A Hierarchical Bayesian Estimation Model for Multienvironment Plant Breeding Trials in Successive Years

Abstract

In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype × environment interaction (G × E). In this study, we propose a hierarchical Bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) G × E term is the matrix von Mises–Fisher (mVMF) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (HPD) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (Zea mays L.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause G × E across years and within years, since the hierarchical Bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about G × E patterns not only for each 1‐yr period of the breeding trials but also for the general process that originates the response across these periods.