The extent of soil loss across the US Corn Belt

Significance

Conventional agricultural practices erode carbon-rich soils that are the foundation of agriculture. However, the magnitude of A-horizon soil loss across agricultural regions is poorly constrained, hindering the ability to assess soil degradation. Using a remote-sensing method for quantifying the absence of A-horizon soils and the relationship between soil loss and topography, we find that A-horizon soil has been eroded from roughly one-third of the midwestern US Corn Belt, whereas prior estimates indicated none of the Corn Belt region has lost A-horizon soils. The loss of A-horizon soil has removed 1.4 ± 0.5 Pg of carbon from hillslopes, reducing crop yields in the study area by ∼6% and resulting in $2.8 ± $0.9 billion in annual economic losses.

Abstract

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 × 10⁵ km² of 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.

Productive agricultural soils are vital for producing food for a growing global population (1–3). However, degradation of soil quality by erosion reduces crop yields, which can result in food insecurity, conflict (3), and the decline of civilizations (4). Degradation of soils leads not only to economic losses for farmers but also a loss in ecosystem services (5), which alters the ability of soils to regulate hydrologic and biogeochemical cycles. Widespread use of synthetic fertilizers to enhance the function of degraded soils increases food production costs (6) and impairs water resources (7), which negatively impacts human health (8) and aquatic ecosystems (9).

Globally, the reservoir of carbon stored in soils is three times that in the atmosphere (10) and given the extent of agricultural land use (11), understanding soil carbon dynamics in agricultural systems is critical to understanding the carbon cycle (12). Whether soil erosion constitutes a net carbon sink or source depends on both the depositional fate of the eroded carbon and the ability to replace carbon in degraded soils (13–15). If biological productivity replaces eroded carbon, and decomposition of carbon stored in sedimentary deposits is halted or slowed, then soil erosion is a net sink of atmospheric carbon (14–17). However, if eroded carbon rapidly decomposes and is not replaced in eroded soil horizons, then soil erosion constitutes a carbon source. Restoring carbon to degraded soils therefore has potential to both reestablish soil function and sequester atmospheric CO₂ (10). However, quantifying the impacts of soil degradation on agricultural productivity and the carbon cycle first requires robust estimates of the magnitude of agriculturally induced soil loss (14, 16).

Although thousands of soil erosion measurements have been made globally (18), the lack of a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes remains a major gap in soil erosion research (19). Large-scale assessments of soil erosion are often based on model predictions (20–22) or qualitative information from soil surveys regarding the degree of soil degradation (23). In the United States, for example, nationwide soil loss trends (24) are simulated using water and wind erosion models that have been calibrated with erosion measurements made on small plots over a period of decades (21, 25). It has been debated whether upscaling such predictions to regional or national scales results in an accurate assessment of the current magnitude of soil loss in the United States (26, 27). Whereas such models are useful for assessing relative rates of erosion for soil conservation planning, the soil loss predictions do not provide information regarding the cumulative soil loss that has occurred since the initiation of cultivation, and hence the overall magnitude of agricultural soil degradation.

To assess the degree of cumulative soil degradation, soil surveys conducted by the US Department of Agriculture have assigned erosion classes to soils based on the percentage of the original A horizon that has been eroded (28). Because the A horizon has the largest fraction of soil organic carbon within the soil profile, it is a key component of water and nutrient retention and soil productivity (29). Soils where 100% of the A-horizon thickness has been removed are designated as Class 4 eroded soils, and other classes represent lesser reductions in A-horizon thickness (<25%, 25 to 75%, and >75% for Classes 1, 2, and 3, respectively). A major disadvantage of the use of erosion classes is that properly assigning classes based on the percentage of A-horizon loss requires accurate determination of the original A-horizon thickness on all topographic positions (30). Hence, although soil erosion classes indicate soil degradation is widespread (23) we do not have a robust, quantitative understanding of how much soil has already been lost.

Here we present results from a remote sensing method used to estimate the spatial extent of agriculturally induced loss of A-horizon soil for a major global agricultural region, the Corn Belt of the midwestern United States. Rather than simulate or measure short-term soil loss rates, we combine measurements of soil-surface reflectance in the visible spectrum (soil color) with high-resolution satellite imagery to directly measure the proportion of the agriculturally cultivated landscape that has completely lost its original A horizon. Combining our spectral analysis with relationships between A-horizon soil loss and topography derived from high-resolution LiDAR topographic data allows us to predict A-horizon soil loss in areas where images are not available. We find that historical soil erosion has completely removed A-horizon soil from approximately one-third of the Corn Belt. The spatial patterns of soil loss suggest that key erosion mechanisms are not simulated in nationwide assessments of soil erosion trends in the United States and that soil survey data greatly underestimate the extent of A-horizon loss.

The Corn Belt Region of the Midwestern United States

Our study focuses on the midwestern United States, on a ∼390,000-km² region that encompasses much of the area colloquially known as the Corn Belt (Fig. 1). The region was glaciated repeatedly during the Pleistocene, with the exception of the Driftless Area (31). The most recent ice sheet advance deposited glacial till in the northern part of the Corn Belt, whereas older glacial deposits to the south are mantled with loess (31). Prior to European settlement in the mid to late 19th century, the vegetation was primarily tallgrass prairie with some savanna and woodlands (32), and mollisols are the dominant soil order in the region (33). The native prairie vegetation fostered the accumulation of thick A-horizon soils (SI Appendix, Fig. S1 and Table S1). In the decades following European settlement, the prairie was plowed, and the landscape was rapidly and extensively converted to row-crop agriculture. For example, in Iowa, Indiana, and Illinois, less than 0.1% of the original tallgrass prairie remains (32). The fertile soils and temperate climate make the Corn Belt one of the world’s most agriculturally productive regions. The United States is the world’s largest producer of corn and soybeans (34), and 75% of the corn and 60% of the soybeans produced in the United States are grown in the Corn Belt (35).

Fig. 1.

Study area in the midwestern United States. The study area is defined by the spatial extent of LiDAR topographic data (gray hillshade). The squares show the locations of 28 sites where 210 km² of imagery from plowed agricultural fields with bare soil was analyzed. The open square indicates the location of the field shown in Fig. 2. The glacial extent prior to the last glacial maximum (LGM) and the extent during the LGM are shown as dashed and solid lines, respectively.

Despite the importance of the region’s agricultural productivity, model predictions indicate the Corn Belt currently has the highest soil erosion rates in the United States (24). The historical magnitude of A-horizon soil loss from the initiation of agriculture to the present is unknown, but prior work in Iowa and Minnesota noted that in some areas the magnitude of soil erosion has been great enough to completely remove dark, carbon-rich A-horizon soil, exposing light-colored B-horizon soil that is poor in organic carbon (36, 37).

A-Horizon Soil Loss in Individual Fields

We combined high-resolution satellite imagery, a newly developed soil organic carbon index (38), and soil spectral data from the US Department of Agriculture’s Rapid Carbon Assessment to develop a logistic regression to differentiate between A- and B-horizon soils exposed in plowed fields. The extent of historical soil loss was measured by the absence of the A horizon, or inversely by the presence of B-horizon soils, which underlie the A horizon. An example field where we applied our method is shown in Fig. 2, where pixels of A- and B-horizon soil are distinguished using the soil organic carbon index (Fig. 2A), and topographic curvature is calculated for each pixel in the field (Fig. 2B). Within the field, A-horizon soil has been completely removed from 34 ± 7% (±1 SD) of the area (Fig. 2C). The fraction of pixels classified as B-horizon soil is highest on convex slopes, indicating topography exerts a strong influence on the spatial distribution of A-horizon loss within the field (Fig. 2D).

Fig. 2.

