[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124213-en":3,"doc-seo-124213-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124213,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival","Study examines physiological, genetic, and environmental factors shaping maternal success in sheep to improve lamb survival and maternal quality. Using native and crossbred prolific ewes from a high-altitude, cold-climate environment, machine learning models predict mothering scores from dam characteristics, birth conditions, and lamb attributes. Pregnant ewes were monitored continuously before parturition with minimal human intervention. Random Forest, Decision Trees, Logistic Regression, and SVM were compared; Random Forest delivered the best accuracy and strongest performance, while feature importance highlighted birth weight and parturition duration as leading predictors.","TYPE Original Research PUBLISHED 14 April 2025  \nDOI 10.3389/fanim.2025.1543490  \nOPEN ACCESS  \nEDITED BY  \nJuan Mauricio Alvarez,  \nNational Institute of Agricultural Technology (INTA), Argentina  \nREVIEWED BY  \nGabriel Ciappesoni,  \nNational Institute for Agricultural Research (INIA), Uruguay  \nDaniel Omar Maizon,  \nNational University of La Pampa, Argentina  \n*CORRESPONDENCE  \nEbru Emsen  \n [ebruemsen@uaeu.ac.ae](ebruemsen@uaeu.ac.ae)  \nRECEIVED 11 December 2024  \nACCEPTED 18 March 2025  \nPUBLISHED 14 April 2025  \nCITATION  \nEmsen E, Odevci BB and Kutluca Korkmaz M (2025) Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival.  \nFront. Anim. Sci. 6:1543490 .  \ndoi: 10.3389/fanim.2025.1543490  \nCOPYRIGHT  \n© 2025 Emsen, Odevci and Kutluca Korkmaz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nUsing machine learning to identify key predictors of maternal success in sheep for improved lamb survival  \nEbru Emsen 1*, Bahadir Baran Odevci 2 and Muzeyyen Kutluca Korkmaz 3  \n1 Integrative Agriculture, College of Agriculture and Veterinary Medicine, United Arab Emirates University, Al Ain, United Arab Emirates, 2 Management Information Systems, Kadir Has University, Istanbul, Türkiye, 3 Department of Animal Science, Faculty of Agriculture, Malatya Turgut Ozal University, Malatya, Türkiye  \nThis 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.  \nKEYWORDS  \nmaternal quality, machine learning, maternal behavior, livestock management, lamb survival  \nFrontiers in Animal Science 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nIt is well-documented that maternal effects play a highly significant role in offspring, even though in sexually reproducing animals, both parents are equally likely to affect the phenotype of offspring (Chenow","cbCairqnBWsS18wp","https://ap.wps.com/l/cbCairqnBWsS18wp","pdf",1330625,1,"English","en",105,"# Introduction\n## Maternal effects and offspring outcomes\n## Maternal quality components in sheep\n## Breed characteristics relevant to maternal success","[{\"question\":\"What factors were modeled to predict maternal success in sheep?\",\"answer\":\"The study used dam characteristics, birth conditions, and lamb attributes, including breed, body weight, age, litter size, lamb genotype, lambing season and time, parturition duration, and lambing assistance.\"},{\"question\":\"Which machine learning algorithms were evaluated and what was the best performer?\",\"answer\":\"Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM) were tested. Random Forest achieved the highest predictive accuracy and overall performance.\"},{\"question\":\"Which variables were identified as the strongest predictors of maternal success?\",\"answer\":\"Feature importance analysis highlighted dam weight at birth, parturition duration, and lamb birth weight as the strongest predictors, with additional decision points revealed by the Decision Tree model.\"}]","Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival | PDF",1785821046,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"using-machine-learning-to-identify-key-predictors-of-maternal-success-in-sheep-for-improved-lamb-survival","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/using-machine-learning-to-identify-key-predictors-of-maternal-success-in-sheep-for-improved-lamb-survival/124213/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What factors were modeled to predict maternal success in sheep?","Question",{"text":74,"@type":75},"The study used dam characteristics, birth conditions, and lamb attributes, including breed, body weight, age, litter size, lamb genotype, lambing season and time, parturition duration, and lambing assistance.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms were evaluated and what was the best performer?",{"text":79,"@type":75},"Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM) were tested. 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