Comparison of non-linear growth models to estimate growth curves and related genetic parameters in Inter-cross sheep.

Abstract

Objective: Evaluation of growth curves is a valuable approach to predict the growth and developmental patterns of different animals. The aim of this study was to monitor the growth curve traits using various non-linear models and related genetic parameters in Inter-cross sheep.

Methods: The information utilized in this study were collected from 743 Inter-cross lambs descended from 139 sires and 414 dams over the period from 2012 to 2022. Five non-linear mathematical models were applied to 3715 body weight records collected at birth, 3, 6, 9 and 12 months of age. The best fitting model was evaluated using various goodness of fit criteria including adjusted coefficient of determination(R2adj.), root means square error(RMSE), Akaike's information criterion(AIC) and Bayesian information criterion(BIC). Genetic parameters were estimated by employing animal model using WOMBAT program.

Results: Analysis revealed that, Brody model was the most suitable for describing the growth curve of both sexes and overall Inter-cross sheep based on its highest R²adj and lowest RMSE, AIC, and BIC values. The models were ranked in the following order of goodness: Brody, Bertalanffy, Gompertz, Logistic and Negative Exponential. Overall estimated values of asymptotic weight(A), inflexion point(B) and maturation rate(k) under best model were 29.81±1.39, 0.87±0.03 and 0.21±0.03, respectively. Least squares analysis revealed significant influence of period of lambing, lamb sex and ewe weight at lambing on estimated traits. Male lambs showed greater mature weight than ewes, while female lambs had high maturation rate. Direct heritability estimates for A, B and k were 0.13±0.06, 0.40±0.07 and 0.11±0.05 respectively. The genetic and phenotypic correlations of A-B were positive and moderate and negative between A-K and B-K.

Conclusion: Limited genetic variability observed in the growth curve parameters indicates the need to control environmental variations through proper measures in order to achieve the desired growth and implement effective selection programs.