Enhancing enviromics based predictions in common bean multi-environment trials.

ABSTRACT: Enviromic approaches enhance predictive models by incorporating environmental data into selection frameworks. By integrating factor analytic (FA) models, enviromics, and Geographic Information Systems (GIS), the GIS-FA method was proposed to improve genotype prediction in untested environments. This study aims to refine GIS-FA by implementing Random Forest Spatial Interpolation for enhanced environmental data interpolation and optimizing spatial sampling to exclude non-agricultural areas, thereby improving environmental characterization. We applied the improved GIS-FA framework to common bean trials conducted across 23 environments in São Paulo, Brazil, evaluating 59 genotypes from the "Carioca" and "Black" market classes. The enhanced method increased empirical Best Linear Unbiased Predic

SUBMITTER: Blasques GM 

PROVIDER: S-EPMC12575806 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature