 2000a,b; Berg and Volden, Berg and Volden, 2005; Volden and Berg, 2005; Berg, unpublished; Havrevoll, unpublished; 2005; Volden and Berg, 2005; Berg, an unpublished data; Havrevoll, unpublished Olsson, unpublished) was used to develop equation to predict AATN efficiency for growthdata; Olsson, unpublished data)variation was used develop an equation to predict AAT for growth (Table 9.11). could be explained with high accuracy (r2 = 0.97 (Table 9.11). The in to AAT N efficiency N efficiency TheRMSE variation in AAT efficiency could be explained with using high accuracy (r2=0.97 and RMSE=2.9) and = 2.9) from NBW, ADG and the ratio of AATN/NEG the following equation:

3195 3196 3196 3197 3197 3198 3198 3199 3199 3200 3200 3201 3201 3202 3202 3203 3203 3204 3204 3205 3205 3206 3206 3207 3207 3208 3208 3209 3209 3210 3210 3211 3211 3212 3212 3213 3213 3214 3214 3215 3215 3216 3216 3217 3217 3218 3218 3219 3219 3220 3220 3221 3221 3222 3222 3223 3223 3224 3224 3225 3225

from BW, ADG and the ratio of AATN/NEG using the following equation: ADG ⎞ ⎛ ⎜⎛1.88 − 0.03821⋅ AATN _ NEG − 0.00176 ⋅ BW − 0.2283 ⋅ ADG ⎟⎞ ⎟ AATN _ Eff = ⎜⎜1.88 − 0.03821⋅ AATN _ NEG − 0.00176 ⋅ BW − 0.2283 ⋅ 1000 1000 ⎟⎟ ⋅⋅100 AATN _ Eff = ⎜ 100 ⎜ + 0.0000019014 ⋅ BW1.83 + 0.000000016 ⋅ AAT _ NEG 5 ⎟ ⎜⎝ + 0.0000019014 ⋅ BW1.83 + 0.000000016 ⋅ AATN _ NEG 5 ⎟⎠ N ⎝ ⎠

[Eq. 9.34]

[Eq. 9.34]

9.34

118

whereAAT AAT _Eff _Effisisthe theefficiency efficiency with which AAT is used for growth, %;isBW the weight of the where with which AAT is used for growth, %; BW the is weight of where AATNNN_Eff is the efficiency with which AATNN isNused for growth, %; BW is the weight of the animal, kg; ADGisisthe theaverage average daily daily live weight gain, g/d and AAT is the ratio animal, kg; ADG live weight gain, g/d and AAT _NEG is the ratio between N_NEG N the animal, kg; ADG is the average daily live weight gain, g/d and AATN_NEG is the ratio available for growth, Equation between AAT N and energy AATN and energy available for growth, Equation 9.35.9.35. between AAT N and energy available for growth, Equation 9.35. The and energy available for growth is calculated as: as: Theratio ratiobetween betweenMP MP and energy available for growth is calculated The ratio between MP and energy available for growth is calculated as: AAT − AATN _ ma int− AATN _ gest AATN _ NEG = AATN N − AATN _ ma int− AATN _ gest  AATN _ NEG = NEG_ gain NEG_ gain

[Eq. 9.35] [Eq. 9.35]

9.35

where AATN_NEG is the ratio between AATN and energy available for growth, g/MJ; AATN is the the ratio between for growth, g/MJ; AATN ison theBW and where Table AAT 9.10.N_NEG AATN isrequirement (g/d) AAT for weight gain available in primiparous cows depending N and energy the feed ration, Equation 8.11; AAT _maint is the AAT required for AATN supplied from AAT from the feed ration, Equation 8.11; AATNN_maint is the AATNN required for N supplied mature BW. maintenance, Equation 9.22; AATN_gest is the AATN required for gestation in heifers, Equation maintenance, Equation 9.22; AATN_gest is the AATN required for gestation in heifers, Equation 9.42; and NEG_gain is the amount of energy used for growth, Equation 9.5. 9.42; and NEG_gain the kg amount of energy used for growth, Equation 9.5. BW mature (kg) is BW, The efficiency of AATN utilization for growth was calculated by dividing the amount of protein The efficiency of AATN utilization for growth was calculated by dividing the amount of protein 400 retained by the amount of AAT available for growth. The amount500 of protein retained was retained by the amount of AATNN available for growth. The amount of protein retained was estimated using Equation 9.8weight (see section 9.1.4). The AATN available for growth was as Daily gain, g/d Daily gain,calculated g/d growth was calculated as estimated using Equation 9.8 (see section 9.1.4). The AATN available for weight the total AATN supply minus the AATN required for maintenance and gestation. Some observations the total AATN supply minus the AATN required for maintenance and gestation. Some observations were omitted from the250 dataset due to unrealistically high AATN_Eff250 (>90%) values. The ratio of 500 ratio of were omitted from the dataset due to unrealistically high AATN_Eff (>90%) values. The500 AATN/NEG for growth was calculated from the amount of AATN available for growth and the AATN/NEG for growth was calculated from the amount of AATN available for growth and the NEG for growth. The relationships AAT efficiency and AAT _NEG, BW 500 requirement 181 between NEG requirement for106 growth. The relationships between AATNN efficiency and AATNN_NEG, BW and ADG are shown in Figures 9.7, 9.8 and 9.9. and Figures 9.7, 9.8 and 9.9. 600ADG are shown in94 170 113 197

700 9.11 Table Table 9.11 Figure 9.7 Figure 9.7

98 

Figure 9.8 Figure 9.8

86

158

101

183

NorFor – The Nordic feed evaluation system

3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233

ADG ⎞ ⎛ ⎜1.88 − 0.03821⋅ AATN _ NEG − 0.00176 ⋅ BW − 0.2283 ⋅ ⎟ 1000 ⎟ ⋅100 AATN _ Eff = ⎜ [Eq. 9.34] 1 Table 9.11. Descriptive statistics of +the dataset ⋅used for developing the equation for predicting ⎜ + 0.0000019014 ⎟ 1.83 5 ⋅ BW 0 . 000000016 AAT _ NEG N ⎝ ⎠

the AATN efficiency for growth in growing cattle. The dataset included trials with Norwegian and Swedish bulls involving 120 treatments.

where AATN_Eff is the efficiency with which AATN is used for growth, %; BW is the weight of the animal, kg; ADG is the averageAverage daily live weightMinimum gain, g/d and AAT N_NEG is the ratio Maximum SD between AATN and energy available for growth, Equation 9.35.

DMI (kg/d) 6.4 2.8 9.9 1.8 BW (kg) 322 113 568 120 ADG (g/d) AAT − AAT _ ma int 1,144 1,567 196 − AATN _ gest 548 N N = AAT [Eq. 9.35] N _ NEG Protein retention (g/d) NEG_ gain173 114 226 26 PBVN (g/kg DM) 22 -29 77 22 AAT /NEG (g/MJ) 16.9 9.7 29.8 is the whereNAATN_NEG is the ratio between AATN and energy available for growth, g/MJ; AATN4.3 AATNNsupplied efficiency (%) 50.7 8.11; AAT 1.4 99.4 23.0 AAT from the feed ration, Equation N_maint is the AATN required for The ratio between MP and energy available for growth is calculated as:

maintenance, Equation 9.22; AATN_gest is the AATN required for gestation in heifers, Equation 1 (Homb, 1974; Hole, 1985; Olsson and Lindberg, 1985; Olsson, 1987; Johansen, 1992; Skaara, 1994; Randby, 2000a; 9.42; and NEG_gain is the amount of energy used for growth, Equation 9.5.

Randby, 2000b; Berg and Volden, 2004; Volden and Berg, 2005; Berg, unpublished data; Havrevoll, unpublished

The of AATN data). utilization for growth was calculated by dividing the amount of protein data;efficiency Olsson, unpublished retained by the amount of AATN available for growth. The amount of protein retained was estimated using Equation 9.8 (see section 9.1.4). The AATN available for growth was calculated as the totalAAT AATN_NEG supply is minus the AAT N required for maintenance and gestation. Some observations where the ratio between AAT and energy available for growth, g/MJ; AATN is were omittedNfrom the dataset due to unrealisticallyNhigh AATN_Eff (>90%) values. The ratio of the AATN supplied from the feed ration, Equation 8.11; AATN_maint is the AATN required for AATN/NEG for growth was calculated from the amount of AATN available for growth and the maintenance, Equation 9.22; AAT the AATAAT for gestation in heifers, Equation N_gest is between N required NEG requirement for growth. The relationships N efficiency and AATN_NEG, BW 9.42; andare NEG_gain the amount energy and ADG shown in is Figures 9.7, 9.8of and 9.9. used for growth, Equation 9.5.

The efficiency of AATN utilization for growth was calculated by dividing the amount of protein Table 9.11

retained by the amount of AATN available for growth. The amount of protein retained was estimated

Figure 9.7 using Equation 9.8 (see Section 9.1.4). The AATN available for growth was calculated as the total

AATN supply minus the AATN required for maintenance and gestation. Some observations were

Figure omitted9.8 from the dataset due to unrealistically high AATN_Eff (>90%) values. The ratio of AATN/NEG

for growth was calculated from the amount of AATN available for growth and the NEG requirement for growth. The relationships between AATN efficiency and AATN_NEG, BW and ADG are shown in Figures 9.7, 9.8 and 9.9. Based on the AAT supply from the feed ration and the AAT _Eff, the amount of AAT retained Figure 9.9

N

N

for growth can be calculated as:

N

Based on the AATN supply from the feed ration and the AATN_Eff, the amount of AATN retained for growth can be calculated as: ADG ⎞ ⎛ ⎜1.88 − 0.03821 ⋅ AATN _ NEG − 0.00176 ⋅ BW − 0.2283 ⋅ ⎟ 1000 ⎟  AATNretained = AATN ⋅ ⎜ ⎜ + 0.0000019014 ⋅ BW1.83 + 0.000000016 ⋅ AAT _ NEG 5 ⎟ N ⎝ ⎠

9.36

[Eq. 9.36] where AATNretained is the AATN retained for growth, g/d; AATN is AATN supplied from the feed ration, Equation 8.11; AATN_NEG is the ratio between AATN and energy available for growth; Equation 9.35; BW is the weight of the animal, kg; and ADG is the average daily live weight gain, g/d. 119

The maximum retention of AATN in growth can be calculated by putting the derivative of this equation equal to 0, which results in: AAT N _ NEG _ Min =

(1.88 − 0.00176 ⋅ BW − 0.2283 ⋅ ADG / 1000 + 0.0000019014 ⋅ BW 1.83 )  0 .03821 ⋅ 2 − 0 .000000016 ⋅ 5 ⋅ AAT N _ NEG 4

9.37

However, the above equation is not suited for calculating the AATN requirement for growing cattle because it is not possible to calculate the minimum AATN requirement (AATN_NEG _Min) using AATN_NEG as an input, since AATN_NEG is a ration variable. Therefore, AATN_NEG was replaced NorFor – The Nordic feed evaluation system

99

AATN efficiency (%)

120 100 80 60 40 20 0

8

10

12

14

16

18

20

22

24

26

28

30

AATN (g/MJ NEG)

Figure 9.7. AATN efficiency (%) in growing cattle in relation to AATN supply. AATN supply is defined as the AATN available for growth per MJ NEG available for growth. A multiple regression with AATN supply, BW (see Figure 9.8) and ADG (see Figure 9.9) as significant explanatory variables explained the variation in AATN efficiency with high accuracy (r2=0.97 and RMSE=2.9). The data are from the trials described in Table 9.11.

AATN efficiency (%)

120 100 80

3234 3235 3236 3237 3238

60the AATN retained for growth, g/d; AATN is AATN supplied from the feed where AATNretained is ration, Equation 8.11; AATN_NEG is the ratio between AATN and energy available for growth; Equation 9.35; BW40 is the weight of the animal, kg; and ADG is the average daily live weight gain, g/d. 20

3239 3240

The maximum retention 0 of AATN in growth can be calculated by putting the derivative of this equation equal to 0, which results in: 50 100 150 200 250 300 350 400 450 500 550 600

3241

AAT N _ NEG _ Min =

3242

(1.88 − 0.00176 ⋅ BW − 0.2283 ⋅ ADG 1000 + 0 .0000019014 ⋅ BW 1 .83 ) BW /(kg) 0 .03821 ⋅ 2 − 0 .000000016 * 5 * AAT N _ NEG 4

Figure 9.8. AATN efficiency (%) in growing cattle in relation to BW based on the trials described in [Eq. 9.37] Table 9.11. A multiple regression with BW, AATN supply (see Figure 9.7) and ADG (see Figure 9.9) However, the above equation is not suited for calculating the AAT requirement for growing cattle N AAT efficiency as significant explanatory variables explained the variation in with high accuracy N because it is not possible to calculate the minimum AAT N requirement (AATN_NEG _Min) using 2 (r =0.97 and RMSE=2.9).

3243 3244 3245 3246 3247 3248 3249 3250

AATN_NEG as an input, since AATN_NEG is a ration variable. Therefore, AATN_NEG was replaced with a value of 14, which corresponds to the requirement of an animal with a BW of 300 with value corresponds to theoptimum requirement ofthe an economic animal with a BWthe of 300 kg and kg anda an ADGofof14, 800which g/d. Since the biological is rarely optimum, an ADG of 800 g/d. Since the biological optimum is rarely the economic optimum, the biological biological optimum (=maximum retention of AATN) was lowered by 1 g AATN/NEG. Therefore, optimum (=maximum of AAT ) was lowered 1 g AAT /NEG. Therefore, the following the following equation isretention a modification of NEquation 9.37, inby which 1 is subtracted and 14 replaces N equation is a modification of Equation 9.37, in which 1 is subtracted and 14 replaces AATN/NEG: AAT N/NEG:

3251

AAT N _ NEG _ Min =

3252

[Eq.is9.38] where AATN_NEG_Min is the minimum AATN requirement (g/MJ NEG); BW the weight of the animal, kg; and ADG is the average daily live weight gain, g/d.

3253 3254 3255 3256 3257 3258

(1.88 − 0.00176 ⋅ BW − 0.2283 ⋅ ADG / 1000 + 0.0000019014 ⋅ BW 1.83 ) − 1 0 .03821 ⋅ 2 − 0 .000000016 ⋅ 5 ⋅ 14 4

9.38

where AATN_NEG_Min is the minimum AATN requirement (g/MJ NEG); BW is the weight of the animal, kg; and ADG is the average daily live weight gain, g/d.

100  NorFor – The Nordic feed evaluation system In contrast to the AATN balance for dairy cows, the AAT N balance for growing cattle is calculated more simply: AATN _ NEG

3234 3235 3236 3237 3238

where AATNretained is the AATN retained for growth, g/d; AATN is AATN supplied from the feed ration, Equation 8.11; AATN_NEG is the ratio between AATN and energy available for growth; Equation 9.35; BW is the weight of the animal, kg; and ADG is the average daily live weight gain, g/d.

120 The maximum retention of AATN in growth can be calculated by putting the derivative of this equation equal to 0,100 which results in:

3241

AAT N _ NEG _ Min =

3242 3243 3244 3245 3246 3247 3248 3249 3250

[Eq. 9.37] 60 However, the above equation is not suited for calculating the AATN requirement for growing cattle because it is not possible 40 to calculate the minimum AATN requirement (AATN_NEG _Min) using AATN_NEG as an input, since AATN_NEG is a ration variable. Therefore, AATN_NEG was replaced with a value20of 14, which corresponds to the requirement of an animal with a BW of 300 kg and an ADG of 800 g/d. Since the biological optimum is rarely the economic optimum, the biological optimum (=maximum retention of AATN) was lowered by 1 g AATN/NEG. Therefore, 0 the following equation is a modification of Equation 9.37, in which 1 is subtracted and 14 replaces 500 600 700 800 900 1000 1,100 1,200 1,300 1,400 1,500 1,600 1,700 1,800 AATN/NEG: ADG (g/d)

3251

AAT N _ NEG _ Min =

3252 3253 3254 3255 3256

AATN efficiency (%)

3239 3240

(1.88 − 0.00176 ⋅ BW − 0.2283 ⋅ ADG / 1000 + 0.0000019014 ⋅ BW 1.83 )

80

0 .03821 ⋅ 2 − 0 .000000016 * 5 * AAT N _ NEG 4

(1.88 − 0.00176 ⋅ BW − 0.2283 ⋅ ADG / 1000 + 0.0000019014 ⋅ BW 1.83 ) − 1 4

0 .03821cattle ⋅ 2 − 0 .in 000000016 5 ⋅ 14 Figure 9.9. AATN efficiency (%) in growing relation⋅to ADG based on the trials described in [Eq. 9.38] Table 9.11. A multiple regression with ADG, AATN supply (see Figure 9.7) and BW (see Figure 9.8) as significant explanatory variables explained the variation in AATN efficiency with high accuracy (r2=0.97 and RMSE=2.9). where AAT N_NEG_Min is the minimum AATN requirement (g/MJ NEG); BW is the weight of

the animal, kg; and ADG is the average daily live weight gain, g/d.

3257 3258

In contrast contrasttotothe theAAT AAT dairy cows, the AAT for growing is calculated N balance N balance for for dairy cows, the AAT for growing cattle iscattle calculated In N balance N balance more simply: more simply:

3259

AATN _ bal =

3260 3261 3262 3263

N N N N where AATfor N_bal is the AATN balance, %; AATN_NEG is the ratio between AATN and energy available growth, Equation 9.35; and AATN_NEG_Min is the minimum AATN requirement, available for growth, Equation 9.35; and AATN_NEG_Min is the minimum AATN requirement, Equation 9.38. Equation 9.38.

AATN _ NEG ⋅100  AATN _ NEG _ Min

[Eq. 9.39]

9.39

where AAT _bal is the AAT balance, %; AAT _NEG is the ratio between AAT and energy

9.2.4 Mobilization Mobilizationand anddeposition deposition 9.2.4

3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275

In dairy cows, mobilized or deposited body protein will affect the amount of AATN available for In dairy cows, mobilizedInorNorFor deposited bodyN protein will affect amount of AATN available for milk mobilization (AATthe milk protein production. the AAT N_mob) or AATN deposition protein production. In to NorFor the AAT (AAT AAT deposition (AATN_dep) (AAT is related the animal's energy balance. This means that or when a Ncow is in negative N_dep) N mobilization N_mob) is related to theprotein animal’s balance.It This meansthat thatbody when a cowreserves is in negative energy balance willenergy be mobilized. is assumed energy containenergy 135 g balance protein/kg (NRC, 1985) and,Itbased on values from INRA (1989), 24.8 MJ energy is supplied per (NRC, protein will be mobilized. is assumed that body energy reserves contain 135 g protein/kg kg mobilized body on mass, and the energy requirement for MJ deposition MJ/kg. The 1985) and, based values from INRA (1989), 24.8 energyisis31.0 supplied per kgutilization mobilized body i.e. The 80%utilization (Tammingaofetmobilized al., 1994),protein of mobilized milk production is assumedisto31.0 be high, mass, and theprotein energyforrequirement for deposition MJ/kg. whereas utilizationisof AATN for deposition in80% adult(Tamminga animals has et been shown towhereas be low the utilization for milkthe production assumed to be high, i.e. al., 1994), (INRA, NRC, 2001); it is assumed to be 120 50%. The amount of AATN originating from of AAT1989; N for deposition in adult animals has been shown to be low (INRA, 1989; NRC, 2001); it mobilized protein is calculated as: is assumed to be 50%. The amount of AAT originating from mobilized protein is calculated as:

3276

AATN _ mob =

3277 3278 3279 3280 3281 3282 3283

from body mass, g/d; NEL_mob is the where N mobilized whereAAT AATNN_mob _mobisisthe theamount amountofofAAT AAT N mobilized from body mass, g/d; NEL_mob is the amount amount of energy mobilized from body Equation 9.18; is the AATcontent perkg kgbody mass; N contentper of energy mobilized from body mass,mass, Equation 9.18; 135135 is the AAT N body mass; 24.8 is the energyper supplied per kg mobilized body MJ NEL/kg; and 0.8torefers 24.8 is the energy supplied kg mobilized body mass, MJmass, NEL/kg; and 0.8 refers the efficiency to the efficiency of mobilized AATN for milk production.

3284

AATN _ dep =

3285 3286 3287 3288

N

135 ⋅ 0.8 ⋅ NEL _ mob  24.8

[Eq. 9.40]

9.40

of mobilized AATN for milk production.

When cows are in positive energy balance the AATN requirement for protein deposition is When cows calculated as:are in positive energy balance the AATN requirement for protein deposition is calculated

as:

135 ⋅ 2 ⋅ NEL _ dep 31.0

[Eq. 9.41]

is the feed amount of AATN required where AAT N_dep NorFor – The Nordic evaluation systemfor protein deposition, g/d; NEL_dep is the amount of energy deposited, Equation 9.17; 135 is the AATN content per kg body mass; 31.0 is the energy content per kg body mass, MJ NEL/kg; and two refers to an efficiency of 50% for AATN deposition.

101

3280 3281 3282 3283

When cows are in positive energy balance the AATN requirement for protein deposition is calculated as:

3284

AATN _ dep =

3285 3286 3287 3288 3289 3290

where g/d; NEL_dep is the whereAAT AATNN_dep _depisisthe theamount amountofofAAT AATNNrequired requiredfor forprotein proteindeposition, deposition, g/d; NEL_dep is the amount amount of energy deposited, Equation the AAT per kg body mass; 31.0isisthe energy N content of energy deposited, Equation 9.17; 9.17; 135 is135 theisAAT content per kg body mass; 31.0 N refers to an efficiency of 50% for the energy content per kg body mass, MJ NEL/kg; and two content per kg body mass, MJ NEL/kg; and 2 refers to an efficiency of 50% for AATN deposition. AATN deposition.

3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310

to the efficiency of mobilized AATN for milk production.

135 ⋅ 2 ⋅ NEL _ dep 31.0



[Eq. 9.41]

9.41

The AATN requirements for deposition and mobilization are illustrated in Table 9.12.

The AATN requirements for deposition and mobilization are illustrated in Table 9.12.

9.2.5 Gestation

Table 9.12

The AATN requirement for gestation in heifers and cows depends on the stage of pregnancy and includes the AATN required for the development and maintenance of all conceptus tissues, including 9.2.5 Gestation the foetus. The AATN requirement equation is modified from on NRC uses mature The AATN requirement for gestation in heifers and cows depends the(1985) stage ofand pregnancy and BW as a scaling factor instead of birth weight. includes the AATN required for the development and maintenance of all conceptus tissues,

including the foetus. The AATN requirement equation is modified from NRC (1985) and uses -0.00262 ∙ gest_day 8.5357 – (13.1201 ) - 0.00262 ∙ gest_day mature BW asBW_mat a scaling factor instead birth weight.∙ e ∙ 34.375 ∙ eof 600 −0.00262⋅ gest _ day  AATN_gest = BW _ mat 8 . 5357 − ( 13 . 1201 ⋅ e ) − 0 .00262⋅gest _ day ⋅ 34.375 ⋅ e 0.5 600 [Eq. 9.42] AATN _ gest =

9.42

5 gestation, g/d; BW_mat is the mature BW, kg (Table where AATN_gest is the daily AAT required0.for 3.2 and 3.5); and gest_day is the actual day of gestation; 0.5 is the assumed AATN utilization for where AATN_gest is the daily AAT required for gestation, g/d; BW_mat is the mature BW, kg gestation. (Table 3.2 and 3.5); and gest_day is the actual day of gestation; 0.5 is the assumed AAT N

utilization for gestation.

The AATN requirement for gestation is illustrated in Figure 9.10.

The AATN requirement for gestation is illustrated in Figure 9.10.

9.3 PBVN

Figure 9.10

The minimum PBVN recommendation for dairy cows is related to milk yield according to the following equation, which is illustrated in Figure 9.11.

9.3 PBVN

-0.14 ∙ ECM) for dairy cows is related to milk yield according to the The PBVN recommendation PBVminimum N_DM_Min = 10 ∙ (1 – e following equation, which is illustrated in Figure 9.11.

9.43

where PBVN_Min is the minimum recommended level of PBVN, g/kg DM; and ECM is the daily 121 energy-corrected milk yield, Equation 3.1 and 3.2. Table 9.12. Weight change and AATN requirement for deposition and mobilization in cows depending on breed and change in BCS. Daily change in BCS1

Weight change, kg/d

AATN requirement, g/d

Large breed

Jersey

Large breed

Jersey

-0.02 -0.01 0 0.01 0.02

-1.2 -0.6 0 0.6 1.2

-0.9 -0.45 0 0.45 0.9

-154 -77 0 192 384

-115 -58 0 144 288

1

One BCS=60 kg for large breeds and 45 kg for Jersey, i.e. 0.01 BCS corresponds to 600 g BW for large breeds and 450 g BW for Jersey.

102 

NorFor – The Nordic feed evaluation system

AATN requirement (g/d)

350

DH Jersey

300 250 200 150 100 50 0

0

50

100

150

200

250

300

Days in gestation

Figure 9.10. Estimated AATN requirement (g/d) for pregnancy depending on days of gestation and mature BW. Jersey has a mature BW of 440 kg; Holstein has a mature BW of 640 kg. Figure 9.11 shows that for cows producing more than 20 kg ECM per day, the minimum PBVN recommendation is 10 g/kg DM, while for cows producing less than 20 kg the minimum PBVN is reduced to zero when the milk production is 0, i.e. for dry cows. If recirculation of urea to the rumen is taken into account, then the minimum PBVN should theoretically be 0, implying that the N available for microbial growth is similar to the N required for microbial growth. The NorFor PBVN recommendation for lactating dairy cows is based on a former Danish recommendation, which was set to 0 g/d (Strudsholm et al., 1999) on the basis of several production trials (Kristensen, 1997; Madsen et al., 2003), but raised to 10 g/kg, for two reasons. Firstly, unlike the former Danish system, NorFor takes into account the recirculation of urea to the rumen in the calculation of PBVN (see Equation 7.35), and secondly the feed passage rate is lower than in the former AAT/PBV system (Madsen et al., 1985), which will result in a higher ruminal degradation of protein. A maximum of 40 g PBVN per kg DM is recommended for both lactating and dry cows

PBVN (g/kg DM)

12 10 8 6 4 2 0

0

10

20

30 40 ECM (kg/d)

50

60

Figure 9.11. PBVN recommendation for dairy cows as a function of ECM yield. NorFor – The Nordic feed evaluation system

103

to avoid increased energy expenditure for the excretion of N via urine (Danfær, 1983; Reynolds, 2000) and decreased fertility (Rajala-Schultz, 2001). For growing cattle the minimum PBVN recommendation is 0 g/kg DM. This recommendation is not based on production trials but on a theoretical approach, i.e. that there is a balance between CP entering the rumen and CP leaving the rumen. The maximum PBVN recommendation is set to 55 g/kg DM based on Olsson (1987). Olsson (1987) conducted several trials with intensively reared bulls and concluded a maximum recommendation of 4 g PBV/MJ ME. Assuming a 60% ME to NEG conversion efficiency, and an average content of 7.5 MJ NEG/kg DM, the maximum recommendation is 50 g PBVN/kg DM (4/0.6=6.7 g PBVN/MJ NEG·7.5). Since the energy content of a ration may be higher than 7.5 MJ NEG/kg DM, it was decided that NorFor has a maximum recommendation of 55 g PBVN per kg DM.

9.4 Rumen load index The maximum recommended RLI (see Chapter 7 and Equation 7.15 for calculation) is set to 0.6, which gives a moderate reduction of NDF degradation rate. A RLI of 0.6 corresponds to a diet with 240-280 g starch per kg DM depending on starch source. The sugar and starch content of a diet will often be 290-320 g/kg DM. These maximum levels of starch and sugar are in accordance with previous Danish recommendations (Strudsholm et al., 1999). The NRC (2001) recommends diets with a non-fibre carbohydrates content ranging from 360-440 g/kg DM, depending on the NDF content of the ration; 360 g/kg DM for diets with a low NDF content (250 g/kg DM), and 440 g/kg DM for diets with a high NDF content (330 g/kg DM). No health problems were reported in Danish trials with dairy cows fed diets containing 330-340 g starch/kg DM using maize silage and barley as starch sources (Hymøller et al., 2005).

9.5 Chewing time Chewing time index (CI) is used for cows and growing cattle in NorFor to ensure good rumen function, see further descriptions in Chapter 11. The minimum recommendation for CI is 32 minutes per kg DM for large breeds and 30 minutes per kg DM for Jersey cows. These recommendations are based on assessments in relation to the former Danish chewing index (Nørgaard, 1986). However, in the future the recommendations will be validated against data where e.g. rumen pH and milk fat content has been measured.

9.6 Intake capacity Calculations of the ration FV and animal IC are described in Chapter 10. The total FV of the ration should not exceed the IC of the animal. When optimising the feed ration FV should be as close to IC as possible in order to ensure the animals satiety. However for practical reasons NorFor has different minimum recommendations for IC for lactating cows, dry cows and growing cattle.

9.7 Fatty acids The type of fat, i.e. chain length, degree of saturation and physical form, is of importance for the response in milk yield (Børsting et al., 2003). Too high fat content in dairy cattle diets will reduce feed intake and fibre degradation in the rumen (NRC, 2001). The NRC (2001) recommends that the CFat content in a ration should not exceed 6-7% of DM, and the Danish maximum recommendation for growing cattle is 50-60 g/kg DM (Strudsholm et al., 1999). A relationship was established between ECM yield and FA/kg DM, based on Danish trials with fat supplementation suggesting that the maximal ECM yield is obtained with 46 g FA/kg DM for fat with a degree of saturation of approximately 50 (Børsting et al., 2003). If the fat supplementation consists of FAs with a high 104 

NorFor – The Nordic feed evaluation system

3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381

The type of fat, i.e. chain length, degree of saturation and physical form, is of importance for the response in milk yield (Børsting et al., 2003). Too high fat content in dairy cattle diets will reduce feed intake and fibre degradation in the rumen (NRC, 2001). The NRC (2001) recommends that the CFat content in a ration should not exceed 6-7% of DM, and the Danish maximum recommendation for growing cattle is 50-60 g/kg DM CFat of DMI (Strudsholm et al., degreeAofrelationship saturation was thenestablished the maximum ECM yield can beFA/kg achieved 75 gonFA/kg DM (Børsting 1999). between ECM yield and DM,atbased Danish trials with supplementation suggesting that the ECM yield is obtained g FA/kg et al.,fat2003). NorFor has implemented a maximal maximum recommendation ofwith 45 g46FA/kg DM for both DM fat growing with a degree of saturation of approximately 50 content (Børstingofet64 al.,g/kg 2003). If the fat cowsforand cattle. This corresponds to a CFat DM, assuming a 50/50 supplementation consists of FAs with degree ofinsaturation the and maximum ECM ratio of roughage to concentrate, anda high FA contents the CFat then of 650 750 g/kg in yield roughage and can be achievedrespectively. at 75 g FA/kg DM (Børsting et al., 2003). NorFor has implemented a maximum concentrates, recommendation of 45 g FA/kg DM for both cows and growing cattle. This corresponds to a CFat content of 64 g/kg DM, assuming a 50/50 ratio of roughage to concentrate, and FA contents 9.8 Minerals in the CFat of 650 and 750 g/kg in roughage and concentrates, respectively.

3382 3383 3384 3385 3386 3387 3388

Mineral requirements are generally based on a factorial approach in which the requirements for 9.8 Minerals

3389 3390 3391 3392 3394 3394 3394 3393 3394 3394 3394 3394 3395 3394 3395 3394 3395 3395 3395 3395 3395 3395 3396 3395 3396 3396 3396 3396 3396 3396 3396 3396 3397 3397 3397 3397 3397 3397 3397 3397 3397 3398 3398 3398 3398 3398 3398 3398 3399 3398 3399 3398 3399 3399 3399 3399 3399 3400 3399 3400 3399 3400 3400 3400 3400 3400 3401 3400 3401 3400 3401 3401 3401 3401 3401 3401 3401 3402 3402 3402 3402 3402 3402 3402 3402 3402 3403 3403 3403 3403 3403 3403 3403 3404 3403 3404 3403 3404 3404 3404 3404 3404 3405 3405 3404 3405 3404 3405 3405 3405 3405 3406 3406 3405 3406 3405 3406 3406 3406 3406 3407 3406 3407 3406 3407 3407 3407 3407 3407 3408 3408 3407 3408 3407 3408 3408 3408 3408 3409

9.8.1 Macro minerals Recommendations for calcium, phosphorus, magnesium, sodium, potassium and chlorine for dairy Recommendations forcattle calcium, sodium, potassium and chlorine for dairy cows and growing arephosphorus, calculatedmagnesium, using the following equations. Note that the absorption cows and growing calculated using the following equations. the absorption coefficients rangecattle fromare16% for magnesium to 90% for sodiumNote andthat potassium:

maintenance, milk production, growth and gestation are considered. The requirements also include a

Mineral requirements are generally based on a factorial approach in which the requirements for safety margin, although this margin notgestation quantified, based onThe therequirements lowest absorption maintenance, milk production, growthisand are but considered. also coefficients from the literature. Furthermore, these low absorption coefficients also apply for mineral supplements, include a safety margin, although this margin is not quantified, but based on the lowest although they have higher true absorption coefficients (NRC, 2001). absorption coefficients from the literature. Furthermore, these low absorption coefficients also apply for mineral supplements, although they have higher true absorption coefficients (NRC, 9.8.1 Macro minerals 2001).

coefficients range_from Ca_maint = factor 1 ⋅ BW16% 0.50for magnesium to 90% for sodium and potassium: [Eq. 9.45] Ca_maint = factor _1 ⋅ BW 00..50 [Eq. 9.45] Ca_maint = factor 1 [Eq. 9.45] Ca_intake_ _ ma0int 9.44] Ca_maint = Min factor=_ _Ca 1 ⋅⋅ BW BW .50 50+ Ca _ milk + Ca _ gain + Ca _ gest  [Eq. 9.45] Ca_maint = factor _ 1 ⋅ BW 0.50 [Eq. 9.45] Ca_maint = factor _ 1 ⋅ BW 0 . 50 [Eq. Ca_maint = factor _ 1 ⋅ BW 0.50 [Eq. 9.45] 9.45] Ca _ milk= =factor 1 . 22_⋅1ECM 0 . 50 [Eq. 9.46] Ca_maint ⋅⋅ BW 9.45] Ca _ milk 1 .. 22 50  [Eq. 9.46] Ca_maint = =factor _⋅1ECM BW 000....50 50 9.45] Ca [Eq. 9.46] Ca _ _ milk milk = =1 1 . 22 22 ⋅⋅ ECM ECM 0 0 . 50 50 [Eq. 9.46] Ca _ milk = 1 . 22 ⋅ ECM 0 . 50 [Eq. 9.46] Ca .. 50 [Eq. 9.46] 9.46] Ca _ _ milk milk = =1 1 .. 22 22 ⋅⋅ ECM ECM 0 00.22 50 [Eq. − 0.22 ⋅ ADG 123 Ca = 1 ⋅⋅ ECM .. 50 [Eq. 9.46] Ca_gain ⋅ BW_mat 0.50 9.47] [Eq. − 0.22 Ca _ _ milk milk= =9.83 1 .. 22 22 ECM 0 00.22 50 ⋅⋅ BW ADG 9.46] 0.22 − 0.22 1000 ADG Ca_gain = 9.83 ⋅ BW_mat BW ⋅ 0.50 [Eq. 9.47] 0.22 ⋅⋅ BW − 0.22 ⋅⋅ ADG1000 0.50 Ca_gain = 9.83 ⋅⋅ BW_mat [Eq. 9.47] Ca_gain = 9.83 BW_mat BW 0.50 [Eq. 9.47] 0.22 − 0.22 1000 ADG Ca_gain = 9.83 ⋅ BW_mat ⋅ BW ⋅ 0.50 1000 [Eq. 9.47] 0.22 − 0.22 ADG 0.22 − 0.22 Ca_gain = = 9.83 9.83 ⋅⋅ BW_mat BW_mat BW ⋅ ADG1000 0.50 0.50  [Eq. 9.47] 9.47] Ca_gain ⋅⋅ 0BW [Eq. .00007 ⋅gest⋅⋅)ADG ⋅gest 1000 0.22 − 1000 0.50 ⎛ 9.83 ⎞ 0.22 − − 0.22 0.22 Ca_gain = ⋅⋅ BW_mat ⋅⋅ 00BW 0.02456 ⋅ e(((000...05581 [Eq. 9.47] 05581 − ..00007 ⋅⋅gest ))ADG ⋅⋅gest ⎛ ⎞ Ca_gain = 9.83 BW_mat BW ⋅ 0.50 05581 − 00007 gest gest [Eq. 9.47] ⎜ ⎟ 1000 ⎛ ⎞ 0.02456 e ⋅ Ca_gest = ⎜⎛⎜ 0.02456⋅ e(0.05581− 0.00007⋅gest)⋅gest 1000 .50 [Eq. 9.48] ⎟⎞⎟⎞⎟ 0 0.02456 e ⋅ ( 0 . 05581 − 0 . 00007 ⋅ gest ) ⋅ gest = Ca_gest 0 . 50 ( 0 . 05581 − 0 . 00007 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) ⎜ ⎛ [Eq. 9.48] Ca_gest 0 . 50 = [Eq. 9.48] 0.02456 e ⋅ ( 0 . 05581 − 0 . 00007 ⋅ gest ) ⋅ gest 0.02456 e − ⋅ ⎜ ⎟ ⎛ ⎞ Ca_gest − 0− .00007 ⋅gest )⋅gest 00..05581 00..00007 ⋅⋅((gest − 1)⋅(gest − 1) ⎠⎟⎞ 0..50 ⎝⎜⎛ −0.02456 [Eq. 9.48] ⋅⋅ ee⋅⋅ (ee0(((.05581 − − 0.02456 0.02456 Ca_gest = = [Eq. 0.05581 05581 −.00007 0.00007 00007 ⋅(gest gest −11))⋅⋅((gest gest − −11)) ⎟ 0 0.02456 − ⋅⋅gest ))⋅⋅gest Ca_gest = ⎝⎜⎜⎝⎜⎛⎝⎛ − 0..50 50  [Eq. 9.48] 9.48] 0.02456 0.05581 0.00007 ⋅(gest −1)⋅(gest −1) ⎠⎟⎠⎟⎞⎠⎟⎞ 0 Ca_gest = 50 05581 − 00− .00007 gest gest 0.02456 ⋅⋅ ee⋅⋅ ((ee00((..05581 [Eq. 9.48] ⎜⎜⎝ − 0.02456 − 0 . 05581 − 0 . 00007 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) 0.02456 ⎟ (0.05581− 0.00007⋅(gest −1)⋅(gest −1) ⎠⎟ 0.50 Ca_gest = ⋅⋅ eeint [Eq. 9.49] 9.48] 0.02456 −in0.02456 ⎠ 0.50 = ⎜⎝⎜⎝ − Ca_gest P_intake_M = P _ ma + P _ milk + P _ gain + P _ gest [Eq. 9.48] ⎟ ⎠ ( 0 . 05581 − 0 . 00007 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) ⎜⎝ −in (0.05581 0.00007 ⋅_ (gest −1+ )⋅(P gest −1) ⎟⎠ 0.02456 ⋅⋅ eeint P_intake_M = P _ ma + P __ −milk + P gain __ gest [Eq. 9.49] P_intake_M in = P _ ma int + P milk + P _ gain + P gest [Eq. 9.49] − 0.02456 P_intake_M in = P _ ma int + P _ milk + P _ gain + P _ gest ⎝ ⎠ [Eq. 9.49] P_intake_M in [Eq. P_intake_M in = =P P_ _ ma ma int int + +P P ___ milk milk + +P P ___ gain gain + +P P ___ gest gest  [Eq. 9.49] 9.49] P_intake_M in = P _ ma int + P milk + P gain + P gest [Eq. 9.49] P_maint = 1.0in⋅ DMI 0ma .70int + P _ milk + P _ gain + P _ gest [Eq. P_intake_M = P _ [Eq. 9.50] 9.49] P_maint = 1 . 0 ⋅ DMI 0 . 70 P_intake_M in = P _ ma int + P _ milk + P _ gain + P _ gest [Eq. 9.50] 9.49] P_maint = = 11..00 ⋅⋅ DMI DMI 0 0..70 70 [Eq. 9.50] 9.50] P_maint [Eq. P_maint = 1 . 0 ⋅ DMI 0 . 70  [Eq. 9.50] 9.50] [Eq. P_maint = 1 . 0 ⋅ DMI 0 . 70 P_maint = 1.0 ⋅ DMI 0.70 [Eq. 9.50] P_milk==factor _ 2 ⋅ MY 0.70 [Eq. 9.50] 9.51] P_maint 1 . 0 ⋅ DMI 0 . 70 [Eq. P_milk = factor _ 2 ⋅ MY 0 . 70 P_maint = 1.0 ⋅ DMI 0.700.70 9.51] [Eq. 9.50] P_milk _ 2 ⋅ MY [Eq. P_milk= = factor factor MY 70 [Eq. 9.51] 9.51] P_milk = factor___ 222 ⋅⋅⋅ MY MY 000...70 70  [Eq. 9.51] P_milk = factor [Eq. P_milk= factor_ 2 ⋅ MY 0.70 0.22 − 0.22 ADG [Eq. 9.51] 9.51] P _ gain =factor 1.2 + ⋅ BW_mat ⋅ BW −− 0.22 ⋅ ADG1000 0.70 [Eq. 9.52] 0.22 P_milk = __ 4.635 22 ⋅⋅ MY 00..70 [Eq. 0.22 P _ gain 1.2 + 4.635 0.70 9.52] P_milk == factor MY⋅⋅ BW_mat 70 [Eq. 9.51] 9.51] 0.22 ⋅⋅⋅ BW − 0.22 0.22 ⋅⋅⋅ ADG P _ gain = 1.2 + 4.635 BW 0.70  9.52] ADG 1000 P _ gain = 1.2 + 4.635 ⋅⋅ BW_mat BW_mat BW [Eq. 9.52] 0.22 − 0.22 1000 0.70 ADG 0.22 − 0.22 P _ gain = 1.2 + 4.635 BW_mat ⋅ BW ⋅ 0.70 1000 [Eq. 9.52] ADG 0.22 − 0.22 [Eq. 9.52] ADG P _ gain = 1.2 + 4.635 ⋅ BW_mat ⋅ BW ⋅ 0.70 1000 0.70 P _ gain = 1.2 + 4.635(⋅0BW_mat ⋅ BW ⋅ [Eq. 9.52] 1000 0.22 − 0.22 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest 1000 ⎛ ⎞ ADG 0.22 − 0.22 P _ gain = 1.2 + 4.635 ⋅ BW_mat ⋅ BW ⋅ 0.70 0.02743 e ⋅ [Eq. 9.52] ADG ( 0 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest ⎛ ⎜ ⎟ ⎞ P _ gain==⎛ 1.2 + 4.635 ⋅(00BW_mat ⋅ BW ⋅ 0.70 [Eq. 9.52] ( . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest 1000 ⎞ ⋅ 0.02743 e P_gest 0 . 70 − 0.000075⋅gest)⋅gest ⋅⋅ ee (0..05527 1000⎟⎟⎞ ⎜⎜⎛⎜⎛ 0.02743 [Eq. 9.53] 0.02743 = 05527 − 0 . 000075 ⋅ gest ) ⋅ gest  P_gest 0 . 70 ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) ⎟ ⎞ [Eq. 9.53] P_gest 0 . 70 = [Eq. 9.53] e ⋅ ( 0 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest ⎟ ⎛⎜⎛ −0.02743 ⎞ P_gest 0 . 70 = ⋅ 0.02743 e ( 0 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) ⎝ ⎠ [Eq. 9.53] ⎜ ⎟ ⎞ 0.70 0.02743 e ⋅ ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) − ⋅ 0.02743 e ⋅ P_gest = 0.02743 e ⎜ ⎟ [Eq. 9.53] ⎝⎝⎜⎜⎛ − ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) 0.02743 e ⋅ ⎠ ⎟ ( 0 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest P_gest 0 . 70 = ⎞ ⎠ ⎟ [Eq. 9.53] 0.02743 e ⋅ ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) P_gest = ⎝⎜⎛ − 0 . 70 ( 0 . 05527 − 0 . 000075 ⋅ gest ) ⋅ gest 0.02743 e ⋅ [Eq. 9.53] ⎞ ⎠ ⎟ 0.02743 e − ⋅ ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) 0.02743 e ⋅ ⎜ ⎟ ⎝ ⎠ ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) ⎜− ⎟ 0 P_gest ..70 = 0.02743 e ⋅ [Eq. 0.02743 e − ⋅ ⎝ ⎠ P_gest 0 70 = [Eq. 9.53] 9.53] ⎝⎜⎜ Min = Mg _ ma ⎠⎟⎟ Mg_intake_ int + Mg _ milk + Mg _ gain + Mg _ gest 9.54] ( 0 . 05527 − 0 . 000075 ⋅ ( gest − 1 ) ⋅ ( gest − 1 ) Mg_intake_ (0.int 05527 − 0_ ⋅Mg (gest −1)⋅(gest −1_ 0.02743 ⋅⋅__eema = Mg + Mg milk + __ gain + Mg [Eq. 9.54] Mg_intake_ Min = Mg int + Mg __.000075 milk + Mg + Mg __) gest gest ⎝⎝ − ⎠⎠  [Eq. 9.54] 0.02743 −Min Mg_intake_ Min = Mg __ ma ma int + Mg milk + Mg __ gain gain + Mg gest [Eq. 9.54] Mg_intake_ Min = Mg ma int + Mg _ milk + Mg gain + Mg _ gest [Eq. 9.54] Mg_intake_ Min = Mg _ ma int + Mg _ milk + Mg _ gain + Mg _ gest [Eq. Mg_intake_Min = Mg _ ma int+ Mg _ milk + Mg _ gain + Mg _ gest [Eq. 9.54] 9.54] [Eq. 9.55] Mg_maint = ⋅  0 . 003 BW 0 . 16 Mg_intake_ Min = Mg ma 9.54] Mg_maint = 0 ..003 [Eq. 9.55] Mg_intake_ Min =⋅⋅ BW Mg 00__..16 ma int int+ + Mg Mg __ milk milk + + Mg Mg __ gain gain + + Mg Mg __ gest gest 9.54] [Eq. 9.55] Mg_maint = 0 003 BW 16 [Eq. 9.55] Mg_maint= 0.003⋅ BW 0.16 [Eq. 9.55] Mg_maint= 0.003⋅ BW 0.16 [Eq. 9.55] Mg_maint = 0 . 003 ⋅ BW 0 . 16 [Eq. 9.55] Mg_maint = 0..15 003 ⋅ BW 00..16 9.56] Mg_milk = ⋅⋅ ECM 16  9.56] Mg_milk ==0 00...15 0...16 16 [Eq. Mg_maint 003 ⋅⋅ BW 9.56] Mg_milk ⋅⋅ ECM ECM 16 [Eq. 9.55] 9.55] Mg_maint 003 BW 000 9.56] Mg_milk= ==0 00..15 15 ECM 0..16 16 [Eq. 9.56] Mg_milk= 0.15⋅ ECM 0.16 [Eq. 9.56] Mg_milk = 0 . 15 ⋅ ECM 0 . 16 9.56] Mg_milk 0.16 0.16  [Eq. 9.57] Mg_gain == 00.15 .45⋅ ECM ⋅ ADG ADG 1000 [Eq. 9.57] 9.56] Mg_milk = 0 . 15 ⋅ ECM 0 . 16 ADG Mg_gain = 0 . 45 ⋅ 0 . 16 [Eq. 9.57] Mg_gain = 0 . 45 ⋅ 0 . 16 9.56] Mg_milk 01000 .16 0.16 [Eq. 9.57] Mg_gain == 00.15 .45⋅ ECM ⋅ ADG 1000 [Eq. 9.57] 1000 0.16 Mg_gain = 0.45 ⋅ ADG ADG [Eq. 9.57] 1000 ADG Mg_gain = 0 . 45 ⋅ 0 . 16 [Eq. 9.58] 9.57] Mg_gain .450⋅ .16 1000 0.16 Mg_gest == 00.33 [Eq. [Eq. 9.58] 9.57] Mg_gain ..45 ⋅ .ADG 0.16 Mg_gest == 000..33 16 1000 [Eq. ADG 9.57] Mg_gest 0 [Eq. 9.58] Mg_gain 450 Mg_gest = == 0 0.33 33 0⋅ ..16 16 1000 0.16 [Eq. 9.58] Mg_gest = 0.33 0.16 1000 [Eq. 9.58] Mg_gest = 0 . 33 0 . 16 [Eq. 9.58] NorFor – The Nordic feed evaluation system Mg_gest = 0Min .33 = 0.16 [Eq. 9.58] Na_intake_ Na _ ma int+ Na _ milk + Na _ gain + Na _ gest 9.59] [Eq. Na_intake_ Min = Na _ ma int+ Na _ milk + Na _ gain + Na _ gest [Eq. 9.59] Mg_gest = ..33 0 Na_intake_ Na [Eq. 9.58] 9.59] Mg_gest =0 0Min 33 = 0..16 16 [Eq. 9.58] Na_intake_ Min = Na _ _ ma ma int int+ + Na Na _ _ milk milk + + Na Na _ _ gain gain + + Na Na _ _ gest gest 9.59] Na_intake_Min = Na _ ma int+ Na _ milk + Na _ gain + Na _ gest [Eq. Na_intake_Min Min = = Na Na _ _ ma ma int int+ + Na Na _ _ milk milk + + Na Na _ _ gain gain + + Na Na _ _ gest gest [Eq. 9.59] 9.59] Na_intake_ [Eq. 9.59] Na_maint= factor_ 3 ⋅ BW 0.90 [Eq. 9.60]

(( ((( ( (( ((( ( ( (((( (

(( ((( (( (( ( (( (( (( ( (( (( ( ((

(( ((( (

(( ((( (

)) (( ))) ((( )(

(( ((( ( ( (((( (

)) ))) )

)) (( ))) ((( )(

(( ((( ( ( (((( (

)) )) ) ))

)) ))) )

)) ))) ) )) )) ))

)) ))) )

)) ))) )

)) ))) )

)) ))) )

9.44 9.45 9.46 9.47 9.48 9.49 9.50 9.51 9.52 9.53 9.54 9.55 9.56 9.57

105

3405 3405 3405

Mg_milk =00...15 15 ECM ..16 Mg_milk ECM 16 Mg_milk Mg_milk== 15⋅⋅⋅⋅⋅ECM ECM0000 0...16 16 Mg_milk ==000..15 15 ECM 16

3406 3406 3406

ADG ADG Mg_gain === 000..45 .45⋅⋅⋅ADG 016 .16 Mg_gain ADG1000 Mg_gain 1000 0000...16 Mg_gain == 00..45 45⋅⋅ADG .16 Mg_gain 45 1000 1000 16 1000

[Eq. 9.57] [Eq. 9.57] [Eq. 9.57] [Eq. 9.57] [Eq. 9.57]

3407 3407 3407

Mg_gest Mg_gest===000..33 .33 33000..16 .16 16 Mg_gest Mg_gest == 00..33 33 00..16 16  Mg_gest

[Eq. 9.58] [Eq. 9.58] [Eq. 9.58] [Eq. 9.58] [Eq. 9.58]

9.58

3408 3408

Na_intake_ Min = Na Na _ ma ma int + Na Na _ milk milk + Na Na _ gain gain Na gest Na_intake_ Min Na ma int Na milk Na gain Na _ gest Na_intake_ ++++ ___gest Na_intake_Min Min=== = Na Na___ _ma maint int+++ +Na Na___ _milk milk+++ +Na Na___ _gain gain +Na Na _gest gest  Na_intake_ Min int Na

[Eq. 9.59] [Eq. 9.59] [Eq. 9.59] [Eq. 9.59] [Eq. 9.59]

9.59

3409 3409 3409

Na_maint = factor factor _ 3 ⋅ BW 0.90 Na_maint factor BW ..90 90 Na_maint Na_maint BW Na_maint=== = factor factor____3333⋅⋅⋅⋅BW BW0000..90 90 

[Eq. 9.60] [Eq. 9.60] [Eq. 9.60] [Eq. 9.60] [Eq. 9.60]

9.60

3410 3410 3410

Na_milk MY 90 Na_milk Na_milk== =000..63 .63 63⋅⋅⋅⋅MY MY0000...90 .90  Na_milk Na_milk== 00..63 63⋅MY MY 0.90 90

[Eq. 9.61] [Eq. 9.61] [Eq. 9.61] [Eq. [Eq.9.61] 9.61]

9.61

3411 3411 3412 3411 3412 3412 3412 3412 3412 3412 3412 3413 3413 3413 3413 3413 3413 3413 3414 3414 3414 3414 3414 3414 3414 3415 3415 3415 3415 3415 3415 3415 3416 3416 3416 3416 3416 3416 3416 3416 3417 3417 3417 3417 3417 3417 3417 3418 3418 3418 3418 3418 3418 3418 3419 3419 3419 3419 3419 3419 3419 3420 3420 3420 3420 3420 3420 3420 3421 3421 3421 3421 3421 3421 3422 3422 3422 3422 3422 3422 3422 3423 3423 3423 3423 3424 3423 3424 3423 3424 3424 3425 3423 3424 3425 3424 3425 3425 3426 3424 3425 3426 3425 3426 3426 3427 3425 3426 3427 3426 3427 3427 3428 3426 3427 3428 3427 3428 3428 3428 3429 3427 3428 3429 3428 3429 3429 3429 3430 3428 3429 3430 3429 3430 3430 3430 3431 3429 3430 3431 3431 3431 3432 3430 3431 3432 3432 3432 3433 3431 3432 3433 3433 3433 3434 3432 3433 3434 3434 3434 3435 3433 3434 3435 3435 3436 3434 3435 3436 3436 3437 3435 3436 3437 3437 3438 3436 3437 3438 3438 3439 3437 3438 3439 3439 3439 3440 3438 3440 3440 3441 3439 3440 3441 3441 3441 3442 3440 3442 3442 3442 3443 3441 3442 3443 3443 3443 3442 3443 3443

ADG Na_gain 90 ADG Na_gain 000..90 Na_gest == = 1111...4.444⋅⋅⋅0⋅ADG .90 1000 Na_gain .90  1000 Na_gain = Na_gain =111..44.400⋅ADG 90 1000 00..90 Na_gest = ..ADG 90 Na_gest = 90 1000 1000 Na_gest = 1 . 4 0 . 90 Na_gest ==11..44 00..90 90 Na_gest Na_gest = 1 . 4 0 . 90 124 124  Na_gest = 1 . 4 0 . 90 Na_gest = 1.4in0.=90K _ ma int + K _ milk + K _ gain 124 K_intake_M + K _ gest 124 K_intake_M = _ int + _ + _ + K K_intake_Min in == K K __ ma ma int ++ K K __milk milk ++K K __gain gain +124 K_ _ gest gest K_intake_M in K ma int K milk K gain gest K_intake_Min in == K K__ma maint int++KK__milk milk++KK__gain gain+++KKK___gest gest K_intake_M K_intake_M in = K _ ma int + K _ milk + K _ gain + K _ K_intake_M gest K_intake_Min in ==KK__ma maint int++KK__milk milk++KK__gain gain++KK_ _gest gest 

(((

[Eq. 9.56] [Eq. 9.56] [Eq. 9.56] [Eq. 9.56] [Eq. 9.56]

))))

(((

))))

( (((

) )))) )

( ((( (

) )))) )

[Eq. 9.62] 9.62 [Eq. 9.62] [Eq. 9.63] 9.62] [Eq. [Eq.9.62] 9.62] [Eq. 9.63] [Eq. 9.63] [Eq. 9.63] [Eq.9.63] 9.63] [Eq. [Eq. 9.63] 9.63 [Eq. 9.63] [Eq. 9.63] 9.64] [Eq. 9.64] [Eq. 9.64] [Eq. 9.64] [Eq. 9.64] [Eq. 9.64] [Eq. 9.64] [Eq. 9.64] 9.64 [Eq. 9.64] K_maint= (0.038⋅ BW + factor_ 4 ⋅ DMI) 0.90 [Eq. 9.65] ( ) K_maint = 0 ..038 ⋅⋅ BW + factor _ 4 ⋅⋅ DMI 0 ..90 [Eq. 9.65] K_maint = 0 038 BW + factor _ 4 DMI 0 90 [Eq. 9.65] ( ) K_maint== (00..038 factor___444⋅⋅DMI ⋅DMI DMI 90 [Eq. 9.65] 038⋅⋅⋅BW BW+ factor ..90 [Eq. 9.65] ))))000..090 K_maint +++factor [Eq. 9.65] K_maint BW factor _ 4 ⋅ DMI = (0.038 9.65 [Eq. 9.65] K_maint ) )0.090 90 [Eq. 9.65] K_maint 038 factor__44⋅ DMI ⋅ DMI .90 [Eq. 9.65] K_milk===1(.(500.⋅.038 MY⋅⋅BW 0BW .90++factor [Eq. 9.66] K_milk = 1 . 5 ⋅ MY 0 . 90 [Eq. 9.66] K_milk= MY 90 [Eq. 9.66] K_milk 90 [Eq. 9.66] =11 1...55 5⋅⋅⋅MY MY000 0....90 90 [Eq. 9.66] K_milk = [Eq. 9.66] K_milk 90 =  [Eq. 9.66] 9.66 K_milk ==111...555⋅⋅⋅MY MY 0 . 90 [Eq. 9.66] K_milk MY 0 . 90 [Eq. 9.66] ADG K_gain = 1.6 ⋅ ADG 0.90 [Eq. 9.67] 1000 ADG K_gain = 1 . 6 ⋅ 0 . 90 [Eq. 9.67] K_gain = 1.6 ⋅ADG 00..90 [Eq. 9.67] ADG1000 K_gain 90  [Eq. 9.67] = 11..66⋅⋅ADG 90 1000 [Eq. 9.67] K_gain = [Eq. 9.67] 9.67 K_gain = 90 [Eq. 9.67] 1000 0000....90 1000 ADG1000 K_gain == 111...666⋅⋅⋅ADG 90 [Eq. 9.67] 1000 K_gain 0 . 90 [Eq. K_gest = 1.0 0.90 1000 [Eq. 9.67] 9.68] 1000 K_gest [Eq. 9.68] K_gest = = 11..00 00..90 90  [Eq. 9.68] 9.68 K_gest [Eq. 9.68] =111...000 000...90 90 [Eq. 9.68] K_gest = [Eq. 9.68] K_gest = 90 [Eq. 9.68] K_gest ==11..00 00..90 [Eq. 9.68] K_gest 90 [Eq. 9.68] Cl_intake_ Min = Cl _ ma int + Cl _ milk + Cl _ gain + Cl _ gest 9.69] Cl_intake_ [Eq. 9.69] Cl_intake_Min Min = = Cl Cl _ _ ma ma int int + + Cl Cl _ _ milk milk + + Cl Cl _ _ gain gain + + Cl Cl _ _ gest gest [Eq. 9.69] Min = = Cl Cl___ma ma int ++Cl Cl milk ++Cl Cl gain ++Cl Cl gest Cl_intake_Min maint int+ Cl____milk milk+ Cl____gain gain+ Cl____gest gest  9.69 [Eq. [Eq. 9.69] 9.69] Cl_intake_ [Eq. 9.69] Cl_intake_ Min = Cl ma int + Cl milk + Cl gain + Cl gest [Eq. 9.69] Cl_intake_ Min = Cl _ ma int + Cl _ milk + Cl _ gain + Cl _ gest [Eq. 9.69] Cl_intake_Min = Cl _ ma int + Cl _ milk + Cl _ gain + Cl _ gest [Eq. 9.69] Cl_maint = 00..0225 0225⋅⋅ BW BW 00..90 90 [Eq.9.70] 9.70] Cl_maint= = [Eq. 9.70 Cl_maint 0225 BW [Eq. 9.70] = 0000....0225 0225⋅⋅⋅⋅BW BW00000....90 90 Cl_maint = [Eq. 9.70] Cl_maint .90 90  [Eq. 9.70] [Eq. 9.70] Cl_maint = 0225 BW 90 [Eq. 9.70] Cl_maint ==00..0225 ⋅⋅BW 00.90 [Eq. 9.70] Cl_maint 0225 BW . 90 [Eq. 9.70] Cl_milk = 11..15 15⋅⋅ MY MY 00..90 90 [Eq.9.71] 9.71] Cl_milk= = [Eq.  9.71 Cl_milk 15 MY 90 [Eq. 9.71] Cl_milk =1111....15 15⋅⋅⋅⋅MY MY0000....90 90 [Eq. 9.71] [Eq. 9.71] Cl_milk= 90 [Eq. 9.71] Cl_milk = 15 MY 90 [Eq. 9.71] Cl_milk= 1.15⋅ MY 0.90 [Eq. 9.71] Cl_milk= 1.15⋅ ADG MY 0.90 [Eq. 9.71] Cl gain = = 11..00 ⋅⋅ ADG 90 [Eq.9.72] 9.72] ADG1000 00..90 9.72 Cl ___ gain gain [Eq. Cl = 1 . 0 ⋅ 0 . 90 [Eq. 9.72] ADG ADG 1000 Cl __ gain 0000...90 [Eq. 9.72] gain = = 111...000⋅⋅⋅ADG 90 1000 [Eq. 9.72] . 90 [Eq. 9.72] Cl gain = 90 [Eq. 9.72] ADG 1000 1000 0.90 Cl _ gain = 1.0 ⋅ ADG1000 [Eq. 9.72] 1000 Cl _ gain==11.0.00⋅ .90 1000 0.90 [Eq. 9.73] 9.72] Cl_gest [Eq. 1000 9.73 Cl_gest 90 [Eq. 9.73] Cl_gest == =111...000 000...90 90 [Eq. 9.73] Cl_gest [Eq. 9.73] =11..00 00..90 90 [Eq. 9.73] Cl_gest = Cl_gest [Eq. [Eq. 9.73] 9.73] Cl_gest = 1.0 0.90 [Eq. 9.73] Cl_gestCa_intake_Min = 1.0 0.90 [Eq. 9.73] where is the the calcium recommendation fordairy dairycows, cows, g/d;Ca_maint, Ca_milk, where Ca_intake_Min is calcium the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, where Ca_intake_Min is recommendation for g/d;Ca_maint, Ca_milk, where Ca_intake_Min is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, where Ca_intake_Min is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, Ca_intake_Min is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, where Ca_intake_Min is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, Ca_gain, Ca_gest is a cows requirement of Ca for maintenance, milk production, gain and gestation, where is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, Ca_gain, Ca_gest is a cows requirement offor Camaintenance, forfor maintenance, milk production, gain and gestation, Ca_gain, Ca_gest is aa cows requirement of Ca milk production, gain and gestation, where Ca_intake_Min is the calcium recommendation dairy cows, g/d;Ca_maint, Ca_milk, Ca_gain, Ca_gest is cows requirement of Ca for maintenance, milk production, gain and gestation, Ca_gain, Ca_gest is requirement of milk production, gain and gestation, Ca_gain, Ca_gest is aa cows cows requirement ofCa Ca for maintenance, milk production, gain and gestation, Ca_gain, Ca_gest is requirement of for maintenance, milk production, gain and gestation, respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are where Ca_intake_Min is the calcium recommendation for dairy cows, g/d;Ca_maint, Ca_milk, Ca_gain, Ca_gest cows requirement Cafor formaintenance, maintenance, milk production, gain and gestation, respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are Ca_gain, Ca_gest isisaaacows cows requirement ofofCa Ca for maintenance, milk production, gain and gestation, respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and Ca_gain, Ca_gest is a cows requirement of Ca for maintenance, milk production, gain and gestation, respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and respectively; P_intake_Min, Mg_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min are recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and recommendations of phosphorus, magnesium, sodium, potassium and for dairy cows and recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and growing cattle, g/d; BW is the the weight of the the animal, animal, kg; BW_mat ischlorine the mature weight ofthe the respectively; P_intake_Min, Na_intake_Min, K_intake_Min and Cl_intake_Min areanimal, recommendations ofBW phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and growing cattle, g/d; is weight of kg; BW_mat is the weight of recommendations of phosphorus, magnesium, sodium, potassium and chlorine dairy cows and growing cattle, g/d; BW is the weight of the animal, kg; BW_mat is the mature weight of the growingcattle, cattle, g/d; BW isMg_intake_Min, the weight ofanimal, the animal, kg; BW_mat ismature thefor mature weight of the growing g/d; BW is the weight of the kg; BW_mat is the mature weight of the growing cattle, g/d; BW is the weight of the animal, kg; BW_mat is the mature weight of the growing cattle, g/d; BW is the weight of the animal, kg; BW_mat is the mature weight of the animal, kg (Table 3.2 and Table 3.5); ECM is the energy-corrected milk yield, Equation 3.1 and 3.2; recommendations of phosphorus, magnesium, sodium, potassium and chlorine for dairy cows and3.2; growing cattle, g/d; BW is the weight of the animal, kg; BW_mat is the mature weight of the animal, kg (Table 3.2 and Table 3.5); ECM is the energy-corrected milk yield, Equation 3.1 and 3.2; growing cattle, g/d; BW is the weight of the animal, kg; BW_mat is the mature weight of the animal, kg (Table 3.2 and Table 3.5); ECM is the energy-corrected milk yield, Equation 3.1 and kg (Table 3.2 and Table 3.5); ECM is theisisweight energy-corrected milk yield, Equation 3.1and and 3.2; MY is animal, kg (Table 3.2 and 3.5); milk Equation 3.1 3.2; animal, kg (Table 3.2 and Table 3.5); ECM the energy-corrected yield, Equation 3.1 and 3.2; animal, kg (Table 3.2 and Table 3.5); ECM the energy-corrected milk yield, Equation 3.1 and 3.2; MY is the milk yield, kg/d; ADG is theECM daily gain ofgrowing growing cattle including primiparous growing cattle, g/d; BW is Table the weight of theis animal, kg;of BW_mat ismilk theyield, mature weight of3.1 the animal, kg (Table 3.2 and Table 3.5); ECM isthe theenergy-corrected energy-corrected milk yield, Equation and 3.2; MY is the milk yield, kg/d; ADG is the daily gain cattle including primiparous animal, kg (Table 3.2 and Table 3.5); ECM isweight the energy-corrected milk yield, Equation 3.1 and 3.2; MY is the the milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous MY is milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous the milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous the milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous MY is the milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous cows, g/d; gest is the actual day in gestation from day 190 of gestation until calving; The animal, kg (Table 3.2 and Table 3.5); ECM is the energy-corrected milk yield, Equation 3.1 and MY isg/d; the milk milk yield, kg/d;ADG ADG isgestation thedaily dailyfrom weight gain of growing cattle including primiparous 3.2; cows, cows, gest is the actual day in day 190 gestation until calving; The MY is the yield, kg/d; is the weight gain of growing cattle including primiparous cows, g/d; gest is the actual day in gestation from day 190 of gestation until calving; The cows, g/d; gest is the actual day in gestation from day 190 ofof gestation until calving; The g/d; isgest is the actual day in gestation from day190 190 of gestation until calving; Theand denominators g/d; gest is the actual day in gestation from day 190 gestation until calving; The cows, g/d; is the actual day in gestation from day of until calving; The denominators equations are absorption coefficients; factor_1 is0.031 0.031 for lactating cows and MY the gest milk yield, kg/d; ADG is the daily weight gain of growing cattle including primiparous g/d; gest is theequations actual day in gestation from day 190 ofgestation gestation until calving; The in the are absorption coefficients; is for lactating cows cows, g/d; gest is the actual day in gestation from day 190 offactor_1 gestation until calving; The denominators in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows and denominators in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows and denominators in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows denominators in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows and in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows and 0.0154 for non-lactating cows, g Ca/kg BW; factor_2 is 0.98 for large breeds and 1.17 for Jersey, cows, g/d; gest is the actual day in gestation from day 190 of gestation until calving; The denominators inthe theequations equations are absorption coefficients; factor_1 0.031 for lactating cows cows,are gCa/kg Ca/kg BW;factor_2 factor_2 is0.98 0.98 forlarge large breeds and 1.17for for Jersey, gg0.0154 for denominators in absorption coefficients; factor_1 is is 0.031 for lactating cows andand 0.0154 for non-lactating cows, Ca/kg BW; factor_2 is 0.98 for large breeds and 1.17 for Jersey, g 0.0154 for non-lactating cows, ggg BW; is for breeds and 1.17 Jersey, g 0.0154 for non-lactating cows, Ca/kg BW; factor_2 is for large breeds and 1.17 for Jersey, g 0.0154 for non-lactating cows, gglactating Ca/kg BW; factor_2 isis0.98 0.98 for large breeds and 1.17 for Jersey, ggP/kg non-lactating cows, g Ca/kg BW; factor_2 is 0.98 for large breeds and 1.17 for Jersey, g P/kg milk; 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, g milk; denominators in the equations are absorption coefficients; factor_1 is 0.031 for lactating cows and 0.0154 for non-lactating cows, Ca/kg BW; factor_2 0.98 for large breeds and 1.17 for Jersey, 0.0154 for non-lactating cows, g Ca/kg BW; factor_2 is 0.98 for large breeds and 1.17 for Jersey, g factor_3 is 0.038 for cows and 0.015 for non-lactating cows and growing cattle, g P/kg milk; milk; factor_3 is 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, g P/kg factor_3 is 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, g P/kg milk; factor_3 is 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, g milk; factor_3 is 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, ggg Na/kg factor_3 is 0.038 for lactating cows and 0.015 for non-lactating cows and growing cattle, Na/kg BW; factor_4 is 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, g 0.0154 for factor_3 non-lactating cows, glactating Ca/kg cows BW; factor_2 is for 0.98 for large cows breeds and 1.17 forcattle, Jersey, milk; factor_3 isis0.038 0.038 forlactating cows and 0.015 fornon-lactating non-lactating cows and growing cattle, P/kg milk; is for lactating 0.015 cows and growing cattle, g 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, g Na/kg BW; factor_4 6.1 for and 2.6 for non-lactating and growing g Na/kg BW; factor_4 is 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, g Na/kg BW; factor_4 is 6.1 for lactating cows and for non-lactating cows growing cattle, g BW; factor_4 is 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, gnonK/kg is dry matter intake which is2.6 set to 3.3% and1.67% 1.67% ofand BW for lactating and P/kg factor_3 0.038 for lactating cows and 0.015 for and non-lactating cows and growing g DM; Na/kg BW;and factor_4 isfor 6.1lactating for lactating cows and 2.6 for non-lactating cows and growing cattle, ggnonNa/kg BW; factor_4 is 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, gcattle, BW;milk; factor_4 is 6.1is cows and 2.6 for non-lactating cows and growing cattle, K/kg DM; and DMI dry matter intake which is set to 3.3% of BW for lactating and DM; and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonK/kg DM; DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonK/kg DM; and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonDM; and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonlactating cows, respectively. For growing cattle, DMI is calculated according to Equations 9.74 and Na/kg BW; factor_4 is 6.1 for lactating cows and 2.6 for non-lactating cows and growing cattle, g DM; and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonK/kg DM; and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and nonrespectively. For growing cattle, DMI is calculated according to Equations 9.74 and and DMI is dry matter intake which is set to 3.3% and 1.67% of BW for lactating and non-lactating lactating cows, respectively. For growing cattle, DMI is calculated according to Equations 9.74 and lactating cows, respectively. For growing cattle, DMI isiscalculated calculated according totoEquations Equations 9.74 and lactating cows, For cattle, DMI is according 9.74 and lactating cows, respectively. For growing cattle, calculated according Equations 9.74 and 9.75. K/kg and respectively. DMI isFor dry growing matter intake which isDMI set toiscalculated 3.3% andaccording 1.67% of to BW for lactating and non-9.75. lactating cows, respectively. For growing cattle, DMI according to Equations 9.74 lactating cows, respectively. Forgrowing growing cattle, DMI calculated according Equations 9.74 and cows,DM; respectively. cattle, DMI is is calculated toto Equations 9.74 and 9.75. 9.75. 9.75. lactating cows, respectively. For growing cattle, DMI is calculated according to Equations 9.74 and

The recommendations formacro macrominerals mineralsare arefrom fromNRC NRC(2001) (2001)with with modifications for phosphorus 9.75.recommendations for macro minerals are from NRC (2001) with modifications for phosphorus recommendations for macro minerals are from NRC (2001) with modifications for phosphorus The for modifications for phosphorus Therecommendations recommendations for macro minerals areNRC from NRC (2001) with modifications for phosphorus The for macro minerals are from (2001) with modifications for phosphorus recommendations for macro minerals are from NRC (2001) with modifications for phosphorus The for minerals are (2001) with modifications for phosphorus sincerecommendations content inmilk milk isdifferentiated differentiated between large breeds and Jersey based on the phosphorus content in milk is differentiated between large breeds and Jersey based on recommendations formacro macro minerals arefrom fromNRC NRC (2001) with modifications for phosphorus the phosphorus content in milk is differentiated between large breeds and Jersey based on since the phosphorus content in is between large breeds and Jersey based on since the phosphorus content in milk is differentiated between large breeds and Jersey based on since the phosphorus content in milk is differentiated between large breeds and Jersey based on the phosphorus content in milk is differentiated between large breeds and Jersey based on since the phosphorus content ininmilk isisSwedish differentiated between large breeds and Jersey based on Danish Aaes, 2004) and Swedish data(Lindmark-Månsson (Lindmark-Månsson et al.,.,2003). .,2003). 2003). In NRC The recommendations for macro minerals are from NRC (2001) with modifications forbased phosphorus et al In NRC (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson the phosphorus content milk differentiated between large breeds and Jersey on et al 2003). In NRC Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson Danish (Sehested and Aaes, 2004) and data et al ., In NRC Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson et al ., 2003). In NRC Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson et al ., 2003). In NRC Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson et al., 2003). In NRC (2001) Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson et al ., 2003). In NRC the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but inin (2001)the forabsorption absorption ofSwedish calciumand andphosphorus phosphorus depend on the but since phosphorus in milk is differentiated between large breeds and Jersey based on Danish (Sehested andcontent Aaes, 2004) and data (Lindmark-Månsson et al .,feedstuff, 2003). In NRC the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but in (2001) the coefficients for of calcium depend on the feedstuff, but in (2001) the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but in the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but inin the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but in NorFor (2001) the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but in these are fixed at 50% and 70%, respectively, based on values from NRC (2001). DMI is NorFor 50% and 70%, respectively, based on values from NRC (2001). DMI is Danish (Sehested and Aaes, 2004) and Swedish data (Lindmark-Månsson et al ., 2003). In NRC (2001) the coefficients for absorption of calcium and phosphorus depend on the feedstuff, but NorFor these are fixed at 50% and 70%, respectively, based on values from NRC (2001). DMI NorFor these are fixed at 50% and 70%, respectively, based on values from NRC (2001). DMI isis NorFor these are at 50% and 70%, respectively, based from NRC (2001). DMI is NorFor these are fixed fixed at 50% and 70%, respectively, basedon onvalues values from NRC (2001). DMI is is required these are fixed at 50% and 70%, respectively, on values from NRC (2001). DMI NorFor these are at 50% and based from NRC DMI isin (2001) the coefficients for absorption ofrespectively, calcium andbased phosphorus depend on the(2001). feedstuff, but NorFor these arefixed fixed at 50% and70%, 70%, respectively, basedon onvalues values from NRC (2001). DMI is 125 125 125 125 NorFor these are fixed at 50% and 70%, respectively, based on values from NRC (2001). DMI is 125 125 125 125 125 NorFor – The Nordic feed evaluation system 106 

3444 3444 3445 3445 3446 3446 3447 3447 3448 3448 3449 3449 3450 3450 3451 3451 3452 3452 3453 3453 3454 3454 3455 3455 3456 3456 3457 3457 3458 3458 3459 3459 3460 3460 3461 3461 3462 3462 3463 3463 3464 3464 3465 3465 3466 3466 3467 3467 3468 3468 3469 3469 3470 3470 3471 3471 3472 3472 3473 3473 3474 3474 3475 3475 3476 3476 3477 3477 3478 3478 3479 3479 3480 3480 3481 3481 3482 3482 3483 3483 3484 3484

required to estimate the maintenance requirement of phosphorus and potassium and was set to 3.3% to BW estimate thecows maintenance requirement of NorFor phosphorus and potassium and wasset settofor to3.3% 3.3% of BW required todairy estimate theaccording maintenance requirement of phosphorus andfor potassium and was of for to NRC (2001). equations DMI were developed of for dairybased cows according toofNRC (2001). NorFor equations for DMI were developed for for growing forBW dairy cows according to NRC NorFor equations for DMIsystem. were developed growing cattle, on a subset the(2001). data used to develop the feed intake growing cattle,on based on a subset the data to develop feedintake intakesystem. system. cattle, based a subset of theofdata usedused to develop thethefeed DMIHeifer Steers = BW / 100 ⋅ (0.000004 ⋅ BW 22 − 0.0049 ⋅ BW + 3.1033) DMIHeifer Steers = BW / 100 ⋅ (0.000004 ⋅ BW − 0.0049 ⋅ BW + 3.1033) 

[Eq. 9.74] [Eq. 9.74]

DMIBulls = BW/100⋅ (2.9 − 0.002⋅ BW)  DMIBulls = BW/100⋅ (2.9 − 0.002⋅ BW)

[Eq. 9.75] [Eq. 9.75]

9.74 9.75

where DMIHeifersSteers and DMIBulls are estimated DMI for heifers, steers and bulls, kg DM/d; BW where DMIHeifersSteers and DMIBulls are estimated DMI for heifers, steers and bulls, kg DM/d; BW theweight weight theanimal, animal, where DMI and DMI HeifersSteers Bulls are estimated DMI for heifers, steers and bulls, kg DM/d; BW isisthe ofofthe kg.kg. is the weight of the animal, kg. Thecalcium calciumrecommendation recommendation growing cattle calculated from tabulated in Pehrson The forfor growing cattle was was calculated from tabulated values values in Pehrson The recommendation forusing growing cattle was calculated etal al. (1975)and andisiscalculated calculated following equation: from tabulated values in Pehrson et .calcium (1975) using thethe following equation: et al. (1975) and is calculated using the following equation: ⎛ 4.744 + 0.0361⋅ BW + 0.00000565⋅ BW2 ⎞ Ca _ int ake _ Min = ⎜⎛ 4.744 + 0.0361⋅ BW + 0.00000565⋅ BW2 ⎟⎞  Ca _ int ake _ Min = ⎜ + 0.0106 ⋅ ADG + 0.00000622⋅ ADG2 ⎟ ⎝⎜ 2 ⎠⎟ ⎠ ⎝ + 0.0106⋅ ADG + 0.00000622⋅ ADG

9.76

[Eq. 9.76] [Eq. 9.76]

where is the calcium recommendation for growing cattle, g/d; BW isg/d; the BW weight whereCa_intake_Min Ca_intake_Min is the calcium recommendation for growing cattle, is of the weight where Ca_intake_Min is the recommendation for growing cattle, g/d; BW the weight of the animal, kg; and is calcium theisaverage daily live weight gain of gain the animal, g/d.isHeifers late of the animal, kg; ADG and ADG the average daily live weight of the animal, g/d.inHeifers in late the animal, kg; and ADG is theCa average daily live weight gain of 9.48. the animal, g/d. Heifers in late gestation require anan additional supply for for gestation, Equation gestation require additional Ca supply gestation, Equation 9.48. gestation require an additional Ca supply for gestation, Equation 9.48. The recommendation for sulfur is set to 2 g/kg DM for dairy cows and growing cattle (NRC, 2001). The recommendation sulfur isto set2 to 2 g/kg DMdairy for cows dairyand cows and growing cattle2001). (NRC, 2001). The forfor sulfur is set g/kg DM cattleon(NRC, The recommendation macro mineral recommendations for dairy cowsfor and growing cattlegrowing that depend the BW and The macro mineral recommendations for dairy cows and growing cattle that depend on the BW and The macro mineral recommendations dairy ECM or ADG are illustrated in Tablesfor 9.13 andcows 9.14.and growing cattle that depend on the BW and ECMororADG ADGareare illustrated in Tables and 9.14. ECM illustrated in Tables 9.13 9.13 and 9.14. Table 9.13 Table 9.8.2 9.13 Micro minerals Table 9.14 Table Micro9.14 mineral recommendations for dairy cows and growing cattle are shown in Table 9.15. The

9.8.2 Micro copper (Cu)minerals requirement in the ration is dependent on the molybdenum and sulfur contents; the 9.8.2 Micro minerals

Micro mineral recommendations cows(Table and growing cattle areifshown in Table 9.15. The recommendation of 10 mg Cufor perdairy kg DM 9.15) is valid the ration contains less than 1 mg Micro mineral recommendations for2dairy cowsper and cattle1989). are shown Table 9.15. of The copper (Cu) requirement in the ration dependent on the(NRC, molybdenum andinsulfur contents; the molybdenum (Mo) and less than g is sulfur kggrowing DM Higher amounts molybdenum copper (Cu) requirement inCu theper ration is dependent on the molybdenum andcontains sulfur contents; recommendation of 10 mg kgcoefficient DM (Table 9.15) is valid ifbetween the ration less thanthe 1 be below and sulfur decrease the absorption of Cu. The ratio Cu and Mo should not recommendation of 10 mg Cu per kg2DM (Table 9.15) is valid if the ration contains less than 1 mg molybdenum (Mo) and less than g sulfur per kg DM (NRC, 1989). Higher amounts of 4:1molybdenum (NRC, 1989).(Mo) Synthesis ofthan the hormone the thyroid gland is inhibited by goitrogenic mg and less 2absorption g sulfurthyroxine per kg DMin(NRC, 1989). Higher amounts of molybdenum and sulfur decrease the coefficient of Cu. The ratio between Cu and Mo substances, which are common in rape seed products thatofcontain (Hermansen molybdenum sulfur the absorption Cu.thyroxine Theglucosinolates ratioinbetween Cu and Mois et al., should not be and below 4:1 decrease (NRC, 1989). Synthesiscoefficient of the hormone the thyroid gland 1995). Since rape seed products are commonly used in feed rations for dairy cows in Nordic should notbybegoitrogenic below 4:1substances, (NRC, 1989). Synthesis of theinhormone thecontain thyroid gland countries, is inhibited which are common rape seedthyroxine products in that the recommended iodine level was set to 1.0 mg/kg DM for dairy cows, although NRC (2001) only inhibited by goitrogenic substances, which are common in rape seed products that contain glucosinolates (Hermansen et al., 1995). Since rape seed products are commonly used in feed rations recommends 0.6Nordic mg/kgcountries, DM. the substantial amounts ofmg/kg goitrogenic substrates, the glucosinolates et al.,If1995). Sincecontains rape seed products commonly used inDM feedforrations for dairy cows (Hermansen in the ration recommended iodine level are was set to 1.0 dairy for dairy cows inNRC Nordic countries, theincreased recommended iodine level wasration set tocontains 1.0 mg/kg DM1999). for dairy iodine recommendation should be 2.0 mg/kg (Strudsholm etsubstantial al., cows, although (2001) only recommends 0.6tomg/kg DM. IfDM the cows, although NRC (2001) only recommends 0.6 mg/kg DM.should If the ration containstosubstantial amounts of goitrogenic substrates, the iodine recommendation be increased 2.0 mg/kg DM amounts of goitrogenic substrates, the iodine recommendation should be increased to 2.0 mg/kg DM (Strudsholm et al., 1999). al., 1999). (Strudsholm Table 9.13.etRecommendations (g/day) for macro minerals for growing cattle gaining 750 g/day1.

The recommendation includes requirements for maintenance and gain. 126

BW, kg

Calcium

Phosphorus126 Magnesium

Sodium

Potassium

200 kg 400 kg 600 kg

24 32 40

12 15 18

5 8 11

22 37 52

6 10 13

1 Phosphorus, magnesium, sodium and potassium recommendations are from NRC (2001). The calcium recommendation

is from Pehrson et al. (1975).

NorFor – The Nordic feed evaluation system

107

Table 9.14. Recommendations (g/day) for macro minerals for dairy cows depending on BW and milk yield (MY) or ECM1. BW, kg

MY or ECM, kg/d

Calcium

Phosphorus2 Magnesium Sodium

Potassium

430 430 430 600 600 600

17/22 24/31 31/40 25/25 35/35 45/45

80 102 124 109 136 163

49 60 72 63 77 91

151 166 181 201 218 235

29 37 46 35 44 53

34 40 46 43 50 57

1 The recommendations are from NRC (2001) with a slight modification for phosphorus, for which the milk content is

from Lindmark-Månsson et al. (2003) and Sehested and Aaes (2004). 2 The P recommendation is dependent on breed. Jersey cows (BW=430 kg) have a content of 1.17 g P/kg milk whereas 0.98 g P/kg milk is used for large breed cows (BW=600 kg). MY and ECM yield were assumed to be equal for large breeds but for Jersey cows MY was multiplied by 1.3 to get ECM yield.

Table 9.15. Recommendations for micro minerals (mg/kg DM) for dairy cows1 and growing cattle2.

Dairy cows Growing cattle

Cobalt

Copper3

Iodine4

Iron

Manganese Selenium5 Zinc

0.1 0.1

10 10

1.0 0.5

50 50

40 20

0.2 0.1

50 30

1 The

recommendations are based on NRC (2001) with the exception of selenium. recommendations are based on NRC (2000). 3 The recommendation is dependent on the content of molybdenum and sulfur in the ration. 4 The recommendation considers some content of goitrogenic substrates, which are sometimes present in rape seed products (Hermansen et al., 1995). 5 The recommendation is from former Swedish recommendations (Spörndly, 2003). 2 The

9.8.3 Cation anion difference In order to reduce the risk of milk fever, the CAD has been introduced as a nutritional variable in feeding, especially for dry cows. The NorFor recommendation for CAD is -150 to 0 mEq/kg DM in the dry period, or at least for the last 3 to 4 weeks of the dry period (Horst et al., 1997; Moore et al., 2000; Kristensen, 2005). For lactating cows the recommendation is 200 to 450 mEq/kg DM since Borucki Castro et al. (2004) found that increasing CAD from 140 to 450 mEq/kg DM had no effect on DMI or milk yield. The CAD should never exceed 450 mEq/kg DM and this is important when adding buffer to the ration (Kristensen, 2005). Research results in the literature are equivocal about whether a reduction in dietary K and a moderate CAD in the dry period are sufficient to avert milk fever (Overton and Waldron, 2004). Potassium is therefore included in the calculation of CAD (see Chapter 6) and NorFor does not provide any recommendations for K in relation to CAD.

9.9 Vitamins Cattle require vitamins A, D, E and K. However, the dietary requirements are only absolute for vitamins A and E, since vitamin K is synthesized by ruminal and intestinal bacteria, and vitamin D is synthesized by the action of ultraviolet radiation in the skin (NRC, 2001). NorFor has 108 

NorFor – The Nordic feed evaluation system

recommendations for the supply of fat soluble vitamins (A, D and E) to dairy cows and growing cattle, which are from NRC (2001) and shown in Table 9.16. The recommendations from NRC (2001) are based on supplemental vitamins and, thus, do not take into account the natural content of vitamins in feedstuffs. NorFor includes the natural content of vitamins A, D and E when calculating the supply of these vitamins from a feed ration and recommendations are therefore a total recommendation. The beta-carotene in feedstuffs is converted to vitamin A such that 1 mg beta-carotene corresponds to 400 IU of vitamin A (Strudsholm et al., 1999). Table 9.16. Recommendations for vitamins A, D and E supplies (IU/kg BW) to dairy cows and growing cattle are based on NRC (2001). Requirements are expressed as total amounts, i.e. supplemental plus vitamins provided by feedstuffs.

Dairy cows Growing cattle

Vitamin A1, 2

Vitamin D3

Vitamin E4

110 80/110

30 10

0.8/1.6 0.3/1.6

1

Beta-carotene can be converted to vitamin A and the total requirement can be met from beta-carotene. 1 mg betacarotene corresponds to 400 IU vitamin A (Strudsholm et al., 1999). 2 The requirement of 80 IU/kg BW is for bulls, steers and non-pregnant heifers, while the requirement of 110 IU/kg BW is for pregnant heifers (NRC, 2001). 3 The requirement is from Strudsholm et al. (1999). 4 The requirement of 0.8 IU/kg BW is for lactating cows, while the requirement of 1.6 IU/kg BW is for dry cows and heifers in late gestation. The requirement for growing cattle is modified from NRC (2001), which suggests a range from 0.26 to 0.8 IU/kg BW, where the lowest value refers to grazing animals and the highest value to animals fed stored forages.

References Berg, J. and T. Matre, 2001. Produksion av storfekjøtt. Landbruktforlaget. Norway. (In Norgwegian). Berg, J. and H. Volden, 2004. Økologisk storfekjøtt. Buskap nr 4. 16-17. (In Norwegian). Borucki Castro, S.I., L.E. Phillip, V. Girard and A. Tremblay, 2004. Altering dietary cation-anion difference in lactating dairy cows to reduce phosphorus excretion to the environment. Journal of Dairy Science 87: 1751-1757. Børsting, C.F., J.E. Hermansen and M.R. Weisbjerg, 2003. Fedtforsyningens betydning for mælkeproduktionen. In: Strudsholm, F. and K. Sejrsen (eds.). Kvægets ernæring og fysiologi. DJF-rapport nr. 54, Danmarks Jordbrugsforskning, Denmark, pp. 133-152. (In Danish). Danfær, A., 1983. Næringsstoffernes intermediære omsætning. In: Østergaard, V. and A. Neimann-Sørensen (eds.). Optimale foderrationer til malkekoen. Foderværdi, foderoptagelse, omsætning og produktion. Beretning nr. 551 fra Statens Husdyrbrugsforsøg, Denmark 12.1-12.65. (In Danish). Garcia, F., J. Agabriel and D. Micol, 2007. Alimentation des bovins en croissance et à l’engrais. In: INRA (ed.). Alimentation des bovin, ovins et caprin. Besoin des animaux - Valeurs des aliments. Edition Quæ, Versaille, France, pp. 89-120. (In French). Hermansen, J., O. Aaes, S. Ostersen and M. Vestergaard, 1995. Rapsprodukter til malkekøer – mælkeydelse og mælkekvalitet. Forskningsrapport nr. 29 fra Statens Husdyrbrugsforsøg, Denmark. (In Danish). Hole, J.R, 1985. The nutritive value of silage made from Poa pratensis ssp. Alpigena and Phlenum pratense. III. Growing bulls fed silage made from the regrowth of Poa pratensis or Phlenum pratense. Report No. 232. Department of Animal Nutrition, Agricultural University of Norway, Aas, Norway. 17 pp. Homb, T., 1974. Grovfôr-kraftfôr-forholdet ved intensivt oppdrett av 14 måneder gamle slakteokser. Institutt for husdyrernæring og fôringslære, Norges landbrukshøgskole. Melding nr. 164, Norway. 18 pp. (In Norwegian). Horst, R.L., J.P. Goff, T.A. Reinhardt and D.R. Buxton, 1997. Strategies for preventing milk fever in dairy cattle. Journal of Dairy Science 80: 1269-1280.

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Hvelplund, T. and J. Madsen, 1990. A study of the quantitative nitrogen metabolism in the gastro-intestinal tract, and the resultant new protein evaluation system for ruminants. The AAT-PBV system. The Royal Veterinarian and Agricultural University, Copenhagen, Denmark. Hymøller, L., M.R. Weisbjerg, P. Nørgaard, C.F. Børsting, and N.B. Kristensen, 2005. Majsensilage til malkekøer. DJF rapport nr. 65. Danmarks Jordbrugsforskning, Denmark. (In Danish). INRA (Institut National de la Recherche Agronomique), 1989. Ruminant nutrition: Recommended allowances and feed tables. John Libbey and Co Ltd, London, UK. 389 pp. Johansen, A., 1992. A comparison between meadow fescue and timothy silage. Doctor Scientarium Thesis. Agricultural University of Norway, Aas, Norway. 108 pp. Kristensen, N.B., 2005. Brug CAB til optimering af foderrationer til malkekøer. I: Fodringsdag - Temadag om aktuelle fodringsspørgsmål. 30 august. Dansk Kvæg, Denmark, pp. 23-42. (In Danish). Kristensen, V.F., 1997. Optimal proteinforsyning. In: Malkekøernes ernæring. Intern rapport nr. 88. Statens Husdyrbrugsforsøg, Denmark, pp. 46-55. (In Danish). Kristensen, V.F., 1995. Forudsigelse af foderoptagelsen hos malkekøer. Intern rapport nr. 61. Statens Husdyrbrugsforsøg, Denmark. 28 pp. (In Danish). Kristensen, V.F., 1983. Styring af foderoptagelsen ved hjælp af foderrationens sammensætning og valg af fodringsprincip. In: Østergaard, V. and A. Neimann-Sørensen (eds.). Optimale foderrationer til malkekoen. Foderværdi, foderoptagelse, omsætning og produktion. Beretning nr. 551 fra Statens Husdyrbrugsforsøg, Denmark. 7.1-7.35. (In Danish). Lindmark-Månsson, H., R. Fondén and H.E. Pettersson, 2003. Composition of Swedish dairy milk. International Dairy Journal 13: 409-425. MacRae, J.C., P.J. Buttery and D.E. Beever, 1988. Nutrient interactions in the dairy cow. In: P.C. Garnsworthy (ed.). Nutrition and lactation in the dairy cow. Butterworths, London, UK, pp. 55-75. Madsen, J., 1985. The basis for the proposed Nordic protein evaluation system for ruminants. The AAT-PBV system. Protein Evaluation for Ruminants. Acta Agriculturae Scandinavica Supplement 25: 13-19. Madsen, J., L. Misciatteli, V.F. Kristensen and T. Hvelplund, 2003. Proteinforsyning til malkekøer. In: Strudsholm, F. and K. Sejrsen (eds.). Kvægets ernæring og fysiologi. DJF-rapport nr. 54. Danmarks Jordbrugsforskning, Denmark, pp. 113-121. (In Danish). Minde, A. and A.J. Rygh, 1997. Metoder for å bestemme nedbrytingshastighet, passasjehastighet og vomfordøyelighet av NDF hos mjølkeku. Master thesis. Norges landbrukshøgskole, Norway. 118 pp. (In Norwegian). Moore, S.J., M.J. Van de Haar, B.K. Sharma, T.E. Pilbeam, D.K. Beede, H.F. Bucholtz, J.S. Liesman, R.L. Horst and J.P. Goff, 2000. Effects of altering dietary cation-anion difference on calcium and energy metabolism in peripartum cows. Journal of Dairy Science 83: 2095-2104. Mydland, L.T., 2005, Molecular and chemical characterization of the rumen microbiota and quantification of rumen microbial protein synthesis. Ph.D. thesis. Norwegian University of Life Sciences, Aas, Norway. 155 pp. Nielsen, H.M., N.C. Friggens, P. Løvendahl, J. Jensen and K.L. Ingvartsen, 2003. Influence of breed, parity, and stage of lactation on lactational performance and relationship between body fatness and live weight. Livestock Production Science 79: 119-133. NRC (National Research Council), 1985. Nutrient Requirement of Dairy Cattle. Fifth revised edition. National Academy Press, Washington, DC, USA. NRC, 1989. Nutrient Requirement of Dairy Cattle. Sixth revised edition. National Academy Press, Washington, DC, USA. NRC, 2000. Nutrient Requirement of Beef Cattle. Seventh revised edition. National Academy Press, Washington, DC, USA. NRC, 2001. Nutrient Requirement of Dairy Cattle. Seventh revised edition. National Academy Press, Washington, DC, USA. Nørgaard, P., 1986. Physical structure of feeds for dairy cows - A new system for evaluation of the physical structure in feedstuffs and rations for dairy cows. In: Neimann-Sørensen A. (ed.). New developments and future perspectives in research of rumen function. Brussels, Belgium, pp. 85-107. Olsson, I., 1987. Effect of protein supply on the performance of intensively reared bulls - evaluation of the DCP and the Nordic AAT-PBV protein evaluation system. Dissertation. Report 164. Swedish University of Agricultural Sciences, Uppsala, Sweden. Olsson, I. and J.E. Lindberg, 1985. Performance of bull calves fed rations with different calculated amino acid flow to the duodenum. Acta Agriculturae Scandinavica Supplement 25.

