Comparison of multivariate methods for studying the G×E interaction

Detalhes bibliográficos
Autor(a) principal: Garbuglio, Deoclécio Domingos
Data de Publicação: 2015
Outros Autores: Ferreira, Daniel Furtado, Araújo, Pedro Mário de, Gerage, Antonio Carlos, Shioga, Pedro Sentaro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Semina. Ciências Agrárias (Online)
Texto Completo: https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/17805
Resumo: The objective of this work was to evaluate three statistical multivariate methods for analyzing adaptability and environmental stratification simultaneously, using data from maize cultivars indicated for planting in the State of Paraná-Brazil. Under the FGGE and GGE methods, the genotypic effect adjusts the G×E interactions across environments, resulting in a high percentage of explanation associated with a smaller number of axes. Environmental stratification via the FGGE and GGE methods showed similar responses, while the AMMI method did not ensure grouping of environments. The adaptability analysis revealed low divergence patterns of the responses obtained through the three methods. Genotypes P30F35, P30F53, P30R50, P30K64 and AS 1570 showed high yields associated with general adaptability. The FGGE method allowed differences in yield responses in specific regions and the impact in locations belonging to the same environmental group (through rE) to be associated with the level of the simple portion of the G×E interaction.
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spelling Comparison of multivariate methods for studying the G×E interactionComparação de métodos multivariados para estudo da interação G×AMultivariate analysisFactor analysisGenotypic effectMaize.Melhoramento VegetalAnálise multivariadaAnálise de fatoresEfeito genotípicoMilho.The objective of this work was to evaluate three statistical multivariate methods for analyzing adaptability and environmental stratification simultaneously, using data from maize cultivars indicated for planting in the State of Paraná-Brazil. Under the FGGE and GGE methods, the genotypic effect adjusts the G×E interactions across environments, resulting in a high percentage of explanation associated with a smaller number of axes. Environmental stratification via the FGGE and GGE methods showed similar responses, while the AMMI method did not ensure grouping of environments. The adaptability analysis revealed low divergence patterns of the responses obtained through the three methods. Genotypes P30F35, P30F53, P30R50, P30K64 and AS 1570 showed high yields associated with general adaptability. The FGGE method allowed differences in yield responses in specific regions and the impact in locations belonging to the same environmental group (through rE) to be associated with the level of the simple portion of the G×E interaction.O objetivo deste trabalho foi avaliar três métodos estatísticos multivariados, para análise de adaptabilidade e estratificação ambiental simultaneamente, utilizando dados de cultivares de milho indicadas para cultivo no estado do Paraná. Nos métodos GGE e FGGE, o efeito genotípico atuou como um coeficiente de ajuste das interações G×A ao longo dos ambientes, implicando em altos porcentuais de explicação, associados a um menor número de eixos. A estratificação ambiental pelos métodos GGE e FGGE apresentou respostas similares, enquanto pelo método AMMI não houve garantia de agrupamento de ambientes. As análises de adaptabilidade apresentaram poucas divergências de resposta, pelos três métodos. Os genótipos P30F35, P30F53, P30R50, P30K64 e AS 1570 apresentaram altas produtividades associadas à adaptabilidade geral. O método FGGE permitiu associar as diferenças de respostas de produtividade entre determinados conjuntos de ambientes e o impacto em localidades pertencentes ao mesmo conjunto ambiental (através de rA), com o auxílio do nível de porção simples atuante da interação G×A.UEL2015-12-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/1780510.5433/1679-0359.2015v36n6p3499Semina: Ciências Agrárias; Vol. 36 No. 6 (2015); 3499-3516Semina: Ciências Agrárias; v. 36 n. 6 (2015); 3499-35161679-03591676-546Xreponame:Semina. Ciências Agrárias (Online)instname:Universidade Estadual de Londrina (UEL)instacron:UELenghttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/17805/17434http://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessGarbuglio, Deoclécio DomingosFerreira, Daniel FurtadoAraújo, Pedro Mário deGerage, Antonio CarlosShioga, Pedro Sentaro2022-12-02T16:04:04Zoai:ojs.pkp.sfu.ca:article/17805Revistahttp://www.uel.br/revistas/uel/index.php/semagrariasPUBhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/oaisemina.agrarias@uel.br1679-03591676-546Xopendoar:2022-12-02T16:04:04Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)false
dc.title.none.fl_str_mv Comparison of multivariate methods for studying the G×E interaction
Comparação de métodos multivariados para estudo da interação G×A
title Comparison of multivariate methods for studying the G×E interaction
spellingShingle Comparison of multivariate methods for studying the G×E interaction
Garbuglio, Deoclécio Domingos
Multivariate analysis
Factor analysis
Genotypic effect
Maize.
