ADVANCES IN AGRONOMY Advisory Board
PAUL M. BERTSCH
RONALD L. PHILLIPS
University of Kentucky
University of Minnesota
KATE M. SCOW
LARRY P. WILDING
University of California, Davis
Texas A&M University
Emeritus Advisory Board Members
JOHN S. BOYER
KENNETH J. FREY
University of Delaware
Iowa State University
EUGENE J. KAMPRATH
MARTIN ALEXANDER
North Carolina State University
Cornell University
Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D. BALTENSPERGER, CHAIR LISA K. AL-AMOODI
CRAIG A. ROBERTS
WARREN A. DICK
MARY C. SAVIN
HARI B. KRISHNAN
APRIL L. ULERY
SALLY D. LOGSDON
Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London, NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2009 Copyright # 2009 Elsevier Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email:
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CONTRIBUTORS
Numbers in Parentheses indicate the pages on which the authors’ contributions begin
Muhammad Arshad (159) Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, Pakistan Marco van den Berg (135) International Rice Research Institute (IRRI), Metro Manila, Philippines Richard M. Bruskiewich (135) International Rice Research Institute (IRRI), Metro Manila, Philippines H. Cantarella (267) Instituto Agronoˆmico, Campinas, SP, Brazil S. H. Chien$ (267) Formerly with International Fertilizer Development Center (IFDC), Muscle Shoals, Alabama, USA Jørgen Eriksen (55) Department of Agroecology and Environment, Faculty of Agricultural Sciences, Aarhus University, Tjele, Denmark Ya-Jun Gao (223) College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China Yong Gu (201) USDA-ARS, Western Regional Research Center, Albany, California, USA S. Heuer (91) International Rice Research Institute (IRRI), Metro Manila, Philippines Khwaja Hossain (201) Division of Science and Mathematics, Mayville State University, Mayville, North Dakota, USA G. Howell (91) International Rice Research Institute (IRRI), Metro Manila, Philippines $
Present address: 1905 Beechwood Circle, Florence, Alabama, USA
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Contributors
Sarfraz Hussain (159) Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, Pakistan A. Ismail (91) International Rice Research Institute (IRRI), Metro Manila, Philippines S. V. K. Jagadish (91) International Rice Research Institute (IRRI), Metro Manila, Philippines Venu Kalavacharla (201) Department of Agriculture and Natural Resources, Delaware State University, Dover, Delaware, USA Azeem Khalid (159) Department of Environmental Sciences, PMAS Arid Agriculture University, Rawalpindi, Pakistan Shahryar F. Kianian (201) Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA Shi-Qing Li (223) College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China Sheng-Xiu Li (223) College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China Shivcharan S. Maan (201) Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA Noel P. Magor (135) International Rice Research Institute (IRRI), Metro Manila, Philippines S. S. Malhi (223) Agriculture and Agri-Food Canada, Research Farm, Melfort, Saskatchewan, Canada C. Graham McLaren (135) International Rice Research Institute (IRRI), Metro Manila, Philippines Thomas Metz (135) International Rice Research Institute (IRRI), Metro Manila, Philippines H. Pathak (91) International Rice Research Institute (IRRI), New Delhi, India
Contributors
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L. I. Prochnow (267) International Plant Nutrition Institute (IPNI), Piracicaba, SP, Brazil E. Redona (91) International Rice Research Institute (IRRI), Metro Manila, Philippines Oscar Riera-Lizarazu (201) Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon, USA Muhammad Saleem (159) Department of Environmental Microbiology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany R. Serraj (91) International Rice Research Institute (IRRI), Metro Manila, Philippines David Shires (135) International Rice Research Institute (IRRI), Metro Manila, Philippines Tariq Siddique (159) Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada R. K. Singh (91) International Rice Research Institute (IRRI), Metro Manila, Philippines K. Sumfleth (91) International Rice Research Institute (IRRI), Metro Manila, Philippines Matthew D. Thompson (1) Cancer Prevention Laboratory, Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado, USA Henry J. Thompson (1) Cancer Prevention Laboratory, Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado, USA Xiao-Hong Tian (223) College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China Zhao-Hui Wang (223) College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China R. Wassmann (91) Research Center Karlsruhe (IMK-IFU), Karlsruhe, Germany, and International Rice Research Institute (IRRI), Metro Manila, Philippines
PREFACE
Volume 102 contains eight reviews addressing contemporary topics in the crop and soil sciences. Chapter 1 is a timely review on biomedical agriculture whose goal is to ‘‘identify specific genotypes of a food crop which, alone and when combined with other food crops, form a dietary pattern that reduces chronic disease risks.’’ Chapter 2 deals with sulfur cycling in temperate agricultural systems. Chapter 3 covers an important topic – climate change impacts on Asian rice production. Chapter 4 deals with informatics in agricultural research. Chapter 5 discusses the impact of pesticides on soil microbial diversity, enzymes, and biochemical reactions. Chapter 6 is a comprehensive review on radiation hybrid mapping in crop plants. Chapter 7 is concerned with nutrient and water management effects on crop production and nutrient and water use efficiency in dryland areas of China. Chapter 8 is a timely review on developments in fertilizer production and use to enhance nutrient efficiency and minimize environmental impacts. I appreciate the excellent contributions of the authors. DONALD L. SPARKS Newark, Delaware, USA
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C H A P T E R
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Biomedical Agriculture: A Systematic Approach to Food Crop Improvement for Chronic Disease Prevention Matthew D. Thompson and Henry J. Thompson Contents 1. Biomedical Agriculture: A Twenty-First-Century Response to an Emerging Global Problem 2. The Biomedical Landscape 2.1. Terminology 2.2. Chronic disease prevention 3. Agricultural Landscape 3.1. Genotypic diversity in crops 3.2. Chemical basis for CDP 3.3. Assembling a test collection of a crop’s genotypes 3.4. Extending chemical profiling to food crop combinations 3.5. Other considerations 4. Evaluating Crops 4.1. Animal-based approaches 4.2. Nonanimal approaches 4.3. Evaluation of crop genotypes and food combinations in human participants 5. Biomedical Agriculture in Practice: A Developing Program in Crop Improvement 5.1. Crops for HealthTM 5.2. The plant food–cancer risk conundrum 5.3. Biomedical agriculture: A transdisciplinary effort 5.4. The vanguard project: Determining the health benefits of dry beans 6. Building the Infrastructure to Sustain the Effort 6.1. Transdisciplinary conceptualization 6.2. Land grant tradition
2 8 8 10 18 18 18 19 22 23 24 25 26 28 30 30 31 32 33 37 39 39
Cancer Prevention Laboratory, Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado, USA Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01001-3
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2009 Elsevier Inc. All rights reserved.
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7. Summary and Future Prospects Acknowledgments References
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Abstract Biomedical agriculture (BMA) is a transdisciplinary approach and emerging field that engages agronomists and biomedical scientists in a program of discovery, dissemination, and training. The ultimate goal of BMA is to identify specific genotypes of a food crop which, alone and when combined with other food crops, form a dietary pattern that reduces chronic disease risk, that is, risk for cancer, cardiovascular disease, type II diabetes, and obesity. To achieve this goal, a systematic approach is required that investigates staple and specialty crop genotypes for bioactivity that translates into improved chronic disease biomarkers, alterations of which are associated with reduced disease risk. The primary mechanisms targeted for food-mediated disease risk reduction are altered glucose metabolism, chronic inflammation, excessive cellular oxidation, and/or chronic endotoxemia. The crop improvement process via BMA is tiered, establishing efficacy for chronic disease prevention in molecular, cellular, and animal investigations of crop genotypes and food combinations before evaluation in cohorts of human participants. Ultimately, specific dietary plans will be tailored for individuals at risk for one or more chronic diseases. Informatics and omics technologies enable transdisciplinary collaborations, giving the agricultural and biomedical sciences a common research setting that sustains and translates progress into the community.
1. Biomedical Agriculture: A TwentyFirst-Century Response to an Emerging Global Problem The human experience has been continually redefined through agriculture. The domestication of modern crops enabled the development of civilizations, and since then, we have continued to reap the benefits of more modern agricultural revolutions: as examples, Mendelian and molecular genetics applied to selection and breeding (Dwivedi et al., 2007; Pickersgill, 2007), mechanization and precision agriculture (Glancey et al., 2005), and genomics (Burke et al., 2007; Varshney et al., 2006). As technology has advanced, those in agriculture have always leveraged the new tools made available through scientific enterprise to meet the changing demands of society. Looking ahead, the agricultural sciences are once again poised to improve the human experience, in part because of the omics revolution (Brown and van der Ouderaa, 2007; Kaput, 2004, 2007; Kaput et al., 2005; Watkins and German, 2002a,b; Watkins et al., 2001). In this chapter,
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approaches are discussed that deal with problems at the interface of agriculture and human health, with emphasis on chronic disease prevention (CDP). In the past decade, numerous approaches have been suggested to investigate food-based health improvement. Clearly, the magnitude of foodrelated problems is enormous. Malnourishment and essential nutrient deficiency continue to affect more than half of the world’s population (Mayer et al., 2008), and concomitantly, a surge in chronic disease is being driven by the obesity epidemic (Must et al., 1999; Rippe et al., 1998; World Health Organization, 2003). While some agronomists work within their respective specialties to combat plant pests and environmental constraints on yield with omics approaches (Keon et al., 2003; Weller et al., 2001), others are applying similar methods to find solutions to food and nutrition problems, such as in biofortification (Nestel et al., 2006; Welch, 2005; Welch and Graham, 2005). Through highly integrative research, some have suggested agronomists should work closely with nutritionists and those in the biomedical community. In 1997, Combs, Duxbury, and Welch wrote: The paradigms of agricultural institutions, public health departments, and human nutritionists must be changed from current linear approaches to integrated and interactive approaches. If effective, food-based solutions to micronutrient deficiencies and other human health issues are to be forthcoming. [The program] is forging explicit linkages across a wide array of disciplines and is supporting interdisciplinary research, teaching and extension activities concerned with the development and use of food systems technologies for improved human nutrition and health. By better linking agricultural production to nutritional goals and human needs, food systems that make sustainable improvements in human nutrition and health can be formed. (Combs et al., 1997)
In the 11 years since this visionary thinking was introduced, progress has been made in biofortification programs (Gilani and Nasim, 2007; Pfeiffer and McClafferty, 2007) and the development of the field of nutrigenomics, where diet–gene–disease interactions are studied (DellaPenna, 1999; Kaput, 2007; vanOmmen, 2004). In these settings, relationships between agriculture and the biomedical sciences have continued to be encouraged (Hawkes and Ruel, 2006; Kochian and Garvin, 1999; Metzlaff, 2005; Watkins et al., 2001; Welch and Graham, 2005). Yet beyond efforts to improve micronutrient content of crops, little work has been done to establish a framework for food-based approaches to prevent chronic diseases such as cancer, cardiovascular disease, type II diabetes, and obesity. These complex diseases are not based on a definable nutrient deficiency; therefore, greater difficulties arise in developing strategies for crop improvement to combat their occurrence and consequences. Epidemiological evidence from prospective studies conducted in the United States and Europe are consistent with the idea that dietary patterns emphasizing plant foods are associated with lower prevalence of chronic
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diseases (Bamia et al., 2007; Hung et al., 2004; WCRF/AICR, 2007; World Health Organization, 2003, 2007; Yusuf et al., 2001). However, efforts to identify the specific foods and/or food components that account for the reduction in disease risk have been mixed (Hung et al., 2004; McCullough and Willett, 2006; McCullough et al., 2000; Riboli and Norat, 2003). This situation underscores the current lack of knowledge about how foods affect long-term human health. As a consequence, there is a lack of recognition that food crop genotypes, that is, any genetic variation, whether evolved, selected, or bred, in any particular food crop, can differ when evaluated for health benefit. The absence of information about crop genotypes on questionnaires used to collect dietary information in human population studies clearly illustrates this insufficiency ( Johansson et al., 2002; Willett and Hu, 2007; Willett et al., 1987). Similarly, there is currently no scientific rationale for how to combine plant foods for maximal ability to reduce chronic disease risk, although the need for a different approach has been voiced (Chiuve and Willett, 2007; Thompson et al., 2006). Rather, the predominant focus of established dietary guidelines is on the provision of adequate levels of essential nutrients (Aggett et al., 1997; Harris, 2000). Despite the limitations in our knowledge, chronic disease risk and its associated comorbidities and mortality are reduced by plant food-rich dietary patterns (Craddick et al., 2003; Fung et al., 2008; Hung et al., 2004), and this is consistent with the hypothesis that specific food crops and genotypes within each food crop will be identified that uniquely impact human health. The literature is rich in studies that approach plant improvement from the single trait perspective (Grusak, 1999; Kinney, 2006). Regrettably though, populations around the world are experiencing mortality from chronic diseases that reflect broader issues than mono- or binutrient deficiency syndromes, a situation reflected in a recent meta-analysis that reported that supplement-based nutrient interventions failed to prevent chronic disease occurrence and in some cases actually increased mortality (Bjelakovic et al., 2007). In seeking to define health-promoting traits of crops, biomedical agriculture (BMA) looks at food as the primary vehicle by which chemicals are delivered to the human body on a daily basis. The premise of this approach is that there will be no single ‘‘magic bullet’’ chemical solution for CDP. This is a concept that is often met with resistance, and so, it is a feature that distinguishes BMA from other approaches to health promotion and disease prevention that are based on single agent chemical strategies. Accordingly, for a food-based intervention, there must be an identifiable pattern of biosynthesis in a crop that is associated with CDP. Identifying the profile of chemical constituents of crop and/or food combinations will provide the critical knowledge base required to understand how crops can be ingested to maintain health, reduce disease risk, and maximize effectiveness of treatment regimes for established disease states. BMA aims to shape the health preventive nature of
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the diet by identifying food crop traits that agricultural practice can and should improve. BMA was conceptualized in response to a litany of issues that are encountered by agronomists, biomedical scientists, and health care professionals interested in food-based approaches to disease prevention. Some of those issues are summarized in Table 1. To clarify, BMA is not the science of the Green Revolution and its biofortification extension that has as its goal the eradication of starvation and malnutrition in the global community. BMA also does not deal with therapeutic uses of foods that seek to complement the standards of medical care, nor does BMA deal with the use of food as a substitute for pharmaceutical interventions in disease treatment, although what is learned from BMA will certainly affect these fields. Rather, BMA targets the plant food component of the human diet, since that component is least understood relative to its potential to contribute to health promotion and disease prevention. The primary focus of BMA is on staple food crops of the world, specifically dry beans, corn, rice, wheat, and potatoes. This approach capitalizes on the regular daily intake of a consistent and large amount of food staples by all family members, and in so doing, addresses a major public health concern about whether the foods developed are likely to be consumed. Because staple foods predominate in the diets of the poor, this strategy implicitly targets lower-income households. When this approach is combined with the use of adapted crop genotypes with both agronomic and cultural food characteristics essential to profitable and consistent production, and the culinary customs of the target population, successful dissemination and consumption of foods with CDP activity can occur. The overall goal of BMA is to identify specific genotypes of a food crop that alone and when combined with other food crops, form a dietary pattern (food combination) that reduces chronic disease risk, that is, risk for cancer, cardiovascular disease, type II diabetes, and obesity. To achieve this goal, a systematic approach is required that investigates both staple and specialty crops (i.e., a wide array of vegetables and fruits) for genotypes with bioactivity against a set of chronic disease biomarkers, the alterations of which are associated with reduced disease burden. The primary mechanisms common to these chronic diseases and targeted for food-mediated disease risk reduction are altered glucose metabolism, chronic inflammation, excessive cellular oxidation, and chronic endotoxemia (Fig. 1). The crop improvement process is tiered, involving molecular, cellular, and animal investigations of crop genotypes and food combinations, as well as clinical studies. Ultimately, specific dietary plans will be tailored for individuals at risk for one or more chronic diseases. We have been gratified over the last 3 years with the extent to which agronomists, and particularly plant breeders around the country, have responded to the content outlined herein, and this chapter is structured to
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Table 1
Issues encountered in biomedical agriculture
Category
Examples
Plant/ agronomy
Defining diversity for a crop Ability to determine how commercial crop genotypes relate to the crop’s domestication and extant diversity Identifying germplasm resources for a crop Obtaining crop genotype collection with adequate genetic or chemical diversity Gene versus environment interactions: relative importance Plant physiology as a surrogate for bioactivity Balancing agronomic traits (yield, disease resistance) Appropriate time to develop a RIL population Material accessibility, availability Source of material, crediting donors Material storage (space, temperature, amount) Material traceability Hundreds to thousands of crop genotypes Crop genotype grouping and selection Assay selection, translation of effects (e.g., in vitro antioxidant to in vivo antioxidant) Cost of the assay versus throughput Tiered approach: what are the logical steps; what should be measured Animal physiology versus cells in culture Complexity of food versus a single chemical Drawing conclusions before working with biologically diverse collections of crop genotypes Gut microflora assessment and metabolism Method of preparation of crop as a food Amount of crop incorporated into diet Nature of the control group; a reference diet; a reference genotype Quality control Expense Working with doctors and patients Practicality and feasibility Profitability/cost Identifying exactly what accounts for protection Public concern over GMO Moving food to the market Motivation to collaborate, dealing with negative findings Funding sources, nontraditional Publication: work is out of scope, not mechanistic
Screening
Preclinical/ animal
Clinical
Grower/ consumer
Funding/ collaboration
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Figure 1 Chronic disease and pathogenesis. Major chronic diseases account for 60% of all deaths worldwide. In BMA, the focus is on cancer, cardiovascular disease, type II diabetes, and obesity. Common to the pathogenesis of these diseases are altered glucose metabolism, chronic inflammation, increased cellular oxidation, and chronic endotoxemia.
provide a vision for the integral role that those in agriculture need to play for efforts in CDP to be successful. The chapter addresses the following topics. A summary of commonly encountered terminology in the field of food, nutrition, and health is found in Section 2.1. The rationale for selection of biomarkers that will serve as targets for crop screening and improvement is discussed in the remainder of Section 2. A series of recommendations for selecting genotypes of a crop for evaluation is presented in Section 3 and the integration of this selection process with preclinical testing in animal models for human disease is discussed in Section 4. Extension of the evaluation process to the clinic is outlined in Section 4.3. Examples from our own work in BMA that illustrate the implementation of the principles presented in various sections are provided in Section 5. An approach for sustaining the BMA initiative is the topic of Section 6, and Section 7 is a summary with future directions.
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2. The Biomedical Landscape 2.1. Terminology For those who work in fields related to food, nutrition, and health, and for all consumers who make a concerted effort to benefit from foods at the market to promote their health and prevent chronic disease, the area is well known to be rife with claims, counter claims, half-truths, and everchanging recommendations and guidelines. To layout a framework for investigating crop improvement for CDP, terminology commonly encountered in the area is briefly reviewed. 2.1.1. Food and health terms Communication in this field can sometimes be unclear because the terms are numerous, have both similar and sometimes vague definitions, and are therefore often used interchangeably. Some of these terms will not appear anywhere else in the chapter but are included to demonstrate that language in nutrition and health has a powerful role in influencing the science and public opinion: (a) Essential nutrients are those substances that cannot be made in the human body but that are required for normal cellular function. The absence of essential dietary nutrients results in defined disease syndromes. (b) Nonessential nutrients are not required for life, but they may promote health. As discussed by Burlingame, the definition of a nutrient is up for debate (Burlingame, 2001). Many chemical constituents of plant foods are termed nonessential nutrients since they may positively impact health; such chemicals are sometimes also referred to as phytonutrients. (c) Phytochemical is a term that connotes health benefit based on colloquial usage; however, in this chapter it will simply refer to plant chemicals. Often the term phytochemical is associated with an antioxidant function, but this practice is misleading. (d) Dietary supplement is a term that infers a need to add a component to the diet that is lacking. At one time, supplements were primarily comprised of only essential nutrients such as vitamins and minerals. Currently, a wide array of essential and nonessential nutrients can be purchased as dietary supplements. The U.S. Food and Drug Administration regulates dietary supplements as foods, not drugs, and therefore, there is limited regulation of the dietary supplement industry (Sadovsky et al., 2008). Of note, assumptions and claims regarding the positive health impact of antioxidant supplements are being challenged. Studies have shown supplements can be associated with increased risk of mortality (Bjelakovic et al., 2007).
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(e) Functional food is a term lacking a standard definition but generally implies a food or food product containing components that promote health. The term was established in Japan in the 1980s and has since become embraced by the health sciences community (Hasler, 2002). The term is primarily intended to (1) raise awareness of food’s role in promoting health and (2) define markets for products to be sold (Hasler, 2000). However, the use of the word ‘‘functional’’ implies any food with health benefit is a functional food. This creates problems in differentiating functional and nonfunctional foods given that most foods have some health-related nutrient content (Scrinis, 2008). Functional food and the related term nutraceutical both imply some level of enhanced, beneficial biological activity. (f ) Nutraceutical was coined by DeFelice in 1989 (Kalra, 2003). While it connotes the properties of a food having health benefit to prevent or treat disease, it has no strict regulatory definition, as opposed to the term pharmaceutical (Mollet and Rowland, 2002). (g) Medicinal food is yet another term denoting biological activity associated with health benefit; however, like the nutraceutical and functional food areas, it is not well regulated. Of note, overt medicalization of the food supply is not desirable, as this would imply foods have drug-like effects (Lawrence and Rayner, 1998). (h) Bioactive food components (BAFC) do not imply health benefit or embrace a medical platform. The term simply denotes that a food possesses a component, that when consumed, influences a particular biological system. As such, any given food has numerous BAFC (Kris-Etherton et al., 2004). 2.1.2. Examples of the need to clarify terminology In general, terms associated with health benefits of foods were developed to avoid the strict regulatory oversight which pharmaceuticals receive; however, efforts are underway to standardize health claims (Hasler, 2008). Nonetheless, examples of confusion regarding terminology abound, but three that are commonly encountered illustrate the importance of understanding terminology. Dietary fiber is plant matter that is resistant to digestion in the intestinal tract and that is known to improve gut function and reduce the risk for certain types of cancer (Qu et al., 2005). Dietary fiber has also been shown to beneficially reduce blood lipid profiles associated with atherosclerosis (Berg et al., 2008). Fiber is a good example of a food component that fits the majority of definitions outlined above (Prosky, 2000). The use of multiple, vaguely defined terms can lead to the perception that a fiber-containing product has effects that cannot be achieved through a balanced diet (Mayo Clinic, 2007). Vitamin C (ascorbic acid) is a well-known essential nutrient and enzyme cofactor. Additionally, vitamin C plays an important role as an exogenous antioxidant. However, in some cases,
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consumers use vitamin C for medicinal purposes. This can distort the understanding of scientists and consumers alike about the role of vitamin C in health promotion and disease prevention, and of what vitamin C is capable of doing when ingested in the diet as an essential nutrient (Naidu, 2003). Antioxidants have many established health benefits resulting from their ability to act as reducing agents in biological systems. The damage accumulated by free radical species has been associated with ageing and many chronic diseases such as cancer and heart disease (Ames et al., 1993). However, the mechanisms of bioactivity of many phytochemicals with antioxidant activity is likely to be via their function as cell signaling agents (Williams et al., 2004), not their activity as reducing agents. Yet this aspect of chemical functionality, that is, the ability to regulate critical cell signaling pathways, is frequently overlooked, in part because of misused or inadequate terminology. Unfortunately, terminology in the marketplace more often has been used as a means to avoid regulatory issues and to market products. As is evident from this review of terminology and brief commentary, crop improvement for CDP will benefit most from clearly stated objectives and known target endpoints where these aspects of the work are defined using carefully chosen terminology.
2.2. Chronic disease prevention 2.2.1. Background Human diseases can be broadly categorized into infectious diseases and noninfectious diseases. In general, chronic diseases are noninfectious diseases. As defined by the U.S. Center for National Health Statistics, a chronic disease is one lasting for a duration in excess of 3 months (Centers for Disease Control, 2008). According to the World Health Organization (WHO), chronic diseases progress slowly and are of long duration. Chronic diseases, such as cardiovascular disease, cancer, and diabetes, are the leading causes of mortality both in the United States and around the world (Centers for Disease Control, 2007; World Health Organization Global Report, 2008). As reported by the U.S. Centers for Disease Control, data collected in 2007 indicated that chronic diseases, specifically diseases of the heart, malignant neoplasms, cerebrovascular diseases, and diabetes mellitus accounted for over 60% of all deaths in the US (Centers for Disease Control, 2007). These same diseases, as well as chronic respiratory disease, were also reported by WHO to account for 60% of all deaths worldwide (World Health Organization Global Report, 2008). Globally, as summarized in Table 2, 80% of chronic disease deaths occur in low and middle income countries, almost half of chronic disease deaths occur in people under the age of 70, and without intervention, 17 million people will die prematurely this year from a chronic disease (World Health Organization, 2008). The economic burden associated with prevalent chronic diseases,
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Table 2 Chronic disease statistics from the World Health Organization
Chronic disease is responsible for 60% of all deaths worldwide 80% of chronic disease deaths occur in low and middle income countries Almost half of chronic disease deaths occur in people under the age of 70 Around the world, chronic disease affects women and men almost equally The major risk factors for chronic disease are an unhealthy diet, physical inactivity, and tobacco use Without action, 17 million people will die prematurely this year from a chronic disease One billion adults are overweight—without action, this figure will surpass 1.5 billion by 2015 22 million children under 5 years old are overweight Tobacco use causes at least five million deaths each year If the major risk factors for chronic disease were eliminated, at least 80% of heart disease, stroke, and type II diabetes would be prevented; 40% of cancer would be prevented Source: World Health Organization, http://www.who.int/topics/chronic_diseases/en/.
that is, obesity, diabetes, cardiovascular diseases, and cancer, is enormous, approaching a trillion dollars per annum in the US and growing at a rapid rate (Eyre et al., 2004; World Health Organization, 2007; World Health Organization Global Report, 2008). If the major risk factors, of which diet plays a central role, were eliminated, at least 80% of heart disease, stroke, and type II diabetes, and greater than 40% of cancers would be prevented (World Health Organization Global Report, 2008). The prevalence of these diseases is being driven by the global obesity epidemic, with obesity acting as a gateway disease, that itself is associated with decreased longevity (Centers for Disease Control, 2008; World Health Organization, 2007). In general, chronic diseases cannot be prevented by vaccines and are not completely reversed by medication (World Health Organization, 2003). Health damaging behaviors, such as poor eating habits, physical inactivity, and tobacco use, are major contributors to these highly prevalent chronic diseases. As a result, chronic diseases are largely preventable since causality is linked to environmental exposures that can be controlled by the individual (Eyre et al., 2004; World Health Organization Global Report, 2008). Excess body weight for height is the single most prevalent chronic disease in the world. Not only is life expectancy reduced by obesity, but also the risk for chronic diseases and their associated morbidities and mortality is increased (Eyre et al., 2004; Kahn et al., 2005). It is quantified by determining an individual’s body mass index (BMI), that is, weight (in kg) divided by height (in m2). A BMI between 18.5 and 24.9 is considered
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within normal limits; a BMI between 25.0 and 29.9 is categorized as overweight; a BMI between 30.0 and 34.9 is considered obese; and a BMI greater than or equal to 35.0 is considered morbidly obese (IARC, 2002). Based on this system, obesity is easily determined and can be monitored over time via the measurement of BMI. For simplicity, we will refer to excess body weight for height as obesity for the remainder of this chapter. In BMA, obesity is viewed as the most important chronic disease to prevent because it impacts the development of the others so profoundly. If food crop genotypes and food combinations can be identified that assist individuals in preventing excess weight gain, then a significant amount of the world’s chronic disease burden would be relieved. A list of candidate crop-related weight prevention strategies is provided in Table 3. Crops clearly have the potential to reduce obesity and its consequences, including the risk for cardiovascular diseases, cancer, and type II diabetes, through weight regulation alone. However, other mechanisms are normally involved in the progression of chronic diseases. 2.2.2. The basis of biomarker-assisted screening for CDP: Mechanisms and markers One of the goals for the biomedical research community, in support of BMA, is to provide agronomists with a set of tools that can be used to screen crop genotypes and food combinations for CDP activity. To achieve this goal, chronic disease risk must be reduced to a simplified, representative set of biological features in animal and human pathophysiology that can be assessed using chemical analyses and that are causally associated with disease occurrence. The tools that result from this effort are referred to as biomarkers. The characteristics of useful biomarkers include (1) causal linkage to the initiation or progression of the disease process, (2) concentrations Table 3 Potential modes of action for food crops to reduce body weight and risk of obesity Mode of action
References
Reducing levels of digestible energy High fiber content that adds bulk to the diet and accelerates and sustains onset of satiety Reestablishing normal function of cellular energy sensors Altering gut microflora to promote negative energy balance Increasing nutrient density, maximizing the nutrient to energy ratio
Englyst and Englyst (2005) Burton-Freeman (2000)
Marshall (2006) Turnbaugh et al. (2006) Drewnowski (2005)
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change in response to changes in disease risk, (3) concentrations respond to changes in the foods consumed, (4) easily assessed using validated chemical assays, and (5) similarly affected in preclinical models for human diseases and in the human disease itself. Identifying a panel of biomarkers that have shared associations with the risk for cardiovascular disease, cancer, type II diabetes, and obesity is based on the common mechanisms that underlie the pathogenesis of these diseases (Biddinger and Kahn, 2006; Eyre et al., 2004; Holmes et al., 2008; Marshall, 2006). 2.2.3. Common mechanisms and biomarkers The goal of BMA is to reduce the risk of four seemingly unrelated chronic diseases. The relationships between these diseases have become better understood as data resulting from omics investigations has provided evidence of a common pathogenic basis for their occurrence (Fig. 2) (Holmes and Nicholson, 2007; Holmes et al., 2008; Li et al., 2008; Marshall, 2006; Martin et al., 2007). Specifically, cardiovascular disease, cancer, type II diabetes, and obesity are metabolic disorders with shared impairments in both cellular processes and metabolism, although each disease also retains unique characteristics. At the cellular level, the pathologies associated with each disease display alterations in cell proliferation, blood vessel formation, and cell death, that is, necrosis, apoptosis, and autophagy. Also common to these diseases are alterations in glucose metabolism, chronic inflammation, and cellular oxidation that is attributed to a common network of cell signaling events that are misregulated in each of these disease states (Marshall, 2006). In addition, emerging evidence indicates the modulation of gut microflora predisposes an individual to each of the disease processes (Li et al., 2008; Martin et al., 2007). Microflora appear to be able to exert effects through either biosynthesis of new compounds or chemical transformations of ingested ones, and as a consequence, influence exposure of the host to gut microflora-associated endotoxins (Li et al., 2008; Martin et al., 2007; Nicholson et al., 2008). Identification of a common set of biomarkers for the shared pathogenic elements among these diseases is a critical step in outlining a systematic approach to crop improvement. A biomarker is objectively measured and evaluated as an indicator of normal biological or pathogenic processes, or pharmacologic responses to a therapeutic intervention (Packard and Libby, 2008). The substance is usually measured in blood or urine. The biomarkers relevant to BMA are biological indicators of disease risk or disease presence (Table 4). The information detailed in the following paragraphs provides a brief overview of each biomarker class. Biomarker-assisted screening in animals on different diets can be used to guide both crop genotype selection and the identification of beneficial food combinations. Additionally, the same biomarkers can be monitored clinically to determine efficacy of plant foodbased interventions in human subjects. Therefore, biomarker-assisted food
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Figure 2 Factors involved in chronic disease development. The pathogenesis of cancer, cardiovascular disease (CVD), type II diabetes, and obesity have substantial cellular and molecular interrelationships. As illustrated, consumption of a nutrientpoor, energy-dense diet leads to altered glucose metabolism and insulin resistance. In addition, Gram-negative bacteria in the gut flourish and die, leaving cell wall components behind to enter the systemic circulation and result in a state of chronic inflammation. Glycation products are formed at increased rates due to elevated blood glucose and may also lead to elevated levels of circulating inflammatory factors. Inflammatory processes are one stimulus for increased production of free radical species in vivo. Production of reactive species leads to oxidation of cellular components and can result in DNA mutation and altered molecular function. In parallel, elevated levels of insulin stimulate release of growth factors like IGF-1. Along with increases in available glucose, this state of positive energy balance perturbs cellular energy sensing mechanisms. Dysfunction occurs as cells lose fine control of proliferation and death pathways. Obesity results from a large number of known and unknown factors that elevate disease risk. The cumulative and interdependent effects of many processes increase risk for the development of chronic diseases. The aim of BMA is to develop crop genotypes that have high chronic disease prevention (CDP) activity and high nutrient density. As outlined, this may significantly reduce risk for disease.
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Table 4 Biomarkers associated with chronic disease risk Biomarker class
Chemical analyses
Glucose metabolism
Fasting glucose Fasting insulin Hemoglobin A1c Insulin-like growth factor-1, total and free C-reactive protein Interleukin-6 Tumor necrosis factor-a 8-Hydroxy-2-deoxyguanosine 8-Isoprostane F2a Oxygen radical absorbance capacity Protein carbonyls Lipopolysaccharide, total and free
Inflammation
Oxidation
Endotoxemia
crop improvement has emerged as an essential component underlying the development of the BMA framework: (a) Glucose metabolism. Glucose is essential for life both as a primary source of energy and as a building block that directly or indirectly supports the biosynthetic processes required for growth and maintenance of cell function. Consequently the supply of glucose to cells within the body in mammalian species is tightly regulated. The pathogenic events that occur in cardiovascular disease, cancer, type II diabetes, and obesity involve alterations in glucose metabolism, a primary manifestation of which is insulin resistance (Misciagna et al., 2005; Zieman and Kass, 2004). Insulin resistance causes increased circulating levels of glucose, insulin, and insulin-like growth factors (Ezzat et al., 2008; Frasca et al., 2008; O’Connor et al., 2008). Elevated levels of circulating glucose also cause glycation of proteins, one of which is hemoglobin A1c (Giugliano et al., 2008; Misciagna et al., 2007). Collectively, elevated levels of fasting glucose and insulin and nonfasting levels of IGF-1 and hemoglobin A1c are biomarkers for alterations in glucose metabolism associated with chronic disease risk (Pirola et al., 2003; Soldatos et al., 2005; Stumvoll et al., 2005). (b) Inflammation. Inflammation is the body’s basic response to infection and injury, but when the signals that regulate this process are continuously activated, a chronic inflammatory process results. Chronic inflammation stimulates processes which underlie chronic disease development, such as the increased oxidation of cellular macromolecules as reviewed elsewhere (Schwartsburd, 2003). Obesity and insulin resistance are also associated with chronic inflammation (Schenk et al., 2008). While the
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mechanisms are not completely elucidated, one hypothesis to explain the association of insulin resistance with inflammation is that chronic hyperglycemia and insulin resistance lead to advanced glycation end products that are able to bind to cell surface receptors that stimulate the production of inflammatory cytokines (Lopez-Garcia et al., 2004). Three circulating factors that are indicative of ongoing inflammation are interleukin-6, C-reactive protein, and TNFa; they serve as biomarkers for chronic disease risk (Casas et al., 2008; Coussens and Werb, 2002). (c) Oxidative damage. Oxidative stress is caused by an imbalance between the production of reactive oxygen species and a biological system’s ability to readily detoxify the reactive intermediates or easily repair the resulting damage. All forms of life maintain a reducing environment within their cells which is largely preserved by enzymes and endogenous reducing agents. Reactive oxygen species can be beneficial, as they are used by the immune system as a way to attack and kill pathogens, as well as participate in cell signaling (Martindale and Holbrook, 2002). However, disturbances in the normal redox state can cause toxic effects through the production of peroxides and free radicals such as hydroxyl (OH) and superoxide (O2 ) radicals, hydrogen peroxide (H2O2), and singlet oxygen (1O2) (Breimer, 1990; Cerutti, 1985; Halliwell and Gutteridge, 1999), particularly in response to inflammation (Coussens and Werb, 2002; Schenk et al., 2008). In humans, oxidative stress is involved in the pathogenesis of a number of disorders including cardiovascular disease, cancer, type II diabetes, and obesity (Collins, 1998; Cooper et al., 2007; Schleicher and Friess, 2007; Vincent et al., 2007). Biomarkers of oxidative damage include 8-oxodG, a marker of DNA damage (Cadet et al., 2002; Cooke et al., 2006), 8-isoprostane F2a, a marker of lipid peroxidation (Milne et al., 2007), and protein carbonyls, a marker of protein oxidation (Hwang and Kim, 2007; Shacter, 2000). (d) Gut microflora. The human intestinal tract houses an ‘‘extended genome’’ (Kinross et al., 2008), the microbiome. A complex symbiosis influences human host metabolism, physiology, and gene expression (Nicholson et al., 2008). Advances in microbiological analysis and systems biology are now beginning to implicate the gut microbiome in the etiology of cardiovascular disease, cancer, type II diabetes, and obesity, in part by a process referred to as chronic (or metabolic) endotoxemia (Cani et al., 2007). Endotoxemia is due to the absorption of lipopolysaccharide (LPS) from the gut (Cani et al., 2008). This endotoxin is continuously produced in the gut from cell wall components of Gram-negative bacteria. Production and absorption of LPS can be modulated via dietary effects on gut microflora composition and inflammatory responses to LPS are influenced by obesity and insulin resistance (Ley et al., 2006). The principal biomarkers for endotoxemia are circulating levels of total and free LPS (Cani and Delzenne, 2007).
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In summary, for reasons not fully understood, chronic positive energy balance and excess accumulation of body fat result in systemic changes in the body such that the factors associated with physiological regulation of glucose metabolism become dysfunctional; this is referred to as altered glucose metabolism. As a component of altered glucose metabolism and fat accumulation, the concentration of proinflammatory molecules in circulation and in various organ sites begin to accumulate despite the lack of an external inflammatory stimulus; this is referred to as chronic inflammation. Elevated levels of reactive oxygen and nitrogen species can result, leading to the oxidative damage of cellular macromolecules. A potential modulator of the inflammatory condition is the presence or absence of populations of microflora in the gut that can lead to increased systemic exposure to microbial cell wall components that promote inflammation and cellular oxidation in the body, a condition referred to as chronic endotoxemia. These four conditions—altered glucose metabolism, chronic inflammation, increased cellular oxidation, and chronic endotoxemia—are widely regarded as playing causal roles in the initiation and progression of obesity, cardiovascular diseases, cancer, and type II diabetes (Grundy et al., 2002). 2.2.4. Strategy for implementing biomarker-assisted crop improvement for CDP Biomarker-assisted crop improvement is envisioned as a dynamic process that will evolve as the field of BMA matures. At the outset, three stages of biomarker evaluation and use are envisioned. In the short term, one or more biomarkers for each altered function associated with a disease condition should be evaluated (Table 4). In the second phase, development of which is ongoing, profiles of mammalian metabolites determined by highthroughput LC or GC mass spectrometry platforms will be used for determining chronic disease risk (Nicholson et al., 2008). These profiles will (1) have greater sensitivity to detect changes in disease risk; (2) have the advantage of requiring a small sample of blood, urine, or tissue for analysis; and (3) be available at a cost affordable for use in crop screening in animals as well as for monitoring human populations. A third phase of biomarkerassisted assessment is envisioned to include the use of genomics technologies to categorize, more specifically, the nature of genetic predisposition of an individual to chronic disease using functional polymorphisms (Ambrosone et al., 1999; Kornman et al., 2004). The end goal is to determine crop genotypes and food combinations that reduce chronic disease risk via understanding how they affect pathogenic mechanisms involved in each disease. Crop genetic and chemical diversity, as discussed in the next section, are critical to understanding how to reduce disease risk by inhibiting cellular and molecular mechanisms underlying chronic disease processes.
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3. Agricultural Landscape 3.1. Genotypic diversity in crops Progress in food crop improvement is driven by screening genotypes for desired agronomic traits (Lenihan et al., 2004; Lindsay et al., 2004; Wu et al., 2005). Worldwide, the total number of genotypes for a given food crop is quite large, probably in excess of 1000 genotypes per species (Food and Agriculture Organization, 1998). For some crops, core collections have been or are being assembled from which to draw germplasm for evaluation for traits of agronomic interest (Fowler and Hodgkin, 2004); rice is a model crop in this regard (Londo et al., 2006; McNally et al., 2006; Monna et al., 2006). However, there have been no systematic efforts to tap global crop diversity for CDP. One only has to review the accomplishments of natural products discovery programs to recognize the potential of this approach (Guilford and Pezzuto, 2008; Kinghorn et al., 2004; Park and Pezzuto, 2002). Crops have undergone a long process of selection for agronomic traits during domestication and genetic improvement for commercial use (Stuber and Hancock, 2008). The scientific basis for proposing to screen diverse food crop germplasm for CDP activity derives from the bottleneck theory of domestication that estimates as much as 95% of the variation in germplasm for many traits was lost during that process (Ahn and Tanksley, 1993; Frary et al., 2000; Gepts and Hancock, 2006; Gepts and Papa, 2003; Lippman and Tanksley, 2001; Ross-Ibarra et al., 2007). Thus, one could hypothesize that significant variation will be identified among genotypes within a crop for CDP if sufficiently diverse germplasm resources are evaluated. On the other hand, if only highly related commercial genotypes are screened for CDP, it is likely that the genotypes tested will be found to have limited variation for CDP. The prevailing practices for dietary assessment in the biomedical sciences assume that different genotypes within a food crop will have similar effects on CDP (discussed in Section 1). Therefore, little has been done to engage agronomists to challenge this assumption and take advantage of plant biodiversity (Keil, 2008). In view of this, a critical step in successful implementation of BMA is that a diverse collection of crop genotypes be evaluated; failure to do so will limit the potential of crop-based research for CDP.
3.2. Chemical basis for CDP The basis for CDP by a food crop is hypothesized to be through the crop’s normalization of glucose metabolism and cellular oxidation and its inhibition of chronic inflammation and endotoxemia (Section 2). While dysregulation of a common cell signaling network has been reported to underlie
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these metabolic alterations (Biddinger and Kahn, 2006; Marshall, 2006; Schenk et al., 2008), that network has multiple regulatory inputs suggesting that a single agent ‘‘magic bullet’’ is not likely to have the same impact on CDP as multiple chemicals that target an array of relevant molecular targets (Duthie et al., 2003; Karamouzis and Papavassiliou, 2004). This argument is supported by the success of multiagent drug-based approaches to the management of chronic diseases like cardiovascular disease, cancer, and diabetes where magic bullets alone failed but were found to be more therapeutically effective when combined (Liby et al., 2007; Sporn, 2006). Since BMA is based on food serving as the delivery vehicle for a milieu of healthpromoting chemicals, the goal is to identify a specific pattern of biosynthetic activity associated with CDP for each crop investigated. The approach that affords the richest opportunity to discover traits for CDP is unbiased chemical profiling of a diverse population of crop genotypes. As a starting point in this process, it is necessary to establish an initial selection of genotypes of a crop for evaluation, whether that material has been organized based on knowledge of the crops center(s) of domestication, its genetic lineage, or other strategies that agronomists use to categorize a crop’s available germplasm resources. From this selection, the chemical diversity of the collection must be determined.
3.3. Assembling a test collection of a crop’s genotypes The objective of the process described below is to obtain a limited number of chemically diverse crop genotypes for initial screening in animal models for human disease as illustrated in Fig. 3. By evaluating genotypes that are the most chemically divergent, there will be a higher probability of identifying chemical profiles that have large differences in CDP activity. The following steps are recommended. 3.3.1. Collecting the crop genotypes The ideal situation is to obtain genotypes from a core collection in which one or more techniques have already been used to establish how the available genotypes relate to one another. If a core collection is not available, then an effort should be made to obtain genotypes from distinct geographical locations from around the globe and to include wild relatives for the crop (Gepts and Papa, 2003; Keil, 2008). Chemical profiling can be performed on as little as 10 g of material. 3.3.2. Deciding on method of preparation and extraction Most food crops are consumed in more than one way. There is no correct preparation/processing method, but there should be a strong rationale for method selection. The method must be uniformly and reproducibly applied to all samples of the crop that are evaluated. The reason for proposing to
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Figure 3 Crop selection. This figure is a depiction of the process proposed in Section 3 for identifying crop genotypes that differ in genetic and chemical composition. The illustration is based on actual data on crop genotypes. Panel A illustrates genetic relationships among 19 genotypes from a model food crop; genotypes are noted as C1–C19. Panel B shows a subset of the crop genotypes shown in panel A that were selected for metabolite analysis because they were in different genetically defined clusters. The panel B crop genotypes were subjected to LC/MS-based chemical profiling. Note that crop genotypes cluster based on chemical similarities; these clusters
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evaluate the crop as it is processed for human consumption is that once an effective genotype is identified, there is more relevance in determining how it works in the form prepared for human consumption. From there, one can work backwards to determine if there are additional means of further enhancing activity in all the steps involved in getting the crop from the field to the table. There are a large number of published methods on food crop extraction (Robbins, 2003; Wu et al., 2004a); there is no single correct method. However, the method must be well justified, practical, give uniform extraction efficiencies, and be appropriate for unbiased chemical profiling using a LC/MS or GS/MS analysis platform. Because unbiased chemical profiling is desired, the analytical approach must be comprehensive, assessing the 14 classes into which plant secondary metabolites are categorized (Table 5). We recommend consideration of the modified Bligh–Dyer extraction technique (Fischer and Sana, 2007).
Table 5 Major classes of plant secondary metabolites
Alkaloids Triterpenes, Saponins, Steroids Sesquiterpenes Diterpenes Flavonoids Polyacetylenes Phenylpropanes Monoterpenes Polyketides Nonprotein amino acids Tetraterpenes Cyanogenic glycosides Glucosinolates Amines Source: Wink (2003).
were determined by principal components analysis (PCA). Based on genetic and chemical analysis, two of the three circled crop metabotypes, C1 and C10, would be evaluated in an animal model for assessing effects of the crop on chronic disease risk biomarkers because both were genetically and chemically distinct. The C16 genotype from the group 3 genetic cluster in panel A was distinct from C18 but further chemical analysis, shown in panel B, revealed C16 was not chemically distinct from C18. Therefore the C16 genotype may not be as ideal for initial evaluation in animals. (This is an original figure using our own data)
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3.3.3. Analysis Because many analytical options are available, we strongly suggest the use of a core facility that routinely profiles plant extracts. This enables working with an analytical team that uses a standardized analysis platform, which can better guarantee elution time and mass consistency. This then allows one to use a library of compounds, validated for the standardized platform, to obtain chemical compound identities from chromatographic features (Dixon et al., 2006). Unsupervised multivariate analysis techniques are used to determine the degree of relatedness of various genotypes. Multiple workflows are available for this purpose depending on the instrumentation used to generate chromatographic data and the bioinformatics support that is available. General steps include subjecting the chromatographic feature data to normalization, filtration for the presence of multiple ions, calculating the magnitude of fold differences and/or statistical significance of fold difference, and subsequent principal components analysis. If there is little or no separation among the genotypes selected for analysis for chemical features (profiles), the process of genotype selection should be repeated using more diverse germplasm with likely greater variation in presumptive chemical composition. If the decision is made to move the work to the next step, then a well-justified process needs to be used by which to select one or a few genotypes from each grouping of genotypes for further analysis in animals. The ultimate goal of this process is identification of biosynthetic profiles that have CDP activity in preclinical models and the use of validated profiles to (1) screen additional selections for greater CDP activity; (2) screen other crops for CDP activity; and (3) evaluate food combinations for complementary, additive, and/or synergistic CDP activity. While there is no direct way to screen germplasm for CDP activity at this time, the approach described here has the potential to evolve into a high-throughput methodology, the principles for which are reviewed and discussed elsewhere (Buxser and Chapman, 2007; Gagarin et al., 2006; Kevorkov and Makarenkov, 2005; Walters and Namchuk, 2003).
3.4. Extending chemical profiling to food crop combinations The occurrence of crop genetic diversity is suggestive of the presence of different patterns of chemical biosynthesis within plants. For many years, there has been interest in the potential value of chemical profiling of plants to assist with plant taxonomy, a field referred to as chemotaxonomy. Wink (2003, 2008) has provided excellent reviews of plant chemical diversity and taxonomy. From such work has emerged an understanding that plants in different botanical families can have more divergent profiles of chemical constituents than those within a family. Further, plants within a botanical family tend to utilize specific classes of chemicals for defense against diseases,
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pests, and other plants as solutions to support their survival. Plants produce an array of antimicrobial chemicals and pesticides as well as chemicals that inhibit the intrusion of other plants into their ecological niche (Bais et al., 2003; Weir et al., 2004). The chemicals that mediate these responses undoubtedly modulate the activity of cell signaling pathways in targeted organisms; interestingly, many of these signaling pathways are conserved in mammalian species (Prithiviraj et al., 2005). Consequently, one can reasonably hypothesize that plant chemicals have the potential to impact human disease mechanisms that share molecular pathways in common with naturally occurring molecular targets for phytochemicals, that is, plant chemicals which offer plant-related protection against microbes, pests, and other plants may also have activity against chronic diseases in animals (Prithiviraj et al., 2005). As such, an understanding of plant chemical diversity and the plant’s natural molecular targets may be leveraged in a systematic manner to provide to the consumer an entire array of diet-derived plant chemicals with proven bioactivity in nature. Combinations of foods that offer complementary chemical profiles may increase the capacity to protect against a spectrum of chronic diseases. Thus, we recommend investigating different botanical families to achieve BMA goals, and in so doing, identify and implement new recommendations for dietary patterns (food combinations) for CDP.
3.5. Other considerations 3.5.1. Genetic modification of crops Developments in producing genetically modified organisms (GMOs), where introduction of specific genes into plants is done to achieve new functionality, have dramatically increased the opportunities to improve crops (Chassy et al., 2005; Food and Agriculture Organization, 2008). There are a host of issues that have been raised about the potential impact of GMO and those questions and concerns are embodied in a number of standards for GMO introduction into a given environment and country (Chassy et al., 2005; Food and Agriculture Organization, 2008). When GMOs are food crops, consumers, in particular, have voiced concerns about potential health impact. Regardless of the science supporting or refuting these concerns, there are other reasons to pursue traditional breeding approaches within BMA at this time (1) single gene, single chemical solutions are not likely to be efficacious for CDP; (2) introduction of one gene is likely to alter the activity of other genes, leading to changes in patterns of biosynthetic activity associated with CDP, both positive and negative; (3) benefits from a GMO crop may be lost when combined with other foods in a typical diet, hence negating the effort that went into producing the GMO crop; and (4) altered agronomic characteristics of the GMO may diminish the likelihood that it will be widely grown. These issues have many parallels to those encountered in the field of biofortification (Nestel et al., 2006).
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3.5.2. Environmental effects In agronomy, there is great attention placed on the impact of the crop environment on trait expression (Dingkuhn et al., 2005; Hammer et al., 2005). However, the goal in BMA should be to identify crop genotypes in which the crop traits associated with CDP are stable despite yearly shifts in growing environment and climate. If too much variation in CDP activity occurs due to the environment, then that crop genotype or trait is less attractive for use. Acceptable crop CDP genotypes/traits will exhibit robust biological effects that ensure crops that go to the market or are used in clinical studies are not prone to fluctuation in CDP activity. This further underscores a central principle in BMA: a combination of chemicals, not a single chemical, will have the most CDP activity.
4. Evaluating Crops As we have discussed, BMA’s distinguishing features are that (1) it is a food-based approach utilizing the chemical diversity present in a crop’s germplasm resources to identify crop genotypes and food combinations with maximal activity for CDP; (2) it targets pathogenic mechanisms common to four chronic diseases, cardiovascular disease, cancer, type II diabetes, and obesity; and (3) it identifies a chemical profile that is associated with CDP and is effective because an array of ingested, absorbed, and metabolized phytochemicals regulate multiple components of a cell signaling network that is dysregulated during the development of these chronic diseases. To fulfill the above set of objectives, a systematic approach to food improvement must be followed (Finley, 2005), requiring evaluation in animals and in human subjects. The number of reported studies which take food-based approaches to the clinic is limited, but fortunately, the few examples, involving evaluation of berries and cruciferous vegetables, demonstrate the appropriate use of animal and clinical components (Cornblatt et al., 2007; Shapiro et al., 2006; Stoner et al., 2007, 2008). Following selection of crop genotypes, BMA proceeds via a tiered, three-stage approach on route to clinical evaluation. The first stage involves assessment of a crop or food combination on biomarker activity in an animal model; the second stage tests effective crop genotypes or food combinations on disease endpoints in one or more appropriate animal models; and the third stage evaluates crop genotypes or food combinations that decrease disease-related endpoints in human participants. Experimental approaches that use cell culture or microplate-based assays of crop extracts complement the animal-based experiments in an effort to confirm candidate mechanisms. Key elements involved in each approach are outlined below.
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4.1. Animal-based approaches 4.1.1. Rapid biomarker assessment Based on the rationale presented in Section 2, an animal model for obesity is used because obese mice demonstrate altered glucose metabolism, chronic inflammation, increased cellular oxidation, and endotoxemia (Buchner et al., 2008; Dumas et al., 2006). Normalization of one or more biomarker categories by a crop genotype or combination of crops in the rapid assessment animal model would be indicative of CDP activity that could be further assessed in an animal model for a specific chronic disease, as described later. Rapid assessment has two notable features. First, the crop is prepared and processed as decided and is freeze dried and milled into a fine powder. The crop powder is then incorporated into the animal diet that contains standard purified components. This ensures animals obtain all essential nutrients and eat in a normal fashion while ingesting the crop. Also, normal metabolism of food chemicals and matrix occurs in the gut by microflora and processes of digestion, absorption, and first-pass metabolism in the liver are carried out in the presence of proteins, carbohydrates, and lipids. Absorbed chemicals are carried by the portal vein to the liver where they are metabolized by phase I and II detoxification systems before being released into general circulation (Prochaska et al., 1992). Second, the mice on control diets establish a biomarker profile of risk due to intake of the high-fat, high-sucrose control diet (Shin et al., 2004; Surwit et al., 1988). The mice fed crop-containing diets, which have the crop substituted into the same control diet, can then be monitored for a reduced biomarker risk profile compared to the control animals. This approach provides a degree of sensitivity across four classes of biomarkers and has clinical relevance. Since mice consume the diet on a daily basis, the risk profile can be assessed after a short period of time. Thus, biologically relevant data are produced with the use of a small amount of crop material for limited cost and investment of time. Disadvantages of this approach include the level of expertise required to do the work, the requirement for an animal facility, and the fact that actual reduction in disease prevalence in a specific animal disease model is not assured. This approach can be further modified so that the rodents consume human diets (McDaniel et al., 2007) as opposed to purified diets with added crop components. The ability to evaluate human menus and recipes as eaten provides a framework for systematically evaluating crop genotypes and food combinations in a manner directly relevant to human populations. Though this additional step would bring more relevance to the animal work, a general caveat with animal studies must be given. Rodent physiology differs from human physiology in certain aspects of digestion, absorption, and xenobiotic metabolism (Lewis et al., 1998). This is why rodent models cannot completely eliminate the need for evaluation in human subjects. However, the use of these rodent systems can assist in developing focused clinical protocols which are most likely to impact human health.
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4.1.2. Assessment in preclinical models for disease If a crop, incorporated into an animal diet, influences the occurrence of a specific chronic disease, this evidence undoubtedly has a high degree of relevance in assessing potential efficacy in the human population. However, well-characterized animal models of human disease must be chosen, and experiments must be designed and executed to standards in the field. For obesity, type II diabetes, and cardiovascular diseases, there are many available models (Russell and Proctor, 2006). Animal models for cancer tend to be more complicated as there are at least 40 types (American Cancer Society, 2008). In general, there are appropriate animal models with which to study each disease; however, results for one cancer site are frequently not directly translatable to other cancer sites. Therefore, care must be taken to develop a strong rationale for the cancer site and animal model chosen for investigation. This process should take into account the characteristics of the crop being investigated and epidemiological evidence relative to the effects of that crop on patterns of cancer occurrence in human populations. Key considerations in designing a robust experiment include (1) selecting an appropriate animal model to evaluate the hypothesis; (2) selecting an appropriate diet formulation into which a crop will be incorporated; (3) procuring materials to formulate the diets or identifying a vendor who custom formulates diets; (4) writing a detailed experimental protocol for use of animals with a well-justified design and powers calculations; (5) devising a plan for general animal care including selection of caging type, frequency of cage changing, and decisions on the frequency of data collection, for example, food intake and/or body weights; (6) duration of the experiment, methods for euthanasia, and sample collection; (7) methods for evaluation of animal tissues; and (8) plan for data analysis and interpretation. Before conducting an experiment, the research protocol must be reviewed and approved by the institution’s animal care and use committee. The primary disadvantages of this approach include the amount of crop material required, which is greater than for the rapid assessment, the number of animals required per treatment group for adequate statistical power, the length of the experiment required to assess disease endpoints, the cost of an experiment, and the technical expertise and physical resources required to do such work.
4.2. Nonanimal approaches 4.2.1. Cell culture methods Results from studies conducted in animal models will likely generate hypotheses about candidate mechanisms of action. Further evaluation of potential mechanisms using an independent line of investigation is advisable.
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Cell culture models are ideal in this regard since the targeted chronic diseases have as a component of their pathogenesis, alterations in cell proliferation, blood vessel formation, and cell death, that is, necrosis, apoptosis, and autophagy. All three of these cellular processes can be investigated using human or rodent cells grown in liquid or gel-based cell culture systems. Cell culture models distinguish themselves in many ways from other approaches (1) they permit detailed investigations of processes fundamentally altered during development of the chronic diseases; (2) they can be performed using rodent and/or human cells thus allowing inferences about what might be observed in animal or human subjects; and (3) they lend themselves to high-throughput platforms which have been effectively used in drug discovery programs for natural products evaluation and bioactivity guided fractionation (Luesch, 2006; Vermeir et al., 2005). Generally, an extract of a crop is prepared, the solvent in which the chemical components were extracted is removed, and the material is redissolved in a solvent that does not itself affect the cultured cells. Parameters that are usually varied are treatment dose and length of exposure. Exposure duration varies from hours to days depending on the characteristics of the cells being investigated. Culture media to which the crop extract is added should be changed daily because of potential instability of compounds in the extract. Endpoints measured are diverse and depend on disease characteristics being investigated. Methods for growing and maintaining rodent and human cells in monolayer or three-dimensional culture are well established and are the topic of many courses and textbooks; general approaches are reviewed elsewhere (Masters and Stacey, 2007). The cell lines which are available through commercial sources are generally well characterized, inexpensive, and pathogen-free. As with the animal experiments, cell-based experiments must be designed and conducted using standardized procedures that meet the norms for mammalian cell culture, including adequate replication, quantification of exposure to crop extracts, and the use of appropriate study endpoints. Unfortunately, even when methodology is correct, the results of cell culture must be interrogated with care. Crop extracts, rather than the whole crop, are evaluated in cell culture. Therefore there is no metabolism of crop components by gut microflora, intestinal cells, or the liver before target cell exposure. In vivo, cells may be exposed to very low concentrations of metabolized chemicals, whereas in cell culture, extract dosing can deliver much higher concentrations. Lastly, cells grown in monolayer culture do not behave as they do in living organisms where growth occurs in a threedimensional matrix under the influence of a heterogeneous population of neighboring cells and growth factors. Though cell culture has been successfully used for drug development, this does not guarantee its effectiveness for the applications described in BMA.
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4.2.2. In vitro assessment Of the metabolic defects mentioned in Section 2, that is, altered glucose metabolism, chronic inflammation, cellular oxidation, and endotoxemia, cellular oxidation has been the focus of many labs investigating the health benefits of crops. The most commonly assessed functional characteristic of a crop is its antioxidant activity, that is, its ability to scavenge free radicals. Antioxidant activity assays generally use a 96-well microplate format to evaluate (1) a chemical extract of a crop, (2) a tissue extract from an animal that has eaten the crop, or (3) animal tissue exposed to the crop extract ex vivo (Lotito and Frei, 2004; Wu et al., 2004a,b). As reviewed by Huang and Prior, standard antioxidant capacity assays include ABTS, FRAP, TRAP, ORAC, and others (Huang et al., 2005). In addition to measuring radical scavenging with antioxidant capacity assays, measures of free radical damage can also be assessed. As discussed in Section 2, measures of oxidative damage are (1) 8-oxodG, (2) 8-isoprostane F2a (8-EPG), and (3) protein carbonyls. Markers like urinary 8-oxodG, 8-EPG, and protein carbonyls can serve as overall ‘‘whole-body indexes’’ of the oxidation of DNA, lipid, and protein, respectively (Haegele et al., 2000). Approaches aimed at measuring antioxidants and measures of oxidative damage may serve as general indicators or be highly specific to a particular biochemical process. This level of specificity often bears greatly on analytical throughput, cost, and technical expertise. Additionally, specific disease outcomes in animal or human studies may be better predicted with different in vitro assessments (DeFlora et al., 1996).
4.3. Evaluation of crop genotypes and food combinations in human participants So far, a process has been described for identifying genotypes within a crop and crop combinations that impact specific chronic disease processes as determined in appropriately designed laboratory experiments using model systems. However, this alone is not sufficient to conclude a certain trait will actually impact a human chronic disease. The food crop genotypes, as well as the combinations of food crops hypothesized to have CDP activity, must also be evaluated in groups of human subjects to demonstrate efficacy. The key elements of the clinical approach are direct extensions of the approaches already discussed regarding preclinical animal models. They include a structured, fully defined food-based dietary plan, a set of disease-related endpoints (biomarkers) that are predictive of chronic disease risk, testing of a crop or crop combination in a human population with an abnormal biomarker profile, and an experimental design that lends itself to rapid evaluation with a reasonably small sample size. The rationale for each element of this translational paradigm is briefly presented.
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4.3.1. A structured, fully defined food-based dietary plan The use of a food-based, versus nutrient-based, dietary plan is consistent with the platform that foods, not nutrients, should be used to structure dietary patterns for CDP ( Jacobs and Tapsell, 2007) and parallels the use of a defined dietary formulation in the animal experiments. The meal plan developed for human participants is recipe and menu defined and meets all current dietary guidelines, that is, the USDA food guide pyramid, the dietary guidelines for Americans, and the recommended dietary intakes, all of which can be viewed on the USDA dietary guidelines website (USDA, 2008). However, the plan, which is divided into breakfast, lunch, dinner, and snack modules of defined caloric content, is structured using the botanical families to create crop-specific modules of defined botanical diversity, an approach that we have used in previous clinical investigations (Thompson et al., 1999, 2005a,b, 2006). For a specific study, the dietary plan that participants follow can be selected so that either genotypes of a specific crop or combinations of food crops could be evaluated. An advantage of this approach is that it provides a means by which to disseminate tested, credentialed diets to the consumer after evaluation in the clinic. Credentialing offers value to various members of both the agricultural and biomedical communities and would benefit consumers as a dietary plan that could be prepared at home or provided as prepared meals in a variety of settings. Credentialing would certify genotypes of a crop that offer demonstrated CDP activity, as well as certify dietary patterns. The anticipated outcome of this effort is the creation of a set of dietary menus and recipes formulated using specific crop genotypes that form a set of breakfast, lunch, dinner, and snack modules (menus) to be used interchangeably in a structured manner to reduce chronic disease risk. 4.3.2. Biomarkers and at-risk populations The rationale underlying the use of biomarkers to monitor disease risk is presented in detail in Section 2. For a crop genotype or food crop combination that is advanced for clinical evaluation, the same biomarker panel shown to be affected in the animal experiments described Section 4.1 would be used in the human intervention study (Table 4). This approach has also been informed by clinical experience, as reported elsewhere (Thompson et al., 1999, 2005a,b, 2006). Assessment of the effects of dietary interventions is likely to be most effective when the individuals assessed have an altered biomarker profile that is associated with increased disease risk. Inclusion criteria for participation in the clinical study should include a predetermined disease risk profile. Because of the high prevalence of overweight and obesity, the majority of adult individuals are likely to have elevated disease risk profiles. An effective approach for recruiting participants is to offer low-cost screening
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for established disease risk markers, which could typically be done at employer or community sponsored health fairs. Individuals identified as at risk can be given the opportunity to participate in a dietary intervention intended to reduce risk. 4.3.3. Clinical trials and follow-up Though many epidemiological studies follow participants for extended periods of time, this need not be the case (Thompson et al., 1999, 2005a,b). Stable responses to a dietary intervention can occur within 2 weeks of diet initiation and others have even reported that a 1-day exposure can be sufficient to obtain the needed information (Loft et al., 1992). Experiments of short duration are desirable for a number of reasons ranging from compliance, cost, and feasibility, to the need for rapid screening and turnaround. However, even if the experiments can be of short duration and with a small number of human participants per treatment condition (N ¼ 10–20), the time and effort required to conduct such a study is not trivial. For this reason alone, a tiered approach to food crop evaluation is vital, with the first real step toward the clinic being evaluation of crop genotypes and crop combinations in appropriate preclinical animal models. Follow-up involves dissemination of results to the agronomist so plant breeding programs can be properly directed. We anticipate multiple genotypes of a given crop will be needed for full coverage of the CDP spectrum. To benefit from these discoveries in the short term, agronomists would identify environments in which the various genotypes of a crop could be grown and in working with other disciplines within BMA, develop feasible approaches by which to provide crop genotype medleys for human consumption. In the intermediate term, being the typical timeframe needed to breed, select, and introduce a new cultivar, plant breeders would use breeding and selection to introduce the desired traits into commercially important genotypes for the various global markets in which that crop is consumed. In the long term, plant breeders would combine biofortified genotypes with CDP-enhanced genotypes to produce crops for health while continuing to ensure that genotypes remain agronomically viable.
5. Biomedical Agriculture in Practice: A Developing Program in Crop Improvement 5.1. Crops for HealthTM The BMA initiative has emerged from a program of training, discovery, and dissemination at Colorado State University referred to as Crops for HealthTM (http://www.cropsforhealth.colostate.edu). The program’s objectives are (1) identification, development, and production of food crop genotypes
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that show maximum potential to benefit human health while retaining adapted traits that make them profitable to grow and distribute in the global market place; (2) development of tools and techniques within the biomedical and allied health sciences that are required to understand how these food crops prevent chronic diseases; and (3) dissemination of this knowledge to the global community using innovative translational public health approaches designed to effectively promote long-term lifestyle changes. The Crops for HealthTM program was initiated in 2003 in response to what we identified at that time as the plant food–cancer risk conundrum.
5.2. The plant food–cancer risk conundrum Although population studies have identified plant food intake as a potential modulator of chronic disease risk, evidence supporting a beneficial role of vegetable and fruit consumption against cancer is conflicting. While the original report of the WCRF/AICR (Potter, 1997) indicated that increased consumption of vegetables and fruit was likely to be protective against cancer at many organ sites, a large prospective study, the European Prospective Investigation into Cancer and Nutrition (EPIC), found no association between fruit and vegetable intake and the occurrence of certain types of cancer (van Gils et al., 2005). A recent evidence-based exhaustive review of the literature on this topic has drawn a similar conclusion (WCRF/ AICR, 2007). This situation constitutes a conundrum since food crops have been well documented to contain a large number of phytochemicals with potential cancer inhibitory activity, and the synergistic combination of these chemicals when consumed as foods has been hypothesized to enhance bioactivity and potential efficacy (Liu, 2004; Potter, 1997). The findings of the epidemiological studies continue to be debated within that scientific community because of recognized problems inherent in measurement of food intake using food frequency questionnaires (Willett and Hu, 2007). While this debate should occur, there are other considerations that have been given minimal attention and that may have a substantial impact on future public health recommendations related to food. As we have reflected on this conundrum, four possible explanations have emerged. They are (1) the epidemiological data indicate that plant food offers little protection against cancer; (2) the epidemiological data indicate that with our current understanding of plant foods, as reflected in the ways we assess plant food intake, protection against cancer is not apparent; (3) the epidemiological data indicate that the plant foods (crop genotypes) to which the consumer has access in the market place are not those with maximum potential for human health benefit; or (4) the epidemiological data indicate that the way populations currently eat plant foods, protection against cancer is not detected. When one looks at these issues, there is recognition that the
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understanding of plant foods in the biomedical community is still quite crude and that this is reflected in terminology (refer to Section 2) and food intake measurements (discussed in Section 1). To begin to interrogate these candidate explanations, it was decided to focus on two issues (1) determine if crop genotypes differ markedly in protective activity against cancer and (2) determine if human eating patterns lack sufficient botanical diversity to promote health and/or prevent disease.