Calculation of A-horizon loss and topographic curvature. (A) True-color image of a field near Clear Lake, IA (open square in Fig. 1); black polygons surround light-colored areas predicted to have exposed B-horizon soil. Gray polygons indicate ±1 SD of the predicted area of B-horizon exposure. (B) Topographic curvature map where red pixels denote negative curvature (convex hillslopes) and blue pixels denote positive curvature (concave hillslopes). (C) Cumulative fraction of area with curvature values for the entire field (black line) and curvature values for the area with exposed B-horizon soil (gray line); 34 ± 7% of the area is exposed B horizon soil. Gray shading shows the ±1 SD prediction of B-horizon exposure. (D) Mean (±1 SD) of the fraction of pixels with a spectral signature of B-horizon soils versus topographic curvature, shaded by median topographic slope per curvature bin, which shows that exposure of B-horizon soil occurs predominantly on convex topography. (Inset) The probability density of topographic slope values for A-horizon pixels and for B-horizon pixels; gray shading shows the ±1 SD prediction of B-horizon exposure. Image credit: ©2013, DigitalGlobe; NextView License/Maxar, Inc.

In 210 km² of agricultural fields across 28 locations, the soil organic carbon index values indicate the mean extent of the agricultural land area with complete A-horizon loss is 34 ± 7% (Fig. 3A). These A-horizon soil loss values are minimum estimates because of the potential for soil moisture to cause misclassification of soil horizons (SI Appendix). At all 28 sites, hillslope topography strongly controls the location of soil loss. B-horizon exposure occurs disproportionately on convex topography, which we quantitatively define as areas with topographic curvature values <0 m⁻¹. Such areas make up only 50% of the area of the fields we analyzed but are the site of 68 ± 9% of the exposed B-horizon soil. On hillslopes with the most convex topography (curvature <−0.02 m⁻¹), 74 ± 8% of the land area has soil organic carbon index values indicative of exposed B-horizon soils. The proportion of the cultivated landscape with complete A-horizon loss decreases to 23 ± 5% for straight slopes (curvature = 0), and 39 ± 8% of the land area on concave topography (curvature > 0 m⁻¹) bears the spectral signature of B-horizon soils (Fig. 3B).

Fig. 3.

Regional-scale A-horizon loss estimate for the Corn Belt. (A) Cumulative distribution of topographic curvature for all (combined A- and B-horizon) soils (black line) and B-horizon soils (gray line) for the 210 km² of fields that were analyzed, which indicates 34 ± 7% of the soil exposed in fields is B-horizon; gray shading shows the ±1 SD prediction of B-horizon exposure. (B) Mean (±1 SD) of the fraction of pixels with a spectral signature of B-horizon soils versus topographic curvature for the 210 km² of fields that were analyzed. Across all sites, convex topography (negative curvature) has experienced the highest proportion of B-horizon exposure and A-horizon loss. Symbol color denotes the median slope angle for each curvature value. (Inset) The probability density of topographic slope values for A-horizon pixels and for B-horizon pixels; gray shading shows the ±1 SD prediction of B-horizon exposure. (C) Cumulative distribution of area as a function of topographic curvature for the entire Corn Belt study area (solid line) and for B-horizon soils (dashed line); gray shading shows the ±1 SD prediction of B-horizon exposure. Our calculation indicates 35 ± 11% of the landscape has completely lost A-horizon soil to erosion, exposing underlying B-horizon soil.

The most convex and concave portions of the cultivated landscape tend to have the greatest proportion of soil loss, due to an association of steeper slopes (3.5 to 4.1°) with highly convex and concave topography (Fig. 3B). However, most of the cumulative A-horizon loss occurs on more modestly sloping topography; 84 ± 2% of the B-horizon exposure occurs in areas with curvature values between −0.02 m⁻¹ and 0.02 m⁻¹, where the mean slope is 2.2°. These results indicate hillslope summit and shoulder positions are prominent locations of soil loss. The implications of the topographic distribution of soil loss for inferring soil erosion mechanisms are discussed below.

The Region-Wide Extent of A-Horizon Soil Loss

Based on the proportion of B-horizon exposure for a given value of topographic curvature for the 210 km² of fields with available satellite imagery (Fig. 3B) and 3.9 × 10⁵ km² of topographic curvature data that span the region, we estimate that A-horizon soil has been completely removed from 35 ± 11%, or 132,738 ± 46,849 km², of the Corn Belt (Fig. 3C). Within the Corn Belt, convex topography occupies roughly 70% of the cultivated landscape (∼273,000 km²), and 68% of the area predicted to no longer have A horizon (∼90,000 ± 33,000 km²) occurs on that convex topography, whereas the remaining 32% (∼42,000 ± 15,000 km²) occurs on concave topography.

The proportion of the study area within each Corn Belt state predicted to have exposed B-horizon soil ranges from 30 ± 10 to 41 ± 13% (Fig. 4A), and county-level estimates range from 24 ± 8 to 47 ± 14% (Fig. 4B). Glacial history influences the extent of soil loss, with greater loss predicted for older, now loess-covered glaciated areas where drainage networks and associated ridge and valley systems are more developed. The extent of A-horizon loss is lower in areas that were covered with ice during the last glaciation because drainage networks are more poorly developed and ridge–valley systems are less established (39).

Fig. 4.

A-horizon loss and associated economic losses within the Corn Belt. (A) Aggregated percent A-horizon loss (black text) and annual economic losses expressed in millions of dollars (M) and as a percentage relative to uneroded soils (green text) for each state within the region (color scale is the same as in B). (B) Percentage of A-horizon loss for each county. (C) Percent annual economic losses for each county relative to predictions for uneroded soils. (D) Predicted mean annual economic losses per farm in each county. The LGM and pre-LGM glacial extent, which define the distribution of glacial till- and loess-derived soils, are shown as solid and dashed lines, respectively.

Our method predicts the land area with complete A-horizon loss, rather than soil erosion rates, so our results cannot be directly compared against regional soil erosion rates modeled using the Revised Universal Soil Loss Equation and the Wind Erosion Equation (24). However, the Class-4 erosion class category from soil surveys (28) is equivalent to the complete loss of A horizon measured by our analysis. US Department of Agriculture soil survey data indicate that none (0%) of our study area has soils with Class-4 erosion or complete A-horizon loss (SI Appendix, Fig. S2) (28). However, we predict the A-horizon has been completely removed from 35 ± 11% of the cultivated area of the Corn Belt. Hence, our results suggest that prior assessment of soil degradation based on erosion classes may have greatly underestimated the extent of A-horizon loss, and therefore the thickness or mass of soil that has been eroded from hillslopes in the Corn Belt.

Soil Loss Mechanisms

Our results indicate that A-horizon soil has been stripped from hilltops and hillslopes. Although our remote sensing method cannot detect soil deposition, prior work indicates that much of the eroded soil has accumulated in topographic concavities (40, 41), though some is ultimately transported out of fields by water erosion (42). Hence, topographic concavities tend to have thicker A horizons, higher soil organic carbon concentrations, and higher crop yields than eroded hilltop summits and shoulder positions (41). Although water erosion contributes to soil loss throughout the cultivated landscape, our observation of widespread loss of soil from low-gradient, convex hilltops and deposition in topographic concavities at the base of hillslopes suggests tillage erosion is also an important driver of soil transport in the Corn Belt.

Tillage erosion is the net downslope movement of soil by repeated tillage operations, such as plowing (43). Soil transport by tillage causes diffusion-like evolution of topography, resulting in erosion of soil from topographic convexities and deposition in concavities (40). The effect of tillage on soil transport can be described with a diffusion-like coefficient that integrates tillage direction, depth, and soil physical properties (44), and measured diffusion coefficients for tillage range from 0.03 to 0.52 m²⋅y⁻¹ (44). Although contour plowing is a common strategy to mitigate soil erosion by water, measured diffusion coefficients for contour plowing still range from 0.03 to 0.2 m²⋅y⁻¹ (44). Such values are one to three orders of magnitude greater than diffusion coefficients measured in nonagricultural settings (45). Hence, order-of-magnitude increases in topographic diffusion due to plow- and tillage-based agriculture provides a mechanistic explanation for the extensive loss of soil from hilltops, especially where the lack of upslope flow accumulation area limits the potential for water erosion by overland flow. The observation that the fraction of a landscape without A-horizon soil increases with increasing topographic convexity is also consistent with a diffusive, tillage erosion soil transport process.

