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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 is the source of the vast majority of food consumed on Earth, and soils constitute the largest terrestrial carbon pool. Soil erosion associated with agriculture reduces crop productivity, and the redistribution of soil organic carbon (SOC) by erosion has potential to influence the global carbon cycle. Tillage strongly influences the erosion and redistribution of soil and SOC. However, tillage is rarely considered in predictions of soil erosion in the U.S.; hence regionwide estimates of both the current magnitude and future trends of soil redistribution by tillage are unknown. Here we use a landscape evolution model to forecast soil and SOC redistribution in the Midwestern United States over centennial timescales. We predict that present‐day rates of soil and SOC erosion are 1.1 ± 0.4 kg ⋅ m^−‐2 ⋅ yr^−‐1and 12 ± 4 g ⋅ m⁻² ⋅ yr⁻¹, respectively, but these rates will rapidly decelerate due to diffusive evolution of topography and the progressive depletion of SOC in eroding soil profiles. After 100 years, we forecast that 8.8 (+1.9/−2.1) Pg of soil and 0.17 (+0.03/−0.04) Pg of SOC will have eroded, causing the surface concentration of SOC to decrease by 4.4% (+0.9/−1.1%). Model simulations that include more widespread adoption of low‐intensity tillage (i.e., no‐till farming) determine that soil redistribution, SOC redistribution, and surficial SOC loss after 100 years would decrease by ∼95% if low‐intensity tillage is fully adopted. Our findings indicate that low‐intensity tillage could greatly decrease soil degradation and that the potential for agricultural soil erosion to influence the global carbon cycle will diminish with time due to a reduction in SOC burial. 

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. 

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.