Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/907473
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorLACA, E. A.pt_BR
dc.contributor.authorGENRO, T. C. M.pt_BR
dc.contributor.authorGENRO NETO, J. S.pt_BR
dc.date.accessioned2011-11-28T11:11:11Zpt_BR
dc.date.accessioned2011-11-28T11:11:11Zpt_BR
dc.date.available2011-11-28T11:11:11Zpt_BR
dc.date.available2011-11-28T11:11:11Zpt_BR
dc.date.created2011-11-28pt_BR
dc.date.issued2011pt_BR
dc.identifier.citationIn: REUNIÓN DE LA ASOCIACIÓN LATINOAMERICANA DE PRODUCCIÓN ANIMAL, 22., 2011, Montevideo, Uruguay. Memorias... Montevideo: Asociación Uruguaya de Producción Animal, 2011.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/907473pt_BR
dc.descriptionAlkane profiles in forage and feces are used to determine diet composition, essentially by inverting a linear mixing equation. Depending on the number of dietary components, number of alkanes and difference in forage profiles, the linear equations can be over or underdetermined. We compared the typical non-negative least squares (NNLS) method against a novel Bayesian hierarchical model (BHM) where diet composition is represented as latent variables modeled with parameters shared by the models for fecal and forage profiles. Forage and fecal profiles were obtained from steers grazing either Brachiaria brizantha cv Marandu, Panicum maximum cv. Mombaça, or Pennisetum purpureum cv. Cameroon at Embrapa Beef Cattle, Campo Grande, MS, Brazil. Herbage and fecal samples were collected in the dry and early wet seasons. Herbage was sampled by 20-cm horizons sorted into stem/sheath and leaf blade as dietary components. Feces were collected from 6 animals in the morning and afternoon. Fecal profiles were corrected by faecal recoveries using mean values from the literature. Distributions of diets estimated by NNLS were obtained by Monte Carlo simulation of profiles using parameters and covariance matrices estimated from data. The BHM yielded posterior distributions directly by using Monte Carlo Markov Chains. NNLS resulted in highly variables diets with distributions that were clearly non-normal. BHM resulted in quasi-normal posterior distributions. We conclude that both approaches are better than the normal approach where diet covariances are calculated ad-hoc. The BHM method has the potential to be vastly superior because it allows the simultaneous integration in a formally correct manner, however, convergence can be difficult.pt_BR
dc.format1 CD-ROM.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectModelo bayesianopt_BR
dc.subjectPerfil alcalinopt_BR
dc.subjectMelhoramento forrageirapt_BR
dc.titleBayesian hierarchical models to improve estimation of diet composition by alkane profiles.pt_BR
dc.typeResumo em anais e proceedingspt_BR
dc.date.updated2012-02-15T11:11:11Zpt_BR
riaa.ainfo.id907473pt_BR
riaa.ainfo.lastupdate2012-02-15pt_BR
dc.contributor.institutionEMILIO ANDRÉS LACA, University of California Davis, CA, USA; TERESA CRISTINA MORAES GENRO, CPPSUL; JAIRO SILVEIRA GENRO NETO, Universidade Federal de Santa Maria, RS, Brasil.pt_BR
Aparece nas coleções:Resumo em anais de congresso (CPPSUL)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
G158pagina173poster.pdf87.81 kBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace