This study uses quantile regressions to estimate historical forecast error distributions for WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence limits for the forecasts based on the empirical distributions. Quantile regressions with fit errors expressed as a function of forecast lead time are consistent with theoretical forecast variance expressions while avoiding assumptions of normality and optimality. Based on out-of-sample accuracy tests over 1995/96–2006/07, quantile regression methods produced intervals consistent with the target confidence level. Overall, this study demonstrates that empirical approaches may be used to construct accurate confidence intervals for WASDE corn, soybean, and wheat price forecasts.