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Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach.

Rakesh Nehra¹, Yogesh C Bangar², C S Patil¹

¹Department of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.

Tropical Animal Health and Production

|December 29, 2025

Summary

This summary is machine-generated.

Machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), accurately predict milk yield in Murrah buffaloes. These algorithms offer valuable tools for enhancing buffalo breeding programs.

Keywords:

Machine learning Milk production Murrah buffaloes Prediction

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Area of Science:

Animal Science
Machine Learning
Quantitative Genetics

Background:

Accurate prediction of milk yield is crucial for efficient livestock management and breeding programs.
Murrah buffaloes are a vital dairy animal, and optimizing their production traits is of significant economic importance.
Machine learning (ML) offers advanced computational approaches for analyzing complex biological data.

Purpose of the Study:

To evaluate and compare the performance of nine ML algorithms for predicting 305-day first lactation milk yield (305FLMY) and total milk yield (TMY) in Murrah buffaloes.
To identify the most effective ML models for genetic improvement strategies in buffalo populations.
To assess the utility of various input variables, including test day milk yields, for predictive modeling.

Main Methods:

Utilized a dataset of 657 Murrah buffaloes with records spanning 24 years (2000-2023).
Input features included animal details, calving year, age at first calving, peak yield, days to peak yield, and test day milk yields (TD1, TD2, TD3).
Compared nine ML algorithms: ANN, BR, GP, GBM, MARS, MLR, RF, SMOreg, and SVM, assessing performance using R², RMSE, MAE, MAPE, and bias.

Main Results:

The Random Forest (RF) model demonstrated superior performance in predicting 305FLMY (R² = 78.43%, MAPE = 9.46%).
The Support Vector Machines (SVM) model achieved the best predictive accuracy for TMY (R² = 71.76%, MAPE = 276.13%).
Artificial Neural Networks (ANN) exhibited the weakest predictive performance for both milk yield traits.

Conclusions:

RF and SVM models show significant potential for accurately predicting complex milk production traits in Murrah buffaloes.
These ML approaches can be integrated into buffalo breeding programs to facilitate genetic selection and improve herd productivity.
Future research should focus on improving model interpretability and computational efficiency for practical on-farm applications.