Advances in Agronomy, Volume 81

Contents CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PREF...

Author: Donald L. Sparks

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Contents CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PREFACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix xi

THE GLOBAL IMPACT OF SOIL EROSION ON PRODUCTIVITY I: ABSOLUTE AND RELATIVE EROSION -INDUCED YIELD LOSSES Christoffel den Biggelaar, Rattan Lal, Keith Wiebe and Vince Breneman I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Data Sources and Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Data Analysis and Interpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Location of, and Methods Employed in, the Erosion– Productivity Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Yield as a Measure of Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Mean Yield Decline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Linear Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Average Bulk Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Relative Yield Decline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Effects on Yield in TSD Experiments . . . . . . . . . . . . . . . . . . . . . . . . . B. Erosional Effects on Yield in Management Practices Studies . . . . . . . . C. Effects of Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 5 5 6 6 11 11 12 12 12 13 14 14 26 32 33 37

THE GLOBAL IMPACT OF SOIL EROSION ON PRODUCTIVITY: II. EFFECTS ON CROP YIELDS AND PRODUCTION OVER TIME Christoffel den Biggelaar, Rattan Lal, Keith Wiebe, Hari Eswaran, Vince Breneman and Paul Reich 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Effect of Erosion on Food Production . . . . . . . . B. Effect of Erosion on Food Security . . . . . . . . . . C. Erosion– productivity Estimates . . . . . . . . . . . . v

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50 51 52 52

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CONTENTS II. Objectives of the Present Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Potential Erosion Rate Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Estimates of Potential Crop Growing Areas . . . . . . . . . . . . . . . . . . . . D. Crop Yield Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Maize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Millet and Sorghum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Potatoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Soybeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Wheat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. Value of Production Losses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Discussion and Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 54 54 55 57 60 64 69 69 73 76 79 82 86 89 92

PLANT GROWTH PROMOTING RHIZOBACTERIA : APPLICATIONS AND PERSPECTIVES IN AGRICULTURE Zahir A. Zahir, Muhammad Arshad and William T. Frankenberger, Jr. I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Mechanisms of Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Biological Nitrogen Fixation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Production of Plant Growth Regulators and Biologically Active Substances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Solubilization and Uptake of Nutrients . . . . . . . . . . . . . . . . . . . . . . . D. Biological Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Screening of PGPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Root/Shoot Growth Under Gnotobiotic Conditions . . . . . . . . . . . . . . B. In Vitro Production of Plant Growth Regulators . . . . . . . . . . . . . . . . C. 1-Aminocyclopropane-1-Carboxylic Acid (ACC) Deaminase Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Application of PGPR in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Effect of PGPR Inoculation on Cereals . . . . . . . . . . . . . . . . . . . . . . . B. Effect of PGPR Inoculation on Other Crops . . . . . . . . . . . . . . . . . . . C. Rhizobia as PGPR in Non-Legumes . . . . . . . . . . . . . . . . . . . . . . . . . D. Co-Inoculation of Legumes with PGPR and Rhizobium . . . . . . . . . . . E. Precursor –Inoculum Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

98 99 100 100 105 107 112 113 113 116 121 124 133 136 137 142 148 149 149

CONTENTS

vii

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS Kanwar L. Sahrawat I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Decomposition of Organic Materials in Submerged Soils . . . . . . . . . . . . A. Factors Affecting Organic Matter Decomposition . . . . . . . . . . . . . . . III. Organic Matter Accumulation in Wetland Soils . . . . . . . . . . . . . . . . . . . A. Factors Influencing Organic Matter Accumulation . . . . . . . . . . . . . . . IV. Mechanisms for Organic Matter Accumulation in Wetlands. . . . . . . . . . . A. Lack of Oxygen or Anaerobiosis . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Deficiency of Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Lack of Terminal Electron Acceptors . . . . . . . . . . . . . . . . . . . . . . . . D. Production of Inhibitors of Microbial Activity. . . . . . . . . . . . . . . . . . E. Formation of Recalcitrant Complexes . . . . . . . . . . . . . . . . . . . . . . . . F. Incomplete Decomposition and Decreased Humification of Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. High Primary Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Modeling Organic Matter in Wetland Soils . . . . . . . . . . . . . . . . . . . . . . . VI. Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

170 172 172 175 175 179 179 180 181 184 184 187 189 192 192 195

POTASSIUM NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM Bijay Singh, Yadvinder Singh, Patricia Imas and Xie Jian-chang I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. The Rice – wheat Cropping Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Potassium Fertilizer Use in the Rice – wheat Cropping Systems . . . . . . . . IV. Potassium Fertility of Soils Under Rice – wheat Cropping Systems . . . . . . A. Mineralogy of Soil Potassium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Forms of Soil Potassium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Potassium Transformations in Soils . . . . . . . . . . . . . . . . . . . . . . . . . D. Assessment of Soil K-supplying Capacity . . . . . . . . . . . . . . . . . . . . . V. Potassium Uptake by Rice – wheat Cropping Systems . . . . . . . . . . . . . . . VI. Response of Rice – wheat Cropping Systems to Applied Potassium. . . . . . A. Time, Source and Method of Potassium Application . . . . . . . . . . . . . B. Interactions of Potassium with Other Nutrients . . . . . . . . . . . . . . . . . C. Effect of Potassium Fertility Status of Soils on Response to Potassium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

204 206 206 207 209 210 211 214 217 220 225 227 231 233 234

viii

VII. VIII. IX. X.

CONTENTS D. Site-specific Potassium Management for Rice and Wheat . . . . . . . . . E. Potassium Use and Resistance to Disease and Pest Incidence . . . . . . . Potassium Balance in Soil– plant Systems. . . . . . . . . . . . . . . . . . . . . . . . Changes in Potassium Fertility in the Soil Under Rice – wheat Cropping Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

236 238 239 243 247 249 251

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

THE GLOBAL IMPACT OF SOIL EROSION ON PRODUCTIVITY I: Absolute and Relative Erosion-induced Yield Losses Christoffel den Biggelaar,1 Rattan Lal,2 Keith Wiebe,3 and Vince Breneman3 1

Department of Interdisciplinary Studies, Appalachian State University, Boone, North Carolina 28608, USA 2 School of Natural Resources, The Ohio State University, Columbus, Ohio 43210, USA 3 USDA Economic Research Service, Washington, District of Columbia 20036, USA I. Introduction II. Data Sources and Analyses A. Data Sources B. Data Analysis and Interpretation C. Location of, and Methods Employed in, the Erosion– Productivity Studies III. Assumptions A. Yield as a Measure of Productivity B. Mean Yield Decline C. Linear Relation D. Average Bulk Density E. Relative Yield Decline IV. Results A. Effects on Yield in TSD Experiments B. Erosional Effects on Yield in Management Practices Studies C. Effects of Inputs V. Discussion and Conclusions References

Published studies relating erosion and productivity have been generally based on information derived from expert opinion on the extent and severity of soil erosion and on limited data on its impact on soil productivity, resulting in widely varying yield and economic loss estimates. In contrast, this report estimates the impact of soil erosion on productivity by collating, synthesizing and comparing the results from published site-specific soil erosion-productivity experiments at a global scale. Using crop yield as a The views expressed here are those of the authors, and may not be attributed to Appalachian State University, The Ohio State University, or the Economic Research Service. 1 Advances in Agronomy, Volume 81 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. DOI 10.1016/S0065-2113(03)81001-5

2

C. DEN BIGGELAAR ET AL. proxy measure for soil productivity, this analysis uses the data from 179 plotlevel studies from 37 countries identified in the soil science literature to calculate absolute and relative yield losses per Mg or cm of soil erosion for various crops, aggregated by continent and soil order. The results show that effects of past erosion on yields differ greatly by crop, continent and soil order. However, aggregated across soils on the continental level, absolute differences in productivity declines Mg21 of soil erosion are fairly small. However, depending on the specific crop and soil, relative erosion-induced yield losses Mg21 or cm21 of soil erosion were two to six times smaller in North America and Europe than in Africa, Asia, Australia and Latin America. The higher losses in the latter continents are due primarily to much lower average yields, so that with identical amounts of erosion, yields decline more rapidly in relative terms. Studies using management practices as their experimental method to determine effects of present erosion showed much greater absolute and relative yield losses, which may be an artefact of the combined effect of erosion and variable management practices. Comparing the results of past and present erosion studies indicates that inappropriate soil management may amplify the effect of erosion on productivity by one or several orders of magnitude. Good soil management for effective erosion control and maintaining productivity, therefore, is imperative to meet the needs of the world’s present and future population. q 2004 Academic Press.

I. INTRODUCTION Soil, a basic resource on which all life depends (Perrens and Trustum, 1984), is degrading in many parts of the world. One of the main processes of soil degradation is accelerated erosion. Erosion is a natural process that has occurred for as long as the earth has been in existence (Larson et al., 1983). Some of the most productive soils in the world (e.g., loess and alluvial soils) are the result of erosional processes. However, human activities have accelerated the naturally occurring rates of erosion (Davis and Browne, 1996). Erosion is both the most visible and the most widespread form of soil degradation. Quantitative, objectively measured data on the dimension and extent of soil erosion are, however, scarce and are still typically lacking in many regions of the world (Erenstein, 1999). Brown (1984) estimated global soil loss to erosion to be 26 billion Mg yr21 (an average of 16 Mg ha21 yr21). Lal and Stewart (1995), Oldeman (1994), and Scherr (1999) estimated that 5 –12 million hectares of land (0.3 –0.8% of the world’s arable area) are rendered unsuitable for agriculture each year due to different forms of soil degradation. In the first attempt to assess the status of soil degradation on a global scale (GLASOD), Oldeman et al. (1991) compiled the opinions of soil experts around the world to create a map of the extent, nature, and severity of human-induced soil degradation. They concluded

GLOBAL IMPACT OF SOIL EROSION I

3

that human-induced soil degradation has affected nearly 2 billion hectares, or 15% of the earth’s total land area since the middle of the twentieth century. Water or wind erosion accounted for about 84% of this area (1094 and 548 million hectares, respectively) (Oldeman, 1994). Fig. 1 provides an overview of the distribution of eroded land areas by continent, and of the extent of soil erosion as a percentage of the total area of degraded soil. Most researchers agree that erosion is a serious problem. There is less agreement with regard to its onsite effect on agricultural production and soil productivity (van Baren and Oldeman, 1998). Productivity can be defined and measured in many ways, such as output per unit of land, labor or other input(s). In the context of soil productivity, it is the productive potential of the soil system that allows the accumulation of solar energy as biomass (Stocking, 1984). Production is the total accumulation of energy, irrespective of how quickly, over what area or with what input it accumulates. Agronomic yield or output per unit area over a given time period, is a measure of production which can be used as an indicator of productivity. However, it is an imperfect indicator as yield is an expression of historical production, whereas productivity is a measure of potential (future) production (Tengberg and Stocking, 1997). Dregne (1995) observed that production (i.e., total biomass) can remain constant or even increase as the soil progressively degrades. Stocking (1994) observed that crop yields may increase even though soil degradation may reduce long-term productivity, causing a loss to future economic returns to production. Oldeman et al. (1991) estimated that “strong” or “extreme” erosion accounted for about 16% of the eroded area (and about 2% of the world’s total land area), but no estimates of impact on productivity were provided. In a separate study, Dregne and Chou (1992) estimated productivity losses due to land degradation on cropland and rangeland in dry areas. Using the range of losses in Dregne and Chou, Crosson (1995) estimated total productivity losses in these areas at about 12%, or approximately 0.3% annually if assumed to occur over a 40-year period (as in Oldeman et al., 1991). Thus, despite millions of dollars invested in erosion research, it is difficult to state precisely what effect the loss of a unit of soil has on crop yield (Lal, 1987a). This is due in part, as Perrens and Trustum (1984) and Erenstein (1999) observed, to the fact that there is no direct, clear-cut relationship between erosion and productivity, making the assessment of the impact of erosion on productivity difficult. Productivity decline may not relate directly to the amount of soil loss (expressed in Mg or cm ha21 yr21), but may be a result of erosion-induced changes in the physical, chemical, and biological qualities of soil that influence production (e.g., water holding capacity, soil organic matter (SOM) and nutrient contents, and bulk density). Moreover, soil is only one of the factors affecting productivity, as crop yield is a function of many variables (Perrens and Trustum, 1984; Lal, 1987a; Rabbinge and van Ittersum, 1994; Erenstein, 1999). Productivity reflects soil erosion if either yield declines with progressive severity of erosion or input use increases to compensate for erosion-caused declines in soil

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Figure 1 Global extent of soil erosion (in Mha and as a percentage of total degraded land) by continent (based on data from Oldeman, 1994).

quality (ERS, 1997). However, soils of poor physical quality (as measured by erosion and erosion-induced changes in texture, water holding capacity, or organic matter, etc.) may produce high yields without large increases in inputs (Vesterby and Krupa, 1993). Soil erosion rates by themselves are, therefore, poor indicators of the loss in productivity (Larson et al., 1983). Much of the debate to date is based on information derived from expert opinion on the extent and severity of soil degradation, and on limited information on its effects on productivity. Few studies have systematically analyzed the data from field experiments relating erosion and productivity. Advances in spatially referenced data and analytical methods permit evaluation of these data more closely, and to draw inferences pertinent to large spatial scales. This report complements a previous review on soil erosion and productivity for North America (den Biggelaar et al., 2001). Its objective is to estimate the impact of soil erosion on productivity by collating, synthesizing and comparing the results from published site-specific soil erosion –productivity experiments on a global scale. The present chapter includes the results from the North American erosion –productivity review to provide a comparative perspective of the impact of erosion in different continents. In accord with most studies reviewed, differential topsoil depth (TSD, in cm) and erosion-induced soil loss (in Mg) are used as independent variables. Crop yield was used as the indicator of soil productivity. This analysis uses the available data to calculate absolute and relative yield losses per megaram (Mg) of soil erosion for various crops,

GLOBAL IMPACT OF SOIL EROSION I

5

aggregated by continent and soil order. This information is then used to make soil-based, continent-level assessments of the impact of soil erosion on crop yields and total production over time. The results of this additional analysis will be presented in a companion chapter (den Biggelaar et al., this volume).

II. DATA SOURCES AND ANALYSES A. DATA SOURCES This review is limited to studies based on field research on soil erosion – productivity that reported quantitative yield results (e.g., bushels per acre, tons per acre, or megagrams (Mg) or kilograms (kg) per hectare (ha)). Studies which reported results only as a percentage decline in yield without specifying those yields were excluded. Also excluded were studies based on simulation models or regression analysis, unless they included data from field studies that were used to develop or test the models. Based on concerns articulated by Boardman (1998) about the “misinterpretation and uncritical use of original field data” in studies using secondary data, and the extrapolation of such data across soils and to all crops, this analysis is based on original studies conducted to determine crop- and soil-specific erosion-induced productivity declines. Information on the area of soil orders by continent was obtained from the Global Soil Regions’ map of NRCS’s World Soil Resources Staff (1997). For the United States, soil series information was translated into the soil subgroup of the US Soil Taxonomy using the USDA-NRCS Soil Survey Division’s Official Soil Series Descriptions on the Internet (Soil Survey Staff, 1999). Soils in other countries are often classified using a different classification system. In some articles and reports, soil classification in either the FAO or US Taxonomy was provided in addition to the local classification. Nomenclature based on the FAO soil classification was converted to the US Taxonomy equivalent using the comparative system provided by Landon (1984). In studies in which only a local classification was provided, the soil order and/or subgroup were derived from the Global Soil Regions’ map (World Soil Resources Staff, 1997) based on the approximate location of the experiments. Latitude and longitude information for the location of the experiments, if not provided in the articles and reports, was obtained from the USGS (2000) Geographic Names database and Natural Resources Canada (1995) Geographic Names of Canada for locations in North America, and from the Getty Thesaurus of Geographic Names (Getty Research Institute, 2000) or the GEOnet Names Server (NIMA, 2000) for experiments elsewhere.

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B. DATA ANALYSIS

AND INTERPRETATION

An Access database was developed to enter the information from the studies identified in the literature. As several studies comprised and reported on experimental results from more than one soil series, a separate record was created for each at the soil subgroup level. The database resulted in a total of 329 separate records, covering 161 soil subgroups from 37 countries (Table I). A total of 38 crops were used in these studies; crop information (i.e., type, yield, and erosion-induced yield loss), together with information on input use (if any), were entered as a nested table within each record. Some studies used differential input levels (such as fertilizers or irrigation) as subplots of the main erosion plots; the various crop-input combinations used in the studies resulted in 572 separate entries nested within the 329 records. The yields reported in the literature were used to calculate absolute and relative mean yield decreases per centimeter or metric ton (Mg) of erosioninduced soil loss. For ease of calculation and comparison of the various studies, we assumed linear yield declines; even though, in most cases, observed yield declines were not linear. For studies using topsoil removal/addition and TSD as experimental methods, actual TSD values were used. To calculate yield impact per centimeter of soil loss in studies using soil phases as the experimental method, we assumed a difference of 7.5 cm between severely and moderately, and moderately and slightly eroded phases, and a difference of 10 cm between slightly eroded and depositional phases (Soil Survey Staff, 1999). Standard conversion factors employed for the US Census of Agriculture (NASS, 1999) were used for weights, measures and yields of various commodities. Yield declines have generally been calculated using uneroded or slightly eroded phases as a reference, which may not be representative of farmers’ conditions that consist of a range of soil depths or phases within one field. We therefore used the mean yields across all experimental plots as the reference yield from which to calculate erosion-induced yield declines. It would be more correct to use the mean yield for the various crops obtained under farmer management for the areas where the experiments were implemented, but such information is not available in the desired format (i.e., disaggregated by country, year, and soil order or subgroup).

C. LOCATION OF, AND METHODS EMPLOYED THE EROSION – PRODUCTIVITY STUDIES

IN,

From a review of the literature, 179 field-based studies on soil erosion and productivity were identified. The studies are not evenly dispersed over the world, however, as shown in Fig. 2 in which the locations of the various experiments are overlaid on a map of soil orders. The majority of studies (59%) were carried out

Table I Soil Orders, Subgroups and Crops Represented in the Studies Reviewed by Continent Soil order

Africa (Benin, Botswana, Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Nigeria, Sierra Leone, Tanzania, Zimbabwe)

Alfisols

Asia (China, India, Indonesia, Pakistan, Philippines, Sri Lanka, Thailand)

Australia (Australia)

Aridisols Entisols Inceptisols Oxisols Ultisols Alfisols Aridisols Inceptisols Oxisols Ultisols Vertisols Alfisols Aridisols Ultisols Vertisols

a

No. of records

No. of soil subgroups

Crops

30

13

2 1 4 7 9 4 2 5

1 1 1 4 5 3 1 5

1 2 2 10 4 1 5

1 2 2 7 2 1 5

Beans, cassava, cotton, cowpeas, forage, maize, millet, pearl millet, peanuts Barley Maize Cotton, maize Cowpeas, maize Cowpeas, maize Cassava, maize, millet, mungbeans Maize, wheat Barley, cabbage, chickpeas, maize, mustard, potatoes, soybeans, wheat Soybeans Maize, tea Soybeans Barley, pasture, wheat Potatoes, wheat Oats, potatoes Wheat (continued)

GLOBAL IMPACT OF SOIL EROSION I

Continent (countries represented)

7

8

Table I (continued) Soil order

No. of records

No. of soil subgroups

Europe (Bulgaria, Germany, Hungary, Russia, Serbia, United Kingdom, Ukraine)

Alfisols Entisols Inceptisols Mollisols

3 1 5 10

3 1 5 6

Latin America (Argentina, Brazil, Colombia, Mexico, Peru, Trinidad, Venezuela)

Alfisols Entisols Inceptisols Mollisols Oxisols Ultisols

2 6 8 2 6 4

2 5 5 2 5 4

North America (Canada, United States)

Alfisols

71

22

Aridisols

2

1

2 4 93

2 4 34

Entisols Inceptisols Mollisols

Crops

a

Potato, rye, triticale, wheat, maize Barley, wheat Barley, maize Barley, millet, mustard, potatoes, rye, sunflower, sw. lupin, sw. sorghum, soybeans, wheat Maize Beans, carrots, cowpeas, maize, potatoes Beans, carrots, cassava, maize, potatoes Maize, soybeans, wheat Beans, maize, soybeans, wheat Cowpeas, crotolaria, maize Beans, barley, hay, maize, oats, soybeans, wheat Alfalfa, barley, beans, maize, potatoes, sugar beets, wheat Maize, soybeans Grapes, potatoes, maize, soybeans Alfalfa, crested wheatgrass, maize, oats, Russian wildrye Sorghum, soybeans, Sudan grass, wheat

C. DEN BIGGELAAR ET AL.

Continent (countries represented)

World (37 countries)

1 2 22

1 2 9

Alfisols Aridisols Entisols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols

120 10 10 26 105 15 2 38 7

48 5 9 21 41 10 2 18 7

Maize Potatoes Cotton, maize, oats, sorghum, soybeans, vetch Crops listed above

a Alfalfa ¼ Medicago spp.; beans ¼ Phaseolus vulgaris; barley ¼ Hordeum vulgare; cabbage ¼ Brassica oleracea spp.; carrot ¼ Daucus carpta; cassava ¼ Manihot esculentum; chickpeas ¼ Cicer arietinum; cotton ¼ Gossypium hirsutum; cowpeas ¼ Vigna ungiculata; crested wheatgrass ¼ Agropyron cristatum; crotolaria ¼ Crotolaria juncea; grapes ¼ Vitis vinifera; maize ¼ Zea mays; millet ¼ Panicum miliaceum; mungbeans ¼ Phaseolus aureus; mustard ¼ Brassica spp.; oats ¼ Avena sativa; peanuts ¼ Arachis hypogea; pearl millet ¼ Pennisetum glaucum; potato ¼ Solanum tuberosum; Russian wildrye ¼ Psathyrostachys juncea; rye ¼ Secale cereale; soybeans ¼ Glycine max; sugar beet ¼ Beta vulgaris; Sudan sorghum ¼ Sorghum saccharatum; tea ¼ Camellia sinensis; triticale ¼ Triticosecale spp.; vetch ¼ Vicia sativa; sorghum ¼ Sorghum bicolor; wheat ¼ Triticum aestivum.

GLOBAL IMPACT OF SOIL EROSION I

Oxisols Spodosols Ultisols

9

10 C. DEN BIGGELAAR ET AL.

Figure 2

Location of the erosion– productivity studies in relation to soil orders. Size of dots is proportional to the number of studies at each site.

GLOBAL IMPACT OF SOIL EROSION I

11

Table 2a Number of Records in the Database by Experimental Methods Used in the Studies Experimental method

No. (%) of records

Erosion phases Topsoil removal and addition Topsoil depth Management practices Depth of fragipan Soil survey Total

116 (35%) 98 (30%) 61 (18%) 38 (12%) 14 (4%) 2 (1%) 329 (100%)

in North America (i.e., the United States and Canada), with a secondary concentration of studies in Australia and Brazil. Mollisols and Alfisols are the most frequently studied soils, followed by Ultisols and Inceptisols (Fig. 2 and Table II). In the United States and Canada, erosion – productivity studies have been carried out primarily on Alfisols and Mollisols, reflecting the importance of these soils for crop production in North America (den Biggelaar et al., 2001). The investigation of yield differences on differentially eroded soil phases was the most commonly used method to determine the effect of erosion. This method was used in 35% of the cases, followed by topsoil removal and addition (29%) and TSD (18%). Depth to fragipan was used in 4% of the studies, whereas soil surveys were used in two studies. Variable management practices (notably tillage, terracing, and contour planting) were used in 37 cases. Experiments using erosion phases, topsoil removal and addition, TSD and depth to fragipan measure the effect of past erosion on crop yields, whereas experiments using variable management practices provide an indication of the effects of present erosion rates on crop yields. However, observed yield differences may not only be due to differential rates of erosion associated with different management practices, but also an artifact of the management systems. The results of studies using this method are, therefore, not comparable with those assessing the effects of past erosion. The results of studies investigating the effect on crop yields of past and present erosion will, therefore, be presented separately in this chapter for comparative purposes.

III. ASSUMPTIONS A. YIELD

AS A

MEASURE

OF

PRODUCTIVITY

As erosion reduces a soil’s capacity to produce biomass, productivity is usually expressed in terms of crop yield or output per unit area over a given

12

C. DEN BIGGELAAR ET AL.

time period (NSE-SPRPC, 1981). Yield data are the way that farmers, policy makers, and the public typically consider agricultural production, and are also a basic measure of productivity in agricultural experiments (Tomlin and Umphrey, 1996). Crop yields are, therefore, used as the measure of productivity in this review.

B. MEAN YIELD DECLINE The results of the studies must be compared cautiously, as they cover a variety of crops, soils, time periods, management practices, and experimental methods (Boardman, 1998). The effects of erosion may also vary from year to year due to fluctuations in climate and other non-controlled variables, so that long-term degradative effects are not easily apparent. We controlled some of the differences by calculating mean erosion-induced yield declines for each crop and soil order across methods, management practices and time periods.

C. LINEAR RELATION Results from field studies and simulation models show that there is a large variation in the way erosion affects soil quality and productivity (Maetzold and Alt, 1986). Some soils experience consistent productivity reductions with progressive soil degradation, while others suffer no loss until some critical point in one (or more) yield-determining factor(s) is reached, at which point significant yield losses begin (Biot and Xi, 1993; Sanders et al., 1995; Hoag, 1998). However, a linear relationship between erosion and productivity was assumed, implying that the loss in productivity remains the same for each Mg or cm of soil erosion over the entire range of soil depths considered in the experiment.

D. AVERAGE BULK DENSITY Nearly all studies investigating the effect of past erosion measured yield losses over different TSDs. Using data in the studies reviewed, the mean yield loss per cm of soil erosion was calculated. In studies using management practices, on the other hand, yield differences between treatments were expressed per Mg of soil erosion. To compare the effects of past and present erosion, the yield losses per cm of soil erosion were converted into yield losses per Mg of soil erosion. For this conversion, an average bulk density of 1.5 Mg m23 was assumed for all soils on all continents. Using this bulk density

GLOBAL IMPACT OF SOIL EROSION I

13

value, 1 cm of soil weighs 150 Mg ha21. Therefore, yield loss per cm of soil was divided by 150 to determine the yield loss per Mg of soil erosion. Based on the previous assumption, it was assumed that the rate of yield loss is uniform over the entire 1 cm depth of soil.

E. RELATIVE YIELD DECLINE To determine the relationship between erosion and productivity, most studies compare yield declines with yields on uneroded or slightly eroded phases, and calculate a relative decrease in yield using the uneroded yield as the denominator. However, erosion is not normally uniform across an experimental plot (except when topsoil is artificially removed to a uniform depth), field or landscape. Therefore, in an effort to more closely approximate conditions faced by farmers, and to better reflect natural circumstances in a field or landscape that includes a range of TSD and erosion-induced soil loss, relative yield decline was calculated using the mean yields across all experimental plots for each crop and soil as the denominator. As the mean experimental yield is usually lower than the yield on the uneroded or slightly eroded plots, this method will lead to an overestimation of relative yield declines as is shown in the hypothetical example in the box below. For ease of analysis and comparison of the data, soil loss due to erosion was assumed to be uniform across each experimental plot.

Example of Yield Loss Calculations TSD removal (cm) 0 10 20 30

Yield (Mg hs21) 10 8 6.5 6

Yield loss (Mg ha21 cm21)

(10 2 8)/10 ¼ 0.20 (10 2 6.5)/20 ¼ 0.18 (10 2 6)/30 ¼ 0.13

Mean 7.6 0.17 Yield loss per Mg soil erosion 0.17/150 ¼ 0.0011 Mg ha21 Mg21 at bd ¼ 1.5 Mg m23: Relative yield loss, using mean experimental yield as the reference yield: per cm soil erosion 0.17/7.6 *100% ¼ 2.2% cm21 per Mg soil erosion 0.0011/7.6 *100% ¼ 0.014% Mg21 Relative yield loss, using the uneroded yield as the reference yield: Per cm Soil erosion 0.17/10*100% ¼ 1.7% cm21 Per Mg Soil erosion 0.0011/10*100% ¼ 0.011% Mg21

14

C. DEN BIGGELAAR ET AL.

IV. RESULTS A. EFFECTS

ON

YIELD

IN

TSD EXPERIMENTS

Erosion – productivity studies have been undertaken worldwide on a variety of crops. Although there are some studies on pasture and fodder crops (United States, Australia, Botswana), vegetables (mustard in India and Hungary, cabbage in Indonesia), and tea (Sri Lanka), the majority of studies involved grain crops, pulses and root crops. This review focuses on these three groups of crops only. 1. Grain Crops Studies on the effect of erosion on grain crops primarily used maize (Zea mais) and wheat (Triticum aestivum) as the experimental crop (Table III). Maize was the predominant crop used in North America and Africa, whereas wheat was predominant in North America (United States and Canada) and Australia. A smaller number of cases involved sorghum (Sorghum bicolor) (US), millet (Panicum miliaceum and Pennisetum glaucum) (Niger, Burkina Faso, Russia and India), rye (Secale cereale) (Bulgaria), oats (Avena sativa) (Australia, US), and barley (Hordeum vulgare) (Australia, US, UK, Hungary, Ukraine, Egypt). a.

Maize

Mean experimental maize yields are the highest in North America (6.2 Mg ha21), and yield losses due to erosion are the lowest (Table III). Maize yields in North America decline 0.092 Mg ha21 cm21. Not only are mean yields in Latin America (i.e., Central and South America and the Caribbean), Africa and Asia less than half of those in North America (2.9, 2.6, and 1.7 Mg ha21, respectively), but erosion-induced yield losses are also higher (0.215, 0.128, and 0.111 Mg ha21 cm21, respectively). Nevertheless, when yield losses per cm of soil erosion are converted to yield losses per Mg of soil erosion (assuming an average bulk density for all soils on all continents of 1.5 Mg m23), the results are similar: maize losses of about 1 kg ha21 Mg21 of soil erosion, ranging from 0.62 kg ha21 Mg21 in North America to 1.44 kg ha21 Mg21 in Latin America. On a relative basis, maize yields in North America decline by 0.01% for each Mg of soil erosion. Compared to North America, even though still small, relative yield declines per Mg of soil erosion are three times higher in Africa (0.03%), four times higher in Asia (0.04%), and fives times higher in Central and South America (0.05%). There are differences in relative yield losses of maize grown on soils of different soil orders. In North America, studies on Entisols showed that yields are

Table III Impact of Past Erosion on the Yield of Grain Crops, by Crop and Continent Erosion-induced yield loss

Continent

Maize

Africa

Asia North America

Mg ha21 cm21 soil erosion, mean (range)

Kg ha21 Mg21 soil erosiona

% Mg21 soil erosion

41

2

2.6

0.128 (0.003 –0.715)

0.86

0.03%

4

2

1.7

0.111 (0.097 –0.143)

0.74

0.04%

131

4

6.2

0.092 ((2 0.080) –1.030)

0.62

0.01%

Sources

15

Abate, 1994; Aune et al., 1998; Azontonde 1993; Boli Baboule and Roose 1998; Boli et al. 1993; Gachene 1995; Hudson and Jackson 1959; Hulugalle 1986; Kilasara et al., 1995; Lal, unpublished data, 1981, 1985, 1987b; Mbagwu, 1981; Mbagwu et al., 1984; Oyedele and Aina, 1998; Seather et al., 1997; Sessay, 1991; Tegene, 1992; Tenge et al., 1998; Vaje et al., 1998 Rimwanich and Na-Thalang, 1978; Shafiq et al., 1988; Sur et al., 1998 Adams, 1949; Alberts and Spomer, 1987; Barre, 1939; Blevins et al., 1987; Carlson et al., 1961; Chengere and Lal, 1995; Fahnestock et al., 1995; Frye et al., 1982, 1983; Gantzer and McCarthy, 1985; Gilliam et al., 1987; Gollany et al., 1992; Hajek and Collins, 1987; (continued)

GLOBAL IMPACT OF SOIL EROSION I

Crop

Mean of mean Mean duration of experimental yield No. of experiments (Mg ha21) (yr) records

16

Table III (continued) Erosion-induced yield loss

Crop

Continent

15

2

2.9

Mg ha21 cm21 soil erosion, mean (range)

0.215 (0.007 –1.640)

Kg ha21 Mg21 soil erosiona

1.44

% Mg21 soil erosion

0.05%

Sources Henning and Kalaf, 1985; Langdale et al., 1979; Lindstrom et al., 1986, 1987; McDaniel and Hajek, 1985; Miller, 1985; Mokma and Sietz, 1992; Murray et al., 1939; Musgrave, unpublished data; Odell, 1950; Olson and Nizeyimana, 1988; Olson and Carmer, 1990; Olson et al., 1999; Olson, 1977; Schertz et al., 1985; Schertz et al., 1989; Schumacher et al., 1994; Shaffer et al., 1994; Smith, 1946; Stallings, 1957; Stone et al., 1985; Swan et al., 1987; Thompson et al., 1991, 1992; Tyler et al. 1989; Xu et al. 1997; Weesies et al., 1994; Wright et al., 1990; Yost et al., 1985 Albuquerque et al., 1996; Coelho Silva et al, 1985; Flores and Fernandez, 1995; Hernani et al., 1997; Nieto et al., 1998; da Silva et al., 1999;

C. DEN BIGGELAAR ET AL.

Latin Americab

Mean of mean Mean duration of experimental yield No. of experiments (Mg ha21) (yr) records

Wheat

Asia Australia

North America

2

3

0.101 (0.097 – 0.143)

0.69

0.02%

16

4

1.2

0.081 ((2 0.003) –0.355)

0.54

0.04%

8

4

3.5

0.026 ((2 0.058) –0.097)

0.17

0.00%

64

5

2.6

0.051 ((2 0.033) –0.225)

0.34

0.01%

GLOBAL IMPACT OF SOIL EROSION I

Europe

4

17

da Silva and Silva, 1997; Sparovek et al., 1991; Tengberg et al., 1997a, b; da Veiga et al., 1998 Agnihotri et al., 1994; Bhatti et al., 1998; Shafiq et al., 1988 Barr, 1957; Davies et al., 1988; Elliott et al., 1988; Hamilton, 1970; Hore and Sims, 1954; Littleboy et al., 1992 Burnham and Mutter, 1993; Duck, 1974; Evans and Nortcliff, 1981; Krisztian et al., 1987; Krumov and Tzvetkova, 1998; Tikhonov, 1960; Vernander et al., 1964 Barre, 1939; Bramble-Brodahl et al., 1985; Carter, et al., 1985; Dormaar and Lindwall, 1984; Dormaar et al., 1986, 1988; Frymire, 1980; Greb and Smika, 1985; Horner, 1960; Ives and Shaykewich, 1987; Izaurralde et al., 1998; Larney and Janzen, 1997; Larney et al., 1991, 1995a, b, 1998; Lowery et al., 1990; Massee, 1990; Massee and Waggoner, 1985; Monreal et al., 1995; Power et al., 1981; Rasmussen and Rohde, 1991; Tanaka, 1995; Tanaka and Aase, 1989; Thompson et al., 1991a; Verity and Anderson, 1990; Wetter, 1977 (continued)

18

Table III (continued) Erosion-induced yield loss

Continent

Barley

Latin Amerciab Asia Australia Europe

Millet

Oats

North America Africa Asia Europe Australia North America

Sorghum North America

Mg ha21 cm21 soil erosion, mean (range)

b

% Mg21 soil erosion

Sources

1

3

2.1

0.062 (n.a)

0.41

0.02%

Tengberg et al., 1998

1 2 11

1 3 3

1.8 2.8 2.5

0.142 (n.a.) 0.040 (0.036 – 0.044) 0.052 ((2 0.023) –0.174)

0.95 0.27 0.35

0.05% 0.01% 0.01%

2

3

3.5

0.057 (0.057 – 0.058)

0.38

0.01%

Agnihotri et al., 1994 Fawcett et al., 1990 Biot and Lu, 1993; Duck, 1974; Dzhadan et al., 1975; Evans, and Nortcliff, 1978; Krisztian et al., 1987; Lu and Biot, 1994; Tikhonov, 1960; Xu and Biot, 1994 Carter et al., 1985; Stallings, 1957

1 2 2 1 5

3 2 4 1 3

0.4 0.3 0.3 0.4 2

0.187 0.015 0.011 0.036 0.045

1.25 0.1 0.08 0.24 0.3

0.29% 0.03% 0.02% 0.06% 0.01%

17

3

4.2

0.014 ((2 0.139) –0.108)

0.09

0.00%

(n.a.) (0.012 – 0.017) (0.005 – 0.018) (n.a.) (0.017 – 0.080)

Assuming a bulk density of 1.5 Mg m23. Latin America includes South America, Central America and the Caribbean.

a

Kg ha21 Mg21 soil erosiona

Buerkert and Lamers, 1999 Vittal et al., 1991 Tikhonov, 1960 McFarlane et al., 1991 Adams, 1949; Barre, 1939; Lamb et al., 1944; Murray et al., 1939; Stallings, 1957 Eck et al., 1965; Eck, 1968, 1987; Langdale et al., 1987

C. DEN BIGGELAAR ET AL.

Crop

Mean of mean Mean duration of experimental yield No. of experiments (Mg ha21) (yr) records

GLOBAL IMPACT OF SOIL EROSION I

19

not affected by erosion (i.e., they decline by less than 0.005% Mg21 of soil erosion). Yields declined by 0.02% Mg21 of soil erosion on Oxisols and Ultisols, and by 0.01% Mg21 on Alfisols and Mollisols. Observed yield declines in Asia are slightly greater on Inceptisols (0.05% Mg21) than on Aridisols (0.04% Mg21). In Africa, relative yield losses are low on Inceptisols and Oxisols (0.01% Mg21), but four to five times greater on Alfisols and Ultisols (0.04 and 0.05% Mg21, respectively). The highest losses in maize yield in the studies reviewed occur on Inceptisols in Central and South America (0.33% Mg21); they are also fairly high for maize grown on Mollisols (0.07% Mg21). Relative yield declines for maize grown on Alfisols and Entisols in this continent, however, are low at 0.01% Mg21 soil erosion.

b.

Wheat

Studies with wheat have been conducted in five continents, with most studies (71%) done in North America (Table III). Mean experimental wheat yields are highest in Europe (3.5 Mg ha21). Yield losses as a result of erosion on this continent are very small (average of 0.026 Mg ha21 cm21); relative yield losses per Mg of soil erosion are negligible for all soils on which wheat yieldproductivity studies have been undertaken (i.e., Alfisols, Entisols, and Mollisols). Mean wheat yield is lowest in Australia (1.2 Mg ha21), declining 81 kg ha21 cm21 or 0.04% Mg21 of soil erosion. In Australia, yield losses are highest for wheat on Alfisols (0.05% Mg21), slightly less on Vertisols (0.04% Mg21), and lowest on Aridisols (0.02% Mg21). In North America, wheat yield averages 2.6 Mg ha21, and declines 51 kg ha21 cm21 or 0.01% Mg21 of soil erosion. Relative yield declines are greater for wheat on Alfisols (0.02% Mg21) than on Entisols and Mollisols (0.01% Mg21). Mean wheat yield in Asia (3.0 Mg ha21) is slightly higher than that in North America, but erosion-induced yield decline is also higher (101 kg ha21 cm21). On a relative basis, wheat yield decreases by 0.02% Mg21 of soil erosion. In Asia, the yield decline of wheat on Aridisols is 50% greater (0.03% Mg21) than on Inceptisols (0.02% Mg21). Only one study was done with wheat in Latin America; the experiment was carried out on a Mollisol in Argentina. Average yield (2.1 Mg ha21) and yield decline (0.062 Mg ha21 cm21) were lower than in Asia, but relative yield decline per Mg of soil erosion was the same (0.02% Mg21).

c.

Barley

Studies on barley have been carried out in Asia (one study in India), Australia (one study), North America (2 studies in the USA), and Europe

20

C. DEN BIGGELAAR ET AL.

(Hungary, Ukraine and the UK). Average barley yields are the highest in North America (3.5 Mg ha21) and lowest in Asia (1.8 Mg ha21), but erosion-induced yield declines are reversed: 142 kg ha21 m21 in Asia and 57 kg ha21 cm21 in North America (Table III). For soil erosion per Mg, the relative yield loss is five times greater in Asia (0.05% Mg21) than in North America (0.01% Mg21). The relative yield loss of barley on Alfisols in North America is twice that on Aridisols (0.02% vs. 0.01% Mg21 of soil erosion). The average relative yield loss in Australia for barley grown on an Aridisol is equivalent to the relative yield loss in North America, although mean yield and yield decline per cm of soil erosion are lower (2.8 Mg ha21 and 40 kg ha21 cm21, respectively). Mean barley yields in Europe are slightly lower than in Australia (2.45 Mg ha21), with yield declines resulting from erosion slightly less than those in North America (5.2 kg ha21 cm21); average yield declines per Mg of soil erosion are negligible (none for barley on Entisols, and 0.01% Mg21 on Inceptisols and Mollisols).

d.

Oats

Oats were used as the experimental crop in four studies, one in Australia on an Ultisol, and three in the United States, one each on an Alfisol, a Mollisol, and an Ultisol (Table III). Mean oats yield in the United States studies was 2.0 Mg ha21, declining at a rate of 45 kg ha21 cm21 or 0.01% Mg21 of soil erosion. Yield decline was highest in oats on the Alfisol (0.03% Mg21) and lowest in oats on the Mollisol (0.01% Mg21); yield decline on the Ultisol was intermediate at 0.02% Mg21 of soil loss. Mean experimental oats yield was low in the Australian study (0.4 Mg ha21); yields in this study declined at the rate of 36 kg ha21 cm21 with a relative loss of 0.06% Mg21.

e.

Millet and Sorghum

Erosional effects on millet yield were investigated on Alfisols in India and Mollisols in Russia. Studies on sorghum were conducted only in the United States. While average millet yields were similar in India and Russia (0.3 Mg ha21, respectively), the decline in yield due to erosion was slightly higher in India than in Russia (15 and 11 kg ha21 cm21 of soil erosion, respectively) (Table III). The relative yield decline was 0.03 and 0.02% Mg21 of soil erosion in India and Russia, respectively. Mean sorghum yield in the experiments in the United States was 4.2 Mg ha21, with (on average) no decline in relative yield. Relative yields declined by 0.01% Mg21 soil erosion on Mollisols, but increased by a similar amount in experiments on Ultisols despite the progressive increase in erosion.

GLOBAL IMPACT OF SOIL EROSION I

21

2. Leguminous Crops With the exception of studies on soybeans in the United States, there have been few erosion – productivity studies on pulses. Besides the United States, soybeans have been used as the experimental crop in Brazil, Hungary, India, and Indonesia, cowpeas in Nigeria, Tanzania, Trinidad, and Brazil, and dry beans in Ethiopia, Brazil, Venezuela, and the United States (Table IV).

a.

Soybeans

Average soybean yields are the same in the Americas (2.1 Mg ha21), but yield declines per cm of soil erosion in Latin America are twice those in North America (0.092 vs. 0.041 Mg ha21 cm21, respectively) and relative yield declines are three times as high (0.01% vs. 0.03% Mg21, respectively) (Table IV). In South America, yield declines were higher on Oxisols (0.03% Mg21) than on Mollisols (0.02% Mg21). In the North American soybean studies, yield declines were highest in studies on Ultisols and Entisols (0.03% Mg21), intermediate on Inceptisols (0.02% Mg21), and lowest on Alfisols and Mollisols (0.01% Mg21 soil erosion). Mean experimental soybean yield is much lower in Asia (0.9 Mg ha21); as a result of erosion, average absolute and relative yields actually increase by 73 kg ha21 cm21 or 0.01% Mg21 of soil erosion. Soybean yields in Asia were not affected by erosion of Inceptisols, decreased by 0.04% Mg21 in experiments on Oxisols, and increased 0.02% Mg21 on Vertisols. The sole study on soybeans in Europe was conducted in Hungary on a Mollisol. Yield and yield decline per cm of soil erosion were low (0.6 Mg ha21 and 20 kg ha21 cm21, respectively). The relative yield declined by 0.02% Mg21 of soil erosion.

b.

Cowpeas

Cowpeas were used as the experimental crop primarily in Africa (Nigeria, Tanzania, and Sierra Leone), and in one study in Brazil (Table IV). The African studies were conducted on Alfisols, Oxisols, and Ultisols, whereas the study in Brazil was done on an Entisol. The mean yield in the studies in Africa was 0.8 Mg ha21. As a result of erosion, yields declined on average at the rate of 44 kg ha21 cm21, or 0.03% Mg21. Yield decline was 25% greater for studies on Ultisols (0.04% Mg21) than on Alfisols and Oxisols (0.03% Mg21). In Brazil, the mean yield (0.3 Mg ha21) and erosion-induced yield decline were lower than in the studies in Africa; absolute yield loss was 6 kg ha21 cm21 or 0.01% Mg21 of soil erosion.

22

Table IV Impact of Past Erosion on the Yield of Leguminous Crops, by Crop and Continent Erosion-induced yield loss

Crop

Continent

Europe North America

Mg ha21 cm21 soil erosion mean (range)

kg ha21 Mg21 soil erosiona

% Mg21 soil erosion

4

1

0.9

20.073 ((20.344)–0.027)

20.49

20.05%

1 43

10 4

0.6 2.1

0.020 (n.a.) 0.041 ((20.001)–0.113)

0.13 0.27

0.02% 0.01%

Sources Shivaramu et al., 1998; Singh et al.,1999; Sudirman et al., 1986; Tiwari and Jain, 1995 Krisztian et al., 1987 Bruce et al., 1995; Ebeid et al., 1995; Fahnestock et al., 1995; Gilliam et al., 1987; Hairston et al., 1989; Hajek and Collins, 1987; Henning and Khalaf, 1985; McDaniel and Hajek, 1985; Pettry et al., 1985; Rhoton, 1990; Salchow and Lal, 1999; Schertz et al., 1985; Schertz et al., 1989; Thompson et al., 1991a, b; Tyler et al., 1987; Weesies et al., 1994; Wetter, 1977; White et al., 1985; Yang et al., 1996

C. DEN BIGGELAAR ET AL.

Soybeans Asia

Mean of mean Mean duration of experimental yield No. of experiments (Mg ha21) (yr) records

Beans

4

4

2.1

0.092 (0.048– 0.134)

0.61

0.03%

Africa North America

3 2

3 2

0.4 1.4

0.009 (0.003– 0.019) 0.035 (0.030– 0.040)

0.06 0.23

0.02% 0.02%

Latin Americab

5

3

1.1

0.055 (0.033– 0.076)

0.37

0.03%

21

1

0.8

0.044 (0.001– 0.124)

0.29

0.03%

1

1

0.3

0.006 (n.a)

0.04

0.01%

Africa

Latin Amerciac a b

Assuming a bulk density of 1.5 Mg m23. Latin America includes South America, Central America and the Caribbean.

Tengberg et al., 1998; da Veiga et al., 1998 Tegene, 1992 Carter et al., 1985; Lamb et al., 1944 Delgado and Lopez, 1998; da Veiga et al., 1998 Aune et al., 1998; Kilasara et al., 1995; Lal, 1981; Mbagwu, 1981; Mbagwu et al., 1984; Sessay, 1991 da Silva and Silva, 1997

GLOBAL IMPACT OF SOIL EROSION I

Cowpeas

Latin Americab

23

24

C. DEN BIGGELAAR ET AL.

c.

Beans

Experiments using beans were conducted on Alfisols in Ethiopia, Alfisols, and Aridisols in the United States, and Inceptisols and Oxisols in Venezuela and Brazil. Mean experimental yields were lowest in Ethiopia (0.4 Mg ha21) and highest in the United States (1.4 Mg ha21) (Table IV). Yield was intermediate in the Brazilian and Venezuelan experiments (1.1 Mg ha21), but the average yield declines due to erosion were highest in both absolute (55 kg ha21 cm21) and relative terms (0.03% Mg21 soil erosion). Relative yield declines in Ethiopia and the United States were identical (0.02% Mg21), although losses per cm of soil erosion were lower in Ethiopia (9 kg ha21 cm21) than in the United States (35 kg ha21 cm21). The yield loss in studies on Alfisols in the United States was three times higher (0.03%) than the yield loss on Aridisols (0.01%). The yield decline on the Oxisol in Brazil was double the loss on Inceptisol in Venezuela (0.04% vs. 0.02%, respectively). 3. Root Crops a.

Potatoes

Very few studies on the impact of erosion on productivity have been done with root crops. Experiments using potatoes were done in Australia on Aridisols and Ultisols, in the United States on an Aridisol, a Spodosol, and an Inceptisol, in Venezuela on an Entisol, and in Bulgaria and Russia on an Alfisol and a Mollisol, respectively (Table V). Mean yields and yield losses due to erosion were lowest in Europe (11.4 and 84 kg ha21 cm21, respectively). Relative yield loss was less than 0.005% Mg21 soil erosion. Average yield in Australia was 54.1 Mg ha21, with a mean yield decline of 542 kg ha21 cm21. However, yield decline occurred only in the experiment on the Aridisol (i.e., 1.084 Mg ha21 cm21, or 0.01% Mg21 of soil erosion); yields remained the same on all depths of soil removal on the Ultisol. The potato study in Venezuela resulted in a mean yield of 20.2 Mg ha21, and decreased 101 kg ha21 cm21 of soil erosion; the relative yield loss was less than 0.003% Mg21. In the United States, the mean potato yield in the experiments was 30.5 Mg ha21; however, yield in the study on the Aridisol was more than twice the yield in the studies on either the Spodosol (14.7 Mg ha21) or the Inceptisol (24.1 Mg ha21). Average erosion-induced yield declined by 0.42% Mg21 of soil erosion, ranging from no loss on the Aridisol to 0.78% Mg21 on the Spodosol and 1.09% Mg21 on the Inceptisol. b.

Cassava

Cassava was used as the experimental crop in two studies in Nigeria (Table V). The mean yield in these experiments was 15 Mg ha21, and declined at an average

Table V Impact of Past Erosion on the Yield of Root Crops, by Crop and Continent

kg ha21 Mg21 soil erosiona

% Mg21 soil erosion

0.101 (n.a.)

0.67

0.00%

Delgado et al., 1998

54.1 11.4

0.542 (0.000–1.084) 0.084 (0.018–0.150)

3.61 0.56

0.01% 0.00%

2

30.5

(n.a.)c

127

0.42%

McFarlane et al., 1991 Krumov and Tzvetkova, 1998; Tikhonov, 1960 Carter et al., 1985; Hepler et al., 1983; Lamb et al., 1944

2

4

15

0.594 (0.535–0.653)

3.96

0.03%

Lal, unpublished data; Lal, 1987b

4

1

27.8

1.323 (0.000–2.678)

8.82

0.03%

Delgado et al., 1998

No. of records

Mean duration of experiments (yr)

Mean of mean experimental yield (Mg ha21)

1

1

20.2

2 2

1 5

North America

3

Cassava

Africa

Carrots

Latin Americab

Crop

Continent

Potatoes

Latin Americab Australia Europe

Mg ha21 cm21 soil erosion, mean (range)

Sources

GLOBAL IMPACT OF SOIL EROSION I

Erosion-induced yield loss

Assuming a bulk density of 1.5 Mg m23. Latin America includes South America, Central America and the Caribbean. c Two studies measured erosion in Mg ha21 and one in cm ha21; therefore, no mean yield losses per cm could be calculated. a b

25

26

C. DEN BIGGELAAR ET AL.

rate of 594 kg ha21 cm21 of erosion. Both of these experiments were conducted on Alfisols. The relative yield loss was 0.03% Mg21 of soil erosion. c.

Carrots

Lastly, two studies in Venezuela investigated the erosional effects on the yield of carrots, one study on an Entisol and another on an Inceptisol. Mean carrot yield in these experiments was 27.8 Mg ha21, and yield losses ranged from 0.0 to 2.7 Mg ha21 cm21 (Table V). The average relative yield loss was 0.03% Mg21 of soil erosion, 0.05% Mg21 on the Entisol and 0.02% Mg21 on the Inceptisol.

B. EROSIONAL EFFECTS ON YIELD IN MANAGEMENT PRACTICES STUDIES In several countries, researchers have investigated the effects of differential management practices on erosion and crop yields. Common management practices used in the studies include soil tillage methods, terracing and bunding, and use of different soil covers and cover crops. These studies measure the effect of management practices on both erosion rates and crop yields (i.e., erosion as it occurs during crop growth), whereas the results presented in the previous section represent the effects on yield of past erosion as reflected in different depths of topsoil, keeping management constant. However, differences in crop yields in studies using different management practices are due both to variable amounts of erosion associated with these management practices, and to other changes in soil properties. In other words, the observed yield differences cannot be attributed solely or entirely to differences in erosion rates. As the relative yield losses per Mg of soil erosion were quite different from relative yield losses in studies investigating the effects of past erosion, the results are presented separately as follows: 1. Grain Crops The relative yield declines in grain crops in studies investigating the effects of past erosion ranged from 0.00 to 0.05% Mg21, whereas relative yield losses in studies evaluating the effect of present erosion rates were much greater, ranging from 0.21 to 11.13% Mg21 of soil erosion (Table VI). a.

Maize

The average experimental maize yields ranged from 2.9 Mg ha21 in Africa to 7.8 Mg ha21 in North America (Table VI). In Africa, this mean yield is

Table VI Impact of Present Erosion on the Yield of Grain Crops, by Crop and Continent Erosion-induced yield loss

Continent

Maize

Africa

Mg21 Mg ha soil erosion, mean (range)

11

5

2.9

0.072 ((20.373)– 0.428)

Asia Europe North America Latin Americaa

2 1 1

4 17 3

3.1 3.7 7.8

0.024 (0.003–0.045) 0.088 (n.a.) 0.790 (n.a.)

5

6

3.9

0.047 ((20.171)– 0.388)

Europe North America Latin Americaa

1 3

17 14

3.1 1.8

0.114 (n.a.) 0.014 (0.000–0.0040)

1

7

2.2

Barley Africa

2

1

Millet

1

4

Wheat

Africa

21

21

kg ha Mg soil erosiona

% Mg21 soil erosion

Sources

72

2.45%

24 87.9 790

0.77% 2.41% 10.12%

46.6

1.18%

Gumbs et al., 1985; Melo Filho and Silva, 1993; Nunes Filho et al., 1987

114 14

3.62% 0.75%

0.009 (n.a.)

9

0.41%

Djorovic, 1990 Horner, 1960; Monreal et al., 1995 Hernani et al., 1997

1.0

0.002 (0.001–0.003)

2.2

0.21%

0.5

0.054 (n.a.)

54

11.13%

Azontonde, 1993; Mensah-Bonsu and Obeng, 1979; Moyo, 1998 Willet, 1994 Djorovic, 1990 Bitzer et al., 1985

GLOBAL IMPACT OF SOIL EROSION I

Crop

Mean duration Mean of mean No. of of experiments experimental records (yr) yield (Mg ha21)

21

Afifi et al., 1992; Wassif et al., 1995 Fournier, 1963

a

Latin America includes South America, Central America and the Caribbean.

27

28

C. DEN BIGGELAAR ET AL.

comparable to the mean yield obtained in studies investigating past erosion (2.9 vs. 2.6 Mg ha21). In other continents, average yields in management practices experiments were higher than those reported in past erosion experiments. Yield decline per Mg of soil erosion was lowest in Asia (0.24 Mg ha21 Mg21) and highest in North America (0.79 Mg ha21 Mg21). The relative yield decline was 0.77% Mg21 in Asia, 1.18% in Latin America, 3.01% in Africa, and 10.12% in the one study using management practices in North America. The average relative yield declines by continent mask differences observed among soils. In Latin America, maize yield declined 33.36% Mg21 of soil erosion on Entisols in Brazil, but yields increased on Ultisols by 3.2% Mg21 in Brazil and 0.19% Mg21 in Trinidad. The loss in yield in Asia was larger on the Ultisol in Thailand (1.79% Mg21) than on Alfisol in the Philippines (0.08% Mg21). In Africa, yields increased by 2.93% Mg21 of soil erosion on Alfisols, but declined 3.1% Mg21 on Entisols, 11.5% Mg21 on Inceptisols, 2.35% Mg21 on Oxisols and 3.14% Mg21 on Ultisols. The sole North American study was done on an Alfisol.

b.

Wheat

Five studies were found that used management practices to investigate the effect of erosion on wheat yields, three in North America (two in Canada and one in the US) and one each in Europe (Serbia) and Latin America (Brazil) (Table VI). Mean yields obtained in these studies were similar to that in past erosion experiments in Brazil (2.2 vs. 2.1 Mg ha21, respectively), but much lower than in the studies in Europe (3.1 vs. 5.2 Mg ha21) and North America (1.8 vs. 2.6 Mg ha21) (comparison of results in Tables III and VI). The yield decline due to erosion was similar in Latin and North America (9 kg ha21 Mg21 in Brazil and 14 kg ha21 Mg21 in North America). In the study in Serbia, however, the decline was much higher at 114 kg ha21 Mg21. The relative yield decline was 0.41% on an Entisol in Brazil, 0.75% in North America (0.04% on Alfisols and 1.09% on Mollisols), and 3.62% in the study on a Mollisol in Serbia.

c.

Barley and Millet

Two studies on barley on Aridisols in Egypt had mean yields of 1.0 Mg ha21, declining by 2 kg ha21 Mg21 of soil erosion, or 0.21% (Table VI). Management practices studies on Alfisols in Burkina Faso and Niger with millet showed comparable yields (485 vs. 437 kg ha21). The decline in yield was, however, much greater in Burkina Faso, both in absolute (54 vs. 1 kg ha21 Mg21 of soil erosion) and relative terms (11.13 vs. 0.29% Mg21 of soil erosion).

GLOBAL IMPACT OF SOIL EROSION I

29

2. Leguminous Crops Studies using management practices on leguminous crops were done on soybeans on a Vertisol in India and an Oxisol in Brazil; beans on Entisols in Brazil and Peru; cowpeas on Ultisols in Trinidad; and peanuts on an Alfisol in Burkina Faso (Table VII). Mean soybean yield was 0.9 Mg ha21 in India and 2.2 Mg ha21 in Brazil. The yield loss was 49 kg ha21 Mg21 of soil erosion or 5.19% in India, and 6 kg ha21 Mg21 of erosion or 0.28% in Brazil. The mean yield of dry beans in the studies in Brazil and Peru was 1.0 Mg ha21; accelerated erosion had no effect on yields, however. In both studies, yields increased in spite of accelerated erosion at an average rate of 25 kg ha21 Mg21 soil erosion. The relative yield increase was 2.46% Mg21 soil erosion. In the study of peanuts on Alfisols in Burkina Faso, yield declined 7.11% Mg21, or 47 kg ha21 Mg21 of soil erosion from a mean yield of 660 kg ha21. Lastly, a study with cowpeas on Ultisols in Trinidad produced an average yield of 1.62 Mg ha21; yields in this study increased with progressive increase in erosion at a rate of 11.36% Mg21 (184 kg Mg21).

3.

Root Crops

Studies on potatoes were conducted on an Aridisol in the US, Inceptisols in Indonesia and Peru, a Spodosol in Canada, and an Entisol in Peru. Mean yields ranged from 20.6 Mg ha21 in Peru to 36.2 Mg ha21 in North America (Table VIII). Erosion-induced yield losses differed among continents and soils. In the Peruvian studies, average yields increased with increase in erosion at a rate of 0.327 Mg ha21 Mg21 or 1.59% Mg21 of soil erosion. Yield on the Entisol in Peru decreased slightly (26 kg ha21 Mg21 or 0.19% Mg21) due to erosion, but increased on the Inceptisol by 0.68 Mg ha21 Mg21 or 2.49% Mg21 soil erosion. In Indonesia, yield declined 9 kg ha21 Mg21 soil erosion from a mean yield of 26.4 Mg ha21 or 0.03% Mg21. In North America, average yield decline was much greater: 2.921 Mg ha21 Mg21 or 8.01% Mg21. However, almost all of this decline was registered in the study on the Aridisol, in which yield declined by 5.845 Mg ha21 Mg21 or 15.05% Mg21 of soil erosion. On a Spodosol in Canada, yield declined only 2 kg ha21 Mg21 or 0.01% Mg21 of soil erosion. In a study with cassava on Hunan Island, China, the mean yield was 24 Mg ha21 (Table VIII), and yield increased slightly with increasing erosion (36 kg ha21 Mg21 or 0.15% Mg21 of soil erosion). In the studies in Colombia on Inceptisols, the mean yield of cassava was 19.4 Mg ha21, and declined by 0.611 Mg ha21 Mg21 or 3.16% Mg21 of erosion (Table VIII).

