In recent years, plot specific crop models have been adapted to national and regional scales to aid policy makers with agricultural decisions concerning climate change (Mearns, Mavromatis, & Tsvetsinskaya, 1999; Southworth, et al., 2000; Jones & Thornton, 2003; Reilly, et al., 2003; Xiong, Matthews, Holman, Lin, & Xu, 2007) and the resulting effects on food security (Parry, Rosenzweig, Iglesias, Fischer, & Livermore, 1999) and future water demands (Liu, Zehnder, & Yang, 2009;Mo, Liu, Lin, & Guo, 2009). These models are often constrained by data to represent geospatially variable inputs as homogeneous data. The impact of these assumptions on model effectiveness is a function of the sensitivity of the input parameter to the model, the scale of data being aggregated, and the scale of the analysis. The impact of aggregation of geospatially variable data at the regional level is loss of calibration and validation effectiveness and thus utility for most modeling efforts (Hasen & Jones, 2000). Regional cropping systems are highly heterogeneous and model inputs should reflect as much. Considering the vast quantities of available input data, decisions must be made about desired spatial and temporal resolution, as well as the amount of generalizations that can be made about the study in question.
Inputs for the crop modeling process can be separated into three major categories: climatic, soil, and management parameters. The purpose of this document is to review current geospatial datasets available as inputs for crop modeling research at the regional scale. This review will also only focus on regional dataset applicable to the US and globe.