Melhoramento Vegetal
Análise multivariada
Análise de fatores
Efeito genotípico
Milho.
title_short Comparison of multivariate methods for studying the G×E interaction
title_full Comparison of multivariate methods for studying the G×E interaction
title_fullStr Comparison of multivariate methods for studying the G×E interaction
title_full_unstemmed Comparison of multivariate methods for studying the G×E interaction
title_sort Comparison of multivariate methods for studying the G×E interaction
author Garbuglio, Deoclécio Domingos
author_facet Garbuglio, Deoclécio Domingos
Ferreira, Daniel Furtado
Araújo, Pedro Mário de
Gerage, Antonio Carlos
Shioga, Pedro Sentaro
author_role author
author2 Ferreira, Daniel Furtado
Araújo, Pedro Mário de
Gerage, Antonio Carlos
Shioga, Pedro Sentaro
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Garbuglio, Deoclécio Domingos
Ferreira, Daniel Furtado
Araújo, Pedro Mário de
Gerage, Antonio Carlos
Shioga, Pedro Sentaro
dc.subject.por.fl_str_mv Multivariate analysis
Factor analysis
Genotypic effect
Maize.
Melhoramento Vegetal
Análise multivariada
Análise de fatores
Efeito genotípico
Milho.
topic Multivariate analysis
Factor analysis
Genotypic effect
Maize.
Melhoramento Vegetal
Análise multivariada
Análise de fatores
Efeito genotípico
Milho.
description The objective of this work was to evaluate three statistical multivariate methods for analyzing adaptability and environmental stratification simultaneously, using data from maize cultivars indicated for planting in the State of Paraná-Brazil. Under the FGGE and GGE methods, the genotypic effect adjusts the G×E interactions across environments, resulting in a high percentage of explanation associated with a smaller number of axes. Environmental stratification via the FGGE and GGE methods showed similar responses, while the AMMI method did not ensure grouping of environments. The adaptability analysis revealed low divergence patterns of the responses obtained through the three methods. Genotypes P30F35, P30F53, P30R50, P30K64 and AS 1570 showed high yields associated with general adaptability. The FGGE method allowed differences in yield responses in specific regions and the impact in locations belonging to the same environmental group (through rE) to be associated with the level of the simple portion of the G×E interaction.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-09
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/17805
10.5433/1679-0359.2015v36n6p3499
url https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/17805
identifier_str_mv 10.5433/1679-0359.2015v36n6p3499
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/17805/17434
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv UEL
publisher.none.fl_str_mv UEL
dc.source.none.fl_str_mv Semina: Ciências Agrárias; Vol. 36 No. 6 (2015); 3499-3516
Semina: Ciências Agrárias; v. 36 n. 6 (2015); 3499-3516
1679-0359
1676-546X
reponame:Semina. Ciências Agrárias (Online)
instname:Universidade Estadual de Londrina (UEL)
instacron:UEL
instname_str Universidade Estadual de Londrina (UEL)
instacron_str UEL
institution UEL
reponame_str Semina. Ciências Agrárias (Online)
collection Semina. Ciências Agrárias (Online)
repository.name.fl_str_mv Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)
repository.mail.fl_str_mv semina.agrarias@uel.br
_version_ 1799306071561469952

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