Abstract
ABSTRACT 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.
Highlights
Body weight is the most common parameter for modeling animal growth due to the ease of measurement in a large number of animals at a low cost
Weights for growth studies using nonlinear models are usually taken before maturity is reached (OLIVEIRA et al, 2009), to what occurs in sheep (SILVA, 2012; SOUZA et al, 2013; HOSSEIN-ZADEH, 2015)
Mean parameter estimates were compared across age groups to determine the optimum age range over which does should be weighed for the analysis of growth curves (Table 3)
Summary
Body weight is the most common parameter for modeling animal growth due to the ease of measurement in a large number of animals at a low cost. Nonlinear models that correlate body weight and age data to provide biologically interpretable parameters, such as the Brody, von Bertalanffy, Richards, logistic, and Gompertz functions, have been widely used to describe animal growth (SOUZA et al, 2013). Selection for body weight may result in delayed maturity and a higher maintenance cost, which is not desirable for production in harsh environments such as semiarid regions, where animals may exhibit reduced performance and have a smaller size to adapt to the environment. Weights for growth studies using nonlinear models are usually taken before maturity is reached (OLIVEIRA et al, 2009), to what occurs in sheep (SILVA, 2012; SOUZA et al, 2013; HOSSEIN-ZADEH, 2015). Breeders can take advantage of the knowledge on the growth dynamics of different tissues, which enables the selection of animals that are more productive at specific age and weight ranges (MOTA et al, 2015)
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