Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability

Detalhes bibliográficos
Autor(a) principal: Teodoro, Paulo Eduardo
Data de Publicação: 2015
Outros Autores: Barroso, Laís Mayara Azevedo, Nascimento, Moysés, Torres, Francisco Eduardo, Sagrilo, Edvaldo, Santos, Adriano dos, Ribeiro, Larissa Pereira
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/22133
Resumo: The objective of this work was to verify the agreement between artificial neural networks (ANNs) and the Eberhart & Russel method in identifying cowpea (Vigna unguiculata) genotypes with high phenotypic adaptability and stability. The experimental design was in a randomized complete block with four replicates. The treatments consisted of 18 experimental lines and two cowpea cultivars. Four value for cultivation and use trials were conducted in the municipalities of Aquidauana, Chapadão do Sul, and Dourados, in the state of Mato Grosso do Sul, Brazil. Grain yield data were subjected to individual and joint variance analyses. Then, the data were subjected to adaptability and stability analyses through the methods of Eberhart & Russell and ANNs. There was a high agreement between the methods evaluated for discrimination of the phenotypic adaptability of semi‑prostrate cowpea genotypes, indicating that ANNs can be used in breeding programs. In both evaluated methods, the BRS Xiquexique, TE97‑304G‑12, and MNC99‑542F‑5 genotypes are recommended for harsh, general, and favorable environments, respectively, for having grain yield above the overall average of environments and high phenotypic stability.
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spelling Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stabilityRedes neurais artificiais para identificar genótipos de feijão‑caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicasVigna unguiculata; artificial intelligence; genotypes x environments interactionVigna unguiculata; inteligência artificial; interação genótipos x ambientesThe objective of this work was to verify the agreement between artificial neural networks (ANNs) and the Eberhart & Russel method in identifying cowpea (Vigna unguiculata) genotypes with high phenotypic adaptability and stability. The experimental design was in a randomized complete block with four replicates. The treatments consisted of 18 experimental lines and two cowpea cultivars. Four value for cultivation and use trials were conducted in the municipalities of Aquidauana, Chapadão do Sul, and Dourados, in the state of Mato Grosso do Sul, Brazil. Grain yield data were subjected to individual and joint variance analyses. Then, the data were subjected to adaptability and stability analyses through the methods of Eberhart & Russell and ANNs. There was a high agreement between the methods evaluated for discrimination of the phenotypic adaptability of semi‑prostrate cowpea genotypes, indicating that ANNs can be used in breeding programs. In both evaluated methods, the BRS Xiquexique, TE97‑304G‑12, and MNC99‑542F‑5 genotypes are recommended for harsh, general, and favorable environments, respectively, for having grain yield above the overall average of environments and high phenotypic stability.O objetivo deste trabalho foi verificar a concordância entre as redes neurais artificiais (RNAs) e o método de Eberhart & Russel na identificação de genótipos de feijão‑caupi (Vigna unguiculata) com alta adaptabilidade e estabilidade fenotípicas. Utilizou-se o delineamento experimental de blocos ao acaso com quatro repetições. Os tratamentos consistiram de 18 linhagens experimentais e duas cultivares de feijão‑caupi. Foram conduzidos quatro ensaios de valor de cultivo e uso nos municípios de Aquidauana, Chapadão do Sul e Dourados, no estado do Mato Grosso do Sul. Os dados de produtividade de grãos foram submetidos às análises de variância individual e conjunta. Em seguida, os dados foram submetidos às análises de adaptabilidade e estabilidade por meio dos métodos de Eberhart & Russell e de RNAs. Houve elevada concordância entre os métodos avaliados quanto à discriminação da adaptabilidade fenotípica dos genótipos de feijão‑caupi semiprostrado, o que indica que as RNAs podem ser utilizadas em programas de melhoramento genético. Em ambos os métodos avaliados, os genótipos BRS Xiquexique, TE97‑304G‑12 e MNC99‑542F‑5 são recomendados para ambientes desfavoráveis, gerais e favoráveis, respectivamente, por apresentarem produtividade de grãos acima da média geral dos ambientes e alta estabilidade fenotípica.