V O LU M E N I N E T Y ADVANCES IN E I G H T AGRONOMY ADVANCES IN AGRONOMY Advisory Board PAUL M BERTSCH RONALD L PHILLIPS University of Kentucky University of Minnesota KATE M SCOW LARRY P WILDING University of California, Davis Texas A&M University Emeritus Advisory Board Members JOHN S BOYER KENNETH J FREY University of Delaware Iowa State University EUGENE J KAMPRATH MARTIN ALEXANDER North Carolina State University Cornell University Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D BALTENSPERGER, CHAIR LISA K AL-AMOODI CRAIG A ROBERTS KENNETH A BARBARICK MARY C SAVIN HARI B KRISHNAN APRIL L ULERY SALLY D LOGSDON V O LU M E N I N E T Y ADVANCES E I G H T IN AGRONOMY EDITED BY DONALD L SPARKS Department of Plant and Soil Sciences University of Delaware Newark, Delaware 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 84 Theobald’s Road, London WC1X 8RR, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA First edition 2008 Copyright # 2008 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: 978-0-12-374355-8 ISSN: 0065-2113 (series) For information on all Academic Press publications visit our website at books.elsevier.com Printed and bound in USA 08 09 10 10 CONTENTS Contributors Preface Advances in Precision Conservation ix xiii Jorge A Delgado and Joseph K Berry Introduction Geospatial Technologies Identifying Spatial Patterns and Relationships Field Level Flows Connection of Field with Off-Site Transport Watershed Scale Considerations Current Applications and Trends Summary and Conclusions References Reaction and Transport of Arsenic in Soils: Equilibrium and Kinetic Modeling 12 17 22 28 39 39 45 Hua Zhang and H M Selim Introduction Environmental Toxicity Arsenic in Soils Biogeochemistry Transport in Soils Modeling Remediation of Contaminated Soils Summary and a Look Ahead References Crop Residue Management for Lowland Rice-Based Cropping Systems in Asia 46 47 48 52 73 81 101 104 105 117 Bijay-Singh, Y H Shan, S E Johnson-Beebout, Yadvinder-Singh, and R J Buresh Introduction Criteria for Evaluating Crop Residue Management Options 118 121 v vi Contents Type and Abundance of Crop Residues Existing and Emerging Residue Management Options Evaluation of Options with Residues Managed During a Rice Crop Evaluation of Options with Residues Managed During a Non-Flooded Crop Crop Residue and Bioenergy Options Summary Acknowledgment References Sampling and Measurement of Ammonia at Animal Facilities 123 125 135 160 181 183 185 186 201 Ji-Qin Ni and Albert J Heber Introduction A General View of Ammonia Determination Ammonia Sampling Ammonia Concentration Measurement Measurement Methods and Devices Ammonia Concentration Data Summary and Conclusions Acknowledgments References Will Stem Rust Destroy the World’s Wheat Crop? 203 205 206 221 225 243 255 257 257 271 Ravi P Singh, David P Hodson, Julio Huerta-Espino, Yue Jin, Peter Njau, Ruth Wanyera, Sybil A Herrera-Foessel, and Richard W Ward Introduction Stem Rust Disease, Pathogen, and Epidemiology Breeding for Resistance Race UG99 and Why it is a Potential Threat to Wheat Production Breeding Strategies to Mitigate the Threat from UG99 and Achieve a Long-Term Control of Stem Rust Conclusion and Future Outlook Acknowledgments References 272 274 277 281 288 305 306 306 Genetic Improvement of Forage Species to Reduce the Environmental Impact of Temperate Livestock Grazing Systems 311 M T Abberton, A H Marshall, M W Humphreys, J H Macduff, R P Collins, and C L Marley Introduction Reducing Diffuse Nitrogenous Pollution of Watercourses 312 315 Contents Reducing P Pollution of Watercourses Reducing Emissions to Air Improving Soil Quality and Reducing Flood Damage Enhancing Persistency and Resilience Enhancing C Sequestration in Grasslands Future Prospects Acknowledgments References Mutagenesis and High-Throughput Functional Genomics in Cereal Crops: Current Status vii 321 325 335 339 341 344 345 345 357 H S Balyan, N Sreenivasulu, O Riera-Lizarazu, P Azhaguvel, and S F Kianian Introduction Insertional Mutagenesis Non-Transgenic TILLING, DEALING, and DeleteageneTM Approaches Phenomics Platform for Screening Mutagenized Population Outlook Acknowledgments References Index 358 361 380 398 399 401 401 415 This page intentionally left blank CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin M T Abberton (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom P Azhaguvel (357) Texas A&M University Agricultural Research and Extension Center, 6500 Amarillo Blvd West, Amarillo, Texas 79106 H S Balyan (357) Department of Genetics and Plant Breeding, Ch Charan Singh University, Meerut 250 004, India Joseph K Berry (1) Berry and Associates, Spatial Information Systems, Fort Collins, Colorado 80525 Bijay-Singh (117) Department of Soils, Punjab Agricultural University, Ludhiana 141 004, Punjab, India R J Buresh (117) International Rice Research Institute, Los Ban˜os, Philippines R P Collins (311) Plant Breeding and Genetics Programme Institute of Grassland Environmental Research, Plas Gogerddan, Aberystwyth, Ceredigion SY23 3EB, United Kingdom Jorge A Delgado (1) USDA-ARS, Soil Plant Nutrient Research Unit, Fort Collins, Colorado 80526 Albert J Heber (201) Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana 47907 Sybil A Herrera-Foessel (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico David P Hodson (271) International Maize and Wheat Improvement Center (CIMMYT), 06600 Mexico, DF, Mexico ix 416 Arsenic (As) biogeochemistry of, 52 binding mechanisms in soils, 54 desorption, 62–64 heterogeneous oxidation, 66–69 pH dependency, 56–57 precipitation and arsenic retention, 55 reduction and oxidation, microbialmediated, 69–72 solution composition, effect of, 57–59 sorption kinetics, 60–62 sulfides, reaction with, 64–66 contaminated soils, remediation of, 101–104 capping of contaminated soils, 102 phytoremediation, 104 PRB and MNA, 103 solidification/stabilization and soil flushing, 102 sorption and precipitation, 103 empirical equilibrium models, 84–86 equilibrium thermodynamic models, 81–84 geochemical models, application of, 99 GLUE methodology, 99, 101 kinetic models kinetic dissolution, 95–96 kinetic reduction–oxidation, 96–97 kinetic retention, 90–95 in soils, 48–52 compounds containing and in poultry industry, 50 leaching and disposal, 51 surface complexation models, 86–89 toxicity of, 47–48 transport of, 73–78 factor affecting mobility, 75–76 mechanisms, 73–78 mobility and field conditions, 78–80 models, 97–99 Arsenicosis, 48 Arsenite (AsO3) See also Arsenic (As) adsorption capacity on minerals and soils, 57 on metal oxides and hydroxides, 53 biotransformation, 76 heterogeneous oxidation on mineral surface, 96–97 kinetics of oxidation in aerated soil and, 69 NOM and sorption kinetic, 59 oxidation kinetics of, 70 simulation competition on Fe and Al oxides and, 88 in soil solution under flooded conditions, 71 sorption on iron sulfides and, 55 toxicity on binding to sulfhydryl groups, 47 weathering process and oxidation, 64 Arsine gas (AsH3), 47 AsO4 and AsO3 sorption edges, 58 Avena sativa, 123 Index B Bacillus benzoevorans, 76 Barley crop, insertional mutagenesis on, 374, 378 Biofuels, Biological N2 fixation (BNF), 159–160 Biomass production, 3, Biomethylation, 52, 72 Black rust See Stem rust, of wheat Brassica napus, 123 B vulgaris, 274 C Caenorhabditis elegans, 385, 390, 397 Carbonate anions in soil, 58 Cauliflower mosaic virus (CaMV), 35S enhancer element, 365, 375 Cereals density of mutations determined in, 389 induce mutations in a variety of plant species, 382 insertion mutagenesis resources in, 376–378 mutagenesis and high-throughput functional genomics in insertional mutagenesis, 362–380 non-transgenic approaches, 381–399 phenomics platform for screening population, 400–401 TILING initiatives in, 393–396 ChemcassetteÒ detection system, 239–240 Chemical deletogens, 385, 397–398 Chemical