We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Multiple-trait model through Bayesian inference applied to flood-irrigated rice (Oryza sativa L).
- Authors
da Silva Junior, Antônio Carlos; de Castro Sant'Anna, Isabela; Peixoto, Marco Antônio; Torres, Lívia Gomes; Silva Siqueira, Michele Jorge; da Costa, Weverton Gomes; Azevedo, Camila Ferreira; Soares, Plínio César; Cruz, Cosme Damião
- 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 (ρ = 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.
- Subjects
BAYESIAN field theory; RICE; MARKOV chain Monte Carlo
- Publication
Euphytica, 2022, Vol 218, Issue 9, p1
- ISSN
0014-2336
- Publication type
Article
- DOI
10.1007/s10681-022-03077-x