%0 Journal Article
%@ 0011-183X
%A Ramirez‐Villegas, J
%A Molero Milan, A
%A Alexandrov, N
%A Asseng, S
%A Challinor, A J
%A Crossa, J
%A Eeuwijk, F
%A Ghanem, M E
%A Grenier, C
%A Heinemann, A B
%A Wang, J
%A Juliana, P
%A Kehel, Z
%A Kholova, J
%A Koo, J
%A Pequeno, D
%A Quiroz, R
%A Rebolledo, M C
%A Sukumaran, S
%A Vadez, V
%A White, J W
%A Reynolds, M
%D 2020
%F icrisat:11426
%I Crop Science Society of America
%J Crop Science (TSI)
%K Crop Improvement, Breeding, Climate Change, Modeling
%P 1-21
%T CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate
%U http://oar.icrisat.org/11426/
%X Crop improvement efforts aiming at increasing crop production (quantity, quality)and adapting to climate change have been subject of active research over the pastyears. But, the question remains ‘to what extent can breeding gains be achievedunder a changing climate, at a pace sufficient to usefully contribute to climate adap-tation, mitigation and food security?’. Here, we address this question by criticallyreviewing how model-based approaches can be used to assist breeding activities, with particular focus on all CGIAR (formerly the Consultative Group on InternationalAgricultural Research but now known simply as CGIAR) breeding programs. Cropmodeling can underpin breeding efforts in many different ways, including assessinggenotypic adaptability and stability, characterizing and identifying target breedingenvironments, identifying tradeoffs among traits for such environments, and mak-ing predictions of the likely breeding value of the genotypes. Crop modeling sciencewithin the CGIAR has contributed to all of these. However, much progress remainsto be done if modeling is to effectively contribute to more targeted and impactfulbreeding programs under changing climates. In a period in which CGIAR breedingprograms are undergoing a major modernization process, crop modelers will needto be part of crop improvement teams, with a common understanding of breedingpipelines and model capabilities and limitations, and common data standards and pro-tocols, to ensure they follow and deliver according to clearly defined breeding prod-ucts. This will, in turn, enable more rapid and better-targeted crop modeling activities,thus directly contributing to accelerated and more impactful breeding efforts.
%Z The authors would like to express their gratitude to USAIDand to the donors to the CGIAR System Council. This workwas supported by the CGIAR research programs (CRPs) onGrain Legumes (GL), RICE, MAIZE, and WHEAT agri-foodsystems, the CGIAR Platform for Big Data in Agriculture,and Excellence in Breeding. JR-V and AJC acknowledge sup-port from the CGIAR Research Program on Climate Change,Agriculture and Food Security (CCAFS) through its Flagship2 on Climate-Smart Practices and Technologies. CCAFS iscarried out with support from CGIAR Trust Fund Donorsand through bilateral funding agreements. For details, pleasevisit https://ccafs.cgiar.org/donors. The views expressed inthis paper cannot be taken to reflect the official opinionsof these organizations. Authors thank Martin J. Kropff forinsightful comments, encouragement and literature on mod-eling G×E×M interactions and gene-to-phenotype mod-els. Authors also thank two anonymous reviewers and CharlieMessina (Editor) for their constructive feedback.