5.3. Biomedical agriculture: A transdisciplinary effort BMA is the outgrowth of our efforts to address two questions (1) do crop genotypes differ markedly in protective activity against cancer? and (2) do human eating patterns lack sufficient botanical diversity to promote health/ prevent disease? The need to address these questions emerged from clinical work performed by one of the authors between 1998 and 2002 (Thompson, 2004; Thompson et al., 1999, 2005a,b, 2006). Approximately 450 women participated in studies designed to discover the effects of plant food-rich diets on biomarkers for cancer risk. In that work, women in the experimental groups generally consumed an average of 12.4 servings per day of vegetables and fruit, and the effects of different diets on markers for oxidative cellular damage were determined. Diets were formulated that varied in the botanical families from which these vegetables and fruits were selected. While statistically significant reductions in levels of oxidative cellular markers were observed (typically 15–20% reductions), the results were surprising in that greater reductions were not detected among individuals consuming 12.4 versus 3.4 servings per day. To investigate this further, discussions commenced with plant scientists at Colorado State University and led to the formation of several hypotheses. One of these was that the most health beneficial genotypes of commonly consumed plant foods are currently not available to the consumer, in part because plant breeders and biomedical scientists have not had the opportunity to work together and establish human health-related characteristics for which plant breeders can select. These discussions ultimately led to Thompson’s laboratory moving to Colorado State University in January 2003. Upon arrival, the Cancer Prevention laboratory, housed within the Department of Horticulture and Landscape Architecture, initiated collaborative research with the laboratory of Mark Brick (Department of Soil and Crop Sciences), a plant breeder with a primary interest in dry beans. Because of progress in investigating the health benefits of dry beans, the physical proximity of the Cancer Prevention Laboratory to other agronomists, and impromptu discussions, collaborations expanded into an organized, systematic effort to discover the human health benefits of most staple food crops. The effort involves teams of investigators who work with wheat, rice, potatoes, corn, and apples. The Crops for Health program also participates in the activities of
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the university’s School of Public Health. Taken together, these events compelled us to define a setting for biomedical and agricultural research of plant foods to take place, hence the name ‘‘biomedical agriculture.’’
5.4. The vanguard project: Determining the health benefits of dry beans 5.4.1. Overview Dry bean (Phaseolus vulgaris L.), a member of the botanical family Fabaceae, is an important staple food. It was selected for the initial work not only because of the limited understanding of its BAFC, but also because it is a model crop for addressing questions about the differential health benefits of crop genotypes. The genetic ancestry of dry beans is well characterized (Gepts, 1998; Kami et al., 1995) and a wealth of variability for seed characteristics exists among different dry bean market classes (seed types). Market classes are collections of bean genotypes from within gene pools that share similar seed size, shape, color, and texture. Thus, market classes are representative of different gene pools that were established in distinct geographic regions of the world thousands of years ago during dry bean domestication (Kami et al., 1995). In our experiments, the primary hypothesis tested was that dry bean genotypes representing six market classes would not have comparable cancer preventive activity; a secondary goal of that work was to determine if typically used assays for antioxidant activity would have value in predicting cancer preventive activity. Using a preclinical model for breast cancer, we showed that significant genetic diversity for anticancer activity was associated with center of domestication rather than measures of antioxidant activity or total content of phenolic compounds (Thompson et al., 2009). 5.4.2. Experimental approach One of the concerns about food-based research is whether results will be reproducible in crops obtained from different locations. Our approach was to represent a broad range of variation in the materials that were evaluated. To do this, we worked in cooperation with Archer Daniels Midland (ADM) and Bush Brothers to obtain dry beans from each market class from the same sources accessed for commercial production. We used identity preserved market classes; this product contains an unspecified number dry bean genotypes of the specified market class and the origin of the beans is from multiple locations. Standard methods for commercial canning of dry beans were used to process the dry beans that we evaluated. This cooked and canned material was sent to Van Drunen Farms, Momence, IL, where it was freeze dried and milled (ground to a fine powder). The resulting powder, which was very homogeneous in appearance, was sent to us and stored at 20 C until it was used to formulate
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rodent diets. We have our own diet mixing facility so that diets were custom formulated on site. Samples of each 25-kg batch of dry bean powder were sent to Warren Analytical Labs in Greeley, CO for proximate analysis. Diets were formulated to have the same crude macronutrient composition. The diet formulation that we used was a modification of AIN-93G (Reeves et al., 1993a,b). Based on preliminary feeding studies, we observed that animal growth rate was unaffected by amounts of dry bean in excess of 60% w/w in the diet. In keeping with usual practices in cancer prevention screening assays, the level of crop in the diet to be tested was chosen to be the highest concentration that did not affect animal growth rate. In this comparative bioassay, with many similar crops, the maximum sensitivity to detect any difference is obtained. Hence, comparisons across dry bean genotypes were made at 60% w/w dry bean powder in the diet. We subsequently conducted a dose–response study at levels of dry bean commonly consumed around the world (Thompson et al., 2008). In developing the carcinogenesis protocol, we used a direct acting carcinogen, 1-methyl-1-nitrosourea, and initiated the feeding of beancontaining diets 1 week after carcinogen administration; this is referred to as the postinitiation phase of the carcinogenic process. We also utilized a rapid emergence model for breast cancer (Thompson et al., 1995) due to limitations in the amount of dry bean available. Using this approach, we were able to quantify the occurrence of both premalignant and malignant mammary pathologies and to complete the animal study within 8 weeks of carcinogen administration since cancer incidence is generally 100% at that time. Female Sprague Dawley rats were obtained from Taconic Farms, Germantown, NY at 20 days of age. They were given a single i.p. injection of MNU (50 mg/kg body weight) at 21 days of age. One week later, rats were randomized into experimental groups. Feeding of the dry bean supplemented diet was initiated beginning 7 days postcarcinogen. The experiments were terminated 7 weeks postcarcinogen. Reported data are based on histopathologically confirmed mammary adenocarcinomas (Thompson et al., 2009). 5.4.3. Results A twofold variation in cancer preventive activity was associated with the center of domestication (geographic origin) of bean genotypes ( p < 0.005) in two independent experiments; no effect was associated with seed coat color or ORAC (Table 6 and Fig. 4). White kidney beans of Andean origin, race Nueva Granada, were found to have twice the cancer inhibitory activity as navy beans from the Middle American region, race Mesoamerica. This effect was also accompanied by a twofold reduction in tumor mass indicating that the protective mechanism is likely to be mediated either via the inhibition of cell proliferation or the induction of apoptotic cell death in
Table 6
Effect of dry bean consumption on induction of breast cancer in an experimental model Dry bean classification
a
Carcinogenic response
Market class
COD
Race
Color
ORAC (TE/g)
Isoflavone (g/kg)
n
Multiplicity (#/rat)a
Tumor mass (g/rat)
Control White kidney Dark red kidney Great northern Small red Navy Black
A A MA MA MA MA
NG NG D D MeA MeA
No Yes No Yes No Yes
9.5 0.3 29.9 1.4 9.5 0.5 36.5 1.6 10.4 0.5 23.2 0.9
0.004 0.006 0.009 0.080 0.005 0.015
30 30 30 30 29 30 29
3.4 (3.1) 1.2 (0.9) 1.2 (1.3) 1.5 (1.3) 1.3 (1.5) 1.9 (1.9) 1.6 (1.3)
1.85 0.46 0.46 0.72 0.66 1.00 0.63
Values in are cancer multiplicity from the second experiment done with bean from a different harvest year. Source: compiled from Thompson et al. (2009). Abbreviations: COD, center of domestication; A, Andean; MA, Middle American; NG, Nueva Granada; D, Durango; MeA, Mesoamerica; ORAC, oxygen radical absorbance capacity; TE/g, mmol TroloxTM equivalents (TE)/g cooked bean.
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Figure 4 Dry bean and breast cancer. Cancer multiplicity (avg cancers/animal) and tumor burden (avg tumor mass, grams per animal) were determined among animals fed six market classes of dry beans; N ¼ 30 rats per group; error bars are 1 s.e.m. Also, antioxidant capacity of dry bean powders used in diets is shown (ORAC, mmol TroloxTM equivalents (TE)/g cooked bean); N ¼ 3 replicates; error bars are 1 s.e.m. Average ORAC was determined for color (30.4 mmol TE/g) and no color groupings (9.8 mmol TE/g). Market classes (MC) were white kidney (WK), great northern (GN), navy (NV), dark red kidney (DK), small red (RD), and black (BL). Races were Nueva Granada (NG), Durango (DU), and Mesoamerica (MS). Centers of domestication (COD) were Andean (AN) and Middle American (MA). Color and no color refer to seed coat. Control rats fed pure AIN-93G diet had significantly higher cancer multiplicity (3.10 vs 1.36, p < 0.0001) compared to rats fed bean diets; control tumor burden was also higher (1.85 vs 0.66). Measures of in vitro antioxidant capacity were not predictive of cancer inhibitory activity (M.D. Thompson et al., unpublished data).
transformed cell populations. The same ranking of bean genotypes for cancer inhibitory activity by center of domestication was observed in two carcinogenesis experiments (N ¼ 30 rats/gp) conducted at a 12-month interval with dry beans harvested from two different years using market class identity preserved seed provided by ADM. The between experiment results did not significantly differ. Evaluation of extracts for antioxidant activity using the ORAC assay and the same cooked bean powders fed to animals in the carcinogenesis experiment indicated that antioxidant activity was not a predictor of cancer inhibitory activity. Isoflavone concentrations of high and low activity dry bean genotypes were 100- to 1000-fold lower than isoflavone concentrations typically reported in soybean flour and
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differed by only 1 mg/kg in white kidney versus navy bean indicating that isoflavones are unlikely to account for cancer inhibitory activity. A dose– response study (7.5–60% w/w small red dry bean in diet vs control, N ¼ 30 rats/gp) was also completed and showed that 7.5% w/w small red dry bean reduced the carcinogenic response (Thompson et al., 2008): (a) Exploiting the extremes: Analysis of low and high activity dry bean genotypes. As noted in the preceding section, navy bean had the lowest cancer inhibitory activity of the dry bean market classes and white kidney bean had the highest activity. We used genomic and metabolic profiling to determine if detectable differences existed between these two dry bean market classes. As shown in Fig. 5 (panel B), principal components analysis of the chromatographic data (panel A) revealed complete separation between the two market classes that have been reported to be genetically distinct. We are now in the process of using a combinatorial approach to determine the key differences in biosynthetic pathways that distinguish between navy bean and white kidney bean. (b) Work on other crops. Investigations on potatoes and apple have now progressed to the stage that initial findings have been recently published or are in press (Thompson et al., 2009). For both crops, genotype differences in cancer inhibitory activity have been confirmed. In addition, neither the results of cell culture experiments nor assays for antioxidant activity have been useful in predicting the cancer inhibitory activity of genotypes of either crop. Investigations of corn, rice, and wheat are proceeding according to the approaches outlined in Sections 3 and 4.
6. Building the Infrastructure to Sustain the Effort Building on the biotechnology revolution of the late twentieth century is essential, but advances in technology alone will not solve many of the problems the world faces. Rather, technology creates opportunities for us to recycle old ideas and come up with new approaches. In this light, BMA distinguishes itself from its counterparts in its conceptualization and transdisciplinary program of training, discovery, and dissemination. A transdisciplinary approach, in contrast to disciplinary, multidisciplinary, and interdisciplinary efforts, is one that calls participants to work together to develop a shared conceptual framework that integrates and extends discipline-based concepts, theories, and methods to address a common goal (Rosenfield, 1992). In BMA, that goal is to improve the chronic disease prevention characteristics of food crops to reduce chronic disease morbidity and mortality worldwide.
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Figure 5 LC/MS of dry bean. Preliminary screening runs using ultra-performance liquid chromatography (UPLC) coupled to Q-TOF-MS were performed on extracts of navy bean (NV) and white kidney bean (WK). Navy bean had the lowest anticancer activity and white kidney bean had the highest anticancer activity in the experimental model for breast cancer. Extracts of cooked, canned, and freeze-dried bean powders that were used in the carcinogenesis experiment were prepared using ultrasonic-assisted extraction (UAE) and acetone (60%) as the solvent. Panel A shows a chromatogram of raw LC/MS data for navy bean (NV), top chromatogram; white kidney bean (WK),
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6.1. Transdisciplinary conceptualization Perhaps, the most appropriate way to enhance interaction among disciplines not commonly associated with one another is to create a place for them to coexist, both intellectually and physically. Putting forth a framework for the conceptualization of BMA clearly underscores this point. As defined by Rosenfield (1992), multidisciplinarity is a process in which scientists from different fields work independently or sequentially, each from his or her own disciplinary perspective, to address a particular problem. Interdisciplinarity entails greater sharing of information and closer coordination among scientists from various fields than occurs in multidisciplinary projects, yet the participants remain anchored in their respective disciplinary models and methodologies as do the members of multidisciplinary teams. Transdisciplinarity is a process by which scientists work together to develop a shared conceptual framework that integrates and extends discipline-based concepts, theories, and methods to address a common problem or goal. BMA is envisioned as a transdisciplinary effort. Transdisciplinary or team science (Stokols et al., 2008) represents a process in which the highest degree of crossfertilization of ideas is likely to occur. Our experience dictates that the process is facilitated by creation of a shared work environment in which the opportunities for informal and spontaneous interactions among individuals can occur regularly and with high frequency.
6.2. Land grant tradition The hallmarks of the land grant university mission are education, research, and extension. For BMA to be successful, especially in a transdisciplinary context, these functions need to be seamlessly integrated with each other. The barriers to such integration are formidable and their discussion is beyond the scope of this chapter. However, several issues are considered in overcoming barriers and successfully implementing a transdisciplinary approach. 6.2.1. Training When it is considered that BMA is a trans-kingdom undertaking (Monera– Plantae–Animalia) and can involve student experiences in the field, the lab, and the clinic, the manner in which the training of students is approached,
middle chromatogram; and the solvent blank, bottom chromatogram. Visually note the differences in the two chromatograms. Upon inspection of these data, it appears that some chromatographic features are more or less abundant in each respective sample. Panel B is an example of the data obtained when performing multivariate analysis on the chromatographic data. Principal components analysis indicates that navy bean and white kidney bean are chemically distinct.
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particularly the training of graduate students, is critical. In our view, graduate training in BMA must embrace the concepts of (1) a competency-based curriculum, (2) comentorship by experts in agriculture and the biomedical sciences, and (3) the completion of a project in which experiments with plants and animals are carried out. Standard components of training include experimental design and statistical methods for agriculture and biomedical research, experience with omics technologies and bioinformatic techniques, and course work that exposes students to ideas from plant breeding, nutrition, and biomedical science. One challenge in developing a curriculum involves a lack of integration among offered courses. A plant breeding course that does not discuss biofortification or a nutrition course that ignores aspects of plant diversity does little to build a broader conceptual framework for the student. At the root of this problem are an insufficient number of broadly trained faculty and few resources to develop course content. Until more faculty receive broader training to develop content for courses, discipline-based course work will have to suffice. Beyond conventional approaches to course work, there exist small group discussions and interuniversity developed course work which can be broadcast over the internet to ensure students gain access to a broad set of perspectives on agriculture and health. 6.2.2. Discovery and dissemination BMA participants should be aware of issues that may be encountered in the discovery and dissemination of results. Several issues will be discussed here. Firstly, obtaining germplasm may involve international collaboration. Upon evaluation of germplasm material, high CDP activity in a crop genotype will usher in questions regarding protecting the rights of entities, potentially the indigenous populations of developing countries, which provided the germplasm (Tripp et al., 2007). Whether discovery of a naturally occurring trait should be handled differently than the active enhancement of a trait will eventually need to be scrutinized with regard to intellectual property and patenting (van Dooren, 2008). These issues will need to be carefully weighed with respect to serving the public good in both developed and developing countries. Secondly, discovery and dissemination of knowledge and materials needs to involve those in agriculture, the biomedical sciences, the private sector, and the public. There is little value in pursuing a crop with a health-promoting trait if health care professionals do not believe it to be of value. If there is no value placed in the trait, then consumers will not demand the crop genotype and growers will not grow it. Therefore, early interaction and ongoing input from all stakeholders is integral to the success of BMA in serving the community. Lastly, though funding sources to support discovery are at this time more limited, even more problematic is the inability of many traditional, discipline-based journals in agriculture, nutrition, or the biomedical sciences to fully appreciate the scope of work
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done at the interface between agriculture and the health sciences. Two issues are of note (1) unfortunately, though food is likely the best medium in which to develop dietary interventions for CDP, food avoids many reductionist efforts. Omics technologies will help deal with this issue (2) reviewers for grants and publications tend to have expertise in one of the two disciplines, agriculture or the health sciences, but few have the background to work across both. The process of discovery and dissemination of results from efforts in BMA will obviously benefit from increased awareness and training.
7. Summary and Future Prospects BMA is an approach built upon the ideas outlined in each section of this chapter. A summary of each section is provided in Table 7. Simply put, every crop contains a complex chemical milieu. A crop is prepared as a food, at which time it may undergo a fundamental change in composition. The food is then combined with other foods to form the overall diet. The diet is the sum of its parts, but may display features not present when foods are consumed in isolation. Over time, typical patterns of food consumption within a population can be categorized and then compared with patterns of chronic disease occurrence in that population. Certain dietary patterns may appear to be associated with health promotion and disease prevention as reflected by changes in biomarkers. Though a person’s genotype is considered in this model, there is virtually no consideration of the different crop genotypes that compose the diet. Without this knowledge, modifying diet to influence chronic disease risk can be viewed as superficial. The emerging solution presented here is to investigate CDP by delving into the crop. The crop is the cornerstone of BMA. As a result of the crop focus, agronomists from around the country have responded favorably to the content outlined in this chapter and they have shown a willingness to become involved in this area. At the request of a number of groups of plant breeders, we have assembled a brief list of key points, found in Table 8, that would facilitate interactions between those in the agricultural and biomedical sciences. The omics and bioinformatics revolutions have provided profound insights regarding the relationships among organisms and across kingdoms; further, these technologies represent an unprecedented opportunity to bring together individuals from all disciplines. As a tool, the omics are redefining biological complexity and reshaping the scientific landscape by removing barriers between our scientific disciplines. As evidence, new areas of research have emerged from omics. Nutrition and genomics have coevolved to form nutrigenomics (Brown and van der Ouderaa, 2007; German et al., 2003; Kaput, 2004, 2005, 2007; Kaput et al., 2005; Watkins et al., 2001),
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Table 7 Key points discussed in each section Section
Points
1. BMA overview
Crop genotypes Food combinations Staple food crops Food and health terminology Use and misuse Rationale for studying chronic diseases Rationale for four diseases Rationale for four biomarker families Underlying mechanisms Organize germplasm resources into groupings Collect crop genotypes from diverse groups Process food component for human consumption Determine chemical diversity of the genotypes evaluated Select a limited number of crop genotypes for further analysis Evaluate in animal biomarker model Evaluate in disease specific animal model(s) In vitro assessment of mechanisms: cell culture, chemical assays Select genotypes for clinical evaluation Test food combinations Components of study design parallel preclinical model Credential genotypes/food combinations for CDP Plant food–health conundrum Dry bean development Define transdisciplinary conceptualization Training concepts Discovery issues Dissemination process Key points Agronomist’s checklist BMA principles
2.1. Definitions 2.2. Biomedical landscape
3. Agricultural landscape
4.1. Preclinical testing
4.2. Evaluation in human participants
5. Examples 6. Infrastructure
7. Summary and future prospects
and other omics like metabolomics are finding applications in agriculture (Dixon et al., 2006). The importance of integrating personalized health care with agricultural innovation is clear. Here, we have put forth our
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Table 8 Facilitating interaction among agronomists and biomedical scientists Category
Example
History
Story of domestication Ethnobotanical evidence Epidemiological evidence Consumer/grower concerns Plant anatomy, relevance to food and nutrients Life cycle, growth, and harvesting (timeline for study) Amount, acquisition, and handling of plant parts Breeding/genetic approaches, advantages/limitations Genetic resources Available genotypes (with classification) Lineage information, relevance to domestication and biodiversity Resistance to weeds, pests, and diseases—chemicals and genes, if known Yield and nutrient composition data—genes, if known Common processing technology Industry International Government Extension
Background
Database
Contacts
crop-centric view of CDP through BMA. The principles that are the foundation for BMA and that have been presented in this chapter are summarized in Table 9. Current food-related health problems continue to drive changes in the practice of agriculture. Populations around the world are experiencing a nutritional dichotomy, as diets composed of nutrient-poor staple foods lead to obesity and undernourishment. In considering how BMA is positioned alongside current efforts in biofortification, the solution is clear. Both of these approaches can be achieved through complementing each other: selecting genotypes of a crop with more nutrient content for a given calorie load and with optimized CDP activity. The convergence of these areas would improve the composition of staple food crops for traits that could impact populations with malnutrition and obesity. We feel those involved in the agricultural sciences have the distinct opportunity to usher in an era in which crops have the capacity to prevent chronic disease and nutrient deficiencies. To achieve this, efforts need to be formalized and supported by institutions such as land grant universities. Creation of transdisciplinary programs
44 Table 9
Matthew D. Thompson and Henry J. Thompson
Unifying principles of biomedical agriculture
Transdisciplinary: building on agriculture, public health, and the biomedical sciences Crop genetic diversity: germplasm resources and screening Crop chemical diversity: many chemicals, many targets Staple food crops: consistently consumed in large amounts Food-based: the vehicle to deliver beneficial array of chemicals Toolbox: informatics, omics, and the scientist in another discipline Biomarkers: assisting crop selection through identifying reduction in disease risk Dietary patterns: food combinations with chemical profiles linked to prevention Crop breeding: balanced approach to produce effective crops for health Translational: research from field to lab to clinic to farm to community to world Sustainable: building a framework for training, discovery, and dissemination
of training, discovery, and dissemination would enable BMA to become a twenty-first-century scientific enterprise with the goal of eradicating dietrelated chronic disease through crop improvement.
ACKNOWLEDGMENTS The authors recognize Mark Brick, Patrick Byrne, Scott Haley, David Holm, Jan Leach, Cecil Stushnoff, and Stephen J. Wallner for their steadfast transdisciplinary participation in the Crops for HealthTM program. We thank Stephen J. Wallner for his critical reading of this chapter, John McGinley and Barbara Wallner for their assistance with manuscript preparation, and the Colorado State University Crops for HealthTM program for support.
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1. Introduction: The Role of Sulfur in Agroecosystems 2. Soil Sulfur Pools 2.1. Soil organic sulfur pools 2.2. Microbial biomass sulfur 2.3. Mineralization of soil organic sulfur 2.4. Soil inorganic sulfur 2.5. Conceptual model for sulfur cycling in temperate agricultural soils 3. Sulfur Amendments to Soil 3.1. Inorganic sulfur fertilizer 3.2. Animal manure 3.3. Other organic materials 4. Soil Sulfur Accumulation and Losses in Farming Systems 4.1. Sulfur mass balances 4.2. Long-term experiments 5. Perspectives for Sulfur Use Efficiency in Agriculture 6. Conclusions Acknowledgments References
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Abstract Despite the essential role of sulfur for plant growth, it has historically received little attention because of an ample supply from the atmosphere and commercial fertilizers. However, during the last 20–30 years the situation has changed dramatically and today we face the challenge of optimizing sulfur availability in cropping systems in synchrony with plant demand and in the required form and quantity. Soil sulfur exists in numerous forms and its dynamics play an important role for the sulfur application to plants. Soil organic sulfur has been
Department of Agroecology and Environment, Faculty of Agricultural Sciences, Aarhus University, Tjele, Denmark Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01002-5
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2009 Elsevier Inc. All rights reserved.
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separated into broad—mostly chemically defined—fractions, reflecting land use and fertilizer practice, but these are of limited value for predicting plant availability. There are several reasons for this: firstly, soil organic sulfur consists of a continuum of fractions with different timescales for mineralization; secondly, the association to soil particles provides physical protection of soil organic sulfur against decomposition; and, finally, mineralization only constitutes 0.5–3% per year of the soil organic sulfur pool. The transient nature of plantavailable sulfur makes soil sulfur testing a difficult task and often sulfur balance considerations provide a better background for fertilizer sulfur recommendations. Compared to other macronutrients, the sulfur use efficiency is low. In Danish agriculture the overall use efficiency is 25%. The sulfur surplus relates to both livestock and arable production. In the arable production there is a potential to reduce the surplus by ensuring a closer and more site-specific match of sulfur with plant demand and by using catch crops to reduce sulfate leaching. Also, the sulfur demand in mineral fertilizer can be reduced in case of manure application. The surplus from animal production is expected to be emitted as gaseous sulfur, mostly from animal manure.
1. Introduction: The Role of Sulfur in Agroecosystems Sulfur is one of the essential elements required for plant growth and it plays a role in many plant processes (Blair, 2002; De Kok et al., 2002) such as:
Synthesis of the essential amino acids cysteine and methionine—both building blocks for protein Synthesis of coenzyme A, biotin, thiamine, and glutathione Synthesis of chlorophyll Synthesis of secondary sulfur compounds (allins, glucosinolates, phytochelatins) primarily in members of the families Cruciferae and Liliaceae Fixation of nitrogen by leguminous plants
Several of the sulfur compounds are of particular importance for plant protection against pests and environmental stress, for food quality and for the production of phytopharmaceutics (De Kok et al., 2002). Sulfur is therefore considered one of the four major nutrients, together with nitrogen, phosphorus, and potassium. Plant sulfur requirements are equal to and sometimes exceed those for phosphorus. For the synthesis of the sulfurcontaining amino acids, assimilatory reduction of both sulfate and nitrate is necessary and therefore nitrogen and sulfur uptake and assimilation are closely linked (Brunold, 1993). Increasing sulfur deficiency in previously sulfur sufficient areas has been reported in many parts of the world. The main reasons are (1) the environmental
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Sulphur deposition (kg ha−1 yr−1)
control of sulfur dioxide emissions in industrial areas, (2) the increasing use of high-analysis, low-sulfur-containing fertilizers, (3) the increase in yields obtained as a result of other technological advances, and (4) the decreasing use of sulfur-containing pesticides and fungicides (Blair, 2002). Historically, many European countries saw rising emissions of sulfur dioxide due to industrialization and the use of coal as the predominant energy source starting in the nineteenth century, but particularly in the twentieth century (McGrath et al., 2002). A similar development in atmospheric sulfur deposition took place in Denmark since year 1800 (Fig. 1). The deposition peaked in 1970, since when legislation to reduce sulfur dioxide emissions was introduced. Interestingly, the atmospheric sulfur deposition today is similar to or approaching preindustrial levels 100–150 years ago. At the peak of deposition the global annual transportation of sulfur to the atmosphere was approx. 405 million tonnes, of which only about 20% originated from anthropogenic activity (Sima´n and Jansson, 1976). However, 93% of the anthropogenic emission was from the Northern hemisphere and 20% was from the industrialized part of Europe that covers only 1% of the Earth’s surface (Saxe and Andersen, 1986). Emissions of sulfur, particularly atmospheric sulfur dioxide, but also dissolved in rain as sulfate, are of concern for two reasons (1) high sulfur dioxide concentrations in air have been responsible for a number of respiratory problems and even deaths and (2) sulfur deposition is one of the most important causes of acidification of natural ecosystems, also known as ‘‘acid rain’’ (McGrath et al., 2002). The use of sulfur in commercial fertilizers has in Denmark like in many other countries dramatically increased from the beginning of the nineteenth century until the mid-1960s (Fig. 2). In this period the consumption was a result of sulfur being part of phosphorus (super phosphate with 13% sulfur) and nitrogen fertilizer (ammonium sulfate with 24% sulfur). The use of 30
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Figure 1 Historic atmospheric sulfur deposition in Denmark. Based on actual values (symbols, Ellermann et al., 2007) and scaling of the relative time series developed by Alveteg et al. (1998).
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Sulfur fertilizer (kg ha−1 yr−1)
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Figure 2 Sulfur consumption in commercial fertilizer 1910–2005 to Danish agricultural soils. Source: Statistics Denmark (1968, 1965–2005).
these increased from 1910, temporarily interrupted by World Wars I and II. The decrease from 1965 was caused by the introduction of compound fertilizer and triple super phosphate containing only 1.3% sulfur. The increase in fertilizer sulfur from 1990 onwards was for the first time in the history of mineral fertilization caused by the deliberate use of sulfur following observations of sulfur deficiency symptoms in crops. Sulfur deficiency in crops started to appear in the late 1980s, first in sulfur-demanding crops such as oilseed rape and later also in cereals, and since the mid-1990s, sulfur fertilization has been recommended for all crops (Pedersen et al., 1998). Similar observations have been made throughout Europe. In a review of crop responses to sulfur fertilization, Zhao et al. (2002) concluded that today sulfur has become one of the most limiting nutrients for agricultural production in many European countries. Sulfur fertilization is not expected to have any adverse environmental consequences as natural ecosystems are usually not sulfate-limited. However, there is an EEC guideline for sulfate in drinking water (250 ppm) and with long-term annual application this limit will eventually be reached, if rates are not adjusted to crop requirements. A key point in estimating the requirement of different crops in different cropping systems is a detailed understanding of the sulfur cycle. The overall objective of the work presented here was to obtain the knowledge required to optimize plant availability of sulfur in appropriate quantities and in synchrony with plant demand. An important part of this was to analyze: Soil sulfur cycling: ability of soils and soil organic matter to accumulate and release sulfur and the supporting mechanisms Organic amendments to soil: the ability of organic manures and crop residues to supply sulfur to plants Sulfur supply in farming systems: soil sulfur accumulation, losses, and balances in different farming systems
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2. Soil Sulfur Pools Soil sulfur exists in organic and inorganic forms. From a plant-nutritional viewpoint inorganic sulfate is the most important, since this is the form assimilated by plant roots. However, sulfate—which is the stable form of inorganic sulfur in aerobic soils—constitutes only a small part of total-S in soils. Generally, more than 95% of soil sulfur is organically bonded with several hundred kilograms of organic sulfur present in the upper horizons of most soils. Although not readily plant available, this large organic sulfur pool may potentially be an important source of sulfur to plants in deficiency situations.
2.1. Soil organic sulfur pools 2.1.1. Characterization Organic sulfur in soils is a heterogeneous mixture of soil organisms and partly decomposed plant, animal, and microbial residues and little is know about the identity of individual compounds. However, choline sulfate, sulfolipids, sulfonic acids, the sulfur-containing amino acids cysteine and methionine, and sulfated polysaccharides have been found in soils (Freney, 1986). Several different approaches have been used to separate soil organic sulfur into broad fractions representing distinct forms and properties. 2.1.2. Separation according to reactivity with reducing agents The traditional way of separating organic sulfur is according to reactivity with reducing agents (Tabatabai, 1982). Two distinct groups of sulfur compounds are obtained (1) organic sulfur not directly bonded to C, which can be reduced to H2S by hydroiodic acid, and (2) organic sulfur, which is directly bonded to C (C–S). The first group is composed primarily of sulfate esters (C–O–S) and the second includes sulfur-containing amino acids, mercaptans, disulfides, sulfones, and sulfonic acids (Freney, 1986). Generally, total sulfur content decreases with depth in line with the organic carbon content, and the percentage of organic sulfur present as sulfate esters increases with depth (Eriksen, 1996; Tabatabai and Bremner, 1972). 2.1.3. Physical separation into mineral and aggregate size fractions Fractionation of soil and organic matter into primary particle size separates has been used as a tool for studying soil organic matter distribution and dynamics, since a significant part of organic matter is closely associated with soil minerals (Christensen, 1992). A considerable sulfur enrichment of the clay fractions has been found similar to that found for C and N. The ratios of C/S decrease dramatically with decreasing particle size and for some soils the N/S ratio also decreases, showing differences in the nature of the
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organic materials associated with different particle size fractions (Anderson et al., 1981; Hinds and Lowe, 1980). The interaction with clay can protect some of the more easily decomposable organic matter from microbial breakdown (Ladd et al., 1993) and this interaction seems even more important for sulfur than for C and N. The increase in the percentage of HI-reducible sulfur (sulfate esters), which is believed to be the more labile form of organic sulfur (Biederbeck, 1978), with decreasing particle size suggests that organic sulfur–clay interactions are the major mechanisms protecting organic sulfur from mineralization. 2.1.4. Chemical extraction followed by physical–chemical separations Soil sulfur has been studied using conventional organic matter fractionation into humic acids, fulvic acids, and humin. By using a sonification procedure in combination with the extraction, humic and fulvic acids are obtained, some of which are intimately associated with clay minerals and humin (Bettany et al., 1979, 1980). Although this type of chemical extraction has been widely used to study soil organic matter dynamics, it is questionable if it is useful for studying soil organic sulfur because of possible artifact formation caused by the strongly alkaline reagent (Freney, 1986). 2.1.5. Molecular weight fractionation Sephadex gel filtration has been used in a number of studies to obtain fractions of organic matter extracts with different molecular size and nominal molecular weight (MW) (e.g., Keer et al., 1990; Scott and Anderson, 1976). Generally, much of the organic sulfur is found in fractions with a very high MW (>100,000 Da), but a significant proportion also has MWs of less than 10,000 Da. Eriksen et al. (1995c) showed that in the short term, little sulfur cycling takes place in fractions >5000 Da. During 8 weeks of incubation, carrier-free 35S was initially incorporated into the <700 Da MW fraction and then recycled into the 700–5000 Da fraction. Keer et al. (1990) noted that high MW organic matter was enriched with compounds in the sulfate ester form. More than 75% of total organic sulfur was in the form of sulfate esters in organic matter with MWs greater than 200,000 Da. This agrees with the finding that the percentage of sulfate esters increased with decreasing particle size, since high MW compounds are better adsorbed to clay particles because of the presumed aliphatic nature of high MW organic matter (Anderson et al., 1974). 2.1.6. Fractionation according to physical protection of soil organic sulfur Extraction of organic sulfur has been carried out using acetylacetone, which works by complexing with the metals that link organic matter and mineral particles together, and the organic matter is then extracted by water
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(Giovannini and Sequi, 1976). Acetylacetone extraction of organic matter in conjunction with ultrasonic dispersion has been used, for example, by Halstead et al. (1966), Keer et al. (1990), and Scott and Anderson (1976). It was suggested by Eriksen et al. (1995b) that organic sulfur that could only be extracted by acetylacetone when subjected to ultrasonic dispersion was physically protected in soil aggregates, and it was shown that sulfur turnover in this fraction was slow. Thus, using the combined dispersion–extraction procedure by Eriksen et al. (1995b), organic matter was assumed to be divided into unprotected, protected, and insoluble organic sulfur, where protected means isolated from decomposers inside water-stable aggregates. It has been demonstrated that physical protection of soil organic sulfur caused by soil aggregation plays an important role in the turnover of soil sulfur (Eriksen et al., 1995c). In an incubation experiment it was found that much of the organic sulfur was physically protected inside aggregates, and that turnover in this protected fraction was slow (Table 1). It has also been demonstrated that sulfur is initially immobilized into organic matter not protected by soil structure and then gradually into physically protected organic matter (Eriksen, 1997b). 2.1.7. XANES spectroscopy A nondestructive technique that has been widely used in speciation studies of environmental and geochemical samples is X-ray absorption fine structure spectroscopy. A version of this, X-ray adsorption near-edge structure (XANES), provides specific information on the functional groups containing sulfur because of its sensitivity to electric structure, oxidation state, and geometry of neighboring atoms (Vairavamurthy et al., 1997), and the technique has been used as a tool for understanding sulfur dynamics in soil organic matter. The advantage over wet chemical methods is that intermediate oxidation states can be identified. Long-term agricultural management has been shown to have a significant effect on sulfur speciation (Solomon et al., 2003) and investigations by Zhao et al. (2006) indicate that sulfur species in the reduced and intermediate oxidation states are the main sources of organic sulfur for mineralization. One problem, however, is that XANES is applied to humic extracts from agricultural soils, which may not reflect the in situ speciation in soil. Soil organic sulfur, moreover, exists in many subcompartments in the soil with widely differing availability, a factor not reflected in the oxidation states. Still, XANES is a promising technique that needs to be tested on more soils and the relation to sulfur mineralization potential should be further investigated.
2.2. Microbial biomass sulfur The microbial biomass plays a major role in soil sulfur cycling and an understanding of the mechanisms and forms of sulfur involved is thus very important. Biomass sulfur has been measured as the flush of extractable
Table 1 Distribution of 35S in sulfur fractions after 8 weeks of incubation at 25 C, calculated as percentage of 35S added at incubation start (Eriksen et al., 1995c) Inorganic sulfur
St. Jyndevad (DK) Lundgaard (DK) Uralla (AUS) Warialda Cult. (AUS) Warialda Native (AUS) Duri (AUS)
Clay (%)
Carbon (%)
3.6 4.6 12.2 13.2 15.3 51.4
1.5 1.4 0.8 0.7 1.5 1.2
Nonprotected organic S Recovery of
73 42 28 44 22 23
32 62 56 50 42 47
Protected organic S 35
Total
S added (%)
1 3 12 14 29 26
106 107 96 108 93 96
Soil Sulfur Cycling
63
sulfur following chloroform fumigation, analogous to the way biomass C is determined (Banerjee et al., 1993; Saggar et al., 1981). Microbial biomass sulfur forms only a small part of soil organic sulfur, accounting for 0.9–2.6% of total organic sulfur in agricultural soils (Chapman, 1987) and 2.2% and 1.2% in a hardwood and a conifer forest, respectively (Strick and Nakas, 1984). It has been suggested that fluctuations in microbial biomass sulfur may be related to levels of inorganic sulfate in soils (Chapman, 1987). Biomass sulfur might become available to plants in a period of decreasing biomass and sulfate may be immobilized during a period of biomass increase. However, results by Wu et al. (1993) show that once sulfur has been immobilized by the microbial biomass, it is directly transformed into soil organic sulfur, remaining unavailable to plants until remineralized. The activity of the microbial pool influences turnover rates rather than the size of microbial biomass sulfur, and in agricultural soils good correlations have been found between sulfur mineralization and microbial activity (Sparling and Searle, 1993). In a forest soil Autry and Fitzgerald (1993) similarly found a significant correlation between organosulfur formation and ATP content. In experiments with 35S-labeling of the sulfate pool, the labeling of microbial biomass sulfur has been shown to be relatively constant over time, both under controlled and field conditions even though immobilization took place (Eriksen, 1997b; Wu et al., 1995). This indicates that 35S immobilized by the microbial biomass is transformed directly into soil organic sulfur.
2.3. Mineralization of soil organic sulfur In soils the processes responsible for sulfur transformations such as mineralization, immobilization, oxidation, and reduction are mainly microbially mediated. Therefore, factors that affect the microbial activity such as temperature, moisture, pH, and substrate availability also affect these processes. In aerobic agricultural soils, the main process of interest is the release of inorganic, plant-available sulfate from organic matter. Since mineralization and immobilization of sulfur happen concurrently (Eriksen, 1997a; Ghani et al., 1993; Maynard et al., 1983), the release or incorporation of inorganic sulfate is the net result of several processes. 2.3.1. Biological and biochemical mineralization McGill and Cole (1981) proposed a conceptual model for the cycling of organic carbon, nitrogen, sulfur, and phosphorus through soil organic matter, in which the mineralization of sulfur involves two different processes— biological and biochemical mineralization. Biological mineralization is believed to be driven by the microbial need for organic C to provide energy, and sulfur released as sulfate is a byproduct of the oxidation of C to carbon dioxide.
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Biochemical mineralization is the release of sulfate from the sulfate ester pool through enzymatic hydrolysis. Whereas mineralization of C-bonded sulfur is strictly dependent on microbial activity, the sulfate esters can be readily hydrolyzed by sulfatase enzymes in the soil, and therefore the biochemical mineralization is controlled by the supply of sulfur rather than the need for energy. In situations where microbial demands cannot be met by soil inorganic sulfate, sulfatase enzymes are used to hydrolyze sulfate esters and, conversely, high levels of sulfate will inhibit biological mineralization. Although the conceptual model can be criticized as an oversimplification of a much more complex system, it does provide insight into the fundamental differences between the processes for different nutrients. Originally, it was thought that because of the close relationship between sulfur and nitrogen in organic matter, the ratio between mineralized nitrogen and sulfur would be the same as in soil organic matter (Walker, 1957; White, 1959). This is in conflict with many studies that have shown considerable deviations from this. Results range from a much wider ratio (Kowalenko and Lowe, 1975) to a narrower (Tabatabai and Al-Khafaji, 1980) N/S ratio in mineralization products than in soil organic matter. Considering the two mineralization mechanisms in the McGill and Cole (1981) model for sulfur, these observations are not surprising. Whereas C-bonded sulfur and nitrogen are stabilized together and released through biological mineralization, the sulfate esters can be mineralized independently. Thus, net mineralization of sulfur depends on the rates of the two reactions and the N/S ratio in mineralized material will vary accordingly. 2.3.2. Sulfatase enzymes Because much of the soil organic sulfur exists as sulfate esters that can be mineralized through enzymatic hydrolysis, the sulfatases responsible have gained some interest. Bacteria and fungi are major sources in the soil, but also plant roots and possibly mammalian urine may contain these enzymes (Fitzgerald, 1976; Klose et al., 1999). Sulfatases are classified according to the sulfate esters they hydrolyze and the main groups are aryl-, alkyl-, steroid, gluco-, condro-, and mycosulfatases, but only arylsulfatases have been measured in soils (Germida et al., 1992). Strong correlations have been found between arylsulfatase activity and soil organic C content (Elsgaard et al., 2002) and good correlations have been found between sulfur mineralization and arylsulfatase activity (Castellano and Dick, 1991; Lee and Speir, 1979), but this may be due to factors that affect enzyme activity in general. Since arylsulfatase is only one of many enzymes involved in the mineralization process, it is unlikely that this alone can explain variations in mineralization of sulfur (Germida et al., 1992). Little is known about the substrate specificity of sulfatases, but the activity of arylsulfatases in the soil does not appear to constitute a rate-limiting factor in the hydrolysis of sulfate esters (Ganeshamurthy and Nielsen, 1990; Houghton and Rose, 1976).
Soil Sulfur Cycling
65
2.3.3. Sulfur mineralization potential The potential mineralization of soil organic sulfur has been estimated by kinetic equations for sulfur mineralization based on the release of sulfur from incubated soils (Ghani et al., 1991; Pirela and Tabatabai, 1988). The validity of this approach may be questionable, since mineralization of sulfur strongly depends on the incubation technique used (Maynard et al., 1983; Valeur and Nilsson, 1993). It is probably also impossible to identify a kinetically homogeneous ‘‘potential mineralizable pool,’’ since the mineralization mechanisms consist of several substrates, biochemical pathways, and microbial communities (Ellert and Bettany, 1988). Besides, kinetic mechanisms change with temperature (Ellert and Bettany, 1992), which implies that a true measure of mineralizable sulfur in soils is best achieved when field conditions are closely simulated. Under such conditions, Eriksen et al. (1995d) found that the contribution from mineralization to the supply of sulfur to plants was small (3–7 mg S g soil 1 yr 1), but differences between soils were very consistent. The amount of sulfur mineralized constituted 1.7–3.1% per year of the organic sulfur pool in the soil, in agreement with other findings on the mineralization of sulfur in the order of 0.5–3% of the soil organic sulfur pool (Freney, 1986; Keer et al., 1986). Mineralization following the addition of organic material to soils is discussed below. 2.3.4. Gross sulfur mineralization–immobilization turnover The release or incorporation of inorganic nutrients is the net result of mineralization and immobilization processes. Quantification of gross nutrient fluxes has been used successfully to understand the fundamentals of these processes for both nitrogen (Murphy et al., 2003) and phosphorus (Di et al., 1994) using isotopic dilution techniques. The principle of this methodology is to label the mineral nutrient pool with an isotopic tracer and measure the change with time. The rates of influx to and outflux from the labeled pool are then calculated using equations based on tracer kinetics. Although these equations were developed as early as the 1950s (Kirkham and Bartholomew, 1954), this technique has not been widely used until recently (Di et al., 2000; Murphy et al., 2003). The 35S tracer has been used to study sulfur transformations in soil without organic matter addition (e.g., Eriksen, 1997a; Ghani et al., 1993; Goh and Pamidi, 2003; Vong et al., 2003), following addition of urinary or fecal sulfur (e.g., Blair et al., 1994; Nguyen and Goh, 1994b; Williams and Haynes, 2000) and plant residues (Wu et al., 1993, 1995), but the use of the tracer dilution method to determine gross sulfur transformation rates has rarely been attempted. Although the use of isotopic dilution techniques has limitations due to the assumptions of the techniques, they have proved valuable for the mechanistic modeling of gross N fluxes and for our understanding of the fundamental
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processes of the soil internal N cycle and individual microbial pathways (Murphy et al., 2003). Some recent studies indicate that the isotopic dilution technique has a similar potential to increase our knowledge of the soil sulfur cycle (Eriksen, 2005; Nziguheba et al., 2005).
2.4. Soil inorganic sulfur Sulfur can have any oxidation number from 2 (sulfide) to +6 (sulfate). In agricultural soils where conditions are mostly aerobic, the dominant and stable form of inorganic sulfur is sulfate, and only negligible quantities of lower oxidation state compounds are present (Bohn et al., 1986). Consequently, sulfate is often referred to as inorganic sulfur in the literature. Concentrations of sulfate in soils fluctuate throughout the year because of changes in the balance between atmospheric inputs, decomposition of plants, fertilizer addition, leaching, plant uptake, and microbial activity. Usually low levels of sulfate are observed over winter and spring due to leaching, plant uptake, and low mineralization rates associated with low temperatures (Castellano and Dick, 1990; Ghani et al., 1990). Sulfate exists as water-soluble salt and as sulfate adsorbed to soil inorganic components. The soluble sulfate plus most of the adsorbed sulfate is generally believed to be plant available. 2.4.1. Adsorption of sulfate The retention of sulfate in soils is dependent on the nature of the colloidal system, the pH, the sulfate concentration, and the concentration of other ions in the solution (Harward and Reisenauer, 1966). Sulfate is adsorbed by hydrous oxides of Fe and Al and by surfaces of clay particles (Parfitt, 1978). It has been proposed that sulfate is adsorbed by purely electrostatic mechanisms (Marsh et al., 1987), but also chemisorption with ligand displacement of –OH and –OH2 has been shown to take place (Parfitt and Smart, 1978). The amount of sulfate adsorbed depends on the surface area of the clay and the surface charge, and therefore the higher the Al content, the greater the anion adsorption (Bohn et al., 1986). The effect of pH on sulfate adsorption is related to the net charge of the Fe and Al oxides. If the pH in the soil is lower than zero point of charge (ZPC), it will lead to a positive surface because of hydration of the metal oxides, and sulfate will be adsorbed in the soil. Thus, acid subsoils (e.g., under forest vegetation) and peat soils typically have large storage capacities for adsorbed sulfate. In agricultural soils, however, the pH is often higher than the ZPC, leading to a low retention of sulfate in the soils. Thus, Curtin and Syers (1990) found that virtually all sulfate in soils with pH > 6 was in solution. Liming, moreover, has been demonstrated to increase sulfur leaching (Bolan et al., 1988; Chao et al., 1962b) because of desorption of sulfate and increased mineralization. Even for soils with a marked capacity
67
Soil Sulfur Cycling
to retain sulfate, the strength of the retention seems weak and Chao et al. (1962a) found that repeated extraction with water removed adsorbed sulfate. Sulfate adsorption is influenced by the presence of other anions. The order of adsorption strength of anions in soils is hydroxyl > phosphate > sulfate > nitrate = chloride (Tisdale et al., 1984). The stronger adsorption of phosphate than sulfate is the basis for extraction of adsorbed sulfate (Tabatabai, 1982) and addition of phosphate to soils has been shown to increase sulfur leaching (Bolan et al., 1988; Chao et al., 1962b).
2.5. Conceptual model for sulfur cycling in temperate agricultural soils Because of the complex nature of soil organic matter, any procedure attempting to divide organic sulfur into a few biologically meaningful fractions is a rough simplification. On the other hand, there is some truth to the various fractions identified above. Clearly the carbon-bonded sulfur and the sulfate esters have chemically distinct properties important in sulfur cycling. In some stable and clay-protected organic sulfur pools, however, these differences are overruled by the physical properties of the soils, as illustrated in Fig. 3, where the organic sulfur compounds of different oxidation states such as sulfate esters and C-bonded sulfur fractions are integrated parts of other mainly physically determined pools. It is, therefore, Atmospheric S
Plants
Fertilizer
Animals
Plant residues Microbial S Reduced S Intermediate S Oxidized S
Solution sulphate Stable Inorganic S
Adsorbed sulphate
Labile organic S
Reduced S Leaching Intermediate S Oxidized S ∼1–10 yr
Figure 3
Physically protected organic S
Stable organic S (residual S)
Reduced S Intermediate S Oxidized S
Reduced S Intermediate S Oxidized S
∼50–100 yr
∼1000 yr
Conceptual diagram of the sulfur cycle.
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J rgen Eriksen
not surprising that carbon-bonded sulfur and sulfate ester determination on whole soils offers little information regarding plant availability of the organic sulfur. On this matter, Scherer (2001) concluded in a review that the complicated dynamics of organic sulfur compounds in soil make it difficult to estimate the sulfur delivery to plants. However, as short-term sulfur cycling probably involves only a small fraction of total organic sulfur, it is essential to identify the more active parts to obtain a better understanding of factors determining plant availability. A key point is the physical protection of soil organic sulfur.
3. Sulfur Amendments to Soil 3.1. Inorganic sulfur fertilizer The use of sulfur fertilizers has been reviewed elsewhere (e.g., Blair, 2002; Messick et al., 2002), but, generally, there are two types of sulfur fertilizers (1) those where sulfur is in the sulfate form and readily available to plants and (2) those where sulfur needs to be oxidized to sulfate to become available to plants. The advantage of sulfate fertilization, in addition to it being in the plantavailable form, is that sulfate is easily incorporated in multinutrient fertilizers, which is a cost-efficient way of fertilizer application. However, in some cases their use may give an unbalanced nutrient supply. One example is the widely used ammonium sulfate, where its use as a nitrogen source applies much more sulfur than typically required. Since sulfate is readily leached from the soil, there is no point in attempting to raise soil sulfur levels by excessive fertilization (Eriksen, 1996; Knights et al., 2000). Studies have shown the importance of the availability of sulfur during grain-filling (Eriksen et al., 2001; Fitzgerald et al., 1999; Monaghan et al., 1999), where shortage is caused by the limited redistribution of sulfur from the vegetative tissue to grain (Eriksen et al., 2001; Zhao et al., 1999). The application of a sulfate-containing fertilizer in the spring carries the risk of sulfur intended for the grain-filling period either being leached, immobilized in soil/microbial biomass or in vegetative plant parts. The use of elemental sulfur fertilizer offers several advantages, including slow release over time as it is gradually converted to sulfate. The effectiveness of elemental sulfur is governed by oxidation carried out principally by bacteria and depends on temperature, moisture, and particle size. Blair et al. (1993) developed a model for matching the oxidation rate of sulfur from elemental sulfur with plant requirements. Elemental sulfur is also the most concentrated form of sulfur, which cuts transport and application costs, it acts as a reservoir because of the oxidation process and it may be incorporated in compound fertilizers (Messick et al., 2002).
Soil Sulfur Cycling
69
3.2. Animal manure On a global scale, total sulfur excretion from domestic animals is estimated at around 8 million tonnes per year, corresponding to about 13% of world sulfur production or 80% of the world consumption for manufactured mineral fertilizer (Eriksen, 2002). However, it is difficult to utilize this potential source of fertilizer sulfur. Of this total, developed countries contribute 2.6 million tonnes and developing countries 5.5 million tonnes. In developed countries, 54% of the excretion occurs during grazing, whereas in the developing countries this figure is 65%. The sulfur excreted during grazing is difficult to utilize. Excremental sulfur may be lost from the main grazing area to unproductive sites by excremental transfer (Nguyen and Goh, 1994a) and even when deposited within the productive area, it may not be efficiently recycled because of nonuniform distribution and losses (Nguyen and Goh, 1994b; Till et al., 1994; Williams and Haynes, 1992). Much of the sulfur excreted within a housing system may be collected and applied to agricultural land as a fertilizer, provided the technical facilities are in place. The sulfur content of manure collected from cattle and pigs (1 million tonnes sulfur per year) has a potential as a sulfur fertilizer especially in the developed countries, as regulations on the utilization of manure N and/or P already stipulate that animal wastes are used as fertilizers. In countries with a large animal production, the potential sulfur contribution can be significant and it is important to establish to what extent the sulfur content in animal manure is available to plants.
3.2.1. Sulfur content of animal manure Animal manure is a very variable substance, and its composition is a product of many factors, for example, animal species, feed composition, production system and time, and conditions of storage. As stored manure may be a mixture of excreta of different ages from animals fed different diets and possibly even from different animal species, the sulfur content can be quite variable. The concentrations of total-S in slurry typically vary between 0.15 and 0.7 kg sulfur m 3 of slurry (Eriksen et al., 1995a) and the mean value in farmyard manure (FYM) is around 1 kg sulfur t 1 FYM. The question is how much does slurry sulfur contribute to the sulfur supply of plants? Average values of around 0.35 kg total sulfur m 3 are commonly found in cattle slurry (Eriksen et al., 1995a; Lloyd, 1994; Watson and Stevens, 1986). Using this value, a dressing of 50 m3 cattle slurry would provide 17.5 kg sulfur ha 1, corresponding to the sulfur requirement of, for example, cereals. However, the total sulfur content is of limited value in predicting fertilizer value.
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3.2.2. Plant availability of manure sulfur in the short term The composition of sulfur in slurry and the content of plant-available sulfate may vary depending on feeding and storage. As a consequence, different slurries may be expected to have different levels of plant availability of sulfur. Unfortunately, there is a lack of information on feeding and slurry storage in the literature covering plant availability of manure sulfur, which makes it difficult to generalize results. Lloyd (1994) found an effectiveness of sulfur in cattle slurry of 55% compared with sulfur in gypsum when applied to grass for silage. This is much higher than the effectiveness of 5% found by Eriksen et al. (1995a) when applying slurry to spring oilseed rape in a pot experiment. The low plant availability of sulfur in slurry was most likely due to transformations in the slurry during storage, where plant-available inorganic sulfate is incorporated into organic sulfur or reduced to sulfide (Fig. 4). Sulfide is expected to be readily oxidized to plant-available sulfate when the slurry is applied to soil. However, the low plant uptake of slurry sulfur suggests this did not happen. Cattle slurry C-bonded
100
µg S g−1 slurry
50
Sulphate Sulphide
0 Pig slurry
300
C-bonded 200 Sulphate 100
Temp. (°C)
Sulphide 0 20 10 0 0
10
20 30 Weeks
40
50
Figure 4 Variations in S composition of slurry during storage from February to November. Error bars: SE.
Soil Sulfur Cycling
71
The possibilities are that sulfide was either emitted from the slurry as H2S or immobilized in the soil as metallic sulfides or by sorption to soil particles (Bremner and Steele, 1978). The differences in effectiveness between studies may be explained by differences in feeding and storage. Often, the sulfur content of feed is tailored to animal requirements and the storage time is usually many months. This combination minimizes the content of inorganic plant-available sulfur. The results from the pot experiment are supported by experiments by Pedersen et al. (1998). In eight field trials, they found a response to a mineral fertilizer application of 40 kg sulfur ha 1 to winter oilseed rape despite applications of animal manure. 3.2.3. Plant availability of manure sulfur in the long term A low efficiency of sulfur in animal slurry is mainly attributed to slurry sulfur being in organic forms not available to plants. This suggests that soils with an annual application of slurry or other organic manures will release more plant-available inorganic sulfate than unmanured soils. To get an idea of the residual effect of sulfur added in animal manure, some plots in the Askov long-term field experiments (Denmark) were examined for their content of organic C and S and inorganic sulfate (Eriksen and Mortensen, 1999). The original experiments started in 1894 on both sandy and loamy soils with the objective of comparing the effect of animal manure with equal dressings of N, P, and K in mineral fertilizers and unmanured treatments (Christensen, 1996). Fertilization with animal manure or NPK fertilizer increased the content of soil organic C compared with unfertilized plots (Fig. 5). On the sandy soil, the buildup of organic C was followed by a similar buildup of organic sulfur. Thus, in the mineral-fertilized plots, organic C and S were on average increased by 26% and 19%, respectively, compared with the unfertilized plots, and in the organic-manured plots organic C and S were increased by 51% and 56%, respectively. On the clay soil, the buildup of organic C—on average 17% in the NPK plots and 24% in organic-manured plots—was followed by insignificant increases in organic sulfur content of 24% and 5%, respectively. The increased organic sulfur content did not significantly affect soil inorganic sulfate levels in the spring. This could be due to leaching losses during the winter, especially in the sandy soil, or alternatively it could indicate that organic sulfur in the soil originating from increased fertilization is no more readily mineralized than the bulk of soil organic sulfur. Similarly, Knights et al. (2001) found that 153 years of manure application in the Broadbalk experiment in England increased soil organic C and S contents, but the long-term application of inorganic sulfur-containing fertilizers had little effect. They found similarly low inorganic sulfate levels in all arable plots. However, they did find increased mineralization rates when incubating soil from the FYM plot compared to the inorganic fertilizer treatments,
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1.6
Organic C
%
1.4 1.2 1.0 0.8 0.6
µg g−1 soil
Organic S 200 150 100
Inorganic S µg g−1 soil
8 6 4 2 0
0 ½ 1 1
0 ½ 1 1½ ½ 1 1½
Sandy soil
Clay soil
Unfertilized NPK fertilizer Organic fertilizer (AM)
Figure 5 The effect of fertilizer history on the soil content of organic C, organic and inorganic S in selected plots in the Askov long-term field experiment. Error bars: SE.
indicating that organic manure application indeed does have a potential longterm effect on the sulfur-supplying capacity to crops. Reddy et al. (2001) found increased sulfur mineralization when incubating soil subjected to 27 years of manure application, but also mineral sulfur applications showed increased mineralization levels. Annual applications of organic manure increase the soil organic sulfur content and thus the sulfur mineralization rate. The extent of this increase depends on soil type, cropping system, and management. Therefore, a residual sulfur effect of long-term organic manure application must be expected, although there is no indication that the sulfur from manure will mineralize more readily than the bulk of soil organic sulfur. The ability of a cropping system to use mineralized sulfur depends on the length of the growing season of the crops, but mineralization in unlikely to fully meet the sulfur demand of a crop.
73
Soil Sulfur Cycling
3.3. Other organic materials Other organic materials such as sewage sludge, green manure, and compost may be applied to agricultural land. The plant availability of organic-bonded sulfur in these depends on the mineralization rates. It is generally believed that sulfate is released from organic material when C/S ratios are less than 200 and is immobilized if C/S is above 400, whereas a C/S between 200 and 400 can cause both mineralization and immobilization (Barrow, 1960). This rule seems to apply across different organic materials such as sludge, animal manure, and plant material. Figure 6 gives data from three studies involving incorporation of different types of organic material in pot or incubation experiments. The regression line explains 65% of the variation in mineralization, which is a rather good correlation considering the comprehensiveness of soils and organic materials used in the experiment. Straw or other strongly immobilizing materials were not included as they do not exhibit the same relationship and it is always advisable to use supplemental sulfur fertilizers during the incorporation of such materials (Chowdhury et al., 2000). For straw, it has been estimated that a C/S ratio of 340 is the critical level for immobilization (Chapman, 1997). Sewage sludge was a good source of sulfur in this example (Fig. 6) as sulfur mineralization was high due to low C/S ratios. The C/S ratios of solid animal manure ranged from 22 for poultry manure to 297 for horse manure. These differences—probably largely due to differences in straw content—resulted in much decreased sulfur mineralization levels in soils amended with a high C/S ratio material. S mineralization (% of added)
100 75 50 25 0 −25 0
100
200 C/S-ratio
300
Sewage sludge Green manure Farmyard manure
Figure 6 Mineralization of S after soil incorporation of sewage sludge (Tabatabai and Chae, 1991), green manure (Eriksen and Thorup-Kristensen, 2002; Reddy et al., 2002; Tabatabai and Chae, 1991) and farmyard manure (Reddy et al., 2002; Tabatabai and Chae, 1991) as a function of total C/S ratio.
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3.3.1. Green manure/catch crop A green manure is a crop grown primarily for the purpose of being ploughed in to add nutrients and organic matter to the soil. It has been demonstrated that a catch crop succeeding the main crop can absorb sulfate from the root zone during autumn and winter and thereby reduce nitrate leaching (Eriksen and Thorup-Kristensen, 2002). The catch crop/green manure is incorporated prior to the sowing of the next crop and ideally the nutrient content of the residues should quickly become plant available as illustrated in Fig. 7. To maintain sufficient soil sulfate levels, it is important not only to capture the sulfate leached in the autumn, but also to make sure that it is released in time for the following crop to use it efficiently. If the mineralization of catch crop sulfur is not synchronized with plant sulfur uptake, there is a risk of leaching losses prior to or after the growing season of the following crop. Most nonlegume species release both N and S soon after incorporation, especially the crucifers having low C/N and C/S ratios (Eriksen et al., 2004). Legumes are generally not as effective as they have high C/S ratios, which may retard sulfur mineralization considerably or even lead to immobilization of sulfur. In sulfur-deficient crop rotations, the release of sulfur from incorporated catch crops will not be able to fulfill the needs of sulfur-demanding crops such as oilseed rape, grass ley, kale, or onion and these will probably always need supplemental sulfur fertilizer. However, the mineralization of catch
Late Autumn (with catch crop)
0
Spring (crop incorporated)
Depth (cm)
25 50 75 100 125 150 0
2
4
6
8 10 12 2 4 µg SO4-S g−1 soil
6
8 10 12
Bare soil Italian ryegrass Fodder radish
Figure 7 Soil sulfate concentrations under different catch crops in autumn and after incorporation the following spring. Error bars: SE.
75
Soil Sulfur Cycling
crop sulfur will be able to contribute considerably to the sulfur nutrition of cereals, especially from cruciferous catch crops (Eriksen and ThorupKristensen, 2002).
4. Soil Sulfur Accumulation and Losses in Farming Systems 4.1. Sulfur mass balances Mass balances may give an indication of the status of a particular crop or farming system, but may also be useful when calculated at a regional or national level. The primary purpose of this is to evaluate the use efficiency of a particular nutrient within the agricultural sector. In Danish agriculture, the sulfur surplus was calculated as described by Kyllingsbæk and Hansen (2007) for nitrogen, phosphorus, and potassium. Using this approach, the sulfur surplus for Danish agriculture (input minus output) decreased from 38 kg S ha 1 in 1980 to 25–30 kg during the last 10–15 years (Fig. 8). As a result the use efficiency of sulfur (total output in % of total input) increased from 13% to 25%. For comparison, the efficiency of nitrogen increased from 20% to 37%, phosphorus from 23% to 52%, and potassium from 18% to 43% from 1980 to 2004 (Kyllingsbæk and Hansen, 2007). The lower efficiency of sulfur compared to other macronutrients is probably the result of less focus on sulfur surplus and also because sulfur is lost relatively easily from agricultural systems. Options for improving sulfur use efficiency are addressed in Section 5.
Input/output (kg S ha−1)
40 Feed stuff etc.
30
Atmospheric deposition
20 Fertilizer
10
0 −10
Plant products Animal products
1980
1985
1990
1995
2000
2005
Figure 8 Sulfur balance for Danish agriculture 1980–2006. Inputs shown as positive bars and outputs as negative.
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Generally, the advantages of the mass balances are their quantitative nature and value as a management tool, but they also have some serious shortcomings, the main one being the inability to estimate internal flows— which is a major drawback when applying nutrient balances to estimate potential losses, and the fact that they only address nutrient amounts and ¨ born et al., 2003). This also applies to sulfur balances, ignore availability (O especially when used at farm or field level. One pitfall is the inclusion in animal manure of sulfur that is partly in organic form and therefore not immediately plant available. The underlying assumption is that annual applications of organic sulfur in animal manure will eventually increase sulfur mineralization. However, the organic fertilizer history may not always be well described and it is virtually impossible to know if equilibrium will ever be reached between organic sulfur input and mineralization for the individual soil. Besides, as mentioned above, the mineralization of organic sulfur, whether in manure or in crop residues, may not be synchronized with plant demand. Much of the mineralization may take place during summer when soil temperatures are highest, which, in the case of cereals, may be too late to prevent sulfur deficiency (Haneklaus et al., 1995). The additional consequence of this situation is that substantial sulfate leaching can take place in the autumn and winter following a sulfur-responsive spring-sown cereal (Eriksen et al., 2002). In the same way that a positive sulfur balance may not always result in crop sulfur sufficiency, a negative balance may not always mean sulfur deficiency. When inputs such as atmospheric deposition are declining, sulfate leaching will for a while—depending on soil type and climatic conditions—reflect previous inputs more than the actual inputs. Obviously, when basing sulfur balances partly on historical data or literature values, great care should be taken that especially sulfate leaching reflects current sulfur inputs. 4.1.1. Example: Organic farming In conventional farming, sulfur deficiency is easily corrected by applying inorganic sulfur fertilizer once a deficiency has been identified. In organic farming systems, this is usually not an option and it seems obvious that sulfur deficiency will eventually occur. However, the timescale for this to happen and the severity depends on a number of aspects that are well illustrated by sulfur balances. Table 2 shows sulfur balances for two organic crop rotations—cereal and dairy production, the cereal rotation being at three sites. Included are the significant parameters described below, whereas parameters such as volatilization from crops or soil and weight gain by cattle are considered insignificant (Eriksen and Askegaard, 2000). Atmospheric Deposition. It used to contribute significantly to the sulfur balance in parts of the world but the inputs have declined and are also variable, even on a smaller geographical scale. Today, the atmospheric input
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Table 2 Sulfur balance (kg ha 1 yr 1) in crop rotations for organic cereal and dairy production, from Eriksen and Askegaard (2000) and Eriksen et al. (2002)
Clay content (%) Organic system Organic since Experiment period
Input Atmosphere Manure Irrigation Output Plants Leaching Balance
Jyndevad
Flakkebjerg
Foulum
Foulum
5
16 Cereal production 1997 1997–1999
9
8 Dairy farming 1987 1994–1997
10 4 15
13 5 0
7 2 0
11 12 10
2 32 6
2 20 4
3 34 28
10 20 3
is only 50–60% of what it was when the balances in Table 2 were established about 10 years ago. Furthermore, the depositions during autumn and winter are prone to sulfate leaching prior to the growing season of plants. Manure. In the cereal production systems, only little sulfur was applied in manure due to restrictions on the import of manure, in contrast to the dairy system where the manure is produced on the farm. It is important to keep in mind, though, that this may not all be plant available. In fact, following from the above, the immediate effect of sulfur in manure must be expected to be low for the cereal rotations, probably less than 1 kg S ha 1 yr 1. Irrigation. On the irrigated sites, the input through irrigation water was significant compared to the sulfur removed in plant material. The sulfur in this case originates from ground water, which typically contains 5–100 mg l 1 of sulfate in temperate regions (Eriksen et al., 1998). Furthermore, irrigation is well timed with plant growth and thus nutrient requirement. Plants. One very distinct difference between the dairy and the cereal systems was the yield levels as determined by N availability. In the dairy system nitrogen-fixing crops such as clover produced an ample supply of N giving high yields, thus creating higher sulfur need and sulfur uptake. Leaching. Sulfur leaching in cereal production was followed for 3 years following conversion to organic farming. The conversion was of pivotal importance for leaching losses. Leaching reflects previous management and it actually fell during the experiment, especially on the lighter soil types.
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Eventually, the leaching losses will approach those of the dairy system that was converted to organic farming 7 years prior to experiment start. Balance. Negative sulfur balances were found in organic cereal crop rotations caused by low inputs in manure and no irrigation at two sites. Leaching losses in the cereal rotation were considerable, but probably reflected site history more than current management, as sulfate leaching decreased considerably following conversion to organic management irrespective of soil type (Eriksen et al., 2002). This would partly have compensated for the reductions in input. One very important aspect is the very low removal of plant sulfur in the cereal rotation. This was caused by low yields due to nitrogen limitation and production of green manure that is recycled in the crop rotation instead of being removed. So despite negative balances, the cereal crop rotations are less sensitive to sulfur shortage than might be expected. However, great care should be taken to ensure availability and synchrony of the limited sulfur resources with plant needs. For the organic dairy farming system the balance was positive, but with declining atmospheric input and low plant availability of manure sulfur, the positive sign of the balance relies on sulfur in irrigation, which will vary between years due to differences in climatic conditions. In contrast to the cereal rotations, the dairy system was organic for many years and therefore plant sulfur removal and sulfate leaching are expected to reflect organic management. So although immediate sulfur deficiency may not occur, in the longer term a negative sulfur balance must be expected in this crop rotation. Thus, it will become necessary to use a plant-available sulfur source approved for organic farming in this crop rotation to avoid the negative effects of sulfur deficiency.
