  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.
