Use of Bayesian probabilistic model approach in common bean varietal recommendation

Recommendation of new varieties is supported by value for cultivation and use (Valor de Cultivo e Uso [VCU]) trials. For a more reliable recommendation, it is necessary to identify methodologies that make better use of the genotype-by-environment interaction (GEI). The methodology proposed by Dias et al. is an alternative to take advantage of the GEI; it considers concepts of Bayesian models and probability methods of adaptation and stability analysis in a single model, classifying the genotypes regarding possible success based on a defined selection intensity. Thus, the aim of the present study was to explore the use of Bayesian probabilistic method for the purpose of recommend common bean (Phaseolus vulgaris L.) varieties. To that end, we used grain yield data from 15 genotypes of common bean evaluated in 42 environments distributed over different crop seasons, years, and locations in regard to VCU trials conducted from 2016 to 2018. Under a predefined selection intensity of 30%, the genotypes with greater marginal probability of superior performance were G01, G14, G07, G11, and G02. The genotypes with greater marginal probability of superior stability were G06, G07, G04, G03, and G12. Considering the joint probability of superior performance and yield stability, the genotypes G07, G14, G01, G11, and G04 stand out. Therefore, the use of the Bayesian probabilistic method showed promise in recommendation of common bean varieties.