Thesis abstract:

«p»The integration of data science in livestock management, particularly within feedlot environments, holds significant potential for enhancing efficiency, productivity, and animal welfare. This thesis explores the application of various data science techniques to optimize feedlot operations. It begins with the implementation of sensors and Internet of Things (IoT) devices for real-time data collection on animal health, behavior, and environmental conditions. The use of predictive analytics is examined to forecast health issues, optimize feed schedules, and improve yield predictions. Additionally, machine learning and artificial intelligence (AI) are employed to detect anomalies, recognize physical changes, and analyze behavioral patterns. These technologies are complemented by data visualization and reporting tools that provide actionable insights through interactive dashboards and custom reports. The findings demonstrate that leveraging data science in feedlot management can lead to more informed decision-making, reduced costs, improved animal health, and enhanced overall productivity. This research underscores the transformative impact of data-driven approaches in modern livestock management.«/p»