Considerable research effort has focused on the forecasting of asset return volatility. Debate in this area centers around the performance of time series models, in particular GARCH, relative to implied volatility from observed option premiums. Existing literature suggests that the performance of any volatility forecast is sensitive to both the data and forecast horizon of interest. This paper rigorously examines the performance of several alternative volatility forecasts for fed cattle, feeder cattle, and corn cash price returns. Forecasts include time series, implied volatility, and composite specifications. The results provide considerable insight into the performance of these alternative volatility forecasting procedures over a range of relevant forecast horizons. The evidence suggests that composite methods be used when both time series and implied volatilities are available. Insight is also gained into the performance of procedures used for scaling 1-period volatility forecasts to longer horizons. However, consistent with the existing volatility forecasting literature, this research confirms the difficulty in finding a "best" volatility forecasting method across alternative data sets and horizons.