Prior work has shown that whereas water and tillage both contribute to soil transport, erosion on upland convex hilltops is dominated by tillage, and erosion by water tends to be dominant in areas with steep, concave slopes (46). Our finding that B-horizon exposure increases with slope for concave topography is consistent with previous work indicating that water erosion is dominant in such landscape positions. About 30% of the observed A-horizon loss has occurred on concave topography where water erosion is expected to dominate soil loss. The remaining ∼70% of landscape with B-horizon exposure occurs on convex hilltops and slopes, where tillage is expected to be a more important mechanism of soil loss than erosion by water (46). Although it has been demonstrated that models that do not simulate tillage erosion underpredict the total magnitude of soil loss (43), tillage erosion is not incorporated into nationwide assessments of soil erosion in the United States (24). Our analysis does not discount the need to quantify and model soil erosion by water but highlights that tillage erosion is an important contributor to widespread removal of soil from hilltops in the Corn Belt that warrants greater recognition in soil erosion prediction and soil conservation efforts in the United States.

Adoption of no-till agriculture greatly reduces soil erosion rates (2) and effectively eliminates tillage erosion. However, less than 15% of the acreage of the upper Mississippi River watershed, the heart of the Corn Belt, is farmed with no-till practices for at least three consecutive years (47). Similarly, nationwide, only 21% of corn, soybean, cotton, and wheat fields are continuously farmed with no- or strip-till practices (48). Hence, widespread adoption of no-till farming methods offers a strategy for preventing further soil loss.

Economic and Soil Organic Carbon Losses due to Erosion of A-Horizon Soil

Using county-level harvest data for corn and soybeans, and crop yield reductions associated with severely eroded soils (37), we estimate that loss of A-horizon soils decreases region-wide crop yields by 6 ± 2%, leading to $2.8 ± $0.9 billion in annual losses across the Corn Belt. Mean annual crop yield decreases, relative to yields from undegraded soils, for each state in the region range from 3 ± 1% to 8 ± 3%, resulting in annual losses of $49 ± $16 million to $793 ± $262 million. The mean crop yield reductions for each county range from 2 ± 1% to 9 ± 3%, equating to annual economic losses of $0.1 ± 0.04 million to $32 ± $11 million (Fig. 4C). The average county-level yield reductions per farm range from 2 ± 1% to 9 ± 3%, leading to losses of $300 ± $100 to $40,000 ± $14,000 (Fig. 4D), and varies as a function of regional differences in soil parent material, crop yields, and the average farm size per county, which ranges from 10 to 718 ha. Because our analysis only identifies areas where the A horizon has been completely removed and not areas where the A horizon has been thinned, which also reduces crop yields (37), our estimates of economic losses are minimum values. Fertilizer is widely applied to degraded soils in the Corn Belt, though it does not restore crop yields to levels measured in noneroded soils (29). Our analysis of economic losses does not account for the cost of fertilizer inputs required to raise crop yields in degraded soils, but others have indicated overfertilization of low-yielding areas in the Midwest alone costs nearly $0.5 billion a year (49).

Global-scale assessments of the influence of soil erosion on the carbon cycle are commonly based on modeled predictions of the extent of soil degradation, which have considerable uncertainty (50). Our remote sensing method provides a means for quantifying the land area of degraded soil. Our method relies on the strong color contrast between the A and B horizons of mollisols but in principle can be applied to other agricultural regions with spectrally distinct soil horizons. In the Corn Belt, we estimate that the regional extent of A-horizon erosion has removed 1.4 ± 0.5 Pg C (10 ± 2 Gg C⋅km⁻²) from hilltops and hillslopes. Within fields (40, 41, 51) and fluvial systems (52, 53) in the Corn Belt there is evidence of widespread storage of the soils that have eroded since European settlement. Due to the greater land area, carbon preservation potential is higher in the hummocky topography of the recently glaciated portion of the Corn Belt, where we estimate erosion of A-horizon soils has removed 0.80 ± 0.3 Pg C, whereas 0.6 ± 0.2 Pg C is predicted to have been lost from the area glaciated earlier in the Pleistocene. Burial of these carbon-rich sediments can act as a carbon sink on timescales of decades to centuries (13), and because the initiation of soil erosion in midwestern United States was relatively recent there is a high potential for the landscape to preserve carbon in young sedimentary deposits (54). Hence, changes in land use (55, 56) or adoption of farming practices (57, 58) that increase soil organic carbon concentrations in areas that have lost A-horizon soils may generate a net sink for atmospheric CO₂.

