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 ood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-ve rice genotypes belonging to the ood-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 ood-irrigated.

Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

“…All gains were favorable towards the selection criteria, with negative gains for IN (−0.8), 50%IN (−1.97) FLO (−1.07), and 50%FLO (−0.77), and an interesting positive gain for GY (18%), which fits the objective of producing early and high-yielding genotypes. Satisfactory results using this method were reported by several authors [38][39][40][41].…”

“…All gains were favorable towards the selection criteria, with negative gains for IN (−0.8), 50%IN (−1.97) FLO (−1.07), and 50%FLO (−0.77), and an interesting positive gain for GY (18%), which fits the objective of producing early and high-yielding genotypes. Satisfactory results using this method were reported by several authors [38][39][40][41]. In total, four genotypes, namely G13, G18, G17, and G11, showed balanced and desirable genetic gains with equal efficiency to simultaneously improve all the traits [30].…”

Bayesian inference applied to soybean grown under different shading levels using the multiple-trait model

“…Values of approximately 95 % distribution credibility for the H 2 parameter were found in the present study (Table 2). In flood-irrigated rice, the H 2 estimate was > 80 % (Silva Junior et al, 2022b). In corn lines, the heritability for N use efficacy was 50 %, considered highly heritable (Torres et al, 2018), indicating that the MTM estimates H 2 more accurately than the individual models.…”

Estratégias e seleção de cultivares de soja em estádios iniciais para tolerância ao déficit hídrico

“…O uso dos índices de seleção é uma estratégia particularmente vantajosa, já que permite a seleção de várias características de interesse agronômico de forma simultânea, contribuindo para o sucesso dos programas de melhoramento Woyann et al, 2020). Índices de seleção como o FAI-BLUP (Factor analysis and ideotype-design), proposto por e o MGIDI (Multitrait Genotype-Ideotype Distance Index) proposto por Olivoto e Nardino (2021) tem ganhado espaço nas pesquisas científicas Woyann et al, 2019;Kistner et al, 2022;Nardino et al, 2022;Silva Junior et al, 2022). Estes índices, além de realizar a seleção para múltiplas características de forma simultânea, lida bem com problemas relacionados à multicolinearidade.…”

“…Posteriormente, é realizado a estimação da distância ideótipo-genótipo, possibilitando o ranqueamento dos genótipos. Muitos trabalhos têm sido realizados envolvendo este índice para seleção de genótipos em diversas culturas, como tomate , Soja (Woyann et al, 2019), Sorgo , milho (Kistner et al, 2022) e arroz (Silva Junior et al, 2022).…”