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 & Agriculture. 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 Hohenheim1, University of Göttingen2, German Institute of Global and Area Studies3, International Maize and Wheat Improvement Center4, University of Bern5, Magister6, University of Waikato7, University of Jena8, Bogor Agricultural University9, University of Kiel10, Leipzig University11, University of the Philippines Los Baños12, Russian Academy of Sciences13, Tadulako University14
TL;DR: Landscape compositions that can mitigate trade-offs under optimal land-use allocation but also show that intensive monocultures always lead to higher profits are identified, suggesting that targeted landscape planning is needed to increase land- use efficiency while ensuring socio-ecological sustainability.
Abstract: Land-use transitions can enhance the livelihoods of smallholder farmers but potential economic-ecological trade-offs remain poorly understood. Here, we present an interdisciplinary study of the environmental, social and economic consequences of land-use transitions in a tropical smallholder landscape on Sumatra, Indonesia. We find widespread biodiversity-profit trade-offs resulting from land-use transitions from forest and agroforestry systems to rubber and oil palm monocultures, for 26,894 aboveground and belowground species and whole-ecosystem multidiversity. Despite variation between ecosystem functions, profit gains come at the expense of ecosystem multifunctionality, indicating far-reaching ecosystem deterioration. We identify landscape compositions that can mitigate trade-offs under optimal land-use allocation but also show that intensive monocultures always lead to higher profits. These findings suggest that, to reduce losses in biodiversity and ecosystem functioning, changes in economic incentive structures through well-designed policies are urgently needed.
697 citations
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TL;DR: This draft genome sequence provides insight into the environmental adaptation of bread wheat and can aid in defining the large and complicated genomes of wheat species.
Abstract: About 8,000 years ago in the Fertile Crescent, a spontaneous hybridization of the wild diploid grass Aegilops tauschii (2n = 14; DD) with the cultivated tetraploid wheat Triticum turgidum (2n = 4x = 28; AABB) resulted in hexaploid wheat (T. aestivum; 2n = 6x = 42; AABBDD). Wheat has since become a primary staple crop worldwide as a result of its enhanced adaptability to a wide range of climates and improved grain quality for the production of baker's flour. Here we describe sequencing the Ae. tauschii genome and obtaining a roughly 90-fold depth of short reads from libraries with various insert sizes, to gain a better understanding of this genetically complex plant. The assembled scaffolds represented 83.4% of the genome, of which 65.9% comprised transposable elements. We generated comprehensive RNA-Seq data and used it to identify 43,150 protein-coding genes, of which 30,697 (71.1%) were uniquely anchored to chromosomes with an integrated high-density genetic map. Whole-genome analysis revealed gene family expansion in Ae. tauschii of agronomically relevant gene families that were associated with disease resistance, abiotic stress tolerance and grain quality. This draft genome sequence provides insight into the environmental adaptation of bread wheat and can aid in defining the large and complicated genomes of wheat species.
689 citations
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TL;DR: A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article, which retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM.
Abstract: Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.
685 citations
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TL;DR: Evaluated parametric and semiparametric models for GS using wheat and maize data in which different traits were measured in several environmental conditions indicate that models including marker information had higher predictive ability than pedigree-based models.
Abstract: The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.
676 citations
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TL;DR: A review of the approaches for addressing the expected effects that climate change may likely inflict on wheat in some of the most important wheat growing areas, namely germplasm adaptation, system management, and mitigation is presented in this paper.
672 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 |