Document Type
Article
Author ORCID Identifier
Ryan Feuz https://orcid.org//0000-0001-9464-201X
Miles Theurer https://orcid.org/0000-0001-8694-1415
Journal/Book Title/Conference
Agricultural and Resource Economics Review
Volume
51
Issue
3
Publisher
Cambridge University Press
Publication Date
9-20-2022
First Page
610
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Last Page
632
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.
Recommended Citation
Fuez, R., Feuz, K., Gradner, J., Theurer, M., & Johnson, M. (2022). Scalability and robustness of feed yard mortality prediction modeling to improve profitability. Agricultural and Resource Economics Review, 51(3), 610-632. doi:10.1017/age.2022.19
