Decision Tree and Automatic Linear Modeling Approaches to Predict Body Weight in Indigenous Sabi Sheep and Matebele Goat Females of Zimbabwe
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Background: Accurate prediction of body weight (BW) in small ruminants is essential for effective herd management, breeding, and nutrition, particularly in resource-limited settings where weighing scales are unavailable. While linear body measurements (LBMs) are commonly used for BW estimation, the comparative performance of machine learning models in indigenous Zimbabwean small ruminants remains underexplored.
Methods: This study evaluated the predictive performance of decision tree algorithms (CHAID, Exhaustive CHAID, CRT) and Automatic Linear Modeling (ALM) for estimating BW from four LBMs—heart girth (HG), body length (BL), withers height (WTH), and rump height (RH)—in 95 ewes and 126 does. Models were assessed using cross-validation, resubstitution error, and relative predictor importance.
Results: Decision trees and ALM showed poor predictive performance in ewes, likely due to low variability in LBMs. In contrast, does exhibited strong predictive relationships: CHAID identified BL, Exhaustive CHAID highlighted HG, and CRT combined HG, BL, WTH, and RH, achieving lower cross-validation errors. ALM corroborated these findings, ranking HG and BL as the most informative traits. These results demonstrate that multivariate machine learning approaches can reliably estimate BW in does using simple, field-measurable traits.
Conclusion: HG and BL consistently emerged as robust predictors of BW in does, while ewes require additional traits for accurate estimation. This study provides the first comparative evaluation of CHAID, Exhaustive CHAID, CRT, and ALM in indigenous Zimbabwean small ruminants, offering practical, cost-effective tools for livestock management and breeding programs in resource-limited settings.
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References
Assan, N. (2012). The effect of non-genetic factors on slaughter weight and carcass traits in indigenous Matebele goats in Zimbabwe. Journal of Animal Production Advances, 2(1), 12–15.
Assan, N., Musasira, M., Mpofu, M., Mwayera, N., Mokoena, K., & Tyasi, T. L. (2024). Relationship between body weight and linear body measurements at various stages of permanent tooth eruption in indigenous Matebele female goats of Zimbabwe. Advances in Animal and Veterinary Sciences, 12(9), 1818–1828. https://doi.org/10.17582/journal.aavs/2024/12.9.1818.1828
Atta, M., Ibrahim, A., & Mahmud, F. (2023). Heart girth and body length as reliable predictors of body weight in sheep. Journal of Small Ruminant Research, 128, 25–32. https://doi.org/10.1016/jsr.2023.01.004
Çelik, Ş., Yılmaz, O., & Gül, S. (2025). Final weight prediction from body measurements in Kıvırcık lambs using machine learning algorithms. Animal Agriculture and Biotechnology, 68, 325. https://doi.org/10.5194/aab-68-325-2025
Day, K. A., Maclaurin, G., Dube, S., et al. (2003). Capturing the benefits of seasonal climate forecasts in agricultural management: Final report for Australian Centre for International Agricultural Research (ACIAR).
Hagreveas, S. K., Bruce, D., & Beffa, L. M. (2004). Disaster mitigation options for livestock production in communal farming systems in Zimbabwe. ICRISAT & FAO.
Hlatshwayo, A. (2007). The effects of soil type and woody cover on grass production on rangelands in Zimbabwe [Bachelor’s thesis]. Zimbabwe Open University.
Hlokoe, M., Muchenje, V., & Dzama, K. (2022). Using multivariate adaptive regression splines to estimate live body weight in Nguni cows. Tropical Animal Health and Production, 54(6), 1–9. https://doi.org/10.1007/s11250-022-03174-3
Kebede, K., Asaminew, M., & Megersa, A. G. (2024). Predicting the body weight of indigenous sheep from linear body measurement traits using classification and regression tree data mining algorithm. Biomed Journal of Scientific & Technical Research, 56(4). https://doi.org/10.26717/BJSTR.2024.56.008875
Kozaklı, Ö., Çelik, Ş., & Yılmaz, Ö. (2024). Comparison of machine learning algorithms and multiple linear regression in predicting post-weaning weights of Akkaraman lambs. Animals, 14(5), 798. https://doi.org/10.3390/ani14050798
Tadesse, T., Alemayehu, M., & Tesfaye, K. (2023). Prediction of body weight using body measurements in sheep and goats in Qatar. Asian Journal of Animal and Veterinary Advances, 18(2), 1–10. https://doi.org/10.3923/ajava.2023.1.10
Tyasi, T. L., & Eyduran, E. (2020). Comparison of artificial neural network and decision tree algorithms in predicting live body weight from morphological traits in indigenous chickens. Tropical Animal Health and Production, 52(8), 2821–2829. https://doi.org/10.1007/s11250-020-02313-1
Van Rooyen, A. F., Freeman, A., Moyo, S., & Rohrbach, D. (2007). Livestock development in Southern Africa: Future research and investment priorities. International Crops Research Institute for the Semi-Arid Tropics.
Yakubu, A., Eyduran, E., Çelik, Ş., & Ishaya, J. O. (2022). Use of linear modeling, multivariate adaptive regression splines, and decision trees in body weight prediction in goats. Genetika, 54(3), 1429–1445. https://doi.org/10.2298/GENSR2203429Y
Yakubu, A., Ibrahim, I., & Aliyu, J. (2023). Use of linear modeling, multivariate adaptive regression splines, and decision trees in body weight prediction in goats. Genetika, 54(3), 1429–1445. https://doi.org/10.2298/GENSR2303429Y
Yilmaz, O., Çelik, Ş., & Gül, S. (2013). Predicting body weight of sheep from linear body measurements using regression models. Small Ruminant Research, 112(1), 36–41. https://doi.org/10.1016/j.smallrumres.2013.03.003
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