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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.

Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 10⁵km²of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.