(Advances in agronomy 116) donald l sparks (eds ) advances in agronomy 116 academic press, elsevier (2012)

273 72 0
  • Loading ...
    Loading ...
    Loading ...

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

Tài liệu liên quan

Thông tin tài liệu

Ngày đăng: 08/05/2019, 14:24

ADVANCES IN AGRONOMY Advisory Board PAUL M BERTSCH RONALD L PHILLIPS University of Kentucky University of Minnesota KATE M SCOW LARRY P WILDING University of California, Davis Texas A&M University Emeritus Advisory Board Members JOHN S BOYER KENNETH J FREY University of Delaware Iowa State University EUGENE J KAMPRATH MARTIN ALEXANDER North Carolina State University Cornell University Prepared in cooperation with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Book and Multimedia Publishing Committee DAVID D BALTENSPERGER, CHAIR LISA K AL-AMOODI CRAIG A ROBERTS WARREN A DICK MARY C SAVIN HARI B KRISHNAN APRIL L ULERY SALLY D LOGSDON Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA 32 Jamestown Road, London, NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2012 Copyright # 2012 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: 978-0-12-394277-7 ISSN: 0065-2113 (series) For information on all Academic Press publications visit our website at elsevierdirect.com Printed and bound in USA 12 13 14 15 10 CONTRIBUTORS Numbers in Parentheses indicate the pages on which the authors’ contributions begin K J Boote (41) Agronomy Department, University of Florida, Gainesville, Florida, USA Jean-Pierre Caliman (71) PT SMART Research Institute (SMARTRI), Pekanbaru, Riau, Indonesia Qing Chen (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Xinping Chen (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Franc¸ois Colin (71) Montpellier-SupAgro, UMR-LISAH (Laboratory on Interactions between Soil, Agrosystem and Hydrosystem), Montpellier cedex, France Irina Comte (71) Department of Natural Resource Sciences, Macdonald Campus of McGill University, Ste-Anne-de-Bellevue, Quebec, Canada, and CIRAD (International Cooperation Centre in Agronomic Research for Development), Montpellier cedex, France Zhenling Cui (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Mingsheng Fan (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Steven J Fonte (123) International Center for Tropical Agriculture (CIAT), Cali, Colombia ¨nberger (71) Olivier Gru IRD (Institut de Recherche pour le De´veloppement), UMR-LISAH, Montpellier cedex, France Rongfeng Jiang (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Xiaotang Ju (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China ix x Contributors Uttam Kumar (41) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Andhra Pradesh, India Patrick Lavelle (125) International Center for Tropical Agriculture (CIAT), Cali, Colombia, and Institut de Recherche sur le De´veloppement (IRD)/Universite´ Pierre et Marie Curie (UPMC), Paris, France Xin Li (219) Department of Agronomy, Kansas State University, Manhattan, Kansas, USA Xuejun Liu (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Guohua Mi (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Pedro Oyarzun (125) EkoRural, Quito, Ecuador Soroush Parsa (125) International Center for Tropical Agriculture (CIAT), Cali, Colombia D Carolina Quintero (125) International Center for Tropical Agriculture (CIAT), Cali, Colombia Idupulapati M Rao (125) International Center for Tropical Agriculture (CIAT), Cali, Colombia Terry J Rose (185) Southern Cross Plant Science, Southern Cross University, Lismore, NSW, Australia Jianbo Shen (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Piara Singh (41) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Andhra Pradesh, India Steven J Vanek (125) Department of Crop and Soil Science, Cornell University, Ithaca, New York, USA Jiankang Wang (219) Institute of Crop Science and CIMMYT China, Chinese Academy of Agricultural Sciences, Beijing, China Contributors xi Joann K Whalen (71) Department of Natural Resource Sciences, Macdonald Campus of McGill University, Ste-Anne-de-Bellevue, Quebec, Canada Matthias Wissuwa (185) Japan International Research Center for Agricultural Sciences (JIRCAS), Crop Production and Environment Division, Ohwashi, Tsukuba, Ibaraki, Japan Jianming Yu (219) Department of Agronomy, Kansas State University, Manhattan, Kansas, USA Fusuo Zhang (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Weifeng Zhang (1) Department of Plant Nutrition, China Agricultural University, Beijing, PR China Chengsong Zhu (217) Department of Agronomy, Kansas State University, Manhattan, Kansas, USA PREFACE Volume 116 contains six excellent reviews dealing with environmental sustainability and food security Chapter is an enlightening review on an integrated nutrient management (INM) approach, developed on more than 20 years of research, to address serious environmental quality challenges, related to excess use of nutrients, in China The INM approach has led to increased nutrient use efficiency and decreased inputs of fertilizers Chapter deals with the effect of climate change factors on crop growth, development, and yield of groundnut Chapter is a comprehensive review on practices used in oil palm plantations and impacts on hydrological changes, nutrient fluxes, and water quality in Indonesia Chapter is an enlightening overview of soil fertility decline in the high Andes of Bolivia, Ecuador, and Peru Approaches are presented to enhance nutrient cycling, crop nutrient uptake, and overall increased productivity Chapter addresses an important global factor affecting future food security, phosphorus utilization efficiency (PUE) by plants The review focuses on grain crops and covers past attempts to improve PUE via plant breeding, and new approaches for improving PUE Chapter is a stimulating review on the importance of computer simulation in plant breeding I am grateful to the authors for their outstanding reviews DONALD L SPARKS Newark, Delaware, USA xiii C H A P T E R O N E Integrated Nutrient Management for Food Security and Environmental Quality in China Fusuo Zhang, Zhenling Cui, Xinping Chen, Xiaotang Ju, Jianbo Shen, Qing Chen, Xuejun Liu, Weifeng Zhang, Guohua Mi, Mingsheng Fan, and Rongfeng Jiang Contents Introduction Principles of INM 2.1 Optimizing nutrient inputs and taking all possible sources of nutrients into consideration 2.2 Dynamically matching soil nutrient supply with crop requirement spatially and temporally 2.3 Effectively reducing N losses in intensive managed Chinese cropping systems 2.4 Taking all possible yield increase measures into consideration Technology and Demonstration of INM in Different Cropping Systems 3.1 INM for intensive wheat and maize system 3.2 INM for paddy rice 3.3 INM for vegetable systems 3.4 INM for orchards Large-Scale Dissemination of INM Summary and Conclusions Acknowledgments References 11 12 15 16 18 21 23 26 29 31 32 32 Abstract While the concept of sustainability as a goal has become widely accepted, the dominant agricultural paradigm still considers high yield and reduced