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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
19/10/2015 |
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Data da última atualização: |
15/05/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
NASCIMENTO, M.; PETERNELLI, L. A.; CRUZ, C. D.; NASCIMENTO, A. C. C.; FERREIRA, R. de P. |
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Afiliação: |
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. |
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Título: |
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Crop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013. |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
Bioinformatic; Data simulation; Eberhart russell. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/131321/1/PROCI-2013.00300.pdf
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Marc: |
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100 1 $aNASCIMENTO, M.
245 $aArtificial neural networks for adaptability and stability evaluation in alfalfa genotypes$h[electronic resource]
260 $c2013
520 $aThe 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.
653 $aBioinformatic
653 $aData simulation
653 $aEberhart russell
700 1 $aPETERNELLI, L. A.
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aFERREIRA, R. de P.
773 $tCrop Breeding and Applied Biotechnology$gv. 13, n. 2, p. 152-156, jul. 2013.
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Embrapa Pecuária Sudeste (CPPSE) |
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