Mexican Creole chickens: effect of data collection periods on goodness-of-fit and parameter precision of growth models.

The objective of this study was to estimate the good-of-fitness and precision of parameters of the Gompertz-Laird, Logistic, Richards, and Von Bertalanffy growth models, using different data collection periods (DCP). Two hundred and sixty-two Mexican Creole chicks (116 females and 146 males), were individually weighed to form the following sets of data for each sex: DCP (weights recorded weekly from hatching to 63 d, and every 2 wk, from 63 to 133 d of age), DCP (weights recorded weekly from hatching to 133 d of age), DCP (weights recorded every third day, from hatching to 63 d, and every 14 d, from 63 to 133 d of age), and DCP (weights recorded every third day, from hatching to 63 d, and weekly, from 63 to 133 d of age). Data were analyzed using the NLIN procedure of SAS (Marquardt algorithm). For all growth models, the width of confidence interval (CI) of each parameter, was estimated (α = 0.05). The adjusted coefficient of determination (AR), as well as the Akaike (AIC) and Bayesian information criteria (BIC) were used to select the best model. The higher the AR, and the lower the width of CI, as well as the AIC and BIC values, the better the model. The Gompertz-Laird model, more frequently showed the highest AR, and the lowest AIC and BIC values compared to the other models. Moreover, for all models, both sexes and all parameters, most confidence interval widths (all with the Gompertz-Laird model) were the lowest with DCP when compared to the other sets of data. In conclusion, the Gompertz-Laird model was the best provided that the chickens are weighed every third day from hatching until 63 d of age, and every 2 wk thereafter.