4.2. Long-term experiments The soil sulfur status caused by a particular land use or fertilizer practice may also be evaluated from the changes in the total soil pool. Since most management-related changes have relatively little effect on the large soil sulfur pool in the short term, long-term experiments offer the best possibility to investigate these changes. Generally, the long-term experiments point in the same direction: a buildup of the soil organic sulfur pool only occurs if there is a concomitant input of C to the soil, be it in the form of manure or plant material. Thus, long-term applications (35–153 years) of inorganic sulfur have in some studies hardly had any effect on soil organic sulfur contents (Kirchmann et al., 1996; Knights et al., 2001), probably because the additions were in excess of crop uptake and therefore lost through leaching. In a situation with sulfur deficiency in a pasture, the inorganic sulfur application has been shown to increase the amount of sulfur in the soil because of increased crop production and hence greater organic C input to the soil (BhupinderpalSingh et al., 2004).
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Changes in land use also affect soil sulfur stocks. When comparing permanent pasture with medium- to long-term cultivation (11 and 30 years, respectively), soil sulfur was reduced and it was significantly related to the decrease in soil C, a well-known consequence of pasture cultivation (Bhupinderpal-Singh et al., 2004). In the Broadbalk experiment, organic sulfur accumulation was found to be much higher in permanent grassland and woodland than in arable cropping systems, but annual applications of FYM equivalent to 7 t DM ha 1 to the arable system was able to maintain soil total sulfur content at the same level as permanent pasture and woodland, which in turn was 2.7 times higher than the sulfur content of mineralfertilized arable land (Knights et al., 2001). In the Askov long-term experiments on animal manure and mineral fertilizer (initiated 1894) and in other studies, soil C and S contents were found to be correlated, but their isotopic signatures were not (Bol et al., 2005). This indicates that although sulfur in the longer term is stabilized in association with C in soil organic matter, the short-term cycles are not necessarily so closely linked.
5. Perspectives for Sulfur Use Efficiency in Agriculture Compared to other macronutrients, the use efficiency of sulfur is low. In the Danish case the overall use efficiency was 25% (Section 4.1). In a situation where the sulfur nutrition of plants is expected to be balanced, this indicates that an extensive loss of sulfur occurs. To reduce these losses, it is necessary to know more about the quantities lost from different compartments. Figure 9 shows a mass flow diagram with current inputs and outputs, again using Danish agriculture as an example. The quantities were calculated by combining numbers in Fig. 8 and separate estimates of the sulfur balance Feed stuff etc. 5
Atmospheric deposition 6
Commercial fertilizer 22
Agriculture
Animal products
6
Livestock
Manure 8
Arable
Surplus: 5
14 Feed
Surplus: 20
2
Plant products
Figure 9 Sulfur mass flow diagram balance for Danish agriculture 2006 (kg S ha 1) with internal flows between arable and livestock sectors.
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of agricultural land (Eriksen, 1997c), making it possible to divide the sulfur surplus into contributions from arable and livestock production. The diagram shows that in 2006 arable land received a total input of 36 kg S ha 1 with commercial fertilizer as the main source (22 kg S). Sulfur in harvested crops was 16 kg ha 1 and most of this sulfur was used for feeding livestock, so only 2 kg S ha 1 was exported from the agricultural sector, reflecting the intensity of livestock farming in Denmark. Overall, arable land had a sulfur surplus of 20 kg S ha 1. Livestock farming received 5 kg S ha 1 in the form of feedstuff and a net input from the arable land of 6 kg S ha 1. As the output in animal products was 6 kg S ha 1, the sulfur surplus of animal production was 5 kg ha 1. Thus, in both livestock and arable sectors, there is a need for increasing the efficiency and also a potential for doing so. Arable Production. Several options exist for reducing the surplus of 20 kg sulfur ha 1 in the field:
Quantitatively, the surplus corresponds to sulfate leaching, as gaseous losses from agricultural land have been found to be insignificant ( Janzen and Ellert, 1998). Our research shows a considerable potential for catch crops to reduce this sulfate leaching, but to have an effect on the use efficiency of sulfur not only should catch crop strategies be employed, but the saved sulfur should also be counterbalanced by reductions in sulfur fertilizer inputs. As sulfur fertilization is not expected to have any adverse natural environmental consequences, application generally has a, in some cases considerable, safety margin. A closer match of sulfur with plant demand and also more site-specific applications based on balance considerations probably have the potential to increase sulfur use efficiency, which will, in the long term, keep us under the limit of 250 ppm for sulfate in drinking water. If the consequence of this is lower—but not critical—concentrations of sulfur in feed crops, it may potentially also increase the sulfur use efficiency of livestock production. There is evidence that low nitrogen availability gives a higher sulfur fertilizer efficiency in plants. This has been demonstrated in both cereals (Eriksen et al., 2001) and legumes (Sunarpi and Anderson, 1997), the reason being that higher in-plant mobility of sulfur during grain-filling increases redistribution from older leaves. This can be utilized in lowinput farming systems and may also explain the modest responses to sulfur application in organic farming systems (Eriksen et al., 2002). However, care should be taken to avoid hidden sulfur deficiency with reduced quality or yield without deficiency symptoms (Schnug, 1991). Furthermore, sulfur shortage also affects the efficiency of other nutrients and given the choice, high nitrogen and phosphorus efficiency would usually be given priority at the expense of sulfur. As a consequence of an expected, low, short-term availability of manure sulfur, Danish farmers are recommended not to reduce the sulfur level in
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mineral fertilizer in the year of manure application, whereas the long-term effect of organic manure to some extent is taken into account (Eriksen, 2002). However, the short-term availability may vary considerably and to ensure plant requirements are met an option would be to include plantavailable sulfate to increase sulfur use efficiency. In this context, there is a need to investigate rapid analysis for sulfate in manure. Livestock Production. The surplus from animal production of 5 kg sulfur ha 1 (Fig. 9) is expected to be emitted as gaseous sulfur mostly from animal manure. Except for the loss of plant-available sulfur from the manure, the consequence may be the development of malodorous, volatile, sulfurcontaining compounds. Several hundred malodorous compounds have been identified in pig manure. They can be divided into four groups (1) volatile fatty acids, (2) indoles and phenols, (3) ammonia and volatile amines, and (4) volatile sulfur-containing compounds (Zhu, 2000). Of the compounds that have the lowest odor detection thresholds for humans, six contain sulfur and the three with the lowest detection threshold all contain sulfur (O’Neill and Phillips, 1992). Hydrogen sulfide and methanethiol are the two sulfurous compounds most commonly causing offensive smells in pig manure (Spoelstra, 1980). Some studies have shown correlations between hydrogen sulfide and odor concentration (Clanton and Schmidt, 2001; Hobbs et al., 2000), whilst others show no such correlations (Le et al., 2005). Apart from odor, hydrogen sulfide is increasingly associated with health hazards. Maximum atmospheric concentrations have been derived based on respiratory effects for inhalation exposures (WHO, 2003). It may be speculated how we can avoid emissions of odorous compounds. Technical solutions are being investigated (Eriksen et al., 2008; Smet et al., 1999), but another option is to reduce sulfur inputs in feed either directly by reducing the content of sulfur amino acids (Le et al., 2006) or indirectly by establishing a closer match of sulfur with plant demand in the field. Currently an investigation is in progress on how emissions of sulfurous compounds may be reduced.
6. Conclusions Despite the essential role of sulfur for plant growth, it has historically received little attention because of an ample supply from the atmosphere and commercial fertilizers. However, during the last 20–30 years, the situation has changed dramatically and it has been necessary to give sulfur in cropping systems greater consideration. Today, we face a challenge of optimizing sulfur availability in cropping systems in synchrony with plant demand and in the required form and quantity.
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Organic sulfur in soil forms a highly complex system of compounds, the mineralization of which occurs along a continuum of different timescales. This makes prediction of the potential plant availability of soil organic sulfur difficult and methods based on identification of compounds or groups of compounds with distinct chemical properties have failed, although they often reflect the long-term agricultural management. This is, at least partly, because association with soil particles and aggregates provides extensive physical protection of soil organic sulfur against decomposition. As an alternative, balance considerations will often be able to provide a better background for fertilizer recommendations, keeping in mind that availability and synchrony are ignored using this approach. The efficiency of sulfur in organic manures, especially animal manure, seems underexplored, considering the focus on animal manure management in large parts of the world. Although part of the sulfur in animal manure is plant available to the crop in the year of application (the extent depending on feeding and storage conditions), most of the residual sulfur will probably not mineralize any more readily than the bulk of soil organic sulfur. Furthermore, as a source of sulfur for plants, mineralization of residual and soil organic sulfur may not be in synchrony with the demand of crops with a short growing season and may even lead to increased leaching losses of sulfate. In some cases catch crops, especially crucifers, have been shown to serve the double purpose of reducing sulfate leaching and increasing synchrony with crop demand. However, when planning catch crop strategies, priority is usually given to maximizing N efficiency, and it is in this context the effect of catch crops on sulfur nutrition should be seen. Compared to other macronutrients, the use efficiency of sulfur is low. In the case of Danish agriculture the overall use efficiency was 25%. Of the sulfur surplus 20% (5 kg ha 1) was related to livestock production and 80% (20 kg ha 1) to arable production. In arable production, there is a potential for a closer and more site-specific match of sulfur with plant demand, catch crops can reduce sulfate leaching, and the sulfur demand in mineral fertilizer can be reduced where animal manure applications are used. The surplus from animal production is expected to be emitted as gaseous sulfur mostly from animal manure. Except for the loss of plant-available sulfur from the manure, the consequence may be development of malodorous, volatile, sulfur-containing compounds. There are ways to avoid this, including technical solutions and a reduction of the sulfur content in animal feeds.
ACKNOWLEDGMENTS Dr. Thomas Ellermann is gratefully acknowledged for advice on estimates of historical sulfur deposition, and Dr. Arne Kyllingsbæk is acknowledged for invaluable assistance in calculation national sulfur balances. Figure 6 is reprinted with permission from The International Fertiliser Society.
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C H A P T E R
T H R E E
Regional Vulnerability of Climate Change Impacts on Asian Rice Production and Scope for Adaptation R. Wassmann,*,† S. V. K. Jagadish,* K. Sumfleth,* H. Pathak,‡ G. Howell,* A. Ismail,* R. Serraj,* E. Redona,* R. K. Singh,* and S. Heuer* Contents 1. Introduction 2. Overview of Climate Change Impacts on Rice Plants 3. Adaptation in Vulnerable Rice-Growing Environments 3.1. Regions with heat stress under current climatic conditions 3.2. Drought-prone regions with rainfed rice 3.3. Deltaic regions 4. A Regional Case Study with High Vulnerability: The Rice–Wheat System in the Indo-Gangetic Plains 4.1. Climate change and climatic variability scenarios for South Asia 4.2. Vulnerability of the rice–wheat system due to climate change 5. Outlook: Current Advances and Future Prospects References
92 93 95 96 103 106 110 111 114 126 127
Abstract Rice is the principle staple crop of Asia and any deterioration of rice production systems through climate change would seriously impair food security in this continent. This review assesses spatial and temporal vulnerabilities of different rice production systems to climate change impacts in Asia. Initially, the review discusses the risks of increasing heat stress and maps the regions where current temperatures are already approaching critical levels during the susceptible stages of the rice plant, namely Pakistan/north India (Oct.), south India (April, Aug.), east India/Bangladesh (March-June), Myanmar/Thailand/Laos/Cambodia (March-June), Vietnam (April/Aug.), Philippines (April/June), Indonesia (Aug.) and * { {
International Rice Research Institute (IRRI), Metro Manila, Philippines Research Center Karlsruhe (IMK-IFU), Karlsruhe, Germany International Rice Research Institute (IRRI), New Delhi, India
Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01003-7
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2009 Elsevier Inc. All rights reserved.
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China ( July/Aug.). Possible adaptation options for heat stress are derived from regions where the rice crop is already exposed to very high temperatures including Iran and Australia. Drought stress is also expected to aggravate through climate change; a map superimposing the distribution of rainfed rice and precipitation anomalies in Asia highlights especially vulnerable areas in east India/ Bangladesh and Myanmar/Thailand. Then, the review gives emphasis to two rice growing environments that have outstanding importance for food supply in Asia and, at the same time, are particularly vulnerable to climate impacts. The mega-deltas in Vietnam, Myanmar and Bangladesh are the backbone of the rice economy in the respective country and will experience specific climate change impacts due to sea level rise. Significant improvements of the rice production systems, that is, higher resilience to flooding and salinity, are crucial for maintaining or even increasing yield levels in these very productive deltaic regions. The other ‘hotspot’ with especially high climate change risks in Asia is the Indo-Gangetic Plains (IGP) which will be affected by the melting of the Himalayan glaciers. The dominant land use type in the IGP is rice-wheat rotation, and we discuss specific vulnerabilities and possible adaptation options in the different sub-regions of the IGP. We conclude that geo-spatial vulnerability assessments may become crucial for planning targeted adaptation programs, but that policy frameworks are needed for their implementation.
1. Introduction Climate change has many facets, including changes in long-term trends in temperature and rainfall regimes as well as increasing year-to-year variability and a greater prevalence of extreme events. The impacts of these changing conditions on agriculture are already being seen, yet considerable gaps remain in our knowledge of how agricultural systems will be affected by both short- and long-term changes in climate, and what implications these changes will have for rural livelihoods, particularly among the most vulnerable people. Despite some projected increase in photosynthesis caused by higher concentrations in CO2, rising temperature will have a far greater detrimental effect, resulting in reduced productivity. For some regions and crops, there will be opportunities for increased production, but for most there is simply not enough information available about the impacts at scales that are relevant for decision making and research prioritization. This review addressing climate change impacts on rice production follows up on a detailed description of stress physiology and adaptation options in terms of both germplasm and crop management development (Wassmann et al., 2009). Based on the ‘‘generic’’ presentation of climate change impacts on rice plants in the previous review, this second review provides a geographic presentation of vulnerability to climate change and potential adaptation. Although climate change is a global phenomenon, it will
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manifest itself as locally variable impacts. This variation breeds uncertainty about the nature of change, preventing people at all levels from making the critical decisions that are necessary for being able to adapt. If future impacts are better anticipated, these decisions could be better managed. The geographic domain of this presentation comprises South, East, and Southeast Asia and corresponds to approximately 90% of the global rice land. We conducted two different approaches of regional assessments. Initially, we identified regions with already existing climatic stresses (heat and drought) as well as deltaic regions as extremely vulnerable rice-growing environments in Asia. Then, we selected one production system with outstanding importance for food security that will encompass high risks under global climate change: the rice–wheat system of the Indo-Gangetic Plains (IGP). For each of these assessments, the objectives are (1) to analyze existing climatic stresses and their trends under climate change, (2) to assess the specific threats to the rice (or rice–wheat) system, and (3) to suggest adaptation strategies for rice production. While this presentation focuses on the technological options of adaptation, the authors recognize that responses to climate change need to encompass several levels, including crop and farm-level adaptations, national-level agricultural and supporting policies and investments, and regional and global policies and investments. Technological progress alone will be insufficient to cope with climate change, but research on germplasm improvement and crop management represents a pivotal component in climate policy. More than 800 million people in tropical and subtropical countries are currently food insecure. Their situation is expected to worsen, and the number of food insecure people is likely to increase as a consequence of climate change impacts—unless drastic measures are implemented to increase their capacity to adapt to climate change.
2. Overview of Climate Change Impacts on Rice Plants Increasing anthropogenic emission of greenhouse gases and their subsequent accumulation in the troposphere are the driving force for future climate change (IPCC 2007). The adverse effects of atmospheric changes are presently felt and are predicted to worsen in the future. Table 1 summarizes the principal findings of the recent Fourth Assessment Report of the IPCC (IPCC, 2007), which ranks the probability of given impacts into different classes. A gradual increase in temperature, as reflected in fewer cold days and more frequent hot days, is already discernible in most regions and will intensify in the future. In turn, the higher temperatures will further increase the intensity and frequency of heat spells. This trend, which is
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Principal conclusions of the IPCC Fourth Assessment Report (IPCC, 2007) Probability of trenda
Climate change impact and direction of trend
Warmer and fewer cold days and nights over most land areas Warmer and more frequent hot days and nights over most land areas Frequency of warm spells/heat waves increases over most land areas Frequency of heavy precipitation events increases over most land areas Areas affected by drought increases in many regions Intense tropical cyclone activity increases in some regions a
Recent decades
Very likely
Future
Likely
Virtually certain Virtually certain Very likely
Likely
Very likely
Likely
Likely
Likely
Likely
Very likely
Probability classes: likely > 66% probability of occurrence; very likely > 90% probability of occurrence; virtually certain > 99% probability of occurrence.
deemed almost certain for future conditions has serious implications for agricultural production and human survival. In the more immediate term, however, changes in precipitation may exert a stronger impact on agricultural production than temperature changes. Similarly, frequent floods due to heavy precipitation may result in higher yield losses under progressing climate change. On the other hand, the predictions of extreme climate events under future climate conditions are attached to considerable uncertainty, so that the Fourth Assessment Report assigned a lower probability to this trend than for the other impacts (Table 1). Moreover, higher temperature will increase sea level due to (1) thermal expansion of sea water and (2) rapid melting of glaciers and ice caps. As a consequence, fragile coastal and highly productive deltaic rice cultivation areas will be more exposed to inundation and salinity intrusion. For rice production, increasing [CO2] leads to a cascade of impact mechanisms that have both beneficial and harmful effects on agricultural production in general and rice production in particular. Figure 1 synthesizes the different impact mechanisms, which are explained in Wassmann et al. (2009). The present [CO2] of 380 ppm, projected to double by the end of the century (IPCC, 2007), could benefit the rice crop by increasing photosynthesis and biomass depending on rice cultivar, growth stage, and environment. Moreover, higher [CO2] beneficially influences stomatal behavior, resulting in less water lost through transpiration, that is, increasing water use efficiency. On the other hand, an increase in [CO2] and other greenhouse gases increases surface air temperature, further aggravating
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Increased [CO2]
Increased [non CO2] Higher air T
Precipitation changes
Sea-level rise/ extreme events
More drought
More salinity
Higher canopy temp/VPD
Partial closure of stomata
Higher photosynthesis
Higher WUE
More biomass production
Lower salt uptake
Higher nutrient demand
Higher respirational losses Shorter growing season
More flooding
Spikelet sterility
Figure 1 Schematic presentation of potential effects of rise in CO2 concentrations and temperatures on rice and its growing environment.
abiotic stress for crop production. At the plant level, higher temperatures enhance the respiratory rates during the day and more so due to increasing night temperatures. Simultaneously, this temperature rise will result not only in reducing the growth duration of the rice crop but also the duration for grain filling, resulting in lower yield and lower quality rice grain. Collectively, the disadvantages largely outweigh the advantages for rice cultivation under predicted future climate change.
3. Adaptation in Vulnerable Rice-Growing Environments This section comprises in-depth assessments of rice environments that are deemed particularly vulnerable to climate change impacts. First we considered regions in Asia where rice is already exposed to high temperatures at the critical plant stages. Thus, any increment in temperatures will directly translate into more heat stress for rice plants. In Section 3.2, we consider drought problems, with an emphasis on rainfed rice. Then, we focus on delta areas because they are highly exposed to sea-level rise and extreme weather events and, at the same time, are enormously important for rice production. Finally, we highlight one region that in most climate change impact projections is identified as a major hotspot of impeded food production (IPCC, 2007): the IGP. The specific vulnerability of agriculture in the IGP stems from its dependence on water released from
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Himalayan glaciers, which cover about three million hectares at present, but that are receding at a very fast pace.
3.1. Regions with heat stress under current climatic conditions Temperature is known to determine the geographic belt where rice is grown, but the principal influence on yield limitation derives from low temperatures. In turn, it can be assumed that heat per se is not usually the limiting factor constraining rice production. The imperatives of staple food production, particularly under multiple cropping systems, have developed heuristically, which maximize yield and minimize risk. Selection and adjustment of cropping calendars have been the key to successful rice production in hot regions. In many parts of Asia, the main rice crop falls into the wet season, which renders the dual benefit of ample water supply and cooler days. The dry season is potentially at risk in many regions (see below), but conventional variety selection and management usually keeps the incidence of heat-induced sterility low. Nevertheless, it seems justified to assume that progressing climate change will soon cause heat-induced losses in those places where the varieties and systems in place are already at their limits. 3.1.1. Geographical analysis of heat stress in Asian rice production Figure 2 maps maximum average daytime temperatures from the period 1950 to 2000 across Asia, where temperatures exceed 33 C during the hottest months (March–October); temperatures from November to February are below 33 C throughout Asia (data not shown). Regions exposed to temperatures >33 C occur in a broad band across continental South and Southeast Asia, in Central China, and in smaller patches of insular Southeast Asia. Temperatures exceeding even 36 C occur in wide parts of the sub-Indian continent and patches in Myanmar and Thailand. The encircled regions in Figure 2 indicates where crops are expected to be in the critical stages, namely, the flowering and maturing stage. This information has been derived from country-specific sections of the Rice Almanac (Maclean et al., 2003) and from Siddiq (2006) for India, compiled in Table 2. The respective harvest month as well as the preceding month have been considered as a heat-sensitive plant stages. Regions where these stages coincided with high temperatures were then encircled on the map. The maps in Figure 2 include Iran, because this chapter elaborates on this country as an example for rice production under extremely high temperatures. The rice crop in Iran is typically harvested in September/October so that high temperatures in August/September denote heat stress for the plants. The climate of the IGP will be discussed in detail in Section 4, so that the presentation here focuses on heat. The climate in Pakistan is characterized by high temperatures from April to October, so that the critical stage of the
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March
April
May
June
July
August
September
October
40 >
33 –3 4 34 –3 5 35 –3 6 36 –3 7 37 –3 8 38 –3 9 39 –4 0
Temperature in degrees C
Figure 2 Asia’s rice-producing regions highlighting maximum temperatures >33 C (monthly averages). Circles depict high-temperature regions where rice crop is at flowering and/or ripening stage in the given month (Datasource: Hijmans, 2005 and Maclean, 2003).
Table 2
Cropping calendars in Asian countries according to Rice Almanac (Maclean, 2003) and Siddiq (2006) for India Harvest
Country
Pakistan India
Region
North, Northwesta West Bengal, Bihar, Jharkhand Orissa Tamil Nadu, Kerala, Karnataka
Sri Lanka Bangladesh Myanmar Thailand North–Central South Laos Cambodia Vietnam Vietnam Malaysia Indonesia Philippines Chinab a b
Peninsular Sabah–Sarawak North South South/Central North
‘‘Wet’’ season crop
‘‘Dry’’ season crop
September–October August/September November December December/January/February August–September November–December November–January November–December March–May November–December November–January September–December
– April May/June April April/May February–March April–May April–May May–June August–September April–June April–May April–June (winter–spring) August–September (summer–autumn) – – Varying May–June March–May October–November (late rice) –
November–March January–April February–June October–December November–December June–October (early or single rice) October
Uttar Pradesh, Haryana, Punjab, Madhya Pradesh, Gujarat, Rajasthan, Himachal Pradesh, Jammu and Kashmir (with only one season). Distinction between wet and dry season not applicable to China since either crop receive considerable amounts of rainfall.
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rice crop (harvested in September/October) also falls into a hot period. Rice production in northwestern India is not so affected at present, because temperatures are still below 33 C during the dry season crop. The western and southern parts of the Indian subcontinent experience high temperatures (>35 C) from March to June. Thus, the dry-season crop in the states of Bangladesh and the Indian states of West Bengal, Bihar, Jharkhand, Orissa, Tamil Nadu, Kerala, and Karnataka will experience aggravating heat stress under rising temperatures. In continental Southeast Asia (Myanmar, Thailand, Cambodia, Laos, and Vietnam) and the Philippines, the hottest months are before the onset of the monsoon season from March to June. This period encompasses the final stage of the dry-season crop, which is typically harvested from April to June (Table 2). Limited areas in central Thailand and Myanmar experience temperatures above 36 C (Figure 2), so the rice crop is already at critical limits of heat tolerance. Rice production in Malaysia and Indonesia appears to have less of a heat stress problem at present because temperatures at the critical stages are generally below 33 C. The highest temperatures in the main rice-growing areas of Indonesia are in August, when a relatively small area of rice is at a sensitive plant stage. For China, the critical months are July and August when large rice areas in central China experience maximum temperatures >35 C. In this part of China, rice is grown either as a double crop (early and late rice) or as a single crop (Table 2). In China, the distinction between the wet and dry season is not applicable since either crop receives considerable amounts of rainfall. High temperatures in July/August coincide with the early (heat-resilient) stage of late rice, but may hit the rather heatsensitive stages of the single crop (Figure 2). Incidences of heat-induced sterility under present climatic conditions are reported for many Asian countries (Yoshida, 1981). However, these losses may not be caused by high temperature alone, but by the concurrence of high temperature and relative humidity. Excessive heat has been shown to affect yield potential by stressing the crop during vulnerable periods of the plant life/production cycle (see Wassmann et al., 2009) and also because temperature in excess of 35 C decreases the efficiency of photosynthetic fixation (Crafts-Brandner and Salvucci, 2000). Documenting the thermal tolerance of rice is difficult because of the interaction of temperature with humidity and wind in causing heat stress as well as cultivar-dependent stress tolerance (Sheehy et al., 1998; Weerakoon et al., 2008). As a result, any consequent yield depression incorporated into computer models is a matter for contention (Sheehy et al., 2006). 3.1.2. Rice farming in high-temperature regions Given a sufficient amount of irrigation water, yield potentials for rice in hot, arid regions can be very high. For example, Egypt has a national average rice yield >10 t ha1, which is twice as much as in most Asian countries
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(IRRI, 2008). Likewise, the Australian rice industry, located mainly in the hot and dry Riverina region, had yields in the range of 7.5 to 9.5 t ha1 (IRRI, 2008) before an extensive drought effectively brought rice production to a halt. What these areas have in common are suitable alluvial soils, access to irrigation water, many days with cloud-free skies, long photoperiod, and high levels of incident radiation, and they are all hot during their rice-growing seasons. In contrast to these arid regions, tropical rice production is subject to a shorter photoperiod, much higher rainfall, and the associated cloud cover decreases incident radiation and increases heat retention at night. To this end, farmers in high temperature regions do successfully produce rice if there is (1) suitable soil, (2) sufficient water, and (3) correct varieties planted in the most favorable cropping season, and (4) the returns on an alternate crop do not preclude rice from being grown. These cropping systems seem to be well adapted to current thermal conditions, but their production potential may dwindle under ongoing warming since temperatures are often already close to critical levels. The Riverina region of Southern NSW Australia played host to one of the most profitable rice industries in the world until 2004, when continuing drought forced it to virtually cease production. The semi-arid conditions of the Riverina proved to be ideal for temperate rice production even though temperatures regularly exceed 35 C during the critical flowering period; however, low humidity increases the efficiency of evapotranspirative cooling and even Japanese-adapted varieties such as Koshihikari can be grown easily when water is available (Matsui et al., 2007). Weerakoon et al. (2008) reported that increasing sterility associated with rice plants exposed to temperatures >36 C (day), >30 C (night) and 85% relative humidity and that the effect declined markedly at lower humidity. Similarly, the fertility response was variety dependent. Ding et al. (2005) postulated that selection for increased evapotranspirative cooling is possible; however, such plants would actually require more water when lack of water is likely to be coincident, if not consequential, to elevated temperatures. Unfortunately, the continuing drought over much of Australia for the last 8 years has severely affected the water supplies for the irrigation schemes that feed the Australian rice industry. It can be argued that this drought in itself is the result of anthropogenic global warming but it is not the heat alone that prevents rice from being grown in the Riverina at present. As water becomes more scarce and expensive, Riverina farmers are looking to less water demanding and higher value crops to get a better return on their remaining water entitlements. It remains to be seen whether eventual rainfall and the current high price for rice will lure growers back into growing rice in the future. The Australian case also offers some insight into possible shifts in ricegrowing regions driven by climate change. Advance planning for the Australian rice industry has concluded that producing in only a single region risks the viability of the industry as has been demonstrated by the current drought, which has brought the industry to its knees. The harvest from the
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first experimental trials conducted by the Rice Growers Cooperative on the north coast of NSW yielded a respectable 8 Mg ha1 in 2008 (Reinke, personal communication). This was achieved using traditional Riverinaadapted cultivars, which were grown in a largely rainfed peaty coastal swamp area (Reinke, personal communication). This move not only shows the resilience and adaptability of the Australian rice industry, but also may indicate a move to lessen reliance on irrigated production in the future. Not all countries, however, will have the flexibility of being able to move their rice industries to different climatic regions as they are physically smaller and have very large populations dependent on local production and distribution. It should also be noted that adjusting varietal selection and husbandry to suit altered climatic conditions will take time, so that the migration of rice production to more suitable areas can only be a mid- or long-term option. Iran and Pakistan rice is grown in places where temperatures during the growing season regularly exceed 40 C (Figure 2). Even in these genuinely hot places, temperatures are still increasing at a significant rate. At the rice region in Ahvaz in southern Iran, minimum temperatures are increasing at a statistically significant trend (R2 ¼ 0.86) at about 0.097 C yr1 (Morardi, personal communication). The trends for temperature maxima are less pronounced (slope ¼ 0.024 C yr1; R2 ¼ 0.12), but temperatures even increase during the months of highest temperatures, that is, July (slope ¼ 0.03 C yr1; R2 ¼ 0.15) and August (slope ¼ 0.05 C yr1; R2 ¼ 0.3) (Morardi, personal communication). Therefore, even cultivars that are well adapted to current heat stress in this region might experience more heat damage in the future. Soltani et al. (2003) concludes that unless new heat tolerant rice varieties are developed expected climate change will halve production in the south of Iran, making rice cultivation unviable. Khakwani et al. (2005) found that crop management during the early plant stages has a profound effect on rice yields in hot areas. Rice variety Koshihikari, yields higher with direct seeding under high temperature and low humidity of Australia, but falters in South Asia, which may be attributed to transplanting shock. In the perennially hot Dera Ismail Khan region of Pakistan, Baloch et al. (2006) reported differences in yield for crops transplanted within a 1–3 week timeframe of almost 75% as late transplanting meant that flowering coincided with unfavorable heat. However, high percentages of panicle sterility could be compensated for by increasing plant density. 3.1.3. The future for rice in warmer regions As previously discussed, accurately predicting climate change impacts is difficult but expected changes driving biosphere responses have largely been agreed upon: it will get hotter, carbon dioxide levels will go up, sea levels will rise and the intensity and frequency of extreme climatic events such as heat waves, droughts, and floods will also increase across the current
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arable regions of the world (IPCC, 2007). With such an array of potential problems, breeders will have to incorporate and stack many desirable genes into new cultivars. To reduce the effects of temperature-induced sterility, several desirable characteristics have been proposed that may overcome projected yield losses if current varieties are retained (Sheehy et al., 2004; Yoshida et al., 1981). A highly heat-tolerant cultivar (N22) has already been identified ( Jagadish et al., 2008; Prassad et al., 2006; Yoshida, 1981), from which temperature resistance can be incorporated into the mega-cultivar. Others have proposed exploiting avoidance via time of day of flowering (Heuer and Howell, 2007; Nishiyama and Blanco, 1980; Sheehy et al., 2005) and more effective transpirative cooling (Ding et al., 2005). Global rice production areas will undergo some realignment but the extent of this will depend on how much change is ultimately experienced. Current models show production trends but are not robust enough to predict a new geography of rice production systems. As was previously discussed, some already hot areas such as Australia’s Riverina may lose production to more favorable regions. Even in the most temperate of rice-producing areas, increased heat stress will lower production if nothing is done to counteract the effects of climate change. A study by Iizumi et al. (2006) predicts that as temperatures rise in Japan, rice yields will rise in the northern part of the county but become lower on average in Southern Japan because of increased heat stress; although newer cultivars may alleviate this. Heat stress is often aggravated by drought and their combination is an excellent example for the simultaneous occurrence of two abiotic stresses (Moffat, 2002). Garrity et al. (1986) concluded that selecting for plants with low panicle transpiration and water use potential causes the plants to overheat and become sterile. Weerakoon et al. (2008) found that different rice varieties vary in their ability to cool themselves. Under drought, innate and heritable differences exist in the ability of varieties to extract the remaining water and continue to cool themselves (Hirayama et al., 2006). Such traits are likely to be incorporated as pressure mounts to move from irrigated to dry-land production systems where drought and heat are much more likely to affect yield. In Indonesia, it has been proposed that adaptation to increased temperatures, brought about by anthropogenic climate change, could be improved if more heat-tolerant varieties were adopted (Amien et al., 1999). However, changes to the pattern and intensity of rainfall may frustrate attempts to develop suitable heat-tolerant cultivars that might ameliorate the effect. In recent times, droughts and floods have had a significant impact on rice production in Java and it has been speculated that change in the distribution and intensity of rainfall has been brought about by global climate change (Rulistia, 2008). So, while the temperature there does not exceed the thermal tolerance for rice, changes to regional weather systems brought
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about by small increases in the global heat budget have lowered production in an area that is highly dependent on rain. Rice will continue to be an important staple throughout Asia. In hot regions, adaptation to even higher temperatures will mean that new traits and practices will have to be incorporated. Even in regions where heat per se may not be limiting rice production, production may decline because of increased crop demand for water and a concomitant decrease in water availability. As Earth warms new areas will open up and many more will have to change their way of growing rice and the varieties used. Rice production areas currently affected by high temperatures such as Australia’s Riverina and Iran offer some insight into what these changes may hold for the future.
3.2. Drought-prone regions with rainfed rice Drought is an important climatic factor that cause substantial yield losses. Even in subhumid rice-growing areas of Asia, Drought regularly affects 23 million ha of rice land (Pandey et al., 2007a) and more than 50% of this area is in China, where droughts cause economic losses that correspond to 0.5–3.3% of the agricultural sector gross domestic product. In India, particular drought years such as 1987 and 2002/2003 have affected more than 50% of the total cropped area and almost 300 million people across the country (Pandey et al., 2007b). In Thailand, the 2004 drought affected 20% of the rice land and more than 8 million people (Pandey et al., 2007b). Even before a complete failure of a crop as in these cases, drought of milder intensity can also lead to substantial losses. The current projections of climate change scenarios include a strong likelihood of a shift in precipitation patterns in many regions. It is predicted that, by 2050s, the area of land subjected to ‘‘increasing water’’ stress due to climate change will be more than double that with ‘‘decreasing water’’ stress (Bates et al., 2008). Increased annual runoff in some areas is projected to lead to an increased total water supply, but this benefit is likely to be counterbalanced in many regions by the negative effects of increased precipitation variability and seasonal runoff shifts in water supply, water quality, and flood risks. Changes in water quantity and quality due to climate change are expected to affect food availability. Drought can be defined in different ways; from an agricultural perspective, drought corresponds to insufficient soil moisture to meet crop water requirements thus leading to yield losses. Since this definition requires the consideration of various factors, that is, actual and potential evapotranspiration, soil water deficit, and production losses, the mapping of drought over larger regions is usually done by simplified indices. Figure 3 displays the Weighted Anomaly Standardized Precipitation (WASP) index, which gives an estimate of the relative deficit or surplus of precipitation for different time intervals (Lyon and Barnston, 2005). WASP assesses the precipitation deficit
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9– 10
7– 8
5– 6
3– 4
1– 2
Weighted anomaly of standardized precipitation (WASP) 1980–2000 [deciles]
1 Dot = 50,000 ha rainfed rice
Figure 3 Weighted Anomaly Standardized Precipitation (WASP) index based on average monthly precipitation data from 1980 to 2000 at a resolution of 2.5 (data sets from Columbia University Center for Hazards and Risk Research, International Research Institute for Climate Prediction, and Center for International Earth Science Information Network and Huke and Huke, 1997).
or surplus over a three-month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation (see http://www. ciesin.columbia.edu/repository/metadata/ig/Browse/GlobalDroughtHazard FrequencyandDistribution.html). The three-month averages are derived from the precipitation data and the median rainfall for the 21-year period of each grid cell. Grid cells where the three-month running average of precipitation is less than 1 mm per day are excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50% of its long-term median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).
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The WASP map is overlaid with the distribution of rainfed rice areas (Figure 3). Thus, the map clearly indicates two broad regions with droughtprone rainfed rice; that is, (1) eastern India/Bangladesh and (2) northern Thailand/southern Myanmar, which are both encircled. Both regions have been included in a recent comparative analysis of drought effects in rainfed rice (Pandey et al., 2007b). In eastern India, the economic cost of drought was found to be substantially higher than in other rainfed areas due to higher probability and greater spatial covariance of drought and less diversified farming systems. In this part of India, rice accounts for a larger share of household income, which implies high risks for food security within the local populations. Although farmers deploy various coping mechanisms, such mechanisms are largely unable to prevent a reduction in income and consumption, especially in eastern India. In the Eastern Indian states of Jharkhand, Orissa, and Chhattisgarh alone, rice production losses during severe droughts (about once every 5 years) average about 40% of total production, with an estimated value of US $800 million (Pandey et al., 2007a). Bangladesh is affected by major country-wide droughts about every 5 years, but local droughts occur frequently and affect crop production life cycles at the respective location. Agricultural drought, related to soil moisture deficiency, occurs in Bangladesh rice production at various stages of crop growth. Monsoon failure often brings yield reduction and famine to the affected regions. A better understanding of the monsoon cycle would clearly be of major scientific and social value. In northwest Bangladesh, the average annual rainfall varies between 1500 and 2000 mm, with more than 200 mm of rainfall per month during the monsoon period ( June–September), when transplanted Aman rice is grown mostly under rainfed conditions. However, the erratic rainfall distribution causes frequent droughts in this region, and results in yield losses that are generally higher than the damage caused by flooding and submergence (Towfiqul Islam, 2008). A recent characterization and modeling study showed that the recurrence interval of drought is around 2 and 3 years, especially during the later part of the Aman crop, generally recognized as terminal drought (Towfiqul Islam, 2008). Short duration varieties such as ‘‘BRRI dhan 39’’ are generally used to escape this specific risk in this region. However, the risk of early drought is also very serious and the return period of 10-mm rainfall deficit is up to 1.3 years in some districts, which requires a new set of drought-adapted Aman rice varieties. Northern Thailand suffers from severe drought in particular years, for example, 2004/2005. In totality, the economic costs of drought in northern Thailand were found to be lower than in eastern India, in both absolute and relative terms, due to a lower frequency and less covariate nature of drought (Pandey et al., 2007b). In addition, household income had a more diversified structure, making them less dependent on rice yields. Figure 3 also highlights areas in Myanmar, Cambodia, and Vietnam as
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potentially drought-prone, but the relatively low abundance of rainfed rice in these regions limits direct drought losses for rice production. Scientific advances in meteorology and informatics have made it possible now to forecast drought with a reasonable degree of accuracy and reliability. Various indicators such as the Southern Oscillation Index are now routinely used in several countries to make drought forecasts. Suitable refinements and adaptations of these forecasting systems are needed to enhance drought preparedness at the national level as well as to assist farmers in making more efficient decisions regarding the choice of crops and cropping practices.
3.3. Deltaic regions The coastlines of South, East, and Southeast Asia comprise several megadeltas (Figure 4), of which nine are larger than 1 million ha (IPCC, 2007). Rice production in these mega-deltas forms the backbone of the agricultural Bangladesh
Ganges-Brahmap. D.
Other regions
Vietnam
30,000
20,000
10,000
0
Mekong D.
Red river D.
Other regions
40,000 Rice production (000 t)
Rice production (000 t)
40,000
1993
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Myanmar
1999 Year
2001
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2005
30,000
20,000
10,000
0 1993
Irrawaddy D.
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1997
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2001
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2005
Other Regions
Rice production (000 t)
40,000
30,000
20,000
10,000
0 1993
1995
1997
1999 Year
2001
2003
− −1 1 − 0 0 − 1 1 − 2 2 − 4 4 − 7 7 − 12 12 − 2 20 0 − 35 35 − 5 50 0 − 70
−
−2
−5
−
−2
Elevation above sea level [m]
Figure 4 Low elevation areas in Asia and rice production data from deltas in Vietnam, Myanmar, and Bangladesh (rice statistics from IRRI, 2008, DEM from SRTM).
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sector in many Asian countries and is responsible for a large share of the rice that is internationally marketed. At the same time, the topographic settings and vicinity to the coast line render delta regions especially vulnerable to the consequences of climate change, namely, those of (1) sea-level rise and (2) storm surge. Observations from tidal gauges indicate that the mean global sea level has risen by about 10–25 cm over the last 100 years (IPCC, 2007). Model projections of future global mean sea-level change, based on temperature change projections, show a rise of between 13 and 94 cm by 2100, with a central estimate of 49 cm (IPCC, 2001). Rice is the predominant form of land use in many coastal and deltaic regions of the tropics. No crop other than rice can be grown under these adverse conditions of unstable water levels and highly saline locations. The elevation map in Figure 4 exhibits the large delta areas in Asia and exemplifies their significance for rice production in Vietnam, Myanmar, and Bangladesh. In Vietnam, the Mekong Delta alone yields 54% of the national rice production, with the Red River Delta adding another 17% (data from IRRI, 2008; for the year 2005). Production growth in the Mekong Delta has been the driver for the steadily increasing rice production in Vietnam over the last decades. The Mekong Delta contributes to the vast share of rice exports of Vietnam, which account for 4.7 million tons of rice per year (in 2007), making it the second largest exporter worldwide (IRRI, 2008). Thus, any shortfall in rice production in this area through climate change would not only affect the economy and food security in Vietnam, but also have repercussions for the international rice market. The deltas of Myanmar (Irrawaddy) and Bangladesh (Ganges–Brahmaputra) provide 68% and 34% of the national rice production, respectively. The rice produced in these deltas is almost entirely used for domestic consumption since Myanmar exports relatively small amount of rice (100,000 t yr1) and Bangladesh is a rice importing country. While rice production in the mega-deltas is currently on the rise across Asia, the comparative advantage of these regions has shifted over recent decades and is still an open question under aggravating climate change (Figure 5). Before the Green Revolution, the Asian delta regions held a comparative advantage in rice production because of fairly productive—by contemporary standards—floating and deepwater rice systems (Dawe, 2005). The early beneficiaries of the Green Revolution, however, were those areas where it was possible to irrigate two crops of rice with the construction of irrigation systems, for example, Punjab in India and Central Luzon in the Philippines. The deltaic regions initially were unable to take advantage of the new rice technologies, but have regained a comparative advantage over the past 15–20 years. Short duration cultivars facilitated an adjustment of cropping calendars to avoid excessive flooding; pumps are becoming increasingly popular to tap the shallow groundwater for irrigation during the dry season.
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New cropping systems (short-duration rice varieties) avoid floods and are irrigated by low-cost pumping
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Deepwater and flooding rice were more productive than other ‘low input’ rice systems
New rice technologies hampered in deltas by inability to control floods
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Green revolution
Figure 5 Schematic presentation of comparative advantage of rice production in Asian delta regions changing over times (phases drawn after Dawe, 2005).
However, rising sea level may deteriorate rice production in a sizable portion of the highly productive rice land in deltas. In the extensive deltas of tropical rivers, sea-level rise will have impacts reaching far inland; for example, in the Mekong Delta higher water levels will be noticeable up to the Cambodian border (Wassmann et al., 2004). Unless preventive measures are taken, this will translate into higher flooding risks for the principal rice crops, that is, in the grain-filling stage of the winter–spring crop and in the vegetative stage of the summer–autumn crop (Wassmann et al., 2004). In Myanmar, higher water levels caused by underlying saltwater intrusion attributed to sea-level rise could detrimentally affect the wet season crop (namely, during seeding and vegetative stage) which accounts for 85% of the national rice production. In Bangladesh, the ‘‘Aman’’ crop, which makes up 55% of the national rice production will be exposed to higher flood risk during the vegetative stage of the rice plants. While sea-level rise is among the most certain consequences of global warming, the projections for tropical cyclones in an environment of higher atmospheric and sea surface temperature are still surrounded with considerable uncertainty. IPCC (2007) states that as of now, ‘‘there is no clear trend in the annual numbers of tropical cyclones,’’ but that ‘‘it is likely that future tropical cyclones will become more intense, with larger peak wind speeds and more heavy precipitation’’ under ongoing global warming. In contrast to the Vietnamese delta regions, the Irrawaddy and Ganges–Brahmaputra deltas have experienced severe cyclones over recent years causing enormous losses in rice production. In Myanmar, tropical cyclone Nargis (during May, 2008) devastated an entire rice crop causing a 4 m high storm surge to
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penetrate 40–48 km inland along the storm’s path (USDA/FAS, 2008). USDA flood impact analysis concluded that 1.75 million hectares of rice land (corresponding to about 30% of the nation’s wet season rice area) were inundated by the storm surge (USDA/FAS, 2008b) of which an area of at least 500,000 ha appeared to be severely affected by salinity (USDA/FAS, 2008). In Bangladesh, cyclone Sidr caused production losses in the range of 800,000 t of rice during 2007 (IRIN, 2008). With higher sea water levels and an increase in storm incidences, flooding and salinity stress for rice in delta areas are likely to worsen. In the Asian mega-deltas, rice is the dominant crop, and in most cases, the only crop that can be grown during the monsoon season. The heavy rains together with poor or non-existant drainage create serious water logging and sometimes complete submergence, or prolonged stagnant floods. In these areas, a slight reduction in rainfall may not affect the water supply and agricultural production in general; however, a small increase in rainfall may strongly affect farming because of the subsequent floods. The adverse effects of flooding elicit complex responses of the rice crop that vary with genotype, energy reserves before and after submergence, developmental stage when flooding occurs, duration, and depth of flooding, as well as conditions of the floodwater, particularly the temperature and degree of turbidity (Das et al., 2005; Jackson and Ram, 2003). Concurrent culm elongation and a reduction in carbon fixation during submergence results in the depletion of carbohydrate reserves with a consequent reduction in plant survival (Ella et al., 2003; Sarker et al., 2006). Only a few lowyielding landraces in these areas are evolved to withstand such conditions. However, prospects to enhance adaptation to these conditions are enormous. Efforts should focus on introducing submergence-tolerance traits during germination in areas where direct seeding is practiced (Ismail et al., 2008). Submergence tolerance during the vegetative stage can now be incorporated into any popular variety or breeding line after the cloning of the Sub1A gene and the development and use of marker-assisted breeding system for its introgression (Septiningsih et al., 2008; Xu and Mackill, 1996; Xu et al., 2006). However, additional genes that are additive to Sub1 are needed to increase the tolerance beyond that conferred by Sub1 alone, particularly to cope with worsening flood situations already being experienced in some areas. Prolonged partial flooding with 30–60 cm water depths reduces rice productivity in vast areas of rainfed lowlands, and sometimes occurs following shorter-term complete submergence. Modern rice varieties are not adapted to these conditions and their yield is severely reduced because of high mortality, suppressed tillering ability, reduced panicle size, and high sterility. These are probably the reasons why farmers in affected areas still rely on low-yielding local landraces. This type of flooding received less attention in the past despite the enormous yield losses and the large areas
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affected. In recent years, efforts were devoted to screening large sets of diverse rice germplasm to identify genotypes with a reasonable level of tolerance (A. M. Ismail, personal communication). Our understanding of the basis of tolerance to these conditions is still inadequate; however, preliminary data showed that significant genetic variability exists in rice that could be explored through breeding. The areas affected by this kind of flooding are expected to increase substantially as a consequence of sea-level rises and increases in rainfall, particularly along tropical coasts. Rice will remain the only choice for farmers in these areas; however, more efforts are needed to develop better adapted high-yielding varieties.
4. A Regional Case Study with High Vulnerability: The Rice–Wheat System in the Indo-Gangetic Plains The intensified rice–wheat system, evolved rapidly since the 1960s after the introduction of modern high-yielding varieties, access to irrigation, fertilizer, and pesticides, provides staple grain for more than 400 million people in Asia. This system is practiced over a variety of soil and climatic conditions with a wide range of input use and management practices. Rice-wheat areas are located within subtropical to warm temperate climates characterized by cool and dry winters and warm and wet summers (Timsina and Connor, 2001). This system occupies about 18 million ha in the IGP and in China and is one of the world’s largest agricultural production systems (Ladha et al., 2003). In South Asia, the system occupies about 13.5 million ha of the most productive land (10 million in India, 2.2 million ha in Pakistan, 0.8 million ha in Bangladesh, and 0.5 million ha in Nepal). In China, rice–wheat is grown on about 3.4 million ha in the provinces of Jiangsu, Zhejiang, Hubei, Guizhou, Yunnan, Sichuan, and Anhui (Dawe et al., 2004). During the past four decades, the system has contributed markedly toward the food security of South Asia. However, of late, there has been a significant slowdown in the productivity growth rate and the sustainability of this important cropping system is at stake. The decline in soil productivity, particularly organic C and N, deterioration in soil physical characteristics, delay in sowing of wheat, decreasing water-availability, and depletion of groundwater, increased soil salinity, water logging, and increased pest incidence and evolution of new, more virulent pests are often suggested as the causes of such a slowdown in productivity. Climate change will have adverse impacts on the productivity of this important cropping system; posing a real threat to food security in Asia. Given the dimensions of the IGP, this region can be further divided into distinct subregions (Figure 6), that is, (a) Trans-Gangetic plains or IGP
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PAKISTAN 2.2 M ha
NEPAL 0.5 M ha 2
1
3 4 5 INDIA 10 M ha BANGLADESH 0.8 M ha
Figure 6 Rice–wheat cropping systems in the Indo-Gangetic plains along a west–east transect (see Table 2 for region-specific descriptions).
subregions 1 and 2 (areas in Pakistan, and parts of Punjab and Haryana in India), (b) Upper-Gangetic plains or IGP subregion 3 (most of Uttar Pradesh and parts of Bihar, India, and parts of Nepal), (c) Middle-Gangetic plains or IGP subregion 4 (parts of Bihar, India, and parts of Nepal), and (d) Lower-Gangetic plains or subregion 5 (parts of Bihar, West Bengal, India, and parts of Bangladesh) (Narang and Virmani, 2001). Solar radiation decreases from IGP subregions 1 to 5 during the rice season while the trend is reversed in the wheat season (Table 3). Minimum temperature in the rice as well as in wheat seasons increases from IGP subregions 1 to 5. This is also true for maximum temperature in the wheat season but in the rice season maximum temperatures remain largely uniform throughout the subregions of the IGP. Rainfall also follows a distinct pattern of increase from subregions 1 to 5 of the IGP (Table 3). Subregions 1 and 2 receive only 650 mm of rainfall per annum, the subregion 5 receives more than 2.5 times as much rainfall. Climatic parameters in the upper subregions of the IGP are basically more favorable for rice and wheat cultivation, except low rainfall. Access to assured irrigation, however, has helped overcome the problem of low rainfall and made the zone very productive, compared to the less favorable climatic conditions and limited irrigation facilities that hamper productivity in the lower subregions of the IGP (Pathak et al., 2003).
4.1. Climate change and climatic variability scenarios for South Asia For South Asia, area-averaged annual mean warming by 2020 is projected to be between 1.0 and 1.4 C and between 2.23 and 2.87 C by 2050, and may rise to 3–4 C toward the end of the twenty-first century (DEFRA, 2005;
Table 3
Soil, climate, irrigation, and potential yield of rice and wheat in various transects of the Indo-Gangetic plains of South Asia IGP transects 1
2
Parameter
Rice
Rainfall (mm) Maximum temperature ( C) Minimum temperature ( C) Solar radiation (M Jm2 d1) Potential yield (t ha1) Irrigated area (%) Soil texture Soil organic C (%)
550 60 34 25 24 14 20 12 10.7 7.9 99 97 Loamy sand 0.3
Source: updated from Pathak et al. (2003).
Wheat
Rice
3 Wheat
550 60 34 24 22 11 23 14 10.7 7.9 99 98 Loamy sand 0.3
Rice
680 34 24 19 9.5 60
4 Wheat
80 25 11 14 7.0 92 Loam 0.3
Rice
5 Wheat
950 100 33 26 27 16 20 15 9.2 6.8 40 88 Silty loam 0.4
Rice
Wheat
1450 150 32 28 25 15 17 16 7.7 5.2 25 73 Sandy loam 0.7
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IPCC, 2007). The warming in South Asia, including the IGP, will be higher during winter than during summer and there will be a more pronounced increase in minimum temperature than maximum temperature on an annual basis (IPCC, 2007). Since temperatures across South Asia are already reaching critical levels during the premonsoon season, any further increment will reduce yields of rice and wheat if measures are not taken to overcome the problem. Temperature measurements presented by Sinha et al. (1998) showed that there has been a 1.5 C increase in the minimum temperature at many places in the IGP. Pathak et al. (2003) also reported an increase in minimum temperature at some sites of the IGP. In Ludhiana, Punjab, in Northwest India, minimum and average temperatures showed increasing trends of 0.06 and 0.03 C yr1, respectively, during the past 32 years (Pathak and Wassmann, 2007). IPCC (2007) envisages temperature increases for all months and the hot premonsoon season will experience an increase of >1 C during 2010–2039, which is likely to result in incidences of extreme heat during April and May. In the IGP, these premonsoon changes will primarily affect the wheat crop, but the rice crop will also be exposed to higher temperatures (>0.5 C increase during 2010–2039) in the wet season (IPCC, 2007). Climate in South Asia in general and in the IGP in particular is dominated by the Asian and Indian summer monsoon, respectively. Monsoon rainfall, though a dependable source of water for South Asia with a coefficient of variation of only 10%, displays a variety of spatial and temporal variations (Pant and Rupakumar, 1997). The simultaneous occurrence of catastrophic floods in some areas and devastating droughts in others is a common feature of the climate of the region. Mean rainfall, as projected by the IPCC (2007), is not expected to change by 2010 but increasing variability of up to 10% during kharif (summer) crop and by 10% during rabi (winter) crop by 2070 is predicted. Several studies showed that during the past decades no significant changes occurred with rainfall on an all-India basis (Mall et al., 2006; Pant et al., 1999; Pathak and Wassmann, 2007; Stephenson et al., 2001), but decreasing/increasing trends were detectable in rainfall on a regional basis (Kripalani et al., 2003; Singh and Sontakke, 2002). Pathak and Wassmann (2007) showed that cropping systems of northwest India are exposed to a very high interannual variability of rainfall; rainfall received during the wheat season (November–March) was 17–260 mm in Ludhiana and <1–155 mm in Delhi. One of the key questions in assessing climate change effects in India is whether monsoon stability will remain or be transformed into more volatile patterns. Most models predict a modest increase in interannual variability but to a differing degree (Turner et al., 2005). May (2002) projected an intensification of rainfall in India during the monsoon season in response to global warming. Whereas an increase in rainfall is projected over the eastern region of India, northwestern deserts would have a small decrease in the absolute amount of rainfall
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(Shivakumar et al., 2004). However, South Asia will experience much larger regional and seasonal variations in rainfall. The occurrence of heavy rainfall events will increase against a decrease in the number of rainy days (Lal et al., 2000). The intensity of extreme rainfall events is projected to be higher in a warmer atmosphere, suggesting a decrease in return period for extreme precipitation events and the possibility of more frequent flash floods in parts of India, Nepal, and Bangladesh. This problem is more severe in Eastern India and Bangladesh. A major impact of climate change on Indian agriculture derives from the expected melting of the Himalayan glaciers (IPCC, 2007). The Ganges River and its tributaries are fed by rains during the monsoon season, but during the dry season they depend heavily on glacial melt water from the Himalayas. Similarly 60% of Pakistan’s population rely on crops irrigated by the Indus River, which is also dependent on melt water from Himalayan glaciers. IPCC (2007) reported that many Himalayan glaciers could disappear by 2035 and global warming could reduce the contribution from glacier melt water to about 30% of its current level over the next 50 years. Severely diminished melt water inflows could make some tributaries seasonal, flowing during the rainy season but not during the dry season when needs of irrigation water are greatest. This will have major implications for water management and irrigated crop production, particularly the rice–wheat system, which depends largely upon irrigation. In the northwestern IGP, water tables are already falling and wells are going dry (World Bank, 2003). Reduced river flow will slow the recharging of underground aquifers and aggravate the problem. Besides a temperature increase and rainfall changes, solar radiation has been declining in the IGP. Sinha et al. (1998) reported a 10% decline in solar radiation in northwestern India from 1980 to 2000. For Delhi (metropolitan) the decrease in solar radiation was 0.09 MJ m2 d1 yr1, apparently influenced by city effects (Pathak and Wassmann, 2007). It is widely perceived that aerosol concentrations have increased in all the major cities of India resulting in decreased solar radiation (Aggarwal et al., 2000). There has also been an increase in the percent of suspended particulate matter in the air around cities. This would attenuate the photosynthetically active light reaching the plant.
4.2. Vulnerability of the rice–wheat system due to climate change Increased temperature will be an important driver of future yields as it plays a crucial role in determining the growing season and yields of rice and wheat. The rate of many developmental processes is a positive linear function of temperature between a base temperature (at and below which
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Table 4 Temperature thresholds and required cumulative daily temperatures above base temperature, for rice and wheat (modified from Roetter and van de Geijn, 1999) Temperature thresholds
Optimum temperature range ( C) Lower temperature range ( C) Upper temperature range ( C) Emergence to preanthesis (day-deg) Postanthesis to maturity (day-deg) Base temperature ( C) Maximum temperature for development ( C)
Rice
Wheat
25–30 7–12 35–38 700–1300 450–850 8 25–31
17–23 0 30–35 750–1300 450–1050 0 20–25
the rate of a particular process is zero) and an optimum temperature, and a negative linear function of temperature between this optimum and a ceiling temperature (Table 4). Generally, the growth rate of rice increases linearly in the temperature range of 22–31 C, depending on genotype, and beyond which growth and productivity rapidly decrease. During flowering and grain filling, high temperature reduces yield by causing spikelet sterility and shortening the duration of grain-filling. An increase in leaf-surface temperature would have significant effects on crop metabolism and yields, and it may make crops more sensitive to moisture stress. Climate change, particularly rising variability in climatic elements associated with global warming, may have serious direct and indirect effects on the rice–wheat system and food security of South Asia. This may be aggravated by water scarcity, drought, flood, and decline in soil organic C content. Simulation models for rice production indicate a reduction in yield of about 5% per degree rise in mean temperature above 32 C (Matthews et al., 1995). This would largely offset any increase in yield as a consequence of increased CO2. Rice is particularly sensitive to hot temperature at anthesis; sterility of some cultivars occurs if temperatures exceed 35 C at anthesis for as little as 1 h, and even in the 1980s hot spells were causing spikelet sterility in dry and monsoon season crops in parts of Asia (Yoshida, 1981). At anthesis, spikelet fertility declines from 90% to 20% after only 2-h exposure to 38 C, and to 0% by <1-h exposure to 41 C. The critical temperature for spikelet fertility (defined as when fertility exceeds 80%) varies between genotypes, but is about 32–36 C (Yoshida, 1981). Below 20 C and above about 32 C, spikelet sterility becomes a major factor, even if growth is sufficient in plant components. Experiments in India showed that higher temperatures and reduced radiation associated with increased cloudiness caused spikelet sterility and reduced yields to such an extent that any increase in dry-matter production as a result of CO2 fertilization proved to be of no advantage in grain productivity (Rao and Sinha, 1994).
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Hundal and Kaur (1996) reported that, if all other climate variables were to remain constant, a temperature increase of 1, 2, and 3 C would reduce the grain yield of rice by 5.4%, 7.4%, and 25.1%, respectively. Matthews et al. (1997) suggested that rice production in the Asian region may decline by 3.8% under changed climate. Lal et al. (1998) observed that, under elevated CO2 yields of rice increased significantly (28% for a doubling of CO2). However, a 2 C rise in temperature canceled out the positive effect of elevated CO2 on rice. Peng et al. (2004) reported that rice yield declined by 10% for each 1 C increase in growing season minimum temperature in the dry season, whereas the effect of maximum temperature was insignificant. Predicted effects of climate change on wheat production include reduced grain yield over most of India, with the greatest impacts in lower potential areas such as the eastern IGP (Ortiz et al., 2008). Physiological traits that are associated with wheat yield in heat-prone environments are canopy temperature depression, membrane thermostability, leaf chlorophyll content during grain filling, leaf conductance and photosynthesis and senescence (Reynolds et al., 1998). Grain growth is shorter with heat stress, thereby influencing grain filling (Wardlaw et al., 1980) and resulting in lower yield. Wheat cultivars capable of maintaining high test weight under heat stress are more tolerant (Reynolds et al., 1994). Hundal and Kaur (1996) observed that in Punjab, India, a temperature increase of 1, 2, and 3 C from present-day conditions, would reduce the grain yield of wheat by 8.1%, 18.7%, and 25.7%, respectively. Lal et al. (1998) found that under elevated CO2, yields of wheat increased significantly (28% for a doubling of CO2). However, a 3 C rise in temperature canceled out the positive effect of elevated CO2 on wheat. The combined effect of enhanced CO2 and imposed thermal stress on the wheat crop is a 21% increase in yield for the irrigation schedule presently practiced in the region. Attri and Rathore (2003) observed that yield enhancements on the order of 29–37% and 16–28% are obtained for different genotype, under rainfed and irrigated conditions, respectively, for a temperature rise coupled with elevated CO2 (Tmax + 1.0 C, Tmin + 1.5 C, and 460 ppmv CO2) compared with the current climate. An increase in temperature on the order of 3 C or more, however, cancels out the beneficial effects of elevated CO2. The impact of modified climate was observed to be higher under rainfed conditions than under irrigated ones for all genotypes. Simulations of the impact of climate change on wheat yields for several locations in India using a modeling approach indicated that, in northern India, a 1 C rise in the mean temperature had no significant effect on potential yields, though an increase of 2 C reduced potential grain yields at most places (Aggarwal and Sinha, 1993). In a subsequent study, Rao and Sinha (1994) using the CERES-Wheat model, showed that wheat yields were lower than those in the current climate, even with the beneficial effects of CO2 on crop yield. Yield reductions were due
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to a shortening of the wheat-growing season, resulting from a temperature increase. Aggarwal and Mall (2002) observed that a 2 C increase resulted in a 15–17% decrease in grain yield of rice and wheat but, beyond that, the decrease was very high in wheat. These decreases were compensated for an increase in CO2 due to the latter’s fertilizing effect on crop growth. However, the CO2 concentration should rise to 450 ppm to nullify the negative effect of a 1 C increase in temperature, and to 550 ppm to nullify a 2 C increase in temperature. In the rice–wheat system of eastern India, at least 60% of wheat areas were planted after November (Chandna et al., 2004), subjecting the crop to suboptimal, often hotter growing seasons and resulting in lower yield. In many of the dry environments that today suffer from severe heat stress during grain filling, it has been shown that the enzyme soluble starch synthase in wheat appears to have limited rates at temperatures in excess of 20 C (Keeling et al., 1994). Furthermore, the grain filling of wheat is seriously impaired by heat stress due to reductions in leaf and ear photosynthesis at high temperatures (Blum et al., 1994). OrtizMonasterio et al. (1994) described the dramatic yield-reducing effects of high temperatures around and after heading for the wheat crop in South Asia. Samra and Singh (2005) analyzed the impact of abnormal temperature rise in March 2004 on the productivity of wheat. A temperature increase above normal ranged from 1 to 12 C in different parts of northern India, resulting in a wheat production loss of 4.6 million tons due to increased incidences of pests and diseases, and advanced maturity of wheat by 10–20 days further reducing grain weight. The decrease in yield was most pronounced in salt-affected/reclaimed salt-affected land due to an accumulation of salts in the root zone soil. Pathak and Wassmann (2007) quantified yield impacts on wheat due to rainfall variability in Northwest India and showed that the years with scarce rainfall resulted in only 34% (Ludhiana) and 35% (Delhi) of the baseline yield. In Ludhiana, high rainfall years resulted in 200% yield as compared with the baseline yield, whereas these years resulted in only 105% yield in Delhi. With an increase in temperature, drought could be come more pervasive in the rice–wheat belt of South Asia. It is estimated that 1 C increase in air temperature will lead to 37 mm more potential evapotranspiration (PET). Similarly, frequent droughts not only reduce supplies but also increase the amount of water needed for plant transpiration. When droughts occur, the drier soil conditions suppress root growth and accelerate the decomposition of organic matter, and thus increase vulnerability to wind erosion. On the other hand, lower rainy days and increased intensity of rainfall events will reduce the amount of water available for crop growth, given increased runoff and drainage (Challinor et al., 2004). The size of the IGP and the heterogeneity of its climate and land use systems defy blanket adaptation strategies and demand for a specific
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assessment of subregions in terms of vulnerability and adaptation. Table 5 summarizes the vulnerability mechanisms and the respective adaptation strategies in different subregions of the IGP (see Figure 6). While the inventory of mechanisms and strategies may look similar throughout the subregions, their internal ranking within the setting of a given subregion varies greatly. For instance, the threat of flooding shows an increasing trend along the west–east transect (Table 5). The current situation of temperature stress for rice production in the IGP is incorporated in the map of Figure 2 (for Asia as a whole) while the maps and diagrams of Figure 7A–D provide a more comprehensive picture in the western versus eastern end of the IGP transect. Figure 7A displays the maximum temperature and humidity levels in the IGP in March which coincides with the mature plant stages of the dry season crop (called Boro in Bangladesh). In March, the eastern part of the IGP has high maximum temperatures between 30 and 34 C. Humidity in the range of 40–70% which is higher than in other parts of the IGP. The diagrams in Figure 7B display the annual course of temperature and relative humidity, of one location in the eastern IGP. The Boro crop is exposed to high temperatures from March to May, but relative humidity is lower than in other months. The heat stress situation of the Aus crop (harvested in July/August) is different because the critical months of June–August have lower temperatures, but higher humidity (Figure 7B). The maps in Figure 7C describe the situation in September which covers the main crop in the western part of the IGP. In this month, the climate in Pakistan is characterized by high temperatures (33–36 C) and relatively low humidity in the range of 10–30%. On the other hand, the humidity diagram in Figure 7D clearly reveals that the relative humidity can reach up to 40–60% on individual days. Thus, any increase in temperature or humidity could translate into aggravating spikelet sterility. According to the recent IPCC assessment, agricultural production in South Asia could fall by 30% by 2050 if no action is taken to combat the effects of increasing temperatures and hydrologic disruption (IPCC, 2007). Adaptive options to deal with the impact of climate change are:
Developing cultivars tolerant of heat and salinity stress and resistant to flood and drought Modifying crop management practices Improving water management Adopting new farm techniques such as resource-conserving technologies (RCTs) Crop diversification Improving pest management Better weather forecasts and crop insurance Harnessing the indigenous technical knowledge of farmers
Table 5 Vulnerability of the rice–wheat system due to climate change and potential adaptation strategies along a west–east transect of the Indo-Gangetic plains Subregion
Vulnerability mechanism
Adaptation strategies
IGP1 and 2: Southern and eastern Pakistan; Punjab and Haryana
1. High temperature-induced sterility in rice 2. Abrupt temperature rise in rabi season
Heat-tolerant rice cultivar
3. Declining soil organic matter 4. Rising salinity 5. Increased pest and diseases 6. Late sowing of wheat 7. Shortage of irrigation water IGP3: Uttar Pradesh and western Bihar; Western Nepal
1. Shortage of irrigation water
2. Abrupt temperature rise in rabi season 3. Rain during maturity of rice 4. Declining soil organic matter 5. Rising salinity and alkalinity 6. Increased pest and diseases 7. Late sowing of wheat
Adjusting sowing date, heat-tolerant cultivar, better weather forecast Residue management Salt-tolerant cultivars Improved pest management No-till wheat Water-saving technologies (laser land leveling, direct-seeded rice, no-till rice and wheat) Water-saving technologies (laser land leveling, direct-seeded rice, no-till rice and wheat) Adjusting sowing date, heat-tolerant cultivar, better weather forecast Adjusting planting date, better weather forecast, crop insurance Residue management Salt and alkali-tolerant cultivars Improved pest management No-till wheat (continued)
Table 5
(continued)
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Subregion
Vulnerability mechanism
Adaptation strategies
IGP4: eastern Bihar; eastern Nepal
1. Widespread flood water
Better weather forecast, crop insurance, flood-resistant cultivar Developing irrigation facilities, droughtresistant cultivar, better weather forecast, crop insurance, indigenous knowledge Adjusting planting date, better weather forecast, crop insurance Residue management Adjusting sowing date, heat-tolerant cultivar, better weather forecast Improved pest management Water-saving technologies (laser land leveling, direct-seeded rice, no-till rice and wheat) Better weather forecast, crop insurance, flood-resistant cultivar Crop diversification, no-till wheat
2. Frequent drought in some areas
3. Rain and storm during maturity of rice 4. Shorter wheat season 5. Rise in temperature during grain filling of wheat 6. Increased pest and diseases 7. Shortage of irrigation water IGP5: Eastern Bihar, West Bengal, and northwestern Bangladesh
1. Frequent flood 2. Water logging and excess soil moisture in wheat 3. Rain and storm during maturity of rice and wheat 4. Rain and storm during maturity in rice and wheat 5. Shorter wheat season 6. Rise in temperature during grain filling of wheat 7. Increased pest and diseases 8. Rising salinity
Adjusting planting date, better weather forecast, crop insurance Adjusting planting date, better weather forecast, crop insurance Residue management Adjusting sowing date, heat-tolerant cultivar, better weather forecast Improved pest management Salt-tolerant cultivar
Temperature
A
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Critical stage of Aus crop
Critical stage of boro crop Critical stage of main crop
B
Bangladesh (Jessore)
D
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Figure 7 Temperature and relative humidity in South Asia in March (A) and September (B) as well as annual course of temperature and relative humidity in Bangladesh (C) and Pakistan (D). Encircled regions in (C) and (D) depict critical stages for the rice plant. (Datasource: The National Climatic Data Center (NCDC), Mitchell and Jones, 2005, Huke and Huke, 1997).
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Potential adaptation strategies in the rice–wheat system in different subregions of the IGP are listed in Table 5. 4.2.1. Cultivars tolerant to aggravating stresses in the IGP One approach in dealing with heat related-constraints is to improve germplasm to provide higher tolerance to stresses associated with global warming (Ortiz et al., 2008). To promote adaptation to an environment of high CO2 and high temperature, plant breeders have suggested selecting of cultivars that exhibit heat tolerance during reproductive development, high harvest index, small leaves, and low leaf area per unit ground. A key mechanism of high temperature-induced floret sterility in rice is the decreased ability of pollen grains to swell, resulting in poor thecae dehiscence (Matsui et al., 2001). The length of basal dehiscence has been proposed as a phenological trait for high-temperature tolerance that could be used in breeding programs (Matsui et al., 2005). There is evidence for genotypic variation in response to increasing CO2 and temperature (Moya et al., 1998) and high night temperature (Counce et al., 2005). Among nine Japonica cultivars, the temperature that caused 50% sterility varied by 3 C between the most tolerant and susceptible cultivars (Matsui et al., 2001). Heat avoidance mechanism (T > 40 C) such as highly efficient transpirational cooling is found in germplasm from some arid environments such as southern Iran, Pakistan, or Australia (Wassmann et al., 2009). Another strategy to overcome high temperatures is to shift the day time of peak flowering to cooler periods. Flowering during the early morning hours could protect rice fertility from future adverse effects of climate change, but the genes involved in determining daytime of flowering need to be identified. Wild rice accessions evaluated at IRRI showed strong variation in the daytime of flowering (Sheehy et al., 2005; Wassmann et al., 2009). Keeping this in view, IRRI’s breeding efforts for rice in hot environments currently pursue the strategies for physiological resilience, reproductive patterns (daytime of flowering), and early maturity. Heat-resistant rice and wheat currently under development at IRRI and CIMMYT, respectively, will provide greater yield reliability, especially in the tropics and subtropics, where many crops are grown at or near their thermal optimum (Ortiz et al., 2008). Under these conditions, any increase in temperature will cause photosynthesis to slow and eventually cease. With increased rainfall and flooding forecast in many parts of Asia, IRRI is incorporating a trait that allows the plant to survive prolonged periods of submergence into popular cultivars for the immediate use of farmers. Other traits are being actively selected for to produce crops that are resistant to lodging (e.g., short rice cultivars) and withstand strong winds during the sensitive stage of crop growth. To meet the world’s need for rice, the yield ceiling must be raised and the yield gap narrowed while
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maintaining sustainable production. Researchers are also looking to boost rice yields from a shrinking land base by reconfiguring the plant’s photosynthetic engine so that it more efficiently converts solar power and atmospheric carbon into grain through the C4 pathway. Advances in the drought-breeding program for rice and wheat are also encouraging (Ortiz et al., 2007; Reynolds and Borlaug, 2006; Wassmann et al., 2009). 4.2.2. Modification of crop management practices Farmers can adapt to climate change to some degree by shifting planting dates, choosing varieties with different growth duration, or changing crop rotations. Adjustment of planting dates to minimize the effect of temperature-induced spikelet sterility can be used to reduce yield instability, by avoiding coincidence of the sensitive flowering stage with the hottest part of the growing season. Shifting sowing dates could be a suitable decision for some farms if the land is not used for any other purpose. A large-scale shift in sowing dates probably would interfere with the agrotechnological management of other crops, grown during the remaining part of the year (Mall et al., 2004). Seasonal weather forecasts could be one supportive measure to optimize planting dates (Gadgil et al., 2002). However, many of these adaptation mechanisms may result in lower yields. In the IGP delayed planting is already one of the major causes of a reduction in crop yields in rice as well as wheat. 4.2.3. Improved water management One of the major impacts of climate change in the rice–wheat system of the IGP is likely to be an acute shortage of water resources associated with (i) significant increases in surface-air temperature and (ii) meltdown of Himalayan glaciers. Increases in temperature alone would enhance evapotranspiration losses. However, there is new evidence that CO2 enrichment can increase water-use efficiency due to a combination of reduced transpiration and increased biomass production resulting from reduced stomatal conductance and increased shading from larger leaves (Yoshimoto et al., 2005). While this effect is beneficial in terms of water savings, it will increase the heat load on the plant by restricting transpirational cooling. Though water and irrigation are major issues throughout the IGP, there are sharp differences in issues facing the different subregions across the IGP. In the western parts, the water supply for irrigation is inadequate and groundwater is depleting, whereas in the eastern parts excessive water supply during the rainy season causes water logging and flooding. Conservation and improved management of water should therefore be given priority. Increasing efforts should be directed towards rainwater harvesting to slow the decline in water levels in the western IGP. There are ample hydrological opportunities for tube well development in the eastern parts (Dinesh Kumar, 2002).
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4.2.4. Resource-conserving technologies RCTs encompass practices that enhance resource- or input-use efficiency and provide immediate, identifiable, and demonstrable economic benefits such as reductions in production costs, savings in water, fuel, and labor requirements, and timely establishment of crops resulting in improved yields (Table 6). Yields of rice and wheat in heat- and water-stressed environments can be raised significantly by adopting RCTs, which minimize unfavorable environmental impacts, especially on small- and medium-scale farms (Kataki et al., 2001). Resource-conserving practices such as zero tillage (ZT) can allow rice–wheat farmers to sow wheat sooner after the rice harvest, so the crop heads and fills the grain before the onset of premonsoon hot weather. On the other hand, ZT can also increase the carryover of pests and diseases where crops are not rotated so its application must be appropriate to the system. As average temperatures in the region rise, early sowing will become even more important for wheat. Field results showed that RCTs are increasingly being adopted by farmers in the rice–wheat belt of the IGP, specifically because they are labor saving, water saving, and early planting of wheat allows the crop’s vulnerable grain-filling stage to avoid the hottest part of the season (Gupta and Sayre, 2007). By the end of 2007, Table 6 Potential benefits of the key resource-conserving technologies (RCTs) in terms of climate change adaptation relative to conventional practices RCTs
Potential benefits relative to conventional practices
Zero tillage
Reduced water use, C sequestration, increases yield and income, reduced fuel consumption, reduced GHG emissions, more tolerant to heat stress Reduced water use, more efficient tractor use, reduced fuel consumption, reduced GHG emissions, increased area for cultivation Less requirement of water, saves time, postharvest condition of field is better for succeeding crop, deeper root growth and better tolerance to water and heat stress, reduced methane emissions Efficient use of water, increased income, increased nutritional security, conserve soil fertility, reduced risk Less water use, improved drainage, better residue management, less lodging of crop, more tolerant of water stress Reduces fertilizer N requirement, reduced N loss and environmental pollution, reduced nitrous oxide emission
Laser-aided land leveling Direct drill seeding of rice
Diversification
Raised-bed planting
Leaf color chart for N management
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approximately 4.0 million ha were under RCTs and 0.5 million farmers were using these technologies (Erenstein et al., 2007). The RCTs in the rice–wheat system have arguably had pronounced effects on the mitigation of greenhouse gas emissions (Table 6). Govaerts et al. (2006) observed that, under ZT combined with residue retention on the soil surface, C sequestered in the uppermost layer was higher than for conventional tillage (CT). Metay et al. (2007) reported a reduced emission of methane under the ZT system whereas Tsuruta et al. (2006) observed increased or similar emissions in continuous ZT direct seeding compared with a puddled transplanting rice field. Similarly, the impact of ZT on N2O emissions has indicated contrasting results, lower (Drury et al., 2006), equal (Lemke et al., 1999), and higher (Rochette et al., 2008) depending upon soil type. RCTs also bring many environmental benefits. For example, using zero tillage for wheat on 1 ha of land in the rice–wheat cropping system of the IGP can save 1 million liter of irrigation water and 98 L of diesel as well as reduce carbon dioxide emissions by 0.25 Mg (Reeves et al., 2001). 4.2.5. Crop diversification Diversification, that is, growing a range of crops suited to different sowing and harvesting times, assists in achieving sustainable productivity by allowing farmers to employ biological cycles to minimize inputs, maximize yields, conserve the resource base, and reduce risk due to both environmental and economic factors (Pal and Gupta, 2005). The farmers of the rice–wheat belt have taken the initiative to diversify their agriculture by including short duration crops such as potato, soybean, mung bean, cowpea, pea, mustard, and maize in different combinations (Pandey and Sharma, 1996). In the Eastern Gangetic Plains, agriculture is more drought- and flood-prone. In flood-prone areas, intercropping and the choice of appropriate crop cultivars would be helpful. It is estimated that more than 4 million ha of land used for rice remain as ‘‘rice fallows’’ in the IGP alone (Chandna et al., 2004). With appropriate crop establishment technologies, it is possible to use this land to raise a second crop of wheat, pulses, maize, or lentils to improve farmers’ income and livelihood in marginal areas. However, there is a need to quantify the impacts of crop diversification on income, employment, soil health, water use, and greenhouse gas emissions. 4.2.6. Improved pest management Changes in temperature and variability in rainfall will affect pest and disease incidences. Some potential adaptation strategies could be: developing cultivar resistance to pests and diseases, integrated pest management with more emphasis on biological control and changes in cultural practices, pest forecasting using innovative tools such as simulation modeling, and alternative production techniques and crops, as well as locations, that are resistant to infestations and other risks.
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4.2.7. Better weather forecasts and crop insurance Forecasting and early warning systems will be very useful in minimizing risks of climatic adversaries. Information and communication technologies (ICT) could greatly help researchers and administrators develop contingency plans. The Indian Meteorological Department has developed improved capabilities for forecasting summer monsoon rainfall and associated variability. Extreme weather events such as Bay of Bengal cyclones are monitored with improved technology. Crop insurance schemes promoted by government policies should be put in place to help farmers reduce the risk of crop failure due to these events. However, it is still an open question how crop insurance can be made a viable option for poor farmers. Subsidies or development programs will be required if the incidence of catastrophic crop failure rises beyond what could be recovered by the premiums. 4.2.8. Harnessing the indigenous technical knowledge of farmers Farmers in South Asia, often poor and marginalized, have been experiencing climatic variability for centuries. There is a wealth of knowledge of a range of measures that can help in developing technologies to overcome climate vulnerabilities. Indigenous knowledge can be harnessed and finetuned to suit adaptation to a rapidly changing world. The integration of local knowledge into climate change adaptation plans might also result in more culturally appropriate options, and present a more holistic and integrated perspective. Farmers of the rice–wheat ecosystem practice an array of adjustments from agronomic practices, family budget adjustments, help from social setup and the acquisition of loans to cope with adverse events such as floods. Locally conducted surveys showed that farmers tried to minimize the losses of recurrent floods by making changes to sowing and planting times, methods of sowing, variety, harvesting time, fertilizer use, and seed rate. In Bihar, for example, local farmers grow several rice varieties that are adapted to depth of flooding. Research on local knowledge for facilitating climate change adaptation requires relatively small resources but might yield a large dividend in furthering sustainable development. Tools such as GIS should be integrated with indigenous knowledge in developing adaptation plans.
5. Outlook: Current Advances and Future Prospects The rice cropping system is the economic backbone of many Asian nations and even a small decrease in productivity will drastically imperil food security. Therefore, the system needs to be modified and diversified
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to increase its resilience to changing climate. While developing more tolerant crop varieties and improving crop management are at the heart of adaptation measures, the efficiency of these approaches can be increased significantly by geographic analysis of vulnerable regions and regional climate modeling to identify locations where stress levels beyond the elasticity window of current systems have to be expected. Thus, site-specific and more targeted adjustments in crop management can be made to optimize the production system. While the resolution of current climate change models may—in most cases—not suffice for identifying regionally defined ‘hot-spots’ of climate change impacts, an analysis of cropping systems in terms of spatial and seasonal vulnerabilities may become crucial for planning targeted adaptation programs. There is a need to develop a policy framework for implementing adaptation options so that farmers are saved from the adverse impacts of climate change. The envisaged adaptation of rice (and rice–wheat) production to climate change will require substantial funds to support vigorous and concerted efforts by national and international research institutions. Given the clear linkage between drought and poverty as demonstrated in this study, it is critically important to include drought mitigation as an integral part of the rural development strategy. The scientific progress made in understanding the physiology of abiotic stresses and in developing of biotechnology tools has opened up promising opportunities for making a significant impact through improved technology. However, as the 2008 rice crisis demonstrated, agricultural research in general, and rice research in particular, has to be stepped up to ensure food security in the developing countries of Asia. More research programs targeting climate change adaptation alongside with adequate investment are required to prevent drastic yield losses that would affect social stability and economic development in wide parts of Asia.
REFERENCES Aggarwal, P. K., and Mall, R. K. (2002). Climate change and rice yields in diverse agroenvironments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climatic Change 52, 331–343. Aggarwal, P. K., and Sinha, S. K. (1993). Effect of probable increase in the carbon dioxide and temperature on the wheat yields in India. J. Agric. Meteorol. 48, 811–814. Aggarwal, P. K., Bandyopadhyay, S. K., Pathak, H., Kalra, N., Chander, S., and Sujith Kumar, S. (2000). Analyses of yield trends of the rice–wheat system in northwestern India. Outlook Agric. 29(4), 259–268. Amien, I., Redjekiningrum, P., Kartiwa, B., and Estiningtyas, W. (1999). Simulated rice yields as affected by interannual climate variability and possible climate change in Java. Climate Res. 12, 145–152. Attri, S. D., and Rathore, L. S. (2003). Simulation of impact of projected climate change on wheat in India. Int. J. Climatol. 23, 693–705.
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Informatics in Agricultural Research for Development C. Graham McLaren, Thomas Metz, Marco van den Berg, Richard M. Bruskiewich, Noel P. Magor, and David Shires Contents 136 137 137 138 138 139 139 141 141 143 145 146 147 148 148 149 153 154 155
1. Introduction 2. The Information Landscape 2.1. Technical developments 2.2. Legal developments 2.3. Ethical developments 2.4. Social developments 3. Enabling Technology 4. Scientific Information 4.1. Quantity and complexity 4.2. Quality 4.3. Capacity 4.4. Relevance 4.5. Intellectual property 5. From Research to Knowledge at the Farmers’ Doorsteps 5.1. Setting the scene 5.2. Examples on the ground 5.3. A commitment to knowledge for farmers 6. Conclusions References
Abstract Technical developments in information and telecommunications technology (ICT), together with associated legal, ethical, and social developments are providing an environment where scientists from developed and developing countries can collaborate to address real problems of food security and development. Aspects of this enabling technology are improved connectivity between developed and developing countries, availability of open-source applications which are both cheap and amenable to innovative local adaptation and the
International Rice Research Institute (IRRI), Metro Manila, Philippines Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01004-9
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emergence of global software and platform services which lower the infrastructure barriers to accessing state-of-the-art computer applications. This technology allows the sharing of the scientific information necessary to address these problems, but there remain issues of quantity relevance and complexity of information, quality and ownership of data, and capacity to use and contribute relevant data and information. ICT also has a role to play in the ‘‘last mile’’ problem of disseminating information to farmers. Innovative strategies for combining Internet, telecommunications, video, and print technologies at appropriate levels are bridging this gap and empowering farmers to make better production and marketing decisions. ICT is, however, no substitute for attention to quality knowledge presented in understandable format.
1. Introduction The rapid development and global spread of modern information and communication technology (ICT) allow the developing world to leapfrog the infrastructural constraints to access and utilize information vital to agricultural research and development. This opportunity obliges developed countries and advanced research institutes to change the way they conduct research and development in collaboration with clients and stakeholders in the developing world. Information and tools vital to research and development can now be shared equitably as global public goods. Communities with common interests in research or development are now able to converse, share knowledge, and collaborate in more effective ways. This chapter reviews the diverse opportunities and constraints for informatics to influence agricultural research development and is divided into several sections as follows:
The Information Landscape. This section will present an overview of the evolution of ICT and the associated legal, ethical, and social impact. Enabling Technology. This section will deal with the new opportunities and challenges of ICT from the perspective of functionality and commercial dynamics. Scientific Information. Global needs for scientific information in agriculture span a wide spectrum, from basic through applied science. This section will focus mainly on the issues of generation, access and efficient usage of more research-focused scientific information, directed toward crop breeding, natural resource management, and socioeconomic analysis. From Research to Knowledge at the Farmers’ Doorsteps. This section will deal with issues relating to the derivation and application of farming practices drawn from scientific research and local experience.
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2. The Information Landscape Informatics opportunities in international agriculture will be shaped by the convergence of developments in four areas that will lead to an unprecedented availability and accessibility of scientific data, information, and knowledge. These areas are technical, legal, ethical, and social. The developments in these areas are driven by forces outside the agricultural research community and therefore present opportunities that need to be recognized and utilized.
2.1. Technical developments The cost of computing has fallen dramatically over the last 25 years, since the emergence of personal computers. Processing speed, data transmission bandwidth, and disk storage capacity have shown exponential growth patterns, with parallel declines in cost. A Seagate 5 MB hard disk in 1981 was listed as US $1700 (Historical Notes about the Cost of Hard Drive Storage space: http://www.littletechshoppe.com/ns1625/winchest.html), equivalent to US $3900 today, when adjusted for inflation. In 2008, the same company offered a 1.5 TB hard disk for US $190 (Seagate Barracuda ST31500341AS as offered on: http://www.amazon.com, October 2008). In 27 years, the capacity of a hard disk has increased by a factor of 300,000 while the price has decreased by about factor of 20. An indicator of the dramatic effects of this is the serious attempt by Google (and others) in collaboration with large libraries to digitize millions of books (Google Press Center: http://www.google.com/press/pressrel/ print_library.html, December 14, 2004) and make them available online, at least for searching (Google Book Search: http://books.google.com/). Cost of computing is no longer a barrier to making all previously printed information digitally available. Intellectual property rights have become the main constraint (Carlson and Young, 2004). Another indicator is the attempt by the OLPC (One Laptop per Child) project to bring cheap, energy-efficient laptop computers to school children in poor countries (One Laptop per Child: http://www.laptop.org/). While the OLPC project was not successful in producing and shipping the numbers envisaged, it has played an important role in establishing a market for this new class of notebook computers. Computer hardware and software are increasingly being commoditized to the level of consumer goods, with prices of the lower-range notebook computers between USD 200 and 300.
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2.2. Legal developments The emergence of easy-to-use legal frameworks for open content publishing, especially the Creative Commons licenses, allow the creation and distribution of information products as global public goods (Creative Commons: http://www.creativecommons.org/). Free software has a longer history (20 years) (Free Software Foundation: http://www.fsf.org/), and open access publishing in scientific journals is quickly gaining traction (Directory of Open Access Journals: http://www.doaj.org/, RoMEO— Publisher copyright policies and self-archiving: http://www.sherpa.ac.uk/ romeo.php). An indicator of this development is the Public Library of Science (PLOS: http://www.plos.org/), a collection of recent open access journals, which aims to make the world’s scientific and medical literature a freely available public resource. Several donors already require their funded research to be published under open access conditions (Wikipedia-Open Access: http://www.en. wikipedia.org/wiki/Open_access# Research_funders_and_universities). Arguably, the release of the Wikipedia articles under open content licenses has contributed to the spectacular growth of its content (Wikipedia Statistics: http://wikisupport.martinkozak.net/~martinkozak/statistics/). Another indicator is the increasing importance of open-source software in business, education, and private use (Wheeler, 2007). As promising as this sounds, significant challenges remain. Computer literacy of many agricultural research scientists in developing countries is still relatively low and publication fees for open access article submissions can be very expensive, thus limiting their participation in the generation and contribution of scientific content for this expanding global public good of scientific information. However, global campaigns to disseminate computers and computer training in developing countries and publication fee discounts for open access article submissions from authors who cannot afford the publication fees (http://www.plos.org/about/faq.html#pubquest) are helping to overcome such inequities.
2.3. Ethical developments The promotion of ICT for development and the equitable access to information and knowledge is increasingly being considered as the basis for an inclusive information society. An indicator for this development is the declaration of principles at the World Summit on the Information Society (WSIS, 2003). At the national level, Freedom of Information Acts in the US (Freedom of Information Act—USA: http://www.usdoj.gov/oip/foi-act. html) and the UK (Freedom of Information Act—UK: http://www.opsi. gov.uk/acts/acts2000/ukpga_20000036_en_1) have been amended or passed in recent years, providing legal frameworks for mandatory disclosure
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procedures for government information and data. Many other countries have similar legislation in place (Wikipedia-Freedom of Information Legislation: http://www.en.wikipedia.org/wiki/Freedom_of_information_ legislation).
2.4. Social developments Recent years have seen the emergence of massive voluntary online collaboration across national borders (Tapscott and Williams, 2006). Again, open-source software development has a longer history for such online collaboration (Raymond, 1999), but the emergence of Wikipedia allowed online collaboration on such a massive scale that it is not possible to manage it in a top-down manner. Social networking sites and massive multiplayer online games are further examples of the possible scale of online collaboration. The term Web 2.0 is used to describe a changing trend in the usage of the World Wide Web, with users increasingly creating and sharing information, and collaborating on content development. There is a huge generation gap in online collaboration attitude and experience. A new generation of Web 2.0 aware and experienced pupils and students is growing up quickly.
3. Enabling Technology The global expansion of ICT and its associated structural changes in information management over the past 25 years is one of the astounding accomplishments of our contemporary world. Furthermore, the convergence of Internet, digital wireless, and microcomputers into steadily expanding mobile phone networks and ever smaller and cheaper (and fully functioning) mobile computing devices means that the potential exists for agricultural researchers in the developing world to obtain broad physical access to scientific information previously denied them due to lack of adequate digital connectivity. Some of the social consequences of this evolution in ICT are outlined above. A steadfast trend toward digital archiving of scientific literature and burgeoning database information online, coupled with widespread open access licensing of scientific information products such as journals, databases, and expert systems, means that global access for national agricultural research scientists to cutting edge information is significantly increased. The open-source movement is rapidly gaining ground in the scientific arena. While often referred to as ‘‘free open-source software (FOSS)’’ the reasons for switching to this type of software are often not related to cost. Governments, universities, and research institutions are increasingly concerned about the security risks of closed, proprietary systems and
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about the long-term availability of their information in undocumented formats. Prime FOSS examples include the various distributions of the Linux operating system and the OpenOffice.org applications. A paradigm that has gained firm ground in the world of business computing is that of ‘‘software as a service.’’ In this model, software does not reside on a user’s workstation but on a server hosted by an applications provider and is accessed over the network, usually by means of a Web browser. Examples of popular business applications that apply this model include the SalesForce.com Customer Relationships Management application and several financial/administrative systems. The main advantage of this model is the managed aspect—which removes the requirement for specialized technical skills for data backup and recovery, database tuning, application patching, etc. A relatively recent development is that of the ‘‘platform as a service’’ which takes the Application Service Provider concept a step further: rather than offering single applications, providers supply their customers with complete ICT platforms that enable interoperability between different applications and technology components. Components that were traditionally part of the desktop or local data center infrastructure such as storage and server farms are increasingly deployed using this service model. These developments are in some ways a journey ‘‘back to the future’’ since traditional mainframes with the ‘‘dumb terminal’’ devices attached to them used to operate similarly. The long-term effect will be an information technology (IT) landscape where software will be ‘‘metered’’ and cost will depend on actual use, with the price of workstations dropping drastically and the requirement for high-performance secure and reliable network connectivity becoming more crucial. Some of the companies that survived the burst of the Internet bubble of 2000 such as Google, Yahoo!, and Microsoft are rolling out platforms such as Google Applications for Domains and MSN Live as part of this new business model in IT. Usually, these services are available free of cost for private users with limited requirements or for nonprofit organizations, and a ‘‘premier’’ edition caters for professional and enterprise users that require guaranteed service levels. A good example is Yahoo!’s Flickr.com, which is available free of cost with certain storage and upload limitations and can be upgraded for a fee to a ‘‘Pro’’ version for unlimited storage and uploads. These developments will have limited impact on agricultural research in developing countries until Internet connectivity in those countries catches up with world standards. This, however, is a priority of governments and international organizations and is happening quickly in most parts of the world (Rice, 2008). When access is sufficient, these developments will allow researchers in developing countries to leapfrog the expensive activities of providing local software and computational capacity, all that will be required is a relatively simple Web browsing machine.
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4. Scientific Information The reality and opportunity access to scientific information as of global public goods implies that the information playing field is becoming increasingly level. In this light, the global challenges and constraints to improving the quality and usability of such information are as much applicable to the developed world as they are to the developing world. The key challenge is to avoid a developed versus developing country mindset and recognize that informatics tools and data are part of the global society. Challenges and constraints to the global applicability and use of scientific information include (but are not limited) to the following:
Quantity and complexity: quantity, complexity, and global access to scientific data and information Quality: incompleteness, low quality, and lack of integration of scientific data and information Capacity: inadequate infrastructure, methodology, and human resources for scientific data and information management Relevance: usability and ‘‘fitness for purpose’’ issues relating to current tools and interfaces accessing and analyzing scientific data and information Intellectual property: public/private scientific data and information sharing barriers
4.1. Quantity and complexity Agricultural research today is both blessed and cursed with the exploding amount and diversity of scientific information, in particular, relating to the biology of the organisms cultivated for human consumption. The availability of scientific data and information across the Internet is unprecedented, but with the increasing and globally dispersed quantity of this information has come a crisis of attention and the sheer difficulty of the task of using those resources:
What data and information are most pertinent to solving the research question at hand? How and where can one find this data and information (on the Internet)? How can one integrate all such data and information into a coherent whole to make it usable? Unfortunately, despite powerful search engines now available to index an ever-expanding list of Web sites, most scientists remain ‘‘hunter gatherers’’ of information with Web browsers. They are faced with tedious manual data entry and integration of the results of their searches into their
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own local project-specific data repositories, be they a simple spreadsheet or a sophisticated relational database. Moreover, the complexity of modern science means that it is becoming increasingly difficult for any one scientist to fully understand the semantics of all relevant data and information and to integrate this to answer even the simplest of research questions beyond their own specific disciplines. A number of promising initiatives are underway, leveraging insights and technology from other fields of science, in particular biology, to apply new strategies and tools to manage and integrate the global tsunami of scientific data and information (Stein, 2003). Although these initiatives are still struggling with the technological complexity of the task and sociological barriers to adoption, they are nonetheless worth highlighting here. Firstly, a number of scientific communities are exerting some effort in developing and managing common dictionaries of interrelated concepts in specified scientific domains, into what are formally called ‘‘ontology.’’ One very successful example of this within biology is the so-called gene ontology (GO: http://www.geneontology.org; Gene Ontology Consortium, 2008) started in the late 1990s and now boasting wide application to many major public genomics and model organism databases. Another more specialized ontology initiative, specifically pertinent to agriculture, is the Plant Ontology Consortium (POC: http://www.plantontology.org; Avraham et al., 2008; Ilic et al., 2007; Plant Ontology Consortium, 2002), which focuses on systematizing descriptive vocabularies for plant anatomy and development stage. POC is also moving to integrate such plant ontology to formal descriptions of plant traits and phenotypes, and increasingly, tying such ontology to annotated data sets. Formalization and adoption of such standards will improve the usability and integration of crop information in the years ahead. The scope, however, of such formal descriptions remains somewhat limited, in many cases to biology of model plants and crop plants growing in standard environments in the developed world. A key challenge will be to extend such standards to describe characteristics of plants growing in the unique, stress-prone environments within the developing world, to ensure a wider impact of such standards on international agriculture. Having started with a few key model plants and crops, including Arabidopsis, rice, and maize, POC is now collaborating with several international research communities, including the CGIAR (Consultative Group for International Agricultural Research) and the Generation Challenge Programme (GCP: http://www.generationcp.org) to expand coverage to other crops and the scope of description to other global environments (Bruskiewich et al., 2006a). On the broader issue of documenting agronomic environments for crop modeling, the Decision Support System for Agrotechnology Transfer (DSSAT) software package of the International Consortium for Agriculture Systems Applications (ICASA) serves as a good example (http://www.icasa.net/dssat/; Hunt et al., 2001).
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Secondly, software engineers are collaborating to develop formal frameworks for gross semantic integration of data. In the field of bioinformatics, standards like BioMoby (BioMoby Consortium, 2008; http://www. biomoby.org; Wilkinson et al., 2005) and SSWAP (http://www.sswap. info) are developing a framework for integration of data resources across the Internet. More specific to crop research, once again the efforts of the GCP are notable: a scientific ‘‘domain model’’ that describes data structure and relationships in a kind of semantic blue print, for use in tying together software tools over the Internet. These efforts have a clear vision of creating a ‘‘next-generation’’ crop information platform to significantly reduce the tedium of discovering, gathering, consolidating, and interpreting crop information (Bruskiewich et al., 2006b, 2008).
4.2. Quality In the area of crop research, progress is still hampered by the relative completeness, low quality, and lack of integration of scientific data and information. To give a feel for what is required here, we can focus on one particular example in the domain of germplasm information. Proper management of germplasm information is essential for the elucidation of genotype–expression–phenotype linkages. Management goals include the accurate tracking of germplasm origin (genealogy), the management of alternate germplasm names, the accurate association of experimental results to applicable genotypes, and the sound material management of germplasm inventories (Bruskiewich et al., 2006b). The critical key to effective management of germplasm information is the assignment of a unique global germplasm identifier (GID) to each distinct sample—seed packages or clonal stocks—of germplasm that needs to be independently tracked (‘‘bar coded’’). GIDs are an essential reference point for all experimental observations made upon a germplasm sample and for crosslinking related samples, for example, the parents (sources) and progeny. Once assigned, a GID is never destroyed, but rather it may persist in the host crop database long after the associated sample has been consumed or otherwise lost. The above GID protocol is a fundamental strategy underlying crop information systems like the International Crop Information System (ICIS: http://www.icis.cgiar.org; McLaren et al., 2005), an open-source crop information system under development since the early 1990s by a collaborative global team of CGIAR, NARES (National Agricultural Research and Extension Services), ARI (Advanced Research Institutes), and private sector partners. ICIS is designed to document germplasm genealogies with associated metadata such as passport data. It couples germplasm entries with their associated experimental observations. ICIS attaches all germplasm names and passport attributes to the GID to which
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it applies and records experimental data against the GID from which it was collected. Furthermore, experimental data is indexed using pertinent controlled vocabulary which allows complete documentation of experimental variables. Systems like ICIS support the full documentation and quality assurance of scientific data, at least with respect to germplasm passport, phenotype, and genotype data. The ICIS genealogy management system approach to germplasm tracking is being adopted by other crop databases, like the Germinate project (Lee et al., 2005). Commercial packages implementing similar crop data management concepts are available (e.g., see Agrobase: http://www.agronomix.mb.ca/), but their cost is likely a constraint to their adoption in developing countries. On a slightly longer time horizon, organizations like the Global Crop Diversity Trust (http://www.croptrust.org/) are guiding efforts to establish crop-specific and regional genetic resource management strategies that include the development of information management tools for genetic resources management in small genebanks, such as an updated, open-source version of the pcGRIN system originally developed in the late 1990s (http://www.ars-grin.gov/npgs/pcgrin.html). Larger genebanks in the developing world may not require such software if they already have a robust existing system in place, like the Genebank Information Management System (GBIMS) of the National Bureau of Plant Genetic Resources (NBPGR) of India (Agrawal et al., 2007). Current activities for genetic resources information systems also include integrated strategies for globally consolidating scientific information about genetic resources. These activities are built on another complementary project funded by the World Bank and hosted by the CGIAR, called ‘‘Global Public Goods Phase 2’’ (GPG2: http://www.sgrp.cgiar.org/ CurrentSGRPInitiatives/GPGProject.htm). The establishment of global crop registries that will compile a global inventory of available accessions for various crops and consolidate their passport data is envisioned. Eventually, such information systems could be extended to the wider pool of breeding stocks, with linkages to experimental evaluation, perhaps by tight linkages to systems like ICIS. Such systems will go a long way toward improving the completeness and quality of crop data in the international crop research community. Similar standards to ensure completeness and quality of data are arising in other related biological domains that are finding increased application in crop research. For example, in the area of functional genomics research, the Microarray Gene Expression Database Consortium (MGED: http://www. mged.org; Brazma et al., 2001) provides standards for microarray-based gene expression data. Completeness and quality of information is not solely driven by technology and standards, but also by target end user familiarity with such systems. In this regard, even crop information systems like ICIS, which
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have a vibrant end user community (see http://www.icis.cgiar.org) still fall short in terms of broad adoption. Unlike widely available conventional software such as word processors and spreadsheets, crop information systems are not simple ‘‘off-the-shelf’’ installations on a user’s computer. Significant investment in configuration and data dictionary (‘‘controlled vocabulary’’ or ‘‘ontology’’) development is still required. It is often hard to convince plant breeders, crop physiologists, and other agricultural scientists to invest in the training and setup required. Even with the tools provided, data curation of accurate germplasm pedigrees and properly documented associated experimental data in such systems remains a tedious, nontrivial, potentially error-prone exercise. The best that can be said is that such training and data curation investments eventually yield fruit and provide new opportunities to analyze historical pedigree and field data in a manner simply not feasible with paper documents, or even spreadsheets. For example, from such databases, reasonable estimates of Coefficients of Parentage (COP) may be undertaken which help in deciphering genotype by environment interactions of germplasm (Burguen˜o et al., 2007). As such systems become more widely adopted within the international agricultural research community, more and more crop scientists will stand to benefit from transparent access to shared information that will guide and optimize plant breeding decisions to accelerate progress in crop improvement globally.
4.3. Capacity The inadequacy of capacity for scientific data and information management is part of the larger challenge of building scientific capacity worldwide (Gelb and Parker, 2008; Uhlir and Page-Shipp, 2006; UNESCO, 1997). The adoption of new informatics and communications technology is accelerated among young people worldwide, especially in developing countries, where mobile phones are swiftly become an accessory of necessity even for individuals living in predominantly farming communities. The task at present becomes one of encouraging agricultural education facilities globally to embrace computer literacy as an essential component of training for the next generation of agricultural scientists in their care. This theme will be developed further in the next section, with respect to extension activities to farmers, but may also be seen to especially benefit young scientists now being trained. Within the specific field of scientific data and information management for agriculture, various opportunities are available for more focused training. For example, the ICIS community annually convenes training workshops on germplasm data management. Although formal courses in agricultural information management remain rare, there are available
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learning resources for the intrepid. One very recent example is an online course on crop bioinformatics (see http://www.mcclintock.generationcp. org) developed with GCP funding. Several commercial software companies offer ‘‘free’’ or ‘‘academically priced’’ versions of their more fully featured scientific software packages, for students or scientists in developing countries, for example, the GenStat Discovery Program (http://www.genstat.co.uk/products/discovery/) and MatLab (http://www.mathworks.com/academia/).
4.4. Relevance Most crop scientists still rely heavily on conventional office tools such as Microsoft Excel to compile their own experimental data. To share their data or access public data, extensive use is made of Web browser access to online public databases. However, the Web interfaces to such resources do not always present and visualize data in a convenient manner supportive of intuitive analysis. Furthermore, the consolidation of such information remains, as noted above, a somewhat labor-intensive exercise across the Internet. If a researcher is fortunate enough, a reasonable open-source software tool to capture, manage, analyze, and visualize data may already be available for the particular classes of data they have compiled or generated within their research objectives. However, the task of crosslinking diverse data types and tools together to develop a coherent resolution of a research hypothesis is generally a significant challenge. Although the current reality is tedious, there is promising progress being made toward a more crop research end user friendly world. This seems especially true in the field of bioinformatics where consortia such as the Generic Model Organism Database (GMOD: http://www.gmod.org) collaborate to improve computing tools for improved management and visualization of sequence and functional genomic data (Stein et al., 2002). Although GMOD targets data management for model organisms, many of its software systems are potentially useful to the crop research community. In a similar vein, the GCP is developing a crop information platform (see http://www.pantheon.generationcp.org) to expand the range and quality of available interfaces to crop scientific information, retrieved in a semiautomated fashion from across the Internet. A key challenge to all of these efforts is that bioinformatics software developers do not typically have a deep appreciation of the specific needs of crop researchers of various kinds. There needs to be a fresh effort to bring together more frequently and closely, the target end users of such systems— plant breeders, crop physiologists, agronomists, and others—with the informatics development community, to ensure that usable tools and interfaces are pertinent and delivered in a cost-effective manner.
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4.5. Intellectual property Although large public scientific databases and open access policies of scientific journals are driving a wedge into intellectual property barriers to access scientific data and information, it is still all too often the case that valuable data sets remain sequestered on computer hard drives, or barricaded behind institutional intellectual property walls. In general, the sociology of global sharing of data and information is still somewhat shackled by a general lack of trust and expertise among many scientists, in particular among national agricultural research scientists, but even among many international agricultural centers and other advanced, publicly funded research centers. In the area of germplasm sharing, the ratification of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGR: http://www.planttreaty.org/) by a great many countries has created a healthier global environment for the sharing of germplasm and associated information, and for benefit sharing, by establishing a legal framework for IP—the so-called Standard Material Transfer Agreement (‘‘SMTA’’)—for germplasm exchanged under the multilateral system. This bodes well for crop research and the revitalization of international networks for the sharing and evaluation of germplasm, so essential for progress in international agricultural research. At the same time, though, genetic resources managers everywhere now face fresh challenges in terms of implementation of effective information management mechanisms to enforce the legal requirements of the ITPGR. Moreover, in the era of plummeting costs for highthroughput genotyping or even routine whole genome sequencing of such germplasm, questions remain about the bounds of the information to be shared under the provisions of the Treaty. Clearly defining the boundaries between ‘‘precompetitive’’ and ‘‘competitive’’ data can help. Software projects like ICIS are considered ‘‘precompetitive’’ in that the real commercial value in such systems is the data itself, not the sharing of best practices for crop data management, embodied in the software. This kind of mindset is permitting private industry to participate as investors and technical contributors alongside public sector institutions, in what is essentially, an open-source software development activity, to the benefit of all concerned. Assuming that editorial quality concerns are properly managed, encouraging collaborative sharing and integration of public knowledge on dynamic repositories such as Wiki’s (Giles, 2007; Salzberg, 2007) hold promise as a source of less formal crop science information. In general, attitudes toward intellectual property in science, and in crop science in particular, are evolving toward a more open environment. The minimum standard for open access to scientific information is now the general availability of scientific citations and abstracts in resources like NCBI PubMed (http://www.ncbi.nlm.nih.gov). Private companies like
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Google are setting up indices of books (http://books.google.com) and other ‘‘scholarly’’ resources (http://scholar.google.com). Increasingly, many journals are moving to the full open access model, spurred ahead by the PLOS (http://www.plos.org/) and equivalent initiatives. A ‘‘Directory of Open Access Journals’’ lists almost 200 open access agricultural, fisheries, forestry, plant, and food journals as of this writing (http://www.doaj.org/ doaj?func=subject&cpid=115, accessed April 23, 2008). Even scientific journals like Nature, despite continuing maintenance of traditional models of subscription-based access to scientific literature, nonetheless provide more and more online public access to indexes and summaries of scientific information, in particular, in the field of genomics (see http://www.nature. com/omics/index.html).
5. From Research to Knowledge at the Farmers’ Doorsteps 5.1. Setting the scene It is easy within advanced research institutions and international centers to lose sight of the reality of the national agriculture research and extension system of less developed countries. In this section, we move beyond the comfort of advanced research institutions with backup IT expertise to institutes with limited Internet capability and even less at the field level for farmer intermediaries. The gap between research and extension institutions is identified as a major disconnection in creating impact with new agricultural technology. A stakeholder analysis in Bangladesh in 1999–2000 identified farmers’ desire for new agriculture knowledge and their lack of access to up to date information as a priority need (Orr et al., 2007). Farmer intermediaries, particularly the direct interface with farmers, are often without extension leaflets on best practices or knowledge of the most recently released varieties. Even with extension leaflets being produced on topics ranging from a new rice variety to a poultry disease, the shelves of extension persons are often empty. This is illustrated from a recent study (Rashid, 2005) in Bangladesh, in which 1800 pieces of extension material from research and extension services and projects were collected and archived. The information was sitting on shelves in the central institutions and not at the field level where it was most needed. Lack of access is greater in countries in which governance is weaker. The research-extension linkage is not only weak but certainly not pro-poor and certainly not meeting the gender equity vision of the Millennium Development Goals.
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In setting the scene for the role of IT in research for development, it is important to acknowledge that the local extension service, regional research stations, nongovernment organizations, government seed agents, local seed and fertilizer dealers, local radio and television, and local government, particularly in a decentralized system, are all actors in the transfer of technology. Van Mele et al. (2005b) highlighted that diversity indicating both the number of different actors and their relative abundance or density in a given area. They conclude that there is no single pathway for flow of information from researchers to farmers. Earlier work by Biggs (1989) coined the phrase ‘‘multiple sources and multiple pathways’’ to describe the flow of innovations to farmers. Therefore, it is important that all actors that may contribute to an innovation system have access to credible information. It will be argued that the first role of IT will not be in the search engines or models outlined earlier but rather in establishing order, practicing simplicity and establishing a recognized reference point for credible information for farmer intermediaries and farmers themselves. It is recognized here that the farmers may be formally uneducated and certainly not computer literate and that the field level intermediaries may lack access to the Internet and computers. It will also be argued that IT can empower the local actors within an innovation system through access to knowledge. A second role, which will vary from country to country, is shifting Internet access closer to the farmer intermediaries and farmers. The two roles complement but the latter without the former may not deliver credible information to clients.
5.2. Examples on the ground Attention to relevant, high-quality content brings the scientists and communication and extension specialists to the fore and not the IT specialists. Initial examples therefore focus on content development and management of credible content. In April 2001, the result of a consultancy into the management of training at the International Rice Research Institute (IRRI) recommended the development of a Training Bank through which all of IRRI’s training could be disseminated. The implementation of this concept led to the emergence of the Rice Knowledge Bank (RKB: http://www. knowledgebank.irri.org/) which included not only IRRI’s training materials but also summaries of its scientific endeavors relevant to the extension/ farmer community. The first version of the RKB was launched in September 2002. IRRI is now using it to:
Create a single, credible repository of rice-farming knowledge for extension and farmer communities Package this knowledge in forms suitable for immediate use in extension
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Integrate local research, local adaptations of IRRI knowledge, and indigenous knowledge into a single repository for each partner country Provide a platform for national systems to manage their information and enhance the research-extension linkage The concept of a platform for national systems to manage their information and enhance the research-extension linkage has been an important step in empowering national research and extension systems with a knowledge management process. IRRI has worked with national rice research partners to prepare their rice management recommendations in local languages for farmer intermediaries and farmers. The country management has reinforced the local pathways of knowledge. For example, in Bangladesh the authority is the committee under the leadership of the Director of Research of the Bangladesh Rice Research Institute. The Bangladesh rice research authority has produced more than 200 fact sheets to cover the two main winter and monsoon rice growing seasons in simple Bangla. The material is available on the Internet (http://www.knowledgebank-brri.org), on CD-ROM and in printed form. Since the country knowledge bank, in this case the Bangladesh Rice Knowledge Bank (BRKB), has national level authority, it can be used by any local organization with confidence. Over time the content can be updated and also, through farmer intermediary and farmer feedback, the content can be revised into a format that is more understandable by farmers. For example, the initial fact sheet may be upgraded through more pictures that are better appreciated by end users. This is considered an ongoing process. The process of localization is being followed in the major rice-producing countries of the region, including Thailand, China, Sri Lanka, Nepal, Myanmar, Indonesia, Vietnam, Cambodia, Laos, and the Philippines. Under the IRRI-CIMMYT Alliance, a similar approach is being followed to introduce wheat and maize and the associated cropping systems. In addition within Bangladesh, for example, nonrice enterprises, such as vegetables, are being introduced on the knowledge bank. For the approach used by IRRI, the focus was on credible content that was understandable by end users. The Internet was the repository for the information with its use being primarily through CD-ROM and also as printed material. IRRI did not focus on strengthening the Internet IT capability of its partners but rather to leave that with the individual countries and the respective organizations. This is illustrated in a small experiment conducted by a local farmer intermediary in Bangladesh (Rashid, 2007). There were 51 service providers (government agriculture extension service, local colleges and schools, tea stalls, CD shops, mosque, government union office, library, tribal academy, agriculture input dealer, deep tube-well club, and NGOs). Figure 1 illustrates a pathway developed by a local mosque committee for the use of the information from the BRKB. Provision of the CD-ROM and selected printed fact sheets provided a range of
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Vita Kazipur Mosque Vita Kazipur, Baraigram, Natore (Moulana Mokbul Hossain, Moazzem)
Imam
Devout Prayers (Regular)
Prayers after discuss
MC members (meeting)
Farmers (Male) at Community
Farmer to farmer knowledge exchange
Figure 1
Uptake pathway for Vita Kazipur mosque in Natore, Bangladesh.
organizations, from mosques to tea stalls to local schools, with access to agriculture information that they would not normally have. This is illustrative of the potential of IT for reaching end users through multiple pathways. A second example concerns IT in the form of video development for women-to-women extension (Van Mele et al., 2005a; Van Mele, 2006). Digital media is not restricted to the Internet. The use of video as a tool for going to scale can enable women to speak beyond their own villages with extension messages learned through participatory research for technology development. An example is the seed health videos developed in Bangladesh in a research project designed to improve seed management practices of women. The women initially involved in the technology development became resource persons for extension messages on good seed management. Videos on seed sorting, seed drying, seed storage, and seed floatation were developed as training tools. The videos have been used extensively in Bangladesh and are available in 15 African languages ( Van Mele, 2008). A third example is E-choupal, an Internet initiative for rural India. E-choupal is an IT initiative of the private sector (ITC enterprise—a large agricultural processing company in India) to link directly, through the Internet, with rural farmers for the procurement of agricultural/aquaculture produce (http://www.en.wikipedia.org/wiki/echoupal). One of the objectives of E-choupal is to deliver real-time information that is customized to
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improve the farmer’s decision making ability through better knowledge of markets (http://www.itcportal.com/ruraldevp_philosophy/echoupal.htm). It purports to cover 38,500 villages through 6500 installations and thereby empowers 3.5 million farmers. The ambition in the next 10 years is to cover 100,000 villages or 10 million farmers (http://www.itcportal.com/ ruraldevp_philosophy/echoupal.htm). A fourth example is the Philippines Information–Communication Technology Project. A project of the Open Academy for Philippine Agriculture (OpAPA: http://www.openacademy.ph/) supported by the Philippine government, with specific objectives:
Test and develop approaches to dealing with the range of problems faced by farmers and how best to make these options more widely known at the village and municipal levels through the use of ICT. Study the effectiveness of computer-based information and knowledge dissemination to rural farmers and extension workers. For this project, the focus was on the use of the Internet and telecommunication tools, such as mobile phones, for dissemination of agriculture information to farmers. The IRRI RKB was one source of information for OpAPA. A mobile Internet bus was used to raise awareness amongst municipalities and villages of the Internet as an information source for agriculture. Text messaging was a method employed to link sources of expertise to farmers in real-time problem solving. As a pilot project, the response of local extension personnel to using the Internet and text messaging has been very positive. The Virtual University for the Semi-Arid Tropics is another example of the use of IT to extend research findings. The virtual academy is driven by the International Center for Research in the Semi-Arid Tropics (ICRISAT) and has focused on connecting farmers with experts using IT. The two principal directions have been the development of semiprocessed or adaptable training material that is not packaged but can be drawn on as bits into locally adapted material. For example, the outputs may be in the form of posters, brochures, conventional print, mobile, Web, radio, television, or performance. The second direction has been the establishment of rural hubs. An example is the rural hub in the Addakal area of Andhra Pradesh, India that covers 37 villages. The hub comprises computer and Internet access along with the capability to develop local content for farmer questions. It could be said that the virtual academy is a vision of the future of fully connected villages with the potential to draw in expert response to technical problems. Another example of community-based information services, this time, in China was reported by Yongling et al. (2005a,b, 2007) who present three case studies. Theses represent farmer associations Fuyu and Lanxi that were self-funded and one in Jinyun that was government funded.
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The objective was to use IT to disseminate market information on inputs and products that enabled collective savings or profits through economies of scale. For Fuyu and Lanxi the level of trained personnel, technical capability of the farmers and quality of content were issues. Farmers were relatively poor and so the resources of the association were limited. For the government-funded service, there were questions of sustainability but the use of qualified extension workers was seen as a plus. The above examples are by no means exhaustive and simply represent a small sample of experimentation that is taking place using IT to empower small and marginal farmers and thereby improve their livelihoods.
5.3. A commitment to knowledge for farmers There is a gap between research and extension hence in the pathway from science through to farmers. IT has an important role to play but the gap will not be bridged simply by making farmer intermediaries or farmers computer literate and hooking them up to the Internet. In the examples of the virtual academy of ICRISAT, E-choupal, and OpAPA, there is a focus on technological opportunities. Hubs, text messaging, and virtual village markets are bringing IT into extension. The virtual academy is equipping village centers with the ability to develop their own training material for extension. OpAPA text messaging is linking farmers to experts on call. E-choupal is focusing on specific information around marketing and production requirements. On the other hand, IRRI through the RKB and the seed health videos has focused more on the quality of the information and its format for being understood by extension workers and farmers. The attention is on the messages that can be used by a multiple of service providers. The material could feed into the virtual academy, OpAPA, and E-choupal. The RKB and video approach has operated under the short-term reality that many farmer intermediaries do not have access to the Internet. For this reason, the RKB reflects an approach of equipping the existing multiple service providers with material that can be used in printed or video format. IT offers opportunities both for packaging information so that it can be accessed by interested farmer intermediaries through the Internet and for presenting that material at the community level in a printed or video format. In other words, reaching farmers uses IT but is not dependent on it at the village interface. The neutrality of IT also allows a focus on poverty alleviation and gender equity which is consistent with the Millennium Development Goals. Knowledge pathways facilitated by IT need to build on those systems which are already in place and accepted (FAO, 2005). A livelihoods framework should be used to identify appropriate networks and partnerships and the IT expertise may be best provided as a member of a team in which the local institution and communication expertise is in the lead.
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In using IT, there is still a need to give attention to content and to keep it simple and understandable by the target audience. In many poorer countries, the government extension service is the principal source of agriculture information. It is also the source of information for most civil society organizations. Linking IT to the central platform of extension will empower government extension services. IT extension messages that are digitized can provide a consistent link to updates in between research and the central platform of government extension services. The localization of knowledge into numerous reproductions at the grass roots level will greatly empower local organizations and individuals. The process as presented by IRRI, ICRISAT, E-choupal, OpAPA, and the China farmer associations supports the innovation systems concept of multiple actors and multiple sources of knowledge.
6. Conclusions ICT is rapidly becoming global and accessible across the development divide. Computing power and storage capacity are within reach of any researcher, including those in the area of agricultural development. Vast amounts of information are becoming freely available to researchers throughout the world via open content licensing and the ethical imperative to share information vital to the well-being of all. This empowers researchers in international agriculture to confront problems where they occur in the developing world. Connectivity throughout the world is allowing research teams to coalesce around critical problems and collaborate through dispersed teams in virtual institutes. Researchers in developing countries can, at last, work on a peer-to-peer basis with the international community and bring their critical problems to global attention. This favorable information scenario has come about through technical advances in ICT, including the global reach of the Internet and the simplicity of connection to and interaction with global communities. Scientific information is readily and affordably available to researchers in developing countries. However, ongoing problems of access and use of this information include its often low quality and lack of coherence, as well as the often limited capacity of researchers to understand and analyze the data. These constraints are being addressed by informatics developments and training for a new generation of researchers. As these constraints are relieved, relevant data and information on problems of international agriculture will become available to the global community, which will then be able to collaborate to solve real development problems.
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Finding technical solutions to development problems is only the first step. These must be translated into useful knowledge in terms of farming practices, which are feasible and compatible with existing methods and socioeconomic conventions. New informatics will also play a profound role in this area by facilitating derivation of best practices from research results and disseminating this knowledge to extension agencies and then on to farmers via appropriate information models. More importantly, it will connect these communities so that successes, failures, and needs of farmer end users can be fed back to the international agricultural research community.
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Impact of Pesticides on Soil Microbial Diversity, Enzymes, and Biochemical Reactions Sarfraz Hussain,* Tariq Siddique,† Muhammad Saleem,‡ Muhammad Arshad,* and Azeem Khalid§ Contents 1. Introduction 2. Effect of Pesticides on Soil Microbial Diversity 2.1. Factors affecting the impact of pesticides on microbial diversity 2.2. Impact of pesticides on root-colonizing microbes 2.3. Impact of pesticides on algae 2.4. Use of molecular techniques in studying effects of pesticides on microbial diversity 3. Effect of Pesticides on Biochemical Reactions in Soil 3.1. Nitrogen fixation 3.2. Mineralization of organic compounds, and availability of nitrogen, phosphorus, and potassium in soil 3.3. Nitrification, denitrification, ammonification, and sulfur oxidation 4. Effect of Pesticides on Soil Enzymes 4.1. Nitrogenase enzyme 4.2. Dehydrogenase enzyme 4.3. Urease and phosphatase enzymes 4.4. b-Glucosidase, cellulase, invertase, and other enzymes 5. Impact of Biopesticides on Microbial Diversity and Enzymes 6. Conclusions References
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Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, Pakistan Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada Department of Environmental Microbiology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany Department of Environmental Sciences, PMAS Arid Agriculture University, Rawalpindi, Pakistan
Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01005-0
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Abstract Pesticides are extensively used in agriculture as a part of pest control strategies. Owing to their xenobiotics characteristics, pesticides may adversely affect the proliferation of beneficial soil microorganisms and their associated biotransformation in the soil. Inactivation of nitrogen-fixing and phosphorussolubilizing microorganisms is observed in pesticide-contaminated soils. Recent studies show that some pesticides disturb molecular interactions between plants and N-fixing rhizobacteria and consequently inhibit the vital process of biological nitrogen fixation. Similarly, many studies show that pesticides reduce activities of soil enzymes that are key indicators of soil health. The applied pesticides may also influence many biochemical reactions such as mineralization of organic matter, nitrification, denitrification, ammonification, redox reactions, methanogenesis, etc. However, a few reports reveal some positive effects of applied pesticides on soil health. In this chapter, we attempt to analyze the impacts of pesticides on soil microbial communities, soil biochemical reactions, and soil enzymes.
1. Introduction Pesticides are widely used against a range of pests infesting agricultural crops. Globally, about 3109kg of pesticides is applied annually with a purchase price of nearly $40 billions each year (Pan-UK, 2003). The amount of applied pesticides reaching the target organism is about 0.1% while the remaining bulk contaminates the soil environment (Carriger et al., 2006; Pimentel, 1995). With the growing use of pesticides in contemporary agriculture, the issue of the impact of these chemicals on the composition of soil microorganisms and the processes they direct has received more attention (Andrea et al., 2000; Baxter and Cummings, 2008). The applied pesticides may harm the indigenous microorganisms, disturb soil ecosystem, and thus, may affect human health by entering in the food chain. Adverse impacts of pesticides on soil microbial diversity and activities have been described by many researchers (Ingram et al., 2005; Littlefield-Wyer et al., 2008; Niewiadomska, 2004; Wang et al., 2006). Similarly, pesticides also influences soil biochemical processes driven by microbial and enzymatic reactions. The microbial mineralization of organic compounds and associated biotransformations such as nutrient dynamics and their bioavailability are also more or less adversely affected by the pesticides (Demanou et al., 2004; Kinney et al., 2005; Mahı´a et al., 2008; Niewiadomska, 2004). The applied pesticides also reduce soil enzymatic activities that act as a ‘‘biological index’’ of soil fertility and biological processes in the soil environment (Antonious, 2003; Monkiedje et al., 2002).
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There are also reports documenting the ability of soil microorganisms to degrade pesticides in the soil environment (Hussain et al., 2007a,b; Kumar and Philip, 2006; Siddique et al., 2003). The degradation products of these pesticides are assimilated by soil microorganisms (Tyess et al., 2006) resulting in increased population sizes and activities of microorganisms (Das and Mukherjee, 2000a,b; Jana et al., 1998). Recently, molecular techniques have been used to elucidate the impact of pesticides on microbial community structure and functioning (Widenfalk et al., 2008). Here, we attempt to describe recent advances in the impact of pesticides on soil microorganisms, soil enzymes, and biochemical changes in soil.
2. Effect of Pesticides on Soil Microbial Diversity Pesticides in soil undergo a variety of degradative, transport, and adsorption/desorption processes depending on the chemical nature of the pesticide (Laabs et al., 2007) and soil properties (Weber et al., 2004). Pesticides interact with soil organisms and their metabolic activities (Singh and Walker, 2006) and may alter the physiological and biochemical behavior of soil microbes. Microbial biomass is an important indicator of microbial activities and provides direct assessment of the linkage between microbial activities and the nutrient transformations and other ecological processes (Schultz and Urban, 2008). Many recent studies reveal the adverse impacts of pesticides on soil microbial biomass and soil respiration (Pampulha and Oliveira, 2006; Zhou et al., 2006). Generally, a decrease in soil respiration reflects the reduction in microbial biomass (Chen et al., 2001a; Klose and Ajwa, 2004) or increase in respiration implies the enhanced growth of bacterial population (Haney et al., 2000; Wardle et al., 1994). Some microbial groups are capable of using applied pesticide as a source of energy and nutrients to multiply, whereas the pesticide may be toxic to other organisms ( Johnsen et al., 2001). Likewise sometimes, application of pesticides reduces microbial diversity but increases functional diversity of microbial communities (Wang et al., 2006) even sometimes demonstrate the tendency of reversible stimulatory/inhibitory effects on soil microorganisms (Pampulha and Oliveira, 2006). Pesticides application may also inhibit or kill certain group of microorganisms and outnumber other groups by releasing them from the competition. For instance, Chen et al. (2001a) reported that fungicides applications killed or inhibited the activity of certain fungi which led to a rapid flush of bacterial activity. Similarly, Lo´pez et al. (2006) reported that heterotrophic mesophilic and psychrophilic aquatic bacteria as well as culturable phosphate-solubilizing microorganisms increased in lake water samples when treated with herbicide simazine.
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Sometimes, initially microbial population is affected by pesticide application but with time after a period of acclimation, the population merely returns to normal or even increases (Fliessbach and Mader, 2004; Niewiadomska, 2004). This is an indication of changes in microbial catabolic capabilities that may be either due to induced pesticide degradation capabilities or due to a change within the microbial community. A detailed description of impacts of various pesticides on soil microbial communities is summarized in Table 1.
2.1. Factors affecting the impact of pesticides on microbial diversity The effect of a pesticide on soil microorganisms is controlled by numerous environmental factors in addition to the persistence, concentration, toxicity of the applied pesticide, and its bioavailability (Abdel-Mallek et al., 1994). One of the major factors contributing to the net impact of applied pesticides on soil microbes is its bioavailability in soil environment. Adsorption and desorption processes regulate concentration of a contaminant in soil solution (Bonczek and Nkedi-Kizza, 2007; Katagi, 2008) and hence its bioavailability, bioactivity, and degradability in soil environment. Menon et al. (2004) reported more inhibitory effect of chlorpyrifos and quinalphos in the loamy sand than in the sandy loam soil due to greater bioavailability (less sorption) of the pesticides in loamy sand, resulting from low clay content and organic carbon. Gundi et al. (2005) studied the effect of three insecticides (monochrotophos, quinalphos, and cypermethrin) on microbial populations in a black clay soil. They observed synergistic effects at the lower level and adverse effects at the highest level of the insecticides. In contrary, toxic effects of pesticides (captan, deltamethrin, isoproturon, and pirimicarb) were observed on freshwater sediment microbial communities even at concentrations predicted to be environmentally safe (Widenfalk et al., 2004). In addition to soil texture, presence of organic matter and vegetation also influences pesticide toxicity to the microbes in the soil environment. Addition of carbon sources—including glucose, acetate, and some amino acids (glutamine, arginine, serine, and tryptophan)—enhances resistance to pesticide toxicity against some fungal species (Mishra and Pandey, 1989). This effect is more pronounced in the soil environment which is tilled or not. Under a no-tilled soil conditions, more retention of organic matter is observed with the increased aggregation of particles (Murage et al., 2007). These phenomena influence the fate of organic matter and affect the cycling of microbial biomass. Thus, the use of pesticides under one of these systems may have a different impact on soil microbial diversity and biomass. Santos et al. (2006) studied the effects of herbicides fluazifopp-butyl and fomesafen and their commercial mixtures on the microbial activity of a soil, cultivated with common bean under no-till (NTS) and conventional-till (CTS) systems. They monitored microbial respiration for
Table 1
Effect of pesticides on soil microorganisms
Pesticide
Microbial species
Comments
References
Atrazine, isoproturon, metribuzin, and sulfosulfuron Phorate, carbofuran, carbosulfan, thiamethoxam, imidacloprid, chlorpyrifos, monocrotophos Methamidophos
Bradyrhizobium sp.
Adversely affected Bradyrhizobium sp.
Khan et al. (2006)
Soil microflora
No significant change in total viable count of bacteria
Sarnaik et al. (2006)
Soil microflora
Decreased microbial biomass (41–83%)
Metsulfuron methyl Metalaxyl
Soil microorganisms Microbial biomass
Inhibited heterotrophic S-oxidizing and S-reducing bacteria but increased fungi Decreased microbial biomass
Wang et al. (2006) He et al. (2006)
Mefenoxam, metalaxyl
Soil microorganisms
Inhibited N-fixing bacteria
Carbendazim, imazetapir, thiram
Soil microorganisms
Carbofuran, ethion, hexaconazole
Soil microorganisms
Combination of fungicide and herbicide reduced while herbicide alone increased soil microorganisms Adversely affected soil microorganisms
Bensulfuron methyl, metsulfuron methyl
Microbial biomass
Decreased microbial biomass-C, and N
Sukul and Spiteller (2001) Monkiedje et al. (2002) Niewiadomska (2004) Kalam and Mukherjee (2001) El-Ghamry et al. (2001) (continued)
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(continued)
Pesticide
Microbial species
Comments
References
HCH, phorate, carbofuran, fenvalerate
Soil microorganisms, N2-fixing bacteria, and P-solubilizing microorganisms Soil microorganism, N2-fixing bacteria, nitrifying and denitrifying bacteria Rhizobium ciceri
Increased soil microbial population
Das and Mukherjee (1998a, 2000a)
Phorate decreased total number of bacteria and N2-fixing bacteria but malathion increased denitrifying bacteria. Nitrifying and fungal populations were not affected Except crown, all fungicides decreased viable counts of rhizobia Inhibited the growth of Rhizobium sp. Inhibited the growth of algae
Gonzalez-Lopez et al. (1993)
Phorate, malathion
Captan, apron, arrest, crown 2,4-D Atrazine
Agroxone, Atranex, and 2,4-Damine Brominal, fenvalerate, Cuprosan g-HCH, 2,4-D, anilofos, carbofuran
Rhizobium sp. Chlamydomonas reinhardtii Pseudokirchneriella subcapitata Rhizobium phaseoli, Azotobacter vinelandii Azotobacter chroococcum, Azospirillum brasilense, Azospirillum lipoferum N-fixing Azotobacter and Azospirillum populations
2,4-Damine was the most toxic of the herbicides and Azotobacter vinelandii was the most sensitive to the herbicides Reduced respiration rate and protein contents of diazotrophs g-HCH stimulated Azotobacter and Azospirillum populations and inhibited anaerobic N-fixing bacteria, while
Kyei-Boahen et al. (2001) Fabra et al. (1997) Reboud et al. (2007) Cedergreen et al. (2007) Adeleye et al. (2004) Omar and AbdAlla (1992) Kanungo et al. (1995) and
Monochrotophos, lindane, dichlorvos, endosulfan, chlorpyrifos, malathion Captan
Gluconacetobacter diazotrophicus
Aerobic N2-fixing, denitrifying, nitrifying bacteria, fungi Methylpyrimifos, chlorpyrifos, Aerobic N2-fixing, profenofos denitrifying, nitrifying bacteria, fungi Diazinon, imidacloprid Urease-producing bacterium Isoproturon Nitroso-, nitro-, ureahydrolyzing bacteria, actinomycetes, fungi Butachlor Anaerobic bacteria
Glufosinate ammonium
Bacteria and fungi
Isoproturon
Actinomycetes and fungi
Methamidophos
Soil microbes
carbofuran 2,4-D and anilofos stimulated anaerobic N-fixing bacteria Affected cell morphology and resulted in large number of pleomorphic cells
Patnaik et al. (1995, 1996) Madhaiyan et al. (2006)
Fungi, nitrifying and N2-fixing bacteria significantly decreased while denitrifying bacteria increased Decreased microbial populations
MartinezToledo et al. (1998) MartinezToledo et al. (1992a,b) Ingram et al. (2005) Nowak et al. (2004)
Inhibited the growth of Proteus vulgaris, a urease-producing bacterium Increased bacterial count and decreased actinomycetes and fungi
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Stimulated anaerobic fermentative and sulfate-reducing bacteria while inhibited acetogenic bacteria in paddy soil The herbicide initially decreased bacterial and fungal populations significantly in clay loam and loam soils Biodegradation of isoproturon favored bacterial growth while suppressed actinomycetes and fungi Population of some microbes increased in soil but the total biomass decreased
Min et al. (2001) Ismail et al. (1995) Nowak et al. (2004) Wang et al. (2006)
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12, 51, and 63 days after application (DAA) of the herbicides. The microbial parameters studied were microbial biomass carbon (MBC), microbial quotient (qMIC), metabolic quotient (qCO2), percentage of bean root colonization by mycorrhizal fungi, and grain yield at the end of the cycle. They reported a greater microbial respiratory rate under no-till, with fluazifopp-butyl providing the lowest respiration. The MBC and qMIC were negatively affected by fomesafen and by combined herbicide mixtures after 12 days of application. The herbicides also affected mycorrhizal colonization only after 12 days of application under CTS. In these periods, the highest values of above-mentioned parameters were observed under NTS. All the herbicides decreased MBC and qMIC values after 51days of applications while the qCO2 demonstrated a greater NTS balance over CTS. Under natural soil conditions, there may be many pesticides together or other contaminants in addition to pesticides at a time in a single locality. The coexistence of different pesticides and other contaminants might have different effects on microbial activity and diversity. Wang et al. (2006) evaluated the effect of methamidophos and urea on microbial diversity in soil by using integrated approaches of soil microbial biomass analysis and community level physiological profiles (CLPPs). They concluded that agrochemicals reduced microbial biomass and enhanced functional diversities of soil microbial communities; that is, some species of bacteria might be enriched in soils under methamidophos stress (Wang et al., 2006). Similarly, Demanou et al. (2006) investigated the effect of a combined application of copper and mefenoxam on the functional diversity of soil microbial communities determined by structural and metabolic profiling (arbitrarily primed and RNA arbitrarily primed PCR). The amoA, a functional molecular marker for b-subgroup ammonia-oxidizing bacteria, was detected in treatments of mefenoxam, and mefenoxamþcopper with higher gene copies in the latter after 60days of applications. During the same period, an increase in nitrification activity in response to application of mefenoxam and mefenoxamþcopper was also recorded. They concluded that Nitrosospira-like organisms were the major nitrifiers under mefenoxam treatments. These results may differ under the conditions when different kinds of pesticides applied simultaneously. For instance, Sa´ez et al. (2006) observed the effect of some pesticides (aldrin, lindane, dimethoate, methyl parathion, methidathion, atrazine, simazine, captan, and diflubenzuron) on growth and denitrifying activity of Xanthobacter autotrophicus CECT 7064. The herbicide atrazine and an insecticide dimethoate completely inhibited growth and biological activity of X. autotrophicus at 10mgl1, while the rest of the tested pesticides delayed the growth of strain CECT 7064 but did not drastically affect the bacterial growth after 96h of culture. The denitrifying activity of X. autotrophicus was negatively affected by the pesticides application with the exception of fungicide captan. The release of N2O was strongly inhibited by several pesticides (aldrin, lindane, methyl parathion,
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methidathion, and diflubenzuron), while dimethoate, atrazine, and simazine inhibited totally the denitrifying activity of the strain (Sa´ez et al., 2006). Wang et al. (2007) investigated the combined effect of cadmium (Cd) and butachlor on microbial activity. They demonstrated that the addition of high concentration of butachlor applied in combination with Cd significantly affected the diversity of microbial community. Moreover, the coexistence of pesticides with fertilizers could also affect microbial diversity and activity in the soil environment. Almost similar results were reported by Chen et al. (2007) and Lin et al. (2007) who investigated the associated impact of inorganic fertilizers, heavy metals, and pesticides on microbial communities in soils. After application to agricultural fields, pesticides undergo to numerous bio- and physiochemical transformations and forms different metabolites which are either more persistent or lethal to target and nontarget organisms or harmless depending upon the victims and metabolites formed. Recently, Vira´g et al. (2007) studied the effects of pesticides and their degradative products, produced by UV treatment, on microbiological activity. They selected five photosensitive pesticides (carbendazim, acetochlor, simazine, EPTC, and chlorpyrifos) and six representative soil microbes (Bacillus subtilis, Pseudomonas fluorescens, Mycobacterium phlei, Fusarium oxysporum, Penicillium expansum, and Trichoderma harzianum) for their experiments. Among them, acetochlor and its degradation products were more toxic to bacteria than fungi. All bacterial strains were sensitive to the parent compound and its degradation products as well. End product of carbendazim was moderately toxic against P. fluorescens and B. subtilis but strongly toxic against T. harzianum. Chlorpyrifos and its metabolites did not inhibit the test organisms. They concluded that the pesticide photodegradation could result in significant changes in soil microbiota in addition to the formation of biologically harmful degradation products. Very recently, Smith and Beadle (2008) also reported the toxicity of 2,4-D and its metabolic intermediates on Burkholderia cepacia.
