Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes

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.

Detalles Bibliográficos

Autores principales:	NASCIMENTO, M., PETERNELLI, L. A., CRUZ, C. D., NASCIMENTO, A. C. C., FERREIRA, R. de P.
Otros Autores:	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.
Formato:	Artigo de periódico biblioteca
Idioma:	Ingles English
Publicado:	2015-10-19
Materias:	Bioinformatic, Data simulation, Eberhart russell,
Acceso en línea:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1026720
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