Year: 2025
Type: article
Abstract: The decline in fed cattle cash sales and its impact on price discovery are concerning. This study extends existing literature by utilizing machine learning to explore factors, particularly decision trees and random forests, to explore factors influencing fed cattle price ranges, complementing traditional regression analyses. These models uncover hidden patterns and provide additional insights into the cattle market. Key variables such as weight range, head count, and trade location, are found to be associated with price ranges. Notably, the weight range emerges as the primary variable influencing the price range, with smaller weight ranges linked to lower price ranges.
Authors: Zuyi Wang, Man-Keun Kim, Hernan Tejeda
Institutions: Clemson University, Utah State University, University of Idaho
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,660
Funder: University of Cambridge
OpenAlex ID: W4411217293