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Related Experiment Video

Updated: Sep 19, 2025

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Predicting genetic merit in Harnali sheep using machine learning techniques.

Spandan Shashwat Dash¹, Yogesh C Bangar², Ankit Magotra³

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

Tropical Animal Health and Production

|June 3, 2025

Summary

This summary is machine-generated.

Gradient Boosting Machine (GBM) accurately predicted genetic merits in Harnali sheep. This machine learning approach enhances animal breeding programs by optimizing selection and accelerating genetic progress.

Keywords:

Breeding values Harnali sheep Machine learning Prediction

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Area of Science:

Animal Science
Genetics
Machine Learning

Background:

Genomic and phenotypic data are crucial for improving animal breeding.
Accurate prediction of genetic merits is essential for efficient livestock improvement.

Purpose of the Study:

To evaluate seven machine learning algorithms for predicting the genetic merits of Harnali sheep.
To identify the most effective algorithm for optimizing breeding programs in this breed.

Main Methods:

Seven machine learning algorithms (KNN, MLR, BR, SVM, ANN, RF, GBM) were tested.
Pedigree and phenotypic data from 2036 Harnali lambs (1998-2021) were used.
Models were trained on 75% of data and tested on 25%, evaluated using R², RMSE, MAE, AIC, BIC, and bias.

Main Results:

Gradient Boosting Machine (GBM) was the top-performing model.
GBM achieved a coefficient of determination (R²) of 0.64 and predictive accuracy (r) of 0.80.
GBM demonstrated superior performance with lower error and bias metrics compared to other algorithms.

Conclusions:

Machine learning, particularly GBM, shows significant potential for enhancing Harnali sheep breeding programs.
Accurate prediction of breeding values using GBM can accelerate genetic progress in sheep populations.
The study highlights the utility of advanced computational methods in modern animal genetics and breeding.