Researchers have developed a hybrid edge cloud digital twin framework for managing poultry facilities. The system integrates sensors, state estimation, and model predictive control to optimize environmental conditions while considering animal welfare. A learned residual is used to capture unmodeled biological variability, allowing the system to adapt to changing conditions. Evaluation in a testbed showed substantial gains over traditional rule-based control, with improved temperature prediction and reduced ammonia constraint violations. --- Why it matters: This matters because current climate control methods in poultry production are often heuristic and don't consider animal welfare. This framework provides a more data-driven approach that can improve both operational efficiency and animal well-being. Source: https://arxiv.org/abs/2608.20367