ADVANCES IN AGRONOMY Advisory Board PAUL M BERTSCH University of Kentucky RONALD L PHILLIPS University of Minnesota KATE M SCOW University of California, Davis LARRY P WILDING Texas A&M University Emeritus Advisory Board Members JOHN S BOYER University of Delaware KENNETH J FREY Iowa State University EUGENE J KAMPRATH North Carolina State, University MARTIN ALEXANDER Cornell University Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D BALTENSPERGER, CHAIR LISA K AL-AMOODI WARREN A DICK HARI B KRISHNAN SALLY D LOGSDON CRAIG A ROBERTS MARY C SAVIN APRIL L ULERY VOLUME ONE HUNDRED EIGHTEEN Advances in AGRONOMY Edited by DONALD L SPARKS Department of Plant and Soil Sciences University of Delaware Newark, Delaware, USA AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA 32 Jamestown Road, London, NW1 7BY, UK The Boulevard, Langford Lane, Kidlington, Oxford, OX51GB, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2013 Copyright © 2013 Elsevier Inc All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: permissions@elsevier.com Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made ISBN: 978-0-12-405942-9 ISSN: 0065-2113 (series) For information on all Academic Press publications visit our website at store.elsevier.com Printed and bound in USA 13 14 15 10 9 8 7 6 5 4 3 2 CONTRIBUTORS Qiang Chai Gansu Provincial Key Laboratory for Aridland Crop Sciences, Gansu Agricultural University, Lanzhou, Gansu, PR China Avishek Datta Agricultural Systems and Engineering, School of Environment, Resources, and Development, Asian Institute of Technology, Klong Luang, Pathumthani, Thailand Yantai Gan Gansu Provincial Key Laboratory for Aridland Crop Sciences, Gansu Agricultural University, Lanzhou, Gansu, PR China Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada Stevan Z Knezevic Department of Agronomy and Horticulture, University of Nebraska, Northeast Research and Extension Center, Concord, Nebraska, USA Sheng-Xiu Li College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry,Yangling, Shaanxi, PR China Xiao-Gang Li School of Life Sciences, Lanzhou University, Lanzhou, PR China Liping Liu Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada Brendan P Malone Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, Sydney NSW, Australia Alex B McBratney Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, Sydney NSW, Australia Budiman Minasny Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, Sydney NSW, Australia Jun-Yi Niu Gansu Provincial Key Laboratory for Aridland Crop Sciences, Gansu Agricultural University, Lanzhou, Gansu, PR China K Raja Reddy Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, USA ix x Contributors Kadambot H M Siddique The UWA Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia Rishi P Singh Birsa Agriculture University, Kanke, Ranchi, Jharkhand, India James E Specht Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, USA B A Stewart Dryland Agriculture Institute, West Texas A&M University, Canyon, TX, USA Robert M Stupar Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, USA Neil C Turner The UWA Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia P. V V ara Prasad Department of Agronomy, Kansas State University, Manhattan, KS, USA Antonio Violante Dipartimento di Agraria, Università di Napoli Federico II, Portici (Napoli), Italy Zhao-Hui Wang College of Resources and Environmental Sciences, Northwest Science and Technology University of Agriculture and Forestry,Yangling, Shaanxi, PR China Ichsani Wheeler Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, Sydney NSW, Australia Chao Yang Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada PREFACE Chapter 118 contains seven comprehensive reviews on contemporary topics in the crop and soil sciences Chapter is a review dealing with digital mapping of carbon, an element of global significance Chapter assesses the impacts of climate change and variability on seed production and the seed industry Chapter provides a thorough review of competitive sorption mechanisms of ions at the mineral/water interface Chapter is a timely review on the soybean genome Chapter covers crop responses to ammonium and nitrate Chapter provides insights on flaming as an approach to control weeds in agronomic crop systems Chapter discusses the role that ridge-furrow mulching systems can play in sustaining agriculture in semiarid environments I appreciate the authors’ outstanding reviews Donald L Sparks Newark, Delaware, USA xi CHAPTER ONE Digital Mapping of Soil Carbon Budiman Minasny*, Alex B McBratney, Brendan P Malone, Ichsani Wheeler Faculty of Agriculture, Food, and Natural Resources, The University of Sydney, Sydney NSW, Australia *Corresponding author: budiman.minasny@sydney.edu.au Contents Introduction R eview of Past Studies 2.1 P ast Studies 2.2 W hat Do We Learn from These Studies? 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6 3 S ources of Data Extent, Resolution, and Sample Density Depth Validation Uncertainty Covariates 5 13 13 13 S oil Carbon Measurement and Depth 3.1 S oil Carbon Concentration Versus Density 3.2 S oil Carbon Variation with Depth 3.3 A nother Issue with Depth: The Mass Coordinate System S ource of Data: Soil Sampling and Legacy Data 4.1 S ampling in the Presence of Covariates 4.2 L egacy Soil Data P rediction and Mapping 5.1 S oil Carbon Variation 5.2 E nvironmental Covariates 5.3 E stimating Bulk Density 5.4 M apping Soil Depth Function 5.5 G lobal Mapping of Soil Carbon 5.6 A Regional Example U ncertainty and Validation 6.1 U ncertainty 6.2 V alidation M apping and Predicting Soil Carbon Change 7.1 M apping Soil Carbon Change 7.2 P redicting Soil Carbon Change C onclusions Acknowledgments References 13 13 14 17 19 19 20 21 21 23 26 27 28 29 31 31 33 34 34 35 39 41 41 © 2013 Elsevier Inc Advances in Agronomy, Volume 118 ISSN 0065-2113, http://dx.doi.org/10.1016/B978-0-12-405942-9.00001-3 All rights reserved Budiman Minasny et al Abstract There is a global demand for soil data and information for food security