Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes.

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dc.contributorMOYSÉS 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.creatorNASCIMENTO, M.
dc.creatorPETERNELLI, L. A.
dc.creatorCRUZ, C. D.
dc.creatorNASCIMENTO, A. C. C.
dc.creatorFERREIRA, R. de P.
dc.date2023-05-15T14:47:33Z
dc.date2023-05-15T14:47:33Z
dc.date2015-10-19
dc.date2013
dc.date.accessioned2026-07-07T05:31:49Z
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.identifierCrop Breeding and Applied Biotechnology, v. 13, n. 2, p. 152-156, jul. 2013.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720
dc.identifier.urihttp://hdl.handle.net/123456789/493962
dc.languageeng
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

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