30

Table VII Impact of Present Erosion on the Yield of Leguminous Crops, by Crop and Continent

Crop

Continent

Mean duration Mean of mean No. of of experiments experimental records (yr) yield (Mg ha21)

Mg ha21 Mg21 soil erosion, Mean (range)

Soybeans Asia Latin Americaa

1 1

3 7

0.9 2.2

0.049 (n.a.) 0.006 (n.a.)

Beans

Latin Americaa

2

11

1.0

Cowpeas

Latin Americaa

1

1

Peanuts

Africa

1

4

a

Kg ha21 Mg21 soil erosion

% Mg21 soil erosion Sources

49 6

5.19% 0.28%

20.025 ((20.034)–(20.015))

224.5

22.46%

1.6

20.184 (n.a.)

2184

211.36%

0.7

0.047 (n.a.)

47

7.11%

Latin America includes South America, Central America, and the Caribbean.

Shivaramu et al., 1998 Hernani et al., 1997 Felipe-Morales et al., 1979; da Silva et al., 1999 Gumbs et al., 1985 Fournier, 1963

C. DEN BIGGELAAR ET AL.

Erosion-induced yield loss

Table VIII Impact of Present Erosion on the Yield of Root Crops, by Crop and Continent

No. of records

Mean duration of experiments (yr)

Mean of mean experimental yield (Mg ha21)

Mg ha21 Mg21 soil erosion, mean (range)

Crop

Continent

Potatoes

Asia

1

2

26.4

0.009 (n.a.)

North America

2

2

36.2

2.921 (0.002–5.845)

Latin Americaa

2

2

20.6

Asia Latin Americaa

1 4

1 2

24 19.4

Cassava

a

Latin America includes South America, Central America, and the Caribbean.

Kg ha21 Mg21 soil erosion

% Mg21 soil erosion

9

0.03%

2921

8.01%

20.327 ((20.680)– 0.026)

2327

21.59%

20.036 (n.a.) 0.611 (0.214–1.026)

235.9 611

20.15% 3.16%

Sources Sinukaban et al., 1994 DeHaan et al., 1999; Sojka et al., 1993 Felipe-Morales et al., 1979 CIAT, 1991 Reining, 1992; Ruppenthal, 1995

GLOBAL IMPACT OF SOIL EROSION I

Erosion-induced yield loss

31

32

C. DEN BIGGELAAR ET AL.

C. EFFECTS

OF INPUTS

The results presented above are based on average yields and yield declines of the experiments reviewed across all levels of input use (e.g., fertilizer, lime, manure, irrigation). To determine the effect of the use of inputs on relative yield losses, we compared the results of studies conducted with and without inputs. The comparative assessment included maize, cowpeas, and cotton in Africa; soybeans, wheat, millet, and maize in Asia; and beans, maize, and soybeans in Latin America (Table IX). The differences shown are only indicative of the effect of inputs, as the number of studies being compared and/or their duration is too small to make definite conclusions. 1.

Africa

In experiments reflecting past erosion, yields of maize and cowpeas were comparable; the yields with and without input were 2.6 and 2.5 Mg ha21 for maize, and 0.76 and 0.89 Mg ha21 for cowpea, respectively (Table IX). Yield declines per Mg soil erosion were similar regardless of input use (1.1 and 0.7 kg ha21 Mg21 for maize, and 0.26 and 0.35 kg ha21 Mg21 for cowpeas with and without inputs, respectively). The relative yield decline for maize, however, was 33% greater in plots without fertilizers compared to plots with inputs (0.04% vs. 0.03%, respectively). The situation was reversed for cowpeas, where relative yield decline was less in plots without inputs (0.03%) than with inputs (0.05%). In management practices experiments, relative yield decline was larger in plots without inputs for maize and seed cotton than with inputs (3.21% vs. 0.61% Mg21 for maize; 20.18% vs. 14.93% Mg21 for seed cotton) (Table IX). For cotton yields, on the other hand, relative yield decline was less on plots without (0.42% Mg21) than with inputs (2.58% Mg21).

2.

Asia

A comparison across input use in Asia is only possible for studies on the effects of past erosion. There was no difference in absolute and relative yield loss in maize. Although average yield was slightly higher on plots with inputs (1.7 vs. 1.5 Mg ha21), yield loss per cm was also higher (Table IX). Yield loss per Mg of soil erosion and relative yield loss were similar: 0.65 and 0.77 kg ha21 Mg21 without and with inputs, respectively, or 0.04% Mg21. The relative yield loss of millet on plots without inputs was twice that on plots with inputs (0.04% vs. 0.02% Mg21). For wheat, losses were 2.5 times larger on plots without than with inputs (0.02% vs. 0.05% Mg21), largely due to the much higher average yields in experiments using fertilizers (3.5 Mg ha21) than in those that

GLOBAL IMPACT OF SOIL EROSION I

33

did not (1.5 Mg ha21). For soybeans, however, yields declined by 0.01% Mg21 on plots with fertilizers, and increased 0.22% Mg21 without fertilizer use.

3.

Latin America

Crop yields were higher in experiments on past erosion effects using inputs (beans 1.1 vs. 0.9 Mg ha21; maize 3.2 vs. 2.0 Mg ha21; and soybeans 2.4 vs. 1.8 Mg ha21). Yield losses per cm of soil erosion in plots with inputs were also higher for maize and soybeans, but slightly lower for beans (Table IX). Relative yield declines were identical for soybeans in experiments with and without fertilizer (0.03% Mg21), but larger for maize in experiments with than without fertilizers (0.05% vs. 0.03% Mg21, respectively). For beans, yield losses were 33% larger in experiments without than with fertilizer (0.04% vs. 0.03% Mg21). The results of studies of present erosion effects in Latin America show that yield of beans and maize increased in spite of accelerated erosion, even when no inputs were used, although actual yields of these crops were much smaller than in experiments with fertilizers (Table IX). Bean yield increased with progressive erosion in both studies with and without fertilizer use, but the increase was larger in studies without (3.42% Mg21) than with fertilizer use (2.14% Mg21). Maize yield in the no-input experiment increased 9.44% or 171 kg ha21 Mg21 of soil erosion, whereas mean yield decreased 2.26% or 101 kg ha21 Mg21 soil erosion in experiments with fertilizers.

V.

DISCUSSION AND CONCLUSIONS

Half of the 179 studies on soil erosion and productivity identified and compared in this review were conducted in North America (the United States and Canada). Even with this comparatively large number of studies, the extrapolation of the results to estimate the production lost as a result of erosion nationally, and to determine the economic value of this production loss, remains a debatable subject because of a statistically small sample (den Biggelaar et al., 2001). As we were able to identify only 89 studies from which one can do similar extrapolations for the rest of the world, estimates of the impact of erosion on productivity at the global scale are even more debatable. Nevertheless, given the paucity of research undertaken on the subject, the present review provides the best information available to date to estimate the potential effects of erosion on productivity on a soil- and crop-specific basis. Our aim for this paper was to undertake the first necessary step for this estimation, namely the determination of erosion-induced yield losses per cm and Mg of soil erosion, both in absolute and

34 Table IX Effect of Inputs (Fertilizers, Manure and/or Irrigation) on Mean Experimental Yield and Erosion-Induced Yield Losses for Selected Crops, by Continent (Results from Present Erosion Experiments in Shaded Rows) Erosion-induced yield loss

Crop

Africa

Maize Cowpeas Maize Cotton Cotton-seed

Asia

Maize Millet Soybeans

N Y N Y N Y N Y N Y N Y N Y N

No. of records

Mean of mean experimental yield (Mg ha21)

Mg ha21 cm21 soil erosion, mean (range)

18 24 14 7 9 2 1 1 1 1 1 3 1 1 1

2 1 1 1 5 3 4 4 2 2 2 1 2 2 1

2.5 2.6 0.9 0.8 2.5 4.8 1.9 2.1 1.1 1.2 1.5 1.7 0.2 0.5 1

0.158 0.106 0.039 0.053 (n.a.) (n.a.) (n.a.) (n.a.) (n.a.) (n.a.) 0.097 0.115 0.012 0.017 20.344

Kg ha21 Mg21 soil erosiona

% Mg21 soil erosion

1.1 0.7 0.26 0.35 81.6 29 8 55 228 171 0.65 0.77 0.08 0.12 22.29

0.04% 0.03% 0.03% 0.05% 3.21% 0.61% 0.42% 2.58% 20.18% 14.93 0.04% 0.04% 0.04% 0.02% 20.22%

C. DEN BIGGELAAR ET AL.

Content

Inputs used

Mean duration of experiments (yr)

3

1

0.9

0.017

0.11

0.01%

Wheat

N Y

1 3

2 1

1.5 3.5

0.106 0.099

0.71 0.66

0.05% 0.02%

Latin AmericabMaize

N Y N Y N Y N Y

4 11 2 2 1 1 1 4

2 2 4 4 20 2 8 6

2 3.2 1.8 2.4 0.4 1.6 1.8 4.5

0.094 0.26 0.083 0.101 (n.a) (n.a) (n.a) (n.a)

0.38 0.36 0.63 1.73 215 234 2171 101

0.03% 0.05% 0.03% 0.03% 23.42% 22.19% 29.44% 2.26%

Soybeans Beans Maize

a b

Assuming a bulk density of 1.5 Mg m23. Latin America includes South America, Central America and the Caribbean.

GLOBAL IMPACT OF SOIL EROSION I

Y

35

36

C. DEN BIGGELAAR ET AL.

relative terms. The extrapolations to calculate the amount of production lost globally due to erosion and its economic value for a selected number of crops constitute Part II of this report (den Biggelaar et al., this volume). The results of the present analysis show that average crop yields and effects of past erosion on yields (measured in Mg yield decline per cm of erosion) differ greatly by crop, continent and soil order. However, aggregated across soils on the continental level, differences in productivity declines per Mg of soil erosion are fairly small. The absolute yield loss ranged between 20.49 and 1.44 kg ha21 Mg21 of soil erosion for grain and leguminous crops, and 0.69 and 127.0 kg ha21 Mg21 for root crops. However, due to differences in mean yields, the relative yield losses per Mg of soil erosion vary more, even though losses were generally small (, ,0.1% Mg21 of soil erosion). The exceptions to this general rule were studies on potatoes in North America, in which yields declined by 0.42% Mg21. In general, relative erosion-induced yield losses for the various crops investigated were smallest in studies in North America (with the exceptions of the potato studies) and Europe. In other continents, relative yield losses were from two to six times greater per Mg of soil erosion depending on the specific crop and soil. The greater relative yield declines were due not so much to differences in the absolute amounts of yield of various crops being lost per cm or Mg of eroded soil, but mostly because of the much lower average yields of similar crops in different continents. With identical amounts of erosion, yields will decline more rapidly in Africa, Asia, Australia, and Latin America than they do in North America and Europe. The concentration of erosion – productivity studies in North America, therefore, appears to be inversely related to the seriousness of the problem of erosion-induced productivity loss at the global level. Nevertheless, the knowledge gained from experiments in North America provides an indication not only of the importance of reducing erosion rates, but also of the possibilities of reducing the relative impact of erosion by increasing crop yields, thereby making it more attractive to farmers to invest in conservation-effective technologies and practices. There is no definite pattern in the relationship between erosion and productivity when comparing relative yield declines across soil orders globally, contrary to findings in North America by den Biggelaar et al. (2001). These authors found that, across the four crops considered (maize, wheat, soybeans, and cotton), yields were least affected by erosion on Mollisols and most on Ultisols. On the global level, there is no soil order that consistently shows small erosional impact. The impact of erosion and the relationship between erosion and productivity depends very much on the particular crop, soil and climate conditions. Averaging relative yield losses across crops and continents show that relative yield decline is generally smallest on Entisols (, 0.01% Mg21) and highest on Spodosols (0.78% Mg21). The soils can be arranged in the following

GLOBAL IMPACT OF SOIL EROSION I

37

order of average relative erosion-induced yield loss: Entisols , Vertisols , Aridisols , Mollisols , Ultisols , Alfisols , Inceptisols , Spodosols. Studies using management practices as their experimental method showed much greater absolute and relative yield losses. However, we cannot directly compare the results of studies investigating the effect of past and present erosion. In past erosion studies, management is the same across all experimental plots, where in present erosion studies management varies. Differences in crop yields between experimental plots may, therefore, be an artifact of the different management techniques being used, rather than (or in addition to) differences in erosion rates between the plots. The much greater yield losses in these studies illustrate the effect that different management practices can have on both erosion rates and crop yield losses. Productivity declines in these studies are a reflection of the combined effect of erosion and variable crop management practices. Studies using various TSD measures reflecting past soil erosion holding management constant provide a more realistic picture of the effect of erosion on productivity. Results of present erosion studies, however, are useful as well, as they indicate that inappropriate soil management may amplify the effect of erosion on productivity by one or several orders of magnitude beyond what can be expected from looking at studies investigating effects of past erosion. Good soil management for effective erosion control and maintaining productivity, therefore, is imperative to feed and cloth the world’s present and future population.

REFERENCES Abate, S. (1994). Land use dynamics, soil degradation and potential for sustainable use in Metu area, Illubabor, Ethiopia. Geographica Bernensia, African Studies Series No. A-13. University of Berne, Berne, Switzerland. Adams, W. E. (1949). Loss of topsoil reduces crop yields. J. Soil Water Conserv. 4(3), 130. Afifi, M. Y., Genead, A. Y., Atta, S. Kh., and Aly, A. A. (1992). Impact of rainfall erosion and management practices on properties and productivity of Maryut soil. Desert Inst. Bull. Egypt 42(2), 173 –184. Agnihotri, R. C., Bushan, L. S., and Singh, S. P. (1994). Productivity loss due to soil erosion and its restoration by conservation practices. Proceedings of the 8th ISCO Conference, December 4–8, 1994, pp. 202 –103. New Delhi, India. Alberts, E. E., and Spomer, R. G. (1987). Corn grain yield response to topsoil depth on deep loess soil. Trans. ASAE 30(4), 977–981. Albuquerque, J. A., Reinert, D. J., and Fiorin, J. E. (1996). Variabilidade de sole e planta em podzolico vermelho-amarelo. Rev. Bras. Ci. Solo 20(1), 151 –157. Alderfer, R. B., and Fleming, H. (1948). Soil factors influencing grape production on well-drained lae terrace areas. Pa. Agric. Exp. Stn. Bull. 495. Aune, J. B., Kullaya, I. K., Kilasara, M., Kaihura, F. S. B., Singh, B. R., and Lal, R. (1998). Consequences of soil erosion on soil productivity and its restoration by soil management in Tanzania. In “Soil Quality and Agricultural Sustainability” (R. Lal, Ed.), pp. 197–213. Ann Arbor Press, Ann Arbor, MI.

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Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

M. Arshad (97), Department of Soil Science, University of Agriculture, Faisalabad38040, Pakistan C. den Biggelaar (1,49), Department of Interdisciplinary Studies, Appalachian State University, Boone, NC 28608 V. Breneman (1,49), USDA Economic Research Service, Washington, DC 20036 H. Eswaran (49), USDA Natural Resources Conservation Service, Soil Survey Division, World Soil Resources, Washington, DC 20013 W. T. Frankenberger, Jr. (97), Department of Environmental Sciences, University of California, Riverside, CA 92521 P. Imas (203), International Potash Institute-Coordination India, co DSW, P. O. Box 75, Beer Sheva 84100, Israel X. Jian-chang (203), Nanjing Institute of Soil Science, Chinese Academy of Sciences, 71 Beijing East Road, Nanjing 210008, People’s Republic of China R. Lal (1,49), School of Natural Resources, The Ohio State University, Columbus, OH 43210 P. Reich (49), USDA Natural Resources Conservation Service, Soil Survey Division, World Soil Resources, Washington, DC 20013 K. L. Sahrawat (169), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India B. Singh (203), Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India Y. Singh (203), Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India K. Wiebe (1,49), USDA Economic Research Service, Washington, DC 20036 Z. A. Zahir (97), Department of Soil Science, University of Agriculture, Faisalabad-38040, Pakistan

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Preface Volume 81 contains five excellent reviews that discuss topics critical to agricultural and environmental sustainability. Chapters 1 and 2 cover the global impact of soil erosion on productivity. Chapter 1, which provides a wealth of data, deals with absolute and relative erosion induced yield losses including data sources and analysis; while Chapter 2 discusses the impact of erosion on yields and production of important crops over time Chapter 3 is a comprehensive treatment on plant growth promoting rhizobacteria. Topics that are covered include: mechanisms of action, screening of PGPR and its application in agriculture, and precursor-inoculum interactions. Chapter 4 is a timely review on organic matter accumulation in submerged soils. Mechanisms and modeling aspects of organic matter accumulation in wetlands are discussed. Chapter 5 provides thorough coverage of potassium nutrition of the rice-wheat cropping system. Distribution and characteristics of the systems, and potassium fertilizer use, fertility, uptake, and crop response in rice-wheat cropping systems are extensively discussed. Many thanks to the authors for their first-rate reviews. DONALD L. SPARKS

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THE GLOBAL IMPACT OF SOIL EROSION ON PRODUCTIVITY * II: Effects On Crop Yields And Production Over Time Christoffel den Biggelaar,1 Rattan Lal,2 Keith Wiebe,3 Hari Eswaran,4 Vince Breneman,3 and Paul Reich4 1

Department of Interdisciplinary Studies, Appalachian State University, Boone, North Carolina 28608, USA 2 School of Natural Resources, The Ohio State University, Columbus, Ohio 43210, USA 3 USDA Economic Research Service, Washington, District of Columbia 20036, USA 4 USDA Natural Resources Conservation Service, Soil Survey Division, World Soil Resources Washington, District of Columbia 20013, USA I. Introduction A. Effect of Erosion on Food Production B. Effect of Erosion on Food Security C. Erosion– Productivity Estimates II. Objectives of the Present Study III. Methods A. Data Sources B. Potential Erosion Rate Estimates C. Estimates of Potential Crop Growing Areas D. Crop Yield Estimation E. Data Analysis IV. Results A. Maize B. Millet and Sorghum C. Potatoes D. Soybeans E. Wheat F. Value of Production Losses V. Discussion and Conclusion References

Soil is one of the most important natural resources and a major factor in global food production. Soil erosion is widely considered the most serious *The views expressed here are those of the authors, and may not be attributed to Appalachian State University, The Ohio State University, the USDA Economic Research Service or the USDA Natural Resources Conservation Service. 49 Advances in Agronomy, Volume 81 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. DOI 10.1016/S0065-2113(03)81002-7

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C. DEN BIGGELAAR ET AL. form of soil degradation, posing a significant threat to world’s food production capacity and global food security. In this report, we combine the estimates of absolute and relative yield declines per unit of soil erosion from the previous report with estimates, by soil order, of the extent of waterinduced erosion, crop production areas and crop yields within a GIS. Analysis of six crops (maize, millet, potatoes, sorghum, soybeans and wheat) generates production loss estimates that vary across crops, soils, and regions but average 0.3%yr21 at the global level, assuming that farmers’ practices do not change. These losses correspond to an estimated economic value of $523.1 million yr21. Reducing production losses by limiting soil erosion would, therefore, go a long way to attain food security, especially in the developing countries of the tropics and subtropics. q 2004 Academic Press.

I. INTRODUCTION As more than 99% of human food is coming from the land (Pimentel and Pimentel, 2000), soil is one of our important natural resources and a major factor in global food production. However, soil management has frequently had major impacts, both positive and negative, on the properties of the soil that govern its productivity. Erosion is widely considered to be the most serious form of soil degradation, undermining the long-term viability of agriculture in many parts of the world (Lal, 1994). Oldeman et al. (1991) estimated that erosion accounts for 84% of the total global area of degraded soils, ranging from 68% in South America to 99% in North America. Some researchers have argued that a significant area of land now being cultivated may be rendered biologically and/or economically unproductive if erosion continues unabated (Brown and Wolf, 1984; Lal, 1994; Pimental et al., 1995; Eaton, 1996). Although erosion is a very widespread phenomenon, it is generally neither assessed nor monitored. There are global and regional estimates of its magnitude. For example, Oldeman (1994), using the data of the Global Assessment of Soil Degradation (GLASOD) study, estimated that 1.6 billion ha of land is affected by erosion globally, 1.1 billion ha by water erosion, and 0.5 billion ha by wind erosion. Estimates of rates of soil loss are derived from few experiments around the world. Global soil loss due to erosion on agricultural land was estimated at 26 billion Mg yr21 (an average of 16 Mg ha21 yr21) by Brown (1984) and Brown and Wolf (1984). Pimental et al. (1995) estimated that the rate of soil erosion is three times higher (75 billion Mg yr21). However, what matters most from a policy standpoint is not how much land has already been lost, but productivity losses related to soil loss and vulnerability of the system to continued degradation (Young, 1999). A recent study by IFPRI (2000), using an overlay of cropland areas and GLASOD data, showed that soil degradation has already had significant impacts on

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the productivity of about 16% of the world’s agricultural land. Lal and Stewart (1990) and Janargin and Smith (1993) estimated that erosion could cause a decline of 19 –29% in food production from rain-fed cropland worldwide during the 25 years from 1985 to 2010 if allowed to continue unchecked. It must be pointed out that these estimates include the consequences of other forms of land degradation although soil loss is a major component. While the literature on the extent and severity of erosion is voluminous, much less is known about the effects of erosion on soil productivity and on crop production (Lal, 1995). In the previous paper (den Biggelaar et al., this volume), we estimated crop yield losses (using crop yield as a proxy measure of soil productivity) per unit of soil erosion from an analysis of 329 records covering 161 soil subgroups from 37 countries from the soil science literature. In the present paper, we will combine those results with soil-based estimates of annual erosion rates, crop yields and production areas to determine annual crop yield, and production losses due to soil erosion by crop, soil order, and region.

A. EFFECT

OF

EROSION

ON

FOOD PRODUCTION

Soil erosion poses a significant threat to the world’s food production capacity to ensure food security in the context of an increasing global population. In addition, rates of soil loss are generally much greater than rates of soil formation. Lack of data has prevented econometric analysis of changes in crop productivity as a function of land degradation; as a result, national, regional, or global estimates of productivity changes are always speculative (van Baren and Oldeman, 1998; Boardman, 1998; Greenland et al., 1998). It is also difficult, if not impossible, to generalize the relationship between soil erosion and productivity because of the location-specific nature of soil erosion (Arifin, 1995). The multiple, location-specific factors affecting productivity (such as inherent soil physical, chemical, and biological properties; climate; management) (Rijsberman and Wolman, 1984; Power, 1990; Ponzi, 1993; Loch and Silburn, 1997; Sccones, 1998) interacting with technological advance may affect the relationship between erosion and productivity over time (Littleboy et al., 1996) and mask the negative impacts of erosion. In general, erosion results in a decline in soil quality leading to a decrease in crop productivity. Crosson (1994) and Lindert (1999), however, argued that effects of soil erosion on productivity are overestimated. Crosson (1997), using data of Dregne and Chou (1982) and Oldeman et al. (1991), determined that the cumulative average degradation-induced loss of global soil productivity was roughly 0.1– 0.2% yr21 during the 1945– 1990 period. In Crosson’s opinion, these estimates support the conclusion that, despite widespread belief to the contrary, losses due to erosion and other forms of

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land degradation do not pose a serious threat to the capacity of the global agricultural systems to increase yields (Crosson, 1997). In some cases, people are able to adapt, cope and overcome severe resource degradation, as, for example, was shown by Tiffen et al. (1994) in Machakos District, Kenya. However, others argue that only some people in Machakos were able to do so using remittances from non-farm income from jobs and access to markets in nearby Nairobi (Rocheleau, 1995; Murton, 1999). In addition, as Young (1999) pointed out, the district was highly favored by foreign aid projects. According to Rocheleau, Murton, and Young, none of these factors were sufficiently taken into account by Tiffen et al. (1994). Further, none of these authors, particularly Crosson, have taken into consideration the fact that degradation – productivity relationships differ between soils. In addition, tropical soils in general have low resilience and a decline in productivity due to a change in soil quality is much greater in comparison to the organic matter and nutrient rich temperate soils. Farmers’ response to changes in productivity has also national and regional differences.

B. EFFECT

OF

EROSION

ON

FOOD SECURITY

Although soil erosion may not pose a threat to food production when looked at from a global perspective, it could still be a potentially serious threat to food security, rural incomes, and rural livelihoods in some parts of the world (Scherr and Yadav, 1996). Food security differs from food production. Food security means ensuring that all people have physical and economic access to the basic food they need to work and function normally (WRI, 1996). USDA’s Economic Research Service (ERS) (1998) estimated that the food gap to maintain per capita consumption at 1995 – 1997 levels in 66 low-income developing countries was 11 million Mg; the gap to meet minimum nutritional requirements was estimated to be much higher at 17.6 million Mg (USDA, 1998). ERS estimates that the food gaps with respect to both consumption indicators would widen to 19.8 and 28.4 million Mg, respectively, by 2008. However, the threat is not uniform, and depends upon a variety of land, environmental, social, economic, demographic, and political circumstances (Tengberg and Stocking, 1997).

C. EROSION – PRODUCTIVITY ESTIMATES Globally, there are few studies on the impact of soil erosion on agricultural production. Soil based estimates of production losses due to water and wind erosion in North America revealed potential losses of 235,000 Mg yr21 of maize

GLOBAL IMPACT OF SOIL EROSION

II

53

(Zea mays L), 60,000 Mg yr21 of soybeans [Glycine max (L). Merr.], 75,000 Mg yr21 of wheat (Triticum aestivum L.), and 2,000 Mg yr21 of cotton (Gossypium hirsutum L.) (den Biggelaar et al., 2001). The annual economic value of these production losses were estimated at US$ 56 million in the United States and US$ 3 million in Canada (additional costs for fertilizers, irrigation, etc. and the off-farm costs of erosion may be much higher, but are not included in these estimates). UNEP (1986) estimated the global costs of soil erosion at US$ 26 billion yr21, of which US$ 12 billion occurred in developing countries. A joint UNDP, FAO and UNEP (1993) study estimated the costs of all forms of land degradation in South Asia alone at between US$ 9.8 and 11 billion yr21. According to Eswaran et al. (2001) the productivity of some lands has declined by 50% due to soil erosion and desertification. Yield reduction in Africa due to past soil erosion may range from 2 to 40%, with a mean loss of 8.2% for the continent. In South Asia, annual loss in productivity is estimated at 36 million tons of cereal equivalent valued at US$ 5,400 million by water erosion, and US$ 1,800 million due to wind erosion. It is estimated that the total annual cost of erosion from agriculture in the USA is about US$ 44 billion per year, i.e., about US$ 247 ha21 of cropland and pasture. On a global scale the annual loss of 75 billion Mg of soil costs the world about US$ 400 billion yr21, or approximately US$ 70 per person per year. Young (1999) concluded that it may not be unreasonable to say that land degradation (which is more inclusive than just soil erosion, although the latter makes up the largest share of it) that has taken place until now is costing developing countries between 5 and 10% of their total agricultural sector production. Tentatively, Young estimated that this rate may be rising by 1% every 5 –10 years. Compared to the total value of global agricultural GDP, estimated at $1.25 trillion in 1997 (World Bank, 2000), the economic value of production lost as a result of erosion is fairly small ($26 billion is about 2% of $ 1.25 trillion).

II. OBJECTIVES OF THE PRESENT STUDY In the previous paper (den Biggelaar et al., this volume), we compiled and synthesized data from published erosion –productivity studies from around the world to estimate absolute and relative crop yield declines per unit of soil erosion. The objective of this report, which compliments the previous one, is to combine the results of the previous paper with estimates of soil erosion rates and crop production areas to make continent-level assessments of the impact of soil erosion on crop yields and production over time.

54

C. DEN BIGGELAAR ET AL.

III. METHODS A. DATA SOURCES To determine the effect of erosion-induced soil productivity loss on global crop production, food security and national economies, multiple types of data are necessary. We used the relative yield losses per Mg of soil erosion from den Biggelaar et al. (this volume) as the basis for our calculations of annual yield and production losses for the selected crops. These relative yield loss estimates assumed that (1) erosion is uniform across a field or landscape; (2) erosioninduced yield declines are constant in percentage terms over time corresponding to a logistic decline in yields, consistent with numerous studies of tropical soils (e.g., Tengberg and Stocking, 1997, 1999); and (3) the impact of erosion on productivity remains constant across differential levels of inputs and management practices. In addition, two additional assumptions (adapted from Lal, 1995) are critical to the estimates generated and their interpretation, namely, that (1) the erosion-induced productivity declines observed at a few sites are applicable across wide geographical and ecological regions within one continent; and (2) the impact of erosion on productivity is identical for different land uses and farming systems. We will estimate the yield loss due to erosion for six crops: maize, wheat, soybeans, potatoes (Solanum tuberosum L.), and sorghum (Sorghum bicolor L.) (in North Americal) or millet (Panicum milicaceum L. and/or Pennisetum glaucum L.) (in Asia and Europe). For these estimations, we will only use the relative yield declines from past erosion studies. Studies that involved yield comparisons across management practices associated with differential rates of erosion (e.g., fertilizer rates, irrigation, tillage, terracing, and contour plowing) are excluded from the analysis in order to avoid confusing the effects of different management practices with the effect of erosion per se. The yield loss estimations for the six selected crops are based on the analysis of 292 soil based erosion – productivity records with 362 nested crop-input combination entries (out of a total 329 records and 576 entries in the entire database, 37 records are based on management practices studies, while 122 and 92 entries involve crop-input combinations in past erosion studies for other crops investigated and in management practices studies, respectively). In the absence of global datasets on soil-specific erosion rates and crop production areas, we estimated this information from existing climate and soil databases within a geographic information system (GIS). We then used the derived potential soil-based erosion rates and crop area data, together with the relative yield losses per Mg of soil erosion from the synthesis of global erosion –productivity studies (den Biggelaar et al., this volume) and FAO production statistics to estimate the impact of erosion on the production of

GLOBAL IMPACT OF SOIL EROSION

II

55

the six selected crops. Applying projected 2000/01 crop prices from the USDA Agricultural Baseline Projections to 2010 (USDA, 2001), we then estimate the value of lost production allowing comparisons of the magnitude of the estimated losses across commodities and soil orders within and between continents. There are, however, additional effects of erosion besides losses in production, such as increased sedimentation downstream, water pollution, extra expenses for fertilizer, irrigation, soil preparation, which will increase both societal and producer costs of erosion significantly. These additional effects will not be considered in this paper. From the review of the impact of erosion on productivity in North America, den Biggelaar et al. (2001) reported that, for the United States, using soil-based extrapolations of productivity declines to determine production and economic losses reduced aggregate estimated losses by 25% compared to using an average national erosion rate that disregards soil-based differences in erosion and crop yield impacts. In the hope that such greater precision can be obtained at the global level as well, we estimated the impact of erosion on soil-based extrapolations of productivity declines at the global level. However, doing so requires global data on erosion rates for each soil order, and on the area of selected major crops produced on each soil order. At present, no global database with this type of data exist. For example, FAO agricultural statistics databases contain information on total land area, agricultural area, arable area, and on area and yield of various crops at country, region or continent levels, but none of the information is disaggregated by soil order (FAO, 2000). To obtain soil-based information on erosion rates, crop production areas and crop yields, we used a multi-step process within GIS to estimate potential average erosion rates, potential crop growing areas, and potential crop yields by soil order and continent. The potential crop growing areas and potential yields are then adjusted using published FAO area and production statistics to better reflect actual crop production areas and yields on the various continents. These procedures are described in Sections II.B, II.C, and II.D, respectively.

B. POTENTIAL EROSION RATE ESTIMATES Information on the global extent of erosion was obtained in a two-step process; details of the procedures and their results can be found in Eswaran et al. (1999) and Reich et al. (2000). First, each soil unit of the Global Soil Regions map (World Soil Resources Staff, 1997) was assigned a vulnerability class to water and wind erosion (Eswaran et al., 1999; Reich et al., 2000). The Global Soil Regions Map is based on a reclassification of soil units of the FAO-UNESCO (1971 – 81) Soil Map of the World. Soil data from the digitized version of this

56

C. DEN BIGGELAAR ET AL.

map was combined with soil climate data to reclassify the soils according to the US Soil Taxonomy suborders on a 2-min grid cell (Soil Survey Staff, 1998). In the Global Soil Regions map, soils are grouped according to the 12 soil orders of the US Soil Taxonomy. Research by Fournier (1960), Charreau (1969), Delwaulle (1973), Wishmeier and Smith (1978), and E1-Swaify and Cooley (1981) has shown that soil loss can be estimated from rainfall induced erosivity. Available data on erosivity was estimated based on published data for specific locations and extrapolating the value to similar soil units. Based on the knowledge of soil behavior under the prevailing climatic conditions, soils were assigned an erosion vulnerability class; catastrophic events were excluded in this assessment (Eswaran et al., 1999). Second, the combination of soil and climate information was used to assign polygons derived from an overlay of the soil map and climate data to one of 25 major land resource stress classes. Knowing the properties of the soils (i.e. soil performance criteria) and the major stresses they experience (expressed as soil resilience), nine inherent land quality classes were created (Eswaran et al., 1999). A matrix was used to estimate magnitudes of potential rates of soil loss due to water and wind erosion as a function of the inherent land quality (ILQ) classes. Due to the paucity of data, values for most of the classes in this matrix were interpolated to represent relative magnitudes. GIS analysis was used to make global estimates of the area occupied by each class; average annual soil losses were then computed from this information. The results were verified by comparing computed values for the US and India with maps based on field measurements of erosion in these countries, the only ones for which national level erosion rate data were available. Reich et al. (2000) calculated the area vulnerable to erosion and total amount of erosion for the African continent using four vulnerability classes, each corresponding to a range of erosion rates: low, medium, high, and very high. For purposes of our analysis, we assumed that erosion occurred at the midpoints of each range. According to Reich et al., most arable land is found in ILQ classes I –VI. Based on the total area vulnerable to water erosion and the total amount of soil erosion in these six ILQ classes, we estimate average annual cropland erosion rates of 9.32, 14.25, 17.20, and 25.78 Mg ha21 for the low, medium, high, and very high vulnerability classes, respectively. For land in the depositional class, we assumed an erosion rate of 0 Mg ha21. From an overlay of the water erosion vulnerability map created by Reich et al. (2000), the map of cropland areas according to the land cover classification of the International Geosphere Biosphere Programme (IGBP) (Belward, 1996), and the Global Soil Regions map, we then estimated the cropland area within each soil order assigned to each vulnerability class. From the soil order areas assigned to the different vulnerability classes and the average erosion rates for these classes, we then calculated average weighted potential cropland water erosion rates for each soil

GLOBAL IMPACT OF SOIL EROSION

II

57

order as follows: n X Ac £ E c Ewp ¼

c¼1

ð1Þ

At

where Ewp is the weighted potential mn erosion rate (Mg ha21 yr21); Ac is cropland area in the erosion class (ha); Ec is the mean erosion rate in the erosion class (Mg ha21 yr21), and At the total cropland area (ha). Crop specific soil erosion rates were estimated by selecting only those polygons that were classified as both IGBP cropland and potentially suitable for the selected crops. The estimated average crop specific erosion rates by soil order and continent resulting form these calculations are given in Table I.

C. ESTIMATES

OF

POTENTIAL CROP GROWING AREAS

Information on potential arable land by soil order was obtained from global land cover, climate, and soil data. We used global land cover data on a continent-by-continent basis, derived from 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning the 12-month period of April 1992– March 1993 (Eidenshink and Faudeen, 1994). In particular, we used land cover classes 12 (cropland) and 14 (cropland-natural vegetation mosaic) of the IGBP Land Cover Classification (Belward, 1996). The World Soil Resources Staff maintains a database of climate with average monthly temperature and precipitation data for about 20,000 stations. A soil water balance model that estimates soil moisture and temperature regimes (Newhall, 1972) was used to obtain soil property information from atmospheric data. The point data was interpolated using a kriging method to create a raster map on a 2-min grid cell. Soil data were derived from the Global Soil Regions map (World Soil Resources Staff, 1997). From the climate and global land cover data, combined with information on growth requirements of the selected crops (maize, wheat, potatoes, soybeans, and millet/sorghum), a determination of areas suitable for these crops was made. Production suitability was classified on a four-point scale (low, moderate, high, and very high) corresponding to the yield ranges for each crop as specified in Table II. This layer was then projected over the Global Soil Regions map to estimate potential arable land for each selected crop by soil order and country. The total area of potential arable land for these crops by soil order at the level of each continent was determined by adding the potential production areas of each crop within each soil order and country. The potential arable land for the selected crops differs from the actual area in those crops, since some land is suitable for the production of a variety of crops or the crops may not be grown for cultural or economic reasons. Accordingly, we

58

Table I Crop-Specific Potential Mean Weighted Erosion Rates (Mg ha21 yr21) for Selected Crops by Continent and Soil Order Erosion rate (Mg ha21 yr21) Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols

Mean

Maize

14.10

13.77

17.17

2.46

12.52

18.75

16.58

12.21

n/a

11.97

18.62

13.68

Asia

Maize Millet Soybean Wheat

12.58 14.12 12.17 10.97

13.21 12.20 13.83 14.34

11.50 19.21 19.49 9.75

1.80 1.03 1.60 1.52

16.45 10.19 10.62 13.71

18.87 11.44 13.48 18.38

13.67 17.17 12.48 13.33

15.77 17.75 14.33 20.92

14.46 n/a n/a n/a

15.09 14.45 16.77 15.25

18.55 18.75 16.69 18.26

15.10 14.45 14.91 14.33

Australia

Potatoes Wheat

12.07 12.34

14.25 14.25

10.58 13.54

8.12 11.84

n/a n/a

22.37 22.55

15.62 15.71

12.83 12.99

0.38 14.48

6.98 14.01

14.07 17.75

12.47 15.13

Europe

Potatoes Millet Soybean Wheat

10.66 13.58 12.26 5.35

11.12 n/a 14.29 11.16

12.60 11.86 n/a 12.07

0.95 0.89 0.79 0.91

3.61 18.09 5.64 3.72

18.13 10.98 10.56 19.22

10.61 14.25 11.99 10.61

9.32 11.13 13.29 n/a

0.04 n/a 7.64 8.88

0.68 12.06 16.66 8.09

21.03 15.87 16.75 21.30

8.89 10.81 11.47 9.09

North America

Maize Potatoes Sorghum Soybean Wheat

11.44 11.14 13.48 10.66 10.74

12.75 5.76 11.97 14.06 5.75

12.55 11.13 11.50 11.54 11.65

1.97 2.26 0.57 12.64 2.28

10.61 5.57 0.00 9.32 8.15

24.00 11.62 13.97 14.50 14.30

13.87 13.27 12.92 14.53 13.21

n/a n/a n/a n/a n/a

15.85 0.02 n/a 10.80 10.81

16.73 14.96 14.27 16.80 15.00

17.31 17.04 17.12 16.80 17.26

14.95 8.73 13.05 14.32 12.08

South þ Central America

Maize Potatoes Soybean Wheat

14.36 10.29 14.36 10.95

14.01 7.61 14.46 11.36

19.50 9.56 23.35 7.28

1.74 1.76 2.09 1.68

14.66 9.32 11.82 9.32

19.21 19.90 14.09 21.27

14.26 14.45 14.39 14.27

12.86 9.60 11.96 9.77

25.78 0.62 n/a 17.42

13.06 14.26 15.72 15.37

17.85 16.42 15.25 17.23

13.99 12.36 13.83 13.22

C. DEN BIGGELAAR ET AL.

Africa

Low Medium High Very high

Maize

Millet

Potatoes

Sorghum

Soybeans

Wheat

0.5 –1.0 (0.75) 1.0 –3.0 (2.0) 3.0 –6.0 (4.5) .6.0 (6.0)

0.5–1.0 (0.75) 1.0–2.0 (1.5) 2.0–3.0 (2.5) .3.0 (3.0)

5.0–20.0 (12.5) 20.0–40.0 (30.0) 40.0–60.0 (50.0) .60.0 (60.0)

0.5 –1.0 (0.75) 1.0 –2.0 (1.5) 2.0 –4.0 (3.0) .4.0 (4.0)

,0.5 (0.25) 0.5–2.0 (1.25) 2.0–4.0 (3.0) .4.0 (4.0)

0.5–1.5 (1.0) 1.5–2.5 (2.0) 2.5–4.0 (3.25) .4.0 (4.0)

GLOBAL IMPACT OF SOIL EROSION

Table II Yield Classes for the Selected Crops and Their Mid-Points (in parentheses) Used to Calculate Mean Weighted Potential Yields (in Mg ha21)

II 59

60

C. DEN BIGGELAAR ET AL.

adjusted the potential area of each crop to the 1998 –2000 average harvested area of that crop within each continent as reported in FAO (2000). Furthermore, since FAO statistics do not disaggregate production data by soil order, it was necessary also to devise a way to estimate how actual production areas for the selected crops are distributed across soil orders. This was done as follows. We assumed that there would be a close correlation between the amount of land within a soil order potentially suitable for the selected crops and the actual amount of land devoted to these crops. Using the relative distribution of land potentially suitable for the selected crops across the 11 soil orders (Gelisols were omitted from all our analyses, as they are not suitable for crop production), we estimated the areas of these crops in each soil order and continent by multiplying the percentage of potential cropland in each soil order with the 1998– 2000 mean harvested areas of these crops (FAO, 2000). The area of land in all four yield classes for each crop was added together, regardless of the potential yields. The results of these calculations are given in Table III; some soil orders are not found on some continents, as indicated by the ‘not applicable’ (n=a) in the table. Non-potential for a particular crop on a specific soil order is indicated by n=p in Table III. The estimated areas of the selected crops in each order in Table III will be used for our calculations of crop losses and their economic value.

D. CROP YIELD ESTIMATION Similar to production areas for various crops, global production statistics (e.g. FAO, World Bank) do not disaggregate crop yields by soil order. In Section II.C, we described how potential crop production areas were determined, and explained how we adjusted the potential area to estimate production areas for each crop within each soil order. Using the potential production areas in each soil order and the midpoints of the yields classes (number in parentheses in Table II) we determined the weighted potential mean yield for each crop in each soil order and continent as follows: n X Apc £ Yc Ywp ¼

c¼1

Atp

ð2Þ

where Ywp is the weighted potential mean yield (Mg ha21); Apc is potential production area in the yield class (ha); Yc is the mean yield in the yield class (Mg ha21); and Atp is the total potential production area (ha). We also calculated the aggregate weighted potential mean yields across soil orders for each continent, and compared these yields with the 1998– 2000 mean yields (FAO, 2000) for the selected crops. Since the actual mean yields reported in FAO (2000) differed substantially from the potential mean yields obtained through our calculations, we decided to normalize the potential weighted mean

Table III Areas in Selected Crops by Soil Order and Continent (103 ha) Estimated from FAO (2000) Harvested Areas and the Relative Distribution of Land Potentially Suitable for the Production of those Crops ( ¼ Potential Area). Total Cropland Area (103 ha) According to IGBP Listed for Comparative Purposes

Africa

Asia

IGBP cropland Maize

IGBP cropland Maize

Millet

Soybeans

Wheat

Australia

IGBP cropland Potatoes

Wheat

Pot. Areab (%) Est. Areab (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha)

Alfisols

Andisols

Aridisols

Entisols

Histosols

Inceptisols

Mollisols

Oxisols

45,248.1 33.70%

1,657.1 0.74%

742.8 0.17%

36,920.0 5,66%

276.1 0.00%

38,737.2 15.41%

3,097.5 0.72%

36,909.4 0.88%

8,567.4

187.3

42.5

1,438.8

0.4

3,917.3

182.1

94,652.0 14.03%

7,985.1 0.62%

8,702.5 0.03%

109,400.7 4.52%

6,657.9 0.05%

230,374.1 20.70%

42,052.4 5.62%

5,311.2 0.04%

6,056.8

266.5

11.4

1,952.4

22.3

8,937.3

2,425.6

19.2

35.07%

0.05%

0.04%

3.29%

0.02%

19.20%

0.02%

0.01%

5,164.4

7.7

5.3

484.6

3.4

2,827.2

3.6

25.41%

1.67%

0.01%

3.47%

0.01%

0.79%

1.69%

0.00%

4,251.3

279.0

1.9

581.1

1.6

132.0

283.5

0.7

21.58%

2.27%

0.06%

6.78%

0.01%

33.59%

20.02%

0.01%

21,304.9

2,236.0

58.6

6,692.9

10.1

33,152.8

19,757.6

9.6

37,109.5 66.13%

13.5 0.62%

5,092.3 1.05%

3,563.0 0.94%

24.6 n/p

1,951.2 10.65%

3,400.9 6.90%

160.5 0.21%

36.8

0.3

0.6

0.5

5.9

3.8

49.54%

0.46%

1.23%

0.71%

7.74%

5.19%

0.15%

5,698.0

52.8

141.8

81.8

889.8

597.5

17.4

n/p

Ultisols

Vertisols

Totala

35,994.6 36.27%

11,476.5 6.45%

211,059.3 100.0%

9,220.6

1,639.4

25,419.7

2,401.7 0.00%

191,227.5 46.37%

59,675.1 8.02%

758,440.3 100.0%

1.4

20,021.3

3,462.5

43,176.8

15.88%

26.42%

100.0%

2,338.2

3,891.0

14727.4

56.37%

10.57%

100.0%

9,429.1

1,768.3

16,728.5

6.16%

9.54%

100.0%

6,076.3

9,412.7

98,711.7

1,411.0 5.62%

722.6 5.65%

6,423.9 2.23%

59,872.9 100.0%

3.1

3.1

1.2

55.7

0.12%

2.21%

32.65%

100.0%

13.4

253.9

3,755.9

11,502.3

Spodosols n/a n/a

223.9

n/p

2.1

0.1

n/p

n/p

(continued)

Table III (continued) Europe

IGBP cropland Potatoes

Millet

Soybeans

Wheat

North America

IGBP cropland Maize

Potatoes

Sorghum

Soybeans

Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha)

Alfisols

Andisols

Aridisols

Entisols

Histosols

Inceptisols

Mollisols

Oxisols

Spodosols

Ultisols

Vertisols

Totala

203,776.1 23.58%

653.7 1.38%

3,015.0 0.01%

43,757.1 5.25%

11,744.3 0.10%

136,211.9 16.38%

127,566.5 28.42%

650.9 0.00%

24,398.7 24.00%

630.1 0.05%

4,894.4 0.82%

557,298.7 100.0%

2,162.1

126.7

1.3

481.3

9.6

1,501.2

2,605.0

0.2

2,199.8

4.8

75.5

9,167.3

47.82%

n/p

0.23%

15.11%

0.07%

33.35%

0.02%

0.97%

n/p

0.40%

2.04%

100.0%

3.0

195.0

0.9

430.5

0.2

5.2

26.3

1,290.7

6.93%

0.05%

0.78%

37.41%

0.14%

0.59%

2.19%

100.0%

84.7

0.6

9.5

457.3

0.5

1.7

7.2

26.8

1,222.4

n/p

0.10%

0.00%

1.50%

100.0%

57.2

1.1

868.6

57,772.0

10,446.2 0.05%

18,832.7 20.74%

4,000.7 1.01%

198,738.2 100.0%

14.1

6,212.3

301.6

29,955.5

27.57%

4.05%

0.56%

100.0%

194.7

28.6

3.9

706.1

12.92%

4.94%

100.0%

428.4

163.9

3,315.9

0.02%

32.65%

2.77%

100.0%

6.9

9,863.9

836.8

30,211.1

617.3 51.63%

0.24%

n/p

631.1

3.0

47.13%

1.39%

0.01%

5.59%

0.10%

15.54%

28.62%

27,229.8

800.6

8.2

3,230.4

59.0

8,980.3

16,536.9

54,168.6 26.15%

282.9 0.04%

896.1 0.03%

9,182.0 0.86%

2,311.1 0.00%

12,594.0 11.73%

86,023.9 39.39%

7,832.5

13.1

10.1

258.5

0.4

3,513.5

11,799.3

28.34%

2.93%

0.07%

0.62%

0.04%

5.13%

30.69%

200.1

20.7

0.5

4.4

0.3

36.2

216.7

32.23%

0.08%

0.06%

3.65%

0.00%

2.06%

44.07%

1,068.7

2.7

1.9

121.0

0.1

68.2

1,461.2

26.05%

0.04%

0.02%

0.10%

0.00%

0.27%

38.07%

7,871.0

13.6

6.2

28.8

0.2

80.9

11,502.7

12.5 0.04%

n/a n/a

n/a

n/a

n/a

n/p

Wheat

South+ Central America

IGBP cropland Maize

Potatoes

Soybeans

Wheat

Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha) Pot. Area (%) Est. Area (ha)

40.16%

3.99%

0.09%

0.84%

0.04%

5.69%

41.93%

13,223.0

1,313.3

29.7

276.3

12.7

1,874.9

13,804.8

87,582.1 25.28%

7,673.0 2.04%

846.3 0.17%

59,456.9 5.07%

957.2 0.02%

69,506.6 12.35%

50,264.1 18.48%

151,113.0 1.08%

6,793.5

549.4

46.8

1,362.1

4.5

3,317.5

4,964.2

24.37%

11.81%

0.15%

6.50%

0.00%

12.73%

42.12%

268.7

130.2

1.6

71.7

0.0

140.4

464.3

47.53%

2.40%

0.24%

5.17%

0.00%

0.98%

33.66%

0.79%

10,923.2

551.9

55.2

1,189.2

0.3

225.3

7,735.4

180.8

18.69%

9.48%

0.16%

8.22%

0.00%

10.34%

49.62%

0.36%

1,632.9

828.3

14.4

718.3

0.1

903.6

4,336.0

31.5

n/a, Soil order not represented on a continent; n/p, Soil order without potential for production of the selected crop. a For the selected crops total areas are equal to the 1998– 2000 mean harvested areas reported in FAO (2000). Pot. Area ¼ Potential Area; Est. Area ¼ Estimated Area.

n/a

0.08%

5.52%

1.66%

100.0%

26.5

1,818.2

545.2

32,924.6

1.1 0.00%

76,620.2 32.15%

11,659.4 3.36%

515,680.0 100.0%

289.0

0.2

8,637.7

903.5

26,868.4

0.47%

0.82%

0.58%

0.45%

100.0%

9.0

6.4

5.0

1,102.5

2.38%

6.84%

100.0%

547.7

1,571.6

22,980.4

0.04%

0.60%

2.48%

100.0%

3.3

52.4

217.0

8,737.6

5.2

n/p

64

C. DEN BIGGELAAR ET AL.

yields around the actual mean yield reported by FAO in order to make better estimations of crop losses and their value. We assumed that if the potential yield of a crop is high on a particular soil order, actual yield will in all likelihood also be high and contribute a greater share to the actual overall continental mean yield than soil orders with low potential mean yields. The normalization was done as shown in Equation (3): Yno ¼

Ypo £ YFAO Ywp

ð3Þ

where Ypo and Yno are the potential and normalized yield (Mg ha21) in a soil order, respectively. The calculated weighted potential mean yields and the normalized yields are given in Table IV; the normalized yields will be used in our calculations of crop losses below. In order to verify the accuracy of the estimated crop areas by soil order and normalized yields obtained in the procedures outlined in Sections II.C and II.D above, we multiplied estimated area data with the normalized mean yields to determine total production of the selected crops by soil order and continent. We then compared the results with the 1998– 2000 average total production reported in FAO (2000). The deviation between estimated and actual production amounts is given as a percentage (plus or minus) in Table V. With the exception of millet in Africa, Asia and Europe, and wheat in North America, the deviations between actual (FAO) and estimated production were # 1.0%. This may not be surprising given that the potential yield and area data were adjusted to FAO statistics. Nevertheless, using the adjusted numbers led to an overestimation of millet production of 7.07, 13.36, and 10.45% in Africa, Asia, and Europe, respectively, and an underestimation of 13.11% for wheat production in North America. The reasons for these deviations is not clear, but we speculate it may be due to disproportionate allocation of land in soil orders of lower or higher yielding potential to those crops using the above outlined procedure. Nevertheless, given the absence of any soil-based data on crop areas and yields outside the United States and the small deviations in production estimated form derived data for most crops and continents, we feel that the procedure enables one to obtain reasonably accurate estimations of yields and crop area by soil order.

E. DATA ANALYSIS To estimate the annual amount of production loss due to water-induced soil erosion, we used four types of data:

1. average relative yield declines for the respective crops and soil orders across experimental methods calculated form the review of soil erosion – soil

Table IV Potential and Normalized Crop Yields (Mg ha21) by Soil Order and Continent Alfisols

Andisols

Aridisols

Entisols

Histosols

Inceptisols

Mollisols

Oxisols

Spodosols

Ultisols

Vertisols

Mean

Potential yield Normalized yield

4.64 3.13

2.62 1.77

4.18 2.82

2.74 1.85

1.48 1.00

1.68 1.13

4.49 3.03

1.90 1.28

n/a

0.79 0.53

1.00 0.68

2.40 1.62

Asia

Maize

Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield

5.18 8.12 1.91 0.92 0.75 0.59 1.99 1.81

2.93 4.59 1.95 0.94 1.56 1.23 1.03 0.94

4.09 6.42 2.09 1.01 0.51 0.40 2.81 2.55

3.93 6.17 2.76 1.33 2.57 2.03 3.27 2.97

1.09 1.71 2.64 1.28 0.93 0.74 1.64 1.49

2.11 3.30 0.98 0.47 1.41 1.12 3.08 2.80

4.64 7.29 2.20 1.06 1.87 1.48 3.48 3.16

0.80 1.25 2.27 1.10 0.38 0.30 2.35 2.13

0.75 1.18 n/p

1.42 2.22 2.53 1.22 2.17 1.71 2.26 2.06

1.34 2.10 1.62 0.78 1.90 1.50 3.08 2.80

2.39 3.75 1.78 0.86 1.77 1.40 2.84 2.58

Potential yield Normalized yield Potential yield Normalized yield

51.03 34.96 2.58 1.78

12.50 8.56 1.00 0.69

53.19 36.44 3.11 2.15

48.07 32.94 2.73 1.89

n/a

49.60 33.99 3.19 2.21

50.85 34.84 3.32 2.30

47.37 32.46 1.86 1.29

49.99 34.25 3.01 2.09

27.80 19.05 2.33 1.61

41.88 28.69 3.19 2.21

49.04 33.60 2.86 1.98

Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield

51.96 15.63 2.32 1.07 2.79 2.22 2.02 2.36

28.00 8.42 n/p

40.04 12.04 1.02 0.47 n/p

49.04 14.75 2.88 1.33 2.92 2.32 3.24 3.78

48.66 14.63 2.83 1.30 3.08 2.45 2.01 2.35

43.34 13.03 1.02 0.47 2.88 2.29 2.80 3.26

52.36 15.74 2.50 1.15 1.77 1.41 3.62 4.23

12.50 3.76 2.63 1.21 0.79 0.63 n/p

50.00 15.03 n/p

30.15 9.07 2.00 0.92 2.24 1.78 2.12 2.48

46.69 49.65 14.04 14.93 2.24 1.78 1.03 0.82 1.44 2.38 1.14 1.89 3.12 2.69 3.65 3.14 (continued)

Millet Soybeans Wheat

Australia

Potatoes Wheat

Europe

Potatoes Millet Soybeans Wheat

1.85 1.47 2.16 2.52

2.75 3.21

n/a

n/p n/p

2.98 2.37 2.68 3.12

II

Maize

GLOBAL IMPACT OF SOIL EROSION

Africa

65

66

Table IV (continued) Alfisols North America

Maize Potatoes Sorghum Soybeans

Central þ South America

Maize Potatoes Soybeans Wheat

Aridisols

Entisols

Histosols

Inceptisols

Mollisols

Oxisols

Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield

5.39 10.74 50.09 37.74 3.62 4.53 3.28 3.17 2.32 1.92

3.30 6.57 33.72 25.40 3.00 3.76 0.72 0.69 2.45 2.02

4.82 9.60 55.94 42.14 3.51 4.39 1.37 1.32 2.53 2.09

4.63 9.23 38.51 29.01 3.11 3.90 2.11 2.04 2.47 2.04

5.10 10.16 51.89 39.09 3.00 3.76 3.00 2.90 1.59 1.31

2.14 4.27 41.88 31.55 1.72 2.15 2.71 2.62 2.97 2.45

5.58 11.13 52.87 39.83 3.94 4.93 2.47 2.39 3.61 2.98

n/a

Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield Potential yield Normalized yield

4.61 4.24 27.22 9.72 0.89 1.25 1.99 1.67

2.78 2.56 20.62 7.37 2.30 3.24 1.81 1.51

4.30 3.96 48.62 17.37 0.52 0.73 3.23 2.71

4.00 3.68 53.38 19.07 1.99 2.80 3.53 2.96

0.89 0.82 12.50 4.46 0.74 1.04 1.00 0.84

1.74 1.60 45.89 16.39 1.00 1.40 3.18 2.67

5.66 5.21 52.50 18.75 2.66 3.74 3.45 2.89

2.17 2.00 14.20 5.07 0.94 1.32 1.16 0.98

n/a n/a n/a n/a

Spodosols

Ultisols

Vertisols

Mean

4.07 8.10 49.98 37.65 n/a

1.98 3.95 21.92 16.51 1.50 1.88 2.52 2.44 2.00 1.65

2.08 4.15 31.32 23.60 1.56 1.96 1.62 1.56 2.25 1.85

4.34 8.65 48.70 36.69 3.32 4.16 2.67 2.58 3.32 2.74

0.84 0.77 26.23 9.37 1.30 1.83 2.16 1.81

1.14 1.05 35.46 12.67 2.48 3.49 2.93 2.46

3.02 2.78 41.35 14.77 1.70 2.39 2.97 2.49

3.60 3.48 2.62 2.16 0.75 0.69 50.03 17.87 n/p 3.32 2.78

n/a, not applicable (soil order not found on a continent); n/p, no potential for that crop on a soil order. Potential yield is the weighted average potential yield calculated from yield classes and potential crop production areas. Normalized yield is the potential yield adjusted around the 1998–2000 average mean yield reported in FAO (2000).