and global environmental management There is also great interest in recognizing the soil system as a significant terrestrial sink of carbon The reliable assessment of soil carbon (C) stocks is of key importance for soil conservation and in mitigation strategies for increased atmospheric carbon In this article, we review and discuss the recent advances in digital mapping of soil C The challenge to map carbon is demonstrated with the large variation of soil C concentration at a field, continental, and global scale This article reviews recent studies in mapping soil C using digital soil mapping approaches The general activities in digital soil mapping involve collection of a database of soil carbon observations over the area of interest; compilation of relevant covariates (scorpan factors) for the area; calibration or training of a spatial prediction function based on the observed dataset; interpolation and/or extrapolation of the prediction function over the whole area; and finally validation using existing or independent datasets We discuss several relevant aspects in digital mapping: carbon concentration and carbon density, source of data, sampling density and resolution, depth of investigation, map validation, map uncertainty, and environmental covariates We demonstrate harmonization of soil depths using the equal-area spline and the use of a material coordinate system to take into consideration the varying bulk density due to management practices Soil C mapping has evolved from 2-D mapping of soil C stock at particular depth ranges to a semi3-D soil map allowing the estimation of continuous soil C concentration or density with depth This review then discusses the dynamics of soil C and the consequences for prediction and mapping of soil C change Finally, we illustrate the prediction of soil carbon change using a semidynamic scorpan approach INTRODUCTION Soil carbon (C) is recognized as the largest store of terrestrial carbon (Batjes, 1996; Jobbagy and Jackson, 2000; Lal, 2004) Globally, its storage capacity is much larger compared with the pools of carbon in the atmosphere and vegetation There is now a large and growing interest in knowing the size of soil carbon pool and its sequestration potential Mapping the spatial distribution of soil carbon has been of great interest as exemplified by the increasing number of publications in mapping soil carbon stock globally and nationally (Grunwald, 2009) This is reflecting the response to the demand for a more accurate assessment of soil carbon pool at a better resolution Many articles have been published, quantifying and mapping soil carbon storage at the field, landscape, regional, continental, and global scales (Bernoux et al., 2002; Post et al., 1982) Conventional methods that used soil maps as the basis of soil carbon estimates are still being used for mapping areas that have a limited number of soil observations (Batjes, 2008b) However, digital soil mapping technology has progressed rapidly in the past Digital Mapping of Soil Carbon decade, making it operational for routine mapping over large areas (Bui et al., 2009; Grunwald et al., 2011; Rawlins et al., 2009; Triantafilis and Buchanan, 2010) Digital soil mapping was identified as one of the emerging research fronts in agricultural sciences in the December 2009 issue of the Thompson Reuters Essential Science IndicatorsSM1 Polygon-based soil maps are now being replaced with digital maps of soil carbon content and their associated uncertainties for new areas or previously mapped areas.These maps are stored and manipulated in digital form within a Geographical Information System (GIS) environment, creating the possibility of vast arrays of data for analysis and interpretation (Grunwald, 2009; Meersmans et al., 2009; Mueller and Pierce, 2003; Triantafilis et al., 2009) This article will review the state of the art in mapping soil carbon and soil carbon change by using digital soil mapping approaches Mapping and the knowledge of the spatial distribution of soil carbon is useful to • Provide a baseline carbon level, which can be useful when soil carbon is included in greenhouse gas emissions trading schemes; • Help localize the variables controlling soil carbon; • Assist in natural resource management and monitoring; • Identify potential project locations for soil-based carbon sequestration; and • Serve as an input into mechanistic simulation models There is, in general principles, an essential difference between mapping of soil carbon and accounting of soil carbon Mapping activity attempts to give an image of the spatial distribution of soil carbon, and while we can use mapping for temporal soil carbon auditing, it will generally be an expensive exercise In auditing, we are only interested in knowing the total amount of carbon over an area for a particular depth at a particular time, and we not need to know the exact spatial distribution of carbon The efficiency of auditing is in the use of statistically design-based sampling strategy (Brus and de Gruijter, 2011) As it is a substantial topic of its own, the issue of auditing will not be discussed here REVIEW OF PAST STUDIES 2.1 Past Studies There have been numerous global estimations of soil carbon stocks, and most of them are derived from existing soil maps The results vary and http://sciencewatch.com/dr/erf/2009/09decerf/ Budiman Minasny et al not state the uncertainty of estimates, for example, the reported estimates for global soil organic carbon (SOC) pool in the upper 1-m profile vary from 1220 Pg (Sombroek et al., 1993), 1395 Pg (Post et al., 1982), 1456 Pg (Schlesinger, 1977), 1462–1548 Pg (Batjes, 1996), 1502 Pg ( Jobbagy and Jackson, 2000), and 1550 Pg (Lal, 2004) These variable results could be due to the effect of different methods used and also to the variability in spatial and temporal status of the data Conventional methods are still being used for the estimation of soil carbon stock for a region or continent; the estimates are based on existing soil maps