Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720 |
Resumo: | The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell. |
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Artificial neural networks for adaptability and stability evaluation in alfalfa genotypesBioinformaticData simulationEberhart russellThe purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell.MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA, VIÇOSA, MG; LUIZ ALEXANDRE PETERNELLI, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; COSME DAMIÃO CRUZ, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; ANA CAROLINA CAMPANHA NASCIMENTO, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; REINALDO DE PAULA FERREIRA, CPPSE.NASCIMENTO, M.PETERNELLI, L. A.CRUZ, C. D.NASCIMENTO, A. C. C.FERREIRA, R. de P.2023-05-15T14:47:33Z2023-05-15T14:47:33Z2015-10-192013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCrop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2023-05-15T14:47:33Zoai:www.alice.cnptia.embrapa.br:doc/1026720Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542023-05-15T14:47:33falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542023-05-15T14:47:33Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
title |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
spellingShingle |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes NASCIMENTO, M. Bioinformatic Data simulation Eberhart russell |
title_short |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
title_full |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
title_fullStr |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
title_full_unstemmed |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
title_sort |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
author |
NASCIMENTO, M. |
author_facet |
NASCIMENTO, M. PETERNELLI, L. A. CRUZ, C. D. NASCIMENTO, A. C. C. FERREIRA, R. de P. |
author_role |
author |
author2 |
PETERNELLI, L. A. CRUZ, C. D. NASCIMENTO, A. C. C. FERREIRA, R. de P. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA, VIÇOSA, MG; LUIZ ALEXANDRE PETERNELLI, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; COSME DAMIÃO CRUZ, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; ANA CAROLINA CAMPANHA NASCIMENTO, UNIVERSIDADE FEDERAL VIÇOSA/ VIÇOSA, MG.; REINALDO DE PAULA FERREIRA, CPPSE. |
dc.contributor.author.fl_str_mv |
NASCIMENTO, M. PETERNELLI, L. A. CRUZ, C. D. NASCIMENTO, A. C. C. FERREIRA, R. de P. |
dc.subject.por.fl_str_mv |
Bioinformatic Data simulation Eberhart russell |
topic |
Bioinformatic Data simulation Eberhart russell |
description |
The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology of Eberhart and Russell. Data from an experiment on dry matter production of 92 alfalfa genotypes (Medicago sativa L.) were used. The experimental design constituted of randomized blocks, with two repetitions. The genotypes were submitted to 20 cuttings, in the growing season of November 2004 to June 2006. Each cutting was considered an environment. The artificial neural network was able to satisfactorily classify the genotypes. In addition, the analysis presented high agreement rates, compared with the results obtained by the methodology of Eberhart and Russell. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 2015-10-19 2023-05-15T14:47:33Z 2023-05-15T14:47:33Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Crop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720 |
identifier_str_mv |
Crop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
_version_ |
1794503544673402880 |