Model Performance and Predictors.

Models explaining SOC density distribution as a function of depth, climate, relief, lithology, land cover, and land use were evaluated using fivefold cross-validation with repeated fitting. For the 0- to 2-m depth interval, R-square was 54% with a root mean square error (RMSE) of 12 kg C⋅m⁻³ based on random forest model, and 45% with a RMSE of 14 kg C⋅m⁻³ based on the gradient boosting model. Correlation plots indicated that the ensemble model for the 0- to 2-m depth interval was unbiased (no overestimation/underestimation) over the whole span of values. Model errors were lowest where sample density was high, and were greatest in regions with sparse data and with high SOC values (SI Appendix, Fig. S4).

While climate and topographic attributes were the most important variables in both models (SI Appendix, Fig. S3), multiple land-use variables have been shown to be also important for explaining the distribution of current SOC. Individual correlation plots for the land-use variables generally decline with increasing intensity of land use within a pixel (SI Appendix, Fig. S3). The relatively high importance of the HYDE land use classes “grazing” and “cropland” in our SOC models validates our conceptual approach to estimating historic SOC stocks in the absence of human land use.

Current SOC Stocks.

Projecting our data-driven statistical model across the globe for the year 2010 suggested that global SOC stocks were 863 Pg C, 1,824 Pg C, and 3,012 Pg C in the upper 0.3 m, 1 m, and 2 m of soil, respectively (SI Appendix, Fig. S5A and Table S3). On a per hectare basis, land classified as cropland [International Geosphere-Biosphere Programme (IGBP) class 12 (24)] contained an average of 62 [31 to 96, 95% confidence intervals (CI)], 127 (61 to 200, 95% CI), and 198 (96 to 315, 95% CI) Mg C⋅ha⁻¹ for the same depth increments. The global total SOC stocks and depth distribution are consistent with most recent estimates of this quantity using a number of approaches (25, 26). This agreement is particularly encouraging because we excluded remote sensing data products characterizing current vegetation, which has been found to be particularly important in explaining SOC distribution (27), because of the requirement to be able to project this model back in time.

Historic SOC Stocks.

Reprojection of our SOC model to a no land-use (NoLU) condition with all other variables held constant resulted in global SOC stocks of 899 Pg C, 1,899 Pg C, and 3144 Pg C in the upper 0.3 m, 1 m, and 2 m of soil, respectively (SI Appendix, Fig. S5B and Table S3), suggesting that human-driven land-use decisions have resulted in substantial reductions in global SOC levels. In the absence of accurate SOC data from past millenia, we have attempted to assess the accuracy of these historic projections by comparing the modeled NoLU SOC stocks to SOC measurements taken in remnant patches of native vegetation which were compiled from the literature (SI Appendix, Fig. S6). Given the limitations of this comparison between point measurements and 10-km model output, model results for NoLU SOC compared favorably to the measured literature values (R-square = 0.33 to 0.34, with RMSE values of 17 Mg C⋅ha⁻¹ and 37 Mg C⋅ha⁻¹ for 0.3- and 1-m depths, respectively).

SOC Loss Due to Land Use.

Subtracting current (2010) SOC stocks from historic (NoLU) SOC stocks, we found that 37 Pg C, 75 Pg C, and 133 Pg C have been lost due to land-use change in the upper 0.3 m, 1 m, and 2 m of soil (Fig. 1B and SI Appendix, Table S3). The mean absolute loss due to land use to 2 m across all pixels with some degree of land use was 17.7 Mg C⋅ha⁻¹ (−22.7 to 54.0, 95% CI). As a percentage of initial SOC stocks (SI Appendix, Fig. S7), this represents a mean loss of 8.1% (−3.9 to 21.8%, 95% CI). While SOC was lost throughout the soil profile, consistent with findings from experimental studies (14), there was an exponential decline in loss with increasing depth (SI Appendix, Fig. S8).

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Fig. 1.

Global distribution of cropping and grazing in 2010 from (A) HYDE v3.2 and (B) modeled SOC change in the top 2 m. In A, color gradients indicate proportion of grid cell occupied by given land use. In B, legend is presented as histogram of SOC loss (Mg C⋅ha⁻¹), with positive values indicating loss and negative values depicting net gains in SOC.