environmental impact being in conflict with one another During the past 49years (1961–2009), the 3.4-fold increase in Chinese agricultural food production can Department of Plant Nutrition, China Agricultural University, Beijing, PR China Advances in Agronomy, Volume 116 ISSN 0065-2113, DOI: 10.1016/B978-0-12-394277-7.00001-4 # 2012 Elsevier Inc All rights reserved Fusuo Zhang et al be partly attributed to a 37-fold increase in N fertilization and a 91-fold increase in P fertilization, but the environment costs have been very high New advances for sustainability of agriculture and ecosystem services will be needed during the coming 50years to improve nutrient use efficiency (NUE) while increasing crop productivity and reducing environmental risk Here, we advocate and develop integrated nutrient management (INM) based on more than 20years of studies In this INM approach, the key components comprise (1) optimizing nutrient inputs by taking all possible nutrient sources into consideration, (2) matching nutrient supply in root zone with crop requirements spatially and temporally, (3) reducing N losses in intensively managed cropping systems, and (4) taking all possible yield-increasing measures into consideration Recent large-scale application of INM for cereal, vegetable, and fruit cropping systems has shed light on how INM can lead to significantly improved NUE, while increasing crop yields and reducing environmental risk The INM has already influenced Chinese agricultural policy and national actions, and resulted in increasing food production with decreased climb of chemical fertilizer consumption at a national scale over recent years The INM can thus be considered an effective agricultural paradigm to ensure food security and improve environmental quality worldwide, especially in countries with rapidly developing economies Abbreviations AEN FNP INM NCP NUE ONR PFPN REN agronomy N efficiency farming practice integrated nutrient management North China Plain nutrient use efficiency optimum N fertilizer rate nitrogen partial factor productivity recovery N efficiency Introduction The Green Revolution helped to create the world’s “Miracle in China,” with 9% of the world’s arable land feeding 22% of the world population In the past 49years (1961–2009), cereal grain yields have increased 3.5-fold from 1.2 to 5.4thaÀ1, while total grain production has increased 3.4-fold from 110 to 483 million ton (MT) (FAO, 2011) In 1998, grain, meat, and egg production per capita in China exceeded the world average The increased demand in Chinese grain production has affected the global food supply and the natural Nutrient Management in China resource bases required for nutrient production (fossil fuels, mineral sources of P and K) and has attained world recognition However, this 3.4-fold increase in Chinese agricultural food production during the past 49years can be partly attributed to a 48-fold increase in chemical fertilizers from to 49MT, including a 37-fold increase in N fertilizer application and a 91-fold increase in P fertilizer use, and a 442-fold increase in the area of irrigated croplands (Fig 1) Total consumption of chemical fertilizers worldwide increased by 3.9-fold from 32 to 164MT, indicating that 36% of the global increase ($132MT) came from China during the past 49years In the past 10years (2000–2009), 54% of the global increase in chemical fertilizer consumption ($27MT) was contributed by China, including 11MT fertilizer N (54% of the global increase), 2.5 MT fertilizer P (52% of the global increase), and 1.1MT fertilizer K (58% of the global increase) (Figs and 2A,B) Cereal yields in the past 10years have continued to increase with no proportional increases in fertilizer use in many developed countries or regions such as Western Europe (rainfed cereal systems), North America (rainfed and irrigated corn), and Japan and South Korea (irrigated rice) (Dobermann and Cassman, 2005) For example, in the past 10years, chemical fertilizer consumption in the United States increased by only 0.04MT with 0.23% of total fertilizer consumption in 2009 and decreased by 0.32 MT in Western Europe (Fig 2A) By contrast, the application rate of 600 400 60 300 40 200 20 100 1960 1970 1980 1990 2000 Fertilizer consumption (MT) Grain production (MT) 500 80 Grain production Total fertilizer N fertilizer P fertilizer K fertilizer 2010 Year Figure The trend of grain production and chemical fertilizer inputs (N, P, and K fertilizers) in China from 1961 to 2009 The P and K fertilizers are calculated by P2O5 and K2O, respectively Fertilizer consumption is defined as the difference between fertilizer production and exports Source: FAO (2011) and IFA (2011) Fusuo Zhang et al 200 80 Global fertilizer (MT) 160 60 120 40 80 20 40 1960 1970 1980 1990 Year 1970 1980 1990 Year 2000 Regional fertilizer (MT) A 2010 600 B -1 Fertilizer rate (kg ) 500 400 300 200 100 1960 2000 2010 Global fertilizer China United States Western Europe Figure Trend of total chemical fertilizer consumption (A) and fertilizer rate per hectare (B) for global scale, China, United States, and Western European Source: IFA (2011) chemical fertilizers in China was continually increasing and reached 448kg haÀ1 in 2009, which is 2.8, 2.9, and 1.4 times the world average and rates in the United States and Western Europe, respectively (Fig 2B) On the other hand, Chinese cereal crop production has stagnated at approximately 450MT since 1998 From 1998 to 2009, grain yields increased 255 Computer Simulation in Plant Breeding Table A list of software commonly used in computer simulation in plant breeding Name Utility References APSIM Crop modeling McCown et al http://www.apsim info/Wiki/ (1996), Keating et al (2003) Frisch et al (2000) Maurer et al http://www.r-project (2008) org/ http://www.uq.edu Podlich and au/lcafs/qugene/ Cooper (1998) PLABSIM Marker-assisted backcrossing PLABSOFT Plant breeding QU-GENE Genotype-byenvironment interaction and plant breeding E-CELL Whole-cell simulation Tomita et al (1999) Online access http://www.e-cell org/ecell/ Commonly used variables for different runs include the number of QTLs and markers, QTL position on the genetic map, genome size, the effect of each QTL, population size, heritability, degree of epistasis, and environment types After meeting the computational challenge, researchers must analyze and interpret computer output As with statistical software, computer simulation gives us only the numbers, but the numbers require interpretation to be meaningful and practical Computer simulation must be coupled with human intelligence to be useful Summary and Perspectives In summary, the main points we presented in this review included: Computer simulation, a bridge between theory and experimentation, has become a powerful tool in scientific research It can be used to conduct pilot or virtual experiments to verify new theories or provide guidelines for empirical experimentation Huge amounts of information have been generated in crop improvement research during the past