CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate
cg.contact | J.R.Villegas@CGIAR.ORG | en_US |
cg.contributor.center | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.contributor.center | International Center for Tropical Agriculture - CIAT | en_US |
cg.contributor.center | International Crops Research Institute for the Semi-Arid Tropics - ICRISAT | en_US |
cg.contributor.center | International Food Policy Research Institute - IFPRI | en_US |
cg.contributor.center | International Maize and Wheat Improvement Center - CIMMYT | en_US |
cg.contributor.center | International Potato Center - CIP | en_US |
cg.contributor.center | United States Department of Agriculture - USDA | en_US |
cg.contributor.center | University of Florida - UF | en_US |
cg.contributor.center | Wageningen University & Research Centre - WUR | en_US |
cg.contributor.center | University of Leeds - UOL | en_US |
cg.contributor.center | International Rice Research Institute - IRRI | en_US |
cg.contributor.center | Empresa Brasileira de Pesquisa Agropecuária - EMBRAPA | en_US |
cg.contributor.center | Mohammed VI Polytechnic University - UM6P | en_US |
cg.contributor.center | Chinese Academy of Agricultural Sciences, Institute of Crop Science - CAAS-ICS | en_US |
cg.contributor.center | Centro Agronómico Tropical de Investigación y Enseñanza - (CATIE) | en_US |
cg.contributor.center | The French Agricultural Research Centre for International Development, UMR: Mediterranean and Tropical Livestock Systems - CIRAD - UMR SELMET | en_US |
cg.contributor.center | São Paulo Research Foundation - FAPESP | en_US |
cg.contributor.crp | CGIAR Research Program on Climate Change, Agriculture and Food Security - CCAFS | en_US |
cg.contributor.crp | CGIAR Research Program on Rice, The Global Rice Science Partnership - GRiSP | en_US |
cg.contributor.crp | CGIAR Research Program on Maize - MAIZE | en_US |
cg.contributor.crp | CGIAR Research Program on Wheat - WHEAT | en_US |
cg.contributor.crp | Big Data in Agriculture - BDA | en_US |
cg.contributor.crp | Excellence in Breeding - EiB | en_US |
cg.contributor.funder | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.contributor.project | CRP WHEAT Phase II | en_US |
cg.contributor.project-lead-institute | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.creator.id | Ramirez-Villegas, Julian: 0000-0002-8044-583X | en_US |
cg.creator.id | Ghanem, Michel Edmond: 0000-0003-0626-7622 | en_US |
cg.creator.id | Kehel, Zakaria: 0000-0002-1625-043X | en_US |
cg.creator.id | Quiroz, Roberto: 0000-0001-8401-2700 | en_US |
cg.creator.id | Vadez, Vincent: 0000-0003-2014-0281 | en_US |
cg.identifier.doi | https://dx.doi.org/10.1002/csc2.20048 | en_US |
cg.isijournal | ISI Journal | en_US |
cg.issn | 0011-183X | en_US |
cg.issue | 2 | en_US |
cg.journal | Crop Science | en_US |
cg.subject.agrovoc | heat tolerance | en_US |
cg.volume | 60 | en_US |
dc.contributor | Molero Milan, Anabel | en_US |
dc.contributor | Alexandrov, Nickolai | en_US |
dc.contributor | Asseng, Senthold | en_US |
dc.contributor | J Challinor, Andrew | en_US |
dc.contributor | Crossa, Jose | en_US |
dc.contributor | van Eeuwijk, Fred | en_US |
dc.contributor | Ghanem, Michel Edmond | en_US |
dc.contributor | Grenier, Cecile | en_US |
dc.contributor | B. Heinemann, Alexandre | en_US |
dc.contributor | Wang, Jiankang | en_US |
dc.contributor | Juliana, Philomin | en_US |
dc.contributor | Kehel, Zakaria | en_US |
dc.contributor | Kholova, Jana | en_US |
dc.contributor | Koo, Jawoo | en_US |
dc.contributor | Pequeno, Diego | en_US |
dc.contributor | Quiroz, Roberto | en_US |
dc.contributor | C. Rebolledo, Maria | en_US |
dc.contributor | Sukumaran, Sivakumar | en_US |
dc.contributor | Vadez, Vincent | en_US |
dc.contributor | W. White, Jeffrey | en_US |
dc.contributor | Reynolds, Matthew | en_US |
dc.creator | Ramirez-Villegas, Julian | en_US |
dc.date.accessioned | 2020-10-07T07:46:30Z | |
dc.date.available | 2020-10-07T07:46:30Z | |
dc.description.abstract | Crop improvement efforts aiming at increasing crop production (quantity, quality) and adapting to climate change have been subject of active research over the past years. But, the question remains ‘to what extent can breeding gains be achieved under a changing climate, at a pace sufficient to usefully contribute to climate adaptation, mitigation and food security?’. Here, we address this question by critically reviewing how model‐based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on International Agricultural Research but now known simply as CGIAR) breeding programs. Crop modeling can underpin breeding efforts in many different ways, including assessing genotypic adaptability and stability, characterizing and identifying target breeding environments, identifying tradeoffs among traits for such environments, and making predictions of the likely breeding value of the genotypes. Crop modeling science within the CGIAR has contributed to all of these. However, much progress remains to be done if modeling is to effectively contribute to more targeted and impactful breeding programs under changing climates. In a period in which CGIAR breeding programs are undergoing a major modernization process, crop modelers will need to be part of crop improvement teams, with a common understanding of breeding pipelines and model capabilities and limitations, and common data standards and protocols, to ensure they follow and deliver according to clearly defined breeding products. This will, in turn, enable more rapid and better‐targeted crop modeling activities, thus directly contributing to accelerated and more impactful breeding efforts. | en_US |
dc.format | en_US | |
dc.identifier | https://mel.cgiar.org/reporting/downloadmelspace/hash/7b47f458245d311d06e8ba2bf9c6941e/v/eafdd729f803a8c753c414e92923e46d | en_US |
dc.identifier.citation | Julian Ramirez-Villegas, Anabel Molero Milan, Nickolai Alexandrov, Senthold Asseng, Andrew J Challinor, Jose Crossa, Fred van Eeuwijk, Michel Edmond Ghanem, Cecile Grenier, Alexandre B. Heinemann, Jiankang Wang, Philomin Juliana, Zakaria Kehel, Jana Kholova, Jawoo Koo, Diego Pequeno, Roberto Quiroz, Maria C. Rebolledo, Sivakumar Sukumaran, Vincent Vadez, Jeffrey W. White, Matthew Reynolds. (1/4/2020). CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate. Crop Science, 60 (2), pp. 547-567. | en_US |
dc.identifier.status | Open access | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.11766/11868 | |
dc.language | en | en_US |
dc.publisher | Crop Science Society of America | en_US |
dc.rights | Copyrighted; Non-commercial educational use only | en_US |
dc.source | Crop Science;60,(2020) Pagination 547-567 | en_US |
dc.subject | physiological traits | en_US |
dc.subject | x environment interaction | en_US |
dc.subject | genome-enabled prediction | en_US |
dc.subject | pedigree-based prediction | en_US |
dc.subject | lens-culinaris medik. | en_US |
dc.subject | dry bean model | en_US |
dc.subject | drought-stress | en_US |
dc.subject | yield gains | en_US |
dc.subject | rice yield | en_US |
dc.title | CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate | en_US |
dc.type | Journal Article | en_US |
dcterms.available | 2020-01-13 | en_US |
dcterms.extent | 547-567 | en_US |
dcterms.issued | 2020-04-01 | en_US |
mel.impact-factor | 1.878 | en_US |