Scalability and robustness of feed yard mortality prediction modeling to improve profitability

Abstract

Cattle feed yards routinely track and collect data for individual calves throughout the feeding period. Using such operational data from nine U.S. feed yards for the years 2016–2019, we evaluated the scalability and economic viability of using machine learning classifier predicted mortality as a culling decision aid. The expected change in net return per head when using the classifier predictions as a culling aid as compared to the status quo culling protocol for calves having been pulled at least once for bovine respiratory disease was simulated. This simulated change in net return ranged from −$1.61 to $19.46/head. Average change in net return and standard deviation for the nine feed yards in this study was $6.31/head and $7.75/head, respectively.