Decision Tree and Automatic Linear Modeling Approaches to Predict Body Weight in Indigenous Sabi Sheep and Matebele Goat Females of Zimbabwe

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.

body weight prediction, decision trees, automatic linear modeling, ewes, does, linear body measurements, small ruminants

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