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Overton, T.R. and M.R. Waldron, 2004. Nutritional management of transition dairy cows: Strategies to optimize metabolic health. Journal of Dairy Science 87: E105-E119. Pehrson, B., V. Kossila, J.T. Hansen and N.E. Hansen, 1975. Förslag til normer för makro- och mikromineraler till nötkreatur och svin. Foderjournalen, pp. 55-106. (In Swedish). Prestløkken, E., 1999. Protein value of expander-treated barley and oats for ruminants. Ph.D. thesis. Agricultural University of Norway, Aas, Norway. 137 pp. Rajala-Schultz, P.J., W.J.A. Saville, G.S. Frazer and T.E. Wittum, 2001. Association between milk urea nitrogen and fertility in Ohio dairy cows. Journal of Dairy Science 84: 482-489. Randby, Å.T., 2000a. Ensilert eller propionsyrekonservert bygg til mjølkekyr og kastrater. Husdyrforsøksmøtet 2000. Institutt for husdyrfag, NLH, Aas, Norway, pp. 409-412. (In: Norwegian). Randby, Å.T., 2000b. Effekten av økende dosering med et maursyreholdig ensileringsmiddel på næringsverdien av surfôret i produksjonsforsøk med kyr og kastrater. Hellerud rapport nr 25, Aas, Norway. 37 pp. (In Norwegian). Randby, Å.T., 2003. Høstetid og fôrkvalitet. Grønn kunnskap. 7: 27-43. (In Norwegian). Reynolds, C.K., 2000. Forage evaluation using measurements of energy metabolism. In: Givens, D.I., E. Owen, R.F.E. Axford and H.M. Omed (eds.). Forage evaluation in ruminant nutrition. CAB International, Wallingford, UK, pp. 95-111. Robelin, J. and R. Daenicke, 1980. Variations of net requirements for cattle growth with live weight, live weight gain, breed and sex. Annales de Zootechnie 29: 99-118. Sehested, J. and O. Aaes, 2004. Fosfor til kvæg. DJF-rapport nr. 60. Danmarks Jordbrugsforskning, Denmark. (In Danish). Schei, I., H. Volden and L. Bævre, 2005. Effects of energy balance and metabolizable protein level on tissue mobilization and milk performance of dairy cows in early lactation. Livestock Production Science 95: 35-47. Sjaunja, L.O., L. Bævre, L. Junkkarainen, J. Pedersen and J. Setälä, 1990. A Nordic proposition for an energy corrected milk (ECM) formula. 26th session of the International Committee for Recording the Productivity of Milk Animals (ICRPMA). Skaara, E., 1994. The effect of N-fertilization on the protein value of silage assist by the AAT/PBV system. Doctor Scientarium Thesis. Agricultural University of Norway, Aas, Norway. 95 pp. Spörndly, R., 2003. Fodermedelstabeller för idisslare. Rapport nr. 257. Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden. 96 pp. (In Swedish). Strudsholm, F., O. Aaes, J. Madsen, V.F. Kristensen, H.R. Andersen, T. Hvelplund and S. Østergaard, 1999. Danske fodernormer til kvæg. Rapport nr. 84. Landbrugets Rådgivningscenter, Århus, Denmark. (In Danish). Subnel, A.P.J., R.G.M. Meijer, W.M. Van Straalen and S. Tamminga, 1994. Efficiency of milk protein production in the DVE protein evaluation system. Livestock Production Science 40: 215-224. Tamminga, S., W.M. van Straalen, A.P.J. Subnel, R.G.M Meijer, C. Steg, J.G. Wever and M.C. Block, 1994. The Dutch protein evaluation system: the DVE/OEB-system. Livestock Production Science 40: 139-155. Thuen, E., 1989. Influence of source and level of dietary protein on milk yield, milk composition and some rumen and blood components in dairy cows. Doctor Scientarium Thesis. Agricultural University of Norway, Norway. 151 pp. Van Es, A.J.H., 1975. Feed evaluation for dairy cows. Livestock Production Science 2: 95-107. Van Es, A.J.H., 1978. Feed evaluation for ruminants. I. The system in use from May 1978 onwards in the Netherlands. Livestock Production Science 5: 331-345. Van Straalen, W.M., 1994. Modelling of nitrogen flow and excretion in dairy cows. Ph.D. thesis, Department of Animal Nutrition, Wageningen Agricultural University, Wageningen, the Netherlands. 205 pp. Volden, H., 1990. Response of dietary AAT density on milk yield - Review of production trials in Norway. Proceedings of the seminar on fat and protein feeding to the dairy cow. October 15-16, Eskilstuna, Sweden, pp. 81-89. Volden, H., 1999. Effects of level of feeding and ruminally undegraded protein on ruminal bacterial protein synthesis, escape of dietary protein, intestinal amino acid profile, and performance of dairy cows. Journal of Animal Science 77: 1905-1918. Volden, H., 2001. Utvikling av et mekanistisk system for vurdering av fôr til drøvtyggere, AAT-modellen. In: Fôropptak og fôrmiddelvurdering hos drøvtyggere. Fagseminar 18.-19. september 2001. Quality Hotell Halvorsbøle, Jevnaker, Norway. 30 pp. (In Norwegian). Volden, H. and O.M. Harstad, 2002. Effects of duodenal amino acid and starch infusion on milk production and nitrogen balance in dairy cows. Journal of Animal Science, Supplement 1 80: 321. Volden, H. and J. Berg, 2005. Nytt fôrvurderingssystem. Buskap nr 5: 12-14. (In Norwegian).

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3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955

10. Prediction of voluntary feed intake 10.0 Prediction of voluntary feed intake

H. Volden, Volden,N. N.II Nielsen, Nielsen,M. M.Åkerlind, Åkerlind,M.M. Larsen, Havrevoll and A.J. Rygh H. Larsen, Ø. Ø. Havrevoll, A. J. Rygh Describingthe thenutrient nutrient variables in aand diettheir andinteractions their interactions is an important part of ration Describing variables in a diet is an important part of ration formulationasasproductivity productivity of the dairy is sensitive the profile of the nutrients formulation of the dairy cow cow is sensitive to the to profile of the nutrients absorbed.absorbed. However, ofof feed intake is probably the most important determinant of production. However,the theprediction prediction feed intake is probably the most important determinant of production. Feed by by animal andand feedfeed characteristics. The most Feedintake intakeisisprimarily primarilyinfluenced influenced animal characteristics. The important most important animal animal characteristics areand BWphysiological and physiological including stage lactation, milkproduction, stage characteristics are BW state,state, including stage of of lactation, milk production, stage of gestation, live weight gain and body condition score. Feed characteristics of gestation, live weight gain and body condition score. Feed characteristics such as digestibility such as digestibility and fiber content have both a strong influence on rumen fill and, hence, feed and fibre content have both a strong influence on rumen fill and, hence, feed intake (Kristensen, intake (Kristensen, 1983). However, several studies (Rinne et al., 2002; Garmo et al., 2007) have 1983). However, several studies (Rinne et al., 2002; Garmo et al., 2007) have shown that cows may shown that cows may stop eating before the fill capacity of the rumen is reached. This has been stop eating before theregulation fill capacity of which the rumen reached. This hastobeen attributed attributed to metabolic (MR), is alsoisan important factor consider when to metabolic regulationfeed (MR), which is alsointake an important factor to consider when predicting intake. predicting intake. Physical regulation is related to the ruminal NDF poolfeed (Bosch et Physical intake regulation ruminal pool (Bosch al., 1992; Rinne et of al.,the 2002) and is al ., 1992; Rinne et is al.,related 2002) to andthe is partly an NDF indirect effect of theetenergy concentration partly an indirect effect ofinthe energy concentration the diet because DMI declines in a curvilinear diet because DMI declines a curvilinear manner withofincreasing energy density of the diet manner with increasing energy density of the diet (Mertens, 1994). (Mertens, 1994). Several toto predict DMI forfor practical ration formulation. These Severalsystems systemshave havebeen beendeveloped developed predict DMI practical ration formulation. These systems systems vary both complexity in their complexity and of choice of variables included (animal, nutritional and vary both in their and choice variables included (animal, nutritional and environmental). environmental). In the Danish1983) (Kristensen, 1983)(Jarrige, and French (Jarrige, 1986) systems, intake from In the Danish (Kristensen, and French 1986) systems, feed intakefeed is predicted is predicted from dietary fill values and animal fill capacity. In the system developed by Mertens dietary fill values and animal fill capacity. In the system developed by Mertens (1987), intake is (1987), intake is regulated both physically by the diet (NDF) and metabolically by the energy regulated both physically by the diet (NDF) and metabolically by the energy demand of the animal. demand of the animal. A similar approach is also used to predict DMI in NorFor. Each individual A similar approach is also used(FV; to predict Each individual is capacity assigned a basic feed is assigned a basic fill value sectionDMI 6.2) in andNorFor. the animal is assigned an feed intake fill value (FV; Section 6.2) and the animal is assigned an intake capacity (IC) expressed in the same (IC) expressed in the same units as the feed. When predicting DMI, the following general units as the When predicting DMI, the following general equation must be fulfilled: equation mustfeed. be fulfilled: IC= FV_ intake 

[Eq.10.1]

10.1

whereIC ICisisthe theanimal animalintake intake capacity FV_intake is total the total intake expressed where capacity andand FV_intake is the feedfeed intake expressed in fill in fill units, whichwhich is calculated as described in Section 6.2. units, is calculated as described in section 6.2. Despite thethe feed FVFV andand thusthus the FV_intake is notisconstant; it varies Despitethis thisgeneral generalrelationship, relationship, feed the FV_intake not constant; it varies with with the concentrate substitution rate(SubR), (SubR),which which affects affects roughage if the the concentrate substitution rate roughageintake. intake.Moreover, Moreover, if the energy energy concentration the ration is high, relative the animal’s requirements, above concentration of the of ration is high, relative to thetoanimal’s requirements, the the above general equation general equation will overestimate feed intake. An MR factor should therefore be included in the will overestimate feed intake. An MR factor should therefore be included in the equation when equation when formulating rations to meet specific animal production levels (e.g. weight gain or formulating rations to meet specific animal production levels (e.g. weight gain or milk yield). milk yield).

In NorFor, NorFor,the theICICofofeach each animal category estimated feeding experiments in which In animal category has has beenbeen estimated from from feeding experiments in which animals were fed total mixed rations ad libitum or fed only roughage ad libitum with fixed animals were fed total mixed rations ad libitum or fed only roughage ad libitum with fixed levelslevels of concentrate. tried to toevaluate evaluatehow howdifferent different types of roughage of concentrate.These Theseexperiments experiments have tried types of roughage are are utilized and also the interaction between roughage and differentcategories categories utilized and also the interaction between roughage andconcentrate concentrate by by different of of animals. From each experiment, the FVs of the different feeds were calculated assuming that the FV_intake represented the IC of the animals.

10.1. Dairy cows

149

A database of 183 dietary treatments was compiled from Norwegian, Danish, Swedish and Icelandic production experiments. (Table 10.1) in which grass or maize silages constituted the sole or dominant forage. The breeds represented in the database were the dominant Nordic dairy breeds, i.e. DH, SR, SH, NR and IB (Table 3.2). Animal and feed characteristics for the large dairy breeds, JER and IB are presented in Table 10.2, 10.3 and 10.4, respectively. The total DMI varied from 9.7 to 32.3 kg/d H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_10, © Wageningen Academic Publishers 2011

113

Table 10.1. Studies used to develop the feed intake module for dairy cows in NorFor. Bertilsson and Murphy, 2003 Bossen et al., 2010a Bossen et al., 2010b Hetta et al., 2007 Hymøller et al., 2005 Johansen, 1992 Kristensen, 1999 Kristensen et al., 2003 Kristensen et al., unpublished data Misciattelli et al., 2003 Mo and Randby, 1986 Mould, 1996 Nordang and Mould, 1994 Randby, 1992

Randby, 1997 Randby, 1999a Randby, 1999b Randby, 2000 Randby, 2002 Randby, 2007 Randby and Mo, 1985 Randby and Selmer-Olsen, 1997a Randby et al., 1999 Rikarðsson, 1997 Rikarðsson, 2002 Schei et al., 2005 Sveinbjörnsson and Harðarsson, 2008 Weisbjerg et al., unpublished data

Table 10.2. Descriptive statistics of data used to develop the equation for predicting intake capacity of dairy cows: large dairy breeds. Item

Average

SD1

Maximum

Minimum

Days in milk Body weight, kg ECM2, kg/d Dry matter intake, kg/d Concentrate proportion, kg/kg DM Roughage intake, kg DM/d Roughage basis fill value, FV/kg DM Starch + sugars, kg/d

123 577 29.9 19.7 0.42 11.2 0.51 2.7

65 59 5.8 3.4 0.101 2.3 0.031 0.7

328 775 48.9 32.3 0.84 19.4 0.76 5.0

7 437 15.1 9.7 0.14 3.7 0.42 1.5

1 SD=standard

2 ECM=energy

deviation. corrected milk.

Table 10.3. Descriptive statistics of data used to develop the equation for predicting intake capacity of dairy cows: Jersey cows. Item

Average

SD1

Maximum

Minimum

Days in milk Body weight, kg ECM2, kg/d Dry matter intake, kg/d Concentrate proportion, kg/kg DM Roughage intake, kg DM/d Roughage basis fill value, FV/kg DM Starch + sugars, kg/d

119 458 25.8 16.0 0.40 9.6 0.44 3.6

77 43 6.0 2.8 0.09 2.3 0.020 0.7

294 598 47.2 23.8 0.59 18.7 0.48 5.9

7 308 4.0 5.3 0.11 3.1 0.41 1.2

1SD=standard

2ECM=energy

114 

deviation. corrected milk.

NorFor – The Nordic feed evaluation system

Table 10.4. Descriptive statistics of data used to develop the equation for predicting intake capacity of dairy cows: Icelandic cows. Data from Baldursdóttir (2010). 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000

1

Item From each experiment, the FVs of the different Averagefeeds were SD calculatedMaximum animals. assuming thatMinimum the FV_intake represented the IC of the animals.

Days in milk 58 34 147 7 Body weight, kg 439 47 619 300 10.1. ECM2Dairy , kg/d cows 20.4 4.7 41.0 7.6 Dry matterofintake, kg/d treatments was compiled15.1 2.1 Danish, Swedish 25.3 and 7.1 A database 183 dietary from Norwegian, Icelandic production experiments. (Table 10.1) in which maize silages constituted the 0.28 Concentrate proportion, kg/kg DM 0.41 grass or0.09 0.49 sole or dominant forage. The breeds represented in the Roughage intake, kg DM/d 9.0 database were 1.8 the dominant 15.6Nordic dairy4.0 breeds, i.e., basis DH, SR, NRFV/kg and IBDM (Table 3.2). Animal characteristics0.62 for the large 0.48 Roughage fill SH, value, 0.53 and feed 0.025 dairy breeds, JER kg/d and IB are presented in Table 10.2, The total DMI Starch + sugars, 2.510.3 and 10.4, 0.5 respectively.3.1 0.5

varied from 9.7 to 32.3 kg/d and the concentrate intake from 2.7 to 19.8 kg DM/d. The roughage FV ranged from 0.42 to 0.76 per kg DM. A multiple regression approach was used to derive the 1SD=standard deviation. equation for predicting animal IC. 2 ECM=energy corrected milk.

Table 10.1 Table 10.2 and the concentrate intake from 2.7 to 19.8 kg DM/d. The roughage FV ranged from 0.42 to 0.76 Table 10.3 per kg10.4 DM. A multiple regression approach was used to derive the equation for predicting animal IC. Table

Based 1995), thethe following modified multiple regression Basedon onthe thework workofofKristensen Kristensen(1983, (1983, 1995), following modified multiple regression equation equation used to describe IC: was usedwas to describe IC: IC _ cow = ( a ⋅ DIM b ⋅ e c ⋅ DIM − DIM − d + e ⋅ ECM + ( BW − f ) ⋅ g ) 

10.2

[Eq. 10.2]

where intake capacity of lactating dairy dairy cows; cows; DIM isDIM days is in milk; ECM is the whereIC_cow IC_cowisisthethe intake capacity of lactating days in milk; ECM is the energy is is thethe animal body weight, kg; and a, b, a, c, b, d, c, e, d, f, ge,are energycorrected correctedmilk, milk,kg/d; kg/d;BW BW animal body weight, kg; and f, g are regression regression coefficients Table 10.3). coefficients (see Table(see 10.5). ® The et et al.,al., 1998) in Microsoft Excel, whichwhich employs a generalized reduced reduced ® Excel, The Solver Solvertool tool(Fylstra (Fylstra 1998) in Microsoft employs a generalized gradient non-linear optimization code (Lasdon et al., 1978; Sveinbjörnsson et al., 2006), was gradient non-linear optimization code (Lasdon et al., 1978; Sveinbjörnsson et al., 2006), was used used to parameterize the equation and fit regression coefficients to the above model by to parameterize themean equation and fit regression coefficients the above model by minimizing the minimizing the root square prediction error (RMSPE) for to animal IC. The regression root mean square prediction error (RMSPE) for animal IC. The regression coefficients for predicting coefficients for predicting the IC of lactating dairy cows are presented in Table 10.5.

the IC of lactating dairy cows are presented in Table 10.5.

Table 10.5

Figure 10.1 illustrates how the IC coefficients of large dairyused breeds varies with lactation period, with a 305-d Table 10.5. Multiple regression to predict dairy cow intake capacity (IC). yield potential of between 6 000 and 10 000 kg ECM. For Icelandic cows the same approach as 1 in Table 10.5 is from described above was used to parameterize the IC,regression and the parameter values Cow category Multiple coefficients the work of Baldursdóttir (2010).

a

b

c

d

e

f

-0.0006 -0.0006 -0.0004 -0.0004 -0.0014 -0.0013

0.55 0.55 0.25 0.15 0.65 0.14

0.091 500 0.091 575 [Eq. 10.3] 0.110 360 0.110 405 0.015 400 0.035 523

The DMI is also affected by animal activity, and when cows are on pasture, or confined in a loose-house system, an IC exercise value is added (Kristensen, 1995):

Primiparous large dairy breeds Multiparous large breeds ) ⋅ factorR IC = (IC _ animal + ICdairy _ exercize Jersey primiparous cows Jersey multiparous cows Icelandic primiparous cows Icelandic multiparous cows

1

2.59 2.82 2.25 2.40 4.07 4.77

0.134 0.134 0.134 0.134 0.087 150 0.071

g 0.006 0.006 0.006 0.006 0.002 0.0013

IC=(a·DIMb·ec·DIM – DIM–d + e·ECM + (BW – f)·g).

NorFor – The Nordic feed evaluation system

115

4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043

used to parameterize the equation and fit regression coefficients to the above model by minimizing the root mean square prediction error (RMSPE) for animal IC. The regression coefficients for predicting the IC of lactating dairy cows are presented in Table 10.5. Table 10.5

Figure 10.1 illustrates how the IC of large dairy breeds varies with lactation period, with a 305-d

Figure 10.1 illustrates how the6,000 IC ofand large10,000 dairy breeds varies with lactationcows period, a 305-d yield potential of between kg ECM. For Icelandic thewith same approach as yield potential of between 6 000 and 10 000 kgthe ECM. For Icelandic cows the sameinapproach as is from described above was used to parameterize IC, and the parameter values Table 10.5 described above was used to parameterize the IC, and the parameter values in Table 10.5 is from the work of Baldursdóttir (2010). the work of Baldursdóttir (2010).

The DMI DMIisisalso alsoaffected affected by ⋅animal when arepasture, or confined (0.45activity, ) ⎞or confined − activity, 0.09 ⋅ pH +when ACF ⋅ (1.cows 5− 0.223 ⋅on pHpasture, ⎛ LAF The and)and cows are on in a in a loose⎟ ⎜ by animal house system, an ICanexercise value is added (Kristensen, 1995): loose-house system, IC exercise value is added (Kristensen, 1995): [Eq. 5.5] DM corr = DM uncorr + ⎜ + PRF ⋅ (1.4 − 0.182 ⋅ pH) + BUF ⋅ (1.9 − 0.272 ⋅ pH)⎟ ⎟ ⎜ ⎟ ⎜ ) ⋅ factorR IC = (IC _ animal + is ICeither _ exercize [Eq. 10.3]  ⋅ 0.6in Equation 10.2 or IC_dry described 10.3 ALF + NH 3N where IC_animal described in Equation ⎠ ⎝ +IC_cow 10.5 or 10.6; IC_exercize is 0 if the cows are tied up and 0.15 if the cows are in a loose housing where IC_animal is either IC_cow in Equation 10.2 or IC_dryindescribed Equation 10.5 system or on pasture; factorR is an addescribed libitum correction factor as described Equationin 10.4. or 10.6; IC_exercize is 0 if the cows are tied150 up and 0.15 if the cows are in a loose housing system Figure 10.1 or on pasture; factorR is an ad libitum correction factor as described in Equation 10.4. ⎛ ACF ⋅ (1.5 − 0.223 ⋅ pH ) + PRF ⋅ (1.4 − 0.182 ⋅ pH )⎞ ⎟ 5.6] = DMfed +⎜ DM corrare If libitum, e.g.e.g. , in in situations where roughage intake is not optimal, due to due to[Eq. ⎜ + BUF If cows cows arenot notuncorr fedadad libitum, situations not optimal, feed )where ⋅ (1.4do − 0not ⋅ pHmaximal + ALFroughage +roughage .182 NH 3 N ⋅ intake 0intake, .6 ⎟⎠ is ⎝ feed shortage or feeding systems that allow an adjustment factor shortage or feeding systems that do not allow maximal roughage intake, an adjustment factor is is introduced: introduced: ⎛ roughage _ appetite ⎞ − 13 ⎟ + 0.8502  factorR = 0.0214 ⋅ ⎜ 5 ⎝ ⎠

[Eq.10.4]

sid _ mAA = r _ mAA ⋅ 0.85

10.4

[Eq. 7.43]

where factorR is an adjustment factor for the intake capacity; roughage_appetite is the appetite

where factorR is an adjustment factor for the intake capacity; roughage_appetite is the appetite proportion,%%ofofexpected expected libitum intake. adad libitum intake. proportion,

In non-lactating non-lactatingcows, cowsone an of equation on the work of based Kristensen used to predict IC: In the twobased alternative equations, on the (1995) work ofisKristensen (1995) are used to predict IC: IC _ dry = (5 + ((BW − f ) ⋅ 0.006)) ⋅ factor _ h  IC _ dry = (5 + ((BW − 575) ⋅ 0.006)) ⋅ factor _ h

[Eq.10.5]

[Eq.10.5] 10.5

where IC_dry is the intake capacity of non-lactating cows, FV/d; BW is the body weight, kg; f is the [Eq.10.6] Jersey=0.83). IC _ dry = (factor _ i f+in (YHerd 6500)factor_h ⋅ 0.0003 + (is BW f ) ⋅ 0.006 ) ⋅ factor _ hfactor (large breeds=1; regression factor Table−10.5; the−breed correction DM _ int ake ⎛ ST _ SUof_ non-lactating ⎞ ⎛ ST _isSU ⎞ where IC_dry the+ intake factor_i the age and−gestation = 0is.97 − 0.2119 cows, ⋅⎜ FV _ SubR 0.562 ⋅ capacity 0.1932 5.122 ⎟ ⋅ 0.05 ⎜ ⎟ ⋅ 0.1 −FV/d; 1000 1000 ⎝ ⎠ ⎝ ⎠ stage factor (less than 260 days of gestation for primiparous cows = 5.3; multiparous cows = 5.8; 10.0 >260 days of gestation: primiparous cows = 4.8; multiparous cows = 5.3);IC BW is the body 10,000 kg ECM weight, kg; f 9.0 is the regression factor f in table 10.5; factor_h is the breed correction factor (large IC 6,000 kg ECM breeds = 1; Jersey = 0.83). YHerd is the yield level in the herd, 305-d average. [Eq.10.8] 8.0 When DMI is calculated, the IC is set equal to the FV_intake. Several factors affect the 7.0 FV_intake and it isPage generally accepted that increases in concentrate supplementation decrease the Equations on 85 in the proof: roughage intake 6.0 (Thomas, 1987). This is not only due to the filling effect of additional concentre but also by the accompanying reduction in ruminal NDF digestibility (Khalili and Huhtanen, 1991; Huhtanen 5.0 and Jaakkola, 1993; Stensig et al., 1998), which results in a higher roughage FV. 4.184FVk mg However, the concentrate substitution rate is not a constant (INRA, 1989); the true roughage ⋅ which for cows factor_1= 0.29256, for heifers and steers factor _ 1 = 90 ⋅ feed but 4.0 size varies with ration and composition. Moreover, MR is not related to each individual 1000 k m , for bulls instead to the entire ration. Bosch et al. (1992), Rinne et al. (2002) and Eriksen and Ness (2007) 3.0 k mgsize, indicating that the DMI is .184 pool have shown that cows do not eat to a constant ruminal4NDF factor _ 1concentration = 91⋅ ⋅ be used metabolically regulated and that dietary NDF can an indirect of of dairy breeds or crossbreed bulls ofmeasure beef breeds 2.0 1000 k m andasfor MR (Mertens, 1987; The same that was the development of animal 0 1994). 25 50 75 dataset 100 125 150 used 175in200 225 250 275 300 IC was k mg also used to establish a relationship between FV_intake and the effects of SubR and MR. When 4.184 Days factorinto _ 1 =account 100 ⋅ SubR⋅and MR, ; the 4.184/1000 converts to MJ;equation: the km is used to taking the factor FV_intake can in bemilk calculated fromkcal the general 1000 k m

Intake capacity (IC)

3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4001 4000 4002 4003 4004 4005 4006 4007 4008 4009 4010

Figure 10.1. throughout a 305-d lactation period at two production levels calculate FV_ intake = ∑Feed DMIi ⋅intake FVi + ∑capacity DMIj ⋅ FV(IC) [Eq.10.7] j ⋅FV_ SubR + FV_ MR (6,000 and 10,000 kg ECM). i j

4044 151

116 

NorFor – The Nordic feed evaluation system

4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4045 4042 4046 4043 4047 4048 4044 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086

where IC_dry is the intake capacity of non-lactating cows, FV/d; factor_i is the age and gestation stage factor (less than 260 days of gestation for primiparous cows = 5.3; multiparous cows = 5.8; >260 days of gestation: primiparous cows = 4.8; multiparous cows = 5.3); BW is the body weight, kg; f is the regression factor f in table 10.5; factor_h is the breed correction factor (large breeds = 1; Jersey = 0.83). YHerd is the yield level in the herd, 305-d average.

When DMI is calculated, the IC is set equal to the FV_intake. Several factors affect the FV_intake

and it DMI is generally accepted increases supplementation decrease the roughage When is calculated, the ICthat is set equal to in theconcentrate FV_intake. Several factors affect the intake (Thomas, Thisaccepted is not only to the in filling effect ofsupplementation additional concentrate FV_intake and it is1987). generally thatdue increases concentrate decrease but the also by roughage intake (Thomas, 1987). is not onlydigestibility due to the filling effect of Huhtanen, additional concentre the accompanying reduction in This ruminal NDF (Khalili and 1991; Huhtanen but by the accompanying in ruminal NDF digestibility (Khalili and Huhtanen, andalso Jaakkola, 1993; Stensigreduction et al., 1998), which results in a higher roughage FV. However, the 1991; Huhtanen and Jaakkola, 1993; et al ., 1998), whichthe results a higherFV roughage concentrate substitution rate is not aStensig constant (INRA, 1989); true in roughage varies FV. with ration However, the concentrate substitution rate notrelated a constant (INRA, 1989); the FVthe entire size and composition. Moreover, MR is is not to each individual feedtrue butroughage instead to varies with ration size and composition. Moreover, MR is not related to each individual feed but ration. Bosch et al. (1992), Rinne et al. (2002) and Eriksen and Ness (2007) have shown that cows instead to the entire ration. Bosch et al. (1992), Rinne et al. (2002) and Eriksen and Ness (2007) do not eat to a constant ruminal NDF pool size, indicating that the DMI is metabolically regulated have shown that cows do not eat to a constant ruminal NDF pool size, indicating that the DMI is and that dietary NDF concentration be⋅ (used measure of MR (Mertens, 1987; ) + ACF ) ⎞ ⋅ (0.45 − 0.09 ⋅ pHcan 1.5 − 0.as 223an ⋅ pHindirect ⎛ LAF metabolically regulated ⎜ and that dietary NDF concentration can⎟be used as an indirect measure of 1994). The same dataset that was used in the development of animal IC wasofalso used establish a [Eq. 5.5] MR (Mertens, 1987; 1994). The same dataset that was used in the development animal ICtowas ⎟ ⎜ ( ) ( ) DM corr = DM uncorr + + PRF ⋅ 1.4 − 0.182 ⋅ pH + BUF ⋅ 1.9 − 0.272 ⋅ pH ⎟ ⎜ relationship between FV_intake and the effects of SubR and MR. When taking into account SubR also used to establish⎜ a relationship between FV_intake and the ⎟effects of SubR and MR. When + NH3 N ⋅ 0.6 ⎠equation: ⎝ + ALF and MR, FV_intake can be calculated fromcan thebe general taking intothe account SubR and MR, the FV_intake calculated from the general equation: where FV_intake is the feed intake expressed in fill units; FVi is the fill value of the i’th concentrate FV/kg DM; FVj is the basic fill value of the j’th roughage, Equation 6.10 and FV_ intake =feed, ∑ DMI [Eq.10.7] 10.6 i ⋅ FVi + ∑ DMIj ⋅ FVj ⋅FV_ SubR + FV_ MR  6.11; FV_SubR rate factor, 0 to1, Equation 10.8 and 10.15; FV_MR is the the i is the substitution j metabolic regulation ⎛factor, 10.9 and ⋅10.16. ) + PRF (1.4 − 0.182 ⋅ pH )⎞ is the fill value of the i’th concentrate ACF ⋅ (1Equations .5 − 0.223 ⋅ pH where ⎟i [Eq. 5.6] = DM uncorr is + ⎜the feed intake expressed in fill units; FV DM corrFV_intake ⎜ + BUF ⋅ (1.4 − 0.182 ⋅ pH ) + ALF + NH N ⋅ 0.6 ⎟

feed, FV/kg DM; the related basis fill value of the j’th Equation 6.10 and 6.11; FV_SubR 3 roughage, ⎝ j is ⎠ Traditionally, SubRFV has been to the effect 151of concentrate per se. However, it may be is the substitution rate factor, 0 to1, Equation and 10.14; FV_MR is the metabolic difficult to define a feedstuff as strictly ‘roughage’10.7 or ‘concentrate’. In NorFor, variations in theregulation factor, Equations 10.8 and 10.15. SubR are explained by changes in the NDF digestion in the rumen and the effect of rapidly degradable CHO’s on digestion in the rumen. This is similar to the effect used to explain the [Eq. sid _ mAA =ofr ruminal _ mAA .85 been correction NDF digestibility, RLI of (Equation 7.15)per in Chapter 7. Therefore, Traditionally, SubR⋅ 0has related toi.e. thethe effect concentrate se. However, it 7.43] may the be difficult suitability includingas dietary ST‘roughage’ and SU in the of theInsubstitution factor (FV_SubR) to define aoffeedstuff strictly or estimation ‘concentrate’. NorFor, variations in the SubR are was assessed, ExcelinSolver tool to develop a SubR function based on CHO’s explained by employing changes inthe theMicrosoft® NDF digestion the rumen and the effect of rapidly degradable the and SU supply and its effect on roughage FV:to explain the correction of ruminal NDF on dietary ruminalSTdigestion. This is similar to the effect used

digestibility, i.e. the RLI (Equation 7.15) in Chapter 7. Therefore, the suitability of including dietary _ SU _ DM_ h ⎞ ⎛ ST _ SU _ int ake ⎞ IC __dry + ((+in BW − f )⋅ ⎛⎜⋅estimation 0ST .006 )) ⋅ factor FV SR ==(SU 05.97 0.562 .2119 − 5.122 05 [Eq.10.5] ⎟ ⋅ 0.1 − 0.1932 ⋅factor ⎜ ⎟ ⋅ 0.assessed, ST and the of− 0the substitution (FV_SubR) was employing the 1000 1000 ⎝ ⎠ ⎝ ⎠ ® Microsoft Excel Solver tool to develop a SubR function based on the dietary ST and SU supply [Eq.10:8] and its effect on roughage FV:

where FV_SubR is the ⎛roughage substitution⎞ correction factor, 0 to 1; ST_SU_DM is the ST _ SU _ DM ⎛ ST _ SU _ int ake ⎞ 5.122 + 0.562 ⋅ ⎜ sugars in the−diet, ⋅⎜ −the FV _ SubR =of 0.97 0.2119 .1 − 0.1932 ⎟ ⋅ 0DM;  sugar ⎟ ⋅ 0.05and proportion starch and g/g and ST_SU_intake is starch 1000 1000 ⎝ ⎠ ⎝ ⎠ intake, g/d.

10.7

where FV_SubR is the roughage substitution correction factor, 0 to 1; ST_SU_DM is the proportion

Figure 10.2 illustrates how FV_SubR is affected by the amount and starch proportion ST +intake, SU in g/d. of starch and sugars in thethe diet, g/g DM; and ST_SU_intake is the and of sugar [Eq.10.8] the ration; an increased proportion of SU + ST in the diet has a negative effect on the roughage FV and 10.2 this effect is alsohow related to the SU +isST intake.by Thethe latter effectand may explain why Figure illustrates theproof: FV_SubR affected amount proportion of ST + SU in the Equations on Pageconcentrate 85 in the replacing a starchy with a soluble fibre-based concentrate has been have a FV and ration; an increased proportion of SU + ST in the diet has a negative effect found on thetoroughage highly variable effects on forage intake (Huhtanen, 1993; Van Vuuren et al., 1993; Petit and this effect is also related to the SU + ST intake. The latter effect may explain why replacing a starchy Tremblay, 1995; Huhtanen et al., 1995; Hymøller et al., 2005). concentrate with a soluble fibre-based concentrate has been found to 4have variable effects .184 ak highly mg factorPetit _ 1 = and 90 ⋅ Tremblay, ⋅ which for cows factor_1= 0.29256, for heifers andetsteers on forage intake (Huhtanen, 1993; Van Vuuren al., 1993; 1995; Huhtanen et , for bulls 1000 k Figure 10.2 m

al., 1995; Hymøller et al., 2005).

4.184 k mg factor _ 1 = 91 ⋅ causing ⋅ In MR isordefined as a regulatory factor cows to stop eating before reaching their of NorFor, dairy breeds crossbreed 1000 k m and for bulls of beef breeds In NorFor, is defined as a regulatory causingrelated cows factor to stop eating before reaching their full ruminal MR FV capacity. Physiologically, thisfactor is an animal rather than a direct k therefore 4.capacity. 184 mg Physiologically, ruminal effect, and it is expected to influence animal IC. However, in NorFor it is full ruminal FV this is an animal related factor rather than a direct factor _ 1 = 100 ⋅ ⋅ ; the factor 4.184/1000 converts kcal to MJ; the km is used to addedruminal to the feed of the equation whentocalculating forIC. computational k m expected effect, andside it is 1000 therefore influence DMI animal However, inreasons. NorForThe it isFV_MR added toisthe feed

calculated side of theas: equation when calculating DMI for computational reasons. The FV_MR is calculated as: calculate ⎛ ⎜ 2.530 FV _ MR = ⎜1.453 − ⎜ ⎛ (0.466 − FV _ r ) ⎞ ⎜ ⎜ 0.065 ⎟⎠ 1 + e⎝ ⎝

⎞ ⎟ ⎟ ⋅ IC ⎟ 8 ⎟ ⎠



[Eq.10.9]

where FV_MR is the roughage metabolic correction factor; FV_r is the mean basis roughage fill value in the diet, FV/kg DM, Equation 6.10; IC is animal intake capacity, Equation 10.3; and the IC/8 ratio is the adjustment factor for animal IC level.

NorFor – The Nordic feed evaluation system 152

10.8

117

1.2

1.1

1 FV_SubRr

Starch + sugar (g/g DM)

0.05

0.40 0.35 0.30 0.25 0.20 0.15 0.10

0.9

1

2

0.8 8 7 5 6 4 3 Starch + sugar (kg/d)

Figure 10.2. A principle figure describing the effect of starch + sugar intake and their proportion in the diet on roughage substitution rate (FV_SubR) factor. where FV_MR is the roughage metabolic correction factor; FV_r is the mean basis roughage fill value in the diet, FV/kg DM, Equation 6.10; IC is animal intake capacity, Equation 10.3; and the IC/8 ratio is the adjustment factor for animal IC level. Dividing IC by 8 adjusts the standard IC so that FV_MR can be used across dairy breeds. Mertens (1994) demonstrated that the threshold between an energy regulated intake and a fill limited intake was dependent on the milk yield. Maximum fill and minimum energy concentration was achieved at a higher NDF diet concentration (440 g/kg DM versus 320 g NDF/kg DM) for low-yielding cows (20 kg ECM) than for high yielding cows (40 kg ECM). Figure 10.3 shows the relationship between FV_MR and FV_r at two levels of IC (6 and 8, which represent different milk yields). At an FV_r of 0.48 the FV_MR is zero, and with a higher and increasing FV_r a low yielding cow will reach its IC relative faster than a high yielding cow. However, at low FV_r values an opposite effect is achieved, which probably is explained by a higher diet energy density and thus a metabolic regulation of the intake (Mertens, 1994). Parameterization of Equation 10.6 showed that FV_MR makes a highly significant contribution to the prediction of roughage intake. The RMSPE was reduced by 19% when the FV_MR was introduced into the FV_intake equation. The need for this correction factor demonstrates that ruminal fill is not the only factor limiting roughage intake, as there are other important metabolic regulations (Forbes, 1995). Since FV_MR is related to roughage FV, and thus to the concentration of NDF in roughage, the rate of rumen particle size reduction may also partly explain this correction factor, since physical particle breakdown is not accounted for when calculating roughage FV. The introduction of FV_SubR and FV_MR in the prediction of DMI demonstrates that roughage FV is not a constant. Ration formulation is therefore performed by an iteration process in NorFor, using a computer program capable of solving non-linear optimization problems (see Chapter 15). The intake sub-model is further evaluated in Chapter 14.

118 

NorFor – The Nordic feed evaluation system

Metabolic regulation factor (FV_MR)

1.5 1.3

IC=8 IC=6

1.0 0.8 0.5 0.3 0.0 0.25 0.30 -0.3

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

-0.5 -0.8 -1.0 -1.3

Roughage basis fill value (FV_r)

Figure 10.3. Relationship between roughage basis fill value (FV_r) and metabolic regulation factor (FV_MR) at two levels of intake capacity (IC=6 and 8).

10.2 Growing cattle The feeding strategies used in Nordic beef production vary widely among countries, from an intensively reared beef animals fed up to 90% concentrates to an extensive system where only small amounts (0 to10%) of concentrates are used. Hence, there are substantial variations in diet composition, which makes it challenging to develop a voluntary feed intake system that is relevant to all production systems. To develop equations for predicting the IC of bulls, steers and heifers, a database was compiled from 32 Danish, Swedish and Norwegian experiments (Table 10.6), in which cattle were fed a wide range of diets, including 145, 36 and 115 treatments for bulls, steers and heifers, respectively. Animal and feed characteristics for the bull, steer and heifer datasets are presented in Tables 10.7, 10.8 and 10.9, respectively. In the bull dataset, DMI varied from 4.5 to 11.7 kg/d (concentrate from 0 to 6.8 kg DM/d) and ADG ranged from 429 to 1,578 g, while in the heifers dataset, ADG varied from 378 to 1196 g/d and the DMI from 3.3 to 11.4 kg/d. 4131 4131 4132 4132 4133 4133 4134 4134 4135 4135 4136 4136 4137 4137 4138 4138 4139 4139 4140 4140 4141 4141 4142 4142 4143 4143 4144 4144 4145 4146 4147 4148 4149 4150 4151

For all all three three animal categories, categories,variations variationsininIC IC wereexplained explainedby bychanges changesininBW BW andADG. ADG.The The For ® changes inand Forequations all threeanimal animal categories, variations in were IC were explained by BW and ADG. The IC ® Excel using the same IC were parameterized using the Solver tool in Microsoft IC equationswere wereparameterized parameterized using the Solver tool in in Microsoft Excel usingusing the same ® Excel equations using the Solver tool Microsoft the same approach approach as for the dairy cows. In bulls the IC is calculated from: approach as for the dairy cows. In bulls the IC is calculated from:

as for the dairy cows. In bulls the IC is calculated from: − 20 IC__ bull bull == 00..006544 006544⋅⋅BW BW++00..0007337 0007337⋅⋅ADG ADG++ − 20 IC .9552⋅ ⋅BW BW 00.9552



[Eq.10.10] 10.10] [Eq.

10.9

whereIC_bull IC_bullisisisthe the feed intake capacity of bulls; BW the animal body weight, and ADG is where IC_bull the feed intake capacity ofbulls; bulls; BWis isthe theisanimal animal bodyweight, weight, kg;and andkg; ADG where feed intake capacity of BW body kg; ADG the average daily gain, g/d. is the average daily gain, g/d. is the average daily gain, g/d. The steer dataset included data obtained from only 3636 dietary treatments andand preliminary Thesteer steerdataset datasetincluded includeddata dataobtained obtained from only dietary treatments a preliminary evaluation The from only 36 dietary treatments and a apreliminary evaluation showed that the IC ICand ofsteers steers and heifers werecomparable. comparable. These twodatasets datasets were combined evaluation showed the of and heifers were These two were showed that the ICthat of steers heifers were comparable. These two datasets were therefore therefore combined in in the theof parameterization ofderived IC.The TheIC ICwas was derived from thesedata datafrom: from: therefore combined parameterization of IC. derived these in the parameterization IC. The IC was from these datafrom from: ⎛ −−33 ⎞ ⎞⎟ ⋅ IC _ gest IC __ heifer heifer == ⎛⎜⎜00..007236 007236⋅⋅BW BW++00..0005781 0005781⋅ ⋅ADG ADG++ IC ⋅ IC _ gest 0 . 9552 BW⎟⎠ ⎠ ⎝ 0 . 9552 ⋅ ⋅BW ⎝



[Eq.10.11] 10.11] [Eq.

where IC_heifer IC_heifer is is the the feed feed intake intakecapacity capacityof ofsteers steersand andheifers; heifers;BW BWisisthe theanimal animalbody bodyweight, weight, and ADG ADG is is the the average average daily dailygain, gain,g/d. g/d.IC_gest IC_gestisisthe theintake intakecapacity capacityadjustments adjustmentsfor forheifers heifers kg; and due to gestation gestation described described in in Equation Equation10.12. 10.12.

NorFor – The Nordic feed evaluation system

The gestation gestation correction correction factor factorisiscalculated calculatedas: as:

10.10

119

Table 10.6. Studies used to develop the feed intake module for growing cattle in NorFor.

