da Silva Junior, A.C.; Sant’Anna, I.D.C.; Peixoto, M.A.; Torres, L.G.; Silva, M.J.; da Costa, W.G.; Azevedo, C.F.; Soares, P.C.; Cruz, C.D. Multiple-Trait Model through Bayesian Inference Applied to Flood-Irrigated Rice (Oryza sativa L). Preprints2022, 2022020151. https://doi.org/10.20944/preprints202202.0151.v1
APA Style
da Silva Junior, A.C., Sant’Anna, I.D.C., Peixoto, M.A., Torres, L.G., Silva, M.J., da Costa, W.G., Azevedo, C.F., Soares, P.C., & Cruz, C.D. (2022). Multiple-Trait Model through Bayesian Inference Applied to Flood-Irrigated Rice (<em>Oryza sativa L</em>). Preprints. https://doi.org/10.20944/preprints202202.0151.v1
Chicago/Turabian Style
da Silva Junior, A.C., Plinio César Soares and Cosme Damião Cruz. 2022 "Multiple-Trait Model through Bayesian Inference Applied to Flood-Irrigated Rice (<em>Oryza sativa L</em>)" Preprints. https://doi.org/10.20944/preprints202202.0151.v1
Abstract
The giant challenge breeding flood-irrigated rice is to identify superior genotypes that present high-yielding with specific grain qualities, resistance to abiotic and biotic stresses, excellent adaptation to the target environment. Thus, the objectives of this study were to propose 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 belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100 grains in the agricultural year 2016/2017. 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 low 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. Genotypes 2 and 15 had similar potential in the three environments, they should be selected as high-performance multi-trait genotypes for the genetic breeding of flood-irrigated rice in the program.
Copyright:
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