weathering, 64 See also Arsenic Chemiluminescence (CL) analyzer, 218, 235–238 Chillgard refrigerant leak detection system, 233, 249 Chromated copper chromate (CCA), 49, 80 Citric acid (CA), 59, 228 Closed sampling method, 211–215 See also Ammonia sampling and measurement C:N ratio, 144, 158, 176, 331, 343 Codon Optimizedto Detect Deleterious LEsions (CODDLE) software, 393 Composting, off-field residue management, 131–132 Condensed tannins (CTs), 332 Conservation management, 3, 9, 16, 22, 28 Contaminant of concern (COC), 47 Crop residues, rice-based cropping systems bioenergy production of, 181 flooded soil and, 120 open-field burning of, 119 residue and straw removal of, 121 Crude protein (CP), 328–329 D Dactylis glomerata, 314 Data quality indicators (DQIs), 244 417 Index DeleteageneTM, 360, 362, 382–383, 386, 390 See also Cereals dealing and, 397–399 Denuder device, 228 Detecting adduct lesions in genomes (DEALING), 360, 362, 381–382, 385, 390, 399 Diepoxybutane (DEB), 384–385, 388, 397–398 Digital elevation models (DEMs), 23–24 Dimethylarsinic acid (DMA), 71–72 Dissolved organic carbon (DOC), 59 Dissolved oxygen (DO), 64–66, 96 DNA pools for Ecotilling, 396 preparation of, 390 to screen deletion lines of C elegans, 397 Doubled haploid (DH) mutation, 388 Draăger sensor, in ammonia measurement, 238239 Drosophila melanogaster, 390 Dry methods, 221 See also Ammonia sampling and measurement Dynamic flows modeling, 10 See also Geographic information systems (GIS) E Eco-TILLING, 396–398 See also Cereals Electrochemical (EC) ammonia sensors, 238–239 Electrophoretic mobility (EM), 53 Emissions black C in China, 154 greenhouse gas and enhance C sequestration, 341 for non-flooded crop, 175–176, 179 for rice, 154, 157–158 reducing to air, 325–335 of trace gases, spatial variability in, 15 Environmental protection agency (EPA), 46, 203 Erosion rates, Escherichia coli, 69 Ethyl methane sulfonate (EMS), 382–383, 385, 388–389, 392, 395–397, 401 N-Ethyl-N-nitrosourea (ENU), 384–385 European Triticeae genomics initiative (ETGI), 358 Eutrophication, 316, 321–322, 328 Evapotranspiration, 2, 24 Extended X-ray absorption fine structure (EXAFS), 51, 53, 55–56, 62, 67 F Ferrihydrite, 54, 56–58, 60–61, 63, 71, 89, 92 Fertilizer, non-flooded crop residue management, 167, 172 Festuca arundinacea, 314 Festuca glaucescens, 329 Festuca pratensis, 314 Festulolium loliaceum, 336 Field level flows See also Precision conservation to reduce N2O emissions, management for, 16–17 variable erosion and transport, 12–16 erosion patterns, 137Cs modeling, 14 predicted NO3-N leaching, spatial distribution of, 16 sand, spatial distribution of, 15 Flood damage control biodiverse mixtures, role of, 338–339 prevention of, 335–337 soil porosity and compaction for, 337–338 Fourier transform infrared spectroscopy (FTIR), 230–231 Full-length cDNA Over-eXpresser (FOX) gene, 377 Fulvic acid (FA), 59 G Gain-of-function mutations, 375–376 Gas detection tubes for ammonia, 228–230 Gasification, 181 Gas manufacturers intermediate standards (GMIS), 250 Gene function determination forward genetics approach, 361 reverse genetics approach, 359–361, 363–364 steps in, 359 Gene trap systems, 365, 376 Geographic information systems (GIS), 10, 12, 14, 20, 23–24, 27, 29 Geospatial technologies See also Precision conservation desktop mapping, 6–8 surface modeling, gibberellin 2-oxidase gene, 377 Global positioning systems (GPS), 4, 25–26, 28, 39 Global rust initiative, 289 See also Stem rust, of wheat Global warming, Granule bound starch synthase (GBSS) I gene, 395 Grassland enhancing C sequestration in, 341–344 management and food production, 313–314 nutrient budget information for, 316 persistency and resilience genetic variation, implementation of, 340–341 interspecific hybridization, role of, 339–340 role of forage legumes, 334 stabilization of, 338 418 Index Greenhouse gas emission from fertilizer production, 334–335 and non-flooded