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CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate

Authors: Roberto Quiroz; Roberto Quiroz; Alexandre Bryan Heinemann; Maria Camila Rebolledo; Maria Camila Rebolledo; Andrew J. Challinor; Sivakumar Sukumaran; +19 Authors

CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate

Abstract

AbstractCrop 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.

Countries
France, France, France, India, France, India, Netherlands, France
Keywords

analysis, [SDV]Life Sciences [q-bio], Crop Modelling, 590, adaptation aux changements climatiques, Wiskundige en Statistische Methoden - Biometris, 630, F30 - Génétique et amélioration des plantes, agricultura, clima, productivité agricole, agriculture, producción, PE&RC, [SDV] Life Sciences [q-bio], Crop Improvement, P40 - Météorologie et climatologie, Climate Change, 333, Production végétale, Life Science, climate, Mathematical and Statistical Methods - Biometris, Changement climatique, amélioration génétique, U10 - Méthodes mathématiques et statistiques, Modélisation des cultures, Biometris (WU MAT), Amélioration des plantes, Plant Breeding, análisis, production, agrovoc: agrovoc:c_5956, agrovoc: agrovoc:c_1374567058134, agrovoc: agrovoc:c_9000024, agrovoc: agrovoc:c_49902, agrovoc: agrovoc:c_1666, agrovoc: agrovoc:c_9fe82378, agrovoc: agrovoc:c_5976

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    popularity
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    Top 1%
    influence
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    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
49
Top 1%
Top 10%
Top 1%
Green
hybrid