using soil–landscape and vegetation associations The resulting maps are usually in the cartographic scale of 1:1,000,000 or coarser, for example, Africa (Henry et al., 2009), Central Africa (Batjes, 2008b), Brazil (Bernoux et al., 2002), and Congo (Schwartz and Namri, 2002) These maps are indeed still useful where there is little soil information for the area These maps were used by Milne et al (2007) in the Global Environment Facility Soil Organic Carbon modeling system to map future SOC stock changes in Brazilian Amazon (Cerri et al., 2007), the Indo-Gangetic plains (Bhattacharyya et al., 2007), and Jordan (AlAdamat et al., 2007) Since the development of digital soil mapping technologies in the late 1990s, and formalization of the discipline by McBratney et al (2003), mapping of soil carbon at the field and regional scales has become an area of active research Table 1.1 summarizes some recent studies of soil carbon concentration and carbon density maps that have been produced using digital soil mapping technology with the scorpan model Here, we only list studies that have used the scorpan approach The approach of digital soil mapping follows the scorpan spatial prediction function: Cx = f (s, c, o, r, p,a,n) + e, (1) where soil carbon C at spatial position x is a function of soil factors (s), climate (c), organisms, which include land use, human effects, and management (o), relief (r), parent materials (p), age or time (a), spatial position (n), and e is the spatially correlated errors Except for the “time” or “age” factor, most digital soil mapping examples have either explicitly or implicitly used these factors for prediction of soil carbon However “time” is also an essential factor in soil carbon prediction Soil carbon observations denoted as “s” on the right-hand side of the equation are 480 Climate change effect on C3/C4 crops and crop/weed interactions, 61–62 benefits, 62 C3 crop and C4 weed competition, 63 response, 63 on crop yield, food security, and national GDPS Asian rice, 53–54 FAO, 53 GDP, 55 IPCC, 53 wheat, 53–54 disease development, 60 causes, 60 increasing pest load, 61 pathogens, 60–61 on geographical crop distribution most-affected group, 56 rising temperatures, 55–56 on insect pest, 56–57 disease, affecting, 58–59 factor, affecting, 57–58 global surface temperature, 58 hydrological cycle intensification, 59–60 population dynamics, 58 on pollination ecosystems and disrupting, 81 FAO, 79 pollinating insects, 78 pollination timing disruption, 80–81 pollination worldwide, 78–79 pollinator biodiversity losses, 79–80 Comparative genomic hybridization (CGH), 191–193 Competitive sorption, 138 anions effect, 142–144 anions involvement arsenic and inorganic anions, 160–161 arsenic and organic anions, 160–161 arsenite and arsenate sorption, 162–163 coprecipitation of arsenic, 161–162 FA, 163, 164f H4SiO4 sorption onto ferrihydrite, 159f LMMOL effect, 162 Index oxyanions fixation on organic matter, 163–164 selenite, arsenate, and vanadate, 160 selenite and silicic acid, 158–160 silicic acid effect, 158 sorption of arsenate, 161t sorption of phosphate, 161t changes in electric potential clay minerals and soils surfaces, 146–147 kinetics reaction of arsenate and phosphate, 147t noncrystalline Al hydroxides, 147–148 phosphate and arsenate, 148 phosphate and selenite, 148 in sorption between two ligands, 147 competition, 138, 145–146 Cu, Pb, and Cr sorption, 140–141 using factors, 138–139 goethite–B-thuringiensis complex, 142 metal cations onto natural organic matter, 141 new surfaces phases, 140 Ni–FA complexes, 141–142 nonelectrostatic equilibrium model, 139–140 single and multisolute systems, 142 solid-state diffusion, 140 sulfate and phosphate involvement, 148 Al-oxide, 151–152 CD-Music model, 151 Fe-oxides, 153f goethite–water interface, 156 inner-sphere surface complexes, 154–155 OH–Al-montmorillonite complexes, 154 phosphate and glyphosate, 156–157, 157f phosphate with inorganic anions, 152 phosphate with organic anions, 152 on soils, phyllosilicates, and variable charge minerals, 148–149 organic acid sorption, 155–156, 155f sorption kinetics, 150 sulfate sorption on Andisol, 149–150, 149f synthetic aluminum hydroxysulfate, 150–151 481 Index Conditioned Latin hypercube sampling design (cLHS design), 19–20 Consultative Group of International Agricultural Research Centers (CGIAR Centers), 94–95 Conventional-flat-planting system (CF system), 432 Crop plant preference N compound effect comparison, 279–280 combinative ratio of NH4+–N to NO3−–N, 282 optimum ratio of NH4+–N to NO3−–N, 280–283 plant growing age effects, 283 protons generation, 281–282 rice cultivar responses to N compounds, 282–283 NH4+–N, 267–268 absorbed and utilized rate, magnitude of, 277 barley, 268–269 calcifuge or calciphobe plants, 268–269 effects, 278–279 field experiments on wheat production, 274–275 grassland plants, 269 maize, 270–271 potato crops, 269–270 rice, 270–271, 276–277 soybean, 271 sugar beet, 276 vegetables, 276 wheat sown, responses of, 275t NO3−–N, 267–268, 271–272 absorbed and utilized rate, magnitude of, 277 dryland crops, 271–272 effects, 278–279 field experiments, on wheat production, 274–275 NO3− accumulation, 278 NO3−–N accumulation in plants, 273–274 POD activity, 277–278 SOD activity, 277–278 sugar beet, 276 vegetables, 276 wheat and maize, 272–273 wheat sown, responses of, 275t reasons for preference, 292–293 aeration effect, 297–298 ammonium concentration in solution, 298 illumination effect, 297 NH4+ to NO3− chemical properties, 294–296 plant characteristics, 293–294 temperature effect, 296–297 Crop straw, 435–437 CTK See Cytokinin Cultivated soybean, 179 germplasm, 180 Cytogenetics, 181–182 Cytokinin (CTK), 247–248, 345–347 Cytosolic induced-enzyme See Nitrate reductase (NR) D Deoxy ribonucleic acid (DNA), 181–182 Desferrioxamine-B (DFOB), 123–124 Desorption process of cations, 116 metal ions, 125–126 phosphate and arsenate, 160–161 DFOB See Desferrioxamine-B Digital soil mapping, 2–3 recent studies on, 5, 6t–12t