2.2. Impact of pesticides on root-colonizing microbes Beneficial root-colonizing microbes such as bacteria and arbuscular mycorrhizal (AM) fungi form symbioses with majority of plants and promote plant growth and development both under stressed and normal conditions (Sainz et al., 2006; Saleem et al., 2007). Exogenous applications of pesticides could influence the functioning of these microbes. Sainz et al. (2006) studied the effects of soil contamination with HCH on vegetation and its associated arbuscular mycorrhizas (AM) in polluted and nonpolluted plots having same plant cover. They selected Plantago lanceolata plants for mycorrhizal analysis because of their presence in both plots and known mycotrophy. The HCH did not have significant effect on the extent of colonization of Plantago roots by AM, but the density of AM spores and viable AM hyphae were much less
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in the HCH-polluted soil (rhizosphere) than in the nonpolluted soil. In a similar study, Verdin et al. (2006) investigated the influence of anthracene on chicory root colonization by Glomus intraradices. The application of anthracene reduced development of extraradical mycelium and caused a marked decrease in sporulation, root colonization, and spore germination. In another study, Vieira et al. (2007) investigated the effect of sulfentrazone on mycorrhizal and rhizobial performance in soybean. They concluded that pesticides markedly decreased microbial infection of soybean roots and consequently adversely affected nitrogen fixation and overall plants growth as well. Recently, Fox et al. (2007) demonstrated that subset of organochlorine pesticides delayed recruitment of rhizobia bacteria to host plant roots, produced fewer root nodules, lowered rates of nitrogenase activity, and resultantly reduced plant yield at time of harvest.
2.3. Impact of pesticides on algae Pesticides also have deleterious impacts on algae by influencing their growth, photosynthesis, nitrogen fixation, biochemical composition, metabolic activities, etc. (Fathi, 2003; Friesen et al., 2003; Ma and Liang, 2001; Ma et al., 2002; Mostafa and Helling, 2002). But little is known about the comparative sensitivity of pesticides toward various algal species (Ma et al., 2004a,b). Ma (2005) investigated the effect of five organotins and pyrethroids pesticides on three cyanobacteria (Anabaena flos-aquae, Microcystis flos-aquae, and Microcystis aeruginosa) and five green algae (Selenastrum capricornutum, Scenedesmus quadricauda, Scenedesmus obliqnus, Chlorella vulgaris, and Chlorella pyrenoidosa) through 96-h acute toxicity tests. They observed wide variations among the eight individual species of cyanobacteria and green algae in response to the tested pesticides. The toxicity of pyrethroids pesticides was lower than that of organotins pesticides while aquatic ecological risk of pyrethroids pesticides was higher than that of organotins pesticides. Such kind of variations in response to pesticides exposure to various algal species could be due to the difference in their metabolic activities. Recently, Rajendran et al. (2007) investigated the effects of a fungicide, an insecticide, and a biopesticide on Tolypothrix scytonemoides. They reported that the rates of photosynthetic oxygen evolution decreased but rates of respiratory oxygen consumption were increased in cells exposed to these pesticides. Moreover, nitrogenase and glutamine synthetase activities were affected in all the pesticide treatments but bavistin enhanced nitrogenase activity. Likewise, the release of ammonia and carbohydrates was increased in pesticides-exposed cells where the release of carbohydrates was reduced in bavistin-treated cells. Somewhat contrarily, Yue et al. (2007) reported that the pesticide bensulfuron methyl accelerated growth of C. pyrenoidosa at lower concentrations (<1mg l1) by increasing chlorophyll or protein contents and inhibited its growth at higher concentrations (>5mg l1). The chlorophyll or
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protein contents in algae cells were reduced with increasing concentration of bensulfuron methyl, exhibiting the good concentration–effect relationship. Jonsson and Aoyama (2007) revealed that surfactant alkyl benzenesulfonate (LAS) and the heavy metals Hg 2þ, Al3þ, and Cu2þ and/or their mixture (all are used as pesticides) markedly altered (50%) acid phosphate activity of Pseudokirchneriella subcapitata.
2.4. Use of molecular techniques in studying effects of pesticides on microbial diversity Recently, molecular techniques such as culture-independent method are used to study the change in the structure and functions of microbial community in response to the application of agrochemicals. Zhang et al. (2006) investigated the long-term effects of methyl parathion contamination on soil microbial diversity estimated by 16S rRNA gene cloning. In the control soil, the dominant bacterial groups included a member of bacterial division, the bacillus genus, and a member of a-proteobacteria, while in methyl parathion-contaminated soil, the dominant phylotypes included a member of the flexibacter–cytophaga–bacteroides division and two members of the g-proteobacteria. This was the first report of the long-term effects of methyl parathion on soil microbial diversity and structure by a culture-independent method, and provided the evidence to assess the long-term environmental toxicological effects of methyl parathion on microbial diversity. Recently, Hoshino and Matsumoto (2007) investigated the effect of two chemical fumigants (chloropicrin and 1,3-dichloropropene) and spinach growth on fungal community structure in a field by developing a nested PCR-DGGE method with a new combination of primer pairs. They showed that the chloropicrin treatment changed DGGE profiles drastically and reduced the diversity index H 0 in both bulk and rhizosphere soils after 2months of fumigation. The profiles and diversity index did not recover completely even after a period of 1year. The DGGE profile of 1,3-dichloropropene demonstrated a smaller change during 2 months of fumigation which became indistinguishable from the control plots after 6months of fumigation. The authors concluded that the rhizosphere may contribute to minimizing the effect of chemical fumigation. The impact of pesticides on the genetic structure of microbial communities and associated degrading microbial biomass has been less investigated. Paul et al. (2006) demonstrated by using 16S rRNA gene sequences that the community structure of the pesticide-contaminated soil was mainly constituted by Proteobacteria and Actinomycetes. Gonod et al. (2006) launched a study to investigate (1) the impact of 2,4-D on the genetic structure of bacterial communities, (2) 2,4-D mineralization and the associated degrading biomass, and (3) the 2,4-D degrading genetic potential. They revealed that genetic structure of bacterial communities was significantly modified in response to 2,4-D application, but only during the intense phase of 2,4-D biodegradation
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and this effect disappeared 7 days after the treatment. The 2,4-D degrading genetic potential increased rapidly following 2,4-D application. There was a concomitant increase between the tfdA copy number and the 14C microbial biomass. The maximum of tfdA sequences corresponded to the maximum rate of 2,4-D mineralization. Moreover, they concluded that in soil, 2,4-D degrading microbial communities seemed to use the tfd pathway preferentially to degrade 2,4-D. Similarly, in another case, Borzı` et al. (2007) investigated the impact of the fungicide fenhexamid (FEX) on the genetic structure of soil bacterial communities already having pcaH sequence. The use of FEX increased the number of the gene copies which implied microbial population of the contaminated soil adapted to the presence of FEX with an increase in degradation potential. Similarly, Su et al. (2007) investigated the toxic effects of acetochlor, methamidophos, and their combination on nifH gene in soil. The acetochlor decreased the nifH gene diversity (gene richness and diversity index values) and changed the nifH gene composition with increasing its concentrations. The methamidophos also reduced nifH gene richness in first 4 weeks. The medium concentrations of methamidophos (150 mg kg1) changed nifH gene diversity in first week, but higher concentrations (250 mg kg1) demonstrated prominent effects on nifH gene diversity in next weeks. The mixture of both acetochlor and methamidophos also affected nifH gene diversity. They reported that different nifH genes (bands) responded differentially to these pesticides and four individual bands were disappeared by the pesticides exposure. Moreover, pesticides stimulated five bands while four bands demonstrated strong resistance against these pesticides. In addition, they partially sequenced 15 prominent bands, yielding 15 different nifH sequences. All these sequences were affiliated with the a- and b-proteobacteria and showed higher similarity to eight different diazotrophic genera. Wang et al. (2006) conducted an experiment to examine the effect of continuous inputs of methamidophos for 4years on the biochemical, catabolic, and genetic characteristics of soil microbial communities through characterizing microbial biomass, PLFA profiles, and CLCPs and ARDRA patterns. They reported that high methamidophos inputs significantly decreased total microbial biomass carbon (Cmic) and fungal biomass, but enhanced Gram-negative bacteria biomass and catabolic activity with no significant effects on the Gram-positive bacteria. These studies support the applications of molecular techniques in studying the response of soil microbes to applied pesticides.
3. Effect of Pesticides on Biochemical Reactions in Soil Soil microorganisms have the ability to carry out biochemical transformations of various elements like nitrogen (N), phosphorus (P), sulfur (S), and carbon (C). Pesticides may directly or indirectly affect the vital
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biochemical reactions such as mineralization of organic matter, nitrogen fixation, nitrification, denitrification, and ammonification by activating/deactivating specific soil microorganisms and/or enzymes (Kinney et al., 2005; Menon et al., 2005; Niewiadomska, 2004). Information on possible effects of pesticides on all biochemical processes is sparse; however, a description of pesticides’ effects on soil biochemical reactions is summarized in Table 2.
3.1. Nitrogen fixation Biological nitrogen fixation (BNF) is an efficient and natural source of nitrogen, and the total BNF has been estimated twice (175 million tones) as compared to the total nitrogen fixation by nonbiological processes. Rhizobial symbioses with over 100 agriculturally important legumes contribute nearly half the annual quantity of BNF entering soil ecosystem (Tate, 1995). Pesticide may influence the nodulation and BNF in legumes either by affecting virulence of attacking nodular bacteria, the root fibers of the plants in which the infection occurs or both. Niewiadomska (2004) and Niewiadomska and Klama (2005) reported the adverse effects of carbendazime, thiram (fungicides), and imazetapir (herbicide) on nitrogenase activity of Rhizobium leguminosarum, Sinorhizobium meliloti, and Bradyrhizobium sp., and clover, lucerne, and serradella plants. Herbicides have been reported to affect Bradyrhizobium japonicum growth in vitro and to reduce the nodulation of soybeans under greenhouse conditions (Malik and Tesfai, 1985, 1993). Singh and Wright (1999, 2002) described the adverse effects of herbicides (terbutryn, simazine, prometryn, and bentazone) on rhizobia at concentrations not normally expected to occur under field condition. Effects of these herbicides on nodulation and nitrogen fixation in peas were, possibly, not due to their effects on rhizobia but to their adverse effects on the plant growth itself. Similar results have been reported by Zawoznik and Tomaro (2005) where growth of B. japonicum in pure culture was not affected by chlorimuron ethyl (herbicide) even when exposed to concentrations 150 times higher than recommended field doses. However, nodulation of soybean plants was impaired at standard application rates. The herbicides (sethoxydim, alachlor, fluazifop-butyl, and metachlor) did not have detrimental effects on N2-fixation or seed yields of soybean at the recommended rates; however, paraquat significantly reduced the N2-fixation by soybean (Kucey et al., 1988). Fungicides are also dangerous to the Rhizobium– legume symbiosis (Fisher and Hayes, 1981; Graham et al., 1980; Heinonen-Taski et al., 1982). Fungicides (captan, apron, and arrest) treatment decreased the number of viable rhizobia on the seed (Kyei-Boahen et al., 2001). In a study conducted with three herbicides (2,4-D, Atranex, and Agroxone), survival of Rhizobium phaseoli and Azotobacter vinelandii decreased with increased concentration and longer incubation period (Adeleye et al., 2004). Different rhizobial growth rates were observed
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Table 2
Effect of pesticides on biochemical reactions in soil
Pesticide
Biochemical reaction
Comments
References
BHC, fenvalerate Organophosphates Captan, benomyl, chlorothalonil, anilazine, dichlofluanid Bensulfuron methyl, metsulfuron methyl Dinoterb Ridomil gold plus copper
C-mineralization N-mineralization N-mineralization
Increased Total soil N decreased Increased
Murthy et al. (1991) Sato (1983) Chen and Edwards (2001), Chen et al. (2001a,b), and Malkomes and Dietze (1998)
N-mineralization
Reduced
El-Ghamry et al. (2001)
N-mineralization N- and organic P-mineralization; ammonification C-, N-, and P-mineralization Plant available N
Increased Increased
Engelen et al. (1998) Demanou et al. (2004)
Increased
Das and Mukherjee (1998b, 2000b) Singh and Singh (2005a)
Phorate, carbofuran Diazinon, imidacloprid, lindane DDT, pentachlorophenol (blue), 2,4-D, 2,4,5-T Carbendazin, imazetapir, thiram Terbutryn, simazine, prometryn, bentazone Butachlor
Nitrogen fixation
Diazinon and imidacloprid increased but lindane decreased plant available N in groundnut field Herbicides inhibited nod-expression by 32–90% by disrupting plant– Rhizobium signaling Inhibited nodulation
Niewiadomska (2004)
Nitrogen fixation
Decreased nodulation and N contents
Singh and Wright (1999)
Nitrogen fixation, nitrification
Increased in the beginning but reduced afterwards in paddy soil
Min et al. (2001)
Nitrogen fixation
Fox et al. (2001) and Mclachlan (2001)
Nitrification
Nitrification was inhibited by the pesticides
Arora et al. (2003) and Kinney et al. (2005)
Reduction in nitrification rates Nitrification and phosphate solubilization decreased
Colores and Schmidt (2005) Ogunseitan and Odeyemi (1985)
Dithiocarbamates
Nitrification
Metalaxyl, mefenoxam
Nitrification, ammonification
Inhibited nitrification and denitrification but stimulated S-oxidation All of the 28 fungicides used inhibited nitrification of urea Fungicides inhibited ammonium oxidation Increased nitrification and ammonification but higher rates significantly decreased these processes Inhibited
Tu (1995)
Fungicides
Nitrification Nitrification, phosphate solubilization Nitrification, S-oxidation, denitrification Nitrification
Mancozeb, prosulfuron, chlorothalonil, metal dithiocarbamates Pentachlorophenol Lindane, malathion, captan Cyfluthrin, imidacloprid
Chlorpyrifos, quinalphos Arginine ammonification Fenpropimorph Denitrification
173
Bromoxynil, methomyl, 2,4-D Chlorinated hydrocarbons Prosulfuron, metachlor
CH4-oxidation
N2O, CH4
Acetamiprid
Respiration
Methanogenesis
Inhibition of denitrification occurred Oxidation of CH4 to CO2 was inhibited Inhibited Stimulated N2O emission and CH4 consumption Reduced respiration
Martens and Bremner (1993) Hansson et al. (1991) Monkiedje et al. (2002)
Menon et al. (2004) Svensson and Leonardsson (1992) Syamsul-Arif et al. (1996) and Topp (1993) Bauchop (1967) and Krumback and Conrad (1991) Kinney et al. (2004) Yao et al. (2006)
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under pesticide treatment. Some pesticides are not detrimental to the growth of rhizobia at field rates (Martensson, 1992; Mogollon et al., 1986; Torralba et al., 1986), whereas other pesticides were toxic to rhizobia at low as well as at high rates (Bandyopadhyay et al., 1979; Ruiz-Sainz et al., 1984). Fabra et al. (1997) reported the effect of 2,4-D on Rhizobium sp. and found the herbicide in cell wall and cytosol of the bacterium. The vital process of nitrogen fixation is coordinated and regulated by phytochemical signaling to Rhizobium (Baker, 1998; Peters et al., 1986). Some pesticides interfere with plant–Rhizobium signaling and affect symbiotic nitrogen fixation by inhibiting nodulation by Rhizobium (Fox et al., 2001, 2007). Detailed description of pesticides’ effects on plant–Rhizobium signaling has been given by Fox et al. (2001). Variation among legume species with regard to nodulation and N2-fixation under pesticide treatment may depend on the type and dose of the pesticide, species of Rhizobium and legume, and stage of development of the Rhizobium–legume symbiosis. Khan et al. (2006) reported that herbicides applied in sandy clay loam soil had an adverse phytotoxic effect on chickpea vitality and subsequently the Mesorhizobium–chickpea symbiosis. Similarly, Madhaiyan et al. (2006) studied the influence of different pesticides on the growth and survival of Gluconacetobacter diazotrophicus strain PAL5. The monocrotophos, lindane and dichlorvos proved lethal to Gluconacetobacter, while endosulfan, chlorpyrifos, and malathion effects were intermediate. The herbicides did not affect the growth and survival of Gluconacetobacter in the medium except for the concentrations exceeding recommended rates while had a slight effect on the growth of Gluconacetobacter at recommended dose except ridomil. Most of the pesticides affected the cell morphology to a larger extent resulting in larger number of pleomorphic cells.
3.2. Mineralization of organic compounds, and availability of nitrogen, phosphorus, and potassium in soil Organic matter is one of the most critical properties of soil that affects soil quality, the productivity of soil, and emission of trace gases to atmosphere. Much changes in the levels and the dynamics of organic matter is controlled by biological activities in soil, and the quantity and quality of plant residues returned to the soil. Researchers have reported inhibitory effects of pesticides on rates of decomposition of organic matter and mineralization in agricultural and grassland ecosystems (Perfect et al., 1981; Pimentel and Warneke, 1989), forest areas (Weary and Merriam, 1978), and a desert ecosystem (Santos and Whitford, 1981). In contrary, others have advocated the stimulatory effect of pesticides on mineralization process. Sukul (2006) revealed that metalaxyl (fungicide) significantly decreased total C and N contents in soil during 0–30 days of incubation. Similar results are described by Das and Mukherjee (1994, 1998a,b, 2000a,b) in laterite (Typic Orchragualf ), Typic Fluvaquent, alluvial and in the rhizosphere soil of rice.
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They reported that the mineralization rate of organic C in soils treated with different insecticides at recommended doses (BHC, phorate, carbofuran, and fenvalerate representing the organochlorine, organophosphate, carbamate, and pyrethroid groups of insecticide) was higher compared to the control. This indicated that biodegradation of insecticides stimulated the growth and activities of heterotrophic microorganisms which favor mineralization of organic matter and biological transformation of other plant nutrients in soil to derive energy, carbon and other elements for microbial metabolism for their cellular constituents, resulting in lower retention of organic C in soil (Bhuyan et al., 1993; Debnath et al., 1994; El-Shahaat et al., 1987; Murthy et al., 1991; Rangaswamy and Venkateswarlu, 1993). Das and Mukherjee (1994, 1998a,b, 2000a,b) and Singh and Prasad (1991) found similar results in case of N-mineralization. The greater mineralization of N following the application of insecticides resulted in an increase in the amounts of mineralized N (NHþ 4 and NO3 ) in soil. Insecticides probably stimulated the growth and activities of ammonifying and nitrifying bacteria which were mainly responsible for mineralization of organic N. Chen and Edwards (2001) and Chen et al. (2001a,b) studied the effect of fungicides (captan, benomyl, and chlorothalonil) on N-dynamics in soils of different textures. All fungicides enhanced rates of net N-mineralization and nitrification initially, but reduced the rates after 20 days. They attributed the increase in N-mineralization to the death of certain fungi and increase in certain bacteria that most likely increased the rates of N-mineralization. Demanou et al. (2004) studied the effect of ridomil gold copper fungicide on N- (ammonification) and P-mineralization and found higher mineralization of both elements, probably occurred through killing of a part of microflora and increasing NHþ 4 N by surviving part of the microflora. The inhibition of nitrification could be another way of NHþ 4 N accumulation in soil. Das and Mukherjee (2000b) also reported higher mineralization of P with the incorporation of insecticides suggesting that insecticides significantly increased the phosphate-solubilizing/mineralizing microorganisms (Das and Mukherjee, 1994). Different results have been reported by Sardar and Kole (2005) who observed significant reduction in N-, P-, and K-mineralization with the application of chlorpyrifos (organophosphate insecticide). The inhibitory effect on available N, P, and K was attributed to the primary and secondary metabolites of chlorpyrifos metabolism rather than chlorpyrifos itself. However, the average N and P status was recovered at 120 days after the disappearance of the metabolites. Arginine deaminase catalyzes the mineralization of nitrogenous compounds in soil to release ammonium and nitrate. Menon et al. (2004) reported that arginine ammonification activity of rhizospheric microbes was inhibited after seed treatment with chlorpyrifos and quinalphos and their principal metabolites. A higher magnitude of inhibition of arginine deamination in the loamy sand than in
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the sandy loam soil could be due to greater bioavailability of the pesticides in the former with lesser sorption due to low clay content and organic C.
3.3. Nitrification, denitrification, ammonification, and sulfur oxidation Applied pesticides can stimulate nitrification, denitrification, and ammonification by switching on bacterial communities responsible for carrying out these biological processes and vice versa (Pell et al., 1998; Rangaswamy and Venkateswarlu, 1993; Sato, 1983). Kinney et al. (2005) observed the toxic effects of mancozeb, chlorothalonil, and prosulfuron (fungicides and herbicides) on nitrification and denitrification at an incubation period of 48h. Nitrous oxide (N2O) and nitric oxide (NO), which are environmentally significant trace gases produced in soil by the processes of nitrification and denitrification, are inhibited by all pesticides at pesticides concentrations from 0.02 to 10 times that of a standard single application. Effect of lindane, captan, and malathion on nitrification, sulfur oxidation, and phosphate solublization in a tropical soil was studied by Ogunseitan and Odeyemi (1985). Nitrification was repressed and phosphate solublization was decreased in the soil for 30 days by all the three pesticides. Only malathion produced breakdown products inhibitory to nitrification. Malathion increased the rate of sulfur oxidation while lindane and captan affected this reaction adversely. Tu (1995) also reported an increase in S-oxidation in sandy soil after 4weeks of insecticides application. It is difficult to quantify the net impact of pesticides on biochemical reactions in soil due to greater soil resilience, nature and concentration of pesticide, its activity and metabolism in soil, and production of metabolites. But in most cases, application of pesticides can disturb microbial biochemical equilibrium and cycling of biological elements.
4. Effect of Pesticides on Soil Enzymes Soil contains free enzymes, immobilized extracellular enzymes, and enzymes within microbial cells (Mayanglambam et al., 2005). They are indicator of biological equilibrium (Frankenberger and Tabatabai, 1991), fertility (Antonious, 2003; Nannipieri, 1994; Schuster and Schroeder, 1990), quality (Bucket and Dick, 1998; Dick, 1994), and changes in the biological status of soil due to pollution (Kucharski et al., 2000; Nannipieri and Bollag, 1991; Nannipieri et al., 1990; Schaffer, 1993; Trasar-Cepeda et al., 2000). The role of soil enzymes and their activities are defined by their relationships with soil and other environmental factors (e.g., acid rain,
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heavy metals, pesticides, and other industrial chemicals) that affect their activities (Burns, 1982). Pesticides reaching the soil may disturb local metabolism or enzymatic activities (Engelen et al., 1998; Liu et al., 2008; Topp et al., 1997). Negative impact of pesticides on soil enzymes like hydrolases, oxidoreductases, and dehydrogenase activities has been widely reported in the literature (Ismail et al., 1998; Malkomes, 1989; Menon et al., 2005; Monkiedje and Spiteller, 2002; Perucci and Scarponi, 1994; Schuster and Schroeder, 1990; Tu, 1992). There is also evidence that soil enzymatic activities and ATP contents are increased by some pesticides (Megharaj et al., 1999; Shukla, 1997). ATP contents are correlated with specific soil enzymes activities and may provide valuable information on trends in transformation of pesticides in soils (Kanazawa and Filip, 1986). A number of factors, for example, chemical nature of pesticides, concentration used, microbial community structure, type of soil, and soil conditions can contribute to divergent research findings. Malkomes (1997) attributed such differences to the dual behavior of pesticides (both harmful and beneficial for soil enzymes), diversity and various stages of the processes taking place in soil that are frequently overlapped. Enzyme activity in soils reflects not only enzymes in soil solution and living tissue, but also enzymes bound to soil colloids and humic substances (Nannipieri et al., 1990; Skujins, 1976). Enzyme activity is influenced by soil conditions such as organic matter content (Kandeler et al., 1999; Lalande et al., 1998; Pancholy and Rice, 1973), moisture (Bergstrom et al., 1998; Ross and Speir, 1984), and temperature (Tscherko et al., 2001). The effect of pesticides on soil enzymes particularly extracellular enzymes are not clear due to their multidimensional behavior in complex soil medium and the greater complexity of soil microbial and biochemical interactions. For these reasons, researchers are faced with difficulties in discerning the effects of pesticides on extracellular enzyme activities in soil (Nannipieri, 1994).
4.1. Nitrogenase enzyme Nitrogenase is the enzyme used by organisms to fix atmospheric nitrogen gas (N2). Application of pesticides affects the efficiency and activity of nitrogenase enzyme. Singh and Wright (1999) observed a decrease in total nitrogenase activity (measured from pots sown with Pisum sativum plants) with the application of herbicides. Adverse effects of pesticides have also been reported on nitrogenase activities of N2-fixing bacteria, purple nonsulfur bacteria, methylotrophic bacteria, and cyanobacteria (Chalam et al., 1997; Durska, 2004; Hammouda, 1999; Martinez-Toledo et al., 1998). In contrary, repeated applications of pesticides significantly stimulated rhizosphere-associated nitrogenase activity (Kanungo et al., 1995; Patnaik et al., 1995, 1996). Effects of different pesticides on nitrogenase enzyme have been summarized in Table 3.
178 Table 3 Effect of pesticides on activity of nitrogenase enzyme in soil Pesticide applied
Comments
References
Carbendazim, imazetapir, thiram
Pesticides reduced nitrogenase activity in Rhizobium leguminosarum bv. trifolii, Sinorhizobium meliloti, and Bradyrhizobium sp. in pot and field conditions Nitrogenase activity of Anabaena doliolum was reduced by 38% after 48h Nitrogenase activity decreased in soils under aerobic conditions Herbicides decreased total nitrogenase activity Pesticides inhibited nitrogenase and hydrogen photoproduction activities of purple nonsulfur bacteria (Rhodobacter sphaeroides and Rhodopseudomonas palustris) Inhibited Fungicide at a rate of 10mgl1 affected the nitrogenase activity of Azospirillum brasilense Cell growth and nitrogenase activity of Azospirillum brasilense were markedly inhibited by low concentrations of the fungicides Field doses of the fungicides had no effect on nitrogenase activity of methylotrophic bacteria but higher doses suppressed the activity
Niewiadomska (2004) and Niewiadomska and Klama (2005) Hammouda (1999)
Carbofuran Captan Terbutryn, simazine, prometryn 2,4-D, quinalphos, monocrotophos, captan, carbendazim Brominal, fenvalerate, Cuprosan Captan Captan, thiram
Oxafun, Funaben, Baytan
Martinez-Toledo et al. (1998) Singh and Wright (1999) Chalam et al. (1996, 1997)
Omar and Abd-Alla (1992) Di Ciocco and Caceres (1997) Gallori et al. (1991)
Durska (2004)
g-HCH, pretilachlor, butachlor, benthiocarb, cinmethylin, 2,4-D, anilofos Carbofuran Methabenzthiazuron, terbutryn, and linuron Atrazine and Northrin
g-HCH stimulated rhizosphere-associated nitrogenase activity. Among herbicides, pretilachlor had no effect while other pesticides stimulated nitrogenase activity Repeated application of carbofuran significantly stimulated rhizosphere-associated nitrogenase activity Pre-emergent application of herbicides adversely affected nitrogenase activity and consequently nodule number and weight Herbicides stimulated dehydrogenase activity of the microbial community at lower and inhibited it at higher concentrations
Patnaik et al. (1995, 1996) Kanungo et al. (1995) Khan et al. (2006) Nweke et al. (2007)
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4.2. Dehydrogenase enzyme Dehydrogenases occur intracellularly in all living microbial cells and it is linked with microbial respiratory processes (Bolton et al., 1985). Dehydrogenase activity in soil is an indicator of overall microbial activity of soils. Mayanglambam et al. (2005) studied the effect of organophosphate insecticide (quinalphos) on dehydrogenase activity (DHA) in soil and observed 30% (p<0.05) inhibition in DHA after 15 days. DHA was recovered after 90 days of treatment which may be due to adaptation of soil microbes to counter the effect of chemical stress in hostile conditions. Similar observation were made with the application of insecticides (Menon et al., 2005; Xie et al., 2004), fungicides (Demanou et al., 2004; Monkiedje et al., 2002), herbicides (Engelen et al., 1998; Nowak et al., 2004), and fumigants (Klose and Ajwa, 2004; Schutter et al., 2001). Klose et al. (2006) reported that soil fumigation reduced DHA activity to 35% over a period of 90 days. However, there are reports which reveal the stimulatory effects of pesticides on soil DHA (Min et al., 2001; Singh and Singh, 2005b). Very recently, Singh and Kumar (2008) revealed that acetamiprid increased dehydrogenase activity up to 22% after first insecticide application. Other reports show variable results. Chen et al. (2001a) studied three fungicides and all three fungicides had different effects on DHA in the amended soils. Metalaxyl (fungicide) application initially increased and then decreased the DHA in fungicide-treated soil (Sukul, 2006). He et al. (2006) did not observe any inhibition of dehydrogenase enzyme with the application of herbicide. Impact of different pesticides on soil DHA has been summarized in Table 4.
4.3. Urease and phosphatase enzymes Hydrolases are of particular importance on account of their role in the soil nitrogen, phosphorus, carbon, and sulfur cycles (Megharaj et al., 1999). Urease is an enzyme that catalyzes the hydrolysis of urea into CO2 and NH3 and is a key component in the nitrogen cycle in soils. Urease activity is found in a large number of soil bacteria and fungi (Sarathchandra et al., 1984). Phosphatase is an exocellular enzyme produced by many soil microorganisms and is responsible for the hydrolysis of organic P compounds to inorganic P (Monkiedje et al., 2002). Several researchers have shown either unchanged, increase or decrease in urease activity following various pesticide applications (Antonious, 2003; Chen et al., 2001b; Ingram et al., 2005; Nowak et al., 2004). Detailed description of pesticides’ effect on soil urease and phosphatase enzymes is given in Table 5. The urease activity in soil is often correlated with the size of the microbial population and activity (Roberge and Knowles, 1967). In soil, hydrolysis of urea, an important fertilizer, with urease yields NHþ 4 ion which is taken up by plants. Nitrifi cation of NHþ 4 ion into NO3 ion in soil is rapid and NO3 can leach down
Table 4 Effect of pesticides on activity of dehydrogenase enzyme in soil Pesticide applied
Comments
References
Glyphosate
Initially inhibited but later on activity was restored
Quinalphos Diazinon, imidacloprid, lindane
Initially inhibited but later on activity was restored Diazinon did not affect, imidacloprid increased while lindane decreased the enzyme when applied as seed treatment in groundnut field Inhibited
Andrea et al. (2000) and Sannino and Gianfreda (2001) Mayanglambam et al. (2005) Singh and Singh (2005b)
F-metalaxyl, P-metalaxyl, metalaxyl, mefenoxam Chlorpyrifos, quinalphos Hexaconazole, carbofuran, ethion Azoxystrobin, tebuconazole, chlorothalonil Isoproturon Ridomil gold plus copper
Monkiedje and Spiteller (2002) and Monkiedje et al. (2002)
Overall inhibition in field
Menon et al. (2005)
Hexaconazole was more toxic than ethion and strongly inhibited dehydrogenase system in bacteria including N-fixing bacteria in soil Fungicides significantly reduced dehydrogenase activity in the low OM/biomass soil but not in the high OM/biomass soil
Kalam and Mukherjee (2001)
A decrease in dehydrogenase activity was observed in sand and loam soils Fungicide inhibited dehydrogenase activity
Nowak et al. (2004)
Bending et al. (2007)
Demanou et al. (2004) (continued)
Table 4
(continued)
Pesticide applied
Comments
References
Triazophos, bensulfuron methyl, chlobenthiazone Methyl bromide, chloropicrin Benomyl, captan, chlorothalonil Dinoterb Metalaxyl Monocrotophos, cypermethrin Butachlor Glyphosate
Increasing concentrations decreased the enzyme activity in paddy soil
Xie et al. (2004)
Inhibited dehydrogenase activity
Schutter et al. (2001)
Benomyl and chlorothalonil increased while captan significantly decreased the activity Decreased enzyme activity Dehydrogenase activity initially increased and then decreased Insecticides stimulated dehydrogenase when applied singly but inhibited in combination Enhanced the enzyme activity No significant correlation was found between 14CO2 production from mineralization of pesticides and dehydrogenase activity Herbicide did not affect the hydrogenase activity
Chen et al. (2001a)
Metsulfuron methyl
Engelen et al. (1998) Sukul (2006) Gundi et al. (2005) Min et al. (2001) Andrea et al. (2003) He et al. (2006)
Table 5 Effect of pesticides on activities of urease and phosphatase enzymes in soil Pesticide applied
Soil enzyme
Comments
References
Diazinon, imidacloprid
Urease
Ingram et al. (2005)
Pyrethrins, Neemix-4E Profenofos
Urease Urease
Bacterial urease from Bacillus pasteurii was unaffected by the insecticides. Only diazinon significantly reduced urease activity in washed cells as well as in Maury soils (fine, mixed, semiactive, mesic Typic Paleudalf ) Inhibited urease activity Inhibited
Isoproturon
Urease
Benomyl, captan
Urease
Glufosinate ammonium
Urease, phosphatase
Metalaxyl
Urease, phosphatase
Imidachlor, cyfluthrin, tebupirimphos, Aztec, Amitraz
Urease, phosphatase
An increase in urease was observed in sand and loam soils Soil urease activity increased with fungicides application Initial inhibition of phosphatase in sandy loam and clay loam soils and urease in sandy loam soil Urease activity continuously decreased while phosphatase activity initially increased and then decreased Insecticides inhibited activities of these enzymes for 1week
Antonious (2003) Abdel-Mallek et al. (1994) Nowak et al. (2004) Chen et al. (2001b) Ismail et al. (1995)
Sukul (2006)
Tu (1995) (continued)
Table 5 (continued) Pesticide applied
Soil enzyme
Comments
References
Chlorpyrifos, dichlorvos, methyl parathion, phorate, methomyl, monocrotophos, quinalphos, cypermethrin, fenvalerate
Phosphatase
Phosphatase activity significantly increased at 20days of incubation and decreased progressively with increasing incubation period in vertisols
2,4-D, Nitrapyrin Mefenoxam, metalaxyl
Phosphatase Phosphatase
Quinalphos
Phosphomonoesterase
Diazinon, imidacloprid, lindane
Phosphomonoesterase
Ridomil gold plus copper
Phosphatase
Validamycin
Phosphatase and urease
Inhibited Inhibited alkaline phosphatase and stimulated acid phosphatase Initially inhibited but later on activity was restored Diazinon did not affect, imidacloprid increased while lindane decreased the enzyme when applied as seed treatment in groundnut field Generally, fungicide did not affect acid and alkaline phosphatases Validamycin affected soil enzyme but recovered soon
Madhuri and Rangaswamy (2002) and Rangaswamy and Venkateswarlu (1996) Tu (1981) Monkiedje et al. (2002) Mayanglambam et al. (2005) Singh and Singh (2005b) Demanou et al. (2004) Qian et al. (2007)
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with water. Decreased urease activity in soil with the application of pesticides reduces urea hydrolysis which is generally beneficial, because it helps to maintain N in a form (NHþ 4 ) less leachable (Antonious, 2003). Yang et al., (2006) showed that chlorimuron ethyl and furadan activated urease in the four soils. The chlorimuron ethyl and furadan enhanced urease activity up to 14–18% and 13–21%, respectively. Contrarily, acetamiprid reduced up to 35% urease activity in soil at 43 days after crop sowing (Singh and Kumar, 2008). Similar observations have been reported in case of phosphatase activity in soil (Demanou et al., 2004; Madhuri and Rangaswamy, 2002; Monkiedje et al., 2002; Rangaswamy and Venkateswarlu, 1996). Acid and alkaline phosphatases are mostly found in microorganisms and animals (Tabatabai, 1982, 1994). Demanou et al. (2004) did not observe significant effect of ridomil fungicide on phosphatases activities in soil. These enzymes may be protected from degradation by adsorption to clays or to humic substances in soil (Boyd and Mortland, 1990). This protection of these exoenzymes may result their insensitivity toward the fungicide application (Demanou et al., 2004). Similarly, Yao et al. (2006) revealed that the activity of dehydrogenase was increased after acetamiprid application for 2 weeks. They demonstrated that the enzyme activities in samples treated with 0.5, 5, and 50 mg kg1 dry soil were about 2.5-, 1.5-, and 2-fold to that of the control at 28 day of application. Contrary to these findings, Klose et al. (2006) reported that soil fumigation reduced the activity of acid phosphatase to 22% over a period of 90 days. However, decrease in this enzyme may be ascribed to the suppression of a sensitive fraction of soil biota.
4.4. b-Glucosidase, cellulase, invertase, and other enzymes b-Glucosidase, cellulase, and invertase enzymes are also very important enzymes involved in the transformation/decomposition of organic matter in soil. b-Glucosidase catalyzes the hydrolysis of disaccharides in soil to form b-glucose. The hydrolysis products of b-glucosidases are believed to be important energy sources for microorganisms (Tabatabai, 1994). Cellulase catalyzes hydrolysis of cellulose to D-glucose. Cellulose is the most abundant polysaccharide of plant cell walls and represents a significant input to soils (Richards, 1987). Invertase hydrolyzes sucrose to fructose and glucose. Invertase is ubiquitous enzyme that occurs in plant tissues and soil organisms (Skujins, 1976). Other enzymes like nitrate reductase, arylsulfatase, amylase, xylanase, catalase, and protease also play important role in biochemical reactions and nutrient cycling. Changes in the activities of these enzymes with the application of pesticides are summarized in Table 6. Application of pesticides increased, decreased, or did not affect activities of these enzymes in soils, depending upon the nature and concentrations of pesticides used, incubation period, status of enzymes in soil, and soil conditions. Ismail et al. (1999) studied the effects of methamidophos on cellulytic activity in three
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Table 6
Effect of pesticides on activities of different soil enzymes
Pesticide applied
Soil enzyme
Comments
References
Ridomil gold plus copper Methyl bromide, chloropicrin, propargyl bromide, Midas, InLine Metalaxyl
b-glucosidase
Fungicide inhibited b-glucosidase
b-glucosidase, arylsulfatase
Fumigants reduced the activities of enzymes by 76% and 28% for b-glucosidase and arylsulfatase, respectively
b-glucosidase, arylsulfatase Cellulase
Activities of enzymes initially increased and then decreased Inhibited cellulase activity in Fusarium moniliforme
Demanou et al. (2004) Klose and Ajwa (2004) and Schutter et al. (2001) Sukul (2006)
Arylsulfatase, cellulase Cellulase Cellulase, invertase
Inhibited
Benlate, calixin, captan Brominal, selecron Methamidophos Tridemorph, captan
Atrazine
Invertase
Atrazine, carbaryl, paraquat Metsulfuron methyl
Invertase Invertase, amylase, xylanase
Cellulase activity was reduced by 38% at week 6 Small doses increased, while higher rates of fungicides and longer incubation period decreased enzymatic activities of both the enzymes in groundnut soil Inhibited invertase activity with respect to the inhibitory effect shown by methanol Inhibited soil invertase activity in some soils of different textures Herbicide caused a significant reduction in enzymes activities for the entire period of study (28days) in loamy sand and clay soils
Arinze and Yubedee (2000) Omar and AbdelSater (2001) Ismail et al. (1999) Srinivasulu and Rangaswamy (2006) Sannino and Gianfreda (2001) Gianfreda et al. (1995) Ismail et al. (2000)
Imidachlor, cyfluthrin, Amylase tebupirimphos, Aztec, Amitraz Imazethapyr Catalase, protease Isoproturon
Nitrate reductase
Profenofos
Nitrate reductase
Imidacloprid, lindane, diazinon
Arginine deaminase
Ridomil gold plus copper Metsulfuron methyl
Validamycin
Extracellular enzymes Hydrogen peroxidase, polyphenol oxidase Catalase
Acetamiprid
Soil enzyme
Insecticides inhibited activity of this enzyme for 1week
Tu (1995)
Decreased the biomass-C content while increased the hydrolytic capacity, protease and catalase activities A decrease in nitrate reductase activity was observed in sand and loam soils Inhibited
Perucci and Scarponi (1994) Nowak et al. (2004)
Diazinon and imidacloprid increased the arginine deaminase activity while lindane had the toxic effects on the enzyme Copper affected the activity of extracellular enzymes by modifying the conformation of the proteins Activity of these enzymes decreased with the application of herbicide
Validamycin decreased 14% of soil catalase activity but recovered soon Nitrate reductase (41%) and arginine deaminase (22%) activities were declined at 43days
Abdel-Mallek et al. (1994) Singh and Singh (2005a) Geiger et al. (1998) He et al. (2006)
Qian et al. (2007) Singh and Kumar (2008)
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soils (loamy sand, clay loam, and clay) and found a correlation between cellulase activity and clay content of the soils. No effect of methamidophos was observed on cellulase activity in clay soil whereas 25% and 38% reduction in cellulase activity was observed in clay loam and loam sand, respectively, after 6 weeks. It is usually reported that high enzymatic activities are associated with high organic matter contents but different results have been reported by Gianfreda et al. (1995) where low invertase concentration was recorded in soil containing high organic matter contents. They also observed greater invertase activity with higher clay content in soil. Recently, Gundi et al. (2007) investigated the effects of the insecticides including monocrotophos and quinalphos (organophosphates), and cypermethrin (pyrethroid), on soil enzyme activities in two agricultural soils— black vertisol soil and red alfisol soil for 30 days under laboratory conditions. Individually applied three insecticides at 5, 10, and 25m gg1 to the soil markedly increased the activities of cellulase and amylase. Interestingly, combinations involving monocrotophos or quinalphos with cypermethrin demonstrated synergistic and antagonistic effects on both enzymes in the soils. At low concentrations (5 and 10m gg1), insecticides in combination demonstrated synergistic effects while at higher concentrations (25m gg1) demonstrated an antagonistic interaction effects on these enzymes. They reported that such diverse effects of insecticides on two enzyme activities was in concomitant to populations of cellulolytic and amylolytic microbes in soils treated with insecticides. Similarly in another study, Klose et al. (2006) reported that soil fumigation reduced the activity of arylsulfatase (62%) and b-glucosidase (6%), implying that S-mineralization in soils and the total oxidative potential of microorganisms were more affected by fumigation. They also indicated that soil fumigation could change microbial communities and important biochemical reactions involved in cycling of elements.
5. Impact of Biopesticides on Microbial Diversity and Enzymes Very recently, use of biopesticide has also received much attention as an alternative to synthetic pesticides as a part of plant pest control strategies (Wang et al., 2007). These biopesticides may also have some impacts on microbial diversity. Sameh et al. (2007) investigated the impact of a microbial biopesticide, Paenimyxin produced by Paenibacillus sp. strain B2 on the genetic structure and density of soil bacterial communities by colony counting and by 16S rRNA and nirK quantitative polymerase chain reaction (PCR). A negative effect of Paenimyxin on the bacterial colonyforming unit (CFU) number was recorded, which was significantly reduced
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2 and 4 days after treatment while its effect on cultivatable bacteria was negligible 7 days after treatment. Moreover, the Paenimyxin did not affect the quantification of 16S rRNA or of the denitrifying bacterial community. The RISA fingerprinting demonstrated that the genetic structure of the bacterial communities was significantly modified 2 days after Paenimyxin application at 50mM and 4 days after treatment at lower concentrations (0.5 and 5mM). As a whole, the effect of Paenimyxin on the genetic structure of soil bacterial communities was transient and recovered to normal after 7, 14, and 28 days of applications. In another study, Gopal et al. (2007) studied the effect of 10% azadirachtin granules (alcoholic extract of neem seed kernel mixed with China clay) on the population of bacteria, actinomycetes, fungi, Azotobacter, and nitrifying bacteria; soil dehydrogenase, phosphatase, and respiratory activities on 0, 15th, 30th, 60th, and 90th days after application in sandy loam soil. Except Azotobacter sp., the azadirachtin at all the doses suppressed microbial communities and enzyme activities in the first 15-day period. The bacterial and actinomycetes populations besides phosphatase and respiratory activities recovered and increased significantly after 60th day. The biopesticide demonstrated a significantly suppressive effect on fungi and nitrifiers reduced them throughout the studies.
6. Conclusions Despite the numerous efforts aimed at understanding the effect of pesticides on soil ecosystem, it is difficult to comprehend the role of pesticides in perturbing soil environment due to divergent research findings reported in the literature. Some pesticides’ residues could be carbon or energy source to microorganisms and are degraded and assimilated by microorganisms, but many reports exhibit their deleterious effects on soil microorganisms as well. Therefore, no definite conclusion can be made on the effect of pesticides on microorganisms and their associated transformation of nutrients in soil since different groups of pesticides exhibit manifold variations in toxicity. The same is true in case of biochemical processes and soil enzymes in soil. The other factors like soil properties, nature and concentration of pesticide used, its activity and production of metabolites during metabolism in soil also contribute to determining the effect of pesticides on soil biological activities. Generally, long-term application of pesticides can disturb biochemical equilibrium which can reduce soil fertility and productivity. Understanding the mechanisms underlying molecular responses in microbes in response to pesticides application could be helpful in elucidating the risk assessment of pesticides contaminations and its consequent adverse impacts on soil microbial diversity, enzymatic activities, and biochemical reactions. For better understanding, this research area merits
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intensive future research based on molecular techniques, contrary to traditional approaches, which are used for quantification of net impact of pesticides on soil biology and biochemistry.
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Radiation Hybrid Mapping in Crop Plants Venu Kalavacharla,*,1 Khwaja Hossain,† Oscar Riera-Lizarazu,‡ Yong Gu,§ Shivcharan S. Maan,} and Shahryar F. Kianian} Contents 1. Introduction and Overview 2. Physical Mapping 2.1. Examples of physical mapping in plant species 3. Radiation Hybrid Mapping in Nonplant Species 4. Radiation Hybrid Mapping in Crop Plants 4.1. Radiation hybrid mapping in maize 4.2. Radiation hybrid mapping in barley 4.3. Radiation hybrid mapping in cotton 4.4. Radiation hybrid mapping in wheat 5. Prospects of Mapping Genes and Genomes Using RH Mapping References
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Abstract A number of recombination-based and physical mapping methods have been developed in order to study and understand the genomic organization of plant species. Of these methods, physical mapping methods provide the best correlation between position on a map and actual physical location on the chromosome. In this review, we discuss differences between maps developed on the basis of recombination and those developed independent of recombination. In the latter method, termed commonly as physical mapping, we discuss methods that have been employed in a number of agriculturally important plant species including rice, the legume model Medicago, tomato, soybean, barley and wheat, and then focus on the radiation hybrid method of physical mapping. After a brief overview of the radiation hybrid methods employed in mammalian species, we discuss radiation hybrid mapping for maize, barley, cotton and wheat in detail. * { { } } 1
Department of Agriculture and Natural Resources, Delaware State University, Dover, Delaware, USA Division of Science and Mathematics, Mayville State University, Mayville, North Dakota, USA Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon, USA USDA-ARS, Western Regional Research Center, Albany, California, USA Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA Corresponding author: Delaware State University; phone: 001-302-857-6492; email:
[email protected]
Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01006-2
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2009 Elsevier Inc. All rights reserved.
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1. Introduction and Overview Mapping determines the linear order of genes and/or molecular markers in the material at hand, and can be performed at several levels of detail (resolution) that traditionally falls into two broad categories: genetic or linkage mapping and more detailed physical mapping. A linkage or genetic map of any plant or animal species denotes the linear order of markers on a particular chromosome, which is determined by the number of crossover events and recombination frequencies between markers (Sturtevant, 1913). During segregation if a block of genes or chromosomal segments do not assort independently, they are described as being linked. This is the basis on which a genetic map is developed. Recombination among polymorphic loci and the likelihood that recombination events occur between two points of a chromosome depends in general on their physical distance: the closer they are located to each other, the more they tend to stay together after meiosis. With the increase of the distance between these points on the chromosome, the probability for recombination increases and genetic linkage tends to disappear. This is why genetic linkage can be interpreted as a measure of physical distance. However, across the whole genome, the frequency of recombination is not constant as it is influenced by chromosome structure which varies across the length of chromosomes. There have been two basic requirements for genetic mapping: a population of individuals or gametes exhibiting marker segregation, and markers that can identify segregating components. Mapping population development is dependent on the modes of reproduction of the species under study. In self-fertilizing plants, mapping populations can consist of F2 plants, recombinant inbred lines (RILs), backcross (BC)-derived populations, introgression lines assembled in different individuals, or doubled haploid (DH) lines. In the case of crossfertilizing species, mapping populations can be derived from heterozygous parental plants such as F1, intermated F2, or BC lines. DNA polymorphism is usually sufficient to function as a molecular marker in mapping. These DNA sequence differences are revealed in the form of restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs), microsatellite or simple sequence repeat polymorphisms (SSRs), single-strand conformation polymorphisms (SSCPs), and single nucleotide polymorphisms (SNPs). Genetic maps have been constructed in many crop plants using a single or a combination of marker systems (Phillips and Vasil, 2001). Although genetic mapping can be used to ascertain the relative location of genes/markers along a chromosome, frequencies of recombination can vary along a chromosome. Thus, it is important to remember that linkage maps are not always the best possible representation of the physical distance among
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genes or markers. Increasingly, genomics research has necessitated the development of physical maps that more accurately reflect the actual position of molecular markers and/or genes on the chromosome(s) and the actual distance in nucleotides between them. In this sense, physical maps provide a high-resolution view of the organization of a given chromosome. In addition, independently derived physical and/or genetic maps can be used to assess the completeness and the integrity of whole-genome sequences. Therefore, the use of a combination of genetic and physical mapping methods has been very useful for advancing knowledge on the number and distance between genes of interest as well as to better understand the topography and organization of the genome.
2. Physical Mapping Genetic maps of moderate resolution can be used to follow the inheritance of any chromosomal region. With an adequate number of markers, it should be possible to survey the entire genome of a species, segment by segment. There are several basic and applied uses of genetic maps, for example, (a) study genomes of related species with common markers, (b) tag important traits of interest with associated markers and follow the transmission and selection of the former in breeding cycles, and (c) with the advent of recombinant DNA technologies, genetic mapping can be carried to its logical conclusion, positional cloning (isolation) of a gene solely on the basis of its chromosomal location without regard to its biological function. On the other hand, a map generated by recombination is rarely sufficient for starting the sequencing phase of a genome project for two main reasons. Firstly, genetic maps have limited resolution because they depend on the number of crossovers. In most eukaryotes, it is simply not possible to obtain a sufficiently large number of meioses to attain resolutions that are greater than 1 Mb. Secondly, genetic maps often have limited accuracy in areas of reduced recombination. Thus, it is difficult to order closely linked markers. These limitations of genetic mapping may be overcome by analyzing more meioses and/or adding more markers. Still, the use of some form of physical mapping in addition to genetic mapping will be necessary before the start of large-scale DNA sequencing. Physical mapping can be performed at various levels of resolution. Classically, physical mapping involved determining the location of a gene or a marker relative to features of a chromosome such as heterochromatic knobs (McClintock, 1929), polytene chromosome bands (Bridges, 1935), or a specific chromosome segment ( Jiang et al., 1993). In wheat, for example, genes or markers have been mapped to chromosomes, chromosome arms, and chromosome segments or bins using aneuploids (e.g., nullisomics,
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monosomics, trisomics, ditelosomics) and a collection of deletion lines (Endo, 1990; Qi and Gill, 2001). Deletion lines have been extensively used in wheat for localizing a large number of genes onto different chromosome bins (Hossain et al., 2004a; Linkiewicz et al., 2004; Miftahudin et al., 2004; Munkvold et al., 2004; Peng et al., 2004; Qi and Gill, 2001; Qi et al., 2004; Randhawa et al., 2004). A more direct way to localize a gene/marker to a chromosomal region involves the use of fluorescent in situ hybridization or FISH (Albini and Schwarzacher, 1992), and chromatin fiber FISH (Florijn et al., 1995). Although these methods have been useful to place genes onto chromosome regions, their resolution has been limited to several Mb of DNA. To attain physical maps of greater resolution, maps based on large-insert DNA clones have been constructed. The reconstruction of chromosomes or genomes from large-DNA insert libraries has been an active area of genomic studies in many species. In humans (McPherson et al., 2001) and several model species (Chang and Karin, 2001; Chen et al., 2002; Gregory et al., 2002; Hoskins et al., 2000; Marra et al., 1997; Tao et al., 2001), the importance of physical maps in large-scale genome sequencing as well as transcript mapping, positional cloning, comparative genome analysis, and geneknockout studies have been well documented (Chaveroche et al., 2000). Physical mapping by fingerprint analysis and end sequencing of large-DNA insert clones does not require a segregating population, and hence allows the construction of genome maps even for species in which development of genetic maps is impossible owing to the absence of appropriate mapping populations.
2.1. Examples of physical mapping in plant species A wealth of physical mapping resources has been generated in major plant species of which several examples are discussed below. Rice has emerged as a model species for studying cereal genomics because of its relatively small genome size (400 Mb) (Arumuganthan and Earle, 1991). In rice, Oryza sativa subspecies japonica and indica are both widely cultivated and equally important to human food supplies. The genomes of both subspecies have been sequenced. The complete sequencing of the japonica genome was the objective of the International Rice Genome Sequencing Project (IRGSP) (Takuji and Burr, 2000). IRGSP (2005) took a clone-by-clone strategy to sequence the japonica genome, while a whole-genome shotgun approach was used in sequencing the indica genome (Yu et al., 2002). To sequence the rice genome using a clone-by-clone approach, a BAC-based physical map for the rice genome was a prerequisite. A total of 73,728 BAC clones were fingerprinted using a single restriction enzyme, with 88% clones successfully fingerprinted, representing 20 genome coverage (Chen et al., 2002). To develop an integrated physical and genetic map of the rice genome, BAC
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contigs were anchored to the rice genetic map. This was accomplished with a combination of approaches, including anchoring genetic markers into BAC clones, contig end walking with overgo primer pairs, and integration of a portion of the Monsanto draft rice genome sequence data into the physical map (Chen et al., 2002). Such analyses allowed the verification of the physical map to enhance its accuracy. The legume model Medicago truncatula which is a relative of the forage crop alfalfa (M. sativa) is currently being sequenced. This species with a 450 Mb genome is autogamous, demonstrates prolific seed production, and has a number of tools that serves as a good model for legume molecular genetics and genomics. Kulikova et al. (2001) constructed a molecular cytogenetic map of M. truncatula based on a karyogram of pachytene chromosomes. This map is especially important because it integrated genetic maps with FISH-based mapping of BAC clones. At least two to five BAC clones were mapped onto each genetic linkage group, thus providing a link between the genetic and physical maps developed via FISH. The resolution for the euchromatic portion of linkage group 5 was estimated to be about 60 kb for its cytological counterpart. This work also demonstrated the use of chromosomes at the meiotic prophase stage for karyotyping rather than chromosomes in meiotic metaphase. Over 190,000 Medicago expressed sequence tags (ESTs) have been generated to date (http://www.medicago.org/MtDB2/ and http://www.tigr.org/tdb/tgi/ plant.shtml), and hybridization of ESTs to BAC clones have helped in the linking of genes to specific BAC clones. These include the hybridization of resistance gene analogs (RGAs) and RFLP clones from related legumes. BAC end sequences (BES) and subsequent contiging in Medicago (ftp.tigr. org/pub/data/m_truncatula) with detailed links to genetic and physical maps are very useful to eventually aid in the linking of genomic sequence data that is currently being generated. Tomato (Solanum lycopersicum) is a very important solanaceous crop second only to potato (S. tuberosum) (Gould, 1992). With a chromosome number of 12, it carries the smallest haploid genome size (953 Mb) in the Solanacea family (Arumuganthan and Earle, 1991). Tomato has been a subject for classical genetic researchers from the beginning of the twentieth century and has excellent morphological maps and high-density molecular maps that contain more than a thousand markers (Broun and Tanksley, 1996; Rick and Yoder, 1988; Tanksley et al., 1992). A large collection of well-characterized phenotypic mutants and near-isogenic lines are available in tomato to help in gene identification and characterization, and to connect phenotypes to sequence data. Muhammad et al. (2000) developed a 15 BAC library from tomato with 129,024 clones and an average insert size of 117.5 kb. BES from 1490 clones were also generated so as to develop a framework of sequence-tagged connectors (STCs) that would aid in whole-genome sequencing.
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Soybean is one of the world’s most important legume crops with a genome size of 1115 Mb/1C (Arumuganthan and Earle, 1991). Approximately 40–60% of its genome contains repetitive sequences and is heterochromatic (Goldberg, 1978; Gurley et al., 1979). Soybean has been classified as a recently diploidized tetraploid species and it has been determined that there are duplicated segments with an estimated six copies per gene (Shoemaker et al., 1996). A number of genetic and physical mapping resources have been developed for this species which include linkage (Cregan et al., 1999a,b; Iqbal et al., 2001; Keim et al., 1997; Lark et al., 1993; Shoemaker and Specht, 1995) (http://soybase.agron.iastate.edu) and physical maps (Marek et al., 2001). Wu et al. (2004) developed an integrated physical map derived from approximately 78,001 clones which represents approximately 9.6 haploid genomes. This map integrated large-insert BAC and BIBAC libraries (developed from three soybean cultivars and highthroughput fingerprinting), with the existing soybean composite genetic map (Marek et al., 2001) and included 781 physical map contigs spanning approximately 59.5% of the total genome size equivalent to 663 Mb. Cultivated barley (Hordeum vulgare L.) is a diploid crop species with an estimated haploid genome size of 5000 Mb (Arumuganthan and Earle, 1991). During the past few years, considerable progress has been made in the establishment of public genomic resources. These include large-insert BAC libraries (Yu et al., 2000), several widely used genetic maps (Behn et al., 2004; Cooper et al., 2004; Hearnden et al., 2007), a large EST collection (Zheng et al., 2004), and Affymetrix arrays to examine genome-wide profiling of gene expression (Shen et al., 2005). A barley BAC library has been constructed from the cultivar Morex containing 313,344 clones with average insert size of 106 kb, representing 6.3 haploid genome equivalents (Yu et al., 2000). The BAC clones from this library have been widely used for a wide range of genomics research, including sequencing large-insert genomic regions for comparative analysis and characterization of genome organization. Despite these efforts, genome-wide physical mapping of the barley genome has not been reported. However, the Morex BAC library has been used to develop physical maps for specific regions. For example, screening the BAC clones using a set of RGA identified 121 RGA-containing clones representing 20 different regions of the genome with an average of 6.1 clones per locus (Yu et al., 2000). Common wheat (Triticum aestivum L.) is an allohexaploid crop species with an estimated haploid genome size of 16,000 Mb (Arumuganthan and Earle, 1991). To develop a physical map of the wheat genome, two approaches have been pursued. In one, a global fingerprinting method (fingerprinted contigs or FPC) is being utilized to construct a physical map of one of wheat’s genomes (the D-genome) derived from Ae. tauschii (Luo et al., 2003). To date, 7447 contigs and 5304 singletons associated with 251,586 BAC/BIBAC clones, and 520 RFLP and 1700 EST markers have
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been assembled (http://wheat.pw.usda.gov/PhysicalMapping). Another approach, utilizing the unique collection of wheat cytogenetic stocks, aims at dissecting the genome one chromosome at a time (Safar et al., 2004). Through this latter approach, individual wheat chromosomes are sorted by flow cytometry and cloned into chromosome enriched BAC libraries. These individual chromosome BAC libraries can then be fingerprinted to develop contig maps of individual chromosomes. As of August 2006, 1997 contigs associated with 67,968 BAC clones for chromosome 3B, with an estimated coverage of 800 Mb (80% coverage), had been developed. Nearly 20,000 BES had also been derived from this library with 2000 BES providing unique retroelement junctions for mapping (10% success rate) (Feuillet, 2006). Only a fraction of these BES (700) yielded a polymorphism and could be mapped genetically. Repetitive and middle repetitive sequences, being significant components of most plant genomes, tend to complicate assembly of BAC contigs by FPC and limit progress toward development of a complete BAC-based physical map. Therefore, other methods that can aid in tackling the large genome size and repetitive nature of most plant genomes are needed. As seen above, a number of physical mapping resources in plants have been developed that have greatly aided in the ability to correlate genetic to actual distances. Since development of physical mapping resources like the BAC- or BIBAC-based libraries requires the random shearing or digestion of genomic DNA and subsequent assembly, and because the assembly of overlapping sequences can be limited in most plant genomes by the presence of repetitive sequences there is a need for another method that will facilitate assembly. To avoid fragmentary, segmented and therefore incomplete physical maps, a recombination-independent strategy can be used to order BAC clones via molecular markers. One such method that has been discussed earlier is deletion-based mapping of molecular markers to large chromosomal bins. A limitation of this latter approach is the verification of order of molecular markers within large chromosomal bins. A very successful approach that has been used in a number of nonplant and a few plant systems is radiation hybrid (RH) mapping which has been effective in the high-resolution ordering of molecular markers.
3. Radiation Hybrid Mapping in Nonplant Species Radiation hybrid mapping involves the use of radiation-induced chromosomal breakage and marker segregation to reconstruct marker order. Physical distance is calculated based on coretention frequencies between chromosomal fragments and molecular markers. RH mapping is
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recombination independent and was first described by Goss and Harris (1975, 1977) and subsequently by Benham et al. (1989) and Cox et al. (1990). In this approach, donor cells are irradiated and fused with recipient cells and irradiated chromosomes were studied in the background of the recipient’s cell (radiation hybrids). A panel of radiation hybrids with random chromosome breaks in the donor genome can be used to precisely place and order molecular markers or genes on a particular chromosome or entire genome (Cox et al., 1990). Markers that are physically close on the chromosome experience less chromosomal breakages during irradiation and will tend to be present together on the same fragment within the same hybrid cells of a panel than the distantly positioned markers (Cox et al., 1990). In this process, a test genome is fragmented into desired resolution and fused with recipient cells to construct an RH mapping panel. The RH mapping panel is assayed usually with PCR-based molecular markers and markers that are present or absent are recorded. Subsequently, the resulting data set is used to determine the order and spacing of markers along each chromosome (Boehnke et al., 1991). In RH mapping, different doses of radiation can be used to construct maps with varying levels of resolution. Although RH mapping is conceptually similar to meiotic mapping, the later cannot easily be used to construct high-resolution maps and is dependent on the frequency of recombination and use of polymorphic markers. Goss and Harris (1975) used RH mapping to localize genes on the human X chromosome. X-ray irradiation of a human cell line containing a single X chromosome resulted in random breakage. The fragmented X chromosomes are reassembled during DNA repair, and the resulting segmented chromosomes are recovered by fusion of the irradiated cell line with a nonirradiated rodent cell line. Although a specific gene in the starting cell line may be used to facilitate hybrid selection, the majority of the radiationinduced fragments are transferred nonselectively. Somatic cell hybrids with human subchromosome fragments were used to develop a high-density map of the human genome using the RH mapping approach (Olivier et al., 2001). Given that a single subchromosome fragment stock may contain multiple noncontiguous pieces of a chromosome, it is not possible to use any single stock as a mapping reagent. However, the frequency of radiation-induced breakage between two markers or loci can be used as a measure of the distance between these markers or loci. Physically close markers will show little breakage and more distant markers will show greater breakage. Thus, a population of subchromosome fragment stocks can be used to calculate the frequency of breakage between all pairwise combination of markers or loci to construct a map analogous to a meiotic linkage map (Cox et al., 1990). RH mapping has been used to develop a high-resolution (100 kb) contiguous map of human chromosomes with 41,000 ordered sequence tagged sites (STSs) that includes 20,000 unique human genes (Schuler et al., 1996;
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Stewart et al., 1997). Subchromosome fragments in radiation hybrids have been excellent vehicles for the production and characterization of libraries enriched in DNA markers and genes for a particular segment (Ledbetter et al., 1990). The success of RH mapping and its application to the human genome has expanded its application in resolving the order of tightly linked loci in mouse (McCarthy et al., 1997), pig (Hawken et al., 1999), dog (Vignaux et al., 1999), zebrafish (Kwok et al., 1999), and rat (Watanabe et al., 1999).
4. Radiation Hybrid Mapping in Crop Plants The increasing repository of genomic resources such as genetic markers, increasing number of molecular markers; integrated genetic and physical maps; large-insert libraries and BAC contigs; and the availability of genomic sequences have expanded the application of RH mapping for localizing and identifying genes with potential application in plant improvement. The potential of this technology has been explored in maize (Kynast et al., 2002; Riera-Lizarazu et al., 2000), barley (Wardrop et al., 2002, 2004), and cotton (Gao et al., 2004). In wheat, this technology has been successfully used for mapping a specific gene (Hossain et al., 2004c) and developing a high-resolution map of a particular chromosomal region in durum wheat (Kalavacharla et al., 2006).
