Application of fuzzy logic for adaptability and stability studies in flood‐irrigated rice (<scp><i>Oryza sativa</i></scp>)

Silva, Antônio Carlos da; Silva, Michele Jorge da; Costa, Weverton Gomes da; Sousa, Ithalo Coelho de; Cruz, Cosme Damião; Nascimento, Moysés; Soares, Plínio César

doi:10.1111/pbr.12973

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Its objective is to recommend the most suitable cultivars for planting, whether from final productivity, plant health, or operational (harvest facilitator). It is necessary to consider the interaction between genotypes and environments (GE) (Silva Júnior et al, 2021). The occurrence of this interaction in multi-trait datasets poses an even more significant challenge for the breeder (Torres et al, 2018;Volpato et al 2019, Peixoto et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

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

Silva¹,

Sant’Anna²,

Peixoto³

et al. 2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Silva¹,

Sant’Anna²,

Peixoto³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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

et al. 2022

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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.

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Prediction and importance of predictors in approaches based on computational intelligence and machine learning

Júnior¹,

Moura²,

Bhering³

et al. 2023

Agron. Sci. Biotechnol.

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Machine learning and computational intelligence are rapidly emerging in plant breeding, allowing the exploration of big data concepts and predicting the importance of predictors. In this context, the main challenges are how to analyze datasets and extract new knowledge at all levels of research. Predicting the importance of variables in genetic improvement programs allows for faster progress, carrying out an extensive phenotypic evaluation of the germplasm, and selecting and predicting traits that present low heritability and/or measurement difficulties. Although, simultaneous evaluation of traits provides a wide variety of information, identifying which predictor variable is most important is a challenge for the breeder. The traditional approach to variable selection is based on multiple linear regression. It evaluates the relationship between a response variable and two or more independent variables. However, this approach has limitations regarding its ability to analyze high-dimensional data and not capture complex and multivariate relationships between traits. In summary, machine learning and computational intelligence approaches allow inferences about complex interactions in plant breeding. Given this, a systematic review to disentangle machine learning and computational intelligence approaches is relevant to breeders and was considered in this review. We present the main steps for developing each strategy (from data selection to evaluating classification/prediction models and quantifying the best predictor).

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Application of fuzzy logic for adaptability and stability studies in flood‐irrigated rice (Oryza sativa)

Cited by 3 publications

References 26 publications

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

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

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

Prediction and importance of predictors in approaches based on computational intelligence and machine learning

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