Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multiple-Trait Model through Bayesian Inference Applied to Flood-Irrigated Rice (Oryza sativa L)

Version 1 : Received: 7 February 2022 / Approved: 10 February 2022 / Online: 10 February 2022 (11:14:21 CET)

How to cite: 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). Preprints 2022, 2022020151 (doi: 10.20944/preprints202202.0151.v1). 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). Preprints 2022, 2022020151 (doi: 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.

Keywords

high-performance; heritable; multi-environments; credibility interval

Subject

MATHEMATICS & COMPUTER SCIENCE, Analysis

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.