Geographic determinants of rural land covers and the agricultural margin in the Central United States

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 by employing Multivariable Fractional Polynomials within a logistic regression framework. It maps the suitability distribution for corn, soybeans, spring and winter wheat, cotton, grassland, and forest, which collectively dominate the central U.S., at a 56 m 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 dependency in land cover dynamics due to crop rotation. This process also aids in the delineation of fields from pixels. When neighboring land covers are added to the models, ROC scores improve only slightly (0.014–0.019), however, indicating a weak spatial dependency or contagious diffusion 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 informs analyses of policies that affect crop prices (e.g., subsidies for crops or biofuels, changes in global supply and demand), by identifying the consequent changes in land use patterns – changes that modify the economic and environmental performance of the landscape.