4131 4131 4132 4132 4133 4133 4134 4134 4135 4135 4136 4136 4137 4137 4138 4138 4139 4139 4140 4140 4141 4141 4142 4142 4143 4143 4144 4144 4145 4145 4146 4146 4147 4147 4148 4148 4149 4149 4150 4150 4151 4151 4152 4152 4153 4153 4154 4154 4155 4155 4156 4156 4157 4157 4158 4158 4159 4159 4160 4160 4161 4161 4162 4162 4163 4163 4164

Bulls

Steers

Heifers

Andersen et al., 1993a Andersen et al., 1993b Berg, 2004 Berg and Volden, 2004 Damgaard and Hansen, 1992 Jørgensen et al., 2007 Kirkland et al., 2006 Matre, 1984 Nadeau et al., 2002 Olsson and Murphy, unpublished data Selmer-Olsen, 1994 Therkildsen et al., 1998 Vestergaard et al., 1993b Vestergaard et al., unpublished data Nadeau, unpublished data

Matre, 1984 Matre, 1987 Randby, 1999c Randby and Selmer-Olsen, 1997b Rustas et al., 2003 Selmer-Olsen, 1994

Andersen et al., 2001 Havrevoll, unpublished data Hessle et al., 2007 Ingvartsen et al., 1988 Mäntysaari, 1993 Rustas, 2009 Sejrsen and Larsen, 1978 Olsson, unpublished data Selmer-Olsen, 1994 Vestergaard et al., 1993a

For all three animal categories, variations in IC were explained by changes in BW and ADG. The For all10.7. three Descriptive animal categories, variations IC were explained changesfor in predicting BW and ADG. Thecapacity Table statistics of datainused to develop theby equation intake IC equations were parameterized using the Solver tool in Microsoft®®Excel using the same IC equations were parameterized using the Solver tool in Microsoft Excel using the same of bulls. approach as for the dairy cows. In bulls the IC is calculated from: approach as for the dairy cows. In bulls the IC is calculated from:

Item Average − 20 − 20 IC _ bull = 0.006544⋅ BW + 0.0007337⋅ ADG + IC _ bull = 0.006544⋅ BW + 0.0007337⋅ ADG +0.9552 ⋅ BW 0.9552 Age at start of experiment, d 224 ⋅ BW

SD1

Maximum

[Eq. 10.10] [Eq. 10.10]

Minimum

109 366 83 Age at end of experiment, d 392 127 676 188 where IC_bull is the feed intake capacity of bulls; BW is the animal body weight, kg; and ADG where IC_bull isweight, the feedkg intake capacity of bulls;362 BW is the animal body 130 body weight, 634kg; and ADG 178 isAverage the average daily gain, g/d. is the average daily gain, g/d. Average live weight gain, g/d 1,165 222 1,578 429 The dataset included obtained from only 367.3 dietary treatments a preliminary Drysteer matter intake, kg/d data 2.1 and 11.7 4.5 The steer dataset included data obtained from only 36 dietary treatments and a preliminary evaluation showed thatkg theDM/d IC of steers and heifers were comparable. These two datasets were 0 Concentrate intake, 3.0 1.9 6.8 evaluationcombined showed that theparameterization IC of steers andof heifers were Thesethese two datasets were therefore in the IC. The IC comparable. was derived from data from: Concentrate proportion, kg/kg DM 0.41 0.29 therefore combined in the parameterization of IC. The IC was derived from these0.95 data from: 0 Roughage intake, kg DM/d 4.3 2.1 9.5 0.2 −3 ⎞ ⎛ IC _ heifer = ⎜⎛basis 0.007236 0005781DM + 0.FV/kg ⋅ ADG + ⎟ ⎞⋅ IC _ gest 0.06 [Eq. 10.11] Roughage fill⋅ BW value, 0.68 0.41 − 3 0.54 [Eq. 10.11] 0.9552 ⋅ BW IC _ heifer = ⎝⎜ 0.007236 ⋅ BW + 0.0005781 ⋅ ADG + ⎠ ⎟ ⋅ IC _ gest 0.9552 ⋅ BW ⎠ ⎝

1

SD=standard deviation. where IC_heifer is the feed intake capacity of steers and heifers; BW is the animal body weight, where is the feed intake capacity of steersisand BW is the animal body kg; andIC_heifer ADG is the average daily gain, g/d. IC_gest theheifers; intake capacity adjustments for weight, heifers kg; and ADG is described the gain, g/d. IC_gest is the capacity heifers where IC_heifer is average the feed intake capacity of steers andintake heifers; BW isadjustments the animalfor body weight, kg; due to gestation in daily Equation 10.12. due gestation Equation 10.12. and to ADG is thedescribed average in daily gain, g/d. IC_gest is the intake capacity adjustments for heifers due The gestationdescribed correctionin factor is calculated to gestation Equation 10.11. as: The gestation correction factor is based on Kristensen and The gestation correction factor is calculated as: Ingvartsen (2003) and calculated as: 1 IC _ gest = [Eq. 10.12] 1 ⎛ gest _ day − 357 ⎞  IC _ gest = 10.11 [Eq. 10.12] ⎜⎛ gest _ day − 357⎟⎞ 36 ⎠⎟ 1 + e ⎝⎜ 36 ⎠ 1 + e⎝ where gestation correction on the capacity; gest_day is the day of gestation. whereIC_gest IC_gestisisthethe gestation correction on intake the intake capacity; gest_day is the day of gestation. where IC_gest is the gestation correction on the intake capacity; gest_day is the day of gestation. Evaluation 10.10 andand 10.11 showed that they for the Jersey breed. EvaluationofofEquations Equations 10.9 10.10 showed thatwere theyunsuitable were unsuitable for the Jersey breed. Evaluationthe of Equations 10.10 andwas 10.11 showed theyyoung were unsuitable for the Jersey Therefore, following equation modified forthat Jersey stock, irrespective of sex:breed. Therefore, the following equation was modified for Jersey young stock, irrespective of Therefore, the following equation was modified for Jersey young stock, irrespective of sex: sex: −2 IC _ jersey = 0.0067 ⋅ BW + 0.0005781⋅ ADG +  10.12 [Eq. 10.13] −2 IC _ jersey = 0.0067 ⋅ BW + 0.0005781⋅ ADG +0.9552 ⋅ BW [Eq. 10.13] 0.9552 ⋅ BW where IC_jersey is the feed intake capacity of Jersey young stock; BW is the animal body weight, 120and  IC_jersey NorFor – The Nordic feed evaluation where is the feed daily intakegain, capacity stock; BW is the animal body weight,system kg; ADG is the average g/d. of Jersey young kg; and ADG is the average daily gain, g/d. The IC of growing cattle is also corrected for activity:

Table 10.8. Descriptive statistics of data used to develop the equation for predicting intake capacity of steers. 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170

1

Item Average SDchanges in BW Maximum For all three animal categories, variations in IC were explained by and ADG.Minimum The IC equations were parameterized using the Solver tool in Microsoft® Excel using the same approach as for dairy cows. from: Age at start of the experiment, d In bulls the IC is calculated 237 125 450 125 Age at end of experiment, d 349 127 555 208 − 20 Average body weight, kg 319 104 470 158 IC _ bull = 0.006544⋅ BW + 0.0007337⋅ ADG + [Eq. 10.10] Average live weight gain, g/d 0.9552 748 ⋅ BW 252 1,196 187 Dry matter intake, kg/d 6.3 1.8 9.5 4.1 where IC_bullintake, is the feed intake capacity of bulls; BW kg; and ADG0 Concentrate kg DM/d 1.0is the animal 0.9body weight,2.6 isConcentrate the average proportion, daily gain, g/d. kg/kg DM 0.21 0.18 0.54 0 Roughage intake, kg DM/d 5.3 2.5 9.5 2.2 The steer dataset included data obtained from only 36 dietary treatments and a preliminary Roughage basis fill value, FV/kg DM 0.58 0.07 0.67 0.48 evaluation showed that the IC of steers and heifers were comparable. These two datasets were therefore combined in the parameterization of IC. The IC was derived from these data from: 1 SD=standard deviation.

−3 ⎞ ⎛ IC _ heifer = ⎜ 0.007236 ⋅ BW + 0.0005781 ⋅ ADG + ⎟ ⋅ IC _ gest 0.9552 ⋅ BW ⎠ ⎝

[Eq. 10.11]

Table 10.9. Descriptive statistics of data used to develop the equation for predicting intake capacity of heifers. where IC_heifer is the feed intake capacity of steers and heifers; BW is the animal body weight, kg; and ADG is the average daily gain, g/d. IC_gest is the intake capacity adjustments for heifers due Itemto gestation described in Equation 10.12. Average SD1 Maximum Minimum The correction factor Agegestation at start of experiment, d is calculated as: 368

124 491 122 Age at end of experiment, d 468 103 634 310 1 Average 379 108 598[Eq. 10.12] 150 IC _ gest =body weight, kg day −g/d 357 ⎞ ⎛ gest _gain, Average live weight 798 205 1,196 378 ⎜ ⎟ 36 ⎠ Dry matter1intake, 7.3 1.6 11.4 3.3 + e ⎝ kg/d Concentrate intake, kg DM/d 1.3 1.3 5.1 0 where IC_gestproportion, is the gestation correction capacity; gest_day Concentrate kg/kg DM on the intake 0.19 0.20 is the day 0.75of gestation.0.0 Roughage intake, kg DM/d 6.0 2.1 11.4 1.2 Evaluation Equations 10.10 and 10.11 they were unsuitable for the0.67 Jersey breed.0.49 Roughageof basis fill value, FV/kg DM showed that 0.54 0.04 Therefore, the following equation was modified for Jersey young stock, irrespective of sex: 1

SD=standard deviation.

IC _ jersey = 0.0067 ⋅ BW + 0.0005781⋅ ADG +

−2 0.9552 ⋅ BW

[Eq. 10.13]

where IC_jersey is the feed intake capacity of Jersey young stock; BW is the animal body weight,

where IC_jersey is the feed intake capacity of Jersey young stock; BW is the animal body weight, kg; and andADG ADGisisthe theaverage average daily gain, kg; daily gain, g/d.g/d.

The IC ICofofgrowing growingcattle cattleis is also corrected activity: The also corrected for for activity: IC = IC _ animal ⋅ IC _ exercize 

[Eq. 10.14]

10.13

where capacity; IC_animal is either IC_bull, IC_heifer or IC_jersey described in whereIC ICisisthe theintake intake capacity; IC_animal is either IC_bull, IC_heifer or IC_jersey described in Equation Equation10.10, 10.9, 10.11 10.10 and and10.13, 10.12,respectively. respectively.

Based on the range of animal characteristics in the datasets, it was decided that the IC equations are 154 with body weights of ≥80 kg, and for Jersey with valid for animals of large dairy and beef breeds body weights ≥60 kg. Figure 10.4 illustrates how the IC in growing bulls changes with BW and ADG over time up to the target slaughter weight of 300 kg at 16 months of age. The ADG changes in a curvilinear manner NorFor – The Nordic feed evaluation system

121

4191 4190 4192 4193 4194 4195 4191 4196 4192 4197 4193 4198 4194 4199 4195 4200 4196 4201 4197 4202 4198 4203 4199 4204 4200 4205 4201 4206 4202 4207 4203 4208 4204 4209 4205 4206 4210 4207 4208 4209 4210

1,400

5.0

Body weight ADG IC

4.5 4.0

1,200

3.5

1000

3.0 2.5

800

Intake capacity

Body weight (kg) or daily weight gain (g)

4171 4172 4173 4174 4175 4171 4176 4172 4177 4173 4178 4174 4179 4175 4180 4176 4181 4177 4182 4178 4183 4179 4184 4180 4185 4181 4186 4182 4187 4183 4188 4184 4189 4185 4186 4187 4190 4188 4189

1,600

Based on the range of animal characteristics in the datasets, it was decided that the IC 2.0equations 600 are valid for animals of large dairy and beef breeds with body weights of ≥ 80 kg, and for Jersey 1.5 with body weights ≥400 60 kg. 1.0 Figureon 10.4 how thecharacteristics IC in growinginbulls changes with and ADG over time up to 200 Based theillustrates range of animal the datasets, it wasBW decided that the IC equations 0.5 the valid targetfor slaughter ofdairy 300 kg at beef 16 months age.body Theweights ADG changes a curvilinear are animalsweight of large and breeds of with of ≥ 80inkg, and for Jersey 0 kg.highest ADG is achieved at an age of 330 days. Since the 0.0 manner with time and IC is with body weights ≥ 60the 91 and 122 ADG, 152 182 213 changes 243 274 over 304 time 334 365 426 456fashion. 486 dependent on both the BW it also in a 395 sigmoidal dayswith BW and ADG over time up to Figure 10.4 illustrates how the IC in growing bullsAge, changes Figure 10.4 the target slaughter weight of 300 kg at 16 months of age. The ADG changes in a curvilinear Figure 10.4. Changes inhighest intake ADG capacity (IC) over time bulldays. withSince a target slaughter weight of manner with time and the is achieved at an agefor ofa330 the IC is 300 kgdairy (587 kg body at 16 months of age.isover As for feed and intake in growing cattle dependent the SubR and MR effects. To dependent oncows, both the weight) BW ADG, it also changes time inon a sigmoidal fashion. parameterize these two variables for the growing cattle, the same approach was used as for lactating dairy However, in the cattle with time and cows. the highest ADGtheis concentrate achieved atcharacteristics an age of 330available days. Since thegrowing IC is dependent on both Figure 10.4 datasets incomplete, made it difficult develop a SubR equation based on starch and the BW were and ADG, it alsowhich changes over time intoa sigmoidal fashion. sugar Consequently, SubR equation developed from the information on the As forinformation. dairy cows, the feed intake the in growing cattle iswas dependent on the SubR and MR effects. To proportion of these in the diet. Thegrowing following equation is used for growing cattle, of all parameterize two for the cattle, theissame approach as and for As for dairy concentrate cows, thevariables feed intake in growing cattle dependent onwas theused SubR MR effects. types: dairy cows. However, the concentrate characteristics available in the growing cattle lactating To parameterize these two variables for the growing cattle, the same approach was used as for datasets were incomplete, which made it difficult to develop a SubR equation based on starch and lactating dairy cows. However, the concentrate characteristics available in the growing cattle 2 sugar information. Consequently, the SubR equation was developed from the information on the ⎛ ⎞ concit_ difficult share datasets were to develop a SubR equation based on starch and ⎜ incomplete, which made ⎟ 0 .9646 0 .706 The ⋅ proportion of⎜ concentrate in the− diet. following equation⎟ is used for growing cattle, of all 100 [Eq.information 10.15] sugar information. Consequently, the SubR equation was developed from the on the FV _ SubR = ⎜ types: 2 ⎟

⎜ concentrate concin_ the sharediet. The following ⎛ conc _ share ⎞ ⎟ proportion of equation is used for growing cattle, of all types: 1 − 0 .7512 ⋅ − 0 .2085 ⋅ ⎜ ⎟

⎜ ⎟ 100 100 ⎝ ⎠ ⎠2 ⎝ ⎛ ⎞ conc _ share ⎜ ⎟ 0 .9646 − 0 .706 ⋅ ⎜ ⎟ 100 correction factor, where FV_SubR is the roughage substitution FV _ SubR =⎜ 2 ⎟ conc _ share _ share proportion of⎜ 1concentrate in the diet on a DM basis, %. ⎛ conc ⎞ ⎟ − 0 .2085 ⋅ ⎜ ⎟ ⎟ ⎜ − 0 .7512 ⋅ 100 100 ⎝ ⎠ ⎠ ⎝

[Eq. 10.15] 0 to 1; and conc_share is the

10.14

Figure 10.5 shows how the FV_SubR factor changes with the proportion of concentrate in the where FV_SubR is the roughage substitution correction factor, 0 to 1; and conc_share is the proportion diet. The growingiscattle diets vary widely in the concentrate to 0roughage (0 to 90is%the where FV_SubR the diet roughage substitution correction factor, to 1; andratio conc_share of concentrate in the on a the DM basis, %. concentrate DM basis),inthus, correction proportion ofonconcentrate the diet FV_SubR on a DM basis, %. factor must be able to correct the roughage intake over a large range of concentrate intake values. The FV_SubR factor, and Figure10.5 10.5shows shows howthethe FV_SubR factor changes with theconcentrate proportion consequently, the roughage FV, increase considerably when inof theconcentrate diet exceeds Figure how FV_SubR factor changes with thethe proportion of concentrate in the in the diet. The The growing cattle diets vary widely in in thetheconcentrate totoroughage (0 to to 90% 60%. Thisgrowing indicates that the voluntary roughage intake is likely to be lowratio at high levels diet. cattle diets vary widely concentrate roughage ratio (0 90 %ofconcentrate on DM basis),on thus, FV_SubR correction mustfactor be able to correct roughage concentrate. concentrate DMthe basis), thus, the FV_SubRfactor correction must be able the to correct the intake over a large range of concentrate values. The FV_SubR factor, consequently, the roughage FV, roughage intake over a largeintake range of concentrate intake values. Theand FV_SubR factor, and Figure 10.5 increase considerably whenFV, theincrease concentrate in the diet exceeds 60%. Thisinindicates the voluntary consequently, the roughage considerably when the concentrate the diet that exceeds 60%. This intake indicates thetovoluntary intake likely to be low at high levels of roughage is that likely be low atroughage high levels of isconcentrate. The FV_MR significantly affected the prediction of DMI in growing cattle, and by including this concentrate. variable in the significantly prediction of FV_intake, MSPE wasofreduced 14% forcattle, the predicted The FV_MR affected thetheprediction DMI inbygrowing and by including this roughage intake. The FV_MR was parameterized independently of animal category and is Figure 10.5 variable in the prediction of FV_intake, the MSPE was reduced by 14% for the predicted roughage calculated as: intake. The FV_MR was parameterized independently of animal category and is calculated as: The FV_MR significantly affected the prediction of DMI in growing cattle, and by including this variable in the MSPE was reduced by 14% ⎛for ⎞ ⎛ prediction of FV_intake,1the .2158 IC the ⎞ predicted ⎟⎟ ⋅ 1 − e − 0.029474088⋅ BW ⋅ ⎜ ⎟  [Eq. 10.16] FV _ MR = ⎜⎜ − 3.8301 + 2.6608 ⋅ FV _ r + 10.15 roughage intake. The FV_MR was parameterized independently of animal category and is FV _ r 3 ⎝ ⎠ ⎠ ⎝ calculated as:

(

)

(

)

⎛ 1.2158 ⎞ 155 − 0.029474088⋅ BW ⎛ IC ⎞ ⎟ ⋅ 1− e FV _ MR = ⎜⎜ − 3.8301 + 2.6608 ⋅ FV _ r + ⋅⎜ ⎟ FV _ r ⎟⎠ ⎝ 3⎠ ⎝

122 

[Eq. 10.16]

NorFor – The Nordic feed evaluation system 155

FV_SubR

6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

0

10

20

30

40

50

60

70

80

90

100

Concentrate in diet, %

Figure 10.5. Effect of proportion of concentrate in the diet on roughage substitution rate in growing cattle. where FV_MR is the roughage metabolic correction factor; FV_r is the diet basis roughage fill value, Equation 6.10; IC is the animal intake capacity, Equation 10.13; BW is the animal body weight, kg; and the IC/3 ratio is an adjustment factor for animal IC level. The computer program TableCurve 2D from SYSTAT was used to resolve the equation profile and the FVL_intake equation was parameterized using the Solver tool in Microsoft® Excel, as described for the dairy cow model. Based on sheep data, Mertens (1994) demonstrated that the dietary NDF concentration at the intersect of metabolic and physical regulation is dependent on animal BW. This may explain why animal BW significantly affected the FV_MR in Equation 10.15. The IC/3 ratio in the equation reflects the different animal categories.

10.3 Implications A primary objective of this work has been to develop a system that can be used to formulate diets that maximize roughage intake while meeting animals’ energy requirements with a minimal dietary energy concentration. It is important to ensure that there is a high proportion of roughage in diets to maintain a favourable rumen environment. The feed intake sub-model in NorFor is complex and consists of several non-linear equations. This comprehensive approach requires DMI to be calculated iteratively by a computer program. Although the system is a simplification of numerous factors and animal-diet interactions affecting voluntary feed intake, it is applicable to diets with a wide range of roughage qualities and diverse feeding situations.

References Andersen, H.R., J. Foldager, P.S. Varnum and S. Klastrup, 1993a. Effects of urea and soybean meal at two levels in a whole crop barley silage ration on performance, carcass and meat quality in young bulls. Forskningsrapport nr. 4 fra Statens Husdyrbrugsforsøg, Denmark. (In Danish with English summary). Andersen, H.R., B.B. Andersen, P. Madsen, P.S. Varnum and L.R. Jensen, 1993b. Effects of maize silage plus urea or soybean versus concentrate on performance, carcass and meat quality of young bulls. Forskningsrapport nr. 5 fra Statens Husdyrbrugsforsøg, Denmark. (In Danish with English summary). Andersen, H.R., B.B. Andersen and H.G. Bang, 2001. Kødracekrydsninger: Kvier kontra ungtyre fodret med forskelligt grovfoder-kraftfoderforhold og slagtet ved forskellig vægt. DJF rapport Husdyrbrug nr. 28. 82 pp. (In Danish).

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Baldursdóttir, R., 2010. Development of the feed intake capacity of Icelandic cows in the NorFor feed evaluation system. Master Thesis, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Norway. 63 pp. Berg, J., 2004. Sluttrapport. Produksjonsegenskapene hos slakteokser av Charolais x NRF, Blond D’ Aquitaine x NRF og NRF ved to ulike fôrstyrkenivåer. Institutt for husdyr og akvakulturvitenskap, UMB. Intern rapport, pp. 11. (In Norwegian). Berg, J. and H. Volden, 2004. Økologisk storfekjøtt. Buskap nr 4. 16-17. (In Norwegian). Bertilsson J. and M. Murphy, 2003. Effects of feeding clover silages on feed intake, milk production and digestion in dairy cows. Grass and Forage Science 58: 309-322. Bosch, M.W., S.C.W. Lammers-Wienhoven, G.A. Bangma, H. Boer and P.W.M. Van Adrichem, 1992. Influence of stage of maturity of grass silages on digestion processes in dairy cows. 2. Rumen contents, passage rates, distribution of rumen and faecal particles and mastication activity. Livestock Production Science 32: 265-281. Bossen, D., M.R Weisbjerg, L. Munksgaard, and S. Højsgaard, 2010. Allocation of feed based on individual dairy cow live weight changes. I: Feed intake and live weight changes. Livestock Science 126: 252-272. Bossen, D. and M.R. Weisbjerg, 2010. Allocation of feed based on individual dairy cow live weight changes. II: Effect on milk production. Livestock Science 126: 273-285. Damgaard, P. and S. Hansen, 1992. Sammenligning af valset og pilleteret kraftfoder til ungtyre. LK-meddelelse nr. 189. 3 pp. (In Danish). Eriksen, J. and S. Ness, 2007. Effet av høstetid for surfôr og kraftfôrnivå på passasjekinetikk of NDF estimert med markørmetoden og vomtømmingsmetoden. Masteroppgave Universitetet for miljø- og biovitenskap. 87 pp. (In Norwegian). Forbes, J.M., 1995. Voluntary Food Intake and Diet Selection in Farm Animals. CAB International, Wallingford, UK. Fylstra, D., L. Lasdon, J. Watson and A. Waren, 1998. Design and use of the Microsoft Excel Solver. Interfaces 28: 29-55. Garmo, T.H., H. Volden, S. Krizsan and S.K. Nes, 2007. Effects of level of concentrate supplementation on nutrient digestion of lactating dairy cows grazing at two pasture allowances. Journal of Animal Science 85: 635, Supplement 1. Hessle, A., E. Nadeau and S. Johnsson, 2007. Beef heifer production as affected by indoor feed intensity and slaughter age when grazing semi-natural grasslands in summer. Livestock Science 111: 124-135. Hetta, M., J.W. Cone, G. Bernes, A.-M. Gustavsson and K. Martinsson, 2007. Voluntary intake of silages in dairy cows depending on chemical composition and in vitro gas production characteristics. Livestock Science 1006: 47-56. Huhtanen, P., 1993. The effects of concentrate energy source and protein content on milk production in cows given grass silage ad libitum. Grass and Forage Science 48: 347-355. Huhtanen, P. and S. Jaakkola, 1993. The effects of forage preservation method and proportion of concentrate on digestion of cell wall carbohydrates and rumen digesta pool size in cattle. Grass and Forage Science 48: 155-165. Huhtanen, P., S. Jaakkola and E. Saarisalo, 1995. The effects of concentrate energy source on the milk production of cows given a grass silage-based diet. Animal Science 60: 31-40. Hymøller, L., M.R. Weisbjerg, P. Nørgaard, C.F. Børsting and N.B. Kristensen, 2005. Majsensilage til malkekøer. DJF Rapport Husdyrbrug nr. 65. 71 pp. (In Danish). INRA (Institute National de la Recherche Agronomique), 1989. Feed intake: The fill unit system. In: Jarrige, R. (ed.). Ruminant Nutrition. Recommended allowances and feed tables. John Libbey and Co Ltd, London, UK, pp. 61-71. Ingvartsen, K.L., J. Foldager, J.B. Larsen and V. Østergaard, 1988. Growth and milk yield by Jersey reared at different planes of nutrition. Beretning nr. 645 fra Statens Husdyrbrugsforsøg. 72 pp. (In Danish with English summary and subtitles). Jarrige, R., 1986. Voluntary intake in cows and its prediction. International Dairy Federation Bulletin 196: 4-16. Johansen, A., 1992. A comparison between meadow fescue and timothy silage. Doctor Scientarium Thesis. Agricultural University of Norway, Aas, Norway. 108 pp. Jørgensen, K.F., J. Sehested and M. Vestergaard, 2007. Effect of starch level and straw intake on animal performance, rumen wall characteristics and liver abscesses in intensively fed Friesian bulls. Animal 1: 797-803. Kirkland R.M., T.W.J. Keady, D.C. Patterson, D.J. Kilpatrick and R.W.J. Steen, 2006. The effect of slaughter weight and sexual status on performance characteristics of male Holstein-Friesian cattle offered a cereal-based diet. Animal Science 82: 397-404. Kristensen, V.F., 1983. Styring av foderopptagelsen ved hjælp af foderrasjonens sammensætning og valg af fodringsprincip. In: Østergaard, V. and A. Neimann-Sørensen (eds.). Optimale foderrationer til malkekoen. Foderværdi, foderopptagelse, omsætning og produktion. Rapport nr. 551. Beretning fra Statens Husdyrbrugsforsøg. pp. 7.1-7.35. (In Danish).

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Kristensen, V.F., 1995. Forudsigelsen af foderoptagelsen hos malkekøer. Intern rapport nr 61. Statens Husdyrbrugsforsøg. 28 pp. (In Danish). Kristensen, V.F., 1999. Grovfoderkildens betydning for malkekoens produktion og effektivitet. Temamøde vedr. Malkekøernes og kviernes fodring. Intern Rapport nr 118, Danmarks Jordbrugsforskning. pp. 18-33. (In Danish). Kristensen, V.F., M.R. Weisbjerg, C.F. Børsting, O. Aaes and P. Nørgaard, 2003. In Malkekoens energiforsyning og produktion. In: Strudsholm, F. and K. Sejrsen (eds.). Kvægets ernæring og fysiologi. Bind 2 - Fodring og produktion. DJF rapport nr. 54. pp. 73-112. (In Danish). Khalili, H. and P. Huhtanen, 1991. Sucrose supplements in cattle given grass silage-based diet. 2. Digestion of cell wall carbohydrates. Animal Feed Science and Technology 33: 263-273. Lasdon, J., A. Waren, A. Jain and M. Ratner, 1978. Design and testing of generalized reduced gradient code for nonlinear programming. ACM Transactions on Mathematical Software 4: 34-49. Mäntysaari, P., 1993. The effects of feeding level and protein source of the diet on growth and development at slaughter of pre-pubertal heifers. Acta Agriculturae Scandinavica. 43: 44-51. Matre, T., 1984. Kjøtproduksjon på oksar og kastratar. I. Forsøk med ulike proteinkjelder Institutt for husdyrernæring, Norges landbrukshøgskole. Melding nr 217. 19 pp. (In Norwegian). Matre, T., 1987. Kjøtproduksjon på oksar og kastratar. III. Ulik fôrstyrke og grovfôrkvalitet til kastratar. Institutt for husdyrernæring, Norges landbrukshøgskole. Melding nr 243. 29 pp. (In Norwegian). Mertens, D.R., 1987. Predicting intake and digestibility using mathematical models of ruminal function. Journal of Animal Science 64: 1548-1558. Mertens, D.R., 1994. Regulation of Forage Intake. In: Fahey Jr. G.C., M. Collins, D.R. Mertens and L.E. Moser (eds.). Forage quality, evaluation, and utilization. American Society of Agronomy, Inc., Crop Science Society of America, Inc. and Soil Science Society in America, Inc., Madison, WI, USA, pp. 450-493. Misciattelli, L., V.F. Kristensen, M. Vestergaard, M.R. Weisbjerg, K. Sejrsen and T. Hvelplund, 2003. Milk production, nutrient utilisation and endocrine responses to increased postruminal lysine and methionine supply in dairy cows. Journal of Dairy Science 86: 275-286. Mo, M. and Å. Randby, 1986. Wilted silage for lactating dairy cows. Proceedings 11th General Meeting of the EGF. Troia, Portugal 4-9. May. Mould, F., 1996. Fôropptak og produksjon hos melkekyr ved et lavt PBV I rasjonen. Husdyrforsølsmøtet. pp. 121127. (In Norwegian). Nadeau, E., A. Hessle, B.-O. Rustas and S. Johnsson, 2002. Prediction of silage intake by Charolais bulls. In: Durand, J-L., J-C Emile, C. Huyghie, and G. Lemaire (eds.). Multi-function grasslands quality forages, Animal products and landscapes. Grassland Science in Europe, Volume 7. Proceedings of the 19th General Meeting of the European Grassland Federation 27-30 May, La Rochelle, France, pp. 220-221. Nordang, L. and F. Mould., 1994. Surfôr med ulikt innhold av PBV til mjølkekyr. Husdyrforsøksmøtet 1994, pp. 99-103. (In Norwegian). Petit, H.V. and G.F. Tremblay, 1995. Ruminal fermentation and digestion in lactating cows fed grass silage with protein and energy supplements. Journal of Dairy Science 78: 342-352. Randby, Å. and M. Mo, 1985. Surfôr tilsatt foraform i sammenlikning med maursyresurfôr til melkekyr, Hellerud høsten 1984. M-141. Rapporter fra forsøksvirksomheten ved Institutt for husdyrernæring, s. 75-78. (In Norwegian). Randby, Å.T., 1992. Grass-clover silage for dairy cows. Proceedings of the 14th General Meeting, European Grassland Federation, Lahti, Finland, 8-11 June, pp. 272-275. Randby, Å.T., 1997. Feeding of silage effluent to dairy cows. Acta Agriculturae Scandinavica Section A. Animal Science 47: 20-30. Randby, Å.T. and I. Selmer-Olsen, 1997a. Formic acid treated or untreated round bale grass silage for dairy cows. 8th International Symposium Forage Conservation. Brno, Czech Republic, pp. 160-161. Randby, Å.T. and I. Selmer-Olsen, 1997b. Formic acid treated or untreated round bale grass silage for steers. Presented on Fifth Research Conference, BGS, University of Plymounth, Devon, UK, 8-10 Sept. 2 pp. Randby, Å.T., 1999a. Grass silage treated with two different acid-based additives for dairy cows. The Fifth International Symposium on the Nutrition of Herbivores. April 11-16, San Antonio, TX, USA. 8 pp. Randby, Å.T., 1999b. Effect of increasing levels of a formic acid based additive on ad libitum intake of grass silage and the production of meat and milk. Proceedings of the XII International Silage Conference, Uppsala, Sweden, pp. 205-206.

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Randby, Å.T., 1999c. Virkning av økende dosering med GrasAAT på surfôropptak og produksjon av mjølk og kjøtt. Forsøk utført på Hellerud Forsøksgård i 1997/98. Institutt for husdyrfag, NLH. Rapport nr 24. Hellerud forsøks- og eliteavslgard. Det kgl. Selskap for Norges Vel. 24 pp. (In Norwegian). Randby, Å.T., 2000. Round bale grass silage treated with an additive containing both lactic acid bacteria and formate for dairy cows and steers. Sixth British Grassland Society Research Conference, SAC, Craibstone, Aberdeen, UK, pp. 121-122. Randby, Å.T., 2002. Silage fermentation by concentrate type interaction. Proceedings of the XIII International Silage Conference, Auchincruive, UK, pp. 312-313. Randby, Å.T., 2007. Effect of propanol and dimethylsulphide in grass silage on organoleptic milk quality. Journal of Animal and Feed Science 16, Suppl. 1, pp. 102-107. Randby, Å.T., I. Selmer-Olsen and L. Baevre, 1999. Effect of ethanol in feed on milk flavour and chemical composition. Journal of Dairy Science 82: 420-428. Rikarðsson, G., 1997. Mismunandi kjarnfóðurgjöf fyrir mjólkurkýr. Ráðunautafundur, pp. 242-254. (In Icelandic). Rikarðsson, G., 2002. Átgeta íslenskra mjólkurkúa. Ráðunautafundur, pp. 125-127. (In Icelandic). Rinne M., P. Huhtanen and S. Jaakkola, 2002. Digestive processes of dairy cows fed silages harvested at four stages of grass maturity. Journal of Animal Science 80: 1986-1998. Rustas, B.-O., 2009. Whole-crop cereals for growing cattle. Effects of maturity stage and chopping on intake and utilisation. Acta Universitatis Agriculturae Sueciae, Doctoral thesis no. 2009:74. Swedish University of Agricultural Sciences, Faculty of Veterinary Medicine and Animal Science, Uppsala, Sweden. Rustas, B.-O., E. Nadeau and S. Johnsson, 2003. Feed consumption and performance in young steers fed whole-crop barley silage at different levels of concentrate. In: Garmo, T.H. (ed.). Proceedings of the International symposium “Early harvested forage in milk and meat production”, 23-24 October 2003, Nannestad, Norway. Agricultural University of Norway, Department of Animal and Aquacultural Sciences, Aas, Norway. Schei, I., H. Volden and L. Bævre, 2005. Effects of energy balance and metabolizable protein level on tissue mobilization and milk performance of dairy cows in early lactation. Livestock Production Science 95: 35-47. Selmer-Olsen, I., 1994. Feed intake, milk yield and liveweight gain with silage from big bales or silos. Poster presented at the 45th Annual Meeting of EAAP, Edinburg, Scotland, 5-8 September. 8 pp. Sejrsen, K. and J.B. Larsen, 1978. Ensilage-kraftfoderforholdets indflydelse på kviers foderoptagelse og tilvækst samt mælkeydelse i første laktation. Beretning nr. 465 fra Statens Husdyrbrugsforsøg. 74 pp. (In Danish). Stensig, T., M.R. Weisbjerg and T. Hvelplund, 1998. Digestion and passage kinetics of fibre in dairy cows as affected by the proportion of wheat starch or sucrose in the diet. Acta Agriculturae Scandinavica Section A. Animal Science 48: 129-140. Sveinbjörnsson, J. and G.H. Harðarsson, 2008. Þungi og átgeta íslenskra mjólkurkúa. Fræðarþing landbúnaðarins 5: 336-344. (In Icelandic). Sveinbjörnsson, J., P. Huhtanen and P. Udén, 2006. The Nordic dairy cow model, Karoline – Development of volatile fatty acid sub-model. In: Kebreab E., J. Dikstra, A. Bannink, J. Gerrits and J. France (eds.). Nutrient Digestion and Utilisation in Farm Animals: Modelling Approaches. CAB International, Wallingford, UK, pp. 1-14. Therkildsen, M., M. Vestergaard, L.R. Jensen, H.R. Andersen and K. Sejrsen, 1998. Influence of feeding intensity, grazing and finishing on growth and carcass quality of young Friesian bulls. Acta Agriculturae Scandinavica 48: 193-201. Thomas, C., 1987. Factors affecting substitution rates in dairy cows on silage based rations. In: Haresign, W. and D.J.A. Cole (eds.) Recent Advances in Animal Nutrition. Butterworths, London, UK, pp. 205-216. Van Vuuren, A.M., C.J. Van Der Koelen and J. Vroons-De Bruin, 1993. Ryegrass versus corn starch or beet pulp fiber diet effects on digestion and intestinal amino acids in dairy cows. Journal of Dairy Science 76: 2692-2700. Vestergaard, M., K. Sejrsen, J. Foldager, S. Klastrup and D.E. Bauman, 1993a. The effect of bovine growth hormone on growth, carcass composition and meat quality of dairy heifers. Acta Agriculturae Scandinavica 43: 165-172. Vestergaard, M., M. Sommer, S. Klastrup and K. Sejrsen, 1993b. Effects of the beta-adrenergic agonist cimaterol on growth and carcass quality of monozygotic Friesian young bulls at three developmental stages. Acta Agriculturae Scandinavica 43: 236-244.

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11. Chewing index system for predicting physical structure of the diet P. Nørgaard, E. Nadeau Å. Randby and H. Volden 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628

An important goal in dairy cow management is, among other things, to develop a feeding strategy

thatChewing ensures goodindex rumen system function, which is vitally important for efficient milk production and healthy 11 for predicting physical structure of the diet

animals. A certain intake physically effective fibre isother essential fortostimulating rumination, salivation An important goal in dairyofcow management is, among things, develop a feeding strategy and rumen motility, and avoiding milk fat depression (Mertens, 1997; De Brabrander et al., that ensures good rumen function, which is vitally important for efficient milk production and 2002). A high intake of rapidly fermentable carbohydrates results production of a high level of volatile healthy animals. A certain intake of physically effective fibreinisthe essential for stimulating fatty acids salivation in the rumen and a motility, subsequent in pH, which increases the1997; risk of rumination, and rumen and reduction avoiding milk fat depression (Mertens, De subacute ruminal acidosis (Krause Infermentable addition to carbohydrates the structural results fibre content Brabrander et al., 2002). A and highOetzel, intake of2006). rapidly in the of a feed, the production of dietary a high level of volatile acids inand theaffects rumenthe andeating a subsequent reduction in pH,time (RT) length of the particles is alsofatty important time (ET), rumination which increases the riskand of subacute ruminal acidosis and Oetzel, 2006). In addition to and, thus, salivation thereby buffering of the (Krause rumen environment (Krause and Oetzel, 2006). the contentobserved of a feed,that the one length the dietary particles is also important and effective for De structural Boever etfibre al. (1993) kgofNDF from late cut grass silage was more affects the eating time (ET), (RT) thus, salivation buffering of stimulating rumination thanrumination NDF fromtime early cutand, grass silage (whichand hasthereby low levels of lignification), the rumen environment (Krause and Oetzel, 2006). De Boever et al. (1993) observed that one kg demonstrating that fibre type affects chewing activity. NDF from late cut grass silage was more effective for stimulating rumination than NDF from early cut grass silage (which has low levels of lignification), demonstrating that fibre type affects The formulation chewing activity. of diets for dairy cows with respect to the fibre content cannot be based solely on

a defined roughage to concentrate ratio or a minimum amount or proportion of roughage NDF in the ration (Mertens, 1997). Balch (1971) fibrousnesses different The formulation of diets for dairy cows withproposed respect toto therank fibrethe content cannot beof based solelyfeeds by a chewing index value that accounts for ETorplus RT. In this chapter, we present model for predicting on a defined roughage to concentrate ratio a minimum amount or proportion of aroughage NDF thethe diet eating index (EI), rumination index (RI) and chewing index (CI). These characteristics in ration (Mertens, 1997). Balch (1971) proposed to total rank the fibrousnesses of different feeds by chewing index value that accounts for ET plus RT. Inofthis we intention present a model areaused in NorFor to optimize the physical structure thechapter, diet. The of thisfor system is to predicting thean diet eating index rumination index and total chewing index (CI).toThese characterize additive CI for(EI), individual feeds and (RI) hence ranking feeds according their ability to characteristics are mastication used in NorFor to optimize physical structure of the diet. The intention of stimulate particle when cows arethe eating and during rumination. The time spent masticating this system is to characterize an additive CI for individual feeds and hence ranking feeds and the intensity of mastication are used to quantitatively rank the biological fibrousnesses of feeds, according to their ability to stimulate particle mastication when cows are eating and during which is closely related to the stimulation of salivation, the frequency of rumen motility and the rumination. The time spent masticating and the intensity of mastication are used to quantitatively formation of a stable flowing layer system in the reticulo-rumen. The NorFor chewing system has rank the biological fibrousnesses of feeds, which is closely related to the stimulation of been developed from the Danishmotility chewing system (Nørgaard, 1986) and usinginnew data salivation, the frequency of rumen andindex the formation of a stable flowing layerby system from Nørgaard et al. the reticulo-rumen. The(2010). NorFor chewing system has been developed from the Danish chewing index system (Nørgaard, 1986) and by using new data from Nørgaard et al. (2010).

11.1 Predicting the chewing index from eating and rumination

11.1 Predicting the chewing index from eating and rumination

Eachfeed feedisisgiven givena aCICIvalue value (min/kg DM), which is estimated the of sum and RI: Each (min/kg DM), which is estimated as theassum EI of andEIRI: CI = EI + RI



[Eq. 11.1]

11.1

where index for for a feedstuff, min/kg DM; DM; EI is the index asindex described in whereCI CIisisthe thechewing chewing index a feedstuff, min/kg EI eating is the eating as described in equation DM, asas described in in equation Equation11.2, 11.2,min/kg min/kgDM; DM;and andRIRIisisthe therumination ruminationindex, index,min/kg min/kg DM, described Equation 11.4. 11.4.

EI and RI describe the recorded ET and RT, respectively, and in NorFor the feed EI and RI values

EI and RI describe the recorded ET and RT, respectively, and in NorFor the feed EI and RI refer to those for a standard cow of a large multiparous dairy breed with a BW of 625 kg, a NDF values refer to those for a standard cow of a large multiparous dairy breed with a BW of 625 kg, a roughage (NDFr) intake of 0.7% of BW andand a daily rumination time of of 400 min. This corresponds to NDF roughage (NDFr) intake of 0.7% of BW a daily rumination time 400 min. This a daily DMI of 20 kg with a CI value of 30 min/kg DM, assuming that rumination time is equivalent corresponds to a daily DMI of 20 kg with a CI value of 30 min/kg DM, assuming that rumination to 2/3 the totaltochewing use time. of a standardized cow impliescow thatimplies the unit time is of equivalent 2/3 of thetime. total The chewing The use of a standardized that‘minutes/kg the DM’“minutes/kg reported inDM” the practical tool to atool standard and thus, unit reported in therefers practical refers tocow a standard cowdoes and not thus,necessarily does not reflects

the actual mean chewing time (CT) (min/kg DM) for that particular cow. The equations for EI and 172 RI were obtained from a meta-analysis of recorded ET and RT from cattle fed 85 different diets consisting of unchopped grass silage, grass hay or lucerne hay with or without supplementation of ground concentrates or rolled barley (Nørgaard et al., 2010). Based on the meta-analysis, the NorFor EI and RI values were standardized to reference values of 50 and 100 min/kg NDFr for unchopped roughage, respectively. In concentrates, the standard EI was set to 4 min/kg DM. As described in H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_11, © Wageningen Academic Publishers 2011

127

4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 4644 4645 4646 4647 4648

equations for EI and RI were obtained from a meta-analysis of recorded ET and RT from cattle fed 85 different diets consisting of unchopped grass silage, grass hay or lucerne hay with or without supplementation of ground concentrates or rolled barley (Nørgaard et al., 2010). Based on the meta-analysis, the NorFor EI and RI values were standardized to reference values of 50 and 100 min/kg NDFr for unchopped roughage, respectively. In concentrates, the standard EI was set to 4 min/kg DM. As described in Chapter 4, concentrates and roughage are defined on the Chapter 4, concentrates and roughage are defined on the basis of the most frequently measured basis of the most frequently measured particle length (PL) that they contain, such that feedstuffs particle (PL) theyas contain, such that feedstuffs with a PL≤6 mmmm areare defined as as concentrates with a PLlength ≤6 mm arethat defined concentrates while feedstuffs with a PL >6 defined while feedstuffs with a PL>6 mm defined as roughage. maize silage are chopped at roughage. Grass and maize silage areare chopped at harvest and theGrass effect and of physical processing on harvest and the effect of physical processing on the EI and RI values is based on the theoretical the EI and RI values is based on the theoretical chopping length (TCL), which corresponds to the chopping length (TCL), which corresponds to theobserved PL value (O´Dogherty, et al. PL value (O´Dogherty, 1984). Nørgaard et al. (2010) that the mean ET 1984). and RT Nørgaard per kg (2010)decreased observedwith thatincreasing the meanBW. ET and RT kgofNDFr decreased withinto increasing BW. Thus, the NDFr Thus, theper size the cow is not taken account when e.g. no distinctions in this respect are drawn between of calculating the values EI orinto RI (account size of the cow is notof taken when calculating the values of EI or RI (e.g.cows no distinctions large breeds small between breeds or cows primiparous vs.dairy multiparous This meansorthat in thisdairy respect arevs.drawn of large breeds breeds). vs. small breeds primiparous vs. Jersey cows have a higher meanJersey ET and RT per kgaNDFr than large breeds same multiparous breeds). Thisrecorded means that cows have higher recorded meanfed ETthe and RT per kg diet. Feeding high moisture silage containing less than 40% dry matter has been shown to NDFr than large breeds fed the same diet. Feeding high moisture silage containing less than 40% increase EThas (Debeen Boever et al.,to1993). However, NorFor et this is 1993). not reflected in a higher EI this is not dry matter shown increase ET (DeinBoever al., However, in NorFor value because the DM content of a feedstuff is not taken into account when calculating the values reflected in a higher EI value because the DM content of a feedstuff is not taken into account when of EI or RI.

4649 4650 4651 4652 4653 4654 4655 4656 4657 4658 4659

11.2 Calculation of the eating and rumination indices

4660

EI = 50 ⋅

4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4672

calculating the values of EI or RI.

11.2 Calculation of the eating and rumination indices

Table 11.1 presents an overview of the equations used to calculate EI and RI depending on PL. The EI describes the eating time per kg DM of a given feedstuff for the standard cow (Nørgaard Table 11.1 presents overview of thefeeds equations used EI DM. and RI depending on PL. The et al., 2010). Ground,an rolled or pelleted are given anto EIcalculate of 4 min/kg The recorded ET EI describes eating time per(Mertens, kg DM of a given feedstuff for the standard cow (Nørgaard et al., decreases as a the result of chopping 1997).

2010). Ground, rolled or pelleted feeds are given an EI of 4 min/kg DM. The recorded ET decreases

Table as a result 11.1 of chopping (Mertens, 1997).

EI of of roughage roughage and to the NDF content and and a particle length factorfactor for EI andby-products by-productsare areproportional proportional to the NDF content a particle length for eating (Size_E) (Nørgaardetetal., al.,2010). 2010). EI EI is is calculated calculated using equation: eating (Size_E) (Nørgaard usingthe thefollowing following equation: NDF ⋅ Size _ E 1000



[Eq. 11.2]

11.2

whereEI EIisisthe theeating eatingindex, index,min/kg min/kgDM; DM;NDF NDF NDF content in the feedstuff, g/kg DM; Size_E where is is thethe NDF content in the feedstuff, g/kg DM; is a correction factor that depends on particle length, as described in Equation 11.3; and Size_E is a correction factor that depends on particle length, as described in equation 11.3; and 50 is the standardized eating time, NDF. NDF. 50 is the standardized eatingmin/kg time, min/kg Size_E is a correction factor for the feed particle length, which ranges from 0.67 in finely chopped feedstuffs to 1 of in the unchopped roughage Figure 11.1). index Size_Esystem. is parameterized from Table 11.1. Overview calculations used(see in the chewing recorded ET data in cattle fed chopped (10 or 20 mm TCL) or unchopped silage (Nørgaard, unpublished) using the following equation: Feed type and is calculated Concentrates Roughages ⋅ PL Size _ E = 1 − 0 .52 ⋅ e − 0 .078 Processing Ground

PL or TCL (mm)1 Size_E Size_R Hardness factor Eating index (EI, min/kg DM) Rumination index (RI, min/kg DM) Chewing index (CI, min/kg DM)

Rolled

Finely

Coarsely

≤2.0

2-4

Chopped

[Eq. 11.3] Long or slightly

chopped

173

4-6

4

6-40 >40 1–0.52·exp(-0.078·TCL) 1 1–exp (-0.173·(TCL/0.7-1)) 1 0.75 + iNDF/1000 50·NDF/1000·Size_E

0

100·NDF/1000·Size_R·Hardness factor

EI + RI

1 PL is the most frequent particle length in a feedstuff. TCL (theoretical chopping length) corresponds to PL in roughage

(O´Dogherty, 1984).

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NorFor – The Nordic feed evaluation system

4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 4714 4715

where EI is the eating index, min/kg DM; NDF is the NDF content in the feedstuff, g/kg DM; Size_E is a correction factor that depends on particle length, as described in equation 11.3; and 50 is the standardized eating time, min/kg NDF.

