Sabiia Seb
        Busca avançada

Botão Atualizar

Botão Atualizar

Registro completo
Provedor de dados:  R. Bras. Zootec.
País:  Brazil
Título:  Ability of non-linear mixed models to predict growth in laying hens
Autores:  Galeano-Vasco,Luis Fernando
Cerón-Muñoz,Mario Fernando
Data:  2014-11-01
Ano:  2014
Palavras-chave:  Chickens
Mathematical models
Regression analysis
Weight gain
Resumo:  In this study, the Von Bertalanffy, Richards, Gompertz, Brody, and Logistics non-linear mixed regression models were compared for their ability to estimate the growth curve in commercial laying hens. Data were obtained from 100 Lohmann LSL layers. The animals were identified and then weighed weekly from day 20 after hatch until they were 553 days of age. All the nonlinear models used were transformed into mixed models by the inclusion of random parameters. Accuracy of the models was determined by the Akaike and Bayesian information criteria (AIC and BIC, respectively), and the correlation values. According to AIC, BIC, and correlation values, the best fit for modeling the growth curve of the birds was obtained with Gompertz, followed by Richards, and then by Von Bertalanffy models. The Brody and Logistic models did not fit the data. The Gompertz nonlinear mixed model showed the best goodness of fit for the data set, and is considered the model of choice to describe and predict the growth curve of Lohmann LSL commercial layers at the production system of University of Antioquia.
Tipo:  Info:eu-repo/semantics/article
Idioma:  Inglês
Editor:  Sociedade Brasileira de Zootecnia
Relação:  10.1590/S1516-35982014001100003
Formato:  text/html
Fonte:  Revista Brasileira de Zootecnia v.43 n.11 2014
Direitos:  info:eu-repo/semantics/openAccess

Empresa Brasileira de Pesquisa Agropecuária - Embrapa
Todos os direitos reservados, conforme Lei n° 9.610
Política de Privacidade
Área restrita

Parque Estação Biológica - PqEB s/n°
Brasília, DF - Brasil - CEP 70770-901
Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC:

Valid HTML 4.01 Transitional