Geographic research on the Corn Belt and other regional landscapes of the central U.S. has not to date identified quantitatively the climatic, edaphic, topographic, and economic characteristics that determine rural land cover, and that therefore govern land cover change. Using the USDA/NASS Cropland Data Layer, this study identifies these characteristics using Multivariable Fractional Polynomials within a logistic regression framework. It maps the suitability distribution for corn, soybeans, spring and winter wheat, cotton, grassland, and forest land covers that dominate the central U.S., at a 56m resolution across 16 central U.S. states. The non-linear logistic regression models are successful in identifying determinants of land cover with relative operating characteristic (ROC) scores that range from 0.769 for soybeans to 0.888 for forest, with a combined corn/soybean model achieving an ROC of 0.871. For corn and soybean models, when prior land cover of a pixel is added, predictability and ROC scores increase substantially (0.07-0.10), indicating a strong temporal feedback in land cover dynamics. This process also aids in the delineation of fields from pixels. Adding neighboring land covers, however, improves predictability and ROC scores only slightly (0.014-0.019), indicating a weak spatial feedback mechanism. By including annual crop prices within the logit models, economically marginal cropland that comes into crop production only when prices are high is identified in a spatially-explicit manner. This capacity improves further analyses of economic and environmental impacts of policies that affect crop prices. The sustainability of current rural land use trends in the central U.S. is highly dependent on the ability to adapt to changing climatic conditions of the 21st century. As the climate begins to shift towards longer growing seasons, more erratic rainfall patterns, and overall warmer temperatures, there is potential for major impacts on seven major land covers of the central U.S. Suitability landscapes of individual land covers (corn, soybeans, spring and winter wheat, cotton, grasslands, and forests) were utilized to determine the influence of climate change on these landscapes. Twenty-seven climate change projection scenarios based on three global climate models, three representative concentration pathways, and three time periods were applied to the land cover suitability maps utilizing raster regression. The area now identified as the Corn Belt is projected to see a dramatic shift in the suitable climate with a potential for a 30 percent increase in summer growing degree days. While the area where conditions are suitable for corn, soybeans and spring wheat are all expected to decrease, winter wheat has the potential to increase in suitable area. In order to maintain current geographic patterns of crop production, corn would need to be adapted to higher temperatures.