Principal component analysis for selection of superior maize genotypes

Detalhes bibliográficos
Autor(a) principal: Carnimeo, Eduardo Sávio Gomes [UNESP]
Data de Publicação: 2020
Outros Autores: Bertasello, Luiz Eduardo Tilhaqui [UNESP], Dutra, Sophia Mangussi Franchi [UNESP], Mõro, Gustavo Vitti [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.15361/1984-5529.2020V48N4P357-362
http://hdl.handle.net/11449/233097
Resumo: Constant advances in studies on the behavior of maize genotypes and their interactions with the environment are of great importance for the best performance of the plant. This study verifies effects and causes of agronomic variables of maize hybrids on grain yields and performs the indirect selection of superior genotypes by principal component analysis (PCA). Two hundred and thirty maize genotypes were used, with two hundred and twenty- -nine topcross hybrids (consisting of crossings of two hundred and twenty-nine partially inbred genotypes with a tester) and one check in a randomized block design with two repetitions. The genotypes were evaluated during the 2016 and 2016/2017 crops considering the agronomic variables plant height, ear insertion height, ear position, lodging, breakage, and grain yield. Data were submitted to analysis of variance and means were compared by the Scott-Knott test (p<0.05) with subsequent multivariate exploratory analysis by PCA. In the principal component analysis, components explained 52.07% and 55.69% of the variance contained in the original variables for the 2016 and 2016/2017 crops, respectively. The variable that was most significant in both crops was ear insertion height, allowing the indirect selection of more productive genotypes. Indirect selection of the most productive genotypes was also conducted through variables that contributed significantly in the principal component analysis. Thus, the use of multivariate exploratory analysis is efficient in the characterization and selection of maize genotypes evaluated in different crop seasons.
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spelling Principal component analysis for selection of superior maize genotypesAnálise de componentes principais para seleção de genótipos superiores de milhoFirst and second crop seasonHybridsPrincipal componentsZea maysConstant advances in studies on the behavior of maize genotypes and their interactions with the environment are of great importance for the best performance of the plant. This study verifies effects and causes of agronomic variables of maize hybrids on grain yields and performs the indirect selection of superior genotypes by principal component analysis (PCA). Two hundred and thirty maize genotypes were used, with two hundred and twenty- -nine topcross hybrids (consisting of crossings of two hundred and twenty-nine partially inbred genotypes with a tester) and one check in a randomized block design with two repetitions. The genotypes were evaluated during the 2016 and 2016/2017 crops considering the agronomic variables plant height, ear insertion height, ear position, lodging, breakage, and grain yield. Data were submitted to analysis of variance and means were compared by the Scott-Knott test (p<0.05) with subsequent multivariate exploratory analysis by PCA. In the principal component analysis, components explained 52.07% and 55.69% of the variance contained in the original variables for the 2016 and 2016/2017 crops, respectively. The variable that was most significant in both crops was ear insertion height, allowing the indirect selection of more productive genotypes. Indirect selection of the most productive genotypes was also conducted through variables that contributed significantly in the principal component analysis. Thus, the use of multivariate exploratory analysis is efficient in the characterization and selection of maize genotypes evaluated in different crop seasons.Universidade Estadual Paulista Júlio de Mesquita Filho Faculdade de Ciências Agrárias e Veterinárias Câmpus de Jaboticabal Departamento de Ciências da Produção AgrícolaUniversidade Estadual Paulista Júlio de Mesquita Filho Faculdade de Ciências Agrárias e Veterinárias Câmpus de Jaboticabal Departamento de Ciências da Produção AgrícolaUniversidade Estadual Paulista (UNESP)Carnimeo, Eduardo Sávio Gomes [UNESP]Bertasello, Luiz Eduardo Tilhaqui [UNESP]Dutra, Sophia Mangussi Franchi [UNESP]Mõro, Gustavo Vitti [UNESP]2022-05-01T03:42:45Z2022-05-01T03:42:45Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article357-362http://dx.doi.org/10.15361/1984-5529.2020V48N4P357-362Cientifica, v. 48, n. 4, p. 357-362, 2020.1984-5529http://hdl.handle.net/11449/23309710.15361/1984-5529.2020V48N4P357-3622-s2.0-85101377098Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCientificainfo:eu-repo/semantics/openAccess2024-06-07T13:57:20Zoai:repositorio.unesp.br:11449/233097Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:00:51.773038Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Principal component analysis for selection of superior maize genotypes