Methods

Study Area and Topographic Data.

The availability of LiDAR-derived digital elevation models (DEMs) dictated the specific extent of the area of the glaciated, former tallgrass prairie region we analyzed. The LiDAR data were clipped to the Herbaceous Agricultural Vegetation layer from the US Geological Survey Gap Analysis Program (59), so that the analysis excluded areas with nonagricultural land use. Topographic slope and curvature were calculated as the first and second derivatives of elevation, respectively, using a 4-m-resolution DEM (SI Appendix, Fig. S3). Details of the topographic analysis are described in SI Appendix.

Differentiating between A- and B-Horizon Soils Using Satellite Imagery.

We used the National Geospatial-Intelligence Agency catalog (60) to identify 28 GeoEye-1, Quickbird-2, WorldView-2, and WorldView-3 satellite images showing plowed fields with exposed bare soil (SI Appendix, Fig. S4). We analyzed 759 individual cropland fields with a total area of 210 km². An example of a study site, which is made up of multiple fields, is shown in SI Appendix, Fig. S5. The spatial distribution of those fields within the study area was primarily dictated by the availability of imagery with plowed fields, as soil horizons could not be distinguished in fields with no-till or conservation tillage practices that left organic carbon-bearing crop residue exposed at the ground surface. Details of the image preprocessing are described in SI Appendix. For each of the 28 images, we calculated the soil organic carbon index [SOCI = ρ_Blue/(ρ_Green·ρ_Red)], where ρ is spectral reflectance in the blue, green, and red bands, respectively (38). We demonstrated the validity of the SOCI by examining the relationship between the SOCI and measured soil organic carbon values at five sites (four within the study area), and the R² for the correlation ranges from 0.63 to 0.72 (SI Appendix, Fig. S6).

The Rapid Carbon Assessment (RaCA), undertaken by the Soil Science Division of the US Department of Agriculture National Resource Conservation Service, collected soil samples at 6,148 sites in the conterminous United States (61). For each sample, the soil horizon was designated and hyperspectral reflectance was measured. Using the laboratory hyperspectral reflectance measurements, we calculated the SOCI at 478 nm (blue), 546 nm (green), and 659 nm (red) for each of the RaCA samples. The RaCA-derived SOCI values are offset from the satellite-derived SOCI values due to atmospheric effects and imperfect radiometric calibration. Hence, the RaCA SOCI values were scaled to the same range as based on a regression relationship between the satellite-derived and laboratory-measured SOCI values (38).

We evaluated the extent of A- and B-horizon exposure in each image by calculating the probability that a pixel has a SOCI signature of B-horizon soil. For each study site, we performed a bootstrapped logistic regression with 500 iterations using the sci-kit-learn module in Python 3.6 to determine the probability that a pixel from a satellite image with a given SOCI value has a B-horizon spectral signature. The soil horizon and SOCI data used in the site-specific logistic regressions were from samples in the RaCA database that were collected within a 50-km radius of individual study sites. We trained the logistic regression model using 20% of the SOCI values for the A- and B-horizon soils. We then tested the power of the logistic regression by using the derived probability function to predict the soil horizon for the remaining 80% of soil samples based on the SOCI value of a sample. To quantify the error in the predicted B-horizon exposure probability values, we generated a probability density function of B-horizon exposure using results from each of the 500 iterations of the logistic regression fit, each based on a different random selection of the 20% of the data used for training (SI Appendix, Fig. S7). We further assessed the validity of the logistic regression model by plotting the receiver operator characteristic curve and calculating the area under the curve (AUC) for each site. An AUC value of 0.5 indicates the logistic regression cannot distinguish between classes, and an AUC value of 1.0 indicates the regression perfectly distinguishes between classes. The AUC values for the classification performed in this study range from 0.52 ± 0.15 to 0.96 ± 0.04, and the mean true-positive classification of samples is 88 ± 8%, indicating a high true-positive classification of soil horizon based on the SOCI values (SI Appendix, Fig. S8). The probability functions derived from the bootstrapped logistic regressions were applied to the SOCI raster calculated for each image, and the mean percentage ±1 SD of exposed B-horizon was calculated from the fraction of pixels with ≥ 50% probability of classification as B horizon.

