Bayesian GGE model for heteroscedastic multienvironmental trials.

Detalhes bibliográficos
Autor(a) principal: OLIVEIRA, L. A. de
Data de Publicação: 2022
Outros Autores: SILVA, C. P. da, SILVA, A. Q. da, MENDES, C. T. E., NUVUNGA, J. J., NUNES, J. A. R., PARRELLA, R. A. da C., BALESTE, M., BUENO FILHO, J. S. de S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142572
https://doi.org/10.1002/csc2.20696
Resumo: The dissection of genotype×environment interaction (GEI) is a crucial aspect ofthe final stages of plant breeding pipelines and recommendation of cultivars. Linear-bilinear models used to analyze this interaction, such as the additive main effectsand multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assumehomogeneity of the residual variances across environments which affects the esti-mates and therefore, interpretations and conclusions. Our main objective was topropose a GGE model that considers heteroscedasticity across environments usingBayesian inference and to evaluate its implications in the interpretation of real andsimulated data. The GGE model assuming common variance was also fitted for com-parison purposes. The great flexibility of the Bayesian inference is transferred to thebiplots, allowing the construction of credible regions for genotypic and environmen-tal scores. The inference on the stability and adaptability of genotypes might changewhen heteroscedasticity is ignored. When real data are used, different patterns of cor-relations between environments also affect the representativeness and discriminationof the target environment. The modeling of heteroscedasticity allowed the clusteringof environments into subgroups, with similar effects for GEI. The proposed GGEmodel was more adequate and realistic to deal with scenarios of heterogeneous vari-ance in multienvironment trials, which can be useful for exploiting the GEI.
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spelling Bayesian GGE model for heteroscedastic multienvironmental trials.Interação meio ambienteModelo mistoEnsaio de rendimentoEnsaio de cultivarEstabilidadeMelhoramento VegetalVariedadeGenótipoThe dissection of genotype×environment interaction (GEI) is a crucial aspect ofthe final stages of plant breeding pipelines and recommendation of cultivars. Linear-bilinear models used to analyze this interaction, such as the additive main effectsand multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assumehomogeneity of the residual variances across environments which affects the esti-mates and therefore, interpretations and conclusions. Our main objective was topropose a GGE model that considers heteroscedasticity across environments usingBayesian inference and to evaluate its implications in the interpretation of real andsimulated data. The GGE model assuming common variance was also fitted for com-parison purposes. The great flexibility of the Bayesian inference is transferred to thebiplots, allowing the construction of credible regions for genotypic and environmen-tal scores. The inference on the stability and adaptability of genotypes might changewhen heteroscedasticity is ignored. When real data are used, different patterns of cor-relations between environments also affect the representativeness and discriminationof the target environment. The modeling of heteroscedasticity allowed the clusteringof environments into subgroups, with similar effects for GEI. The proposed GGEmodel was more adequate and realistic to deal with scenarios of heterogeneous vari-ance in multienvironment trials, which can be useful for exploiting the GEI.LUCIANO ANTONIO DE OLIVEIRA, Universidade Federal da Grande Dourados; CARLOS PEREIRA DA SILVA, Universidade Federal de Lavras; ALESSANDRA QUERINO DA SILVA, Universidade Federal da Grande Dourados; CRISTIAN TIAGO ERAZO MENDES, Universidade Federal de Lavras; JOEL JORGE NUVUNGA, Universidade Eduardo Mondlane; JOSÉ AIRTON RODRIGUES NUNES, Universidade Federal de Lavras; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARCIO BALESTE, Universidade Federal de Lavras; JÚLIO SÍLVIO DE SOUSA BUENO FILHO, Universidade Federal de Lavras.OLIVEIRA, L. A. deSILVA, C. P. daSILVA, A. Q. daMENDES, C. T. E.NUVUNGA, J. J.NUNES, J. A. R.PARRELLA, R. A. da C.BALESTE, M.BUENO FILHO, J. S. de S.2022-06-15T10:20:15Z2022-06-15T10:20:15Z2022-05-022022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCrop Science, v. 62, p. 982-996, 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142572https://doi.org/10.1002/csc2.20696enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-06-15T10:20:24Zoai:www.alice.cnptia.embrapa.br:doc/1142572Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-06-15T10:20:24falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-06-15T10:20:24Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Bayesian GGE model for heteroscedastic multienvironmental trials.
title Bayesian GGE model for heteroscedastic multienvironmental trials.
spellingShingle Bayesian GGE model for heteroscedastic multienvironmental trials.