covariates, 13 depth, extent, resolution, and sample density, sources of data, uncertainty, 13 validation, 13 Dissociation (Ds), 195–196 DNA See Deoxy ribonucleic acid Dose–response curves, 403–404 Dry weight (DW), 447 Dynamic–mechanistic simulation model, 35 E EANN See Enhancement of ammonium N nutrition ED See Effective dose 482 EDTA See Ethylenediaminetetraacetic acid Effective dose (ED), 403–404 Electron spin and paramagnetic resonance (ESR), 113 EMS See Ethyl methanesulfonate Enhancement of ammonium N nutrition (EANN), 283–284 Enhancement of nitrate N nutrition (ENNN), 284–285 effect on N assimilation and utilization, 289–290 13N-labeled experiment, 290 NH4+–N absorption, 290–291 NH4+–N and NO3−–N mixed nutrition, 291–292 NO3−–N reduction, 291 rice seedlings, fresh weight of, 292t nitrification in rice rhizosphere, 285–286 nitrifying bacteria, distribution of, 286–287 NO3−–N effect on rice, 287–288 rice field, space variation in, 286 root growth promotion, 288 carbohydrate uses, 288–289 root-split experiments, 288 ENNN in stimulation, views on, 289 ENNN See Enhancement of nitrate N nutrition Environmental covariates, 23–24 CEC, 25 large terrain variations, 24–25 NIR reflectance, 25 and soil carbon, 25–26 spatial topsoil carbon pattern, 24 Equivalent soil mass approach (ESM approach), 17–18 ESM approach See Equivalent soil mass approach ESR See Electron spin and paramagnetic resonance Ethyl methanesulfonate (EMS), 194 Ethylenediaminetetraacetic acid (EDTA), 121 EXAFS spectroscopy See Extended X-ray absorption fine structure spectroscopy Extended X-ray absorption fine structure spectroscopy (EXAFS spectroscopy), 113 Index F FA See Fulvic acid FAO See Food and Agricultural Organization Ferredoxin generation, 246 Flame weeding, 420 Flaming, 405 advantages, 420 agronomic crops, 411–412 maize types tolerance, 412–416 sorghum tolerance, 416–417 soybean tolerance, 419–420 winter wheat tolerance, 417–418 disadvantages, 420–421 environmental impacts, 421–422 chemical weed control methods, 422–423 CO2 emissions, 422, 423t glyphosate, 422 nonchemical weed control methods, 422–423 propane dose effects on dry matter reduction of barnyardgrass, 409f on dry matter reduction of green foxtail, 408f on field maize yield loss, 414f on pop maize yield loss, 415f on sorghum yield loss, 418f on soybean yield loss, 421f on sweet maize yield loss, 416f on winter wheat yield loss, 419f recommendations and future research flame weeding, 424 IWM program, 424–425 maize and soybean, 424 organic crop production system, 424 propane per hectare, 423–424 weed species tolerance broadleaf and grass weed species, 407t–408t, 409–410 environmental factors, 411 plant role, 405–406 residual soil activity, 410–411 Food and Agricultural Organization (FAO), 28, 51–52, 79 Fourier transform infrared (FTIR) spectroscopy, 113 483 Index FTIR spectroscopy See Fourier transform infrared spectroscopy Fulvic acid (FA), 119 Future fertilizer See NH4NO3 G γ-aminobutyric acid (GABA), 221–222 GDH See Glutamate dehydrogenase GDP See Gross domestic product GE crops See Genetically engineered crops Genetically engineered crops (GE crops), 77–78 Genetically modified organism (GMO), 87 Genetically modified seed (GM seed), 86 crops yield, 89–90 environmental issues, 90–91 net farm income, 91 with GM seed, 86 in market-driven agriculture, 86–87 Genomic era, 184–185 See also Postgenomic era genome resequencing, 187–188 divergence time estimation, 188 G soja genome, 188 iceberg, 189 lower-coverage resequencing, 188–189 molecular genetic information, 185 soybean genome sequence, 185 Arabidopsis thaliana, 187 euchromatin chromosomal arms, 186–187 organized landscape, 185–186 whole-genome duplications, 187 Genomic structural variation diversity in G max, 193 RGAs, 191–193 within Soybean, 191 soybean and G soja comparisons, 191–193 Germplasm haplotyping genetic mapping, 189 multiplexed marker systems, 190 NAM approach, 190–191 soybean RIL population, 191 soybean SNP-based genotyping, 189–190 Glutamate dehydrogenase (GDH), 221–222 Glutamate synthase See Glutamine (z-) oxoglutarate aminotransferase pathway (GOGAT pathway) Glutamic oxaloacetic transaminase (GOT), 217–218 Glutamic-pyruvic transaminase (GPT), 217–218 Glutamine, 265 Glutamine (z-) oxoglutarate aminotransferase pathway (GOGAT pathway), 242–243 Glutamine synthetase (GS), 262 CO2 concentration effect, 255 on NO3−–N concentrations, 256f CO2 enrichment effect, 257t light effect, 257t Gly See Glycine Glycine (Gly), 217–218 Glycine max See Soybean genome sequence Glycine soja (G soja), 188 GM seed See Genetically modified seed GMO See Genetically modified organism Goethite–B-thuringiensis complex, 142 GOGAT pathway See Glutamine (z-) oxoglutarate aminotransferase pathway GOT See Glutamic oxaloacetic transaminase GPT See Glutamic-pyruvic transaminase Gravel–sand mulches, 437, 438f Greenhouse gas emissions, 464–465, 468 Gross domestic product (GDP), 54–55 GS See Glutamine synthetase GS–GOGAT enzyme system, 262 H H+-adenosine triphosphate (ATP), 239–240 H+/NO3− cotransport mechanism, 246 HA See Humic acid Harvest index (HI), 64–65 Herbicide tolerance, 420 Herbicides, 401 HI See Harvest index 484 High-molecular-weight organic N compound (HMWONC), 264, 266 See also Low-molecular-weight organic N compound (LMWONC) N components, 267 NH4+ assimilation, 267 synthesization, 266–267 HNO2 See Nitrous acid Hormones, N forms effect ABA, 343–345 auxins, 343 chemicals, 343 CTK, 345–347 factors, affected, 342–343 IAA content, 343 NO3-reduction, 343–344 potato tubers development, 347–348 ZOG, 347 Humic acid (HA), 115 I IAA See Indole-3-acetic acid IFAD See International Fund for Agricultural Development Improved water use efficiency mechanisms, 467 Indole-3-acetic acid (IAA), 288–289 Inorganic ligands effect, 126 asymmetric stretching mode, 129f ATR-FTIR spectroscopies, 127 cadmium sorption data for goethite, 130f using EXAF spectroscopy, 126–127 EXAFS spectroscopies, 127 inner-sphere sorbed sulfate, 127–128, 128f outer-sphere sorbed sulfate, 127–128, 