Discrimination of forage pea seed lots by means of multivariate techniques

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dc.contributor.authorMachado, Carla Gomes
dc.contributor.authorMartins, Cibele Chalita [UNESP]
dc.contributor.authorDa Silva, Givanildo Zildo
dc.contributor.authorCruz, Simério Carlos Silva
dc.contributor.authorGama, Gabriela Fernandes
dc.contributor.authorCoelho, Mirelle Vaz
dc.contributor.institutionUniversidade Federal de Goiás (UFG)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-12T02:32:50Z
dc.date.available2020-12-12T02:32:50Z
dc.date.issued2019-01-01
dc.description.abstractMultivariate techniques allow to understand the structural dependence contained in the variables, as well as to characterize groups of seed lots according to specific standards. Thus, this study analyzes the efficiency of multi-variate exploratory techniques in discriminating forage pea seed lots as a function of the physiological potential of seeds. We evaluated ten seed lots of forage pea in a completely randomized design, considering the following variables: thousand seed weight, germination, first germination count, electrical conductivity, and accelerated aging. Moreover, seedling emergence, first count of seedlings in the field, and seedling emergence speed index in the field were added to randomized blocks with four replications per lot. Initially, the data obtained in each test were analyzed separately by means of analysis of variance, and the means of the treatments were compared by the Scott Knott test at 5% probability. Exploratory multivariate statistical techniques were applied by means of Cluster Analysis and Principal Components Analysis to discriminate seed lots with better physiological quality and to characterize the variables responsible for the differentiation between them. Multivariate analysis of principal components is efficient in discriminating vigor and seed germination tests in Pisum sativum subsp. Arvense, which help in identifying lots of superior performance in the field.en
dc.description.affiliationUniversidade Federal de Goiás - UFG Campus Jatobá
dc.description.affiliationEngenheira Agronoma Prof-Essora Livre-Docente Faculdade de Ciěncias Agrárias e Veterinárias - UNESP
dc.description.affiliationUnespEngenheira Agronoma Prof-Essora Livre-Docente Faculdade de Ciěncias Agrárias e Veterinárias - UNESP
dc.format.extent321-326
dc.identifierhttp://dx.doi.org/10.15361/1984-5529.2019v47n3p321-326
dc.identifier.citationCientifica, v. 47, n. 3, p. 321-326, 2019.
dc.identifier.doi10.15361/1984-5529.2019v47n3p321-326
dc.identifier.issn1984-5529
dc.identifier.lattes9669833663325445
dc.identifier.orcid0000-0002-1720-9252
dc.identifier.scopus2-s2.0-85077526835
dc.identifier.urihttp://hdl.handle.net/11449/201450
dc.language.isoeng
dc.relation.ispartofCientifica
dc.sourceScopus
dc.subjectArvense
dc.subjectCluster analysis
dc.subjectGermination
dc.subjectPisum sativum subsp
dc.subjectPrincipal component analysis
dc.subjectVigor
dc.titleDiscrimination of forage pea seed lots by means of multivariate techniquesen
dc.typeArtigo
unesp.author.lattes9669833663325445[2]
unesp.author.orcid0000-0002-1720-9252[2]
unesp.departmentProdução Vegetal - FCAVpt

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Jaboticabal - FCAV - Faculdade de Ciências Agrárias e Veterinárias