Abstract
Key message
Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time.
Abstract
Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors are grateful to the students (Claire Barbet-Massin, Ewen Gery, Bertrand Haquin) and staff from INRAE (Céline Colombet, Didier Rafaillac, Colette Quinquiry), ENSAT (Michel Labarrère, Pierre Maury) and Terres Inovia (Frédéric Bardy, Philippe Christante, André Estragnat, Pascal Fauvin, Céline Motard, Jean-Pierre Palleau, Frédéric Salvi) that helped to constitute the phenotypic database. We also wish to thank the INRA RECORD team (Hélène Raynal, Eric Caselas) for modeling and simulation, and the INRA AgroClim team (Benoît Persyn, Patrick Bertuzzi) that provided gridded climate datasets.
Funding
Grants were provided by the French Ministry of Agriculture (CASDAR C-2016-03 CARAVAGE) and the French Ministry of Research (ANR SUNRISE ANR-11-BTBR-0005). This work was also supported by the French National Research Agency under the Investments for the Future Program, referred to as ANR-16-CONV-0004.
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PC, NL, RT, and PD designed and planned the study; PC, AL, EM, and JS provided data; PC, AG, JS, and RT analyzed the data; PC wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Communicated by Dragana Miladinovi.
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Casadebaig, P., Gauffreteau, A., Landré, A. et al. Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale. Theor Appl Genet 135, 4049–4063 (2022). https://doi.org/10.1007/s00122-022-04072-5
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DOI: https://doi.org/10.1007/s00122-022-04072-5