Fitting Growth Curve in Japanese quail (Coturnix Coturnix Japonica) using Nonlinear and Nonlinear Mixed-Effects Models

The growth models own a very great importance in biological systems.  For example, by analyzing the growth curve of ruminant animals and poultries, it is possible to fastener and to manage their rearing, nutritional and behavioral requirements, based on their growth routines and principles. On the other hand the growth pattern of animals might be used in evaluating their genetic potential for breeding purposes. Thus, to evaluate and describe the growth pattern of Japanese quail, this study aimed to evaluate the capabilities and advantages of non-linear and nonlinear mixed models to describe and evaluate the growth pattern of Japanese quail.

In order to assess and designate the growth pattern of Japanese quail, we used the growth data of three groups; high weight (HW), low weight (LW) and the control lines of Japanese quail. Different models, including logistic, Gompertz and Richard's both non-linear and nonlinear mixed models were fitted to the data. For evaluation of models three criteria including coefficient of determination (R2), mean square error (MSE) and Akaike's information criterion (AIC) were employed as criteria to compare the mentioned six different models.

Values of the coefficient of determination (R2), for logistic, Gompertz and Richard's nonlinear models were 0.954, 0.957 and 0.951, mean square error (MSE) for three models were 74.034, 72.560 and 72.730, and Akaike's information criterion (AIC) for the models were 65829, 65307 and 65349, respectively. The results for non-linear mixed models, in the same order mentioned above and for R2, MSE and AIC, were 0.976, 0.978 and 0.978; 33.400, 31.658 and 31.849; 61449, 60641 and 60591 respectively.

The results showed that nonlinear mixed models had higher accuracy and less mean square error, compared to nonlinear models and Richard’s model is more capable (better) to the predict growth pattern of Japanese quail. Also among non-linear models Gompertz model had a better fit to the purpose. In general it can be said that the parameters determined by the functions inspected in this study, are not much different.