Comparison of model results with the native remnant database suggests that modeled SOC loss due to land use was likely a conservative estimate. First, comparing measured SOC in native patches to predicted SOC stocks with NoLU, we found a negative bias of −10 Mg C⋅ha⁻¹ (SI Appendix, Fig. S6). Second, it was found that there was 13.8 to 28.0% (95% CI, n=62) less SOC to 1 m under agricultural land compared with paired remnant native vegetation plots (see SI Appendix, Fig. S2 and extended discussion); however, modeled loss (NoLU – 2010) for the same sites was only 7.7 to 12.8% (95% CI, n= 62) of the NoLU SOC to 1 m.

This analysis clearly demonstrates that, while, on average, agricultural land use leads to SOC loss, there are important spatial patterns and contrasts (Fig. 1) suggesting that simple accounting methods for SOC change [e.g., Intergovernmental Panel on Climate Change default emission factors (28)] will misrepresent SOC change in many regions of the world. The majority of data used to generate published emission factors come from North America and Europe (29). In our own metaanalysis (SI Appendix), 82 of the 140 paired comparisons were from these two regions. Model results from the agricultural heartland of the United States and much of Europe showed large losses (Fig. 1B), primarily from cropping, that are consistent with the average documented changes in SOC stocks from field investigations (14, 15).

The largest per pixel losses were found to coincide with cropping regions (Fig. 1B); however, grazing, especially in arid and semiarid regions, with its larger spatial extents (Fig. 1A), was responsible for at least half of the total SOC loss (SI Appendix, Table S3). The grassland and savanna IGBP land classification categories collectively lost more SOC than the cropland and crop/natural vegetation mosaic categories (48 Pg C vs. 35 Pg C). In particular, the rangelands of Argentina, southern Africa, and Australia stand out as hotspots of SOC loss when viewed as a percent of historic SOC (SI Appendix, Fig. S7).

While land use is the underlying anthropogenic driver of SOC loss, the degree to which land use results in SOC loss is at least partially dependent upon the degree to which the soil resource has been exploited (2). Using the Global Land Degradation Information System biophysical status of land index, a quantitative expression of a given land area’s ability to provide four categories of ecosystem services (biomass, soil, water, and biodiversity) (30), we found a strong correlation between land degradation and SOC loss (SI Appendix, Fig. S9). Results from SI Appendix, Fig. S9, coupled with the finding that grazing was the single most important land-use variable in the model (SI Appendix, Fig. S3), suggest that grazing of relatively unmanaged rangelands may be a stronger driver of SOC loss than previously acknowledged.

Agricultural land uses do not always result in large losses of SOC. For example, the 2-million-km² seasonally dry moist savanna region in Brazil known as “the Cerrado” was thought to have soils too poor to support intensive agriculture but, over the past few decades, has been transformed into one of the most productive agricultural regions of the world, with ∼750,000 km² of crops and 800,000 km² of pasture, through liming, fertilization, and weed control (31). Because of the naturally infertile soil state in the Cerrado, agricultural expansion has resulted in little loss in SOC (Fig. 1B), and there are large areas that have actually accumulated modest amounts of SOC after the advent of agriculture, which is consistent with results from field studies (17, 32, 33).

Historic SOC Loss Trend.

By using the HYDE v3.2 land-use dataset, we predicted the temporal evolution of SOC stocks due to changes in land use alone (Fig. 2). Globally, SOC loss follows the exponential rise in used land, but not in a linear fashion (Fig. 2, Inset). There were low annual rates of SOC loss (<0.05 Pg C⋅y1−) until AD 1800, followed by a century of losses >0.3 Pg C⋅y1−, with a slight moderation of this rate during the last century (0.13 Pg C⋅y1−). Comparison of this historic trend among the 10 countries with the greatest losses reveals some interesting contrasts (SI Appendix, Fig. S10), with old-world countries (e.g., India, Kazakhstan, and Germany) showing large losses before AD 1000 but new-world countries (e.g., Brazil and Argentina) only losing significant amounts of SOC in the past century.

