Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Detalhes bibliográficos
Autor(a) principal: Leite, Regina Maria Villas Bôas de Campos
Data de Publicação: 2015
Outros Autores: Oliveira, Maria Cristina Neves de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/20785
Resumo: The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty‑nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.
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spelling Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplotAgrupamento de genótipos de girassol quanto à produtividade, ao teor de óleo e à reação à mancha de alternária por GGE biplotAlternariaster helianthi; Helianthus annuus; disease resistance; principal component analysisAlternariaster helianthi; Helianthus annuus; resistência a doenças; análise de componentes principaisThe objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty‑nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.O objetivo deste trabalho foi avaliar a adequação do uso do método multivariado de análise de componentes principais (ACP), com uso do programa GGE biplot, para o agrupamento de genótipos de girassol quanto à sua reação à mancha de alternária (Alternariaster helianthi), e quanto à produtividade e ao teor de óleo. Sessenta e nove genótipos foram avaliados quanto à severidade da doença em campo, na fase de desenvolvimento R3, em sete safras, em Londrina, PR, com uso de uma escala diagramática própria, desenvolvida para esta doença. A produtividade e o teor de óleo também foram avaliados. Os dados foram padronizados com o programa Statistica, e o GGE biplot foi utilizado para ACP e exibição gráfica dos dados. Os dois primeiros componentes principais explicaram 77,9% da variação total. De acordo com o biplot poligonal, obtido com os dois primeiros componentes principais e as três variáveis resposta, os genótipos foram divididos em sete setores. Os genótipos alocados nos setores 1 e 2 apresentaram alta produtividade e alto teor de óleo, respectivamente, e os agrupados no setor 7 apresentaram tolerância à doença e alta produtividade, apesar de elevada severidade da doença. O método de análise de componentes principais com uso do GGE biplot é eficiente para agrupar genótipos de girassol com base nas variáveis estudadas.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraLeite, Regina Maria Villas Bôas de CamposOliveira, Maria Cristina Neves de2015-09-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/20785Pesquisa Agropecuaria Brasileira; v.50, n.8, ago. 2015; 649-657Pesquisa Agropecuária Brasileira; v.50, n.8, ago. 2015; 649-6571678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAenghttps://seer.sct.embrapa.br/index.php/pab/article/view/20785/13002https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/20785/13012info:eu-repo/semantics/openAccess2015-09-03T19:44:38Zoai:ojs.seer.sct.embrapa.br:article/20785Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2015-09-03T19:44:38Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
Agrupamento de genótipos de girassol quanto à produtividade, ao teor de óleo e à reação à mancha de alternária por GGE biplot
title Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
spellingShingle Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
Leite, Regina Maria Villas Bôas de Campos
Alternariaster helianthi; Helianthus annuus; disease resistance; principal component analysis
Alternariaster helianthi; Helianthus annuus; resistência a doenças; análise de componentes principais
title_short Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
title_full Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
title_fullStr Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
title_full_unstemmed Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
title_sort Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot
author Leite, Regina Maria Villas Bôas de Campos
author_facet Leite, Regina Maria Villas Bôas de Campos
Oliveira, Maria Cristina Neves de
author_role author
author2 Oliveira, Maria Cristina Neves de
author2_role author
dc.contributor.none.fl_str_mv

dc.contributor.author.fl_str_mv Leite, Regina Maria Villas Bôas de Campos
Oliveira, Maria Cristina Neves de
dc.subject.por.fl_str_mv Alternariaster helianthi; Helianthus annuus; disease resistance; principal component analysis
Alternariaster helianthi; Helianthus annuus; resistência a doenças; análise de componentes principais
topic Alternariaster helianthi; Helianthus annuus; disease resistance; principal component analysis
Alternariaster helianthi; Helianthus annuus; resistência a doenças; análise de componentes principais
description The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty‑nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.
publishDate 2015
dc.date.none.fl_str_mv 2015-09-03
dc.type.none.fl_str_mv
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://seer.sct.embrapa.br/index.php/pab/article/view/20785
url https://seer.sct.embrapa.br/index.php/pab/article/view/20785
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/20785/13002
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/20785/13012
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
dc.source.none.fl_str_mv Pesquisa Agropecuaria Brasileira; v.50, n.8, ago. 2015; 649-657
Pesquisa Agropecuária Brasileira; v.50, n.8, ago. 2015; 649-657
1678-3921
0100-104x
reponame:Pesquisa Agropecuária Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Agropecuária Brasileira (Online)
collection Pesquisa Agropecuária Brasileira (Online)
repository.name.fl_str_mv Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pab@sct.embrapa.br || sct.pab@embrapa.br
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