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Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.

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Τίτλος: Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.
Συγγραφείς: Jat N; Animal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India. nitesh.bana78@gmail.com., Kumar V; Animal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India., Chopra A; Animal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India., Lehga RA; Animal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India., Pannu U; Animal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India.
Πηγή: Tropical animal health and production [Trop Anim Health Prod] 2026 Jul 10; Vol. 58 (6). Date of Electronic Publication: 2026 Jul 10.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer Country of Publication: United States NLM ID: 1277355 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-7438 (Electronic) Linking ISSN: 00494747 NLM ISO Abbreviation: Trop Anim Health Prod Subsets: MEDLINE
Imprint Name(s): Publication: 2005- : Heidelberg : Springer
Original Publication: Edinburgh, Livingstone.
Ιατρικοί όροι (MeSH): Sheep, Domestic*/physiology , Sheep, Domestic*/growth & development , Body Weight* , Machine Learning*, Sheep/physiology ; Animals ; Prediction Algorithms ; Female ; Boosting Machine Learning Algorithms ; Predictive Learning Models ; Bayes Theorem ; Support Vector Machine ; Male ; Linear Models ; Neural Networks, Computer ; Birth Weight
Περίληψη: This study was designed to predict the Twelve-month body weight from live body weights at different age of Magra sheep, reared on Arid Region Campus (ICAR-Central Sheep and Wool Research Institute), Bikaner, Rajasthan using machine learning algorithms. Twelve-month body weight (MWT12) was predicted using several predictors including pedigree records, sex, period, season, dam age at lambing, dam weight at lambing, and early body weight traits such as birth weight (BWT), weaning weight (MWT3), and six-month weight (MWT6). Data of 5,470 Magra sheep born from 1998 to 2023 were used. Comparative analysis was conducted by using Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Bayesian Regression (BR), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The algorithms were compared based on goodness of fit criteria including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and bias. The results revealed GBM as the top-performing model, achieving an R2 value of 0.75 and demonstrating superior predictive accuracy (r = 0.87) with lower values of RMSE, MAE, and bias. Support Vector Machine (SVM) and Artificial Neural Network (ANN) also demonstrated strong predictive capabilities, surpassing traditional approaches such as Multiple Linear Regression (MLR) and Bayesian Regression (BR) in accuracy and overall performance. The present study showed the reliability of advanced machine learning models, particularly GBM, for accurate body weight prediction in sheep breeding and it could be use of potential in genetic selection and flock management in Magra sheep.
(© 2026. The Author(s), under exclusive licence to Springer Nature B.V.)
Competing Interests: Declarations. Ethical approval: Not applicable. Consent for publication: The authors give their consent for publication. Competing interests: The authors declare that they have no conflict of interest.
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Contributed Indexing: Keywords: Gradient boosting machine; Machine learning; Magra sheep; Prediction
Entry Date(s): Date Created: 20260710 Date Completed: 20260710 Latest Revision: 20260729
Update Code: 20260729
DOI: 10.1007/s11250-026-05196-2
PMID: 42429911
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1573-7438
DOI:10.1007/s11250-026-05196-2