Hajjarpoor, A and Nelson, W C D and Vadez, V (2022) How process-based modeling can help plant breeding deal with G x E x M interactions. Field Crops Research (TSI), 283. pp. 1-14. ISSN 0378-4290
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Abstract
Genotype-by-Environment-by-Management (GxExM) interactions represent many unknowns for crop improvement programs, which hampers the development of improved varieties, especially for highly variable environments like those limited by rainfall. While breeding programs have traditionally used statistical tools to deal with these interactions, process-based crop modeling has recently become an alternative and powerful approach. Overall, while statistical methods remain the most optimal solution to deal with GxExM interactions when many production datasets across time and space are available from multi-environment trials (MET), in silico methods like crop modeling can be used if such data is lacking, or if MET data don’t cover the entire target region. Yet, despite several reviews on the potential uses of process-based modeling tools to aid such issues, their practical use in helping breeding programs is still in its infancy. After exposing the pros and cons of process-based modeling, this paper presents the step-by-step process that would allow breeding programs to harness this tool to help guide their breeding decisions. We also argue that the issue of GxExM interactions should be tackled in a co-construction process, involving breeders, agronomists, extensionists, and modelers from the beginning, and this would bring crop models one step closer to being used to help make plant breeding decisions.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Crop improvement Multi-Environment Trials(MET), Process-based modeling, Statistical analysis, Target Population of Environment (TPE) |
| Subjects: | Others > Crop Modelling Others > Plant Breeding |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 16 Jan 2024 10:59 |
| Last Modified: | 16 Jan 2024 10:59 |
| URI: | http://oar.icrisat.org/id/eprint/12366 |
| Official URL: | https://www.sciencedirect.com/science/article/pii/... |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | UNSPECIFIED |
| Links: |
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