Identification of superior genotypes and soybean traits by multivariate analysis and selection index

Página do item simplificado
dc.contributor.authorLeite, Wallace de Sousa
dc.contributor.authorUneda-Trevisoli, Sandra Helena [UNESP]
dc.contributor.authorSilva, Fabiana Mota da [UNESP]
dc.contributor.authorSilva, Alysson Jalles da [UNESP]
dc.contributor.authorDi Mauro, Antonio Orlando [UNESP]
dc.contributor.institutionInst Fed Educ Ciencia & Tecnol Piaui
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T17:52:06Z
dc.date.available2018-11-26T17:52:06Z
dc.date.issued2018-07-01
dc.description.abstractThe selection of superior genotypes of soybean is a complex process, thus exploratory multivariate techniques can be applied to select genotypes analyzing the agronomic traits altogether, increasing the chance of success of a breeding program. Thus, the objective of this study was to select soybean genotypes carrying the RR gene with good agronomical performance through of multivariate analysis and selection index and identify those traits that influence, also verifying the agreement of multivariate techniques and selection index in the selection process. The experiment was conducted in an increased block experimental being evaluated 227 genotypes of F-5 generation, which 85 of those were detected to be glyphosate-resistant by PCR. The following traits were evaluated: number of days to maturity, plant height at maturity, lodging, agronomic value, number of branches per plant, number of pods per plant, hundred seeds weight and grain yield. The principal components analysis resulted in the selection of sixteen genotypes with higher grain yield. The traits related to the production of components exerted great influence on grain yield. The clustering by K-means and Ward's methods were similar because they clustered the specific genotypes for the selected traits in the principal components analysis in the same group. There was an agreement on the results of the multivariate analysis in the selection index of Mulamba and Mock in relation to the selected genotypes. The methodologies applied are efficient for selecting genotypes.en
dc.description.affiliationInst Fed Educ Ciencia & Tecnol Piaui, Campus Urucui,Rodovia PI 247,Km 7 S-N, BR-64860000 Urucui, PI, Brazil
dc.description.affiliationUniv Estadual Paulista, Fac Ciencias Agr & Vet, Dept Prod Vegetal, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Fac Ciencias Agr & Vet, Dept Prod Vegetal, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil
dc.format.extent491-500
dc.identifierhttp://dx.doi.org/10.5935/1806-6690.20180056
dc.identifier.citationRevista Ciencia Agronomica. Fortaleza: Univ Federal Ceara, Dept Geol, v. 49, n. 3, p. 491-500, 2018.
dc.identifier.doi10.5935/1806-6690.20180056
dc.identifier.fileS1806-66902018000300491.pdf
dc.identifier.issn1806-6690
dc.identifier.scieloS1806-66902018000300491
dc.identifier.urihttp://hdl.handle.net/11449/164315
dc.identifier.wosWOS:000435145200016
dc.language.isoeng
dc.publisherUniv Federal Ceara, Dept Geol
dc.relation.ispartofRevista Ciencia Agronomica
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectGlycine max
dc.subjectClustering analysis
dc.subjectPrincipal components
dc.subjectSelection gain
dc.subjectGrain Yield
dc.titleIdentification of superior genotypes and soybean traits by multivariate analysis and selection indexen
dc.typeArtigo
dcterms.rightsHolderUniv Federal Ceara, Dept Geol
unesp.author.lattes1275652518822095[5]
unesp.author.orcid0000-0003-1662-5745[5]

Arquivos

Pacote Original
Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
S1806-66902018000300491.pdf
Tamanho:
132.11 KB
Formato:
Adobe Portable Document Format
Baixar

Coleções

Artigos - Produção Vegetal - FCAV