ArticleBMC veterinary research2026
Application of XGBoost and Random Forest algorithms for body weight prediction in Blackbelly sheep using biometric measurements.
Article in BMC veterinary research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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3 citing papers in PubMed.
- Heart girth-based bodyweight prediction for Sub-Saharan African ruminants: updated equations by species, breed, and system.Veterinary and animal science · 2026Article
- Correction: Application of XGBoost and Random Forest algorithms for body weight prediction in Blackbelly sheep using biometric measurements.BMC veterinary research · 2026Article
- Body Weight Prediction in Karayaka Lambs Using Morphometric Measurements: A Comparison of Regression and Machine Learning Approaches.Animals : an open access journal from MDPI · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReliable and cost-effective live weight determination is critical for improving both management and selection efficiency in small-scale sheep farming. In recent years, machine learning approaches based on phenotypic biometric measurements have emerged as a practical alternative for producers with limited access to weighing infrastructure. This study aimed to compare the performance of two machine learning algorithms (XGBoost and Random Forest) for estimating live weight based on biometric measurements in Blackbelly sheep (60 females, 60 males) raised in humid tropical climates.
resultsUsing the obtained biometric measurements, both algorithms were evaluated separately for training and test sets. The XGBoost algorithm demonstrated high accuracy on the training data (R2 = 0.981; MSE = 0.416; RMSE = 0.645; MAE = 0.486; AIC = 150.513 and BIC = 157.841) but had limited generalization to the test data (R2 = 0.813; MSE = 3.296; RMSE = 1.816; MAE = 1.440; AIC = 146.125 and BIC = 150.791). In contrast, the Random Forest algorithm produced more balanced and stable predictions on both training (R2 = 0.969; MSE = 0.630; RMSE = 0.794; MAE = 0.617; AIC = -31.230 and BIC = -21.459) and test data (R2= 0.873; MSE = 2.188; RMSE = 1.479; MAE = 1.105; AIC = 35.401 and BIC = 41.623). Optimal hyperparameters were determined for both models, and model fit criteria were compared. The results revealed that Random Forest offers a more reliable and stable option for estimating live weight.
conclusionsThe current study demonstrates that machine learning models based on phenotypic biometric measurements can be used in decision-support processes in small-scale sheep farming operations. The Random Forest algorithm stands out as a suitable tool for improving production and selection efficiency in live weight estimation. Furthermore, validation studies encompassing different sheep breeds and larger data sets will strengthen the reliability and generalizability of the developed models.
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