101 Evaluation of Growth Curve Functions for predicted weight in small holder crossbred dairy cattle in Tanzania

Abstract

Abstract Modelling the growth curve of animals provides information on growth characteristics and is important for optimizing management in different livestock systems. This study evaluated the growth curves of crossbred calves from birth to 30 months of age in small holder dairy farms in Tanzania using a two parameter (exponential), four different three parameters (Logistic, von Bertalanffy, Brody, Gompertz), and three polynomial functions. Predicted weights based on heart girth measurements of 623 male and 846 female calves born between 2016 and 2019 used in this study were from the African Dairy Genetic Gains (ADGG) project in selected milk sheds in Tanzania, namely Tanga, Kilimanjaro, Arusha, Iringa, Njomba and Mbeya. Each function was fitted separately to weight measurement of males and females adjusted for the effect of ward and season of birth using the nonlinear least squares (nls) functions in R statistical software. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, all three polynomial and four parameter functions performed better and did not differ enough from each other in both males and females compared to the two-parameter exponential model. Predicted weight varied among the models and differed between males and females. The highest estimated weight was observed in the Brody model for both males (278.09 kg) and females (264.10 kg). Lowest estimated weight was observed in the exponential model. Estimated growth rate varied among models. For males, it ranged from 0.04 kg-0.08 kg and for females, from 0.05 kg-0.09 kg in the Brody model and logistic model respectively. Predictive ability across all fitted curves was low, ranging from 25% to approximately 29%. This could be due to the huge range of breed compositions in the evaluated crossbred calves which characterizes small holder dairy farms in this system and different levels of farm management.