254 Feedlot economic visualization decision support tool for sustainable beef production.

Abstract The beef cattle industry contributes over $86 billion annually to the United States agriculture GDP. However, fluctuating market prices, volatile feed costs, and resource management inefficiencies threaten beef production sustainability. To address this issue, we developed the Feedlot Economic Decision Visualization Tool (FEDVT), a data-driven decision-support system that integrates real-time data analytics to optimize feedlot productivity and profitability. Data visualization and analysis were conducted using a custom-built tool integrating Microsoft Excel and Tableau. Excel was employed for data preprocessing, statistical summarization, and preliminary trend analysis, while Tableau facilitated interactive and dynamic visualization of key metrics. The tool integrates historical feedlot performance data from trusted sources to generate predictive economic insights. A moving average model was applied to analyze trends in feed costs, operational expenses, and market price fluctuations. Real-time cattle futures data were incorporated to support the tool’s predictive capabilities. FEDVT features an interactive dashboard for enhanced usability and is designed in collaboration with the Texas A&M Institute of Data Science. The dashboard allows users to input key operational parameters and simulate various economic scenarios through “what-if” analyses, empowering feedlot stakeholders to assess potential financial outcomes before making strategic decisions. Using historical dataset, the tool was tested against real-world economic conditions, and preliminary validation was conducted through industry stakeholder feedback. Results indicate that FEDVT can potentially improve economic decision-making by providing real-time data visualization and predictive analytics tailored to feedlot operations. The system enhances transparency in cost-benefit analyses and enables data-driven resource allocation strategies. To further refine the tool’s decision-making capabilities, future iterations will incorporate additional economic and environmental sustainability metrics, and integrate Artificial intelligence powered predictions to enhance the quality of insights and recommendations.