C. DEN BIGGELAAR ET AL.

Wheat

Andisols

Table V Deviation Between Actual Production Statistics from FAO (2000) and Calculated Production Using Normalized Yields and Estimated Production Area by Soil Order

Potatoes

Sorghum/millet

Soybeans

41,198.1 162,288.9 259,121.8 74,608.3 1,872.0 136,831.9 25,903.0 16,281.4 12,692.7 1,059.8 13,810.9 23,492.6 2,313.8 77,878.5 55,425.5 254,338.3 22,739.0 181,517.3 90,360.1 21,719.9

41,227.5 161,585.7 258,939.8 74,762.5 1,871.5 136,858.4 25,906.3 16,328.2 14,387.9 1,170.5 13,809.7 23,430.7 2,313.9 78,058.8 54,784.8 254,983.3 22,746.1 181,288.9 78,510.9 21,768.3

Deviation (%) 0.07% 20.43% 20.07% 0.21% 20.03% 0.02% 0.01% 0.29% 13.36% 10.45% 20.01% 20.26% 0.00% 0.23% 21.16% 0.25% 0.03% 20.13% 213.11% 0.22%

II

Wheat

Africa Asia North America Central þ South America Australia Europe North America Central þ South America Asia Europe North America Asia Europe North America Central þ South America Asia Australia Europe North America Central þ South America

Calculated production (103 Mg)

GLOBAL IMPACT OF SOIL EROSION

Maize

Actual Production (103 Mg)

67

68 Table VI Summary of the Relative Water Erosion Induced Yield Declines of the Selected Crops by Continent and Soil Order, (the Number in Parentheses Refers to the Number of Records on Which the Yield Declines are Based (for details, see Tables III–V in den Biggelaar et al. (this volume)

Alfisols

Aridisols

Entisols

Inceptisols

Mollisols

Oxisols

Spodosols

Ultisols

Maize

0.04% (24)

n/a

n/a

0.01% (3)

n/a

0.01% (5)

n/a

0.05% (9)

Asia

Maize Millet Soybeans Wheat

n/a 0.03% (2) n/a n/a

0.04% (2) n/a n/a 0.02% (2)

n/a n/a n/a n/a

0.05% (2) n/a 0% (1) 0.02% (2)

n/a n/a n/a n/a

n/a n/a 0.04% (2) n/a

n/a n/a n/a n/a

n/a n/a n/a n/a

Australia

Potatoes Wheat

n/a 0.05% (8)

0.01% (1) 0.02% (3)

n/a n/a

n/a n/a

n/a n/a

n/a n/a

n/a n/a

0% (1) n/a

Europe

Potatoes Millet Soybeans Wheat

0.001% (1) n/a n/a 0% (1)

n/a n/a n/a n/a

n/a n/a n/a 0% (1)

n/a n/a n/a n/a

0.01% (1) 0.02% (2) 0.02% (1) 0.01% (6)

n/a n/a n/a n/a

n/a n/a n/a n/a

n/a n/a n/a n/a

North America

Maize Potatoes Sorghum Soybeans Wheat

0.01% (66) n/a n/a 0.01% (21) 0.01% (4)

n/a 0% (1) n/a n/a 0.01% (2)

0.005% (1) n/a n/a 0.03% (1) n/a

n/a 1.09% (1) n/a 0.02% (1) n/a

0.01% (50) n/a 0.01% (13) 0.01% (6) 0.01% (56)

0.02% (2) n/a n/a n/a n/a

Central þ South America

Maize Potatoes Soybeans Wheat

0.01% (2) n/a n/a n/a

n/a n/a n/a n/a

0.01% (2) 0.001% (1) n/a n/a

0.33% (1) n/a n/a n/a

0.07% (1) n/a 0.02% (1) 0.02% (1)

0.03% (9) n/a 0.03% (3) n/a

n/a

n/a n/a n/a n/a

0.03%

n/a 0.04% n/a 0.03% 20.22% (1) 20.05% n/a 0.02%

n/a 0.02% (12) 0.78% (1) n/a n/a 20.01% (4) n/a 0.03% (13) n/a n/a n/a n/a n/a n/a

Weighted mean

n/a 0.04% (5)

0.01% 0.04%

n/a n/a n/a n/a

0.00% 0.02% 0.02% 0.00%

n/a n/a n/a n/a n/a

0.01% 0.42% 0.00% 0.01% 0.01%

n/a n/a n/a n/a

0.05% 0.001% 0.03% 0.02%

C. DEN BIGGELAAR ET AL.

Africa

Vertisols

GLOBAL IMPACT OF SOIL EROSION

II

69

productivity studies (see den Biggelaar et al., this volume). Table VI summarizes the relative yield declines for the selected crops by soil order and continent; 2. the crop specific estimated mean annual erosion rates for various soil orders by continent (see II.B); 3. the estimated area of various crops by soil order and continent (see II.C); and 4. the normalized mean crop yields by soil order and continent (see II.D). To estimate soil-based production losses due to erosion-induced productivity declines, soil orders for which there are no crop-specific soil erosion productivity studies within a continent are assigned the weighted mean relative yield decline of the studies reviewed in Part I (den Biggelaar et al., this volume). However, if there were no studies for a selected crop in a continent, no production loss estimates were attempted. The mean relative yield declines used in our production loss calculations for soil orders for which there were no specific studies are listed in the last column in Table VI. The following equations (Equations 4 –7) show the calculations made to determine annual production losses for each soil order: La ¼ L £ Ewp

ð4Þ 21

where La is the relative annual yield loss (% yr ) and L is the relative yield loss per Mg of soil erosion (% Mg21). We will use the crop-specific mean annual erosion rates by soil order provided in Table I in our calculations. C ¼ La £ Yn

ð5Þ

where C is crop loss (Mg ha21 yr21), the normalized mean yield (Mg ha21), T the total production loss (Mg yr21); and Ae the estimated crop area (ha). T ¼ C £ Ae

ð6Þ

To aggregate production losses across crops, we multiplied the annual production loss estimates for each crop, soil order, and continent by the 2000/01 crop prices from the USDA Agricultural Baseline Projections to 2010 (USDA, 2001). V ¼T £P

ð7Þ

Where V is the value of production lost ($ yr

21

), and P the price ($ Mg

21

).

IV. RESULTS A. MAIZE The mean relative yield losses of maize range from 0.15% yr21 in North America to 0.94% yr21 in Central and South America. Losses in Africa and Asia

70

C. DEN BIGGELAAR ET AL.

are intermediate at 0.49 and 0.59% yr21, respectively. In Africa, the highest relative yield losses occur on Alfisols, Vertisols and Ultisols at 0.56. 0.56 and 0.60% yr21, respectively (Table VII). Relative yield losses are lowest on Entisols (0.07% yr21) due to the low erosion rate of these soils (estimated at 2.46 Mg ha21 yr21). Many of the Entisols are on valley floors and instead of soil loss, there is soil gain through sedimentation. Despite low erosion induced yield losses on Inceptisols and Oxisols in Africa (0.01% ha21 yr21), relative annual yield losses are somewhat greater (0.19 and 0.12% yr21, respectively) as a result of higher erosion rates. The loss in maize production in Africa is estimated at about 200,000 Mg yr21, with about two-thirds of these losses being realized on Alfisols and about 14.5% on Ultisols. Relative yield losses for maize in Asia were estimated to be $ 0.5% yr21 on all soil orders except Entisols (Table VII). On Entisols, relative yield losses were estimated at 0.07% yr21 as a result of its low estimated erosion rate of 1.80 Mg ha21 yr21. The highest yield loss occurred on Inceptisols (0.94% yr21). Relative annual yield losses were also high on Vertisols (0.74% yr21), Histosols (0.66% yr21), Oxisols (0.63% yr21), and Ultisols (0.60% yr21). Total production losses of maize in Asia were estimated to be about 960,000 Mg yr21, about 57% of which were lost on Inceptisols and Ultisols, and 25% on Alfisols (Table VII). Although the mean relative annual production losses in North America were fairly low at 0.15% yr21 (Table XII), losses were significant in several soil orders. Relative annual production losses were at or below the average on Alfisols, Andisols, Aridisols, Entisols, Histosols, and Mollisols, but were greater than average on Inceptisols, Spodosols, Ultisols, and Vertisols at 0.24, 0.16, 0.33, and 0.17% yr21, respectively. The total loss of maize production in North America was estimated at 400,000 Mg yr21, comprising 46% on Mollisols (200 £ 103 Mg yr21) and 24% on Alfisols (100 £ 103 Mg yr21) (Table VII). The relative annual yield losses for maize in Central and South America were more than 0.5% per year on almost all soil orders. The exceptions are Entisols, Alfisols, and Oxisols at 0.02, 0.14, and 0.39% yr21, respectively (Table VII). Relative annual yield losses of . 1% yr21 were found for Aridisols, Mollisols, Spodosols, and Inceptisols at 0.98, 1.0, 1.29, and 6.34% yr21, respectively (Table VII). The very high relative production losses on Inceptisols (6.34% yr21) are a result of both the high erosion rates on these soils (estimated at 19.21 Mg ha21 yr21), and the high erosion-induced yield declines reported in erosion –productivity studies on these soils. Although only about 12% of total maize area in Central and South America is grown on Inceptisols, erosion on these soils contribute 48% of total crop loss of maize about 700,000 Mg yr21 in this continent. The 1.29% yr21 production loss of maize on Spodosols is less problematic, since only about 200 ha of these soils is estimated to being used for maize production. Relative and absolute production losses of maize on Mollisols are 1.0% yr21 and 258 Mg yr21, respectively; the absolute production loss is

TableVII Estimated Absolute and Relative Annual Losses in Maize Production by Continent and Soil Order Erosion-induced Crop-specific estimated Relative annual Normalized Estimated production Total Production yield loss erosion rate yield loss yield area Production loss (Mg ha21 yr21) (% yr21) (Mg ha21) (103 ha) (103 Mg) (103 Mg yr21) (% Mg21) 0.04% 0.03% 0.03% 0.03% 0.03% 0.01% 0.03% 0.01% 0.05% 0.03%

14.10 13.77 17.17 2.46 12.52 18.75 16.58 12.21 11.97 18.62

0.56% 0.41% 0.52% 0.07% 0.38% 0.19% 0.50% 0.12% 0.60% 0.56%

3.13 1.77 2.82 1.85 1.00 1.13 3.03 1.28 0.53 0.68

8,567.4 187.3 42.5 1,438.8 0.4 3,917.3 182.1 223.9 9,220.6 1,639.4 25,419.7

26,812.0 331.4 119.9 2,657.2 0.4 4,441.7 552.4 287.3 4,915.9 1,109.3 41,227.5

Asia

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.04% 0.04% 0.04% 0.04% 0.04% 0.05% 0.04% 0.04% 0.04% 0.04% 0.04%

12.58 13.21 11.50 1.80 16.45 18.87 13.67 15.77 14.46 15.09 18.55

0.50% 0.53% 0.46% 0.07% 0.66% 0.94% 0.55% 0.63% 0.58% 0.60% 0.74%

8.12 4.59 6.42 6.17 1.71 3.30 7.29 1.25 1.18 2.22 2.10

6,056.8 266.5 11.4 1,952.4 22.3 8,937.3 2,425.6 19.2 1.4 20,021.3 3,462.5 43,176.8

49,180.4 1,223.2 73.2 12,053.6 38.1 29,521.8 17,672.5 24.0 1.7 44,511.7 7,285.5 161,585.7

151.3 1.4 0.6 2.0 0.0 8.3 2.7 0.4 29.4 6.2 202.2

71

247.5 6.5 0.3 8.7 0.3 278.5 96.6 0.2 0.0 268.7 54.0 961.2 (continued)

II

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Ultisols Vertisols Total

GLOBAL IMPACT OF SOIL EROSION

Africa

72

TableVII (continued) Total Production Erosion-induced Crop-specific estimated Relative annual Normalized Estimated production yield loss erosion rate yield loss yield area Production loss (Mg ha21 yr21) (% yr21) (Mg ha21) (103 ha) (103 Mg) (103 Mg yr21) (% Mg21) North America

0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.02% 0.01%

11.44 12.75 12.55 1.97 10.61 24.00 13.87 15.85 16.73 17.31

0.11% 0.13% 0.13% 0.01% 0.11% 0.24% 0.14% 0.16% 0.33% 0.17%

10.74 6.57 9.60 9.23 10.16 4.27 11.13 8.10 3.95 4.15

7,832.5 13.1 10.1 258.5 0.4 3,513.5 11,799.3 14.1 6,212.3 301.6 29,955.5

84,133.4 86.1 96.8 2,387.0 4.5 15,008.4 131,327.1 114.4 24,530.5 1,251.5 258,939.8

96.2 0.1 0.1 0.2 0.0 36.0 182.2 0.2 82.1 2.2 399.3

Central þ South Alfisols America Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.01% 0.05% 0.05% 0.01% 0.05% 0.33% 0.07% 0.03% 0.05% 0.05% 0.05%

14.36 14.01 19.50 1.74 14.66 19.21 14.26 12.86 25.78 13.06 17.85

0.14% 0.70% 0.98% 0.02% 0.73% 6.34% 1.00% 0.39% 1.29% 0.65% 0.89%

4.24 2.56 3.96 3.68 0.82 1.60 5.21 2.00 0.69 0.77 1.05

6,793.5 549.4 46.8 1,362.1 4.5 3,317.5 4,964.2 289.0 0.2 8,637.7 903.5 26,868.4

28,801.5 1,405.9 185.1 5,018.7 3.7 5,323.2 25,851.9 577.4 0.1 6,648.2 946.8 74,762.5

41.4 9.8 1.8 0.9 0.0 337.5 258.1 2.2 0.0 43.4 8.4 703.6

C. DEN BIGGELAAR ET AL.

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Spodosols Ultisols Vertisols Total

GLOBAL IMPACT OF SOIL EROSION

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about 37% of the total maize production loss due to erosion in Central and South America (Table VII).

B. MILLET

AND

SORGHUM

Studies on the effect erosion on millet yields were carried out in Asia and Europe, whereas studies using sorghum were undertaken only in the United States. It should be noted, though, that very few studies on erosion-induced soil productivity declines have been undertaken using millet (two each in Europe and Asia) or sorghum (17 records in the US) as an indicator crop, the results presented below should therefore be interpreted with caution and not taken as absolutes. However, they do provide an indication of the seriousness of the problem and, especially for millet, should be used only to identify the primary soil orders used for its production as priority areas to reduce erosion. Mean relative annual yield losses on millet were estimated at 0.51 and 0.23% yr21 in Asia and Europe, respectively. The relative annual yield loss of millet in Asia ranges from 0.03% yr21 on Entisols to 0.58% yr21 on Aridisols. Millet in Asia is grown primarily on, in order of importance, Inceptisols, Ultisols, Entisols, Alfisols, and Vertisols. Relative production losses on the these soils were estimated at 0.34, 0.43, 0.03, 0.42, and 0.56% yr21, respectively (Table VIII). Total production losses were estimated at about 64,000 Mg yr21, with most of the losses (42%) incurred on Vertisols. In spite of a much greater area of cropland in millet on Alfisols than on Vertisols, production losses on these soils (20.2 Mg yr21 or 31% of the total losses) are lower due to the lower erosion rate (14.12 and 18.75 Mg ha21 yr21 on Alfisols and Vertisols, respectively) (Table VIII). Millet is only a small crop in Europe, primarily produced in southern and Eastern Europe on about 1.3 Mha. Nearly half the millet area in Europe is grown on Alfisols, with about one-third of the area being on Inceptisols. The estimated average annual relative yield loss ranged from at 0.02% yr21on Entisols to 0.36% yr21 on Histosols (Table VIII). Production loss of millet on this continent was estimates at 2,400 Mg yr2, almost all of it due to erosion-induced production losses on Alfisols (75%) and a much smaller amount (17%) being contributed by losses on Inceptisols. A review of erosion – productivity studies conducted on sorghum in North America showed relative erosion-induced yield declines of 0.01% Mg21 on Mollisols. However, on Ultisols, yield increased by a similar percentage with increasing erosion. The mean relative yield loss in sorghum in North America was 0.06, 0.13% yr21 on Millisols and 2 0.13% on Ultisols, with losses of less than 0.00% yr21 for sorghum grown on other soil orders (Table VIII). The apparent beneficial effect of soil erosion on Ultisols is interesting and perhaps suggests the sensitivity of the model. The organic-rich surface layers are more

74

TableVIII Estimated Absolute and Relative Annual Losses in the Production of Millet (Asia and Europe) and Sorghum (North America) by Continent and Soil Order Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha21)

Estimated production area (103 ha)

Total Production (103 Mg)

Production loss (103 Mg yr21)

Asia

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Ultisols Vertisols Total

0.03% 0.03% 0.03% 0.03% 0.03% 0.03% 0.03% 0.03% 0.03% 0.03%

14.12 12.20 19.21 1.03 10.19 11.44 17.17 17.75 14.45 18.75

0.42% 0.37% 0.58% 0.03% 0.31% 0.34% 0.52% 0.53% 0.43% 0.56%

0.92 0.94 1.01 1.33 1.28 0.47 1.06 1.10 1.22 0.78

5,164.4 7.7 5.3 484.6 3.4 2,827.2 3.6 2.1 2,338.2 3,891.0 14,727.4

4,765.7 7.2 5.4 646.2 4.3 1,338.6 3.8 2.3 2,858.1 4,756.2 14,387.9

20.2 0.0 0.0 0.2 0.0 4.6 0.0 0.0 12.4 26.8 64.2

Europe

Alfisols Aridisols Entisols Histosols

0.02% 0.02% 0.02% 0.02%

13.58 11.86 0.89 18.09

0.27% 0.24% 0.02% 0.36%

1.07 0.47 1.33 1.30

617.3 3.0 195.0 0.9

659.7 1.4 258.7 1.1

1.8 0.0 0.0 0.0

C. DEN BIGGELAAR ET AL.

Erosion-induced yield loss (% Mg21)

0.02% 0.02% 0.02% 0.02% 0.02%

10.98 14.25 11.13 12.06 15.87

0.22% 0.29% 0.22% 0.24% 0.32%

0.47 1.15 1.21 0.92 1.03

430.5 0.2 12.5 5.2 26.3 1,290.7

202.3 0.3 15.1 4.8 27.1 1,170.5

0.4 0.0 0.0 0.0 0.1 2.4

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Ultisols Vertisols Total

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 20.01% 0.00%

13.48 11.97 11.50 0.57 0.00 13.97 12.92 14.27 17.12

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.13% 2 0.14% 0.00%

4.53 3.76 4.39 3.90 3.76 2.15 4.93 1.88 1.96

1,068.7 2.7 1.9 121.0 0.1 68.2 1,461.2 428.4 163.9 3,315.9

4,841.6 10.2 8.2 471.9 0.2 146.5 7,205.8 804.9 320.4 13,809.7

0.0 0.0 0.0 0.0 0.0 0.0 9.3 21.1 0.0 8.2

GLOBAL IMPACT OF SOIL EROSION

North America

Inceptisols Mollisols Oxisols Ultisols Vertisols Total

II 75

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C. DEN BIGGELAAR ET AL.

acid than sub-surface layers in Ultisols. Removal of the acid surface layers is beneficial to sorghum, which responds better. Similar site-specific processes also operate in other soils and crops respond differently. In Alfisols of the semi-arid regions, for example, the subsoil retains more moisture and nutrients than the lighter-textured surface soil. In some of these Alfisols, removal of the topsoil may have beneficial effects with respect to crop performance. The specific soil –crop relationships cannot be included in this global generalization, but must be considered in more detailed analysis. In North America, we estimate that about half the total area in sorghum is grown in Mollisols and about one-third on Alfisols. Although the production of sorghum increases by 1,100Mg yr21 on Ultisols in spite of erosion, North America experiences a net decline in production of 8 200 Mg yr21 due to the production losses of 9,300 Mg yr21 incurred on Mollisols (Table VIII).

C. POTATOES Erosion – productivity studies using potatoes were conducted in Australia, Europe, North America, and Central and South America. As shown in Part I of this review (den Biggelaar et al., this volume), observed erosion-induced yield losses for potatoes were very small in Australia, Europe, and Central and South America (# 0.01% Mg21 soil loss), but much larger in North America, especially on the Inceptisols on this continent. Mean relative annual yield losses of potatoes were small in Australia, Europe and Central and South America at 0.12, 0.04, and 0.01% yr21, respectively, but very large in North America at 3.98% yr21. Given that the number of studies having investigated the effect of erosion using potatoes is small (eight worldwide), these results may, therefore, not be an accurate reflection of the real impact of erosion on this crop. The relative yield loss of potatoes in Australia ranged from 0.00% yr21 on Spodosols and Ultisols to 0.22% yr21 on Inceptisols (Table IX). On Alfisols, which make up 62% of Australia’s cropland and 69% of the area in potatoes, the relative yield loss for potatoes was 0.12% yr21. The loss of potato production as a result of erosion amounts to about 2,300 Mg yr21, 69% of which is due to erosion-induced yield losses on Alfisols (Table IX). In Europe, we estimate that there are no relative production losses for potatoes on Entisols, Histosols, Spodosols and Ultisols, and very small losses of 0.01% yr21 on Alfisols, Andisols, Aridisols and Mollisols and of 0.02% yr21 on Inceptisols and Vertisols (Table IX). We calculated that the largest losses would occur on Mollisols at 0.11% yr21. Production losses on Mollisols contribute about 85% of the total annual production loss of 51,100 Mg yr21 of potatoes in Europe (Table IX). The mean relative yield losses of potatoes in North America are estimated to be high, ranging from 0.00% yr21 on Aridisols to 12.66% yr21 on Inceptisols. Most potatoes in North America are grown on Alfisols, Mollisols and Spodosols, with estimated yield declines of 4.68, 5.57, and 0.02% yr21, respectively. The low

Table IX Estimated Absolute and Relative Annual Losses in Potato Production by Continent and Soil Order Erosion-induced yield loss (% Mg21)

Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha21)

Estimated production area (103 ha)

Total Production (103 Mg)

Production loss (103 Mg yr21)

0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.00% 0.01%

12.07 14.25 10.58 8.12 22.37 15.62 12.83 0.38 6.98 14.07

0.12% 0.14% 0.11% 0.08% 0.22% 0.16% 0.13% 0.00% 0.00% 0.14%

34.96 8.56 36.44 32.94 33.99 34.84 32.46 34.25 19.05 28.69

36.8 0.3 0.6 0.5 5.9 3.8 0.1 3.1 3.1 1.2 55.7

1,287.9 3.0 21.2 17.2 201.7 133.9 3.9 107.2 60.0 35.7 1,871.5

1.6 0.0 0.0 0.0 0.5 0.2 0.0 0.0 0.0 0.1 2.3

Europe

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.00% 0.00% 0.00%

10.66 11.12 12.60 0.95 3.61 18.13 10.61 9.32 0.04 0.68 21.03

0.01% 0.01% 0.01% 0.00% 0.00% 0.02% 0.11% 0.01% 0.00% 0.00% 0.02%

15.63 8.42 12.04 14.75 14.63 13.03 15.74 3.76 15.03 9.07 14.04

2,162.1 126.7 1.3 481.3 9.6 1,501.2 2,605.0 0.2 2,199.8 4.8 75.5 9,167.3

33,783.3 1,066.2 15.4 7,096.8 140.2 19,565.8 41,012.9 0.6 33,074.1 43.2 1,060.0 136,858.4

3.6 0.1 0.0 0.1 0.0 3.5 43.5 0.0 0.0 0.0 0.2 51.1

77

(continued)

II

Alfisols Andisols Aridisols Entisols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

GLOBAL IMPACT OF SOIL EROSION

Australia

78

Table IX (continued) Erosion-induced yield loss (% Mg21)

Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha21)

Estimated production area (103 ha)

Total Production (103 Mg)

Production loss (103 Mg yr21)

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Spodosols Ultisols Vertisols Total

0.42% 0.42% 0.00% 0.42% 0.42% 1.09% 0.42% 0.78% 0.42% 0.42%

11.14 5.76 11.13 2.26 5.57 11.62 13.27 0.02 14.96 17.04

4.68% 2.42% 0.00% 0.95% 2.34% 12.66% 5.57% 0.02% 6.28% 7.16%

37.74 25.40 42.14 29.01 39.09 31.55 39.83 37.65 16.51 23.60

200.1 20.7 0.5 4.4 0.3 36.2 216.7 194.7 28.6 3.9 706.1

7,552.2 524.8 20.6 126.9 11.4 1,143.3 8,630.5 7,330.9 472.6 93.2 25,906.3

353.4 12.7 0.0 1.2 0.3 144.8 480.9 1.3 29.7 6.7 1,030.9

Central +South America

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

10.29 7.61 9.56 1.76 9.32 19.90 14.45 9.60 0.62 14.26 16.42

0.01% 0.01% 0.01% 0.00% 0.01% 0.02% 0.01% 0.01% 0.00% 0.01% 0.02%

9.72 7.37 17.37 19.07 4.46 16.39 18.75 5.07 17.87 9.37 12.67

268.7 130.2 1.6 71.7 0.0 140.4 464.3 5.2 9.0 6.4 5.0 1,102.5

2,611.9 959.1 28.6 1,367.1 0.0 2,301.2 8,707.4 26.1 161.1 84.4 81.2 16,328.2

0.3 0.1 0.0 0.0 0.0 0.5 1.3 0.0 0.0 0.0 0.0 2.1

C. DEN BIGGELAAR ET AL.

North America

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annual yield loss on Spodosol is due to the very low estimated erosion rate of Spodosols in potatoes (0.02 Mg ha21 yr21), as these are sandy soils. The total production loss of potatoes was estimated at about 1.0 million Mg yr21, 35% coming from losses on Alfisols and 48% on Mollisols. Mechanized land preparation and harvesting perhaps contributed to the larger losses in North America. In Central and South America, potatoes were used only in one erosion – productivity study on an Entisol. The mean yield decline in this study was 0.001% Mg21 of erosion. If we accept that this study is representative for soils in all soil orders on which potatoes are grown in this continent, relative annual yield losses of potatoes are small, ranging from 0.00 to 0.02% yr21. The higher rates of yield decline were found on Inceptisols and Vertisols, resulting from the high erosion estimates for land under potatoes on these soils. Based on the relative annual yield losses on the various soil orders, we estimate that farmers lose about 2,100 Mg yr21of potatoes due to erosion, with 1,300 Mg yr21 being lost due to erosion on Mollisols.

D. SOYBEANS Soybeans are the only leguminous crop included on our calculations. The effect of erosion on soybean yield and production varies across continents; annual loss of soybean yield ranges from 2 1.08% yr21 in Asia to 0.33% yr21 in Central and South America. The small number of studies on soybeans in Asia (4) reviewed in Part I (den Biggelaar et al., this volume) showed no effect of erosion on soybean yield on Inceptisols, small losses of 0.04% Mg21 of soil loss on Oxisols, but an increase in yield of 0.22% Mg21 on Vertisols (Table X). Annual yield losses of 0.57% yr21 are estimated for soybeans grown on Oxisols, with no losses recorded for soybeans on Inceptisols. All other soils show an increase in yield with progressive erosion, ranging from 0.08% yr21 on Entisols to 3.67% yr21 on Vertisols. For most soil orders, the production increases ranged from 0.5 to 1.0% yr21. Overall, we therefore estimate that erosion has no deleterious effect on the yield of soybeans in Asia; on the contrary, our calculations show a general increase in total soybean production of about 254,000 Mg yr21 (Table X). Most of the increase (53%) occurs on Ultisols, which are the primary soil type on which soybeans are produced in Asia (56% of soybean is grown on Ultisols). In Europe, soybeans are a minor crop, produced on about 1.2 Mha, primarily on Alfisols (52% of soybean area) and Mollisols (37% of the area) (Table X). The production loss amounts to 5,200 Mg yr21, or 0.22% yr21. Relative annual yield losses range from 0.02% yr21 on Entisols to 0.33% yr21 on Ultisols and Vertisols (Table X). For the soil orders on which most soybeans are produced in Europe, the annual losses are 0.25 and 0.24% yr21 for Alfisols and Mollisols,

80

Table X Estimated Absolute and Relative Annual Losses in Soybean Production by Continent and Soil Order Erosion-induced yield loss (% Mg21)

Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha2)

Estimated production area (103 ha)

Total Production (103 Mg)

Production loss (103 Mg yr2)

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Ultisols Vertisols Total

20.05% 20.05% 20.05% 20.05% 20.05% 0.00% 20.05% 0.04% 20.05% 20.22%

12.17 13.83 19.49 1.60 10.62 13.48 12.48 14.33 16.77 16.69

20.61% 20.69% 20.97% 20.08% 20.53% 0.00% 20.62% 0.57% 20.84% 23.67%

0.59 1.23 0.40 2.03 0.74 1.12 1.48 0.30 1.71 1.50

4,251.3 279.0 1.9 581.1 1.6 132.0 283.5 0.7 9,429.1 1,768.3 16,728.5

2,526.3 344.2 0.8 1,180.7 1.2 147.2 418.2 0.2 16,153.8 2,658.2 23,430.7

215.4 22.4 0.0 20.9 0.0 0.0 22.6 0.0 2135.4 297.6 2254.3

Europe

Alfisols Andisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02%

12.26 14.29 0.79 5.64 10.56 11.99 13.29 7.64 16.66 16.75

0.25% 0.29% 0.02% 0.11% 0.21% 0.24% 0.27% 0.15% 0.33% 0.33%

2.22 1.47 2.32 2.45 2.29 1.41 0.63 2.37 1.78 1.14

631.1 3.0 84.7 0.6 9.5 457.3 0.5 1.7 7.2 26.8 1,222.4

1,398.4 4.3 196.6 1.4 21.7 643.6 0.3 4.1 12.8 30.7 2,313.9

3.4 0.0 0.0 0.0 0.0 1.5 0.0 0.0 0.0 0.1 5.2

C. DEN BIGGELAAR ET AL.

Asia

0.01% 0.01% 0.01% 0.03% 0.01% 0.02% 0.01% 0.01% 0.03% 0.01%

10.66 14.06 11.54 12.64 9.32 14.50 14.53 10.80 16.80 16.80

0.11% 0.14% 0.12% 0.38% 0.09% 0.29% 0.15% 0.11% 0.50% 0.17%

3.17 0.69 1.32 2.04 2.90 2.62 2.39 3.48 2.44 1.56

7,871.0 13.6 6.2 28.8 0.2 80.9 11,502.7 6.9 9,863.9 836.8 30,211.1

24,922.2 9.4 8.2 58.6 0.7 211.9 27,494.6 24.2 24,021.6 1,307.3 78,058.8

26.6 0.0 0.0 0.2 0.0 0.6 39.9 0.0 121.1 2.2 190.7

Central +South America

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Ultisols Vertisols Total

0.03% 0.03% 0.03% 0.03% 0.03% 0.03% 0.02% 0.03% 0.03% 0.03%

14.36 14.46 23.35 2.09 11.82 14.09 14.39 11.96 15.72 15.25

0.43% 0.43% 0.70% 0.06% 0.35% 0.42% 0.29% 0.36% 0.47% 0.46%

1.25 3.24 0.73 2.80 1.04 1.40 3.74 1.32 1.83 3.49

10,923.2 551.9 55.2 1,189.2 0.3 225.3 7,735.4 180.8 547.7 1,571.6 22,980.3

13,676.5 1,785.6 40.3 3,331.3 0.3 316.3 28,908.5 238.7 1,000.9 5,486.3 54,784.8

58.9 7.7 0.3 2.1 0.0 1.3 83.2 0.9 4.7 25.1 184.2

II

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Spodosols Ultisols Vertisols Total

GLOBAL IMPACT OF SOIL EROSION

North America

81

82

C. DEN BIGGELAAR ET AL.

respectively (Table X). Almost all production losses in Europe occurred on Alfisols (65%) and Mollisols (29%). Overall relative yield loss of soybeans in North America is 0.24% yr21, ranging from 0.09% yr21 on Histosols to 0.50% yr21 on Ultisols (Table X). High relative annual yield losses were also found for soybeans on Entisols (0.38% yr21) and Inceptisols (0.29% yr21). Relative yield losses were intermediate on the other soil orders, ranging from 0.11 2 0.17% yr21. In North America, soybeans are produced on about 30.2 Mha; primary soil orders on which soybeans are produced are Mollisols (11.5 Mha or 38% of the total area), Ultisols (9.9 Mha or 33%) and Alfisols (7.9 Mha or 26 %). Due to the high erosion rate of Ultisols and the high erosion-induced yield loss, most soybean production loss occurs on Ultisols (121,000 Mg yr21 out of a total loss of 191,000 Mg yr21, or 63% of total losses). Production losses on Mollisols and Alfisols are much smaller, contributing about 21 and 14% of the total annual production losses, respectively. Central and South America is the second soybean production region in the world, with a land area of about 23 Mha in soybeans. Nearly half of this area is estimated to be on Alfisols, and another one-third on Mollisols. The average relative annual yield loss in this continent was estimated at 0.33% yr21, with a range of 0.06 –0.70% yr21 (Table XII). The lowest relative losses are found on Entisols, while the highest relative loss occurs on Aridisols. However, few soybeans are produced on the latter (552,000 ha) (Table X). Relative yield losses on the major soybean producing soils were estimated at 0.43, 0.29, and 0.46% yr21 on Alfisols, Mollisols and Vertisos, respectively (Table X). Total production losses in Central and South America were estimated at about 184,000 Mg yr21. Although Mollisols are not the major soils on which soybeans are grown in this continent, the amount of production loss is greatest on these soils with 83,000 Mg yr21, or 45% of the total loss in production. Losses on Alfisols constitute about 32% of the total loss of soybean production, with about 14% of the losses attributed to erosion on Vertisols (Table X).

E. WHEAT Estimates on the erosion-induced losses in the production of wheat were made for five continents. Average relative wheat yield losses across soil orders range from 0.04% yr21 in Europe to 0.67% yr21 in Australia. Relative annual yield losses in Asia and Central and South America are similar at 0.29 and 0.27% yr21, respectively, with average relative losses in North America estimated at less than half those rates at 0.11% yr21. Relative yield losses across soil orders in Asia ranged from 003% yr21 on Entisols to 0.42% yr21 on Oxisols (Table XI). For the soil orders on which most of the wheat is grown in Asia (i.e. Alfisols, Inceptisols, and Mollisols), relative yield losses were estimated at 0.22, 0.37, and 0.27% yr21, respectively. The total

Table XI Estimated Absolute and Relative Annual Losses in Wheat Production by Continent and Soil Order Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha21)

Estimated production area (ha)

Total production (103 Mg)

Production loss (103 Mg yr21) 84.7 6.0 0.3 6.0 0.0 341.4 166.6 0.1 38.1 96.3 739.5

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Ultisols Vertisols Total

0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02%

10.97 14.34 9.75 1.52 13.71 18.38 13.33 20.92 15.25 18.26

0.22% 0.29% 0.20% 0.03% 0.27% 0.37% 0.27% 0.42% 0.31% 0.37%

1.81 0.94 2.55 2.97 1.49 2.80 3.16 2.13 2.06 2.80

21,304.9 2,236.0 58.6 6,692.9 10.1 33,152.8 19,757.6 9.6 6,076.3 9,412.7 98,711.7

38,583.8 2,101.2 149.6 19,897.7 15.1 92,861.3 62,504.3 20.6 12,489.9 26,359.8 254,983.3

Australia

Alfisols Andisols Aridisols Entisols Inceptisols Mollisols Oxisols Spodosols

0.05% 0.04% 0.02% 0.04% 0.04% 0.04% 0.04% 0.04%

12.34 14.25 13.54 11.84 22.55 15.71 12.99 14.48

0.62% 0.57% 0.27% 0.47% 0.90% 0.63% 0.52% 0.58%

1.78 0.69 2.15 1.89 2.21 2.30 1.29 2.09

5,698.0 52.8 141.8 81.8 889.8 597.5 17.4 13.4

10,163.8 36.5 305.3 154.6 1,962.3 1,374.3 22.5 28.0

62.7 0.2 0.8 0.7 17.7 8.6 0.1 0.2 (continued)

II

Asia

GLOBAL IMPACT OF SOIL EROSION

Erosion-induced yield loss (% Mg21)

83

84

Table XI (continued) Crop-specific estimated erosion rate (Mg ha21 yr21)

Relative annual yield loss (% yr21)

Normalized yield (Mg ha21)

Estimated production area (ha)

Total production (103 Mg)

Production loss (103 Mg yr21)

Ultisols Vertisols Total

0.04% 0.04%

14.01 17.75

0.56% 0.71%

1.61 2.21

253.9 3,755.9 11,502.3

409.9 8,288.9 22,746.1

2.3 58.9 152.2

Europe

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Spodosols Ultisols Vertisols Total

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.00% 0.00%

5.35 11.16 12.07 0.91 3.72 19.22 10.61 8.88 8.09 21.30

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.11% 0.00% 0.00% 0.00%

2.36 2.52 3.21 3.78 2.35 3.26 4.23 3.12 2.48 3.65

27,229.8 800.6 8.2 3,230.4 59.0 8,980.3 16,536.9 57.2 1.1 868.6 57,772.0

64,317.5 2,015.3 26.4 12,226.6 138.4 29,318.1 69,897.1 178.7 2.8 3,168.1 181,288.9

0.0 0.0 0.0 0.0 0.0 0.0 74.2 0.0 0.0 0.0 74.2

North America

Alfisols Andisols Aridisols Entisols Histosols Inceptisols

0.01% 0.01% 0.01% 0.01% 0.01% 0.01%

10.74 5.75 11.65 2.28 8.15 14.30

0.11% 0.06% 0.12% 0.02% 0.08% 0.14%

1.92 2.02 2.09 2.04 1.31 2.45

13,223.0 1,313.3 29.7 276.3 12.7 1,874.9

25,356.4 2,652.4 62.2 564.0 16.7 4,597.9

27.2 1.5 0.1 0.1 0.0 6.6

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Erosion-induced yield loss (% Mg21)

0.01% 0.01% 0.01% 0.01%

13.21 10.81 15.00 17.26

0.13% 0.11% 0.15% 0.17%

2.98 2.16 1.65 1.85

13,804.8 26.5 1,818.2 545.2 32,924.6

41,184.7 57.3 3,008.6 1,010.7 78,510.9

54.4 0.1 4.5 1.7 96.3

Alfisols Andisols Aridisols Entisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols Total

0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02%

10.95 11.36 7.28 1.68 9.32 21.27 14.27 9.77 17.42 15.37 17.23

0.22% 0.23% 0.15% 0.03% 0.19% 0.43% 0.29% 0.20% 0.35% 0.31% 0.34%

1.67 1.51 2.71 2.96 0.84 2.66 2.89 0.98 2.78 1.81 2.46

1,632.9 828.3 14.4 718.3 0.1 903.6 4,336.0 31.5 3.3 52.4 217.0 8,737.6

2,725.4 1,253.8 39.0 2,127.9 0.1 2,407.2 12,546.8 30.8 9.1 94.9 533.4 21,768.3

6.0 2.8 0.1 0.7 0.0 10.2 35.8 0.1 0.0 0.3 1.8 57.9

GLOBAL IMPACT OF SOIL EROSION

Central þ South America

Mollisols Spodosols Ultisols Vertisols Total

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annual amount of production lost as a result of erosion in Asia was estimated at 740,000 Mg yr21, 46% from erosion on Inceptisols, and 23% on Mollisols. Erosion on Alfisols and Vertisols generated about 11 and 13% of total production losses of wheat in Asia (Table XI). Average relative annual yield losses in wheat were highest in Australia at 0.67% yr21. Comparing relative losses across soil orders shows that the lowest losses occurred on Aridisols (0.27% yr21) and the highest losses on Inceptisols (0.90% yr21) (Table XI). The major wheat production zone of Australia is on Alfisols and Vertisols with 5.7 and 3.8 Mha, respectively; relative yield losses on these two soil orders were estimated at 0.62 and 0.71% yr21, respectively. Soil erosion on these two soil orders combined lead to 80% of the loss of wheat production of about 152,000 Mg yr21 (Table XI). Studies on the effect of erosion on wheat yields in Europe showed that it has little to no effect on this crop. The mean relative yield loss across soil orders was estimated at 0.04% yr21. Of the 10 soil orders with a potential for wheat production in Europe, no losses occurred on nine soils orders; only Mollisols experienced a relative loss of 0.11% yr21 (about 74,000 Mg yr21) (Table XI). Relative yield losses were also low in North America, declining at an average rate of 0.11% yr21 across soil orders. Yield losses were estimated at less than 0.1% yr21 on Andisols, Entisols and Histosols; on other soil orders, production losses ranged between 0.11 (Alfisols and Spodosols) and 0.17% yr21 (Vertisols) (Table XI). Most wheat in North America is produced on Alfisols and Mollisols with 40 and 42% of the total area of 32.9 Mha; Inceptisols and Ultisols each have about 5.5% of the total wheat area (Table XI). The total amount of production lost due to erosion was estimated at 96,000 Mg yr21, with 56% of that total due to wheat production losses on Mollisols and 28% on Alfisols. Relative annual yield losses of wheat in Central and South America range from 0.03% yr21 on Entisols to 0.43% yr21 on Inceptisols (Table XI). Relative loss on Mollisols, which represent half of the wheat area in Latin America, was estimated at 0.29% yr21, while losses on Alfisols (having the second largest area in wheat) amount to 0.22 % yr21. The total loss of production was estimated to be 58,000 Mg yr21; of this total, 62% was due to erosion-induced yield declines on Mollisols, with 18% due to yield decreases on Inceptisols. Although Inceptisols represent only about 10% of the area in wheat in Central and South America, they have the highest estimated erosion rate under wheat on this content (21.27 Mg ha21 yr21).

F. VALUE

OF

PRODUCTION LOSSES

To aggregate production losses due to induced soil productivity declines across crops and continents, we used the 2000/01 prices of the USDA baseline projections to 2010 (USDA, 2001). We used the same prices for crops across

Table XII Value of Erosion-Induced Production Losses, by Continent and Crop Production loss (103 Mg yr21)

Priceb (US$ Mg21)

Value of total production (106 US$)

Value of production loss (103 US$)

Estimated mean production/value loss (% yr21)

Maize Subtotal

41,198.1

202.2

$72.83

$3,000 $3,000

$14,726 $14,726

0.49% 0.49%

Asia

Maize Millet Soybeans Wheat Subtotal

162,288.9 12,692.7 23,492.6 254,338.3

961.2 64.2 2254.3 739.5

$72.83 $72.75 $180.04 $93.96

$11,820 $923 $4,230 $23,898 $40,870

$70,004 $4,671 2$45,784 $69,483 $98,374

0.59% 0.51% 21.08% 0.29% 0.24%

Australia

Potatoes Wheat Subtotal

1,872.0 22,739.0

2.3 152.2

$129.00 $93.96

$241 $2,137 $2,378

$297 $14,301 $14,597

0.12% 0.67% 0.61%

Europe

Millet Potatoes Soybeans Wheat Subtotal

1,059.8 136,831.9 2,313.8 181,517.3

2.4 51.1 5.2 74.2

$72.75 $129.00 $180.04 $93.96

$77 $17,651 $417 $17,055 $35,200

$175 $6,592 $936 $6,972 $14,675

0.23% 0.04% 0.22% 0.04% 0.04%

II

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GLOBAL IMPACT OF SOIL EROSION

Total productiona (103 Mg yr21)

(continued)

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Table XII (continued) Total productiona (103 Mg yr21)

Production loss (103 Mg yr21)

Priceb (US$ Mg21)

Value of total production (106 US$)

Value of production loss (103 US$)

Estimated mean production/value loss (% yr21)

Maize Potatoes Sorghum Soybeans Wheat Subtotal

259,121.8 25,903.0 13,810.9 77,878.5 90,360.1

399.3 1030.9 8.2 190.7 96.3

$72.83 $129.00 $64.96 $180.04 $93.96

$18,872 $3,341 $897 $14,021 $8,490 $45,622

$29,081 $132,986 $533 $34,334 $9,048 $205,982

0.15% 3.98% 0.06% 0.24% 0.11% 0.45%

Central þ South America

Maize Potatoes Soybeans Wheat

74,608.3 16,281.4 55,425.5 21,719.9

703.6 2.1 184.2 57.9

$72.83 $129.00 $180.04 $93.96

$5,434 $2,100 $9,979 $2,041

$51,243 $271 $33,163 $5,440

0.94% 0.01% 0.33% 0.27%

Subtotal Maize Potatoes Millet Sorghum Soybeans Wheat Total

537,217.1 180,887.9 13,752.0 13,810.9 159,110.4 570,674.6

2,266.3 1086.4 66.6 8.2 125.8 1120.1

$72.83 $129.00 $72.75 $64.96 $180.04 $93.96

$19,554 $39,126 $23,335 $1,000 $897 $28,646 $53,621 $146,624

$90,118 $165,055 $140,146 $4,845 $533 $22,649 $105,245 $438,472

0.46% 0.42% 0.60% 0.48% 0.06% 0.08% 0.20% 0.30%

Global total

a b

Production data from FAOStat (2000). Prices based on the projected 2000/01 crop prices from the USDA Agricultural Baseline Projections to 2010 (USDA, 2001).

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North America

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continents, namely $72.83 Mg21 for maize, $93.96 Mg 21 for wheat, $180.04 Mg21 for soybeans, $72.75 Mg21 for millet, $64.96 Mg21 sorghum, and $129.00 Mg21 for potatoes. The estimated value of the losses by crop and continent are shown in Table XII. The value of annual production losses for the selected crops amounts to $14.7 million in Africa, $98.4 million in Asia, $14.6 million in Australia, $14.7 million in Europe, $205.9 million in North America and $90.1 million in Central and South America (Table XII). Globally, the losses are estimated at $165.1 million for maize, $4.9 million for millet, $140.1 million for potatoes, $533 thousand for sorghum (North America only), $22.7 million for soybeans and $105.2 million for wheat (Table XII). These losses represent an annual decline of 0.3% in the value of 0.3% of the value of the global production of the selected crops, ranging from 0.04% yr21 in Europe to 0.61% yr21 in Australia. These figures represent a rough estimate of the potential annual value of crop production losses to soil erosion for the selected crops. The true value of production losses is indeterminate. We have not been able to estimate the losses for all crops and regions in the absence of any erosion –productivity studies on which to base the estimates. For example, sorghum in North America represents only 23% of the total global production of this crop. It is, however, a major staple crop in both Africa and Asia, but no erosion –productivity studies using this crop have been done on these continents; hence it is not possible to provide a true global estimate of sorghum production losses. The production loss estimates for millet, potatoes, maize, wheat and soybeans represent about 49, 60, 89, 97, and 100% of the total global production of these crops, respectively. Our estimates do not include the additional production costs incurred by farmers to offset the loss of soil, and of societal costs to mitigate sedimentation, water pollution and other off-site damages caused by soil erosion. On the other hand, the estimated potential production losses reported here may overstate actual losses to the extent that farmers’ actions reduce losses to soil erosion. The true economic costs resulting from soil erosion are, therefore, likely to be significantly different from our estimated global total value of $438.5 million (Table XII).

V. DISCUSSION AND CONCLUSION In the companion paper (den Biggelaar et al., this volume), we presented a review of studies in the soil science literature on the effects of erosion on soil productivity on a soil and crop specific basis. The aim of the present paper was to extrapolate from the yield losses per cm or Mg of soil erosion determined in the previous report to estimate the annual impact of soil erosion on crop yields and production at various scales. Our aim was not to determine definite, final answers as to the amount or the value of crop production lost as a result of erosion-induced

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soil productivity declines, but mainly to (1) identify priority areas where, based on importance to crop production, erosion rates and erosion-induced yield impacts, erosion most affects crop yields, production, and food security; and (2) help guide policy makers in developing appropriate response to erosion accordingly. As we estimated production losses only for crops for which regional soilspecific studies were available, the global total production losses and their values are not inclusive totals. However, the estimates do suggest the relative differences in the magnitude of the problem by crop, soil order, and location at both continental and global scales, and provide an indication of the losses incurred by the farm sector as a result of erosion-induced production declines. It should be noted that the additional costs associated with erosion at the farm level (such as extra costs for fertilizers, irrigation, soil preparation, and pesticides to maintain yields or, at least, limit their decline), and at the level of societies (such as costs of water pollution and sedimentation), are not included in our estimate of the economic costs of soil erosion. Extrapolations from yield decline data per cm or Mg soil erosion on a soilspecific basis requires the availability of soil-based erosion, crop yield and crop area data. International statistics (for example, those of FAO and the World Bank) do not provide this detail, and are limited to only providing aggregate statistical data on a national level. In absence of global soil-based databases on soil erosion and crop production, our analyses are necessarily empirical and dependent upon the assumptions made in the methods employed to arrive at our estimates of potential soil erosion and crop areas by soil order. A further weakness in our analysis stems from the paucity of erosion-productivity studies which we used as the basis for the assessment of production losses and their values. This study takes the conclusion of Reich et al. (2000) that soil erosion and the resulting degradation remain a threat to world food production one step further by estimating the actual effects of erosion on crop production at various scales. The estimation of erosion rates by soil order and continent shows that there is a large variation in erosion rates among soil orders, and according to the crops being grown on them. For policy making to combat erosion, the use of a global or continental average erosion rate (e.g. Brown, 1984; Brown and Wolf, 1984; Pimental et al., 1995), an average erosion for a specific soil order across an entire continent or country for all crops, or an average erosion rate for a specific crop across soil orders is, therefore, neither justified nor recommended. For example, in Central and South America, erosion rates for land under maize on most orders are higher than erosion rates for land under wheat on those same soil orders. In Central and South America, maize land has erosion rates ranging from 1.74 Mg ha21 in Entisols to 25.78 Mg ha21 on Spododols, whereas for wheat they range from 1.68 Mg ha21 on Entisols to 21.27 Mg ha21 on Inceptisols (with the rate on Spodosols being 30% less than those under maize at 17.45 Mg ha21). In addition, high average erosion rates on a particular soil order do not necessarily indicate a serious problem, as the order may be only a marginal one for crop

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production in general, or for a specific crop in particular. Globally, very little land under Andisols, Aridisol, Histosols, and Spodosols is used for crop production, with the exception of Spodosols in Europe and North America where about one-quarter of the potatoes are grown on these soils. On most continents, our estimates indicate that less than 0.5% of cropland consists of Aridisols and Histosols. It may, therefore, be environmentally and economically better to limit use of certain soil orders for agricultural production, or to restrict the production of specific crops on these orders. For example, potential average erosion rates on Spodosols in Central and South America are 25.78, and 17.42 Mg ha21 under maize and wheat, respectively, but only 0.62 Mg ha21 under potatoes. As the estimated areas in these crops on Spodosols are small (in Central and South America, 200, 9000, and 3300 ha of maize, potatoes, and wheat are grown on these soils, respectively), discouraging their cultivation, especially for maize and wheat, and encouraging their cultivation on less erosion prone soils would not greatly affect overall production and food security. On the other hand, soil conservation measures should in preference be concentrated and encouraged on soil orders that are important in terms of both extent and production of particular crops, but presently prone to high erosioninduced production losses (for example, soybeans on Ultisols in North America and on Alfisols and Mollisols in Central and South America). We therefore recommend that our assessments primarily be used to identify specific soil orders that, according to our estimations, are important for the production of some of the world’s major staple crops in terms of both area and production, and identify specific soil – crop combinations in which erosion has particularly large impacts in terms of lost production and value. Policy measures and soil conservation interventions should therefore be aimed, in particular, at producers growing those crops on soils on which they are particularly vulnerable to erosion. Partial global production loss estimates (partial because estimates are made only on continents on which erosion – productivity studies have been done for a specific crop) suggest that, each year, farmers lose about 2.2 million Mg maize, 0.07 million Mg millet, 1.1 million Mg potatoes, 0.1 million Mg soybeans and 1.1 million Mg wheat as a result of erosion. Losses of sorghum in just North America amount to 8,200 Mg yr21. These partial production losses are 33 – 55% of the estimated gap between production and the amount necessary to maintain per capita consumption at 1995 – 1997 levels in 66 low-income developing countries (11 million Mg), or to meet minimum nutritional requirements (17.6 million Mg) (USDA, 1998). Reducing production losses by limiting erosion would, therefore, go a long way to attain food security. The economic value of this production loss is estimated at $489.9 million. Three main conclusions can be drawn from our analyses: First, estimated annual losses at a global scale for the crops and continents considered in our analyses are small relative to the total agricultural production and value of the

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selected crops. The losses are likely to be masked over the short term by market fluctuations, weather, and other environmental perturbations, diminishing incentives for farmers to adopt conservation practices. Moreover, erosion’s impacts are cumulative and may cause more serious losses if it continues unabated over a long period of time. Second, our estimated global annual losses in crop yields and production are at the lower end of the range of previously published estimates of erosion-induced productivity losses (Lal and Stewart. 1990; Janargin and Smith, 1993; Crosson, 1997; Lal, 1998; Young, 1999). Of more interest, especially for soil conservation policy is the finding that losses vary widely between crops, soil orders and regions, and in selected situations can be quite substantial. In general, though, little is known about these losses for many important crops in many developing countries. Third, estimated losses in productivity are probably small in relation to offsite impacts (such as sedimentation). These findings underscore the importance of continued policy measures to encourage soil conservation. They also underscore the importance of improved understanding of erosion and its impacts for these crops, soils, and regions where its impacts are most severe or least understood. Finally, more precise estimation of actual losses due to erosion (as opposed to the potential losses estimated here) depends on improved understanding of farmers’ optimal response in the face of changing physical, market, and policy environments.

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USGS (United States Geological Survey) EROS Data Center, University of Nebraska-Lincoln and the Joint Research Centre of the European Commission. (2000). Global Land Cover Characterization. Internet URL ,http://edcdaac.usgs.gov/glcc/globdic1_2.html . Version 1.2 (Feb 17, 2000). Wishmeier, W. H., and Smith, D. H. (1978). “Predicting Rainfall Erosion Losses – A Guide to Conservation Planning”. “Agric. Handbook No. 537”. US Department of Agriculture, Washington, DC. World Bank, (2000). “World Development Indicators 2000”. World Bank, Washington, DC. World Soil Resources Staff, (1997). “Global Soil Regions Map”. USDA Natural Resources Conservation Service, Soil Survey Division, World Soil Resources, Washington DC. WRI (World Resources Institute), (1996). “Food and Agriculture. World Resources 1996–97: The Urban Environment”. WRI, Washington, DC, Chapter 10. Young, A. (1999). “Land Degradation.” In “Land Resources: Now and for the Future.” Cambridge University Press, Cambridge, UK, pp. 101–133.

PLANT GROWTH PROMOTING RHIZOBACTERIA: APPLICATIONS AND PERSPECTIVES IN AGRICULTURE Zahir A. Zahir,1 Muhammad Arshad,1 and William T. Frankenberger, Jr.2 1

Institute of Soil & Environmental Science, University of Agriculture, Faisalabad-38040, Pakistan 2 Department of Environmental Sciences, University of California, Riverside, California 92521, United States

I. Introduction II. Mechanisms of Action A. Biological Nitrogen Fixation B. Production of Plant Growth Regulators and Biologically Active Substances C. Solubilization and Uptake of Nutrients D. Biological Control III. Screening of PGPR A. Root/Shoot Growth Under Gnotobiotic Conditions B. In Vitro Production of Plant Growth Regulators C. 1-Aminocyclopropane-1-Carboxylic Acid (ACC) Deaminase Activity IV. Application of PGPR in Agriculture A. Effect of PGPR Inoculation on Cereals B. Effect of PGPR Inoculation on Other Crops C. Rhizobia as PGPR in Non-Legumes D. Co-Inoculation of Legumes with PGPR and Rhizobium E. Precursor – Inoculum Interactions V. Concluding Remarks References

Rhizobacteria that exert beneficial effects on plant growth and development are referred to as plant growth promoting rhizobacteria (PGPR). In recent years, the use of PGPR to promote plant growth has increased in various parts of the world. PGPR can affect plant growth by production and release of secondary metabolites (plant growth regulators/phytohormones/biologically active substances), lessening or preventing deleterious effects of phytopathogenic organisms in the rhizosphere and/or facilitating the availability and uptake of certain nutrients from the root environment. Selection of effective 97 Advances in Agronomy, Volume 81 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. DOI 10.1016/S0065-2113(03)81003-9

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Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER PGPR is the most critical aspect to have maximum benefits from this technology. Researchers are using different approaches for screening rhizobacteria to select effective PGPR including promotion of root/shoot growth under gnotobiotic conditions, in vitro production of plant growth regulators/biologically active substances and assessing of ACC-deaminase activity of the rhizobacteria. However, the combined use of two or more approaches for screening of rhizobacteria could be more useful to select effective PGPR. These screening approaches and practical applications of PGPR in agriculture are the major focus of this review. q 2004 Academic Press.

I. INTRODUCTION A major strategy to counteract the rapid decline in environmental quality is to promote sustainable agriculture. This demands continuous advances in biological productivity, achieved in an ecologically sustainable manner (Swaminathan, 1991). The gradual reduction in the use of pesticides and fertilizers and greater use of the biological and genetic potential of plant and microbial species may alleviate the high demand of agrochemicals in sustaining high production in agriculture. Considering the competitive nature of indigenous soil bacteria and their population densities, it has proven difficult to make any long lasting phenotypic changes in the composition of bacteria within any given soil community. One strategy that may contribute to the establishment of pre-selected beneficial organisms in the root zone of soils is through early establishment of selected communities of bacteria in the rhizosphere at the plant seedling stage providing a continuous supply of carbon and energy sources. Altering the rhizosphere microflora by seed or root inoculation with specific organisms has long been recognized as a practical possibility. The objective of this practice is to modify plant growth in a desired direction as a result of a beneficial interaction between the plant and the inoculated/introduced microorganism(s). During the last couple of decades, the use of rhizobacteria as inocula has been dramatically increased. The effects of rhizobacteria on an inoculated host may be neutral, deleterious, or beneficial. The beneficial rhizobacteria have been classified as plant growth-promoting rhizobacteria (PGPR) by Kloepper and Schroth (1978). The list of PGPR is increasing every day as more research groups are engaged in screening rhizobacteria for growth promotion. For instance, Gram positive PGPR taxa include coryneform bacteria, Bacillus cereus, B. cirulans, B. subtilis and Bacillus spp, while gram negative PGPR include fluorescent as well as non-fluorescent Pseudomonas (P. gladioli and P. cepacia) and various members of the family Enterobacteriaceae (Kloepper, 1994). Frankenberger and Arshad (1995) are of the view that all rhizobateria that promote plant growth upon inoculation through any mechanism of action should be classified as PGPR.

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99

In recent years, the use of PGPR to promote plant growth has increased dramatically in various parts of the world. Several authors have reported that inoculation with PGPR can result in increased germination, seedling emergence and modify growth and yield of various cereal and non-cereal crops (Kloepper and Schroth, 1978; Suslow, 1982; Kloepper et al.,1986; Hassouna, 1990; Xia et al., 1990; deFreitas and Germida, 1992; Chen et al.,1994; Javed and Arshad, 1997; Mehnaz et al., 1998; Biswas et al., 2000a, b; Dobbelaere et al., 2001; Van et al., 2000). It has been reported that these growth responses to microbial inoculation involves strain to crop and even variety and site specificity. Chen et al. (1994) reported that PGPR used in a geographical area outside the isolated source of enrichment had variable effects for improving crop yields. Chanway and Holl (1992) also reported specific responses of PGPR in correspondence to certain geographical areas. Similarly, Nowak (1998) found that inoculation benefits of certain strains varied with plant species, cultivar, and growth conditions. Chanway et al. (1988) further confirmed the specificity of PGPR for certain soils and cultivars. Furthermore, we have observed different responses of plants to inoculation with PGPR in regards to timing, mode of application, and different soils (unpublished).

II.

MECHANISMS OF ACTION

Several mechanisms have been postulated to explain how PGPR may affect the growth and development of inoculated plants. These can be broadly categorized as either direct or indirect mechanisms of growth stimulation. Direct growth promotion occurs when a rhizobacterium produces a metabolite(s) i.e. phytohormones or improves nutrient availability that directly promotes plant growth (Kloepper et al., 1989, 1991). In contrast, antibiotics, siderophores and hydrogen cyanide (HCN), which decrease the activities of pathogens or deleterious microorganisms and thereby increase plant growth are examples of indirect growth promotion. Thus, the mechanisms reported by different workers may include direct or indirect growth promotion or both. However, Kloepper (1993) reported that there is often no clear separation between direct growth promotion and biological disease control promoting indirect growth. Rather, growth promotion and biological control of disease should be viewed as two sides of the same coin. Bacterial strains selected initially for in vitro antibiosis as part of evaluating biological control activity frequently demonstrate growth promotion in the absence of pathogens. Similarly, PGPR selected initially for growth promotion in the absence of pathogens may demonstrate biological control of disease when challenged with the pathogen. Plant response is obviously a very complex phenomenon and each complementary study provides

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new resources to understand this complexity. Literature available till date has allowed us to gain a better understanding of the mechanisms responsible for plant growth promotion via both direct and indirect effects.

A. BIOLOGICAL NITROGEN FIXATION Biological N2-fixation (BNF) by soil microorganisms is considered one of the major mechanisms by which plants benefit from the association of micropartners. One of the benefits that diazotrophic microorganisms provide to plants is fixed nitrogen in exchange for fixed carbon released as root exudates (Glick, 1995). The beneficial effects of the symbiotic association between rhizobia and legumes are well known and these have been intensively investigated. Moreover, previous studies have shown that free-living bacteria as well as rhizobial strains can promote the growth of cereal plants by contributing to N-economy through their ability to fix N2 (Hassouna and Wareing, 1964; Hassouna, 1973; Malik et al., 1997; Antoun et al.,1998; Biswas et al., 2000a, b). Ladha et al. (1998) reported that nitrogen fixation by PGPR may stimulate the growth of lowland rice plants. Using a 15N isotopic dilution technique, Malik et al. (1997) investigated the nitrogen fixing ability of Azospirillum strain N-4 in rice and found a significant contribution of nitrogen fixed by the PGPR. Similarly, Biswas et al. (2000a) reported that biological nitrogen fixation by diazotrophic PGPR strains may be a contributing factor of rice growth promotion in addition to other mechanisms. While conducting a field study on rice inoculated with Rhizobium leguminosarum strain E11, Dazzo et al.(2000) monitored BNF activity by the 15 N natural abundance technique. They found increased N content in the harvested rice plant at maturity. However, the contribution of BNF in plant growth was found non-significant. The increased N content may have possibly been due to other plant growth stimulating mechanisms as result of inoculation. More detailed information on the BNF contribution in promoting plant growth is reviewed by Ladha and Reddy (2000), Yanni et al. (2001), Kennedy and Islam (2001) and Benavides-Mendoza et al.(2001). Perhaps too much credit is given to BNF in stimulating plant growth while other mechanisms of action also need to be investigated.

