We forecast the realized and median realized volatility of agricultural commodities using\ud variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data\ud for five widely traded agricultural commodities (Corn, Rough Rice, Soybeans, Sugar,\ud and Wheat) from the CME/ICE. Real out-of-sample forecasts are produced for 1- up\ud to 66-days ahead. Our in-sample analysis shows that the variants of the HAR model\ud which decompose volatility measures into their continuous path and jump components and\ud incorporate leverage effects offer better fitting in the predictive regressions. However, we\ud convincingly demonstrate that such HAR extensions do not offer any superior predictive\ud ability in the out-of-sample results, since none of these extensions produce significantly\ud better forecasts compared to the simple HAR model. Our results remain robust even when\ud we evaluate them in a Value-at-Risk framework. Thus, there is no benefit by adding more\ud complexity, related to volatility decomposition or relative transformations of volatility, in\ud the forecasting models.