Size_Eisisaacorrection correctionfactor factor feed particle length, which ranges 0.67 in finely chopped Size_E forfor thethe feed particle length, which ranges from from 0.67 in finely feedstuffs to 1 in unchopped roughage (see Figure 11.1). 11.1). Size_E is parameterized fromfrom recorded ET chopped feedstuffs to 1 in unchopped roughage (see Figure Size_E is parameterized recorded ET data cattle fed(10 chopped 20 mm or unchopped silage (Nørgaard, data in cattle fedinchopped or 20 (10 mmorTCL) or TCL) unchopped silage (Nørgaard, unpublished data) unpublished) and isusing calculated using the equation: following equation: and is calculated the following Size _ E = 1 − 0 .52 ⋅ e − 0 .078 ⋅ PL  [Eq. 11.3] 11.3 where Size_E is the correction factor for the eating index due to the chopping or processing of the feedstuff. The Size_E rangesfactor from 0.67 for fine chopped to 1chopping for unchopped where Size_E is the factor correction for the eating indexroughage due to the or processing of roughages; and PL is the most frequent particle length in the feedstuff, mm. PL corresponds 173 the feedstuff. The Size_E factor ranges from 0.67 for fine chopped roughage to 1 fortounchopped TCL for roughage (O´Dogherty, 1984) and is therefore used as the input for the equation.

roughages; and PL is the most frequent particle length in the feedstuff, mm. PL corresponds to TCL for roughage (O´Dogherty, 1984) therefore usedthe as value the input for thewhich equation. Figure 11.1 shows how the length ofand feedisparticles affects of Size_E, approaches 1 for PL values > 50 mm, showing that slight chopping of roughage with TCL values higher than Figure 11.1 not shows how length of feed particles affects the value of Size_E, which approaches 1 50 mm does affect thethe value of EI.

for PL values >50 mm, showing that slight chopping of roughage with TCL values higher than 50

mm does Figure 11.1not affect the value of EI.

RI activity during rumination perper kg DM of aof given feedstuff in in the RI describes describesthe theassociated associatedchewing chewing activity during rumination kg DM a given feedstuff the standard cow. determinedfrom fromthe the feed feed NDF NDF content, factor forfor rumination standard cow. RIRI is is determined content,aaparticle particlelength length factor rumination (Size_R) and a hardness factor,iswhich is to related to the content iNDF content the feedstuff (Size_R) and a hardness factor, which related the iNDF in theinfeedstuff (Nørgaard et ., 2010). RI is using calculated using the following (Nørgaard al., 2010).etRIalis calculated the following equation:equation: RI = 100 ⋅

NDF ⋅ Size _ R ⋅ Hardness _ factor 1000



[Eq. 11.4]

11.4

where RI is the rumination index, min/kg DM; NDF is the NDF content in the feedstuff, g/kg

where RI is the rumination index, min/kg DM; NDF is the NDF content in the feedstuff, g/kg DM;Size_R Size_Risisa correction a correction factor depends on particle as described in Equation 11.5; DM; factor thatthat depends on particle lengthlength as described in equation 11.5; the Hardness_factor Hardness_factoris is described in Equation andis 100 is the standardized rumination time, the described in equation 11.6; 11.6; and 100 the standardized rumination time, min/kgNDF. NDF. min/kg If lower than 2.02.0 mmmm (as in processed concentrates), then the RI the RI is If the thePL PLofofthe thefeedstuff feedstuffis is lower than (asfinely in finely processed concentrates), then is assumedtotobebezero zero(see (seeTable Table 11.1). 11.1). Nørgaard Nørgaard and shown that thethe most assumed andSehic Sehic(2003) (2003)have have shown that most frequent frequent particle length in the faeces of cattle fed solely roughage is 0.7 mm, consequently, feedstuffs with a PL>0.7 mm are considered to stimulate rumination. However, some concentrates have 1.0 a PL>0.7 mm but are not considered to stimulate rumination. Therefore, the borderline value for PL was set to 2.0 mm to ensure that all finely ground concentrates are given an RI of zero. Thus, Size_R is zero for feeds with a PL ≤2.0 mm and their CI is 4 min/kg DM corresponding 0.8 to eating index (Table 11.1). Coarsely processed concentrates with a PL between 2 Size_R to and 6 mm (e.g. rolled grains, coarsely ground grains, oil cakes and hulls) are considered Size_E stimulate rumination. Several studies have shown that chopping roughage at TCL values higher 0.6 not decrease the recorded RT (De Boever et al., 1993; Garmo et al., 2008). than 20 mm does Furthermore, Nørgaard and Sehic (2003) showed that the PL of particles in swallowed boli from cows eating grass silage chopped at 19 mm TCL was the same as the PL of boli particles from 0.4 cows eating unchopped grass silage. Therefore, the RT for roughage chopped at a TCL higher than 20 mm is considered to have the same RT as unchopped roughage, and consequently Size_R at 20 mm TCL is set to 0.99. Size_R decreases exponentially when TCL is below 20 mm, as 0.2 11.1. illustrated in Figure

Correction factor

4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4673 4672 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685 4686 4687 4688

⎛ ⎛ PL ⎞ ⎞ ⎜⎜0.0 −0.173⋅⎜ −1⎟ ⎟ 0.7 ⎠ ⎟⎠ ⎝ Size _ R = 1 − e 0 ⎝

10

20

30

40

50 [Eq. 11.5] 60

PL (mm)

Figure 11.1. Effect of the most frequent particle length (PL) in a feedstuff on the correction factors 174 Size_E and Size_R. Size_E and Size_R are used to adjust the eating and rumination index due to chopping or processing of feedstuffs (see Table 11.1). NorFor – The Nordic feed evaluation system

129

4693 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 4716 4717 4714 4718 4715 4719 4716 4720 4717 4721 4718 4722 4719 4723 4720 4724 4721 4725 4722 4726 4723 4727 4724 4728 4725 4729 4726 4730 4727 4731 4728 4732 4729 4733 4730 4734 4731 4735 4732 4733 4736 4734 4737 4735 4738 4739 4736 4740 4737 4741 4738 4742 4739 4743 4740 4744 4741 4745 4742 4746 4743 4747 4744 4748 4745 4749 4746 4750 4747 4748 4751 4749 4750 4752 4753 4751 4754 4755 4752 4756 4753 4754

the Hardness_factor is described in equation 11.6; and 100 is the standardized rumination time, min/kg NDF. If the PL of the feedstuff is lower than 2.0 mm (as in finely processed concentrates), then the RI is assumed to be zero (see Table 11.1). Nørgaard and Sehic (2003) have shown that the most particle length the faeces cattle solely roughage is 0.7ismm, consequently, feedstuffs with a frequent particleinlength in the of faeces offed cattle fed solely roughage 0.7 mm, consequently, feedstuffs with PL>0.7 mmto arestimulate considered to stimulate rumination. However, some have a PL>0.7 mm PL>0.7 mm area considered rumination. However, some concentrates concentrates have a PL>0.7 mm but are not considered to stimulate rumination. Therefore, the set to 2.0 but are not considered to stimulate rumination. Therefore, the borderline value for PL was borderline valuethat for all PL finely was setground to 2.0 mm to ensure that finely concentrates are given mm to ensure concentrates are all given an ground RI of zero. Thus, Size_R is zero for an RI of zero. Thus, Size_R is zero a PLDM ≤2.0corresponding mm and their CI 4 min/kg DM feeds with a PL≤2.0 mm and theirforCIfeeds is 4 with min/kg to is eating index (Table 11.1). corresponding to eating index (Table 11.1). Coarsely processed concentrates with a PL between 2 Coarsely processed concentrates with a PL between 2 and 6 mm (e.g. rolled grains, coarsely ground and 6 mm (e.g. rolled grains, coarsely ground grains, oil cakes and hulls) are considered to grains, oil cakes and hulls) are considered to stimulate rumination. Several studies have shown that stimulate rumination. Several studies have shown that chopping roughage at TCL values higher chopping roughage TCL values higher than 20 mm does the recorded RT (De Boever et not al., decrease 1993; Garmo et al., 2008). than 20 mm does not at decrease the recorded RT (De Boever et al., 1993; Garmo et al., 2008). Furthermore, Nørgaard and Sehic (2003) showed Furthermore, Nørgaard and Sehic (2003) showed that the PL of particles in swallowed bolithat fromthe PL of particles in grass swallowed boli fromatcows eating silage mm TCL was cows eating silage chopped 19 mm TCL grass was the samechopped as the PLatof19 boli particles fromthe same as the PL of boli particlesgrass fromsilage. cowsTherefore, eating unchopped silage. Therefore, the RT for roughage cows eating unchopped the RT forgrass roughage chopped at a TCL higher than 20 mm considered have20 themm same as unchopped roughage, andRT consequently Size_R chopped at is a TCL highertothan is RT considered to have the same as unchopped roughage, at 20consequently mm TCL is setSize_R to 0.99.atSize_R exponentially when TCL is below 20 mm, as when TCL and 20 mmdecreases TCL is set to 0.99. Size_R decreases exponentially illustrated in Figure is below 20 mm, as11.1. illustrated in Figure 11.1. ⎛ PL ⎞ ⎞ factor for the rumination index, which depends on chopping or where Size_R⎛⎜ is the correction ⎜ −0.173⋅⎜ 0.7 −1⎟ ⎟⎟ ⎝ ⎠ ⎠ Size_R ⎝ processing The factor ranges from 0 for fine processed feeds[Eq. to 111.5] for  11.5 Size _ R = 1 −the e feedstuff. unchopped roughage or roughage chopped with a PL higher than 20 mm. PL is the most frequent particle length (mm) in correction the feedstufffactor and for PL corresponds to TCL depends (theoretical whereSize_R Size_R forroughage the rumination index,depends which on or chopping or where is is thethe correction factor for the rumination index, which on chopping chopping length, mm; O'Dogherty, 1984) andranges can therefore used as the inputfeeds for the equation; processing thefeedstuff. feedstuff. TheSize_R Size_R factor from 0be for fine processed 1 for unchopped processing the The factor ranges from 0 for fine processed feeds to 1tofor 0.7 is the most frequent particle length in faeces174 from cattle, meaning that feedstuffs with a unchopped or chopped roughagewith chopped a PL higher than PL 20 mm. is the most frequent roughage orroughage roughage a PLwith higher than 20 mm. is thePL most frequent particle length PL>0.7 stimulate rumination (Nørgaard and Sehic, 2003). particle length (mm) in the feedstuff and for roughage PL corresponds to TCL (theoretical (mm) in the feedstuff and for roughage PL corresponds to TCL (theoretical chopping length, mm; chopping length, mm;and O'Dogherty, 1984)beand can therefore be used the input for the equation; O’Dogherty, can therefore used the input theas 0.7relatively is the most frequent The structural 1984) NDF in immature spring grass andas chopped beetfor pulp isequation; soft and has 0.7 is thelength most frequent particle length inmeaning faeces from cattle, meaning that feedstuffsstimulate with a rumination particle in faeces from cattle, that feedstuffs with a PL>0.7 little resistance to physical breakdown during chewing compared with mature grass, barley straw, PL>0.7 stimulateSehic, rumination and Sehic, 2003). (Nørgaard 2003).(Nørgaard lucerne hay and cut after blooming or whole cotton seeds. The content of iNDF in a feedstuff is closely related to the lignification of the NDF fibres (Weisbjerg and Soegaard, 2008) and The structural NDF in immature spring grass and chopped beet pulp is soft and has relatively lignification is known increase resistance to physical breakdown. to has physical The structural NDF intoimmature spring grass and chopped beet The pulpresistance is soft and relatively little little resistance to physical breakdown during chewing compared with mature grass, barley straw, breakdown is physical ranked bybreakdown a hardness during factor, which hascompared been givenwith a value of 1 grass, for typical grass resistance to chewing mature barley lucerne hay cut after blooming or whole cotton seeds. The content of iNDF in a feedstuff isstraw, lucerne silage with an blooming iNDF content of 250cotton g/kg NDF. The hardness increases from 0.8 for hay cut after or whole seeds. The contentfactor ofand iNDF in a feedstuff is closely related closely related to the lignification of the NDF fibres (Weisbjerg Soegaard, 2008) and immature spring grass with an iNDF content of approximately 50 g/kg NDF to 1.25 for late cut to the lignification NDF resistance fibres (Weisbjerg and Soegaard,The 2008) and lignification lignification is knownoftothe increase to physical breakdown. resistance to physical is known lucerne hay with an iNDF content of approximately 500 g/kg NDF. The hardness factor is to increaseisresistance physicalfactor, breakdown. Thebeen resistance physical is ranked by breakdown ranked by to a hardness which has given a to value of 1 forbreakdown typical grass calculated as: silage with an iNDFwhich content of been 250 g/kg NDF. The hardness factor increases fromwith 0.8 for a hardness factor, has given a value of 1 for typical grass silage an iNDF content immature spring grass an iNDF content of approximately 50 immature g/kg NDF to 1.25 for latewith cut an iNDF of 250 g/kg NDF. Thewith hardness factor increases from 0.8 for spring grass ⎛ iNDF ⎞ [Eq. 11.6] Hardnesshay _offactor 0.75 + ⎜ content lucerne with= an iNDF of approximately 500 late g/kgcut NDF. The hardness factor is ⎟ g/kg content approximately 50 NDF to 1.25 for lucerne hay with an iNDF content of ⎝ 1000 ⎠ calculated as: 500 g/kg approximately NDF. The hardness factor is calculated as:

where the Hardness_factor is used for calculating the rumination index and ranges from 0.8 to ⎛ iNDF ⎞ 11.6]  iNDF content in the feedstuff, g/kg[Eq. Hardness _ factor = 0.75 + ⎜feedstuffs; ⎟ 1.25 for typical Nordic iNDF is the NDF. 1000 ⎝

⎠

11.6

Table shows examples ofisEI, RI for andcalculating CI values for concentrates ranging where11.2 the Hardness_factor used thesome rumination indexand androughages ranges from 0.8 to 1.25 where the Hardness_factor is used for calculating the rumination index and ranges from 0.8 to from 4 to 22Nordic min/kgfeedstuffs; DM. The main reason foriNDF the much higher CI of dried beet pulpNDF. compared to for typical iNDF is the content in the feedstuff, g/kg 1.25 for typical Nordic feedstuffs; iNDF is the iNDF content in the feedstuff, g/kg NDF. barley and rape seed cake is its higher PL, which causes a higher RI. The higher CI of grass silage compared to examples maize silage is due to its CI higher TCL and NDF content, which result in higher Table 11.2 11.2shows shows EI,RI RI and values some concentrates roughages. The main Table examples ofofEI, and CI values forfor some concentrates and and roughages ranging EI and RI (Table 11.2). reason for the much higher CI of dried beet pulp compared to barley and rape seed cake is its from 4 to 22 min/kg DM. The main reason for the much higher CI of dried beet pulp compared to higher PL, which causes higher RI.higher The higher CI of grassa silage to maize barley and rape seeda cake is its PL, which causes higher compared RI. The higher CI of silage grass is due to its Table 11.2 silage maize silage iswhich due toresult its higher TCL and NDF highercompared TCL andtoNDF content, in higher EI and RIcontent, (Table which 11.2). result in higher EI and RI (Table 11.2). The EI and RI are additive values and the CI of a total diet (min/kg DM) is considered to be the The of EIthe andindividual RI are additive sum feed CIvalues values:and the CI of a total diet (min/kg DM) is considered to be the sum Table of the11.2 individual feed CI values: ∑ DMI ⋅ CI

i i The EI andi RI are additive values and the CI of a total diet (min/kg DM) is considered to be the [Eq. 11.7]  CI _ DM = sum of the individual ∑ DMI i feed CI values:

11.7

i

∑ DMI i ⋅ CI i

where CI_DM is the chewing index for the total diet, min/kg DM; DMIi is the dry[Eq. matter intake 11.7] CI _ DM = i ∑ DMI i kg DM/day; and CI is the chewing index for the i-th feedstuff, min/kg DM for the i-th feedstuff, i i as described in Equation 11.1. 130  NorFor – The Nordic feed evaluation system where CI_DM is the chewing index for the total diet, min/kg DM; DMIi is the dry matter intake for the i-th feedstuff, kg DM/day; and CIi is the chewing index for the i-th feedstuff, min/kg DM

Table 11.2 Estimated chewing index values for different feedstuffs in the NorFor feed stuff table.

Barley RSC DBP GS MS

Feed code

NDF g/kg DM

iNDF g/kg NDF

PL1 mm

001-0008 002-0044 004-0020 006-0241 006-0308

206 268 382 417 364

158 511 89 151 187

2.1 2.0 4.0 20 10

Size_E Size_R Hardness EI2 RI2 CI2 min/kg min/kg min/kg DM DM DM 0.29 0.89 0.76

0.56 0.99 0.90

0.91 1.26 0.84 0.90 0.94

4 4 4 19 14

5 0 18 37 31

9 4 22 56 45

1

PL is the most frequent particle length in concentrates and corresponds to TCL (theoretical chopping length) in roughage (O´Dogherty, 1984). 2 See Equations 11.1 to 11.6. RSC, rape seed cake; DBP, dried beet pulp; GS, grass silage; MS, maize silage.

where CI_DM is the chewing index for the total diet, min/kg DM; DMIi is the dry matter intake for the i-th feedstuff, kg DM/day; and CIi is the chewing index for the i-th feedstuff, min/kg DM as described in Equation 11.1.

11.3 Implications The objectives of the work described in this chapter were to develop a system for ranking the structural value of different feedstuffs by calculating an additive chewing index, which could be used as a tool to minimize the risk of digestive disorders and ensure proper rumen function of a diet fed to dairy cows. The minimum recommended chewing index (32 min/kg DM) is intended to ensure a high degree of fibre digestion, a high acetate to propionate ratio and an acceptable milk fat content even at a high DMI.

References Balch, C.C., 1971. Proposal to use time spent chewing as an index to which diets for ruminants possess the physical property of fibrousness characteristic of roughages. British Journal of Nutrition 26: 383-392. De Boever, J.L., A. De Smet, D.L. De Brabander and C.V. Boucqué, 1993. Evaluation of physical structure. 1. Grass silage. Journal of Dairy Science 76: 140-153. De Brabrander, D.L., J.L. De Boever, J.M. Vanacker, C.V. Boucqué and S.M. Botterman, 2002. Evaluation of physical structure in cattle nutrition. Recent developments in ruminant nutrition. Wiseman, J. and P.C. Garnsworthy (eds.). Nottingham University Press, Nottingham, UK, pp. 47-80. Garmo, T.H., A.T. Randby, M. Eknaes, E. Prestløkken and P. Nørgaard, 2008. Effect of grass silage chop length on chewing activity and digestibility. In: Hopkins, A., T. Gustafsson, J. Bertilson, G. Dalin, N. Nilsdotter-Linde and E. Spörndly (eds.). Grassland Science in Europe, volume 13, pp. 810-812. Krause, K.M. and G.R. Oetzel, 2006. Understanding and preventing subacute ruminal acidosis in dairy herds: a review. Special Issue: Feed and animal health. Animal Feed Science and Technology 126: 215-236. Mertens, D.R., 1997. Creating a system for meeting the fiber requirements of dairy cows. Journal of Dairy Science 80: 1463-1481. Nørgaard, P., 1986. Physical structure of feeds for dairy cows. A new system for evaluation of the physical structure in feedstuffs and rations for dairy cows. New developments and future perspectives in research of rumen function, pp. 85-107.

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Nørgaard, P. and A. Sehic, 2003. Particle size distribution in silage, boluses, rumen content and faeces from cows fed grass silage with different theoretical chopping length. Proceedings from the 6th International Symposium on the Nutrition of Herbivores, pp. 457-460. Nørgaard, P., E. Nadeau and A. Randby, 2010. A new Nordic structure evaluation system for diets fed to dairy cows: a meta analysis. In: Sauvant, D., J. Van Milgen, P. Faverdin and N. Friggens (eds.). Modelling nutrient digestion and utilization in farm animals. Wageningen Academic Publishers, Wageningen, the Netherlands, pp. 112-121. O’Dogherty, M, 1984. The description of chop length distribution from forage harvesters and a geometric simulation model for distribution generation. In: Kennedy, P. (ed.). Canadian society of animal science, occasional publication, Edmonton, Alberta, Canada, pp. 62-75. Weisbjerg, M.R. and K. Soegaard, 2008. Feeding value of legumes and grasses at different harvest times. In: Hopkins, A., T. Gustafsson, J. Bertilsson, G. Dalin, N. Nilsdotter-Linde and E. Spörndly (eds.). Biodiversity and animal feed: Future challenges for grassland production. Grassland Science in Europe, volume 13, pp. 513-515.

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4825 4825 4826 4825 4826 4827 4827 4826 4828 4828 4827 4829 4828 4830 4829 4831 4830 4829 4832 4831 4830 4832 4831 4833 4832 4833 4834 4835 4834 4833 4836 4835 4834 4837 4836 4835 4837 4838 4836 4837 4838 4839 4838 4840 4839 4841 4840 4839 4842 4841 4840 4843 4842 4841 4844 4843 4842 4845 4844 4843 4846 4845 4844 4847 4846 4845 4848 4847 4846 4849 4848 4847 4850 4849 4848 4850 4851 4849 4850 4851 4852 4851 4853 4852 4854 4853 4852 4855 4854 4853 4856 4855 4854 4856 4855 4857 4856 4858 4857 4859 4858 4857 4860 4859 4858 4861 4860 4859 4862 4861 4860 4863 4862 4861 4864 4863 4862 4865 4864 4863 4865 4866 4864 4865 4866 4867 4866 4868 4869 4870 4871 4872 4873 4874 4875

12.0 Prediction of milk yield, weight gain and utilisation of N, P 12.0 milk yield, weight gain and utilisation of N, P and KPredictionofofmilk 12. Prediction yield, weight gain and utilisation of N, P and K and KPrediction 12.0 of milk yield, weight gain and utilisation of N, P M. Åkerlind and H. Volden M. and K and M. Åkerlind Åkerlind andH. H.Volden Volden

NorFor predicts ECM and protein yield for dairy cows and weight gain for growing cattle. M. Åkerlind Volden Excretion of and N, PH. and K can also be predicted for a given diet and production level. Keep NorFor NorForpredicts predictsECM ECMand andprotein proteinyield yieldfor fordairy dairycows cowsand andweight weightgain gainfor forgrowing growingcattle. cattle. Excretion track on excreted N, PK and Kalso are of interest when estimating plant fertilising value and also Excretion of N, P and can be predicted for a given diet and production level. Keep of N, P and K can also be predicted for a given diet and production level. Keep track on excreted NorFor predicts ECM and protein yield for dairy cows and weight gain for growing cattle. evaluating the impact of excreted N interest and P onwhen eutrophication of the fertilising environment. track on excreted N, P and K are of estimating plant value and also Excretion ofare N, Pofand K canwhen also be predictedplant for afertilising given diet value and production level. Keep N, P and K interest estimating and also evaluating the impact of evaluating the impact excreted N interest and P onwhen eutrophication the fertilising environment. track on excreted N, Pof and K are of estimating of plant value and also

excreted N and P on eutrophication of the environment. 12.1 Prediction of milk yield evaluating the impact of excreted N and P on eutrophication of the environment. 12.1 Prediction Prediction of ECMof yield milk is estimated yield from the total supply of NEL minus basal energy 12.1 Prediction offorismilk yield requirements corrected any changes in BCS (seesupply chapterof9). Since 1 kgbasal ECMenergy contains Prediction of ECM yield estimated from the total NEL minus 12.1 Prediction of milk yield

3.14 MJ (Sjaunja et al., for 1990), the estimated ECM isSince calculated as: contains requirements corrected changes in BCS (seeproduction chapterof9). 1 kg ECM Prediction of yield isisany estimated from total supply minus Prediction ofECM ECM yield estimated fromthe the total supply ofNEL NEL minusbasal basalenergy energy requirements 3.14 MJ (Sjaunja et al., 1990), the estimated ECM production is calculated as: requirements corrected for any changes in BCS (see chapter 9). Since 1 kg ECM contains

corrected for anyNEL changes (see _Chapter Since 1 kg ECM contains − NE _in maBCS int − NEL gain − NE9). _ gest − NEL _ dep + NEL _ mob 3.14 MJ (Sjaunja et

ECMMJ _ response = et al., 1990), the estimated ECM production is calculated as: 3.14 (Sjaunja al., 1990), the estimated ECM production is calculated as: ECM _ response =

14 _ gest − NEL _ dep + NEL _ mob NEL − NE _ ma int − NEL _ gain −3.NE 3 . 14 _ gest − NEL _ dep + NEL _ mob[Eq. 12.1] NEL − NE _ ma int − NEL _ gain − NE

 12.1] 12.1 ECM _ response = [Eq. 3.14 where ECM_response is the predicted energy corrected milk yield, kg/day; NEL is the net [Eq. 12.1] energy lactation obtained from the diet,energy Equation 8.3; NE_maint, NEL_gain, NE_gest, whereECM_response ECM_response thepredicted predicted energy corrected milk yield, kg/day; NEL the net energy where isisthe corrected milk yield, kg/day; NEL is theisnet NEL_dep and NEL_mob is energy required of 8.3; maintenance, gestation, growth of primiparous energy lactation lactation obtained obtained from from the diet, the diet, Equation Equation 8.3; NE_maint, NE_maint, NEL_gain, NEL_gain, NE_gest, NE_gest, NEL_dep and where ECM_response is thefrom predicted energy corrected milk yield, kg/day; NEL9.18 is the net cows, and supply of energy mobilisation, Equation 9.1, 9.4, 9.3, 9.17 and NEL_dep and NEL_mob is energy required of maintenance, gestation, growth of primiparous NEL_mob is energy required of maintenance, gestation, growth of primiparous cows, and supply of energy lactation obtained from the diet, Equation 8.3; NE_maint, NEL_gain, NE_gest, respectively; and 3.14 is the energy content of 19.3, kg ECM. cows, and supply of energy from mobilisation, Equation 9.1, 9.4, 9.3, 9.17 and 9.18 energy from mobilisation, Equation 9.1, 9.4, 9.17 and 9.18 respectively; and 3.14 is the energy NEL_dep and NEL_mob is energy required of maintenance, gestation, growth of primiparous respectively; andECM. 3.14 is the from energy content of 1Equation kg ECM.9.1, 9.4, 9.3, 9.17 and 9.18 content ofsupply 1 kg cows, and of energy mobilisation, Milk protein response is calculated by multiplying available AATN by the efficiency of AA respectively; and is thesynthesis. energy content of 1 kg AAT ECM. is the total AAT minus basal utilisation forresponse milk3.14 protein The available N N Milk protein is calculated by multiplying AAT Milk protein response is calculated by multiplyingavailable available AATNNby bythe theefficiency efficiencyof ofAA AA utilisation AAT Response in milkThe protein production isiscalculated as: N minus basal N requirement. utilisation for milk protein synthesis. available AAT the total AAT N for milk protein synthesis. The available AATN isavailable the totalAAT AATNNbyminus basal AAT requirement. Milk protein response is calculated by multiplying the efficiency of NAA AAT N requirement. Response in milk protein production is calculated as: utilisation for milk protein protein production synthesis. The available AAT Response in milk is calculated as: is the total AAT minus basal N N AAT _ Eff [Eq. 12.2] AAT100 N _ Eff Pr otein _ response = (Avail _ AATN ) ⋅ [Eq. 12.2]  12.2 AAT100 _ Eff N where Pr oteinProtein_response _ response = (Availis_ the AAT predicted milk protein production, g/day; Avail_AAT [Eq.N 12.2] is the N )⋅ 100 where Protein_response is the in predicted milk protein g/day;Equation Avail_AAT the available available amino acid absorbed the small intestine forproduction, milk production, 9.26 is and

N Pr otein _ response = (Avail _ AAT ) ⋅ protein AAT Response inNmilk production is calculated as: N requirement.

where Protein_response is the predicted milk protein production, g/day; Avail_AATNNis the amino acid in theofsmall intestine for milkfor production, Equation 9.26 and AAT AAT isabsorbed theacid efficiency amino acid utilization for milkproduction, protein production, Equation N_Eff available amino absorbed in the small intestine milk Equation 9.26 andN_Eff is the where Protein_response isutilization the predicted milk protein production, g/day; Avail_AAT N is the efficiency of amino acid for milk protein production, Equation 9.24. 9.24. AAT N_Eff is the efficiency of amino acid utilization for milk protein production, Equation available amino acid absorbed in the small intestine for milk production, Equation 9.26 and 9.24. AAT _Eff is the efficiency of amino acid utilization for milk protein production, Equation

12.2 of weight gain gain 12.2NPrediction Prediction of weight 9.24. For cattle,of ADG can be predicted from either the energy available for growth or the 12.2growing Prediction weight gain AAT forof growth. Since the energy utilization kg_corr (Equation 9.6) and kormgthe AATN For growing growing cattle, ADG can bepredicted predicted from either the energy available growth N available For cattle, ADG can be from either thefactors energy available forfor growth or the 12.2 Prediction weight gain (Equation 8.5),growth. both depend on and ADG, they cannot used to (Equation predict daily weight available for SinceSince theBW energy utilization factorsbe kg_corr (Equation 9.6) and k kmg(Equation AAT for ADG growth. the energy utilization kg_corr 9.6) and N available For growing cattle, can be the predicted from either thefactors energy available for growth or mg the gain for a given diet.on Therefore, predicted requirement for ADG was assumed toweight vary with 8.5), both depend BW and ADG, they cannot be used to predict daily weight gain for a given (Equation 8.5), both depend on BW and ADG, they cannot be used to predict daily AAT N available for growth. Since the energy utilization factors kg_corr (Equation 9.6) and kmg animal type and diet. BW. gain for a given Therefore, the predicted requirement for ADG was assumed to vary diet. Therefore, the predicted requirement for ADG was assumed to vary with animal type (Equation 8.5), both depend on BW and ADG, they cannot be used to predict daily weightwithand BW. animal and diet. BW. Therefore, the predicted requirement for ADG was assumed to vary with gain fortype a given For growing bulls ofofdairy gain is is predicted from available energy: For growing bulls dairybreed breedweight weight gain predicted from available energy: animal type and BW. For growing bulls of dairy breed weight gain is predicted from available energy: NEG - NE_maint

 gaingrowing _ response 1000breed ⋅ For bulls of =dairy weight gain is predicted from available energy: [Eq. 12.3] NEG 0 .0243 + 7.7315 NEG ⋅-BW NE_maint gain _ response NEG = 1000 ⋅ [Eq. 12.3]

12.3

0 .0243 ⋅-BW +gain 7.7315 For growing bulls of beef breed NEG weight NE_maint is predicted from available energy:

gaingrowing _ response 1000breed ⋅ For bulls of =beef weight gain is182 predicted from available energy: [Eq. 12.3] NEG 0 .0243 ⋅ BW + 7.7315 NEG - NE_maint 182  gain _ response NEG = 1000 ⋅ [Eq. 12.4] 0 .0116 ⋅ BW + 9.7214 182

where gain_response

12.4

is the predicted weight gain based on the energy supply, g/d; NEG is the

NEG where gain_responseNEG is the predicted weight gain based on the energy supply, g/d; NEG is energy gain, Equation 8.8;8.8; NEG_maint is the energy requirement of of maintenance, Equation 9.1; BW the energy gain, Equation NEG_maint is the energy requirement maintenance, is current9.1; body weight, kg; body and the numerator calculates the energy content 1 kg content of weight gain. Equation BW is current weight, kg; and the numerator calculates the of energy of 1 kg of weight gain.

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_12, © Wageningen Academic Publishers 2011 AAT =

⋅

133

4870 4872 4871 4871 4873 4872 4872 4874 4873 4873 4875 4874 4874 4876 4875 4875 4877 4876 4876 4877 4877 4878 4878 4879 4878 4880 4879 4879 4881 4880 4880 4882 4881 4881 4883 4882 4882 4884 4883 4883 4885 4884 4884 4885 4885 4886 4886 4887 4886 4888 4887 4887 4889 4888 4888 4890 4889 4889 4890 4890 4891 4891 4892 4891 4893 4892 4892 4894 4893 4893 4895 4894 4894 4895 4896 4895 4896 4896 4897 4898 4897 4897 4899 4898 4898 4900 4899 4899 4901 4900 4900 4902 4901 4903 4901 4902 4903 4902 4904 4903 4905 4904 4904 4906 4905 4905 4907 4906 4906 4908 4907 4907 4908 4909 4908 4909 4910 4910 4909 4911 4911 4912 4912 4913 4913 4914 4914 4915 4915 4916 4916 4917 4917 4918 4918 4919 4919 4920 4920 4921 4921 4922 4922 4923 4923 4924 4924 4925 4925 4926 4926 4927 4927 4928 4928 4929 4929

gain, Equation is thegain energy requirement of maintenance, the NEG_maint predicted weight based on the energy supply, g/d; NEG is where gain_response the energy NEG is8.8; the predicted gain on thecalculates energy supply, g/d; NEG is where gain_response Equation 9.1; BW is current body weight,weight kg; and thebased numerator the energy content the energy gain, Equation NEG_maint is the energy requirement of maintenance, NEG is8.8; the gain, Equation 8.8; NEG_maint is the requirement of maintenance, of 1energy kg of9.1; weight gain. Equation BW is current body weight, kg; andenergy the numerator calculates the energy content Equation BWgain. is current body weight, kg; and the numerator calculates the energy content of 1 kg of9.1; weight of 1 kg of weight gain. for heifers and steers are calculated from available AATN: Weight gain prediction Weight gain gainprediction predictionfor forheifers heifersand andsteers steers calculated from available AAT Weight areare calculated from available AAT N: N: Weight gain prediction for heifers and steers are calculated from available AAT _Eff AATN: N (AATN - AATN _maint - AATN _gest) ⋅ gain _ responseAAT = 1000 ⋅ (AAT - AAT _maint - AAT _gest) ⋅ AATN _Eff 100 [Eq. 12.5] N N − 0.1262 ⋅ BWN+ 194.2 12.5 gain _ responseAAT = 1000 ⋅ (AATN - AATN _maint - AATN _gest) ⋅ AATN _Eff 100  [Eq. 12.5] 100 − 0.1262 ⋅ BW + 194.2 [Eq. 12.5] gain _ responseAAT = 1000 ⋅ 0.1262gain ⋅ BW based + 194.2on the amino acid supply, g/day; where gain_ responseAAT is the predicted −weight where gain_responseAAT is the predicted weight gain based on the amino acid supply, g/day; AATN is the response amino acid in the small intestine, Equation AAT the AATN gain_ is the predicted weight gain based on the8.11; amino acid supply,isg/day; where N_maint AATabsorbed is the amino acidrequirement absorbed infor the small Equation 8.11; _maint isprotein theisanimal’s protein NAAT where is the predicted weight gain based onAAT theAAT amino acid supply, g/day; animal’s maintenance, Equation 9.22; is Nthe AAT is protein the response amino acid in the intestine, small intestine, Equation 8.11; _maint the AATabsorbed N_gest N gain_ requirement for maintenance, Equation 9.22; AAT _gest is the protein requirement of gestation for AATN is protein the of amino acid absorbed in Equation the small 9.42; intestine, 8.11; AAT _maint N Equation is the efficiency of theis the requirement gestation for heifers, AAT is Nthe protein animal’s requirement for maintenance, Equation 9.22; N_EffAAT N_gest heifers, Equation 9.42; AAT _Eff is the efficiency of the utilisation of the available amino acids protein animal’s protein requirement for Equation 9.22; utilisation ofof thegestation availablefor amino acids for protein gain, 9.34; BWisisthe the current BW, requirement heifers, Equation 9.42; AATEquation is the efficiency of the N maintenance, N_gest N_EffAAT for and protein gain, Equation 9.34; BW isfor theprotein current BW, kg; numerator calculates the protein requirement of for heifers, Equation 9.42;gain, AAT _Eff is the the efficiency of the kg; theofnumerator calculates the protein content in 1Equation kg ofand weight gain. utilisation thegestation available amino acids 9.34; BW is the current BW, N content inof 1numerator kg weight gain. the utilisation the of available amino acids for protein gain, 9.34;gain. BW is the current BW, kg; and the calculates protein content in 1Equation kg of weight kg; 12.3and Prediction the numerator of N, calculates P and K theutilisation protein content andinexcretion 1 kg of weight gain. 12.3 Prediction N, and K utilisation and 12.3amount Prediction ofKPN, P and utilisation excretion The of N, Pofand excreted areK calculated as excretion the and difference between ingested amounts 12.3 Prediction N, and K in utilisation and excretion and incorporated in milk, used and The incorporation of N, amounts P and K The that amount of N, Pofand KPexcreted aregestation calculated asfor thebody. difference between ingested The amount of PPand KKexcreted calculated thebody. difference between The amount ofN, N,gestation and excreted are as the difference between ingested amounts and in milk, gain and corresponds tocalculated the requirements ofThe protein, P and ingested K described in K and that incorporated in milk, used inare gestation andasfor incorporation of N, amounts P and and that incorporated in milk, used gestation andforforbody. body.of The incorporation ofN, N,PPand and K in milk, chapter 9. in milk, gain and gestation corresponds to the and requirements protein, P and K of described in K that incorporated in milk, used in in gestation The incorporation in milk, gain and gestation corresponds to the requirements of protein, K described in chapter gain and9. gestation corresponds to the requirements of protein, P andPKand described in Chapter 9. chapter Nitrogen 9. 12.3.1 utilisation and excretion 12.3.1 Nitrogen in utilisation excretion The N efficiency ruminantsand is low. The utilisation is higher in lactating animals than in 12.3.1 Nitrogen utilisation and excretion 12.3.1 Nitrogen utilisation excretion growing cattle andin non-producing animals. N efficiency is calculated as theanimals total amount The N efficiency ruminantsand is low. The utilisation is higher in lactating than inof N The ruminants isislow. is is higher in lactating thanthan inofin utilized incattle relation to its total intake: growing andinin non-producing animals. Nutilisation efficiency ishigher calculated as theanimals total amount N growing The NNefficiency efficiency ruminants low.The Theutilisation in lactating animals growing cattle and non-producing animals. N efficiency is calculated as the total amount of N utilized in relation to its total intake: cattle and non-producing animals. N efficiency is calculated as the total amount of N utilized in utilized intorelation Ntointake: _ its u total intake: relation its total [Eq. 12.6] ⋅ 100 N _ u _ pct = u i ⋅ 0.16 DMIN i ⋅ _CP [Eq. 12.6] ⋅ 100 N _ u _ pct = ∑ _ u i DMIN ⋅ CP ∑ i i ⋅ 0.16 ⋅ 100  [Eq. 12.6] N _ u _ pct = 12.6 i DMIi ⋅ CPi ⋅ 0.16 ∑

i is the N utilization as a proportion of the total intake, %; N_u is the amount of where N_u_pct where N_u_pct the utilization a proportion ofofthe intake, %; N_u isamount theCP amount of N N utilized, Equation 12.7; DMIi is the matter intake thetotal i=1...n'th is where N_u_pct isisthe NNutilization as as adry proportion of the total intake, %;feedstuff, N_u is thekg/d; i of utilized, Equation 12.7; DMI is the dry matter intake of the i=1...n’th feedstuff, kg/d; CP is where N_u_pct iscontent the12.7; N utilization as adry proportion of theof total intake, N_u thekg/d; amount the crude protein inDMI theiii=1...n'th feedstuff, g/kg DM; 0.16%; isfeedstuff, the Nisproportion N utilized, Equation is the matter intake theand i=1...n'th CPin is i the i of crude protein content in the i=1...n’th feedstuff, DM; and 0.16 is the N proportion in in N utilized, Equation 12.7; is the dry matter g/kg intake of the i=1...n'th kg/d; CP is protein. the protein crude protein content inDMI the feedstuff, g/kg DM; and 0.16 isfeedstuff, the N proportion ii=1...n'th ithe the crude proteinprotein content in the i=1...n'th feedstuff, g/kg DM; and 0.16 is the N proportion in N _ protein u = N _ milk + N _ gest + N _ gain  the 12.7 [Eq. 12.7] N _ u = N _ milk + N _ gest + N _ gain [Eq. 12.7] N _ u = N_u N _ milk + Ntotal _ gestamount + N _ gain where the utilised g/d; N_milk, N_gest N_gain are amount the amount of N 12.7] where N_u isisthe total amount ofofutilised N,N, g/d; N_milk, N_gest andand N_gain are [Eq. the incorporated in milk, foetus and weight gain, Equation 12.8, 12.9 and 12.10 of N incorporated in milk, foetus weight gain, Equation 12,8, 12.9 and 12.10 respectively. where N_u is the total amount of and utilised N, g/d; N_milk, N_gest and N_gain arerespectively. the amount where N_u is the total amount of and utilised N, g/d; N_gest are respectively. the amount of N incorporated in milk, foetus weight gain,N_milk, Equation 12,8, and 12.9N_gain and 12.10 of incorporated milk, foetus and weight gain, Equation 12,8, 12.9 [Eq. and 12.8]12.10 respectively. N _Nmilk = MPY ⋅ 0.in 15674 12.8 [Eq. 12.8] N _ milk = MPY ⋅ 0.15674 [Eq. 12.8] N _ milk = MPYis⋅ 0the .15674 where N_milk the amount of of N N incorporated incorporated into into milk, milk, g/d; g/d; MPY MPY is the milk protein yield, Equation where is where N_milk N_milk is is the amount amount of N incorporated into milk, g/d; MPY is the the milk milk protein protein yield, yield, 183 3.4; and 0.15674 is the N content in milk protein (ISO 8968-5|IDF 020-5:2001). Equation 3.4; 3.4; and and 0.15674 0.15674 is is the the N N content content in in milk milk protein protein (ISO (ISO 8968-5|IDF 8968-5|IDF 020-5:2001). 020-5:2001). Equation 183 183  [Eq. 12.9 N _ gest = AAT _ gest ⋅ 0 . 16 ⋅ 0 . 5 N [Eq. 12.9] 12.9] N _ gest = AAT _ gest ⋅ 0.16 ⋅ 0.5 N

whereN_gest N_gestis theN incorporated into foetus, AAT is the amino required for where N_gest isisthe the NNincorporated incorporated into thethe foetus, g/d;g/d; AAT _gest is the the amino acidacid required N N_gest where into the foetus, g/d; AAT is amino acid required N_gest for gestation, Equation 9.42; 0.16 is the N proportion in the protein; and 0.5 is the utilisation gestation, Equation 9.42; 0.16 is the N proportion in the protein; and 0.5 is the utilisation for gestation, Equation 9.42; 0.16 is the N proportion in the protein; and 0.5 is the utilisation factor for factor for the the amino acids1985). (NRC, 1985). 1985). the amino acids (NRC, factor for amino acids (NRC, N_ _ gain gain = gain _ _ prot prot ⋅⋅ 00..16 16 N = gain



[Eq. 12.10] 12.10] [Eq.

12.10

where into the BW gain, g/d; gain_prot is gain, whereN_gain N_gainis theN incorporated into BW gain, gain_prot is protein the protein where N_gain isisthe the NNincorporated incorporated into thethe BW gain, g/d;g/d; gain_prot is the the protein gain,gain, Equation Equation 9.8; 0.16 is in 9.8; and 0.16 is the N proportion in the protein. Equation 9.8; and and 0.16 is the the N N proportion proportion in the the protein. protein. Total amounts amounts of of excreted excreted N N is is assumed assumed to to be be the the difference difference between between the the N N in in the the CP CP intake intake Total from from the the ration ration and and N_u: N_u: N = N_ _ excreted excreted =∑ DMI ii ⋅⋅ CP CPii ⋅⋅ 00..16 16 − −N N __ uu ∑ DMI 134  i i

12.11] [Eq. 12.11] system NorFor – The Nordic feed [Eq. evaluation

where N_excreted N_excreted is is the the total total amount amount of of N N excreted, excreted, g/day; g/day; DMI DMIi is is the the DM DM intake intake of of the the of of where

4923 4923 4919 4922 4924 4924 4920 4923 4925 4925 4921 4924 4926 4926 4922 4925 4927 4927 4923 4926

4924 4927 4928 4928 4925 4929 4929 4928 4926 4930 4930 4929 4927 4931 4931 4930 4932 4932 4931 4928 4933 4933 4929 4932 4934 4934 4933 4930 4935 4935 4934 4931 4936 4936 4935 4932

4936 4933 4937 4937 4934 4937 4938 4935 4938 4939 4936 4939 4938 4940 4940 4939 4937 4941 4941 4940 4942 4942 4938 4941 4943 4943 4939 4942 4944 4944 4943 4940 4945 4945 4944 4941 4946 4946 4945 4942 4947 4947 4946 4943 4948 4948 4944 4947 4949 4949 4945 4948 4950 4950 4946 4949 4951 4951 4947 4950 4952 4951 4948 4952 4949 4952 4953 4953 4954 4950 4955 4951 4953 4956 4952 4957 4953 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968 4969 4970

N _ gain =9.8; gainand _ prot ⋅ 0.16 [Eq. intake 12.10] Equation 0.16 is the is N proportion in the protein. Total Total amounts amounts of of excreted excreted N N is assumed assumed to to be be the the difference difference between between the the N N in in the the CP CP intake from the ration and N_u: from rationis and N_u: wherethe N_gain the N incorporated into to thebeBW g/d; gain_prot gain, Total amounts of excreted N is assumed thegain, difference between is thethe N protein in the CP intake

Equation 9.8;= and 0.16 is the from the ration N_u: N _ excreted DMI ⋅ CP ⋅ 0N .16proportion − N _ u in the protein. ∑and N _ excreted = ∑ DMI ii ⋅ CPii ⋅ 0.16 − N _ u

[Eq. [Eq. 12.11] 12.11]

ii Total amounts of excreted isassumed difference between theinNthe in[Eq. theintake CP intake Total amounts excreted be be thethe difference between the N CP N _ excreted = ∑of DMI ⋅ 0Nis .16 −assumed N _ u to to 12.11] i ⋅ CPi N the ration and iN_u: from the ration andisN_u: where N_excreted the total amount of N excreted, g/day; DMI is the DM intake of the of

where N_excreted is the total amount of N excreted, g/day; DMIii is the DM intake of the of content in i=1...n'th feedstuff, the i=1...n'th feedstuff, kg DM/day; CP isNthe the crude crude protein protein content in the the i=1...n'th feedstuff, the i=1...n'th feedstuff, kgtotal DM/day; CPofii is is the DM intake of the of where N_excreted is ithe amount g/day; DMI iamounts N _ excreted = ∑isDMI ⋅ CP −N _inu CP;  excreted, [Eq. 12.11] g/kg DM; 0.16 the proportion N and N_u is the total of N utilized, i ⋅ 0.16 of g/kg DM; 0.16 is the proportion of N in CP; and N_u is the total amounts of N utilized, is the crude protein content in the i=1...n'th feedstuff, the i=1...n'th feedstuff, kg DM/day; CP i i Equation 12.15. Equation g/kg DM;12.15. 0.16 is the proportion of N in CP; and N_u is the total amounts of N utilized,

from

12.11

where N_excreted is the total amount of N excreted, g/day; DMI is the DM intake of the of the

i Equation 12.15. intake of the of where N_excreted is the total amount ofisN excreted, DMI i is the Excreted N in the assumed to be the difference between N and apparent i=1...n’th kgis CP the crude g/day; protein content inDM the i=1...n’th feedstuff, g/kg Excreted Nfeedstuff, infeedstuff, the faces faces isDM/day; assumed to be difference between N intake intake and apparent i isthe the i=1...n'th kg DM/day; CP the crude protein content in the i=1...n'th feedstuff, i digested N. DM; 0.16 is the proportion of N in CP; and N_u is the total amounts of N utilized, Equation 12.15. digested Excreted N0.16 in the faces is assumed to in beCP; the and difference intake and g/kg DM;N. is the proportion of N N_u isbetween the total N amounts of Napparent utilized, digested EquationN. 12.15. ⎛ ⎞ Excreted N⎛inDMI the feaces−istdassumed apparent digested N. ⎞ .16to be the difference between N intake and [Eq. N N_ _ faeces faeces = = ⎜⎜ ∑ CPii − td _ _ CP CP⎟⎟⎟ ⋅⋅ 0 0.16 ∑ DMIii ⋅⋅ CP [Eq. 12.12] 12.12] ⎠⎠⎞ to be the difference between N intake and apparent Excreted N ⎝⎜⎝⎛inii the faces is assumed ⎜ ⎟  12.12 N _ faeces = DMI ⋅ CP − td _ CP ⋅ 0 . 16 ∑ [Eq. 12.12] i i digested N. ⎜ ⎟ ⎝ i is the amount of ⎠N excreted in the faeces, g/d; DMI is the dry matter intake where N_faeces where N_faeces is the amount of N excreted in the faeces, g/d; DMIii is the dry matter intake where N_faeces is the amount of inprotein the faeces, g/d; matter g/kg intake of the the content in the feedstuff, of the kg/d; i is the dry ⎛ feedstuff, ⎞ iiNis isexcreted the crude crude protein content in DMI theisi=1...n'th i=1...n'th feedstuff, g/kg of the i=1...n'th i=1...n'th feedstuff, kg/d;ofCP CP where N_faeces istotal amount N⋅ 0excreted in7.52; the faeces, g/d; DMI the dry matter intake ⎜⎜is∑ DMI ⎟⎟the N _ faeces =feedstuff, ⋅kg/d; CP .Equation 16 i [Eq. 12.12] i=1...n’th CP_iCP isCP, crude protein content in is the i=1...n’th feedstuff, g/kg DM; td_CP ithe i − td DM; td_CP the digested and 0.16 the proportion of N in CP. DM; td_CP is the total digested CP, Equation 7.52; and 0.16 is the proportion of N in CP. ⎝ i feedstuff, kg/d; CP ⎠ is the crude protein content in the i=1...n'th feedstuff, g/kg of the i=1...n'th

is the total digested CP, Equation i7.52; and 0.16 is the proportion of N in CP.

