Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720
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dc.contributor.authorNASCIMENTO, M.
dc.contributor.authorPETERNELLI, L. A.
dc.contributor.authorCRUZ, C. D.
dc.contributor.authorNASCIMENTO, A. C. C.
dc.contributor.authorFERREIRA, R. de P.
dc.date.accessioned2023-05-15T14:47:33Z-
dc.date.available2023-05-15T14:47:33Z-
dc.date.created2015-10-19
dc.date.issued2013
dc.identifier.citationCrop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720-
dc.descriptionThe 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectBioinformatic
dc.subjectData simulation
dc.subjectEberhart russell
dc.titleArtificial neural networks for adaptability and stability evaluation in alfalfa genotypes.
dc.typeArtigo de periódico
riaa.ainfo.id1026720
riaa.ainfo.lastupdate2023-05-15
dc.contributor.institutionMOYSÉS 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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