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Fig. 2.

Historic reconstruction of loss in SOC relative to 10,000 BC (assumed NoLU). Temporal evolution of cropland and grazing land is given in stacked area plots. (Inset) Biplot of SOC loss (Pg C) v. total used land area (10⁶ km²) for each predicted time interval.

Limitations of This Study.

While great care has been taken to ensure that the input data were of the highest quality possible (23, 27), there remain several limitations in the underlying datasets and therefore predicted SOC change. First, the training dataset used to build spatial predictions models was not ideal for testing the hypotheses. ISRIC’s soil profile dataset is a compilation of national inventories from a large number of nations with different reasons for undertaking soil surveys and different methods of laboratory analysis. Data were collected over a 50-y period, which is likely smoothing out some of the SOC loss in the model. In addition, the mismatch in scale between a soil pedon (0.5 m on a side) and the pixel size of the HYDE v3.2 land-use data (10 km) can create situations where the dominant soil properties of a pixel are not represented by the particular soil pedons that were sampled within that pixel (34). Relatedly, regions with low sampling density may be overly influenced by a few data points that may not be representative of that region as a whole. These last two issues are the major drivers of the spatial distribution of model error (SI Appendix, Fig. S4).

The HYDE dataset itself also presents a few limitations. First, these land-use data are a combination of national and subnational level statistics and remotely sensed land-use change (4), which, in some cases, can create artificial changes in SOC along political boundaries. This became particularly apparent when we used the reconstructed land-use histories in prior centuries to estimate SOC stocks for those time periods (Fig. 2). Second, only very coarse land-use categories are represented, so management-specific practices which can influence SOC levels (13), such as tillage practices, rotations, and cover crops, are not represented. Relatedly, HYDE does not contain direct information on forest or wetland loss, both known drivers of SOC loss. Third, in our model formation, there is no indication of the duration of a given land use. SOC stocks, while often declining most rapidly in the first decade after land-use change (35), often take many decades to over a century to reach a new steady state (36, 37). Finally, the HYDE dataset describes the extent of land use but not the intensity. This limitation may be particularly important for the grazing category, as SOC levels have been shown to decline with increasing grazing pressure (38), although this effect appears to be dependent upon grass species composition, with C3 grasses showing large declines and C4 grasses showing small gains in SOC with increased grazing pressure (39). It is very likely that, taking these limitations together, our estimate of soil carbon debt covers only a smaller fraction of the actual debt due to human influence.

Implications.

This analysis indicates that the majority of the used portions of planet Earth have lost SOC, resulting in a cumulative loss of ∼133 Pg C due to agricultural land use. These SOC losses are on par with estimates of carbon lost from living vegetation primarily due to deforestation (40) and are nearly 100 Pg C higher than earlier estimates of land use and land use change-driven losses of SOC (41). Importantly, as Fig. 1 demonstrates, there are hotspots of SOC loss, associated with extensive cropping regions but also with highly degraded grazing land (SI Appendix, Fig. S9), suggesting that there are identifiable regions which should be targets for SOC restoration efforts.

The potential to recover lost SOC may be more limited than is often assumed. The amount of SOC that has been lost historically can be thought of as the carbon sink potential of the soil (42). Our analysis has found that this sink potential is ∼133 Pg C (SI Appendix, Table S3). A widely repeated figure is that, with adoption of best management practices, two thirds of lost SOC can be recovered (42). If the two-thirds figure is accurate, then SOC sequestration has the potential to offset 88 Pg C (322 Pg CO₂) of emissions. However, bottom-up estimates of the maximum biophysical potential for carbon sequestration on cropping and grazing land range from 0.4 Pg C⋅y⁻¹ to 1.4 Pg C⋅y⁻¹ (20, 43). Assuming SOC reaches a new steady state in 20 y (35, 44), this calculation suggests that 8 Pg C to 28 Pg C can be recaptured. Even the range of 8 Pg C to 28 Pg C is likely overly ambitious given the various social, economic, and technical constraints on universal adoption of best management practices (45), suggesting that the amount of the carbon sink that can be filled is on the order of, at best, 10 to 30% globally and may well be <10%.