several decades, especially significant advances in molecular dissecting of complex traits and high-throughput genotyping techniques Computer simulation can transfer these advancements into plant-breeding practice Computer simulation can compare different breeding strategies, incorporating gene information, cross scheme, propagation method, population 256 Xin Li et al size, selection intensity, and number of generations simultaneously; thus, we can use computer simulation to decide which breeding strategy could lead to higher genetic gain Computer simulation can be applied to gene mapping to validate the effectiveness of new mapping methods or assess the factors influencing mapping power (e.g., population type and size, marker number and density, heritability, and number of QTLs) Computer simulation can also help us determine the significance threshold and CI, which otherwise would be difficult to calculate analytically Plant-breeding simulation platforms are potent tools that can simulate the whole plant-breeding process They use genetic and GEI information to, for instance, predict cross performance and compare selection methods, thus enhancing our ability to make decisions about plant breeding Computer simulation can integrate crop physiological models, environmental information, and genetic composition of different crops to fill the gap between genotype and phenotype We can use computer simulation to predict the performance of different cultivars in TPEs and thus facilitate the plant-breeding process When coupled with climate simulation models, crop models can be used to predict the possible influences of climate change on crop production, which can subsequently provide guidelines for plant breeding With the exponential increase in computational power and decrease in the price of that power, computer simulation will become more common, but custom programs tailored for specific purposes will still need to be developed At the beginning of the second decade of the twenty-first century, we are closer than at any other point in history to deciphering various mysteries in life science Huge amounts of information in genetics, genomics, biochemistry, molecular biology, and bioinformatics are now available, but only some of this information has been applied to plant breeding The practical goal of scientific research is more than just explaining the mechanisms underlying life phenomena—it is learning to manipulate those mechanisms to benefit humankind Plant breeders have the challenge of determining how to take advantage of this knowledge to make crop improvement more efficient and enhance genetic gain Research in establishing genotype–phenotype relationship and developing new breeding methods has been proposed to realize the potential brought about by ultrahigh-throughput genomic technologies in plant breeding (Yu, 2009), and computer simulation undoubtedly will be a key part of this process As a tool, computer simulation will aid decision-making and resource allocation through transferring experimental results from laboratory to agricultural production and by predicting the outcome of breeding decisions, directing gene mapping, and tackling GEI and climate change Computer Simulation in Plant Breeding 257 ACKNOWLEDGMENTS This work was supported by the Agriculture and Food Research Initiative Competitive Grant (2011-03587) from the USDA National Institute of Food and Agriculture, the Plant Feedstock Genomics Program (DE-SC0002259) of the U.S Department of Energy, and the Plant Genome Program (DBI-0820610) of the National Science Foundation, the Targeted Excellence Program of Kansas State University, and the Kansas State University Center for Sorghum Improvement REFERENCES Ahuja, I., de Vos, R C H., Bones, A M., and Hall, R D (2010) Plant molecular stress responses face climate change Trends Plant Sci 15(12), 664–674 Allard, R W (1960) Principles of Plant Breeding John Wiley and Sons Inc., New York Asseng, S., Turner, N C., Botwright, T., and Condon, A G (2003) Evaluating the impact of a trait for increased specific leaf area on wheat yields using a crop simulation model Agron J 95(1), 10–19 Barczi, J F., Rey, H., Caraglio, Y., De Reffye, P., Barthelemy, D., Dong, Q X., et al (2008) AmapSim: A structural whole-plant simulator based on botanical knowledge and designed to host external functional models Ann Bot 101(8), 1125–1138 Bauer, A M., Hoti, F., Korff, M.v., Pillen, K., Leon, J., and Sillanpaa, M J (2009) Advanced backcross-QTL analysis in spring barley (H vulgare ssp spontaneum) comparing a REML versus a bayesian model in multi-environmental field trials Theor Appl Genet 119(1), 105–123 Beavis, W D (1998) QTL analyses: Power, precision, and accuracy In “Molecular Dissection of Complex Traits” (A H Paterson, Ed.), pp 145–162 CRC press, New York Beavis, W (1994) The power and deceit of QTL experiments: Lessons from comparative QTL studies In “Proceedings of the 49th Annual Corn and Sorghum Industry Research Conference,” pp 250–266 Bernacchi, D., Beck-Bunn, T., Emmatty, D., Eshed, Y., Inai, S., Lopez, J., et al (1998a) Advanced backcross QTL analysis of tomato II Evaluation of near-isogenic lines carrying single-donor introgressions for desirable wild QTL-alleles derived from lycopersicon hirsutum and L pimpinellifolium Theor Appl Genet 97(1), 170–180 Bernacchi, D., Beck-Bunn, T., Eshed, Y., Lopez, J., Petiard, V., Uhlig, J., et al (1998b) Advanced backcross QTL analysis in tomato I Identification of QTLs for traits of agronomic importance from lycopersicon hirsutum Theor Appl Genet 97(3), 381–397 Bernardo, R., and Yu, J (2007) Prospects for genomewide selection for quantitative traits in maize Crop Sci 47(3), 1082–1090 Bernardo, R (1999a) Marker-assisted best linear unbiased prediction of single-cross performance Crop Sci 39(5), 1277–1282 Bernardo, R (1999b) Selection response with marker-based assortative mating Crop Sci 39 (1), 69–73 Bernardo, R (2001) What if we knew all the genes for a quantitative trait in hybrid crops? Crop Sci 41(1), 1–4 Bernardo, R (2002) Breeding for Quantitative Traits in Plants Stemma Press, Woodbury, MN Bernardo, R (2003) Parental selection, number of breeding populations, and size of each population in inbred development Theor Appl Genet 107(7), 1252–1256 Bernardo, R (2004) What proportion of declared QTL in plants are false? Theor Appl Genet 109(2), 419–424 258 Xin Li et al Bernardo, R (2008) Molecular markers and selection for complex traits in plants: Learning from the last 20 years Crop Sci 48(5), 1649–1664 Bernardo, R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize Crop Sci 49(2), 419–425 Bernardo, R (2010) Genomewide selection with minimal crossing in self-pollinated crops Crop Sci 50(2), 624–627 Bernardo, R., and Charcosset, A (2006) Usefulness of gene information in marker-assisted recurrent selection: A simulation appraisal Crop Sci 46(2), 614–621 Bernardo, R., Moreau, L., and Charcosset, A (2006) Number and fitness of selected individuals in marker-assisted and phenotypic recurrent