Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival

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dc.contributor.authorEmsen, Ebru
dc.contributor.authorOdevci, Bahadir Baran
dc.contributor.authorKorkmaz, Muzeyyen Kutluca
dc.date.accessioned2026-06-19T06:37:57Z
dc.date.available2026-06-19T06:37:57Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis study investigates key physiological, genetic, and environmental factors influencing maternal success in sheep to enhance lamb survival and maternal quality. Using data from native and crossbred prolific ewes in a high-altitude, cold-climate region, we applied machine learning models to predict mothering scores based on dam characteristics, birth conditions, and lamb attributes. Pregnant ewes were monitored 24 hours per day, beginning three days before parturition, with minimal human intervention. Predictor variables included dam breed, body weight, age, litter size, lamb genotype, lambing season, time of lambing, parturition duration, and lambing assistance. Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. The Random Forest model achieved the highest accuracy (67.2%) and demonstrated the best overall performance with a 0.41 Kappa statistic and the lowest mean absolute error (0.59). Feature importance analysis identified dam weight at birth, parturition duration, and lamb birth weight as the strongest predictors of maternal success. The Decision Tree model highlighted time of lambing, lamb genotype, and lambing assistance as key decision points for classifying mothering ability. Further analysis revealed that shorter parturition durations (<= 38 min), unassisted lambing, and smaller litter sizes were associated with higher mothering scores. Breed-specific maternal differences were also observed, with crossbred prolific ewes exhibiting stronger maternal instincts. These findings provide actionable insights for precision livestock farming, emphasizing the importance of genetic selection, birthing management, and environmental monitoring to enhance maternal efficiency and lamb survival.
dc.description.sponsorshipInnovation for Sustainable Sheep and Goat Production in Europe [iSAGE-679302]
dc.description.sponsorshipThe author(s) declare that financial support was received for the research and/or publication of this article. This research was partly funded by the Innovation for Sustainable Sheep and Goat Production in Europe (iSAGE-679302).
dc.identifier.doi10.3389/fanim.2025.1543490
dc.identifier.issn2673-6225
dc.identifier.orcid0000-0003-1791-2535
dc.identifier.scopus2-s2.0-105003818534
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fanim.2025.1543490
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5304
dc.identifier.volume6
dc.identifier.wosWOS:001476935100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Animal Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectMaternal Quality
dc.subjectMachine Learning
dc.subjectMaternal Behavior
dc.subjectLivestock Management
dc.subjectLamb Survival
dc.titleUsing machine learning to identify key predictors of maternal success in sheep for improved lamb survival
dc.typeArticle

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