Effect of Moisture on the SOCI and Soil Horizon Classification.

Soil moisture causes soils to appear darker and can obscure the spectral signature of soil organic carbon (62). We performed two analyses to assess the potential impact of soil moisture on our estimation of A-horizon soil loss. We evaluated whether soil moisture influenced the spectral reflectance of soils in the images that we used, then we estimated the potential magnitude of a soil moisture impact on our analyses.

The soil surface, which is imaged by satellites, has been shown to become completely dry after 3 to 4 d after rainfall (63). To assess the surface soil moisture condition when the images we used were acquired, we analyzed precipitation data from National Oceanic and Atmospheric Administration weather stations nearest to each of our 28 sites. We found that the minimum time between image acquisition and prior rainfall was 20 h, with a mean of 73 h, and the mean magnitude of precipitation for all events was 10 mm (SI Appendix, Fig. S9). These results suggest that the soil surface would have been dry when the images were acquired. A more detailed description of the precipitation analysis is in SI Appendix.

To determine the influence of soil moisture on the SOCI and the potential impact on the differentiation of soil horizons from spectral data we conducted a laboratory experiment to measure the spectral reflectance of 26 soil samples with a range of soil organic carbon concentrations collected from the Corn Belt. The reflectance was measured when the samples were dry and again when the samples were saturated with moisture. When moisture is added to the soils, the largest increase in the SOCI occurs for samples with the greatest soil organic carbon concentration (SI Appendix, Fig. S10). We used the relationship between soil organic carbon and the maximum change in SOCI due to moisture saturation to simulate the effect of soil moisture on the threshold distinguishing A- and B-horizon SOCI values. We scaled the RaCA SOCI values by the percent change in the SOCI due to moisture saturation. For each of the 28 sites, our analysis indicates that any addition of moisture to the RaCA samples increases the threshold SOCI value that distinguishes between A and B horizons (SI Appendix, Fig. S11). When the threshold that accounts for SOCI changes due to addition of soil moisture is applied to the image where the SOCI values have not been adjusted for moisture, a higher fraction of pixels are classified as B horizon, relative to the threshold based on dry calibration samples (SI Appendix, Fig. S12). Because the simulated effect of soil moisture consistently results in an increase in the fraction the pixels predicted to have B-horizon soils, our estimate of A-horizon loss, which is based on dry calibration samples, is a minimum. A detailed description of the moisture experiment and sensitivity analysis is given in SI Appendix.

Upscaling Soil Loss Estimates to the Corn Belt Area Using Topographic Curvature.

