Institution
International Maize and Wheat Improvement Center
Nonprofit•Texcoco, Mexico•
About: International Maize and Wheat Improvement Center is a nonprofit organization based out in Texcoco, Mexico. It is known for research contribution in the topics: Population & Quantitative trait locus. The organization has 1976 authors who have published 4799 publications receiving 218390 citations.
Papers published on a yearly basis
Papers
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University of Florida1, University of Bonn2, Blaise Pascal University3, Institut national de la recherche agronomique4, Stanford University5, Prince of Songkla University6, Agricultural Research Service7, University of Arizona8, International Maize and Wheat Improvement Center9, Kansas State University10, International Water Management Institute11, Washington State University12, Michigan State University13, University of Leeds14, CGIAR15, Counterintelligence Field Activity16, Spanish National Research Council17, University of Tübingen18, University of Guelph19, Texas A&M University20, University of Maryland, College Park21, Aarhus University22, Potsdam Institute for Climate Impact Research23, Indian Agricultural Research Institute24, Goddard Institute for Space Studies25, Rothamsted Research26, University of Hohenheim27, Wageningen University and Research Centre28, Chinese Academy of Sciences29, Commonwealth Scientific and Industrial Research Organisation30, China Agricultural University31, Nanjing Agricultural University32
TL;DR: The authors systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating.
Abstract: Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.
1,461 citations
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United States Department of Agriculture1, Cornell University2, North Carolina State University3, University of Missouri4, University of Wisconsin-Madison5, Monsanto6, Beijing Normal University7, Purdue University8, University of Illinois at Urbana–Champaign9, Spanish National Research Council10, International Maize and Wheat Improvement Center11, Cold Spring Harbor Laboratory12, Kansas State University13
TL;DR: A simple additive model accurately predicts flowering time for maize, in contrast to the genetic architecture observed in the selfing plant species rice and Arabidopsis.
Abstract: Flowering time is a complex trait that controls adaptation of plants to their local environment in the outcrossing species Zea mays (maize). We dissected variation for flowering time with a set of 5000 recombinant inbred lines (maize Nested Association Mapping population, NAM). Nearly a million plants were assayed in eight environments but showed no evidence for any single large-effect quantitative trait loci (QTLs). Instead, we identified evidence for numerous small-effect QTLs shared among families; however, allelic effects differ across founder lines. We identified no individual QTLs at which allelic effects are determined by geographic origin or large effects for epistasis or environmental interactions. Thus, a simple additive model accurately predicts flowering time for maize, in contrast to the genetic architecture observed in the selfing plant species rice and Arabidopsis.
1,342 citations
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TL;DR: It is concluded that agriculture in the next decade will have to sustainably produce more food from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands.
Abstract: The paper focuses on conservation agriculture (CA), defined as minimal soil disturbance (no-till, NT) and permanent soil cover (mulch) combined with rotations, as a more sustainable cultivation system for the future. Cultivation and tillage play an important role in agriculture. The benefits of tillage in agriculture are explored before introducing conservation tillage (CT), a practice that was borne out of the American dust bowl of the 1930s. The paper then describes the benefits of CA, a suggested improvement on CT, where NT, mulch and rotations significantly improve soil properties and other biotic factors. The paper concludes that CA is a more sustainable and environmentally friendly management system for cultivating crops. Case studies from the rice-wheat areas of the Indo-Gangetic Plains of South Asia and the irrigated maize-wheat systems of Northwest Mexico are used to describe how CA practices have been used in these two environments to raise production sustainably and profitably. Benefits in terms of greenhouse gas emissions and their effect on global warming are also discussed. The paper concludes that agriculture in the next decade will have to sustainably produce more food from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands. Promoting and adopting CA management systems can help meet this goal.
1,259 citations
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TL;DR: Recent advances in field HTPPs are reviewed, which should combine at an affordable cost, high capacity for data recording, scoring and processing, and non-invasive remote sensing methods, together with automated environmental data collection.
1,206 citations
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TL;DR: QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations and to perform analysis of variance for multi-environmental trials.
Abstract: QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations. Eight functionalities are integrated in this software package: (1) BIN: binning of redundant markers; (2) MAP: construction of linkage maps in biparental populations; (3) CMP: consensus map construction from multiple linkage maps sharing common markers; (4) SDL: mapping of segregation distortion loci; (5) BIP: mapping of additive, dominant, and digenic epistasis genes; (6) MET: QTL-by-environment interaction analysis; (7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and (8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL, and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci, and to perform analysis of variance for multi-environmental trials.
1,133 citations
Authors
Showing all 2012 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rajeev K. Varshney | 102 | 709 | 39796 |
Scott Chapman | 84 | 362 | 23263 |
Matthew P. Reynolds | 83 | 286 | 24605 |
Ravi P. Singh | 83 | 433 | 23790 |
Albrecht E. Melchinger | 83 | 398 | 23140 |
Pamela A. Matson | 82 | 188 | 48741 |
José Crossa | 81 | 519 | 23652 |
Graeme Hammer | 77 | 315 | 20603 |
José Luis Araus | 62 | 226 | 14128 |
Keith Goulding | 61 | 262 | 17484 |
John W. Snape | 61 | 214 | 13695 |
Bruce R. Hamaker | 61 | 333 | 13629 |
Zhonghu He | 59 | 245 | 10509 |
Rosamond L. Naylor | 59 | 155 | 30677 |
Wei Xiong | 58 | 364 | 10835 |