Application of the bayesian-AMMI model in the study of genotypic stability and adaptability in mustard data (Research, Society and Development, 2020)
The analysis of data sets from multienvironmental trials is of fundamental importance in the final stages of plant breeding programs. In this context, the Additive Main Effects and Multiplicative Interaction (AMMI) model has become a popular method for evaluating genotype responses in different environments. In the present work, the AMMI model was applied, under the bayesian approach, to a set of data from a randomized block experiment with 12 mustard genotypes (varieties) in 6 different environments. The objective was to analyze genotypic stability and adaptability through the AMMI-2 biplot representation, highlighting differences in this approach in relation to the classical AMMI analysis. The results showed the great flexibility of the bayesian method to incorporate a random effect for genotypes, as well as inference to the biplot through regions of credibility for genotypic and environmental scores that describe the effect of the interaction between genotypes by environments (GEI). The regions of credibility built for main effects and bilinear parameters allowed to identify more productive genotypes and to visualize homogeneous subgroups of genotypes and environments in relation to the effect of GEI. The more productive genotypes were G8 and G10 and only G2 was considered statistically stable.