Abstract Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961 to 2014. To do this, pixels are assigned into probability clusters based upon crop-specific pixel suitability (based on mean climate and soil characteristics) and gridded historical agricultural areas. PCAM maps compare relatively well with an existing gridded dataset of crop-specific areas circa 2000 (simple matching coefficient value >0.8 for all crops). PCAM estimates compare less well with time series county-level agricultural census data for the United States. Importantly, deviations between census data and PCAM benchmark estimates (driven by soil and climate suitability) can be used to infer the importance of other factors of agricultural production (e.g. labor, agricultural policy, extreme climate) in future work. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century.