The last two farm bills have used moving averages and Olympic moving averages in computing revenue benchmarks and hence payments in the Average Crop Revenue Election (ACRE) program in the Food, Conservation, and Energy Act of 2008 and its more recent version, the Agricultural Risk Coverage (ARC) program in the Agricultural Act of 2014. Accurate revenue forecasting is important to farmers and agribusiness managers because of the variety of risks associated with farming including price and yield variability, which are often negatively correlated. This paper therefore assesses the performance of various speci_x000C_cations of simple and Olympic moving averages in forecasting U.S. crop year revenue for the program crops of corn, soybeans, wheat, rice, and sorghum over the 1974 through 2013 crop years. In general, forecast error is found to be lower for the moving average than for the Olympic moving average technique. It was also generally lower for the technique of forecasting revenue directly than for forecasting separating the price and yield components of revenue. Last, forecast error was generally smaller for calculation windows smaller than the 5 years used as the underlying method by the ARC farm support program.