DM; td_CP isprotein the total CP, Equation 7.52; and 0.16 is the proportion of N in CP. Overfeeding or digested pourly ration N in urine.The difference between Overfeeding protein pourly balanced balanced rationinincreases increases N g/d; in the the urine.The difference between where N_faeces is theoramount oftheNNexcreted thethe faeces, DMI dry matter intake i is the total amount of excreted N and excreted in faeces is assumed to be the N excreted in Overfeeding protein or pourly balanced rationinincreases Nisinassumed the urine.The difference between total total amount of excreted N and the N excreted the faeces to be the N excreted in of Overfeeding the i=1...n'th protein feedstuff, or pourly kg/d; balanced CP ration increases N in the urine.The difference between is the crude protein content in the i=1...n'th feedstuff, g/kg i the urine: amount of excreted N and the N excreted in the faeces is assumed to be the N excreted in the urine: the urine: total excreted N and the excreted7.52; in theand faeces to be the DM; amount td_CP isofthe total digested CP,NEquation 0.16isisassumed the proportion of N N excreted in CP. in the urine: N _ urine = N _ excreted − N _ faeces [Eq. N _ urine = N _protein excreted N _ faeces [Eq. 12.13] 12.13]  Overfeeding or−pourly balanced ration increases N in the urine.The difference between 12.13 total amount excreted the N excreted in the faeces is assumed to be the N excreted N _ urine = N _ofexcreted − NN_and faeces [Eq. 12.13]in where N_urine N_excreted total amount amount of of N N excreted, excreted, Equation where N_urine is is the the N N excretion excretion in in urine, urine, g/d; g/d; N_excreted is the the total where N_urine is the N excretion N_excreted is the urine: Equation 12.11; and N_faeces is the amount of N excreted in faeces, Equation 12.12. Equation 12.11; is the amount of N_excreted N excreted in 12.12. 12.11;N_urine and N_faeces the amount of N excreted in faeces, Equation 12.12.of where isand theN_faeces Nisexcretion in urine, g/d; is faeces, the totalEquation amount N excreted, N _ urine = N _ excreted − N _ faeces [Eq. 12.13] Equation 12.11; and N_faeces is the amount of N excreted in faeces, Equation 12.12. N _ urine N _ urine [Eq. N  12.14 [Eq. 12.14] 12.14] N __ urine urine __ pct pct = = N _ excreted ⋅⋅ 100 100 N _excreted urine where N_urine isN_the N excretion in urine, g/d; N_excreted is the total amount of [Eq. N excreted, 12.14] N _ urine _ pct = ⋅ 100 where N_urine_pct isN_faeces excreted ininurine thethe total NN excretion, where N_urine_pct isthe theNN excreted urine aproportion proportion of total excretion, Equation 12.11;Nand is the amount ofasas Na excreted in of faeces, Equation 12.12. %;%; N_urine is _ excreted N_urine the NEquation in urine, 12.13; Equation and N_excreted is the total amount of N excreted, the N in isurine, and12.13; N_excreted is the total amount of N excreted, Equation 12.11. Equation 12.11. N _ urine [Eq. 12.14] N _ urine _ pct = ⋅ 100 N _ excreted 12.3.2 Phosphorus utilisation and excretion 184 12.3.2 Phosphorus utilisation and excretion 184 The 1980). Therefor The excessary excessaryfed fedPPover overrequirement requirementisisexcreted excreted thefaeces faeces(ARC, (ARC, 1980). Therefore overfeeding 184 ininthe overfeeding P is useless. P is useless. PP efficiency efficiencyisiscalculated calculatedas: as: P _ u _ pct =

P_u

∑ DMI i ⋅ Pi i

184

⋅100 

[Eq. 12.15]

12.15

where P_u_pct P_u_pctisisthe thePPutilization utilizationas asaa proportion proportionof oftotal totalPPintake, intake,%; %;P_u P_uisisthe theamount amountof ofPP utilized, where EquationEquation 12.16; DMI the iDM intake of the of of the theof i=1...n’th feedstuff, kg DM/day; and Pi is the utilized, 12.16; is the DM intake the i=1...n'th feedstuff, kg DM/day; i isDMI P content inPthe i=1...n’th DM. g/kg DM. and Pi is the content in thefeedstuff, i=1...n'thg/kg feedstuff, P _ u = (P _ milk + P _ gest + P _ gain ) ⋅ 0.7 

[Eq. 12.16]

12.16

4971 4972 4973 4974 4975 4976 4977 4978

whereP_u P_uisisthe thetotal totalamount amountofofutilised utilisedP,P,g/d; g/d;P_milk, P_milk,P_gest P_gestand andP_gain P_gain where areare thethe requirement of P for milk, gestation weight gain,and Equation and 9.53 requirement of P forand milk, gestation weight9.51, gain, 9.52 Equation 9.51,respectively; 9.52 and 9.530.7 is the absorption coefficient. 0.7 is the absorption coefficient. respectively;

4979

P _ excreted = ∑ DMI − P _ uevaluation system i ⋅ Pi feed NorFor – The Nordic

4980 4981

where P_excreted is the total amount of P excreted, g/d; DMIi is the DM intake of the of the

Phosphorus excretion occurs normally in the faeces and only a minor part excretes in the urine (ARC, 1980). The excretion is estimated as the difference of total P intake and the amounts of P incorporated in milk, foetus and weight gain. i

[Eq. 12.17]

135

4970 4970 4971 4971 4972 4972 4973 4973 4974 4974 4975 4975 4976 4976 4977 4977 4978 4978

[Eq. 12.16] where where P_u P_u is is the the total total amount amount of of utilised utilised P, P, g/d; g/d; P_milk, P_milk, P_gest P_gest and and P_gain P_gain are are the the requirement of of P P for for milk, milk, gestation gestation and and weight weight gain, gain, Equation Equation 9.51, 9.51, 9.52 9.52 and and 9.53 9.53 requirement respectively; respectively; 0.7 0.7 is is the the absorption absorption coefficient. coefficient.

Phosphorus excretionoccurs occurs normally in the faeces and only a minor part excretes in the urine Phosphorus Phosphorus excretion excretion occurs normally normally in in the the faeces faeces and and only only aa minor minor part part excretes excretes in in the the (ARC, 1980). The The excretion is estimated as as thethe difference urine (ARC, 1980). The excretion is estimated estimated as the differenceofof oftotal totalPP Pintake intake and and the urine (ARC, 1980). excretion is difference total intake and the amounts of P incorporated in milk, foetus and weight amounts of P P incorporated incorporated in milk, milk, foetus gain. and weight weight gain. gain. amounts of in foetus and

4979 4979

P =∑ DMI ⋅ P − P _ u ∑ P __ excreted excreted = ∑ DMI iii ⋅ Piii − P _ u 

4980 4980 4981 4981 4982 4982 4983 4983

i the DM intake of the of the where P_excreted is excreted, g/d; DMI is the DM intake the ofand the P_u is the where P_excreted is the the total total amount of P excreted, g/d;i=1...n’th DMIiii is feedstuff, kg DM/day; and Pamount is the of PP content in the feedstuff, g/kgofDM; i i=1...n'th feedstuff, feedstuff, kg kg DM/day; DM/day; and and P Pii is is the the P P content content in in the the i=1...n'th i=1...n'th feedstuff, g/kg DM; i=1...n'th i and amount of P utilised in milk, gestation gain, Equation 12.16. feedstuff, g/kg DM; and and P_u P_u is is the the amount amount of of P P utilised utilised in in milk, milk, gestation gestation and and gain, gain, Equation Equation 12.16. 12.16.

4984 4984 4985 4985 4986 4986 4987 4987 4988 4988 4989 4989 4990 4990 4991 4991 4992 4992 4993 4993

4994 4994 4995 4995 4996 4996

4997 4998 4999 5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013 5014 5015

[Eq. 12.17] 12.17] [Eq.

iii

12.17

where P_excreted is the total amount of P excreted, g/d; DMI is the DM intake of the of the i=1...n’th

12.3.3 Potassium utilisation utilisation andexcretion excretion 12.3.3 12.3.3 Potassium Potassium utilisation and and excretion

Cattle normally ingest more K than the requirement. Potassium utilisation is as: Cattle Potassium utilisation is calculated calculated as: as: Cattle normally normallyingest ingestmore moreKKthan thanthetherequirement. requirement. Potassium utilisation is calculated K_ _u u K = ⋅⋅ 100 100  K __ uu __ pct pct = K ∑ DMI ∑ DMIii ⋅⋅ K K ii ∑ i

iii

[Eq. 12.18] 12.18] [Eq.

i

12.18

where K_u_pct isisthe the KKutilization utilization asas proportion of of total K intake, intake, %; %; K_uK_u is the the total whereK_u_pct K_u_pctis theK utilizationas a proportion total K intake, is total the total amount of where aa proportion of total K %; K_u is amount of utilized, Equation 12.19; DMI the DM of the offeedstuff, the i=1...n'th feedstuff,and Ki is K utilized, DMI DM intake ofintake the i=1...n’th kg DM/day; ii is amount of K KEquation utilized, 12.19; Equation 12.19; DMI i is the i is the DM intake of the of the i=1...n'th feedstuff, isi=1...n’th the K K content content in the the g/kg i=1...n'th feedstuff, g/kg g/kg DM. DM. kg DM/day; and Kthe ii is theDM/day; K content inK feedstuff, DM.feedstuff, the in i=1...n'th kg and i

K_ _u u= = (K K_ _ milk milk + +K K_ _ gest gest + +K K_ _ gain gain) ⋅⋅ 0 0..9 9  K

12.19 [Eq. 12.19] 12.19] [Eq. where K_u K_u is is the the total total amount amount of of utilised utilised K, K, g/d; g/d; P_milk, P_milk, P_gest P_gest and and P_gain P_gain are are the the where requirement K milk, gestation and gain, Equation 9.66, and where K_u of is the total amount of utilised K, g/d; P_gest and P_gain requirement of K for for milk, gestation and weight weight gain,P_milk, Equation 9.66, 9.67 9.67 and 9.68 9.68are the requirement respectively; 0.9gestation is the the absorption absorption coefficient. of K for milk, and weight gain, Equation 9.66, 9.67 and 9.68 respectively; 0.9 is the respectively; 0.9 is coefficient. absorption coefficient.

185 185

The total totalamount amountofofexcreted excretedKKisiscalculated: calculated: The K _ excreted = ∑ DMI i ⋅ K i − K _ u  i

[Eq. 12.20]

12.20

where K_excreted is the K excreted, g/day; DMI is the DM intake of the of the i=1...n’th feedstuff,

i DM intakeg/kg of the of and the i=1...n'th where K_excreted thethe K excreted, i is thefeedstuff, kg DM/day; and Kisi is K contentg/day; in theDMI i=1...n’th DM; K_u is the amount of feedstuff, kg DM/day; and Ki is the K content in the i=1...n'th feedstuff, g/kg DM; and K_u is K utilized in milk, gestation and gain, Equation 12.19. the amount of K utilized in milk, gestation and gain, Equation 12.19.

References 12.4 References ARC (Agricultural Reasearch Council), 1980. The nutrient requirements of ruminant livestock. Commonwealth

ARC – Agricultural Reasearch Council, 1980. The nutrient requirements of ruminant Agricultural Bureaux, Slough, UK. livestock. Slough England: Commonwealth Agricultural Bureaux.

ISO 8968-5/IDF 020-5:2001. Milk - Determination of nitrogen content. International Dairy Federation, Brussels, ISO Belgium. 8968-5/IDF 020-5:2001. Milk - Determination of nitrogen content. International Dairy Sjaunja, L.-O., L. Bævre,Belgium. I. Junkkarainen, J. Pedersen and J. Setälä, 1990. A Nordic proposition for an energy corrected Federation, Brussels, milk (ECM) formula. 26th session of the International Committee for recording the productivity of Milk Animals (ICRPMA). Sjaunja, L.-O., L. Bævre, I. Junkkarainen, J. Pedersen and J. Setälä, 1990. A Nordic

proposition for an energy corrected milk (ECM) formula. 26th session of the International Committee for recording the productivity of Milk Animals (ICRPMA).

136 

NorFor – The Nordic feed evaluation system

5016 5017 5018

13.0 Standardfeed feedvalue value 13. Standard

5019 5020 5021 5022 5023 5024 5025 5026 5027

Traditional feed evaluation systems determine fixed and additive feed values of feedstuffs. The NorFor system, however,systems is a rationdetermine evaluationfixed system in principle individual Traditional feed evaluation andand additive feed each values of feedstuffs. The feedstuff has no fixed feed value because of the interactions in the digestion and metabolism NorFor system, however, is a ration evaluation system and in principle each individual feedstuff of there is aofneed compare feedstuffs for trading or in of thenutrients. hasnutrients. no fixedNevertheless, feed value because the to interactions in the digestion andpurposes metabolism optimization concentrate mixtures by linear programming in thepurposes feedstuff or industry. Nevertheless,ofthere is a need to compare feedstuffs for trading in the optimization of Therefore, we calculate feed values based on standardized conditions, where important model concentrate mixtures by linear programming in the feedstuff industry. Therefore, we calculate feed variables are fixed, e.g. fractional passage rates and efficiency in the microbial protein values based on standardized conditions, where important model variables are fixed, e.g. fractional synthesis (see Table 13.1). These fixed values represent feed rations for diets used in passage rates and efficiency inSweden. the microbial protein synthesis (see Table 13.1). These fixed values Denmark, Iceland, Norway and

5028 5029 5030 5031 5032 5033 5034 5035 5036 5037

Standard feed values for NEL, AATN and PBVN were calculated in the following way. First Standard feed values for NEL,proportion AATN andand PBV in the animal BW, DMI, concentrate content ofcalculated CP, NDF, ST andfollowing RestCHOway. wereFirst set animal N were BW, DMI,toconcentrate proportion and of CP, NDF, STthe andstandard RestCHO according Table 13.1 to standardize thecontent feed ration from which feedwere valueset is according calculated. Then for RLI, fractional rates and to Table 13.1 to values standardize thecorrNDF_fac, feed ration from which passage the standard feedr_emCP value isare calculated. Then determined usingcorrNDF_fac, Equations 13.1fractional to 13.8 (Table 13.1). values substitute the values for RLI, passage ratesThese and r_emCP are determined using Equations corresponding equations in chapter 7 whensubstitute calculating standard feed equations value of a in feedstuff 13.1 to 13.8 (Table 13.1). These values thethe corresponding Chapter 7 when with specificthe dietary characteristics. of NEL, AAT N and PBVN The standard calculating standard feed value The of a standard feedstufffeed withvalues specific dietary characteristics. (Equation 8.3, and 13.9, and respectively) receive a8.3, suffix thatand tells which standardisedreceive DMI, a suffix feed values of8.11 NEL, AAT PBV (Equation 8.11 13.9, respectively) N N 8that or 20 kgwhich DM, the calculations are based upon: NEL8the , NEL PBV and , NEL , 20, PBVN8,are N20, AAT tells standardised DMI, 8 or 20 kg DM, calculations based upon:N8NEL 8 20 AAT PBVN20,. PBV , AAT and AAT .

5038 5039

Standardised RLI, corrNDF_fac, fractional passage rates and r_emCP are calculated as:

5040

RLIstd =

5041 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055 5056

M. Åkerlind and H. Volden

M. Åkerlind and H. Volden

represent feed rations for diets used in Denmark, Iceland, Norway and Sweden.

N8

N20

N8

N20

Standardised RLI, corrNDF_fac, fractional passage rates and r_emCP are calculated as: 280 DMIstd ⋅ 370

where, RLI



[Eq. 13.1]

13.1

is the rumen load index; 280 is the sum of rumen degraded ST and RestCHO in the

std where, RLIDM; rumen loadstandardized index; 280 isDM the sum of rumen RestCHO in and 370 std is the diet, g/kg DMI the intake, which degraded is either 8ST orand 20 kg DM/day; std is is the diet, g/kg DM; DMI the standardized DM intake, which is either 8 or 20 kg DM/day; std is the NDF content, g/kg DM. and 370 is the NDF content, g/kg DM.

The NDF correction factor is calculated as:

Table 13.1. Fixed values for the listed parameters used for determining standard feed values. corrNDF Parameter_ fac = e

(

− 0 .24 ⋅ RLI

)

2 .9

std

Equation

8 kg DMI[Eq. 13.2]20 kg DMI

where, is kg the correction factor for the NDF degradation rate; CurrentcorrNDF_fac body weight, 600and RLIstd is the600 rumen load index, Dry matter intake,Equation kg/d 13.1. 8 20

Concentrate proportion of the ration, % DMIstd ⋅1000 DM rCP, _ kplg/kg = 2.45 + 0.055 ⋅ + 0.0004 ⋅ 502 NDF, g/kg DM 6000.75 ST+RestCHO, g/kg DM DMI DM std ⋅ 1000 − 0.02 ⋅ 50 rrdST+rdRestCHO, _ kpc = 2.504 + 0.1375g/kg ⋅ Correction factor for NDF600 degradation rate Passage rate for liquid, %/h Passage rate for CP and ST in concentrate, %/h Passage rate for NDF in concentrate, %/h 187 Passage rate for CP and ST in roughage, %/h Passage rate for NDF in roughage, %/h Efficiency of microbial CP synthesis, g/kg rd_OM

13.2 13.3 13.4 13.5 13.6 13.7 13.8

50 50 160 160 [Eq. 13.3] 370 370 310 310 280 [Eq. 13.4]280 0.89857 0.89857 7.07943 12.5236 3.33733 6.08733 1.43505 2.61755 2.30177 4.47943 0.998465 1.64242 133.383 184.329

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_13, © Wageningen Academic Publishers 2011

137

5041 5042 5042 5043 5043 5044 5044 5045 5045 5046 5046 5047 5047 5048 5048 5049 5049 5050 5050 5051 5051 5052 5052

5053 5053 5054 5054 5055 5057 5055 5057 5056 5058 5056 5058 5057 5059 5059 5060 5058 5060 5059 5060 5061 5061

5061 5061 5062 5062 5063 5063 5064 5062 5064 5065 5062 5063 5065 5066 5064 5066 5067 5065 5067 5068 5066 5068 5069 5067 5069 5070 5068 5070 5071 5069 5071 5072 5070 5072 5071 5073

5073 5074 5072 5074 5075 5073 5075 5076 5074 5076 5077 5075 5077 5078 5076 5078 5079 5077 5079 5080 5078 5080 5081 5079 5081 5082 5080 5082 5083 5081 5083 5084 5082 5084 5085 5083 5085 5086 5084 5086 5087 5085 5087 5088 5086 5088 5089 5087 5089 5090 5088 5090 5091 5089 5091 5092 5090 5092 5093 5091 5093 5094 5092 5094 5095 5093 5095 5096 5094 5096 5097 5095 5097

where, RLIstd is the rumen load index; 280 is the sum of rumen degraded ST and RestCHO in is the rumen load index; 280 is the sum of rumen degraded ST and RestCHO in where, RLIstd the diet, g/kg DM; DMIstd is the standardized DM intake, which is either 8 or 20 kg DM/day; the diet, g/kg DM; DMIstd is the standardized DM intake, which is either 8 or 20 kg DM/day; and 370 is the NDF content, g/kg DM. and 370 is the NDF content, g/kg DM. The NDF correction factor is calculated as: The NDF correction factor is calculated as:

The NDF correction factor is calculated as:

((

− 0 .24 ⋅ RLI

− 0 .24 ⋅ RLI std corrNDF fac e − 0 .24 ⋅ RLI stdstd corrNDF __ fac == e

))

2 2 ..9 9 2 .9



[Eq. 13.2] [Eq. 13.2]

13.2

where, corrNDF_fac is the correction factor for the NDF degradation rate; and RLIstd is the where,corrNDF_fac corrNDF_facisisthe thecorrection correctionfactor factor NDF degradation rate; is the rumen the where, forfor thethe NDF degradation rate; andand RLIRLI std isstd rumen load index, Equation 13.1. rumen load index, Equation load index, Equation 13.1. 13.1. DMIstd ⋅ 1000 ⋅ 1000 + 0.0004 ⋅ 5022  r _ kpl = 2.45 + 0.055 ⋅ DMIstd r _ kpl = 2.45 + 0.055 ⋅ + 0.0004 ⋅ 50 0.75 600 0 . 600 75

[Eq. 13.3] [Eq. 13.3]

13.3

⋅ 1000 DMIstd DMI ⎞ ⎛ 0.1375 ⋅ DMI std ⋅1000 13.4 − 0.02 ⋅ 50 [Eq. 13.5] 13.4] r _ kpNDFc kpc = 2.504 .02 ⋅50⎟ ⋅ 0.43 = ⎜ 2++.504 + 0.1375⋅std ⋅ 1000 [Eq. rr __ kpc = 2.504 − 0.02−⋅050 [Eq. 13.4] DMIstd ⋅1000 ⎞⎠ ⎛⎝ 0.1375 ⋅ 600 600 600 r _ kpNDFc= ⎜ 2.504 + 0.1375⋅ − 0.02 ⋅ 50⎟ ⋅ 0.43 [Eq. 13.5] 600 ⎠ ⎝ DMIstd 1000 ⋅ ⎞ ⎛⎛ ⋅ DMI 1000 ⎞ std kpNDFc= 504 + 1375std ⋅⋅ ⋅ 1000 − [Eq. 13.5] 13.5 0..02 02 2⋅⋅ 50 50⎟⎟ ⋅⋅ 0 0..43 43  = ⎜⎜+220..504 + 00DMI −0 rrr ___ kpNDFc ..1375 kpr = 0.35 [Eq. 13.6] 600+ 0.0002 ⋅ 50187 ⎠⎠ ⎝⎝ .022 ⋅ DMIstd0.⋅75 600 1000 2 r _ kpr = 0.35 + 0.022 ⋅ 600 + 0.0002 ⋅ 50187 [Eq. 13.6] 600 0.75 DMI ⋅ 1000 DMI std std ⋅ 1000 + 13.6 rr __ kpr 0..0002 0002 ⋅⋅ 50 50 22  [Eq. 13.6] kpr = = 00..35 35 + + 00..022 022 ⋅⋅ +0 0 ..75 0 75 600 600 0.78 ⋅1.93668 r _ kpNDFr = 0.480339 + [Eq. 13.7] 0.78 ⋅1.93668 − 3.19822 ⎛ DMI std ⋅ 370 ⎞ r _ kpNDFr = 0.480339 +  13.7 [Eq. 13.7] 1+ ⎜ − 3.19822 ⎟ DMI⋅ 7std ⋅ 370 ⎠⎞ ⎛ 600 . 48383 ⎝ ⋅1.93668 ⎟ r _ kpNDFr = 0.480339 + 1 + ⎜⎝ 6000⋅.78 [Eq. 7.48383 ⎠ −− 33..19822 [Eq. 13.7] 13.7] ⋅ DMI 370 ⎛ ⎞ 19822 std std where, r_kpl is the passage 1 +rate ⎜ of the liquid ⎟ phase out of the rumen; DMIstd is the .48383 where, r_kpl r_kplDMI, thepassage rate the phase outthe ofrumen; the DMI where, isisthe rate of⋅ 7of the liquid outBW, of DMI ⎝ 600 ⎠ phase is the is standardized 8 passage or 20 kg DM; 600 isliquid the current kg; 50 isrumen; the proportion of the standardized std std

standardized 8%;or600 20 is kgthe 600 isBW, the current kg; 50 is the of in diet, %; r_kpc concentrate diet, r_kpc isDM; the passage rate protein starch inofproportion concentrates; DMI, 8 or 20inDMI, kg DM; current kg;of50 isBW, the and proportion concentrate where, r_kpl the passage rate of the liquid phase out of DMI ispassage theroughage std concentrate diet, %; r_kpc isofand the passage of protein and starch in is concentrates; r_kpNDFc isinis the passage rate NDF in concentrate; isrumen; the passage rate of where, r_kpl is the passage rate of the liquid phase outr_kpr of the the rumen; DMI is the passage rate of protein starch inrate concentrates; r_kpNDFc the rate of NDF in std standardized DMI, 8isor kg of DM; 600 isconcentrate; therate current BW, 50starch; is the proportion of r_kpNDFc the passage NDF inof r_kprkg; is the passage rate protein andisstarch; r_kpNDFr is the passage of roughage NDF; and 370 is of theroughage NDF concentrate; r_kpr the20rate passage rate roughage protein and r_kpNDFr is the passage rate concentrate inNDF; diet, %; r_kpc the passage rate andNDF; starchand in concentrates; protein and starch; theNDF passage rateof of roughage 370 is the NDF content in diet, g/kgr_kpNDFr DM. of roughage and 370 isisisthe content inprotein diet, g/kg DM. r_kpNDFc is theg/kg passage content in diet, DM. rate of NDF in concentrate; r_kpr is the passage rate of roughage protein and starch; is r_kpNDFr is from the passage rate of equation: roughage NDF; and 370 is the NDF The The fixed fixed r_emCP r_emCP iscalculated calculated fromthe thefollowing following equation: content in r_emCP diet, g/kgisDM. The fixed calculated from the following equation: ⎛1000 1000⋅⋅ DMI DMI

⎞

std ⎞ ⋅ 55.6 + 0.4166 ⋅ 310 − 0.0008868⋅31022 −54.56 ⎛ std rr __ emCP ⎟⎟ ⋅ 55.6 +from emCP ln⎜⎜ 0.4166 − 0.0008868 ⋅ 310 − 54.56  The fixed== ln r_emCP is calculated the⋅ 310 following equation: DMI ⎛⎝⎝1000⋅600 2 600 std ⎠⎞⎠

[Eq. 13.8]

13.8

r _ emCP= ln⎜ ⎟ ⋅ 55.6 + 0.4166⋅ 310 − 0.0008868⋅ 310 − 54.56 [Eq. 13.8] 600 ⎝ ⎠ ⋅ 1000 DMI ⎛ DMIefficiency 2 − 54.56 DMI is the std ⎞⎞ ⋅ 55.6 +of where, r_emCP isis⋅ the rumen microbial protein = ln _ emCP emCP ln⎛⎜⎜ 1000 ..4166 ⋅⋅ 310 − 0 ⋅⋅protein 310 ⎟⎟ ⋅ 55.6 + 0 [Eq. 13.8] std = rr _ 0of 4166 310 −microbial 0..0008868 0008868 3102synthesis; − 54 .56 where, r_emCP thestd efficiency rumen synthesis; DMI std is the standardized 600 ⎝⎝ DMI ⎠⎠kg DM; 600 where, r_emCP is the efficiency of rumen microbial protein synthesis; DMI standardized 8 or 20 600 is the standardized BW, kg;the and 310 std is is thethe of ST + RestCHO DMI 8 or 20 kg DM; 600 is the standardized BW, kg; and 310 is concentration

standardized DMI 8+ orRestCHO 20 kg DM; is theg/kg standardized BW, kg; and 310 is the concentration of ST in 600 the diet, DM. in the diet, g/kg DM. where, r_emCP the+ efficiency rumen concentration ofisST RestCHO of in the diet,microbial g/kg DM.protein synthesis; DMIstd std is the standardized DMI or 20 kgfeed DM; 600 is the8,standardized kg;AAT and N20 310uses is the The calculation of 8standard value, NEL NEL20, AATBW, the Equations N8 and The calculation of standard feedinvalue, NEL , AATN8 and AATN20 uses the Equations 7.6 8, NEL 20to concentration ofof ST + RestCHO the7.36 diet, DM. NEL AAT Equations The calculation standard value, NEL 7.6 to7.14, 7.17 to 7.23. 7.30feed to 7.34, tog/kg 7.40 7.51, 7.54, 7.55, 7.57,the 7.59, 8.1 to 8,7.38, 20, AAT N8 and N20 uses to7.14, 7.177.17 to 7.23. 7.30 to to 7.34, 7.36 to 7.38, 7.40 to 7.51, 7.54, 7.55, 7.57, 7.59, 8.1 to 8.3 and 7.6 and to7.14, to 7.23. the 7.30equations 7.34, 7.36 7.38, 7.40 to 7.51, 7.54, 7.55, 7.59, 8.1isto 8.3 8.11. However, 7.1 toto7.5 is changed to equation 13.37.57, to 13.7, 7.16 8.11. However, Equations 7.1 to 7.5 are changed to Equation 13.3 to 13.7, 7.16 is changed The calculation of standard feed value, NEL , NEL and AAT13.3 the Equations 8 20 N8 N20 8.3 and 8.11. However, to 7.5 isnutrient changed to 13.7, 7.16i,isSTi to 13.2 changed to 13.2 and 7.29the is equations changed to7.1 13.8. composition (CP CFat i,to i, NDF 8The 20, AAT N8 equation N20 uses and 7.29 is changed to 13.8. The nutrient composition (CP , CFat , NDF , ST , etc.) is the composition 7.6 to7.14, 7.17 to 7.23. 7.30 to 7.34, 7.36 to 7.38, 7.40 to 7.51, 7.54, 7.55, 7.57, 7.59, 8.1 to i i i i i changed to composition 13.2 and 7.29ofisthe changed 13.8. Thetonutrient composition (CP etc.) is the specifictofeedstuff which the feed values are calculated for i, CFat i, NDF i, ST of the feedstuffof tothe which the7.1 feed are calculated for and DMI kg.foris 8.3 and 8.11. the equations to values 7.5 is to feed equation 13.3 tocalculated 13.7, etc.) isspecific the composition specific feedstuff tochanged which the values are and DMI 1However, kg. i is 17.16 i is changed and DMIto 1 kg.and 7.29 is changed to 13.8. The nutrient composition (CPii, CFatii, NDFii, STii i is13.2 etc.) is the composition the specific feedstuff which the feed values calculated for The standardized standardized valueofofPBV PBV PBV in Equation 13.9 assumes a recirculation fixed recirculation of The value andand PBV intoEquation 13.9 assumes aare fixed N8N8 N20 N20 and DMI 1 kg. urea, based a fixed intake, totoavoid overestimating it: ii is on The standardized ofCP PBV PBV in Equation 13.9 of urea, based on avalue fixedCP intake, avoid overestimating it: assumes a fixed recirculation N8 and N20 of urea, based on a fixed CP intake, to avoid overestimating it: The and PBVN20 PBVstandardized 160 ⋅ 0.of 046PBV ) − r _N8 [Eq. 13.9] 13.9 N8mCP N20 in Equation 13.9 assumes a fixed recirculation N = rd _ CP + (value of urea, fixed CP) −intake, [Eq. 13.9] PBV rd _ CPon + (a160 ⋅ 0.046 r _ mCPto avoid overestimating it: N =based where, PBVN is the standard feed value for protein balance in the rumen, g/day; rd_CP is the (160 ) −−feed [Eq. 13.9] PBV =PBV rd ⋅⋅ 00..046 rr __ mCP PBVdegraded rd __NCP CP 160 046of mCP N is ++the the standard valueoffor balance in the rumen, 7.8; g/day; is the where, CP in rumen 1kg DM theprotein specific feedstuff, Equation 160rd_CP is a fixed N =PBV where, N is the standard feed value for protein balance in the rumen, g/day; rd_CP is the CP CP degraded rumen of 1kg DM of the specific feedstuff, 7.8; 160 a of fixed value for CP in the standardised ration (Table 13.1); 0.046 is Equation theEquation assumed proportion Nvalue that for CP degraded in the rumen of 1kg DM of the specific feedstuff, 7.8; 160 is is a fixed where, PBV is the the standard value forisprotein balance theassumed rumen, g/day; isNthe N value for CPback standardised ration (Table 13.1); 0.046 isinthe proportion that is recycled to the rumen;feed and r_mCP the rumen microbial CP yielded by rd_CP 1 kg of DM of Nin CPrecycled degraded in the rumen of 1kg DM of the specific feedstuff, Equation 7.8; by 1601 iskga DM fixedof is back to the rumen; and r_mCP is13.1). the rumen microbial CP yielded the specific feedstuff, Equation 7.30 (Table value in the standardised ration (Table 13.1); 0.046 is the assumed proportion of N that the Equation 7.30 (Table 13.1). 138specific  for CPfeedstuff, NorFor – The Nordic feed evaluation system is recycledfeed back to theofrumen; and r_mCP is the rumen microbial yielded 1 kg Standard values some common feedstuffs are presented inCP table 13.2. by Note thatDM theof the specific feedstuff, Equation 7.30 than (Table 13.1). Standard feed valuesAAT of some feedstuffs presented table 13.2. Note that the lower AAT mainly due toin increased microbial standard feed value N8 is common N20 are standard feed value AAT is lower than AAT mainly due to increased microbial

in the standardised ration (Table 13.1); 0.046 is the assumed proportion of N that is recycled back to the rumen; and r_mCP is the rumen microbial CP yielded by 1 kg DM of the specific feedstuff, Equation 7.30 (Table 13.1). Standard feed values of some common feedstuffs are presented in Table 13.2. Note that the standard feed value AATN8 is lower than AATN20 mainly due to increased microbial efficiency with higher feed intake and therefore higher supply of AA from microbial protein. PBVN8 is higher than PBVN20, also due to increased microbial efficiency which incorporates more N into the microbial protein and therefore contributes less to the PBVN value. NEL8 is higher than NEL20, because increased DMI results in a higher passage rates and lowered digestibility and thus lower energy supply. Table 13.2. Standard feed values in some feedstuffs. Feedstuff

Barley Oat Triticale Brewers grain, dried Distillers grain, dried Rape seed meal Soy bean meal Vegetable fat Sugar beet pulp, dried unmolassed Grass silage very high OMD Grass silage high OMD Grass silage low OMD Maize silage high OMD Maize silage low OMD Straw, spring barley Glycerin, glycerol Urea

NorFor AATN8 PBVN8 NEL8 ID g/kg g/kg MJ/kg DM DM DM

AATN20 PBVN20 NEL20 g/kg g/kg MJ/kg DM DM DM

1-16 1-17 1-15 1-68 1-38 2-42 2-53 2-26 4-20

83 66 79 125 92 102 154 17 70

-6 20 -10 97 176 236 289 -6 -22

7.97 7.31 8.21 6.99 7.30 6.90 8.74 20.80 6.93

110 87 106 157 127 144 218 20 96

-44 -11 -50 55 127 180 210 -11 -63

7.49 6.78 7.80 6.38 6.69 6.52 8.32 18.98 6.26

6-460 6-461 6-463 6-307 6-308 6-386 12-12 13-1

60 63 62 68 66 39 82 0

92 76 49 -30 -30 -25 -120 2,922

7.73 7.50 6.64 7.36 7.05 3.44 8.12 0.00

79 80 75 87 84 44 109 0

62 49 28 -59 -56 -34 -163 2,922

7.00 6.69 5.74 6.60 6.21 2.47 7.49 0.00

NorFor – The Nordic feed evaluation system

139

5111 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147

14.0 System evaluation H. N. I. evaluation Nielsen, M. Åkerlind and A. J. Rygh 14.Volden, System This chapterN.I. focuses on the of different aspects within the NorFor system. This H. Volden, Nielsen, M.evaluation Åkerlind and A.J. Rygh includes sensitivity analyses with respect to feed characteristics and model tests of total tract digestion, milk production, the energy system for growing cattle, RLI, feed intake and This chapter focuses on the evaluation of different aspects within the NorFor system. This includes chewing index.

sensitivity analyses with respect to feed characteristics and model tests of total tract digestion, milk production, the energy system for growing cattle, RLI, feed chewing 14.1 Sensitivity of feed degradation characteristics to intake rationand energy andindex. protein

values

14.1 Sensitivity of feed degradation characteristics to ration energy and

When the NorFor values system is used for on-farm ration optimization, forages are mainly analysed protein at commercial feed laboratories while for concentrate ingredients, values from the NorFor feed table are normally used (www.norfor.info). Within forage type, there are large variations When the NorFor system is used for on-farm ration optimization, forages are mainly analysed at in nutrient degradation characteristics, which affect (to varying degrees) the nutritional value commercial feed laboratories while for concentrate ingredients, values from the NorFor feed table of the feed ration. A sensitivity analysis is therefore useful for evaluating effects of changes in are composition normally used (www.norfor.info). forage there are large variations in nutrient the of feed ingredients on theWithin NEL and AATtype, N contents of the diet. A sensitivity degradation characteristics, affect (tothe varying degrees) the nutritional of the feed ration. analysis is also advantageouswhich for evaluating usefulness and errors related tovalue different A sensitivity analysis is therefore useful for evaluating effects of changes in the composition of feed routine analyses.

ingredients on the NEL and AATN contents of the diet. A sensitivity analysis is also advantageous

for evaluating the usefulness andiserrors to different routine in analyses. The sensitivity analysis presented basedrelated on a dairy cow simulation which a standard diet was fed and the influence of forage and concentrate degradation characteristics were tested. The sensitivity analysis presented based cow simulation in 140 which a standard Effects were tested at a DM intake ofis20 kg/don fora adairy cow of 600 kg BW and days in milk. diet was The diet the consisted of grass silageand (50% of diet DM) with an OMD of 71.4% and fed and influence of forage concentrate degradation characteristics werethe tested. Effects were concentrate mixture consisted of barley grain beet140 pulpdays (12%), maizeThe grain tested at a DM intake of 20 kg/d for a cow of(40%), 600 kgdried BW and in milk. diet consisted of (10%), soybean meal (16%), molasses (6%), rape seedmixture (5%) and mineralof barley grass silage (50% of diet DM)rapeseed with an meal OMD(9%), of 71.4% and the concentrate consisted and vitamins wasgrain (g/kg(10%), DM): CP, 179; NDF, 347; ST,rapeseed 191; CFat, grain (40%),(2%). dried The beetdiet pulpcomposition (12%), maize soybean meal (16%), meal (9%), 45; FPF, 56 and rape RestCHO, 105.and Input degradation characteristics tested sCP, iCP, molasses (6%), seed (5%) mineral and vitamins (2%). The dietwere: composition was (g/kg DM): kdCP, iNDF and347; kdNDF, and CFat, their basal, minimum maximum values arecharacteristics CP, 179; NDF, ST, 191; 45; FPF, 56 and and RestCHO, 105.parameter Input degradation presented in Table 14.1.kdCP, WheniNDF sCP and were changed, corresponding of pdCP parameter tested were: sCP, iCP, andiNDF kdNDF, and their basal, minimum values and maximum and pdNDF were changed simultaneously to maintain the same concentrations of CP and values are presented in Table 14.1. When sCP and iNDF were changed, corresponding values of NDF in the diet. The input parameter of interest was changed for one characteristic at a time pdCP and pdNDF were changed simultaneously to maintain the same concentrations of CP and and correlated responses were ignored to facilitate interpretation. For example, iNDF and NDF inare theoften diet.negatively The input correlated parameterwithin of interest changed for onetest, characteristic kdNDF feeds was but in the sensitivity they were at a time and correlated responses were to facilitate interpretation. example, iNDF and kdNDF are changed independently. Theignored sensitivity of diet energy and proteinFor values to parameter often negatively correlated within feeds but in the sensitivity test, they were changed variation was calculated as the change in the response variable relative to the change in independently. The sensitivity parameter value:of diet energy and protein values to parameter variation was calculated as the change in the response variable relative to the change in parameter value:

5148

⎛ ⎛ rmin − rmax ⎞ ⎞ ⎜ ⎜ ⎟ ⎟ ⎜ ⎜⎝ rbasal ⎟⎠ ⎟ Sensitivity = ⎜ ⎟ ⋅100  ⎜ ⎛⎜ p min − p 2 max ⎞⎟ ⎟ ⎟⎟ ⎜⎜ p basal ⎠⎠ ⎝⎝

5149 5150 5151 5152 5153

wheresensitivity sensitivityisiscalculated calculated in %, the minimum response rmaxmaximum is the maximum where in %, rmin ris theisminimum response value, value, rmax is the min response response value, pminpmin is the minimum parameter value, pmax is responsevalue, value,rbasal rbasalisisthethebasal basal response value, is the minimum parameter value, pmax is the the maximum parameter value, and pbasalisisthe thebasal basalparameter parameter value. value. maximum parameter value, and pbasal

[Eq. 14.1]

14.1

Changes in diet composition, i.e. proportions of CP, ST, NDF, SU and CFat are of major importance since they affect (inter alia) diet energy and191 metabolizable protein value (Fox et al., 2003). In the presented sensitivity analysis (Table 14.1), only the effects of variations in nutrient degradation characteristics were evaluated, since they may have an impact on the reliability of feed analyses. Both NEL and AATN are sensitive to changes in forage iNDF and kdNDF. The pdNDF from forage constitutes a large proportion of the NDF pool, and changes in both iNDF concentration and kdNDF strongly influence the energy supply for microbial growth, and hence AATN from microbial protein H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_14, © Wageningen Academic Publishers 2011

141

Table 14.1. Results of the sensitivity analysis in NorFor to changes in feed characteristics with a lactating dairy cow diet. Variables

Forage sCP1, g/kg CP iCP2, g/kg CP iNDF3, g/kg NDF kdCP4, %/h kdNDF5, %/h Concentrate sCP, g/kg CP iCP, g/kg CP iNDF, g/kg NDF kdCP, %/h kdNDF, %/h kdST6, %/h kdRestCHO7, %/h

Ranges

Sensitivity, %

Basal

Minimum

Maximum

NEL8 AATN9 MJ/kg DM g/kg DM

600 38 160 11.0 4.7

500 19 80 5.5 2.4

700 57 240 16.0 7.1

-0.1 -0.6 -5.3 -0.1 7.6

-5.1 -1.7 -3.4 -3.0 5.7

247 40 190 6.0 3.0 15.0 150

124 20 80 3.0 1.5 7.5 75

371 60 240 9.0 4.5 22.5 225

-0.1 -0.8 -2.4 -0.1 2.4 -0.9 0.3

-7.5 -3.4 -3.0 -12.8 0.5 1.5 -1.0

1

sCP = soluble crude protein. iCP = total indigestible crude protein. 3 iNDF = total indigestible NDF. 4 kdCP = fractional degradation rate of potentially degradable crude protein. 5 kdNDF = fractional degradation rate of potentially degradable NDF. 6 kdST = fractional degradation rate of potentially degradable starch. 7 kdRestCHO = fractional degradation rate of residual carbohydrates. 8 NEL = net energy lactation. 9 AAT = amino acids absorbed in the intestine. N 2

and total tract digestion of OM, which provide the basis for ME predictions. The work of Nordheim et al. (2007) demonstrated the difficulties of using NIRS to measure kdNDF. Due to the importance of kdNDF for the nutritive value of the diet and it was, therefore, decided in NorFor to calculate the kdNDF from NIRS or in vitro measurements of OMD and iNDF (see Section 5.2.1; Eriksson, 2010a). The AATN was sensitive to the proportion of sCP in the forage, although the relative difference between the selected minimum and maximum parameter values was less than for the other variables (Table 14.1). In grass silage, sCP is the dominating protein fraction, which explains why sCP has more impact on AATN than kdCP (Volden et al., 2002). Diet NEL is not sensitive to changes in forage or concentrates sCP and kdCP. This is consistent with expectations, because the segmental digestion of degradable CP has limited effects on the total tract digestion of CP. The sensitivity analysis indicates that predicted energy values are more sensitive to the chemical composition of concentrates, (e.g. ST vs. NDF) than to degradation characteristics (data not shown). However, predictions of the AATN are sensitive to kdCP, sCP and iCP. Nevertheless, this sensitivity analysis demonstrates that the NorFor system is sensitive to CHO and CP degradation characteristics in both roughage and concentrates.

142 

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14.2 Evaluation of the digestive tract sub-model Two important sub-models in NorFor are the digestive tract sub-model (which predicts the ration digestibility) and the metabolism sub-model (which predicts energy and protein supply and animal responses). When evaluating a complex model, it may be useful to evaluate several subdivisions separately, to reveal strengths and weaknesses in different parts of the system. This section summarizes the test of total tract digestion of major nutrients. Sources of data used in the evaluation of dairy cow digestion are presented in Table 14.2. The published studies were supplemented with measurements from trials conducted in connection with a student course in ruminant nutrition and physiology at the Norwegian University of Life Sciences. Total tract digestion was selected because there are minimal methodological differences between relevant studies. It is relatively simple to measure, compared to ruminal and intestinal flow measurements, which are heavily dependent on marker techniques and calculation methods used. The model was evaluated by regressing predicted values against observed values, and several statistical measures were used to assess model adequacy and behaviour, including coefficients of correlation (r) and determination (r2), mean square prediction error (MSPE) and the partitioning of MSPE (Bibby and Toutenburg, 1977). The MSPE was decomposed into errors due to: overall bias (deviation of the intercept from 0), line bias (deviation of the slope from unity) and disturbance (lack of correlation). The square root of the MSPE (RMSPE) expressed as a percentage of the observed mean was used as a measure of the prediction error. High correlations (r>0.97) between predicted and observed values were found for all the digestion variables tested (td_OM, td_CP, td_NDF and td_ST) (Figure 14.1, 14.2, 14.3, 14.4). NorFor accounted for 96% of the variation in td_OM with a mean bias of 221 g/d (1.9%). Corresponding values for td_CP, td_NDF and td_ST were 68, 68 and 106 g/d (3.3, 1.6 and 3.9%), respectively. The model slightly overpredicted td_OM, td_NDF and td_ST, while td_CP was underpredicted (Table 14.2). Except for td_ST, most of the errors in the MSPE were related to disturbance. The analysis of residuals between observed and predicted td_ST indicated that the higher general bias (0.43) was largely due to an underestimation of iST in the feedstuffs. Regression statistics (Mitchell, 1997) showed that the fitted line between predicted and observed values did not differ significantly from 1, indicating that there was no systematic deviation in digestion in the interval examined. Prediction errors were generally low, with RMSPE of 560, 167, 264 and 161 g/d for td_OM, td_CP, td_NDF and td_ST, respectively. The difference between observed and predicted values for td_CP, td_NDF and td_ST and were within 5% of the observed values for 45, 58 and 70% of the observations, respectively. Deviations were less than 10% for more than 80% of the observations. This demonstrates the ability of the NorFor model to predict ration digestion over large ranges of diet composition and feed intake. This ability is crucial for calculation of energy supply, which is based on total apparent digestion of CP, CFat and CHO in NorFor. From the mean prediction error for OM, and assuming an average ME factor of 15.5 MJ/kg digestible OM, the NEL (utilization of ME=0.6) prediction error was estimated to be 5.2 MJ, corresponding to 1.7 (5.2/3.14) kg ECM. For testing digestion in the growing cattle sub-model, a small data set from three studies (Olson and Lindberg, 1985; Olson and Lindberg, unpublished data; Wallsten, 2010) was available. The data set included results from 18 treatments, applied to both bulls (n=10) and heifers (n=8). The total apparent digestion of td_OM, td_CP, td_NDF was evaluated (Figures 14.5, 14.6 and 14.7). The coefficient of determination was high (r2>0.96) for all digestion variables tested (Table 14.3). NorFor overpredicted td_OM (6.4%) and td_NDF (9.0), while td_CP was underpredicted (13.2%). The MSPE consisted mainly overall bias, while the proportion of MSPE related to line bias was low. The regression slope between predicted and observed digestion did not differ significantly from 1. The data set was, however, too small for further analysis of the residuals.