crop, 175–179 rice and, 154–158 Ground covering rice production system (GCRPS), 131 H Happy Seeder machine, 175 Humic acid (HA), 55 Hybrid single-particle lagrangian integrated trajectory (HYSPLIT), 284 Hydrous ferric oxide (HFO), 58, 72, 89 I In-field residue management, in rice cropping systems, 127 Infrared gas analyzer, 231–234 Insertion mutagenesis, 362–364 See also Cereals activation tagging, 375 gene trap systems, 376 T-DNA and, 364–369, 378–379 and transposon elements, 369–375, 379–381 Intergovernmental panel on climate change (IPCC), 313 International barley sequencing consortium (IBSC), 358 International rice functional genomics consortium (IRFGC), 376 International rice genome sequencing project, 358 International wheat genome sequencing consortium (IWGSC), 358 Iris fulva, 336 Italian ryegrass (IRG), 314, 329–330 K Knock-out mutation, 364–365 See also Cereals; Mutation T-DNA insertion mutation, 364–365 vs allelic mutation, 399–400 L Leaf protein, half-life values, 330 Lolium multiflorum, 314, 329–330 Lolium perenne, 314, 330 Long-term experiments, on rice See also Rice crop with continuous cropping of flooded rice in Philippines, 159 with removal of all above-ground biomass, 160 on rice–rice cropping systems, 152 and soil organic C, 180 Lotus corniculatus, 334 Lotus japonicus, 344 Lowland rice CH4 and N2O emissions from, 154–156 ecosystems in Asia, 124 effect of residue incorporation on yield of, 144 mulch as weed suppressant, importance of, 135, 151 variety under non-flooded conditions, 131 yeild, 144 Lr19 gene, 290 Lr24 gene, 289 Lupinus albus, 323 M Maize crop insertional mutagenesis on, 377–378 mutator transposon insertions in, 380 transposable elements in, 369–370 transposon insertion populations on, 371–372 Maize gene discovery project, 370, 377 Maize genetics cooperative stock center’s Web site, 377 Maize targeted mutagenesis (MTM) project, 377 Maximum contaminant level (MCL), 46 Mean annual nutrient productivity (aNP), 317 Mean residence time (MRT), 317 Medicago sativa, 314 Medicago truncatula, 344 Metal oxides, 53, 56–57, 60, 103, 105 Methane (CH4) contributor to climate change, 154 reduction of emissions, 331–332 tanniferous forage species, role of, 332–334 uptake and, 15–16 Micrometeorological technique for ammonia sampling, 215 Miran 203 infrared analyzer, 207, 234 Molybdopterin synthase gene, 380 Monitored natural attenuation (MNA), 103–104 Monomethylarsonic acid (MMA), 71–72 Mulching, rice-based cropping systems biomass transfer in, 134–135 conventional tillage and, 134 direct drilling and, 133 Happy Seeder approach in, 134 soil puddling and, 130 Multimedia mapping, 10, 12 Multi-point sampling system (MPSS), 215–216 Mutagenesis-based reverse genetics, 360–362 Mutagens for development of mutagenized population, 382–386 point-mutations-inducing chemical, 399 screening mutagenized population, 400–401 (see Cereals) treatment and population size, 386–390 Mutation See also Mutagens allelic series vs knockout, 399–400 419 Index analysis, identification of, 388 deletion mutations in Caenorhabditis elegans, 385 density determined in cereal crop species, 389 detection technique in TILING, 390–393 expression of tagged gene causing knock-out, 365 induced by Mu elements, 370 insertion of nDart1 in OsClpP5 in rice, 379 knockout mutations of Arabidopsis transcription factors, 397 toward more complex virulence, 282 waxy loci with severe, 395 Mu transposon, 369–370, 377 N National air emission monitoring study (NAEMS), 203 National institute of standards and technology (NIST), 249–250 Natural organic matter (NOM), 59 N fertilizer, 17, 150, 172, 316, 320, 334–335 Nitrate leaching, 3, 316, 320, 325 Nitrogen pollution, in watercourses causes of, 315–316 forage legumes, role of, 320 mapping techniques, 318–320 NUE, characteristics of, 316–318 red clover, losses from, 321 Nitrogen trading, 27 3-Nitro-4-hydroxyphenylarsonic acid (Roxarsone), 50 Nitrous oxide (N2O), 9, 154 