Classifying Soybean Cultivars Using an Univariate and Multivariate Approach

Detalhes bibliográficos
Autor(a) principal: Carvalho, João Paulo Santos
Data de Publicação: 2020
Outros Autores: Bruzi, Adriano Teodoro, Silva, Karina Barroso, Soares, Igor Oliveri, Bianchi, Mariane Cristina, Vilela, Nelson Junior Dias
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/46266
Resumo: Selection indices are good for classification because they consider several evaluated traits simultaneously to identify superior cultivars with a combination of the traits of interest. Adaptability/stability methods enable determining contributions to the genotype-by-environment (G × E) interaction and the risk associated with each cultivar. This study used a univariate and multivariate strategy to identify commercial soybean cultivars that presented both precocity and good productive performance and studied the G × E interaction considering all cultivars both simultaneously and by maturation groups. The experiments were conducted in the agricultural years 2014/15 and 2015/16 in seven distinct environments in southern Minas Gerais State, Brazil, considering a combination of locations and seasons. A randomized complete block design was used, and the treatments included 35 commercial soybean cultivars. In the univariate analysis, were evaluate several traits. Selection indices were calculated considering yield, harvest index, plant height, first pod insertion height and absolute maturation. The selection strategy efficiencies were quantified using the coincidence index. Each cultivar’s contribution to the G × E interaction and associated risk were determined using the ecovalence and confidence index methods, respectively. The results showed that the NS 7000 IPRO and NS 7209 IPRO cultivars were the most productive. The NS 7000 IPRO cultivar, although obtaining a good yield, contributed greatly to the G × E interaction when considering the maturation groups. The low coincidence in ranking the strategies indicates that more than one agronomic trait should be used to classify the superior cultivars.
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spelling Classifying Soybean Cultivars Using an Univariate and Multivariate ApproachAdaptability and stabilityCoincidence indexGenotype-by-environment interactionGlycine max (L.) MerrSelection indexInteração genótipo por ambientesAdaptabilidade e estabilidadeSoja - CultivoSoja - Índice de seleçãoSelection indices are good for classification because they consider several evaluated traits simultaneously to identify superior cultivars with a combination of the traits of interest. Adaptability/stability methods enable determining contributions to the genotype-by-environment (G × E) interaction and the risk associated with each cultivar. This study used a univariate and multivariate strategy to identify commercial soybean cultivars that presented both precocity and good productive performance and studied the G × E interaction considering all cultivars both simultaneously and by maturation groups. The experiments were conducted in the agricultural years 2014/15 and 2015/16 in seven distinct environments in southern Minas Gerais State, Brazil, considering a combination of locations and seasons. A randomized complete block design was used, and the treatments included 35 commercial soybean cultivars. In the univariate analysis, were evaluate several traits. Selection indices were calculated considering yield, harvest index, plant height, first pod insertion height and absolute maturation. The selection strategy efficiencies were quantified using the coincidence index. Each cultivar’s contribution to the G × E interaction and associated risk were determined using the ecovalence and confidence index methods, respectively. The results showed that the NS 7000 IPRO and NS 7209 IPRO cultivars were the most productive. The NS 7000 IPRO cultivar, although obtaining a good yield, contributed greatly to the G × E interaction when considering the maturation groups. The low coincidence in ranking the strategies indicates that more than one agronomic trait should be used to classify the superior cultivars.Canadian Center of Science and Education2021-05-11T19:07:44Z2021-05-11T19:07:44Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCARVALHO, J. P. S. et al. Classifying Soybean Cultivars Using an Univariate and Multivariate Approach. Journal of Agricultural Science, Ontario, v. 12, n. 11, p. 190-199, 2020. DOI:10.5539/jas.v12n11p190.http://repositorio.ufla.br/jspui/handle/1/46266Journal of Agricultural Sciencereponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessCarvalho, João Paulo SantosBruzi, Adriano TeodoroSilva, Karina BarrosoSoares, Igor OliveriBianchi, Mariane CristinaVilela, Nelson Junior Diaseng2022-09-13T19:43:43Zoai:localhost:1/46266Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-09-13T19:43:43Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
title Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
spellingShingle Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
Carvalho, João Paulo Santos
Adaptability and stability
Coincidence index
Genotype-by-environment interaction
Glycine max (L.) Merr
Selection index
Interação genótipo por ambientes
Adaptabilidade e estabilidade
Soja - Cultivo
Soja - Índice de seleção
title_short Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
title_full Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
title_fullStr Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
title_full_unstemmed Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
title_sort Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
author Carvalho, João Paulo Santos
author_facet Carvalho, João Paulo Santos
Bruzi, Adriano Teodoro
Silva, Karina Barroso
Soares, Igor Oliveri
Bianchi, Mariane Cristina
Vilela, Nelson Junior Dias
author_role author
author2 Bruzi, Adriano Teodoro
Silva, Karina Barroso
Soares, Igor Oliveri
Bianchi, Mariane Cristina
Vilela, Nelson Junior Dias
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Carvalho, João Paulo Santos
Bruzi, Adriano Teodoro
Silva, Karina Barroso
Soares, Igor Oliveri
Bianchi, Mariane Cristina
Vilela, Nelson Junior Dias
dc.subject.por.fl_str_mv Adaptability and stability
Coincidence index
Genotype-by-environment interaction
Glycine max (L.) Merr
Selection index
Interação genótipo por ambientes
Adaptabilidade e estabilidade
Soja - Cultivo
Soja - Índice de seleção
topic Adaptability and stability
Coincidence index
Genotype-by-environment interaction
Glycine max (L.) Merr
Selection index
Interação genótipo por ambientes
Adaptabilidade e estabilidade
Soja - Cultivo
Soja - Índice de seleção
description Selection indices are good for classification because they consider several evaluated traits simultaneously to identify superior cultivars with a combination of the traits of interest. Adaptability/stability methods enable determining contributions to the genotype-by-environment (G × E) interaction and the risk associated with each cultivar. This study used a univariate and multivariate strategy to identify commercial soybean cultivars that presented both precocity and good productive performance and studied the G × E interaction considering all cultivars both simultaneously and by maturation groups. The experiments were conducted in the agricultural years 2014/15 and 2015/16 in seven distinct environments in southern Minas Gerais State, Brazil, considering a combination of locations and seasons. A randomized complete block design was used, and the treatments included 35 commercial soybean cultivars. In the univariate analysis, were evaluate several traits. Selection indices were calculated considering yield, harvest index, plant height, first pod insertion height and absolute maturation. The selection strategy efficiencies were quantified using the coincidence index. Each cultivar’s contribution to the G × E interaction and associated risk were determined using the ecovalence and confidence index methods, respectively. The results showed that the NS 7000 IPRO and NS 7209 IPRO cultivars were the most productive. The NS 7000 IPRO cultivar, although obtaining a good yield, contributed greatly to the G × E interaction when considering the maturation groups. The low coincidence in ranking the strategies indicates that more than one agronomic trait should be used to classify the superior cultivars.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021-05-11T19:07:44Z
2021-05-11T19:07:44Z
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 CARVALHO, J. P. S. et al. Classifying Soybean Cultivars Using an Univariate and Multivariate Approach. Journal of Agricultural Science, Ontario, v. 12, n. 11, p. 190-199, 2020. DOI:10.5539/jas.v12n11p190.
http://repositorio.ufla.br/jspui/handle/1/46266
identifier_str_mv CARVALHO, J. P. S. et al. Classifying Soybean Cultivars Using an Univariate and Multivariate Approach. Journal of Agricultural Science, Ontario, v. 12, n. 11, p. 190-199, 2020. DOI:10.5539/jas.v12n11p190.
url http://repositorio.ufla.br/jspui/handle/1/46266
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Canadian Center of Science and Education
publisher.none.fl_str_mv Canadian Center of Science and Education
dc.source.none.fl_str_mv Journal of Agricultural Science
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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