400 Design and development of artificial intelligence driven decision support systems for sustainable livestock systems in United States.

Abstract Bovine respiratory disease (BRD), enteric emissions, and economic efficiency represent three critical sustainability challenges in global beef cattle production. BRD, a polymicrobial and multifactorial disease, incurs annual losses of $2 billion in the U.S. cattle industry and is the primary driver of antibiotic use across food animal production. Despite livestock emissions accounting for only 4% of total U.S. greenhouse gas emissions, beef cattle contribute 55% of these emissions, totaling 300 million tons annually. Additionally, optimizing economic outcomes across the beef value chain is essential for the industry’s sustainability. Our Artificial Intelligence for sustaianble livestock systems laboratory is developing an Hybrid-Intelligent Mechanistic Modeling-based Decision Support System (HIMM-DSS) designed to address these challenges through real-time interventions within cattle herds. Leveraging the extensive precision livestock farming infrastructure of Texas A&M System, including video surveillance and biosensor-based data collection, the HIMM-DSS integrates multiple PLF sensors with both mechanistic and artificial intelligence-based modeling. For BRD mitigation, traditional methods of monitoring depression, appetite, respiration, and temperature is being enhanced using PLF technologies such as activity sensors, depth cameras, lung-ultrasonography, and infrared thermography. Studies across the U.S. have demonstrated that boosted decision trees applied to sensor data provide a reliable positive predictive value for disease detection. The HIMM-DSS facilitates early BRD prediction, reducing its prevalence and the need for antibiotics. For emission reduction, the HIMM-DSS employs advanced sensors like Growsafe, greenfeed systems, and trutest based body weight prediction for real-time gas-flux versus animal performance analysis. By combining mechanistic modeling, such as the cattle value discovery system, with sensor data, we are developing systems that can accurately quantify greenhouse gas emissions and animal performance. This system supports the use of methane-mitigating feed additives, providing real-time evidence for progress towards carbon-neutrality benchmarks in the cattle industry. Additionally, we incorporate an economic analysis of the beef value chain, encompassing cow-calf, stocker, and feedlot economics. By applying machine learning techniques, we develop decision tools that analyze and optimize economic outcomes across these segments. These tools enable stakeholders to make informed decisions, enhancing profitability and sustainability throughout the beef production system. The HIMM-DSS represents a significant advancement in sustainable beef cattle production, offering practical solutions to the industry’s most pressing challenges in disease management, emissions reduction, and economic efficiency.