selection Crop Sci 46(5), 1972– 1980 Bogdan, M., Ghosh, J K., and Doerge, R W (2004) Modifying the schwarz bayesian information criterion to locate multiple interacting quantitative trait loci Genetics 167(2), 989–999 Botstein, D., White, R L., Skolnick, M., and Davis, R W (1980) Construction of a genetic-linkage map in man using restriction fragment length polymorphisms Am J Hum Genet 32(3), 314–331 Chapman, S., Cooper, M., Podlich, D., and Hammer, G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects Agron J 95(1), 99–113 Chapman, S C (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials Euphytica 161 (1), 195–208 Chapman, S C., Cooper, M., Hammer, G L., and Butler, D G (2000) Genotype by environment interactions affecting grain sorghum II Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields Aust J Agr Res 51(2), 209–221 Chapman, S C., Wang, J., Rebetzke, G J., and Bonnett, D G (2007) Accounting for variability in the detection and use of markers for simple and complex traits In “Scale and Complexity in Plant Systems Research: Gene-Plant-Crop Relations” ( J H J Spiertx, P Struik, and H H van Laar, Eds.), pp 37–44 Springer, Dordrecht, Netherlands Charmet, G., Robert, N., Perretant, M R., Gay, G., Sourdille, P., Groos, C., et al (1999) Marker-assisted recurrent selection for cumulating additive and interactive QTLs in recombinant inbred lines Theor Appl Genet 99(7–8), 1143–1148 Chen, X., Zhao, F., and Xu, S (2010) Mapping environment-specific quantitative trait loci Genetics 186(3), 1053–1066 Chenu, K., Chapman, S C., Tardieu, F., McLean, G., Welcker, C., and Hammer, G L (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: A “gene-to-phenotype” modeling approach Genetics 183(4), 1507–1523 Chesler, E J., Rodriguez-Zas, S L., and Mogil, J S (2001) In silico mapping of mouse quantitative trait loci Science 294(5551), 2423 Christensen, J., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, R., et al (2007) Regional climate projections In “Climate change, 2007: The physical science basis Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change” (S Solomon, D Qni, M Manning, Z Chen, M Marquis, K B Averyt, M Tignor, and H L Miller, Eds.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA Churchill, G A., and Doerge, R W (1994) Empirical threshold values for quantitative trait mapping Genetics 138(3), 963–971 Cooper, M., Chapman, S C., Podlich, D W., and Hammer, G L (2002) The GP problem: Quantifying gene-to-phenotype relationships In Silico Biol 2(2), 151–164 Computer Simulation in Plant Breeding 259 Cooper, M., Podlich, D W., and Luo, L (2007) Modeling QTL effects and MAS in plant breeding In “Genomics-Assisted Crop Improvement” (R K Varshney and R Tuberosa, Eds.), pp 57–95 Springer, Dordrecht, The Netherlands Crepieux, S., Lebreton, C., Servin, B., and Charmet, G (2004) Quantitative trait loci (QTL) detection in multicross inbred designs: Recovering QTL identical-by-descent status information from marker data Genetics 168(3), 1737–1749 Darvasi, A., and Soller, M (1997) A simple method to calculate resolving power and confidence interval of QTL map location Behav Genet 27(2), 125–132 Darvasi, A., Weinreb, A., Minke, V., Weller, J I., and Soller, M (1993) Detecting markerqtl linkage and estimating qtl gene effect and map location using a saturated genetic-map Genetics 134(3), 943–951 de Dorlodot, S., Forster, B., Pagcˇs, L., Price, A., Tuberosa, R., and Draye, X (2007) Root system architecture: Opportunities and constraints for genetic improvement of crops Trends Plant Sci 12(10), 474–481 Doerge, R W (2002) Mapping and analysis of quantitative trait loci in experimental populations Nat Rev Genet 3(1), 43–52 Dupuy, L., Gregory, P J., and Bengough, A G (2010) Root growth models: Towards a new generation of continuous approaches J Exp Bot 61(8), 2131–2143 Edwards, M D., and Page, N J (1994) Evaluation of marker-assisted selection through computer-simulation Theor Appl Genet 88(3–4), 376–382 Evers, J B., Vos, J., Yin, X., Romero, P., van der Putten, P E L., and Struik, P C (2010) Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation J Exp Bot 61(8), 2203–2216 Falconer, D S (1960) Introduction to Quantitative Genetics Oliver and Boyd, London Frisch, M., Bohn, M., and Melchinger, A (2000) Computer note PLABSIM: Software for simulation of marker-assisted backcrossing J Hered 91(1), 86 Frisch, M., Bohn, M., and Melchinger, A E (1999) Comparison of selection strategies for marker-assisted backcrossing of a gene Crop Sci 39(5), 1295–1301 Frisch, M., and Melchinger, A E (2001) Marker-assisted backcrossing for simultaneous introgression of two genes Crop Sci 41(6), 1716–1725 Frisch, M., and Melchinger, A E (2005) Selection theory for marker-assisted backcrossing Genetics 170(2), 909–917 Gimelfarb, A., and Lande, R (1994) Simulation of marker-assisted selection in hybrid populations Genet Res 63(1), 39–47 Gimelfarb, A., and Lande, R (1995) Marker-assisted selection and marker-qtl associations in hybrid populations Theor Appl Genet 91(3), 522–528 Grafahrend-Belau, E., Schreiber, F., Koschutzki, D., and Junker, B H (2009) Flux balance analysis of barley seeds: A computational approach to study systemic properties of central metabolism Plant Physiol 149(1), 585–598 Grupe, A., Germer, S., Usuka, J., Aud, D., Belknap, J K., Klein, R F., et al (2001) In silico mapping of complex disease-related traits in mice Science 292(5523), 1915–1918 Hammer, G., Kropff, M., Sinclair, T., and Porter, J (2002) Future contributions of crop modelling—From heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement Eur J Agron 18(1–2), 15–31 Han, L., and Xu, S (2010) Genome-wide evaluation for quantitative trait loci under the variance component model Genetica 138(9–10), 1099–1109 Hodson, D., and White, J (2010) GIS and crop simulation modelling applications in climate change research In “Climate Change and Crop Production” (M P Reynolds, Ed.), pp 245–262 CABI, Oxfordshire, UK Holland, J B (2004) Implementation of molecular markers for quantitative traits in breeding programs—challenges and opportunities In “New Directions for a Diverse Planet: Proceedings for the 4th International Crop Science Congress, Brisbane, Australia, 26” 260 Xin Li et al Hoogenboom, G., White, J W., and Messina, C D (2004) From genome to crop: Integration through simulation modeling Field Crop Res 90(1), 145–163 Hospital, F., and Charcosset, A (1997) Marker-assisted introgression of quantitative trait loci Genetics 147(3), 1469–1485 Hospital, F., Chevalet, C., and Mulsant, P (1992) Using markers in gene introgression breeding programs Genetics 132(4), 1199–1210 Hospital, F., Moreau, L., Lacoudre, F., Charcosset, A., and Gallais, A (1997) More on the efficiency of marker-assisted selection Theor Appl Genet 95(8), 1181–1189 Hospital, F (2001) Size of donor chromosome segments around introgressed loci and reduction of linkage drag in marker-assisted backcross programs Genetics 158(3), 1363–1379 Jannink, J L., and Jansen, R (2001) Mapping epistatic quantitative trait loci