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Öğe Artificial Intelligence-Assisted Selection Strategies in Sheep: Linking Reproductive Traits with Behavioral Indicators(Mdpi, 2025) Emsen, Ebru; Kutluca Korkmaz, Muzeyyen; Odevci, Bahadir BaranReproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts.Öğe Designing Biomimetic Learning Environments for Animal Welfare Education: A Gamified Approach(Mdpi, 2025) Emsen, Ebru; Odevci, Bahadir Baran; Korkmaz, Muzeyyen Kutluca; Alshamsi, Fatma; Alkaabi, AlyaziyaAnimal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems evolve through feedback and cooperation. These principles were translated into an instructional model that integrates a core pathway (Pre-Test, Levels 1-4, Post-Test) with optional enrichment tasks and a role-specific Reward Marketplace. Question formats are constant across levels (MCQ, image-based, video-based) while cognitive difficulty increases, culminating in Positive Welfare scenarios. We describe the learning design structure and report preliminary implementation observations using a mixed-methods evaluation plan (pre/post knowledge assessments and engagement indicators). Results from early deployment indicate strong usability and engagement, with high voluntary uptake of enrichment tasks and positive learner feedback on role-tailored rewards; full empirical testing is in progress. Findings support the feasibility and pedagogical promise of biomimetic gamification to enhance knowledge, motivation, and intended practice in animal welfare education. GamifyWELL offers a replicable framework for nature-inspired instructional design that can be extended to allied sustainability domains.Öğe Developing a Practical Welfare Assessment Tool for Intensive Sheep and Goat Farming in Hot-Arid Regions: Pilot Validation in the United Arab Emirates(Mdpi, 2026) Emsen, Ebru; Korkmaz, Muzeyyen Kutluca; Odevci, Bahadir; Alnuaimi, Aysha; Almarzooqi, Maryam; Alketbi, Anoud; Alhammadi, DanaIntensive sheep and goat farming in hot-arid regions faces unique welfare challenges that differ substantially from those encountered in cooler climates; however, few practical and validated assessment tools are specifically designed to assess welfare under such extreme conditions. In this study, the term practical refers to field feasibility under routine farm conditions, limited assessment time, and suitability for reliability-based application, rather than comprehensive validation of welfare outcomes. This study aimed to develop and pilot-test a simplified welfare assessment protocol, based on a reduced set of clearly defined, field-applicable indicators supported by explicit operational definitions and standardized scoring criteria, tailored for the United Arab Emirates, with a specific focus on extreme heat and intensive husbandry conditions. Candidate indicators were identified from validated international sources and screened for applicability to arid climates, meat-oriented production, and intensive systems. The refined indicator set was converted into operational scoring sheets and applied by trained undergraduate animal science students as assessors to 100 animals at an intensive research farm. Inter-observer reliability was calculated using Fleiss' Kappa to evaluate consistency across assessors. Most behavioural and health indicators demonstrated substantial to almost perfect inter-observer agreement (kappa-based), while environmental and some tactile indicators, such as body condition and hydration tests, showed moderate reliability. Based on the most reliable indicators, a climate-sensitive Arid-Hot Small Ruminant Welfare Index (ASR-WI) was developed by weighting four welfare domains-Behaviour and Mental State, Environment, Nutrition, and Health. The findings confirm that a simplified welfare assessment protocol can be reliably implemented under intensive hot-arid conditions when clear scoring criteria and structured assessor training are provided. The resulting protocol and index offer a practical foundation for routine welfare monitoring under intensive hot-arid conditions, as well as for policymaking and future longitudinal research.Öğe Farklı yaşlarda sütten kesimin prolifik ve terminal ırk melez kuzuların büyüme özellikleri ve yaşama güçleri üzerine etkisi(2020) Korkmaz, Müzeyyen Kutluca; Emsen, EbruBu çalışmada, farklı yaşlarda sütten kesilen Romanov × Morkaraman (F1 Rom), Romanov × F1 Romanov (G1 Rom) ve Charollais × F1 Romanov (Charom) melez kuzularının büyüme ve yaşama gücü özellikleri karşılaştırılmıştır. Doğumdan sonra üç genotipten kuzular 50 ve 75 günlük iki ayrı yaşta sütten kesilmiş ve sütten kesim sonrası büyüme performansları 120 günlük yaşa kadar incelenmiştir. Doğum ağırlığına, üç ayrı genotip ve doğum şeklinin etkisi çok önemli, cinsiyetin etkisi önemsiz bulunmuştur. Sütten kesim ağırlığında ise sadece doğum şeklinin etkisi önemli bulunmuştur. Kuzu doğum ve sütten kesim ağırlıkları Charom, F1 Rom, G1 Rom kuzularında sırasıyla 3,38-15,28; 3,98-15,04; 3,21-15,55 kg tespit edilmiştir. Doğum şekli kuzu doğum ağırlıkları bakımından varyasyon göstermiş ve doğumda yavru sayısı arttıkça kuzu doğum ağırlıklarında (Tekiz: 4,19 kg; İkiz: 3,42 kg; Üçüz: 3,15 kg ve Dördüz: 2,88 kg) düşüş gözlenmiştir. Doğum şeklinin sütten kesim ağırlıklarına etkisi ise tekiz ve üçüzlerde benzer bulunmuştur. Tekiz doğan kuzular ikiz ve dördüz doğanlardan daha yüksek sütten kesim ağırlığına ulaşmışlardır. Sütten kesime kadar günlük canlı ağırlık arıtışı (G.C.A.A) tekiz doğan kuzularda 247,37 gr ile ikiz (190,67 gr), üçüz (201,44 gr) ve dördüz (178,57 gr) doğanlardan daha yüksek bulunmuştur. Sütten kesim yaşının sütten kesim ağırlıklarına etkisi önemsiz bulunmuştur. Sütten kesim sonrası ilk ay ağırlığına kuzu genotipi, doğum şekli ve cinsiyetin etkisi önemsiz iken, sütten kesim yaşının sütten kesim ve sütten kesim sonrası ilk aya kadar G.C.A.A’na etkisi önemli bulunmuştur. Geç sütten kesilen kuzular, daha yüksek sonraki ilk ay canlı ağırlık (18,62-17,89 kg) ve G.C.A.A (179,45- 81,32 g ) sahip olmuşlardır. Kuzuların 120. gün canlı ağırlıkları benzer bulunmuştur ve incelenen faktörlerden hiç birisinin etkisine rastlanmamıştırÖğe Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival(Frontiers Media Sa, 2025) Emsen, Ebru; Odevci, Bahadir Baran; Korkmaz, Muzeyyen KutlucaThis 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.