4.1. Radiation hybrid mapping in maize The first attempt to implement RH mapping in plants was described by Riera-Lizarazu et al. (2000) where a genetically buffered (allohexaploid) oat (Avena sativa) line carrying a single maize chromosome 9 (monosomic maize chromosome 9 addition line) was used as source material for the production of chromosome-specific radiation hybrids. A monosomic maize chromosome 9 addition line seed was treated with g-rays (30–50 krad), subsequently planted, and surviving plants were self-pollinated. Progeny from self-pollination (the radiation hybrids) possessing maize chromatin including plants with an apparently normal maize chromosome 9 were recovered. Plants with various maize chromosome 9 rearrangements (intergenomic translocations, deletions, and a combination of both) were selected for further analysis. Radiation hybrids with a range of 1–10 radiation-induced chromosome breaks were identified. Since the average number of chromosome breaks per line was 3, it was estimated that a panel of 100 radiation hybrids would permit mapping this 191-Mb maize chromosome at the 0.6-Mb level of resolution.
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The average marker retention frequencies in the oat–maize radiation hybrids ranged from 75% to 85% (Riera-Lizarazu et al., 2000). These marker retention frequencies were higher than the 10–50% of donor genome retention that are typically cited as being best suited for RH mapping in mammalian systems (Stewart et al., 1997). Thus, the level of chromosome breakage and mapping resolution in the oat–maize radiation hybrid system may be lower than that attainable in reports using human/ rodent radiation hybrids. This difference has been attributed to the way mapping panels are developed. Human/rodent radiation hybrids are the product of in vitro rescue by cell fusion of lethally irradiated donor cells with nonirradiated recipient cells whereas the oat–maize radiation hybrid system involves live self-propagating plants derived from irradiated seed. Thus, the need for morphogenetic capacity and fertility may limit the level or mode of chromosome breakage that is tolerated and sexually transmitted. On the other hand, a recent study with durum wheat, Triticum turgidum, that is discussed later (Kalavacharla et al., 2006), suggests that high mapping resolutions can be obtained. Another benefit of working with live and fertile plants is the availability of material for comprehensive mapping and future use. Vales et al. (2004) showed that most maize chromosome 9 rearrangements (simple or complex) can be maintained in subsequent generations by self-fertilization.
4.2. Radiation hybrid mapping in barley Barley RH panels were developed by fusing irradiated transgenic barley protoplasts, harboring the bar transgene as selectable marker, and conferring resistance to bialaphos, with untreated tobacco protoplasts (Wardrop et al., 2002). In this strategy, the barley protoplasts were first irradiated with X-rays with different dosages, prior to electrofusion, to induce varying amounts of chromosome fragmentation. Putative fusion hybrids were selected by culture in a medium containing bialaphos. Hybrid calli from fusion protoplasts carrying the bar transgene were maintained under selection up to 12 weeks to yield enough tissue for DNA preparation. Although hybrid production was feasible, the efficiency was low compared to the technique used to generate mammalian RH panels (Olivier et al., 2001). It was suggested that multiple fusion experiments be conducted to generate a suitable number of putative hybrids for RH panel construction. The experimental results showed that exposure of cells to an X-ray irradiation dose of 5 krad generated the largest number of putative hybrids. About 200 putative calli exhibiting resistance to bialaphos were generated from 10 fusion experiments. Among them, 40 calli were verified to be of RH status as assessed by PCR for the presence of the bar gene. Validation of the RH panel was also carried out by studying coretention of a marker, Xpsr145, which was genetically linked (3.7 cM) to the bar gene on chromosome 5H. This
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marker was retained in fusion hybrids at a frequency of 77%, confirming the physical linkage of Xpsr145 to the bar gene. To evaluate the genome coverage of the RH panel, somatic hybrids were characterized using 35 barley-specific microsatellite markers, which offered genome-wide coverage and gave no amplification with recipient tobacco DNA. The marker retention frequency (%) was calculated based on the presence and absence of individual markers in the RH panel. In the barley RH panel, the average marker retention frequency, which is the number of hybrids retaining individual markers, was 26%. Although no statistically significant differences in marker retention were found between the different linkage groups, a number of individual loci did appear to be retained at both very low and very high frequencies. Those with high retention frequency could be explained by the possibility that they might be responsible for the specific biological functions associated with the survival of hybrid calli. In addition, it is not surprising that markers that were tightly linked to the bar gene exhibited a high retention rate. The first barley RH panel consisting of 40 hybrids provided a new resource in terms of physical mapping of the whole barley genome. One technical challenge of RH panels generated by protoplast fusion is the low efficiency associated with hybrid production. Furthermore, the technique can only been applied to the plant species for which efficient protoplast isolation and fusion have been well developed.
4.3. Radiation hybrid mapping in cotton There have been numerous efforts devoted to the development of genetic linkage maps in cotton, Gossypium hirsutum (Lacape et al., 2003; Mei et al., 2004; Reinisch et al., 1994; Shappley et al., 1998; Ulloa and Meredith, 2000; Yu et al., 1998; Zhang et al., 2002). Since linkage groups in cotton exceed the chromosome number (n = 26), there is a need for the integration of linkage groups to actual physical locations, and thus chromosomes in the genome. Additionally, since there is a difficulty in correlating information provided by various genetic linkage maps, there is a need for the development of detailed physical maps that would aid in this effort. Traditional RH mapping has dealt with one single chromosome at a time, but to overcome this limitation, Gao et al. (2004) used whole-genome radiation hybrid mapping (WGRH) in cotton, which is a modified method of RH mapping, first used by Walter et al. (1994). In this method, the RH material is generated from a diploid cell line instead of a somatic single-chromosome addition line. This is especially useful if the intent is to construct maps of all of the chromosomes of the species of interest. Although useful, this approach is limited by the ability to rescue the irradiated material. In cotton, Gao et al. (2004) irradiated pollen from cultivated cotton (G. hirsutum) with 5 krad of g-rays, and then rescued by in vivo fertilization with egg cells from
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the related species G. barbadense. This method termed wide-cross RH (WWRH) mapping demonstrated the first use of RH mapping in cotton and permitted the placement of 102 SSR markers to chromosome segments. These researchers tested a wide range of radiation dosages and found that the F1 seeds that were obtained from the wide cross after irradiation were viable at the lower doses (1.5 and 5 krad) but were unviable at the higher dosages (15 and 30 krad). To assay for deletions, 22 plants each were randomly selected from the two lower doses and screened with 33 SSR markers. It was seen that the 5-krad panel showed greater number of deletions as well as more variation in the type of deletion (with respect to size or chromosomal locations). The authors also found that the marker retention frequency was high in the 1.5-krad lines which coincided with the lower incidence of deleted markers. The retention frequency for individual molecular markers ranged from 77% to 100% with an average across all assayed loci at 93%. Therefore, in this work, the 5-krad panel performed best, and the authors concluded that the optimal RH dosage could be between 5 and 15 krad. Given these results, the researchers generated additional 5-krad RH lines and used a total of 93 of the 101 individual lines to use as a WWRH panel. To assay the lines for the nature and amount of deletions, the authors used markers from cotton linkage group 9 (LG9) because of the even spread of eight SSR markers across this LG. When assayed with these eight markers, 20 of the 101 lines were identified as carrying deletions in the region represented by this LG and eight SSR markers. A clear use of the RH mapping effort in breaking linkages observed during recombination-based map construction was demonstrated by the cotton WWRH panel. The cotton SSR markers BNL0625 and BNL2805 cosegregated in the recombination-based linkage map but were separated in the two WWRH lines GH6550 and GH6707. Additionally, molecular markers that map separately onto two cotton linkage groups (LG9 and LG13) were seen to map onto the same RH syntenic group because of the use of the WWRH panel.
4.4. Radiation hybrid mapping in wheat The availability of the appropriate cytogenetic material, which will help assay for deletions due to irradiation, is a very important component of RH mapping. The dosage that is used for RH mapping needs to be balanced and adjusted to aid in the recovery of the maximum number of viable plants containing deletions. Another critical feature that must be considered is the ability to separate out portions of the genome and assay these portion(s) in an independent fashion that will enable the construction of a high-resolution map of that area. Wheat, as indicated earlier, is uniquely suitable for RH mapping because of the vast collection of cytogenetic stocks. An alloplasmic durum wheat line which contained the nucleus from cultivated
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durum and the cytoplasm from A. longissimum (S. & M.) carrying a portion of hexaploid wheat chromosome 1D was identified and characterized by Hossain et al. (2004b) in their analysis of the scsae gene. This gene is responsible for compatibility between the nucleus and cytoplasm in wheat lines which contain a nucleus from one species and the cytoplasm from another species. Therefore, this line is uniquely suitable as a test case for RH mapping in wheat because of the presence of this D-genome fragment in a AB genome background, as well as the target of analysis of the phenotype conferred by the scsae gene (plump seed contain the gene, whereas shriveled seed do not contain the gene). Hossain et al. (2004c) used this line to demonstrate the feasibility of RH mapping in wheat by using g-irradiation of seeds. The male-sterile hemizygous (lo) scsae line was crossed with the durum wheat line LDN16. Seed from this cross segregated both plum seed and shriveled seed. Plump seed (which therefore contain the scsae gene) were then irradiated with 35-krad g-rays, after which they were germinated and planted in the growth room. The RH0 plants derived from these treated seed were again crossed to the euplasmic durum wheat line LDN16 to obtain RH1 seed. Plump seed from this cross were selected and 87 RH1 plants were grown in the greenhouse, and used for extraction of DNA and analysis using molecular markers that were specific to the homeologous group 1 of chromosomes of wheat. This set of 87 durum wheat RH lines carrying deletions in the 1D chromosome constitutes the durum wheat radiation hybrid-1D (DWRH-1D) panel. These authors used a combination of RFLP, SSR, and EST markers. The RFLP markers were selected based on the order on genetic and physical maps, while the SSR markers genetically mapped onto chromosome 1D. The EST markers were selected based on their assignment to the chromosomal deletion breakpoints of chromosome 1D as part of the wheat EST genomics project (http:// wheat.pw.usda.gov/NSF). Hossain et al. (2004c) observed that the use of radiation doses exceeding 40 krad resulted in a dramatic reduction in survival and plant vigor. It was also observed that 60% of the RH1 plants carried deletions of molecular markers with number of markers lost ranging from one to six, with 69% (27 of 39 markers) of the latter identifying deletion breakpoints which ranged from 1 to 11. Since the alloplasmic wheat line carries a portion of the 1DL chromosome that carries the scsae gene, Hossain et al. (2004c) estimated the size of this portion to be approximately 464.8 Mb (excluding the missing telomeric portion of 1DL), and therefore the average distance between the radiation-induced breaks for the 1D portion of this line is estimated to be 5.3 Mb. The average marker retention frequencies for this population with this set of molecular markers used ranged from 87% to 100%. There were two regions in the RH population which retained all molecular markers, one of which is of particular interest to the isolation of the scsae gene. A region containing four molecular markers in the short arm of 1D (which might contain a factor that
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is important for seed plumpness) and the region of the 1DL4–1DL2 deletion breakpoint. This latter retention of molecular markers is likely due to the selection for the scsae gene in the scheme to develop the RH population. Therefore, this method of physical mapping in wheat allowed the localization of the scsae gene and serves as a model by which future wheat genes of interest may be localized. Since Hossain et al. (2004c) demonstrated the initial use of RH mapping in wheat using a limited number of molecular markers, Kalavacharla et al. (2006) set out to develop a high-resolution RH map of the chromosome 1D region that carries the scsae gene using the DWRH-1D panel by expanding the number and types of molecular markers. A total of 378 markers were used for this analysis. This marker set included 209 AFLPs, 134 ESTs, and 24 RFLP and SSR markers that were previously localized onto the group 1 homeologous chromosomes. Therefore, this group of molecular markers, believed to be derived from both gene-rich and gene-poor regions, provided even more coverage of chromosome 1D. The analysis of the DWRH-1D panel with the 378 molecular markers showed that 77% of the 87 lines used in this study lost at least one marker and that anywhere from 18 to 134 markers were missing per individual line, which meant that of the 378 total markers, 96% were lost at least once. This study also detected the deletion of at least one marker that was determined to be retained by Hossain et al. (2004c). The EST marker BE444505 which was previously placed at the proximal end of the long arm of 1D. The average marker retention frequencies seen across the DWRH-1D panel was estimated to be around 74%, and is lower than the Hossain et al. (2004c) study since AFLP loci that were retained were excluded from analysis as they did not provide useful mapping information. The retention frequencies that were observed are similar to that observed in the cotton and maize RH panels. These high retention frequencies may be due to the use of seed or pollen as the target of irradiation in the plant systems, compared to the use of somatic cells used in the mammalian and nonmammalian systems where the frequencies of retention have ranged from 5% to 45%. Since 368 of the 378 markers used in this study detected at least one break, Kalavacharla et al. (2006) estimated the resolution of the RH map in terms of the average distance between breaks. Hossain et al. (2004c) had identified 88 breaks using 39 markers, while the subsequent study with 378 markers showed an increase in the number of obligate chromosome breaks by 26-fold, thereby detecting 2,312 breaks. In the prior study, the RH map resolution (size of chromosome/total number of breaks) was estimated to be 5 Mb/break, while the Kalavacharla et al. (2006) study showed a resolution of 199 kb/break or a 27-fold increase. Given the size of the chromosome 1D region of interest (464 Mb) and the estimated resolution of 199 kb/break, >2300 molecular markers would need to be assayed to completely cover the chromosome. Since only 378
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markers were used in this study (16% of total number of markers needed), the authors estimated that there would be a number of linkage groups and unlinked markers that would be seen after analysis. At the two-point LOD of 4, five major linkage groups consisting of a total of 158 markers, and 14 linkage groups with 2–4 markers totaling 33 additional markers were observed, while 187 markers were shown to be unlinked. Interestingly, four of the major linkage groups consisted entirely of AFLP markers, whereas one major linkage group consisted entirely of EST markers, and these linkage groups covered a total of 3341 cR35,000 (defined as a unit of distance for a RH panel generated with 35 krad of radiation). The comprehensive linkage map generated at a lower confidence of a two-point LOD of 2.0 included all the markers used in the study and comprised a total length of 11,737 cR35,000. RH mapping can also be used to order molecular markers within large linkage groups. In the wheat deletion bin mapping method, large number of molecular markers can be effectively placed into chromosomal regions, but the order of the markers cannot be effectively determined. To determine the effectiveness of high-resolution RH mapping, Kalavacharla et al. (2006) demonstrated that the EST markers derived from the chromosomal deletion bin 1DL2–0.50–0.80 can be ordered effectively. Therefore, high-resolution mapping can be effectively performed for complex plant species using the RH method as demonstrated in durum wheat (Kalavacharla et al., 2006).
5. Prospects of Mapping Genes and Genomes Using RH Mapping The success of RH mapping depends on the ability of having a system where one can induce a desired level of chromosomal breakage, the ability to recover or isolate these events, and a robust genotyping platform. The ease of developing maps of varying levels of resolution and availability of genomic resources such as a diverse array of molecular markers, genomic sequences, and BAC contigs in major animal and plant species have increased the application of this technology in various ways. Recent advances in individual chromosome sorting technology (Kubala´kova´ et al., 2005) and isolation and fusion of microprotoplasts containing one or few chromosomes (Saito and Nakano, 2002), have broadened the scope of using the RH method for chromosomal mapping. Development of individual chromosome maps and integration of these maps to dense genetic maps and BAC contigs may help in the sequencing of an individual chromosome and eventually the whole genome especially for plant species with large genomes or higher ploidy levels. Systematic sequencing of complex genomes involves the clone-by-clone approach, the whole-genome shotgun sequencing (WGS), or a combination
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of both (Green, 2001). For efficient WGS, high-quality physical maps are essential especially for the larger genomes including wheat (Meyers et al., 2004). In addition to recombination-based maps, RH mapping may be an alternative for developing a robust physical map. Molecular markers developed from ESTs usually show lower levels of polymorphism and are therefore not suitable for meiotic mapping. The advantage of the RH strategy is that this technology requires only that a molecular marker be amplified to be placed on an RH map (Bentley and Dunham, 1995). Additionally, sequences of markers used in the RH map can potentially be used to screen BAC libraries and assign contigs to chromosome regions. The potential of using this strategy of ordering BAC contigs using RH maps has recently been explored in wheat (Kalavacharla et al., 2006). RH mapping technology does not depend on meiotic recombination and high-resolution maps can be generated with relatively fewer (100) lines (Green, 2001; Meyers et al., 2004) compared to the thousands of individual lines to achieve a similar level of resolution by recombination-based mapping methods. Comparative gene mapping studies provide a picture of the evolutionary history of chromosomes and it allows the extrapolation of information from one genome to another. The latter is extremely important and useful for eukaryotic species that are related to a model species with substantial genomics resources and information. Comparative mapping of human and mouse genomes with cattle genomes (Chowdhary et al., 1996; Solinas-Toldo et al., 1995; Womack and Moll, 1986) demonstrated that rearrangement of gene order within segments of conserved synteny is very common and must be addressed for effective trans-species shuttling of information between the species being compared (Yang and Womack, 1998). Using comparative RH mapping of 24 orthologous genes between human chromosome 17 (HSA17) and bovine chromosome 19, Yang and Womack (1998) revealed internal structural rearrangements between these chromosomes. In wheat, over 8000 ESTs have been localized into chromosome deletion bins spanning all of the chromosomes. However, the order of these ESTs is unknown which limits the use of these gene sequences in comparative gene discovery. RH mapping in wheat has recently shown that ordering of genes in a specific bin is possible (Kalavacharla et al., 2006). Thus, coupling RH mapping to bin mapping information would add value to this resource. Genome sequencing of both plants and animals involves the assembly of overlapping sequence reads generated from random genomic fragments. To accomplish most of the sequencing efforts, redundant sequencing is required. In plants and animals, reference genomes have been sequenced most extensively, with each base represented by an average of >8 reads (i.e., >8 coverage). Consequently, the genomes of species that comprise large section of phylogenetic trees of major plant and animal remained unexplored. The sequencing of all species belonging to the phylogenetic
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trees of all major plant and animal species is impractical. However, survey sequencing (1 to 2 coverage) of the unexplored plant and animal species can provide enough data to understand genomic infrastructure. It is also possible to generate molecular markers for mapping in the genome of interest, and high-resolution physical maps can be constructed by combining RH mapping technology with survey sequence data. By anchoring mapping data with BAC inserts of the related major species, it is possible to understand sequence organization and structure as well as functional integrity of the unexplored species. The prospect of this strategy has been recently investigated in dog using human and mouse genomes (Hitte et al., 2005) and in the canine genome using the human genome sequence (Sidjanin et al., 2003), and this is a viable strategy that can be explored in plants.
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Nutrient and Water Management Effects on Crop Production, and Nutrient and Water Use Efficiency in Dryland Areas of China Sheng-Xiu Li,* Zhao-Hui Wang,* S. S. Malhi,† Shi-Qing Li,* Ya-Jun Gao,* and Xiao-Hong Tian* Contents 227
1. Introduction 2. Effects of Nutrient Input on Root Growth, Plant Physiology, Soil Water Storage, Water Use Efficiency, and Crop Yield 2.1. Root growth 2.2. Plant physiological activities 2.3. Photosynthesis 2.4. Soil water storage 2.5. Reduction of water loss by evaporation and increase of water transpiration 2.6. Improving water use efficiency 2.7. Soil erosion control 3. Effects of Soil Water Supply on Nutrient Utilization 3.1. Nutrient availability 3.2. Nutrient movement 3.3. Nutrient efficiency 3.4. Nutrient distribution in plants 4. Effect of Water and Nutrients Interaction on Crop Yield 5. Research Accomplishments, Gaps, and Future Needs 6. Summary and Conclusions Acknowledgments References
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College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry, Yangling, Shaanxi, People’s Republic of China Agriculture and Agri-Food Canada, Research Farm, Melfort, Saskatchewan, Canada
Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01007-4
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2009 Elsevier Inc. All rights reserved.
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Abstract Located in the northern territory of China, the vast semiarid and subhumid regions referred to as dryland areas are stressed by two major constraints for crop production: shortage of water supply and deficiency of nutrients in soil. Low precipitation and its uneven distribution have resulted in soil water, surface water and groundwater deficit, and made crops being under water stress in most cases. As a direct result, except for a few places that can conduct irrigation, most regions remain rainfed agriculture. In addition to shortage of water supply, serious wind and water erosion derived from sparse vegetation coverage, windy climate and frequent rainstorms plus human activities have led to serious soil degradation and nutrient stress. Deficiency of N can be found everywhere and that of P occurs at least in one third of the arable lands, this leading to low productivity. However, the limited water resources have not been fully used and the nutrient use efficiency by crops is very low, both having a certain potential for use and a large room for improvement. Management of water and nutrients are extremely important not only for crop production, but for environmental concern in these areas. Water and nutrients have great interactions that may gain either positive or negative effects on crop production, depending on crop growth stages, amounts, combinations and balance. In the dryland areas, the effect of nutrients and that of water are often limited to each other. Remarkable variations in precipitation from year to year significantly influence soil water and nutrient status, and so do the nutrient input effect. Nutrient input may obtain a good harvest in one year while a poor harvest in another. Considering the precipitation changes and taking effective measures to regulate nutrient supply, crops may not suffer from water limitation in a dry year and from nutrient deficiency in a wet year, and in this way we cannot lose the opportunity to obtain good harvest in both dry and wet year. Nutrient input is the key for crop production. Roots are essential for taking up water and nutrients to support crop growth, and the significance of roots becomes even more important on drylands, since the topsoil is often dry and nutrients are often unavailable, and plants need to extend their roots into deep layer to obtain available nutrients in the moist soil. It has been found that in most cases, crop yield is highly correlated with crop root mass almost in a linear shape. Addition of organic fertilizers can enhance soil organic matter, raise soil water storage capacity, reduce soil bulk density, and therefore create good conditions for root penetration into deep layer. Both organic and chemical fertilizer can provide nutrients for forming strong root system and for roots having a higher capacity to absorb nutrients and water, improve root activities such as raising the root synthetic ability of amino acids by rational N fertilization. Different nutrients have different functions on root growth and its distribution. Nutrient input is also essential for improvement of plant physiological activities. Regulating plant water status and osmotic pressure, increasing the activity of nitrate reductase in plant leaves and raising photosynthesis and transpiration intensity whereas decreasing evaporation constitute some important aspects. All these benefit plants in
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optimization of the use efficiency of water and nutrients. Experimental results show that the osmotic regulation effect is higher with fertilization. The increase of N-supply level reduces disorder of N metabolism in plants deficient in water and increases plant resistance to drought. Under water stress, rational N supply could make wheat leaves to have high activity of nitrate reductase, high levels of proteins, and better water status. Bleeding sap amount increase per plant by N fertilization provides evidence that water intake by plants is increased. Addition of K can make leaf stomata quickly closed under dry and hot wind conditions. With normal water supply, transpiration rate is increased by fertilization while reduced in a water deficit case. Due to vigorous growth, rapid leaf emergency, large leaf area and high coverage rate of plants on the ground with fertilization, soil surface evaporation is reduced and more water is used by transpiration. It has been found that by rational N fertilization, the ratio of water lost by transpiration to that by evapotranspiration was increased from 0.32 to 0.65, and water loss by evaporation was decreased by 1/3, the water use efficiency (WUE) for both grain and dry matter production being increased. Addition of nutrients, particularly K, can increase chlorophyll, protect the photosynthetic organs from dryness and make the photosynthetic organs fully played their role, and therefore increase the photosynthesis that is regarded as the main cause for crop yield reduction under dry conditions. All these have made the dryland crop production increased. Wise input of fertilizer and manure may do more to prevent soil erosion than some of the more obvious mechanical means of control, since the growing of bumper crops by fertilization not only gives a maximum ground cover but supplies sufficient organic matter to aid in the maintenance of all important soil constituents; and the increase of soil permeability to water under such conditions is certainly a factor of major importance. Effective water management can increase nutrient availability, transformation of nutrients in soil or from fertilizers. Mineralization of organic N is proportional to soil water, and the net mineralized nitrate-N is increased with the increase of water content in an adequate range under suitable temperature. A very closely linear relationship has been found between water content and mineralized N. Due mainly to good aeration induced by deficit of water on drylands, ammonium-N both from soil and fertilizers can be quickly nitrified into nitrateN. Thus, a large amount of nitrate-N often accumulates in soil profile that has been used as a good index for reflecting soil N-supplying capacity. Adequate water content can promote nitrification of ammonium N while the process is inhibited when moisture content is too high or too low. Water influences mineral nutrient movement from soil to roots and then from roots to aboveground parts of plants. The difference of nitrate N concentrations at different distance points of soil from a plant being greatly declined by adequate irrigation is a typical example showing that some nutrients could be transferred as solute to plant roots with water movement. Adequate soil water content can significantly transfer a large portion of N to aboveground part, and increase N contents in seeds.
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All in all, water promotes total nutrient uptake by plants and nutrient use efficiency, and affects nutrient composition of plants. It has been reported that N recovery was increased about 20% at any N rate by an adequate supply of water. Water deficit, on the other hand, not only causes water stress to plants, inhibits plant root growth, reduces roots-absorbing area and capacity, increases the viscosity of sap in hadromestome, and thereby decreases nutrient transfer, but also reduces the availability of soil nutrients, nutrient movement in soil, and nutrient uptake and efficiency. Plant growth and crop yield are thus reduced. However, the reduction rate of plant growth is more serious than nutrient uptake, leading to a relative increase in nutrient concentration. Too much supply of water may cause nitrate N leaching and decrease N recovery. Since water supply and nutrient efficiency are closely related, balanced application of nutrients, and determination of their types, ratios, amounts, timing and methods should be based not only on the nutrient-supplying capacity, but also on water status of soil. Rational combinative supply of water and nutrients can increase efficiency of both and produce good interaction. When available water supply is less than a certain range, crops may have little response to fertilizers at any rate, and with sufficient supply of water, nutrient efficiency is increased. An intense interaction exists between available water and fertilizer, and one being changed will likewise lead to the change of the other. The interaction of water and fertilizer is time dependant, and application of water and fertilizer at different stages of plant growth may produce different interaction effects. Oversupply of either or both may delay crop maturation by encouraging excessive vegetative growth, while deficit of water may result in high nutrient concentration in soil, making it difficult for crops to take up and use both water and nutrients, and in a worst case, plants may die resulting in “haying off” effect. The different results obtained for the optimal time of application of water and fertilizer may relate to soil water and nutrient supply at different time. For promotion of water and nutrient fully playing their role and realization of maximum yield, high quality and high efficiency, while protection of the environment from fertilizer ill impact, one important thing is to fully understand and utilize their positive interaction, and attention should be paid not only to input of water and nutrients, but to their rational combination. That is, for addition of water, one should consider nutrient supply, and for addition of nutrients, one should consider water coordination, so that limited nutrient and water can produce optimum effect. Short supply of fresh water and fertilizer pollution has promoted investigations into the interaction effects of water and nutrients on crop yield and nutrient efficiency and WUE, and some achievements have been made. However, there still exist a large number of issues that need further studies in the future. Delineating drylands into different regions and determining the priority issue in each region, determination of most efficient time or growth stage for input of nutrients and water to different crops, and interaction mechanism of water and nutrients are some important aspects.
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1. Introduction The vast regions of northern territory of China located in arid, semiarid, and subhumid areas are prone to drought (Li and Xiao, 1992; Working Committee of Natural Regionalization, 1959). These areas are referred to as drylands. In these areas, low precipitation per event results in low infiltration and high evaporation loss from surface, and consequently, soil water, surface water, and groundwater are all in deficit. Only a few places conduct irrigation and most regions remain rainfed agriculture. Shortage of water supply is the major constraint to obtain high crop yields. The scarce vegetation coverage induced directly by the sparse precipitation plus windy climate causes severe wind erosion. The impact of sparse precipitation is further intensified by its uneven distribution. Precipitation is mostly concentrated in 3 months ( July, August, and September) and only partly meets the water requirements of the crops that grow in the rainy season. Because of this, most crops are under water stress, and this, in turn, increases the threat of water shortage for agricultural production. In addition, frequent storms during the rainy period often lead to serious runoff and water erosion on sloping lands, resulting in serious soil degradation and nutrient losses. Therefore, two serious constraints for crop production in dryland areas are shortage supply of water and nutrient deficiency by soil degradation derived from severe wind and water erosion. The two characteristics are common in dryland regions not only in China, but also in the world (Stewart, 1988). Like water, lack of nutrients for optimum crop growth is widespread. For example, deficiency of N could be found everywhere (Li et al., 1976a,b,c; Liang et al., 1987; Wu, 1989), and that of P occurs at least in one third of the agricultural lands ( Jin, 1989; Li et al., 1978, 1979, 1987; Shao and Zhen, 1989). Because deficiency of nutrients exerts a detrimental impact on plant growth (Arnon, 1975), fertilization produces remarkable results in most cases (Li and Zhao, 1990; Li et al., 1987, 1990a,b, 1991, 1992; Lu¨ and Li, 1987; Lu¨ et al., 1989; Ma, 1987; Yao and Yang, 1989; Zhang, 1984). Nutrient input promotes root growth, makes roots absorb more water from deep soil layers (Shan, 1983a,b), and therefore increases plant tolerance ability to drought, all being beneficial for crop production (Cao, 1987; Chen et al., 1989; Hu et al., 1989). However, crop nutrient use efficiency is extremely low. In China, the average recovery of P fertilizer in a two-crops-per-year system ranges from 10% to 15% for the first crop that receives the fertilizer, and only about 25% for two crops. The N fertilizer recovery varies from 28% to 41% for crops receiving the fertilizer, and the residual effect is negligible in some cases. The insufficient use of fertilizers becomes even more serious on drylands due to water supply limitation. The poor nutrient use efficiency has not only led to
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low economic returns, but also has detrimental impact on the environment, such as nitrate pollution of groundwater (Ma, 1992), nitrate accumulation in vegetables (Wang and Li, 1996), nutrient enrichment of surface water (Ma, 1992), and emission of greenhouse gas as N2O (Bowman, 1989). Although each of water and nutrients has its own function, they are related and interact with each other (Brown, 1972). One can supplement or constrain the other by controlling, restricting, or checking function in plant. Their interaction may gain either positive or negative effects on crop production, depending on crop growth stages, amounts, combinations and balance. Only in the case of rational input of nutrients and water, based on water status to supply nutrients, can the synergic and supplementary effect be achieved, making both playing a much higher role than their sequential additive effect. This is their positive interaction, a bonus by adding to outcome but not in costs. Since both nutrients and water are deficient in dryland areas, the effect of nutrients and that of water is often limited to each other (Cheng et al., 1996). Due to scarce precipitation and drought occurrence, nutrient input under different dryland conditions has different results, depending on the degree of water deficit and timing of fertilization. It has been found that with the increase of available P in soil, plants took up more P in a wide range of soil water contents. However, fertilization causes overconsumption of water by producing abundant vegetative growth at early stages, and can lead to a significant reduction in seed yield (Wang et al., 2004). Under lower soil water content, nutrient availability and its use efficiency are all decreased. Nutrient input may obtain a good harvest in one year, but a poor harvest in another (Eck and Stewart, 1954). Previous research on the effect of fertilization on millets (Pennisetum typhoides L.) in 4 successive years at Jodhpur, India, found that under an extremely dry condition, fertilization had no effect, while under a sporadic drought condition, fertilization was certainly beneficial to crop growth and seed yield (Du et al., 1995). Russell (1979) reported that fertilization was of little significance when precipitation was less than 120 mm during spring wheat-growing period. Dai and Yang (1995) revealed a negative effect of N and P fertilization on winter wheat with precipitation less than 109 mm during the crop-growing period. Precipitation varies from year to year, so does the fertilizer effect. In an extremely dry year, the more fertilizer is applied, the more serious is the reduction in crop yield (Chen et al., 1992). Since remarkable variations in precipitation on drylands significantly influence soil water and nutrient status, considering the precipitation changes and taking effective measures to regulate nutrient supply, crops may not suffer from water limitation in a dry year and from nutrient deficiency in a wet year, so that we cannot lose the opportunity to obtain good harvest in both dry and wet year. The purpose of this review paper is to summarize research information related to nutrient and water management on root growth, plant physiological
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activities, soil water storage, crop yield, nutrient and water use efficiency (WUE), effect of water on nutrient behaviors, and water and nutrient interaction on crop yield in dryland areas of China, and most of important results are obtained from our research group.
2. Effects of Nutrient Input on Root Growth, Plant Physiology, Soil Water Storage, Water Use Efficiency, and Crop Yield 2.1. Root growth Roots are a major part of a plant and play a very important role in plant growth. In addition to fixing and supporting plants, the main function of roots is to absorb water and nutrients. Li et al. (1978, 1979, 1994) showed that rational input of N together with P fertilizer increased wheat root growth and yield, the yield increase being highly correlated with crop root mass in a linear shape. Although water and nutrients can move from one point to another, the function of roots cannot be replaced by their movement. Significance of roots becomes even more important on drylands, since the topsoil is often dry and nutrients are often unavailable, while the deep soil is usually moist with some available nutrients. Consequently, plants may depend more on deep soil layers for water and nutrient supply than topsoil. For surviving in such a condition, plants extend their roots into deep layers and form large root branches and root surface areas, which may be responsible for some crops that have a certain drought resistance. Smith (1954) demonstrated that in dry areas, fertilization extended roots quickly into deep soil layers to take up water stored during the summer-fallowing period. Brown (1972) found that water absorbed by wheat was limited to 91-cm depth without fertilization, while doubling the water intake soil depth and increasing its use efficiency by 56% with N application. The magnitude of fertilization role is closely related with soil fertility. In a soil poor in plant nutrients, rational addition of either organic or inorganic fertilizer can make root growth stronger and have a higher capacity to penetrate through the compact, hardpan layer to absorb nutrients and water from the subsoil (Marschner, 1986). Taylor and Gardner (1963) claimed that a soil with high compaction caused by high bulk density, high cohesion and firmly combined clods could seriously change the normal growth patterns of roots, and reduce the elongation rate of either main roots or their branches. This could be changed by application of organic manure. Jamison (1953) concluded that increasing organic manure rate to a fertile soil was not as effective as to an infertile sandy soil. Different nutrients have different functions on root growth and its distribution (Anghinoni and Barber, 1980). When roots penetrate into
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areas of the soil containing abundant mineral elements, they branch profusely (Weaver and Clements, 1938). The addition of commercial fertilizers to the upper few centimeters of soil layer undoubtedly favors the concentration of crop roots near the surface. Not so much is known about the specific effects of various nutrient elements on roots as on shoots, but it is recognized that P stimulates root growth, and deficiencies of boron and calcium produce short, stubby branches, while the root tips often die (Kramer, 1983). In general, an abundance of essential mineral elements, particularly N, stimulates root growth, but shoot growth is increased even more, so the ratio of shoot to root is usually higher in fertile than in infertile soil. Subsoiling caused little increase in depth of rooting, but fertilizer addition to the subsoil caused deep rooting (Bushnell, 1941). Placement of P stimulated maize root growth in the fertilized portion of the soil, while N, particularly nitrate, which moves readily in the soil, influenced root distribution (Claassen and Barber, 1977). Nutrient supply also influences root distribution. Li et al. (1982) found that on average wheat root length was only 1.45 m with N addition, almost equal to that without fertilization (1.4 m), while P application increased root length to 2.7 m. Water content in the 140–200 cm layer was 17% for plots with or without N application, but almost no water was left in the 200-cm layer with P fertilizer. In north Syria, Gregory (1988) found that application of P fertilizer alone only increased root growth in the surface soil, but application of P and N fertilizers together extended the root distribution to the entire soil profile where plant roots could penetrate. Combined application of N and P fertilizers increased root length in both surface and deep soil layers (Brown et al., 1987). Nutrient input rates and timing affect root-growing time, distribution space, and activities. Comfort et al. (1988) demonstrated that high N rate inhibited wheat root extension into deep soil layer, and reduced water and N utilization from that soil. Zhao and Huang (1993) concluded that high N rate increased aboveground biomass of plants, but reduced their root biomass. Liu (1992) and Liu and Shi (1993) found that dressing an adequate amount of N increased root mass, secondary root number, and root activities, but it was not so when N rate reached to a higher level. Controversy reviews also exist, the argument being in that due to extensive root growth, the soil water is quickly exhausted, and this can cause death of roots in the water-depleting zone (Taylor and Hlepper, 1973).
2.2. Plant physiological activities A rational nutrient input can improve plant physiological activities in various manners. Li et al. (1994) found that in addition to NO 3 N and NHþ 4 N, the bleeding sap contained remarkable amino acids that increased with increasing N rate (Table 1). Although amino acids exist in
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Table 1 Effect of rate of applied N to soil on ammonium–N (NHþ 4 N), nitrate–N 1 1 d ) in bleeding sap (NO 3 N), and amino acid–N (A.A.–N) content (mg N plant of plant Rate of N (kg N ha1) N form
Tillage system
0
30
60
90
120
NO 3
Conventional Plastic mulching Conventional Plastic mulching Conventional Plastic mulching
0.06 0.90 0.96 1.96 1.24 1.63
0.64 1.25 1.59 2.18 1.39 1.64
1.17 1.30 1.87 2.28 1.12 2.05
1.43 2.48 2.80 4.20 2.40 2.46
1.65 3.13 2.93 4.39 2.45 2.71.
N
NHþ 4 N A.A.–N
Modified from Li et al. (1994).
soil (Wen, 1992), the amount is little, and cannot be increased with N rate increase. This shows that the amino acids were synthesized in plant roots, and N fertilization raised root synthetic ability. Nutrient input affects plant water status and its tolerance to drought. Some reports showed that under dry soil conditions, application of organic or inorganic fertilizer increased plant water potential (Xu, 1985), made plants maintain higher water content in tissue, increased the proportion of free water to bound water, and therefore improved plant water status (Cao et al., 2002; Zhang and Li, 2005). However, other results showed that fertilization decreased leaf water potential (Shan, 1983a,b; Xu and Shan, 1991; Yanbao and O’Toole, 1984). The relative leaf water contents may be decreased or increased (Xie and Chen, 1990), and the proportion of free water to bound water is not changed by fertilization (Zhang and Shan, 1995). Li et al. (1994) revealed that the crop growth was much better and leaf water content was higher with N fertilization, but the leaf water potential was decreased with N rate increase due to the high solute concentration in leaves. Zhao et al. (1991) suggested that plant leaf water potential (PLWP) was mainly related to atmospheric water potential in addition to soil water content. When soil and atmospheric water potentials were lower, fertilization decreased PLWP, while increasing it when both higher. In contrast, when soil water potential was higher and atmospheric water potential lower, atmospheric water potential was the major factor affecting PLWP (Mei and Tao, 1993). Osmotic regulation is an adaptive mechanism of plants under water stress. The rise of cell solutes leading to decline of osmotic pressure is beneficial to maintain turgor pressure and its relevant cell elongation, stoma opening, and photosynthesis (Shan, 1985; Xu and Shan, 1988). A high capacity to regulate the osmotic pressure was found in leaves of
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rice seedlings cultured at high N solution (Yanbao and O’toole, 1984) and in wheat with N fertilization (Xie and Chen, 1990). However, Radin and Parker (1979) argued that there was no such relation between N nutrition and osmotic pressure regulation. Morgan (1986) reported that during the process of PLWP declining, the leaf osmotic potential of wheat was lower with low N rate than with high N rate, thus maintaining higher turgor pressure. Phosphorus did not affect cotton leaf osmotic regulation (Ackerson, 1985), while K supply increased wheat osmotic regulation under dry conditions, but no effect with normal water supply (Chen, 1993). With soil water content varying from 40% to 70% of the maximum capillary water-holding capacity (WHC), N, P, and K fertilization had almost no effect on osmotic regulation; when soil water content declined to 20–30%, the osmotic regulation effect was higher with fertilization (Xu and Shan, 1988). Zhang and Wang (1988a,b) found that increasing N supply level reduced disorder of N metabolism in plants deficient in water and increased plant resistance to drought. Also, under water stress, rational N supply could make wheat leaves to have high activity of nitrate reductase, high levels of proteins and RNA, and better water status. Regulation of transpiration intensity reflects another function of fertilization in improving plant physiological activities (Du et al., 1995). Transpiration has both negative and positive impact on plant growth. Water loss from plants is the major negative impact and water uptake increase is the other. Without transpiration, the process of water absorption by transpiration-pulling force would not be performed. Transpiring more water is able to reduce the possibility of water loss by evaporation. Experiments from Li et al. (1994) showed that under a moderate water supply, input of N fertilizer significantly increased transpiration intensity (Table 2; Fig. 1). Since nutrient input improves the capacity of crop to absorb and transfer water and nutrients from soil, water intake by plants is significantly increased. This can be seen clearly from plant bleeding sap amounts (Du et al., 1995; Li et al., 1994). Compared to no fertilization, roots in fertilized plots were not only stronger, but also had higher capacities to take up water and nutrients. With either mulching or conventional tillage, the amount of bleeding sap, although no difference at the early stage of maize growth, was increased with N rate increase at vigorously growing stages (Table 3). Stomata of N-deficient plants could not so freely open and close as those with sufficient N supply (Shimshi, 1970). Deficient in water and N, fescue grass (Festuca Linn.) increased stoma resistance and made leaves rolled up seriously. With a normal irrigation, WUE was higher with sufficient N supply, but under a dry condition, WUE was increased more by low rate of N supply (Ghashagaie and Saugier, 1989). Potassium also plays a great role in regulating stoma resistance. The function of K in regulation of stomata is regarded as the major mechanism in controlling water status of
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Table 2 Effect of rate of applied N to soil on transpiration intensity of maize (g plant1 h1) under mulching and conventional tillage Rate of N (kg N ha1) Tillage system
0
30
60
90
120
Conventional Plastic film mulching
18.1 13.7
26.7 19.1
31.7 19.9
31.6 21.8
30.6 21.5
90
120
Transpiration intensity (g plant−1 d−1)
Modified from Li et al. (1994).
35 30
Non-mulching Mulching
25 20 15 10 5 0
0
30
60
Rate of N (kg N ha−1)
Figure 1 Effect of N fertilizer application on transpiration intensity of maize. Drawn with data from S. X. Li (unpublished results).
Table 3 Effect of rate of applied N to soil on bleeding sap content (g plant1 d1) determined at vigorously growing stage of maize Rate of N (kg N ha1) Tillage system
0
30
60
90
120
Conventional Plastic sheet mulching
0.90 0.97
1.25 1.09
1.30 1.19
2.48 1.34
3.13 1.53
Modified from Li et al. (1994).
higher plants; for K-deficient plants, stoma opening was seriously affected (Marschner, 1986). Skogley (1976) found that under dry and hot wind conditions, supplied with favorable K, barley leaves closed their stomata in 5 min while 45 min were needed for those deficient in K. Wheat experiments showed that under a normal supply of water or under a moderate soil dryness, leaf stoma resistance was significantly lower
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for plants with fertilization than those with no fertilization, the latter significantly reducing water loss per unit area of leaves by transpiration (Zhang and Shan, 1995). At the late growth stages of spring wheat, under a serious soil dryness or normal water supply, WUE by a single leaf was raised by 20–40% compared to that without fertilization. Zhao et al. (1991) concluded that when both soil and atmospheric water potentials were lower, fertilization increased the stoma resistance but decreased the transpiration intensity. The effect of fertilization on transpiration was different under different water supply conditions: with normal water supply, transpiration increased by fertilization; in a water deficit case, fertilization reduced the transpiration rate. Due to vigorous growth, rapid leaf emergency, large leaf area, and high coverage rate of plants on the ground with fertilization (Muchow, 1988; Shangguan and Chen, 1990), soil surface evaporation was reduced and more water was used by transpiration.
2.3. Photosynthesis Water stress resulting in weakness of photosynthesis may be the main cause for crop yield reduction under dry conditions (Guo et al., 2004; Wang et al., 1992; Xie et al., 1992). However, there are different views on what causes the photosynthetic weakness. Some investigators (Baker and Musgrave, 1964) indicated that decline of stoma conductivity was the cause, but others showed that it was due to the influence of water stress on mesophyll cell or on photosynthetic activity of green chloroplast (Boyer, 1976). Xu and Shan (1992) and Zhang and Shan (1995) demonstrated that the net rate of photosynthesis was not all determined by stoma resistance. When soil was extremely dry, the net rate of photosynthesis of fertilized spring wheat was decreased to a large extent, but the absolute amount was still higher than that not fertilized. Such a good effect of high N rate on reduction of the injury by dry stress might be related with the high activity of mesophyll cell (Xie and Chen, 1990). Li (1993) demonstrated the effects of K addition to maize on increasing chlorophyll, postponing the injured time and magnitude of photosynthetic organs suffered from dryness, and protecting the photosynthetic organs from dryness. Pier and Berkowitz (1987) also drew up such conclusions. Yao et al. (1989) confirmed the effects of K application to rice on increasing chloroplast grana, intensifying Hill reaction, raising activity of photosynthetic phosphorylation (photophosphorylation), forming large and numerous mammillas inside of leaves, and significantly increasing the degree of siliconization. As a direct result, leaves grew straighter, the light condition was improved, and the photosynthetic organs fully played their role under a more suitable condition of light. Zhou et al. (1993) have proven that improvement of P nutrition to tobacco resulted in decline of compensation point of carbon dioxide, rise of photosynthetic rate, and reduction of photorespiration.
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2.4. Soil water storage Well related with soil organic matter, water storage capacity is a major nature of soil property linked to crop production. Research has shown that WHC is highly correlated with organic matter content in soil ( Jamison, 1953; Unger, 1975). Addition of fertilizers, particularly organic fertilizer can increase soil organic matter, and thereby increases soil water storage capacity. Zhao et al. (1991) found that soil water potential during winter wheat-growing period increased with fertilizer rate increase. The increase occurred both in top and in deep soil layers, and such a trend did not change approximately until maturity. Zhang et al. (1982) reported that by applying 7.5-Mg organic manure ha1 to a dry, infertile soil, the amount of water stored in 2-m layer was 44.7 mm more than that with no manure application. Cheng et al. (1987) found that application of organic manure increased the amount of stored water by 30 mm within 2-m layer of a manured loessial soil. Ma et al. (1984) claimed that application of organic fertilizer together with mineral fertilizers did not only increase soil nutrients and improve plant nutrition, but also enhanced soil organic carbon. This promoted formation of organic– mineral colloidal complex, improved soil structure and its water-holding capacity, resulting in increased soil fertility, crop yield, and WUE. Li (1982) studied WUE of wheat on drylands and concluded that WUE was increased with soil fertility improvement. The preceding findings have led the authors to suggest that application of fertilizers, especially organic fertilizers/manures, to dryland soil could increase water content and water potential at early growth stages of plants, making part of the ineffective water available to plants, resulting in a mobilizing effect of fertilization on WUE. Yao et al. (1994) also drew up the same conclusion.
2.5. Reduction of water loss by evaporation and increase of water transpiration Due to rise of water transpiration and reduction of water evaporation, rational nutrient input leads to an effective use of water by plants and thereby increases dryland agricultural production. Li et al. (1995) conducted a field experiment with and without plastic film mulching using maize as a test crop. During plant-growing period of the experiment, water content in the 0–200 cm soil layer and maize biomass were determined every 10 days. The plastic film was lifted away from the land just before rainfall or irrigation for the soil to receive water. For plots with plastic film mulching, the water loss during plant-growing period is only caused by transpiration, while for plots without mulching, both transpiration and evaporation cause water loss. The difference of water loss between mulched and nonmulched plots cannot be regarded as evaporation loss in the case without mulching, since water, nutrients, aeration, and temperature in mulched plots are different from those
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of the nonmulched ones. However, all the differences would be reflected in the crop growth status or the biomass that can indicate the leaf area, and thus the function of transpiration. Based on such a consideration, a good-fitting linear regression equation was established for transpiration amount against maize biomass using data obtained from mulched plots (Fig. 2). Using the equation and the biomass data of nonmulched plots, water loss by transpiration in the field was calculated, and that by evaporation was estimated (Table 4). Results showed that with N fertilization, water loss by transpiration was increased, and that by evaporation decreased, and the ratio of transpiration to evapotranspiration significantly increased. Both decrease of evaporation and increase of transpiration were linearly related to N rate increase. The reason is clear: with N fertilization, the crop grew vigorously, and took up more water from soil, reducing water loss by evaporation. On the other hand, larger leaf areas covered the soil surface, lowered its temperature, and therefore reduced water evaporation rate (Fig. 3). Also, rational fertilization made the transpired water more effective, transforming it into photosynthetic products, as evidenced by the higher sugar concentration in crop leaves at different N rates (Li et al., 1994).
2.6. Improving water use efficiency Rational nutrient input enhances crop intake of total water, especially from deep layer, increases WUE, brings about full utilization of soil water, and thereby decreases the possibility of crop suffering from drought during dry spell. Brown (1972) increased seed yield of winter wheat from 1610 to 3090, and to 3630 kg ha1, while water uptake amount from soil increased from 61 to 112, and to 155 mm by applying 0, 67, and 268 kg N ha1, respectively.
Transpiration amount (mm)
350 300 250 200 150 100 50 0 0
50
100 Biomass (g plant−1)
150
200
Figure 2 Relationship between transpiration amount and shoot biomass. Drawn with data from Li et al. (1994).
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Table 4 Effect of rate of applied N to soil on transpiration, evaporation, and water use efficiency (WUEET and WUET) of spring maize Rate of N (kg N ha1) Determination
Plastic sheet mulching Grain yield (kg ha1) Dry matter (kg ha1) Transpiration (mm) WUET for grain WUET for dry matter Without plastic sheet mulching Grain yields (kg ha1) Dry matter (kg ha1) Total water loss (evapotranspiration) (mm) Transpiration (mm) Evaporation (mm) Water use efficiency T/ET WUEET for grain production WUEET for dry matter production WUET for grain production WUET for dry matter production
0
30
60
90
120
2180 5715 178.3 12.3 32.1
3265 8058 225.3 15.8 35.7
5040 9680 248.6 20.4 39.0
6234 12170 250.8 24.9 48.5
4910 10227 280.5 17.6 36.5
1749 5715 330.3
2394 5991 312.1
3264 7409 332.5
3950 8168 319.5
4241 8262 323.7
106.0 224.2
132.1 180.0
173.1 159.4
198.6 120.9
210.5 113.2
0.32 5.3
0.42 7.7
0.52 9.8
0.62 12.3
0.65 13.1
15.0
19.2
22.4
25.5
25.5
16.5
18.2
19.2
20.0
20.1
46.9
45.3
42.8
41.1
39.3
Modified from Li et al. (1994).
Ramig and Phoades (1963) observed that under water stress of more than 15 atmospheric pressures, N-fertilized wheat took up 25–50 mm more water from 2-m soil layer than that without N fertilization. Shan (1985) revealed that maize grown in fertilized fields utilized 60-mm more water (almost equal to one third of the water consumed by the crop from 0- to 300-cm soil layer) than that in an unfertilized field. In Guyuan, Ningxia Hui Autonomous Region, production of 15 kg grain ha1 consumed 2.99-mm water on fertile lands while consuming 5.5-mm water on unfertile lands. Ma et al. (1984) showed that winter wheat absorbed 21–77 mm more water from 0- to 100-cm soil layer with fertilization than that without fertilization, increasing WUE by 40–120% in a semiarid area. Han et al. (1990) demonstrated that wheat yield following pea reached 4.77 Mg ha1, and WUE was increased to 15 kg mm1 ha1 by fertilization. Cheng et al. (1987) increased wheat yield
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Water consumption (mm)
400 320 240 160 80 0
0
20
40
60
80
100
120
Rate of N (kg N ha−1) Total water consumption Evaporation
Transpiration
Figure 3 Relationship between N rate and transpiration (mm) under conditions without plastic sheet mulch. Drawn with data from Li et al. (1994).
to 6.6 Mg ha1 and wheat precipitation use efficiency (WPUE) to 15.6 kg mm1 ha1 by applying organic and inorganic fertilizers together while WPUE was less than 10 kg mm1 ha1 without fertilization. Li (2008) cited a report that application of 60 kg N and 60 kg P ha1 increased water uptake amount by 40–70 mm, and WUE by almost 100%. Gao et al. (1995) further demonstrated the great role of nutrient input in improving WUE. WUE is a term used to describe the relation of plant biomass to water use. Strictly, it is not the ‘‘efficiency’’ because a true efficiency is a comparative term (i.e., dimensionless) requiring a theoretical maximum value. For this reason, some researchers prefer to use other terms such as water use ‘‘coefficient’’ or ‘‘ratio.’’ Further, as Sinclair et al. (1984) pointed out, ‘‘WUE has been used interchangeably to refer to observations ranging from gas exchange by individual leaves for a few minutes, to grain yield response to irrigation treatments through an entire season.’’ Despite some arguments, the term is still adopted by agronomists in China and elsewhere. The quantity of water used to produce crop yield may be expressed in several ways. Commonly, it is measured as the residual term in water balance equation and expressed as total water use, evaporation directly from the soil surface (E) plus transpiration, or the evapotranspiration (ET) during plant-growing season. It may also be defined as transpiration alone or as the total water input (precipitation only on drylands) to the system in plant-growing season. These can be expressed in the following equations:
WUEET ¼ Y=ET;
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WUET ¼ Y=T; WUEP ¼ Y=TP; where Y is the crop yield, either total dry matter production or economical products; ET is the evapotranspiration; T is the total transpiration; and P is the total precipitation in the plant-growing season. The WUE calculated by evapotranspiration (WUEET) may be used for evaluating effects of cropping practices on the difference of water loss by both evaporation and transpiration under different crop production systems as well as on the degree of effectiveness of water use. The WUE calculated by transpiration (WUET) may be used for providing information on water metabolism function of a plant and on the relation between plant growth and water use. The WUE calculated by total precipitation (WUEP), including water loss in different ways and residual water still existing in soil profile, during plantgrowing period, may be of significance for a long-term study in evaluation of effects of cropping measures on the bioavailability of water resources. Based on our experimental results, first two equations have been utilized to provide an overall evaluation of both agricultural measures and plant water metabolism. Results presented in Table 4 showed that there was almost no difference in the consumption of total water between fertilized and nonfertilized plots. However, due to significant increase in crop yield by N fertilization, magnitude and stability of WUEET was greatly increased for both grain and dry matter production with N rate increase. The function of fertilization was not due to consumption of more water, but due to increase in crop yield per unit water use (Fig. 4). Results of the WUET more clearly reveal the effects of N fertilization on WUE. Without N fertilization, 1-mm water transpiration produced only 14.3 kg ha1 of grain and 23.4 kg ha1 of dry matter; while with N fertilization at appropriate rate, the corresponding average values were increased to 16.8 and 36.5 kg ha1. The increase in harvest index benefits grain production (Li et al., 1994). Transpiration coefficient is the ratio of water consumed to the product obtained in same weight unit, and this can express WUE in another way. Transpiration coefficient was greatly reduced with N fertilization (Fig. 5).
2.7. Soil erosion control For sustainable agriculture, the most important and at the same time the most underrated means are the maintenance of high soil fertility and productivity. The growing of bumper crops not only gives a maximum ground cover but also supplies sufficient organic matter to aid in the maintenance of all important soil constituents. The increased permeability of soils to water under such conditions is certainly a factor of major importance. Although not usually recognized as erosion-control features, wise input of fertilizer and manure may do more to prevent soil erosion than some of the more obvious
240 Water use efficiency (kg ha−1 mm−1)
Sheng-Xiu Li et al.
40 Biomass
Grain
30 20
10 0
0
30 60 90 Rate of N (kg N ha−1)
120
Figure 4 Relationship between N rate and water use efficiency of maize. Drawn with data from Li et al. (1994).
Transpiration coefficient (g water g−1 dry matter)
800 Biomass
Grain
600 400 200 0
0
30 60 Rate of N (kg N ha−1)
90
120
Figure 5 Relationship between transpiration coefficient of maize and N rate. Drawn with data from Li et al. (1994).
mechanical means of control (Brady, 1974). Zheng et al. (1987) demonstrated that adequate input of nutrients significantly reduced sheet and rill erosion, thus improving soil quality, fertility, and productivity.
3. Effects of Soil Water Supply on Nutrient Utilization 3.1. Nutrient availability Organic matter and minerals in soil contain nutrient elements. Mineralization of organic matter and weathering of minerals provide available nutrients plants need, and are the basis of natural soil fertility and major sources of
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plant nutrients. Fertilizers applied to soil can be changed into either available or unavailable forms. Many factors affect the availability of soil nutrients, and soil water is the most important one. Water affects original nutrient transformation in soil turning unavailable nutrients into available forms. It also affects the rate of transformation of fertilizers added to the soil. Consequently, it affects absorption of nutrients, total nutrient uptake and nutrient composition of plants. Dry climate or water stress influences the availability of nutrients to crop plants, and thus the total uptake amount. In general, water stress reduces both plant growth and nutrient uptake, but the reduction rate of net assimilation is more serious than nutrients, leading to a relative increase in nutrient concentration. Marschner (1986) pointed out that in any case, water supply changes resulted in corresponding changes in distribution of roots in soil profile and nutrient uptake amounts from different layers. Grimme et al. (1981) demonstrated the differences in nutrient uptake from different soil layers under different water conditions. For example, spring wheat grown in a soil developed on loess parent material took up 50% K on average from deep soil layer at its late growth stages. However, this was changed from year to year with variation in precipitation. In a dry year it was 60%, while in a wet year it was only 30%. Even in topsoil containing highly available P, spring wheat still took up 30–40% of P from deep soil layer under a waterlimiting condition. Water status does not only influence the availability of nutrients existing in the topsoil layer, but also in the deep soil layer. This is more obvious for N transformation. Stanford and Epstein (1974) found relationship between amount of mineralized N in a 2-week period at 35 C and water content was as follows:
Y ¼ 1:02x 4; where Y is the relative amount of N mineralized in term of percentage (mineralized N under optimum water content was set to 100%), and x is the relative water content (optimum water content for N mineralization was also set to 100%). In the equation, the constant (i.e., intercept) was not large, and the slope was approximated to 1, indicating that relationship was similar to one to one (y = x). This means that mineralization of organic N was proportionally increased with soil water. In an aerobic incubation experiment over 147 days at four temperatures (5, 15, 25, and 35 C), and four soil water levels (8%, 15%, 22%, and 29% by weight of oven-dried soil with maximum WHC of 32%), Ju and Li (1998) showed that net mineralized nitrate–N increased with the increase of water content from 8% to 29% of oven-dried soil weight, the magnitude depending on temperature. This indicated that only under suitable combinations of water and temperature could the soil organic N be well mineralized,
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and provided much more nutrients for plant use (Table 5). A very closely linear relationship was found between water content and mineralized N (Table 6; Fig. 6) for both the short-term (2 weeks) and long-term (21 weeks) incubation. For the 2-week incubation, the relation was
Y ffi 3:68 þ 1:06x
ðr ¼ 0:99Þ
and for the 21-week incubation, it was
Y ffi 9:59 þ 0:90x
ðr ¼ 0:99Þ;
where Y is mineralized N in mg N kg1 soil, and x is the soil water content (%). The two equations are very similar to that of Stanford and Epstein (1974), and the regression coefficients almost approximate to 1, further showing the one-to-one relationship. Ammonium and nitrate are two major forms of N that can directly be taken up by plants. Of these, nitrate is more important for supplying N to plants than ammonium in dryland areas. Due to mainly good aeration on lands, ammonium–N from both soil and fertilizers can be quickly transformed into nitrate–N. Thus, a large amount of nitrate–N often accumulates in soil profile while ammonium–N in a small amount. Most of dryland soils are calcareous with high pH, and transformation of ammonium–N into nitrate–N can reduce N volatilization whereas leaching of nitrate–N by scarce rainfall is negligible. It has been proven that nitrate–N content in soil profile is a good index for reflecting soil N-supplying capacity (Hu and Li, 1993a,b,c,d). Water content effectively affects ammonium–N nitrification. In the range of 12–27% of water content in soil, nitrification rate was linearly correlated with the increase in soil water content. Flower and Challagha (1983), Li (1990), and Malhi and McGill (1982) revealed that nitrification reached maximum when soil water content ranged from 50% to 70% of maximum WHC, and it was inhibited when moisture content was higher or lower than this range. In an incubating experiment on nitrification of 3 N fertilizers applied at 0.19 g N kg1 to soil at 12–27% water contents of a soil with 32% WHC and 26 C showed that net nitrification of ammonium–N from fertilizers was closely related with soil water content (Tables 7 and 8). In this water range, the higher the water content, the fewer the ammonium–N left and the higher the nitrate– N content occurred in the soil. A close linear relationship was found between rate of nitrification and soil water content (%) for each fertilizer as shown below: for ammonium bicarbonate
Y ffi 10:07 þ 78:13X
ðr ¼ 0:9733 Þ;
Table 5
Effect of temperature and water content on net mineralized N (mg N kg1)a in soil after certain days of incubation
Temperature ( C)
5
15
25
35
a
Days of incubation
Soil water content (%)
7
14
21
35
47
71
91
119
147
8 15 22 29 8 15 22 29 8 15 22 29 8 15 22 29
0.9 1.9 3.8 6.7 2.1 4.5 7.4 9.6 2.3 5.0 8.4 11.1 2.8 8.6 12.9 14.8
1.2 3.2 5.6 9.4 2.5 5.7 9.2 10.6 2.9 6.4 9.8 12.6 3.9 10.7 14.4 18.3
1.6 4.0 6.5 9.8 3.1 7.1 11.4 13.3 4.0 8.2 14.7 14.8 6.7 13.6 20.4 26.7
1.9 4.9 7.9 12.8 4.0 9.5 12.4 16.6 6.1 11.0 15.6 21.0 10.1 18.0 23.4 33.5
2.1 5.2 8.5 12.6 4.5 10.3 14.1 18.9 8.4 13.3 19.0 24.1 12.5 21.6 30.3 40.2
2.2 5.7 9.5 14.9 5.9 12.5 18.1 23.1 9.7 16.6 23.5 33.2 15.7 26.5 40.0 52.1
2.3 5.9 10.1 15.5 6.2 14.2 20.0 25.6 13.0 20.8 27.1 38.8 24.3 31.9 51.7 61.4
2.6 6.2 10.3 15.7 7.8 16.7 22.5 29.1 14.6 23.3 34.2 46.0 25.6 39.7 61.1 74.4
2.9 6.5 10.3 15.9 10.2 18.8 24.3 33.3 18.9 29.6 37.6 50.6 28.4 49.2 68.9 84.6
Mineralized N was obtained by subtracting the initial nitrate–N, 6.67 mg N kg1 soil, from the total amount of nitrate–N after given days of incubation at 5 C and 8% soil moisture content. Modified from Li et al. (1995).
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Table 6 Effect of temperature and water content on mineralized N (mg N kg1) in soil after certain time of incubation Amount of N mineralized at different water content (%)
Temperature Time ( C) (week)
15
Mineralized N 1
2
Amount (mg N kg ) Relative amount (%) Amount (mg N kg1) Relative amount (%) Amount (mg N kg1) Relative amount (%) Amount (mg N kg1) Relative amount (%) Amount (mg N kg1) Relative amount (%) Amount (mg N kg1) Relative amount (%)
21 25
2 21
35
2 21
8
15
22
29
2.5 23.9 10.2 30.7 2.9 23.4 18.9 37.4 3.9 21.5 28.4 33.6
5.7 53.6 18.8 56.3 6.4 50.5 29.6 58.6 10.7 58.3 49.2 58.1
9.2 87.2 24.3 72.9 9.6 77.6 37.6 74.4 14.4 78.6 68.9 51.4
10.6 100.0 33.3 100.0 12.6 100.0 50.6 100.0 18.3 100.0 84.6 100.0
Modified from Li et al. (1995).
Mineralized N (mg N kg−1)
40 Incubation for 2 weeks Incubation for 21 weeks
30 20
10 0
5
10
15 20 Water content (%)
25
30
Figure 6 Relationship between mineralized N and soil water content. Drawn with data from Ju and Li (1998).
for urea
Y ffi 5:61 þ 63:00X
ðr ¼ 0:8918 Þ;
and for ammonium chloride
Y ffi 4:62 þ 46:09X
ðr ¼ 0:8825 Þ;
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Nutrient and Water Management Effects on Crop Production
Table 7 Effect of water content on nitrification of chemical fertilizer N in soila after incubation for certain days at 26 C NHþ 4 N after given days of Water incubation (mg N kg1) content in soil (%) 1 3 6 11 17
No application of chemical fertilizers 12 32.0 10.7 3.5 0.0 15 29.8 5.3 3.4 0.0 18 28.3 4.8 2.9 0.0 21 28.4 2.2 3.5 0.0 24 27.8 2.4 3.7 0.0 27 26.7 0.8 3.1 0.0 Application of ammonium chloride 12 59.0 75.8 47.0 38.7 15 54.2 61.5 24.9 0.0 18 39.6 44.2 5.0 0.0 21 43.9 48.2 7.3 0.0 24 37.2 35.2 8.1 0.0 27 35.5 28.3 3.7 0.0 Application of urea 12 35.6 69.6 41.5 27.3 15 33.4 55.8 18.8 0.0 18 33.2 59.0 11.3 0.0 21 32.1 44.4 12.3 0.0 24 31.2 31.5 5.0 0.0 27 31.0 22.6 4.2 0.0 Application of ammonium bicarbonate 12 52.7 57.6 38.2 19.5 15 49.3 54.8 20.3 0.0 18 40.8 39.3 5.2 0.0 21 30.4 22.0 4.2 0.0 24 34.2 13.7 4.0 0.0 27 25.7 10.9 4.0 0.0
NO 3 N after given days of incubation (mg N kg1) 1
3
6
11
17
0.0 0.0 0.0 0.0 0.0 0.0
42.2 45.2 46.0 47.8 48.8 50.3
62.0 67.8 69.4 74.0 76.3 77.8
74.1 74.1 77.3 80.1 81.3 82.8
76.5 76.9 78.5 82.5 82.5 84.3
76.5 79.2 81.3 82.4 85.1 88.7
0.0 0.0 0.0 0.0 0.0 0.0
38.9 42.8 45.7 47.3 48.5 50.7
60.3 74.1 86.8 92.6 94.1 98.0
77.9 114.5 141.1 145.2 146.5 157.3
94.5 163.4 168.9 167.1 167.2 172.6
114.4 172.3 174.9 182.5 185.4 187.0
0.0 0.0 0.0 0.0 0.0 0.0
43.0 47.0 48.6 50.7 50.8 52.7
67.2 81.2 82.8 104.4 97.0 114.9
92.6 126.5 139.1 161.2 153.5 163.8
130.1 164.2 169.9 171.0 175.9 184.2
161.4 170.7 179.8 185.4 184.1 188.8
0.0 0.0 0.0 0.0 0.0 0.0
44.0 44.5 47.6 48.9 50.9 52.1
60.7 98.4 85.1 92.8 105.1 108.2
89.4 115.2 146.4 149.6 165.5 162.4
128.5 164.4 167.0 165.6 171.4 175.4
139.3 169.3 178.2 181.8 186.9 190.5
a
Initial soil ammonium–N was 24.8 mg N kg1 soil. Modified from Li et al. (1995).
where Y is the nitrification rate (mg N kg1 d1), X is the soil water content (%), and * and ** indicate significant at p < 0.05 and p < 0.01, respectively. Regression coefficients in the equations indicate the contribution of water increase by 1% to the increase of nitrification rate. Clearly, in the range of water contents of the experiment, the higher the water content, the faster
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Sheng-Xiu Li et al.