with onedimensional genome searches Genetics 157(1), 445–454 Jannink, J L., Lorenz, A J., and Iwata, H (2010) Genomic selection in plant breeding: From theory to practice Brief Funct Genomics 9(2), 166–177 Jansen, R C (1993) Interval mapping of multiple quantitative trait loci Genetics 135(1), 205–211 Jansen, R C., Jannink, J L., and Beavis, W D (2003) Mapping quantitative trait loci in plant breeding populations: Use of parental haplotype sharing Crop Sci 43, 829–834 Jiang, C J., and Zeng, Z B (1997) Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines Genetica 101(1), 47–58 Johnson, R (2004) Marker-assisted selection Plant Breed Rev 24(1), 293–309 Kang, M S (2001) Quantitative genetics, genomics and plant breeding In “Quantitative Genetics Genomics and Plant Breeding” (M S Kang, Ed.) xvi ỵ 400 pp CABI, Wallingford, UK Kao, C H., Zeng, Z B., and Teasdale, R D (1999) Multiple interval mapping for quantitative trait loci Genetics 152(3), 1203–1216 Keating, B A., Carberry, P., Hammer, G., Probert, M E., Robertson, M., Holzworth, D., et al (2003) An overview of APSIM, a model designed for farming systems simulation Eur J Agron 18(3–4), 267–288 Kuchel, H., Fox, R., Reinheimer, J., Mosionek, L., Willey, N., Bariana, H., et al (2007) The successful application of a marker-assisted wheat breeding strategy Mol Breed 20(4), 295–308 Kuchel, H., Ye, G., Fox, R., and Jefferies, S (2005) Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy Mol Breed 16(1), 67–78 Lande, R., and Thompson, R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits Genetics 124(3), 743–756 Lander, E S., and Botstein, D (1989) Mapping mendelian factors underlying quantitative traits using rflp linkage maps Genetics 121(1), 185–199 Letort, V., Mahe, P., Cournede, P H., De Reffye, P., and Courtois, B (2008) Quantitative genetics and functional-structural plant growth models: Simulation of quantitative trait loci detection for model parameters and application to potential yield optimization Ann Bot 101(8), 1243–1254 Leuning, R., Kelliher, F., Pury, D., and Schulze, E D (1995) Leaf nitrogen, photosynthesis, conductance and transpiration: Scaling from leaves to canopies Plant Cell Environ 18 (10), 1183–1200 Li, H H., Ye, G Y., and Wang, J K (2007) A modified algorithm for the improvement of composite interval mapping Genetics 175(1), 361–374 Lobell, D B., Burke, M B., Tebaldi, C., Mastrandrea, M D., Falcon, W P., and Naylor, R L (2008) Prioritizing climate change adaptation needs for food security in 2030 Science 319(5863), 607–610 Computer Simulation in Plant Breeding 261 Loffler, C M., Wei, J., Fast, T., Gogerty, J., Langton, S., Bergman, M., et al (2005) Classification of maize environments using crop simulation and geographic information systems Crop Sci 45(5), 1708–1716 Lynch, M., and Walsh, B (1997) Genetics and Analysis of Quantitative Traits Sinauer Associates, Inc., Sunderland, MA Mackay, T F C (2001) The genetic architecture of quantitative traits Annu Rev Genet 35, 303–339 Manichaikul, A., Moon, J Y., Sen, S., Yandell, B S., and Broman, K W (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis Genetics 181(3), 1077–1086 Masutomi, Y., Takahashi, K., Harasawa, H., and Matsuoka, Y (2009) Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/ parameter uncertainty in general circulation models Agric Ecosyst Environ 131(3–4), 281–291 Mather, K (1949) Biometrical Genetics Methuen, London Maurer, H P., Melchinger, A E., and Frisch, M (2008) Population genetic simulation and data analysis with Plabsoft Euphytica 161(1–2), 133–139 Mayor, P J., and Bernardo, R (2009) Genomewide selection and marker-assisted recurrent selection in doubled haploid versus F-2 populations Crop Sci 49(5), 1719–1725 McCown, R., Hammer, G., Hargreaves, J., Holzworth, D., and Freebairn, D (1996) APSIM: A novel software system for model development, model testing and simulation in agricultural systems research Agric Syst 50(3), 255–271 Messina, C., Jones, J., Boote, K., and Vallejos, C (2006) A gene-based model to simulate soybean development and yield responses to environment Crop Sci 46(1), 456–466 Meuwissen, T H E., Karlsen, A., Lien, S., Olsaker, I., and Goddard, M E (2002) Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping Genetics 161(1), 373–379 Meuwissen, T H E., Hayes, B J., and Goddard, M E (2001) Prediction of total genetic value using genome-wide dense marker maps Genetics 157(4), 1819–1829 Myles, S., Peiffer, J., Brown, P J., Ersoz, E S., Zhang, Z W., Costich, D E., et al (2009) Association mapping: Critical considerations shift from genotyping to experimental design Plant Cell 21(8), 2194–2202 Ooijen, J W (1992) Accuracy of mapping quantitative trait loci in autogamous species Theor Appl Genet 84(7), 803–811 Parisseaux, B., and Bernardo, R (2004) In Silico Mapping of Quantitative Trait Loci in Maize Springer, Berlin/Heidelberg Peccoud, J., Vander Velden, K., Podlich, D., Winkler, C., Arthur, L., and Cooper, M (2004) The selective values of alleles in a molecular network model are context dependent Genetics 166(4), 1715–1725 Peleman, J D., and van der Voort, J R (2003) Breeding by design Trends Plant Sci 8(7), 330–334 Pillen, K., Zacharias, A., and Leon, J (2003) Advanced backcross QTL analysis in barley (Hordeum vulgare L.) Theor Appl Genet 107(2), 340–352 Podlich, D., Cooper, M., Basford, K., and Geiger, H (1999) Computer simulation of a selection strategy to accommodate genotype environment interactions in a wheat recurrent selection programme Plant Breed 118(1), 17–28 Podlich, D W., and Cooper, M (1998) QU-GENE: A simulation platform for quantitative analysis of genetic models Bioinformatics 14(7), 632–653 Podlich, D W., Winkler, C R., and Cooper, M (2004) Mapping as you go: An effective approach for marker-assisted selection of complex traits Crop Sci 44(5), 1560–1571 262 Xin Li et al Price, A L., Patterson, N J., Plenge, R M., Weinblatt, M E., Shadick, N A., and Reich, D (2006) Principal components analysis corrects for stratification in genomewide association studies Nat Genet 38(8), 904–909 Pritchard, J K., Stephens, M., Rosenberg, N A., and Donnelly, P (2000) Association mapping in structured populations Am J Hum Genet 67(1), 170–181 Prusinkiewicz, P., Hammel, M., Hanan, J., and Mech, R (1996) L-systems: From the theory to visual models of plants In “Proceedings of the Second CSIRO Symposium on Computational Challenges in Life Sciences” Prusinkiewicz, P (2004) Art and science for life: Designing and growing virtual plants with L-systems In “Nursery Crops Development, Evaluation, Production and Use” (C Davidson and T Fernandez, Eds.), Vol 630, pp 15–28 ISHS, Leuven, Belgium Reymond, M., Muller, B., Leonardi, A., Charcosset, A., and Tardieu, F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit Plant Physiol 131(2), 664–675 Ribaut, J., Jiang, C., and Hoisington, D (2002) Simulation experiments on efficiencies of gene introgression by backcrossing Crop Sci 42(2), 557–565 Risch, N., and Merikangas, K (1996) The future of genetic studies of complex human diseases