Optimizing feedlot placement weights using simulation-based mathematical programming

Abstract

This thesis develops a mathematical programming model to determine the optimal feeder placement weight that maximizes net returns in commercial feedlot operations. Feedlot profitability is affected by numerous biological and economic variables that include feed efficiency, yardage costs, health risks, mortality, feeder cattle purchase prices, and carcass-based revenue. Linear programming has been implemented within the industry before, but linear programming is rarely used to directly model how placement weight influences profitability over a defined feeding period. This study addresses that gap by integrating a flexible mathematical framework with real-world data and biologically informed performance data. The model simulates feeding outcomes across a range of initial weights while incorporating price slides, dressing percentage, feed-to-gain efficiency, mortality rates, and culling probabilities. Feeder cattle prices and grid-based carcass discounts are included to reflect the real-world market profits that feedlots could experience. The results of this analysis provide feedlot operators with a decision-support tool that can capture the trade-offs between animal growth and economic constraints. The results demonstrate how market-based carcass valuation can incentivize particular feeding durations. By offering a replicable and data driven optimization tool, this thesis contributes practical insights to feedlot decision-making and expands the agricultural economic literature on production modeling.