Bayesian segmented regression model to evaluate the adaptability and stability of maize in Northeastern Brazil

Abstract

Although maize is one of the main crops in the Northeast region, yield is still considered low when compared to other regions. One of the main solutions to increasing yield is the selection of cultivars adapted to the conditions of the Northeast region. Thus, the present study aims to use the Bayesian segmented regression model to evaluate the adaptability and stability of maize. The experiment was set up in a randomized block design with two repetitions, where 25 maize hybrids were evaluated in different states. Initially, the analysis of variance was performed. Then, the Bayesian approach of the segmented regression method was used to select the hybrids regarding adaptability and stability. There was a difference between the genotypes indicated using the a priori distribution and those indicated by the minimally informative a priori distribution. Hybrids 20A55HX, 2B433HX, 2B512HX, and P2830H were considered ideal for the Northeast region.