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)Fundação Arthur Bernardes (Funarbe)Teodoro, Paulo EduardoBarroso, Laís Mayara AzevedoNascimento, MoysésTorres, Francisco EduardoSagrilo, EdvaldoSantos, Adriano dosRibeiro, Larissa Pereira2015-12-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/22133Pesquisa Agropecuaria Brasileira; v.50, n.11, nov. 2015; 1054-1060Pesquisa Agropecuária Brasileira; v.50, n.11, nov. 2015; 1054-10601678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://seer.sct.embrapa.br/index.php/pab/article/view/22133/13119https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/22133/14174Direitos autorais 2015 Pesquisa Agropecuária Brasileirainfo:eu-repo/semantics/openAccess2015-12-09T18:15:31Zoai:ojs.seer.sct.embrapa.br:article/22133Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2015-12-09T18:15:31Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
Redes neurais artificiais para identificar genótipos de feijão‑caupi semiprostrado com alta adaptabilidade e estabilidade fenotípicas
title Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
spellingShingle Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
Teodoro, Paulo Eduardo
Vigna unguiculata; artificial intelligence; genotypes x environments interaction
Vigna unguiculata; inteligência artificial; interação genótipos x ambientes
title_short Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
title_full Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
title_fullStr Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
title_full_unstemmed Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
title_sort Artificial neural networks to identify semi‑prostrate cowpea genotypes with high phenotypic adaptability and stability
author Teodoro, Paulo Eduardo
author_facet Teodoro, Paulo Eduardo
Barroso, Laís Mayara Azevedo
Nascimento, Moysés
Torres, Francisco Eduardo
Sagrilo, Edvaldo
Santos, Adriano dos
Ribeiro, Larissa Pereira
author_role author
author2 Barroso, Laís Mayara Azevedo
Nascimento, Moysés
Torres, Francisco Eduardo
Sagrilo, Edvaldo
Santos, Adriano dos
Ribeiro, Larissa Pereira
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)
Fundação Arthur Bernardes (Funarbe)
dc.contributor.author.fl_str_mv Teodoro, Paulo Eduardo
Barroso, Laís Mayara Azevedo
Nascimento, Moysés
Torres, Francisco Eduardo
Sagrilo, Edvaldo
Santos, Adriano dos
Ribeiro, Larissa Pereira
dc.subject.por.fl_str_mv Vigna unguiculata; artificial intelligence; genotypes x environments interaction
Vigna unguiculata; inteligência artificial; interação genótipos x ambientes
topic Vigna unguiculata; artificial intelligence; genotypes x environments interaction
Vigna unguiculata; inteligência artificial; interação genótipos x ambientes
description The objective of this work was to verify the agreement between artificial neural networks (ANNs) and the Eberhart & Russel method in identifying cowpea (Vigna unguiculata) genotypes with high phenotypic adaptability and stability. The experimental design was in a randomized complete block with four replicates. The treatments consisted of 18 experimental lines and two cowpea cultivars. Four value for cultivation and use trials were conducted in the municipalities of Aquidauana, Chapadão do Sul, and Dourados, in the state of Mato Grosso do Sul, Brazil. Grain yield data were subjected to individual and joint variance analyses. Then, the data were subjected to adaptability and stability analyses through the methods of Eberhart & Russell and ANNs. There was a high agreement between the methods evaluated for discrimination of the phenotypic adaptability of semi‑prostrate cowpea genotypes, indicating that ANNs can be used in breeding programs. In both evaluated methods, the BRS Xiquexique, TE97‑304G‑12, and MNC99‑542F‑5 genotypes are recommended for harsh, general, and favorable environments, respectively, for having grain yield above the overall average of environments and high phenotypic stability.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-09
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/22133
url https://seer.sct.embrapa.br/index.php/pab/article/view/22133
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/22133/13119
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/22133/14174
dc.rights.driver.fl_str_mv Direitos autorais 2015 Pesquisa Agropecuária Brasileira
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Direitos autorais 2015 Pesquisa Agropecuária Brasileira
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.11, nov. 2015; 1054-1060
Pesquisa Agropecuária Brasileira; v.50, n.11, nov. 2015; 1054-1060
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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