Comparative Analysis of Growth Function Models for Merino Crossbred Sheep in West Java, Indonesia - Putra, WPB; Margawati, ET; Raadsma, HW; Tyasi, TL; Tırınk, C (Journal of the Hellenic Veterinary Medical Society, 2025)
This study aimed to compare the performance of five growth function models (Brody, Von Bertalanffy, Logistic, Gompertz, and Morgan-Mercer-Flodin) in predicting the growth patterns of Merino crossbred sheep in West Java, Indonesia. The analysis was conducted separately for male and female sheep, and the models were evaluated based on various statistical indicators. The Brody model yielded the highest coefficient of determination (R2) of 0.64 for male sheep, with an associated root mean square error (RMSE) of 6.825. The AIC and BIC values were 16413.30 and 16429.56, respectively, indicating the goodness of fit for the model. The Durbin-Watson (DW) statistic was 1.52, suggesting the absence of autocorrelation. Among the other models, Von Bertalanffy, Logistic, Gompertz, and Morgan-Mercer-Flodin also exhibited reasonably good fits, with comparable R2 values and model evaluation criteria. In the case of female sheep, the growth function models showed different performance characteristics compared to males. The Logistic model produced the highest R2 value of 0.63, indicating a good fit to the data. The RMSE was 5.036, and the AIC and BIC values were 10127.14 and 10143.25, respectively. The DW statistic was 0.93, indicating the absence of autocorrelation. Similar to the male sheep, the other models (Brody, Von Bertalanffy, Gompertz, and Morgan-Mercer-Flodin) also demonstrated reasonable performance. These results provide valuable insights into the growth patterns of Merino crossbred sheep in West Java, Indonesia, and can assist in optimizing breeding and management strategies. The findings highlight the importance of selecting appropriate growth function models based on sex-specific characteristics to accurately predict sheep populations’ growth trajectories. Further research is recommended to validate and refine these models for broader application in the sheep farming industry.