Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are often derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Validation exercises show that our approach is effective at predicting cropland allocation at multiple levels of resolution. In addition, the model provides marginal effects of changes in climate and biophysical factors on cropland allocation at the pixel level that can be used in a variety of research and policy contexts.