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    Designing Biomimetic Learning Environments for Animal Welfare Education: A Gamified Approach
    (Mdpi, 2025) Emsen, Ebru; Odevci, Bahadir Baran; Korkmaz, Muzeyyen Kutluca; Alshamsi, Fatma; Alkaabi, Alyaziya
    Animal 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.
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    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, Dana
    Intensive 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.
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    Effect of fish and soybean oils feed supplementation on the characteristic of Romanov crossbred lamb meat
    (Polish Soc Veterinary Sciences Editorial Office, 2022) Korkmaz, Muzeyyen Kutluca
    This study was conducted to investigate the effects of roughage and/or fattening feed rations and CLA-rich fish oil and soybean-supplemented diets on meat quality parameters (pH, color and fatty acids) of crossbred Romanov lambs. As compared to the control group, soybean and fish oil-supplemented groups had almost 3 times greater feed conversion ratios. The amount of feed consumed for 1 kg of live weight during the fattening period was 4.77 kg in the soybean oil-supplemented group, 5.70 kg in the fish oil-supplemented group and 13.33 kg in the control group. In terms of M. longissimus dorsi (MLD) area and thickness, treatment groups all had similar values. Soybean and fish oil-supplemented groups had superior pH and color (L*, a*, b*) values. In the thiobarbituric acid (TBA) test, measuring malonaldehyde (MDA) produced due to the oxidation of fatty acids, results revealed that soybean and fish oil-supplemented groups yielded more ideal outcomes for TBARS values and fatty acid profiles.
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    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 Kutluca
    This 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.