Optimal selection of common bean genotypes under genotype × environment interaction and its environmental drivers

Genotype-by-environment interaction (GEI) complicates variety recommendations for common bean ( Phaseolus vulgaris L.) in regions with high agroclimatic variability. This study aimed to dissect GEI in Brazilian multi-environment trials (METs) using a linear mixed models with factor analytic (FA) variance structures for the genotype-by-environment effects (FA mixed models) to identify high-yielding, stable genotypes and to characterize the environmental drivers underlying this interaction. Eleven genotypes were evaluated across 32 environments (combinations of location, year, and season). The FA model with four factors (FA4) efficiently captured 81.9% of the GEI variance, showing high accuracy (0.93) and generalized heritability (0.88). Using Factor Analytic Selection Tools (FAST), the model outputs were synthesized into metrics for overall performance (OP) and stability (RMSD). Genotypes CNFP16379 and CNFP16404 were identified as superior candidates for broad adaptation, combining high OP with small RMSD. In contrast, genotypes like CNFP16830 and IPR UIRAPURU, with high OP but low stability, were considered suitable for specific environments. To interpret the environmental basis of GEI, correlations were performed between the factor loadings and climatic covariates, revealing that the interaction was primarily driven by gradients related to water balance (precipitation, humidity, vapor-pressure deficit) and solar radiation. The results demonstrate that the FAST framework is a powerful tool for common bean breeding, enabling data-driven selection for both broad and specific adaptation while providing valuable insights into the environmental factors that modulate genotype performance. This integrated strategy enhances breeding efficiency and supports more precise cultivar deployment across diverse agroecosystems.