Assessment of Smart Flock Manager: Artificial Intelligence-Driven Decision Support for Sheep Farming in Türkiye

Abstract Smart Flock Manager (SFM) is an artificial intelligence (AI)-driven decision support system (DSS) designed to enhance precision livestock farming (PLF) through machine learning, cloud computing, and Radio Frequency Identification technology. It enables real-time data collection, predictive analytics, and automated decision making to optimize reproductive efficiency, health monitoring, and economic sustainability while ensuring animal welfare. A comparative analysis of flock performance before and after SFM implementation highlights key trade-offs. While pre-SFM conditions resulted in higher birth (256%) and weaning (225%) rates, they also increased metabolic stress on ewes, shortening reproductive lifespan and raising replacement rates. In contrast, SFM improved reproductive efficiency and lamb survival, reducing stillbirths (4 to 1%), lamb mortality (12.22 to 6.78%), and increasing pregnancy rates (79 to 85.29%). However, earlier weaning (69 to 52 days) led to reduced average daily gains (222.46 g/day to 196.15 g/day) prior to weaning, emphasizing the need for optimized post-weaning nutrition. This study highlights the importance of balancing high output with ewe longevity and welfare. A moderate prolificacy rate (200–220%) with improved weaning strategies enhances sustainability and productivity. Future recommendations include optimizing reproductive selection, maternal nutrition, and weaning protocols. SFM’s AI-driven insights offer a scalable solution for sustainable and economically viable intensive sheep farming. Information © The Authors 2025