A COMPARISON OF NONLINEAR MODELS FOR DESCRIBING WEIGHT-AGE DATA IN ANGLO-NUBIAN DOES

• Autor(a) principal: Arré, Francisco Arthur
• Data de Publicação: 2019
• Outros Autores: Campelo, José Elivalto Guimarães, Sarmento, Jose Lindenberg Rocha, Figueiredo Filho, Luiz Antônio Silva, Cavalcante, Diego Helcias
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Caatinga
• Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/6976
• topic: Anglo nubian. Weight gain. Age. Asymptotic weight. Growth rate.; Anglo nubiana. Ganho de peso. Idade. Peso assintótico. Taxa de crescimento.

The objective of this study was to determine the optimum age at last weighing and compare the goodness of fit of nonlinear models used to fit longitudinal weight-age data to describe the growth pattern of Anglo-Nubian does. Weights of 104 animals from birth to 60 months of age were grouped into 10 age groups at six-month intervals. In each age group, parameters A (asymptotic weight), B (integration constant), and K (maturity index) were estimated using the Brody, Gompertz, logistic, and von Bertalanffy models. Data were analyzed using analysis of variance in a factorial design (10 age groups × 4 nonlinear models). The age group × model interaction was not significant. Mean estimates of A, B, and K were significantly different between age groups up to 30 months (p < 0.05), indicating that the estimated curve is affected by weights taken before this age independent of the model. The values of mean squared error (MSE), mean absolute deviation (MAD), coefficient of determination (R²) and Rate of convergence (RC) at each age group up to 30 months were compared to determine the goodness of fit of nonlinear models. The ranking of fit was logistic, Brody, von Bertalanffy, and Gompertz. The logistic and Brody models respectively estimated the smallest and largest asymptotic weight. Longitudinal weight records taken until 30 months of age are most appropriate for estimating the growth of Anglo-Nubian does using nonlinear models.