B. PRODUCTION OF PLANT GROWTH REGULATORS BIOLOGICALLY ACTIVE SUBSTANCES

AND

Plant growth regulators (PGRs) are organic compounds that influence the physiological processes in plants at extremely low concentrations. Production of PGRs by inocula has been suggested as one of the most plausible mechanisms of action affecting plant growth. Numerous studies have shown

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an improvement in plant growth and development in response to seed or root inoculation with various microbial inoculants capable of producing PGRs. There are five classes of well-known PGRs, namely, auxins, gibberellins, cytokinins, ethylene, and abscisic acid. Soil microbiota, particularly the rhizosphere microflora are potential sources of these PGRs (Frankenberger and Arshad, 1995; Costacurta and Vanderleyden, 1995; Patten and Glick, 1996; Arshad and Frankenberger, 1998). Microorganisms known to produce PGRs are listed in Tables I and II. The type and amount of PGR production by microorganisms is variable. Barea et al. (1976) found that among 50 bacterial isolates collected from the rhizosphere of various plants, 86, 58, and 90% produced auxins, gibberellins, and kinetin-like substances, respectively. Similarly, Mansour et al. (1994) tested 24 isolates of thallobacteria belonging to the genus Streptomyces for their potential to produce PGRs. All the isolates were capable of producing auxins (ranging from 10.5 to 39.0 mg mL21), gibberellins (1.21 to 13.3 mg mL21) and cytokinins (2.5 to 14.9 mg mL21) in liquid medium. Different amounts of PGRs produced by PGPR were also confirmed by Gracia de Salamone et al. (2001). They reported that five plant growth promoting rhizobacteria strains produced the cytokinin, dihydrozeatin riboside (DHZR) in pure culture. Ps. fluorescens G20-18 produced higher amounts of three cytokinins, isopentyl adenosine (IPA), trans-zeatin ribose (ZR) and DHZR than three mutants. Isopentyl alcohol was the major metabolite produced, but the proportion of ZR and DHZR accumulated by the mutants (CNT1 and CNT2) increased with time. It is now known that microbial production of PGRs is one of the major mechanisms in modifying the growth and yield of plants. Addition of sterile spent medium of Ps. fluorescens 2137 increased the growth of B. japonicum A1017 in yeast mannitol broth indicating that the Ps. fluorescens released a substance that increased the rhizobial population (Chebotar et al., 2001), beneficial to plant growth. Under gnotobiotic conditions, Noel et al.(1996) showed the direct involvement of IAA and cytokinin production by PGPR in the growth of canola and lettuce. Similarly, Young et al. (1991) screened the PGPR strains, Pseudomonas and Serratia for PGRs and observed a good correlation between induction of rice root elongation and phytohormone production. Likewise, Asghar et al. (2002) reported a highly significant correlation between L-TRPderived auxin production by PGPR in vitro and grain yield ðr ¼ 0:77p Þ; number of pods ðr ¼ 0:78p Þ and number of branches ðr ¼ 0:77p Þ per plant of Brassica juncea. It was hypothesized that the PGPR influenced the growth and yield of inoculated plants by producing auxins in the rhizosphere upon the release of tryptophan in the root exudates, although other mechanisms of action might have also contributed. Gutie´rrez-Manˇero et al. (2001) isolated Bacillus pumilus and Bacillus lichenoformis from the rhizosphere of Alnus glutinosa L. which had strong growth promoting activity on alder. Bioassay data indicated that the dwarf phenotype of alder seedlings induced by paclobutrazol (an inhibitor of

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Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER Table I In Vitro Production of Plant Growth Regulators by Rhizobacteria

PGPR (if identified) Arthrobacter mysorens 7, Flavobacterium sp. L30, Klebsiella CIAM 880 Azotobacter beijernickii A. beijernickii A. chroococcum

A. chroococcum A. chroococcum A. chroococcum A. chroococcum A. paspali

A. A. A. A.

vinelandii vinelandii vinelandii vinelandii

Azotobacter sp. Azotobacter sp. A. brasilense A. brasilense A. brasilense A. brasilense A. lipoferum A. lipoferum Azospirillum sp. Azospirillum sp. Azospirillum sp. Azospirillum sp. Aeromonas sp. Azospirillum sp. Bacillus licheniformis B. licheniformis B. pumilus B. subtilis B. mycoides

PGR

Reference

Indole-3-acetic acid, ethylene

Pishchik et al. (2002)

Cytokinin-like substances Auxins, gibberellin-like substances Gibberelin-like substances, gibberellic acid, indole-3-acetic acid Gibberellin-like substances Gibberellin-like substances t-Zeatin, ribosylzeatin, isopentyl adenine, dihydrozeatin riboside Indole-3-acetic acid Cytokinin-like substances, indole-3-acetic acid, gibberellin-like substances Cytokinin-like substances t-Zeatin, isopentyl adenosine Indole-3-acetic acid Indole-3-acetic acid, gibberellin-like substances Indole-3-acetic acid, gibberellin-like substances Indole-3-acetic acid

Nieto and Frankenberger (1989) Azco´n and Barea (1975) Brown and Burlingham (1968)

Cytokinin-like substances, gibberellin-like substances Isopentyl adenine, isopentyl adenosine, zeatin Gibberellins, iso-gibberellic acid, gibberellic acid Indole-3-acetic acid Indole-3-acetic acid Gibberellin, gibberellic acid, iso-gibberellic acid Gibberellin-like substances Gibberellic acid Indole-3-acetic acid Indole-3-acetic acid Ethylene Ethylene Ethylene Physiologically active gibberellins Physiologically active gibberellins Ethylene Ethylene

Martinez-Toledo et al. (1988) Salmeron et al. (1990) Nieto and Frankenberger (1989) Mu¨ller et al. (1989) Barea and Brown (1974)

Nieto and Frankenberger (1989) Taller and Wong (1989) Lee et al. (1970) Gonzalez-Lopez et al. (1986) Mahmoud et al.(1984) Zahir et al.(1998a, b, 2000); Khalid et al. (2001) Tien et al. (1979) Horemans et al. (1986) Janzen et al. (1992) Martin et al. (1989) Martin et al. (1989) Bottini et al. (1989) Hubbell et al. (1979) Lucangeli and Bottini (1997) Dobbelaere et al. (2001) Lambrecht et al. (2000) Billington et al. (1979) Strzelczyk et al. (1994) Fukuda et al. (1989) Gutie´rrez-Manˇero et al. (2001) Gutie´rrez-Manˇero et al. (2001) Mansouri and Bunch (1989) Billington et al. (1979)

PGPR APPLICATION IN AGRICULTURE

Enterobacter aerogens Pseudomonas sp. Pseudomonas sp. Ps. Aeruginosa Ps. fluorescens

Ethylene Ethylene Auxins Ethylene Ethylene

Ps. fluorescens G20-18

Ps. Putida

Isopentyl adenosine, t-zeatin ribose, dihydrozeatin riboside Ethylene

Ps. Syringae

Ethylene

Ps. Tabaci Rhizobacterial isolates

Ethylene Auxins

103

Mansouri and Bunch (1989) Primorse (1976) Pal et al. (2000) Mansouri and Bunch (1989) Pazout et al. (1981); Swanson et al. (1979) Garcı´a de Salamone et al. (2001)

Pazout et al. (1981); Fukuda et al. (1989) Sato et al. (1997), Swanson et al. (1979) Swanson et al. (1979) Asghar et al. (2000, 2002); Khalid et al. (2001a, b)

gibberellin [GA] biosynthesis) was effectively reversed by applications of extracts from a medium incubated with both bacteria and also by exogenous GA3. Full-scan gas chromatography – mass spectrometery analysis of the extracts of this medium showed the presence of GA1, GA3, GA4, and GA20 in addition to the isomers 3-epi-GA1 and iso-GA3. Non-pathogenic rhizosphere microorganisms can be detrimental to plant growth. Suslow and Schroth (1982) first highlighted the importance of bacteria responsible for the inhibition of root growth and they proposed the term “deleterious rhizobacteria” or “DRB”. Subsequently, the term “deleterious rhizosphere microorganisms” or “DRMO” has been used to include nonpathogenic fungi detrimental to root growth (Cherrington and Elliott, 1987). The mechanisms by which DRMOs adversely affect plants include high levels of IAA, siderophore-mediated competition for iron, ethylene (C2H4), HCN, and unidentified phytotoxins. Barazani and Friedman (1999) studied the phytotoxic and promoting effect of bacterial secretions on root growth of young lettuce (Lactuca sativa) under axenic conditions. It was assumed that the inhibitory or promoting effects of either DRB or PGPR were auxin mediated. Thin layer chromatography combined with Salkowski’s reagent indicated that the DRB examined (Micrococcus luteus, Streptoverticillum sp., Ps. putida, and Gluconobacter sp.) produced and released high levels of IAA (76.6 mM) during 48 h of incubation. High concentrations of IAA released by the DRB accounted for the suppression of root growth. Like the DRB, four isolates of PGPR (Agrobacterium sp., Alcaligenes piechaudii, and two different strains of Comamonas acidovorans) secreted IAA, but at lower levels (16.4 mM) during a similar period of incubation.

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Table II Rhizobial Production of Plant Growth Regulators

Bradyrhizobium sp. B. japonicum B. japonicum Rhizobium sp. R. japonicum R. leguminosarum R. leguminosarum R. leguminosarum R. leguminosarum R. lupini R. meliloti R. meliloti R. meliloti R. phaseoli R. phaseoli R. phaseoli R. phaseoli R. phaseoli R. phaseoli R. trifolii

R. trifolii R. trifolii R. trifolii

PGR Isopentyl adenine, zeatin, methylthiozeatin a-naphthalene acetic acid, indole-3-acetamide, indole-3-acetic acid Indole-3-acetic acid Isopentyl adenine, isopentyl adenosine Indole-3-pyruvic acid, indole-3-acetic acid Indole-3-aldehyde, tryptophol, indole-3-carboxylic acid, indole-3-glycolic acid, indole-3-lactic acid, indole-3-glyoxylic acid, indole-3-acetic acid Indole-3-acetic acid Indole-3-acetic acid Isopentyl adenosine, zeatin, methylthiozeatin Indole-3-acetic acid, tryptophol Isopentyl adenine, ribosylzeatin Indole-3-acetic acid Isopentyl adenosine, methylthiozeatin Gibberellins, GA1, GA4, GA9, GA20 Indole-3-acetic acid, tryptophol Indole-3-acetic acid Indole-3-acetic acid Indole-3-acetic acid, tryptophol Isopentyl adenine, zeatin, methylthiozeatin Indole-3-acetaldehyde, tryptophol, indole-3-carboxylic acid, indole-3-glycolic acid, indole-3-lactic acid, indole-3-glyoxylic acid, indole-3-pyruvic acid, indole-3-acetic acid Indole-3-acetic acid Gibberellin-like substances Isopentyl adenine, isopentyl adenosine, ribosylzeatin, methylthiozeatin

Reference Taller and Sturtevant (1991) Sekine et al. (1988) Hunter and Kuykendall (1990) Sturtevant and Taller (1989) Kaneshiro et al. (1983) Badenoch-Jones et al. (1982a) Badenoch-jones et al. (1982a), Dazzo et al. (2000) Wang et al. (1982) Taller and Sturtevant (1991) Dullaart (1970) Taller and Sturtevant (1991) Kittell et al. (1989) Taller and Sturtevant (1991) Atzorn et al. (1988) Ernesten et al. (1987) Atzorn et al. (1988) Wheeler et al. (1984) Ernesten et al. (1987) Taller and Sturtevant (1991) Badenoch-jones et al. (1982b)

Badenoch-jones et al. (1982b) Williams and de Mallorca (1982) Taller and Sturtevant (1991)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Rhizobium

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PGPR can modify plant growth by producing and releasing substances other than IAA as well (Barazani and Friedman, 1999). Baker and Schippers (1987) and Schippers et al. (1987) established that 4% of the total aerobic bacteria and 40% of the pseudomonads from the rhizosphere of potato grown in short rotation were able to produce HCN in vitro. They demonstrated that the cytochrome respiratory pathway of potato roots was particularly sensitive to cyanide. The review by Schippers (1988) provides a model in which the DRMOs use glycine and proline in potato root exudates to synthesize HCN, which is taken up by the root. This process requires Fe2þ, but when PGPR are introduced into the rhizosphere, they deprive the DRMOs of Fe2þ by producing siderophore with stronger iron-chelating power than that produced by the DRMOs. Several other workers have reported that C2H4 and abscisic acid (ABA) production is a common characteristic of soil and rhizosphere microflora (Primorse, 1976; El-Sharouny, 1984; Arshad and Frankenberger, 1989; Crocoll et al., 1991). Glick and his co-workers have suggested the involvement of an enzyme, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase produced by Pseudomonas putida GR12-2 in modifying the root growth of different plants (Glick et al., 1994a, b; Hall et al., 1996; Glick et al., 1998; Li et al., 2000; Belimove et al., 2001, 2002). They found that this bacterium hydrolyzes ACC, the immediate precursor of C2H4 in higher plants. ACC deaminase might act to stimulate plant growth by sequestering and then hydrolyzing ACC from germinating seeds, thereby lowering the endogenous levels of ACC which subsequently results in plant growth promotion. Arshad and Frankenberger (1993, 1998) and Frankenberger and Arshad (1995) have published many studies demonstrating the involvement of phytohormones in inoculation-evoked plant responses.

C. SOLUBILIZATION

AND

UPTAKE

OF

NUTRIENTS

An adequate supply of mineral nutrients is necessary for optimum plant growth. However, when adequate amounts of essential nutrients are present in soil, plants may still show deficiencies due to the non-availability of these mineral nutrients. Solubilization of mineral nutrients such as phosphorus and iron by PGPR makes them more readily available for plant uptake, and this should be considered as a mechanism for enhanced plant growth (Glick, 1995). Several reports have suggested that PGPR can stimulate plant growth by increasing solubilization (via releasing siderophores or organic acids) and facilitate the uptake of mineral nutrients by the plant (Kloepper et al., 1987; Glick, 1995; Chabot et al., 1996a, b; Biswas et al., 2000b; Dazzo et al., 2000). Chabot et al. (1996a) and Dazzo et al. (2000) reported that certain strains of rhizobia are able

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to solubilize phosphorus. Phosphorus uptake was increased (13 – 23%) significantly in response to rhizobial inoculation of rice compared to a non-inoculated control (Biswas et al., 2000b). Toro et al. (1997) evaluated the interactive effect of phosphate-solubilizing bacteria (Enterobacter sp. and Bacillus subtilis) and arbuscular mycorrhizal (AM) fungi (Glomus intraradices) on onion (Allium cepa L.) with a soil of low P content. Co-inoculation of G. intraradices and B. subtilis significantly increased the vegetative biomass and N and P accumulation in plant tissues. This study revealed that the mycorrhizosphere interaction between bacteria and fungi can affect P cycling, thus promoting a sustainable nutrient supply to plants. Several other researchers have reported a similar role of other bacteria in P and other mineral nutrient solubilization enhancing nutrient uptake and subsequently plant growth (Belimov et al.,1995; Noel et al.,1996; Glick et al., 1998; Biswas et al., 2000a). PGPR may also alter the solubility of mineral nutrients by releasing organic acids, sugar acids and creating acidity via CO2 (respiration). The rhizosphere is a favorable habitat for acid-producing bacteria (Rouatt and Katznelson, 1961; Louw and Webley, 1959). Pietr et al. (1990) tested 748 bacterial strains isolated from the rhizoplane of different field crops and found that 25.6% strains were capable of dissolving calcium phosphate. They suggested that production of organic acids was the major mechanism of action by which insoluble phosphorus compounds were converted to more soluble forms. Many other scientists have also reported that PGPR can create an acidic environment to promote mineral nutrient solubilization (Webley and Duff, 1962; Moghimi et al.,1978; Alexander, 1977). More studies are indicating that PGPR may increase the mobility and availability of micronutrients by the formation of high affinity siderophores. Siderophores are low-molecular weight compounds that complex with Fe2þ and render it available to microorganisms (Leong, 1986). Some fluorescent pseudomonads produce a yellow-green pigment, a siderophore which Kloepper et al. (1980) designated as “pseudobactin”. The role of pseudobactin in promoting the growth of potato was demonstrated when 10 mg of pseudobactin increased plant growth to the same extent as when the fluorescent pseudomonad was applied to potato seed pieces. The widespread production of siderophores by microbes at low iron levels is reviewed by Neilands (1986). Organisms as diverse as Bacillus, Rhizobium, Pseudomonas, Agrobacterium, Escherichia coli, and many fungi produce a wide range of iron-chelating compounds. Numerous plants are capable of using bacterial Fe siderophore complexes as a means of obtaining Fe from soil (Wang et al.,1993). This view is supported by the findings of Hughes et al. (1992) who reported enhanced Fe uptake in oat due to siderophore production. More details on microbial production of siderophores and their role in enhancing Fe uptake has been reported by Loper and Schroth (1986) and Mori et al. (1991); Biswas et al. (2000a). Further work is needed to

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determine the quantity, quality, and optimal conditions for microbial production of siderophores and their influence on plant growth.

D. BIOLOGICAL CONTROL Soil-borne pathogens are well known for their devastating effects on plant health and yield. For successful disease management, it is important to find the most effective and economical ways to protect the plant from various pests or diseases. In recent years, the use of PGPR as an inducer of systemic resistance in crop plants against different pathogens has been demonstrated under field conditions (Wei et al.,1991 and 1996; Vidhayasekaran and Muthamilan, 1999; Viswanathan and Samiyappan, 1999). The use of natural PGPR strains in plant defense may offer a practical way to deliver immunization. In recent years, biological control of disease has emerged as an important mode of action through which PGPR may benefit plant growth. PGPR have been reported to increase the resistance in plants against fungal, bacterial, and viral diseases (Liu et al.,1995a; Maurhofer, et al.,1998), insects (Zehnder et al.,1997a,b) and nematodes (Sikora, 1988). Table III cites important studies of biological control by PGPR against certain diseases, pathogens and insects in different crops. Mode of action studies reveal that biological control by PGPR involves production of bacterial metabolites which reduce the population or activities of pathogens or deleterious rhizosphere microflora (Kloepper, 1993, 1994, 1996; Glick, 1995). These metabolites may include siderophores that bind Fe making it less available to certain members of the native pathogenic microflora (Berthelin et al., 1991; Kloepper et al., 1987; Subba Rao, 1993). Berthelin et al. (1991) reported that production of siderophores chelating iron in the plant rhizosphere makes it unavailable to harmful microflora in a sterilized medium. Certain fluorescent pseudomonads, particularly Ps. fluorescens and Ps. putida were adapted to colonize plant roots which promoted plant growth under field conditions most likely through siderophore formation (Kloepper et al., 1987). Similarly, Subba Rao (1993) suggested that fluorescent pseudomonads producing siderophores which specifically recognize and sequester the limited supply of iron in the rhizosphere may reduce the availability of iron for the growth of pathogens (Subba Rao, 1993). Antibiotic production is one of the most intensively studied aspects of biocontrol, but in many cases it is difficult to distinguish between antibiosis and competition. When antibiotic-negative mutants are inoculated in the rhizosphere and they do not affect the target organism as does the parent antibiotic producer, it is a good indication that antibiosis is a major factor. Several studies have demonstrated that production of antibiotics (e.g. pyrrolnitrin, phycocyanin, 2,4-diacetyl phloroglucinol) by microbial inocula can cause suppression of pathogens

108

Crop Barley Beans

Disease/pathogen/insect

PGPR

Carnation Cotton

Powdery mildew Halo blight Sclerotium rolfsii Fusarium wilt Damping off

B. subtilis Ps. fluorescens strain 97 Ps. Cepacia Pseudomonas sp. (WCS 417r) Ps. flourescens

Cucumber

Meloidogyne incognita, M. arenaria Rhizoctonia solani Helicoverpa armigera Cucumber anthracnose

B. subtilis Ps. Cepacia Ps. Gladioli Ps. putida (89B-27), Serratia marcescens (90-166) Ps. Cepacia Ps. putida (89B-27), S. marcescens (90-166) Ps. putida (89B-27), Flavomonas oryzihabitans INR-5, S. marcescens (90-166), Bacillus pumilus (INR-7) Ps. putida (89B-27), S. marcescens (90-166) Ps. putida (89B-27), S. marcescens (90-166) Ps. putida (89B-27), Flavomonas oryzihabitans INR-5, S. marcescens (90-166), B. pumilus (INR-7) Mixture of Paenibacillus sp. 300, Streptomyces sp. 385

Pythium ultimum Bacterial wilt Bacterial angular leaf spot Fusarium wilt Cucumber mosaic virus Striped Cucumber beetle Spotted Cucumber beetle Fusarium wilt

Reference Scho¨nbeck et al. (1980) Alstrom (1991) Fridlender et al. (1993) Van peer et al. (1991) Howell and Stipanovich (1979; 1980) Sikora (1988) Fridlender et al. (1993) Qingwen et al. (1998) Wei et al. (1991, 1996) Fridlender et al. (1993) Kloepper et al. (1993) Kloepper et al. (1993) Liu et al. (1995b) Raupach et al. (1996) Zehnder et al. (1997a) Singh et al. (1999)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Table III Biological Control by PGPR Against Certain Diseases, Pathogens and Insects in Different Crops

Green gram

Maize

Sugar beet

Sugarcane Tobacco

Root rot, Root knot Rice sheath blight Rice sheath blight Rhizoctonia solani (sheath blight pathogen) Rice root nematode Cyst nematode Pythium ultimum, Phona beta, Rhizopus stolonifer, Fusarium oxysporum Red rot Tobacco necrosis virus Wild fire (Ps. Syringae pv. tabaci) Blue mold

Tobacco horn worm

Pseudomonas sp.

Sindhu et al. (1999)

Ps. maltophila Ps. cepacia strain 526 and 406, Enterobacter agglomerans strain 621 Ps. aeruginosa, B. subtilis Streptomyces spp. and Bacillus cereus in combination with Ps. fluorescens and Burkholderia Combination of Ps. fluorescens strains Pf1 and Fp7 Ps. fluorescens Strains Pf1 and Fp7

Bong and Sikorowski (1991) Hebbar et al. (1992)

PGPRa Ps. fluorescens Pseudomonas sp. strain F113

PGPRa Ps. fluorescens strain PGPRa S. marcescens 90-116, B. pumilus SE 34, Ps. fluorescens 89B-61, B. pumilus T4, B. pasteurii C-9 Transgenic Ps. cepacia strain 526

Siddiqui et al. (2001) Sung and Chung (1997) Nandakumar (1998) Vidhayasekaran and Muthamilan (1999) Swarnakumari et al. (1999) Oostendorp and Sikora (1989, 1990) Shanahan et al. (1992)

Viswanathan and Samiyappan (1999) Maurhofer et al. (1994; 1998) Jetiyanon (1997); Park and Kloepper (2000); Press et al. (1997) Zhang et al. (2002)

PGPR APPLICATION IN AGRICULTURE

Mung bean Rice

Aspergillus sp., Curvularia sp., Fusarium oxysporum, Rhizoctonia solani Corn ear worm Fusarium miniliformes

Stock et al. (1990) (continued)

109

110

Crop Tomato

Disease/pathogen/insect Root knot nematode Cucumber mosaic virus

Tomato mottle virus Wheat

Take all disease

Septoria tritici a

PGPR not identified.

PGPR Ps. chitinolytica B. pumilus, Kluyvera cryocrescens, B. amyloliquifacians strain 1N 937a, B. subtilis strain 1N 937b B. amyloliquifacians strain 1N 937a, B. subtilis strain 1N 937b Bacillus, Pseudomonas, Penicillium, Beauveria, Rhodococcus Mixture of Pseudomonas sp. Ps. aeruginosastrain Leci Ps. putida strain BK8661

Reference Speigel et al.(1991) Raupach et al. (1996); Zehnder et al. (2000) Murphy et al. (2000) Renwick et al. (1991) Pierson and Weller (1994); Duffy and Weller (1995) Leavy et al. (1992) Flaishman et al. (1996)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Table III (continued)

PGPR APPLICATION IN AGRICULTURE

111

(Pierson and Thomashow, 1992; Kloepper, 1993, 1994; Subba Rao, 1993; Glick, 1995; Thomashow and Weller, 1995). Glick (1995) was of the view that the most effective mechanism that a PGPR can employ to prevent proliferation of phytopathogens is the synthesis of antibiotics. Thomashow and Weller (1995) also reported that production of antibiotic compounds is a common feature of inhabitating fluorescent pseudomonads in the rhizosphere. Suppression of takeall in wheat by Pseudomonas strains selected from disease suppressive soils was attributed to their production of phenazine antibiotics. Other mechanisms for biological control of disease may include: competition for infection sites and nutrients, parasitism on pathogens i.e. destruction of fungal pathogens by the action of lytic enzymes (e.g. chitinase and b-1, 3-glucanase) that degrade fungal cell wall, and uncharacterized antifungal factors (Lim et al., 1991; Fridlender et al., 1993; Kloepper, 1993, 1994, 1996; Potgieter and Alexander, 1996; Glick, 1995; Velazhahan et al.,1999). Buchenauer (1998) reported various mechanisms for biological control such as competition for space and nutrients in the rhizosphere and spermosphere, lytic enzymes, HCN, and many other metabolites produced by rhizobacteria. Subba Rao (1993) believes in multifarious mechanisms for biocontrol including competition for substrate and niche exclusion. He further explains that the points of emergence of lateral roots are favorable sites for deleterious rhizobacteria and PGPR appear to compete for those sites very effectively. In addition, several microorganisms are able to hydrolyze the compound fusaric acid, which is a causative agent in the damage of plants infected with Fusarium and, thus may prevent plant disease (Thomashow and Weller, 1995). A consortium of PGPR may often have more influence on biological control and plant growth than a single strain (Keel and Defago, 1997; Kim et al., 1997; Krishnamurthy and Gnanamanickam, 1998; Schmiedeknecht et al., 1998; Bapat and Shah, 2000). Renwick et al. (1991) used a screening program to search for biocontrol agents against Gaeumannomyces graminis causing take-all disease of wheat. Of 1800 rhizosphere microorganisms tested, 10% controlled the disease in a secondary screen. Of all the isolates selected, 63% belonged to the genera Bacillus, Pseudomonas and Penicillium. Fluorescent pseudomonads, all producing siderophores in low-iron medium, accounted for 23% of the isolates. Over 50% of the strains produced b-glucanases and chitinases. In a gnotobiotic system, the Pseudomonas strains were faster in colonizing wheat roots than the majority of Bacillus and fungal strains. Renwick et al. (1991) concluded that most of the naturally occurring biological controls result from a mixture of antagonists rather than from a high density of a single antagonist. The application of a mixture of introduced biocontrol agents would more closely mimic the natural environment and might broaden the spectrum of biological activity (Duffy and Weller, 1995). The work of various scientists have recently supported this broad spectrum of biocontrol activity by a mixture of PGPR strains (Raupach and Kloepper, 1998; Sung and Chung, 1997; Singh et al.,1999; Nandakumar, 1998). However, in

112

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

some cases, mixtures of different strains had no synergistic effect. Furthermore, a consortium that improves efficacy under one set of conditions or on one host may not perform under a different set of conditions and also on different hosts (Schisler et al., 1997). Recent work on the broad spectrum of PGPR-mediated induced systemic resistance against different pathogens in different crops has gained importance. Seed treatment with Ps. fluorescens was effective not only against the fungal root pathogen, Fusarium oxysporum f. sp. raphani, but also against the bacterial leaf pathogen, Ps. syringae pv. tomato and fungal leaf pathogen, F. oxysporum (Hoffland et al., 1996). Induced systemic resistance by Ps. putida strain 89B-27 and S. marcescens strain 90-166 was effective against anthracnose of cucumber (Wei et al., 1991), angular leaf spot caused by Ps. syringae pv. lachrymans (Liu et al., 1993a), Fusarium wilt caused by F. oxysporum f. sp. cucumerinum (Liu et al., 1993b) and cucurbit wilt caused by Erwinia tacheiphila (Kloepper et al., 1993). Similarly, Ps. fluorescens strain Pf1 induces resistance against different pathogens in different crops, viz. Rhizoctonia solani (Nandakumar, 1998) and Colletotrichum falcatum in sugarcane (Viswanathan and Samiyappan, 1999). The broad spectrum of biocontrol by PGPR has many applications in disease protection. Hence, selecting suitable strains having the potential to induce systemic resistance against multiple pathogens and pests is an important task in the delivery of microbial inoculants to the field. Further details on the induction of systemic resistance in plants by PGPR can be found in a comprehensive review by Ramamoorthy et al. (2001). Various workers have emphasized that plant growth promotion by PGPR in the field is likely to require the coordination of a cascade of events affecting plant growth promoting activity by a variety of different mechanisms (Lifshitz et al., 1988; Bayliss et al., 1993; Frankenberger and Arshad, 1995; Glick, 1995; Arshad and Frankenberger, 1998). However, the precise mechanism by which PGPR stimulates plant growth needs to be known to optimize each mechanism.

III. SCREENING OF PGPR Soils host a complex biological community where microorganisms prevail in great numbers and diversity. Many prokaryotes reside within the rhizosphere and/or the rhizoplane as an ecological niche, where they survive, multiply, and find protection against antagonists in the surrounding area. Such microorganisms have been generically called “rhizobacteria”. More attempts need to be made to screen rhizobacteria with beneficial effects on plant growth. Some classic and potential approaches to search for these beneficial bacteria to be used as PGPR are reviewed below.

PGPR APPLICATION IN AGRICULTURE

113

A. ROOT/ SHOOT GROWTH UNDER GNOTOBIOTIC CONDITIONS This is the most widely used approach for preliminary screening of rhizobacteria for selection of PGPR. In the past, when sensitive analytical techniques were not available, this approach was commonly employed for preliminary selection of PGPR from a large number of rhizobacterial isolates. In most cases, the mechanism(s) of action of PGPR isolates collected by this approach was not well understood. The rhizobacterial isolates are inoculated onto the seed and/or roots of the host plants under axenic conditions and the effects of inoculation on various growth parameters such as root/shoot growth are monitored. The rhizobacteria exhibiting positive effects on the growth parameters of inoculated host plants are then selected and sometimes subjected to further screening under repeated trials to confirm their growth promoting activities. Such selected rhizobacteria could be designated as PGPR and may be used as inocula under natural field conditions. Although this approach does not elucidate the mechanism of action of the PGPR, it does provide a good preliminary selection in testing of a high number of isolates under natural field conditions. However, this approach is extremely laborious and time consuming. Several workers have used this approach for preliminary selection of PGPR and some of these studies are summarized in Table IV.

B. IN VITRO PRODUCTION

OF

PLANT GROWTH REGULATORS

Although the role of microbially produced biologically active substances in evoking a physiological response in inoculated plants was speculated long ago, it was not until the 1970s that these substances were unequivocally identified upon the availability of state-of-the-art analytical instruments. During last couple of decades, the approach of screening microflora for in vitro production of PGRs has become very popular. Different rhizobacteria vary greatly in their potential to produce PGRs (Lebuhn and Hartmann, 1993; Sarwar and Kremer, 1995a; Zahir et al., 1998a; Pal et al., 1999; Almonacid et al., 2000; Biswas et al., 2000b; Dobbelaere et al., 2001; Khalid et al.,2001a,b; Padua et al., 2000). Several studies have demonstrated a strong relationship between in vitro production of PGRs by PGPR and their plant growth-promoting activity in various plants. Glick (1995) reports that the primary mechanism most commonly invoked by PGPR on plants is the production of phytohormones, and IAA may play the most important role in plant growth promotion. Under gnotobiotic conditions, Noel et al. (1996) demonstrated the direct involvement of PGRs including, IAA and cytokinin in modifying the growth of canola and lettuce. Similarly, Xie et al. (1996) reported that plant growth stimulation of canola by P. putida GR12-2 and other PGPR was due to IAA released under gnotobiotic conditions. Asghar et al. (2002) found

114

Table IV Preliminary Screening of Rhizobacteria for Their Growth Promoting Activities Under Gnotobiotic Conditions Crop/plants inoculated

31

Spring wheat

111

Winter wheat

7

Spring wheat

28

Rice

23

Maize

38

Maize

17

Maize

120

Maize

Specific comments Inoculation with some of the rhizobacterial isolates significantly promoted root and shoot growth of different cultivars, however, the efficacy of various isolates in promoting growth varied with cultivars. Based on the growth promoting activity, four isolates were selected as PGPR for further experimentation. Fourteen isolates stimulated the growth of wheat seedlings. Nine isolated rhizobacteria significantly increased winter wheat height, root and shoot biomass. Six strains of Bacillus rhizobacteria exhibited a positive effect on root weight and shoot height of wheat seedlings. Root to shoot ratio was also greater due to inoculation. Majority of rhizobacteria (, 89%) showed stimulatory effect on root elongation (up to 233%) and weight (150%) of rice seedlings. Cultivar-strain specificity was observed in many cases. Nine isolates caused significant increases in maize root/shoot length and weight. Eleven isolates significantly enhanced germination of maize seeds. Root and shoot weight of maize seedlings were increased up to 68.4 and 42.6%, respectively, in response to inoculation over a non-inoculated control. Culture supernatants of some bacteria significantly stimulated the germination of seeds. Three strains (two Bacillus and one Pseudomonas) showed growth promoting effects on maize seeds sown in sand culture.

Reference Khalid et al. (2002)

deFreitas and Germida (1990) Chanway et al. (1988) Khalid et al. (2001)

Thuar et al. (2000) Javed et al. (1998)

Hussain and Vancura (1970) Gadille (1991)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

No. of rhizobacterial isolates tested

Maize

121

Mungbean

88

Sunflower

100

Brassica juncea

69

Tomato

152

Tomato

152 273

Tomato Canola

111

Canola

116

Soybean

Several strains

Rapeseed

One prolific auxin producer increased the length and weight of maize seedling roots and shoots. Application of 16 isolates resulted in better growth of mungbean producing 62 – 106% higher dry mass compared to control. Positive response on plant growth was observed only with one isolate.

Gupta et al. (2001) Hanada and Romeiro (1998) Asghar et al. (2002)

Kumar and Dube (1991) Deuner et al. (2000) Romeiro et al. (2000) Grayston and Germida (1991)

deFreitas et al. (1997)

Cattelan and Hartel (1998)

Guaiquil and Ciampi (1992)

115

Several isolates of rhizobacteria (. 50%) promoted the root and shoot growth of various species of B. juncea. Ten isolates were selected as PGPR in evaluating their response under natural field conditions. One isolate of fluorescent Pseudomonas showed a significant increase in emergence of tomato seedlings. Eight isolates demonstrated in vitro antibiosis against some plant pathogens. Nearly 70% of the total isolates were capable of promoting the growth of tomato plants, and 26 isolates were found as active root colonizers. Twenty-six isolates were found to have plant growth promoting activity. Fourteen isolates of sulfur-oxidizing bacteria promoted the leaf size of canola at the bud stage of growth while seven isolates enhanced root and pod dry weight at maturity. Three isolates were also able to stimulate leaf area and suppress the growth of fungal pathogens. Nine strains belonging to the genus Bacillus exhibited a positive influence on plant growth of canola, and were selected as PGPR. Some strains of Bacillus significantly increased plant height and pod yield. One strain caused a significant increase in number and weight of pods and seed yield. Two isolates of phosphorus-solubilizing bacteria had a positive influence on the early growth of soybean. Some isolates were capable of producing siderophores and b-1, 3-glucanase, respectively, but did not show any significant growth promoting effect on soybean seedlings. Twelve bacterial strains significantly promoted plant growth of rapeseed. Screening of native strains of PGPR was thought to be more effective in increasing the growth of host plants.

Zahir et al. (2000)

PGPR APPLICATION IN AGRICULTURE

10

116

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

a significant correlation between in vitro production of auxin by PGPR and growth promotion in inoculated Brassica juncea plants. This premise was supported by the observations that cell-free separates of PGPR influenced plant growth similarly to that of inoculation with living cells (Azc´on et al., 1978; Tien et al., 1979; Hubbel et al., 1979). In many cases, exogenous application of PGRs causes changes in treated plants similar to that induced by inoculation with PGPR (Hubble et al., 1979; Tien et al., 1979; Kolb and Martin, 1985; Barbieri et al., 1986; Martin et al., 1989). Table V provides a summation of the response of various plants to microbially produced PGRs under gnotobiotic conditions.

C. 1-AMINOCYCLOPROPANE- 1-CARBOXYLIC ACID (ACC) DEAMINASE ACTIVITY Microbial inoculation has been shown to alter the endogenous levels of the plant hormone, C2H4, which subsequently leads to changes in growth and development of inoculated plants. Ethylene, which is believed to be produced by all plants, mediates a wide range of different plant responses and developmental processes. In some instances, the presence of C2H4 is stimulatory while in others it is inhibitory. Recently, it has been discovered that certain bacteria (such as Ps. putida) contain an enzyme, 1-aminocyclopropane-1-carboxylate (ACC)deaminase which hydrolyses ACC (an immediate precursor of C2H4), into ammonia and a-ketobutyrate. A model describing the role of bacterial ACC-deaminase in facilitating plant growth implies that bacteria inhabiting roots or seeds cause a decrease in the levels of endogenous ACC in plant roots. Decreased levels of ACC result in lower levels of endogenous C2H4 and subsequently promote root growth (Glick et al.,1998). Better root growth often results in better shoot growth. Glick and his co-workers (Glick et al., 1998) support this premise by providing the following evidence: (1) C2H4 has previously been found to be an inhibitor of plant root elongation in several different systems (Abeles et al.,1992). However, soil bacteria that contain the enzyme, ACC deaminase can lower C2H4 levels by decreasing the plant ACC content (Glick et al.,1995). Thus, the roots of C2H4 sensitive plants are invariably longer when ACC deaminase-containing bacteria are present (Hall et al.,1996); (2) three mutants of Ps. putida GR12-2 that were all devoid of any ACC deaminase activity, unlike the wild-type Ps. putida GR12-2, were unable to promote the elongation of canola roots in growth pouches under gnotobiotic conditions (Lifshitz et al.,1987; Glick et al.,1994a, b). Similarly, with tomato, lettuce and wheat, as well as canola, wild-type Ps. putida GR12-2, but not the ACC deaminase minus mutants of Ps. putida GR12-2 promoted root elongation under gnotobiotic conditions (Hall et al.,1996); (3) the ACC level in extracts of roots from canola seedlings as measured by HPLC were lower when the seeds were treated with Ps. putida GR12-2 than in seedlings from

Table V Production of Biologically Active Substances by Microorganisms and Plant Responses Bacteria

A. brasilense

A. brasilense

A. brasilense

Crop plants

Responses

Reference

Indole-3-acetic acid, gibberellin-like substances, cytokinin-like substances Indole-3-acetic acid, gibberellin-like substances, cytokinin-like substances Indole-3-acetic acid, indole-3-pyruvic acid, indole-3-lactic acid, indole-3acetaldehyde, tryptophan

Pearl millet

Combination of IAA, GA3 and kinetin produced changes in root morphology similar to those produced by the inoculum.

Tien et al. (1979)

Pearl millet, sorghum

Combination of IAA, GA3 and kinetin produced changes in root morphology similar to those produced by the inoculum.

Hubbell et al. (1979)

Wheat

Zimmer et al. (1988)

Indole-3-acetic acid

Wheat, alfalfa

Substances (identified as auxins) excreted from Azospirillum were tested on cereals after purification. These were reported to cause the formation of additional root hairs and lateral roots. Also, IAA completely substituted for inoculation in an assay where the increase in dry weight of intact wheat roots was determined after an incubation of 10 days. Inoculation of A. brasilense and exogenous application of IAA had similar effects on the number of lateral roots, total root length and number of root hairs of wheat and on nodule numbers in alfalfa, suggesting that IAA was an important factor in the effect observed after inoculation.

Martin et al. (1989)

117

(continued)

PGPR APPLICATION IN AGRICULTURE

Azospirillum brasilense

PGRs detected

118

Table V (continued) Bacteria

PGRs detected

Crop plants

Indole-3-acetic acid

Wheat

A. brasilense

Indole-3-acetic acid

Wheat

A. biejernickii

Medicago sativa

A. brasilense

Indole-3-acetic acid, gibberellin-like substances, cytokinin-like substances Indole-3-acetic acid

Maize

A. brasilense

Indole-3-acetic acid

Wheat

Reference

Inoculation of wheat with the tri5-induced mutant, A. brasilense SpM7918 (a very low IAA producer) promoted less root development than did the wild-type strain Sp6. The number and length of roots were significantly increased by inoculation with IAA-producing strains, whereas, strains unable to produce IAA did not cause the same effect. Treatment with different concentrations of IAA showed a dose effect on the root system development. Cell-free supernatants and whole bacterial cultures behaved as pure hormones (IAA, GA3, kinetin) in improving dry weight and infection with micro symbionts. Inoculated roots had higher amounts of both free and bound IAA when compared to the control. The amount of free IAA significantly increased in the inoculated roots after two weeks of sowing. GCMS analysis revealed the presence of both IAA and IBA in two-week-old inoculated seedling roots. Inoculation with a wild-type strain active in IAA production caused an increase in number and length of lateral roots. A Nif strain (a low producer of IAA) did not affect root development.

Barbieri and Galli (1993)

Barbieri et al. (1988)

Azco´n et al. (1978)

Fallik et al. (1989)

Barbieri et al. (1986)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

A. brasilense (low and high IAA producing strains)

Responses

Indole-3-acetic acid, gibberellic acid

Tomato

A. chroococcum

Indole-3-acetic acid, gibberellin-like substances Gibberellic acid, Isogibberellic acid, indole-3-acetic acid

Wheat

A. lipoferum

A. paspali

Indole-3-acetic acid, gibberellin-like substances, cytoki nin-like substances

A. vinelandii

Indole-3-acetic acid, gibberellin-like substances, cytoki nin-like substances Indole-3-acetic acid, gibberellin-like substances, cytoki nin -like substances Indole-3-acetic acid, gibberellin-like substances Indole-3-acetic acid

A. vinelandii

Azomonas macrocytogenes Azospirillum spp.

Maize

Lycopersicon esculen tum, Paspalum not tum, Triticum vul gare, Lactuca sativa, Centrosema pubescens, Lolium perenne Tomato

Lavandula, Lycoper sicon esculentum

Wheat

Wheat

Exogenous application of IAA and GA3 at an amount similar to those present in bacterial cultures produced an effect similar to inoculation. A five-day-old culture increased the root and shoot length, most likely through production of PGRs (IAA, GLS, CLS). Inoculation of maize significantly enhanced root growth, and the effect resembled with the effects obtained with GA3 and IAA at specified concentrations. Inoculation affected plant growth and development significantly. Since there was no N2 fixation, the pronounced effect was attributed to PGRs (IAA, GLS, CLS).

Brown et al. (1968)

Pati et al. (1995)

Fulchieri et al. (1993)

Barea and Brown (1974)

Treating roots with bacterial cultures accelerated plant growth and increased the yield of fruit. Effects were most likely caused by plant hormones (IAA, GLS, CLS). Cell-free supernatants and whole bacterial cultures behaved as pure hormones (IAA, GA3, kinetin) in improving dry weight and infection.

Azco´n and Barea (1975)

A five-day-old crude culture increased the root and shoot length, most likely through production of PGRs (IAA, GLS, CLS). Growth of wheat seedlings increased upon inoculation in a Petri dish bioassay.

Pati et al. (1995)

Azco´n et al. (1978)

PGPR APPLICATION IN AGRICULTURE

A. chroococcum

Dobbelaere et al. (2000)

119

(continued)

120

Table V (continued) Bacteria

PGRs detected

Crop plants

Indole-3-acetic acid, abscisic acid

Beta vulgaris (spp. cicla) and wheat

Azotobacter

Indole-3-acetic acid

Maize

Azotobacter spp.

Indole-3-acetic acid, gibberellin-like substances Indole-3-acetic acid

Barley

Rhizobacteria (unidentified)

Indole-3-acetic acid

Wheat, Rice

Rhizobium, Azospirillum Rhizobium leguminosarum (strain E11)

Indole-3-acetic acid

Rice

Indole-3-acetic acid

Rice

Rhizobacteria (unidentified)

Brassica juncea

Reference

Root elongation of B. vulgaris spp. cicla was stimulated and the number of lateral roots was increased in response to inoculation. Exogenous application of IAA to wheat plants caused a similar response. Since the inocula produced IAA in high amounts, growth promotion caused by inoculation was speculated to be due to IAA excretion. Inoculation with strains efficient in IAA production had significant growth-promoting effects on maize seedlings. Bacterial metabolites showed a stimulatory effect on plant height and dry weight of barley.

Kolb and Martin (1985)

There was a significant correlation between auxin production by PGPR in vitro and growth promotion of inoculated rapeseed seedlings in the modified jar experiments. Rhizobacterial strains active in IAA production had relatively more positive effects on inoculated seedlings. Inoculation with diazotrophs had significant growthpromoting effects on rice seedlings. Growth promoting effects upon inoculation on axenically-grown rice seedlings were observed.

Zahir et al. (2000)

Mahmoud et al. (1984)

Asghar et al. (2002)

Khalid et al. (2002)

Biswas et al. (2000b) Dazzo et al. (2000)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

A. brasilense

Responses

PGPR APPLICATION IN AGRICULTURE

121

the untreated seeds. In addition, the ACC levels in root extracts from the seedlings in which the seeds were treated with an ACC deaminase minus mutant of Ps. putida GR12-2 were similar to those of the untreated seedlings; (4) the increase in the length of the roots of young (five- to seven-day-old) seedlings of canola, tomato, lettuce, wheat, oats, and barley following treatments of the seeds with the wild-type Ps. putida GR12-2 was similar to the response of these plants when their seeds were treated with the C2H4 inhibitor, AVG (Hall et al., 1996). Similarly, plants that were stimulated to the greatest extent by treatment with either Ps. putida GR12-2 or AVG were those that were the most sensitive to root length inhibition by ethephon (Hall et al., 1996). Thus, as far as root length is concerned, only C2H4 sensitive plants responded to the presence of PGPR that contained ACC deaminase; (5) following transposon mutagenesis and selection for IAA overproducing mutants of Ps. putida GR12-2, it was found that a mutant that overproduced IAA to the greatest extent was inhibitory to root elongation (Xie et al.,1996). An explanation for the inhibitory effect on root growth by the IAA overproducing mutant of Ps. putida GR12-2 is that the increased level of IAA secreted by the mutant bacterium is taken up by the plant and interacts with the enzyme ACC synthase, stimulating the synthesis of excess ACC which in turn gets converted to C2H4 (Yang and Hoffman, 1984; Kende, 1993); (6) it was observed that with transgenic tomato plants, the expression of the bacterial enzyme, ACC deaminase dramatically lowered C2H4 levels in ripening tomato fruit (Klee et al., 1991; Sheehy et al., 1991). Although, this situation is different from the root –bacterial interaction, it confirms that this enzyme can in fact lower C2H4 levels in plants; and (7) it was recently observed that when canola seeds were treated with E. coli cells expressing a cloned Enterobacter cloacae ACC deaminase gene, these E. coli cells were able to promote root elongation (Shah et al., 1998). This demonstrates that enhanced plant root elongation was a direct consequence of the presence of the enzyme, ACC deaminase. Other studies indicating that inoculation with bacteria carrying the ACC-deaminase enzyme promotes root growth of developing seedlings of various crops under gnotobiotic conditions are summarized in Table VI. Enrichment for soil bacteria that can utilize ACC as a sole N source is a means of isolating organisms with the presence of the enzyme, ACC deaminase. This trait appears to be limited to rhizobacteria that are capable of stimulating plant growth. Based on the interaction of ACC-deaminase carrying bacterial strains with plant roots, it is highly likely that manipulation of the ACC-deaminase gene could be used to improve agricultural production.

IV. APPLICATION OF PGPR IN AGRICULTURE PGPR have become a new class of biofertlizers and physiological stimulators in recent years. There has been a renewed interest in the use of these rhizobacteria

122

Table VI Physiological Responses of Plants to Inoculation with Bacteria Containing ACC-Deaminase Activity

Spring rapeseed

Indian mustard and rapeseed

Bacteria

Plant responses to inoculation

Ps. putida Am2, Ps. putida Bm3, Alcaligenes xylosoxidans cm4, Pseudomonas sp. Dp2 Pseudomonas spp. Rhodococcus spp.

Significant increase in root elongation of phosphorus sufficient seedlings of rapeseed in a growth pouch culture experiment was observed in response to inoculation. Out of 15 bacterial strains containing ACC-deaminase, 8 and 13 strains caused significant increase in root elongation of Indian mustard and rapeseed seedlings, respectively. The strains also enhanced tolerance to cadmium toxicity and stimulated root elongation of rape seedlings even in the presence of 300 mM CdCl2 in nutrient solution. A wild-type strain promoted root growth of developing seedlings of canola under gnotobiotic conditions while its mutants lacking ACC-deaminase did not show such a response. Seed inoculation with a wild-type strain containing ACCdeaminase stimulated root growth of canola seedlings under gnotobiotic conditions while its mutant did not show any positive effect. Only the wild-type strain promoted the root growth of inoculated seedlings. Inoculation with the wild-type strain exhibited a positive influence on root and shoot growth. ACC-deaminase carrying strain increased root length of canola seedlings under gnotobiotic conditions. This strain also exhibited the ability to protect cucumber against Pythium damping off and potato tubers against Erwinia soft rot in a hermetically sealed container. Strains deficient in ACC-deaminase activity did not show any positive effect on canola seedlings.

Canola

Ps. putida GR 12-2 and its ACC-deaminase minus mutant

Canola

Enterobacter cloacae UW4 wild-type and ACC-deaminase minus mutant

Canola, lettuce, tomato, wheat Canola

Ps. putida GR 12-2 and ACCdeaminase lacking mutants Ps. puitida (wild type and over-producing IAA mutants) Ps. fluorescens strains CHA0 and CHO96 (ACC-deaminase lacking strains)

Canola

Reference Belimov et al. (2002)

Belimov et al. (2001)

Glick et al. (1994b)

Li et al. (2000)

Hall et al. (1996) Glick et al. (1997) Wang et al. (2000)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Plant

Canola

Flower petals (Zinnia, white sin canation, maltese cross) Mung bean

Ps. putida GR 12-4 Ps. putida UW1

Tomato

Ps. putida

Ps. putida GR 12-2 (wild type), GR12-2/acd36 (ACCdeaminase minus mutant), GR 12-2/aux1 (IAA-over producers)

Strains exhibiting ACC-deaminase activity promoted the elongation of canola roots under gnotobiotic conditions.

Shah et al. (1998)

The bacterium with ACC-deaminase activity partially protected plants against nickel toxicity under gnotobiotic conditions most likely by lowering the level of stress C2H4 induced by nickel. Inoculation with ACC-deaminase containing PGPR was effective in prolonging the petal life of C2H4-sensitive flowers.

Burd et al. (1998)

Only the wild-type strain influenced the development of longer roots. IAA-over producing mutants did not influence the number of roots. The results were consistent with the model that bacterially produced IAA stimulated the synthesis of ACC and hence C2H4, which affects the initiation and elongation of adventitious roots in mung bean cuttings. Inoculation with Ps. putida carrying ACC deaminase noticeably stimulated the development of both adventitious roots and stem aerenchyma in flooded tomato plants compared to the noninoculated flooded tomato plant or flooded plant inoculated with Ps. putida strain which did not carry ACC deaminase.

Mayak et al. (1999)

Nayani et al. (1998)

Grichko and Glick (2001)

PGPR APPLICATION IN AGRICULTURE

Canola, Tomato

Enterobacter cloacae, Escherichia coli, Ps. putida, Ps. fluorescens Kluyvera ascorbata SUD165

123

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Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

for inoculation of agricultural crops. PGPR hold great promise as potential agricultural and forestry inoculants and if effective, could reduce the use of agrochemicals including chemical fertilizers and pesticides. Extensive studies commencing in the1960s, have been performed by scientists in China, who refer to PGPR as yield-increasing bacteria (YIB). A review by Chen et al. (1994) gives an excellent overview of the Chinese work. Over 300 scientists were involved in YIB research and development in 28 provinces, ranging from basic studies to product formulation and distribution to farmers. Microbial cultures that promoted growth in greenhouse screening studies were field tested for at least 2 years in several provinces in statistically designed trials, and if consistently positive they were then commercialized. The bacteria currently in use are B. cereus, B. firmus, and B. lichniformis, but new cultures are being tested and developed. In many instances, mixtures of different isolates are used. Plants are treated in several ways such as seed coating, rootdips for transplants, and watering into soil. The greatest responses were obtained upon seed treatment followed by one or more foliar sprays while in a nursery bed, root dipping at the time of transplanting, and then possibly one or two sprays as the plants developed was frequently employed. In 1990, YIBs were used over an area of 3.3 million hectares in 18 provinces on wheat (with 8.5– 16% yield increase), rice (8.1 – 16%), corn (6 – 11%), sorghum (5 – 10%), sweat potato (15 –19%), cotton (6 –13%), rapeseed (11 –18%), bean (7 – 16%), sugar beet (15 –20%), watermelon (16 – 18%), peanut (10 – 15%), and vegetables (13 – 35%). A study in Canada with over 4000 bacterial isolates from the rhizosphere of different plants showed that 222 isolates increased crop growth of canola upon initial screening (Kloepper et al., 1987). After further greenhouse screening of the active isolates, 2 years of field trials were conducted with the most promising isolates increasing seedling vigor and yield increases of 5 –29% in canola. Overall, there were positive yield responses in two-thirds of the field trials (Kloepper et al.,1987). Similarly, various researchers have reported beneficial effects of PGPR inoculation on many different crops (Table VII).

A. EFFECT

OF

PGPR INOCULATION 1.

ON

CEREALS

Wheat

Several studies indicate that PGPR may act as natural elicitors for improving the growth and yield of wheat. Javed and Arshad (1997) isolated 38 cultures of rhizobacteria from a rhizosphere soil and screened them on the basis of their ability to produce IAA in vitro. Seeds of two wheat cultivars (Inqlab and LU-26S) were inoculated with these isolates and sown in the field under optimum fertilized

Table VII Response of Different Crops to PGPR Inoculation Crop PGPR Ps. putida R-20 A. chroococcum A. brasilense Canola

Chick pea Cotton Jojoba

Maize

Ps. migulae, Agrobacterium tumefaciens, Phyllobacterium myrsinacearum, Phyllobacterium rubiacearum, Variovorax paradoxus, A. brasilense cd, A. lipoferum 4B A. brassilense

Reference

Shoot mass Root dry weight Root length Root dry weight Root length Root dry weight

36 71 117 58 114 11–52

Staley et al. (1992) Carletti et al. (1994)

Seed dry weight

22.7 (Experiment 1) 411 (Experiment 2) 8 –40 52 50 43.0 150 19.8 19.6 16.0 14.8 14.0 18.9 4.6

Hamaoui et al. (2001)

Plant growth Root surface area Root surface area Root surface area Root length Grain yield Grain yield Plant dry weight Leaf area Plant dry weight Grain yield Grain yield

Carletti et al. (1994) Bertrand et al. (2001)

Sakthivel et al. (1986) Carletti et al. (1994) Carletti et al. (1994) Carletti et al. (1994) Carletti et al. (1994) Zahir et al. (1998a) Hussain et al. (1987) Dobbelaere et al. (2001) Pan et al. (1999) Pan et al. (1999) Javed et al. (1998) Pietr et al. (1990) (continued)

125

Ps. fluorescens A. brasilense A. lipoferum A. chroococcum Lipoferum Azotobacter þ Pesudomonas Azotobacter A. irakense Serratia liquefacians S. liquefacians Pseudomonas Phosphate-solubilizing

% increase over non-inoculated control

PGPR APPLICATION IN AGRICULTURE

Alfalfa Barley

Parameter

126

Table VII (continued)

Pepper Potato

Rice

PGPR rhizobacteria A. lipoferum CRT1 B. circulan RSA19 A. chroococcum Pseudomonas sp. Azotobacter

P-uptake Grain yield Grain yield Shoot dry weight Shoot dry weight Tuber yield

PGPRa

Tuber yield

Burkholderia vietnamiensis

Shoot weight Root weight Grain yield

A. brasilense

Rapeseed

Parameter

Mixture of Azospirillum, Azoarcus, Pseudomonas and Zoogloea Alcaligenes xylosoxidans Alcaligenes xylosoxidans Ps. marginalis (DP1) Ps. putida (Am2) Pseudomonas sp. DP2 Ps. brassicacearum (Am3) B. cereus 83-10 Ps. Putida, Ps. fluorescens, S. liquefacians, Ps. putida biovar. B, Arthrobacter cetreus PGPRa

Grain yield

Root weight Shoot weight Root dry weight Root dry weight Root dry weight Shoot dry weight Grain yield Grain yield

Grain yield

% increase over non-inoculated control 15.2 9.16 6.72 24.0 61.0 45.3 (Pots) 32.3 (Field) 47.5 (Pots) 25.8 (Field) 33.0 57.0 15–21 (Inoculation þ N) 5–6 (Inoculation alone) 11.7

2.0 7.9 21.9 25.0 25.5 21.2 17.2 57.0

11.5

Reference

Berge et al. (1990) Carletti et al. (1994) Carletti et al. (1994) Zahir and Arshad (1996) Javed and Arshad (1999) Van et al. (2000) Omar et al. (1989) Mehnaz et al. (1998)

Belimov et al. (2002) Belimov et al. (2002) Belimov et al. (2001) Belimov et al. (2001) Belimov et al. (2001) Belimov et al. (2001) Mei (1989) Kloepper et al. (1987)

Chen et al. (1994)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Crop

Azotobacter Achromobacter B. strains L6-16R B. strain PW-2R A. brasilense strain cd

Tomato

Ps. fluorescens A. chroococcum

Wheat

Pseudomonassp. Azotobacter Pseudomonas sp. Azotobacter

Not identified.

A. brassilense

Grain yield Grain yield Plant dry weight

A. brassilense

Grain weight per ear

PGPRa

Grain yield

PGPRa Pseudomonas sp. B. cereus strain A-47

Grain yield Grain yield Grain yield

P-solubilizing bacteria isolate I1M12

Shoot weight P-uptake

Zahir et al. (1998a) Bertrand et al. (2000) Shishido and Chanway (1998) Shishido and Chanway (1998) Itzigsohn et al. (2000) Gagne et al. (1993) Carletti et al. (1994) Carletti et al. (1994) Zahir et al. (1996) Javed et al. (1996) Khalid et al. (1997) de Freitas and Germida (1992) Dobbelaere et al. (2001) Dobbelaere et al. 0(2001)

Javed and Arshad (1997) Chen et al. (1994) Khaliq et al. (1996) Xia et al. (1990) Domey and Lippman (1989)

127

a

Fruit weight/plant Shoot dry weight Total root length Shoot dry weight Grain yield Grain yield

34.4 22– 33 6 –21 20.5 16.0 166 (after 35 days) 87.5 (after 105 days) 18.2 71.0 55.0 81.0 30.5 15.3 (cv. Inqlab) 18.5 (cv. Lu26S) 19.3 11.0 62 (No added N) 3.65 (with 60 kg ha-1 N) 22.0 (with 50 kg ha-1 N) 27.6 (with 60 kg ha-1 N) 29.6 (with 110 kg ha-1 N) 15.3 (cv. Inqlab) 18.5 (cv. Lu 26S) 6.3– 15.0 Up to 20 Up to 11.4 (1st year) Up to 14.7 (2nd year) 31.4 30.7

PGPR APPLICATION IN AGRICULTURE

Spruce seedlings

Grain yield Shoot dry weight Root dry weight Root biomass Root biomass Yield

128

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

conditions (NPK, 150– 75– 50 kg ha21). Grain yields of Inqlab and LU-26S due to inoculation with the PGPR isolates were increased by 5.3 and 18.5%, respectively, compared with the non-inoculated control. Inoculation with PGPR also significantly increased the number of tillers, straw weight and 1000-grain weight in both cultivars. Plant height was increased only in LU-26S variety. Chen et al. (1994) isolated PGPR from the roots and rhizosphere of 57 crops and selected five strains for seed inoculation of 12 crops. These bacteria were grown for 24 h in nutrient broth at 288C and then mixed with sterile peat at 100 mL kg21 peat. They treated plant seeds by mixing with peat in ratio of 100:1 (seed : inoculant) and the control consisted of seeds treated with non-inoculated peat. Inoculation significantly promoted the yield of wheat with an average increase ranging from 6.3 to 15.0% compared to the non-inoculated control. Pseudomonas is one of the most exensively studied PGPR which has been reported to improve plant growth and yield of crops when used alone or in combination with other PGPR. These plant growth responses are often variable depending upon the bacterial strain, crop variety, and experimental site. Khaliq et al. (1996) conducted a field experiment to evaluate the potential of two isolates of Pseudomonas (isolated from maize rhizosphere soil) on wheat under fertilized conditions. Bacterial inoculation promoted grain and straw yields, tillering, N concentration in grains, and total N uptake up to 20, 14, 17, 41, and 65%, respectively, over the non-inoculated control. Germida and Walley (1996) studied the impact of PGPR inoculants on the growth and yield of spring wheat in field trials. The Pseudomonas inoculants (Ps. cepacia R55, R85, Ps. aeruginosa R80, Ps. fluorescens R92, and Ps. putida R104) were applied at a rate 107 –108 cfu seed21 of wheat at two different sites in Saskatchewan. Plant growth responses were variable and dependent on the inoculant strain, harvest date and growth parameter evaluated. However, harvest index was consistently increased by all pseudomonad inoculants; the responses to strain R55 and R104 were highly significant. Root distribution and root length were also significantly altered by the inoculants. Strains R85 and R92 significantly increased root dry weight in the 0- to 15-cm zone. Kropp et al. (1996) assessed the effects of inoculation with Pseudomonas chlororaphis strain 2E3 on spring wheat at two different sites in northern Utah. They found a 6 – 8% increase in the emergence of spring wheat by inoculation at both sites compared to the non-inoculated controls. Similar results of increased wheat yield have also been reported with Bacillus sp. Two years of replicated field trials were conducted on wheat at two locations and up to 11.4 – 14.7% increases in grain yields over the control for each year were observed in response to inoculation with Bacillus cereus strain A-47 (Xia et al., 1990). In addition to yield promotion, PGPR inoculation also enhanced rate of seedling emergence, tiller formation and plant dry weight. Domey and Lippman (1989) tested various isolates of phosphate-solubilizing bacteria to evaluate their effects on wheat seedlings grown under gnotobiotic conditions and in pots. They reported that P-solubilizing bacteria were able to improve P

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129

nutrition of wheat and thus could stimulate plant growth under conditions of P deficiency. In a pot trial of the same study, root dipping of a bacterial suspension containing 109 cell mL21 before transplanting increased shoot weight of wheat up to 31.4% and P uptake of the shoot was increased by 30.7% with isolate I1MI12 compared with the non-inoculated control. However, there was no correlation between the P-solubilization ability of the isolates and their effects on wheat growth. They suggested that the ability of bacteria to solubilize P alone could not account for the bacterial effect on plant growth (Domey and Lippman, 1989). Several studies have demonstrated the effectiveness of Azotobacter in improving growth and yield of inoculated wheat plants. Zahir et al. (1996) conducted a field experiment to study the effect of Azotobacter inoculation on growth and yield of wheat (cv. Inqlab) in the presence of NPK at 150 –75 – 50 kg ha21. Seed inoculation with various Azotobacter cultures significantly increased the grain yield (38.5%), straw yield (15.3%), number of tillers (12.5%), spikelets (10.7%), and 1000 grain weight (7.3%) compared to the non-inoculated control. Similarly, Khalid et al.(1997) studied the effects of inoculation of wheat seeds (cv. Inqlab) with two isolates of Azotobacter in the field receiving fertilizer NPK at 120 – 75 – 50 kg ha21. Inoculation significantly increased grain yield, straw yield, number of tillers, N concentration in grains and total N uptake up to 19.3, 19.5, 16, 48, and 58%, respectively, over the non-inoculated control. Lakshminarayana et al. (1992) evaluated the potential of Azotobacter strains influencing the N economy in wheat. Wheat seeds were inoculated with Azotobacter chroococcum and its mutant strains (Mac3, Mac27, Mal3). All strains tested produced greater grain yields than the non-inoculated control. The effectiveness of native strains of Azospirillum brasilense have been reported to be more effective in promoting the yield of wheat than non-native strains. Field experiments were conducted by Pozzo et al. (1993) in an area with low rainfall and neutral sandy soil during 1988– 1990. Inoculation of wheat with the native strain of A. brasilense produced higher grain yields and grain protein content than inoculation with the non-native strains or when compared to the noninoculated control. Similarly, in a greenhouse trial, plants were grown under conditions that mimicked the external field conditions. The effect of inoculation with the wild type strain A. brasilense Sp245 was most pronounced at intermediate N levels and inoculum concentrations of 105 – 106 cfu per plant. Inoculated plants were characterized by better germination and early development, earlier flowering and an increase in dry weight of both the root system and the upper plant parts. The total amount of N present in the plants was, in contrast to the above mentioned plant parameters, highest at the high N-levels and high inoculum concentrations (Dobberlaere et al., 2001). Barassi et al. (2000) found that A. brasilense BR1005 exerted a positive effect on wheat seedlings growing under salt-stressed conditions.

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Many other recent studies have confirmed positive effects of rhizobacterial inoculation on the growth and yield of wheat (Zahir et al., 1998b; Gerk et al., 2000; Remus et al., 2000; Sherchand, 2000).

2.

Rice

Beneficial effects of inoculation with PGPR on the growth and yield of rice has been demonstrated. Sakthivel et al. (1986) isolated PGPR (Pseudomonas fluorescens) strains from the rhizosphere of various crops and used them for seed coating of rice. Results of pot trials showed 12– 27% greater plant height in response to inoculation than the non-inoculated control. Similarly, Van et al. (2000) conducted pot- and field-trials of rice to assess the effect of a PGPR on four rice cultivars sown at different times. The isolated bacterial strain, Burkholderia vietnamiensis was used to inoculate rice cultivars in two pot experiments and four field trials at different sites in Vietnam. Inoculation had a significant positive effect on shoot weight (33%), root weight (57%), leaf area (30%), and number of tillers hill21 (13%) compared to the non-inoculated control. Pot and lysimeter experiments were carried out to study the effects of root inoculation with Rhodobacter capsulatus in combination with graded levels of nitrogen fertilizer on the growth and yield of rice variety Giza 176 (Elbadry et al., 1999a). Inoculation with this PGPR significantly increased plant dry weight, number of productive tillers, and grain and straw yields when compared to the non-inoculated control. The results also suggested that inoculation along with moderate N fertilizer levels can save up to 50% of the nitrogen fertilizer needed for optimum G176 rice crop yield (Elbadry et al., 1999a). Rice seedlings were inoculated with A. brasilense immediately after germination and just before transplanting into nursery beds (Omar et al., 1989). These seedlings were grown at two sites given either 0, 38, 76, kg N ha21 or 0, 48, 96 kg N ha21. Results showed that grain yield and 1000-grain weight were increased upon inoculation at both sites. There was positive interaction between N application and inoculation with a yield enhancement of 15– 21% upon inoculation and applying the highest N rate, compared with 5 –6% upon inoculation and no N application (Omar et al., 1989). Chi et al. (1998) conducted field trials to assess the response of rice to Azospirillum strains. Germinated rice seeds were inoculated with Azospirillum Mat 2-1b and DA 9-3a and transferred into the field at several places along the Red River Delta of North Vietnam. After 30 – 35 days, the inoculated seedlings appeared to be taller and more vigorous than the non-inoculated controls. The inoculated seedlings also showed a high survival rate in comparison to the non-inoculated seedlings. In another study, Mehnaz et al. (1998) conducted a field trial to assess the potential of PGPR to

PGPR APPLICATION IN AGRICULTURE

131

promote the growth of rice. A mixture of five PGPR strains belonging to the genera Azospirillum (A. lipoferum and A. brasilense), Azoarcus, Pseudomonas and Zoogloea were used to inoculate rice seedlings growing in microplots. Seed inoculation with PGPR significantly increased root weight (41%), straw yield (12%), grains yield (11.7%) and total biomass (18.7%) compared to the noninoculated control. Prayitno et al. (1999) investigated the interaction between two groups of rice endophytic bacterial strains and several rice cultivars. Inoculation experiments showed that these rice-associating bacteria could promote, inhibit, or have no influence on plant growth. Furthermore, the growth effects were greatly influenced by the environmental growth conditions. It was suggested that some of these rice associating bacteria possess important genes that enhance their ability to intimately colonize on and/or within rice tissues promoting plant growth of rice (Prayitno et al.,1999). Padua et al. (2000) inoculated rice seedlings (cv. Guarani and IR 42) with endophytic bacteria (capable of producing IAA) such as Herbaspirillum seropedicae (ZAE 94), Burkholderia brasilensis (M-130) and Herbaspirillum mutant (M2) under aseptic conditions. Inoculation resulted in a significant increase in root (53%) and shoot (42%) fresh weights compared to the non-inoculated control. Inoculation also caused enhancement in the growth of axillary roots and root hairs compared to the control (Padua et al., 2000). Sherchand (2000) assessed the response of rice to bacterial inoculation under field conditions. Rice yield was increased by 15 –20% in response to inoculation over the non-inoculated control. Elbadry et al. (1999b) used phototrophic purple non-sulphur bacteria (PPNSB) as well as Azospirillum, Azotobacter, Clostridium, and cyanobacteria for the inoculation of rice. This experiment was conducted at two rice fields in Egypt. Rice plants showed a positive response to PPNSB, Clostridia, Azotobacter and Azospirillum clearly reflecting the potential of PGPR to promote the growth and yield of rice.

3.

Maize

In response to inoculation with PGPR in a field experiment of maize, grain yield was increased up to 18.9%, while cob weight, cob length, 1000-grain and straw weight was significantly enhanced up to 20.8, 11.6, 17.2, and 27.1%, respectively (Javed et al., 1998). Of the 11 isolates tested in this study, five were consistent in improving maize growth and all five were identified as Pseudomonas. In another study, seeds inoculated with four isolates of PGPR (two each of Azotobacter and Pseudomonas) were sown in the field receiving NPK at 150 –100– 100 kg ha21 (Zahir et al., 1998a). Results revealed that the combined inoculation of Azotobacter and Pseudomonas significantly increased

132

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

grain yield (19.8%), cob weight (21.3%), cob length (20.6%), 1000 grain weight (9.6%), plant height (8.5%) and N content in the grain and straw (19.8 and 18%, respectively) compared to the non-inoculated control. Inoculation with a single strain of Azotobacter or Pseudomonas also gave promising results. Berthelin et al. (1991) investigated the effect of phosphate-solubilizing rhizobacteria (PSR) on maize growth and showed that inoculation with PSR resulted in a significant stimulation of growth of maize seedlings (shoot and root). Inoculation with Bacillus spp. increased the P content (34% higher than the control) in the maize shoot. In greenhouse trials, Pietr et al. (1990) observed that inoculation of maize seeds with PSR strain P-221 increased the grain yield by 4.6% and P-uptake by 15.2% over the non-inoculated control, when P was supplied as rock phosphate. Fages et al. (1991) found that inoculation of maize seeds with Azospirillum strain SAB2 significantly increased the primary root length (27.6% more than the control). Field studies in Israel showed that Azospirillum affected both the vegetative and reproductive growth of maize under different levels of N fertilization (Kapulnik et al., 1982). Inoculation of seeds with this bacterium resulted in increased plant height, fresh and dry weight of plant foliage, number of ears per plant, and total ear yield of maize. The increase in total yield of marketable ears at all levels of N fertilization ranged from 8.3 to 16.0%. Likewise, Vedderweiss et al. (1999) found that inoculation with Azospirillum (106 cfu mL21) promoted fresh root weight as well as shoot weight of maize seedlings. Stancheva et al. (1992) reported that inoculation with Azospirillum brasilense strain 1774 caused greater dry matter production of maize than the non-inoculated control in a field receiving 0, 100, and 200 kg N ha21. The influence of inoculation on dry matter accumulation was the greatest with 100 kg N ha21. Dry matter yields with this combination were equivalent to those with 200 kg N ha21 alone. Dobbelaere et al. (2001) evaluated the effect of Azospirillum lipoferum (AZOGREEN-m) on Zea mays cv. Kajak sown in a sandy loam soil in Belgium. Six different levels of N fertilizer were applied @ 0, 50, 100, 150, 200, and 250 kg N ha21. On an average, inoculation of maize with AZOGREEN-m resulted in a higher N content of the plants and a higher total N yield per hectare. However, these differences were non-significant at the 5% level. Analysis of the soil samples taken at harvest time clearly indicated that less N remained in the soil in the inoculated plots compared to the control plots. Similarly, Jacoud et al. (1998) assessed the influence of A. lipoferum CRT1 on maize growth under field conditions. Three plant parameters including plant height, primary root length and root fresh weight were monitored. The results showed that the growth promoting effect of A. lipoferum CRT1 began early (from day 14) in plant development and increased in spite of a rapid decrease in bacterial density. From day 14 onward, primary roots of the inoculated plants were significantly longer than the noninoculated controls. Similarly, root fresh weight values were twice as much for

PGPR APPLICATION IN AGRICULTURE

133

the inoculated plants 21 and 28 days after sowing. However, no difference was observed in the inoculated and control plant height. In another study, Jacoud et al. (1999) evaluated the effect of A. lipoferum CRT1 inoculation on the surface area of maize roots in a pot experiment under greenhouse conditions. They inoculated maize seeds with commercial inoculants containing 1.3 £ 107 Azospirillum lipoferum CRT1 cells. After 24 – 48 h, seeds were washed and sown in pots containing perlite. They found that the contact period (48 h) and density of inoculant were both important affecting the surface area of roots. Berge et al. (1990) isolated B. circulan RSA19 and A. lipoferum CRT1 from maize rhizosphere and roots, respectively, and applied these isolates to maize (cv. Sirena) seeds at 1.5 £ 108 cfu per seed. Results showed that Azospirillum inoculation increased grain yield per plant, total dry matter yield and 1000-kernal weight by 9.16, 16.6, and 1.38%, while B. circulans inoculation increased these parameters by 6.72, 3.08, and 4.83%, respectively, compared to the non-inoculated control.

B. EFFECT

OF

PGPR INOCULATION

ON

OTHER CROPS

The effectiveness of seed inoculation with Azotobacter for improving potato yield under optimum fertilized conditions (NPK, 250– 150– 150 or 200– 100 –100 kg ha21) was studied in pot and field experiments by Zahir and Arshad (1996). In pot experiments, inoculation significantly increased tuber yield (45.3%), straw yield (61.9%) and number of tubers per plant (82.4%) compared to the non-inoculated control. In field conditions, inoculation with Azotobacter was also effective in enhancing tuber yield (32.3%), straw yield (15.9%) and number of tubers per plant (50%) compared to the non-inoculated control. Javed and Arshad (1999) conducted pot and field trials to test 11 selected rhizobacterial isolates for yield promotion of potato. In both trials, tuber yield, number of tubers and shoot plus root weight were significantly increased in response to inoculation. Tuber yield was increased up to 47.5 and 25.8% in pot and field trials, respectively, in response to PGPR inoculation. Similarly, the number of tubers and shoot plus root weight were enhanced up to 56.2 and 27.6% in the pot trial and up to 27.1 and 23.1% in the field, respectively. Klebsiella mobilis strains CIAM880 and CIAM853 were used for inoculating potato cultivars at low doses of nitrogen fertilizer (Pishchik et al.,1998). The yield of various potato cultivars increased 1.2- to 1.4-fold compared to the non-inoculated control. Inoculation with K. mobilis significantly increased starch, nitrogen, phosphorus, and potassium in the potato tubers. It was concluded that K. mobilis strains CIAM 880 and CIAM853 should be considered as associative nitrogen fixers since their acetylene reducing activity was 329 –407 nmol mL21 h21. Both strains were able to produce IAA. Gagne et al. (1993) conducted a greenhouse experiment to study the effect of PGPR on tomato yield. The bacteria were inoculated into a commercial

134

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

peat-based substrate. The bacterial strains increased fruit yield up to 9.6% in the spring crop, but these results were statistically non-significant. However, in the fall experiment where plants were grown under suboptimum environmental conditions, Ps. fluorescens strain 63-28 significantly increased fruit yield by 18.2%. The average size of the harvested fruit was also increased by 11.1%. Highly significant increases in tomato and pepper transplants were also reported by Kokalis-Burelle et al. (2002) in response to a formulation of PGPR. Transplant vigor and survival in the field was improved by PGPR treatments in both tomato and pepper with an increase of 395% in dry root weight of tomato and 565% in dry root weight of pepper compared to the non-inoculated control. Many researchers have reported pronounced effects of PGPR inoculation on the growth and yield of rapeseed. According to Mei (1989), rapeseed is the second most important oil seed crop in China. PGPR were used on 200,000 ha in 13 provinces during 1980 –87 with Bacillus cereus strains 83-10 and 83-6 being the most commonly used cultures. Results of multi-year trials indicated that application of PGPR increased the yield of rapeseed up to 11.5%. In repeated field trials at four locations, inoculation with B. cereus strain 83-10 increased the grain yield of rapeseed by 17.2% compared to the non-inoculated control (Xia et al., 1990). In this study, yield increase resulted from more pods per plant (33.1%), but not the number of seeds per pod and 1000-grain weight compared to the non-inoculated control. Xia et al. (1990) also found that PGPR treated plants usually exhibited increased emergence and vegetative growth. The mean increase in total plant weight was 26.3 and 23.7% during flowering and pod filling stages, respectively. Yield increase of rapeseed in response to PGPR inoculation was also associated with increased oil content (15.2%), root system activity and nutrient uptake. Kloepper et al. (1987) reported that 35 strains of PGPR increased grain yield of rapeseed up to 57% compared to the non-inoculated control in one of the two years tested. All site averages indicated that PGPR increased rapeseed yields by 6 – 13% during these trials. The PGPR strains included Ps. putida, Ps. fluorescens, S. liquefaciens, Ps. putida biover B and Arthrobacter cetreus. In another study, Kloepper et al. (1991) found that inoculation with PGPR increased the emergence and dry weight of rapeseed seedlings (12 day after sowing) by 123 and 79%, respectively, over the non-inoculated control. A significant increase in the emergence rate was also observed due to inoculation with certain root colonizing bacteria, including Pseudomonas as a PGPR (Kloepper et al., 1986). Chen et al. (1994) isolated PGPR strains from roots of rapeseed and rhizosphere soil and added them to sterile peat. They then inoculated rapeseed with five strains for several years from 1979 to 1990 and observed a significant increase in yield (11.5% over the control). The application of these PGPR improved seedling emergence and vegetative vigor of rapeseed in addition to enhancing yields. Zahir et al. (1998a) conducted an experiment in which

PGPR APPLICATION IN AGRICULTURE

135

19 Azotobacter cultures were isolated from the rhizosphere soil of maize and their ability to produce auxins (IAA-equivalents) was measured in vitro. Six isolates were obtained on the basis of their auxin production and tested for their growth promoting effects. Seeds of Sarsoon (Brassica campestris) were inoculated with these isolates and sown in pots. Results of this study revealed that yield was significantly increased up to 34.4% over the non-inoculated control. Lifshitz et al. (1987) assessed the growth promoting activity of Ps. putida strain GR12-2 in a gnotobiotic growth pouch assay and reported that inoculation of rapeseed with strain GR12-2 significantly increased root length and shoot height compared to the control. Ps. putida GR12-2 and E. coli had the ability to promote root elongation of canola seedlings under gnotobiotic conditions. The activity was mainly attributed to the enzyme, 1-aminocyclopropane-1-carboxylate (ACC) deaminase (Tang et al.,1995; Shah et al.,1998). Glick et al. (1995) reported that 11 strains of Pseudomonas had the ability to promote root elongation of canola under gnotobiotic conditions. Root and shoot lengths as well as fresh and dry weights of shoots of canola were improved by inoculation with Ps. putida GR12-2 (Glick et al.,1997). Bayliss et al. (1997) also reported that Ps. putida GR12-2R3 promoted the emergence and growth of canola. deFreitas et al. (1997) isolated 111 bacteria from the rhizosphere of field-grown plants and a collection of nine bacteria (PGPR) were screened for P-solubilization in vitro. The P-solubilizing isolates were identified as two Bacillus brevis strains, B. megaterium, B. polymyxa, B. sphaerricus, B. thuringiensis and Xanthomonas maltophilia (PGPR strains R85). These bacteria were tested for their effects on growth and P-uptake of canola plants. Several of these rhizobacteria increased plant height or pod yield with the most effective inoculant being B. thuringiensis which significantly increased the number and weight of pods and seed yield. Xanthomonas maltophilia increased plant height, whereas, the other bacilli increased the number and weight of pods. None of these isolates increased P-uptake. Hall et al. (1996) conducted an experiment to study the effect of PGPR on root elongation of various agronomic crops. Seeds of canola, lettuce, tomato, barley, wheat, and oat were inoculated with either the wild type Ps. putida GR12-2 or the mutant Ps. putida GR12-2/acd68 lacking in ACC deaminase. They reported an increase in root length when seeds were treated with the wild type Ps. putida GR12-2. Chiarini et al. (1998) tested Burkholderia cepacia strain PHP7 for its ability to colonize roots promoting the growth of Sorghum bicolor alone or in combination with Enterobacter spp. strain BB23/T4d or Ps. fluoroscens strain A23/T3c. All three strains were able to colonize the root system of sorghum, but only PHP7 and A23/T3c promoted plant growth in the single strain inoculation tests. Dual strain inocula were no more effective than single strain inoculum. Several strains of PGPR have been isolated to test their efficiency in enhancing the yield of graminaceous crops in pot and field conditions (Subba Rao, 1996). Experiments under axenic as well as pot and field conditions often show

136

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

enhancement of root biomass as well as yield of fodder and grain upon inoculation with Azospirillum brasilense and A. lipoferum isolates. It is believed that the observed beneficial effects of Azospirillum are due to better nutrient absorption through augmented root biomass.

C. RHIZOBIA

AS

PGPR

IN

NON- LEGUMES

The beneficial effects of Rhizobium and Bradyrhizobium on legumes in terms of nitrogen fixation are well known. However, recent reports also indicate that these symbiotic bacteria may have the potential to be used as PGPR with non-legumes. Root colonization is an important first step in the interaction of beneficial bacteria with plants (Kloepper and Beauchamp, 1992). To act as PGPR, rhizobia like other PGPR are capable of colonizing roots of non-legumes forming an associative interaction as they produce phytohormones, siderophores, and HCN. Some rhizobial strains are antagonistic towards plant pathogenic fungi. Rhizobial attachment to roots of asparagus (Asparagus officinalis L.), oat (Avena sativa L.), rice (Oryza sativa L.) and wheat (Triticum aestivum L.) has been reported by Shimshick and Herbert (1979) and Terouchi and Syono (1990). Moreover, Penˇa-Cabriales and Alexander (1983) found that strains of rhizobia and bradyrhizobia grew readily in the presence of germinating seeds and developing root systems of soybeans (Glycine max L. Merr.), kidney beans (Phaseolus vulgaris L.), red clover (Trifolium pratense L.), cowpeas (Vigna unguiculata L.) and oat, wheat and corn. Recently, Wiehe and Hoflich (1995) demonstrated that the strain R39 of Rhizobium leguminosarum bv. trifolii, multiplied under field conditions in the rhizosphere of host legumes (lupine and pea) as well as non-legumes including corn, rape (Brassica napus L.) and wheat. Many studies indicate that non-legumes interact with bradyrhizobia and rhizobia in the rhizosphere. Root hair curling induced by these symbiotic bacteria was observed on maize, rice and oat plants (Plazinsky et al.,1985; Terouchi and Syono, 1990). Other PGPR traits of rhizobia and bradyrhizobia include phytohormone production (Lippmann et al.,1995; Arshad and Frankenberger, 1998), siderophore release (Guerinot, 1991; Plessner et al.,1993; Jadhav et al., 1994), solubilization of inorganic phosphorous (Halder and Chakrabartty, 1993; Abd-Alla, 1994; Chabot et al., 1996a) and antagonism against plant pathogenic microorganisms (Antoun et al.,1978, Buonassisi et al.,1986; Malajczuk et al., 1984; Ehteshamul-Haque and Ghaffar, 1993). The effect of R. leguminosarum bv. trifolii on non-legume plant growth has been reported to compare similar to Ps. fluorescens as a PGPR in its colonization on certain plant roots. Strain R39 of Rhizobium leguminosarum bv. trifolii is an effective strain isolated in Germany from red clover. In field experiments performed from 1985 to 1993 on a loamy sand, inoculation with R39 significantly increased the shoot dry matter yield of maize (7%), spring wheat (8%) and spring

PGPR APPLICATION IN AGRICULTURE

137

barley (6%) (Hoflich et al.,1994). In these experiments, the stimulatory effect of R39 on maize growth compared favorably with strain PsIA12 of Ps. fluorescens which caused an increase of 6% in shoot dry matter yield of maize. However, some maize cultivars did not respond to inoculation with R39 (Hoflich, 2000). In general, Rhizobium strain R39 colonized the rhizosphere of pea, maize and sugar beet better than strain PsIA12 of Ps. fluorescens (Hoflich et al., 1995). However, the leguminous plants (Pisum sativum and Lupinus albus) were colonized in higher numbers by R39 than non-leguminous plants (Triticum aestivum and Zea mays). Strain R39 was found in the rhizosphere soil and rhizoplane of maize 4 weeks after inoculation and colonized inner of the root tissues. Strain R39 did not have any free-living nitrogenase activity in the laboratory culture; however, nitrate reductase activity was observed and it produced the phytohormones, cytokinin, and auxin. This strain also showed in vitro antagonistic activity against the Fusarium spp., Rhizoctonia solani, Helminthosporium sativum and Gaeumannomyces graminis plant root pathogens (Hoflich, 2000). The plant growth promoting ability of rhizobia inoculation varies with soil properties and crop rotation. Recently, Hilali et al. (2000) isolated several strains of R. leguminosarum bv. trifolii from the roots of wheat cultivated in rotation with clover in Morocco. In a greenhouse trial performed with two different soils, strain IAT 178 of R. leguminosarum bv. trifolii increased grain yield by 12 and 18% compared to the non-inoculated control. One hundred strains of R. leguminosarum bv. trifolii were screened for their effect on the growth of the cultivar, Rihane of wheat grown in an agricultural soil under greenhouse conditions. After 5 weeks, Hilali and his colleagues (2001) selected 14 strains of PGPR stimulating the fresh or dry matter yield of shoots. Strain IAT 168 increased shoot dry matter and grain yield by 24% compared to the control. However, in a second pot inoculation trial, the rhizobial strains behaved differently in the soil they used. In western Canada, Biederbeck et al.(2000) did not find any rhizobia in the rhizosphere of wheat continuously cultivated as a monoculture. However, when wheat was cultivated in rotation with lentil for 20 years, the rhizosphere of wheat contained between 2 and 7 £ 105 g21 rhizobia. About 6.8 £ 104 endophytic rhizobia were present in wheat when put in rotation with lentil. Table VIII provides citations in which rhizobia may act as PGPR for many crops other than legumes when applied as inoculants or when legume crops are rotated with non-legumes.

D. CO-INOCULATION OF LEGUMES AND RHIZOBIUM

WITH

PGPR

Co-inoculation of legumes with PGPR and Rhizobium has received increasing attention in recent years. Co-inoculation with symbiotic bacteria and rhizosphere

138

Crops

Bacteria

Barley

Bradyrhizobium japonicum

Brassica campestris cv. Tobin, Brassica napus cv. Westar Maize Lettuce

Rhizobium leguminosarum

Maize, Letuce

R. leguminosarum bv. phaseoli

R.. leguminosarum bv. phaseoli

Response

References

Bacterization with B. japonicum increased root length by 12.9% and root fresh weight by 6.3% compared to the non-inoculated control. Early seedling root growth of canola (Brassica campestris cv. Tobin, Brassica napus cv. Westar) was significantly promoted by inoculation of seeds with some strains of R. leguminosarum under gnotobiotic conditions.

Carletti et al. (1994)

Two selected strains, P31 and R1 exhibited the highest in vitro dicalcium phosphate solubilization activity. A field inoculation trial of forage maize with strains P31 and R1 showed a significant interaction between inoculation treatments and P-fertilization effects on dry matter yield of shoots. Strain P31 significantly stimulated maize shoot dry matter yield (8% increase) only when the 100% recommended rate of P fertilizer was applied. In a poorly P fertile soil, inoculation of lettuce with strain P31 in the presence of 50% of the recommended P fertilization rate (35 kg P ha-1) caused a significant increase (15%) in lettuce dry matter yield. In addition to their P-solubilization activity, both rhizobial strains produced siderophores and IAA or an IAA-analog. P31 was also a HCN producer. Comparison of different strains indicated that R1 was the best root colonizer of maize and lettuce.

Noel et al. (1996)

Chabot et al. (1993)

Chabot et al. (1996b)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Table VIII PGPR Activity of Rhizobium and Plant Responses

Rhizobium, Bradyrhizobium

Radishes

Rhizobium and Bradyrhizobium strains

Rice

R. leguminosarum bv. trifolii

Rhizobium spp.

In cereal-legume crop rotation systems, inoculation of the preceding cereal crop of maize significantly increased nodule volume, dry weight of shoots, number of pods and final grain yield of the following green gram and groundnut legume crop. Some of the strains examined displayed PGPR activity on radishes. Among the 10 best strains promoting more than 50% increase in radish dry matter yield in a loam agricultural soil, two strains were Bradyrhizobium japonicum and another strain was a Bradyrhizobium sp. No correlation was found between in vitro HCN, IAA or siderophores production and the P-solubilizing activtiy of the strains vs. their PGPR effect. Quantitative measurement of plant growth responses to inoculation revealed that certain rhizobial endophytes significantly promoted growth of rice shoots and roots, the extent of which was influenced by the rice cultivar, inoculated strain, plant growth medium and the growth parameters measured in the growth chamber experiments. Substantial increases in grain yield and N-content were observed under field conditions. Inoculation promoted rice plant growth. It was suggested that these bacteria possess important genes that enhanced their ability to intimately colonize on and within rice tissues promoting the growth of rice.

Gaur et al. (1980)

Antoun et al. (1998)

Yanni et al. (1997)

PGPR APPLICATION IN AGRICULTURE

Maize, Green gram, Groundnut

Prayitno et al. (1999)

(continued)

139

140

Table VIII (continued) Crops

Bacteria

Rhizobium leguminosarum bv. trifolii E11, Rhizobium sp. IRBG74, Azospirillum Azorhizobium caulinodans strains (TAL 1926 and IBRG-42)

Azorhizobium caulinodan Sunflower

Rhizobium sp. strain (YAS34)

Tomato

B. japonicum

Wheat

Rhizobium

References

Inoculation increased grain (8 to 22%) and straw (4 to 19%) yield of rice at different N rates compared to non-inoculated controls. N, P and K uptakes were increased by 10 to 28% upon rhizobial inoculation. Inoculation with strain E11 and IRBG74 stimulated early rice growth resulting in significant increases in grain and straw yields at maturity. Growth responses to inoculation also involved strain (PGPR)-variety (rice) specificity. The inoculant, TAL1926 caused an 120% increase in shoot dry weight, 28% increase in %N and 140% increase in total N content plant-1 over the non-inoculated control. The inoculant, IBRG-42 caused a 58% increase in shoot dry weight, 19% increase in %N and 68% increase in total N content plant21 over the noninoculated control. Inoculation had a significant effect on plant height (13%), and dry (8%) and wet weights (7%) over the non-inoculated control. Inoculation of sunflower seeds and soil with strain YAS34 caused a significant effect on shoot diameter (up to þ 50%) and root diameter (up to þ70%) in normal and water stress conditions. Total root length of tomato seedlings inoculated by B. japonicum was increased by 87% compared to the non-inoculated control. Inoculation increased grain yield by 106% over the control.

Biswas et al. (2000a)

Biswas et al. (2000b)

Khokhar and Qureshi (1998)

Nieuwenhove et al. (2000) Alami et al. (2000)

Carletti et al. (1994) Amara and Dahdoh (1997)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

R. leguminosarum bv. trifolii E11, Rhizobium sp. IRBG74, and Bradyrhizobium sp. IRBG271

Response

PGPR APPLICATION IN AGRICULTURE

141

bacteria may increase nodulation through a variety of mechanisms. For example, PGPR can produce phytoalexin, antibiotics against pathogenic organisms, siderophores chelating insoluble cations and colonize root surfaces, thereby out-competing pathogens (Parmar and Dadarwal, 1999). Dual inoculation with Rhizobium and PGPR can stimulate or inhibit nodule formation and growth in a given symbiotic relationship, depending upon the nature and concentration of secondary metabolites released by the non-rhizobial PGPR. Rhizobium infection takes place by the formation of infection threads in root hairs. Stimulation of nodulation upon inoculation with Azospirillum may be due to an increase in production of lateral roots, root hair density and root hair branching (Okon and Kapulnik, 1986), but also by the differentiation of a greater number of epidermal cells into infected root hairs (Yahalom et al.,1991). When inoculated with rhizobia, Azospirillum lipoferum stimulates the formation of epidermal cells that becomes infected root hair cells, creating additional infection sites that are later occupied by rhizobia (Plazinski and Rolfe, 1985; Tchebotar et al.,1998). It has been demonstrated with Fa˚hraeus slides, that Azospirillum brasilense causes an increase in the number of root hairs and root diameter in alfalfa (Itzigsohn et al., 1993). Inoculation of common bean with A. brasilense also promotes root hair formation in seedling roots (Burdman et al.,1996). In experiments carried out with a hydroponic system, A. brasilense caused an increase in secretion of the nod gene inducing flavonoids by common bean seedlings, as observed by nod gene induction assays of root exudates fractionated by HPLC (Burdman et al.,1996). In a parallel study on alfalfa, different flavonoid profiles were observed when root exudates of A. brasilense- and yeast extracttreated plants were compared, indicating that changes in root metabolism caused by A. brasilense were not a defense-like response (Volpin et al.,1996). The effects of PGPR on both root hair formation and secretion of nod gene inducers by the roots could explain, at least in part, the appearance of a greater number of nodules in several legumes following co-inoculation, as compared to plants inoculated with Rhizobium alone. The increase in the number of nodules may indicate that an earlier nodulation event and/or a greater susceptibility of roots to nodulation occurs. Co-inoculation of several legumes with Azospirillum and Rhizobium, or inoculation with Azospirillum alone in the case of naturally nodulated legumes, were shown to benefit plant growth in greenhouse and field experiments. The increase in dry-matter production and N content of dually inoculated plants may be attributed to earlier and enhanced nodulation, higher N2-fixation rates and a general improvement of root development (Okon and Itzigsohn, 1995). Oliveira et al. (1997) investigated the potential of three PGPR strains for nitrogen fixation and growth of clover plants under greenhouse conditions. They found that Azospirillum brasilense Sp7 and a local rhizobacteria promoted nodulation and nitrogenase activity. In general, co-inoculation affects many plant growth parameters including nodulation (nodule number and weight,

142

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Fe-content in nodules and earlier nodulation); nitrogenase activity, root dry weight, root length and surface area, shoot dry weight and height, N and mineral content and grain yield. This area of research is becoming very popular over the recent years. Table IX provides positive results observed for several legumes following co-inoculation with PGPR and Rhizobium in greenhouse and field experiments.

E. PRECURSOR – INOCULUM INTERACTIONS The beneficial effects of inoculation with PGPR on plant growth are well documented; however, there is often a lack of consistency in reproducible results during growth promotion of plants. To improve the effectiveness of inoculation and enhance reproducibility, Frankenberger and his co-workers (1987 to present) have developed an approach based upon the hypothesis that inoculation of PGPR in the presence of a specific physiological precursor of a PGR is often more effective in promoting plant growth than inoculation alone (Frankenberger and Arshad, 1995; Arshad and Frankenberger, 1993, 1998). By using this approach, the synthesis of a particular PGR in the rhizosphere can be controlled to evoke a physiological response. Several published studies have demonstrated the success of this approach, as discussed below. L -Tryptophan (L -TRP) is considered the physiological precursor of auxin for both plant and soil microorganisms. Its addition to soil has been shown to increase the auxin content and influence various physiological effects on plant growth via microbially derived auxins. Frankenberger and his co-workers have conducted many experiments to evaluate this hypothesis. Low availability of L -TRP could be the most limiting factor for auxin production in the rhizosphere of plants. Root exudates are the only natural source of tryptophan for rhizosphere microflora. Recently, detectable amounts of L -TRP in root exudates of some, but not in all varieties of wheat were reported by Kravchenko et al. (1991) and Martens and Frankenberger (1992, 1994). This indicates that not all plants release adequate quantities of L -TRP into the rhizosphere for microbial production of auxins. Auxin production in the root zone is limited by the genetic and physiological properties of both the microorganism(s) and the plant(s). Thus, effective utilization of microbial-produced auxins by the plant requires careful selection of both partners. Moreover, the presence of other compounds (e.g., glutamine, a-ketoglutaric acid and pyruvic acid) and growth factors may affect auxin production in the rhizosphere. Rhizosphere microflora are also capable of catabolizing auxins. Further work is needed to explore auxin production in the rhizosphere. After confirming L -TRP-dependent IAA synthesis by an ectomycorrhizal fungus, Pisolithus tinctorius, using TLC, HPLC, ELISA, and GC – MS,

Table IX Co-inoculation of Legumes with PGPR and Rhizobium

Crop

Coinoculating PGPR

Rhizobium meliloti

Azospirillum brasilense

R. meliloti

Pseudomonas

Bean

R. phaseoli

Pseudomonas putida

Chickpea

Indigenous Rhizobium

A. brasilense

Mesorhizobium

Pseudomonas

Rhizobium

Pseudomonas, Bacillus Pseudomonas, Bacillus

Rhizobium

Plant responses to inoculation Combined inoculation increased fresh and dry matter, and protein yields in a field experiment carried out in a calcareous soil of Northwestern Egypt. Upon co-inoculation, a significant increase in plant growth, nitrogenase activity, nodule number, total nodule weight and total plant nitrogen was reported. Inoculation with P. putida markedly increased nodulation compared to a R. phaseoli control. Also, 2-ketogluconic acid, a phosphate-solubilizing compound, was detected in P. putida. Inoculation with A. brasilense has the potential to prevent or at least diminish the effects caused by salinity stress in greenhouse experiments with chickpeas irrigated with saline water. A significant increase in nodule weight and shoot biomass was observed when co-inoculated with Mesorhizobium and Pseudomonas in sterilized chillum jar conditions. In pot experiments, co-inoculation significantly increased root and shoot biomass. Upon co-inoculation, reduction in wilt incidence and an increase in nodulation were observed. A significant increase in nodule weight, root and shoot biomass and total plant nitrogen were reported due to co-inoculation.

References Hassouna et al. (1994)

Knight and Langston-Unkefer (1988) Grimes and Mount (1984)

Hamaoui et al. (2001)

Sindhu et al.(2002a)

PGPR APPLICATION IN AGRICULTURE

Alfalfa

Rhizobia

Khot et al. (1996) Parmar and Dadarwal (1999)

143

(continued)

144

Table IX (continued)

Rhizobia

Coinoculating PGPR

Chickpeas, Common beans

Rhizobium,

A. brasilense

Clover

R. leguminosarum bv. trifolii 24

Common bean

R. phaseoli Rhizobium

Pseudomonas sp. Strain 267 and R. Ps. putida A. brasilense

Green gram

Bradyrhizobium spp. (Vigna) strains Cog15 and S24

Bacillus

Bradyrhizobium

Bacillus

Plant responses to inoculation Inoculation with A. brasilense increased nodule dry weight, various plant growth parameters and seed yield of nodulated chickpeas in the field. In common bean, inoculation with Rhizobium meliloti and R. tropici increased seed yield, while combined inoculation with Rhizobium and A. brasilense resulted in a further increase. Plants inoculated with A. brasilense alone did not differ in yield from the noninoculated controls, despite a relative increase in shoot dry weight. Co-inoculation of clover plants significantly increased shoot weight and nodule weight in comparison with control plants infected only with R. leguminosarum bv. trifolii. Co-inoculation caused a marked increase in nodulation. Co-inoculation promoted root hair formation and an increase in secretion of the nod gene induced flavonoids resulting in greater numbers of nodules. Co-inoculants of the PGPR, Bacillus and Bradyrhizobium strains failed to show any conclusive influence on nodulation and ARA at 50 days of plant growth. Bacillus had a direct effect on shoot biomass development, N-content and grain yield of green gram when co-inoculated with Bradyrhizobium strain S24 at 50 and 80 days of plant growth. Also, single inoculation of Bacillus isolates significantly increased grain yield over the non-inoculated control. Co-inoculation enhanced nodulation and plant growth of green gram.

References Burdman et al. (1996)

Derylo and Skorupska (1993)

Grimes and Mount (1984) Burdman et al. (1996)

Gupta et al. (1998)

Sindhu et al. (2002b)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Crop

Lentil/Peas

Peas, Chick peas, Vetch Soybean

Pseudomonas

R. leguminosarum biovar Viceae Indigenous Rhizobium R. leguminosarum

PGPRa

Pseudomonas

Rhizobium

A. brasilense

B. japonicum

B. japonicum

S. lequefacians 2-68, S. protea maculans 1-102 PGPRa

B. japonicum

Ps. fluorescens

B. japonicum

Azospirillum

B. japonicum

S. liquefaciens 2-68, S. proteamaculans I-102 PGPRa

B. japonicum

A. brasilense

Co-inoculation resulted in significant increases in nodule weight, plant dry weight and total plant N compared to inoculation with Bradyrhizobium alone. Co-inoculation enhanced plant emergence, vigor, nodulation, C2H2 reduction activity and root fresh weight. Inoculation with A. brasilense showed a similar effect to that of phosphate fertilization. Numbers of nodules were greater in co-inoculated plants than with R. leguminosarum inoculation alone. Co-inoculation significantly increased seed yield, but did not affect dry matter yield of garden peas and chickpeas. Vetch co-inoculation significantly increased N2-fixation, nitrogen content and dry matter yield. Nodulation, nitrogen fixation, protein and N yield were increased by co-inoculation. Co-inoculation resulted in increased nodulation, nitrogen fixation and plant growth. Co-inoculation increased colonization of Bradyrhizobium japonicum on soybean roots, nodule number and the acetylene reduction assay. Co-inoculation promoted nodulation, nitrogenase activity and plant growth. Co-inoculation increased grain yield, grain protein and total plant protein production under short season conditions.

Chanway et al. (1989) Itzigsohn et al. (2000) Bolton et al. (1990) Sarig et al. (1986)

Dashti et al. (1998)

Dashti et al. (2000) Chebotar et al. (2001)

Iruthayathas et al. (1983); Polonenko et al. (1987); Zhang et al. (1996) Dashti et al. (1997)

Verma et al. (1986); Yahalom et al. (1987); Li and Alexander (1988) (continued)

145

Co-inoculation increased the root and shoot weight, grain yield, plant vigor, nodulation and N2 fixation.

Sindhu et al. (1999)

PGPR APPLICATION IN AGRICULTURE

Natural pasture Pea

Bradyrhizobium

146

Crop

White clover

a

Not identified.

Rhizobia

Coinoculating PGPR a

B. japonicum

PGPR

B. japonicum

Ps. putida, Ps. fluorescens, Aeromonas hydrophia

R. legumino Sarum bv. trifolii

Azospirillum lipoferum

Plant responses to inoculation

References

Co-inoculation caused an increase in dry weight of plant tissue and leaf and pod number at low nitrogen levels. Co-inoculation promoted a significant increase in the weight and number of nodules. Several strains increased the dry weight of shoots and roots when inoculated with B. japonicum,but these effects did not correlate with changes in number and weight of nodules. Co-inoculation enhanced the number of nodules by 2–3 times from 5 to 20 days after inoculation. Acetylene reduction activity was also increased by 2.3- to 2.7-fold at 20 days after inoculation.

Souleimanov and Smith (2000) Polonenko et al. (1987)

Tchebotar et al. (1998)

Z. A. ZAHIR, M. ARSHAD, AND W. T. FRANKENBERGER

Table IX (continued)

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Frankenberger and Poth (1987) studied the influence of L -TRP and the fungus as an inoculum on Douglas fir (Pseudotsuga menziesii). We found that P. tinctorius stimulated the growth of potted seedlings of Douglas fir only when supplied with ng to mg quantities of L -TRP. There was basically no difference in growth between the inoculated and non-inoculated treatments in the absence of L -TRP. Zahir et al.(1997) also investigated the effectiveness of precursor-inoculum interactions. After confirming the ability of Azotobacter to convert L -TRP into auxins in vitro, we studied the effect of Azotobacter inoculation, both in the presence and absence of L -TRP, on potato yield under fertilized conditions. The combined application of Azotobacter inoculation and L -TRP was more effective than their application alone, increasing the tuber and straw yield of potato. Similarly, Khalid et al. (1999) conducted a field experiment on wheat and found that the combined application of Azotobacter (capable of producing auxins) and L-TRP (1023 M) increased wheat yield (21.3 %) compared to the untreated control. Zahir et al. (2000) conducted Leonard Jar experiments to study the response of root/shoot growth of maize seedlings to Azotobacter inoculation and L-TRP applications separately and in combination with each other. Results indicated that maximum length and weight of roots/shoots were recorded by applying Azotobacter in combination with L -TRP at 1023-M concentration. Precursor – inoculum interaction has also been studied for lentil in a pot experiment where Rhizobium inoculation alone increased the grain yield by 20.9% compared to the non-inoculated control, while application of 1.7 mg L -TRP kg21 soil along with the Rhizobium inoculation increased the yield by 30.6% (Hussain et al., 1995). Sarwar and Kremer (1995b) employed the precursor-inoculum approach for biological control of weeds. They found that Enterobacter taylorae with high 21 L -TRP-derived auxin producing potential (72 mg L IAA equivalents) inhibited the root growth of field bindweed seedlings by 77.4%. Application of L -TRP (1025-M) alone reduced the root growth by 18.3%, whereas, supplementing 25 L -TRP (at 10 M) with the inoculum (E. taylorae) further inhibited root growth by 90.5%. They suggested that providing L -TRP with the selected auxinproducing inoculum may be a practical biological control strategy against weeds. A similar response was observed in the case of other plants, including red clover, wheat, velvet leaf, pigweed, green foxtail, morning glory, corn, and soybean, most likely because of the higher-than-optimum production of auxins as a result of the precursor – inoculum interactions. These studies demonstrate the superiority of precursor-inoculation interactions over inoculation alone for improving crop yields. Nieto and Frankenberger (1990) investigated the effect of adenine (ADE), isopentyl alcohol (IA) and the cytokinin-producing bacterium, A. chroococcum on the morphological plant characteristics of Raphanus sativus (radish) in sand under axenic-inoculated conditions and in soil under glasshouse and field conditions. The combined application of ADE, IA and the bacterial inoculum

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enhanced the dry weight of root and shoot tissues, leaf area and chlorophyll a content of radish to a much greater degree than in the presence or absence of the cytokinin precursors (ADE or IA) or the bacterium alone. Although the addition of the inoculum without precursors was also stimulatory, growth promotion was not stimulated to the same degree as in the presence of the two precursors and A. chroococcum. Greater enhancement of plant growth was observed following the application of ADE, IA and A. chroococcum, which was attributed primarily to the increase in bacterial production of cytokinins within the rhizosphere. In another study, Nieto and Frankenberger (1991) evaluated the effect of ADE, IA and A. chroococcum applied to soil on the vegetative growth of Z. mays studied under glasshouse conditions. A combined treatment of ADE, IA and the bacterial inoculum enhanced the vegetative growth of maize to a greater degree than did the application of ADE plus IA; ADE plus A. chroococcum; or ADE, IA, or A. chroococcum alone. The dry mass of the root and shoot tissues was enhanced up to 5.6- and 5.0-fold, respectively, in comparison to the controls; however, the root-shoot ratios were similar. The increase in shoot height, inter-nodal distance, and stem and leaf width over the controls under the optimal treatment (ADE, IA, and A. chroococcum) were 2.07-, 2.82-, 1.46-, and 1.70-fold, respectively. These studies clearly demonstrate that the effectiveness of PGPR can be enhanced by the simultaneous applications of physiological precursor(s) of specific PGRs.

V. CONCLUDING REMARKS The rhizosphere of a plant is a zone of intense microbial activity. Rhizobacteria that exert beneficial effects on plant growth and development are referred to as plant growth promoting rhizobacteria (PGPR) because their application is often associated with increased rates of plant growth, development and yield. PGPR can affect plant growth directly or indirectly. Indirect promotion of plant growth occurs when introduced PGPR lessens or prevents deleterious effects of one or more phytopathogenic organisms in the rhizosphere. The direct promotion of plant growth by PGPR may include the production and release of secondary metabolites such as plant growth regulators (phytohormones) or facilitating the uptake of certain nutrients from the root environment. There is increasing evidence that rhizobia can promote the growth of nonlegumes via direct or indirect mechanisms. Furthermore, co-inoculation of legumes with rhizobia and PGPR is even more effective for improving nodulation and growth of legumes. The effect of PGPR strains on plant growth is strongly influenced by environmental factors including soil characteristics, plant species, and even plant

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genotypes within a species, and other rhizosphere microflora. Less than optimal or unfavorable conditions may lead to little or no synthesis of biologically active substances in the root zone resulting in the failure of PGPR to promote plant growth. Application of PGPR strains can provide an effective, economical and practical way of plant protection via disease suppression. PGPR strain mixtures often show synergistic action in plant protection and growth promotion involving many mechanisms. Selecting a combination of strains may be beneficial in crop production. Researchers are using different plant growth promoting techniques (e.g. growth promotion under gnotobiotic conditions, in vitro production of PGRs and screening for ACC deaminase activity) for selection of rhizobacteria and beneficial PGPR. In addition to these approaches, novel molecular and biochemical technologies are needed to screen rhizobacteria as PGPR in promoting the yield of agronomic crops and sustainable agriculture.

ACKNOWLEDGEMENTS We acknowledge and appreciate the help from Dr Azeem Khalid, Dr Hafiz Naeem Asghar and Ben Johanson for preparing this manuscript.

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ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS Kanwar L. Sahrawat International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India

I. Introduction II. Decomposition of Organic Materials in Submerged Soils A. Factors Affecting Organic Matter Decomposition III. Organic Matter Accumulation in Wetland Soils A. Factors Influencing Organic Matter Accumulation IV. Mechanisms for Organic Matter Accumulation in Wetlands A. Lack of Oxygen or Anaerobiosis B. Deficiency of Nutrients C. Lack of Terminal Electron Acceptors D. Production of Inhibitors of Microbial Activity E. Formation of Recalcitrant Complexes F. Incomplete Decomposition and Decreased Humification of Organic Matter G. High Primary Productivity V. Modeling Organic Matter in Wetland Soils VI. Perspectives References

The decomposition and accumulation of organic materials in submerged (anaerobic) soils and sediments differ considerably from those in their aerobic counterparts. This is caused by the lack of oxygen or anaerobiosis. Compared to aerobic soils, there is preferential accumulation of organic matter in submerged rice soils. This paper reviews the current literature to establish basis or bases for organic matter accumulation in wetland soils and sediments. The decomposition or destruction of organic materials is lessened and incomplete, and the humification of organic matter is decreased under flooded conditions. Consequently, the overall organic matter decomposition rates are slower in submerged soils than those in aerobic soils and this results in net accumulation of organic matter in soils or sites that remain flooded for several years. Also, high organic matter soils or Histosols are developed in permanently waterlogged sites or soils because the rate of organic matter destruction is slower than its accumulation. The balance between organic matter inputs and decomposition is the primary determinant of organic matter accumulation or depletion. 169 Advances in Agronomy, Volume 81 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. DOI 10.1016/S0065-2113(03)81004-0

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K. L. SAHRAWAT Several hypotheses have been postulated to explain the accumulation of organic matter in submerged soils. They include the deleterious effects on microbial activity of reduction products produced such as hydrogen sulfide or volatile fatty acids and toxic concentrations of ammonia, aluminum, iron, and other cations in soil solution. The absence of electron acceptors such as iron oxides and hydroxides in submerged soils and sediments slows down organic matter oxidation and mineralization. Formation of recalcitrant complex organic molecules with organic matter fractions, e.g., through enrichment of organic matter with phenolics in intensified irrigated rice production system, may render them less available for microbial attack and utilization. Moreover, the net primary productivity of wetlands is higher than other ecosystems. There is need for further research to fully understand the mechanism(s) involved in the accumulation of organic matter in submerged soils as wetlands offer an excellent example of conservation and maintenance of organic matter and storage of organic C. q 2004 Academic Press.

I. INTRODUCTION Physical, chemical, and biological processes continually transform organic materials added to soil in the form of plant or animal detritus. Plant litter and the biomass are major contributors to the formation of soil organic matter. Soil organic matter includes decomposition products at various stages of decomposition of organic tissues and products synthesized by soil fauna. Soil organic matter has two major types of compounds: (1) non-humic substances, belonging to identifiable chemical classes such as carbohydrates, and (2) humic substances consisting of a series of brown to dark-brown, high-molecular weight biopolymers (Quideau, 2002). The importance of organic matter in soil cannot be overemphasized in view of its role in the maintenance of soil fertility and crop productivity and for maintaining soil’s inherent capacity to perform its crucial functions for ecological and environmental integrity. The interest in submerged soils or wetlands stems from their importance in agricultural and social productivity and environmental protection. There are various types of wetlands which includes swamps, marshes, shallow lakes and rivers, bogs, mire, salt marshes, mangroves, floodplains, fens, and other ecosystems saturated by water during all or part of the growing season. Wetlands are aquatic to semi-aquatic ecosystems where permanent, prolonged, or periodic inundation creates environment conducive for establishment of aquatic life. Wetlands are referred to as ecotones or transitional communities as they are often located between land and water, although many wetlands are not ecotones as they are not associated with a lake, river, or stream. For nomenclature, definitions and global distribution of wetlands, the reader is referred to Tiner (2002). Acharya (2002) has discussed the economic benefits and value of wetlands. The extent of

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world’s wetland is estimated to be 7 –9 million km2 and occupy about 4– 6% of the earth surface (Lefeuvre and Bouchard, 2002). For the purpose of this chapter two definitions have been adopted. For general purpose, the submerged soils or wetlands “are those areas that are inundated or saturated by surface or ground water at a frequency and duration, sufficient to support and that under normal circumstances do support a prevalence of vegetation typically adapted for life in saturated conditions” (Reddy and Patrick, 1993). For the purpose of agricultural production systems and related aspects, the definition given by Brinkman and Blokhuis (1986) seems more appropriate. According to this definition, submerged soils or wetland soils are those which “have free water at the soil surface for at least during the growing season of arable crops, or for at least 2 months of the growing season of perennial crops, grasslands, forest, or other vegetation,”. Wetlands thus support wetland rice crops or wetland rice and dryland crops. The source of water could be rainwater and/or irrigation water. The wetland rice production systems in Asia support two or three crops per year under irrigated condition. Wetland rice-based system has been intensified rapidly during the last four decades and covers 22 million ha of South Asia and accounts for about 50% global rice supplies (Cassman and Pingali, 1995). These intensified irrigated rice systems or the double-crop rice and wheat systems contribute handsomely to global food supply. Unlike arable crop production systems, the wetland-based systems are relatively robust and sustainable in the maintenance of soil fertility. It has been argued that wetland soils are better endowed in maintaining fertility, especially their organic matter status (Sahrawat, 1994). Organic matter is the major source of N to wetland rice because 50– 75% of the N in the rice crop even in fertilized rice paddies comes from organic matter (Sahrawat, 1983). Moreover, results with 885 diverse soil samples from wetland rice fields in the Philippines showed that N-supplying capacity of the soils, as measured by anaerobic incubation method, was highly significantly ( p , 0.01) correlated to soil organic matter content (Sahrawat, 1983). Also, accumulated organic matter in wetland soils improves their general fertility status, cation exchange capacity, and nutrient retention capacity, especially of highly weathered, low activity tropical soils (Sahrawat, 1994). Thus, it is not surprising that plant productivity is linked to organic matter content of the soil (Bauer and Black, 1994). Moreover, organic matter has been proposed as a potential sink for storing atmospheric C in soil profile for mitigating global warming and at the same time increasing soil fertility and productivity (Lal and Bruce, 1999; Izaurralde et al., 2001). The chemistry of submerged soils and sediments differs considerably from that of their aerobic counterparts. This distinction is mainly caused by the lack of oxygen or anaerobiosis under submerged conditions. Anaerobiosis in submerged soils greatly affects the chemistry, microbiology and fertility of soils

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(Ponnamperuma, 1972, 1984a; Patrick and Reddy, 1978; Sahrawat, 1998a; Narteh and Sahrawat, 1999; Kimura, 2000; Liesack et al., 2000). A distinct characteristic of soils or sites that has been under submerged or waterlogged (anaerobic) condition for several years, is the accumulation of organic matter. Accumulation of organic matter, especially in tropical rice paddies is significant when compared to soils under arable cropping under similar conditions, and has often been cited as the basis for sustainable maintenance of fertility of wetland rice soils (Sahrawat, 1994). However, compared to arable crop production systems, little attention has been paid to understand the mechanisms or bases of accumulation and maintenance of organic matter in wetland rice-based production systems. Various authors have proposed hypotheses for the accumulation of organic matter in wetland paddy soils and natural wetlands. However, no effort has been made to critically review and analyze the literature to establish basis or bases for organic matter accumulation in wetland soils. But synthesis of such information could provide leads for improved and sustainable management of fertility in wetlands, especially those supporting wetland rice based production systems. The question to be asked is why wetland soils accumulate organic matter at relatively higher rates and help maintain soil fertility on a long-term basis. The objective of this chapter, therefore, is to critically review recent literature on accumulation of organic matter in wetland rice soils and develop a conceptual framework for the basis or bases of organic matter accumulation in various wetland soils and sediments. The need for future research is also examined.

II. DECOMPOSITION OF ORGANIC MATERIALS IN SUBMERGED SOILS A. FACTORS AFFECTING ORGANIC MATTER DECOMPOSITION Studies on the decomposition of plant materials in anaerobic environment showed that the decomposition rates are slower in the absence of oxygen than in aerobic environment. The reduction in oxygen concentration decreases the decomposition rate of plant residues in soil (Wershaw, 1993). Although the decomposition rates are lessened under anaerobic condition, the release of N occurs at a relatively higher C:N ratio than in aerobic environment because the energy requirement for the bacteria, the main decomposers in anaerobic conditions, is lower than those of heterotrophs in aerobic medium (Acharya, 1935a,b,c; Sahrawat, 1980; Ponnamperuma, 1984 a,b). A comparative evaluation of decomposition of 14C-labeled plant materials in diverse soils from England, Nigeria and four South Australian field sites, showed

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that the decomposition pattern was very similar in all soils, except that the net decomposition rate doubled approximately for every 8 –108C rise in mean annual air temperature (Jenkinson and Ayanaba, 1977; Ladd et al., 1985; Ayanaba and Jenkinson, 1990). The results suggest that decomposition rates of plant materials in tropical arable or submerged soils are much faster than in temperate soils because of higher air temperatures. Recent experimental evidence on the relationship between temperature and litter or organic matter decomposition suggests that the simple assumption that temperature affects the rate constant of the processes may not be valid. This was due to the effect of thermal conditions on the kinetics of C mineralization by changing the estimated percentages of initial material that behave as labile or recalcitrant. According to this finding, the utilization of temperature response functions by simulation models may lead to significant overestimations of soil C losses due to temperature increase (Dalias et al., 2003). Soil moisture affects the rate of decomposition of plant residues and the rate decreases under flooded conditions after peaking at moisture content of 60% of water holding capacity (Pal and Broadbent, 1975). Doran et al. (1988) showed that the optimum water content for aerobic microbial activity is 60% of soil pore space filled with water. This applied to diverse soils with various textural classes. The increase in soil water content above 60% of soil pore space filled with water leads to increased anaerobiosis and decline in aerobic microbial activity. That is the reason why the decomposition and mineralization of plant residues in soils are slower and less complete under anaerobic than aerobic conditions (Reddy et al., 1980; Neue and Scharpenseel, 1987; Murthy et al., 1991; Kretzschmar and Ladd, 1993). Lack of oxygen has a major effect on microbial physiology and decomposition of organic materials in soil. For example, the first-order rate constant for decomposition of the easily decomposable fraction of rice straw was 0.0054 day21 under aerobic condition compared to 0.0024 day21 under anaerobic conditions. The decomposition rates for the slowly decomposable straw fractions were 0.0013 and 0.0003 day21 for aerobic and anaerobic conditions, respectively (Reddy et al., 1980). The decomposition of alfalfa (Medicago sativa L.) in soil under aerobic condition, as measured by carbon dioxide evolution, had rate constants of 0.123 and 0.059 day21, respectively, for rapid and intermediate phases of decomposition. The decomposition rate constants under anaerobic condition, as measured by the sum of water-soluble C, carbon dioxide and methane production, for the rapid and intermediate phases were 0.118 and 0.024 day21, respectively (Gale and Gilmour, 1988) (Table I). Transformation of several pyridine derivatives was most efficient under aerobic condition with oxygen as an electron acceptor. Under anaerobic conditions, most of the pyridine derivatives persisted and were often transformed only after a prolonged or no lag period (Kaiser and Bollag, 1992). Polysaccharides and protein polymers undergo depolymerization reactions and

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K. L. SAHRAWAT Table I First-Order Rate Constants for Anaerobic and Aerobic Decomposition of Alfalfa in a Silt Loam Typic Fragiudult Rate constants (per day) Decomposition phase Rapid Intermediate

Anaerobic

Aerobic

0.118 (0.016)a 0.024 (0.005)

0.123 (0.011) 0.059 (0.005)

Adapted from Gale and Gilmour (1988). a Standard error.

structural components such as polyphenols are degraded mainly by oxidation reactions (Wershaw, 1993). While carbohydrates and amino acids from fresh plant materials and litter decompose equally fast under aerobic and anaerobic conditions, structural component mineralization under anaerobic or reduced oxygen levels is hampered by inefficient and slow bacterial hydrolysis (Kristensen et al., 1995). Similarly, the rates of soil organic matter decomposition and N mineralization are lessened in ill-drained lowland rice fields, apparently due to excessively reduced conditions (Takai and Wada, 1977; Kanke and Kanazawa, 1986; Watanabe, 1984). Providing drainage to ill-drained fields results in enhanced rates of organic matter decomposition and release of mineral N (Kanke and Kanazawa, 1986). A collaborative study on the decomposition of rice straw in submerged wetland rice fields at seven sites in South Korea, Philippines, China, and Thailand showed that there were no great differences in the decomposition rates. The decomposition rates during the cropping season at seven sites in four countries varied from 1.2 to 1.9% day21 (Watanabe, 1984). Chimmer and Cooper (2003) studied the effect of water table levels on organic matter decomposition and carbon dioxide emissions in a Colorado subalpine fen for two summers in microcosms installed in the fen. It was found that carbon dioxide emissions were lowest at the highest water (6 – 10 cm standing water above the soil surface) which corresponded to submergence of soil under water. The emissions of carbon dioxide increased with the lowering of water table and were highest when the water table was 0– 5 cm below the soil surface. From this discussion it can be concluded that the rates of decomposition of added organic materials as well as of soil organic matter are slower under anaerobic conditions than under aerobic conditions. As soil water level increases, all soil pores are filled with water and the diffusion of air is extremely slow. The diffusion coefficient of oxygen in water is 10,000 times slower than in air and even a modest oxygen demand for microbial activity cannot be met if large pores are filled with water (Jenkinson, 1988).

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Obviously, the lack of oxygen in submerged soils is the most important and dominant factor controlling the rate and pattern of organic material decomposition in anaerobic environments.

III. ORGANIC MATTER ACCUMULATION IN WETLAND SOILS The accumulation of organic matter in soils is controlled by environmental and pedogenic processes. According to Jenny (1941), organic matter content of a soil is the function of climate, parent material, time, organisms, and topography. The rate of organic matter accumulation also greatly varies among soils (Vijre et al., 2003).

A. FACTORS INFLUENCING ORGANIC MATTER ACCUMULATION The accumulation of organic matter in arable soils differs considerably from that in submerged or flooded (anaerobic) soils. This difference is caused mainly by lack of oxygen or anaerobiosis. Wetland rice soils have a relatively greater tendency to accumulate organic matter. For example, formation of high organic matter soils including organic soils or Histosols occurs under permanently waterlogged conditions. Organic soils are formed when the rate of organic matter production exceeds the rate of its destruction or decomposition. For this, an anaerobic or low oxygen environment is required (Gorham, 1957). The amount of organic matter formed in wetland soils depends on the amount of organic material added and its humification coefficient (Wen, 1984). Humification coefficient is defined as the fraction of organic C left or retained in the soil after 1 year of decomposition. The humification coefficient of organic materials varies with their chemical composition. As a general rule, higher the lignin content higher the humification coefficient of the organic material. In addition, soil properties and prevailing climatic conditions also affect humification of organic materials (Wen, 1984). The data in Table II show the range in C content and humification coefficients of some crop residues. Rice roots have the highest humification coefficient (50%) and rice straw/stubble the lowest (23%). Soil fertility research in paddy soils of China showed that the content of organic matter in paddy soil is related to the soil water regime. The drainage conditions of the soil invariably affect the decomposition and accumulation of organic matter. There is greater accumulation of organic matter in waterlogged or poorly drained soils than in the freely drained soils. These results support

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K. L. SAHRAWAT Table II Humification Coefficient and Carbon Contents of Organic Materials in Submerged Soils in China Plant residue

C content (%)

Humification coefficienta

46 43 43 52 37 43

0.50 0.23 0.23 0.31 0.32 0.43

Rice root Rice stubble Rice straw Wheat, barley and millet stubble Wheat, barley and millet root Azolla

Adapted from Wen (1984). a The fraction of C in the soil after one year of decomposition.

the conclusion that there is preferential accumulation of organic matter in waterlogged soils compared to well-drained soils (Table III). Analysis of soil samples from a long-term experiment at the International Rice Research Institute in the Philippines revealed that soils in plots with higher intensity of irrigated wetland rice cropping had significantly higher content of organic C and total N than those with less intensity of wetland rice (Table IV). Soil samples from plots with triple-crop rice had the highest concentration of organic C and total N, followed by double-crop rice, rice-soybean and dryland rice in the descending order of organic C and total N contents. A survey of soils in South China showed that the amount of organic matter is higher in paddy soil under continuous wetland rice cultivation than in the soil under wetland rice – dryland crop system, evidently as the result of different water regimes (Table V). In a long-term experiment conducted for 7 years, Ponnamperuma (1984b) found that water regime, dry fallow or flood fallow, and application of rice straw influenced the accumulation of N in a clay soil in a double wetland rice system. Highest N is in the soil accumulated under flood fallow with application of rice

Table III Effect of Water Regime on Organic Matter Contents of Paddy Soils in China Location

Water regime

Organic matter (%)

Jiangsu

Waterlogged Well-drained Waterlogged Well-drained Waterlogged Well-drained

3.47 2.20 3.23 3.08 3.12 2.12

Jiangxi Guangdong

Adapted from Cheng (1984).

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 177 Table IV Organic C and Total N Content of Four Soils from the Philippines Varying in Intensity of Irrigated Rice Cropping Cropping system

Organic C (g kg21)

Total N soil (g kg21)

C:N ratio (g kg21)

Dryland rice Rice–soybean Double-crop rice Triple-crop rice

13.0ba 13.3b 22.5 (0.31)b 28.8a

1.25b 1.19b 1.89 (0.03) 2.45a

10.4c 11.2b 11.9 11.8a

Adapted from Olk et al. (1996). a Means within a column followed by the same letter do not differ significantly (P , 0.05). b Standard deviation.

straw and lowest in the treatment with dry fallow, without application of rice straw (Table VI). Aeration of waterlogged soils through sub-surface or surface drainage enhances the rates of soil organic matter decomposition and N mineralization (Sahrawat, 1983). The soil-drying effect, for example, was marked in four permanently waterlogged Philippine Histosols (pH 5.6– 6.2; organic matter 2.2 – 4.2 g kg21 soil). There was virtual absence of organic matter decomposition and mineralization of organic N in these permanently waterlogged soils, but air drying the soils prior to flooding caused a surge in release of ammonium N (Sahrawat, 1981). Deep marsh vegetation not subject to drying allows organic sediments to accumulate. Further anaerobic conditions in sediments of discharge wetlands with inflow of ground water persist, leading to organic matter accumulation by a decrease in decomposition rates of organic matter. It has been observed that

Table V Effects of Rice-Based Cropping Systems on Organic Matter Content of Paddy Soils in South China Location Hubei Zhejiang Taihu Lake Region Sanghai suburbs

Adapted from Cheng (1984). a Standard deviation.

Cropping system

Organic matter (%)

Continuous rice Rice– dryland crop Continuous rice Rice– cotton Rice– rice– wheat Rice– wheat Rice– rice– wheat Rice– wheat

2.03 – 2.15 1.85 – 1.94 3.11 – 5.21 2.01 – 2.87 2.74 (0.94)a 2.45 (1.04) 2.14 (0.19) 1.58 (0.14)

178

K. L. SAHRAWAT Table VI Nitrogen Accumulation in Maahas Clay as Influenced by Water Regime and Application of Rice Straw in an Experiment Conducted for 7 Years

Treatment Two wetland rice crops with dry fallow Two wetland rice crops plus rice straw with dry fallow Two wetland rice crops with flood fallow Two wetland rice crops plus rice straw with flood fallow

N accumulation (kg ha21 per year) 117 208 217 317

From Ponnamperuma (1984b).

the oxidation of organic matter results from aeration during frequent draw down and sulfate reduction during anaerobiosis which in turn results in the consumption of organic matter (Komor, 1992). Also, it has been suggested that if wetlands are drained, much of the oxidizable C is oxidized and the remnant is tightly bound to clay and is inert (Richardson and Bigler, 1984). Analysis of soil samples from a long-term experiment at the International Rice Research Institute farm in Los Banos, Philippines, which began in 1963, showed that intensive rice ecosystems (two or three crops of rice) could maintain organic matter. Infact, the concentrations of organic matter increased by 5 –10% in the past 15 years, despite removal of all above-ground crop residues from the plots during the study. The capacity of the soil to supply indigenous N to rice was sustained as the yield of rice in plots not receiving N fertilizer has remained constant for the past 25 years (Buresh, 2002). These results are in agreement and supplement earlier results obtained at several sites in tropical Asia, that soil organic C levels are maintained or even increased in double and triple cropped long-term rice experiments under irrigated conditions (Cassman et al., 1996). The high losses of soil organic C in the rice –wheat (upland) system contrast with results from continuous double and triple crops of wetland rice due to differences in organic matter decomposition pattern and products under more continuous maintenance of anaerobic conditions (Duxbury et al., 2000). Craft and Chiang (2002) measured organic matter accumulation and the forms and amounts of soil N and P across transects from fresh water depressional wetlands into longleaf pine-wiregrass forests of southwestern Georgia to evaluate changes in labile vs. recalcitrant N and P. Plant available (nitrate), organic and total N decreased and C:N increased from wetland to upland soils. Nearly all N (97 –98%) and most P (50 –82%) existed in recalcitrant forms, regardless of landscape position. Wetland soils concentrated total N at higher levels than

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 179 Table VII Water Level, Organic C, Total N and Total P in Surface Soil (0– 5 cm) Along a Depressional Wetland-Ecotone-Upland Continuum at Ichauway, Georgia, USA

Water level, cma Organic C,% Total N, % Total P, mg kg21

Wetland

Ecotone

Upland

20 –40 3.1 (0.3)b 0.23 (0.02) 79 (13)

Saturated 3.4 (0.5) 0.13 (0.02) 51(11)

,230 3.4 (0.3) 0.10 (0.01) 48 (3)

Adapted from Craft and Chiang (2002). a Water levels are measured relative to the soil surface. b Standard error.

upland soil even though organic C and organic matter was uniform across the gradient (Table VII). The results indicated that periodic waterlogging favors sequestration of organic forms of N and P in soil. Wetness also favors N retention more than P resulting in preferential accumulation of organic N over P. From this brief discussion it can be concluded that under similar conditions, wetland rice soils have a greater tendency to accumulate organic matter compared to soils cropped to rice-arable or dryland crop systems. The organic matter level in soil under intensified wetland rice production system can be maintained without any external input of nutrients. Cropping the soil with dryland crop following wetland rice results in the depletion of organic matter accumulated during the wetland rice crop phase. This effect is equal to the air drying effect on organic matter decomposition and the release of mineral N (Sahrawat, 1983).

IV. MECHANISMS FOR ORGANIC MATTER ACCUMULATION IN WETLANDS A. LACK

OF

OXYGEN

OR

ANAEROBIOSIS

Since oxygen is absent in wetland soils and sediments, this has profound effect on the biogeochemical processes which take place under anaerobic conditions or anaerobiosis (processes that occur in the absence or very low supply of oxygen) in soils. Most soil microorganisms are aerobes including common bacteria and fungi (Coyne, 1999). Thus, under lack of oxygen or anaerobic conditions of wetland soils and sediments, microbial activity is greatly affected. Lack of oxygen is responsible for lessened rate of organic matter decomposition in submerged soils (Howeler and Bouldin, 1971). For example, Tate (1979) while studying organic matter decomposition in peat soils, found that the catabolism

180

K. L. SAHRAWAT

rate of several C substrates markedly decreased under anaerobic condition compared to rates in the same soil for an aerobic environment. The catabolism rate for amino acids, glucose and acetate, however, was less sensitive to submergence than aromatic compounds such as salicylate. There is a shift in the activity of microbial population and the activity of anaerobic and fermentative microorganisms increase at the expense of aerobes. The consequence of this shift in microbial activity is that the biodegradation of organic matter is lessened compared to that under aerobic conditions. Moreover, the decomposition of organic matter via anaerobic respiration and fermentation processes is metabolically less efficient and results in slower decomposition of organic substrates. This leads to a greater net accumulation of organic materials in wetland soils and sediments (Duchaufour, 1998). Cycling and turnover of organic matter is directly related to oxygen availability in soils. Since biodegradation of organic materials is retarded, organic matter and N accumulate relatively rapidly under anaerobic conditions compared to those in well-drained upland soils (Axt and Walbridge, 1999; Craft, 2001). The accumulation of organic matter in submerged soils leads to formation of a dark surface horizon in mineral soils. It was found that soil profile darkening index had strong correlation with the duration of saturated or anaerobic condition. The thickness and color of surface horizons are strong indicators of landscape hydrology (Reuter and Bell, 2003). Under prolonged waterlogged or anaerobic conditions, organic materials may accumulate to the extent that organic soils or Histosols are developed (Soil Survey Staff, Soil Taxonomy, 1999; Collins and Kuehl, 2001). To sum up, the absence or low concentration of free oxygen is the most important single factor controlling the rate, pattern and ultimate fate of organic matter in submerged soils and sediments.

B. DEFICIENCY

OF

NUTRIENTS

It has been observed that decomposition of organic materials is faster in fertile than in infertile soils (Neue et al., 1997). Carbon cycling in soils is closely coupled to those of other nutrients and their availability plays an important role in C decomposition, accumulation and storage. Vijre et al. (2003) found that C storage in Danish forest soils is facilitated because of impeded decomposition of the litter in nutrient- poor soils and is not driven by high productivity in nutrientrich soils. Nitrogen deficiency (Regan and Jeris, 1970), phosphorus deficiency (Sundareshwar et al., 2003), sulfur deficiency (Golhaber and Kaplan, 1975) have been proposed as the factors for lessened rates of destruction of organic materials in wetland soils and sediments. Deficiency of nutrients such as N, P, and S affect the growth of bacteria, which in turn affect C fixation, storage, and release in wetland ecosystems. Recent study of coastal wetlands showed that P

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 181

limitation of microbial growth impacted the transformation and availability of N, resulting in influencing C fixation, storage, and release mediated by plants for ecosystem management (Sundareshwar et al., 2003). Meli et al. (2003) studied the microbial respiratory responses to the addition of simple and complex model substrates in soil. They found that the availability of nitrogenous substrate facilitated the utilization of starch by soil microorganisms and reduced the time taken for the maximum respiratory response to occur. Martin and Fitzwater (1988) found that iron deficiency is limiting the growth of phytoplankton, single-celled photosynthetic organisms that convert carbon dioxide to organic C in the surface oceans, in high-nutrient, low-chlorophyll oceanic regions. As a result of scarcity of biologically available iron, phytoplanktons cannot use excess N and P available in the northeast Pacific subarctic. Since this finding, there has been interest in ocean iron fertilization for increasing dioxide fixation by phytoplanktons. It is postulated that enhanced supply of iron would stimulate photosynthesis, which in turn would lead to draw down in atmospheric dioxide levels during glacial maxima (Martin and Fitzwater, 1988). However, concerns have been raised about the practical feasibility and likely side effects associated with iron fertilization of oceans (Chisholm et al., 2001; Lawrence, 2002; Sahrawat, 2002).

C. LACK

OF

TERMINAL ELECTRON ACCEPTORS

Principal redox couples in sequence in submerged soils and wetland sediments 22 are: O2/H2O, NO2 3 /N2, Mn (IV,III)/Mn (II), Fe (III)/Fe (II), SO4 /H2S and CO2/CH4. The main electron acceptors in submerged soils and wetland sediments 22 include dissolved oxygen (O2), NO2 3 , Fe (III), SO4 and CO2. The final products of reduction in submerged soils are Fe (II), H2S and CH4, although intermediate products such as dissolved H2 and H2S are also found in submerged soils and wetland sediments (Gao et al., 2002). Lack of terminal electron acceptors such as ferric iron and sulfate has been proposed as a factor in slowing down the destruction of organic materials in submerged soils and sediments (Lovley, 1995; Sahrawat and Narteh, 2001; Roden and Wetzel, 2002). Lovley (1995) proposed a model for the oxidation of complex organic matter to carbon dioxide with Fe (III) as the sole electron acceptor (Fig. 1). Recent research with tropical wetland soils from Africa provided indirect evidence on the involvement of electron acceptors such as iron oxides and hydroxides in the mineralization of organic N (Sahrawat and Narteh, 2001). The results showed that N mineralization under flooded conditions was highly significantly correlated to the amount of reducible iron extracted by ammonium oxalate or EDTA ( p , 0.01, n ¼ 15). Multiple regression of mineralizable N on organic C and EDTA (EDTA – Fe) or ammonium oxalate extractable iron

182

K. L. SAHRAWAT

Figure 1 Model for microbial oxidation of organic matter in wetland soils and sediments with Fe (III) serving as the electron acceptor. From Lovley, 1995.

(Amox –Fe) showed that ammonium production in soils under submerged condition can be predicted from organic C and extractable iron by the following regression equations: Mineralizable N or ammonium N produced ðmg kg21 soilÞ ¼ 16:4 þ 1:320 Organic C ðg kg21 Þ þ 0:0369 EDTA 2 Fe ðmg kg21 Þ; R2 ¼ 0:85

ð1Þ

Mineralizable N or ammonium N produced ðmg kg21 Þ ¼ 11:14 þ 1:805 Organic C ðg kg21 Þ þ 0:00469 Amox 2 Fe ðmg kg21 Þ; R2 ¼ 0:81

ð2Þ

Sahrawat and Narteh (2003) proposed that organic C and reducible iron could be used as N index for assessing the supply of N in submerged rice soils. It was found that soils with higher N supplying capacity, as measured by ammonium released under waterlogged incubation of soils, had relatively higher contents of organic C and reducible iron. On the other hand, soils low in organic C or

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 183 Table VIII Distribution of 15 West African Rice Soils According to Mineralizable N (Min-N) Produced Under Anaerobic Incubation and Associated Organic C and Reducible Iron Extracted by EDTA (EDTA–Fe) or Ammonium Oxalate (Amox–Fe) Min-Na (mg kg21soil) 86–166 55–77 21–50

No. of soils

Organic C (g kg21)

EDTA–Fe (mg kg21)

Amox–Fe (mg kg21)

4 5 6

23.0–46.0 9.2–23.2 7.4–15.6

150 –2200 325 –800 125 –600

1875–11,412 1100–6750 925–3562

Adapted from Sahrawat and Narteh (2003). a Ammonium produced in soils incubated under waterlogged condition for two weeks at 308C.

reducible iron released relatively lower amounts of ammonium under anaerobic incubation (Table VIII). Studies with fresh water wetland sediments demonstrated a direct correlation between first-order Fe (III) reduction rate constants and initial rates of organic C mineralization or decomposition as measured by the amount of carbon dioxide and methane accumulated (Roden and Wetzel, 2002). From the review of recent research on the role of iron as electron acceptor, it was concluded that reducible iron influences organic matter oxidation (Lovley, 1995) and N mineralization or ammonium production (Sahrawat, 2002) in wetland soils and sediments. It was indicated that lack of electron acceptors might lead to slow down in the destruction of carbonaceous materials and increase the accumulation of organic matter in submerged soils and sediments. The reduction of Fe (III) oxides to Fe (II) is a redox reaction in which Fe (III) oxides serve as the source of reducible iron (electron acceptor) and soil organic matter (CH2O) serves as electron donor. In this redox reaction, organic matter is oxidized and Fe (III) is reduced to Fe (II) and can be represented by the following equation: Fe2 O3 þ 1=2CH2 O þ 4Hþ ¼ 2Fe2þ þ 5=2H2 O þ 1=2CO2

ð3Þ

Precipitation reaction tends to increase Fe (III)/Fe (II) and this drives the reduction reaction ðFe3þ ¼ Fe2þ þ Hþ Þ forward. Iron reduction drives the reduction process in submerged tropical rice soils in the humid savanna and forest zones. For example, Narteh and Sahrawat (1999) showed that 4 weeks after flooding of 15 West African rice soils, the soil solution redox potential (Eh in mV) can be predicted from the concentration of Fe (II) (mg L21) in soil solution and solution pH: Eh ¼ 409 2 4:09 log Fe ðIIÞ 2 59 pH;

R2 ¼ 0:99

ð4Þ

Moreover, it has been observed that intensive cropping of soils with wetland rice and submergence decrease the amount of free or easily reducible iron in

184

K. L. SAHRAWAT

the soil (Mahieu et al., 2002). The implications of these results are that soils under long-term submergence and intensive rice cropping are liable to become deficient in easily reducible iron or electron accepting iron. And this may lead to lessened rates of soil organic matter oxidation and mineralization of soil organic N in submerged soils and fresh water wetlands (Sahrawat and Narteh, 2001; Sahrawat, 2002; Roden and Wetzel, 2002). Reducible iron participates in the redox reactions involved in soil organic matter oxidation and N mineralization in anaerobic soils and sediments.

D. PRODUCTION

OF INHIBITORS OF

MICROBIAL ACTIVITY

It has been postulated that solubilization of iron (Fe), manganese (Mn), aluminum (Al) and other cations in toxic concentrations in soil solution under reduced conditions or their high concentration in dissolved organic matter might account for slow down in organic matter decomposition in soils and sediments (Hewitt and Nicholas, 1963; Marschner and Kalbitz, 2003). However, Al concentration is unlikely to be in the toxic concentration range in the flooded mineral soils, except perhaps in acid sulfate soils. Because flooding of soils tend to bring the soil pH in the neutral range (Ponnamperuma, 1972; Sahrawat, 1998a). Also, products of anaerobic metabolism in soil such as hydrogen sulfide (H2S), ammonia (NH3) or volatile fatty acids have been implicated for deleterious effect on microbial activity and consequent lessened rate of destruction of organic materials in wetland soils (Hallberg, 1973; Ko and Chow, 1977; Takai and Kamura, 1966). The existence of compounds or substances that inhibit microbiological activity is common, especially, in peat soils or Histosols (Gorham, 1957). Kilham and Alexander (1984) conducted experiments under controlled conditions to establish the factors contributing to the slow turnover of organic materials in flooded soils. The factors investigated included potentially limiting plant nutrients and possible toxicants that might affect microbial activity and organic matter destruction. They concluded that the inhibitory effect of organic acids (acetic, formic, and propionic acids) at low pH values is the reason for the accumulation of organic matter in the soils studied. It is known that undissociated volatile fatty acids at low pH are inhibitory to microbial activity in flooded rice soils (Stevenson, 1967).

E. FORMATION

OF

RECALCITRANT COMPLEXES

Furthermore, Alexander (1965) suggested that the resistance of some organic substances to microbial degradation is associated with formation of complex molecules with the substrates, which render them less available for microbial utilization and decomposition. Moreover, forming complexes with cations,

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 185

sesquioxides, and clay minerals in the soil stabilizes the humus fraction of soil organic matter physically and chemically (Oades, 1988; Feller and Beare, 1997; Baldock and Skjemstad, 2000). Wang et al. (2003) showed that protective effect of clay on soil organic matter decomposition in a range of Australian soils became significant as the substrate supply and microbial demand approached to an equilibrium state. After this stage soil respiration was dependent on the replenishment of the labile substrate from the bulk soil organic C pool. Kiem and Koegel-Knabner (2003) provided evidence to show that polysaccharides, mainly those of microbial origin, are stabilized in the long-term within fine separates of arable soils from European long-term agro-ecosystem experiments. On the other hand, lignin (determined by the copper oxide technique) is associated mainly with the coarse fractions of the soil and does not contribute to the refractory C pool. Olk et al. (1996, 2002) provided evidence, which showed that with increased frequency of irrigated rice there was a large increase in phenolic content of organic matter and decreasing abundance of heterocyclic N compounds in Philippine soils. It was suggested that slower lignin decomposition caused by the deficiency of oxygen leads to incorporation of phenolic functional groups in to young soil organic matter fractions. It has been suggested that increased phenolic character of organic matter influence N mineralization, N supplying capacity, and cycling in lowland soils supporting two or three rice crops under irrigated conditions. The results of this research indicated that mobile humic acid and calcium humate fractions of the humic acid were comparatively more sensitive to management of intensified rice paddies than total C or N. In another study of the nature of organic matter in lowland soil under intensive rice cropping under irrigated conditions in the Philippines, it was revealed that there was consistent enrichment of young soil organic matter with phenols in the two humic acid fractions isolated from lowland soils that have been continuously cropped to rice over the past 11– 34 years. However, soil properties, hydrology in the fallow period and the use of mineral fertilizers or green manures had little effect on the accumulation of phenols, although other properties of the humic acid were markedly affected (Olk et al., 1998). It is known that phenolic content and the lignin:N ratio affect the rate of N mineralization from incorporated crop residues and green manures in submerged soils (Watanabe et al., 1991; Becker et al., 1994). It would thus appear that the presence of phenolics in soil organic matter makes it less labile and retards its decomposition and mineralization in wetland rice soils. The decomposition of leaf litter from beech (Fagus sylvatica L.) and oak (Quercus robur L.) trees, as measured by mass losses from litter species and litter types, in three soil types was best predicted by initial concentration of lignin in the litter (Sariyildiz and Anderson, 2003). However, the intra-specific variation in rates of litter decomposition of beech and oak litters from different sites, and

186

K. L. SAHRAWAT

differences in their interactions with the two floor materials became too complex for a reliable prediction of the decomposition rates. The initial decomposition of rice straw in submerged soils of the Philippines was rapid followed by a slow phase. Ultimately, rice straw becomes a part of the soil organic matter with a mean half-life of about 2 years. Moreover, with time rice straw becomes increasingly recalcitrant to further degradation (Neue and Scharpenseel, 1987). The hydrolysis of polysaccharides during anaerobic decomposition of rice straw is limited by the accessibility of the polymers formed rather than by the activity of the hydrolytic enzymes (Glissman and Conrad, 2002). A diversity of physical, chemical, and biological mechanisms selectively protect different fractions of soil organic matter by microbial activity. Most of the recalcitrant C destined for sediment accretion is derived from heavily lignified biomass. Carbon is even better protected in acidic environments, in marine sediments and under low temperature conditions (Bouchard and Cochran, 2002). In anaerobic environment, organic matter fractions form complexes with ions such as Fe2þ (Schnitzer and Skinner, 1966; Jansen et al., 2003) which are stable and resistant to microbial attack. Also, lignins and phenols form stable complexes with amino acids and other N organic compounds (Bondietti et al., 1972; Verma et al., 1975; Martin et al., 1980). Lignins and phenolic subunits of lignins are highly resistant to degradation in anaerobic environment without oxygen (Zeikus, 1980, 1981; Colberg, 1988). Spaccini et al. (2002) studied the effect of humified organic matter, extracted from compost and lignite, on the mineralization of a labile, labeled organic compound (13C-labeled 2-decanol) in soil. It was found that after 3 months of incubation, a higher proportion of C was sequestered in the soil treated with humic acids from lignite compared to humic acids isolated from compost. The higher C sequestration in the lignite humic acid treated soil was due to the hydrophobic nature of the humic acid, which effectively protected mineralization of labile organic compound. It was suggested that the use of hydrophobic substances might increase the biological stability of soil organic matter and also decrease the emission of carbon dioxide from agricultural soils (Spaccini et al., 2002). Moreover, it has been observed that high contents of carbohydrates, organic acids, and proteins, for which the hydrophilic neutral fraction is a good estimate, enhances the biodegradability of dissolved organic matter in soils. In contrast, aromatic and hydrophobic structures, estimated by UV absorbance, decrease the biodegradability of dissolved organic matter, either due to their recalcitrance or due to effects on enzyme activity (Marschner and Kalbitz, 2003). Kalbitz et al. (2003) also found that the biodegradability of dissolved organic matter in agricultural soils, peats, and organic residues was greatly affected by the organic matter properties such as UV absorbance and synchronous and emission fluorescence. The extent and rate of dissolved organic matter biodegradation from less humified organic materials such as maize straw, litter and fermented layers of forest floors were high resulting in 61 –93% being mineralized

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 187

(measured by carbon dioxide production during 90 days). The dissolved organic matter extracted from agricultural soils was of intermediate biodegradability (17 – 32% dissolved organic C mineralized). Dissolved organic matter extracted from peats and forest floor layers was relatively stable (4 – 9% of dissolved organic C mineralized). It would appear from this discussion that molecular recalcitrance of organic matter fractions stabilizes organic matter, makes it less accessible for decomposition by microbial activity. This results in the preferential accumulation organic matter in submerged soils and wetland sediments.

F. INCOMPLETE DECOMPOSITION AND DECREASED HUMIFICATION OF ORGANIC MATTER Although organic matter plays a predominant role in the supply of N to wetland rice (Sahrawat, 1983), our understanding of N cycling in relation to organic matter status of wetland soils under intensified rice cropping remains inadequate (Cassman et al., 1996). For example, in tropical wetland soils under intensified rice cropping system, the amount of mineralized N produced or N taken up by rice were not significantly correlated to organic matter status of the soil (Cassman et al., 1996). A number of studies made on the chemistry of soil organic matter from a diverse group of soils under diverse land use, showed that the incomplete soil organic matter humification was associated with soil submergence. This conclusion was supported by the results of research which showed that the mobile fraction of soil organic matter had less visible light absorption, enhanced presence of lignin residues, lower concentrations of oxygen-containing functional groups, and higher concentrations of hydrogen (Mitsuchi, 1974; Tsutsuki and Kuwatsuka, 1978; Yonebayashi and Hattori, 1988; Ye and Wen, 1991). Maie et al. (2000) studied the humus composition of subsoil of Japanese lowland paddy and upland soils. Differences were observed in the composition of the humus from the paddy and upland soils. Paddy soils had a lower proportion of extractable humus than upland soils, but the amount of sodium pyrophosphateextractable humic acids was larger in the paddy soils than in the upland soils. The lower content of extractable humic acids in paddy soils was due mainly to the lower amount of sodium hydroxide-extractable fulvic acids. Paddy soils also had lower proportion of extractable humus in total C than upland soils. Paddy and upland soils differed in the composition of humic acids in soil layers below the layer that had accumulated iron. Humification degree of the sodium pyrophosphate-extractable humic acids decreased with depth in the upland soils, but was high even in deeper layers in the paddy soils. Olk et al. (2000) studied the influence of continuous lowland rice cropping on soil organic matter chemistry. It was revealed that the chemical nature of soil

188

K. L. SAHRAWAT Table IX Elemental Composition of the Mobile Humic Acid Fraction from Soils of Varying Intensity of Irrigated Rice Cropping Without Application of Fertilizer N Cropping system Dryland rice Double-cropped rice Triple-cropped rice

C (g kg21) H (g kg21) N (g kg21) O (g kg21) S (g kg21) 518 547 553

52.0 59.3 59.8

46.6 45.8 44.1

377 337 330

6.0 10.8 13.1

Adapted from Olk et al. (2000).

organic matter became less humified with the increasing intensity of irrigated rice cropping and soil submergence. With increasing submergence, the humic acid fractions became less polycondensed and less oxidized or humified with higher sulfur and hydrogen and lower oxygen concentrations (Table IX), lower levels of free radicals, and fewer COOH groups (Table X). Free radical concentrations for the two humic acid fractions studied were highly correlated with the indices of humification. The humic acid extracted from submerged soils had greater capacity for complexing Cu (II), Fe (III) and VO (II) than did humic acid from aerated soils. Mahieu et al. (2002) conducted a comprehensive study of wetland rice soils in the Philippines to identify the long-term effects of intensive lowland rice cropping and soil submergence on soil organic matter properties including its humification. For this purpose, soil samples were collected from fields varying in their previous history of soil submergence. The results showed that soils with two or three rice crops contained 17 – 29 g C kg21, while soils with one or no rice crop contained only 13– 15 g C kg21. Intensively cropped soils contained less free or active Fe (10 –23 g kg21) than did soils with one or no wetland rice crop (31 –32 k kg21). Multinuclear magnetic resonance analysis of two humic acid fractions (mobile humic acid and calcium humate) from lowland rice soils clearly demonstrated that regular aeration of soils promoted soil organic matter humification and that the humic acid fractions were less humified with increasing intensity of irrigated Table X Acid Functional Group Composition of the Mobile Humic Acid Fraction from Soils of Varying Intensity of Irrigated Rice Cropping Without Application of N Fertilizer

Cropping system

Total acidity (mol kg21)

COOH (mol kg21)

Phenolic OH (mol kg21)

5.18 3.81 4.33

3.10 2.00 2.36

2.08 1.81 1.93

Dryland rice Double-cropped rice Triple-cropped rice Adapted from Olk et al. (2000).

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 189

rice cropping. The more humified character of the calcium humate fraction relative to the mobile humic acid fraction was confirmed (Mahieu et al., 2002). The mobile humic acid fraction had higher N and H concentration, lower free radical concentration, and visible light absorption than did the calcium humate fraction in all soils (Table XI). The C : N ratio was larger for the calcium humate fraction (mean 14.0) than for the mobile humic acid fraction (mean 10.9). The humification process in soils is affected by climatic conditions, especially temperature and soil water regime and the humification processes in soils of the tropical climates may differ from those in temperate climates (Haider, 1992). Nevertheless, soil submergence has an over-riding effect on humification of soil organic matter.

G. HIGH PRIMARY PRODUCTIVITY The net primary productivity of plant communities in an ecosystem is greatly affected by soil, especially its organic matter content, temperature, water, light, and availability of nutrients. Wetland soils store higher amount of organic matter in their profiles than done by soils in other ecosystems (Table XII). The amount of organic matter in wetland soils usually exceeds the amount contained in the living and dead vegetation. Some of the wetland Histosols may contain 10 –30% or more of their dry mass as C (Paustian, 2002). In the case of lowland soils, the primary productivity is mainly controlled by nutrient supply (Brinson et al., 1981). The primary production in the flood water of tropical rice paddies and fresh water lakes depends on P concentrations and N is seldom a limiting factor. For example, Sahrawat (1998b) found that natural growth of Azolla in irrigated rice fields in West Africa was influenced by P concentration in the floodwater, which in turn was affected by the available P status of the soil. When N becomes limited in the flood water, algae populations shift to nitrogen fixing blue-green algae. In wetland rice paddies, biological nitrogen fixation may contribute 30 kg N ha21 each cropping season. It has been suggested that significant nitrogen fixation occurs only when the N:P ratio is less than 16 (Howarth et al., 1988). Higher primary productivity of tropical wetlands has been cited as an important factor for the accumulation of organic matter in natural and agricultural wetlands. Many natural and cultivated tropical wetlands have a net primary productivity of more than 1000 g C m22 per year, which is greater than that of any other ecosystem (Neue et al., 1997). The net primary production in floodwater, in addition to that of the soil, is considered as organic matter input to soil. In a study made at the International Rice Research Institute in the Philippines, it was estimated that the organic matter production in flood water constituted 10 to 15% of the rice plant total gross primary production in a fertilized

190 Table XI Concentration of N, H, Organic Free Radicals and Visible Light Absorption at 465 nm (E465) for the Mobile Humic Acid (MHA) and Calcium Humate (CaHA) Fractions Extracted from Soils Varying in Intensity of Irrigated Rice Cropping without Application of Fertilizer N in the Philippines N conc.

Maize Dryland rice Rice –maize Rice –soybean Double cropped rice Triple cropped rice

Free radicals

E

465

MHA CaHA MHA CaHA MHA CaHA MHA (OD units CaHA (OD units (g kg21 HA) (g kg21 HA) (g kg21 HA) (g kg21 HA) (spins g21 £ 1017) (spins g21 £ 1017) g HA-C dm23) g HA-C dm23) 48.6 47.7 47.0 48.2 47.0 44.6

Adapted from Mahieu et al. (2002). a Not determined.

40.2 36.4 40.9 33.9 37.7 36.0

–a 52.0 – 51.2 56.6 60.4

– 45.1 – 40.8 49.3 53.7

– 1.51 – 2.45 1.36 0.81

– 5.26 – 6.71 3.13 2.13

– 8.0 – 7.1 3.4 3.1

– 14.4 – 16.9 10.8 9.3

K. L. SAHRAWAT

Cropping system

H conc.

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 191 Table XII Global Carbon Stocks in Soil in Major Ecosystems

Ecosystem Tropical forests Temperate forests Boreal forests Tropical savannas Temperate grasslands Deserts and semi-deserts Tundra Wetlands Croplands

Area (106 km2)

Average C density (Mg ha21)

Soil C stock (Pga)

17.6 10.4 13.7 22.5 12.5 45.5 9.5 3.5 16.0

123 96 344 117 236 42 13 642 80

216 100 471 264 295 191 121 225 128

Adapted from Paustian (2002). a Pg is Petagram ¼ 1015 g ¼ billion t.

and non-fertilized treatments, respectively (Saito and Watanabe, 1978). Primary productivity and turnover of the aquatic community in a rice field were higher than those of rice roots. A gross primary production of 60 –70 g C m22 in 120 days was recorded (Saito and Watanabe, 1978). Yamagishi et al. (1980) also reported an average gross organic production of 71 g C m22 in rice paddy fields in Japan, which is similar to the value obtained by Saito and Watanabe (1978). It has been suggested that the photosyntheic biomass production contributes to readily decomposable matter (Saito and Watanabe, 1978). It is estimated that flood water provides a biomass of 1– 2 t ha21 per season to fertile wetland rice soils (Neue et al., 1997). Also, flooding increases the readily decomposable soil N in the surface layer (Kobo and Uehara, 1943). A submerged soil is an ideal medium for both aerobic and anaerobic nitrogen fixation, especially in the presence of rice plants (Ponnamperuma, 1972, 1984a). The surface soil is immediately below the partially oxygenated free water in contact with the atmosphere and immediately above saturated soil devoid of oxygen. The soil below is anaerobic and serves as a source of electron donors to the upper side of the boundary, the electron sink. The boundary itself serves as the interface (Bouldin, 1968). Organic products of anaerobic respiration diffuse upward and provide readily available reduced C energy needed by the nitrogenfixing organisms living on the aerobic side of the interface, which is an optimum environment for nitrogen fixation by non-symbiotic microorganisms (Reddy and Patrick, 1979). Nitrogen fixation in the anaerobic zone, supplied with cellulose as the energy source, was found to be directly proportional to the interfacial area in soil containers (Magdoff and Bouldin, 1970). Soil submergence and the presence of the rice plants enhance biological nitrogen fixation in wetland rice (Charyulu and Rao, 1979;

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Yoshida and Ancajas, 1971). The maintenance of soil fertility in wetland rice has been believed to be largely due to the fixation of atmospheric nitrogen by diverse nitrogen fixers in the soil. The soil’s capacity to supply nutrients, especially N is maintained (De, 1936; Grant, 1965; Grist, 1965; Takahashi, 1965). Moreover, the organic matter formed is conserved under submerged soil conditions.

V.

MODELING ORGANIC MATTER IN WETLAND SOILS

Compared to upland or arable soils, relatively little research effort has been devoted to modeling organic matter in wetland soils, although rice is a major food crop and wetland rice contributes handsomely to global rice supply. Moreover, wetlands are considered of critical importance in global C cycle (Paustian, 2002). Nevertheless, initial results with tropical wetland rice soils indicate that N mineralization or ammonium production under submerged conditions can be predicted from organic C and free or reducible iron contents of the soils (Narteh and Sahrawat, 2000; Sahrawat and Narteh, 2001; 2003) (see Section IV.C). Significant progress has, however, been made in modeling organic matter in arable soils (Coleman and Jenkinson, 1996; Molina and Smith, 1998; Ruehlmann, 1999; Falloon and Smith, 2000; Smith, 2002). For modeling organic matter in wetland soils, useful leads could perhaps be taken from the advances made in modeling organic matter in upland ecosystem. However, for modeling organic matter in wetland soils, consideration must be given to the simulation of oxygen concentration in soil because water and oxygen have a major effect on microbial physiology and decomposition of organic materials and accumulation of organic matter. There is an obvious need for research on modeling organic matter in wetland soils. Because apart from their predictive value, soil organic matter models are important research tools which can be used for testing various hypotheses on dynamics of C and N in time and space in soils. Soil organic matter models also have application in improving agronomic efficiency and environmental quality by incorporating them into decision support systems (Smith, 2002).

VI. PERSPECTIVES Unlike in aerobic soils, the destruction of organic materials is slower in the absence or low concentration of oxygen in wetland soils and sediments. Under permanent or long-term waterlogged conditions the rate of accumulation of organic matter is greater than its decomposition and leads to the formation of high

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 193 Table XIII Various Factors or Mechanisms Proposed for Accumulation of Organic Matter and Its Turnover in Wetland Soils and Sediments Factors involved in lessened and incomplete decomposition, and decreased humification and net accumulation of organic matter Deficiency of oxygen Deficiency of nutrients (N, P, S) Lack of terminal electron acceptors (Fe3þ, SO22 4 ) Decreased humification of organic matter High net primary productivity Production of inhibitors of microbial activity and formation of complex, recalcitrant compounds Formation of complex and stable complexes recalcitrant to microbial degradation Production of reduction products (H2S, NH3, volatile fatty acids) deleterious to microbial activity Toxic cations and anions in soil solution

organic matter soils or Histosols. Several factors are involved in the accumulation of organic matter in wetland soils and sediments (Table XIII). Under anaerobiosis or absence of oxygen, electron-acceptors such as iron oxides and hydroxides are important in organic matter oxidation and ammonium production in submerged soils and sediments (Lovley, 1995; Sahrawat and Narteh, 2001; Sahrawat, 2002). However, the mechanism(s) involved is not fully understood. The reduction products such as hydrogen sulfide or volatile fatty acids produced during anaerobic metabolism and toxic concentrations of elements such as iron, aluminium, and other cations in soil solution may have a deleterious effect on microbial activity. Complex organic compounds formed with organic matter fractions may render them less available for microbial utilization and destruction. Various authors have ascribed the lessened rates of organic material decomposition to one or the other discussed mechanisms for the accumulation of organic matter in submerged soils. Evidently, there is a preferential accumulation of organic matter in submerged soils and sediments, although the mechanisms involved are not fully understood. However, there is enough evidence to show that lack of electron acceptors under prolonged waterlogging conditions brings organic matter oxidation and N mineralization to a virtual halt. Since iron oxides and hydroxides are the predominant source of electron acceptors in wetland soils and sediments, they play a dominant role in organic matter oxidation and N mineralization in wetland soils and sediments (Lovley, 1995; Sahrawat, 2002). The availability of iron as electron acceptor in submerged soils also has been reported to suppress methane formation (Watanabe and Kimura, 1999; Jaeckel and Schnell, 2000; Furukawa and Inubushi, 2002; Conrad, 2002).

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Thus the availability of electron acceptors, especially iron in submerged soils can have an important influence on the fate of organic materials added or synthesized and the overall pathways of C cycling in submerged soils and sediments. There is need for further research in this important area. The absence of oxygen in the intensified submerged rice soils slows down the decomposition of lignin from crop-derived materials, leading to incorporation of phenolic moieties into young soil organic matter fractions. This may slow down the oxidation and mineralization of organic matter and N cycling in the intensified irrigated rice-based systems (Olk et al., 1996, 2002). Additionally, the formation of volatile fatty acids produced during anaerobic metabolism of submerged soils has inhibitory effect on microbial activity. The fatty acids have been implicated in the lessening of soil organic matter destruction and hence in its preferential accumulation in flooded mineral and organic soils (Gorham, 1957; Stevenson, 1967; Kilham and Alexander, 1984). Higher net primary productivity has been ascribed as the important factor for increased soil organic matter in tropical wetland soils (Neue et al., 1997). Flooded soils provide an ideal environment for aerobic and anaerobic microbial activity in its flood water and soil for contributing to higher net primary productivity.

Table XIV Distribution of Carbon Pools in the Major Reservoirs on Earth Pools Atmosphere Oceans Total inorganic Surface layer Deep layer Total organic Lithosphere Sedimentary carbonates Kerogens Terrestrial biosphere (total) Living biomass Dead biomass Aquatic biosphere Fossil fuels Coal Oil Gas Peat Adapted from Falkowski et al. (2000). Gt is Giga t, which is 109 t.

a

Quantity of C (Gta) 720 38,400 37,400 670 36,730 1000 . 60,000,000 15,000,000 2000 600 –1000 1200 1–2 4130 3510 230 140 250

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 195

It would appear that higher primary production, incomplete and retarded decomposition, and decreased humification of organic matter cause increased organic matter accumulation in many wetland soils (Olk et al., 2000; Mahieu et al., 2002). Obviously, there is need for future research to fully understand and establish the factors responsible for and the mechanisms involved in the accumulation of organic matter. Mechanisms apart, the wetland soils offer an excellent example of conservation and maintenance of organic matter and storage of organic C. Wetlands are important for sequestering C from atmosphere under anaerobic metabolism. Protection of existing wetlands and creation and restoration of new wetlands will contribute to C sequestration for mitigating greenhouse emissions (Vloedbeld and Leemans, 1993; Mitsch et al., 1998; Bouchard and Cochran, 2002). However, it is important to note that the total amount of dissolved inorganic C in the oceans is 50 times that of atmosphere (Table XIV) and on time scales of millenia the oceans determine atmospheric carbon dioxide concentration and not vice versa (Falkowski et al., 2001). The dissolved inorganic C in the ocean is 19 times that of terrestrial biosphere. In comparison, the quantity of C in the aquatic pool is tiny (Table XIV). Finally, there is an urgent need for research for organic matter modeling in wetland rice soils by taking possible leads from the advances made for modeling soil organic matter in upland or arable soils.

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Reddy, K. R., and Patrick, W. H. Jr (1979). Nitrogen fixation in flooded soil. Soil Sci. 128, 80–85. Reddy, K. R., and Patrick, W. H. Jr (1993). Wetland soils—opportunity and challenges A guest editorial. Soil Sci. Soc. Am. J. 57, 1145–1147. Reddy, K. R., Khaleel, R., and Overcash, M. R. (1980). Carbon transformations in the land areas receiving organic wastes in relation to nonpoint source pollution: A conceptual model. J. Environ. Qual. 9, 434–442. Regan, R. W., and Jeris, J. S. (1970). A review of decomposition of cellulose and refuse. Compost. Sci. 11, 17– 20. Reuter, R. J., and Bell, J. C. (2003). Hillslope hydrology and soil morphology for a wetland basin in south-central Minnesota. Soil Sci. Soc. Am. J. 67, 365– 372. Richardson, J. L., and Bigler, R. J. (1984). Principal component analysis of prairie pothole soils in North Dakota. Soil Sci. Soc. Am. J. 48, 1350–1355. Roden, E. E., and Wetzel, R. G. (2002). Kinetics of microbial Fe (III) oxide reduction in fresh water sediments. Limnol. Oceanogr. 47, 198–211. Ruehlmann, J. (1999). A new approach to estimating the pool of stable organic matter in soil using data from long-term field experiments. Plant Soil 213, 149 –160. Sahrawat, K. L. (1980). Effects of rice straw on transformation of soil and fertilizer N in tropical flooded rice soils. Agrochimica 24, 149–153. Sahrawat, K. L. (1981). Ammonification in air-dried lowland Histosols. Soil Biol. Biochem. 13, 323–324. Sahrawat, K. L. (1983). Nitrogen availability indexes for submerged rice soils. Adv. Agron. 36, 415–451. Sahrawat, K. L. (1994). State of the Art Paper “Fertility and Chemistry of Rice Soils in West Africa”. West Africa Rice Development Association (WARDA), Bouake, Cote d’Ivoire. Sahrawat, K. L. (1998a). Flooding soil: a great equalizer of diversity in soil chemical fertility. Oryza 35, 300 –305. Sahrawat, K. L. (1998b). Soil phosphorus status and natural growth of Azolla in irrigated lowland rice. Current Sci. 75, 548. Sahrawat, K. L. (2002). Reducible iron affects organic matter oxidation and ammonium production in submerged soils and sediments. Current Sci. 83, 1434– 1435. Sahrawat, K. L., and Narteh, L. T. (2001). Organic matter and reducible iron control of ammonium production in submerged soils. Commun. Soil Sci. Plant Anal. 32, 1543–1550. Sahrawat, K. L., and Narteh, L. T. (2003). A chemical index for predicting ammonium production in submerged rice soils. Commun. Soil Sci. Plant Anal. 34, 1013–1021. Saito, M., and Watanabe, I. (1978). Organic matter production in rice field floodwater. Soil Sci. Plant Nutr. 24, 427 –440. Sariyildiz, T., and Anderson, J. M. (2003). Interactions between litter quality, decomposition and soil fertility: a laboratory study. Soil Biol. Biochem. 35, 391 –399. Schnitzer, M., and Skinner, S. I. M. (1966). Organo-metallic interaction in soils. 5. Stability constants of Cu2þ-, Fe2þ- and Zn- fulvic acid complexes. Soil Sci. 102, 361 –365. Soil Survey Staff, Soil Taxonomy, (1999). “A Basic System of Soil Classification for Making and Interpreting Soil Surveys”, 2nd. “Agric. Handbook no. 436”. USDA-NRCS, U.S. Govt. Printing Office, Washington, D.C. Smith, P. (2002). Organic matter modeling. In “Encyclopedia of Soil Science” (R. Lal, Ed.), pp. 917 –924. Dekker, New York. Spaccini, R., Piccolo, A., Conte, P., Haberhauer, G., and Gerzabek, M. H. (2002). Increased soil organic carbon sequestration through hydrophobic protection by humic substances. Soil Biol. Biochem. 34, 1839–1851. Stevenson, F. J. (1967). Organic acids in soils. In “Soil Biochemistry” (A. D. McLaren and G. H. Peterson, Eds.), pp. 119– 146. Dekker, New York.

ORGANIC MATTER ACCUMULATION IN SUBMERGED SOILS 201 Sundareshwar, P. V., Morris, J. T., Koepfler, E. K., and Fornwalt, B. (2003). Phosphorus limitation of coastal ecosystem processes. Science 299, 563–565. Takahashi, J. (1965). Natural supply of nutrients in relation to plant requirements. In “The Mineral Nutrition of the Rice Plant” (International Rice Research Institute, Ed.). Johns Hopkins University Press, Baltimore, MD, pp. 271–293. Takai, Y., and Kamura, T. (1966). The mechanism of reduction in waterlogged paddy soil. Folia Microbiol. 11, 304 –313. Takai, Y., and Wada, H. (1977). Effects of water percolation on fertility of paddy soils. In “Proceedings of International Seminar on Soil Environment and Fertility Management in Intensive Agriculture”. Society of the Science of Soil and Manure, Tokyo, Japan, pp. 216–222. Tate, R. L. (1979). Effect of flooding on microbial activities in organic soils: Carbon metabolism. Soil Sci. 128, 267 –273. Tiner, R. W. (2002). Wetlands. In “Encyclopedia of Soil Science” (R. Lal, Ed.), pp. 1437–1442. Tsutsuki, K., and Kuwatsuka, S. (1978). Chemical studies on soil humic acids. II. Composition of oxygen-containing functional groups of humic acids. Soil Sci. Plant Nutr. 24, 547 –560. Verma, L., Martin, J. P., and Haider, K. (1975). Decomposition of carbon-14 labelled proteins, peptides, and amino acids-, free and complexed with humic polymers. Soil Sci. Soc. Am. Proc. 39, 279 –294. Vijre, H., Callesen, I., Vesterdal, L., and Raulund-Rasmussen, K. (2003). Carbon and nitrogen in Danish forest soils—contents and distribution determined by soil order. Soil Sci. Soc. Am. J. 67, 335 –343. Vloedbeld, M., and Leemans, R. (1993). Quantifying feedback processes in the response of the terrestrial carbon cycle to global change: The modeling approach of Image-2. In “Terrestrial Biospheric Carbon Fluxes: Quantification of Sinks and Sources of CO2” (J. Wisniewski and R. N. Sampson, Eds.), pp. 615 –628. Kluwer Academic Publishers, London. Wang, W. J., Dalal, R. C., Moody, P. W., and Smith, C. J. (2003). Relationships of soil respiration to microbial biomass, substrate availability and clay content. Soil Biol. Biochem. 35, 273–284. Watanabe, I. (1984). Anaerobic decomposition of organic matter. In “Organic Matter and Rice”. International Rice Research Institute, Manila, Philippines, pp. 237– 258. Watanabe, A., and Kimura, M. (1999). Influence of chemical properties of soils on methane emission from rice paddies. Commun. Soil Sci. Plant Anal. 30, 2449– 2463. Watanabe, I., Padre, B., and Ramirez, C. (1991). Mineralization of Azolla N and its availability to wetland rice. I. Nitrogen mineralization of different Azolla species as affected by their chemical composition. Soil Sci. Plant Nutr. 37, 679 –688. Wen, Q. X. (1984). Utilization of organic materials in rice production in China. In “Organic Matter and Rice”. International Rice Research Institute, Manila, Philippines, pp. 45–56. Wershaw, R. L. (1993). Model for humus in soils and sediments. Environ. Sci. Technol. 27, 814–816. Yamagishi, T., Okada, K., Hayashi, T., and Murata, Y. (1980). Cycling of carbon in paddy field. Jpn J. Crop Sci. 49, 135– 145. Ye, W., and Wen, Q. X. (1991). Characteristics of humic substances in paddy soils. Pedosphere 1, 229 –239. Yonebayashi, K., and Hattori, T. (1988). Chemical and biological studies on environmental humic acids: I. Composition of elemental and functional groups of humic acids. Soil Sci. Plant Nutr. 34, 571 –584. Yoshida, T., and Ancajas, R. (1971). Nitrogen fixation by bacteria in the root zone of rice. Soil Sci. Soc. Am. Proc. 35, 156 –158. Zeikus, J. G. (1980). Fate of lignin and related aromatic substrates in anaerobic environments. In “Lignin Biodegradation Microbiology, Chemistry, and Potential Applications” (T. Higuchi and H. Chang, Eds.), 1, pp. 101– 109. CRC Press, Boca Raton, FL. Zeikus, J. G. (1981). Lignin metabolism and the carbon cycle. Polymer biosynthesis, biodegradation, and environmental recalcitrance. Adv. Microbial Ecol. 5, 211 –243.

POTASSIUM NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM Bijay-Singh,1 Yadvinder-Singh,1 Patricia Imas,2 and Xie Jian-chang3 1

Department of Soils, Punjab Agricultural University, Ludhiana 141 004, India 2 International Potash Institute-Coordination India, c/o DSW, P.O. Box 75, Beer Sheva 84100, Israel and 3 Nanjing Institute of Soil Science, Chinese Academy of Sciences, 71 Beijing East Road, Nanjing 210008, People’s Republic of China

I. Introduction II. The Rice – Wheat Cropping Systems A. Distribution B. Characteristics III. Potassium Fertilizer Use in the Rice – Wheat Cropping Systems IV. Potassium Fertility of Soils Under Rice – Wheat Cropping Systems A. Mineralogy of Soil Potassium B. Forms of Soil Potassium C. Potassium Transformations in Soils D. Assessment of Soil K-supplying Capacity V. Potassium Uptake by Rice – Wheat Cropping Systems VI. Response of Rice – Wheat Cropping Systems to Applied Potassium A. Time, Source and Method of Potassium Application B. Interactions of Potassium with Other Nutrients C. Effect of Potassium Fertility Status of Soils on Response to Potassium D. Site-specific Potassium Management for Rice and Wheat E. Potassium Use and Resistance to Disease and Pest Incidence VII. Potassium Balance in Soil– Plant System VIII. Changes in Potassium Fertility in the Soil Under Rice – Wheat Cropping Systems IX. Research Needs X. Conclusions References Among the cropping systems commonly followed in the Indo-Gangetic plain of South Asia and in China, rice-wheat cropping system occupies more 203 Advances in Agronomy, Volume 81 Copyright q 2004 by Academic Press. All rights of reproduction in any form reserved. DOI 10.1016/S0065-2113(03)81005-2

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BIJAY-SINGH ET AL. than 26 M ha of cultivated land and removes the highest amount of potassium. To a large percentage of area under rice-wheat cropping system, particularly in the Indo-Gangetic plains, very little or no potassium fertilizers are being applied and thus most of it comes from potassium reserves of the soil. Each harvest leaves the soil poorer with respect to potassium. Imbalance in the use of nitrogen, phosphorus and potassium is further creating situations, which may lead to reduced sustainability of the rice-wheat cropping system. Whereas illite is the dominant potassium bearing clay mineral in soils in the Indo-Gangetic plains, clay minerals in soils under ricewheat system in China are at a more advanced stage of weathering than illite so that responses of both rice and wheat to applied potassium are substantial in China. Response of sequentially grown rice and wheat to applied potassium is influenced by time and method of application of different sources of potassium and interaction of potassium with other nutrients. Issues pertaining to sustainability of rice-wheat system have been examined in terms of potassium fertility of soils, mineralogy and forms of soil potassium, longterm potassium balances and changes in soil potassium. In spite of potassium incorporation through irrigation, crop residues and fertilizers, the occurrence of negative potassium balance in soils in the Indo-Gangetic plains has serious implications on mineralogy of potassium in soils in terms of advancement of weathering front in illite-vermiculite or illite-vermiculite-smectite phases. In China, most of the soils under rice-wheat system are already in kaolinite and vermiculte-smectie phases and thus application of potassium leads to increased yields of both rice and wheat. Substantial potassium applications will have to be made to sustain high production levels of the rice-wheat cropping systems and to avoid further advancement of weathering front of potassium bearing minerals in the soil. q 2004 Academic Press.

I. INTRODUCTION Rice (Oryza sativa L.) and wheat (Triticum aestivum L.) grown sequentially in an annual rotation constitute a rice – wheat cropping system. In annual cycle, suitable thermal conditions for both rice and wheat exist in warm-temperate and subtropical areas and at high altitudes in the tropics. The rice – wheat rotation is one of the world’s largest agricultural production systems, occupying more than 26 Mha of cultivated land in the Indo-Gangetic Plains in South Asia and in China. It accounts for about one-third of the area of both rice and wheat grown in South Asia and its production provides staple grains for more than one billion people, or about 20% of the world’s population. Irrigated rice– wheat cropping systems have remained the major source of the marketed surplus of food grain for feeding the growing urban population in South and East Asia. From 1960 to 1990, genetic improvements leading to development of highly fertilizer responsive rice and wheat varieties and improved management

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strategies resulted in a dramatic rise in productivity and production from rice – wheat systems. Both rice and wheat are exhaustive feeders, and the double cropping system is heavily depleting the soil of its nutrient content. A rice – wheat sequence that yields 7 t ha21 of rice and 5 t ha21 of wheat removes more than 300 kg nitrogen, 30 kg phosphorus, and 300 kg ha21 of potassium from the soil. Even with the recommended rate of fertilization in this system (straw taken out of the fields), a negative balance of the primary nutrients still exists, particularly for nitrogen and potassium. The system in fact, is now showing signs of fatigue and is no longer exhibiting increased production with increases in input use. Evidence of declining partial or total factor productivity is already becoming available (Hobbs and Morris, 1996). Causes for this decline include changes in biochemical and physical composition of soil organic matter, and a gradual decline in the supply of soil nutrients causing macro- and micronutrient imbalances due to inappropriate fertilizer applications (Ladha et al., 2000). Depletion of soil potassium seemed to be a general cause of yield decline in 23 rice – wheat long-term experiments in the Indo-Gangetic plains investigated by Ladha et al. (2003). Before the advent of high yielding varieties of rice and wheat or when increasing areas were brought under a rice – wheat system, indigenous sources could supply appreciable quantities of nutrients. For example, irrigation and flood water provided significant amounts, particularly where erosion is active. This was the case during early years of irrigation schemes, as in China (Greenland, 1997), and continues on the flood plains of large rivers such as Jamuna and Meghna of Bangladesh (Whitton et al., 1988). Although in several rice– wheat areas where ground water is used for irrigation, potassium inputs may be more than 30 kg ha21 year21 (Pasricha, 1998), yet importance of potassium nutrition of rice – wheat systems stems from two facts: (1) the removal of potassium by above-ground plant parts and losses through leaching far exceeds the small additions through fertilizers and manures and it should have serious implications on the sustainability of the system on a long-term basis; and (2) lack of balanced availability of nitrogen, phosphorus, and potassium to rice and wheat that may hinder achieving the potential yields (Singh and Singh, 2002). Balanced application of nitrogen, phosphorus, and potassium means replenishing the soil potassium reserves which are being continuously mined by following high intensity rice –wheat cropping sequence. As exhaustion of potassium influences mineralogical transformations in soils, potassium nutrition of rice – wheat cropping systems is also important in terms of defining quality of the soil to be transferred to future generations. We have attempted to address these issues in this chapter and discussed potassium nutrition of sequentially grown rice and wheat in China and the Indo-Gangetic plains of India, Pakistan, Bangladesh and Nepal in terms of producing high yields from this system and maintenance of soil fertility on sustainable long-term basis.

