Predicting crop production plays a critical role in food price forecasting and mitigating potential food shortages. Crop models may require parameters from, for example, weather, crop genotype, farm management, and soil. Sources for these data are often found in very different places. Researchers spend a significant amount of time to collect and curate them. In addition, in order to scale yield forecasts from the single-farm level up to the continental scale, crop models have to be coupled with a geospatial big data platform to provide the required data inputs. In a proof-of-concept case study, we investigate the coupling of a scalable geospatial big data platform, Physical Analytics Integrated Repository and Services (PAIRS), to the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. We envision running this system on a global scale. For geospatial analytics, PAIRS provides curation of heterogeneous data sources to simulate crop models using hundreds of terabytes of data.