Artificial intelligence in the selection of common bean genotypes with high phenotypic stability
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | LOCUS Repositório Institucional da UFV |
Texto Completo: | http://dx.doi.org/10.4238/gmr.15028230 http://www.locus.ufv.br/handle/123456789/12869 |
Resumo: | Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability. |
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Corrêa, A.M.Teodoro, P.E.Gonçalves, M.C.Barroso, L.M.A.Nascimento, M.Santos, A.Torres, F.E.2017-11-08T10:49:36Z2017-11-08T10:49:36Z2016-04-2716765680http://dx.doi.org/10.4238/gmr.15028230http://www.locus.ufv.br/handle/123456789/12869Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability.engGenetics and Molecular Researchv. 15, n. 2, gmr.15028230, Apr. 2016Artificial neural networksEberhart and Russell methodGenotype x environment interactionPhaseolus vulgaris L.Artificial intelligence in the selection of common bean genotypes with high phenotypic stabilityinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALgmr8230_1.pdfgmr8230_1.pdftexto completoapplication/pdf467114https://locus.ufv.br//bitstream/123456789/12869/1/gmr8230_1.pdf2374e86f9a3600ea6c4983208078e8deMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/12869/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILgmr8230_1.pdf.jpggmr8230_1.pdf.jpgIM Thumbnailimage/jpeg4250https://locus.ufv.br//bitstream/123456789/12869/3/gmr8230_1.pdf.jpgcc29a7a5e7cb8391a0ebb40dfb86914aMD53123456789/128692017-11-08 22:00:47.461oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452017-11-09T01:00:47LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false |
dc.title.en.fl_str_mv |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
title |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
spellingShingle |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability Corrêa, A.M. Artificial neural networks Eberhart and Russell method Genotype x environment interaction Phaseolus vulgaris L. |
title_short |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
title_full |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
title_fullStr |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
title_full_unstemmed |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
title_sort |
Artificial intelligence in the selection of common bean genotypes with high phenotypic stability |
author |
Corrêa, A.M. |
author_facet |
Corrêa, A.M. Teodoro, P.E. Gonçalves, M.C. Barroso, L.M.A. Nascimento, M. Santos, A. Torres, F.E. |
author_role |
author |
author2 |
Teodoro, P.E. Gonçalves, M.C. Barroso, L.M.A. Nascimento, M. Santos, A. Torres, F.E. |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Corrêa, A.M. Teodoro, P.E. Gonçalves, M.C. Barroso, L.M.A. Nascimento, M. Santos, A. Torres, F.E. |
dc.subject.pt-BR.fl_str_mv |
Artificial neural networks Eberhart and Russell method Genotype x environment interaction Phaseolus vulgaris L. |
topic |
Artificial neural networks Eberhart and Russell method Genotype x environment interaction Phaseolus vulgaris L. |
description |
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016-04-27 |
dc.date.accessioned.fl_str_mv |
2017-11-08T10:49:36Z |
dc.date.available.fl_str_mv |
2017-11-08T10:49:36Z |
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/10.4238/gmr.15028230 http://www.locus.ufv.br/handle/123456789/12869 |
dc.identifier.issn.none.fl_str_mv |
16765680 |
identifier_str_mv |
16765680 |
url |
http://dx.doi.org/10.4238/gmr.15028230 http://www.locus.ufv.br/handle/123456789/12869 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.pt-BR.fl_str_mv |
v. 15, n. 2, gmr.15028230, Apr. 2016 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
Genetics and Molecular Research |
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Genetics and Molecular Research |
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