OLIVEIRA, L. A. de
Interação meio ambiente
Modelo misto
Ensaio de rendimento
Ensaio de cultivar
Estabilidade
Melhoramento Vegetal
Variedade
Genótipo
title_short Bayesian GGE model for heteroscedastic multienvironmental trials.
title_full Bayesian GGE model for heteroscedastic multienvironmental trials.
title_fullStr Bayesian GGE model for heteroscedastic multienvironmental trials.
title_full_unstemmed Bayesian GGE model for heteroscedastic multienvironmental trials.
title_sort Bayesian GGE model for heteroscedastic multienvironmental trials.
author OLIVEIRA, L. A. de
author_facet OLIVEIRA, L. A. de
SILVA, C. P. da
SILVA, A. Q. da
MENDES, C. T. E.
NUVUNGA, J. J.
NUNES, J. A. R.
PARRELLA, R. A. da C.
BALESTE, M.
BUENO FILHO, J. S. de S.
author_role author
author2 SILVA, C. P. da
SILVA, A. Q. da
MENDES, C. T. E.
NUVUNGA, J. J.
NUNES, J. A. R.
PARRELLA, R. A. da C.
BALESTE, M.
BUENO FILHO, J. S. de S.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv LUCIANO ANTONIO DE OLIVEIRA, Universidade Federal da Grande Dourados; CARLOS PEREIRA DA SILVA, Universidade Federal de Lavras; ALESSANDRA QUERINO DA SILVA, Universidade Federal da Grande Dourados; CRISTIAN TIAGO ERAZO MENDES, Universidade Federal de Lavras; JOEL JORGE NUVUNGA, Universidade Eduardo Mondlane; JOSÉ AIRTON RODRIGUES NUNES, Universidade Federal de Lavras; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; MARCIO BALESTE, Universidade Federal de Lavras; JÚLIO SÍLVIO DE SOUSA BUENO FILHO, Universidade Federal de Lavras.
dc.contributor.author.fl_str_mv OLIVEIRA, L. A. de
SILVA, C. P. da
SILVA, A. Q. da
MENDES, C. T. E.
NUVUNGA, J. J.
NUNES, J. A. R.
PARRELLA, R. A. da C.
BALESTE, M.
BUENO FILHO, J. S. de S.
dc.subject.por.fl_str_mv Interação meio ambiente
Modelo misto
Ensaio de rendimento
Ensaio de cultivar
Estabilidade
Melhoramento Vegetal
Variedade
Genótipo
topic Interação meio ambiente
Modelo misto
Ensaio de rendimento
Ensaio de cultivar
Estabilidade
Melhoramento Vegetal
Variedade
Genótipo
description The dissection of genotype×environment interaction (GEI) is a crucial aspect ofthe final stages of plant breeding pipelines and recommendation of cultivars. Linear-bilinear models used to analyze this interaction, such as the additive main effectsand multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assumehomogeneity of the residual variances across environments which affects the esti-mates and therefore, interpretations and conclusions. Our main objective was topropose a GGE model that considers heteroscedasticity across environments usingBayesian inference and to evaluate its implications in the interpretation of real andsimulated data. The GGE model assuming common variance was also fitted for com-parison purposes. The great flexibility of the Bayesian inference is transferred to thebiplots, allowing the construction of credible regions for genotypic and environmen-tal scores. The inference on the stability and adaptability of genotypes might changewhen heteroscedasticity is ignored. When real data are used, different patterns of cor-relations between environments also affect the representativeness and discriminationof the target environment. The modeling of heteroscedasticity allowed the clusteringof environments into subgroups, with similar effects for GEI. The proposed GGEmodel was more adequate and realistic to deal with scenarios of heterogeneous vari-ance in multienvironment trials, which can be useful for exploiting the GEI.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-15T10:20:15Z
2022-06-15T10:20:15Z
2022-05-02
2022
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Crop Science, v. 62, p. 982-996, 2022.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142572
https://doi.org/10.1002/csc2.20696
identifier_str_mv Crop Science, v. 62, p. 982-996, 2022.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1142572
https://doi.org/10.1002/csc2.20696
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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