Table 8 Residuala ammonium–N (NHþ 4 N) and nitrate–N (NO3 N) in soil produced from added N fertilizers after certain days of incubation at different water contents
Water content in soil (%)
NHþ 4 N after given days of incubation (mg N kg1) 1
3
6
Application of ammonium chloride 12 27.0 65.1 43.5 15 24.4 56.2 21.5 18 11.3 39.4 2.1 21 15.5 46.0 3.8 24 9.4 32.8 4.4 27 8.8 27.5 0.6 Application of urea 12 3.6 58.9 38.0 15 3.6 50.5 15.4 18 4.9 5.4 8.4 21 3.7 42.2 8.8 24 3.4 29.1 1.3 27 4.3 21.8 1.1 Application of ammonium bicarbonate 12 20.7 46.9 34.7 15 19.5 49.5 16.9 18 12.5 34.5 2.3 21 2.0 19.8 0.7 24 6.4 11.3 0.3 27 1.0 10.1 0.9
NO 3 N after given days of incubation (mg N kg1)
11
17
1
3
6
11
17
38.7 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
3.3 2.4 0.3 0.5 0.3 0.4
1.7 6.3 17.4 19.6 17.8 20.2
3.8 40.4 63.8 65.1 65.2 74.5
18.0 86.5 90.4 84.6 84.7 88.3
37.9 93.1 93.6 100.1 100.3 98.3
27.3 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.8 1.8 2.6 2.9 2.0 2.4
5.2 13.4 13.4 30.4 20.7 37.1
18.5 52.4 61.8 81.1 72.2 81.0
53.6 87.3 91.4 88.5 93.4 99.9
84.9 91.5 98.5 103.0 99.0 100.1
19.5 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
1.8 0.7 1.6 1.1 2.1 1.8
1.3 0.6 15.7 18.8 28.8 30.4
15.3 41.1 69.1 69.5 84.2 79.6
52.0 87.5 88.5 38.1 88.4 91.1
62.8 90.1 96.9 99.4 101.8 101.8
a
Residual N was obtained by subtracting the ammonium–N and nitrate–N in soil without fertilization from the corresponding amounts with fertilization under the same water content and at the same time. Modified from Li et al. (1995).
the nitrification rate, and the more the transformation of ammonium–N into nitrate–N (Fig. 7). The above results were obtained in the laboratory where soil moisture and temperature conditions were ideal and could be controlled, and are different from field conditions. To find if water has the same function for promoting the availability of nutrients in farm fields under natural conditions, Ju and Li (1998) conducted a field experiment, including irrigation and nonirrigation, and each at 5 N rates. Results claimed that irrigation increased crop yield and nutrient uptake, and therefore the differences in nitrate–N produced in the soil by irrigation were not comparable with that
247
Nitrate N nitrified from fertilizer (mg kg−1)
Nutrient and Water Management Effects on Crop Production
100 80 60 40 Incubation for 2 weeks Incubation for 21 weeks
20 0 10
15
20 Water content (%)
25
30
Figure 7 Relationship of nitrification of ammonium–N to soil water content at certain days of incubation. Drawn with data from Li et al. (1995). Table 9 Mineral N (mg N kg1) after crop harvest under irrigation and nonirrigation conditions Time of soil sampling
Water treatment
After maize harvesting After wheat harvesting
Nonirrigation Irrigation Nonirrigation Irrigation
Rate of N added to soil (kg N ha1) 0
37.5
75
112.5
150
5.6 5.0 23.3 23.7
4.0 4.0 23.2 25.8
3.3 9.2 23.8 26.7
4.6 5.3 23.1 24.2
5.9 10.1 23.2 25.6
Modified from Ju and Li (1998).
in incubation. Even so, the irrigated field still retained more mineral N (total of ammonium–N and nitrate–N) than that without irrigation after wheat and maize harvest, respectively (Table 9). Obviously, the effect of water on the nutrient availability is the basis for supplying nutrients to plants, and adequate supply of water can make crops obtain sufficient nutrients, and thereby promote crop production.
3.2. Nutrient movement During growing period, crops continuously absorb nutrients and water from soil with their roots, forming a depleted zone of nutrients and water around roots. This causes different content of water and nutrients between root zone soil and bulk soil, and thus water from the higher potential point with a distance from roots moves to the lower potential point close to roots, finally reaching its balance. Nutrients dissolved in soil water also move with water, leading to a decrease of nutrient concentration gradient. Water influences nutrient movement in three ways, interception, mass flow and diffusion,
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Sheng-Xiu Li et al.
and thus influences nutrient uptake by plants directly or indirectly. Barber (1984) pointed out that some nutrients such as Ca, Mg, and N were transferred by mass flow, whereas others such as P and K mostly by diffusion. Comprehensive review of literature by Mengel et al. (1969) concluded that mass flow, diffusion, and interception, respectively, contributed 82%, 7%, and 11% of the total N uptake of wheat on drylands. For sugar beet, spring wheat and spring barley, Reager et al. (1981) reported that the contribution of nitrate–N by mass flow to these crops occupied 100%, 40%, and 110%, respectively, of their total N uptake. The movement of mineral nutrients in soil is essential for crop uptake. Water deficit does not only affect nutrient amount in solution, but also the movement rate of mass flow and diffusion. The effect of water deficit on mass flow depends on the degree of decline in soil water potential, while that of diffusion largely on water potential changes. Role of water on nutrient movement can be studied by determining nutrient concentration changes at different points of soil by using different water treatments over a period of time. If the nutrient concentration is different from the original, this shows nutrients being moved by water from one point to another. Otherwise, there is no movement or the movement cannot be measured (Barber, 1984). However, this method is only suitable for soil without plants. If plants are grown, their roots can extend toward different areas and directly absorb nutrients from points where roots reach, and this may drive nutrient movement in the soil. For this reason, separation of the contribution of water with that of plant roots to nutrient movement should be considered. Song and Li (2006) conducted a field experiment on maize to determine the effect of water movement and root uptake of nutrients on NO 3 N and NHþ 4 N transfer with four treatments: with and without limiting root-grown space, and with and without supplemental supply of water, using the method described by Li et al. (1994). Their results showed that both the root-grown space and water supply had a significant effect on reduction of nitrate–N concentration, but their effect on NO 3 N movement was different (Fig. 8). With irrigation, NO 3 N concentrations at all points measured were small with a difference of 6.5 mg N kg1 between the highest and the lowest. This indicated that nitrate–N could be transferred as solute to plant root systems with water movement. Without irrigation, NO 3 N concentration sharply decreased from one point to another and the difference between the highest and lowest was 26 mg N kg1. The nitrate–N concentration distribution was conformed to root distribution. On the average of three sampling times at 0–2, 2–4, 4–6, 6–8, and 8–10 cm distances from plants, root mass in the 20-cm topsoil layer were 13.9, 13.8, 7.5, 3.2, and 1.1 g m2, and root areas were 45.6, 48.5, 37.8, 17.6, and 6.8 m2, respectively. Clearly, root mass and area were significantly decreased with distance from the plants, and so was nitrate–N concentration.
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Nutrient and Water Management Effects on Crop Production
B
NO3−–N concentration (mg N kg−1)
A 40
60
35
50 40
30
30 25 Non irrigation Irrigation
20 15
2
4
6
8
10
20
Non irrigation Irrigation
10 0
2
4 6 8 Distance from plant (cm)
10
Figure 8 Effect of soil water on nitrate–N (NO 3 N) with root grown naturally (A) and grown in nylon bag (B). Drawn with data from Song and Li (2006).
However, without supplemental water supply, there was a sharp decrease in NO 3 N concentration from one point to another. The NO3 N decrease at different points was made by root direct uptake, and the great difference between adjacent points indicated that it was impossible for roots to promote nutrient movement from one point to another. This is more clearly for limited root-grown space treatments. Without irrigation, nitrate– N concentration difference between the highest and lowest was 43 mg N kg1; with irrigation, it decreased to 23 mg N kg1. Two obviously parallel curves formed between with and without supplemental water supply. In the two treatments, roots were almost entirely concentrated in 0–4 cm distance soil where water was exhausted. Therefore, there was a remarkably sharp decrease of nitrate–N concentration from 6 to 4 cm as shown on the abscissa, while beyond 6 cm, some water still existed, and thus nitrate–Nconcentration difference at points 6, 8, and 10 cm was relatively small. The irrigation treatment had the same trend, but the difference between 4 and 6 cm was greatly decreased, and beyond point 6 cm, there was almost no difference. This again showed that roots promoted nutrient uptake, but was unable to directly promote nutrient movement or such role was too small to be measured. In contrast to nitrate–N, the transfer and distribution of ammonium–N were not influenced by root growth and soil water supply (Table 10). This may be caused by its movement mainly by diffusion, not by mass flow.
3.3. Nutrient efficiency Although the nutrient uptake and water intake are two independent processes in nature, water status in soil greatly affects nutrient uptake and efficiency. Water deficit causes water stress to plants, inhibits plant root growth, reduces
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1 Table 10 Effect of root growth on ammonium–N (NHþ 4 N) migration (mg N kg )
Sampling distance from plant (cm) Root grown naturally
Root grown in nylon bag
Time (m–d)
2
4
6
8
10
2
4
6
8
10
7–30 8–28 9–30 Average
6.2ab 8.5a 0.4a 5.2a
6.6a 8.8a 0.3a 5.2a
5.6b 8.2a 0.4a 4.7a
6.3ab 8.6a 0.2a 5.0a
6.4a 8.1a 0.2a 5.3a
8.3a 13.2a 0.2a 7.2a
8.4a 13.0a 0.3a 7.2a
8.1a 13.0a 0.3a 7.1a
7.8a 12.5a 0.2a 6.8a
8.0a 12.8a 0.2a 7.0a
Data in the same line with the same letter indicate not significant at 0.05 level. Modified from Song and Li (2006).
absorbing area and capacity of plant roots, increases the viscosity of sap in hadromestome, and thereby, decreases nutrient uptake and transfer. Rego (1988) found that water stress reduced sorghum N uptake by 40%, and decreased N fertilizer use efficiency, as crop uptake of N from fertilizers was much lower than that without water stress. Zhao and Zhang (1979) demonstrated that when soil water content in pots was below wilting point, N fertilizer added to soil was almost useless to plants, the N recovery being only 1.9%, while when water content was adequate, the N recovery was increased to 35–39%. Li et al. (1994) showed that an adequate supply of water significantly increased N recovery by about 20% at any N rate (Fig. 9). Torbert et al. (1992) found that too much supply of water decreased N recovery. Overall, water content and its availability have a direct, great influence on plants themselves and on their nutrient uptake.
3.4. Nutrient distribution in plants Soil water content also influences nutrient movement from roots to aboveground part of plants. The influence of water deficit on N distribution was different for different crops. Despite roots being mainly responsible for supply of N in most cases, water contributes a large portion of N to aboveground part. Dong (1992) found in apple trees that water deficit decreased leaf N content, promoted N transferring from new branches to old organs in trees without fructification; and due to different distribution, new organs contained much less N. Such changes were only found in newly growing branches of old trees with fructification. For cereals and leguminous crops, water deficit at later stages only reduced transfer of photosynthesis products from leaves, but N transferred to seeds and storage proteins were much less influenced. Singandhupe and Rajput (1990) revealed that extending time interval of irrigation for producing water stress resulted in a
251
Nutrient and Water Management Effects on Crop Production
decrease of N distributed to rice grain by 17%, but had no influence on its distribution to stems and leaves. Wang and Gao (1988) applied 60 mm water to winter wheat, and 70 mm to summer maize, and added N fertilizer at a rate of 113 kg N ha1 to each in a semiarid area. Their results showed that irrigation increased grain N percentage of winter wheat by 0.07% and summer maize by 0.03%, while reduced that of stem by 0.12% and leaf by 0.14%. Li (2007) showed N allocation in wheat grain through mass flow by irrigation in a lysimeter experiment and revealed that wheat seeds obtained much higher N by the contribution of mass flow with irrigation compared to that without irrigation (Fig. 10). This indicates that water could significantly transfer N from other organs to seeds. Li et al. (1995) obtained similar results in a semiarid area. Whether irrigation increases or decreases grain N content, also depends on other nutrient supply and soil water amount. 80 N fertilizer recovery (%)
70 60 50 40 30 20
Non-irrigation Irrigation
10 0 30
50
70
90
110
130
150
Rate of N (kg N ha−1)
Figure 9 Relationship of N fertilizer recovery to water supply. Drawn with data from Li et al. (1994).
Irrigated water 81% (urea) N uptake from mass flow 88% (ammonium bicarbonate) Nitrate concentration in leached water
Figure 10 Contribution of nitrate–N to N uptake of wheat through mass flow. Drawn with data from S. X. Li et al. (unpublished results).
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Sheng-Xiu Li et al.
Liu and Zhang (1991) reported that by irrigating 70 mm water and applying 239 kg N ha1, grain N increased by 0.37% when adding 30 kg P2O5 ha1, and 0.07% when adding 120 kg P2O5 ha1. The reason for reduction of irrigation rate while increasing grain N content is assumed in that N uptake by winter wheat was higher than dry matter increase, and water deficit limited plant growth rate much higher than it did N uptake rate.
4. Effect of Water and Nutrients Interaction on Crop Yield In dryland areas, the key point for crop production is rational combinative use of water and fertilizers (Guo et al., 1999; Zheng and Liu, 1995). Shimshi (1970) pointed out that the combined effect of water and N could be estimated based on Liebig’s law of the minimum to obtain approximate values. Assume that crop biomass production was YW under a water-limiting condition and that N supply was YN under an N-limiting condition. Under extremely low precipitation (less than 200 mm), YW < YN, showing the biomass production being limited by water supply; and under precipitation in range from 200 to 400 mm, YN < YW, showing N being the major factor affecting biomass production. Smike et al. (1965) found that when available water supply was less than 250 mm, grass production was very low, and the crop had little response to N fertilizer at any rate. However, when available water supply was increased to more than 400 mm, crop yield was raised, and the slope of a linear regression between yield and water amount was increased with the increase of N rate. Li and Zhao (1993) studied the relation of maize response to N and P fertilizers with water amount, and found that nutrient efficiency was increased by sufficient supply of water. Under water deficit, P fertilizer efficiency was more significant, while N fertilizer efficiency was sharply decreased. It is evident that there is an intense interaction between available water and fertilizer, and one being changed will likewise lead to the change of the other (Fan et al., 2003; Zhou and Li, 1995). As the mineralization of organic N and the nitrification of ammonium–N are linearly related with soil moisture under given conditions, water has double, or direct and indirect functions on plant growth and soil fertility (Standford and Epstein, 1974). Power (1971) considered that deficiency of water and N had an additive effect on crop yield decline. For example, smooth bromegrass (Bromus inermis Leyss) yield was reduced by 32% with water deficit, by 24% with N deficiency, and by 54% with both, the latter being almost equal to the addition. Rational fertilizer rate was determined to a large extent by soil water supply. Leggett (1970) and Leggett et al. (1959) suggested a recommendation rate of N fertilizer for wheat based on precipitation in areas along the coast of the west Pacific Ocean. That is, rates being 20–40 kg N ha1 for
Nutrient and Water Management Effects on Crop Production
253
those with annual precipitation <250 mm, 30–60 kg N between 250–380 mm and 60–80 kg N for those >380 mm. Dang et al. (1991) conducted field experiments in Weibei drylands, Shaanxi Province, to study the relation of water stored in soil profile before wheat being sown to N and P fertilizer rates in successive 8 years. Based on their results, they recommended to apply 69–98 kg N and 56–86 kg P2O5 ha1 in a precipitation deficit year (<200 mm), 93–137 kg N and 66–86 kg P2O5 ha1 in a normal precipitation year (200–250 mm), and 113–137 kg N and 68–99 kg P2O5 ha1 in a precipitation abundant year (>250 mm). Having adopted these rates, wheat yields were increased by 15–28% compared to that without fertilization. For rational estimation of N input, Li and Zhao (1993) suggested a precise method considering available water including water stored in soil before wheat being sown and precipitation infiltrated into soil during wheat growth period, WUE, degree of nutrient deficiency and current yield level. This estimation agreed well with the practical results. For successful dryland agriculture, full use of groundwater and precipitation including eliminating runoff water or harvesting it for later use, and joint application of fertilizers with water-saving irrigation to play their synergic role have become a focusing point for investigation (Huang et al., 1990; Li et al., 1990c, 2001, 2005; Zhao et al., 1990). Li (1985) carried out an experiment with low irrigation norm in Luochuan County, Shaanxi Province, and found that declining the irrigation norm from 200 to 100 mm, wheat yield was not affected, and the irrigation result was to a great extent dependant on irrigation timing. Of the three irrigation times, prevernal (early spring) irrigation was better than that in winter and at grain filling stage. Mei and Tao (1993) found that with irrigation norm of 90 mm at four times, prewinter, elongation, booting, and filling stage, increased wheat yields by 27%, 45%, 8%, and 1%, respectively, thus elongation stage being the best. However, Chen et al. (1992) proved that irrigation just before sowing was superior over other stages during wheat-growing period. The interaction of water and fertilizer is time dependant. Research has shown that irrigation of 60-mm water combined with application of 105 kg N and 52 kg P2O5 ha1 before sowing of wheat resulted in an increase of 585 kg grain ha1 as an interaction effect and, the interaction effect increased to 857 kg ha1 when irrigation amount was increased to 120 mm (Chen et al., 1992). However, irrigation with 75–220 mm water at other time for maize and wheat, the interaction effect was small, being 11–30 kg ha1 for winter wheat and 3–364 kg ha1 for maize. In some cases, negative interaction may occur. Although early application of water and fertilizer has been practiced, recently some results revealed that elongation stage might be the high efficient stage for winter wheat (Zhai and Li, 2005) and maize (Gao and Li, 2002; Gao et al., 2006). The different results obtained for the optimal time of application of water and fertilizer may relate to soil water and nutrient supply at different time, and without consideration
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of their status at different time, it is impossible to obtain a common pattern for practical use. Wang et al. (1993) concluded that the significant interaction could not be realized for winter wheat and spring maize until high rate of fertilizer is applied. Li and Li (1994) showed that with adequate irrigation norm, interaction of water and nutrient increased with fertilizer rate increase, and high norm of irrigation was in a reverse situation. For promotion of water and nutrient fully playing their role and realization of maximum yield, high quality, and high efficiency, while protection of the environment from fertilizer ill impact, one of important things is to fully understand and utilize their positive interaction (Fang et al., 2006; Li et al., 2001a,b, 2002a,b; Shaaban, 2006). Irrational combination may produce detrimental effect; the worst situation is negative interaction. For example, oversupply of water and N may delay crop maturation by encouraging excessive vegetative growth, weakening stems and subsequently lodging, in addition to wasting water and N by excessive uptake and overconsumption. Oversupply of water may lead to nutrient loss by leaching, while shortage of water supply may bring about high nutrient concentration in soil, leading to difficulties for crops to take up water and nutrients; and even making plants die before grain filling (‘‘haying-off’’ effect). Based on these considerations, attention should be paid not only to input of water and nutrients, but also to their rational combination. That is, for addition of water, one should consider nutrient supply, and for addition of nutrients, one should consider water coordination, so that limited nutrient and water can produce optimum effect. One should keep such an objective in mind and implement from beginning to the end in one’s planning and in production process.
5. Research Accomplishments, Gaps, and Future Needs Studies related to the interaction effects of water and nutrients on crop yield and nutrient and WUE were initiated much later, because such research concerns multiple factors, and needs a number of treatments that are difficult to manage. Because of short supply of fresh water and fertilizer pollution becoming more serious in the world, there has been a focus on such investigations, and some achievements have been made in many countries, either in rainfed areas or in irrigated areas. One of the main nutrients for concern is N. In rainfed areas, the studies concentrate on N fertilizer and soil water interaction, attempting to mobilize soil water playing full role by rational N fertilization, and producing maximum positive interaction effect. In irrigated areas, however, the major emphasis lies on effects of irrigation and N fertilizer to obtain high sustainable yields, while saving water, controlling groundwater pollution, and reaching high efficiency.
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By now, a variety of studies have been conducted on different aspects, and great progress has been made. Even though, there still exist a large number of issues that need further studies in the future. Delineating drylands into different regions, and determining the priority issue in each region. Although water shortage is regarded as main constraint in dryland areas, it is not always true. In some regions or some fields in the same region, crops have no response to water application, but have a remarkable response to fertilization. This shows that nutrient deficiency is the major constraint; without supply of sufficient nutrients, water cannot play its role. For this reason, delineation of drylands into different regions, and determination of the major constraints related to water or nutrients in a given region are in the priority. It is impossible to have good harvest while reduction of pollution by application of either fertilizer or irrigation until the major constraint is found. Determination of most efficient time or growth stage for input of nutrients and water to different crops. Each crop has different requirement and sensitivity to water and nutrients at different growth stages, and thus input of water and nutrients at different stages will have different effects. It has been reported that at early stage of a crop, deficiency of water produced a compensatory effect, and crop growth, photosynthetic rate, osmotic regulating ability, water-holding capacity of plant cell, energy metabolism, and physiological synthesis were all better in restoring water supply after a dry spell than those continuously retaining high water supply. For this reason, input of nutrient and water could achieve the high efficient use of water only at the most suitable time and nutrients. Based on such considerations, we should investigate the crop response to water and nutrient supply at different times, and determine the optimum efficient stage, and the stage at which deficiency of water or nutrients has no serious influence on crop yield, and the degree of deficiency that crops can tolerate or can allow to a certain extent. In such a way, we can take adequate measures to regulate water supply and to use suitable level of water and nutrients together at the most efficient stage. Such research has been done for some crops, but not for others. Soil is the basic medium for plant growth, and is an important pool for supply of water and nutrients to plants. It is the soil that can change nutrient and water forms and availability, and only through which the water and nutrient role can be displayed. The input amount and frequency of water depend on soil water-holding capacity, and that of nutrients on soil nutrient-supplying capacity and the available quantity. Therefore, the effect of water and nutrient input is closely related with soil properties. Revealing available nutrient quantities, their release process, time, and the harmonious degree of its supply from the soil and that taken up by plants under various water conditions can provide basic information for guiding fertilization and determination of fertilizer amount and application time. Likewise, revealing soil capacity for water conservation and the availability of water added to
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soils with different textures and its use efficiency under given conditions can clarify the direction and strategies for management. In addition to soil itself, tillage systems can change soil properties and thus play a great role on water storage, utilization, and availability. In dryland areas, both conventional and mulching tillage are used and such characteristics should be linked with water and nutrient management and strategies. Interaction mechanism of water and nutrients, especially that of nutrients in improving WUE. Although a large number of studies have been carried out on the mechanism of the two factor interaction, such studies are often limited in a narrow range, most only in one aspect, and thus results are difficult to interpret the phenomenon of entire plant behavior. In fact, any stress from water or nutrients not only affects one property, but the entire process in plants. For instance, any stress can influence plant growth, decrease biomass, reduce photosynthetic efficiency, enzyme activity, and metabolism process and others. As a result, these studies can not reveal which was the primary cause and which was the secondary cause induced by the primary. For an understanding of the effect of nutrient deficiency on plant water, more attention should been paid on two aspects: water intake and plant retaining water. Radin and Boyer (1982) reported that N deficiency reduced the root membrane permeability of sunflower by 50%, leading to a serious inhibition of leaf swelling pressure and expanse at daytime. Recently, we observed that the same water stress had different effect on plant growth. That is, at high temperature, the influence was serious while at low temperature it was slight. At high temperature, the leaf temperature with fertilization was lower than that without fertilization while the transpiration velocity (rate) may not be higher. Obviously, in addition to the influence of water intake and consumption, fertilization may also maintain a proper temperature of plants. This phenomenon makes us assume that one of the main aspects by water stress may be caused by the reduction of transpiration and the rise of temperature in leaf. This may induce disorder of metabolism, and an important role of nutrient supply may be associated with the regulation of plant temperature and maintenance of plant normal metabolism. Investigation starting from such phenomena, we may reveal the nature of water to nutrient supply relation, and that of the supply of nutritional materials in eliminating the dryness stress.
6. Summary and Conclusions China’s drylands cover vast regions in northern territory. Low precipitation and uneven distribution result in deficit of water. Sparse vegetation coverage plus windy climate and frequent rainstorms have led to serious wind and water erosion, which is further intensified by human activities. Two constraints exist for crop production: shortage of water supply, and
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nutrient stress by soil degradation derived from severe erosion. Limited water resources have not been fully used in the areas, and have a certain potential for agricultural production. Nutrient input is essential for crop production on drylands. Addition of fertilizers, particularly organic fertilizers, can enhance soil organic matter, raise soil water storage capacity, promote root growth, increase root length, make roots absorb more water from deep soil layers, and thereby increase plant tolerance to drought, and improve plant physiological activities such as plant water status, osmotic pressure regulation, high activity of nitrate reductase in plant leaves, and high photosynthesis and transpiration intensity but decrease evaporation. All these benefit plants in absorbing water and nutrients and optimize their use efficiency. Effective water management can increase nutrient availability, transformation of nutrients in soil or from fertilizers, promote nutrient uptake by plants and nutrient use efficiency, transfer a large portion of N to aboveground part, affect plant nutrient composition, and mineral nutrient movement from soil to roots and then from roots to aboveground parts of plants. Water deficit causes water stress in plants, inhibits plant root growth, reduces absorbing areas and capacities of plant roots, increases the viscosity of sap in hadromestome, and thereby decreases nutrient uptake, transfer, and efficiency. Adequate supply of water and nutrient can increase efficiency of both and produce good interaction. However, oversupply of either or both may delay crop maturation by encouraging excessive vegetative growth, while deficit of water may result in high nutrient concentration in soil, making it difficult for crops to take up and use both water and nutrients, and in a worst case, plants may die resulting in ‘‘haying-off ’’ effect. Since water supply and nutrient efficiency are closely related, balanced application of nutrients, and determination of their types, ratios, amounts, timing, and methods should be based on the nutrient-supplying capacity and water status of soil.
ACKNOWLEDGMENTS This work was the key project (30230230), important project (49890330), project toward agriculture (30070429) and common projects (40671107 and 30871596) supported by the National Natural Science Foundation of China (NSFC). The authors would like to take the opportunity to express their thanks to the NSFC, for its kindness of supporting such projects in succession.
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C H A P T E R
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Recent Developments of Fertilizer Production and Use to Improve Nutrient Efficiency and Minimize Environmental Impacts S. H. Chien,*,1 L. I. Prochnow,† and H. Cantarella‡ Contents 1. Introduction 2. Improving the Efficiency of Nitrogen Fertilizers 2.1. Controlled-release coated urea products 2.2. Slow-release urea–aldehyde polymer products 2.3. Urea supergranules for deep placement 2.4. Reducing nitrate leaching/denitrification by nitrification inhibitors 2.5. Reducing ammonia volatilization by urease inhibitors 2.6. Reducing ammonia volatilization and nitrate leaching/denitrification by combining urease and nitrification inhibitors 2.7. Use of ammonium sulfate to enhance N efficiency of urea 3. Improving the Efficiency of Conventional Phosphorus Fertilizers 3.1. Coated water-soluble phosphorus fertilizers 3.2. Urea supergranules containing phosphorus and potassium nutrients 3.3. Fluid versus granular water-soluble phosphorus fertilizers 4. Use of Nonconventional Phosphorus Fertilizers 4.1. Phosphate rock for direct application 4.2. Mixture of phosphate rock and water-soluble P 4.3. Calcined nonapatite phosphate rock for direct application 4.4. Agronomic effectiveness of nonconventional acidulated phosphate fertilizers 5. New Granular Nitrogen and Phosphorus Fertilizers Containing Sulfur Nutrient * { { 1
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Formerly with International Fertilizer Development Center (IFDC), Muscle Shoals, Alabama, USA International Plant Nutrition Institute (IPNI), Piracicaba, SP, Brazil Instituto Agronoˆmico, Campinas, SP, Brazil Corresponding author: 1905 Beechwood Circle, Florence, Alabama, USA; email:
[email protected]
Advances in Agronomy, Volume 102 ISSN 0065-2113, DOI: 10.1016/S0065-2113(09)01008-6
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6. Cadmium Uptake by Crops from Phosphorus Fertilizers 6.1. Effect of acidulation levels of phosphate rock on cadmium uptake by crops 6.2. Cadmium uptake by crops from granulated versus bulk-blended phosphorus and potassium fertilizers 7. General Conclusions References
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Abstract This chapter provides information on some recent developments of fertilizer production and use that improve nutrient efficiency and minimize environmental impact. The nutrients discussed are mainly N, P, and S. Improving N nutrient efficiency includes use of (1) controlled-release coated urea products, (2) slowrelease urea–aldehyde polymer products, (3) urea supergranules for deep placement, (4) nitrification inhibitors to reduce nitrate leaching and denitrification, (5) urease inhibitors to reduce ammonia volatilization from urea, and (6) ammonium sulfate to enhance N efficiency of urea. Improving efficiency of conventional P fertilizers includes use of (1) coated water-soluble P fertilizers, (2) urea supergranules containing P and K nutrients, and (3) fluid P fertilizers. Use of nonconventional P fertilizers includes (1) phosphate rock (PR) for direct application with a newly developed computer-based phosphate rock decision support system (PRDSS), (2) a mixture of PR and water-soluble P sources, (3) calcined nonapatite PR for direct application, and (4) nonconventional acidulated P fertilizers containing water-insoluble but citrate-soluble P compounds. The agronomic effectiveness of newly developed granular NP fertilizers containing elemental S to provide S nutrient is discussed. Two processes of producing (1) partially acidulated P fertilizers and (2) compound fertilizers of NP and K by bulk blending are recommended for reducing Cd uptake from P fertilizers by crops. The use of these nonconventional fertilizers may result in an increased relative economic benefit with respect to the use of conventional fertilizers in terms of saving fertilizer cost, enhancing nutrient efficiency, or increasing crop yield.
1. Introduction For many years, the main goal of applying fertilizers was to provide nutrients to plants to increase or sustain optimal crop yield. Thus, improving fertilizer use efficiency in terms of nutrient uptake and crop yield is important to fertilizer producers and users. However, any fertilizer, whether in the natural, inorganic, or organic form, can harm the environment if misused. Recently, fertilizer use has been labeled by environmentalists as one source of polluting soil, water, and air environments. The main environmental impacts associated with fertilizer use have been linked to nitrate leaching into ground water, emission of greenhouse gases (nitrous oxides), soils
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polluted with toxic heavy metals, and surface runoff of N and P nutrients causing aquatic eutrophication. To ensure that proper use of fertilizer is beneficial to both crop production and the environment, researchers and fertilizer producers have tried to find ways to achieve the newly defined goal of fertilizer use, that is, improving fertilizer nutrient use efficiency and minimizing environmental impacts. The purpose of this chapter is to examine literature reports of recent research and developments in technology for fertilizer production and use to improve nutrient efficiency and minimize environmental impacts. The elements to be discussed in this chapter are limited to only three important plant nutrients, N, P, and S. Possible toxic Cd associated with P fertilizer use will also be discussed.
2. Improving the Efficiency of Nitrogen Fertilizers Nitrogen use efficiency is usually low. Dobermann (2005) used data from over 800 experiments to estimate that, on average, only 51% of the N applied was recovered by the aboveground parts of cereals. Similar results were compiled by Cantarella (2007) using 15N data for maize in Brazil. However, N recovery may be even lower under certain management conditions. Fan et al. (2004) reported that the average N fertilizer recovery in cereals in China was 30–35%. Available data for perennial crops, such as citrus, show the same range of results; between 25% and 50% of the applied N was taken up by the plants (Quaggio et al., 2005). Nitrogen derived from fertilizers and not taken up by plants may be immobilized in soil organic matter or may be lost to the environment. In this case, it has the potential to become a pollutant of ground or surface waters or to contribute to the greenhouse effect. Loss of N to the environment usually takes place when high concentrations of soluble N forms are present in the soil solution in excess compared to the amount that plants can take up or when in periods or positions in the soil profile where there are no plants or roots to make use of the available N. These problems can be largely overcome with good management practices, which include selecting a rate of application compatible with plant needs, placing the fertilizer where plants can easily reach the nutrients, and choosing the right application time. In many cases for N, this implies splitting the application in two or more time intervals. Management practices will not be discussed in this text, except urea supergranules (USG) for deep placement in lowland rice soils. Recently, the Association of American Plant Food Control Officials (AAPFCO) has adopted the term ‘‘enhanced efficiency fertilizers’’ (EEF) to
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characterize products that can minimize the potential of nutrient loss to the environment, as compared to reference soluble sources (Hall, 2005). EEF includes ‘‘slow-release’’ or ‘‘controlled-release’’ fertilizers, which comprise coated, water-insoluble or slowly water-soluble products, and ‘‘stabilized’’ fertilizers which are those amended with additives that reduce the transformation rate of fertilizer compounds, resulting in an extended time of availability in the soil. The terms adopted by AAPFCO have shown wide international acceptance. There are two important groups of fertilizers classified as slow- or controlled-release fertilizers (Trenkel, 1997). One group is formed by condensation products of urea and urea aldehydes, of which the most significant types on the market are urea formaldehyde (UF), isobutylidene diurea (IBDU), and crotonylidene diurea (CDU). The second group is comprised of coated or encapsulated fertilizers, such as S-coated urea (SCU) or polymer-coated urea (PCU). Examples of the stabilized nitrogen fertilizers are those treated with inhibitors, such as nitrification or urease inhibitors, that may avoid the rapid transformation of N into forms that are less stable in certain environments.
2.1. Controlled-release coated urea products Although the terms slow-release and controlled-release are used interchangeably, it has become acceptable recently to apply the term controlled-release to coated or encapsulated fertilizers for which the factors determining the rate, pattern, and duration of release are known and regulated during fabrication, and slow-release be used for microbial decomposed N products such as UF (Shaviv, 2005; Trenkel, 1997). SCU was developed by the Tennessee Valley Authority (TVA) and extensively studied compared to uncoated urea for different crops in the late 1960s to early 1980s as a controlled-release N fertilizer. Urea granules are coated with elemental S melted at about 156 C, followed by a wax layer that acts as a sealant to cover fissures or cracks in the S coating, and finally by a conditioner layer. The pattern of N release of SCU depends on the thickness and quality of the coating. Cracks in the coatings, excess manipulation, and flaws in the manufacturing process may jeopardize the slow-release properties of the fertilizer (Gould et al., 1986; Shaviv, 2005). SCU with 7-day dissolution rates in water of 10%, 20%, and 30% were used in most of the agronomic tests. Despite the favorable results often reported in field trials (Allen, 1984; Gould et al., 1986), the TVA SCU products required a 20–23% coating weight that resulted in a lower N content (35–37%) than uncoated urea (46% N) (Young, 1974), which would increase transportation costs on top of the additional cost for S coating. Consequently, SCU has not been accepted by farmers, especially
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those growing food crops in developing countries. Lately some fertilizer companies (e.g., Regal, Simplot) have produced SCU products using a thin coating containing a higher N content (42–44%). Some commercialized products have a double coating of urea involving polymer-sealed S coating (e.g., ‘‘TriKote’’ by Pursell, ‘‘Poly-S’’ by Scotts, and ‘‘Poly-Plus’’ by Lesco) to reduce coating weight and maintain a higher N content. Several companies have marketed thin PCU products as controlledrelease N sources (e.g., ‘‘POLYON’’-coated urea by Pursell, ‘‘ESN’’ by Agrium, ‘‘Osmocote’’ by Scotts, Meister by Chisso-Asahi, and many others) (Trenkel, 1997). The coatings are usually resins or thermoplastic materials and their weight can be as low as <1% of the granule mass without significantly reducing the N content. Unlike SCU which releases urea through small pinholes that can result in a more difficult controlled-N release pattern, PCU releases N by diffusion of urea through the swelling polymer membrane. The release pattern is related to the coating composition and usually depends on soil moisture and temperature (Christianson, 1988), although some products are reported to be affected little by soil moisture content, pH, soil microbial activity, and even by temperature (Shaviv, 2005). It is possible, by changing or combining coatings, to formulate fertilizers which release 80% of their nutrients in pre-established time intervals such as 80, 120, 180, or even 400 days (Shaviv, 2005; Shoji et al., 2001; Trenkel, 1997; Wen et al., 2001). There are many reports of favorable, as well as not so encouraging, results for the coated N fertilizers in the literature. In field trials, Singh et al. (1995) reported that grain yield of lowland rice from a single application of PCU was equivalent to or better than 3–4 well-timed split urea application. Fertilizer recovery with PCU was 70–75% compared with 50% with prilled urea (PU). The higher recovery of N from two PCU products was related to N release and subsequent N uptake by rice during the postanthesis stage (Fig. 1). A one-time application of PCU may have distinct advantages over prilled urea, not just in terms of labor saving, but also because PCU may provide a more stable and sustained N release in rainfed crop systems where well-timed split N applications may not be feasible due to variability in rainfall and soil moisture (Singh et al., 1995). Coated urea also performed better than regular fertilizers by promoting increased grain yield and N uptake in rice in Spain (Carreres et al., 2003), winter wheat in China (Fan et al., 2004), peanuts in Japan (Wen et al., 2001), potatoes in the USA (Munoz et al., 2005), and maize in Japan (Shoji et al., 2001). On the other hand, Carreres et al. (2003) found that some formulations of PCU were not efficient in increasing grain yield and N recovery by flooded rice in Spain, probably because of insufficient coating of the urea granule.
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2.2. Slow-release urea–aldehyde polymer products Another approach for controlling N release to improve N efficiency and minimizing environmental impacts is to use urea formaldehyde-based polymer–N products. They can be either water-soluble (e.g., ‘‘Nitamin’’ by Georgia-Pacific) or water-insoluble (e.g., ‘‘Nutralene’’ by Agrium) or a mixture of both (‘‘Sazolene’’ by Sadepan Chimica). UF, IBDU, and CDU are marketed in several countries, but UF (38% N) is the most important fertilizer of this class. Urea–aldehyde condensation products may be combined with regular urea or with other fertilizers resulting in products with various N contents and release patterns. Urea–aldehyde polymers will become plant available only when mineralized to NH4–N in the soil. However, there are complex factors such as solubility of polymer–N, soil temperature, moisture, microbial activity, texture, organic matter, etc., that can affect the mineralization rate and agronomic effectiveness of polymer– N (Christianson et al., 1988). One distinct advantage of polymer–N products over urea is a reduction in NH3 volatilization. The effectiveness in reducing NH3 volatilization and also N release rate greatly depends on the ratio of urea and paraformaldehyde (PFA) used in the reaction (Carter et al., 1986; Christianson et al., 1988). For example, Christianson et al. (1988)
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reported that NH3 volatilization decreased as the percentage of PFA used increased from 0% to 6% (UF6) and 12% (UF12). The agronomic performance of urea–aldehyde polymers has been studied by several authors (Allen, 1984; Gould et al., 1986). In general, this class of fertilizer is more efficient than soluble sources when the gradual supply of N is an advantage to crops such as grains, perennials, pastures, and turf grasses. However, Cahill et al. (2007) reported that the N of a fluid commercial UF polymer was released and hydrolyzed in only 1–2 weeks, and this UF formulation was equal to or outperformed with respect to urea ammonium nitrate (UAN) solution in eight field experiments with wheat and maize grown in the USA. More information on controlled-release polymer-sealed S-coated urea products, polymer-coated urea products, and polymer–N products can be found in recent review papers by Shaviv (2000, 2005). Despite the potential to increase N use efficiency due to the gradual supply of N of the slow- or controlled-release fertilizers, the use of such products in commercial agriculture is limited by their cost compared to conventional fertilizers. SCU is probably the least expensive, but still costs twice as much as regular urea. The price of other slow- or controlled-release fertilizers varies from 2.4 to 10 times that of conventional soluble N sources, per unit of N (Shaviv, 2005; Trenkel, 1997). Therefore, currently the slowor controlled-release fertilizers are marketed in niches such as nurseries, turf grass, and gardening. It has been estimated that slow-release fertilizers comprise only 8–10% of the fertilizers used in Europe (Lammel, 2005; Shaviv, 2005), 1% in the USA and only 0.25% in the world (Hall, 2005). However, this may change in the future. There is an effort by the fertilizer industry to search for less expensive EEF products, and world concerns about the environment may help to promote the use of less soluble N fertilizers.
2.3. Urea supergranules for deep placement Work done by many researchers, especially by the International Fertilizer Development Center (IFDC), have conclusively demonstrated that compacted USG, that is, urea with 1–3 g granules, are an effective N source (Savant and Stangel, 1990). In general, one or more USG are deep placed (7–10 cm depth) by hand at the center of every four rice seedling hills in rice soils during or after rice transplanting. Savant and Stangel (1998) have shown that N loss is significantly reduced, which results in a significant increase in rice grain yield under flooded conditions compared with splitapplied PU. For example, the average rice grain increase over control with USG was significantly greater than that with split-applied PU in 29 irrigated rice trials (Fig. 2). Deep placement of USG essentially cuts off NH3 volatilization and also significantly reduces denitrification N loss compared to
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surface application of PU. Furthermore, the N concentration of flooded water is greatly reduced when USG is deep placed, so that any water runoff from rice paddies does not contribute to N loss or to potential eutrophication problems (Savant and Stangel, 1990). The reason for producing USG is that it makes it easier for farmers to apply USG by hand. Use of USG has one great advantage in that it requires only one-time application after rice transplanting, whereas surface application of PU requires two to three split applications that can still result in significant N loss through NH3 volatilization. One drawback of applying USG is that it is a labor-intensive practice that some rice farmers in developing countries are not willing to adopt, for example, China. Also, it is not an alternative to commercial rice farms in the USA, Europe, and Latin America due to high labor costs. However, the use of USG has been successfully promoted in several Asian countries, notably in Vietnam and Bangladesh. The Government of Bangladesh has announced that it will expand the use of USG to almost 1 million hectares of rice land, reaching about 1.6 million farm families (IFDC, 2007). In contrast to flooded rice, little study has been done on the use of USG for upland crops, presumably due to difficulty in deep placement of USG in upland soils. Nevertheless, supergranules of NPK compound fertilizers containing urea have been commercially marketed by some fertilizer companies for tree crops, particularly fruits. Once the problem of deep placement in upland soils is solved, it is expected that deep placement of USG should also perform well as an N source for upland food crops.
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2.4. Reducing nitrate leaching/denitrification by nitrification inhibitors As early as the 1960s and 1970s, various chemical compounds were extensively tested for their nitrification retarding properties to inhibit oxidation of NH4–N to NO3–N, and thus reduce NO3–N leaching/denitrification N losses. Decreasing denitrification of NO3–N to N2O and NO can also benefit the environment, as N2O and NO are now considered a part of the greenhouse gases that are responsible for global warming (Snyder et al., 2007). There are many compounds known as nitrification inhibitors (Trenkel, 1997), but three products have come out on a commercial basis. These are (1) 2-chloro-6-(trichloromethyl) pyridine (Nitrapyrin) with the trade name ‘‘N Serve,’’ (2) dicyandiamide (DCD, H4C2N4), which is available with several commercial names, and (3) 3,4-dimethylpyrazole phosphate (DMPP) with the trade name ‘‘ENTEC.’’ A different type of potent nitrification inhibitor, acetylene gas, was reported by Hynes and Knowles (1982) and Walter et al. (1979). The addition of wax-coated calcium carbide (CaC2), which reacted with water to produce acetylene gas to urea fertilized soil, reduced nitrification and increased yield, or recovery of N, in irrigated wheat, maize, and cotton, and flooded rice as summarized by Freney (1997). Numerous reviews on nitrification inhibitors have been reported in the literature (e.g., Amberger, 1986; Edmeades, 2004; Freney et al., 1995a; Hoeft, 1984; Prasad et al., 1971; Scharf and Alley, 1988; Trenkel, 1997), and therefore there will be no further discussion on nitrification inhibitors in this chapter.
2.5. Reducing ammonia volatilization by urease inhibitors Urea-based N products (e.g., urea, UAN) are N fertilizers used worldwide for crop production today, especially urea due to its high N content (46% N). However, NH3 volatilization can be a significant N loss mechanism for urea when applied to the surface, especially for neutral, alkaline, and flooded soils, at the early stage of plant growth. Hydrolysis of urea [(NH2)2CO] to NH4HCO3 produces high pH that induces NH3 volatilization under conditions of high wind, moistened soil surface, low plant canopy, high temperature, etc. The use of urease inhibitors to reduce NH3 volatilization from urea hydrolysis has thus been considered one effective strategy to increase N efficiency of urea-based N products, and more than 14,000 compounds or mixtures of compounds with a wide range of characteristics have been tested (Kiss and Simihaian, 2002) and many patented as urease inhibitors. Many metals are able to inhibit urease activity, among them Ag, Hg, Cd, Cu, Mn, Ni, and Zn (Bayrakly, 1990; Reddy and Sharma, 2000; Shaw, 1954; Tabatabai, 1977; Tyler, 1974). Boric acid was also reported to have an inhibitory effect on urease (Benini et al., 2004). It appears that metals react with sulfhydryl groups of the urease enzyme rendering it inactive
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(Shaw, 1954; Tyler, 1974), whereas boric acid appears to show competitive inhibition with urea (Benini et al., 2004). However, the effectiveness of these inorganic products is somewhat low (Bayrakly, 1990; Bremner and Douglas, 1971; Reddy and Sharma, 2000; Tabatabai, 1977) and some of them are heavy metals which have restrictions for soil application. Furthermore, in some studies the rates of application were too high to justify their use in commercial fertilizers (Bayrakly, 1990; Purakayastha and Katyal, 1998; Tabatabai, 1977). On the other hand, micronutrients added to urea at rates compatible with nutrient recommendations may have some appeal if they could show urease inhibition in addition to that of more effective organic inhibitors. Ammonium thiosulfate, which is an S and N fertilizer, also presents a capacity to inhibit nitrification and urea hydrolyses (Goos, 1985), but its effectiveness is low and the compound is required at high rates (Goos and Fairlie, 1988). Many organic compounds are capable of inhibiting urease activity. Up to the early 1970s, hydroquinone and some benzoquinones were shown to be urease inhibitors (Bremner and Douglas, 1971), but later studies showed that the compounds of the group of structural analogues of urea were more effective (Martens and Bremner, 1984a,b; Radel et al., 1988; Watson, 2000). One potent urease inhibitor that received extensive investigation in the early days, after it had been patented by East German researchers in 1976, was phenyl phosphorodiamidate (PPDA). Experiments were conducted with this product under laboratory conditions (e.g., Martens and Bremner, 1984a,b) and also in different field and greenhouse studies (e.g., Broadbent et al., 1985; Byrnes et al., 1983; Fillery et al., 1986; Snitwongse et al., 1988; Vlek et al., 1980). In a review article on urease inhibitors, Byrnes and Freney (1995) reported that application of PPDA significantly increased rice grain yields in only two out of eight flooded-field rice trials. One of the reasons they attributed was a rapid degradation of PPDA due to high pH or temperature of flooded water. Broadbent et al. (1985) found that adding PPDA to a urea solution applied to corn did not affect the rate of urea hydrolysis, N uptake of corn or corn yield. However, Joo and Christians (1986) found that 2% PPDA added to liquid urea increased the fresh weight of Kentucky bluegrass turf by 20–31%. Lately attention has been focused on the most widely tested urease inhibitor, N-(n-butyl) thiophosphoric triamide (NBTPT), trade named ‘‘Agrotain.’’ In a greenhouse study, Byrnes (1988) reported that NBTPT was more effective than PPDA at retarding urea hydrolysis (Fig. 3A) and reducing the ammonium–N concentration in flooded water (Fig. 3B). Similar results that showed NBTPT was more effective than PPDA in retarding urea hydrolysis were also reported by Beyrouty et al. (1988), Bremner and Chai (1986), Bremner et al. (1991), Bronson et al. (1989), Buresh et al. (1988), and Lu et al. (1989). Addition of NBTPT increased flooded rice grain yield by an average of 40% over unamended urea
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A 350 No inhibitor
PPDA
NBTPT
300
Urea-N (mg L−1)
250 200 150 100 50 0 0 B
2
4
6
8
10
12
14
16
60 No inhibitor
PPDA
NBTPT
50
NH4-N (mg L−1)
40
30
20
10
0 0
2
4
6
8
10
Time (days)
Figure 3 Concentrations of (A) urea–N and (B) NH4–N in floodwater treated with urea as affected by urease inhibitors. Source: Byrnes (1988).
(Byrnes, 1988). However, the yield provided by NBTPT use was not significantly greater than that of PPDA. In field experiments, application of NBTPT seldom resulted in significant increases in flooded rice grain yields as summarized by Byrnes and Freney (1995). They attributed this to the requirement for the conversion of NBTPT in sulfur-analog form
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[NBTPT(s)] (I), which is not an effective urease inhibitor per se, to NBPT [N-(n-butyl) phosphoric triamide] in oxygen-analog form [NBPT(o)] (II), which becomes active to retard urea hydrolysis in soil (McCarty et al., 1989) as shown in the following reaction: S CH3
(CH2)3
N
P
H
NH2
NBTPT(s) (I)
O NH2
CH3
(CH2)3
N
P
H
NH2
NH2
NBPT(o) (II)
However, NBPT(o) (II) is too unstable to be used to treat urea directly (Quin et al., 2005), so the NBTPT(s) (I) form is employed in the commercial formulation. The rate of conversion of NBTPT(s) (I) to NBPT(o) (II) in soil depends on several factors, including soil properties, moisture, temperature, microbial activity, and concentration of inhibitors (Byrnes and Freney, 1995; Carmona et al., 1990; McCarty et al., 1989). Under flooded conditions, the conversion of NBTPT(s) (I) into NBPT(o) (II) may be impaired (Byrnes and Freney, 1995). In many situations, it appears that much of the urea has been hydrolyzed before sufficient conversion of NBTPT(s) (I) to NBPT(o) (II) has occurred (Freney, 1997). This may explain the inconsistent results of the agronomic beneficial effect of NBTPT(s) on increasing crop yield from application of urea-based products reported in the literature. One potential way to enhance the effectiveness of urease inhibitors in paddy rice is by combining PPDA and NBTPT (Luo et al., 1994). It appears that during the time when PPDA actively retarded urea hydrolysis, part of NBTPT(s) was being converted to NBPT(o), which inhibited urease activity as the concentration of PPDA declined later due to degradation. Phongpan et al. (1995) showed that daily NH3–N volatilized in a floodedfield rice soil was higher with NBTPT than PPDA for the first 7 days, whereas the reverse was observed after 8 days (Fig. 4). A combination of NBTPT and PPDA reduced NH3–N volatilization more than either NBTPT or PPDA alone. The rather low NH3–N volatilized from urea (15%) in the study was, in part, due to the addition of algicide to all treatments that controlled algal growth and maintained floodwater below pH 8.3. Without algicide, the pH of floodwater reached 8.7–9.3 in the daytime, which could have induced higher NH3–N volatilization from urea hydrolysis. Lu et al. (1989) reported that the effectiveness of urease inhibition of NBTPT was enhanced by aerobic, in contrast to anaerobic, conditions. In fact Byrnes and Freney (1995) found that the conversion of NBTPT(s) to NBPT(o) could be detected within a few minutes in four aerobic soils in
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Daily ammonia volatilized (% of applied N)
3.5 No inhibitor
Total N loss (% of applied N) 3.0
Control = 15.0 NBTPT = 5.4 PPDA = 7.3 NBTPT + PPDA = 3.0
2.5 2.0 1.5 1.0
NBTPT 0.5
PPDA NBTPT + PPDA
0.0 0
1
2
3
4
5
7 6 Time (days)
8
9
10
11
12
Figure 4 Ammonia volatilization as affected by urease inhibitor treatments in flooded rice soil. Source: data adapted from Phongpan et al. (1995).
the USA. Byrnes and Freney (1995) summarized that in 21 field experiments with maize from 1989, mostly in the Midwestern United States, NBTPT increased average grain yields by 750 kg ha1 (9%) at an average fertilization rate of 100 kg ha1 N. The yields with NBTPT were equivalent to those resulting from 80 kg ha1 N of additional urea–N without NBTPT. In southern Illinois, maize yields were increased by an average of 525 kg ha1 (9%) for 13 field experiments with broadcast urea and 750 kg ha1 (14%) for nine experiments in which urea was placed in bands on the soil surface. Trenkel (1997) summarized the results of over 400 field trials with NBTPT and showed that on average, treating urea or UAN with NBTPT brought about maize grain yield increases of 0.89 and 0.56 t ha1 compared to yields obtained with untreated fertilizers (Table 1). In the same way as many inhibitors of the urea analogue family, NBTPT is efficient at low concentrations (Keerthisinghe and Blakeley, 1995; Watson et al., 1994). Also, NBTPT presents solubility and diffusivity properties similar to those of urea (Radel et al., 1988; Watson, 2000), which are important characteristics for a fertilizer additive. NBTPT has been tested in several countries, usually with satisfactory results (Antisari et al., 1996; Grant and Bailey, 1999; Rawluk et al., 2001; Watson, 2000; Watson et al., 1994). Recent studies in New Zealand also showed that the coating of urea with NBTPT or adding NBTPT to urea suspensions significantly increased pasture yields (Quin et al., 2005; Zaman et al., 2005). In Brazil, Cantarella et al. (2005) reported that the reduction of
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Table 1 Response of maize to urea or UAN treated with the urease inhibitor NBTPT in the USA Grain yield (t ha1) N source
Number of field trials
With NBTPT
Without NBTPT
Yield increase due to NBTPT
Urea UAN
316 119
8.02 8.21
7.13 7.62
0.89 0.56
Source: data adapted from Trenkel (1997). Data are an average of 11 years of field research.
Table 2
Ammonia volatilization losses evaluated in field trials carried out in Brazil NH3 volatilization (% of applied N)
Crop/site
Urea
Urea–NBTPTa
Maize (site 1) Maize (site 2) Maize (site 3) Maize (site 4) Pasture (site 1) Pasture (site 2) Pasture (site 3) Pasture (site 4) Average
45 37 64 48 18 51 18 18 37
24 (47) 5 (85) 22 (65) 34 (29) 6 (69) 22 (56) 3 (83) 2 (89) 15 (60)
a Values in parentheses are % reduction compared with urea. Source: Cantarella et al. (2005). Fertilizers were surface applied to no-till maize or to Brachiaria pastures.
NH3 volatilization from surface-applied urea due to NBTPT ranged from 29% to 89%, with an average of 60%, compared to no inhibitor, in eight field trials (Table 2). Most of the volatilization with untreated urea took place within 2–3 days after fertilizer application. The addition of NBTPT postponed volatilization by 2–8 days, depending on the soil moisture and temperature conditions, and slowed down the rate of ammonia loss. In another set of seven trials with N fertilizers applied on top of sugarcane trash blankets by Cantarella et al. (2008), an average reduction in NH3 volatilization of 26% was observed when NBTPT-treated urea was compared to regular prilled urea. Under prevailing dry weather conditions, the effect of NBTPT tended to be smaller than that observed in the rainy season, because after the inhibitory effect on urease subsided, urea fertilizer remained unincorporated into the soil and was still subject to volatilization losses (Fig. 5). The effect of NBTPT on increasing crop yields obtained with
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45
3
1
7
47 mm
Cumulative NH3 losses (% of applied N)
40 35 UR
30
UR-NBPT
25
AN 20 15 10 5 0 0
7
28 14 31 Days after N application
35
42
Figure 5 Cumulative ammonia loss from urea (UR), ammonium nitrate (AN), and NBTPT-treated urea (UR–NBTPT) surface applied to a trash-covered sugarcane soil. Arrows indicate the amount (mm) and the date of rain events after N application. Source: Cantarella et al. (2008). Table 3 Grain yield response to N as urea, NBTPT-treated urea, or ammonium nitrate surface applied to maize grown under no-till conditions in southeastern Brazil N source
Urea Urea + NBTPT Ammonium nitrate
Grain yielda (kg ha1)
Grain yield increase (kg ha1)
7054a 7405b 7526b
– 351 472
a Means followed by the same letter are not significantly different at p 0.05 by the Tukey test. Source: H. Cantarella (unpublished data). Data are average of three N rates (40, 80, and 120 kg ha1) in seven N-responsive sites.
urea in no-till field trials conducted in Brazil is shown in Table 3 (H. Cantarella, unpublished data). In laboratory studies, Chien et al. (1988a) and Christianson et al. (1990) found that cyclohexylphosphoric triamide (CHPT) in oxygen-analog form was a very effective inhibitor of urease activity, and this finding was confirmed in a field experiment with flooded rice that showed CHPT was even more effective than NBTPT in reducing daily NH3–N volatilization (Fig. 6) and total N uptake, but not in rice grain yield (Freney et al., 1995b).
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S. H. Chien et al.
Daily ammonia volatilized (% of applied N)
2.5 No inhibitor
Total N loss (% of applied N) Control = 14.5 NBTPT = 7.6 CHPT = 1.1
2.0
1.5 NBTPT 1.0
0.5 CHPT 0.0 0
1
2
3
4
5
8
6 7 Time (days)
9
10
11
12
Figure 6 Ammonia volatilization from urea as affected by urease inhibitors in flooded rice soil. Source: data adapted from Freney et al. (1995a,b).
Both NBTPT and CHPT retarded NH3–N volatilization for the first 3 days, but CHPT was more effective than NBTPT between 4 and 10 days. For the whole course of 11 days, CHPT was able to sustain lower NH3–N volatilization losses than NBTPT. To compare the effectiveness of sulfur and oxygen analogs of two urease inhibitors, Christianson et al. (1990) synthesized four compounds: two sulfur analogs, NBTPT(s) and CHTPT(s), and two oxygen analogs, NBPT(o) and CHPT(o). At 0.1% concentration (w/w in urea), NH3–N volatilized in 12 days from urea applied to the surface of a soil with pH 6.2 were as follows: control CHTPTðsÞ > NBTPTðsÞ ¼ NBPTðoÞ ¼ CHPTðoÞ. At a concentration lower than 0.01%, the order was as follows: control > CHTPT(s) > NBTPT(s) > NBPT(o) = CHPT(o). The results showed that, similar to NBTPT(s), CHTPT(s) was a weak urease inhibitor and became more effective when it was converted to CHPT(o) in soil as shown in the following reaction: S C6H11
N
P
H
NH2
CHTPT(s) (III)
O NH2
C6H11
N
P
H
NH2
NH2
CHPT(o) (IV)
Both NBPT(o) (II) and CHPT(o) (IV) were equally effective in reducing NH3–N volatilization. Unlike NBPT(o) (II), which is an unstable
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283
compound, CHPT(o) (IV) is rather stable. However, there are no commercial products with either CHTPT(s) (III) or CHPT(o) (IV) available yet. New urease inhibitors of the phosphoroamide family are being developed. Domingues et al. (2008) reported that eight phosphoroamide-derived compounds presented higher in vitro urease inhibitory activities than NBTPT, the commercial product used as a reference; two of the compounds tested (4-methyl-2-nitrophenyl phosphoric triamide and 2-nitrophenyl phosphoric triamide) showed marked differences from NBTPT in acidic soils, whereas the commercial inhibitor is reported to be less effective. The interest in urease inhibitors is well justified because urea is the most important conventional N fertilizer worldwide and the risks of NH3 volatilization loss significantly contribute to low fertilizer use efficiency. However, the data available show that urease inhibitors cannot completely control NH3 loss when urea is surface applied to soils because the inhibitory effect depends on soil physical and chemical characteristics and also on environmental conditions. The urease inhibitors available so far can prevent urea hydrolysis for at most 1 or 2 weeks, during which time the fertilizer should ideally be incorporated into the soil by water (rain or irrigation) or mechanical methods; however, that does not always happen. The shortlived effect of the inhibitor is also a limitation when urea is surface applied to flooded rice. Nevertheless, despite only a partial capacity for reducing NH3 losses, urease inhibitors represent an alternative that cannot be disregarded due to the growing presence of urea in the fertilizer market.
2.6. Reducing ammonia volatilization and nitrate leaching/denitrification by combining urease and nitrification inhibitors Because ammonia volatilization and nitrate leaching/denitrification are mainly responsible for potential N losses from application of urea-based products, it seems logical to expect that combining urease inhibitors and nitrification inhibitors may yield the least amount of N loss. In an interesting field experiment in New Zealand, Zaman et al. (2005) compared the treatment of combined NBTPT (urease inhibitor) and DCD (nitrification inhibitor) against NBTPT or no inhibitor in terms of N loss in a soil (pH 5.7) fertilized with urea for pastures comprised mainly of perennial ryegrass and white clover. The results (Table 4) showed that NBTPT reduced more NH3 volatilization than no inhibitor did, whereas DCD resulted in less NO3–N leaching and denitrification loss from the soil fertilized with urea. The authors explained that the overall low losses of NO3–N leaching for the treatments were due to inadequate drainage (30 mm) to remove the bulk of the nitrate from the 30-cm soil depth during the period of the trial. Combining NBTPT and DCD, however, somehow enhanced NH3 volatilization comparing to NBTPT alone. Similar results
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S. H. Chien et al.
Table 4 Effect of different inhibitors on N status, losses, and dry-matter yield in a soil fertilized with urea for pasture
Measurement
No N
No inhibitor
NBTPT
NBTPT + DCD
Soil ammonium–N (mg kg1 N) (on 14th day) Soil nitrate–N (mg kg1 N) (on 14th day) Ammonia volatilized (kg ha1 N) (over 14 days) Nitrate leaching (g ha1 N) (over 82 days) N2O flux (kg ha1 N) (over 82 days) Total dry-matter yield (t ha1) (three cuts)
5
52
23
95
2.0
24.5
15.5
3.8
1.2
7.7
4.2
10.0
5
375
200
50
0.8
2.4
2.3
1.5
7.5
9.5
11.0
10.8
Source: data adapted from Zaman et al. (2005).
were reported by Gioacchini et al. (2002) and Nastri et al. (2000). It is not clear whether DCD affects the inhibiting properties of NBTPT or if by decreasing denitrification DCD enhanced NH3 volatilization loss due to an increase in NH4–N concentration on the soil surface. However, Clay et al. (1990) found that DCD did not affect NH3 volatilization when mixed with urea or urea plus NBTPT; however, in their work the ratio of DCD to urea was somewhat low (2:160). In the study of Zaman et al. (2005), the drymatter yield of pasture follows the order: no N < no inhibitor < NBTPT = NBTPT + DCD. In this study, no additional benefit was shown for combining a urease inhibitor and a nitrification inhibitor compared to urease inhibitor alone in terms of pasture production with urea application. Radel et al. (1992) found that thiophosphoryl triamide [(NH2)3PS or TPTA] had a dual effect on inhibition of urea hydrolysis and nitrification. They compared TPTA with DCD (nitrification inhibitor) and NBTPT (urease inhibitor) in terms of retardation of urea hydrolysis or nitrification in a soil treated with urea or (NH4)2SO4 during a 5-week incubation. Urea was completely hydrolyzed in 1 week in the treatment without inhibitor, in 2 weeks with DCD, and in 5 weeks with TPTA, whereas NBTPT effectively retarded urea hydrolysis for the incubation period (Fig. 7). TPTA was 95% and 81% as effective as NBTPT for weeks 1 and 2, respectively, on retarding urea hydrolysis. Since NH3 volatilization from urea hydrolysis generally occurs during the first 2 weeks after urea surface application, TPTA seems to be an effective urease inhibitor for reducing NH3 volatilization. As expected, NH4–N from added (NH4)2SO4 significantly decreased
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Recent Developments of Fertilizer Production and Use
A
250
NBTPT
Urea-N (mg)
200
150
TPTA
100
DCD
50 No inhibitor 0 0 B
1
2
3
4
5
180 160
DCD
140
NH4-N (mg)
120 TPTA
100 80 60 40 NBTPT
20
No inhibitor
0 0
1
2
3
4
5
Time (weeks)
Figure 7 Effect of inhibitors on urea hydrolysis and nitrification in a soil treated with (A) urea and (B) ammonium sulfate during incubation. Source: data adapted from Radel et al. (1992).
after 2 weeks in the soil treated without inhibitors or with NBTPT (Fig. 7). DCD effectively retarded nitrification during the course of incubation, whereas TPTA was 55% as effective as NBTPT after 5 weeks. When the same soil was treated with urea, Radel et al. (1992) found that the percentage
286
S. H. Chien et al.
of nitrification inhibition for NBTPT was 85%, for DCD was 90%, and for TPTA was 97% after the 5-week incubation. Thus TPTA could be an effective dual inhibitor for urea hydrolysis and nitrification, although slightly less effective than NBTPT and DCD with regard to their sole inhibition properties. However, no agronomic studies of TPTA versus DCD and NBTPT in soil treated with urea have been reported in the literature.
2.7. Use of ammonium sulfate to enhance N efficiency of urea Ammonium sulfate [(NH4)2SO4 or AS] is a weakly acidic salt that is not prone to NH3 volatilization in acidic and neutral soils. It is also a common source of N fertilizer, and therefore several studies have been conducted to investigate whether use of AS could enhance the agronomic N efficiency of urea by reducing NH3 volatilization. Fleisher and Hagin (1981) hypothesized that pretreatment of soil with an NHþ 4 salt could increase the population of nitrifiers that could reduce NH3 volatilization from subsequently applied urea. They found that pretreatment with AS reduced NH3 loss from a subsequent urea application by half in a neutral soil (pH 6.2). In a field study in India, Kumar and Aggarwal (1988) also found that pretreatment of soil (pH 8.2) with AS 2–4 weeks prior to urea application reduced NH3 loss by half, and led to a yield increase of pearl millet. Recently, Goos and Cruz (1999) observed a similar effect of AS pretreatment 2 weeks before urea on reducing NH3 volatilization from the subsequent urea application to soils varying widely in soil properties. The concept of this approach could be utilized in crop systems that receive more than one urea topdressing if AS is used before the first application (Goos and Cruz, 1999), although it is not always possible to coincide the fertilizer applications in the same spots under field conditions. Another approach to enhance the N efficiency of urea is to partially substitute AS for urea in the mixture. Several studies have shown that mixing AS with urea reduced NH3 volatilization losses (Lara-Cabezas et al., 1992, 1997; Oenema and Velthof, 1993; Vitti et al., 2002). Lower NH3 losses are expected when AS is added to urea because AS is unlikely to contribute to NH3 losses under neutral or acidic soil conditions; moreover, the dilution effect of AS–N should be taken into account since NH3 volatilization is greater with increased urea concentration and/or rate of application (Cantarella et al., 2003; Nelson, 1982). However, in some studies the decrease in NH3 volatilization loss due to the addition of AS is greater than the proportion of NHþ 4 N in the AS–urea mixture (LaraCabezas et al., 1992; Vitti et al., 2002). For instance, in a recent study, Vitti et al. (2002) mixed different amounts of AS (0, 75, 150, 225, and 300 mg N) with a constant amount of urea–N (330 mg N). After 23 days of soil incubation, the total amounts of NH3–N volatilized from urea decreased with increasing amounts of AS added, along with a decreasing soil pH from 6.2 to 5.2. Apparently the acidic nature of AS caused a reduction in NH3
Recent Developments of Fertilizer Production and Use
287
volatilization from urea in the mixtures (Lara-Cabezas et al., 1992; Oenema and Velthof, 1993; Vitti et al., 2002). Studies with plants have also shown that the mixture of urea and AS may increase fertilizer N efficiency. Watson (1988), in a pot experiment, measured N uptake by ryegrass from mixtures of AS and urea using 15N-labeled fertilizers, and observed that N derived from urea increased by 38% when AS was present in the same granule, as compared with urea alone; however, the uptake of AS-derived N decreased 14% in the presence of urea. The net effect of the fertilizer mixture on N uptake was still positive. In another study with maize in a sandy soil in Brazil, Villas Boas (1990) concluded that the mixture of AS and urea in the same granule tended to promote higher recovery of N fertilizer and increased grain yield compared to plants treated only with urea; the N fertilizers were surface applied to soils with no incorporation. The extent of the response to the addition of AS to urea is not always significant and there are reports showing no effect of the fertilizer mixture compared with urea alone. Lara-Cabezas et al. (1992) in a field experiment observed a reduction of NH3 volatilization due to the addition of AS to urea in a sandy Oxisol with 14.9% and 25.3% moisture content, but not with 18.3% moisture content. In another study with maize under field conditions, Lara-Cabezas et al. (1997) found lower NH3 losses with AS + urea compared with urea alone when the fertilizers were applied before, but not after, sprinkle irrigation (28 mm). The reasons for these results are not clear, but could be associated with experimental error in field trials. In an experiment with sugarcane, Costa et al. (2003) reported no difference in NH3 volatilization loss in plots treated with urea or with a mixture of urea and AS (loss of about 35% of the surface-applied N). In the same way, Villas Boas et al. (2005) did not find differences in uptake by maize plants of 15Nlabeled AS + urea and urea alone, although in their experiment conditions were not conducive to NH3 volatilization losses. In some cases, it appears that the proportion of AS in the fertilizer mixture may affect N fertilizer efficiency when N is surface applied. For instance, Watson (1987, 1988) observed an increase in urea–N uptake by ryegrass when the amounts of urea and AS (w/w) were 1 + 1 (Watson, 1988), but not when they were 3 + 1 (Watson, 1987). Great reductions of NH3 volatilization reported by Lara-Cabezas et al. (1992) were obtained when AS accounted for 42% N of the urea + AS mass (25% of total N as NHþ 4 N), but the differences were less pronounced compared to those of urea when the mixture contained only 21% AS (11% of total N as NHþ 4 N). Similarly, the greater the amount of AS added, the lower the volatilization loss of urea–N observed in the study of Vitti et al. (2002). Although high amounts of AS in urea + AS formulations do not always guarantee decreased NH3 loss or increased N uptake by plants, in most studies with positive results reported in the literature, the proportion of N
288
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from AS in the urea + AS mixture was relatively high: 50% (Watson, 1988), 42% (Lara-Cabezas et al., 1992), 65% (Lara-Cabezas et al., 1997), and 41–48% (Vitti et al., 2002). It should be pointed out that the reason for urea being widely used as an N fertilizer worldwide is its lower price per unit N due to its high N content (46% N), compared to those of ammonium nitrate (35% N) and AS (21% N), which reduces the transportation cost of urea–N fertilizer. Thus, any attempts to use AS with urea to enhance the N efficiency of urea should also consider the economic aspects, including (1) increase in the transportation cost for AS compared to urea, (2) saving the cost of urea–N by reducing NH3 volatilization loss, and (3) a possible increase in crop yield by mixing AS and urea compared to urea alone. An additional reason to mix AS and urea is to supply S along with N. AS has an N:S ratio much higher than that of most plants (N:S ratios 8:1–10:1), but urea lacks S. However, it takes only about 21% AS in the AS + urea mixture to produce a formulation with an N:S ratio of 8:1, or approximately 5% S. Mixtures containing 42% AS and 58% urea (about 10% S and an N:S ratio of 3.7:1) could be justified for situations with high S demand.
