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This study adjusted different regression models to describe the growth pattern of meat quails from birth to 42 days of age. Data of 300 male quails were used. Weight and height information of all quails were collected weekly from the 1 st to the 42 nd day of age. Body weight of poultry was subjected to the polynomial, logistic, Gompertz, Weibull, and log-normal regression models. The criteria used to choose the best model to explain the growth curve of quails were the coefficient of determination of the model, Akaike’s information criterion, sum of squared residuals and Willmott’s index. For all the models used, the variables age and height were significant to explain the weight of quails. The polynomial (R² = 99.99%, AIC = 24.68, SSR = 27.5, d = 0.9999) and log-normal (R² = 99.60%, AIC = -17.5, SSR = 107.15, d = 0.9989) models presented the best fit criteria and were recommended to explain the growth of quails.

Adjusting weight growth curve of male quails <i>Coturnix japonica</i> reared in the semi-arid region of the State of Pernambuco