and CH4 emission, 158, 177–179 emissions control, in atmosphere, 325–326 in oxidation of ammonia, 334 Nondispersive infrared analyzers (NDIR), 231 Non-flooded crop, residue management bioenergy implications for biopower options in, 181 straw characteristics, 182–183 fertilizer efficiency and, 167, 172 grain yield for mulching crop residues effect of, 168–172 N immobilization in, 161 residual effect of rice, 162–163 residue incorporation effects of, 164, 167 rice-wheat systems in, 165–166 Happy Seeder approach in, 175–176 N2O and CH4 emission and, 177–179 P and K management of, 179–180 pest and disease pressure for, 173–174 SOM in, 180–181 water use efficiency for, 173 NO3-N leaching, 14, 20, 29, 31 See also Field level flows; Off-site transport, connection of field with predicted spatial distribution, 16 N use efficiency (NUE), 316–318 Nutrient cycling, crop residue management, 120–121 Nutrient uptake efficiency (NUpE), 317 Nutrient utilization efficiency (NUtE), 317 O Occupational safety and health administration (OSHA), 203 Off-site transport, connection of field with flows from field to nonfarm areas, 17–20 effective erosion buffers, 19 pollutants in vadose zone, GIS software and models for, 18 PSMs, for capturing runoff phosphorus, 20 transport of chemicals in shallow underground tile, 18 ideal buffer width and riparian zones, 21–22 RUSLE and VFSMOD, to determine locations, 21 On-field residue management, rice cropping systems, 126 Open-field burning, rice-based cropping systems, 119 banning of, 122 residue incorporation, 129 Open-path Fourier transform infrared system (OP-FTIR), 218 Open-path sampling, 217–218 See also Ammonia sampling and measurement Opsis AR-500 UV open-path monitor, 235 OryGenesDB database, 376 Oryza sativa, 118 P Passive measurement device, 221–222 See also Ammonia sampling and measurement Path-weighted average (PWA), 217 Permeable reactive barrier (PRB), 103 Phenotype screening system, for mutagen population, 400–401 Phleum pratense, 314 Phosphate (PO4) in soils, 57 Phosphorus pollution control, in watercourses causes of, 321–323 PUE, in rumen, 324–325 P use efficiency, 323–324 Photoacoustic spectrophotometer (PAS), 232–233, 248, 252 Photosynthetic mutant screen (PMS), 377 Phyllosilicates, 57 Pioneer Hi-Bred’s trait utility system of corn, 377 Poa pratensis, 314 Point sampling method, 215–217 See also Ammonia sampling and measurement Point-zero-charge (PZC), 53 Polyphenol oxidase (PPO), 321 420 Index Population growth, Potential conservation practices, 30–38 Precision agricultural-landscape modeling system (PALMS), 24 Precision conservation buffers and riparian zones, 20–21 different degrees of, environmental impacts and production systems sustainability, 24 on field scale, 13 to generate maps for use in analysis in field of, 10 GIS mapping approach and map analysis, 6–8 hydrologically sensitive area and, 22 to identify hot spots on farm and watershed, 28 to increase for soil and water, conservation practices, 30–38 integration of information and locations for riparian buffers, 29 management and conservation, integration and maps for, for management of flows, 16–17 manure management, technology for, 17 modeling approach to, 23 patterns and relationships, identification of, 9–12 GIS research for, 10, 12 Map analysis procedures, 10–11 multimedia mapping and Cartesian coordinate system, 10 static coincidence analysis vs dynamic three-dimensional flows, 10, 12 and potential for site-specific applications, to reduce the transport of nutrients, 20 site-specific and three-dimensional scale approach, 4, 13 at watershed scale, 24–27 for animal management and soil and water conservation, 26 Precision conservation management zones (PCMZ), 16 Precision farming, Project aligned related sequences and evaluate SNPs (PARSESNP), 393 Pteris vittata, 104 Puccinia graminis, 273 Puccinia graminis tritici, 272 Pyrolysis, 181 Q Quality assurance and quality control (QAQC), 