NorFor – The Nordic feed evaluation system

143

144 

NorFor – The Nordic feed evaluation system

n

11,841 1993 4235 2860

831 -134 377 24

Intercept

Observed Predicted

11,620 2061 4167 2754

Regression

Digested, g/day 0.948 1.033 0.926 1.030

Slope 0.967 0.955 0.966 0.993

r2 560 167 264 161

RMSPE 4.8 8.1 6.3 5.8

Prediction error, % 0.165 0.168 0.067 0.431

0.009 0.100 0.044 0.099

0.836 0.732 0.889 0.470

Overall bias Regression Disturbance

Proportion of MSPE

2

td_OM = apparent total tract digestion of organic matter, g/day. td_CP = apparent total tract digestion of crude protein, g/day. 3 td_NDF = apparent total tract digestion of NDF, g/day. 4 td_ST = apparent total tract digestion of starch, g/day. 5 The following trials were used: Alne, 2000; Aston et al., 1994; Bertilsson and Murphy, 2003; Eriksson et al., 2004; Kjos, unpublished data; Larsen et al. 2009b; Lund et al., 2004; Lund, unpublished data; Murphy et al., 1993; Prestløkken, 1999; Prestløkken et al., 2007; Rinne et al., 1999a, 2002; Selmer-Olsen, 1996; Tothi et al., 2003; Volden, unpublished data.

1

td_OM1 107 td_CP2 95 td_NDF3 111 td_ST4 67

Item

Table 14.2. Accuracy and precision of NorFor’s ability to predict digestion in dairy cows5.

Predicted OM digestion (g/d)

18,000

Observed - predicted

4,000

16,000 14,000 12,000 10,000 8,000 6,000 2,000 0 -2,000

0

2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000

-4,000

Observed OM digestion (g/d)

Observed - predicted

Predicted CP digestion (g/d)

Figure 14.1. Predicted vs. observed organic matter (OM) digestion in dairy cows.

4,500 4,000 3,500 3,000 2,500 2,000 1,500 1000 500 0 -500

0

500

1000

1,500

2,000

2,500 3,000

3,500 4,000

4,500

-1000

Observed CP digestion (g/d)

Figure 14.2. Predicted vs. observed crude protein (CP) digestion in dairy cows.

NorFor – The Nordic feed evaluation system

145

Predicted NDF digestion (g/d) Observed - predicted

8,000 7,000 6,000 5,000 4,000 3,000 2,000 1000 0 -1000

0

1000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

-2,000 Observed NDF digestion (g/d)

Observed - predicted

Predicted ST digestion (g/d)

Figure 14.3. Predicted vs. observed NDF digestion dairy cow. 6,000 5,000 4,000 3,000 2,000 1000 0

0

1000

2,000

3,000

4,000

5,000

6,000

-1000 Observed ST digestion (g/d)

Figure 14.4. Predicted vs. observed starch (ST) digestion in dairy cows.

146 

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NorFor – The Nordic feed evaluation system

147

n

3,290 386 1,044

79 -24 100

Intercept

Observed Predicted

3,090 445 958

Regression

Digested, g/day 1.047 0.922 0.986

Slope 0.981 0.968 0.990

r2 230 64 62

RMSPE

2

td_OM = apparent total tract digestion of organic matter, g/day. td_CP = apparent total tract digestion of crude protein, g/day. 3 td_NDF = apparent total tract digestion of NDF, g/day. 4 The following trials were used: Olson and Lindberg, 1985; Olson and Lindberg, unpublished data; Wallsten, 2010.

1

td_OM1 18 td_CP2 18 td_NDF3 18

Item

Table 14.3. Accuracy and precision of NorFor´s ability to predict digestion in growing cattle4.

7.5 14.5 11.1

Prediction error, % 0.753 0.838 0.657

0.035 0.010 0.001

0.212 0.152 0.342

Overall bias Regression Disturbance

Proportion of MSPE

Predicted OM digestion (g/d)

5,000

Observed - predicted

1000

4,500 4,000 3,500 3,000 2,500 2,000 1,500 500 0

-500 0

1000

-1000

2,000

3,000

4,000

5,000

Observed OM digestion (g/d)

Predicted CP digestion (g/d)

Figure 14.5. Predicted vs. observed organic matter (OM) digestion in growing cattle.

800 700 600 500 400

Observed - predicted

300 200 100 0

0

100

200

300 400 500 600 Observed CP digestion (g/d)

700

800

Figure 14.6. Predicted vs. observed crude protein (CP) digestion in growing cattle.

148 

NorFor – The Nordic feed evaluation system

Observed - predicted

Predicted NDF digestion (g/d)

3,000 2,500 2,000 1,500 1000 500 0 -500

0

500

1000

1,500

2,000

2,500

3,000

Observed NDF digestion (g/d)

Figure 14.7. Predicted vs. observed NDF digestion in growing cattle.

14.3. Evaluation of milk production in lactating dairy cows A Nordic dataset, which had not been used for model construction, was compiled. Data originated from published and unpublished trials performed in Denmark, Norway, Sweden, and Finland mainly from the 1990’s and 2000’s (Table 14.4). No selection was made among the experiments and data from all identified experiments providing information on ECM or MY, fat and protein contents, DMI and BW were included. Not all studies included information on BW or BCS changes during the trial period. Lactose content in milk was only reported in a few studies, thus a fixed content of 48 g/kg was used. The dataset included information from both long- and short-term studies, i.e. data both from continuous lactation experiments and cross-over type experiments. A further criterion was availability of data on most nutrient characteristics for each feedstuff. Experiments that only provided data on nutrient characteristics for the whole TMR were not included. Most studies did not include iNDF, sCP, kdST, kdNDF and kdCP measurements on single feedstuffs. When nutrient composition data were missing, values from the NorFor feedstuff table were used (www.norfor.info). The dataset, described in Table 14.5, consists of information from 53 trials (see Table 14.4) with five breeds (205 NRF, 77 DH, 38 SR, 5 RD and 4 JER) with a total of 329 treatments. As described in Chapter 12, ECM is predicted from the calculated total energy intake minus the estimated energy usage for maintenance, growth (only primiparous cows), pregnancy and mobilization/deposition (see Equation 12.1). Energy usage for maintenance is estimated from BW. Information on days in gestation was often not available and was therefore set to zero. Mobilization/ deposition was included via information on changes in BW or BCS. As not all studies included data on BW or BCS changes during the trial period, mobilization/deposition was assumed to be zero. Energy requirement for growth in primiparous cows was included indirectly via changes in BW or BCS. The prediction of milk protein yield is based on model estimates of AATN supply to the mammary gland and the efficiency of AATN use in synthesis of milk protein (see Equation 12.2).

NorFor – The Nordic feed evaluation system

149

Table 14.4. Studies used to evaluate the prediction of milk production, roughage intake and total dry matter intake in dairy cows. Aaes, 1991 Aaes, 1993 1,2 Bertilsson, 2007 Bertilsson, 2008 Bertilsson and Murphy, 20031 Eriksson, 2010b Eriksson et al., 20041 Hymøller et al., 20052 Ingvartsen et al., 20011, 2 Johansen, 19921 Kjos, unpublished data Kristensen, 19992 Lund, 20021 Minde and Rygh, 1997 Misciattelli et al., 20031 Mo and Randby, 19861 Mogensen and Kristensen, 20021,2

Mogensen et al., 20082 Mould, 19961 Murphy et al., 2000 Nielsen et al., 20072 Nordang and Mould, 19941 Prestløkken, 1999 Prestløkken et al., 20071 Randby, 19921 Randby, 19971 Randby, 1999a1 Randby, 1999b1 Randby, 2000a1 Randby, 2000b1 Randby, 20021 Randby, 2003 1 Randby, 20071 Randby and Mo, 19851

Randby and Selmer-Olsen, 19971 Rinne et al., 1999a1 Rinne et al., 1999b1 Sairanen, 20011 Schei et al., 20051 Shingfield et al., 2003 Steinshamn et al., 20061 Thorhauge and Weisbjerg, 20092 Thuen, 19891 Volden, 19901 Volden, 1999 Volden and Harstad, 2002 Wallsten and Martinsson, 20101 Weisbjerg, 2007 2 Weisbjerg et al., 20082 Åkerlind et al., 19992 Österman, 20031, 2

1 The

study is used for roughage intake evaluation. study is used for total dry matter intake evaluation. Studies marked with superscripts 1 and 2 included treatments with separate and TMR and feeding, respectively. 2 The

Table 14.5. Descriptive statistics of the 329 treatments from 53 Nordic trials used to evaluate prediction of ECM and milk protein yield. Item

Average

SD1

Maximum

Minimum

Days in milk ECM2, kg/d MY3, kg/d Fat, g/kg Protein, g/kg Protein yield, g/d

121 27.1 27.1 41.1 32.6 882

44 5.3 5.2 4.7 1.8 174

255 49.7 47.0 58.3 41.1 1,607

31 12.6 13.1 26.9 29,3 422

1

SD = standard deviation. ECM = energy corrected milk. 3 MY = milk yield. 2

Figures 14.8 and 14.9 illustrate the relationships between observed and predicted ECM and milk protein yields, respectively. Table 14.6 shows that across all experiments and treatments, the observed and predicted yields were similar (27.1 vs. 26.4 kg ECM/d), especially for milk protein (882 vs. 900 g protein/d). The correlation between observed and predicted milk protein values was high (r=0.97), but somewhat lower for ECM (r=0.88). Predicted ECM and milk protein values were higher than observed for 43% and 68% of the observations, respectively, with RMSE’s of 2.7 kg ECM and 48 g milk protein, corresponding to prediction errors of 9.8 and 5.5%, respectively. Predicted ECM yield was within 5% of observed values for 47% of the observations and 73% of the predicted ECM yield values were within 10% of observed values. Predicted milk protein yield was within 5% and 10% 150 

NorFor – The Nordic feed evaluation system

Predicted ECM (kg/d)

50

40

30

Observed - predicted

20

10

0

0

10

20

30

40

50

-10 Observed ECM (kg/d)

Predicted milk protein yield ( g/d)

Figure 14.8. Predicted vs. observed ECM yield.

1,800 1,600 1,400 1,200 1000 800

Observed - predicted

600 400 200 0 -200

0

200

400

600

800

1000 1,200 1,400 1,600 1,800

Observed milk protein yield (g/d)

Figure 14.9. Predicted vs. observed milk protein yield.

NorFor – The Nordic feed evaluation system

151

152 

NorFor – The Nordic feed evaluation system

2

26.4 900

2.6 39

Intercept

Observed Predicted

27.1 882

Regression

Production

ECM = energy corrected milk yield, kg/day. MPY = milk protein yield, g/day.

329 329

ECM1 MPY2

1

n

Item 0.88 0.98

Slope 0.78 0.94

r2 2.7 48

RMSPE

Table 14.6. Accuracy and precision of NorFor´s ability to predict ECM and milk protein yield.

9.8 5.5

Prediction error, % 0.07 0.14

0.05 0.02

0.88 0.84

Overall bias Regression Disturbance

Proportion of MSPE

of observed values for 69% and 92% of the observations, respectively. The overall and regression biases were minor for ECM and milk protein, since more than 80% of the MSPE was characterised as disturbance. The regression slopes between predicted and observed milk production variables were not significantly different from 1.

14.4 Evaluation of the energy requirement for growing cattle The estimated energy requirement for growing cattle is based on the French system (Chapter 9), modified with a 10% increase in the energy requirement for gain based on a dataset compiled from Nordic experiments with bulls. In order to perform an independent test of the NorFor energy system for growing cattle, an independent dataset containing heifer data from the Nordic countries and the US was used. Criteria for including data were heifers of dairy breeds and measurements of ADG, DMI and nutrient characteristics for each feedstuff. Hence, experiments where nutrient characteristics were available only for the whole diet were excluded. When nutrient composition data were missing, values from the NorFor feedstuff table were used (www.norfor.info). The reason for including US data was that a limited number of experiments from the Nordic countries were available. No selection was made among the experiments that were found in the literature. The dataset is described in Table 14.7 and consist of 9 trials with 54 treatments of which 47 include large breeds and 7 include Jersey. Ration energy supply was calculated using NorFor and were regarded as observed values. The energy requirements were calculated from reported BW and ADG and were regarded as predicted values. The relationship between energy supply and requirement is shown in Figure 14.10. Across all experiments and treatments, the energy supply and requirement were fairly similar (31.9 vs. 33.7 MJ NEG) with an RMSPE of 3.4 MJ NEG, corresponding to a prediction error of 10.0%. The correlation (r=0.96) was stronger than that obtained from the data used to develop the energy system for growing cattle (see Figure 9.2).

14.5. Evaluation of the rumen load index (RLI) RLI was included in the model to establish a relationship between the level of starch and sugars in the ration and their influence on digestion of NDF in the rumen (see Section 7.1.3). Therefore, RLI was evaluated against data from two studies in which rumen pH was measured (Lund, unpublished data; Larsen et al., 2009a) and where kdNDF was estimated on the basis of digestibility trials with lactating Holstein cows (Larsen et al., 2009a). The cows were fed ad libitum with grass-clover silage as the sole roughage and the concentrate consisted of barley, wheat, oat, maize, soybeans, faba beans, peas or lupins and most treatments also included soybean meal as a protein supplement in the ration.

Table 14.7. Descriptive statistics of the dataset1 used for evaluating the energy system for growing cattle, including data from nine trials with heifers and 54 treatments. Item

Average

SD2

Maximum

Minimum

DMI, kg/d BW, kg ADG, g/d Concentrates (% of DMI)

5.3 253 724 35

1.9 102 203 28

9.9 489 1,180 92

2.2 88 396 4

1

Olsson et al., 1984; Andersen et al., 1986; Ingvartsen et al., 1988; Mäntysaari, 1993; Andersen and Foldager, 1994; Van Amburgh et al., 1998; Andersen et al., 2001; Whitlock et al., 2002; Vestergaard, 2006. 2 SD = standard deviation.

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153

70

NEG requirement (MJ/d)

60 50 40 30 20 10 0

0

10

20

30 40 NEG supply (MJ/d)

50

60

70

Figure 14.10. The relationship between ration energy supply calculated in NorFor and estimated energy requirement in heifers according to NorFor (r2=0.92; RMSPE=3.4 MJ NEG; RMSPE=10.0%). The data are from the trials described in Table 14.8. In contrast to expectations, RLI was not negatively correlated to pH in the rumen (Figure 14.11). Indeed, the data in Figure 14.11 show a positive relationship between RLI and rumen pH in the data from Lund (unpublished data) and no relationship in the data from Larsen et al. (2009a). As RLI was included in NorFor to estimate the effect of starch and sugars on the digestion of NDF in the rumen, the main interest is in evaluating RLI was as a predictor for kdNDF in the rumen, rather than as a predictor of rumen pH. This relationship is illustrated in Figure 14.12, where a negative correlation (r=-0.82) between RLI and kdNDF is evident. Thus, even though RLI was not correlated with rumen pH, increases in RLI were associated with reductions in kdNDF. This confirms that RLI is a useful term for correcting kdNDF, as described in Section 7.1.3, although this evaluation was only based on one study. However, the relationship shown in Figure 14.12 is supported by the findings of Danfær et al. (2006).

14.6. Evaluation of roughage and total dry matter intake in dairy cows Data from Nordic experiments were used to evaluate roughage and total DMI predictions. For this purpose, the data were divided in two sub-sets (Tables 14.8 and 14.9). The first (n=226) consisted of data from studies where roughage was fed ad libitum and separately, and the concentrate was fed either according to MY or as fixed amounts (Table 14.8). This dataset was used to evaluate roughage intake. In the second dataset (n=62), cows were fed total mixed rations (TMR) ad libitum and total DMI was evaluated (Table 14.9). Both datasets showed wide variations in both DMI and diet composition, and the data were in the range of those used for parameterization of the intake sub-model. Published nutrient compositions of the individual feeds were used. When nutrient composition data were missing, values from the NorFor feedstuff table were used (www.norfor.info). Predicted vs. observed roughage intake and the evaluation of the prediction errors are presented in Figure 14.13 and Table 14.10, respectively. Mean observed roughage DM intake was 11.0 kg/d 154 

NorFor – The Nordic feed evaluation system

6.8 min_pH_Lund

6.6

mean_pH_Lund

Rumen pH

6.4

min_pH_Larsen mean_pH_Larsen

6.2 6.0 5.8 5.6 5.4 5.2 5.0 0.0

0.2

0.4

0.6

0.8

1.0

Rumen load index

Figure 14.11. The relationship between rumen load index calculated in NorFor and mean and minimum pH measured in the rumen. The data are from Lund (unpublished data, n=8) and Larsen et al. (2009a, n=13). 4.0 3.5

kdNDF (%/h)

3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.0

0.2

0.4

0.6

0.8

1.0

1.2

Rumen load index

Figure 14.12. The relationship between rumen load index calculated in NorFor and trial estimates of NDF degradation rate in the rumen (kdNDF) of lactating Holstein cows (n=13; r2=0.68; RMSPE=0.33%/h). The data are from Larsen et al. (2009a).

NorFor – The Nordic feed evaluation system

155

Table 14.8. Descriptive statistics of the dataset used for evaluating feed intake of cows, including data from 36 trials3 and 226 treatments where roughage and concentrate were fed separately. Item

Average

SD1

Maximum

Minimum

Days in milk Body weight, kg ECM2, kg/d Dry matter intake, kg/d Concentrate proportion, kg/kg DM Roughage intake, kg DM/d Roughage basis fill value, /kg DM Starch + sugars, g/kg DM Starch + sugars, kg/d

124 567 25.3 18.2 0.40 10.9 0.50 183 3.3

35 37 4.5 2.4 0.12 2.6 0.06 69 1.2

214 667 45.0 28.4 0.80 18.2 0.70 426 6.9

42 481 17.1 13.1 0.14 3.2 0.35 60 0.9

1

SD = standard deviation. ECM = energy corrected milk. 3 The trials are listed in Table 14.5. 2

Table 14.9. Descriptive statistics of the dataset used for evaluating the feed intake system for cows, including data from 12 trials3 and 62 treatments where the ration was fed as a total mixed ration. Item

Average

SD1

Maximum

Minimum

Days in milk Body weight, kg ECM2, kg/d Dry matter intake, kg/d Concentrate proportion, kg/kg DM Roughage basis fill value, /kg DM Starch + sugars, g/kg DM Starch + sugars, kg/d

96 588 33.2 21.2 0.54 0.46 208 4.4

43 48 4.6 2.1 0.1 0.04 59 1.2

200 659 49.7 25.1 0.70 0.60 419 7.8

31 441 22.4 16.6 0.27 0.42 94 1.7

1

SD = standard deviation. ECM = energy corrected milk. 3 The trials are listed in Table 14.5. 2

and mean predicted intake was 10.1 kg DM/d. The regression slope was 0.88 and not significantly different from 1. The RMSPE was 1.3 kg DM/d and 0.52 of the prediction error was related to disturbance. The overall bias accounted for 0.48 of the total MSPE, and showed that the intake model underpredicts roughage intake. Predicted roughage intake was within ±10% of observed intake for 58% of the observations. Predicted vs. observed TMR intake and results of the evaluation of the prediction error are presented in Figure 14.14 and Table 14.10, respectively. The mean observed and predicted TMR intakes were 21.2 and 21.1 kg DM/d, respectively, the regression slope was not significantly (P<0.05) different from 1 and 0.62 of the MSPE was explained by disturbance error. The RMSPE was 1.6 kg DM, corresponding to a prediction error of 7.7%. Predicted intake was within 10% of observed intake for 79% of the observations.

156 

NorFor – The Nordic feed evaluation system

Predicted roughage intake (kg DM/d) Observed - predicted

20 18 16 14 12 10 8 6 4 2 0 -2

0

2

4

6

-4

8

10

12

14

16

18

20

Observed roughage intake (kg DM/d)

Oberved - predicted

Predicted TMR intake (kg DM/d)

Figure 14.13. Predicted vs. observed roughage intake. 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 -2 0 -4 -6

2

4

6

8

10

12

14

16

18

20

22

24

26

28

Observed TMR intake (kg DM/d)

Figure 14.14. Predicted vs. observed DMI of a TMR.

NorFor – The Nordic feed evaluation system

157

158 

NorFor – The Nordic feed evaluation system

10.1 21.1

0.49 0.48

0.876 0.972

Intercept Slope

Observed Predicted

11.0 21.2

Regression

Intake, kg DM/day

2 TMR

Roughage intake in experiments where roughage and concentrate were fed separately. = total mixed ration.

229 62

Roughage1 TMR2

1

n

Item 0.866 0.593

r2

Table 14.10. Accuracy and precision of NorFor´s ability to predict feed intake

1.3 1.6

RMSPE 11.6 7.7

Prediction error, % 0.468 0.006

0.001 0.371

0.531 0.623

Overall bias Regression Disturbance

Proportion of MSPE

14.7. Evaluation of chewing index (CI)

5395 5395 5396 5395 5395 5396 5397 5396 5395 5396 5397 5398 5397 5396 5397 5398 5399 5398 5397 5398 5399 5400 5399 5398 5399 5400 5401 5400 5399 5400 5401 5402 5401 5400 5401 5402 5403 5402 5401 5402 5403 5403 5404 5402 5403 5404 5403 5404 5405 5404 5405 5404 5405 5405 5406 5406 5405 5406 5407 5406 5407 5406 5407 5408 5407 5408 5407 5408 5409 5408 5409 5410 5408 5409 5409 5410 5411 5410 5409 5410 5411 5412 5411 5410 5411 5412 5413 5412 5411 5412 5413 5414 5413 5412 5413 5414 5415 5414 5413 5414 5415 5416 5415 5414 5415 5416 5417 5416 5415 5416 5417 5418 5417 5416 5417 5418 5419 5418 5417 5418 5419 5420 5419 5418 5419 5420 5421 5420 5419 5420 5421 5421 5420 5422 5421 5422 5421 5422 5423 5422 5423 5422 5423 5423 5424 5423 5424 5424 5425 5424 5425 5424 5425 5426 5425 5426 5425 5426 5427 5426 5427 5428 5426 5427 5427 5428 5429 5428 5427 5428 5429 5430 5429 5428 5429 5430 5431 5430 5429 5430 5431 5432 5431 5430 5431 5432 5433 5432 5431 5432 5433 5434 5433 5432 5433 5434 5435 5434 5433 5434 5435 5436 5435 5434 5435 5436 5437 5436 5435 5436 5437 5438 5437 5436 5437 5438 5439 5438 5437 5438 5439 5440 5439 5438 5439 5440 5440 5439

This section describes an independent test of predicted EI, RI and CI values of various, typical Nordic roughages against recorded ET, RT and CT values. The independent test set included data from studies with dairy cows and growing steers fed grass, maize or whole crop barley silage harvested at different times with various degrees of chopping, i.e. PL. The studies included a Danish study with lactating dairy cows fed two types of maize silage (Hymøller et al., 2005), a Norwegian study with lactating dairy cows fed unchopped grass silage harvested at two stages of maturity (Garmo et al., 2008), a Swedish study with lactating dairy cows fed grass silage chopped by two different choppers (Bertilsson, studyestimated with growing fed whole cropand barley silage Size_R value, Hardness2008), factorand and aRISwedish values were using steers Equations 11.5, 11.6 Size_R value, Hardness factor andwere RI values were estimated using Equations 11.5, 11.6 and harvested at two stages ofvalues maturity et al., 2010) (Table 14.11). 11.4, respectively. The CI estimated from Equation 11.1. Size_R value, Hardness factor and RI(Rustas values were estimated using Equations 11.5, 11.6 and

Size_R value, Hardness factor andwere RI values werefrom estimated using Equations 11.5, 11.6 and 11.4, respectively. The CI values estimated Equation 11.1. 11.4, The values estimated Equation 11.1. Size_R value, Hardness factor andwere RI values werefrom estimated using Equations 11.5, 11.6 and 11.4, respectively. respectively. The CI CI values were estimated from Equation 11.1. Table 14.11 value and The size_E EI values ofestimated the roughages in Table 11.4, Thethe CI values were from (listed Equation 11.1. 14.11) were estimated from the Tablerespectively. 14.11 Table 14.11 content of NDF and the PL according to Equations 11.3 and 11.2, respectively. The Size_R value, Table 14.11 Hardness Table 14.11factor and RI values were estimated using Equations 11.5, 11.6 and 11.4, respectively. The (ETestimated (RTo)Equation per kg intake roughage NDF (NDFIr) for each of the o) and RT from The observed CI valuesET were 11.1.of The observed ET (ET RTestimated (RTo) per according kg intake of roughage NDFequations: (NDFIr) for each of the o) and roughages in Table 14.11 were to the following The observed ET (ET ) and RT (RT ) per kg intake of roughage NDF (NDFIr) o The observed ET (ET RTestimated (RToo) per according kg intake of NDFequations: (NDFIr) for for each each of of the the roughages in Table 14.11 were to roughage the following o) and roughages in Table 14.11 were estimated according to the following equations: The observed ET (ET ) and RT (RT ) per kg intake of roughage NDF (NDFIr) for each of each the of the o ) were o The observed ET (ET and RT (RT ) according per kg intake offollowing roughageequations: NDF (NDFIr) for roughages in Table 14.11 estimated to the o o ET − 4 ⋅ DMIc [Eq. 14.2] roughages Table 14.11 were estimated according to the following equations: ETo = ET − 4in roughages in Table 14.11 were estimated according to the following equations: ⋅ DMIc [Eq. 14.2] ETo = ET −NDFr 4 ⋅ DMIc [Eq. ETo = ET −NDFr 4 ⋅ DMIc [Eq. 14.2] 14.2] ETo = ET −NDFr 4 ⋅ DMIc [Eq. 14.2] ETo = RT −NDFr 14.2 ∑ DM i ⋅ RI i [Eq. 14.3] RTo = RT −NDFr ∑ DM i ⋅ RI i [Eq. 14.3] RTo = RT − NDFIr ∑ DM i ⋅ RI i [Eq. ∑ DM i ⋅ RI i RTo = RT − NDFIr [Eq. 14.3] 14.3]  14.3 RTo = RT − NDFIr ∑ DM i ⋅ RI i NDFIr [Eq. RT = [Eq. 14.3] 14.4] CToo = ETo +NDFIr RTo [Eq. 14.4] CTo = ETo + RTo [Eq. CTo = ETo + RTo  14.4 [Eq. 14.4] 14.4] CTo = ETo + RTo where ET the [Eq.of14.4] CTo = ET RTthe o o+is o observed eating time, min/kg NDFr; ET is the recorded eating time where ET is the observed eating time, min/kg NDFr; ismin/kg the recorded time of NDFIr the whole ration, min/d; 4·DMIc refers to an assumed ET ET of 4ET DMI ofeating concentrate; where whereET ETooois isthe theobserved observed eating eating time, time, min/kg min/kg NDFr; NDFr; ET is the is the recorded recorded eating eating time of the where ET observed eating time, min/kg NDFr; ET ismin/kg the recorded eating timetime of NDFIr theof the whole whole ration, min/d; 4·DMIc refers to an assumed ET of 4 DMI of concentrate; o is the is the intake of NDF from roughage, kg/d; RT is the observed rumination time, min/kg o whole ration, min/d; 4·DMIc refers to an assumed ET of 4 min/kg DMI of concentrate; NDFIr ration, min/d; 4·DMIc refers to an assumed ET of 4 min/kg DMI concentrate; NDFIr is the is the observed eating time, min/kg NDFr; ET is the recorded eating time of the where ET o of whole ration, min/d; 4·DMIc refers an assumed ETobserved of 4DM min/kg DMI of concentrate; is the intake NDF from RT roughage, kg/d; RT rumination time, min/kgNDFIr o is the NDFr; RT isof the recorded of kg/d; theto whole ration, min/d; the DMI of the i=1...n'th i is is the intake NDF from roughage, kg/d; RT the observed rumination time, min/kg whole ration, min/d; 4·DMIc refers to an assumed ET of 4 min/kg DMI of concentrate; NDFIr o is observed intake of NDF from roughage, RT is the rumination time, min/kg NDFr; RT is the is the intake from RT roughage, kg/d;oRT observed rumination time, min/kg the DMI the i=1...n'th NDFr; RT isof theNDF recorded ofofthe whole ration, min/d; DM o is the i is is the RI value the i=1...n'th concentrate, Equation 11.4,of 11.5 and 11.6, RI andis the RI concentrate; RI is the DMI of the i=1...n'th NDFr; RT is the recorded RT of the whole ration, min/d; DM is the intake of from roughage, kg/d; RT is the observed rumination time, min/kg i the recorded RT ofiiNDF the whole ration, min/d; DM DMI of i=1...n’th concentrate; o concentrate, NDFr; RT is the recorded RT of the whole ration, min/d; DM is the DMI of the i=1...n'th concentrate; RI is the RI value of the i=1...n'th Equation 11.4, 11.5 and 11.6, and i is the i i Table is the observed chewing time, min/kg NDFr. concentrate; RI value the i=1...n'th concentrate, Equation 11.4, and and NDFr; RTthe isCT theoi is recorded RT ofof the whole ration, min/d; DM DMI of11.5 the i=1...n'th i is the value 11.1; of i=1...n’th concentrate, Equation 11.4, 11.5 and 11.6, and Table 11.1; CT11.6, the RI RI value of the i=1...n'th concentrate, Equation 11.4, 11.5 and 11.6, andobserved concentrate; RI observed chewing time, min/kg NDFr. Table 11.1; CT o is the oi is the observed chewing time, min/kg NDFr. Table 11.1; CT RI value of the i=1...n'th concentrate, Equation 11.4, 11.5 and 11.6, and concentrate; RIoimin/kg chewing time, NDFr. Table 11.1; CT is the observed chewing time, min/kg NDFr. o The RT values were corrected a standard size of 625 kg and a standard o andCT TableET the observed chewingto min/kgcow NDFr. o oisvalues The ET11.1; RT werethe corrected totime, aequations, standard cow size of 625akg and a standard o and o BW using NDFIr of 0.7%/kg following obtained from meta-analysis of data The ET were corrected to a standard cow size of 625 kg and aa standard o and RTo values The ET RT were corrected to a standard cow size of 625 kg and standard The ET and RT values were corrected to a standard cow size of 625 kgdata and a standard NDFIr of NDFIr of 0.7%/kg BW using the following equations, obtained from a meta-analysis ofthe data ooand oovalues from 80of dietary treatments by Nørgaard et al. (2010), which did not include from four NDFIr 0.7%/kg BW using the following equations, obtained from a meta-analysis of data The ET and RT values were corrected to a standard cow size of 625 kg and a standard o dietary o BW using NDFIr 0.7%/kg following equations, obtained from a meta-analysis data from 80of treatments bythe Nørgaard et al. (2010), which not include data the four 0.7%/kg BW using the following equations, obtained fromdid a meta-analysis of from dataoffrom 80 dietary studies listed in Table 14.11. from 80 dietary treatments bythe Nørgaard et al. (2010), which did not include data from the four NDFIr 0.7%/kg BW14.11. using following equations, obtained a meta-analysis data from 80of dietary treatments Nørgaard al. (2010), which didfrom not include fromof the fourlisted in studies listed Table treatments byin Nørgaard etbyal. (2010), etwhich did not include data from data the four studies studies in Table 14.11. from 80listed dietary treatments by Nørgaard et al. (2010), which did not include data from the four studies listed in Table 14.11. Table 14.11. NDFr studies Table [Eq. 14.5] (ETo +in0.21 ETcorr =listed ⋅ (BW14.11. − 625 )) ⋅ NDFr [Eq. 14.5] 1000 ETcorr = (ETo + 0.21 ⋅ (BW − 625 )) ⋅ NDFr [Eq. ETcorr = (ETo + 0.21 ⋅ (BW − 625 )) ⋅ NDFr 1000 [Eq. 14.5] 14.5] ETcorr = (ETo + 0.21 ⋅ (BW − 625 )) ⋅ NDFr 14.5 1000  [Eq. 14.5] ETcorr = ⎛(ETo + 0.21 ⋅ (BW − 625 )) ⋅ 1000 NDFIr ⋅ 100 ⎞ NDFr ⎛ ⎞ 1000 [Eq. 14.6] RTcorr = ⎜⎛ RTo + 0.20 ⋅ (BW − 625 ) + 52 ⋅ ⎜⎛ NDFIr ⋅ 100 − 0.7 ⎟⎞ ⎟⎟⎞ ⋅ NDFr [Eq. 14.6] BW⋅ 100 − 0.7 ⎠⎞⎟ ⎠⎟⎞⎟ ⋅ NDFr 1000  RTcorr = ⎜⎝⎛⎜⎜ RTo + 0.20 ⋅ (BW − 625 ) + 52 ⋅ ⎝⎛⎜ NDFIr 14.6 [Eq. ⋅ 100 − 0.7 ⎞⎟⎠ ⎞⎟⎟⎠ ⋅ NDFr RTcorr = ⎛⎜⎜⎝ RTo + 0.20 ⋅ (BW − 625 ) + 52 ⋅ ⎛⎜⎝ NDFIr BW 1000 [Eq. 14.6] 14.6] − ⎟ ⋅ RTcorr = ⎜⎜⎝⎛ RTo + 0.20 ⋅ (BW − 625 ) + 52 ⋅ ⎜⎝⎛ NDFIr 0 . 7 ⎟ BW⋅ 100 1000 ⎞ NDFr ⎞ ⎠ ⎟ ⎠ [Eq. 14.6] BW − 0.7 ⎠⎟ ⎠⎟⎟ ⋅ 1000 RTcorr = ⎝⎜ RTo + 0.20 ⋅ (BW − 625 ) + 52 ⋅ ⎝⎜ [Eq. 14.7] 14.7 CTcorr =⎜⎝ETcorr + RTcorr  BW ⎝ ⎠ ⎠ 1000 [Eq. 14.7] CTcorr = ETcorr + RTcorr [Eq. 14.7] CTcorr = ETcorr + RTcorr [Eq. 14.7] CTcorr = ETcorr + RTcorr where ET the observed eating corrected for and BWNDFIr, and NDFIr, DMr; ETo is the where ET is+isthe observed eating timetime corrected for BW min/kgmin/kg DMr; [Eq.ET 14.7] CTcorr = ET RT corr o is the corr corr observed eating time corrected forweight BW and min/kg DMr; ET where ETeating corr is the o is the observed time, min/kg NDFr; BW is is thethe body of NDFIr, thethe animal, kg;kg; NDFr is the observed eating time, min/kg NDFr; BW body weight of animal, NDFr is the observed eating time corrected for BW and NDFIr, min/kg DMr; ET where ET corr o is the content where ETeating observed eating time corrected forthe BW and min/kg DMr; ET observed time, min/kg NDFr; BW is RT the body weight of NDFIr, therumination animal, kg;time NDFr is the corr is the o is the content ofin NDF in the roughage, g/kg DM; observed corrected of NDF the roughage, g/kg DM; RTcorrected iscorr theisfor observed rumination time corrected observed eating time, min/kg NDFr; BW is the body weight of the animal, kg; NDFr is the is the observed eating time BW and NDFIr, min/kg DMr; ET theBW and where ET corr o isfor corr observed eating time, min/kg NDFr; BW is the body weight of the animal, kg; NDFr is the content of NDF in the roughage, g/kg DM; RT is the observed rumination time corrected corr is the observed eating time, min/kg NDRr; NDFIr/BW for BW of and NDFIr, min/kg DM; RT oBW content NDF in the roughage, g/kg DM; RT is the observed rumination time corrected NDFIr, min/kg DM; RT is the observed eating time, min/kg NDRr; NDFIr/BW is the intake of observed eating time, min/kg NDFr; is the body weight of the animal, kg; NDFr is the corr o DM; g/kg content of in the roughage, DM; RT observed rumination timeNDFIr/BW corrected for BW andNDF NDFIr, min/kg RTo% is theBW; observed eating min/kg NDRr; corr is the is the intake of NDF from roughage, of and CT istime, the chewing observed chewing time for BW and corr for BW and NDFIr, min/kg DM; RT the observed eating time, min/kg NDRr; NDFIr/BW content of NDF in the roughage, g/kg DM; RT is the observed rumination time corrected NDF from roughage, % of BW; and CT is the observed time corrected o is corr corr forthe BW and min/kg DM; RTo% is theBW; observed eating min/kg NDRr; NDFIr/BW the observed chewing time is intake of NDF from roughage, of and CT corr istime, corrected forNDFIr, BW and NDFIr, min/kg DMr. is the intake of NDF from roughage, of and the observed chewing time for BW and min/kg DM; RTo% is theBW; observed eating min/kg NDRr; NDFIr/BW corr istime, NDFIr, min/kg DMr. is the intake of NDF from roughage, % of BW; and CT CT corrected forNDFIr, BW and NDFIr, min/kg DMr. corr is the observed chewing time corrected for BW and NDFIr, min/kg DMr. is the intake of NDF from roughage, % of BW; and CT is the observed chewing time corr corrected for BW andthe NDFIr, min/kg between DMr. observed and predicted eating, rumination Figure 14.15 shows relationship and corrected for BW and NDFIr, min/kg DMr. Figure shows the relationship observed predicted eating, rumination chewing14.15 activity obtained from thebetween four Nordic trialsand listed in Table 14.11. Across and all Figure 14.15 shows the relationship between observed and predicted eating, rumination and Figure 14.15 theobserved relationship observed andlisted predicted eating, and chewing activity obtained from CT thebetween fourpredicted Nordic trials in (67 Table Across all treatments theshows average and CI was similar vs. 14.11. 64rumination min/kg DMr). chewing activity obtained from the four Nordic trials listed in Table 14.11. Across all Figure 14.15 shows the relationship between observed and predicted eating, rumination and chewing activity obtained from the four Nordic trials listed in Table 14.11. Across all treatments the average observed CT and predicted CI was similar (67 vs. 64 min/kg DMr). values for the roughages However, in theaverage study by Hymøller al.predicted (2005), ETcorr treatments the observed CT and CI was similar vs. 64 min/kg DMr). chewing activity obtained from theet four Nordic observed trials listed in (67 Table 14.11. Across all treatments the average observed CT and predicted CI was similar (67 vs. 64 min/kg DMr). However, in the study by Hymøller et al. (2005), observed ET values for the roughages NorFor – The Nordic feed evaluation system 159 corr were nearly twice as high as the EI values, whereas ET values fitted well with the EI values corr However, in the study by Hymøller et al. (2005), observed ET values for the roughages treatments the average observed CT and predicted CI was similar vs. 64 min/kg DMr). corr (67 However, in the study by Hymøller et al. (2005), observed ET values for the roughages were nearly twice as high as the EI values, whereas ET values fitted well with the EI values corr corr in thenearly studyintwice by et (2008) and Rustas et al.ET (2010). The fairly goodfor prediction of RT were as as the whereas fitted well with the EI values the However, theGarmo study byal. Hymøller et al. (2005), observed ETcorr corr values fitted well theroughages EI values values were as high high as the EI EI values, values, whereas in thenearly study twice by Garmo et al. (2008) and Rustas et al.ET (2010). The fairly goodwith prediction of RT corr values in thenearly study twice by Garmo et al. and Rustas et al.ET (2010). The fitted fairlywell goodwith prediction of RT were as high as (2008) the EI values, whereas the EI values corr values

Table 14.11. Comparison of observed chewing activity (ETcorr, RTcorr, CTcorr) with predicted chewing index values (EI, RI, CI) in four Nordic studies with dairy cows or steers. Experiment Roughage Animals

Hymøller et al. (2005) Maize silage Dairy cows

Treatment

Pretti early cut

Banguy early cut

Body weight, kg Concentrate, kg DM/d Roughage intake, kg DM/d NDFr intake, kg/d NDFr intake, % of BW NDF in roughage, g/kg DM Particle length (PL/TCL), mm Size_E factor1 Size_R factor1 iNDF, g/kg NDF Hardness factor1 Predicted chewing index EI2, min/kg DMr RI2, min/kg DMr CI2, min/kg DMr 2 Observed chewing activity Observed ET4, min/kg NDFr Observed RT4, min/kg NDFr Observed CT4, min/kg NDFr Observed ETcorr5, min/kg DMr Observed RTcorr5, min/kg DMr Observed CTcorr5, min/kg DMr

625 8.4 11.7 5.4 0.86 462 9.3 0.75 0.88 230 0.98

625 8.9 12.3 5.2 0.82 419 10.3 0.77 0.91 144 0.89

17 403 57

16 343 50

64 90c 154 30 45 75

67 85c 152 28 38 66

1

Size_E, Size_R and Hardness factor were estimated using Equations 11.3, 11.5 and 11.6, respectively. EI, RI and CI were estimated using Equations 11.2, 11.4 and 11.1, respectively. DMr: DMI of roughage. 3 Corrected for RI values of 10, 4 and 9 min/kg DM for rolled barley (30%), rape seed meal (50%) and pelleted beetpulp (20%), respectively. 4 Observed ET and RT were calculated using Equations 14.1 and 14.2, respectively. NDFr: DMI of NDF from roughage. 5 ET corr and RTcorr are observed values corrected for BW and NDFr intake using Equations 14.4 and 14.5. 6 In this trial only total chewing time was recorded. 2

Figure 14.15 shows the relationship between observed and predicted eating, rumination and chewing activity obtained from the four Nordic trials listed in Table 14.11. Across all treatments the average observed CT and predicted CI was similar (67 vs. 64 min/kg DMr). However, in the study by Hymøller et al. (2005), observed ETcorr values for the roughages were nearly twice as high as the EI values, whereas ETcorr values fitted well with the EI values in the study by Garmo et al. (2008) and Rustas et al. (2010). The fairly good prediction of RT compared with the less well predicted ET is in accordance with the results from the meta-analysis by Nørgaard et al. (2010). Predicted CI was

160 

NorFor – The Nordic feed evaluation system

Garmo et al. (2008) Grass silage Dairy cows

Bertilsson (2008) Grass silage Dairy cows

Rustas et al. (2009) Whole crop barley silage Steers

Early cut

Normal cut

Pöttinger cutter

Taarup wagon

Early cut

Late cut

618 5.2 16.4 7.0 1.17 427 >100 1 1 113 0.86

618 5.3 14.5 7.7 1.25 532 >100 1 1 160 0.91

625 8.9 15.3 6.9 1.10 450 32 0.96 1 120 0.87

625 10.4 17.1 7.7 1.23 450 25 0.92 1 120 0.87

350 0.3 7.8 3.8 1.08 486 >100 1 1 192 0.94

350 0.3 7.0 3.3 0.95 476 >100 1 1 366 1.12

21 37 58

27 48 75

24 46 70

24 53 77

51 70 121 21 40 61

54 71 125 28 52 80

99 130 229 20 46 66

100 148 248 20 51 71

22 39 61

21 39 60

1136

976

606

566

within ±5% of observed CTcorr for six out of eight observations. The two observations with large deviations (>15%) were from the study of Hymøller et al. (2005) and are largely responsible for a relatively low coefficient of determination (r2 = 0.21) between observed CT and predicted CI. The RMSPE was 9.2 min/kg DMr corresponding to a prediction error of 13.8%.

NorFor – The Nordic feed evaluation system

161

Predicted EI, RI and CI (min/kg DMr)

100 CT ET RT

80

60

40

20

0

0

20

40 60 80 Observed ET, RT and CT (min/kg DMr)

100

Figure 14.15. The relationship between observed eating (ET), rumination (RT) and chewing time (CT) vs. corresponding predicted indexes (EI, RI, CI). The data are from trials described in Table 14.11.

References Aaes, O. 1991. Grøncobs som erstatning for kraftfoder til malkekøer. In: Foderværdi og anvendelse af kunsttørret grønfoder til malkekøer. Statens Husdyrbrugsforsøg, Denmark. 5-19. (In Danish). Aaes, O. 1993. Total mixed rations versus separate feeding of concentrate rich rations fed restrictively or ad libitum to dairy cows. Forskningsrapport no 16 fra Statens Husdyrbrugsforsøg, Denmark. (In Danish with English summary). Åkerlind, M., K. Holtenius, J. Bertilsson and M. Emanuelson, 1999. Milk composition and feed intake in dairy cows selected for high or low milk fat percentage. Livestock Production Science 59: 1-11. Alne, J., 2000. Effects of duodenal starch and amino acid infusion on milk yield, protein content and utilization of feednitrogen in dairy cows. Master of Science Thesis. Agricultural University of Norway, Aas, Norway. (In Norwegian). Andersen, H.R. and J. Foldager, 1994. Protein requirement for replacement heifers in the period from 3 to 12 months of age. Forskningsrapport no. 26 fra Statens Husdyrbrugsforsøg, Denmark. (In Danish with English summary). Andersen, H.R., Foldager, J., Varnum, P.S. and T. Hvelplund, 1986. Forskellige proteinmængder og proteinkilder til kvieopdræt. Meddelelse no. 645 fra Statens Husdyrbrugsforsøg. (In Danish). Andersen, H.R., B.B. Andersen and H.G. Bang, 2001. Beef crossbreeding: Heifers versus bulls fed different concentrate/ roughage ratio and slaughtered at different live weight. DJF rapport no. 28. (English summary). Aston, K, C. Thomas, R. Daley, J.D. Sutton and M.S. Dhanoa, 1994. Milk production from grass silage diets: effects of silage characteristics and the amount of supplementary concentrate. Animal Production 59: 31-41. Bertilsson, J., 2007. Agrodrank som foder till mjölkkor. Kungsängendagarna 2007. Rapport nr 267. Swedish University of Agricultural Sciences, Uppsala, Sweden. pp. 21-23. (In Swedish). Bertilsson, J., 2008. Effects of chop length of silage on chewing time, feed consumption and milk production. In: Hopkins, A., T. Gustafsson, J. Bertilsson, G. Dalin, N. Nilsdotter-Linde and E. Spörndly (eds.). Biodiversity and animal feed: Future challenges for grassland production. Grassland Science in Europe volume 13, Uppsala, Sweden, pp. 783-785. Bertilsson, J. and M. Murphy. 2003. Effects of feeding clover silages on feed intake, milk production and digestion in dairy cows. Grass and Forage Science 58: 309-322.

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Danfær, A., P. Huhtanen, P. Udén, J. Sveinbjörnsson and H. Volden, 2006. Karoline – A Nordic model for feed evaluation. Model description. In: Kebreab, E., J. Dijkstra, A. Bannink, J. Gerrits and J. France (eds.). Nutrient digestion and utilisation in farm animals: Modelling approaches. CAB International, Wallingford, UK, pp. 383-406. Eriksson, T. 2010a. In vitro methods for indigestible Neutral Detergent Fiber (iNDF). Proceedings of the 1th Nordic Feed Science Conference, Report no. 274, Swedish University of Agricultural Sciences, Uppsala, Sweden, pp. 41-45. Eriksson, T. 2010b. Nitrogen metabolism in dairy cows fed restricted amounts of grass–clover silage supplemented with seeds from narrow-leafed lupin or pea. Livestock Science 131: 39-44. Eriksson, T., M. Murphy, P. Ciszuk and E. Burstedt, 2004. Nitrogen balance, microbial protein production. and milk production in dairy cows fed fodder beets and potatoes or barley. Journal of Dairy Science 87: 1057-1070. Fox, D., G.L.O. 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Effects of pattern of concentrate allocation in the dry period and early lactation on feed intake and lactational performance in dairy cows. Livestock Production Science 71: 207-221. Ingvartsen, K.L., J. Foldager, J.B. Larsen and V. Østergaard, 1988. Growth and milk yield by Jersey reared at different planes of nutrition. Beretning no. 645 fra Statens Husdyrbrugsforsøg. (In Danish). Johansen, A., 1992. A comparison between meadow fescue and timothy silage. Doctor Scientarium Thesis. Agricultural University of Norway, Aas, Norway. Kristensen, V.F., 1999. Grovfoderkildens betydning for malkekoens produktion og fodereffektivitet. Temamøde Malkekøernes og kviernes ernæring April 1999. Statens Husdyrbrugsforsøg rapport 118, pp. 18-33. (In Danish). Larsen, M., N.B. Kristensen and T. Hvelplund, 2009a. Stivelsen til malkekøer – perspektivering af resultaterne fra stivelsesprojektet. In: Fodringsdag - Temadag om aktuelle fodringsspørgsmål. 1 September 2009. 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Milk production, nutrient utilization and endocrine responses to increased postruminal lysine and methionine supply in dairy cows. Journal of Dairy Science 86: 275-286. Mitchell, P.L. 1997. Misuse of regression for empirical validation of models. Agricultural Systems 54: 313-326. Mo, M. and Å.T. Randby, 1986. Wilted silage for lactating dairy cows. Proceedings of 11th General Meeting of European Grassland Federation. Troia, Portugal, 4-9 May. Mogensen, L. and T. Kristensen, 2002. Effect of barley or rape seed cake as supplement to silage for high-yielding organic dairy cows. Acta Agriculturae Scandinavica. Section A - Animal Science 53: 243-252. Mogensen, L., P. Lund, T. Kristensen and M.R. Weisbjerg, 2008. Effects of toasting blue lupins, soybeans or barley as supplement for high-yielding organic dairy cows fed grass-clover silage ad libitum. Livestock Science 115: 249-257. Mould, F., 1996. Fôropptak og produksjon hos melkekyr ved et lavt PBV i rasjonen. Husdyrforsøksmøtet, pp. 121127. (In Norwegian). Murphy, M., H. Khalili and P. Huhtanen, 1993. The substitution of barley by other carbohydrates in grass silage based diets to dairy cows. Animal Feed Science and Technology 41: 279-296.

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Murphy, M., M. Åkerlind and K. Holtenius, 2000. Rumen fermentation in lactating cows selected for milk fat content fed two forage to concentrate ratios with hay or silage. Journal of Dairy Science 83: 756-764. Mäntysaari, P. 1993. The effects of feeding level and protein source of the diet on growth and development at slaughter of prepurbertal heifers. Acta Agriculturae Scandinavica Section A - Animal Science 43: 44-51. Nielsen, N.I., N.C. Friggens, T. Larsen, J.B. Andersen, M.O. Nielsen and K.L. Ingvartsen, 2007. Effect of changes in diet energy density on feed intake, milk yield and metabolic parameters in dairy cows in early lactation. Animal 1: 335-346. Nordang, L. and F. Mould, 1994. Surfôr med ulikt innhold av PBV til mjølkekyr. Husdyrforsøksmøtet, pp. 99-103. (In Norwegian). Nørgaard, P., E. Nadeau and A. Randby, 2010. A new Nordic structure evaluation system for diets fed to dairy cows: a meta analysis. In: Sauvant, D., J. Van Milgen, P. Faverdin and N. Friggens (eds.). Modelling nutrient digestion and utilization in farm animals. Wageningen Academic Publishers, Wageningen, the Netherlands, pp. 112-121. Olsson, K., I. Olsson and L. Lindell, 1984. Effect of the amount of roughage fed to heifers during the rearing period on feed consumption and milk production during the first lactation. Report no. 131. Swedish University of Agricultural Sciences Österman, S., 2003. Extended calving interval and increased milking frequency in dairy cows. Acta Universitatis Agriculturae Suecia. Agria 383. Swedish University of Agricultural Sciences, Uppsala, Sweden. Prestløkken, E., 1999. Protein value of expander-treated barley and oats for ruminants. Doctor Scientarium Thesis, Agricultural University of Norway, Aas, Norway. 137 pp. Prestløkken, E., Å.T. Randby, M. Eknæs and T. Garmo, 2007. Tidlig og normalt høstet gras. Opptak av surfôr og produksjon hos mjølkekyr. Husdyrforsøket, Thon Hotel Arena 14-15 February 2007, pp. 25-28. (In Norwegian). Randby, Å.T., 1992. Grass-clover silage for dairy cows. Proceedings of 14th General Meeting, European Grassland Federation, Lahti, Finland, 8-11 June, pp. 272-275. Randby, Å.T., 1997. Feeding of silage effluent to dairy cows. Acta Agriculturae Scandinavica Section A - Animal Science 47: 20-30. Randby, Å.T., 1999a. Grass silage treated with two different acid-based additives for dairy cows. The Fifth International Symposium on the Nutrition of Herbivores. San Antonio, Texas, USA. 8 pp. Randby, Å.T., 1999b. Effect of increasing levels of a formic acid based additive on ad libitum intake of grass silage and the production of meat and milk. Proceeding of the XII International Silage Conference, Uppsala, Sweden, pp. 205-206. Randby, Å.T., 2000a. Round bale grass silage treated with an additive containing both lactic acid bacteria and formate for dairy cows and steers. Sixth British Grassland Society Research Conference, SAC, Craibstone, Aberdeen, Scotland, pp. 121-122. Randby,Å.T., 2000b. Effekten av økende dosering med et maursyreholdig ensileringsmiddel på næringsverdien av surfôret i produksjonsforsøk med kyr og kastrater. Hellerud Forsøksgård, Rapport nr. 25. 37 pp. (In Norwegian). Randby, Å.T., 2002. Silage fermentation by concentrate type interaction. Proceedings of the XIII International Silage Conference, Auchincruive, Scotland, pp. 312-313. Randby, Å.T., 2003. Høstetid og fôrkvalitet. Grønn kunnskap 7: 27-43. (In Norwegian). Randby, Å.T. 2007. Effect of propanol and dimethylsulphide in grass silage on organoleptic milk quality. Journal of Animal and Feed Science 16, Supplement 1: 102-107. Randby, Å. and M. Mo, 1985. Surfôr tilsatt foraform i sammenlikning med maursyresurfôr til melkekyr, Hellerud høsten 1984. M-141. Rapporter fra forsøksvirksomheten ved Institutt for husdyrernæring, pp. 75-78. (In Norwegian). Randby, Å.T. and I. Selmer-Olsen, 1997. Formic acid treated or untreated round bale grass silage for dairy cows. 8th International Symposium of Forage Conservation. Brno, Czech Republic, pp. 160-161. Randby, Å.T., I. Selmer-Olsen and L. Baevre, 1999. Effect of ethanol in feed on milk flavor and chemical composition. Journal of Dairy Science 82: 420-428. Rinne M., P. Huhtanen and S. Jaakkola. 1997. Grass maturity effects on cattle fed silage-based diets. 2. Cell wall digestibility, digestion and passage kinetics. Animal Feed Science and Technology 67: 19-35. Rinne M., S. Jaakkola, K. Kaustell, T. Heikkilä and P. Huhtanen, 1999a. Silage harvested at different stages of grass growth versus concentrate foods as energy and protein sources in milk production. Animal Science 69: 251-263. Rinne, M., S. Jaakkola, T. Varvikko and P. Huhtanen, 1999b. Effects of type and amount of rapeseed feed on milk production. Acta Agriculturae Scandinavica, Section A - Animal Science, 49: 137-148.

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Rinne M., P. Huhtanen and S. Jaakkola. 2002. Digestive processes of dairy cows fed silages harvested at four stages of maturity. Journal of Animal Science 80: 1986-1998. Rustas, B.O., P. Nørgaard, A.R. Jalali and E. Nadeau, 2010. Effects of physical form and stage of maturity at harvest of whole-crop barley silage on intake, chewing activity, diet selection and faecal particle size of dairy steers. Animal 4: 67-75. Sairanen, A., J.I. Nousiainen and H. Khalili, 1999. Korkean väkirehumäärän vaikutus maitotuotokseen ja tuotannon kannattavuuteen. In: Mitä Suomi syö - ja millä hinnalla? Agro-Food ‘99, Helsinki. pp. 7. (In Finnish). Selmer-Olsen, I., 1996. Microbial protein synthesis in the rumen of cows fed extensively or restricted fermented grass silage. Proceedings of the XIth International Silage Conference, Aberystwyth, Wales, pp. 226-227. Schei, I., H. Volden and L. Bævre, 2005. Effects of energy balance and metabolizable protein level on tissue mobilization and milk performance of dairy cows in early lactation. Livestock Production Science 95: 35-47. Shingfield, K.J., A. Vanhatalo and P. Huhtanen, 2003. Comparison of heat-treated rapeseed expeller and solvent-extracted soya-bean meal as protein supplements for dairy cows given grass silage-based diets. Animal Science 77: 305-317. Steinshamn, H., L.T. Mydland, H. Volden and E. Thuen. 2006. The effect of red clover proportion in timothy silage on nitrogen utilization in lactating dairy cows. In: Wachendorf, M, Á. Helgadóttir and G. Parente (eds.). Sward dynamics. N-flows and forage utilisation in legume-based systems. Grado. Italy. 10-12 November. 2005. ESAAzienda regionale per lo sviluppo rurale, Italy, pp. 293-297. Thorhauge, M. and M.R. Weisbjerg, 2009. Effekt af fodring med mættet eller umættet fedt hos SDM-DH, RDM og Jersey. Kvæginfo no. 1938. Dansk Landbrugsrådgivning, Aarhus, Denmark. (In Danish). Thuen, E., 1989. Influence of source and level of dietary protein on milk yield, milk composition and some rumen and blood components in dairy cows. Doctor Scientarium Thesis. Agricultural University of Norway, as, Norway. 151 pp. Tothi, R, P. Lund, M.R. Weisbjerg and T. Hvelplund, 2003. Effect of expander processing on fractional rate of maize and barley starch degradation in the rumen of dairy cows estimated using rumen evacuation and in situ techniques. Animal Feed Science and Technology 104: 71-94. Van Amburgh, M.E., D.G. Fox, D.M. Galton, D.E. Bauman and L.E. Chase, 1998. Evaluation of National Research Council and Cornell net carbohydrate and protein systems for predicting requirements of Holstein heifers. Journal of Dairy Science 81: 509-526. Vestergaard, M., K.F. Jørgensen, I. Fisker, H.R. Andersen and K. Sejrsen, 2006. Forhøjet proteintildeling til store kvier - Resultater fra forsøg på Kvægbrugets ForsøgsCenter. In: Fodringsdag - Temadag om aktuelle fodringsspørgsmål. 29 august. Dansk Kvæg, pp. 41-45. (In Danish). Volden, H. 1990. Response of dietary AAT density on milk yield - Review of production trials in Norway. Proceedings of the seminar on fat and protein feeding to the dairy cow, 15-16 October, Eskilstuna, Sweden, pp. 81-89. Volden, H., 1999. Effects of level of feeding and ruminally undegraded protein on ruminal bacterial protein synthesis, escape of dietary protein, intestinal amino acid profile, and performance of dairy cows. Journal of Animal Science 77: 1905-1918. Volden, H. and O.M. Harstad, 2002. Effects of duodenal amino acid and starch infusion on milk production and nitrogen balance in dairy cows. Journal of Animal Science 80, Supplement 1. 321 pp. Volden, H.L., T. Mydland, V. Olaisen, 2002. Apparent ruminal degradation and rumen escape of soluble nitrogen fractions in grass, and grass silage administered intraruminally to lactating dairy cows. Journal of Animal Science 80: 2704-2716. Wallsten, J., J. Bertilsson, E. Nadeau and K. Martinsson, 2010. Digestibility of whole-crop barley and oat silages in dairy heifers. Animal 4-3: 432-438. Weisbjerg, M.R. and K. Soegaard, 2008. Feeding value of legumes and grasses at different harvest times. In: Hopkins, A., T. Gustafsson, J. Bertilsson, G. Dalin, N. Nilsdotter-Linde and E. Sporndly (eds.). Biodiversity and animal feed: Future challenges for grassland production. Grassland Science in Europe, volume 13, pp. 513-515. Weisbjerg, M.R., 2007. Lucerneensilage til malkekøer – resultater fra nye forsøg. In: Fodringsdag - Temadag om aktuelle fodringsspørgsmål. 5-12. Dansk Kvæg, Aarhus, Denmark. (In Danish). Weisbjerg, M.R., L. Wiking, N.B. Kristensen and P. Lund, 2008. Effects of supplemental dietary fatty acids on milk yield and fatty acid composition in high and medium yielding cows. Journal of Dairy Research 75: 142-152. Whitlock, B.K., M.J. Vandehaar, L.F.P. Silva and H.A.Tucker, 2002. Effect of dietary protein on prepurbertal mammary development in rapidly growing dairy heifers. Journal of Dairy Science 85: 1516-1525.

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15. The NorFor IT system, ration formulation and optimisation H. Volden, H.D. Rokkjær, A. Göran and M. Åkerlind This chapter describes how we have converted a whole-animal model into a commercial computer tool to be used by advisors and farmers to formulate and optimise diets for dairy cows and growing cattle. The NorFor system is a semi-mechanistic model that has many theoretical advantages for the formulation and evaluation of feed rations. The system represents a significant development in models of nutritional formulation. It incorporates a detailed description of the protein and carbohydrate composition of the ration, which is used in the algorithms to predict degradation, digestion and metabolism in the gastro-intestinal tract and also the supply of metabolizable energy and protein. It can be used not only to estimate nutritional requirements but also provides substantial benefits for predicting nutrient utilization and the environmental impact from different diets. A complex model with semi-mechanistic algorithms and non-linear functions requires the development of a powerful, user-friendly computer tool before it can be used in practical ration formulation and optimisation. Feed is one of the major costs in modern cattle production, so it is important to consider both nutritional and economic aspects when formulating optimal rations. A key objective in modern, computerised ration formulation is auto-balancing (Boston et al., 2000), i.e. identification of the least expensive combination of feed ingredients that provides all considered nutrients within specified ranges. Use of an optimisation (auto-balancing) approach in the formulation of feed rations for cattle requires detailed nutritional and economic knowledge, including information regarding available feeds, the animals and management practices. Some feeds are home-grown while others are purchased as individual ingredients or as concentrate mixtures, and knowledge of their characteristics, obtained from either analyses or tables are needed. In addition, information on feed prices is required. Important animal information includes lactation stage, lactation number, BW, gestation day and age. Management factors include housing (tied up or loose housed) and feeding system (total mixed ration or separate feeds; pasture or indoor). All of these factors affect feed requirements and it is therefore essential that all relevant information is readily available. For these reasons, NorFor has put substantial efforts and resources into the development of an effective, economic IT platform and computer tool that can be used in practical ration evaluation and optimisation.

15.1 IT platforms NorFor is used by nutrition advisors in Denmark, Iceland, Norway and Sweden. An objective in the development of the computer tools was to use an IT platform that allows operators to access the latest version of the model. Hence, all users had to be able to access it via the same server system. Two IT platforms were developed: one web-based and one in which operators use an offline client, synchronized with the NorFor sever before starting the ration formulation. Denmark, Iceland and Norway have chosen to use an online solution, while Sweden is using an offline solution. Although, all countries use the common NorFor severs, each country, except Iceland, has developed their own national client and interface, to enable the NorFor system to be integrated with other national advisory tools. Figure 15.1 presents an overview of the common IT system, which consists of a server application, a homepage, an administration tool and Web services for dairy management software (National herd recording system), laboratories and feed companies. The NorFor servers host four web services: the feed analysis system (FAS), the feedstuff table (FST), the feed ration calculator (FRC), and the one-day feeding control (OFC) system.

15.2 The feed analysis system (FAS) NorFor has developed a web-client that is used by linked laboratories to report results from herd level feed analyses (Figure 15.2). The laboratories connect to the system and upload analytical results, H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9_15, © Wageningen Academic Publishers 2011

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Figure 15.1. An architectural overview of the NorFor IT system.

Figure 15.2. An architectural overview of the feed analysis system (FAS). and the system calculates feed characteristics (using the FST Calculator) from other analytical data, as described in Chapter 6. Based on the NorFor feed code system, tabulated values are added to the feed samples if absent. If the feed sample is from a herd registered in the national herd recording system (Figure 15.3), it is assigned a herd-specific id, making it automatically available for use by the FRC for ration optimisation or by the OFC for calculating herd feed efficiency. In the NorFor server, each herd has its own feedstuff table, in which the analysed feeds and those copied from the FST are saved. When roughage is analysed at the laboratory, the farmer or advisor reports additional information about the feed sample. This information included date of harvest, cutting number, botanical composition, feed additives, storage system for silage and theoretical cutting length. These additional values are stored, together with the feed characteristics, in a common feed database, which can be used for 168 

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Figure 15.3. An architectural overview of the link between NorFor and the national client. statistical evaluation of factors such as the annual roughage quality and the relationships between feed additives and silage fermentation quality. Approximately 25,000 roughage samples are analysed according to NorFor specifications annually.

15.3 Feedstuff table (FST) NorFor has developed a comprehensive feedstuff table (FST) that is continuously updated. Updating and monitoring the FST is done by use of the NorFor administration tool. From the NorFor sever, the FST is available to each national client and when working on herd-specific feed plans, feedstuffs are copied from the FST into the herd feedstuff table for further use. For external users, the FST is available at www.norfor.info. Figure 15.4 shows an overview of the NorFor FST, which is organized in a hierarchal directory system, in which the highest level is region, dividing the feedstuffs into country specific and common NorFor categories. The feed directory is further divided into feed groups, by 19 sub-directories, and the feed groups are divided into feed types. For example, the feed group ‘grains, is divided into ‘grains’, ‘dry grain by-products’ and ‘wet grain by-products’. The ‘forages and roughage’ group is divided into seven feed types: ‘pasture grass and clover-grass’, ‘grass and clover-grass’, ‘whole crop’, ‘grass and clover-grass silage’, ‘whole crop silage’, ‘hay and ‘straw’, and ‘grass pellets’. Figure 15.4 also shows an example of choosing a parameter setting (‘NDF characteristics’). These settings are used to generate reports on variables such as NDF, starch, protein, amino acids fatty acids, fermentation products, minerals, vitamins or total characteristics. A part of the feed table system is also information on commercial compound feeds including prices, reported by the feed industry (Figure 15.5). When feed rations are formulated, the operators copy NorFor – The Nordic feed evaluation system

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Figure 15.4. Overview of the NorFor feedstuff table and example of NDF characteristics in feedstuffs from the feed group forages and roughage.

Figure 15.5. An architectural overview of the feed company web-service in NorFor. 170 

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actual company products into the herd specific feedstuff table. NorFor has developed a web-service to help the feed industry report these details to the FST.

15.4 Dairy management system Each Nordic country has its own national herd recording system, in which both records for individual cows (e.g. lactation number, age, days in milk, milk yield, milk composition, BCS, fertility and health) and herd information (e.g. 305-d milk yield, milk composition, slaughter weight and slaughter age) are reported. When formulating diets in the NorFor system, herd information is available (Figure 15.3), which means that rations can be formulated for either individual cows or groups of cows in the herd.

15.5 Feed ration calculator (FRC) A requirement for the NorFor IT system was that it should allow both the calculation and optimisation of rations. The chosen solution was three calculator add-inns and a black-box optimisation approach (Figure 15.6). The FRC Calculator is used to evaluate feed rations and to predict production responses when the feed input is known. The FAS/FST Calculator is used to calculate standard feed values for a single feedstuff (see Chapter 13) by combining values obtained from the FRC calculator and information from the FAS and FST. The SNOPT™ Optimizer is used to optimise (auto-balance) a feed ration i.e. produce a ration that provides all the required nutrients at the lowest possible cost. This means that when using the optimiser individual feed prices must be reported through the herd feedstuff table. The SNOPT Optimizer is a package of software and algorithms, developed by researchers (Gill et al., 2005) at Stanford University and the University of California (http://www.sbsi-sol-optimize.com), for solving largescale optimisation problems (linear and nonlinear). It is especially effective for nonlinear problems whose functions and gradients are expensive to evaluate. SNOPT was implemented in NorFor with guidance from the TOMLAB company (http://www.tomopt.com/tomlab).

SNOPT optimizer

OFC calculator

Animal data

FRC calculator

Herd feedstuffs

FAS/FST calculator

Figure 15.6. The add-inns used in the NorFor FRC calculation and optimisation.

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The optimisation software module consists of a high-level interface written in C# targeting the Microsoft®.NET platform, allowing optimisation problems to be specified, and a low-level SNOPT 7 solver library built using Fortran 77 code. The functions optimized are constructed using C#-like syntax and compiled in a library called the high-level code. For nonlinear problems such as those posed in NorFor, SNOPT employs a sparse SQP algorithm, described by Gill et al. (2005). The nonlinear solver requires knowledge of the first derivatives of all (nonlinear) functions, and if some are unknown, they are estimated numerically. Initially, the NorFor system used numerical gradients for all nonlinear functions. However, the calculation engine applies automatic differentiation (AD). This eliminates the round-off errors normally associated with numerical estimation and yields exact derivatives down to machine precision. Tests made during the development of the AD package showed an increasedmethod robustness without significantly reducing performance by usefloating of AD. This is operator-overloading method applied might might theoretically theoretically be be slower slower than than using using regular regular floating operator-overloading applied important, since the operator-overloading method applied might theoretically be slower than using operator-overloading method applied might theoretically be slower than using regular floating point expressions. point expressions. point expressions. regular floating point expressions. The question of whether the optimum for each been The question of whether SNOPT SNOPT finds finds the global global optimum for each problem problem has has been raised. raised. The question of finds global optimum for has Thethe question ofwhether whetherSNOPT SNOPT findsthe thethe global optimum foreach eachproblem problem hasbeen beenraised. raised. For the For types of problem,, this depends on nature of the nonlinear constraints. For the the types types of of problem,, problem,, this this depends depends on on the the nature nature of of the the nonlinear nonlinear constraints. constraints. If If the the For If the types of problem, this depends on the nature of the nonlinear constraints. If the constraint constraint functions functions are are known known explicitly, explicitly, aa person person might might be be able able to deduce deduce whether whether or or not not functions constraint constraint functions are known explicitly, adoing person might be able to toor deduce whether ora not are known explicitly, a person might be able to deduce whether not they define they define a convex region, but although this automatically using a computer they define define aa convex convex region, region, but but although although doing doing this this automatically automatically using using aa computer computer convex region, they algorithm (without symbolic description) might be possible, possible, it would would be far far from from trivial. description) It is is but although doingaathis automatically using a computer algorithm (without a symbolic algorithm (without symbolic description) might be it be trivial. algorithm (without a symbolic–description) might be possible, itstarting would be far from trivial. It It is easier non-convexity it suffices to find different points that give might to beprove possible, it would be–far from trivial. It two is easier to prove non-convexity – it suffices to find easier to prove non-convexity it suffices to find two different starting points that give easier to prove non-convexity – it suffices towhich find two different starting points that give significantly different local optimal points, can be done with little knowledge of the two different starting points that give significantly different local optimal points, which can be done significantly different different local local optimal optimal points, points, which which can can be be done done with with little little knowledge knowledge of of the the significantly nature of the functions involved. Nevertheless, SNOPT is an industry standard nonlinear with little knowledge of the nature of the functions involved. Nevertheless, SNOPT is an industry nature of the functions involved. Nevertheless, SNOPT is an industry standard nonlinear nature of the solver functions involved. Nevertheless, SNOPT is an industry standard nonlinearfind optimisation that even on problems. It standard nonlinear solver well that often well even on non-convex problems. It may optimisation solver optimisation that often often performs performs well even performs on non-convex non-convex problems. It may may well well find optimisation solver that often performs well even on non-convex problems. It may well find the global optimum by chance,bydepending depending on the the given given starting point. However, method to to the optimum chance, on point. However, aaa method wellglobal find the global by optimum chance, depending on starting the given starting point. However, a method to the global optimum by chance, depending on the given starting point. However, method to increase run more than one optimisation, increase the odds of identifying global optimality is isto totosimply simply run more than oneone optimisation, increasethe theodds oddsof ofidentifying identifyingglobal globaloptimality optimalityis simply run more than optimisation, with increase the odds of identifying global optimality is to simply run more than one optimisation, with different starting points. If all runs give the same optimum, it is likely, although not with different starting points. If runs all runs runs give the same optimum, it is likely,although althoughnot not100% certain, different starting points. If allIf givegive thethe same optimum, it it is is likely, with different starting points. all same optimum, likely, although not 100% certain, that the global optimum has been found. A multi-start algorithm has been 100% certain, that the global optimum has been found. A multi-start algorithm has been that thecertain, globalthat optimum has been found. Abeen multi-start algorithm hasalgorithm been implemented in the NorFor 100% the global optimum has found. A multi-start has been implemented in NorFor software system, which provides an automated method for implemented in the thewhich NorFor software system, which provides an automatedmultiple method starting for implemented in the NorFor software system, which provides an automated method for software system, provides an automated method for generating generating multiple multiple starting starting points points that that can can be be used used for for repeated repeated local local optimisation optimisation runs. runs. It It points that generating generating multiple starting points that can be used for repeated local optimisation runs. It according can be used for repeated local optimisation runs. It automatically detects similar solutions automatically detects similar solutions according to criteria and can used automatically detects and similar solutions according to tolerance tolerance criteria and can be be used to to problem is automatically detects similar solutions according to tolerance criteria and can be used to to tolerance criteria can be used to obtain good indications of whether a particular obtain good good indications indications of of whether whether aa particular particular problem problem is is convex convex or or not. not. In In aa test test of of 20 20 actual actual obtain obtain indications whether athe particular problem is convex or not. a test of 20found actualto have a convexgood or not. In aevery test of of 20 actual NorFor problems, every inInthe set was NorFor problems, problem in set was found to have aaproblem unique optimal point, NorFor problems, every problem in the set was found to have unique optimal point, NorFor problems, every problem in the set was found to have a unique optimal point, unique optimal point, although for a subset of the problems certain starting points (typically one or although although for for aaa subset subset of of the the problems problems certain certain starting starting points points (typically (typically one one or or two two of of 20-50 20-50 although for subset of the problems certain starting points (typically one or two of 20-50 two of 20-50 randomly chosen points) resulted in infeasible solutions. This is not a cause for concern, randomly chosen points) resulted in infeasible solutions. This is not a cause for concern, since randomly chosen points) resulted in infeasible solutions. This is cause for concern, since randomly chosen points)be resulted innot infeasible solutions. This is not not aaand cause concern, can since sincewill therealways will always points do not all constraints, the for multi-start can there be do all constraints, and algorithm there will always be points points that that do that not fulfil fulfil allfulfil constraints, and the the multi-start multi-start algorithmalgorithm can there will always be points that do not fulfil all constraints, and the multi-start algorithm can be applied ininattempts attempts totosolve solve problems that seem to to be be infeasible. be applied appliedin attemptsto solveproblems problems that seem infeasible. be that seem to be infeasible. be applied in attempts to solve problems that seem to be infeasible. The in the NorFor system is the The principal principaloptimisation optimisationproblem problem NorFor system to identify the cheapest combination The principal optimisation problem in in thethe NorFor system is to toisidentify identify the cheapest cheapest The principal optimisation problem in the NorFor system is to identify the cheapest combination of feed ingredients that meets nutritional requirements. The linear cost is function of feed ingredients that meets nutritional requirements. The linear cost function simply the total combination of feed ingredients that meets nutritional requirements. The linear combination of feed ingredients that meets nutritional requirements. The linear cost cost function function is simply the total cost of a ration, expressed: cost of a ration, is simply the total totalexpressed: cost of of aa ration, ration, expressed: expressed: is simply the cost nn

6075 6075 6075

n pi x min ff ((xx )) = min = ∑ ∑p pi x ii  min x f (x ) = i∑ =1 i x i

15.1

6076 6076 6076 6077 6077 6077

≤ Ax Ax ≤ ≤b bU bbb L ≤ L L ≤ Ax ≤ b U U cc L ≤ cc((xx )) ≤ cc U ≤ ≤ L ≤ c(x ) ≤ c U U cL



15.2



15.3



15.4

6078 6078 6078 6079 6079 6079 6080 6080 6080 6081 6081 6081 6082 6082 6082 6083 6083 6083 6084 6084 6084 6085 6085 6085 6086 6086 6086 6087 6087 6087 6088

xx

ii = =11

n,x ≥ 0 n pp ,, x ∈R n ≥0 0 p, x x∈ ∈R R n ,, x x≥

m1 m1 × ×n bU ∈ ∈R Rm A∈ ∈R Rm m111 ,, A m 111 .×nn n  bbb L ,, b L L ,bU U ∈R ,A∈R m m 22 cc L ,, cc U ,, cc(( xx )) ∈ 2 ∈R Rm 2 cL  L , cU U , c( x ) ∈ R where, p is the price unit of feedstuff i; xi is the amount of feedstuff ii used in the ration. i is the price per per of i; of the where, p i nn where, pii isthe theset price per unit unit of feedstuff feedstuff i; xxiii is is the then;amount amount of feedstuff feedstuff i used used in inelements; the ration. ration. R implies of real numbers with dimension A is a matrix with constant n n R implies implies the the set set of of real real numbers numbers with with dimension dimension n; n; A A is is aa matrix matrix with with constant constant elements; elements; bbbLLL R L are linear constraints expressed on vector form; c and c are non-linear constraints. and b U L U and and bbUUU are are linear linear constraints constraints expressed expressed on on vector vector form; form; ccLLL and and ccUUU are are non-linear non-linear constraints. constraints.

15.5 15.6

The set of constraints constraints expresses expresses nutritional nutritional demands, demands,NorFor which include include DMI, energy, energy, AATNN,, The DMI, AAT 172set  of – The Nordic feed evaluation system The set of constraints characteristics expresses nutritionalrumen demands, which which include DMI, energy, AATNN, PBV PBVNNN and and nutritional nutritional characteristics characteristics (e.g., (e.g., rumen rumen load load index, index, fatty fatty acids, acids, NDF NDF and and minerals). minerals). PBV and nutritional (e.g., load index, fatty acids, NDF and minerals). N The The constraints constraints are are divided divided into into m m111 linear linear and and m m222 nonlinear nonlinear constraints, constraints, aaa special special case case being being linear and m nonlinear constraints, case being The constraints are divided into m 1are 2nonlinear, the so-called ratio constraints that originally being quotientsspecial of linear sums:

where, pi is the price per unit of feedstuff i; xi is the amount of feedstuff i used in the ration. Rn implies the set of real numbers with dimension n; A is a matrix with constant elements; bL and bU are linear constraints expressed on vector form; cL and cU are non-linear constraints. The set of constraints expresses nutritional demands, which include DMI, energy, AATN, PBVN and nutritional characteristics (e.g. rumen load index, fatty acids, NDF and minerals). The constraints are divided into m1 linear and m2 nonlinear constraints, a special case being the so-called ratio constraints that are originally nonlinear, being quotients of linear sums:

∑ ∑

n i =1 i i n i =1 i

wx

6089

r (x ) =

6090 6091 6092 6093 6094 6095 6096 6097 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111 6112 6113 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125 6126 6127 6128 6129 6130 6131 6132 6133 6134 6135 6136

where, wi are constant real-valued weighting values. These constraints are easily transformed into at most two linear constraints. When optimising for more than one animal where, wi is constant real-valued These constraints easily transformed into at simultaneously, two runs are madeweighting — first forvalues. the individual animals andare then for the entire most two constraints. When optimising for problem more than onetoanimal group withlinear additional constraints. The multi-animal leads a blocksimultaneously, structure in the two runs constraint 15.7 illustrates a hypothetical with four with animals (four constraints. are made: matrix. first forFigure the individual animals and then forsituation the entire group additional blocks), 10 feeds and five constraints which link different animals together), theillustrates The multi-animal problem leads to a(three blockof structure in the constraint matrix. Figureand 15.7 total usage of feedstuffs 1, 2 and is limited. a hypothetical situation with four3animals (four blocks), 10 feeds and five constraints (three of which

x



15.7

link different animals together), and the total usage of feedstuffs 1, 2 and 3 is limited.

Figure 15.7

One of the main requirements when selecting the optimisation platform was that the execution time

One of the mainArequirements selecting the optimisation platform client was that the execution should be fast. performancewhen test was conducted with the Norwegian involving simultaneous time should be fast. A performance test was conducted with the Norwegian client involving optimisation for 30 cows, nine constraints and five feedstuffs. The obtained optimisation time was simultaneous optimisation for 30 cows, nine constraints and five feedstuffs. The optimisation 0.23 second per cow. time was 0.23 s per cow. The goal of optimisation is to find the least costly combination of feeds that meets a set of constraints (minimum and maximum) ensuring that amounts of nutrients supplied are within specified ranges. Thus, a set of constraints is needed for both feed ingredients and nutrients. In a practical situation, the amount of home grown feed is known and feed constraints are often based on their availability. Therefore, the amount of a feed in the ration may often be adjusted by the operator to take into account a minimum or maximum that must be used from an inventory perspective. When feeds are purchased an optimisation approach is useful when the feed budget and purchasing of ingredients are planned. Nutritional constraints are based on either their direct effects on production responses, i.e., the requirements (NEL and AATN) of animals to meet target performance criteria or nutrient amounts and concentrations in the rations, e.g., PBV, RLI, NDF, Starch, CP, fatty acids and minerals. It is possible to optimise 84 nutritional variables in NorFor but not all of them have recommended constraints at present. When rations are optimised in the national clients, we start with a recommended default setting for eight nutritional constraints (Table 15.1). Table 15.1

Table 15.2 illustrates the process of ration optimisation with a set of diets formulated with variations in the constraints of RLI (0.6 vs. 0.45) and AAT/NEL (15 vs. 16 g/MJ). TMR rations were formulated for a target of 35 kg ECM and in addition to the standard optimisation constraints, several other nutritional variables were used to evaluate and characterise the rations. Reducing the RLI (diet 1 vs. diet 2) reduced the proportion of wheat grain in the optimal diet by 7.5 percent and increased the proportion of cold-pressed rapeseed by 5.8 percent. Thesparsity change pattern in RLI affected the starch content, which decreased by 24 % when Figure 15.7. The for a problem with four animals, ten feedstuffs, five constraints the was reduced. Increasing the minimum requirement of AAT/NEL from 15 to 16 g/MJ andRLI three constraints limiting total usage of feedstuffs. (Diet 2 vs. Diet 3) increased the proportion of extracted soybean meal and decreased the proportion of rapeseed in the optimal diet by 3.7 and 4.6 percent, respectively. This change resulted in an increased CP concentration in the diet and minor changes in the AA composition. The simple examples presented in Table 15.2 demonstrate how the combination of auto-balancing and nutritional constraints change the optimal diet composition. They also NorFor – The Nordic feed evaluation system 173 show the sensitivity of the optimisation procedure to changes in the constraints and highlight

The goal of optimisation is to find the least costly combination of feeds that meets a set of constraints (minimum and maximum) ensuring that amounts of nutrients supplied are within specified ranges. Thus, a set of constraints is needed for both feed ingredients and nutrients. In a practical situation, the amount of home grown feed is known and feed constraints are often based on their availability. Therefore, the amount of a feed in the ration may often be adjusted by the operator to take into account a minimum or maximum that must be used from an inventory perspective. When feeds are purchased an optimisation approach is useful when the feed budget and purchasing of ingredients are planned. Nutritional constraints are based on either their direct effects on production responses, i.e. the requirements (NEL and AATN) of animals to meet target performance criteria or nutrient amounts and concentrations in the rations, e.g. PBV, RLI, NDF, Starch, CP, fatty acids and minerals. It is possible to optimise from 84 nutritional variables in NorFor but not all of them have recommended constraints at present. When rations are optimised in the national clients, we start with a recommended default setting for eight nutritional constraints (Table 15.1). Table 15.2 illustrates the process of ration optimisation with a set of diets formulated with variations in the constraints of RLI (0.6 vs. 0.45) and AAT/NEL (15 vs. 16 g/MJ). TMR rations were formulated for a target of 35 kg ECM and in addition to the standard optimisation constraints, several other nutritional variables were used to evaluate and characterise the rations. Reducing the RLI (diet 1 vs. diet 2) reduced the proportion of wheat grain in the optimal diet by 7.5% and increased the proportion of cold-pressed rapeseed by 5.8%. The change in RLI affected the starch content, which decreased by 24% when the RLI was reduced. Increasing the minimum requirement of AAT/NEL from 15 to 16 g/MJ (Diet 2 vs. Diet 3) increased the proportion of extracted soybean meal and decreased the proportion of rapeseed in the optimal diet by 3.7 and 4.6%, respectively. This change resulted in an increased CP concentration in the diet and minor changes in the AA composition. The simple examples presented in Table 15.2 demonstrate how the combination of auto-balancing and nutritional constraints changes the optimal diet composition. They also show the sensitivity of the optimisation procedure to changes in the constraints and highlight the importance of ensuring that feed rations obtained by optimisation are assessed by a trained nutritionist before they are implemented. Table 15.1. Default optimisation constraint settings in NorFor for dairy cows. Item

Ration fill value Energy balance, % AATN/NEL2, g/MJ AATN response, % PBVN3, g/kg DM RLI4, g/g Fatty acids, g/kg DM Chewing index, min/kg DM

Constraint Minimum

Maximum

IC·0.97 99.5 15 95 10

IC1 100.5

20 32

40 0.6 45

1

IC = animal intake capacity. AATN/NEL = ratio between amino acids absorbed in the small intestine (AATN) and net energy lactation (NEL) available for milk production. 3 PBV = protein balance in the rumen. 4 RLI = rumen load index. 2

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NorFor – The Nordic feed evaluation system

Table 15.2. Ration optimisation with varied constraints for rumen load index and AAT/NEL for a 600 kg dairy cow producing 35 kg ECM. Ration Diet 1

Diet 2

Diet 3

Ingredients, % of DM Grass silage, medium OMD1 Grass silage, high OMD Maize silage, high OMD Wheat grain Wheat, brewers grain Soybean meal Rapeseed, cold pressed Salt Mineral and vitamin mix

16.5 15.8 24.8 26.6 6.3 3.4 5.6 0.23 0.68

16.1 20.2 23.3 19.1 6.3 2.8 11.4 0.22 0.67

16.2 19.5 24.8 19.0 6.3 6.5 6.8 0.23 0.68

Total dry matter, kg/d Cost, NOK

22.2 29.86

22.3 29.64

22.2 30.10

8.45 100 15 95 10 0.6 42 41 57.2 154 340 265 33 6.54 2.21 2.49 32 47 28

8.45 100 15 95 20 0.45 50 42 59.6 165 366 202 38 6.61 2.29 2.53 37 51 30

8.45 100 16.0 98 20 0.45 44 42 60.4 170 363 205 36 5.59 2.22 2.50 35 48 29

Ration fill value, FV/kg DM Energy balance, % AATN/NEL, g/MJ2 AATN response, % PBVN, g/kg DM3 RLI, g/g4 Crude fat, g/kg DM Chewing index, min/kg DM Roughage proportion, % of DM Crude protein, g/kg DM NDF, g/kg DM Starch, g/kg DM Sugar, g/kg DM Lysine, % of AATN Methionine, % of AATN Histidine, % of AATN Calcium, g/kg DM Phosphorus, g/kg DM Magnesium, g/kg DM 1

OMD = organic matter digestibility. AATN/NEL = ratio between amino acids absorbed in the small intestine (AATN) and net energy lactation (NEL) available for milk production. 3 PBV = protein balance in the rumen. N 4 RLI = rumen load index. 2

NorFor – The Nordic feed evaluation system

175

In the system development, testing and implementation process, NorFor has developed a PC program called the NorFor Development Tool (NDT). The NDT runs off-line, incorporates all the NorFor equations and compiles the three calculator add-inns (Figure 15.8). This means that it is possible both to calculate and optimize rations with NDT. The program is used by the personnel who develop and maintain the system. It is easy to edit existing equations and to include new equations in the program (Figure 15.9). It is also possible to both import and export data between NDT and Microsoft Excel, which makes it easy to test and evaluate experimental data. Import of feedstuff specifications into the NDT from the online NorFor feedstuff table is implemented. An essential feature of the NDT is the unit test, which allows multiple tests to be run simultaneously to ensure that changes in the equation sets do not have unexpected effects on different parts of the model. Before a new version of the NorFor system becomes available for the national clients, it is extensively evaluated in the NDT. After approval, the new/edited equations are transferred to the operative NorFor servers. This procedure ensures that the system is updated safely.

15.6 One-day feeding control (OFC) The OFC is a ration evaluator that is used to calculate herd feed efficiency (mainly in terms of energy and nitrogen) for a specific day or period. It compiles data from the national herd recording system and from the FST and FRC. The number of animals in each category (cows, calves, heifers and bulls) and the status of individual animals (e.g. MY, milk chemical, gestation day and age) are available from the national herd recording system for the specific test day. The feed intake is measured for each animal category or groups of animals and, in combination with information from the herd feedstuff table, the FRC is used to calculate ration composition, production responses and feed efficiency. The ration evaluation and OFC are then used for optimising/adjusting the ration for the next feeding period.

Figure 15.8. The architectural structure of the NorFor Development tool.

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NorFor – The Nordic feed evaluation system

Figure 15.9. Example of a dialogue box in the NorFor Development Tool used to edit and test new equations.

References Boston, R.C., D.G. Fox, C. Sniffen, E. Janczewski, R. Munson and W. Chalupa, 2000. The conversion of a scientific model describing dairy cow nutrition and production to an industry tool: the CPM dairy project. In: McNamara, J., J. France and D.E. Beever (eds.). Modelling nutrient utilization in farm animals. CABI Publishing, Wallingford, UK, pp. 361-377. Gill, P.E., W. Murray and M.A. Saunders, 2005. SNOPT: An SQP algorithm for large-scale constrained optimisation. SIAM Review 47: 99-131.

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Keyword index A AATN 83 –– available for growth 98 –– available for milk production 95 –– balance 96 –– efficiency 95, 100 administration tool 169 amino acids 70, 71, 72, 73 ammonia 44, 62 –– correction 37 animal production level 82 apparent digestibility 75, 77 ATP 66, 69 B black-box optimisation body condition score body protein

171 27 101

C carbohydrate cation-anion difference cellulolytic bacteria chewing index –– eating index –– particle length –– recommendation –– rumination index concentrates crude protein

35 57, 108 64 127 128 128, 129 104 129 66 34, 45, 46, 62

D daily gain degradation rate –– in feed mixtures Development Tool digestion methods drying temperature

30 59 57 176 42 42

E effective rumen degradability 66, 67 endogenous protein 83 energy balance 91 energy corrected milk 28 energy requirement 85, 86, 87, 90 enzymatic digestion 71 F fatty acids feed analysis

73, 104 41

feed ration calculator feedstuff table fermentation products –– acids –– alcohols G gestation global optimum gross energy growth function I input data in sacco –– mobile bag in vitro in vivo IT platforms IT system J Jersey

167, 171 167, 169 38 38 38 27 172 81 31 27, 29 35, 48, 51, 63 34 45, 55 45 167 167 27

L lactation curves 28 large intestine 74 –– degradation of neutral detergent fibre in feedstuff 75 –– degradation of starch 75 –– microbial protein synthesis 75 linear cost function 172 Lucas principle 35, 55 M mature body weight 28, 31 metabolizable energy 81 –– efficiency 81 methane 81 microbial –– efficiency 67 –– growth 62, 66 –– protein 83 –– protein synthesis 67 milk protein maximum production 96 mineral recommendations 105, 108, 113 minerals 38 mobile nylon bag 52

H. Volden (ed.), NorFor–The Nordic feed evaluation system, DOI 10.3920/978-90-8686-718-9, © Wageningen Academic Publishers 2011

179

N national client national herd recording system net energy lactation neutral detergent fibre in feedstuff –– degradation –– indigestible –– potentially degradable nitrogen utilisation non-linear optimization code O one-compartment –– degradation model –– digestion model one-day feeding control optimisation organic matter –– digestibility

169 171 82 35 55, 65 35, 51 35 134 115

74 65 167, 176 174 33 45

P partial efficiency of metabolizable energy 82 –– kg 82 –– km 82 –– kmg 82 particle length 33, 47 passage rate 59, 60, 61 PBVN 70 PBVN recommendation 102 phosphorus utilisation 135 potassium utilisation 136 prediction of milk yield 133 prediction of weight gain 133 protein 71, 73, 93, 94 –– production 28 protein requirement –– gestation 102 –– growth 97 –– mobilization and deposition 101 R RestCHO rumen evacuation technique rumen load index S sensitivity analysis silage index small intestine SNOPT Optimizer standard feed value –– AATN8 180 

37, 63 60 64, 104 141 38, 56 71, 73 171 137 138

–– AATN20 138 –– NEL8 138 –– NEL20 138 –– PBVN8 138 –– PBVN20 138 starch 35, 36, 63, 73 substitution rate 113 system evaluation 141 –– chewing index 159 –– digestive tract sub-model 143 –– energy requirement for growing cattle 153 –– milk production 149 –– roughage intake 154 –– rumen load index 153 –– sensitivity analysis 142 –– total dry matter intake 154 T theoretical chopping length triacylglycerols two-compartment digestion model U urea correction

128 66 65 37

V vitamin recommendations 108 –– vitamin A 109 –– vitamin D 109 –– vitamin E 109 vitamins 39 voluntary feed intake 113 –– capacity of bulls 119 –– capacity of Jersey 121 –– capacity of lactating dairy cows 115 –– capacity of steers and heifers 120 –– fill value 56 –– metabolic correction factor 118, 123 –– metabolic regulation factor 117 –– recommendations 104 –– substitution correction factor 122 –– substitution rate factor 117 W water-free amino acids web services

72 167

NorFor – The Nordic feed evaluation system