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The United States (US) swine industry plays an important role in providing a safe and reliable source of animal proteins for a growing world population. As the industry evolves and society advances, producers face new and complex challenges such as optimizing animal production, welfare, and health. This dissertation contributes novel evidence-based knowledge to address current swine housing and management challenges in several key areas that formed the objectives of this dissertation, which were to: develop a computer vision system to monitor sow behavior in farrowing stalls (Chapter 2), evaluate the impacts of farrowing stall layout and number of heat lamps on sow and piglet productivity (Chapter 3) and behavior (Chapter 4), quantify the static and dynamic space usage of late gestation sows (Chapter 5), and determine supplemental heat requirements to implement ventilation shut down plus and virus inactivation (Chapter 6). The research presented in this dissertation contains the following discoveries. In Chapter 2, a large-scale computer vision system was established and implemented to simultaneously and continually monitor 60 farrowing stalls. The semi-automatic image processing algorithm achieved sow posture classification accuracies of >99.2% (sitting: 99.4%, standing: 99.2%, kneeling: 99.7%, lying: 99.9%) and >97% accuracy for sow behaviors (feeding: 97.0%, drinking: 96.8%, other: 95.5%). The computer vision system provided the foundation for carrying out the subsequent study concerning the impact of farrowing stall layout and management strategies. It was revealed in Chapter 3 that farrowing stall physical dimensions and number of heat lamps for localized heating did not significantly impact the percentage of pre-weaning mortality, overlay, number of piglets born alive, number weaned, average daily weight gain, or litter

Evaluation of swine gestation-farrowing facility space and management for improving production, welfare, and infectious disease containment