Análise de componentes principais para seleção de genótipos superiores de milho
title Principal component analysis for selection of superior maize genotypes
spellingShingle Principal component analysis for selection of superior maize genotypes
Carnimeo, Eduardo Sávio Gomes [UNESP]
First and second crop season
Hybrids
Principal components
Zea mays
title_short Principal component analysis for selection of superior maize genotypes
title_full Principal component analysis for selection of superior maize genotypes
title_fullStr Principal component analysis for selection of superior maize genotypes
title_full_unstemmed Principal component analysis for selection of superior maize genotypes
title_sort Principal component analysis for selection of superior maize genotypes
author Carnimeo, Eduardo Sávio Gomes [UNESP]
author_facet Carnimeo, Eduardo Sávio Gomes [UNESP]
Bertasello, Luiz Eduardo Tilhaqui [UNESP]
Dutra, Sophia Mangussi Franchi [UNESP]
Mõro, Gustavo Vitti [UNESP]
author_role author
author2 Bertasello, Luiz Eduardo Tilhaqui [UNESP]
Dutra, Sophia Mangussi Franchi [UNESP]
Mõro, Gustavo Vitti [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Carnimeo, Eduardo Sávio Gomes [UNESP]
Bertasello, Luiz Eduardo Tilhaqui [UNESP]
Dutra, Sophia Mangussi Franchi [UNESP]
Mõro, Gustavo Vitti [UNESP]
dc.subject.por.fl_str_mv First and second crop season
Hybrids
Principal components
Zea mays
topic First and second crop season
Hybrids
Principal components
Zea mays
description Constant advances in studies on the behavior of maize genotypes and their interactions with the environment are of great importance for the best performance of the plant. This study verifies effects and causes of agronomic variables of maize hybrids on grain yields and performs the indirect selection of superior genotypes by principal component analysis (PCA). Two hundred and thirty maize genotypes were used, with two hundred and twenty- -nine topcross hybrids (consisting of crossings of two hundred and twenty-nine partially inbred genotypes with a tester) and one check in a randomized block design with two repetitions. The genotypes were evaluated during the 2016 and 2016/2017 crops considering the agronomic variables plant height, ear insertion height, ear position, lodging, breakage, and grain yield. Data were submitted to analysis of variance and means were compared by the Scott-Knott test (p<0.05) with subsequent multivariate exploratory analysis by PCA. In the principal component analysis, components explained 52.07% and 55.69% of the variance contained in the original variables for the 2016 and 2016/2017 crops, respectively. The variable that was most significant in both crops was ear insertion height, allowing the indirect selection of more productive genotypes. Indirect selection of the most productive genotypes was also conducted through variables that contributed significantly in the principal component analysis. Thus, the use of multivariate exploratory analysis is efficient in the characterization and selection of maize genotypes evaluated in different crop seasons.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2022-05-01T03:42:45Z
2022-05-01T03:42:45Z
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/10.15361/1984-5529.2020V48N4P357-362
Cientifica, v. 48, n. 4, p. 357-362, 2020.
1984-5529
http://hdl.handle.net/11449/233097
10.15361/1984-5529.2020V48N4P357-362
2-s2.0-85101377098
url http://dx.doi.org/10.15361/1984-5529.2020V48N4P357-362
http://hdl.handle.net/11449/233097
identifier_str_mv Cientifica, v. 48, n. 4, p. 357-362, 2020.
1984-5529
10.15361/1984-5529.2020V48N4P357-362
2-s2.0-85101377098
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Cientifica
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 357-362
dc.source.none.fl_str_mv Scopus
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
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