128f phosphate and cadmium, 128–131 sorption o chromate, 131 phosphate, 131 XAS and electrophoretic mobility, 128 Integrated weed management (IWM), 400 flaming efficacy determination, 405 flaming specifications and equipment calibration procedure, 404–405 flaming treatments, 404–405 North American agronomic cropping systems, 401 objective, 403–404 Index organic agriculture, 402 organic crop production, 401–402 propane flaming, 401 rationale cell membranes and subsequent tissue desiccation, 403 flame weeding efficacy, 403 flaming control weeds, 403 flaming effects on plants, 403 weeds pose, 402 Intellectual property rights (IPRs), 85 biotech-stacked traits and patent infringements, 92 giant transnational corporations, 92 patent infringement, 93–94 stewardship, 93 environmental issues, 90–91 GM crops yield, 89–90 IPRs and technology use agreements related issues, 91–92 issues, 85 and monocultures, 86 with GM seed, 86 in market-driven agriculture, 86–87 monopolization by transnational corporations large-scale capital accumulation, 88 multinational pharmaceutical firms, 87 spreading, belatedly, 88 net farm income, 91 seed price of conventional vs GE seeds, 88–89 Intergovernmental Panel on Climate Change (IPCC), 51 families, 51 FAO estimation, 51–52 food production growth, 51 International Fund for Agricultural Development (IFAD), 52 International Rice Research Institute (IRRI), 53–54 International Union for Protection of New Varieties of Plants Convention (UPOV Convention), 85 iPAs See Isopentenyladenine IPCC See Intergovernmental Panel on Climate Change IPRs See Intellectual property rights Index IRRI See International Rice Research Institute Isopentenyladenine (iPAs), 345 IWM See Integrated weed management K K, radiometric, 25 Kaolinite, 121–123, 134 KNO3, 211, 213–214 Kinetics, of arsenite reactions, 147t of phosphate, 147t L Lagrange system, 18 Law of the Minimum, 211–212 Layered double hydroxide (LDH), 117–118 LD See Linkage disequilibrium LDH See Layered double hydroxide Lineweaver–Burk equation, 222 Linkage disequilibrium (LD), 188–189 LMMOL See Low-molecular mass organic ligand LMWONC See Low-molecular-weight organic N compound Low-molecular mass organic ligand (LMMOL), 119 Low-molecular-weight organic N compound (LMWONC), 264 arginine, 265 aspartate, 264 betaine, 265 entry points of NH3, 264 glutamine, 265 NH4+ assimilation in roots, 264 nicotianamine, 265–266 M Maize absorption kinetic parameters, 231–232, 231t dry biomass weight, 228 Ds transposon system, 195–196 NH4+–N and NO3−–N effects, 227–228, 229t responses to N forms, 228 uptake ratio, 314 Malondialdehyde (MDA), 336–337 485 Mapping soil carbon change, 34 C stock over successive periods, changes in, 35f digital soil mapping, 35 legacy soil data, 34 depth function equal-area spline function, 28 negative exponential depth model, 27 pseudo-3-D soil carbon map, 27 Mass coordinate system carbon stock accounting and C density, 18 cumulative carbon density, 18 ESM approach, 17–18 Lagrange system, 18 soil bulk density information, 17 soil carbon observations, 19f MAT See Mean annual temperature Material coordinate system See Lagrange system MBC See Microbial biomass carbon MDA See Malondialdehyde Mean annual temperature (MAT), 37–39 Metal oxides, 116–117 Michaelis–Menten equation, 222–223 NH4+–N absorption kinetic parameters on crops, 231–232, 231t biomass of crops, 228–230 crops responses, 228 different ratios in crops, effects of, 227–228, 229t, 230–231 uptake velocity for, 227 NO3−–N absorption kinetic parameters on crops, 231–232, 231t biomass of crops, 228–230 crops responses, 228 different ratios in crops, effects of, 227–228, 229t, 230–231 uptake velocity for, 227 utilization in nutrient uptake by plants, 223 hybrid rice and rice, 225 ion uptake kinetics, 223–224 ion-depleted technique, 224–225 kinetic parameters, 223–225 NH4+ effects, 226–227 NO3− uptake by roots, 225–226 486 Microbial biomass carbon (MBC), 457 DRM, RM and NM, 458 plastic-mulched RF system, 457–458 soil microorganisms, 458–459 soil moisture, 458 Microfibrils, 234 Mineral N compounds, 217 apoplast and cytoplast pathways, 220 contribution to plant uptake, 217 Mineral/water interface, 124 Mineralized N (MN), 460–461 MLG See Molecular linkage group MN See Mineralized N Molecular linkage group (MLG), 182–183 Molecular marker linkage maps classical markers, 182 QTLs, 184 RFLP markers, 182–183 SNP markers, 184 SSR, 183–184 Molybdate, 119 Monopolization in seed industry, 89 by transnational corporations spreading, belatedly, 88 large-scale capital accumulation, 88 multinational pharmaceutical firms, 87 Mulching system, 432, 433 See also Ridge-furrow system (RF system) crop straw, 435–437, 436f gravel–sand mulches, 437, 438f mulching techniques, 437–439 plastic film, 433, 440f rainwater conservation, 439–441 rainwater runoff, 441–443, 442t ridge furrow, 433–435 with ridge furrowing, 432 soil temperature, 443–445, 444t Mulching techniques, 437–439 Multiplexed marker systems, 190 N N forms effect See also Ammonium N (NH4+–N); Nitrate N (NO3−–N) on carbohydrate and nutrient accumulation, 328, 330–331, 332t carbon metabolism influence, 328–330 effect on mineral ion uptake, 330 Index on chlorophyll contents and photosynthesis, 327 on crop root morphology, 326–327 hormones ABA, 343–345 auxins, 343 chemicals, 343 CTK, 345–347 development of potato tubers, 347–348 factors, affected, 342–343 IAA content, 343 NO3-reduction, 343–344 ZOG, 347 on N-containing compounds, 327–328 NRA, 331–333 N forms relation to, 334 NO3−–N application effect, 333 reductive amounts, rate of, 333 storage form in plants, 334–335 on physiological characteristics of wheat, 329t protective enzymes, 336, 338, 341t CAT activities, 337–342 MDA amount, 336–337 physiological and biochemical functions, 339–340 POD activities, 337–340 ROS, 336–337 SOD activities, 337–339, 342 on turgor pressure, 335–336 NAD See Nicotinamide adenine dinucleotide NADP See Nicotinamide adenine dinucleotide phosphate NAM See Nested Association Mapping NDVI See