Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment...

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• Climate change necessitates crop varieties adapted to diverse environmental stresses.
• Genomic predictions coupled with environmental characterization aid in identifying suitable gene combinations.
• Understanding genotype × environment interactions (G × E) is key for efficient crop production.

Purpose of the Study:

• To develop and compare methods for predicting genotype × environment interactions in winter bread wheat.
• To enhance the accuracy of genomic predictions by incorporating environmental covariates and advanced modeling techniques.
• To identify optimal strategies for selecting environmental covariates and modeling G × E in plant breeding.

Main Methods:

• Utilized a multi-environment trial of 220 European elite winter bread wheat varieties across 42 environments.
• Compared reference regression models with alternative models incorporating environmental covariates (ECs).
• Applied AMMI decomposition to estimate covariance matrices and analyzed the impact of different kinship matrices on G × E prediction.

Main Results:

• Selecting a subset of environmental covariates significantly improved prediction accuracy for G × E.
• Estimating covariance matrices using AMMI decomposition, leveraging training set phenotypic data, proved effective.
• Using distinct kinship matrices for genetic and G × E effects enhanced prediction accuracy.
• Integrative stress indexes from crop growth models were more effective for capturing G × E than climatic covariates.

Conclusions:

• New methods involving selected environmental covariates and AMMI decomposition enhance genotype × environment interaction prediction.
• Optimized modeling of genetic and G × E effects, alongside the use of integrative stress indexes, is crucial for breeding climate-adapted wheat varieties.
• These approaches offer a pathway to develop more efficient and resilient crop varieties for challenging agricultural conditions.