ASAS-NANP SYMPOSIUM: mathematical modeling in animal nutrition: the importance of animal science flight simulators to enhance the competitiveness and sustainability of livestock production

Complex livestock production problems are barriers to achieving long-term sustainability and profitability. Mathematical nutrition models are an integral component of daily livestock feeding systems, as they help improve animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Different actors, such as research institutions, single-owner farmers or ranchers, integrated livestock producers, or technology providers, have different needs for mathematical models. Many actors use spreadsheets and existing empirical models for nutrition and management, while others use mechanistic, dynamic, or artificial intelligence models. Regardless of model type, understanding the complexity of livestock systems is important, especially as many modeling projects often fail to give new insights to experienced actors within these systems. Flight simulators, also known as decision support tools, help identify high-leverage solutions while causing learning that leads to insight. However, understanding interrelationships between the parts of a system is difficult because intended and unintended consequences are often delayed. Our article reviews the pipeline of developing a flight simulator and contextualizes it within relevant technological-economic-agriculture issues, such as the growing use of precision livestock farming and associated cyberinfrastructure concerns. Fully documented and easy-to-use flight simulators provide a means to introduce stakeholders (actors) to a methodology for generating insights, as users design and run model scenarios for themselves, thereby challenging their mental models. This experiential learning is often more impactful (compared with someone telling them) because insight is gained as the actors challenge their own mental models through inductively or deductively assessing equations, parameters, and outcomes. As precision livestock technology increases on-farm, so do the intended and unintended consequences of data collection, maintenance, model development, deployment, and scale for enhancing animal production. Flight simulators help actors anticipate desired reinforcing actions such as increasing efficiencies and balancing actions within systems that slow the growth of efficiencies such as new training, labor, or infrastructure to manage large datasets or cyberinfrastructure concerns.