Rafael Tassinari Resende

This paper develops a bivariate enviromic reaction-norm model to predict breeding genotypes as commercial cultivars by integrating genomic and environmental data, showing it can generate spatial recommendation maps for genotype deployment.

This paper develops an enviromic framework to predict tree performance and guide clonal deployment in Eucalyptus spp., achieving higher accuracy than traditional GxE models.

This study evaluates enviromic models for predicting upland rice productivity in Brazil by integrating environmental and genetic data, finding that comprehensive environmental covariates improve model stability and accuracy.

The paper introduces an R-based GIS function to define Target Population of Environments for enviromics studies in plant breeding, enhancing GxE interaction analysis.