Classificação multivariada de modelos de crescimento para grupos genéticos de ovinos de corte

• Ano de defesa: 2010
• Autor(a) principal: Silveira, Fernanda Gomes da
• Orientador(a): Silva, Fabyano Fonseca e
• Banca de defesa: Cecon, Paulo Roberto , Carneiro, Antônio Policarpo Souza , Nascimento, Carlos Souza do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Modelo não-linear; Análise de agrupamento; Ovinos de corte
• Palavras-chave em Inglês: Nonlinear models; Cluster analysis; Beef lambs
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS
• Link de acesso: http://locus.ufv.br/handle/123456789/4027

The main objective of this work was used the cluster analysis in order to classify nonlinear growth models in relation to different quality fit evaluators when utilized data from the following beef lambs genetic groups: Dorper x Morada Nova (DMN), Dorper x Rabo Largo (DRL) e Dorper x Santa Inês (DSI). After the choice of the best model, we aimed also apply the model identity in order to identify the most efficient group. The proposed methodology was considered in two experimental conditions: with repetitions, using data of all animals from each group; and without repetitions, using average data from each group. Twelve nonlinear models were used, whose fit quality was measured by determination coefficient (R2 aj), Akaike information criterion (AIC), Bayesian information criterion (BIC), mean quadratic error of prediction (MEP) and predicted determination coefficient (R2 p). The Richards and von Bertalanffy models, respectively, presented the best fit for the mean and individual data sets. The model identity tests revealed that the DSI group presented higher adult weight, therefore this group is recommend for meat production.