Comparative application of machine learning approaches for body weight prediction in non-descript indigenous goats at different growth stages.

Background and Aim: Accurate prediction of body weight (BW) in goats is vital for breeding, feeding, drug administration, and marketing decisions, particularly in resource-limited farming systems where weighing scales are often unavailable. Traditional regression models have been applied but are limited by multicollinearity and non-linearity in body measurement data. This study aimed to evaluate the predictive performance of two machine learning (ML) approaches - Classification and Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS) - for estimating BW in non-descript indigenous goats across birth, weaning, and yearling stages, compared with stepwise regression models. Materials and Methods: A total of 100 goats were assessed at three growth stages: Birth (24 h), weaning (4 months), and yearling (12 months). Linear body measurements, body length (BL), sternum height, heart girth (HG), rump height, and withers height, were recorded alongside BW. Correlation analyses, stepwise regression, CART, and MARS models were developed. Model performance was evaluated using the coefficient of determination (R Results: BW showed strong positive correlations with HG and BL across all stages, while associations varied with other morphometric traits. Stepwise regression models exhibited lower predictive power, as indicated by reduced R Conclusion: ML techniques, particularly CART, provide robust and reliable prediction of BW in non-descript indigenous goats, surpassing conventional regression methods. These approaches can guide practical herd management decisions, including optimized feed allocation, drug dosage, and breeding selection, especially in resource-limited settings. While the study underscores CART's effectiveness, further validation with larger datasets and additional morphometric traits is recommended to enhance generalizability.