Over the last decade, U.S. crop prices have become significantly more volatile. Volatile markets pose increased risks for the agricultural market participants and create a need for reliable price forecasts. Research discussed in this paper aims to find different approaches to forecast crop cash prices based on the prices of related futures contracts. Corn, soybeans, soft red winter wheat, and cotton are the focus of this research. Since price data for these commodities is non-stationary, this paper used two approaches to solve this problem. The first approach is to forecast the difference in prices between current and future period and the second is to use the regimes. This paper considers the five-year moving average approach as the benchmark when comparing these approaches. This research evaluated model performance using R-squared, mean errors, root mean squared errors, the modified Diebold-Mariano test, and the encompassing test. The results show that both the difference model and the regime model render better performance than the benchmark in most cases, but without a significant difference between each other. Based on these findings, the regime model was used to make forecasts of the cash prices of corn and soybeans, the difference model was used to make predictions for cotton, and the benchmark was used to forecast the SRW cash price. / Master of Science