The behavior of agricultural commodity markets can arguably result in markedly asymmetric price cycles, that is, downward cycles of substantially different length and breadth than upward cycles. This study assesses whether asymmetric-cycle models can enhance the understanding of the dynamics and provide for a better forecasting of U.S. soybeans and Brazilian coffee prices. The forecasts from asymmetric cycle models are found to be substantially mode precise than those obtained from standard autoregressive models. The asymmetric cycle models also provide useful insights on the markedly different dynamics of the upward versus the downward cycles exhibited by the prices of these two commodities.