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Abstract Erosion degrades soils and undermines agricultural productivity. For agriculture to be sustainable, soil erosion rates must be low enough to maintain fertile soil. Hence, quantifying both pre-agricultural and agricultural erosion rates is vital for determining whether farming practices are sustainable. However, there have been few measurements of pre-agricultural erosion rates in major farming areas where soils form from Pleistocene deposits. We quantified pre-agricultural erosion rates in the midwestern United States, one of the world's most productive agricultural regions. We sampled soil profiles from 14 native prairies and used in situ–produced 10Be and geochemical mass balance to calculate physical erosion rates. The median pre-agricultural erosion rate of 0.04 mm yr–1 is orders of magnitude lower than agricultural values previously measured in adjacent fields, as is a site-averaged diffusion coefficient (0.005 m2 yr–1) calculated from erosion rate and topographic curvature data. The long-term erosion rates are also one to four orders of magnitude lower than the assumed 1 mm yr–1 soil loss tolerance value assigned to these locations by the U.S. Department of Agriculture. Hence, quantifying long-term erosion rates using cosmogenic nuclides provides a means for more robustly defining rates of tolerable erosion and for developing management guidelines that promote soil sustainability. 

Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,L_p, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.