An estimation of cattle movement parameters in the Central States of the US

TL;DR

This paper introduces a large-scale maximum entropy optimization method to estimate cattle movement parameters across 10 US states, providing detailed county-level data crucial for livestock modeling and risk assessment.

Contribution

It presents a novel approach to estimate cattle movements using optimization and data recovery, addressing data privacy and regional characterization challenges.

Findings

Estimated cattle movement parameters reveal significant risk levels.

Method effectively recovers non-disclosed data elements.

Provides detailed county-level cattle movement characterization.

Abstract

The characterization of cattle demographics and especially movements is an essential component in the modeling of dynamics in cattle systems, yet for cattle systems of the United States (US), this is missing. Through a large-scale maximum entropy optimization formulation, we estimate cattle movement parameters to characterize the movements of cattle across $10$ Central States and $1034$ counties of the United States. Inputs to the estimation problem are taken from the United States Department of Agriculture National Agricultural Statistics Service database and are pre-processed in a pair of tightly constrained optimization problems to recover non-disclosed elements of data. We compare stochastic subpopulation-based movements generated from the estimated parameters to operation-based movements published by the United States Department of Agriculture. For future Census of Agriculture distributions, we propose a series of questions that enable improvements for our method without compromising the privacy of cattle operations. Our novel method to estimate cattle movements across large US regions characterizes county-level stratified subpopulations of cattle for data-driven livestock modeling. Our estimated movement parameters suggest a significant risk level for US cattle systems.