Reproductive 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.

Reproductive 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 algorithms, provide new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes current research on the use of AI-powered behavioral monitoring tools to enhance selection accuracy and breeding outcomes. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring of individual animals, allowing for the early detection of reproductive events and maternal performance. We also present a conceptual model, ReproBehaviorNet, that maps age and sex specific behaviors to bio-logical processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. This approach not only improves breeding pre-cision and genetic progress but also contributes to better animal welfare and sustainable management. Our findings underscore the importance of interdisciplinary collaboration and data-driven tools in the evolution of livestock selection strategies.

Bu 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 rastlanmamıştır.

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.

This study investigated the reproductive performance, lamb survivability, and growing performance of Morkaraman ewes, the estrus of which was induced by natural and synthetic progesterone and which were inseminated by laparoscopic technique at the end of the mating season (December). In the study, 5 different estrus synchronization methods (CIDR, Crestar, Natural progesterone, Cronolone and MAP), 100 Morkaraman ewes, and lambs born to them were used. After the completion of the estrus synchronization methods, the first sign of estrus was detected earliest in the natural progesterone group (39.53±1.87 hours), and there was a significant difference between the natural progesterone and vaginal sponges containing MAP and Cronolone concerning the time to the first hour of estrus. Estrus rates were found significantly different across the study groups, with the shortest time to estrus having been achieved by MAP. The lambing rates for CIDR, Cronolone, Natural progesterone, MAP, and Crestar groups were 74%, 61%, 56%, 33%, and 10%, respectively. There was no significant difference between the treatment groups concerning birth weight, weight at various periods, and survivability of lambs although a significant difference was found in their weaning weight.

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 (κ-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.