Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms

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dc.contributor.authorMehmet Eroğlu
dc.contributor.authorAli Osman Turgut
dc.contributor.authorMürsel Küçük
dc.contributor.authorMuhammed Furkan Önen
dc.date.accessioned2025-06-02T11:06:41Z
dc.date.available2025-06-02T11:06:41Z
dc.date.issued2025-05-28
dc.departmentFakülteler, Veteriner Fakültesi, Zootekni ve Hayvan Besleme Bölümü
dc.description.abstractBackgroundMachine learning systems learn from historical data to forecast future outcomes. In the context of livestock farming, machine learning can be utilized to predict variables such as growth rates, milk production and breeding success by analysing data related to animal health, nutrition and environmental conditions.ObjectiveThis study aimed to investigate the performance of different machine learning algorithms in predicting weaning weight based on biometric measurements of Romanov lambs at 30 days of age.MethodsThe biometric traits of the lambs, including body length (BL), chest circumference (CC), chest depth (CD), chest width (CH), withers height (WH), rump height (RH), rump width (RW) and sex were used to construct predictive models. The study employed random forest (RF), classification and regression trees (CART), gradient boosting (GB), eXtreme gradient boosting (XGBoost) and CatBoost algorithms. The data was standardized to eliminate scale differences and divided into training (80%) and test (20%) sets. GridSearchCV was utilized for hyperparameter optimization. The performance of the models was evaluated using various goodness-of-fit metrics, including RMSE, MAE, R2, MAPE, RAE, MAD and SD ratio.ResultsThe gradient boosting and XGBoost models performed the highest R2 values and the lowest RMSE, MAE and MAPE values in the test data. In contrast, the random forest and CatBoost models showed lower predictive performance, with higher errors in the test data.ConclusionThe study suggests that machine learning algorithms, particularly gradient boosting and XGBoost, show promising potential in predicting the weaning weight of lambs. These insights may facilitate more informed decision-making in animal breeding and selection, potentially contributing to enhanced livestock management practices.
dc.identifier.citationEroğlu, M., Turgut, A. O., Küçük, M., & Önen, M. F. (2025). Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms. Veterinary Medicine and Science, 11(4), e70420.
dc.identifier.doi10.1002/vms3.70420
dc.identifier.issn2053-1095
dc.identifier.issn2053-1095
dc.identifier.issue4
dc.identifier.pmid40434929
dc.identifier.urihttps://doi.org/10.1002/vms3.70420
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8699
dc.identifier.volume11
dc.identifier.wos001497663800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorEroğlu, Mehmet
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofVeterinary Medicine and Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectXGBoost
dc.subjectgradient boosting
dc.subjectmachine learning
dc.subjectpredict
dc.subjectweaning weight.
dc.titlePredicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms
dc.typejournal-article
oaire.citation.issue4
oaire.citation.volume11

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