Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099791
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dc.contributor.authorCARVALHO, L. P. de
dc.contributor.authorTEODORO, P. E.
dc.contributor.authorBARROSO, L. M. A.
dc.contributor.authorFARIAS, F. J. C.
dc.contributor.authorMORELLO, C. de L.
dc.contributor.authorNASCIMENTO, M.
dc.date.accessioned2018-11-25T23:30:28Z-
dc.date.available2018-11-25T23:30:28Z-
dc.date.created2018-11-21
dc.date.issued2018
dc.identifier.citationCrop Breeding and Applied Biotechnology, v. 18, p. 200-204, 2018.
dc.identifier.issn1518-7853
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1099791-
dc.descriptionFiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability.
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectInteligência artificial
dc.titleArtificial neural networks classify cotton genotypes for fiber length.
dc.typeArtigo de periódico
dc.date.updated2018-11-25T23:30:28Zpt_BR
dc.subject.thesagroAlgodão
dc.subject.thesagroGossypium Hirsutum
dc.subject.thesagroGossypium Hirsutum Marie Galante
dc.subject.thesagroGenótipo
dc.subject.nalthesaurusCotton
dc.subject.nalthesaurusArtificial intelligence
dc.subject.nalthesaurusGenotype-environment interaction
riaa.ainfo.id1099791
riaa.ainfo.lastupdate2018-11-21
dc.identifier.doi10.1590/1984-70332018v18n2n28
dc.contributor.institutionLUIZ PAULO DE CARVALHO, CNPA; PAULO EDUARO TEODORO, UFMS - CHAPADÃO DO SUL, MS; LAÍS MAYARA AZEVEDO BARROSO, UFV; FRANCISCO JOSE CORREIA FARIAS, CNPA; CAMILO DE LELIS MORELLO, CNPA; MOYSÉS NASCIMENTO, UFV.
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