Because high-resolution satellite imagery for plowed fields in the Midwest is limited by both spatial coverage and by seasonal crop, snow, and cloud cover, region-wide estimates of B-horizon exposure and A-horizon loss based solely on high-resolution satellite imagery is not possible. However, analysis at four sites throughout the Corn Belt (SI Appendix, Figs. S13 and S14) indicates that satellite-derived SOCI is related to topographic curvature, where pixels with low SOCI values are observed on topographic convexities and high SOCI values are located in topographic concavities (SI Appendix, Fig. S14). Hence, we use the relationship between B-horizon exposure and topographic curvature from the 210 km² of analyzed fields to upscale our estimate of soil loss to the entire Corn Belt region. We extracted the SOCI and topographic curvature values from colocated pixels within each of the 28 study sites. Using data from all 210 km² of fields, we calculated the fraction of area with SOCI values diagnostic of exposed B-horizon soil. We treated each study site (each made up of 6 to 109 fields) as an individual measurement and calculated the mean and one SD of B-horizon exposure as a function of curvature for the 28 sites. The relationship between soil loss and curvature (Fig. 3B), which includes uncertainty in B-horizon pixel classification from the bootstrapped logistic regression, was used to calculate the mean and SD of the land area with curvature values indicative of B-horizon soil exposure. Pixels classified as B-horizon soil are disproportionately located where curvature values are <−0.02 m⁻¹. Compared to the analyzed fields, the full Corn Belt study area has a slightly larger fraction of curvature values between −0.02 m⁻¹ and 0.02 m⁻¹ (SI Appendix, Fig. S15), where most erosion is predicted to occur (Fig. 3A). Hence, the estimated percentage of the Corn Belt with B-horizon exposure is slightly higher than the B-horizon exposure determined for the analyzed fields.

Calculation of Economic Losses.

Our estimate of the magnitude of annual economic losses incurred from the loss of the A-horizon relies on the similarity between yield reductions reported for corn and soybeans in severely eroded soils (64). Corn yields were previously evaluated at 569 sites in 44 counties in Iowa on both glacial till and loess soil parent materials, where categorical measurements of soil erosion were also evaluated (37). In severely eroded soils, where the A-horizon was completely removed, corn yields decreased by 137,300 kg⋅km⁻² in soils derived from glacial till, whereas yields decreased by 67,100 kg⋅km⁻² in loess-derived soils. We estimated the total area of loess-derived soils by assuming that all soils south of the last glacial maximum (LGM) ice limit are derived from loess, and the areas north of the LGM ice limit have soils formed from glacial till; soils in the Driftless Area were classified as loess-derived. We calculate that there are 235,632 km² and 154,775 km² of glacial till- and loess-derived soils in our study area, respectively. The area of till-derived soils that no longer has A-horizon is estimated to be 80,114 ± 28,275 km² and the area of loess-derived soils with no A-horizon is estimated to be 52,623 ± 18,573 km². Based on the reductions in corn and soybean yields due to complete A-horizon loss for each parent material, predicted areas of A-horizon loss (mean loss ±1 SD), hectares planted of corn and soybeans (SI Appendix, Fig. S16), and the average corn and soybean prices from 2012 to 2017, we estimate mean economic losses for each county. The uncertainties we report are based on the 1 SD uncertainties in the percentage of A-horizon soil loss for each county. We estimate total loss with the glacial till-derived soils to be $1,683 ± $575 million and losses from loess-derived soils to be $762 ± $246 million. The mean per-farm economic loss for glacial till-derived soils is $15,200 ± $5,200 and is $5,900 ± $1,900 for loess-derived soils.

Estimation of Soil Organic Carbon Erosion.

To estimate the magnitude of soil organic carbon erosion, we multiplied the mean value of carbon stocks (10.5 billion g C⋅km⁻²) in the upper 30 cm of soil samples collected on native prairie hillslopes (50) from locations with convex topography (curvature values between −0.01⁻¹ and −0.09 m⁻¹) by the area of A-horizon loss. We also compiled published measurements of A-horizon thickness for native tallgrass prairies within our study area (SI Appendix, Table S1). The mean A-horizon thickness was 37 cm, indicating a carbon stock value measured to 30 cm depth reasonably approximates the A-horizon carbon stock. The loss of 30 cm of soil is also consistent with our spectral measurements of soils on convex hilltops in a prairie and adjacent field in Iowa (SI Appendix, Fig. S1).

Data Availability

Data are cataloged at the Oak Ridge National Laboratory Distributed Active Archive Center (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1774) (65). The archive includes spatial raster data for topographic metrics (elevation, slope, and curvature), soil organic carbon index values, and the probability of B-horizon soil. Spatial vector data and tabular data with county-, state-, and farm-level erosion and economic loss values are also archived. The soil organic carbon index values derived from the RaCA samples and used to develop the logistic regression and receiver operator characteristic curve for each site (such as those shown in SI Appendix, Fig. S8) are also archived.