CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate
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2020-03Date Online
2020-03Language
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Ramirez‐Villegas, J.; Molero Milan, A.; Alexandrov, N.; Asseng, S.; Challinor, A.J.; Crossa, J.; van Eeuwijk, F.; Ghanem, M.E.; Grenier, C.; Heinemann, A.B.; Wang, J.; Juliana, P.; Kehel, Z.; Kholova, J.; Koo, J.; Pequeno, D.; Quiroz, R.; Rebolledo, M.C.; Sukumaran, S.; Vadez, V.; White, J.W.; Reynolds, M. 2020 CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. Crop Science ISSN: 0011-183X 21 p.
Permanent link to cite or share this item: https://hdl.handle.net/10568/108316
Abstract/Description
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.
CGIAR Author ORCID iDs
Julian Ramirez-Villegashttps://orcid.org/0000-0002-8044-583X
Cecile Grenierhttps://orcid.org/0000-0001-5390-8344
Jose Crossahttps://orcid.org/0000-0001-9429-5855
Philomin Julianahttps://orcid.org/0000-0001-6922-0173
Matthew Paul Reynoldshttps://orcid.org/0000-0002-4291-4316

