2021
DOI: 10.1590/1984-70332021v21n1a11
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Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach

Abstract: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus, the objectives of this study were: i) propose the Multi-Environment Index Based on Factor Analysis and Ideotype-Design/Markov Chain Monte Carlo (FAI/ MCMC index), and ii) apply it for soybean genotypes recommendation. To this end, a data set with 30 soybean genotypes evaluated in 10 environments for grain yield trait was used. Variance components, genetic… Show more

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Cited by 3 publications
(2 citation statements)
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“…To query a given canopy, a fingerprint of the canopy was generated and then compared with the existing database of fingerprints to identify the possible match. This capability could enable further in-depth development and exploration of the germplasm resources for ideotypes ( Kokubun, 1988 ; Evangelista et al., 2021 ; Lukas et al, 2022 ; Roth et al., 2022 ). While this work evaluated a single time point, a more in-depth and temporal fingerprint could be developed to evaluate the canopy growth and development across time.…”
Section: Resultsmentioning
confidence: 99%
“…To query a given canopy, a fingerprint of the canopy was generated and then compared with the existing database of fingerprints to identify the possible match. This capability could enable further in-depth development and exploration of the germplasm resources for ideotypes ( Kokubun, 1988 ; Evangelista et al., 2021 ; Lukas et al, 2022 ; Roth et al., 2022 ). While this work evaluated a single time point, a more in-depth and temporal fingerprint could be developed to evaluate the canopy growth and development across time.…”
Section: Resultsmentioning
confidence: 99%
“…CF Azevedo et al Bayesian approach is used for several studies in genetic breeding (Azevedo et al 2015, Evangelista et al 2021. Specifically, Bayesian methods applied genomic prediction differ regarding the distribution assumed for marker effects, and the assumptions encompass the genetic architecture of the desirable trait (Gianola 2013).…”
Section: Introductionmentioning
confidence: 99%