3. Improving the Efficiency of Conventional Phosphorus Fertilizers It is known that water-soluble P (WSP) can be converted to waterinsoluble P after reaction with soil minerals, which can result in a decrease of P availability. Several terminologies, such as P sorption, adsorption, retention, fixation, precipitation, and immobilization, have been used to describe this process. The forms of reaction products depend on P sources and soil minerals. In general, Fe–Al–P minerals form in acidic soils containing Fe–Al–oxide minerals, whereas Ca–P minerals form in alkaline or calcareous soils. The P reaction process involves surface adsorption and/or precipitation. Often the Fe–Al–P precipitates, if they indeed occur, are in the amorphous instead of crystalline form, which makes identification of mineral species rather difficult (Hsu, 1982a,b). In alkaline and calcareous soils, often more crystalline Ca–P and/or NH4–Ca–P compounds from TSP, SSP, DAP, and MAP can be physically identified (Lindsay et al., 1989). There has been some interest in research and development on modifying the physical characteristics of conventional WSP fertilizers to reduce P fixation by soil, and thereby increase the P efficiency for plant uptake. Some of the recent findings are discussed below.
3.1. Coated water-soluble phosphorus fertilizers Recently, some fertilizer companies have developed thin coating of WSP fertilizers (DAP, MAP, TSP) with water-insoluble polymers, with or without S (e.g., trade name ‘‘DAP-Star’’ by Hi Fert), as a slow-release
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P fertilizer. Another type is coated with water-soluble polymers (e.g., trade name ‘‘Avail’’ by SFP) to reduce the rate of WSP conversion to waterinsoluble P by soil fixation. Gordon and Tindall (2006) claimed that Avail is a polymer with a very high surface charge density (about 1800 cmol kg1 of cation exchange capacity) that can inhibit P precipitation by acting as a platform for sequestration of P-fixing cations, such as Ca and Mg in high pH soils and Fe and Al in low pH soils. One study conducted by the University of Georgia (G. J. Gascho, unpublished data) showed that MAP coated with this polymer performed significantly better than uncoated MAP when MAP was broadcast, but it did not when banded (Table 5). Since soil fixation of WSP is higher when broadcast than when banded, as evidenced by the lower grain yield of uncoated MAP when broadcast than when banded, the result showed the benefit of MAP by polymer coating. However, there is little information on the soil chemistry of this polymer-coated P fertilizer published in the peer-reviewed scientific journals and questions remain unanswered regarding the mechanisms of reducing P fixation by the polymer. For instance, it is known that WSP is adsorbed or precipitated on the solid surfaces of Fe and Al oxide minerals in acid soils and CaCO3 in alkaline soils (Lindsay et al., 1989). What mechanism causes these Fe, Al, and Ca cations to dissolve from their minerals and diffuse to the dissolved polymercoated WSP granule sites, be adsorbed by the polymer via ionic exchange, and thereby protect the WSP from precipitation? Furthermore, shouldn’t the soluble cations associated with the WSP fertilizers (Ca ions from SSP and TSP, and NH4 ions from MAP and DAP) be first adsorbed by the polymer and thereby reduce the polymer’s capacity to sequester soil Fe, Al, and Ca ions? Research is needed to address these questions in order to understand the merit of using WSP fertilizers coated with water-soluble or water-insoluble polymers. If indeed P release meets the crops’ needs, and at the same time minimizes P fixation, the coated WSP fertilizers may be effective for crop production provided the cost/benefit is feasible compared to the uncoated WSP fertilizers. Table 5 Grain yield of maize obtained by polymer-coated and -uncoated MAP as influenced by the placement method P rate (kg ha1 P)
Method of placement
P source
Grain yield (t ha1)
0 11.6 11.6 11.6 11.6 LSD (0.10)
NA Band Band Broadcast Broadcast NA
Control MAP Polymer-coated MAP MAP Polymer-coated MAP NA
3.7 8.5 9.2 7.0 10.1 1.2
Source: G. J. Gascho (unpublished data).
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3.2. Urea supergranules containing phosphorus and potassium nutrients Savant and Chien (1990) first showed that available P from DAP in USG by deep placement was as effective as broadcast and incorporation of DAP or TSP for flooded rice. Although initial P accumulation in young rice leaves from deep-placed USG-containing DAP was lower than that of incorporated TSP, P uptake from both P sources was the same 40 days after rice transplanting (Savant et al., 1997). In the same study, practically no 32P activity was detected in the floodwater when USG-containing 32P-tagged DAP was deep placed. This observation clearly suggests that runoff losses of P in solution and/or P adsorbed on clays suspended in the flowing floodwater would be reduced substantially, thus practically eliminating the problem of water pollution or eutrophication due to P runoff from paddy fields. Results of several farmer-managed field trials conducted in India demonstrate that USG–DAP management can make the fertilizer agronomically more efficient, economically more attractive with less risk, and reduce the loss of nutrients compared to the conventional use of PU and WSP fertilizers (Savant and Stangel, 1998). For example, the grain yield of rainfed rice obtained with USG–DAP by deep placement in an Ultisol was higher than that obtained with basal incorporated SSP plus split-applied PU (Fig. 8). 3.0
Rice grain yield (t ha−1)
2.5
Deep-placed USG containing DAP
2.0
1.5
Split-prilled urea + incorporated SSP
1.0
Deep-placed USG
0.5
LSD (0.05) = 0.2
0 0
5
10 15 20 Rate of P applied (kg ha−1)
25
30
Figure 8 Grain yield of flooded rice obtained with different NP treatments (each N rate = four times each P rate). Source: Savant and Stangel (1998).
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A recent study (Kapoor et al., 2008) showed that deep placement of USG-containing DAP and KCl performed better than broadcast application of urea (three splits), DAP, and KCl for rainfed rice in a Vertisol. Significantly higher grain yields and straw yields, total N, P, and K uptake, and N and P use efficiencies were observed with deep placement of N–P–K compared to broadcast of N–P–K. Furthermore, the amounts of N, P, and K in the floodwater in the deep-placement treatments were negligible—similar to floodwater N, P, and K contents without fertilizer application. Thus, ureabased N–P–K compound fertilizers may be agronomically and economically feasible in supergranule form by deep placement for flooded rice production.
3.3. Fluid versus granular water-soluble phosphorus fertilizers Numerous studies have been reported in literature on the comparison of agronomic effectiveness of fluid versus granular or nongranular WSP fertilizers. As Engelstad and Terman (1980) pointed out, for a valid comparison of fluid and solid fertilizer P, the P should be supplied in the same chemical compounds in both cases and be similarly placed. However, many reported greenhouse and field trials do not comply with these requirements and often the results are conflicting. For example, many studies claimed that fluid ammonium polyphosphates (APP) are agronomically superior to ammonium phosphates when based on different P compounds in the two P sources (polyphosphates vs orthophosphates). Most results in the USA and other countries show equal P availability from these two P sources to crops grown on most soils (Terman, 1975). A recent study by Ottman et al. (2006) also showed that there were no significant differences in alfalfa yield obtained with fluid APP and granular MAP on a calcareous soil over 3 years. According to Engelstad and Terman (1980), some favorable results with APP on neutral to alkaline soils may have been caused by appreciable amounts of micronutrients such as Fe or Zn in the APP. On the other hand, P of APP would become plant available only when polyphosphates are hydrolyzed to orthophosphate in soils that depends on soil biological activity. For example, Engelstad and Allen (1971) reported that APP was less effective than MAP at a colder temperature (16 C), but there was no difference at a warmer temperature (24 C). Earlier, after summarizing many field trials in the USA, Lathwell et al. (1960) also concluded that P in solution form is as satisfactory as in comparable solid sources. Recently, renewal interest in research work on fluid versus granular forms of the same P fertilizers has been reported, especially in Australia. Holloway et al. (2001) showed that commercial fluid P fertilizers, for example, MAP, DAP, and APP, were more effective than the same commercial granular P fertilizers in increasing crop yield in calcareous and alkaline soils. Hettiarachchi et al. (2006) and Lombi et al. (2004) used highly
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sophisticated instruments to study P mobility of surface-applied granular versus fluid MAP in a calcareous soil at 60% of field capacity. They found that total and labile P from the liquid MAP diffused farther (1.35 cm) than did the granular MAP (0.75 cm) from the site of P application. This may explain the better agronomic performance of fluid MAP over granular MAP in field trials as reported by Holloway et al. (2001). They claimed that P diffusion and isotopic lability from granular MAP were reduced compared with equivalent liquid MAP because precipitation reactions osmotically induced the flow of soil moisture into the MAP granule. Also, a significant amount of the initial P still remains in the granule even after some time of dissolution, partly due to the presence of water-insoluble Fe–Al–P minerals in the granule, and also due to the precipitation in situ of similar minerals resulting from the diffusion of Ca and Al into the granule. In contrast, there is significantly less fixation of P from the fluid P fertilizers, and hence a greater concentration of labile P (Lombi et al., 2004). Black (1968), however, showed that P could diffuse up to 1.50 cm from the site of solid KH2PO4 applied to the surface of a calcareous soil at 100% field capacity. Apparently, P diffusion greatly depends on soil moisture content (% of field capacity) and water content of the liquid P fertilizers. Furthermore, in the studies conducted in Australia, the researchers only reported total P content, but did not report the proportion of WSP and water-insoluble P content, when comparing the same fluid with granular P fertilizers. For example, Lombi et al. (2004) compared commercial granular MAP with commercial fluid technical grade MAP, but they did not report whether the two sources had the same P compounds or water solubility, a caution that was emphasized by Engelstad and Terman (1980). In short, a comparison of fluid with granular WSP fertilizers in agronomic effectiveness depends on many factors. Some of them are (1) chemical compounds of P sources, (2) proportion of WSP and water-insoluble P in P sources, (3) soil pH, (4) soil P-fixing capacity, (5) soil biological activity, (6) soil moisture or rainfall, (7) crop species (e.g., rooting system), (8) rate of P applied, (9) P placement method (e.g., incorporation vs band, no-till vs till), (10) initial versus residual P effect, and (11) cropping systems. One benefit of applying fluid ortho- or polyphosphate sources as compared with solid P sources may be that fluid P sources can increase soil concentration of available P with depth. Kovar (2006) suggested that fluid P sources, especially polyphosphates, which are known to sequester from soil adsorption, applied to the soil surface after crop harvest would move into the soil profile where it may be less subject to loss in runoff or by erosion, which may minimize the environmental impact of P during the winter months, and yet be available to plants the following growing season. In light of the recent renewal of interest in research comparing the agronomic effectiveness of fluid to granular WSP fertilizers in Australia (Evans, 2008; Holloway et al., 2006), it may be worthwhile to take up similar research for different soils in other countries.
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4. Use of Nonconventional Phosphorus Fertilizers 4.1. Phosphate rock for direct application Direct application of phosphate rock (PR) can be an effective agronomic and economic alternative to the use of more expensive WSP fertilizers for crop production under certain conditions, especially in acidic soils of tropical and subtropical developing countries. The agronomic use of PR has been extensively studied and reported over the past 50 years. Some PR sources have been commercialized for export from Tunisia, Jordan, Algeria, Egypt, Morocco, Israel, China, and Christmas Island (Australia) to Malaysia, Indonesia, New Zealand, and Brazil, mainly for pastures and tree crops. Some indigenous PR sources are marketed for local use, for example, Burkina Faso, Colombia, Chile, Nigeria, Mali, and Tanzania (Kuyvenhoven et al., 2004). Recent work done by researchers, particularly the work done by the IFDC, has provided more insight on the agronomic effectiveness of PR compared with the use of WSP fertilizers. Several in-depth reviews of this subject have been reported by Chien (2003), Chien and Menon (1995b), Hammond et al. (1986), Khasawneh and Doll (1978), Rajan et al. (1996, 2004), and Truong (2004). Therefore, there will be no further discussion on the agronomic use of PR in this chapter, except the most recent developments on PR use as described below. The major factors affecting the agronomic effectiveness of PR are (1) chemical and physical properties of PR that affect the solubility of PR, (2) soil properties, (3) management practices, (4) climate, and (5) crop species. Despite hundreds of agronomic trials that have been conducted worldwide in the past, there is a need to integrate all of these factors in a comprehensive system to understand how these major factors affect the agronomic effectiveness of PR. Use of a phosphate rock decision support system (PRDSS) is a means to solve this problem. Because it is designed to be practical, PRDSS can be used in developing countries, especially those countries with endowed indigenous PR deposits, to assist in making decision to use WSP fertilizers or PR to supply P needed by crops. Researchers in New Zealand (e.g., Metherell and Perrott, 2003) and Australia (e.g., Gillard et al., 1997) have developed different versions of PRDSS. However, their models are mainly aimed at the use of reactive PR sources for pasture production. Recently, IFDC has developed and published its own PRDSS model for PR sources varying in reactivity for different crop species (Smalberger et al., 2006). Based on this model, the FAO/IAEA has posted the PRDSS on the IAEA Web site (http:// www-iswam.iaea.org/dapr/srv/en/resources). The current PRDSS version, however, only applies to the initial relative agronomic effectiveness
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(RAE) of PR with respect to the conventional WSP fertilizers. To optimize agronomic and economic use of PR, IFDC, and FAO/IAEA have advanced the current PRDSS version by incorporating the residual RAE of PR, since the residual PR effect is very important when comparing the use of PR versus WSP. By including the residual PR effect and further validating or modifying the current PRDSS version, it will make the future FAO/IAEA Web-based PRDSS a more powerful and useful tool to researchers, extension workers, fertilizer companies, and government decision makers on the feasibility of the agronomic use of PR, whether locally produced or imported, compared to the use of WSP fertilizers. Figure 9 shows that the latest validation of an updated PRDSS in initial and residual RAE agrees well between the predicted and observed RAE values within 10% of a 1:1 line across different PR sources, types of soil, and crop species (U. Singh and S. H. Chien, unpublished data). The updated PRDSS version will be posted on the same IAEA Web site later. It should be noted that the current residual RAE of PR of the updated PRDSS version represents only the average of residual RAE values of a PR over several years or crops. In the future, more work will be needed to model a residual RAE of PR for a given residual crop in a given time. Recently, eutrophication of aquatic environments (creeks, ponds, rivers, lakes, etc.) caused by excessive P from soil surface runoff has drawn many researchers to find strategies to mitigate the P pollution problem. Preliminary studies done in New Zealand and the USA have suggested that the use of reactive PR can not only sustain crop productivity but also may minimize the eutrophication problem compared to the use of WSP sources, because of lower P availability from PR for algal growth (Hart et al., 2004; Shigaki et al., 2006, 2007). Table 6 shows that both cumulative total and dissolved reactive P losses from surface runoff from three soils were significantly lower with reactive North Carolina PR than TSP, indicating less eutrophication would be encountered with reactive PR (Shigaki et al., 2007). However, more work, including field studies, is needed to validate this supposition. Another new field of PR research is the increasing use of PR for organic farming worldwide, since chemical P fertilizers are not allowed to be used on organic farms (Nelson and Mikkelsen, 2008). Mixing elemental S, which is also allowed as an input for organic farming, with PR may be a potential source of P and S for organic crop production. Oxidation of elemental S to H2SO4 may enhance PR dissolution, which has been studied for many years with inconclusive results due to many variables (Rajan et al., 1996). It should be pointed out that use of PR with or without elemental S for organic farming, similar to conventional farming, greatly depends on the reactivity of PR sources used. Thus organic farmers should be aware that not all PR sources are the same in reactivity. The general rule is that the higher the reactivity of PR, the better it is as a P source for organic farming. For example, an indigenous igneous PR from Ontario, Canada, with very low reactivity has
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A 140 120 100 80 60 40 Comparison of initial Relative Agronomic Effectiveness (RAE)
Predicted RAE (%)
20 0 0
20
40
60
80
100
120
140
B 140 120 100 80 60 40 Comparison of residual Relative Agronomic Effectiveness (RAE)
20 0 0
20
40
100 80 60 Observed RAE residual (%)
120
140
Figure 9 Comparison of observed and predicted relative agronomic effectiveness (RAE) for (A) initial and (B) residual applications of PR and WSP. The spread (10%) along the one-to-one line (dashed line) is shown by dotted lines. Source: U. Singh and S. H. Chien (unpublished data).
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Table 6 Cumulative loss of dissolved reactive and total P from surface runoff from three soils treated with phosphate rock (PR) or triple superphosphate (TSP) P sources Soils
Control
North Carolina PR
Cumulative dissolved reactive P (DRP) loss (kg ha1) Alvira 0.28 0.52 Berks 0.18 0.39 Watson 0.23 0.43 Averagea 0.23c 0.45b Cumulative total P loss (kg ha1) Alvira 0.35 0.83 Berks 0.30 0.68 Watson 0.31 0.72 Averagea 0.32c 0.74b
TSP
32.2 14.5 16.2 20.9a 33.2 15.5 19.6 22.7a
a Average DRP and total P loss followed by the same letter are not significantly different at p < 0.05. Source: Shigaki et al. (2007).
been marketed for organic farming due to its high P content. However, the total P content is irrelevant to PR reactivity for direct application (Chien, 1995). In fact, most igneous PR sources are high in P content but very low in reactivity due to little carbonate substitution for phosphate in apatite structure (Chien, 2003), and therefore they are not suitable for direct application. Another example is an organic strawberry farm in Canada once imported a highly reactive PR from North Africa, but the crop was grown on alkaline soils, and therefore the PR was not effective as a P source for the organic crop. Factors affecting the agronomic effectiveness of PR for organic farming should be considered more or less the same way as for conventional farming. One major exception is that in the case where PR is used with composting for organic farming, it is likely that the chelation of organic matter with Ca ions derived from apatite is the main mechanism responsible for PR dissolution (Chien, 1979), rather than soil acidity in the case of conventional farming. Thus the PR reactivity and the effectiveness of composts to chelate with Ca ions are important factors to the success of organic farming using PR as a P source. However, little information on the use of PR for organic farming has been published in the peer-reviewed scientific journals.
4.2. Mixture of phosphate rock and water-soluble P Under certain conditions, such as low PR reactivity, high soil pH, or shortterm crop growth, agronomic use of PR may not be as feasible as WSP (Chien and Menon, 1995a). Mixing PR with WSP can sometimes be an agronomically and economically effective P source under these conditions.
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Partial acidulation of low-reactive PR (PAPR), which consists of unacidulated PR and acidulated WSP, is one way to achieve this goal (Chien and Hammond, 1988). Another way is to mix PR with WSP by dry granulation (compaction) (Menon and Chien, 1996). For example, the agronomic effectiveness of a low-reactive Patos PR (Brazil) compacted with SSP at a 50:50 P ratio was as good as SSP in dry-matter yield of wheat and ryegrass (Prochnow et al., 2004). The explanation is that WSP can provide initially available P to plants (starter effect) that results in better plant root development that in turn may utilize PR more effectively later than if PR is applied alone (Chien and Hammond, 1988). The direct evidence of the starter effect of WSP on increasing PR effectiveness was reported by Chien et al. (1996) using an isotopic dilution technique with radioactive 32P-tagged P sources. In the study by Prochnow et al. (2004), P uptake from Patos PR in the presence of WSP was higher than that from Patos PR alone, indicating the starter effect of water-soluble P on improving the effectiveness of Patos PR (Fig. 10). The results of many studies, particularly those of the IFDC, have provided valuable information on the factors affecting the agronomic effectiveness of mixtures of PR and WSP. These include (1) PR reactivity, (2) the degree of acidulation (IFDC’s recommendation is 50% with H2SO4 or 20% with H3PO4, based on agronomic and economic considerations), (3) the degree of Fe and Al impurities of PR [if they are high, e.g., Capinota PR (Bolivia), the compaction process is preferable because the effectiveness of PAPR is lower than that of compacted PR with water-soluble P, whereas the two types of mixtures would have the same effectiveness if PR has low Fe and Al impurities, e.g., Huila PR (Colombia), shown in Fig. 11 as reported by Menon and Chien (1990).], (4) the effect of soil properties such as pH and P-fixing capacity (more favorable for soils with high P-fixing capacity) (Chien and Hammond, 1989), (5) the starter effect of water-soluble P on PR effectiveness (Chien et al., 1996), (6) the effect of crop species (Chien and Menon, 1995b), and (7) the initial and residual P effect. Figure 12 shows that RAE of PAPR increases with soil P-fixing capacity and it can be even more effective than TSP in soils with very high P-fixing capacities (Chien and Hammond, 1989). More information on the use of mixtures of PR and water-soluble P in terms of production, soil chemistry, and agronomic effectiveness can be found in several reviews (Chien, 2003; Chien and Hammond, 1989; Chien and Menon, 1995a; Hammond et al., 1986; Menon and Chien, 1996).
4.3. Calcined nonapatite phosphate rock for direct application Most PR sources used for the chemical acidulation process or direct application contain Ca–P minerals in the form of apatite. There are limited PR deposits in the world that contain Ca–Fe–Al–P minerals in the form of crandallite. Due to its high Fe and Al content, the nonapatite PR is not
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16 PR alone
PR in compacted (PR + SSP)
P uptake by wheat (mg pot −1)
14 12 10 8 6 4 2 0 0
P uptake by ryegrass (mg pot−1)
B
10
20
30
40
50
40
50
12 PR alone
PR in compacted (PR + SSP)
10 8 6 4 2 0 0
10
20 30 PR-P applied (mg kg−1)
Figure 10 Phosphorus uptake by (A) wheat and (B) ryegrass from PR alone or PR in compacted (PR + SSP) applied at the same PR–P rate. Source: Prochnow et al. (2004).
suitable for the conventional chemical acidulation process. The natural crandallite PR sources are very low in reactivity and therefore, not suitable for direct application. The reactivity, however, can be significantly increased upon calcination at temperatures ranging from 450 to 700 C, after the hydrated water molecule of the crandallite structure is driven off and the structure becomes amorphous (Hoare, 1980). In the past, commercial calcined crandallite PR products were marketed from Christmas Island (trade named ‘‘Calciphos’’ and ‘‘Citrophos’’) and Senegal (trade named ‘‘Phospal’’), but without success due to high energy costs and poor agronomic response for upland crops (Bolland and Gilkes, 1987).
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A 35 Huila PR Fe2O3 + Al2O3 = 2.3%
30 25 20 15
Dry matter yield of mayze (g pot−1)
10 5 TSP
PAPR
Compacted (PR+TSP)
PR
0 0
50
100
150
200
250
TSP
PAPR
300
350
400
B 35 Capinota PR Fe2O3 + Al2O3 = 8.8%
30 25 20 15 10 5 0
0
50
100
150
Compacted (PR+TSP)
200 300 250 P applied (mg kg−1)
350
PR
400
Figure 11 Dry-matter yield of maize obtained with TSP and modified PR products made from (A) Huila PR and (B) Capinota PR. Source: Menon and Chien (1990).
Chien (1998) reported that the calcined Christmas Island PR performed well compared to TSP for flooded rice. The explanation was that flooding could (1) create reduced soil conditions that convert Fe3+ to Fe2+, which could in turn increase Fe–P solubility, and (2) increase soil pH, which could increase the solubility of Fe–Al–P. Later, S. H. Chien (unpublished data) found that a calcined crandallite PR was able to provide both P and Fe
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140 130 120 110 RAE (%)
SSP = 100 100 90 80
P uptake y = 1.611x + 42.10 R2 = 0.974
Dry-matter yield y = 0.745x + 79.38 R2 = 0.903
70 60 50 40 0
10
20
30 40 P-fixing capacity (%)
50
60
Figure 12 Relative agronomic effectiveness (RAE) in dry-matter yield and P uptake of maize obtained with PAPR-50% H2SO4 as related to soil P-fixing capacity. Source: Chien and Hammond (1989).
nutrients to upland rice, whereas TSP failed due to Fe deficiency in an alkaline soil deficient in both P and Fe nutrients. Recently, two studies were reported by Francisco et al. (2008a,b) on the characterization and agronomic evaluation of several calcined crandallite PR sources from Brazil. In the study by Francisco et al. (2008b), the solubility of two calcined PR sources ( Juquia and Sapucaia) in neutral ammonium citrate (NAC) increased and reached a maximum at 500 C upon calcination (Fig. 13). Their RAE values in terms of rice grain yield compared to WSP (RAE ¼ 100%) were 66–72% for flooded rice, whereas the RAE of highly reactive Gafsa PR (Tunisia) was 0%. For upland rice grown in an acidic soil (pH 5.3), the RAE values of these two calcined PR sources were 83–89%, whereas the RAE was 95% for Gafsa PR. The results showed that the calcined PR sources can indeed be used for upland and flooded rice. However, economic analysis is needed to assess the feasibility of using these nonconventional P sources compared to conventional WSP fertilizers or other highly reactive PR sources containing apatite.
4.4. Agronomic effectiveness of nonconventional acidulated phosphate fertilizers The amounts of premium quality PR available to produce conventional acidulated P fertilizers (SSP, TSP, MAP, and DAP) are rapidly decreasing worldwide. As the P industry becomes more dependent on lower quality
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120
NAC solubility (g kg1 P)
100
Juquiá Sapucaia
80
60
40
20
0 300
400
500 600 Temperature (⬚C)
700
800
Figure 13 Effect of thermal treatment on neutral ammonium citrate (NAC) solubility of crandallite minerals from Juquia PR and Sapucaia PR deposits, Brazil. Source: Francisco et al. (2008b).
PR ore, higher levels of P impurity compounds can be expected in the final acidulated P fertilizers (Lehr, 1980; Mullins and Sikora, 1995). These impurities are generally water-insoluble forms of Ca–P or Fe–Al–P, and their composition is determined by the mineralogical constitution of the ore and also by the process of fertilizer production (Gilkes and Lim-Nunez, 1980; Lehr, 1980). There has been some concern regarding the effect these impurity compounds may have on P fertilizer effectiveness. Gilkes and Lim-Nunez (1980), for example, stated that raw materials and manufacturing procedures used to produce superphosphates should be studied to limit the development of impurities, mainly the compounds Ca(Fe,Al)H(HPO4)2F22H2O and (Fe, Al)(K,Na)H8(PO4)66H2O. In fact, preliminary agronomic studies showed that some Fe–Al–P compounds in acidulated P fertilizers, when applied per se, were less effective when compared to the WSP compounds normally found in superphosphates and ammonium phosphates (Bartos et al., 1991; Gilkes and Lim-Nunez, 1980; Gilkes and Mangano, 1983; Mullins et al., 1990; Prochnow et al., 1998; Sikora et al., 1989). Legislation in some parts of the world has established the minimum legal content of WSP in acidulated P fertilizers. As an example, Brazil set the minimum content levels of WSP in acidulated P fertilizers to approximately 90% available P [NAC plus WSP as adopted by AOAC (1999)], which amounts to 7.0%, 16.2%, 16.6%, and 19.2% WSP in SSP, TSP, DAP, and MAP, respectively (Brasil, 1982). Also, for many years Europe adopted a minimum of 93% WSP in the available P fraction for TSP (European Community, 1975).
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Although the impurity compounds do not provide P to plants as WSP compounds when they are mixed with reagent-grade MAP or MCP, under experimental conditions that simulated acidulated P fertilizers, the results suggested that these fertilizers may contain higher proportions of impurity compounds than those expected and normally used in commercial fertilizers (Bartos et al., 1992; Mullins and Sikora, 1995; Mullins et al., 1995; Prochnow et al., 2003a,b, 2008). Bartos et al. (1992) isolated water-insoluble impurity compounds from five MAP fertilizers, representing the primary U.S. sources of PR (Florida, North Carolina, and Idaho). These impurities were used to simulate MAP fertilizers containing 0%, 20%, 40%, 60%, 80%, and 100% of the total P as reagent-grade MAP (i.e., 0–100% WSP). When a Mountview silt loam soil with a pH of 6.7 was evaluated under greenhouse conditions, the MAP fertilizers, at 80 mg kg1 P, did not require more than 75% WSP to obtain 90% of the maximum sorghum–sudangrass forage yield. Mullins and Sikora (1995) conducted a similar study utilizing waterinsoluble impurities isolated from two commercial TSP fertilizers (manufactured from Floridian and Moroccan PR) to determine if soil pH would affect the requirement for WSP to reach maximum yield. TSP fertilizers were simulated by mixing the fertilizer impurities with MCP to supply approximately 0%, 20%, 40%, 60%, 80%, and 100% of the AOAC-available P as MCP (AOAC, 1999). In a greenhouse study using wheat, the simulated TSP fertilizers required as little as 37% WSP to reach maximum yields when applied at a soil pH of 5.4, whereas at soil pH 6.4 the fertilizers required at least 63% WSP for maximum yields. To achieve 90% of maximum yield, the values were only 23% and 38% for pH 5.4 and 6.4, respectively. The authors stated that the pH dependence suggests that some of the P in the water-insoluble P fractions of TSP fertilizers may be some type of Ca–P compound(s), although no chemical characterization was provided on these compounds. Prochnow et al. (2008) conducted another study with four nonconventional acidulated P sources produced from Brazilian PR. The requirement for WSP was once again P source and pH dependent. At a soil pH of 5.2, the fertilizers required 73–95% WSP to reach the maximum dry-matter yield, while they required 60–86% WSP at pH 6.4. To reach 90% of the maximum yield, all superphosphate fertilizers required <50% WSP (Fig. 14; Table 7). It should be noted that the results show that higher levels of waterinsoluble P as compounds of the Fe–Al–P type can be tolerated in acidulated P fertilizers when applied to slightly acid soils than when applied to strongly acid soils, while, as shown by Mullins and Sikora (1995), the opposite was true when the water-insoluble P fraction is composed of forms of Ca–P. It may be noted that the results of several studies consistently show that it is not always necessary to have high water solubility as required by legislation in many countries. Research has provided already valuable information regarding the possible agronomic use of some nonconventional acidulated P fertilizers.
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A
120
Relative yield of wheat (%)
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100
pH = 5.2
pH = 6.4
Model pH = 5.2
Model pH = 6.4
80 60 40 20 0 120
Relative yield of wheat (%)
0
B
100
20 pH = 5.2
40 pH = 6.4
60
80
Model pH = 5.2
100 Model pH = 6.4
80 60 40 20 0 120
Relative yield of wheat (%)
0
C
100
20 pH = 5.2
40 pH = 6.4
60
80
100 Model pH = 6.4
Model pH = 5.2
80 60 40 20 0 120
Relative yield of wheat (%)
0
D
100
20 pH = 5.2
40 pH = 6.4
60
80
Model pH = 5.2
100 Model pH = 6.4
80 60 40 20 0 0
20
40
60
80
100
Percent WSP
Figure 14 Relative dry-matter yield of 101-day-old wheat plants at initial soil pH values of 5.2 and 6.4, as affected by the percentage of water-soluble phosphorus (WSP) for the P sources (A) triple superphosphate (TSP) produced from Tapira phosphate rock (PR), (B) TSP produced from Jacupiranga PR, (C) low-quality single superphosphate (SSP) produced from Araxa PR, and (D) low-quality SSP produced from Patos de Minas PR, applied at a rate of 40 mg available P kg1 soil. Arrows show the WSP percentage needed to reach the plateau. Source: Prochnow et al. (2008).
304
pH
5.2
5.2
6.4
6.4
5.2
6.4
5.2
6.4
5.2
P sourcea
MCP–DMY
MCP–RY
MCP–DMY
MCP–RY
TSP1–RY
TSP1–RY
TSP2–RY
TSP2–RY
SSP1–RY
Y = 0.94 + 0.957X 8.8 103X2 (0.98) Y = 3.27 + 3.337X 30.0 103X2 (0.97) Y = 0.70 + 1.447X 19.3 103X2 (0.97) Y = 2.44 + 5.047X 67.4 103X2 (0.95) Y = 35.44 + 1.249X 7.9 103X2 (0.97) Y = 34.13 + 1.830X 15.3 103X2 (0.96) Y = 47.98 + 0.745X 3.9 103X2 (0.97) Y = 42.97 + 1.161X 6.8 103X2 (0.96) Y = 17.93 + 1.705X 11.4 103X2 (0.97)
Quadratic equation (R2)
Segmented regression model
26.9 93.9 27.8 96.9 84.9 88.8 83.5 92.9 81.6
4.37 1.45 5.05 1.57 2.66 2.14 5.04 3.62
Plateau
1.25
SEd
37.4
37.4
54.3
54.3
Plateau
25.4
25.4
36.8
36.8
90% of plateau
P rate (mg kg1) required to reachb
75
86
95
60
79
Plateau
48
48
49
36
46
90% of plateau
WSP (%) required to reachc
Table 7 Segmented regression models for P sources in each soil pH condition describing the relationship between dry-matter yield of wheat (DMY; Y ¼ g pot1) or relative yield of wheat (RY; Y = %) and the rate of P applied (X = mg kg1) for the MCP and relative yield of wheat (RY; Y ¼ %) and the percentage of water-soluble P (X = %) for the P sources TSP1, TSP2, SSP1, and SSP2
305
5.2
6.4
SSP2–RY
SSP2–RY
Y = 24.42 + 1.897X 13.1 103X2 (0.97) Y = 58.76 + 0.683X 4.7 103X2 (0.96) Y = 60.97 + 0.926X 6.9 103X2 (0.95) 93.2 83.7 92.1
2.21 4.02 5.24
67
73
72
31
31
46
P Source: MCP, reagent-grade monocalcium phosphate; TSP1, triple superphosphate produced from Tapira PR; TSP2, triple superphosphate produced from Jacupiranga PR; SSP1, low-quality single superphosphate produced from Araxa PR; SSP2, low-quality single superphosphate produced from Patos de Minas PR. b Rate of P (mg kg1) needed to obtain the plateau or 90% of the plateau of the segmented model. c Percentage of water-soluble P (WSP) needed to obtain the plateau or 90% of the plateau of the segmented model. d Standard error for comparing predicted values. Source: Prochnow et al. (2008).
a
6.4
SSP1–RY
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As a result, legislation was modified to make the presence of water-insoluble phosphate compounds more flexible. In Europe, the legislation was changed to decrease the requirement for water solubility in TSP. Instead of 93%, the limit was lowered to 85%. Also in Brazil, the SSP water solubility requirement was lowered from 16% to 15%, and new categories of P fertilizers, such as multimagnesium phosphates (MMP) fertilizers, were added to the legislation to legally permit the commercialization of fertilizers containing lower WSP amounts compared to conventional SSP, TSP, MAP, and DAP. MMP fertilizers are P sources containing as low as 50% water solubility in the total amount of P soluble in water and NAC. It is necessary to recognize that a great variety of new, nonconventional acidulated P products may be offered in the future due to differences in PR chemical composition and process of production. An example of differences in chemical composition of SSP sources, and how to evaluate on a percent basis the different compounds in this type of fertilizer, can be found in the publication of Prochnow et al. (2003c). Accessing this type of information for the various fertilizers will be essential. Due to distinct chemical compositions, the fertilizers will have to be tested and approved individually. A better understanding of how the water-insoluble P compounds will form, the final chemical composition of the P fertilizers, and also how these different nonconventional acidulated P fertilizers will react in the soil is essential for P fertilizer producers, legislators and final users to obtain and manage these heterogeneous fertilizers in a cost-effective manner. Only agronomic research will provide the necessary guidance. Better utilization of PR is anticipated as a result of this type of research.
5. New Granular Nitrogen and Phosphorus Fertilizers Containing Sulfur Nutrient Soil S deficiency has become a major problem for crop production in many countries due to the extensive and popular use of high-analysis NP fertilizers, for example, urea, MAP, DAP, and TSP that contain little or no S nutrient. Because elemental S (ES) is almost 100% S, incorporation of ES will not significantly decrease N and P contents of these NP fertilizers compared to the incorporation of SO4–S, such as (NH4)2SO4 or CaSO4. Thus, some fertilizer companies have been marketing products by incorporating ES to NP fertilizers. For example, ES-enriched urea fertilizer was introduced to the market over 25 years ago. The product is manufactured by injecting molten ES into liquid urea and prilling the melt; it contains 36% N and 20% S. Boswell and Friesen (1993) provided a comprehensive review on the use of ES fertilizers, including effects of incorporation of ES into NP fertilizers on crops and pastures. The present chapter, therefore,
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will discuss only the most recent developments in new NP fertilizers containing ES. It is known that ES is not plant available unless it is oxidized to SO4–S by soil microbes, and the rate of S oxidation greatly depends on the particle size of ES (Boswell and Friesen, 1993). For this reason, some fertilizer companies have been developing processes to micronize the particle size of ES being incorporated into granular NP fertilizers. The new idea assumes that once the fertilizer granules dissolve and ES disperses back to the original fine particle size, it may rapidly enhance rate of S oxidation in soils. However, it should be pointed out that there is still the so-called ‘‘locality’’ effect; that is, all the very fine ES particles will still be localized at the applied site when the granule disintegrates. The rate of S oxidation will still be slowed due to limited contact between ‘‘clustered’’ ES particles and soil microbes, unless the ES particles are thoroughly mixed with the soil. Figure 15 shows little S oxidation of granulated ES with bentonite clay (90% ES) during incubation compared to powdered ES in a sandy soil, despite the fact that the granular ES disintegrated and ES particles dispersed into the soil (S. H. Chien, unpublished data). Chien et al. (1987) reported that the grain yield of flooded rice decreased from 96% with powdered ES to 58% with a granular urea–ES melt when surface applied with respect to that of gypsum. When all S sources were incorporated, the effectiveness was 87% with powdered ES and 61% with granular urea–ES melt. Therefore, S-enriched NP granular fertilizers may not provide adequate initial S nutrient for the first crop. However, once the soil is mixed (e.g., plowing) 60
Amount of SO4-S (mg kg−1)
50
40
30
20 No S
Powdered ES
Granulated ES
10
0 0
2
4 6 8 Time of incubation (weeks)
10
12
Figure 15 Amounts of SO4–S obtained with check (no S), powdered ES, and granulated ES with bentonite. Source: S. H. Chien (unpublished data).
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between the first and second crops, ES applied to the first may become available to the second crop due to S oxidation by mixing ES with the soil, as reported by Chien et al. (1988b). In this study, incorporation of powdered ES was as effective as gypsum for flooded rice, whereas deep-placed powdered ES was ineffective due to a lack of S oxidation under reduced soil conditions. The soil was subsequently mixed after the first rice crop and the results showed that both previously deep placed and incorporated powdered ES were equally effective as gypsum for the second flooded rice crop. The negative ‘‘locality’’ effect of granular S-carrier products on S oxidation when incorporated can be seen in Fig. 16 as reported by Friesen (1996). In this study, ES was cogranulated with TSP, DAP, urea, or bentonite. All granules had the same size (1.68–3.36 mm diameter) and contained fine ES (<0.15 mm) homogeneously mixed throughout each product. The results showed that all granular ES-carrier products were much less effective than gypsum in dry-matter yield of maize, despite all the granules of disintegrated ES particles being dispersed in the soil. Furthermore, P carriers (TSP, DAP) were significantly better than a non-P carrier (urea), which was better than an inert carrier (bentonite), presumably attributable to the effect of P and N nutrients on S-oxidizing microorganisms. Surface application of granular S-enriched NP fertilizers may perform better than incorporation because rainfalls may break up the granules and disperse S particles, which may enhance S oxidation. However, this still may
Dry matter yield (g pot−1)
40
30
20
10 Gypsum
ES-TSP
ES-DAP
ES-Urea
ES
0 0
20
40
60
80
100
120
140
Rate of S applied (mg kg−1)
Figure 16 Dry-matter yield of maize obtained with different granular S fertilizers. All the S fertilizers had the same granule size (1.68–3.36 mm diameter). Source: data adapted from Friesen (1996).
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Table 8 Dry-matter yield of maize obtained with different granular S fertilizers during consecutive maize cropping Dry-matter yield of maize (g pot1)a Granular S source
First
Second
Third
Fourth
Fifth
No S ES–bentonite (ES + AS)–MAPb AS
10.6b 10.6b 13.4a 13.8a
1.6c 2.1c 14.2b 17.8a
31.0a 31.5a 33.1a 27.2a
4.1c 5.3c 17.1a 12.8b
5.7b 8.4ab 10.1a 5.2b
a
Means followed by the same letter are not significantly different at p < 0.05. ES, elemental S; AS, ammonium sulfate. S ratio of ES:AS = 50:50. Source: Prochnow et al. (2007). b
not be effective for the initial S effect for deep-rooted cereal crops, for example, maize, sorghum, etc., usually the consequence of inadequate downward movement of SO4–S to plant roots caused by restricted rainfall (Boswell and Friesen, 1993). The exception may be grazed pastures with shallow-rooted crops. Grazing may also affect the rate of S oxidation, probably attributable to the roaming of grazing animals, which may help break up and disperse the S-enriched NP granules to facilitate S oxidation. To overcome the relatively low agronomic effectiveness of granular ES-enriched NP fertilizers due to slow S oxidation, these fertilizers may be required to include some SO4–S for early plant establishment. In other words, SO4–S can provide for early S requirement, while ES can provide available S later after S oxidation to SO4–S in the soil. The concept is similar to that of mixing WSP and PR to enhance the agronomic effectiveness of PR, as discussed previously. In a recent greenhouse study, Prochnow et al. (2007) compared granular AS, granular MAP containing 7.5% S as AS and 7.5% as ES, and ES granulated with bentonite (90% S) as the S source for five consecutive maize crops. Table 8 shows that granular S-enriched MAP was as effective as or better than AS, whereas granular ES was not effective due to slow S oxidation of ES in increasing the dry-matter yield of maize. Field trials of these new granular NP fertilizers containing ES or a mixture of ES and SO4–S are needed to assess factors affecting their agronomic potential to provide plant-available S nutrient.
6. Cadmium Uptake by Crops from Phosphorus Fertilizers Cd is one of the heavy metals that may be potentially toxic to human health. There is an increasing concern over the use of Cd-containing P fertilizers for crop production because Cd uptake by plants can be one
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possible avenue of Cd entry into the human food chain through consumption of plants directly or indirectly by man. Depending on the sources of PR, Cd content associated with apatite minerals can vary widely. If a PR source contains a significant amount of Cd, a significant amount of Cd can be found in SSP or H3PO4 upon acidulation of the PR with H2SO4. If the H3PO4 is used to acidulate the same PR, the resultant TSP would have a high Cd content. Ammoniation of the Cd-containing H3PO4 would also result in Cd-containing DAP and MAP.
6.1. Effect of acidulation levels of phosphate rock on cadmium uptake by crops A study by the IFDC showed that Cd uptake by upland rice was increased by raising the degree of acidulation of Togo PR that contained a significant Cd content (Iretskaya et al., 1998). The results showed that Cd concentration in upland rice grain was 0.051 mg g1 Cd with 50% acidulation with H2SO4, compared to 0.114 mg g1 Cd with 100% acidulation, whereas the two P sources produced the same rice grain yield (Table 9). Cd was associated with Ca and P in the apatite structure based on the data of P and Cd uptake (Iretskaya et al., 1998). The study also showed that a high Cd-containing reactive North Carolina PR was agronomically as effective as fully acidulated SSP produced from the same PR in increasing rice grain yield, but Cd uptake was lower from the directly applied PR (Table 9). The results suggest that if PR and PAPR are as agronomically effective as fully acidulated P sources, the former may also contribute less to Cd uptake by crops than the use of WSP sources.
Table 9 Upland rice grain yield and Cd uptake by grain from various P sources applied at 200 mg kg1 P to Wavely soil (pH 5.6)
a
P sourcea
Cd rate added (mg g1)
Grain yieldb (g pot1)
Uptake of Cd by grainb (mg pot1)
Concentration of Cd in grainb (mg pot1)
NC–PR NC–SSP Togo–PAPR Togo–SSP
70.5 79.0 57.8 69.3
25.3a 24.5a 27.6a 25.6a
1.68b 3.25a 1.44b 2.88a
0.066b 0.135a 0.051b 0.114a
NC = North Carolina; PAPR = PR acidulated at 50% with H2SO4; SSP = 100% acidulation of PR with H2SO4. Means followed by the same letter in each column are not significantly different at p < 0.05. Source: Iretskaya et al. (1998). b
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6.2. Cadmium uptake by crops from granulated versus bulk-blended phosphorus and potassium fertilizers Recently, field experiments conducted in Australia have shown that increased chloride (Cl) content in irrigated waters would have a high risk of producing crops with high Cd concentrations. These researchers have proposed that Cl ions form relatively strong complexes with Cd2+ in the form of CdCl1+ and CdCl02 in solution, and results in enhanced Cd uptake (McLaughlin et al., 1997; Smolders et al., 1998). Based on these observations, it would be expected that if a WSP fertilizer contains a high Cd content, granulation of this WSP fertilizer with KCl may result in a higher Cd uptake by crops compared to the same, but bulked-blended, PK fertilizer. The explanation is that, in the granulated PK fertilizers, KCl- and Cd-containing P fertilizers are in the same granule and thus are in close contact, thereby increasing the possibility of forming CdCl02 and CdCl1+ complexes. Likewise, it would be less likely that the complexes would form when KCl and Cd-containing P granules are physically separated in bulk-blended PK fertilizers. The above hypothesis was tested and confirmed by Chien et al. (2003) in a preliminary greenhouse study. In this study, all P and K sources produced by either granulation or bulk blending had the same granule size (1.68–3.36 mm diameter). Upland rice and soybean were grown to maturity and Brachiaria grass was cut four times in an acidic soil (pH 5.2). The results showed that the agronomic effectiveness in increasing crop yield was the same with Cd-containing SSP and the reagent-grade MCP (0% Cd), whether granulated or bulk blended with KCl (Tables 10–12). Concentrations of Cd in plant tissue samples of all crops were much lower for MCP than for SSP. In all the plant tissue samples, except grain samples of upland rice, Cd concentrations obtained with granulated (SSP + KCl) fertilizers were significantly higher than that with bulk-blended (SSP) + (KCl) fertilizers. Bulk blending of Cd-containing P fertilizers with KCl can thus reduce Cd uptake by crops compared to the same, but granulated, PK Table 10 Grain yield of upland rice and Cd concentrations in rice, grain, and straw Cd concentrationb (mg kg1) PK source
Grain yielda,b (g pot1)
Granulated (SSP + KCl) 22.6a Bulk-blended (SSP) + (KCl) 23.1a Bulk-blended (MCP) + (KCl) 25.1a a
Grain
Straw
0.13a 0.12a 0.01b
1.53a 1.18b 0.20c
Grain yield of check (no P and K = 1.2 g pot1). Means followed by the same letter within the columns are not significantly different at p < 0.05. Source: Chien et al. (2003). b
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Table 11 Grain yield of soybean and Cd concentrations in soybean, grain, straw, and root
PK source
Grain yielda,b (g pot1)
Granulated (SSP + KCl) 27.1a Bulk-blended (SSP) + (KCl) 29.5a Bulk-blended (MCP) + (KCl) 23.8a
Cd concentrationb (mg kg1) Grain
Strawc
Root
0.54a 0.35b 0.06c
1.66a 0.88b 0.26c
1.34a 0.99b 0.27c
a
Grain yield of check (no P and K) = 1.4 g pot1. Means followed by the same letter within the columns are not significantly different at p < 0.05. c Combined leaf, stem, and pod samples. Source: Chien et al. (2003). b
Table 12 Dry-matter yield and Cd concentration of Brachiaria grass
PK source
Granulated (SSP + KCl) Bulk-blended (SSP) + (KCl) Bulk-blended (MCP) + (KCl)
Dry-matter yielda,b (g pot1)
Cd concentrationb (mg kg1)
43.9a 42.8a
0.24a 0.21b
41.5a
0.08c
a
Dry-matter yield of check (no P and K) = 4.1 g pot1. Means followed by the same letter within the columns are not significantly different at p < 0.05. Source: Chien et al. (2003). b
fertilizers. Although PK sources, instead of NPK sources, were used in the study, it is expected that inclusion of N will not affect the results. If this is proven to be true, the process of bulk blending, compared to that of granulation in decreasing Cd uptake, would also apply to NPK compound fertilizers. Due to the simplicity of the bulk-blending process and its relatively low investment and operating cost, bulk blending has become popular worldwide. This is an area that some researchers and fertilizer companies may want to pursue for future production and use of NPK compound fertilizers, and at the same time, minimize Cd uptake by crops.
7. General Conclusions Fertilizers, whether from inorganic or organic sources, will be continuously used to increase and sustain crop production in order to meet the demand of the growing population worldwide in the future. At the same
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time, however, the potential impacts of fertilizer use on environmental quality due to inappropriate application of fertilizers should also be addressed. Research and development shall continue to pursue new alternative innovative technology in terms of fertilizer production and use to achieve these goals, that is, sustaining crop production and minimizing environmental impacts. Another issue that should be pointed out is that the prices of conventional fertilizers have steadily increased. For example, urea increased from US $277/t in early 2007 to US $672/t in early 2008, and DAP increased from US $252/t to US $1230/t (IFDC, 2008). The use of the nonconventional fertilizers discussed in this chapter may result in an increased relative economic benefit with respect to the use of conventional fertilizers in terms of saving fertilizer cost, enhancing nutrient efficiency, or increasing crop yield. More detailed economic analysis is needed to address this issue.
REFERENCES Allen, S. E. (1984). Slow release nitrogen fertilizers. In ‘‘Nitrogen in Crop Production’’ (R. D. Hauck, Ed.), pp. 195–206. American Society of Agronomy, Madison, WI. Amberger, A. (1986). Potential of nitrification in modern N-fertilizer management. Z. Pflanzenern. Bodenk. 149, 469–484. Antisari, L. V., Marzadori, C., Gioacchini, P., Ricci, S., and Gessa, C. (1996). Effects of the urease inhibitor N-(n-butyl) phosphorothioic triamide in low concentrations on ammonia volatilization and evolution of mineral nitrogen. Biol. Fertil. Soils 22, 196–201. AOAC. (1999). “Official Methods of Analysis”, Vol. I, 16th edn., 5th revision. Association of Official Agricultural Chemists, Arlington, VA. Bartos, J. M., Mullins, G. L., Sikora, F. J., and Copeland, J. P. (1991). Availability of phosphorus in the water-insoluble fraction of monoammonium phosphate fertilizers. Soil Sci. Soc. Am. J. 55, 539–543. Bartos, J. M., Mullins, G. L., Williams, J. C., Sikora, F. J., and Copeland, J. P. (1992). Waterinsoluble impurity effects on phosphorus availability in monoammonium phosphate fertilizers. Soil Sci. Soc. Am. J. 56, 972–976. Bayrakly, F. (1990). Ammonia volatilization losses from different fertilizers and effect of several urease inhibitors, CaCl2 and phosphogypsum on losses from urea. Fertil. Res. 23, 147–150. Benini, S., Rypniewski, W. R., Wilson, K. S., Mangani, S., and Ciurli, S. (2004). Molecular details of urease inhibition by boric acid: Insights into the catalytic mechanism. J. Am. Chem. Soc. 126, 3714–3715. Beyrouty, C. A., Sommers, L. E., and Nelson, D. W. (1988). Ammonia volatilization from surface-applied urea as affected by several phosphorothioic triamide compounds. Soil Sci. Soc. Am. J. 52, 1173–1178. Black, C. A. (1968). ‘‘Soil–Plant Relationships’’, pp. 558–653. John Wiley & Sons, New York. Bolland, M. D. A., and Gilkes, R. J. (1987). How effective are Calciphos and Phospal? Fertil. Res. 12, 229–239. Boswell, C. C., and Friesen, D. K. (1993). Elemental sulfur fertilizers and their use on crops and pastures. Fertil. Res. 35, 127–149.
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Index
A Acetamiprid, 180, 185 Ammonification, 176 Animal-based approaches, BMA preclinical disease models, 26 biomarker assessment, 25 Antioxidants, 10 Atmospheric sulfur deposition, 57, 76–77 Azadirachtin, 189 B Barley physical mapping, 206 radiation-hybrid mapping, 210–211 Bioactive food components (BAFC), 9 Biological nitrogen fixation (BNF), 171 Biomarkers, 12–17 glucose metabolism, 15 gut microflora, 16 inflammation, 15–16 oxidative damage, 16 Biomedical agriculture (BMA) chronic disease prevention biomarker-assisted crop improvement strategies, 17 biomarker-assisted screening, 12–13 definition, 10 mechanisms and biomarkers, 13–17 obesity, 11–12 crop improvement program Crops for HealthTM, 30–31 dry beans, health benefits determination, 33–37 plant food–cancer risk conundrum, 31–32 transdisciplinary effort, 32–33 crops evaluation, 24 animal-based approaches, 25–26 crop genotypes evaluation, 28–30 nonanimal approaches, 26–28 food and health terminologies, 7–10 global problems, twenty-first century response chronic disease and pathogenesis, 7 crop improvement, 3 food and nutrition problems, 3 food as substitute for pharmaceutical interventions, 5 plant food-rich dietary patterns, 4
infrastructure, 37–41 discovery and dissemination, 40–41 training, 39–40 transdisciplinary conceptualization, 39 landscape chemical basis for CDP, 18–19 chemical profiling, food crop combinations, 22–23 environmental effects, 24 genetic modification, 23 genotypic diversity, 18 test collection, crop’s genotypes, 19–22 Biopesticides, 188–189 C Cadmium (Cd) acidulation levels, 310 granulated vs. bulk-blended phosphorus and potassium Brachiaria grass, 311 bulk-blending process, 312 Carbendazim, 167, 171 Cell culture models, BMA, 26–27 Cellulase, 185–188 Cereal systems, 77 Chronic disease prevention (CDP) biomarker-assisted crop improvement strategies, 17 biomarker-assisted screening, 12–13 deaths, 10 definition, 10 economic burden, 10–11 obesity, 11–12 Chronic inflammation, 17 Climate change impacts, rice production anthropogenic emission, 93 CO2 concentrations, 94–95 rice-growing environments deltaic regions, 106–110 drought-prone regions, rainfed rice, 103–106 heat stress region, 95–103 rice–wheat system advantages, 108 climate change adaptation, 126 climate change and variability, 113–115 crop diversification, 124–125
323
324
Index
Climate change impacts, rice production (cont.) crop management practices modification, 123 cultivars tolerant, 122–123 pest management, 125–126 productivity growth rate, 110 rainfall problem, 111 resource-conserving technologies (RCTs), 124–125 temperature thresholds, 113 tolerant crop varieties, 127 water management improvement, 123–124 weather forecasts and crop insurance, 126 Community level physiological profiles (CLPPs), 166 Controlled-release fertilizers, 270 Cotton, radiation-hybrid mapping, 211–212 Crop genotype, CDP evaluation, 28 biomarkers and at-risk populations, 29–30 clinical trials and follow-up, 30 structured, fully defined food-based dietary plan, 29 testing analysis, 22 collection, 19 decision on preparation and extraction method, 19–21 Crop rotations, 77 Crops for HealthTM, 30–31 D Dehydrogenase activity (DHA), 180 Dehydrogenases, 177–179 Deletion-based mapping, 207 2,4-Dichlorophenoxyacetic acid (2,4-D), 167, 169–171, 174 Dietary fiber, 9 Dietary supplement, 8 Directory of Open Access Journals, 148 Dry beans, health benefits determination experimental approach, 33–34 overview, 33 results breast cancer, 34–36 dose–response study, 37 E Endotoxemia, 16 Evapotranspiration (ET), 225, 236, 238–239 F Farming systems, soil sulfur accumulation and losses long-term experiments, 78–79 sulfur mass balances, 75–78
Fertilizer production cadmium (Cd) acidulation levels, 310 granulated vs. bulk-blended phosphorus and potassium, 311–312 conventional phosphorus coated water-soluble, 288–289 fluid vs. granular water-soluble, 291–292 urea supergranules, 290–291 nitrogen fertilizers efficiency ammonia volatilization and nitrate leaching/ denitrification, 283–286 ammonium sulfate, 286–288 controlled-release coated urea products, 270–272 greenhouse effect, 269 nitrification inhibitors, 275 slow-release urea-aldehyde polymer products, 272–273 urease inhibitors, 275–283 urea supergranules, 273–274 nonconventional phosphorus agronomic effectiveness, 300–306 calcined nonapatite phosphate rock, 297–300 mixture and water-soluble P, 296–297 phosphate rock (PR), 293–296 sulfur nutrient deficiency, 306 effects, 308 ES particles, 307 oxidation, 309 Flood and drought resistant, 122–123 Functional food, 9 Fungicide fenhexamid (FEX), 170 G Generic Model Organism Database (GMOD), 146 Genetically modified organisms (GMOs), 23 Genetic mapping basic and applied uses, 203 definition, 202 limitations, 203 requirements, 202 Global germplasm identifier (GID), 143 Glucose metabolism, 15 b Glucosidase, 185–188 GMOD. See Generic Model Organism Database I ICT. See Information and Communication Technologies Indo-Gangetic Plains (IGP), 92, 96, 111, 113, 118 Inflammation, 15–16 Information and Communication Technologies (ICT), 126
325
Index
enabling technology free open-source software (FOSS), 139–140 platform and software service models, 140 farmers knowledge commitment, 153–154 content development and management, 149–151 E-choupal, 151–152 innovation systems concept, 154 intermediaries, 148 IT role in research for development, 148–149 market information, 153 Open Academy for Philippine Agriculture (OpAPA), 152 technology transfer, 149 video development, 151 information landscape ethical developments, 138–139 legal developments, 138 social developments, 139 technical developments, 137 scientific information capacity, 145–146 intellectual property, 147–148 quality, 143–145 quantity and complexity, 141–143 relevance, 146 Intergovernmental Panel on Climate Change (IPCC), 93–94, 106 International Center for Research in Semi-Arid Tropics (ICRISAT), 152 International Rice Research Institute (IRRI), 149–150 International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGR), 147 Invertase, 185–188 IPCC. See Intergovernmental Panel on Climate Change ITPGR. See International Treaty on Plant Genetic Resources for Food and Agriculture L Legume genome model, 205 M Maize, radiation-hybrid mapping, 209–210 Medicinal food, 9 Methamidophos, 166, 170, 185, 188 Mineralization biological and biochemical, 63–64 kinetic equations, 65 mineralization–immobilization turnover, 65–66 pesticide impacts arginine deaminase, 175
C and N contents, 174–175 P and K in, 175 and organic matter, 174 sulfatase enzymes, 64 Moisture stress, 115 Multidisciplinarity, 39 N Negative sulfur balances, 78 nifH gene, 170 Nitrification, 176 Nitrogenase, 177–179 Nitrogen fertilizers efficiency ammonium sulfate acidic and neutral soils, 286 sprinkle irrigation, 287 controlled-release coated urea products dissolution rates, 270 lowland rice, 272 swelling polymer membrane, 271 denitrification DCD, 284 nitrate leaching, 283 urea hydrolysis, 285 greenhouse effect, 269 nitrification inhibitors, 275 slow-release urea-aldehyde polymer products, 272–273 urea-aldehyde polymer products NH3 volatilization, 272 UF polymer, 273 urease inhibitors beneficial effect, 278 characteristics, 279 cyclohexylphosphoric triamide (CHPT), 281 effects, 280 N-(n-butyl) thiophosphoric triamide (NBTPT), 277 oxygen analogs, 282 phenyl phosphorodiamidate (PPDA), 276 phosphoroamide family, 283 sulfhydryl groups, 275 urea supergranules (USG), 272 International Fertilizer Development Center (IFDC), 273 potential eutrophication problems, 274 Nonanimal approaches, BMA cell culture methods, 26–27 in vitro assessment, 28 Nonessential nutrients, 8 Nutraceutical, 9 Nutrient and water management effects crop production fertilizer rate, 252 grain filling, 254 future needs
326
Index
Nutrient and water management effects (cont.) fertilizer pollution, 254 mulching tillage, 256 osmotic regulating ability, 255 transpiration velocity, 256 nutrient availability soil water content, 242 water stress, 241 nutrient distribution fructification, 250 by irrigation, 251 water deficit, 250 nutrient efficiency, 249–250 nutrient input evaporation, 235–236 photosynthesis, 234 plant physiological activities, 230–234 root growth, 229–230 soil erosion control, 239–240 soil water storage, 235 water use efficiency, 236–239 transpiration, 235–236 nutrient movement irrigation treatment, 249 nutrient concentration, 248 Nutrient input effects evaporation and transpiration, 235–236 photosynthesis, 234 plant physiological activities mulching, 232 N rate, 230 osmotic pressure regulation, 232 plant leaf water potential (PLWP), 231 water-holding capacity (WHC), 232 root growth subsoiling, 230 summer-fallowing period, 229 soil erosion control, 239–240 soil water storage, 235 water use efficiency evapotranspiration (ET), 238 transpiration coefficient, 239 wheat precipitation use efficiency (WPUE), 238 O Obesity, 11–12 One Laptop Per Child (OLPC) project, 137 Oxidative stress, 16 P Paenimyxin, 188 Particle size separation, 59–60 Pesticide impacts biochemical reactions, soil mineralization, 174–176
nitrogen fixation, 171–174 biopesticides, 168, 188–189 soil enzymes b glucosidase, cellulase and invertase, 185–188 dehydrogenases, 177–179 fumigation, 188 microbial respiratory process, 180 multidimensional behavior, 177 nitrogenase, 177–179 synergistic and antagonistic effects, 188 urease and phosphatase, 180–185 soil microbial diversity acetochlor, 167 algae, 168–169 atrazine and dimethoate, 166 bioavailability, 162 denitrifying activity, 167 fluazifopp-butyl and fomesafen, 162, 166 methamidophos and urea, 166 molecular techniques, 169–170 Nitrosospira, 166 root-colonizing microbes, 167–168 PET. See Potential evapotranspiration Phaseolus vulgaris. See Dry beans, health benefits determination Phosphatase, 180–185 Phosphate rock (PR) agronomic effectiveness, 293 calcined nonapatite chemical acidulation process, 297 crandallite structure, 298 Fe deficiency, 300 eutrophication problem, 294 initial and residual applications, 295 mixture and water-soluble, 296–297 organic and conventional farming, 296 thermal treatment effects, 301 Phosphorous fertilizers conventional coated water-soluble, 288–289 fluid vs. granular water-soluble, 291–292 urea supergranules, 290–291 nonconventional agronomic effectiveness, 300–306 calcined nonapatite phosphate rock, 297–300 phosphate rock (PR), 293–296 PR and water-soluble mixture, 296–297 Photosensitive pesticides, 166 Physical properties, soil, 67–68 Plant leaf water potential (PLWP), 231–232 Plant Ontology Consortium (POC), 142 PLOS. See Public Library Of Science POC. See Plant Ontology Consortium Potential evapotranspiration (PET), 117 Public Library Of Science (PLOS), 138
327
Index R Radiation-hybrid mapping barley, 210–211 cotton, 211–212 maize, 209–210 levels of resolution, 203 mapping genes and genomes, 215–217 need for, 207 nonplant species, 207–209 rice, 204–205 soybean, 206 tomato, 205 wheat, 206–207, 212–215 Resource-conserving technologies (RCTs), 124–125 Rhizosphere-associated nitrogenase activity, 177 Rice Knowledge Bank (RKB), 149–150 Rice, physical mapping, 204–205 Rice–wheat system climate change and variability glacier melt water, 114 premonsoon changes, 113 rainfall changes, 114 solar radiation, 114–115 IGP irrigation, and potential yield, 112 productivity growth rate, 110 subregions types, 111 temperature thresholds, 113 vulnerability CO2 elevated effects, 116–117 crop diversification, 124–125 crop management practices modification, 123 crop metabolism and yields, 115 global warming, 114–115 grain filling, flowering, 115 harnessing, 126 heat and salinity stress, 122–123 heat-prone environments, 116 humidity levels, 118, 121 information and communication technologies (ICT), 126 pest and disease incidences, 125–126 photosynthesis and senescence, 116 potential adaptation strategies, 119–120 potential evapotranspiration (PET), 117 resource-conserving technologies (RCTs), 124–125 thermal stress, 116 water management, 123–124 S Sea water levels, 108–109 Simazine, 161, 166–167, 171 Slow-release fertilizers, 270 Soil amendments
animal manure fertilizer history effect, 72 plant availability, 70–72 sewage sludge mineralization, 73 slurry composition, 70 sulfur content, 69 inorganic sulfur fertilizer, 68 organic material green manure/catch crop, 74–75 mineralization rates, 73 Soil enzymes, pesticide impacts dehydrogenases, 177–179 fumigation, 188 microbial respiratory process, 180 multidimensional behavior, 177 nitrogenase, 177–179 synergistic and antagonistic effects, 188 urease and phosphatase, 180–185 Soil inorganic sulfur pH effect, 66 sulfate adsorption, 66–67 Soil microbial diversity, pesticide impact algae, 168–169 factors, 162–167 molecular techniques fungicide fenhexamid (FEX), 170 nested PCR-DGGE, 169 16S rRNA gene cloning, 169 root-colonizing microbes, 167–168 Soil organic matter dynamics, 60 Soil organic sulfur pools characterization, 59 chemical extraction, 60 fractionation molecular weight, 60 physical separation, 59–60 fraction distribution, 61 physical protection, fractionation, 60–61 reducing agents, separation, 59 XANES spectroscopy, 61 Soil sulfur cycling agriculture efficiency arable production, 80–81 livestock production, 81 mass flow diagram balance, 79 agroecosystems consumption, fertilizer, 57–58 historic atmospheric sulfur deposition, 57 plant processes role, 56–57 sulfur dioxide emissions, 57 amendment animal manure, 69–72 green manure/catch crop, 74 inorganic fertilizer, 68 sewage sludge mineralization, 73 farming systems accumulation and losses long-term experiments, 78–79 mass balances, 75–78
328
Index
Soil sulfur cycling (cont.) sulfur pools conceptual model, diagram, 67–68 inorganic, 66–67 microbial biomass, 61–63 mineralization, 63–66 organic, 59–61 Soil water supply nutrient availability soil water content, 242 water stress, 241 nutrient distribution fructification, 250 by irrigation, 251 water deficit, 250 nutrient efficiency, 249 nutrient movement irrigation treatment, 249 nutrient concentration, 248 Soybean, physical mapping, 206 Sulfate fertilization, 68 Sulfur leaching, 77–78 Sulfur oxidation, 176 T Tomato, physical mapping, 205 Transdisciplinarity, 39 Transpiration, 235–236 U Urease, 180–185 U.S. Center for National Health Statistics, 10 V Vitamin C, 9–10 Vulnerable rice-growing environments adaptation, 95 geographical analysis, 96
Asia’s rice producing regions, 97 cropping calendars, 98 flowering and maturing stages, 96 plant life/production cycle, 99 heat stress regions, 96 rice farming, high-temperature regions crops transplanted yield, 101 rice yield potentials, 99 rice, warmer regions carbon dioxide levels, 101 temperature-induced sterility effect, 102 water availability, 103 W WASP. See Weighted Anomaly Standardized Precipitation Water and nutrient interaction, crop production crop biomass production, 252 fertilizer rate, 252 haying-off effect, 254 Water-holding capacity (WHC), 232, 241–242 Water irrigation, 77 Weighted Anomaly Standardized Precipitation (WASP), 103–105 WHC. See Water-holding capacity Wheat physical mapping, 206–207 radiation-hybrid mapping, 212–215 Whole-genome radiation hybrid (WGRH) mapping, 211 Whole-genome shotgun sequencing (WGS), 215 Wide-cross radiation hybrid mapping, 212 World Health Organization (WHO), 10–11 X X-ray adsorption near-edge structure (XANES), 61
