Growth curve modeling is essential for understanding livestock development, productivity, and efficiency. This study evaluated the growth patterns of Bali cattle, a resilient and economically significant breed in Indonesia, using five non-linear growth models: Brody, Gompertz, Logistic, Von Bertalanffy, and Modified Von Bertalanffy. Body weight data were collected from 256 males and 279 females at key growth stages from birth to 730 days. Goodness-of-fit criteria including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R²), and correlation coefficient (r) were applied to identify the most suitable model for describing growth curves. The Gompertz model exhibited the best fit for males, with the lowest AIC (29.76) and BIC (28.58) and highest R² (0.9913) and r (0.9956). For females, the Modified Von Bertalanffy model performed best, with superior goodness-of-fit metrics. Growth parameter analysis revealed that males achieved higher mature weights (A) and slower growth rates (K), whereas females exhibited faster growth rates but matured at smaller sizes. These findings indicate distinct growth dynamics between sexes, influenced by genetic and physiological factors. This research emphasizes the importance of selecting appropriate models to understand critical growth stages, optimize nutrition, and enhance management and breeding strategies. The results offering valuable insights for breeders, farmers, and policymakers aiming to bolster beef production.
The ethical approval from the committee of Animal Care and Welfare for this research was not necessary due to the use of secondary data only and there was no field experiment conducted.
| Primary Language | English |
|---|---|
| Subjects | Animal Growth and Development, Animal Science, Genetics and Biostatistics |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | June 20, 2025 |
| Publication Date | June 30, 2025 |
| Submission Date | February 25, 2025 |
| Acceptance Date | April 24, 2025 |
| Published in Issue | Year 2025 Volume: 35 Issue: 2 |