Ongole Grade cattle have high growth potential, particularly for beef production, with a remarkable mean daily gain. Our study is the most recent analysis of the growth curve of Ongole Grade cattle and has the largest number of samples thus far (1,500 cattle) (Table 2). The mean birth, weaning, and yearling weights determined here (25.02, 122.40, and 147.97 kg, respectively) were higher than the values reported by Kurniawan et al. (2021). Furthermore, the mean body weights of 2- and 3-year-old cattle were higher in our study than in the work by Maharani et al. (2017) (290 ± 59.8 and 330 ± 34.8 kg, respectively). Our results are consistent with the potential mature body weight of Ongole Grade cattle, which can exceed 400 kg, as indicated by Astuti (2004). Because intensive management can optimize cattle growth potential (Laya et al. 2020), the rearing system at BCATRES (where our samples were obtained) might have contributed to the high mean body weights in our study.
We found significant differences in the values of A, B, and k (Table 3). Differences in mature weight (A) were also found by Budimulyati et al. (2012) and Tutkun (2019) for Friesian Holstein cattle, Bahaswan et al. (2015) for Dhofari cattle, and Selvaggi et al. (2016) for Podolica bulls; the von Bertalanffy method yielded the highest values. For parameter B, similar results were obtained by Selvaggi et al. (2016), Maharani et al. (2017), and Tutkun (2019), who stated that the estimated value was highest using a logistic function. The logistic model also yielded the top growth rate (k) in our study, similar to the findings in other studies (Bahaswan et al. 2015; Maharani et al. 2017; Tutkun 2019). Various results for each parameter were obtained, as indicated by the correlations among parameters (Table 4). Selvaggi et al. (2016) reported that the von Bertalanffy and logistic functions usually provide the best results for Bos indicus, although there are variations according to cattle breed and population structure.
The models can be accurately compared by the evaluating the overall growth curve analysis processes, as well as differences between actual and generated data. The number of iterations can be used to compare models; the von Bertalanffy and logistic models required more iterations than did the Gompertz model (Table 3). A greater number of iterations indicates that the model has more difficulty achieving convergence (Inounu et al. 2007). The age at puberty was younger with the von Bertalanffy model than with the Gompertz and logistic models; notably, the Gompertz and logistic models also yielded the lowest estimated weight at puberty. The actual age and weight at puberty obtained from the database averaged 12.57 months and 205.86 kg, respectively; the results of Gompertz and logistic models were closest, while the von Bertalanffy model tended to overestimate these values. These conclusions were confirmed by the R2 and RMSE analyses (Table 4). Based on the accuracy of each model parameter, there were no differences in the R2 values. However, the RMSE value was lowest for the Gompertz model, indicating high accuracy. Furthermore, the deviation between actual and estimated weights at different ages helps to compare models (Fig. 2). All deviation lines tend to point outwards from the beginning of the growth period. However, the deviation line of the Gompertz model most closely matched the real data. Therefore, we consider the Gompertz model to have better accuracy. Maharani et al. (2017) obtained different results for Ongole Grade cattle, which might have been related to differences in study environment. Body weight data obtained from identical environments would influence the goodness-of-fit of the mathematical models for explaining the variation in cattle body weight (Hafiz et al. 2015).
In conclusion, the Gompertz model estimated the parameters most rapidly, while the von Bertalanffy model required the most computation. All three models had high degrees of accuracy, but the Gompertz model provided better estimates based on the low deviation value. Therefore, we recommend using the Gompertz model to predict the growth rate of Ongole Grade cattle during puberty.