Science 273(5281), 1516–1517 Sahana, G., Guldbrandtsen, B., Janss, L., and Lund, M S (2010) Comparison of association mapping methods in a complex pedigreed population Genet Epidemiol 34(5), 455–462 Satagopan, J M., Yandell, Y S., Newton, M A., and Osborn, T C (1996) A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo Genetics 144 (2), 805–816 Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., et al (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement Nat Biotechnol 24(4), 447–454 Schon, C C., Utz, H F., Groh, S., Truberg, B., Openshaw, S., and Melchinger, A E (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits Genetics 167(1), 485–498 Slafer, G A (2003) Genetic basis of yield as viewed from a crop physiologist’s perspective Ann Appl Biol 142(2), 117–128 Soller, M., Brody, T., and Genizi, A (1976) Power of experimental designs for detection of linkage between marker loci and quantitative loci in crosses between inbred lines Theor Appl Genet 47(1), 35–39 Stich, B (2009) Comparison of mating designs for establishing nested association mapping populations in maize and Arabidopsis thaliana Genetics 183(4), 1525–1534 Takahashi, K., Ishikawa, N., Sadamoto, Y., Sasamoto, H., Ohta, S., Shiozawa, A., et al (2003) E-cell 2: Multi-platform E-cell simulation system Bioinformatics 19(13), 1727– 1729 Tanksley, S., and Nelson, J (1996) Advanced backcross QTL analysis: A method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines Theor Appl Genet 92(2), 191–203 Tomita, M (2001) Whole-cell simulation: A grand challenge of the 21st century Trends Biotechnol 19(6), 205–210 Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T S., Matsuzaki, Y., Miyoshi, F., et al (1999) E-CELL: Software environment for whole-cell simulation Bioinformatics 15(1), 72–84 van Berloo, R., and Stam, P (1998) Marker-assisted selection in autogamous RIL populations: A simulation study Theor Appl Genet 96(1), 147–154 Computer Simulation in Plant Breeding 263 van Berloo, R., and Stam, P (2001) Simultaneous marker-assisted selection for multiple traits in autogamous crops Theor Appl Genet 102(6–7), 1107–1112 Van Ooijen, J W (1999) LOD significance thresholds for QTL analysis in experimental populations of diploid species Heredity 83, 613–624 Vanoeveren, A J., and Stam, P (1992) Comparative simulation studies on the effects of selection for quantitative traits in autogamous crops—Early selection versus single seed descent Heredity 69, 342–351 Vieland, V J (1998) Bayesian linkage analysis, or: How I learned to stop worrying and love the posterior probability of linkage Am J Hum Genet 63(4), 947–954 Visscher, P M., Haley, C S., and Thompson, R (1996a) Marker-assisted introgression in backcross breeding programs Genetics 144(4), 1923–1932 Visscher, P M., Thompson, R., and Haley, C S (1996b) Confidence intervals in QTL mapping by bootstrapping Genetics 143(2), 1013–1020 Wang, J K., Chapman, S C., Bonnett, D G., and Rebetzke, G J (2009a) Simultaneous selection of major and minor genes: Use of QTL to increase selection efficiency of coleoptile length of wheat (Triticum aestivum L.) Theor Appl Genet 119(1), 65–74 Wang, J K., and Pfeiffer, W H (2007) Simulation modeling in plant breeding: Principles and applications Agric Sci China 6(8), 908–921 Wang, J K., Singh, R P., Braun, H J., and Pfeiffer, W H (2009b) Investigating the efficiency of the single backcrossing breeding strategy through computer simulation Theor Appl Genet 118(4), 683–694 Wang, J K., Eagles, H A., Trethowan, R., and van Ginkel, M (2005) Using computer simulation of the selection process and known gene information to assist in parental selection in wheat quality breeding Aust J Agric Res 56(5), 465–473 Wang, J K., Chapman, S C., Bonnett, D G., Rebetzke, G J., and Crouch, J (2007) Application of population genetic theory and simulation models to efficiently pyramid multiple genes via marker-assisted selection Crop Sci 47(2), 582–590 White, J W., and Hoogenboom, G (2003) Gene-based approaches to crop simulation: Past experiences and future opportunities Agron J 95(1), 52–64 Winsberg, E (2010) Science in the Age of Computer Simulation University of Chicago Press, Chicago Wong, C., and Bernardo, R (2008) Genomewide selection in oil palm: Increasing selection gain per unit time and cost with small populations Theor Appl Genet 116(6), 815–824 Wu, R L., and Zeng, Z B (2001) Joint linkage and linkage disequilibrium mapping in natural populations Genetics 157(2), 899–909 Xu, S Z (2010) An expectation-maximization algorithm for the lasso estimation of quantitative trait locus effects Heredity 105(5), 483–494 Xu, S Z (1998) Mapping quantitative trait loci using multiple families of line crosses Genetics 148(1), 517–524 Xu, S Z (2003) Estimating polygenic effects using markers of the entire genome Genetics 163(2), 789–801 Xu, Y (2010) Bayesian mapping In “Molecular Plant Breeding” (Y Xu, Ed.), pp 219– 223 CAB International, Wallingford, UK Yang, J., Zhu, J., and Williams, R W (2007) Mapping the genetic architecture of complex traits in experimental populations Bioinformatics 23(12), 1527–1536 Yin, X., Kropff, M J., and Stam, P (1999) The role of ecophysiological models in QTL analysis: The example of specific leaf area in barley Heredity 82(4), 415–421 Yin, X., Stam, P., Kropff, M J., and Schapendonk, A (2003) Crop modeling, QTL mapping, and their complementary role in plant breeding Agron J 95(1), 90–98 Yin, X., Struik, P C., and Kropff, M J (2004) Role of crop physiology in predicting geneto-phenotype relationships Trends Plant Sci 9(9), 426–432 264 Xin Li et al Yin, X., Struik, P C., Van Eeuwijk, F A., Stam, P., and Tang, J (2005) QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley J Exp Bot 56(413), 967 Yin, X., and Struik, P C (2010) Modelling the crop: From system dynamics to systems biology J Exp Bot 61(8), 2171–2183 Yu, J (2009) Realizing the potential of ultrahigh throughput genomic technologies in plant breeding Plant Genome 2, Yu, J., Holland, J B., McMullen, M D., and Buckler, E S (2008) Genetic design and statistical power of nested association mapping in maize Genetics 178(1), 539 Yu, J., Pressoir, G., Briggs, W H., Bi, I V., Yamasaki, M., Doebley, J F., et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness Nat Genet 38(2), 203–208 Yu, J., Zhang, Z., Zhu, C., Tabanao, D A., Pressoir, G., Tuinstra, M R., et al (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping Plant Genome 2(1), 63–77 Yu, J., Arbelbide, M., and Bernardo, R (2005) Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program Theor Appl Genet 110(6), 1061–1067 Zeng, Z B (1994) Precision mapping of quantitative trait loci Genetics 136(4), 1457 Zeng, Z B., Kao, C H., and Basten, C J (1999) Estimating the genetic architecture of quantitative traits Genet Res 74(3), 279–289 Zhang, L Y., Wang, S Q., Li, H H., Deng, Q M., Zheng, A P., Li, S C., et al (2010) Effects of missing marker and segregation distortion on QTL mapping in F-2 populations Theor Appl Genet 121(6), 1071–1082 Zhang, W., and Smith, C (1992) Computer-simulation of marker-assisted selection utilizing linkage disequilibrium Theor Appl Genet 83(6–7), 813–820 Zhang, W., and Smith, C (1993) Simulation of marker-assisted selection utilizing linkage disequilibrium—The effects of several additional factors Theor Appl Genet 86(4), 492–496 Zhu, C., Gore, M., Buckler, E S., and Yu, J (2008) Status and prospects of association mapping in plants Plant Genome 1(1), 5–20 Zhu, C., and Yu, J (2009) Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types Genetics 182(3), 875–888 Zou, W., and Zeng, Z B (2009) Multiple interval mapping for gene expression QTL analysis Genetica 137(2), 125–134 Index Note: Page numbers followed by “f ” indicate figures, and “t” indicate tables A Active landscape management, 161 Agricultural Production Systems sIMulator (APSIM), 249–251, 250f Agroecological intensification Andean region, 163–165 plant breeding, 152–157 Alternative glycolytic pathways, PUE, 206 American Palm Oil Council (APOC), 85 Andean soil fertility biological function biochar, 150–151 composts, 151 import substitution, 151–152 inoculation, 144 microbial and faunal communities, 144–145 microbial inoculants, 147 organic matter management, 144 P-solubilizing activity, 149 soil fauna, 152 symbioses, 145–146 biophysical limitations and risks climate, 127–128, 128f soil environment, characterization of, 127–128, 129f challenges and threats, 131–132 crop growth factors, 162 cropping systems, 126–127 ecologically based intensification, 132–133 mass balance erosion, 135–137 fertilizer application, 138–139 nitrogen flow diagram, 133–135, 134f nutrient availability/uptake vs time, 137, 137f nutrient dynamics and synchronization composting, 140–141 fertilizer application, 139 nutrient management strategies, 142 organic resources, 139–140 physiochemical environment aggregation, 142–143 liming, 143–144 pH, 143–144 plant breeding, 152–157 socioeconomic and cultural setting, 129–130 spatial and temporal organization, farms, 157–161 APSIM See Agricultural Production Systems sIMulator (APSIM) Arachis hypogaea L See Groundnut crop Arbuscular mycorrhizae (AM), 145–146 Assess site-specific yield potential (ASYP), 89 Average yields, palm oil production, 92–93, 92t B Barley seed metabolism, 252 Bayesian mapping, 245–246 Best linear unbiased prediction (BLUP), 228, 229 Biochar, 143–144, 150–151 Biological function, soil biochar, 150–151 composts, 151 import substitution, 151–152 inoculation, 144 microbial and faunal communities, 144–145 microbial inoculants, 147 organic matter management, 144 P-solubilizing activity, 149 soil fauna, 152 symbioses, 145–146 Bornean orangutans, 75–76 Breeding methods early selection, 225 genome-wide selection goal of, 235 vs MARS, 235–236, 236f marker-assisted backcross, 231–234 marker-assisted selection influencing factors, 226–228 vs phenotypic selection, 228–230 MARS, 231 MODPED, 225–226 SELBLK, 225–226 SSD, 225 C Cangahua soils, 142–143 Cation exchange capacity (CEC), 143–144 Composite interval mapping (CIM), 243–244 Composting, anerobic methods, 140–141 Computer simulation, plant breeding breeding methods early selection, 225 genome-wide selection, 234–237 265 266 Computer simulation, plant breeding (cont.) marker-assisted backcross, 231–234 marker-assisted selection, 226–230 MARS, 231 MODPED, 225–226 SELBLK, 225–226 SSD, 225 classification of, 222 climate change, 252–254 computation and software issues, 254–255, 255t crop modeling advantages, 248–249 APSIM, 249–251, 250f uses, 248 example, 222 gene mapping association mapping, 243–244 Bayesian mapping, 245–246 confidence interval, 243 joint linkage and linkage disequilibrium mapping, 245 linkage analysis, 243–244 meta-analysis, 240–241 missing markers, 240 population size and marker density, 238–239, 239f significance threshold, 241–242 gene network and genotype-by-environment interaction E(N:K) model, 246–247 QU-GENE, 247 origin of, 221 random number generation, 223f virtual plants and E-cell, 251–252 Critical tissue P concentration, 191 Crop breeding, 155 CROPGRO models, groundnut climatic effects on root growth, 64 pod addition, seed growth, and partitioning intensity, 63–64 reproductive progression, 61–62 vegetative development, 61 vegetative expansion and photosynthesis processes, 62–63 Crop modeling advantages, 248–249 APSIM, 249–251, 250f uses, 248 Cropping system, INM fertilizer rates, 16–17 management strategy, 17 nitrogen efficiency, 16 nutrient resource characteristics, 16, 17t orchards, 26–29 paddy rice, 21–22 vegetable systems, 23–26 wheat and maize, 18–21 Crude palm oil (CPO), 74 Index D Deforestation, 75–76 Deterministic simulation, 222 E E-cell system, 252 Elaeis guineensis cultivation See Oil palm cultivation E(N:K) model, 246–247 Environmental stakes, oil palm cultivation agricultural policies, 80–81 deforestation and loss of biodiversity, 75–76 GHG emissions and carbon storage, 78 peatland degradation, 76–77 water pollution, 79–80 F False discovery rate (FDR), 242 Fertilizer management, oil palm cultivation, 86 Food production, 30–31 Forest clearing, hydrological impacts of, 111, 112f Foster system, 90 G Gene mapping, plant breeding association mapping, 243–244 Bayesian mapping, 245–246 confidence interval, 243 joint linkage and linkage disequilibrium mapping, 245 linkage analysis, 243–244 meta-analysis, 240–241 missing markers, 240 population size and marker density, 238–239, 239f significance threshold, 241–242 General circulation models (GCMs), 253 Grain crop genotypes, PUE, 187, 188t Grain PUE, screening for, 200 Greenhouse gas (GHG) emissions, 78 Groundnut crop canopy expansion and growth processes leaf area and stem elongation, 46–47 leaf senescence, 47 leaf thickness, 45 net assimilation and growth rates, 49–50 photosynthesis, 48–49 stomatal conductance and transpiration, 47–48 CROPGRO models climatic effects on root growth, 64 pod addition, seed growth, and partitioning intensity, 63–64 reproductive progression, 61–62 vegetative development, 61 267 Index vegetative expansion and photosynthesis processes, 62–63 harvest index, 58–59 reproductive development and growth appearance of flowers, pegs, and pods, 50–51 number of pegs, pods, and seeds, 54–55 pod and seed growth rates and their size, 55–56 pollen production and viability and fruit-set, 53–54 rate of flower production, 51–52 root growth, 59–60 root-to-shoot ratio, 60–61 shelling percentage, 59 total dry matter, pod, and seed yield, 56–58 vegetative development germination and emergence processes, 43–44 leaf appearance and leaf number, 44–45 H Harvest index (HI), 189–191 Hydrological processes, oil palm evapotranspiration, 95–96 forest clearing, hydrological impacts of, 111, 112f hydrological cycle, 93, 94f interception, 94–95 leaching and goundwater facility annual rainfall, annual runoff and nutrient losses, 97–98, 99t, 100t plot-scale study, 101–103 soil texture, 98–101, 102t precipitation, 94 qualitative description, 111, 113t soil infiltration deforestation, 96–97 hydraulic conductivities, 96 soil types and locations, 97, 97t stream flow, 