Classifying Soybean Cultivars Using an Univariate and Multivariate Approach
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
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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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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1823242054234275840 |