Bayesian AMMI applied to food-type soybean multi-environment trials

Abstract

A complicating factor for the selection of plant strains is the influence of a genotype-environment (GE)
interaction. The Bayesian approach is a tool to increase the efficiency of adaptability and stability methodologies. In this context,
the objective of this study was to evaluate the linear and bi-linear parameters of the additive main effects and multiplicative
interaction (AMMI) analysis using the Bayesian approach for selection of food-type soybean genotypes in multi-environment
trials. The grain yields of five lipoxygenase-free lines intended for human consumption of from the soybean breeding program
of the Londrina State University and two commercial standards (BRS 257 and BMX Potência RR) were evaluated in four
counties of the State of Paraná, Brazil, in the 2014/15, 2015/16 and 2016/17 growing seasons. Of the evaluated lines, only UEL
110 and UEL 122 had positive posterior genotypic effects, exceeding a probability of 95% against the commercial standard
BRS 257. Only lines UEL 115 and UEL 123 did not contribute significantly to the GE interaction. Lines UEL 110 and UEL
122 proved adaptable to the largest number of environments with significant GE interaction and are therefore promising for
the development of new food-type soybean cultivars. The use of AMMI1 (PC1 vs. effects genotypes) showed results for the
stability of genotypes similar to AMMI2 (PC1 vs PC2), allowing a direct selection by the biplot for productivity and stability.