Normalized Difference Vegetation Index Near infrared (NIR) spectroscopy, 13 Nested Association Mapping (NAM), 190–191 New South Wales (NSW), 37–39 NH4+ toxicity, 298–299 appearance, 299–300 ATPase in membranes, 305 carbohydrate amount effect, 311–312 cation deficiencies effect, 306–307 calcium ion role, 307 NH4+ and NO3− effect, 307–308 Index characterization, 300 effects on plants, 299 ion leakages out of roots, 305–306 mechanisms assimilation and transport processes, 304 energy-intensive NH4+ efflux, 301–302 NH3 toxicity, 302–303 NH4+–N preference, 303–304 relative preference to NO3−–N, 303–304 membrane stability, 305 N fertilizer application, 299 NO3− nutrition, 306 pH changes effect, 308–309 media acidification, 310–311 in rhizosphere, 309 in root cells, 309–310 solution-moving culture experiment, 311 restriction of root growth, 300 species tolerances, 301 symptoms, 300 wilting phenomenon, 301 NH4+/NH3 assimilation, 262 See also Ammonium N (NH4+–N) ammonia assimilation, 262 cytosolic GS, 263 glutamate dehydrolysis, 263 GS–GOGAT enzyme system, 262 HMWONC, 264, 266–267 LMWONC, 264–266 NH4+/NH3 uptake, 240 active uptake, 243–244 diurnal pattern, 244–245 efflux rate, 242–243 GOGAT pathway, 242–243 passive uptake, 241–242 rhizosphere pH, changes in, 245 by root cells, 244f transport systems for, 243–244 NH4+–N See Ammonium N NH4+–N and NO3−–N nutrition amounts and ratios, 315t–316t buckwheat absorbed ratio, 317–318 crop responses, 312–313 maize uptake ratio, 314 487 media pH effect on wheat uptake, 321 cumulative N amount, 322f, 324f nutritional solution pH effect, 325t–326t pH treatments, 325–326 solution culture experiment, 321–322 solution pH change, 322–323, 322f total uptake amount of N compounds, 325t uptake ratios of N compounds, 323–325 NH4+–N concentrations, 313t, 314 NO3−–N concentrations, 313t, 314 Pakchoi absorbed ratio, 317 proso absorbed ratio, 314–317 rice responses, 318–319 absorption kinetic parameters, 320–321 absorption velocity of rice seedlings, 320t preference of rice, 321 uptake ratio and amount of, 319–320, 319t tested tomato ratio, 317 wheat absorbed ratio, 317–318 NH4NO3, 212–215 Nicotinamide adenine dinucleotide (NAD), 246 Nicotinamide adenine dinucleotide phosphate (NADP), 246 NiR See Nitrite reductase NIR spectroscopy See Near infrared spectroscopy Nitrate N (NO3−–N), 210–211 See also Crop plant preference absorption, 212–213 absorption kinetic parameters, 231–232, 231t assimilation, 252 biomass of crops, 228–230 crops responses, 228 different ratios in crops, effects of, 227–228, 229t, 230–231 ENNN, 284–285 leaching, 294–295 in maize sap at growing stages, 250t movement to plant roots diffusion, 232–233 mass flow, 232–233 root interception, 232–233 488 Nitrate N (NO3−–N) (Continued ) NH4NO3, experiments with, 214–215 NO3− assimilation, 252–253 amino acid regulation, hypothesis of, 258 carbohydrate, 252–253 change of cell pH, 258 CO2 concentration effect, 253–256, 256f decreasing plant shoot NO3−–N concentration, 255–256 NO3−–N transport and amino acids, 256–257 saturation of light, experiment under, 253–254 NO3− reduction, 245, 258–259 cytosolic-induced enzyme, 247–248 energy and C skeleton requirement, 248 environmental conditions impact, 249–251 ferredoxin generation, 246, 246f NO3− photosynthesis, 246 process, 245–246 reduction to NH3, 246 rhizosphere pH, increase in, 251 roots and shoots, 248–249 nutritional function, 212–213 solution culture techniques, 213–214 storage in vacuoles, 259–260 metabolic and storage pool, 260–261 NO3−–N accumulation, 260–262 NRA, 260–261 uptake, 234, 252 cells, 234 ion movement, 235 NO3− uptake, 236–240 by plant roots, 235–236 roots, 234 symplast, 234–235 velocity, 227 Nitrate reductase (NR), 238 Nitrate reductase activity (NRA), 217–218 N forms effect, 331–333 relation to, 334 NO3−–N application effect, 333 plants, storage form in, 334–335 reductive amounts, rate of, 333 Index Nitrification in rice rhizosphere, 285–286 nitrifying bacteria, distribution of, 286–287 NO3−–N effect on rice, 287–288 space variation in rice field, 286 Nitrite reductase (NiR), 246 Nitrogen (N), 210 See also Ammonium N (NH4+–N); Organic N nutrition for plants complicated N compound synthesization, 210–211 inorganic to organic form conversion, 210–211 nutrition theory, 211–212 organic N substances, 210 Nitrogen fertilizer, 464 Nitrous acid (HNO2), 246 NO3− uptake, 236–237 affecting factors, 251 cotransport mechanism, 237 efflux into soil solution, 240 by plant root cells, 239–240, 240f proton–anion cotransport system, 237–239 transportation, 238 NO3−–N See Nitrate N Nonchemical weed control, 424–425 Nonerodible materials, 465 Normalized Difference Vegetation Index (NDVI), 13 North American agronomic cropping systems, 401 NR See Nitrate reductase NRA See Nitrate reductase activity NSW See New South Wales Nucleic acids, 210 O Organic crop production, 401–402 Organic N nutrition for plants, 216 mineral N compounds, 217 apoplast and cytoplast pathways, 220 contribution to plant uptake, 217 organic N compounds, 216–217 amino acid hypothesis, passive uptake of, 221 apoplast and cytoplast pathways, 220 assimilation, 221–222 Index effects, 218–219 plant species responses, 217–219 plant uptake, contribution to, 217 sugar/proton cotransport hypothesis, 220–221 Organic weed control, 406–409 Outer-sphere complexes, 113 OX acid See Oxalic acid Oxalic acid (OX acid), 135 P Pakchoi absorbed ratio, 317 absorption kinetic parameters, 231–232, 231t fresh biomass and root weights, 230 NH4+–N and NO3−–N effects, 227–228, 229t responses to N forms, 228 Patent infringement, 93–94 PCR See Polymerase chain reaction PEP carboxylase See Phosphoenolpyruvate carboxylase Perennial crops, 447–448 Peroxidase (POD), 277–278, 337 Peroxide enzyme See Peroxidase (POD) Pest control, 462–464 Phenology, 63 Phosphoenolpyruvate carboxylase (PEP carboxylase), 247 Phosphorus, 461 Phyllosilicates, 118–119 Phytohormones, 343 Plants flaming effects on, 403 N source for, 216 NH4+–N nutritional function in, 212–213 obtaining minerals, 210–211 physiology, 467 rooting characteristics, 452 sexual reproduction in, 65 Plasma membrane, 235 Plastic film, 433 POD See Peroxidase Polygon-based soil maps, 2–3 Polymerase chain reaction (PCR), 182–183 Polyvalent cations, 117 489 Postgenomic era, 189 See also Pregenomic era gene function, 193–194 CGH platform, 197 CGH/PCR approach, 195 DNA base changes, 194 genome engineering, 196–197 irradiation-based mutagenesis, 194–195 maize Ds transposon system, 195–196 model genetic systems, 194 PTGS method, 196 genomic structural variation, 191–193 germplasm haplotyping and association mapping genetic mapping, 189 multiplexed marker systems, 190 soybean NAM population, 190–191, 192t soybean RIL population, 191 soybean SNP-based genotyping, 189–190 soybean genomic research community, 197 Posttranscriptional gene silencing method (PTGS method), 196 Precipitation use efficiency (PUE), 431–432 Predicting soil carbon change, 35, 36f dynamic–mechanistic simulation model, 35 empirical approach, 39 NSW, 37–39, 38f using rule-based model, 37–39 SOC simulation models, 35–36 soil carbon dynamic models, 37 static-empirical model, 37 STEP-AWBH, 39 Pregenomic era, 178 See also Genomic era cytogenetics, 181–182 molecular marker linkage maps classical markers, 182 QTLs, 184 RFLP markers, 182–183 SNP markers, 184 SSR, 183–184 phenotypic diversity, domestication, and breeding cultivated soybean, 179 cultivated soybean germplasm, 180 domestication time frames, 179 490 Pregenomic era (Continued ) G max landraces, 180 G soja landraces, 180 soybean breeding efforts, 180–181 soybean cooking oil and forage crop, 179 Propane dose effects on dry matter reduction of barnyardgrass, 409f of green foxtail, 408f on field maize yield loss, 414f on pop maize yield loss, 415f on sorghum yield loss, 418f on soybean yield loss, 421f on sweet maize yield loss, 416f on winter wheat yield loss, 419f Propane flaming, 401 in organic cropping systems, 402 Proso (Panicum miliaceum) absorbed ratio, 314–317 absorption kinetic parameters, 231–232, 231t biomass, 228 NH4+–N and NO3−–N effects, 227–228, 229t responses to N forms, 228 Protective enzymes N forms effect, 336, 338, 341t CAT activities, 337–342 MDA amount, 336–337 physiological and biochemical functions, 339–340 POD activities, 337–340 ROS, 336–337 SOD activities, 337–339, 342 Proteins, 210 PTGS method See Posttranscriptional gene silencing method PUE See Precipitation use efficiency Q Quantitative trait loci (QTL), 184 R Rainwater channeling percentage (RWCP), 442–443 Rainwater conservation, 439 heavy rainfall, 441 RF system, 441 Index Rainwater runoff, 441–442 infiltration and sediment load, 442t RWCP, 442–443 Reactive oxygen species (ROS), 336–337 Recombinant inbred line population (RIL population), 190–191 RF mulching See Ridge-furrow mulching RFLP markers, 182–183 Ridge-furrow mulching (RF mulching), 432–435 components, 432 cover types, 435t crop productivity covered RF systems, 446t crop growth characteristics, 447 crop production, 445–448 crops PUE, 451t factors, 448–449 fertilizers, 449 perennial crops, 447–448 plastic-covered plots, 447 water use efficiency, 449–452 yield components, 448 effects on environments fertilizer input and carbon footprint, 464 greenhouse gas emissions, 464–465 pest control, 462–464 soil and water erosion, 465–466 effects on soil attributes microbial biomass carbon, 457–459 relation, 462 soil microorganisms and biodiversity, 459 soil nitrogen, 459–461 soil organic carbon, 454–457, 455t–456t soil phosphorous, 461–462 maize plantation in, 434f revolutionizing agricultural systems, 432–433 ridge-to-furrow ratios, 435 ridges covered with plastic film, 436f rooting characteristics field crops, 452 mulched and unmulched wheat, 452–453 plants, 452 Index root development, 453 root DW distribution, 453f in semiarid environments, 454 suggestions for future research, 466–467 detailed relationships between soil attributes, 468 greenhouse gas emission and carbon footprints, 468 improved water use efficiency mechanisms, 467 plant physiology and signaling systems, 467 risk of pollution, 468–469 soil N and leaching quantification, 468 system sustainability evaluation, 467 RIL population See Recombinant inbred line population RNA interference (RNAi), 196 RNAi See RNA interference Root growth promotion, 288 carbohydrate uses, 288–289 ENNN in stimulation, views on, 289 root-split experiments, 288 ROS See Reactive oxygen species RWCP See Rainwater channeling percentage S Scorpan approach, changes in soil properties, 37 spatial prediction function, Seed industry, 52–53 Seed production See also Climate change effect anthesis/pollen viability heat stress, 67–68 pollen grains, 68–69 pollen impairment, 67–68 temperature, 67–68 crop phenology flowering and fruiting phenology, 63–64 plant phenology, 63 temperatures, 64 flowering, 65–67 GE crops, 77–78 pollen germination, 69–70 reproduction 491 heat stress, 64–65 reproductive processes, 65, 66t temperature, 65, 67t seed dormancy, 74 seed quality α-tocopherol, 77 aspects, 74–75 harvest maturity, 76–77 seed vigor, 76 soybean plants production, 75–76 temperature effects, 77 seed size density, 71 grain growth, 72 heat stress, 71–72 seed yield, 72–73 high temperature stress, 73 pod setting period, 73–74 seed/grain filling duration, 70 cool season pulses, 71 heat stress, 70–71 Seed vigor, 76 Selenite, 119 Signaling systems, 467 Single-nucleotide polymorphism markers (SNP markers), 184 SNP markers See Single-nucleotide polymorphism markers SOC See Soil organic carbon SOD See Superoxide dismutase Soil erosion, 465–466 microorganisms, 459 nitrogen, 459–461 phosphorous, 461–462 Soil, topography, ecological and geographic properties–atmospheric, water, biotic, and human (STEP-AWBH), 39 Soil carbon (C), 2–3 bulk density estimation, 26 concentration vs density laboratory measurement, 14 mapped inorganic C concentration and C fractions, 14 soil carbon concentration or content, 13–14 in soil carbon density, 14 492 Soil carbon (C) (Continued ) conventional