Multiple-trait model through Bayesian inference applied to flood-irrigated rice (Oryza sativa L)

Abstract

The objectives of this study were to use a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes and six traits belonging to the flood-irrigated rice improvement program were evaluated. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: ${h}^{2}$: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a weak correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from ($\rho$= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated.