Author ORCID Identifier
Date Available
4-30-2023
Year of Publication
2023
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Agriculture, Food and Environment
Department/School/Program
Agricultural Economics
First Advisor
Dr. Carl R. Dillon
Abstract
This dissertation contains three essays on select economic components of the U.S. beef industry. The first and second essays concentrate on the different economic problems in beef cattle production. The third essay evaluates the price dynamics and the impact of COVID-19 along the beef supply chain.
The first essay explores the economics of culling decisions in cow-calf operations in the U.S. with a novel application of a dynamic mathematical programming model. The results provide an optimal culling strategy under the base model and a range of optimal strategies that vary with respect to different components such as fertility probabilities, market prices, production and replacement heifer costs, calf weights, and pregnancy check use. The results suggest that producers should cull all cows that are older than age 10 considering their productivity and production costs in light of base product prices. The model recommends culling open cows earlier (at age 7) given their productivity status and probabilities. To measure the sensitivity of the optimal results with respect to components, several experiments are run, and outcomes underline the sensitivity of the optimal strategies to market conditions, cost structure, cow fertility, and pregnancy check use.
The second essay aims to contribute to the U.S. beef cattle price forecasting literature with its model selection framework which compares traditional time series techniques and machine learning algorithms to select the best technique to provide one-week-ahead steer, heifer, and cull cow price forecasts. The study performs these techniques using weekly Kentucky cattle auction prices with lagged variables and dummy variables for weekly seasonal structure. The results demonstrate that while ARIMA models without seasonality has better performance in forecasting steer prices, the LASSO regression provides better forecasts for heifer and cull cow prices. The model selection results point to the superiority of machine learning techniques over standard ARIMA models when forecasting U.S. livestock prices in larger samples.
The third essay investigates the price dynamics along the U.S. beef supply chain and the impact of the COVID-19 shock on the dynamics of vertical price transmission using monthly farm, wholesale, and retail prices for the period 1970-2021. A vertical error correction model along with historical decomposition graphs is employed to measure the impact of the pandemic on price adjustment. The results reveal that the impact of COVID-19 has been uneven across the beef marketing channel, with farmers taking the burden of the shock. The results underline that in the case of the COVID-19 shock, wholesale prices adjusted more quickly than both farm (threefold) and retail prices (tenfold). Historical decomposition graphs also show that the COVID-19 pandemic caused retailers and wholesalers to have higher prices, while farmers received lower prices than their predicted values. The results indicate that the U.S. beef markets were resilient enough to absorb the shocks and return to their pre-shock patterns in 4 to 6 months.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2023.106
Recommended Citation
Erol, Erdal, "THREE ESSAYS ON THE U.S. BEEF SUPPLY CHAIN: PRODUCTION, MARKETING, AND PRICE DYNAMICS" (2023). Theses and Dissertations--Agricultural Economics. 102.
https://uknowledge.uky.edu/agecon_etds/102
