This report examines the potential for statistical forecast models to improve the performance of the U.S. Department of Agriculture’s (USDA) long-term agricultural baseline projections for harvested area for U.S. corn, soybeans, and wheat. After-the-fact analysis for years 1997 to 2017 reveals the baseline projections have, historically, consistently overestimated the harvested area of wheat and underestimated soybean area. The baseline projections also tend to underestimate the corn area, though to a lesser degree. Part of the difference between the projections and realized values is likely attributable to policy, program, weather, and other unforeseen changes when USDA developed the projections. Still, the results of quantitative forecast models show there may be substantial potential for improvement on the existing methodology. Forecasts generated using 3 econometric time-series models did not improve performance relative to the current baseline approach for nearer forecast horizons but improved performance for projection horizon lengths of 8-10, 2-10, and 4-10 years for harvested area of corn, soybeans, and wheat, respectively, when using 1 of our statistical measures. The forecasts generated using the econometric models produce predictions with an average absolute forecasting error 10 years out that is between 26 percent to 60 percent smaller than those provided by baseline projections. The results suggest that econometric models offer the potential to improve the performance of forecasting long-term trends in agricultural markets. As of 2020, USDA begun using statistical forecast models such as these when developing its long-term agricultural projections as complements to the existing process. USDA is also in the process of testing these models for additional commodities to improve the long-term projections for all commodities.