244, 250–251, 256 R Race Ug99, 273, 278–279 See also Stem rust gene Sr, immunity to, 291 and long-term control, 288–305 markers associated to stem rust resistance genes, 293–294 pandemic, prediction of, 287–288 threat to wheat production, 281–288 Red clover, 313, 315, 321, 333, 338 Remote sensing (RS), 4, 24–25, 28, 39 RescueMu project See Mu transposon Retrotransposon-tagged mutation, 380 Reverse genetics approach, gene function determination, 359–361 T-DNA insertion line in, 363–364, 378–379 transposon insertion lines in, 379–381 Rice-based cropping systems in Asia, residue management decision tree, 184–185 in-field residue management practices, 127 monocropping systems in, 128 non-flooded crop following rice biomass, transfer of, 134–135 incorporation, 132–133 mulching, 133–134 non-flooded crop, rice following rice composting, 131–132 incorporation, 129–130 mulching, 130–131 nutrient cycling in, 120–121 on-field residue management practices, 126 production area and grain yield in, 123–124 productivity, profitability and environmental impact, 122 residue production and area for, 124 soil puddling in, 125 Rice crop, evaluation for residue management options biological N2 fixation and, 159–160 grain yield effect of incorporation of upland crop residue on, 141–143 effect of rice residue incorporation on, 135–140 incorporation, profitability, 152–153 insertional mutagenesis on, 376–377 monocropping systems, 128, 151–152 residue incorporation effects of, 136–140 T-DNA knockout mutant lines in, 366–368 water use efficiency, 151 yield and residual effect relationship, 145 Rice yield in a barley–rice rotation, 144 with and without fertilizer application, 149 Riparian ecosystem management model (REMM), 21 Riparian zones denitrification of N in, 27 precision conservation and, 3–4 RNAi technique, 360–361 See also Cereals Rosemount gas analyzer, 233–234 421 Index S Seattle TILLING Project (STP) See Arabidopsis TILLING Project (ATP) Secale cereale, 281 Semidwarf wheat varieties, 278–279 Septoria nodorum, 273 Septoria tritici, 273 Silicic acid, 59 Site-specific management zones (SSMZ), 16 Smart dust, Smoke particles, 153–154 Soil aggregation, 12 Soil and water assessment tool (SWAT), 23 Soil erosion, 2, 18, 336, 338 Soil organic matter (SOM), 55, 178, 180–181 Soil puddling, 125 mulching and, 130 Soil quality, improvement of biodiverse mixtures, 338–339 flood tolerance and prevention, 335–337 soil porosity and compaction, 337–338 Solid-state/electronic ammonia sensor, 240–241 Sorghum crop See Cereals Sorting intolerant from tolerant (SIFT) program, 400 Spatial patterns and relationships See also Precision conservation GIS research and changes in geo-referencing and, 12 map analysis procedures, 10 maps of surface flow, 11 Sr gene, 278, 280, 290–291 See also Stem rust Staphylococcus aureus, 69 Staphylococcus xylsis, 69 Static coincidence modeling, 10 See also Geographic information systems (GIS) Stem rust, of wheat breeding for resistance, 277–281, 288–289 future perspectives of, 305–306 high-yielding wheat, 296–300 race-specific resistance genes for Ug99, 290–292 race-specific resistance genes in wheat, 292–295 resistant wheat varieties development, 304–305 Sr24 gene breakdown, 289–290 Ug99 resistance of plant, 295–296 world wheat area reduction, 300–304 occurrence of, 273–274 pathogens and epidemiology of, 274–277 race Ug99 of avirulent and virulent genes in, 281–282 epidemic prediction of, 287–288 geographical distribution of, 282–283 migration of, 284–285 wheat germplasm resistance/susceptibility, 285–287 resistance genes, PCR-based markers, 293–294 Surface mulching, 132 CH4 production and, 179 residue incorporation and, 158 and rice residue incorporation in no-till sown wheat, 176 Surfurosprillum barnesii, 76 Syngenta GeneChipÒ , 399 T Tanniferous forage species, 332–334 See also Methane (CH4) Targeting induced local lesions in genomes (TILLING) See also Cereals; Mutagen; Arabidopsis TILLING Project (ATP); Eco-TILLING creation of mutagenized populations, different schemes of, 387 DNA pool preparation, 390 mutagen agents for, 382–386 mutagen treatment, 386–390 mutation detection technique in CELI enzymatic mismatch cleavage of DNA, 392–393 PCR amplification of DNA pools, 390–391 software’s used and emerging techniques, 393–394 T-DNA insertion mutagenesis See also Cereals; Mutation; Mutagens gene knock-out mutation, 364–365 insertion lines in, 378–379 tagging mutation, 365–369 Thinopyrum elongatum, 289 Thinopyrum ponticum, 278 Tilletia indica, 174 Time weighted average (TWA), 215 Total ammoniacal-N (TAN), 327 Transposon-based gene tagging, 381 Transposon insertion mutagenesis See also Maize crop Ac/Ds transposons, uses of, 373 barley transposons, 374 insertion lines in, 379–381 maize transposon insertion populations, 371–372t Mu transposons, 370 transposable elements, 369 Trifolium ambiguum, 329 Trifolium nigrescens, 334 Trifolium pratense See Red clover Trifolium repens L See White clover Trimethylarsine oxide (TMAO), 72 Triticum aestivum, 119, 272 422 Index Triticum monococcum, 385, 390, 399 Triticum turgidum, 291 Triticum ventricosum, 278, 281 U Ultraviolet differential optical absorption spectrometer (UV-DOAS), 217–218, 234–235 Universal soil loss equation (USLE), 23 Upland (non-flooded) crop residue incorporation, 141–143 V VECHTA air sampling system, 210 Vegetative filter strip model (VFSMOD), 21 Vesicular arbuscular mycorrhizal fungi (VAM), 323 W Water and tillage erosion model, 13–14 Water erosion prediction project (WEPP), 24 Watershed scale See also Precision conservation models and tools, 22–24 DEMs, AGNPS and SWAT models, 23–24 WEPP and PALMS, 24 variable hydrology, 22 Water soluble carbohydrate (WSC), 328–329 waxy gene See Granule bound starch synthase (GBSS) I gene Wet methods, 221, 225–228 See also Ammonia sampling and measurement Wheat See also Stem rust, of wheat breeding for rust resistance, 277–281, 288–289 future perspectives of, 305–306 high-yielding wheat for, 296–300 race-specific resistance genes for Ug99, 290–292 resistant wheat varieties development, 304–305 Sr24 gene breakdown, 289–290 Ug99 resistance of plant, 295–296 wheat improvement strategies, race-specific resistance genes in, 292–295 world wheat area reduction, 300–304 crop, insertional mutagenesis on, 378 (see also Cereals) fungal diseases in, 273 production of, 272–273 rust pathogens, dispersal modes of, 275–277 stem rust (see Stem rust, of wheat) White clover, 314, 320, 323, 325, 329, 338 X X-ray absorption near-edge spectroscopy (XANES), 51, 65, 67, 69 Z Zea mays, 119 Precision conservation Precision Ag Wind erosion Chemicals Soil erosion Runoff Leaching Leaching Terrain Leaching Soils Yield Potassium 3-dimensional Flows Cycles Coincidence CIR image 2-dimensional Interconnected perspective Isolated perspective Jorge A Delgado and Joseph K Berry, Figure The site-specific approach can be expanded to a three-dimensional scale approach that assesses inflows and outflows from fields to watershed and region scales (From Berry et al., 2003.) Surface modeling Point samples are spatially interpolated into a continuous surface 53.2 ppm 4.2 ppm Field sample locations Phosphorus surface Discrete data spikes Min = 4.2 Max = 53.2 Avg = 13.4 SDev = 5.2 Spatial data mining 32c,62r 45c,18r Map surfaces are clustered to identify data pattern groups P 53.2 Relatively low responses in P, K, and N Relatively high responses in P, K, and N 11.0 Cluster Cluster N K 412.0 177.0 27.9 32.9 N K P Geographic space Data space Clustered data zones Jorge A Delgado and Joseph K Berry, Figure Surface modeling is used to derive map surfaces that utilize spatial data mining techniques to investigate the numerical relationships in mapped data (From Berry et al., 2005.) Map analysis Desktop mapping Field data Standard normal curve fit to the data Spatially interpolated data 34.1% 34.1% 68.3% +/−1 standard deviation Average = 22.0 StDev = 18.7 22.0 28.2 Discrete spatial object (generalized) 80 60 40 20 −20 −40 −60 High = 50 80 60 40 20 Average = 22.0 −20 −40 −60 N Continuous spatial distribution (detailed) Jorge A Delgado and Joseph K Berry, Figure Desktop mapping uses aggregated, nonspatial statistics to summarize spatial objects (points, lines, and polygons), whereas map analysis uses continuous spatial statistics to characterize gradients in geographic space (surfaces) Inclination of a fitted plane to a location and its eight surrounding elevation values 2418 2404 2393 2409 2395 2341 2383 2373 2354 Slope(47,64) = 33.23% 35% 30% 25% 20% 15% 10% 5% 1% 0% Steep Moderate Gentle flat Slope map draped on elevation Slope map Elevation surface Flow(28,46) = 451 paths 537 Paths Heavy 256 Paths 123 Paths 64 Paths 32 Paths 16 Paths Moderate Paths Paths Light Paths Paths minimal Total number of the steepest downhill paths flowing into each location Flow map draped on elevation Slope map Jorge A Delgado and Joseph K Berry, Figure Maps of surface flow confluence and slope are calculated by considering relative elevation differences throughout a project area (From Berry et al., 2005.) Tillage erosion Water erosion Tillage–water erosion Total erosion (cesium-137 measurements) −1 −1 Mg yr −33 Soil loss net erosion −22 Accelerated erosion −11 Soil loss T value = −11 Mg ha−1 yr−1 11 22 33 120 Soil gain net deposition Slope % map and cesium137 sampling sites Slope % Elevation contour lines are overlaid on all maps elevation labels are shown only on total erosion map Jorge A Delgado and Joseph K Berry, Figure Erosion patterns developed from tillage, water, tillage-water, and total erosion (137Cs) modeling of the research field are displayed Cesium sampling sites are also displayed on a contour map of slope percentage for the field (From Schumacher et al., 2005.) A 100 100 90 80 70 70 60 Sand (%) 90 80 60 50 50 200 200 150 150 100 100 50 50 kg NO3-N/ha 0–1.5m B Jorge A Delgado and Joseph K Berry, Figure Spatial distribution of sand content in the top 1.5 m of soil across different productivity zones (A) Spatial distribution of observed residual soil NO3 -N in the top 1.5 m of soil for study one across the different productivity zones during the 2000 growing season (B) (From Delgado and Bausch, 2005.) 250 200 150 150 100 100 50 50 kg NO3-N/ha 0–1.5m 250 200 Jorge A Delgado and Joseph K Berry, Figure Spatial distribution of predicted NO3-N leaching from the root zone of corn (1.5 m depth) in study one across the different productivity zones during the 2000 growing season (From Delgado and Bausch, 2005.) Erosion potential Slopemap Reclassify Overlay Reclassify steep moderate gentle Flow/slope Slope_classes Reclassify 33 heavy flow: steep 33 heavy flow: moderate 33 heavy flow: gentle 23 moderate flow: steep 22 moderate flow: moderate 21 moderate flow: gentle 13 light flow: steep 12 light flow: moderate 11 light flow: gentle Flowmap Erosion_potential High Moderate Low Flow_classes heavy moderate light Effective erosion buffers Effective erosion potential distance Erosion_potential Far Distance Close Erosion buffers Streams Jorge A Delgado and Joseph K Berry, Figure Effective erosion buffers around a stream expand and contract depending on the erosion potential of the intervening terrain (From Berry et al., 2005.) ... patterns inherent in mapped data using surface modeling and spatial data mining These approaches can be used to explain variance by mapping and analyzing spatial distributions (Berry, 2002) Identifying... surface modeling, spatial data mining, and map analysis In this paper, we are refining the definition as follows: Precision Conservation is technologically based, requiring the integration of... have to meet the increasing food demands for this increasing population, especially because of an increasing demand for land area to be used for biofuels These increases in intensive production