Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S0100-204X2018001000002 http://hdl.handle.net/11449/186516 |
Resumo: | The objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h(2)mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials. |
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Repositório Institucional da UNESP |
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Bayesian approach, traditional method, and mixed models for multienvironment trials of soybeanGlycine maxmathematical modelingprior distribution in plant breedingThe objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h(2)mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Nova Amer Agr Ltda, Fazenda Nova Amer S-N, BR-19820000 Taruma, SP, BrazilUniv Estadual Paulista, Fac Ciencias Agr & Vet, Campus Jaboticabal, BR-14884900 Jaboticabal, SP, BrazilUniv Fed Vicosa, Ave PH Rolfs S-N,Campus Univ, BR-36570900 Vicosa, MG, BrazilUniv Fed Sergipe, Nucleo Grad Agron, Campus Sertao,Rodovia Engenheiro Jorge Neto,Km 3, BR-49680000 Nossa Senhora Da Gloria, SE, BrazilUniv Estadual Paulista, Fac Ciencias Agr & Vet, Campus Jaboticabal, BR-14884900 Jaboticabal, SP, BrazilEmpresa Brasil Pesq AgropecNova Amer Agr LtdaUniversidade Estadual Paulista (Unesp)Universidade Federal de Viçosa (UFV)Universidade Federal de Sergipe (UFS)Silva, Alysson Jalles daSanches, Adhemar [UNESP]Bastos Andrade, Andrea CarlaFerreira de Oliveira, Gustavo HugoDi Mauro, Antonio Orlando [UNESP]2019-10-05T04:10:41Z2019-10-05T04:10:41Z2018-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1093-1100application/pdfhttp://dx.doi.org/10.1590/S0100-204X2018001000002Pesquisa Agropecuaria Brasileira. Brasilia Df: Empresa Brasil Pesq Agropec, v. 53, n. 10, p. 1093-1100, 2018.0100-204Xhttp://hdl.handle.net/11449/18651610.1590/S0100-204X2018001000002S0100-204X2018001001093WOS:000452380700002S0100-204X2018001001093.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPesquisa Agropecuaria Brasileirainfo:eu-repo/semantics/openAccess2024-06-06T13:43:43Zoai:repositorio.unesp.br:11449/186516Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-06T13:43:43Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
title |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
spellingShingle |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean Silva, Alysson Jalles da Glycine max mathematical modeling prior distribution in plant breeding |
title_short |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
title_full |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
title_fullStr |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
title_full_unstemmed |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
title_sort |
Bayesian approach, traditional method, and mixed models for multienvironment trials of soybean |
author |
Silva, Alysson Jalles da |
author_facet |
Silva, Alysson Jalles da Sanches, Adhemar [UNESP] Bastos Andrade, Andrea Carla Ferreira de Oliveira, Gustavo Hugo Di Mauro, Antonio Orlando [UNESP] |
author_role |
author |
author2 |
Sanches, Adhemar [UNESP] Bastos Andrade, Andrea Carla Ferreira de Oliveira, Gustavo Hugo Di Mauro, Antonio Orlando [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Nova Amer Agr Ltda Universidade Estadual Paulista (Unesp) Universidade Federal de Viçosa (UFV) Universidade Federal de Sergipe (UFS) |
dc.contributor.author.fl_str_mv |
Silva, Alysson Jalles da Sanches, Adhemar [UNESP] Bastos Andrade, Andrea Carla Ferreira de Oliveira, Gustavo Hugo Di Mauro, Antonio Orlando [UNESP] |
dc.subject.por.fl_str_mv |
Glycine max mathematical modeling prior distribution in plant breeding |
topic |
Glycine max mathematical modeling prior distribution in plant breeding |
description |
The objective of this work was to compare the Bayesian approach and the frequentist methods to estimate means and genetic parameters in soybean multienvironment trials. Fifty-one soybean lines and four controls were evaluated in a randomized complete block design, in six environments, with three replicates, and soybean grain yield was determined. The half-normal prior and uniform distributions were used in combination with parameters obtained from data of 18 genotypes collected in previous and related experiments. The genotypic values of the genotypes of high- and low-grain yield, clustered by the Bayesian approach, differed from the means obtained by the frequentist inference. Soybean assessed through the Bayesian approach showed genetic parameter values of the mixed model (REML/Blup) close to those of the following variables: mean heritability (h(2)mg), accuracy of genotype selection (Acgen), coefficient of genetic variation (CVgi%), and coefficient of environmental variation (CVe%). Therefore, the mixed model methodology and the Bayesian approach lead to similar results for genetic parameters in multienvironment trials. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-01 2019-10-05T04:10:41Z 2019-10-05T04:10:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/S0100-204X2018001000002 Pesquisa Agropecuaria Brasileira. Brasilia Df: Empresa Brasil Pesq Agropec, v. 53, n. 10, p. 1093-1100, 2018. 0100-204X http://hdl.handle.net/11449/186516 10.1590/S0100-204X2018001000002 S0100-204X2018001001093 WOS:000452380700002 S0100-204X2018001001093.pdf |
url |
http://dx.doi.org/10.1590/S0100-204X2018001000002 http://hdl.handle.net/11449/186516 |
identifier_str_mv |
Pesquisa Agropecuaria Brasileira. Brasilia Df: Empresa Brasil Pesq Agropec, v. 53, n. 10, p. 1093-1100, 2018. 0100-204X 10.1590/S0100-204X2018001000002 S0100-204X2018001001093 WOS:000452380700002 S0100-204X2018001001093.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1093-1100 application/pdf |
dc.publisher.none.fl_str_mv |
Empresa Brasil Pesq Agropec |
publisher.none.fl_str_mv |
Empresa Brasil Pesq Agropec |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803045483731484672 |