108–109 stream water quality, 109–111, 110t surface runoff and soil erosion, 103–108, 104t, 106t, 107t I Import substitution, 151–152 Inclusive composite interval mapping (ICIM), 244 Industrial vs smallholder plantations, oil palm, 82 In silico mapping, 244–245 Integrated nutrient management (INM) chemical fertilizer consumption, 3–4, 4f conceptual model, 6, 6f in cropping system fertilizer rates, 16–17 management strategy, 17 nitrogen efficiency, 16 nutrient resource characteristics, 16, 17t orchards, 26–29 paddy rice, 21–22 vegetable systems, 23–26 wheat and maize, 18–21 fertilizer rate per hectare, 3–4, 4f grain production and chemical fertilizer inputs, 2, 3f large-scale dissemination environmental pollution, 30–31 factors, 29–30 partial factor productivity, 30, 30f principles of crop yields, 15–16 mobilization and acquisition, 6–7 nitrogen fertilization, 7–8 N loss reduction, Chinese cropping systems, 12–15 NO-3, 7–8 nutrient input optimization, 9–11 rhizosphere/root-zone nutrient management, 8, 9f soil nutrient supply matching, 11–12 L Leaf analysis, 89 Least square method, 226–227 LOD drop-off method, 243 Lupinus mutabilis, 145–146, 155–156 M Mapping as you go (MAYG) method, 227–228 Marker-assisted backcross (MABC), 231–234 Marker-assisted recurrent selection (MARS), 231 Marker-assisted selection (MAS) breeding methods influencing factors, 226–228 vs phenotypic selection, 228–230 PUE, 209–210 MARS See Marker-assisted recurrent selection (MARS) Microbial symbioses, 154–155 Mitochondrial electron transport pathways, PUE, 206 Modified pedigree/bulk selection method (MODPED), 225–226 Multiple interval mapping (MIM), 244 Mycorrhizal inoculation, impacts of, 147–149, 148t N Nested association mapping (NAM), 245 Nucleus estate scheme (NES), 82 Nutrient dynamics and synchronization, soil fertility 268 Index Nutrient dynamics and synchronization, soil fertility (cont.) composting, 140–141 fertilizer application, 139 nutrient management strategies, 142 organic resources, 139–140 Nutrient use efficiency (NUE), 15 O Off-the-shelf software, 254 Oil palm cultivation climate and soil conditions, 81–82 environmental stakes agricultural policies, 80–81 deforestation and loss of biodiversity, 75–76 GHG emissions and carbon storage, 78 peatland degradation, 76–77 water pollution, 79–80 expansion of future expansion, 74–75 in Indonesia, 74, 75f palm oil utilization, 74 fertilizer management chemical fertilizer, 90 organic fertilizer, 90–92 hydrological processes evapotranspiration, 95–96 forest clearing, hydrological impacts of, 111, 112f hydrological cycle, 93, 94f interception, 94–95 leaching and goundwater facility, 97–103 precipitation, 94 qualitative description, 111, 113t soil infiltration, 96–97, 97t stream flow, 108–109 stream water quality, 109–111, 110t surface runoff and soil erosion, 103–108, 104t, 106t, 107t land clearing and site preparation, 82–83, 84f nutrient-demand assessment fertilizer management, 86 soil nutrient supply, 86–89, 88t positive and negative aspects, 73 production systems, 82 synthesis, 92–93 water and soil management, 83–85 On-field crop residue retention, 137–138 Orchards basal fertilization, 28 controlling N and P fertilization, 27–28 deficiency, 27 management, 28 misuse of fertilizers, 27 nutrient inputs, 27 P Paddy rice basal and top-dressing, 22 fertilizer N application rate, 21 INM performance, China, 22, 23f soil N supply capacity, 22 Palm kernel oil, 74 Palm oil mill effluent (POME), 79–80 P-deficiency stress response mechanisms, 206–207 Peatland degradation ecological functions, 77 oil palm conversion, 77 peatland formation, 76 PER See Phosphorus-efficiency ratio (PER) Phalaris sp, 136–137 Phosphorus acquisition efficiency (PAE) crop cultivars, 206–207 vs PUE, 208 Phosphorus-efficiency ratio (PER), 189, 195–196 Phosphorus utilization efficiency (PUE) agronomic implications, 189–191 classification, 189 definitions, 189, 190t, 195–196 grain crop genotypes, 187, 188t marker-assisted selection, 209–210 mechanism and physiology alternative glycolytic pathways and mitochondrial electron transport pathways, 206 P-deficiency stress response mechanisms, 206–207 remobilization and scavenging, of P, 205–206 vs PAE, 208 physiological implications reproductive stage, 195 vegetative stage, 192–194, 193t screening grain PUE, 200 vegetative PUE, 197–200 Physiochemical environment, soil aggregation, 142–143 liming, 143–144 pH, 143–144 Plant-growth-promoting microbes (PGPM), 146, 149–150 Plasma, 82 P-LIM-GROW model, 202–204, 203f Potassium fluxes, oil palm plantation, 86–89, 89f Pseudorandom number generator, 254–255 P-stress levels outcomes, PUE, 202–204 P-deficient crops, 201–202 PUE See Phosphorus utilization efficiency (PUE) 269 Index R Remobilization and scavenging, of P, 205–206 Root PUE definition, 192 variation, vegetative crop growth, 193t Roundtable on Sustainable Palm Oil (RSPO), 80 S Screening grain PUE, 200 P-stress levels, 201–204 vegetative PUE P uptake nullification, genotypes, 198–200 shoot PUE vs shoot P content, 197, 198f, 199f Selected bulk selection method (SELBLK), 225–226 Shoot PUE control of, 192 correlation coefficients, 196t vs shoot P content, 198f, 199f variation, vegetative crop growth, 193t Single seed descent (SSD), 225 Soil aggregation, 142–143 Soil degradation, 131 Soil infiltration deforestation, 96–97 hydraulic conductivities, 96 soil types and locations, 97, 97t Spatial and temporal organization, farms, 157–161 SSD See Single seed descent (SSD) Standing stock biomass, in oil palm plantation, 86, 87t Stochastic simulation, 222 Sumatran orangutans, 75–76 T Trichoderma, 145–146 Trifolium spp, 145–146 V Vegetable systems characteristics, 23–24 chili pepper and cucumber yield, 24–25, 24f fertigation techniques, 26 in-season root-zone N management, 25, 25f P management strategy, 26 Vegetative PUE, screening P uptake nullification, genotypes, 198–200 shoot PUE vs shoot P content, 197, 198f, 199f Vermicomposting, 140–141 Virtual plants, 252 W Wacho rozado, 136–137 Water management, oil palm cultivation, 83–85 Wheat and maize system factors, 20–21 grain yields, 18 INM performance, North China Plain, 19–20, 20f model of, in-season root-zone, 18–19, 19f soil N tests, 18–19 ... elementsa a Management strategy Trace elements include boron (B), chlorine (Cl), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni), and zinc (Zn), respectively In China, maintenance... chlorine (Cl), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni), and zinc (Zn) is based on their plant availability in both soil and plant Their available contents in soil and... Tropical Agriculture (CIAT), Cali, Colombia D Carolina Quintero (12 5) International Center for Tropical Agriculture (CIAT), Cali, Colombia Idupulapati M Rao (12 5) International Center for Tropical
- Xem thêm -

Xem thêm: (Advances in agronomy 116) donald l sparks (eds ) advances in agronomy 116 academic press, elsevier (2012) , (Advances in agronomy 116) donald l sparks (eds ) advances in agronomy 116 academic press, elsevier (2012)

Mục lục

Xem thêm