methods, data source cLHS design, 19–20 legacy soil data, 20 using numerical methods or cluster analysis, 19 reliability and quality, 20 sampling in presence of covariates, 19 digital mapping studies, 5, 6t–12t covariates, 13 depth, extent, resolution, and sample density, sources of data, uncertainty, 13 validation, 13 digital soil mapping, 2–3 dynamic models, 37 environmental covariates, 23–24 CEC, 25 environmental covariates and soil carbon, 25–26 large terrain variations, 24–25 NIR reflectance, 25 spatial topsoil carbon pattern, 24 global mapping FAO–UNESCO map, 28 GlobalSoilMap.net, 28–29 in North America, 29f scorpan prediction function, 29–30 mapping SC change, 34 C stock over successive periods, changes in, 35f digital soil mapping, 35 legacy soil data, 34 mapping soil depth function equal-area spline function, 28 negative exponential depth model, 27 pseudo-3-D soil carbon map, 27 using mass coordinate approach, 30 mass coordinate system carbon stock accounting and C density, 18 cumulative carbon density, 18 ESM approach, 17–18 Lagrange system, 18 soil bulk density information, 17 Index soil carbon observations, 19f past studies, 3–4 predicting SC change, 35, 36f dynamic–mechanistic simulation model, 35 empirical approach, 39 NSW, 37–39, 38f using rule-based model, 37–39 SOC simulation models, 35–36 soil carbon dynamic models, 37 static-empirical model, 37 STEP-AWBH, 39 pseudo-3-D soil carbon mapping, 30 results from studies, 13f scorpan approach, soil carbon stock map, 31f spatial distribution, uncertainty, 31–33, 32f usage, validation, 33 crossvalidation, 33 crossvalidation and random holdback, 33–34 independent sampling, 33 internal validation, 33 variation biological and properties, 15 with depth, 14–15 equal-area spline fit, 17f at global scale, 21–23, 22f negative exponential depth function, 15 nonparametric depth function, 16 purposive-designed surveys, 15 spatial, 21 statistical procedure, 16–17 variogram of surface soil carbon, 21–23, 23f variograms of topsoil organic carbon, 21f Soil carbon change mapping, 34–35 in C stock over successive periods, 35f digital soil mapping, 35 legacy soil data, 34 predicting, 35–39 dynamic–mechanistic simulation model, 35 493 Index empirical approach, 39 NSW, 37–39, 38f by rule-based model, 37–39 SOC simulation models, 35–36 soil carbon dynamic models, 37 static-empirical model, 37 STEP-AWBH, 39 Soil organic carbon (SOC), 3–4 See also Microbial biomass carbon (MBC) dependency, 457 effects, 454 RF system effects, 454–457, 455t–456t simulation models, 35–36 Sorption, 112–113 onto phyllosilicates and organic matter bacteria, 124 EDTA and HA, 124 inner-sphere complexes, 123 inorganic and organic ligands, 121, 122f involving outer-sphere complexation, 123 mineral–water interface, 124 outer-sphere complexes, 123 pyromorphite, 123 rhizosphere, 123–124 sorption of Zn as function, 125f onto variable charge minerals and soils, 125–126 inorganic ligands effect, 126 organic ligands effect, 131–132 sorbent–metal–ligand or sorbent– ligand–metal, 126 Soybean genome sequence, 178 genomic era, 184–185 genome resequencing, 187–189 soybean genome, 185–187 postgenomic era, 189 gene function, 193–197 beyond genome, 197 genomic structural variation, 191–193 germplasm haplotyping and association mapping, 189–191 pregenomic era, 178 classical and molecular marker linkage maps, 182–184 cytogenetics, 181–182 phenotypic diversity, domestication, and breeding, 179–181 SoyTEdb database, 185–186 Special Report on Emissions Scenarios (SRES), 51 Spectroscopic techniques, 113 SRES See Special Report on Emissions Scenarios Static-empirical model, 37 Stele, 234 STEP-AWBH See Soil, topography, ecological and geographic properties–atmospheric, water, biotic, and human Stewardship, 93 Sugar/proton cotransport hypothesis, 220–221 Superoxide dismutase (SOD), 277–278 Symplasm See Symplast Symplast, 234–235 System sustainability evaluation, 467 T TCA See Tricarboxylic acid TN See Total soil nitrogen Tomato (Solanum tuberosum L.) absorption kinetic parameters, 231–232, 231t averaged ratio, 317 biomass, 230 NH4+–N and NO3−–N effects, 227–228, 229t responses to N forms, 228 Total soil nitrogen (TN), 459–460 Tricarboxylic acid (TCA), 234 Turgor pressure, 335–336 U United Nations Educational, Scientific and Cultural Organization (UNESCO), 28 United States Department of Agriculture research (USDA research), 179 UPOV Convention See International Union for Protection of New Varieties of Plants Convention USDA research See United States Department of Agriculture research 494 V Vegetative storage protein (VSP), 256–257 VIGS See Virus-induced gene silencing Virus-induced gene silencing (VIGS), 196 Visual crop injury symptoms, 412–413 VSP See Vegetative storage protein W Water erosion, 465–466 Water use efficiency (WUE), 449–450 in arid and semiarid rain-fed areas, 450 growing-season precipitation, 450–452 under RF systems, 450 in semiarid rain-fed areas, 450 Weed control, 462 Wheat absorbed ratio, 317–318 absorption kinetic parameters, 231–232, 231t N forms, responses to, 228 Index NH4+–N and NO3−–N effects, 227–228, 229t Winter wheat (T aestivum L.), 255 WUE See Water use efficiency X X-ray absorption near edge structure (XANES), 113 X-ray absorption spectroscopy (XAS), 113 Y Yield, climate change effect on of crop, 53–55 of seeds, 72–74 Z Z-O-glucosides See Z-O-xylosides (ZOG) Z-O-xylosides (ZOG), 347 Zeatin (ZT), 345 Zeatin riboside (ZR), 345 ... cover, lithology Ordinary kriging Linear mixed model Regression kriging Regression kriging Linear model Digital Mapping of Soil Carbon Hebei 187,693 province, China (Rawlins et al., 201 1) (Rawlins... swelling soils The approach is relatively well known in soil physics and has been applied in the calculation of water flow in swelling soils (McGarry and Malafant, 198 7) For carbon accounting,... soil mineral materials This is done in the following manner: first, the mineral mass of each sampling layer is calculated from the bulk density ρb (in kg m− 3), mineral fraction fmin (kg kg− 1),