In this study, we analyzed 50,238 records of the body weight of the Murciano-Granadina goat breed from birth to 360 days of age. The data were collected from a private dairy farm located in Ghale-Ganj city, which is in the southern part of Kerman province, in the south of Iran. The records were collected between 2016 and 2022. Our goal was to evaluate the suitability of non-linear models for characterizing growth curves from birth to 360 days of age and to estimate genetic parameters for these growth curve traits. Five non-linear mathematical models namely Brody, Negative exponential, von Bertalanffy, Logistic, and Gompertz were compared by using Akaike's information criterion (AIC), root mean square error (RMSE), and Durbin-Watson statistic (DW) to determine the most suitable function for characterizing the growth curve. Among the investigated models, the von Bertalanffy model exhibited the lowest values for both AIC and RMSE. Additionally, we observed positive autocorrelations among residuals for all of the investigated non-linear models, with the lowest value being observed for the von Bertalanffy model. As a result, we selected the von Bertalanffy as the most suitable model for characterizing the growth curve of the Murciano-Granadina goat breed. To estimate genetic parameters for the growth curve traits, including parameters A (estimated mature weight), B (an integration constant related to initial animal weight), K (maturation rate), inflection age (IA), and inflection weight (IW), we utilized a Bayesian multivariate animal model that accounted only for direct additive genetic effects. The posterior means for heritabilities of A, B, K, IA, and IW were significant values of 0.11, 0.13, 0.03, 0.11, and 0.17, respectively. Parameter A had significant and positive genetic and phenotypic correlations with parameters B, IA, and IW. The posterior means for genetic and phenotypic correlations between parameters A and K were negative estimates of -0.58 and -0.17, respectively, implying that the kids with slower maturation rates had higher mature weights. Positive and medium estimates were obtained for posterior means of phenotypic (0.04) and genetic (0.29) correlations between parameters B and K. Both posterior means for phenotypic and genetic correlations of B with IA were 0.32 while those of B with IW were 0.51 and 0.50, respectively. We found high and positive genetic (0.51) and phenotypic (0.50) correlations between IA and IW. However, we observed low levels of additive genetic variation for all of the studied growth curve traits. In conclusion, our analysis suggests that the growth curve traits of the Murciano-Granadina goat breed are highly influenced by non-additive genetic and environmental effects. Therefore, it is essential to consider these effects when designing strategies to improve these traits and develop an appropriate breeding scheme that can achieve the desired shape of the growth curve.