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II. THE RICE– WHEAT CROPPING SYSTEMS A. DISTRIBUTION In the Indo-Gangetic plains, rice– wheat cropping systems are spread over a vast area spanning from Punjab in Pakistan in the west to the Brahmputra flood plains of Bangladesh in the east (Fig. 1). More than 85% of the rice – wheat system practised in South Asia is located in the plains of the Indus and Ganges, conveniently divided into four transacts. The trans-Indo-Gangetic plains occupy large areas of Punjab in Pakistan and Punjab and Haryana in India. Upper and middle Indo-Gangetic plains comprise the areas of western-central and eastern Uttar Pradesh, Bihar, and the Tarai in Uttaranchal in India and in Nepal. The Tarai belt is an extension of the Indo-Gangetic plains and has an altitude of 100 –200 m above mean sea level. The lower parts of the Indo-Gangetic plains are located in West Bengal in India, and parts of Bangladesh (Fig. 1). In more than 10 Mha in India occupied by rice – wheat systems, about 23% of the total rice area produce wheat and approximately 40% of the wheat area produce rice (Table I). In China, the rice – wheat systems are located primarily in the Yangtze river basin around 308 ^ 48N latitude (Zheng, 2000; Huke et al., 1993c) and are widely practised in the provinces of Jiangsu, Zhejiang, Hubei, Guizhou, Yunnan, Sichuan and Anhui (Fig. 1) (Timsina and Connor, 2001). Although most

Figure 1 Distribution of rice–wheat production areas in South Asia and China. The curve passing from northeast to southwest China represents the limits for growing rice–wheat sequences in China. [adapted from Timsina and Connor (2001); based on data from Huke and Huke (1992); Huke et al. (1993a,b,c); Woodhead et al. (1993, 1994)].

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Table I Area Under Rice–Wheat Systems in the Indo-Gangetic Plain and China, and Contribution of Rice Plus Wheat to Total Cereal Production and Total Caloric Intake in Different Countries Area (%)

Country China India Pakistan Bangladesh Nepal

Contribution (%)

Area (Mha)

Rice

Wheat

Total cereal production

Total national caloric intake

13.0 10.3 2.3 0.5 0.6

31 23 72 5 35

35 40 19 85 84

72 85 92 100 71

56 60 62 94 63

Based on data from Singh and Paroda (1994); Aslam (1998); Yadav et al. (1998) Source: Timsina and Connor (2001).

rice – wheat cropping systems are found below 358N latitude in the plains and below 288N in the highlands, these are also located as far as 408N across the Huihe and Yellow rivers (Lianzheng and Yixian, 1994; Zheng, 2000). Out of total area of 26 Mha under rice – wheat cropping system, around 13, 10 and 2.2 Mha are located in China, India and Pakistan, respectively (Table I). Proportion of the total rice or wheat areas also varies considerably in different countries. Rice and wheat together contribute 70 – 100% of total cereal production and 56 – 94% of the national calorie intake in China and South Asian countries in the Indo-Gangetic plain (Table I). Since good lands are being diverted to other sectors of national economies, the prospects for further expansion of the rice and wheat area seem remote (FAO, 1999). Additional sources of productivity growth in rice –wheat would have to come through enhanced overall system productivity.

B. CHARACTERISTICS In the trans- and western parts of the upper Gangetic plains, rice– wheat system mostly includes an indica-type monsoon rice and a spring wheat, because there is generally insufficient time for a third crop. In the eastern part of the upper Gangetic plains, and in the middle and lower Gangetic plains, where temperatures are generally higher, the rice – wheat systems often include an additional crop (such as mungbean, cowpea, jute) after wheat or before rice. In the Tarai of Nepal, a rice – rice –wheat cropping system is also followed. The Indo-Gangetic plains have a continental monsoonal climate. In the northwest trans-Gangetic plains, the average annual precipitation ranges from 400 to 750 mm year21 and increases toward the warm and humid parts of the lower Gangetic plains of West Bengal and Bangladesh, where annual rainfall is

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as high as 1800 mm year21. Nearly 85% of the total precipitation is received during the warm humid/subhumid monsoon season from June to September when rice is grown. In winter months, only a few showers are received from December to February. The weather is cool and dry during November to March when wheat is grown. The Indus plain drains into the Arabian Sea, whereas the Gangetic plains gradually slope from the northwest towards the Bay of Bengal. There are wide variations in soil types, generally coarser in the trans- and upper Gangetic plains and becoming finer with the run of the river systems. Soils are primarily calcareous and micaceous alluviums with sandy loam to loam in the upper reaches becoming finer textured in the distal plains close to the mouth of the river systems (Gupta et al., 2002). Irrigated (and partially irrigated) and rainfed agriculture co-exist in several districts. This has led to a mosaic pattern of agricultural development in the Indo-Gangetic plains. Most soils in the IndoGangetic plains are deficient in nitrogen. Deficiency of phosphorus is next in order of importance. Because of the micaceous nature of soils, deficiency of potassium is starting to emerge in several areas (Ladha et al., 2002). In China, the growing season for wheat is shorter in the south (early November to mid-May) than in the north (early October to mid-June). Thus, longer duration wheat cultivars are combined with shorter duration rice cultivars of japonica type during the cool dry winter in northern China. In southern and southeastern China, farmers grow a second rice crop in the rice –wheat system and sweet potato replaces the second rice crop in southeastern and southwestern China (Timsina and Connor, 2001). Whereas the average productivity of the rice crop in China ranges from 6.0 to 8.2 t ha21, wheat yields are 2.1– 3.2 t ha21 (Gupta et al., 2002). In regions where the rice– wheat system is practised in China, annual precipitation varies from 650 to 1400 mm, increasing from the south to southeast. Most precipitation is received during April and October. The annual total sunshine hours are more in the river plains (about 2000 hr) than in the plateau region in the south or in southeastern China (1100 – 1400 hr). In the plateau region, red earths are low in soil fertility. In the valley and low mountainous regions, purple earths are fertile soils (Gupta et al., 2002). The two crops in rice –wheat cropping systems have contrasting edaphic requirements. Wheat is grown in upland well-drained soils having good tilth, whereas rice is commonly transplanted into puddled soils and prefers continued submergence. Thus, a dominating feature of the rice –wheat cropping system is the annual conversion of soil from aerobic to anaerobic and then back to aerobic conditions. Flooding of rice fields causes several chemical and biochemical changes in the soil, which regulate transformations and availability of nutrients (Ponnamperuma, 1972; 1985; Cao and Hu, 1995). The flooded soil is characterized by larger amounts of exchangeable potassium and sodium compared with the upland soil, particularly in the cultivated layer (Zhang, 1985). Submerged soils differ from others in the control of acidity and alkalinity because the partial pressure of CO2 in flood water buffers carbonate and lowers pH. The pH changes

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alter chemical equilibria and consequently the availability of different nutrients. However, most chemical changes are reversible on draining, which suggests important implications for nutrient management in rice –wheat systems.

III. POTASSIUM FERTILIZER USE IN THE RICE– WHEAT CROPPING SYSTEMS Data pertaining to application of potassium fertilizers to rice and wheat in China and in the four countries of the Indo-Gangetic plains are given in Table II. Although the data for the two crops do not necessarily pertain to when these are sequentially grown in an annual rotation, the data do provide a fairly good estimate of the extent of potassium fertilization. While on average more than 25 kg ha21 of potassium is being applied to both rice and wheat in China, the range in the IndoGangetic plain is 0.4 – 8.3 kg ha21. However, to a large percentage of area under rice – wheat system, particularly in the Indo-Gangetic plain, no potassium fertilizer is being applied. In Bangladesh, application of potassium amounts to only 27% of the total removal by rice –wheat cropping systems (Saunders, 1990).

Table II Potassium Fertilizer Use in Rice and Wheat in China, India, Pakistan, Bangladesh and Nepal

China (1997) Rice Wheat India (1998) Rice Wheat Pakistan (1999) Rice Wheat Bangladesh (1998) Rice Wheat Nepal (1989) Rice Wheat

Area (Mha)

Fertilizer potassium usea (kg ha21)

Fertilizer potassium consumption (£103 Mt)

30.17 28.98

33.2 26.6

600.8 76.9

43.45 26.70

7.8 3.2

253.4 74.9

2.42 8.33

0.4 5.1

0.2 0.6

10.12 0.88

8.3 6.6

84.0 5.9

1.43 0.60

0.8 1.7

0.5 0.4

Source: IFA, IFDC, IPI, PPI and FAO (2002); IFA, IFDC and FAO (1999). a To calculate average fertilizer potassium use, the area under rice and wheat receiving no potassium fertilizers was not taken into account.

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In the Indo-Gangetic plains in India, the general recommendation for rice is to apply 25 kg ha21 of potassium in Punjab (trans-Gangetic plains) and up to 50 kg ha21 in the middle and lower Gangetic plains (Uttar Pradesh and West Bengal). For wheat, the range for potassium application is 21 –58 kg ha21 (Tiwari, 2000). Diagnostic surveys (Yadav et al., 2000b) have indicated that rice – wheat farmers in the Indo-Gangetic plain seldom adopt recommended fertilizer doses and potassium fertilizers are rarely used. Fertilizer use pattern for rice – wheat systems in the Indo-Gangetic plains varies greatly from one part to another. For example, in out of 36 districts in Punjab and Haryana states in northwestern India, 34 districts consumed more than 100 kg (N þ P2O5 þ K2O) ha21. On the other hand, 95 out of 155 districts of the eastern part comprising Uttar Pradesh, Bihar and West Bengal consumed 100 kg ðN þ P2 O5 þ K2 OÞ ha21 or less. While fertilizer nitrogen remained heavily subsidized, reduction in subsidies of phosphate and potash in India adversely affected their consumption. This resulted in continued imbalance in fertilizer use (Singh and Singh, 2001b). In 1998, the N:K2O ratio was wider in northwestern states of Punjab (45.2) and Haryana (171.5) consuming the highest amount of fertilizer per unit area as compared to in eastern states (11.5 and 3.2 in Bihar and West Bengal, respectively) of the Indo-Gangetic plain (Fertiliser Association of India, 1999). Thus, the highest amounts of potassium fertilizers are being applied in West Bengal followed by Bihar, Uttar Pradesh, Punjab, and Haryana. The percentage of total potassium fertilizer applied in the summer season when rice is grown was, however, in reverse order—highest in the Punjab and lowest in West Bengal. In China too, fertilizer use is highly imbalanced in favor of nitrogen. For example, in 1993 the N:K2O ratio for fertilizer consumption was 8.3:1 (Xie, 1995). In rice – wheat cropping systems in South China, a monitoring of 10 sites for 10 years revealed that on average while organic sources contributed 89 kg K ha21, contribution of chemical fertilizers was only 38 kg K ha21 (Wang et al., 2002).

IV. POTASSIUM FERTILITY OF SOILS UNDER RICE– WHEAT CROPPING SYSTEMS Total potassium in alluvial soils of the Indo-Gangetic plains in India ranges from 1.28 to 2.77%; the range for exchangeable potassium contents is from 78 to 273 mg K kg21 soil (Tandon and Sekhon, 1988). Soils in the Indus plain in Pakistan contained 2.65– 3.55% K (Zia and Rahmatullah, 1998). In large tracts of tropical and subtropical soils of China supporting rice –wheat cropping systems, total potassium varied from 1.06 to 2.02% (Cao and Hu, 1995). Although soils under rice– wheat systems contain large amounts of potassium as an essential part of their matrix, many times the soil fails to supply adequate amounts of the nutrient to meet the normal needs of the plant. Even two soils that contain the same amount of total potassium reserves and water soluble potassium may differ widely in their

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behavior in supplying the needs of the plants. Potassium fertility of soils can be defined better by understanding the mineralogy of soil potassium, forms of potassium, and different kinds of potassium transformations occurring in the soil.

A. MINERALOGY

OF

SOIL POTASSIUM

Potassium feldspars and micas are the potassium minerals present in the soils of Indo-Gangetic alluvial plains in India (Sidhu, 1984). Potassium feldspar species present in these soils are microcline and orthoclase. Mica minerals present are muscovite and biotite in the coarser fractions and illite in the finer fractions. Illite, a mixed layer mica-monmorillonite, is partially weathered muscovite mica with layer charges less than for muscovite; part of its charge originates in the octahedral layer, unlike the muscovite. Sand fractions are dominated by quartz, micas, and feldspars in decreasing order (Sidhu and Gilkes, 1977; Kapoor et al., 1982; Pundeer et al., 1978). The silt fraction resembled the sand fraction in mineralogical makeup. Illite, vermiculite, and different amounts of smectite, chlorite, and kaolinite are common clay minerals. The illites are predominantly dioctahedral (Kapoor et al., 1981; 1982; Sidhu and Gilkes, 1977). Soils in western and central Uttar Pradesh (Upper Gangetic plain) have illite and chlorite as the dominant clay minerals (Ghosh and Bhattacharya, 1984). Tarai soils contain largely illite and chlorite but also some mixed layer minerals, kaolinite and quartz. In western Uttar Pradesh, smectite was found to be the dominant clay mineral along with illite, chlorite, kaolinite, quartz, feldspar, and allophane. The salt affected alluvial soils in the Indo-Gangetic plain were found to contain smectite – mica and chlorite –vermiculite interstratified minerals. In the lower Gangetic basin, illite or smectite are the dominant minerals in the soils. Mishra et al. (1996) found that whereas smectite – illite – chlorite is the most common clay mineral phase in the terraces, soil clays of flood plains are dominated by an illite – smectite – chlorite phase in the middle and lower Gangetic plains. Sekhon et al. (1992) carried out a systematic study of mineralogical composition of silt and clay fractions in soil samples collected from eight soil series in the rice– wheat regions in the Indo-Gangetic plain in India. The results of this study as described in Table III reveal that except in two series from lower Gangetic plains in West Bengal, illite is the dominant clay mineral in the seven soil series spread over the states of Punjab, Uttar Pradesh, and Bihar. Dominant minerals in the silt fraction in the entire Indo-Gangetic plain are quartz – feldspar, quartz – mica or quartz alone (Table III). In Pakistan, containing the western part of the Indo-Gangetic plains, soils under rice –wheat systems contain large amounts of mica (about 50%) in sand and silt fractions and illite (about 50%) in clay fractions (Akhtar and Jenkinson, 1999). Besides illite, the clay fraction contained kaolinite, montmorillonite, chlorite, and vermiculite (Bajwa, 1989). These soils experience moderate levels

212 Table III Mineralogical Composition of Clay and Silt Fractions in Soil Samples Collected from Rice–Wheat Growing Regions of the Indo-Gangetic Plains in India Clay fraction Soil series and locationa

Dominant mineral Illite

Khatki (Meerut, Uttar Pradesh)

Illite

Akbarpur (Etah, Uttar Pradesh)

Illite

Rarha (Kanpur, Uttar Pradesh)

Illite

Jagdishpur Bagha, (Muzaffarpur, Bihar) Raghopur (Muzaffarpur, Bihar) Hanrgram (Bardhaman, West Bengal) Kharbona, Birbhum, West Bengal

Dominant mineral

Illite

Vermiculite, chlorite, quartz, feldspar, kaolinite Chlorite, vermiculite, quartz, feldspar, kaolinite Smectite, vermiculite, chlorite, kaolinite, quartz, feldspar Vermiculite, chlorite, quartz, feldspar, kaolinite Chlorite, smectite, quartz, feldspar

Illite

Chlorite, smectite, quartz, feldspar

Quartz

Smectite, Illite

Vemiculite, kaolinite, quartz, feldspar, chlorite Illite, smectite, quartz, feldspar

Kaolinite

Source: Sekhon et al. (1992). a Listed in order of location from trans- to lower Indo-Gangetic plains.

Associated mineral

Quartz, mica

Vermiculite, feldspar

Quartz

Mica, vermiculite

Quartz, mica

Vermiculite, feldsapr

Mica, quartz

Vermiculite, feldspar

Quartz, mica

Quartz

Feldspar, chlorite, vermiculite, 2:1–2:2 intergrades Mica, feldspar, chlorite, vermiculite, 2:1–2:2 intergrades Mica, vermiculite, feldspar

Quartz

Mica, vermiculite, feldspar, kaolinite

BIJAY-SINGH ET AL.

Nabha (Ludhiana, Punjab)

Associated mineral

Silt fraction

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213

Table IV Potassium-supplying Potential of Major Soil Groups in China

Category

Slowly available Ka (mg K kg21 soil)

Very low

,66

Low

66–166

Predominant clay minerals Kaolinite Kaolinite, hydrous micas

Moderate–low

166–332

Vermiculite, kaolinite

Moderate

332–498

Hydrous micas, vermiculite, kaolinite

Moderate–high

498–747

High

747–1162

Hydrous micas, vermiculite (chlorite) Hydrous micas, montmorillonite

Very high

.1162

Hydrous micas

Major soil groups Latosols and laterite red earths and their paddy soils Red earths, yellow earths and their paddy soils Paddy soils in Taihu Lake and Zhujiang River valleys, sandy soils alongside the Changjiang River Alluvial paddy soils in Dongting Lake and Gan River valleys, yellow brown earth, sandy fluvo-aquic soil, brown earths Purplish soils, castanozems, and meadow soils Dark brown earths, chernozem, cinnamon soil, and clayey fluvo-aquic soils Grey desert soil, brown desert soil

Source: Xie et al. (1982; 1990). a K extractable with 1M HNO3.

of weathering of provenance K-minerals as a large amount of applied Kþ gets fixed (Ranjha et al., 1992). The mica content in the rice – wheat continuous cropping area in China is about 14% (Li et al., 1992). Predominant clay minerals present in seven categories of soils in China classified on the basis of potassium-supplying potential are listed in Table IV. The soils in regions where the rice– wheat cropping system is commonly practised in China fall into the categories of low to moderate – high potassium supplying potential (Xie et al., 1990). Thus, kaolinite and vermiculite are the dominant clay minerals in red earths, yellow earths and their paddy soils, paddy soils in Taihu Lake and Zhujiang River valleys, and in sandy soils alongside the Changjiang River. On the other hand, alluvial paddy soils in Dongting Lake and Gan River valleys, yellow brown earth, sandy fluvo-aquic soil, brown earths and the purple soils derived from purple sandstone mostly distributed in Sichuan and other provinces in subtropical wet regions in South China contained hydrous micas as the dominant clay mineral and thus highest potassium-supplying potential as based on 1M HNO3 extractable potassium.

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Depending upon climate, vegetation, and drainage, minerals continue to weather and proton-exchange constitutes an important means for potassium release from micas. The degraded micas thus formed acquire inter-layer space from which more potassium can be released over time. However, if application of potassium fertilizer increases the concentration of potassium in soil solution, Kþ may enter expanded inter-layer spaces and become fixed by reversing the weathering process. Since a hydrated form of Ca2þ, the dominant cation in the solution of most soils under rice – wheat systems in the Indo-Gangetic plain, is larger than Kþ, it enlarges the interlayer space releasing more Kþ in the process. When plant roots remove potassium from the soil solution, more potassium continues to be released from the clay minerals by cation (including proton) exchange. The gradual release of potassium from positions in the mica lattice results in the formation of hydrous mica (6 – 8% K) or illite (4 – 6% K). Further release of potassium due to weathering, including excessive mining by rice –wheat cropping systems, converts illites to transitionary clay minerals (2 –3% K) such as expanding illites and inter-stratified minerals and ultimately leads to formation of montmorillonite/vermiculite (, 1% K).

B. FORMS

OF

SOIL POTASSIUM

Soil potassium is often considered to exist in solution, and in exchangeable and non-exchangeable (fixed and structural potassium) forms. The amount of solution and exchangeable potassium is usually a small fraction of total potassium (1 –2% and 1 –10%); the bulk of soil potassium exists in potassiumbearing micas and feldspars (Sekhon, 1995). The amount of potassium present in the soil solution is often smaller than the crop requirement for potassium. Thus continuous renewal of potassium in the soil solution for adequate nutrition of high yielding varieties of rice and wheat is obvious. Similarly, the exchangeable potassium component has to be continuously replenished through the release of fixed potassium and weathering of potassium minerals. Hence, potassium nutrition of crops is a function of the amounts of different forms of potassium in soil, their rates of replenishment and the degree of leaching. Brar and Sekhon (1986) studied four loam soils from Indo-Gangetic alluvium and found that desorption of potassium by electroultrafiltration (EUF) differed considerably, although the soils tested similarly for exchangeable potassium. Thus, for a given amount of exchangeable potassium, one soil may supply more potassium to plants than another. In general, illite dominant soils have a larger proportion of water soluble to exchangeable potassium than smectite dominant soils. Sekhon et al. (1992) determined different forms of potassium in samples collected from eight well-defined benchmark soil series in the Indo-Gangetic plain of India. The water soluble potassium content in the soil varied from 14 to 31 mg K kg21 (Table V). Exchangeable potassium content was influenced by the clay

Water soluble K Soil series and locationa Nabha (Ludhiana, Punjab), Udic Ustochrept, pH 7.7–9.8 Khatki (Meerut, Uttar Pradesh), Typic Haplustalf, pH 7.2 –8.7 Akbarpur (Etah, Uttar Pradesh) Udic Haplustalf, pH 7.7– 9.8 Rarha (Kanpur, Uttar Pradesh), Udic Ustochrept, pH 8.1–8.8 Jagdishpur Bagha (Muzaffarpur, Bihar), Typic Ustifluvent, pH 7.8–9.4 Raghopur (Muzaffarpur, Bihar), Aquic Eutrochrept, pH 7.8–9.0 Hanrgram (Bardhaman, West Bengal), Veric Eutrochrept, pH 5.0 –5.9 Kharbona (Birbhum, West Bengal), Typic Haplaquept, pH 4.5 –6.8

Exchangeable Kb

Non-exchangeable Kc

(mg kg21)

% of total

(mg kg21)

% of total

(mg kg21)

% of total

Total K (%)

27

0.10

57

0.22

1334

5.05

2.64

14

0.05

70

0.26

1548

5.73

2.70

14

0.07

55

0.28

1330

6.68

1.99

15

0.05

67

0.24

1856

6.75

2.75

26

0.14

39

0.22

1923

10.62

1.81

31

0.12

53

0.20

2200

8.46

2.60

18

0.15

87

0.71

601

4.89

1.23

18

0.51

27

0.77

98

2.80

0.35

Source: Sekhon et al. (1992). a Listed in order of location from trans- to lower Indo-Gangetic plains. b 1M ammonium acetate extractable potassium minus water soluble potassium. c 1M boiling HNO3 extractable potassium minus 1M ammonium acetate extractable potassium.

K NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM

Table V Forms of Soil Potassium in Samples Collected from Eight Soil Series in Rice–Wheat Growing Regions of the Indo-Gangetic Plain in India

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mineralogy of the series. The soils from Punjab, Uttar Pradesh and Bihar with illite as the dominant clay mineral contained 39 –70 mg K kg21. But the two soils from West Bengal with smectite (Hanrgram) and kaolinite (Kharbona) as the dominant clay minerals contained 87 and 27 mg K kg21 exchangeable potassium, respectively. Effect of clay mineralogy was also very striking in influencing the non-exchangeable potassium content of the soils in the Indo-Gangetic plains. The two soils from West Bengal contained only 601 and 98 mg K kg21 nonexchangeable potassium, whereas all the remaining six soil series with illite as the dominant clay mineral showed very high content of non-exchangeable potassium varying from 1330 to 2200 mg K kg21. Trends in total potassium content were also similar to that for non-exchangeable potassium; minimum potassium contents were observed in soils from West Bengal in the lower Indo-Gangetic plains. Different forms of potassium in tropical and subtropical soils in China are listed in Table VI. Rice – wheat systems are practised in red soil, yellow soil, yellow brown earth, purple soil, and paddy soil. The weathering intensity of these soils decreased in the order: red soil . yellow soil . yellow brown earth . purple soil and thus potassium content of these soils also increased in the same order (Cao and Hu, 1995). Purple soils mostly distributed in Sichuan province possessed the highest potassium-supplying and buffering capacity. However, soils under rice – wheat systems in China contained conspicuously less

Table VI Forms of Soil Potassium in Samples Collected from Tropical and Subtropical Soils Developed on Different Parent Materials in China

Soil Latosols Lateritic red earth Red soil Yellow soil Yellow brown earth Purple soil Paddy soil

Parent material

Total K (%)

Non-exchangeable Ka (mg K kg21 soil)

Water soluble and exchangeable Kb (mg K kg21 soil)

Basalt Sea deposit Granite

0.20 0.31 0.38

37 44 64

55 44 65

Red clay Granite Arenaceous shale Arenaceous shale

0.95 2.72 1.06 1.28

163 198 75 362

66 76 98 80

Arenaceous shale Sediment Alluvial deposits Lake deposits

2.02 1.68 1.43 1.71

483 224 314 685

132 124 82 173

Source: Cao and Hu (1985). a 1M HNO3 extractable K. b 1M Ammonium acetate extractable K.

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217

exchangeable, non-exchangeable or total potassium than in soils in the IndoGangetic plains of South Asia. As shown in Table IV, the clay mineralogy of Chinese soils is also in accord with the magnitude of different forms of potassium in the soils. The potassium content of paddy soils also varied widely depending upon parent material (Table VI).

C. POTASSIUM TRANSFORMATIONS

IN

SOILS

Dynamic equilibrium reactions occurring between different forms of potassium have a profound effect on the potassium nutrition of rice –wheat cropping systems. The direction and rate of these reactions determine the fate of applied potassium and release of non-exchangeable potassium. Under certain conditions, added potassium is fixed by the soil colloids and is not readily available to plants. The most important aspect of the potassium transformations in soils under rice – wheat cropping systems is the rate at which the non-exchangeable portion is released to the exchangeable and soluble forms. The rate and magnitude of release are primarily dependent on the level of potassium in the soil solution and the type and amount of clay minerals present (Martin and Sparks, 1985; McLean, 1978). The rate of release of non-exchangeable potassium is also influenced by the degree of exposure of edges of clay mineral to the soil solution, and the position of non-exchangeable potassium with respect to outer edges. Thus in some soils, the rate of release of non-exchangeable potassium may be slow enough to restrict yield, whereas it may be rapid enough to meet the potassium needs of the entire crop. Sekhon et al. (1992) studied release kinetics of nonexchangeable potassium using the hot HNO3 method as described by Pieri and Oliver (1987) in samples from eight well-defined benchmark soil series in the Indo-Gangetic plain of India. An estate of step-K was also obtained by repeated extractions with boiling 1M HNO3 at 10-minute intervals, using a soil solution ratio of 1:10 (Haylock, 1956). The data are listed in Table VII. Kaolinitedominant alluvial soils (Kharbona) and smectitic acidic alluvial soils (Hanrgram) in the lower Gangetic plain showed lower rates of potassium release from the nonexchangeable fractions than the illitic alluvial soils. Potassium release rates were nearly proportional to the size of the non-exchangeable pool in different soils. Results from a large number of exhaustion experiments carried out in China reveal that rice and wheat absorbed a large proportion of the potassium from slowly available potassium in soils. Following several successively continued cultivations, 60 –80% potassium absorbed by crops came from slowly available potassium (Xie and Li, 1987). After four successive crops, the amounts of potassium absorbed by rice plants from the no-K treatment varied greatly with the soil. In no-K plots, the lowest removal of potassium by rice (17 mg K kg21 soil) was observed in latosols; the highest (up to 439 mg K kg21 soil) removal was from fluvo-aquic soils. With an increasing number of crops, the contribution of

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Table VII Potassium Release Characteristics following Hot 1M HNO3 Method in Samples Collected from Eight Soil Series in Rice– Wheat Growing Regions of the Indo-Gangetic Plain in India K release rate (mg kg21 h21)d Soil series and locationa Nabha, Ludhiana, Punjab Khatki, Meerut, Uttar Pradesh Akbarpur, Etah, Uttar Pradesh Rarha, Kanpur, Uttar Pradesh Jagdishpur Bagha, Muzaffarpur, Bihar Raghopur, Muzaffarpur, Bihar Hanrgram, Bardhaman, West Bengal Kharbona, Birbhum, West Bengal

Exchangeable K (mg kg21)b

Non-exchangeable K (mg kg21)c

P1

P2

67 72 79 72 70

860 1300 1340 1470 1636

356 635 469 494 543

55 324 144 127 39

70 130

1936 360

745 66

59 18

63

156

25

10

Source: Sekhon et al. (1992). a Listed in order of location from trans- to lower Indo-Gangetic plains. b 1M ammonium acetate extractable potassium minus water soluble K. c 1M boiling HNO3 extractable potassium minus 1M ammonium acetate extractable K. d Pieri and Oliver (1987). Potassium was dissolved by 1M HNO3 at 858C over periods ranging from 15 min to 8 h. Potassium dissolved by the same reagent in 5 min at room temperature is taken as the extraction at zero time. P1 is the gradient of the first linear segment and represents dissolution of external K; P2 is the gradient of the second linear segment and represents the destruction of the lattice.

ammonium acetate extractable K was reduced, whereas increasing amounts of potassium were derived from a pool of slowly available potassium. Studying the release kinetics of potassium under a continuous rice – wheat cropping system, Singh et al. (2002b) and Pannu et al. (2003) observed that application of organic manures (farmyard manure and green manure) along with urea-N increased the cumulative non-exchangeable potassium release and could maintain large amounts of potassium in soil solution and on exchange sites by reestablishing the equilibrium among different forms of potassium. Increased plant growth due to application of organic manures and NHþ 4 -forming fertilizers applied to rice and wheat encourages acidification which in turn results in release of non-exchangeable potassium. Under NHþ 4 -N nutrition, interlayer potassium ions that are similar to Kþ in ionic size. can also be replaced by NHþ 4 Acidification may also dissolve mineral potassium, a process that is irreversible (Tandon and Sekhon, 1988). According to Wihardjaka et al. (1999), mobilization of non-exchangeable potassium in flooded rice is due to root induced acidification, coupled with potassium removal from the soil solution by the roots.

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The hydronium ion ðH3 Oþ Þ is very effective in acting as a counter-ion to replace structural potassium. Under flooded rice conditions, hydronium ions are generated by: (1) the release from roots to balance excess intakes of cation 2þ over anions under NHþ due to released 4 -N nutrition, (2) oxidation of Fe oxygen, and (3) decomposition of applied organic manures and root residues (Kirk et al., 1993). The acidification increases with increase in root biomass due to better crop growth on application of fertilizers and organic manures. Thus, increased release of non-exchangeable potassium when rice and wheat are adequately fertilized is partly plant induced and partly due to solubilization of potassium caused by acidification. Experiments carried out by Rahmatullah and Mengel (2000) on release of potassium from micaceous mineral structures in five soils in Pakistan by Hþ ion resin revealed that Kþ release from the inter-layers of Kþ bearing minerals is initiated by a low Kþ concentration near the mineral surfaces. On average, quantities of Kþ released from clay and silt fractions were comparable and twice as high as from sand fractions. Srinivasa Rao and Khera (1994) studied the potassium replenishment capacity of eight soil series in the Indo-Gangetic plain with varying illite content of their clay fraction at their minimum exchangeable K. Average daily rates of potassium replenishment of soils varied from 0.25 to 0.67 mg K kg21. Srinivasa Rao et al. (2000) studied fixation of potassium by soils differing in mineralogical make up and found that illitic soils fixed 23 – 29% of applied potassium; the values for smectitic and kaolinitic soils were 26– 32% and 17– 23%, respectively. The water regime is highly dynamic in rice– wheat systems and it may influence availability and fixation of potassium in soils. Flooding of dry lowland soils containing vermiculite, illite, or other 2:1 layer clay minerals may result in increased potassium fixation and reduced solution concentration, so that rice depends on non-exchangeable reserves for potassium uptake. In a long-term experiment with rice –wheat rotation in the Tarai plain of southern Nepal, the proportion of added potassium that was fixed in the soil ranged from 46 to 56% in a wet/dry equilibration, and fixation was linear with addition rates of up to 25 mM K kg21 soil (Regmi, 1994). Since both Kþ and NHþ 4 are fixed by the same mechanism, potassium competes with ammonium for fixing sites. More potassium fixation has been reported when NHþ 4 -N was applied before potassium application and less when KCl was applied alone (Singh and Singh, 1979). The work of Luo and Bao (1988) suggests that K-fixing capacity of some Chinese soils under rice – wheat systems is in the order: red soil , loess , yellow brown earth and the amount of potassium fixed by different soils was well correlated with clay content. Since both release and fixation of potassium in soils under flooded rice take place depending upon water regimes including continuous submergence or alternate flooding and drying, and the periodicity of such cycles (Kadrekar, 1975; Kadrekar and Kibe, 1973), measurements of non-exchangeable potassium should prove useful in studying potassium nutrition of rice – wheat cropping systems.

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Soil solution potassium is kept at relatively high levels in flooded soils because large amounts of soluble Fe2þ and Mn2þ ions brought into solution displace cations from the clay complex, and exchangeable potassium is then released into the soil solution (Ponnamperuma, 1972). In fields with adequate drainage, potassium and other basic cations are lost via leaching. Leaching losses of potassium should be substantial, particularly in highly permeable wetland rice soils with low cation exchange capacity. In lower Gangetic plains in Bangladesh, Abedin et al. (1991) recorded leaching losses of potassium as high as 0.1–0.2 kg K ha21 day21. Leaching losses of potassium will depend on soil solution concentration and percolation rates. Singh et al. (2003b) found that leaching losses of potassium in sandy loam and loam soil profiles maintained at submerged moisture regimes were 22 and 16% of the applied K, respectively. Singh and Sekhon (1977, 1978) studied leaching of potassium in illitic alluvial soils under different crop rotations. Estimates based on potassium saturation in subsoil layers indicated that potassium released from illitic minerals during the rainy season in the Indo-Gangetic plain can be lost via leaching beyond more than 2 m depth. In Chinese red soils under rice–wheat rotation having limited potassium absorption capabilities, leaching of potassium beyond the rooting zone constitutes a serious problem. The work of Shen (1993) carried out in field lysimeters during September 1991 to August 1992 revealed that the extent of leaching of potassium decreased in the following order of parent materials: granite . quaternary red earth . basalt . red sandstone. It was also observed that leaching of potassium decreased with cropping. Leaching as well as potassium removal by rice and wheat in large quantities enhances release of potassium from micas by removing the reaction products and accelerates the weathering/transformation of micas to expansible 2:1 layer silicates and other weathering products. Keeping in view the existence of illite – vermiculite, illite – vermiculite – smectite or illite – smectite/chlorite– kaolinite phases, the hidden hunger for potassium and more importantly farm practices leading potassium development vis-a`-vis weathering of potassium minerals in the trans-, upper and middle Gangetic plain to a point of no return may soon pose a great threat (Mukhopadhyay and Datta, 2001).

D. ASSESSMENT

OF

SOIL K-SUPPLYING CAPACITY

Using published data from several field experiments conducted during the years 1970 to 1998 across wheat growing environments in the Indo-Gangetic plains in India, Pathak et al. (2003) observed a relationship ðR2 ¼ 0:82Þ between potassium uptake in no-K plots, a real measure of soil potassium supply and exchangeable soil potassium. Use of 1M ammonium acetate at pH 7.0 to extract plant available potassium (exchangeable þ water soluble K) is still the most used soil potassium availability index for rice – wheat cropping systems. But its suitability as a measure of plant available potassium remains controversial, especially when soils with

K NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM

221

different textures and clay mineralogy are considered together (Kemmler, 1980; Dobermann et al., 1996a,b). For example, in trans-Gangetic plains (Gurdaspur, India) 40% of soil samples were found to be deficient in potassium while only 7% of the plant samples from rice – wheat cropping systems could be called deficient in potassium (Tandon and Sekhon, 1988). The rice– wheat continuous cropping areas mostly lie in the eastern, central and northern parts of China, where the ammonium acetate extractable potassium content varies between 50 and 100 mg K kg21, whereas content of slowly available potassium (HNO3 extractable) ranges between 300 and 800 mg K kg21 (Xie et al., 2000; Shen et al.,1998). Soils in the Indo-Gangetic plains of India have been grouped into three categories of low, medium and high on the basis of soil test values. Usually soils analyzing , 55 mg K kg21 soil by 1M ammonium acetate solution are rated as low in available potassium and soils analyzing . 110 mg K kg21 soils are rated as high in available K. Depending on soil texture, clay mineralogy, and potassium input from natural resources, however, critical levels of ammonium acetate extractable potassium can vary from 39 to 156 mg K kg21 soil (Singh and Singh, 2001a). In China, the yield of rice and wheat increased due to application of fertilizer potassium in soils testing , 100 mg K kg21 of ammonium acetate extractable potassium. At values of available potassium . 200 mg K kg21, no response to applied potassium was observed (Wu and Sun, 2002). Similar observations for wheat were recorded by Zhang (2002) in Henan province but responses were higher on loamy sandy soils compared to clay soil when available potassium was . 100 mg K kg21. In soils with high potassium fixation and release characteristics (for example, vermicullitic soils), 1M ammonium acetate extractable potassium is often small (, 78 mg K kg21) and not a reliable soil test to assess potassium supply. Potassium saturation (% of total CEC) is often a better indicator of soil potassium supply than the absolute amount of potassium extracted with 1M ammonium acetate, because it takes into account the relationship between potassium and other exchangeable cations (Ca, Mg, Fe). The ranges for rice as suggested by Dobermann and Fairhurst (2000) are: † K saturation , 1.5%—low potassium status, response to potassium fertilizer is certain, † K saturation 1.5 –2.5%—medium potassium status, response to potassium fertilizer is probable, and † K saturation . 3.5%—high potassium status, response to potassium fertilizer is unlikely. A measure of non-exchangeable potassium in soil is determined by boiling 1M HNO3, but results are not always correlated to grain yields and total potassium uptake. The critical level for hot 1M HNO3 extractable potassium (slow release K) is 98 mg K kg21. The high root density, relatively high maximum influx and low minimum solution concentrations for potassium uptake indicate that rice and

222

BIJAY-SINGH ET AL.

wheat depend on the non-exchangeable fraction for much of their potassium supply in such soils (Meelu et al., 1995). Thus, it appears desirable to include a measure of non-exchangeable potassium in our estimate of plant available potassium. Subba Rao et al. (1993) categorized soils on the basis of how potassium release rate was related to non-exchangeable potassium reserves in soils with different mineralogical compositions. The limits of non-exchangeable potassium for categories of very low to very high worked out by Subba Rao et al. (1993) were very close to those proposed by Xie et al. (1982; 1990) for soils in China (Table IV). Tandon and Sekhon (1988) suggested that soils with low available potassium (, 100 mg kg21 K soil) are expected to readily respond to potassium application. Soils with low available potassium and high reserves of potassium (. 1000 mg K kg21 soil) status will need lower rates of potassium application and soils with high available potassium (. 100 mg K kg21 soil) and low reserve potassium status can support crops for some years without potassium fertilizer application. Soils containing a (Ca þ Mg):K ratio . 100 may indicate low soil potassium availability to rice (Tandon and Sekhon, 1988). There is now considerable information showing that sub-soil potassium fertility makes a significant contribution to plant nutrition, and differences in the mineralogy and reserve potassium and relationships between exchangeable potassium and water soluble potassium among soil series and soil types suggest the need for different rates of critical limits for different soils (Sekhon, 1995). In addition, ammonium acetate extractable potassium for soil testing should include soil properties such as clay content, cation exchange capacity, and organic matter content. The EUF technique of Nemeth (1979) involving extraction of seven successive fractions of soil potassium at different voltages and temperatures over a 35-minute period has been used to study availability of potassium in soil samples collected from eight benchmark soil series in the Indo-Gangetic plain (Sekhon et al., 1992). Amounts of water soluble potassium in soils should have been comparable to that of those desorbed using EUF during the first 10 minutes (EUF10) (Table VIII). However, in illite dominant alluvial soils where the rice – wheat system is practised in the Indo-Gangetic plains, water soluble potassium was generally more than EUF10; the reverse was true in smectite and kaolinite dominant soils from Hanrgram and Kharbona in lower Gangetic plains. These trends were due to the higher affinity of illitic minerals for potassium as compared to those of smectite and kaolinite. The EUF-K quotient (EUF30 – 35/EUF30) estimates the preponderance of difficult-to-extract potassium and is therefore considered a measure of buffering power. This quotient is generally higher in illite and smectite dominant soils than in kaolinite dominant ones (Brar and Sekhon, 1986; Subba Rao et al., 1988). Buffering capacity of soils in the Indo-Gangetic plains to maintain concentration of potassium in soil solution at a satisfactory level has also been studied using the concept of quantity –intensity relations. Soils containing predominantly illitic or smectitic clays possessed higher buffering capacity and lower potassium activity ratios than the corresponding kaolinite-dominant soils.

Water soluble 1M ammonium acetate 1M HNO3 extractable K (mg K kg21) Soil seriesa K (mg K kg21) extractable K (mg K kg21) Nabha Khatki Akbarpur Rarha Hanrgram Kharbona

35.7 15.4 26.3 15.3 9.1 15.6

119 91 134 88 131 45

990 1278 1440 1480 446 125

EUF10b EUF30 EUF 30 – 35 EUF 35 (mg K kg21) (mg K kg21) (mg K kg21) (mg K kg21) EUF 2 KQc 29.2 13.4 25.2 14.0 23.9 18.1

Source: Sekhon et al. (1992). a Listed in order of location from trans- to lower Indo-Gangetic plains. b Subscripts of EUF indicate time for which the soil sample was subjected to electroultrafiltration. c EUF 2 KQ ¼ EUF30 – 35/EUF30.

60.9 34.4 61.0 35.2 61.0 27.6

25.4 23.6 39.4 27.8 21.6 4.9

86.3 58.0 100.4 63.0 82.6 32.5

0.45 0.68 0.71 0.83 0.37 0.21

K NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM

Table VIII Availability Indices of Soil Potassium as Estimated by Different Extractants and Following Electroultrafiltration (EUF) Technique in Samples Collected from Six Soil Series in Rice–Wheat Growing Regions of the Indo-Gangetic Plain in India

223

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BIJAY-SINGH ET AL.

Also, fine textured soils exhibited higher potassium buffering capacity than those with coarse textures (Sekhon et al., 1992). A significant positive relationship was observed between clay content and buffer capacity in illitic alluvial soils; potassium activity ratios were negatively correlated with clay content of soils (Subba Rao and Sekhon, 1989). Although quantity – intensity relationships involve probably only planar and edge K, these indicate that for a given level of soil solution K, the quantity varies widely for soils with different mineralogy. Thus to obtain a given amount of potassium in soil solution, more than double the amount of adsorbed potassium is needed in smectitic soils than in illitic or kaolinitic soils. The critical levels of available potassium would, therefore, be different for soils of widely different mineralogical composition. The standard approach to soil fertility management revolves around identification of K-deficient soils or plant potassium deficiency using rapid chemical tests with empirical critical threshold ranges. This approach relies on a large number of field experiments to establish calibrations between a given soil potassium test value and the profitability of a response to applied potassium. On soils with relatively high native fertility and little potassium fixation character, this approach may be adequate to provide reasonable recommendations for potassium input requirements. However, such an approach is greatly inadequate for intensively irrigated rice –wheat systems in Asia. This is because these systems are very highly K-demanding. Extractable potassium levels can fluctuate enormously and many soils under rice – wheat systems also have strong K-fixation properties. Dobermann et al. (1996b) evaluated several chemical tests for assessing the K-supplying power of rice soils. They observed a positive correlation between potassium uptake and extractable K, potassium saturation of CEC and CEC of the clay fraction. Despite the significant correlations, none of these static measures provided a reliable estimate of potassium uptake across the different soils. These chemical tests of potassium availability were not suitable indicators of potassium status in soils with K-fixing clay minerals. Extractable potassium alone explained only 53% of total potassium uptake in the NP and NPK treatments. Several regression models were evaluated to improve the prediction of total potassium uptake by incorporating other static soil tests that seemed to provide additional factors affecting soil potassium supply in rice soils. A regression model combining commonly used static soil test parameters that appeared to integrate measures of potassium release from non-exchangeable forms as well as chemical factors affecting potassium activity in soil solution explained 72% of the crop potassium uptake by rice. The integrative term accounts for (1) potential potassium release from non-exchangeable fractions, and (2) potassium exchange equilibrium and potassium activity as affected by Ca and Mg. However, this approach would require determination of six soil properties and it does not provide a direct measure of potassium release dynamics. All chemical soil tests used for potassium for rice and wheat production have theoretical limitations, including that (1) nutrient availability in irrigated

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rice – wheat ecosystems is extremely dynamic and tests on air-dried soil may not fully reflect nutrient status after submergence, (2) differences in clay mineralogy and physical properties have a strong impact on desorption characteristics and plant availability, (3) unextracted nutrient pools may also contribute to plant uptake, (4) diffusion is a key process of potassium transport to the root surface, (5) external mechanisms such as root-induced solubilization of P by acidification contribute significantly to uptake by rice and wheat roots, and (6) kinetics of nutrient release are not measured. Traditional methods are based on extractants’ snapshots of the amounts of nutrients in specific nutrient pools. These give little information on the dynamics of inter-pool conversions and the potential rate of nutrient supply to a growing root surface. Therefore, ion exchange resins have attracted considerable attention as an alternative method for estimating bioavailable potassium in a more dynamic manner. Resins maintain low ion concentrations in solution, thereby stimulating further release from soil solids. Compared with static extraction soil tests, resin incubation techniques assess the nutrient supply to a strong resin sink over a longer time period and, in most cases, they can account for diffusion as one process controlling the amount of potassium adsorbed on the resin. Dobermann et al. (1996b) assessed the soil potassiumsupplying capacity using mixed-bed ion exchange resin capsules. The resin method was sensitive to past fertilizer history and the resulting build-up and depletion of soil potassium reserves and it was a better predictor of total potassium uptake (K uptake; kg ha21 ¼ 2 11 þ 83aK þ 227bK; r 2 ¼ 0.82, where aK ¼ initial resin K adsorption rate; bK ¼ resin K adsorption rate coefficient) than static soil tests. The coefficient aK expresses the rapid potassium adsorption by the resin capsule. The coefficient bK characterizes the capability of a soil to maintain a nutrient flux to a strong sink such as a resin or a plant root. The resin capsule method also known as a phytoavailability soil test has the potential to become a valuable method for assessing soil nutrient-supplying capacity, not only for K, but practically all essential plant nutrients across a wide range of soil types.

V. POTASSIUM UPTAKE BY RICE– WHEAT CROPPING SYSTEMS Field crops generally absorb potassium faster than they absorb nitrogen or phosphorus or build up dry matter. The removal of potassium depends on the production level, soil type and whether crop residues are removed or recycled in the soil. When crop residues are retained in the field, large amounts of potassium are recycled. Optimum application of nitrogen increased potassium uptake by 57% over control plots and nitrogen and phosphorus application increased potassium uptake by 145% (Tandon and Sekhon, 1988). The amount of potassium removed by rice – wheat cropping systems from the soil can be as high as 325 kg K ha21 in the Indo-Gangetic plains (Table IX). In China, Nepal

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Table IX Potassium Removal by Rice–Wheat Cropping Systems in the Indo-Gangetic Plains and in China

Cropping system Rice –wheat Rice –wheat–cowpea Rice –wheat–jute Rice –wheat Rice –wheat–mungbean Rice –wheat Rice –wheat Rice –wheat Rice –rice–wheat Aman rice– wheat Aman rice– wheat– Aus rice

Total productivity (t ha21)

K uptake (kg ha21)

13.2 9.6 þ 3.9 (dry) 6.9 þ 2.3 (fibre) 8.8 11.2 10.7 9.9 10.7 8.1 5.7 8.1

287 324 212 280 279 238 152 142 150 132 185

Reference Kanwar and Mudahar (1986) Nambiar and Ghosh (1984) Sharma and Prasad (1980) Meelu et al. (1979) Bhandari et al. (2002) Bao and Xu (1993) Regmi et al. (2002b) Saunders (1990)

and Bangladesh, uptake of potassium by rice –wheat cropping systems has been reported around 150 kg K ha21, generally due to low wheat yields. Table X lists the typical ranges and averages for potassium uptake and content in grain and straw of modern rice and wheat varieties. Grains of wheat contain more potassium than rice; the opposite is true for straw. It has been shown that nutrient requirement per unit of grain of rice is nearly the same for all dwarf varieties although variations between varieties of a crop and variations of the same variety between two different seasons have also been observed. The pattern of K uptake follows most closely that of vegetative growth. Even before the booting stage, 75% of the maximum K content has been taken up, and most of the remaining uptake is completed before grain formation begins, very

Table X Uptake and Content of Potassium in Modern Rice and Wheat Varieties

Typical range Plant part

Rice

Wheat

Observed average Rice

Potassium uptake (kg t 21of grain or straw) Grain 2–3 4–5 2.5 Straw 12–17 9–12 14.5 Potassium concentration (%) Grain 0.22–0.31 0.35–0.50 0.27 Straw 1.17–1.68 0.85–1.15 1.39 Source: Singh and Singh (2001a); Dobermann and Fairhurst (2000).

Wheat

4.5 10.5 0.43 1.00

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similar to the pattern of K uptake in wheat. About 20% of the K taken up before full heading is translocated to the panicles and the rest remains in the vegetative parts at maturity (De Datta and Mikkelsen, 1985). Out of the total potassium uptake by rice, about 55% of potassium is absorbed during the early panicle initiation stage. About 60% of potassium uptake is completed by the heading stage (Pillai and Kundu, 1993). In China, 78% and 76% of potassium uptake was completed by the jointing stage in rice and the booting stage in wheat, respectively (Lu, 1998).

VI. RESPONSE OF RICE– WHEAT CROPPING SYSTEMS TO APPLIED POTASSIUM Yield response to applied potassium is a function of crop, variety, soil characteristics, attack of pests and diseases, and application of other nutrients. Rice tends to respond more to potassium than wheat. Possibly, due to retarded respiration rates of roots under anaerobic soil conditions, adequate absorption of potassium by rice roots can only be ensured by high potassium levels in the soil. Studies carried out on a large number of on-farm locations showed that application of 50 kg K ha21 produced a grain yield response of 290 and 240 kg ha21 in wheat and rice, respectively (Randhawa and Tandon, 1982). Average agronomic response of 6 kg grain kg21 K to an application of 37.5 kg K ha21 was observed in rice and wheat. In later studies carried out in Punjab, Haryana and Uttar Pradesh in the trans-Indo-Gangetic plains, response of rice to 25 – 50 kg K ha21 ranged from 210 – 370 kg grains ha21 (Meelu et al., 1992). In the western part of the Indo-Gangetic plains located in Pakistan, Zia et al. (2000) observed 11 and 19% increases in grain yields of rice and wheat, respectively, due to application of 62 kg K ha21 as potassium chloride in a 7-year experiment on a rice– wheat cropping system. In on-farm experiments carried out at several locations in Punjab in Pakistan, average response of rice and wheat to application of 50 kg K ha21 was only 1.3 and 0.12 t ha21 (NFDC, 2001). Dobermann et al. (1995) observed significant yield increase of 12% to potassium application in rice at Pantnagar. In a 5-year field study on a sandy loam soil (ammonium acetate extractable potassium; 123 kg ha21), application of 25 kg ha21 resulted in a mean increase in yield of rice and wheat by 280 and 160 kg grain ha21, respectively, (Meelu et al., 1995). In a number of long-term experiments on rice– wheat systems located all over the Indo-Gangetic plain (Table XI), average response to application of 33 kg K ha21 over 120 kg N and 35 kg P ha21 to each crop ranged from 0 to 0.5 t ha21 in rice and 0 to 1.3 t ha21 in wheat. The low responses to fertilizer potassium observed in rice and wheat on alluvial soils of the IndoGangetic plain suggest that release of native potassium from illitic minerals in these soils could meet the potassium needs of these crops.

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Table XI Response of Sequentially Grown Rice and Wheat to Application of Potassium in Long-term Experiments in the Indo-Gangetic Plains of India Grain yield (t ha21) Location

Years

Crop

No NPK

N

NP

NPK

Barrackporea

1972–97

Pantnagara

1972–96

R.S. Pura

1981–90

Palampur

1978–89

Faizabad

1977–90

Kanpur

1977–87

Pantnagar

1977–90

Varanasi

1977–88

Rewa

1978–90

Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat Rice Wheat

1.6 0.8 3.4 1.6 2.1 1.1 2.3 1.2 1.0 0.8 1.7 1.2 2.3 1.4 2.1 1.3 2.0 1.0

3.5 2.1 5.0 3.8 4.2 1.9 4.1 1.3 3.9 3.6 3.5 3.5 4.0 3.5 4.1 3.1 3.9 1.5

3.9 2.3 5.0 3.8 4.8 3.1 4.0 2.4 4.7 4.5 4.2 4.1 4.2 3.5 3.7 3.5 4.1 2.7

4.0 2.4 5.4 3.9 4.8 3.5 4.5 3.7 4.8 5.5 4.4 4.2 4.4 3.5 3.8 3.6 4.2 2.9

Source: Hegde and Sarkar (1992). a Swarup (1998).

Using time series analyses, Bhargava et al. (1985) showed that response to potassium has been increasing with time. The response of wheat to potassium in different agro-ecological regions was in the range of 6.7– 12.7 kg grain kg21 K during 1977– 1982 as against 2.0 –5.0 kg grain kg21 K during 1969 – 1971. The corresponding values for rice were 6.5– 10.7 kg and 1.8– 8.0 kg grain kg21 K (Table XII). The increasing trend in response to potassium over the years suggests the need for its application in intensive rice – wheat cropping systems. A large proportion of area (about 2.8 Mha) in the Indo-Gangetic plain is highly alkaline (pH . 8.5) and contains an excessive concentration of soluble salts, a high exchangeable sodium percentage (. 15%) and CaCO3. Swarup and Singh (1989) found that application of fertilizer potassium did not significantly increase crop yields in rice – wheat rotation on reclaimed sodic soils in Haryana even after continuous cropping for 12 years. However, in salt affected soils of Kanpur, application of 25 kg K ha21 to both crops produced additional grain yield of 0.50 and 0.61 t ha21 of rice and wheat, respectively (Tiwari et al., 1998).

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Table XII Response of Rice and Wheat over Different Periods to Applied Potassium in Different Agro-ecological Regions in India Response of 50 kg K ha21 (kg grain kg21K) Rice Region Humid, western Himalayan Subhumid, Satluj-Ganga Alluvial Plain Subhumid to humid Eastern Uplands Arid western Plains

Wheat

1969–71

1977–1982

1969– 71

1977–82

8.0 4.8 4.4 1.8

10.7 7.0 9.8 6.5

5.0 3.4 2.0 2.6

12.7 7.8 7.1 6.7

Source: Bhargava et al. (1985).

In a 20-year long-term experiment with rice – rice – wheat rotation in the Tarai region of Nepal, the average yield of wheat increased from 1.2 t ha21 in NP treatment to 2.3 t ha21 in NPK (100 kg N þ 18 kg P þ 25 kg K ha21) (Regmi et al., 2002b). Response of first and second crops of rice to application of 25 kg K ha21 on the top of 100 kg N and 13 kg P ha21 was only 0.2 and 0.6 t ha21. Similar increases in grain yield of rice and wheat during 1995 –1999 when potassium was applied to a plot receiving only N and P fertilizers during 1988– 1994 were also observed (Regmi 2002a). A wide range of responses of rice – wheat cropping systems to application of potassium were also observed in China. Application of 90 kg K ha21 to rice as well as wheat resulted in a 12 – 29% increase of total grain yield in Jiangsu province. The increase was 29 –55% with application of only 37.5– 56 kg K ha21 to rice as well as wheat in Sichuan province (Table XIII). Similar increases in total productivity of a rice –wheat cropping system were observed when only potassium was applied to rice (180 kg K ha21) in Jiangsu province and to wheat (75 to 112 kg K ha21) in Sichuan province (Table XIII). However, within Jiangsu province, application of potassium exhibited a higher direct effect on wheat and residual effect on rice in Entisols rather than in Alfisols (Table XIV). In a longterm experiment in Hubei province, Chen (1997) observed that the direct response of wheat to potassium application was larger than that of rice, while the residual response of rice was larger than that of wheat. In a large number of balanced fertilization demonstration trials carried out during more than a decade in southern China, application of 48 –75 kg K ha21 to rice resulted in grain yield responses of 7.9– 61.3% and application of 46– 62 kg K ha21 to wheat increased the grain yield by 6.9– 23.2% (Scientific Technology Department of Ministry of Agriculture, 1991). In general, potassium application shows larger yield responses on wheat than on rice. Thus when potassium fertilizer is not available in sufficient quantity, it is preferably applied to wheat (Xie et al., 2000).

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Table XIII Response of Rice–Wheat systems to Application of Potassium in Different Modes in Northern Jiangsu Province and Sichuan Province of China

Location and K level (kg K ha21) b

Siyang (Jiangsu) 1.0 ¼ 180 kg K ha21

Liyang (Jiangsu)c 1.0 ¼ 180 kg K ha21

Emei (Sichuan)d 1.0 ¼ 75 kg K ha21

Leshan (Sichuan)e 1.0 ¼ 75 kg K ha21

Qingshen (Sichuan)f 1.0 ¼ 112 kg K ha21

Mode of K application 0 0.5 1.0 1.0 0 0.5 1.0 1.0 0 0.5 1.0 1.0 0 0.5 1.0 1.0 0 0.5 1.0 1.0

to rice, 0.5 to wheat to wheat to rice to rice, 0.5 to wheat to wheat to rice to rice, 0.5 to wheat to wheat to rice to rice, 0.5 to wheat to wheat to rice to rice, 0.5 to wheat to wheat to rice

Yield of rice (t ha21)

Yield of wheat (t ha21)

Yield of rice þ wheat (t ha21)a

8.2 9.1 8.4 9.5 7.6 9.0 8.0 9.3 5.3 5.8 5.7 1.1 5.1 6.5 6.4 0.6 5.1 5.8 5.5 1.1

6.0 6.8 6.9 6.7 1.6 2.9 3.0 2.5 0.9 2.2 2.9 6.0 0.4 1.7 2.6 6.8 0.7 3.2 3.7 5.9

14.2d 15.9b 15.3c 16.2a 9.2c 11.9a 11.0b 11.8a 6.2 8.0 8.6 7.1 5.5 8.2 9.0 7.4 5.8 9.0 9.2 7.0

Per cent increase over no-K plot

12.0 7.7 14.1 29.3 19.6 28.3 29.0 38.7 14.5 49.1 63.6 34.5 55.2 58.6 20.7

Source: Chen and Zhou (1999); Nong et al. (1993). a Values in a column for a particular site followed by same letter are not significantly different at p ¼ 0.05. b pH 8.1, ammonium acetate-K 47.3 mg kg21, HNO3-K 546.4 mg kg21. c pH 8.3, ammonium acetate-K 65.6 mg kg21, HNO3-K 153.6 mg kg21. d Alluvial yellow earth, ammonium acetate-K 46 mg kg21. e Acid purple soil, ammonium acetate-K 11 mg kg21. f Old alluvial yellow earth, ammonium acetate-K 52 mg kg21.

