Unprecedented changes in commodity prices since 2004 have had worldwide repercussions, often acting as a destabilizing economic and political influence. In this paper, we use a recently developed multiple bubble testing procedures to detect and date-stamp bubbles in corn, soybean, and wheat futures markets. To account for conditional heteroskedasticity and small sample bias, inferences are derived using a recursive wild bootstrap procedure. We find that the markets experienced price explosiveness about 2% of the time. Using a logit model which accounts for bias due to the rare occurrence of an event, we find that bubbles are more likely to occur in the presence of large aggregate global demand, low stocks to use ratios, and a weak US dollar. While commodity index traders had no effect on the probability of an explosive episode, speculative activity exceeding the minimum level required to absorb hedging activities as measured by the Working’s T reduces considerably the probability of a bubble.