Predictive Ability of Enviromic Modeling in G×E Interactions for Upland Rice Site Recommendations

Enviromics is an omics approach that investigates a phenomenon using all available environmental information. This study explores the use of enviromic covariates in studies of genotype × environment (G×E) interactions in upland rice in Brazil, utilizing a field trial dataset from 143 locations over 27 years, covering diverse environmental conditions. The platforms WorldClim, NASA POWER, and SoilGrids were used to extract data, resulting in 383 environmental covariates. The objective of this study was to evaluate the use of enviromic kernels to integrate GIS and genetic data for predicting upland rice productivity across Brazil and to determine the optimal number of environmental covariates required to ensure model accuracy and stability. The predictive abilities of the enviromic model peaked with around 81 covariates, stabilizing when all 383 were included, suggesting the importance of a comprehensive dataset for accurate predictions. Analysis reveals that environmental dissimilarities are more critical than geographical distance for genotypic variability, reinforcing the need to consider multiple covariates in predictive models. Heritability mapping revealed spatial variations, with regions of high heritability concentrated in southern Brazil, where genetic selection may be more efficient. The clustering of mega-environments was not efficient, highlighting the complexity of G×E interactions, and confirming that pixel-by-pixel enviromic models are a safer approach for recommending breeding actions for upland rice. This study suggests strategies to improve genotype selection for specific conditions, guiding the expansion of rice cultivation into new agricultural areas in Brazil. The findings also contribute to rice-growing regions worldwide, especially in countries cultivating upland rice under diverse conditions.