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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/4962
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dc.contributor.authorBoligon, A. A.-
dc.contributor.authorMercadante, M. E. Z.-
dc.contributor.authorLobo, R. B.-
dc.contributor.authorBaldi, F.-
dc.contributor.authorAlbuquerque, Lucia Galvão de-
dc.date.accessioned2014-05-20T13:19:11Z-
dc.date.available2014-05-20T13:19:11Z-
dc.date.issued2012-02-01-
dc.identifierhttp://dx.doi.org/10.1017/S1751731111001534-
dc.identifier.citationAnimal. Cambridge: Cambridge Univ Press, v. 6, n. 2, p. 212-220, 2012.-
dc.identifier.issn1751-7311-
dc.identifier.urihttp://hdl.handle.net/11449/4962-
dc.description.abstractThe objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.en
dc.format.extent212-220-
dc.language.isoeng-
dc.publisherCambridge University Press-
dc.sourceWeb of Science-
dc.subjectGrowth curveen
dc.subjectCovariance functionsen
dc.subjectSegmented polynomialsen
dc.subjectNellore cattleen
dc.titleRandom regression analyses using B-spline functions to model growth of Nellore cattleen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionEstacao Expt Zootecnia Sertaozinho-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.description.affiliationUniv Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 São Paulo, Brazil-
dc.description.affiliationEstacao Expt Zootecnia Sertaozinho, Inst Zootecnia, BR-14160000 São Paulo, Brazil-
dc.description.affiliationUniv São Paulo, Dept Genet, Fac Med Ribeirao Preto, BR-14049900 São Paulo, Brazil-
dc.description.affiliationUnespUniv Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 São Paulo, Brazil-
dc.identifier.doi10.1017/S1751731111001534-
dc.identifier.wosWOS:000299437200004-
dc.rights.accessRightsAcesso restrito-
dc.identifier.fileWOS000299437200004.pdf-
dc.relation.ispartofAnimal-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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