Interestingly, the F1 hybrid rice cultivars take up more K due to a well-developed root system and vigorous growth than do the ordinary rice varieties (Xu and Bao, 1995). For example, at the same yield of 7.5 t ha21 of rice grains, the K uptake by hybrid rice was 218 kg ha21 compared to only 156 –187 kg ha21 by ordinary rice cultivars. The yield potential of hybrid rice is greater than that of ordinary varieties, when soil fertility is high or large amounts of fertilizers are used, but hybrid varieties often yield less than most of the ordinary varieties when grown on K-deficient soils. The beneficial effect of K is more likely with hybrid rice than with ordinary varieties (Fan and Tao, 1981).

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Table XIV Direct and Residual Effects of Application of Different Levels of Potassium to Sequentially Grown Rice and Wheat in Alfisols and Entisols of Jiangsu Province of China Experiment 1 K levels applied to rice (kg ha21) Alfisola 0 83 166 Entisolb 0 83 166

Experiment 2

Direct effect rice yield (t ha21)

Residual effect wheat yield (t ha21)

K levels applied to wheat (kg ha21)

Direct effect wheat yield (t ha21)

Residual effect rice yield (t ha21)

7.3 7.8 8.1

3.7 4.0 4.2

0 83 166

3.9 4.5 4.6

6.8 7.8 8.0

7.4 7.7 7.9

4.0 4.4 4.6

0 83 166

3.6 4.3 4.7

6.2 6.7 7.1

Source: Zhu et al. (2000). a pH 6.3, ammonium acetate-K 116 mg kg21, HNO3-K 725 mg kg21, clay 28.2%. b pH 8.0, ammonium acetate-K 67 mg kg21, HNO3-K 625 mg kg21, clay 21.2%.

A. TIME, SOURCE

AND

METHOD

OF

POTASSIUM APPLICATION

The common recommendation is to apply a full dose of potassium as basal at puddling for rice and at sowing for wheat. When cation exchange capacity of the soil is low and drainage in soil is excessive, basal application of potassium to rice should be avoided. As rice and wheat require large quantities of potassium, a sustained supply is necessary up to heading stage when the reproduction stage is complete. On coarse textured soils, split application of fertilizer potassium in both rice and wheat may give higher nutrient use efficiency than its single application due to reduction in leaching losses and luxury consumption of potassium (Tandon and Sekhon, 1988). Tiwari et al. (1992) have cited several references showing a distinct benefit of applying potassium in split doses. In trans-Indo-Gangetic plains, Kolar and Grewal (1989) reported a yield advantage of 250 kg grains ha21 by split application of potassium (half at transplanting þ half at active tillering stage) as compared with single application at transplanting. Similarly, in a sandy loam soil of Uttar Pradesh, Singh and Singh (1987) reported a yield advantage of 440 –490 kg grain ha21 in wheat by split application of potassium as compared to a single application. At sowing of wheat and transplanting of rice, potassium fertilizers are normally applied by drilling, placement or broadcast followed by incorporation.

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In China, a number of experiments carried out in provinces where a rice – wheat cropping system is followed have shown that application of potassium to each of the two crops can be more beneficial than applying the total quantity to one of the crop (Scientific Technology Department of Ministry of Agriculture, 1991). However, in some regions where one of the two crops shows very high response to potassium application than the other, application of whole of potassium to one crop can prove more beneficial (Table XIII). Application of potassium fertilizers as basal manure or combining basal manuring with early top dressing has shown better results in both rice and wheat. In sandy soils, top dressing of potassium has been preferred. Shallow tillage has been recommended when potassium fertilizer is applied before planting of rice or wheat. Top dressing of potassium fertilizer to rice is done when there is no standing water in the field (Xie et al., 2000; Shen et al., 1998; Xie and Zhou, 1999). Murate of potash (KCl) is a major fertilizer potassium source for rice and wheat because of its low cost and high potassium analysis. However, its use in salinity affected areas is discouraged. Potassium sulphate may be used in areas with S deficiency (Zia et al., 2000). In the Indo-Gangetic plains, 99% of the total fertilizer potassium applied is KCl and no overall significant difference was observed between KCl and potassium sulphate (Tandon and Sekhon, 1988). Foliar application involves the use of K fertilizer in solution. It results in fast K absorption and utilization and has the advantage of quickly correcting deficiencies diagnosed by observation or foliar analysis. Other advantages are low application rates, and uniform distribution of fertilizer. However, foliar fertilization is supplementary to and cannot replace the basal fertilization (Kafkafi et al., 2001). In rice, a foliar application of 10 kg KCl m23 at panicle initiation, boot leaf and 50% flowering stages, both in the monsoon and winter seasons, significantly increased seed yield and improved quality (seed germination and 100-seed weight) (Jayaraj and Chandrasekharan, 1997). Splitting a total of 95 kg ha21 of KCl to rice, a third at sowing in soil, a third as a foliar spray at flag leaf stage and a third as foliar spray at grain development, gave larger yields than a soil application all at sowing (Narang et al., 1997). In China, a foliar spray applying 3.9 kg K ha21 (as 10 kg KCl m23) three times at one-week intervals from full head of rice cv. Wuyuegen increased grain yield from 7850 kg ha21 in the control plots, sprayed only with water, to 8500 kg ha21 (Kadrekar, 1975). It is unclear whether K or Cl contributed to the increased grain yield. Foliar spray of 10 kg KCl m23 and 10 kg urea m23 from the jointing stage and the full heading stage increased the N and K content in the plants and stimulated N translocation to the grain, increasing the protein content of wheat grain by 15 g kg21. However, only the grain yield of wheat was significantly increased by the foliar spray (Xu et al., 1999).

K NUTRITION OF THE RICE – WHEAT CROPPING SYSTEM

B. INTERACTIONS

OF

POTASSIUM

WITH

233

OTHER NUTRIENTS

The interaction among plant nutrients is a common feature of crop production. Potassium plays an important role in ensuring efficient utilization of nitrogen. Large quantities of nitrogen used in intensive rice –wheat cropping systems encourage crop uptake of nitrogen and potassium and in turn heavy depletion of soil potassium. Application of nitrogen and phosphorus resulted in 145% increase in potassium uptake as compared to a control (Tandon and Sekhon, 1988). If insufficient nitrogen and phosphorus or other essential plant nutrients restrict the crop development, the amount of potassium present even at low soil test values may be sufficient to meet crop needs. Tiwari et al. (1992) reported that response to potassium application in rice as well as wheat increased with increasing rate of nitrogen application. In order to obtain high yields of rice and wheat, application of an increasing rate of potassium with increasing levels of nitrogen was suggested. A positive N£K interaction observed for rice in the lower Indo-Gangetic plains revealed that at higher rates of fertilizer nitrogen, higher levels of potassium application were needed to achieve high yields and that the N£K interaction was more important in the dry season rather than the wet season (Mondal, 1982). A marked interaction between nitrogen and potassium applied to rice has been observed even in the following crop of wheat to which no potassium was applied (Table XV). As potassium, calcium and magnesium perform both specific and nonspecific functions in plants, in the event of better supplies of one, the uptake of other nutrients may be reduced. In the Indian Punjab, Stillwell et al. (1975) observed luxury consumption of potassium by wheat beyond an application of 26 kg K ha21 and this was accompanied by a reduction in the uptake of Ca þ Mg.

Table XV Residual Effect of Applying Different Levels of Nitrogen and Potassium to Rice on the Following Crop of Wheat in Entisol and Alfisol Soils in Jiangsu Province in China Wheata grain yield in Alfisol (t ha21)

Wheata grain yield in Entisol (t ha21)

N levels (kg N ha21)

N levels (kg N ha21)

K level (kg K ha21)

200

250

300

200

250

300

0 83 166

3.61 3.65 3.78

3.41 3.68 3.89

3.46 3.74 3.93

4.49 4.77 4.81

4.73 5.04 5.12

4.68 5.05 5.57

Source: Zhu et al. (2000). a A uniform dose of 250 kg N ha21 and no potassium was applied to wheat.

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C. EFFECT

OF POTASSIUM FERTILITY STATUS OF ON RESPONSE TO POTASSIUM

SOILS

On-farm studies suggest large variability in soil nutrient supply and response of rice and wheat to applied nutrients. The uniform adoption of blanket fertilizer recommendations, therefore, does not ensure economy and efficiency of potassium use since the variation in soil fertility is not taken into account. There will be wastage of fertilizers in some cases while under-usage in others. Responses of rice and wheat to potassium application are expected to be high on soils testing low in 1M ammonium acetate extractable potassium than on high potassium soils (Tandon and Sekhon, 1988). Significant responses of wheat to applied potassium were observed up to 25 kg K ha21 on soils testing low in available potassium in Punjab, but no significant increase in wheat yield was observed on soils testing medium and high in available potassium (Sharma et al., 1978; Stillwell et al., 1975; Singh and Khera, 1998). Rana et al. (1985) observed that rice responded to 50 kg K ha21 on soils testing low and medium in available potassium, but no significant response to applied potassium was observed on soils testing high in available potassium. Experiments carried out by Kapur et al. (1984) revealed that wheat responded up to a dose of 75 kg K ha21 on low potassium soils and up to 50 kg K ha21 on medium and high potassium soils. On the same lines, Azad et al. (1993) observed that wheat yield increased significantly up to 75 kg K ha21 on soils testing low in available potassium, whereas significant increase in wheat yield was observed only at 25 kg K ha21 on soils testing medium as well as high in available K. Based on results of more than 2200 trials with wheat, a similar relationship was observed by Tandon (1980). Tandon and Sekhon (1988) concluded that the response of high yielding varieties of rice and wheat to potassium application in soils rated medium in available potassium were only marginally lower than responses in low potassium soils. Such results emphasize the need for a fresh look at soil fertility limits used for categorizing soils into low, medium and high with respect to available K, particularly for highly productive rice – wheat cropping systems. A large proportion of the total removal of potassium by 2 or 3 cycles of wheat –rice rotations at two locations in Taihu region of south Jiangsu province in China was found to come from non-exchangeable or interlayer pools of potassium in the soil (Bao and Xu, 1993). When fertilizer potassium was applied, contribution of non-exchangeable potassium to total potassium removal was reduced. Experiments carried out in the Indo-Gangetic plains (Tiwari et al., 1992) also suggest that contribution of non-exchangeable potassium fractions to the nutrition of rice and wheat was 89% when no potassium was applied and 56% when fertilizer potassium was applied at 50 kg K ha21 to both rice and wheat. In a long-term experiment on a rice – wheat system initiated in 1977– 78 at Faizabad in the middle Indo-Gangetic

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235

plains, both the crops did not respond to applied potassium in the first 10 years. Thereafter, responses to applied potassium started increasing; higher response was observed in wheat (Yadav et al., 2002). The release of potassium from a non-exchangeable pool was responsible for the lack of response during the initial years. Response of rice to application of potassium in Chinese soils was found to be strongly influenced by the HNO3 extractable potassium content in the soils (Table XVI). Response of rice to applied potassium also increased substantially when soil potassium was exhausted by continuous cropping (Xie and Li, 1987). Field experiments conducted at different locations in the Indian Punjab showed that rice responded more to applied potassium in northeastern districts (Gurdaspur, Amritsar, Kapurthala, and Hoshiarpur) than in central and southwestern districts (Ludhiana, Bathinda, Sangrur, and Ferozepur) (Singh and Bhandari, 1995). The values of available potassium in soil ranged from 150– 180 kg K ha21 in northwestern districts to 112 –165 kg K ha21 in central and southwestern districts. A recent 6-year study conducted at two locations in northwestern India showed that both rice and wheat responded significantly to potassium application up to 50 kg K ha21 on loam soil at Gurdaspur, whereas no significant increase in rice yields was observed on sandy loam soil at Ludhiana (Singh et al., 2002a). Wheat started responding to potassium application at Ludhiana two years after the initiation of the experiment. Although soils at both the locations tested low in ammonium acetate extractable potassium, higher response at Gurdaspur was due to high K-fixation capacity and slow K-release rate of the loam soil.

Table XVI Response of Three Continuous Crops of Rice to Applied Potassium in Soils Containing Different Amounts of Slowly Available Potassium (HNO3 Extractable Potassium) Increase in dry matter yield of rice due to potassium application (% over no-K control) Slowly available K 21 (mg K kg ) ,66 66 , 166 166 , 330 330 , 500 500 , 750 750 , 1160 .1160

Number of soil samples

1st crop

2nd crop

6 4 6 5 5 6 3

237 117 53.6 15.8 4.3 2.8 20.1

–a 1870 392 98.0 72.8 57.2 10.6

Source: Xie and Li (1987). a Plants died in the control treatment.

3rd crop – – – 631 504 187 39.6

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These results suggest that potassium supplying capacity of different soils is governed by pools of potassium other than water soluble plus exchangeable potassium. Key components of potassium management should include: (1) an estimate of crop potassium demand, potential indigenous potassium supply, and recovery of potassium from applied inorganic and organic sources to predict the potassium inputs required to maintain a targeted yield level, (2) a schedule for timing potassium applications depending on soil potassium buffering characteristics and an understanding of the relationship between potassium nutrition and pest incidence and (3) knowledge of the relationship between the potassium budget, residual effects of potassium fertilizers, and changes in soil supply over time. Among the new approaches, the target yield approach has found popularity in India (Srivastava and Subba Rao, 2000). This method not only estimates soil test based fertilizer dose but also the level of yield the farmer can achieve with a particular dose. Velayutham et al. (1985) described the use of the targeted yield approach for making fertilizer recommendations to different crops. This approach as based on soil tests has three basic steps: (1) the potassium requirements for specific yield targets are worked out from the nutrients removed by the above ground harvested biomass; (2) using soil test values the proportion of nutrient extracted by the suitable extractant that actually becomes available to crops during the growth period is estimated; and (3) the fertilizer recommendations are based on the apparent recovery of applied nutrient under a standard set of agronomic practices. Fertilizer doses are worked out by subtracting the amount that is likely to be made available by the soil to crop at a known soil test value. Based on the above basic data, fertilizer doses are worked out for a target yield relationship. In this approach, it is assumed that there is a linear relationship between grain yield and nutrient uptake by a crop. Quantitative fertilizer requirements based on this approach have been estimated for specific yield targets of rice and wheat (Velayutham et al., 1985; Ahmed et al., 1999; Srivastava and Subba Rao, 2000). Sharma et al. (2000) evaluated the targeted yield approach for fertilizer recommendations in wheat vis-a`-vis general fertilizer recommendations at three locations in Delhi state. The results revealed that the moderate yield target of 5 t ha21 could be achieved with a deviation of ^10% along with higher response ratio and net profit as compared to general recommended dose and farmers’ practice.

D. SITE-SPECIFIC POTASSIUM MANAGEMENT FOR RICE AND WHEAT Site-specific nutrient management focuses on developing a nutrient management program taking into account: (1) regional and seasonal differences in the climatic yield potential and crop nutrient demand, (2) between field

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spatial variability in indigenous nutrient supply, (3) field-specific within-season dynamics of crop nutrient demand, and (4) location-specific cropping systems and crop management practices. The basic data required for formulating fertilizer recommendation using this approach are: (1) climatic yield potential, (2) yield goal, (3) definition of the relationship between grain yield and plant nutrient accumulation, (4) field-specific estimates of the indigenous nutrient supplies, and (5) estimated recovery efficiencies of fertilizer. The distinct characteristic of the site-specific nutrient management approach developed for rice and wheat is the use of crop-based estimates of the indigenous nutrient supply instead of relying on soil tests. The modification of the QUEFTS (Quantitative Evaluation of Fertility of Tropical Soils) model developed by Janssen et al. (1990) has been used to work out field-specific recommendations for nitrogen, phosphorus and potassium for each site at the beginning of each season. It has been established that crop yields, profit, plant nutrient uptake, and nutrient use efficiencies can be significantly increased by applying fertilizers on a field-specific and cropping season specific basis (Dobermann and White, 1999). On-farm experiments conducted at 179 locations over a period of four cropping seasons during 1997 – 1999 in Asia have shown that the improved techniques of site-specific nutrient management can contribute to productivity increases of 6– 8% (0.31 –0.41 t ha21) in rice. Yield and income gains were much higher in well managed farms (Dobermann et al., 2002). Fertilizer potassium rates predicted by the QUEFTS model to achieve the target yields and maintain the soil indigenous potassium supply were, on average, higher than the amounts currently applied by the farmers. Potassium rates in site-specific nutrient management plots ranged from 50 to 66 kg K ha21 per crop, while the average farmer fertilizer potassium rate was 30 kg K ha21. Wang et al. (2001) evaluated the site-specific nutrient management approach for irrigated rice in southern China on 21 sites. The indigenous potassium supply ranged from 70 to 180 kg K ha21. The extractable potassium ranged from 47 to 330 mg K kg21. Compared with farmers’ practice, site-specific nutrient management significantly increased grain yields and potassium uptake. Potassium use in farmers’ plots ranged from 44 to 63 kg K ha21 and fertilizer potassium use was 4 –21 kg less in site-specific nutrient management treatments compared with farmers’ plots. Lower fertilizer use rates in site-specific nutrient management plots resulted from model-based predictions that accurately accounted for the high native soil fertility status measured as plant nutrient uptake in omission plots. Pathak et al. (2003) made modifications in the QUEFTS model for its use in wheat in rice –wheat systems in South Asia. A relationship between potassium uptake in minus potassium plots, a measure of soil potassium supply and exchangeable soil potassium was established. The required potassium accumulation by wheat for 1 ton grain yield was 28.5 kg. A preliminary

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validation of the model in wheat showed good agreement with grain yield values observed and predicted by the model, indicating that the model can be used for predicting fertilizer recommendations to achieve a yield target of wheat. Estimated potassium requirement (kg per ton grain yield) using the QUEFTS model ranged from 14.5 to 15.7 kg for rice (Witt et al., 1999) and 28.5 to 32.0 kg for wheat (Pathak et al., 2003). Site-specific nutrient management has potential for improving yields and nutrient use efficiency in irrigated rice –wheat systems to close existing yield gaps.

E. POTASSIUM USE AND RESISTANCE AND PEST INCIDENCE

TO

DISEASE

Potassium has been known to impart resistance against diseases and a high concentration of K ions in the cell sap restricts attack by insects. Reviewing relationships between use of potassium fertilizers and incidence of plant diseases, Perrenaud (1977) observed that potassium reduced bacterial and fungal diseases in 70% of instances, insects and mites 60% and nematodes and virus influences in the majority of the cases. Influence of potassium on crop yields varied according to the parasite group, as accumulating nitrogen compounds and sugars are frequently accompanied by improved conditions for parasite development. Since tissue hardening and stomatal opening patterns are closely related to infestation intensity, nitrogen balance with potassium is significant to disease susceptibility. Vaithilingam and Baskaran (1985) examined the mechanism of induced resistance to insect pests in rice plants with enhanced potassium application and observed that rice plants receiving high amounts of potassium accumulated more total phenols and ortho-dihydroxy phenols. Accumulation of phytophenols which are the precursors for synthesis of several toxic compounds in the plant system render the plant resistant to pests. Further, it was found that the amino nitrogen content, which is the basic dietary requirement of many insect species, was drastically reduced at high potassium dose in the tested rice plants. Increase in lignification and scleranchymatous layer in rice supplied with adequate potassium fertilizers also acts as a mechanical barrier to pest invasion in potassium treated soils. Prasad and Misra (1983) observed that the population of hoppers in rice was significantly higher in no-K plots than in potassium treated ones receiving the same level of nitrogen and phosphorus. Mondal and Mia (1985) studied the effect of potassium application on bacterial blight by inoculating rice plants with Xanthomonas campestris at maximum tillering and at flag leaf stage. The results (Table XVII) showed that average lesion length was significantly lower in potassium treated series than on soils to which less potassium was applied.

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Table XVII Effect of Potassium Application on the Response of Rice to Bacterial Blight Inoculation Average lesion length (cm) K level (mg K kg21 soil)

Inoculation at maximum tillering stage

Inoculation at flag leaf stage

30 22 20 19 16

13 8 6 6 5

100 183 266 349 432 Source: Mondal and Mia (1985).

VII. POTASSIUM BALANCE IN SOIL– PLANT SYSTEMS Introduction of modern production technologies for rice and wheat with high nitrogen responsive high yielding varieties has resulted in increased annual removal of potassium by above-ground portions of the crops. Long-term studies have indicated that continuous rice – wheat cropping will lead to depletion of potassium in soil even when optimum levels of fertilizer potassium have been applied. From the nutrient removal data (Table IX) it is evident that annual removal of potassium by rice – wheat cropping system equals or exceeds that of nitrogen, while the replacement of potassium by fertilizer represents only a fraction of nitrogen (Table II). Furthermore, most of the potassium uptake in rice and wheat crops is stored in straws, which is mostly removed from the field as animal feed and is not directly returned to the soil. Long-term studies have shown that potassium balance in rice – wheat system is highly negative even when recommended doses of potassium are applied to rice – wheat cropping system. Data obtained from two long-term experiments at Ludhiana in western India and Bhairahawa in Nepal (Table XVIII) shows that highly negative potassium balance in NPK treatments was substantially improved by application of farmyard manure or returning wheat residues. In Fig. 2 are shown potassium balances from two long-term experiments in middle- and lower Gangetic plains in which treatments consisted of increasing levels of NPK (Nambiar and Ghosh, 1984). Interestingly at Barrackpore in West Bengal, higher potassium levels applied due to smectitic nature of clay minerals resulted in potassium balance ranging from 0 to – 75 kg K ha21. In sharp contrast, potassium balances in illitic soils in Pantnagar were highly negative even under 150% NPK treatment as removal of potassium by rice and wheat was high even at low potassium application levels (Fig. 2). In a long-term experiment at Ludhiana,

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BIJAY-SINGH ET AL. Table XVIII Annual Potassium Balance in Two Long-term Experiments Progressing at Ludhiana, Northwestern India and Bhairahwa, Nepal in the Indo-Gangetic Plains Output (kg K ha21 yr21)

Input (kg K ha21 yr21)

Treatment

Manure/ Fertilizer

Irrigation

Rain

Seed

Crop removal

Rice– rice–wheat long-term experiment at Bhairahwa, Nepal (1978–98) Control 0 20.4 5.0 4.9 36.4 NPK 75 20.4 5.0 4.9 150.3 FYM 120 20.4 5.0 4.9 148.8 Rice– wheat long-term experiment at Ludhiana, India (1988– 2000) NPK 50 100 5.0 2.7 285 Wheat straw þ NPK 111 100 5.0 2.7 281 FYM þNPK 131 100 5.0 2.7 271

Leaching loss

Balance (kg K ha21 year21)

6.0 17.3 16.7

212.1 262.3 215.3

19 28 31

2151 290 263

Source: Regmi et al. (2002b); Yadvinder-Singh et al. (2003a).

net negative potassium balance of more than 200 kg K ha21 year21 was observed when no potassium was applied to rice or wheat (Table XIX). Application of fertilizer potassium to rice, wheat or both resulted in less negative potassium balance. Removal of all the straw from the fields leads to potassium mining at alarming rates because 80 – 85% of the potassium absorbed by rice and wheat crops is in the straw. At Rangpur in Bangladesh, potassium balance in a rice –mungbean – wheat rotation ranged from 2 152 to 2 98 kg K ha21 with total

Figure 2 Potassium balance (applied minus removed by rice and wheat) in different treatments in long-term experiments at Barrackpore and Pantnagar [adapted from Nambiar and Ghosh (1984)].

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Table XIX Annual Potassium Balance (Applied as Fertilizer Minus Removed by Plants) as Influenced by Direct, Residual and Cumulative Application of Potassium in a Rice–Wheat System at Ludhiana in Northwestern India K applied (kg K ha21) Rice 0 0 0 0 25 50 75 25 50 75

Wheat

Mean grain yield of rice (1990–2000) (t ha21)

Mean grain yield of wheat (1990–2000) (t ha21)

0 25 50 75 0 0 0 25 50 75

5.30 5.36 5.38 5.45 5.32 5.42 5.53 5.39 5.59 5.53

4.70 4.90 5.02 4.98 4.87 4.75 4.84 4.96 5.07 4.97

Mean annual K balance (kg K ha21) 2215 2198 2182 2162 2211 2188 2173 2202 2161 2103

Source: Bijay-Singh, Yadvinder-Singh and C.S. Khind, Department of Soils, Punjab Agricultural University, Ludhiana, India; unpublished data.

potassium application to the system ranging from 33 to 83 kg K ha21 (Abedin and Mukhopadhay, 1990). It may be interesting to note that potassium rates applied by most farmers are lower than those used in the long-term experiments. Ladha et al. (2003) analyzed 33 rice – wheat long-term experiments in the Indo-Gangetic plains of South Asia, non-Indo-Gangetic plains in India, and in China to monitor yield trends, and identify possible causes of such yield trends. In treatments where recommended rates of nitrogen, phosphorus and potassium were applied, yields of rice and wheat stagnated in 72 and 85% of the long-term experiments, respectively, while 22 and 6% of the long-term experiments showed a significant (P , 0.05) declining trend for rice and wheat yields, respectively. In over 90% of the long-term experiments, the fertilizer potassium rates used were not sufficient to sustain a neutral potassium input – output balance (Fig. 3). All the long-term experiments with a significant yield decline had large negative balances of potassium. The potassium balances were consistent with the changes in soil potassium status in the Ludhiana and Bhairahwa long-term experiments. In these two experiments, soil potassium declined by 62 and 33%, respectively, after 10 years of cultivation (Bhandari et al., 2002; Regmi et al., 2002b). Similar observations were made by Dawe et al. (2000), Duxbury et al. (2000), and Yadav et al. (2000a) in rice – wheat systems. Xie et al. (1991) have reported negative potassium balance in different regions of China, where rice – wheat cropping system is predominantly practised (Table XX). In a study based on several on-farm locations under rice – wheat cropping system in Jiangsu and Sichuan

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Figure 3 Apparent K balances in rice– wheat cropping systems. Long-term experiments (LTE) in Asia [adapted from Ladha et al. (2003)].

provinces of China, negative potassium balances were observed in the range of 2 160.6 to 2 12.0 kg K ha21 (Table XXI) (Zhou et al., 2000). In a Vertisol, 33 kg K ha21 was applied to rice as well as wheat grown in a sequence for 8 years (Singh et al., 2002b). Application of increasing levels of fertilizer N resulted in an increase in the negative potassium balance from 56 kg K ha21 in no-N control to 103 kg K ha21 at 90 kg N ha21 and 156 kg K ha21 at 180 kg N ha21. The negative potassium balances mean that it will be impossible to maintain the present production levels of the rice–wheat system. Results from long-term fertility experiments in India show that crop response to potassium application start appearing over a period of time in soils which were initially well supplied with potassium (Nambiar and Ghosh, 1984). Such responses to potassium started appearing after 3 years in rice and 11 years in wheat at Pantnagar (Uttar Pradesh) and after 3 and 7 years, respectively, at Barrackpore (West Bengal). Long-term studies suggest that application of farmyard manure and recycling of crop residues can help improve the potassium balance in the rice–wheat cropping system. There is,

Table XX Potassium Balance in Different Regions of China where the Rice–Wheat Cropping System is Predominantly Practised

Regions Purplish fluvo-aquic soil around Donting Lake, Jiangxi Guizhou Hang Jia Hu plain Jiangsu Taihu Lake region Source: Xie et al. (1991).

K balance (kg K ha21 year21) 2153 230 238 290 262

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Table XXI Potassium Balance in the Soil under Rice–Wheat Cropping Systems at Several On-farm Locations in Jiangsu and Sichuan Provinces in China

County and Province

Number of on-farm locations

Potassium balance (kg K ha21)

Siyang, Jiangsu Qidong, Jiangsu Changshu, Jiangsu Chongzhou, Sichuan Fushun, Sichuan Jianyang, Sichuan

18 20 18 30 27 29

212.0 248.4 287.1 275.7 266.0 2160.6

Source: Zhou et al. (2000).

however, a need to work out long-term potassium balances in the rice–wheat system based on precise data on potassium removal from a field or region through straw, potassium inputs from irrigation or rain water besides the well-defined inputs and outputs such as fertilizers, manures, and grains. Straw management can strongly influence potassium budgets and can help in efficient management of potassium for a sustainable rice–wheat system in the Indo-Gangetic plain.

VIII. CHANGES IN POTASSIUM FERTILITY IN THE SOIL UNDER RICE– WHEAT CROPPING SYSTEMS Deficiency of potassium in the Indo-Gangetic plain is not as wide spread as nitrogen and phosphorus but soils testing high with respect to available potassium some years ago are becoming potassium-deficient due to heavy removal by rice and wheat and inadequate potassium application. Since depletion of soil potassium reserves is a matter of deep concern from the point of view of sustainability of rice –wheat system, it is important to analyze the data from longterm experiments so as to plan efficient management of both potassium fertilizers and soil potassium reserves. In six out of eight benchmark soil series in the IndoGangetic plain studied by Sekhon et al. (1992) for detailed characterization of potassium, measurements were made again after 10 years to assess changes in potassium fertility of soils. The data pertaining to changes in ammonium acetate and HNO3 extractable potassium are listed in Table XXII. Both the indices show considerable decrease in availability of potassium in a span of 10 years thereby suggesting that crops may start responding to potassium fertilizer in course of time. Tiwari (1985) observed a decline in available potassium and nonexchangeable by 17% and 2.8% after two cropping cycles measured on 14 fields at Kanpur (middle Indo-Gangetic plains). In long-term experiments progressing

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Table XXII Changes Observed in Potassium Fertility in some Soil Series in Rice–Wheat Growing Regions of the Indo-Gangetic Plains Ammonium acetate-K (mg kg21)

HNO3-K (mg kg21)

Soil series and location

First sampling

After 10 years

First sampling

After 10 years

Nabha, Ludhiana, Punjab Akbarpur, Etah, Uttar Pradesh Rarha, Kanpur, Uttar Pradesh Hanrgram, Bardhaman, West Bengal Kharbona, Birbhum, West Bengal

104 ^ 54

63 ^ 41

965 ^ 255

875 ^ 230

125 ^ 41

71 ^ 23

1448 ^ 203

1231 ^ 188

95 ^ 33

79 ^ 20

1531 ^ 353

1497 ^ 180

132 ^ 53

93 ^ 16

425 ^ 160

400 ^ 191

42 ^ 17

29 ^ 16

119 ^ 34

109 ^ 26

Source: Sekhon (1999).

at different locations in the Indo-Gangetic plain, a decrease in available potassium has been observed at all sites in treatments where no potassium has been applied during 13 to 14 year period (Table XXIII). Except at Ludhiana, a decrease in available potassium content of soil was noticed even in treatments receiving potassium for both wheat and rice. These data suggests that fertilizer

Table XXIII Changes in Available Potassium in Soils in Different Treatments (no NPK, 50% NPK, 100% NPK, 50% NPK þ FYM, 50% NPK þ Crop Residues, 50% NPK þ Green Manure) in Long-term Fertility Experiments on Rice– Wheat Systems at Various Locations in the Indo-Gangetic Plains 1M ammonium acetate extractable K (mg kg21) Location

Duration of the experiment

At beginning

After 12–15 years

Ludhiana

1983–84 to 1997–98

46

Pantnagar Kanpur Faizabad Sabour

1983–84 1985–86 1984–85 1984–85

4–17% increase (except in no NPK treatment) 17–34% decrease 10–22% decrease 10–30% decrease 7–14% decrease except in 50% NPK þ FYM treatment

to 1997–98 to 1997–98 to 1997–98 to 1997–98

100% NPK ¼ 120 kg N þ 26 kg P þ 33 kg K ha21. Source: Yadav et al. (2000a).

65 82 161 58

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doses considered as optimum can still result in potassium depletion from the soil at high productivity levels and in the process become sub-optimal doses. In rice –wheat system, potassium is readily displaced from the exchange complex due to increased concentrations of Fe (II), Mn (II) and ammonium during flooding phase (rice) (Ponnamperuma, 1972). Though the displacement of potassium from the exchange complex ceases during aerobic phase (wheat), Kadrekar and Kibe (1973) and Singh and Ram (1976) have shown that alternating wetting and drying increases the availability of exchangeable potassium in the soil. Nevertheless, as discussed by Dobermann et al., (1996a) for a Pantnagar soil, perhaps due to unfavourable ratios of potassium to other cations (Ca, Mg, Fe) in the soil, potassium nutrition of rice – wheat system in the Indo-Gangetic plains is not assured. The rapid decline in plant available potassium after flooding of dry soil (Cassman et al., 1995; Olk et al., 1995) some what similar in mineralogy to those found in the Indo-Gangetic plain contrast with the general view that flooding a soil increases the solution potassium. Rice – wheat cropping system has tremendous capacity to draw large quantities of potassium and soils are often the exclusive suppliers of potassium nutrition to plants in the Indo-Gangetic plains. Warning signals are already emanating from present potassium management practices with respect to sustainability of the system as soil deterioration with respect to potassium supplying power is being largely overlooked. About a quarter century ago, Singh and Brar (1977) could show that continuous cropping without potassium dressing decreased 1M ammonium acetate extractable potassium in a soil from transGangetic plains in Northwest India from 165 to 85 kg ha21. Still there was no response of crops to potassium application; obviously 90% of potassium demand was met by release of potassium from non-exchangeable pool. In a 8-year experiment on a Vertisol, Singh et al. (2002b) applied 33 kg K ha21 to both rice as well as wheat and in view of a large negative potassium balance found sustainability of the system at threat as a distinct depletion of potassium from the sum of changes in HNO3 þ HClO4 extractable potassium after 8 years (16 crops) in 0– 15, 16– 30, and 31 –45 cm layers was observed. In no-N treatment, the total depletion of potassium was 54 kg K ha21 year21, and it increased to 102 and 145 kg ha21 year21 on application of 90 and 180 kg N ha21 to rice. In the 20-year long-term experiment at Bhairawah, Nepal, total potassium showed a significant decline in the NPK and FYM treatments over the last nine years of the experiment (Regmi et al., 2002b). During the first 10 years as well (when no soil analyses were made), a greater potassium extraction from soil may have occurred because of higher biomass removal. The FYM treatment had significantly higher total soil potassium than the control and NPK treatments in four out of seven sampling years (Fig. 4a). But when extracted with ammonium acetate, the potassium pool did not differ among treatments (Fig. 4b). The average annual decline of total potassium was 180 and 92 mg kg21 in the NPK and FYM treatments, respectively. It suggests that the application of 25 and 40 kg K ha 21crop21 in

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Figure 4 (a) Total soil K and (b) Ammonium acetate extractable K in control, NPK, and farmyard manure (FYM) treatments of the long-term plots maintained at Bhairawah, Nepal after 10, 13, 14, 15, 16, 18, and 19 years. Different letters indicate significant differences between treatments within a year at P , 0.05 [adapted from Regmi et al. (2002b)].

the NPK and FYM treatments, respectively, was not sufficient to maintain the soil potassium level in the rice – rice – wheat rotation and should also lead to irreversible adverse changes in soil potassium pools. The negative K-balance has serious implications on mineralogy of potassium in soils under rice–wheat cropping system in the Indo-Gangetic plains. Due to incorporation of potassium through canal and tube well water containing substantial amounts of potassium, weathering of potassium containing minerals, particularly

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Figure 5 Mean potassium uptake by rice from ammonium acetate and HNO3 extractable K fractions of soils as influenced by continuous cropping from 26 soil types in different parts of China [adapted from Xie and Li (1987)].

illite is minimal. Mukhopadhyaya et al. (1992) observed formation of edge-wedge sites in potassium bearing minerals when Kþ was removed through 18 successive crops. After 28 crops, there was about 1% conversion of illite to vermiculite. The X-ray diffractograms of 1.0 nm peaks showed broadening towards low angle suggesting loss of interlayer Kþ. A co-occurrence of illite and vermiculite indicated that in spite of potassium incorporation through irrigation, crop residues and fertilizers, the minerals exist under Kþ-loss domain. The scenario is alarming in view of advancement of weathering font in illite–vermiculite or illite–vermiculite– smectite phases. In China, most of the soils under rice–wheat system are already in kaolinite and vermiculte–smectie phases and thus application of potassium leads to increased yields of both rice and wheat. Exhaustion experiments conducted on 26 soil collected from different parts of China revealed that with just four continuous crops of rice (no potassium was applied) potassium uptake by crops was drastically reduced and 60–80% potassium absorbed by crops came from slowly available potassium (HNO3 extractable potassium) (Fig. 5). Negative potassium balances in these soils further suggest that potassium application rates will have to be increased to sustain high production levels in the rice–wheat systems and to avoid further aggravating the situation.

IX. RESEARCH NEEDS The soil moisture regimes in rice –wheat system show tremendous variation but the effects of such moisture changes on potassium availability and crop

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responses have not received much attention. Research is needed to clarify processes of fixation and release of potassium during drying and wetting cycles, and extent of impeded potassium diffusion in the rhizosphere of rice and its effect on potassium uptake at very high yield levels. A better understanding is needed of the processes affecting long-term fate of fertilizer potassium in irrigated rice – wheat system, including more information on the recovery and leaching of potassium (Dobermann et al., 1998). There exist a large number of publications on forms of potassium, availability in soils and response of rice and wheat to potassium application. Most of these publications will become redundant for future planning if matching information about soil mineralogy is not available. To work out management practices for achieving sustainable rice – wheat cropping systems in the Indo-Gangetic plains and in China, it will be necessary to understand potassium availability in the context of mineralogical composition of the soils. Research should be initiated to predict the time taken for soils currently well supplied with potassium for rice and wheat to become deficient in potassium and it should be backed-up by adequate input from the clay mineralogical investigations. The critical limits distinguishing soils that are likely to profit from supplementary application of potassium from those which are not, are currently uniform for all the soils in both Indo-Gangetic plains and in China. The differences in mineralogy and forms of potassium provide strong indications regarding desirability of different sets of critical limits. Research should be able to propose and test the most appropriate sets of such limits. Dynamic soil test methods assaying the potassium supplying power of soils under rice – wheat system should be developed. An integrated approach to potassium fertilizer recommendation may be developed based on site factors, laboratory analysis (texture, exchangeable and non-exchangeable), and data from weather records, soil survey (clay mineralogy, soil depth), probable yield and crop response, and processed by computer models that would generate a fertilizer recommendation derived almost entirely from site-specific data. In view of imbalanced use of nitrogen, phosphorus and potassium in rice – wheat cropping system in both South Asia and China, adequate data are not available on rates of potassium depletion due to continuous applications of nitrogen with or without phosphorus or potassium. Interaction of potassium with other nutrients including micronutrients also needs to be studied more thoroughly. Data on interactions of potassium with other nutrients and inputs can help improve strategies for the integrated management of different inputs. Models for estimating crop nutrient requirements based on interactions of N and potassium and for predicting the long-term fate of added potassium fertilizers should be developed for rice – wheat system. These may provide a basis for introducing farm or field-specific nutrient management approaches. Sufficient information is not available on the positive impact of potassium in improving grain quality of rice and wheat. Role of potassium in combating

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disease and pest incidence in rice –wheat system has also not been adequately researched. Information needs to be generated on potassium dynamics and longterm responses of rice and wheat to applied potassium in changing scenario of tillage systems, such as increasing adoption of minimum/zero tillage in wheat and direct seeding of rice involving no puddling. Applied research must provide the tools necessary for practical use of long-term strategies for potassium management based on nutrient balance concept. It should be useful to develop potassium balance sheets for all typical soils under rice – wheat cropping system. This will need adequate data on the gross and net contributions from irrigation water, crop residues, organic manures, and removals through runoffs and erosion. Soils, most off-balance should receive particular attention for studies on potassium needs of rice –wheat cropping system. Recycling of crop residues and other organic inputs influence nutrient supplying capacity of the soil. We need an improved understanding of how crop residue management affects potassium cycling and different pools of potassium in the soil. This will facilitate the development of budgets to balance potassium removal with nutrient application of potassium at different yield targets which seems necessary in sustainable, high yielding rice– wheat production systems. In long-term experiments, potassium is being continuously applied in some plots but still there is no buildup of available potassium. Research philosophy in long-term experiments should be more analytical rather than exploratory and documentary. Also, to obtain a more meaningful picture of the changes taking place in the soil potassium status in much of the root zone, soil layers deeper than 0– 15 cm should be taken into account.

X. CONCLUSIONS A better understanding of soil potassium in relation to productivity is immensely important to develop sustainable rice – wheat cropping systems in the Indo-Gangetic plains and in China. Due to nitrogen remaining heavily subsidized, there exists a continued imbalance in the use of nitrogen, phosphorus and potassium fertilizers. Farmers in the Indo-Gangetic plains apply very small amount of potassium fertilizers to rice –wheat cropping systems as compared to those in China. Most of the soils in the Indo-Gangetic plain contain illite as dominant clay mineral and are medium to high in ammonium acetate (1M, pH 7.0) extractable potassium. Therefore, responses of rice and wheat to applied potassium are generally small. Total annual potassium removal by rice – wheat system exceeding 200 kg K ha21 and negligible fertilizer potassium application are causing depletion of soil potassium supply. Alluvial soils in most of the Indo-Gangetic plain are less weathered and potassium availability deciphered through ammonium acetates

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extractant was rarely low, although the suitability of ammonium acetate extractable potassium as an index of plant available potassium for different soils varying in texture and clay mineralogy remains controversial. Rate and extent of release of nonexchangeable potassium play a crucial role in meeting the potassium requirements of the rice–wheat cropping systems. Continuous negative potassium balances (applied through fertilizers minus removal by crops) mean soil potassium mining and ultimately loss in soil fertility and it will be impossible to maintain the present production levels of the rice–wheat system. Potassium balances worked out after taking into consideration management of straw and potassium inputs from irrigation water may, however, suggest means and ways to achieve sustainability of rice– wheat cropping system. Soils under rice – wheat system in China contain clay minerals that are at more advanced stage of weathering than illite. Thus responses of both rice and wheat are substantial as compared to in the Indo-Gangetic plains. However, farmers are not able to apply amounts of fertilizer potassium equal to or more than the removal of potassium by the rice – wheat system. Measure of slowly available potassium based on HNO3 extraction of soils is thus a reliable index of potassium availability in Chinese soils. Continuous negative potassium balance is possibly accelerating the depotassification and weathering of clay minerals and as a consequence, large dressings of potassium fertilizers will be required to meet the requirements of rice – wheat cropping system at high productivity levels. The activity of Kþ ions in soil solution around mica particles is a factor in determining the release of potassium from the micas. Thus, leaching as well as potassium removal by rice and wheat in large quantities enhances release of potassium from micas by removing the reaction products and accelerates the transformation of micas to expansible 2:1 layer silicates and other weathering products. Keeping in view the existence of illite – vermiculite, illite – vermiculite – smectite or illite – smectite/chlorite –kaolinite phases, the hidden hunger for potassium and more importantly farm practices leading potassium development vis-a`-vis weathering of potassium minerals in the trans-, upper- and middle Gangetic plain to a point of no return may soon pose a great threat. Clay minerals in Chinese soils being already in smectite/chlorite-kaolinite phase of weathering, are creating unfavorable situations with respect to potassium nutrition of rice – wheat cropping system. Conventionally, blanket fertilizer potassium recommendations are often made for over large areas without taking into account the wide variability in soil nutrient supplies, fertilizer efficiency, and site- and season-specific crop nutrient requirements within each recommendation domain. This is not adequate to sustain higher yields and maintain or build-up soil fertility at a level that ensures maximum efficiency from potassium inputs. In this context, interaction of potassium with other nutrients and inputs is also important. It is argued that a sustainable fertilizer management strategy must ensure high and stable overall

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productivity with optimum economic return and sufficient nutrient supply for potential yield increases with minimal leakage of nutrients into the environment. This can be achieved if exogenous potassium supply is matched with the nutrient supplies from soil and crop demand.

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Index A abscisic acid (ABA) 105 absolute annual losses 69–86 absolute erosion induced yield losses 1– 37 absolute yield losses 1– 37, 50, 53 ACC see 1-aminocyclopropane-1-carboxylic acid accumulation of organic matter 169 –95 acid function group composition 188 adenine (ADE) 147 –8 aeration 177 Africa erosion productivity impact 58, 61, 65, 67 –71, 87, 89 erosion-yield relationship 7, 18, 21, 23 –8, 30 –2, 34 organic matter accumulation 182– 3 alfalfa 117, 125, 143, 173– 4 alkalinity 228 alluvial soils 210–14 aluminum 184 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase activity 116–21, 122 –3 ammonia, microbial activity 184 ammonium acetate 245–6, 249–50 ammonium oxalate extractable iron (Amox-Fe) 181 –2 anaerobic soils 169–95 anaerobiosis 179 –80 annual erosion rates 68, 69 antibiotic production 107, 111 applied potassium 227 –39 Asia erosion productivity impact 58, 61, 65, 67 –71, 73– 4, 79–80, 82–3, 87, 89 erosion-yield relationship 7, 14–15, 17 –22, 27 –8, 30–5 rice-wheat cropping system 203–51 Australia erosion productivity impact 58, 65, 67–8, 76 –7, 82–4, 86–7, 89 erosion-induced yield losses 7, 25 availability indices 223 average bulk density 12–13 Azospirillum brasilense 129, 130 –1, 132, 141 –2

Azospirillum lipoferum 132 –3 Azotobacter 129, 131–2, 133, 147–8 B Bacillus sp. 128–9, 132, 134 bacteria 97–149 bacterial diseases 238–9 balance, potassium 239–43, 246–7 Bangladesh 206–7, 209–51 barley rhizobacteria 108, 120, 125, 135, 138 yield-differential topsoil depth 18, 19–20 yield-management practice 27, 28 beans rhizobacteria 108, 143–4 yield-differential topsoil depth 23, 24 yield-management practice 29, 30 beech 185–6 Beta vulgaris 120 biodegradability 186– 7 biological controls 99–100, 107–12 biological nitrogen fixation (BNF) 100 biologically active substances 100 –5, 117–20 blanket fertilizer potassium 250–1 BNF see biological nitrogen fixation Brassica campestris 138 Brassica juncea 115 bulk density 12–13 C calcium 188– 9, 190, 233 Canada 124, 125–7 canola 115, 122, 125, 135 carbohydrates 186 carbon cycling 180–1, 194 nitrogen ratio 172, 189 organic matter accumulation 170–9, 182–3, 185–9, 194, 195 carbon dioxide emissions 174 carnations 108 carrots 25, 26 cassava 24–6, 29, 31 cations 184–7

261

262

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Central America, erosion productivity impact 58, 63, 66 –70, 72, 78 –9, 81– 2, 85–6, 88–9 cereals see grain crops chemical tests 224–5, 248 chemistry of submerged soils 171–2 chick peas 125, 143–5 China 124, 175–6, 203–51 clay fractions 185, 211 –14, 219, 250 climate 207–8 clover 144, 146 co-inoculation 137, 141 –2, 143– 6 complex recalcitrant molecules 170, 184 –7 continents erosion productivity impact 52–3, 55 –63, 65–89 erosion-yield relationship 5, 7–11, 14–37 organic matter accumulation 176– 9, 182– 3, 185–6, 188–91 rhizobacteria 124, 125–7 rice-wheat cropping system 203 –51 cotton 108, 125 cowpeas 21, 23, 29, 30 crop residue recycling 249 crop response 125–7 crop yields erosion productivity impact 49–92 erosion-induced losses 1–37, 86–9 potential arable land estimates 57–60, 61– 3 soil productivity 1–37 cropping systems, potassium nutrition 203–51 cucumber 108 cytokines 101, 147–8 D data analysis 6, 64, 69 data sources 5 decomposition 172 –5, 187– 9, 190, 195 deep marsh vegetation 177–8 deleterious rhizobacteria (DRB) 103 differential topsoil depth (TSD) 4, 6, 11, 13, 14–26 direct growth mechanisms, rhizobacteria 99 diseases 99 –100, 107 –12, 238– 9 displacement, potassium 245 dissolved inorganic carbon 186–7, 194, 195 dissolved organic matter 186–7, 194, 195 double cropping systems 203–51 DRB see deleterious rhizobacteria

E Economic Research Service (ERS) 52 edaphic requirements 208 –9 electron acceptors 170, 181–4, 193–4 electron donors 191 electroultrafiltration (EUF) 214, 222 –4 Enterobacter taylorae 147 erosion productivity impact absolute erosion induced losses 1–37 crop yields 49 –92 over time relationship 49– 92 relative erosion induced losses 1–37 erosion-induced losses 1–37, 86 –9 ERS see Economic Research Service ethylene 105 EUF see electroultrafiltration Europe erosion productivity impact 58, 62, 64–5, 67– 8, 73–7, 79–80, 82 –4, 86–9 erosion-induced yield losses 8, 17 –22, 24 –5, 27– 8 exchangeable soil potassium 214– 18, 220– 5 extractable potassium 235 –6 F farmyard manure (FYM) 244 –6 fatty acids 184, 194 feldspars 211 –14 ferric iron 181 –4, 186, 193–4 fertility 175– 6, 224–5, 234–6, 243 –7 fertilizers erosion-yield relationship 32–3, 34–5 organic matter accumulation 188–9, 190 potassium nutrition 204, 209–10, 234 –6, 250– 1 first-order rate constant 173, 174 flooded soils 169 –95 flower petals 123 foliar applications 232 food productivity 51–92 food security 51– 2, 54 free oxygen absence 179–80 free radicals 188–9, 190 fresh water wetland sediments 183 fungal diseases 238– 9 FYM see farmyard manure

INDEX G Gangetic plains 203– 51 geographic information systems (GIS) 50, 54–5 global impacts of soil erosion 1–37, 49 –92 Global Soil Regions map 55–7 gnotobiotic conditions 113, 114 –15 grain crops erosion productivity impact 57– 63, 65–76, 82 –6 potassium nutrition 203 –51 rhizobacteria 124, 128 –33, 135– 6 rice-wheat cropping system 203–51 yield-differential topsoil depth 14–20 yield-management practice 26 –8 green gram 109, 139, 144–5 groundnut 139 growing seasons 208 growth promotion 97–149 H high primary productivity 170, 189–92, 194 –5 Histosols 169 hot nitric acid method 217– 18, 221–2, 235 humic substances 170, 188–9, 190 humification 175– 6, 186, 187 –9, 190 humification coefficients 175 –6 humus composition 187 hybrid rice 229 –31 hydrogen 189, 190 hydrogen sulfide 184 hydronium ions 219 hydroxide electron acceptors 170, 181, 193 –4 I IA see isopentyl alcohol IAA (indole-3-acetic acid) 101, 103, 105, 142, 147 –8 IGBP see International Geosphere Biosphere Programme illite 211–14 ILQ see inherent land quality in vitro production 101 –3, 113, 116, 117 –20 incomplete decomposition 187–9, 190 India 203– 51 indirect growth mechanisms 99 Indo-Gangetic plains 203–51 indole-3-acetic acid (IAA) 101, 103, 105, 142, 147 –8

263

inherent land quality (ILQ) 56 inhibitor production 184, 194 inoculation 98–149 inoculum interactions 142, 147 –8 inorganic carbon 194, 195 input effects on erosion-yield relationship 32–3, 34–5 intensive cropping 183–4 International Geosphere Biosphere Programme (IGBP) 56–7 International Rice Research Institute, Philippines 176–9, 185 –6, 188 –91 iron organic matter accumulation 170, 181–4, 186, 193– 4 rhizobacteria 105– 7 rice-wheat cropping system 245 iron oxide electron acceptors 170, 181 –4, 193–4 irrigation erosion-yield relationship 32 –3, 34– 5 organic matter accumulation 176 –7, 185 rice-wheat cropping system 204, 237–8 isopentyl alcohol (IA) 147–8 J Japanese paddy soils 187 jojoba 125 K KCl see murate of potash L Latin America, erosion-yield relationship 8, 14–18, 21, 23–31, 33, 35 Lavandula 119 leaching 204, 220, 250 leaf litter decomposition 185 –6 leguminous crops co-inoculation 137, 141–2, 143–6 erosion productivity impact 57 –9, 60– 3, 65–8, 79 –82 yield-differential topsoil depth 21– 4 yield-management practice 29, 30 lentils 145 lettuce 122, 135, 138 lignins 185, 186, 194 linear relationships 12 litter-temperature relationship 173

264

INDEX

locations, erosion productivity impact studies 6–11 Lycopersicon esculentum 119 M Maahas clay 176 –7, 178 magnesium 233 maize erosion productivity impact 57–9, 60 –3, 65–8, 69–73 rhizobacteria 109, 114–15, 118–20, 125, 131–3, 138–9 yield-differential topsoil depth 14 –16, 19 yield-management practice 26–7 management diseases 99 –100, 107– 12 site-specific potassium 236–8 yield-management practices 26–30 manganese 184, 245 manure 32 –3, 34– 5, 244–6 marsh vegetation 177–8 mean erosion induced yield losses 12 mean experimental yield losses 32 –3, 34–5 mean weighted erosion rates 58 mean weighted potential yields 59, 60, 64 mean yield decline 12, 32–3, 34 –5 Medicago sativa 118 micas 211–14, 250 microbials organic matter accumulation 181–2, 184, 194 rhizobacteria 98– 9, 101 microorganisms 101, 102 –4, 117– 20 millet erosion productivity impact 57–9, 60 –3, 65–8, 73–6 rhizobacteria 117 yield-differential topsoil depth 18, 20 yield-management practice 27, 28 mineral nutrients 105 –7 mineralogy 211 –14, 246 –7, 248, 249–51 mobile humic acid fractions 188 –9, 190 modeling organic matter 192 molecular recalcitrance 170, 184– 7 multinuclear magnetic resonance analysis 188–9 mung beans 109, 115, 123 murate of potash (KCl) 232 mustard 122

N national economies 54 negative potassium balances 239–43, 246–7 Nepal 206–7, 209 –51 net primary productivity 170, 189–92, 194–5 nitric acid 217–18, 221–2, 235 nitrogen fixation 100, 191 –2 mineralization 174, 181, 193 –4 organic matter accumulation 171–4, 176– 83, 185, 188–94 potassium nutrition 233, 248 nod gene inducers 141 non-exchangeable soil potassium 214– 18, 221– 5 non-humic substances 170 non-legumes 136 –7, 138 –40 non-pathogenic rhizobacteria 103 normalized yields 64, 65–7 North America erosion productivity impact 62–3, 66–70, 72– 7, 81–2, 84–6, 88– 9 erosion-induced yield losses 8 –9, 14–31 NPK potassium nutrition treatments 244 –6 nutrients organic matter accumulation 180–1 potassium interactions 233 rhizobacteria 105–7 solubilization and uptake 105–7 O oats 18, 20, 135 oceans, organic matter accumulation 194, 195 organic acids 184, 186 organic carbon 182 –3 organic matter accumulation 169–95 decomposition 172–5 mechanisms 179–92 modeling 192 oxygen 169–95 P paddy soils see rice Pakistan 206–7, 209–51 partial global production losses 91 pathogens 112 peanuts 29, 30 pearl millet 117

INDEX peas 145 peppers 126 pest incidence 238–9 petals 123 PGPR see plant growth promoting rhizobacteria PGRs see plant growth regulators pH 184 phenolics 185, 186, 194 Philippines 176 –9, 185–6, 188–91 phosphorus organic matter accumulation 178– 9, 181, 189 potassium nutrition 233, 248 rhizobacteria 105–7 physiological responses 122–3 phytoplankton 181 plant growth promoting rhizobacteria (PGPR) 97 –149 action mechanisms 99–112 applications 121, 124–48 biological control 99 –100, 107– 12 co-inoculation 137, 141–2, 143 –6 disease management 99–100, 107–12 non-legumes 136–7, 138 –40 precursor-inoculum interactions 142, 147–8 responses 117–20, 136 –7, 138– 40 screening 112–21 plant growth regulators (PGRs) 100–5, 113, 116 –20 plant responses 117–20, 136 –7, 138 –40 polysaccharides 185 potassium 203 –51 balance 239–43, 246 –7 diseases 238–9 fertilizers 204, 209–10, 234 –6, 250 –1 ion activity 250 management 236–8 mineralogy 211–14, 246–7, 248, 249–51 nutrition 203–51 pest incidence 238–9 rice-wheat cropping system 203–51 uptake 225– 7, 247 potatoes erosion productivity impact 57– 63, 65–8, 76 –9 rhizobacteria 126, 133 yield-differential topsoil depth 24, 26 yield-management practice 29, 31 potential arable land estimates 57– 60, 61–3 potential crop yields 59, 60, 64, 65–7 potential erosion rates 55–7, 58

265

precipitation reactions 183– 4 precursor-inoculum interactions 142, 147 –8 primary productivity 170, 189–92, 194–5 production erosion-yield-production 51, 86–9 plant growth regulators 100–5 productivity erosion-yield relationship 49 –92 organic matter accumulation 170, 189–92, 194–5 soil erosion 1 –37 prokaryotes 112– 21 proteins 186 Pseudomonas fluorescens 101, 128, 130 –3, 134 pulses erosion productivity impact 57 –9, 60– 3, 65–8, 79 –82 yield-differential topsoil depth 21– 4 pyridine derivatives 173 R radishes 139 rainfall induced erosion 56 rapeseed 115, 122, 126, 134–5 recalcitrant complex organic molecules 170, 184–7 redox reactions 181 –4 relative annual losses 69–86 relative erosion induced yield losses 1–37 relative yield decline 1– 37, 50, 53 –4, 64, 69 release kinetics 217–20 research needs, potassium nutrition 247–9 resin incubation 225 responses applied potassium 227 –39 rhizobacteria 117– 20, 136–7, 138–40 rhizobacteria 97– 149 rhizosphere co-inoculation 137, 141–2, 143–6 rice organic matter accumulation 171 –80, 182–5, 187 –92 rhizobacteria 109, 114, 120, 126, 130–1, 139–40 straw 175–6, 179, 186 rice-wheat cropping system 203– 51 root crops erosion productivity impact 57 –9, 60– 3, 65–8, 76 –9 yield-differential topsoil depth 24– 6 yield-management practice 29, 31

266

INDEX

roots hair formation 141 rhizobacteria 98, 113, 114– 15, 136–7, 141 S sand fractions 211–14 saturation, potassium 221 screening 112 –21 sediments 171–2, 182 –3 seed inoculation 98 sesquioxides 185 shoot growth 113, 114–15 siderophores 106 –7 silt 173–4, 211–14 site-specific potassium management 236–8 soil erosion 1–37, 49– 92 fertility 175–6, 224–5, 234 –6, 243– 7 moisture 173 orders erosion productivity impact 50, 58, 61– 3, 65–6, 68–86 global impact of soil erosion 5, 7–11, 14–28 organic matter accumulation 169–95 potassium forms 214 –17 mineralogy 211 –14, 246– 7, 248, 249–51 supply capacity 220 –5, 249 productivity 1–37 submerged 169–95 solubility, soil potassium 214–17 solubilization cations 184 nutrients 105 –7 sorghum erosion productivity impact 57–9, 60 –3, 65–8, 73–6 yield-differential topsoil depth 18, 20 South America, erosion productivity impact 58, 63, 66 –70, 72, 78 –9, 81– 2, 85–6, 88–9 South Asia 203–51 soybeans erosion productivity impact 57–9, 60 –3, 65–8, 79–82 rhizobacteria 115, 145–6 yield-differential topsoil depth 21, 22 yield-management practice 29, 30

split potassium application 231 –2 spruce seedlings 127 static extraction tests 225, 248 straw 175 –6, 179, 186 submerged soils 169– 95 subtropical soils 216–17 sugar beet 109 sugar cane 109 sulfates 181 sunflowers 115, 140 supply capacity, potassium nutrition 220–5, 249 symbiotic bacteria co-inoculation 137, 141–2, 143– 6 T temperature 173 terminal electron acceptors 181–4 test methods 224–5, 248 time, potassium application 231– 2 time-production relationship 49–92 tobacco 109 tomatoes 110, 115, 119, 122 –3, 127, 133– 5, 140 tropical soils 216–17 tropical wetlands 189, 194 L-tryptophan (L-TRP) 142, 147 –8 TSD see differential topsoil depth U uptake nutrients 105–7 potassium 225– 7, 247 V visible light absorption 190 volatile fatty acids 184, 194 W water induced erosion 50, 68 organic matter in wetlands 175–6, 179 soluble soil potassium 214 –17 table levels 174 weighted potential mean yields 59, 60, 64 West Africa 182 –3 wetland soils 169 –95

INDEX wheat erosion productivity impact 57– 9, 60–3, 65 –8, 82–6 rhizobacteria 110, 114, 117–22, 124, 127 –30, 135, 140 rice-wheat cropping system potassium nutrition 203 –51 yield-differential topsoil depth 17–18, 19 yield-management practice 27, 28

267 Y

yield applied potassium response 227 –39 differential topsoil depth 14–26 erosion productivity impact 49 –92 erosion-induced losses 1–37, 86–9 potential arable land estimates 57–60, 61–3 soil productivity 1–37