A Framework for Building Enviromics Matrices in Mixed Models

Abstract

This study introduces a framework for constructing enviromics matrices in mixed models to integrate genetic and environmental data to enhance phenotypic predictions in plant breeding. Enviromics utilizes diverse data sources, such as climate and soil, to characterize genotype-by-environment (GxE) interactions. The approach employs block-diagonal structures in the design matrix to incorporate random effects from genetic and envirotypic covariates across trials. The covariance structure is modeled using the Kronecker product of the genetic relationship matrix and an identity matrix representing envirotypic effects, capturing genetic and environmental variability. This dual representation enables more accurate crop performance predictions across environments, improving selection strategies in breeding programs. The framework is compatible with existing mixed model software, including rrBLUP and BGLR, and can be extended for more complex interactions. By combining genetic relationships and environmental influences, this approach offers a powerful tool for advancing GxE studies and accelerating the development of improved crop varieties.