Land management and the related land use changes have an effect on the spatial patterns and magnitude of accelerated soil erosion which can affect land productivity and food security12,18, biological diversity39 and carbon cycling13,38,40. Global soil erosion dynamics have been previously quantified based on scientific soil expert judgments4,5, through the extrapolation of plot and river sediment data41,42 and RUSLE-based modelling36,37. While these approaches range in their degree of complexity, their lumped or coarse resolution modelling (ca. 10–60 km cell size) with a static observation approach limits their predictive power to assess the effects of frequent land use changes and to identify soil erosion hotspots. Our study investigates the global soil erosion dynamics by means of high-resolution spatially distributed modelling (ca. 250 × 250 m cell size). The proposed geo-statistical approach, allows for the first time, the thoroughly incorporation of land uses and their changes, the extent, types, spatial distribution of global croplands, and the effects of the different regional cropping systems into a global soil erosion model. This, coupled with an improved global assessment of the global rainfall erosivity dynamics and the latest globally consistent dataset paved the path towards a state-of-the-art global RUSLE-based model.

The results of this study shed light on the impacts of the 21st century global land use change on soil erosion, providing insights into the potential mitigating effects attributable to conservation agriculture. The strong bond between remote sensing and inventory statistics formed the basis for globally consistent characterizations of soil erosion with local importance and utility (Fig. 5). The knowledge derived from this global assessment can thus improve our understanding in both global and regional land degradation dynamics and forms an important starting point to develop concepts for a better management of the land and an effective mitigation of land degradation.

Fig. 5 Examples of the local relevance and utility of the global soil erosion estimates. a Representation of the land use/land cover data employed in this study. The area reported in the image is a region of Mato Grosso in Brasil. The light green indicates forest loss between 2001 and 2012. b Representation of the high spatial detail of the soil erosion model predictions, capable to represent the effect of the forest loss in the Mato Grosso Full size image

The limited availability of globally consistent data on the amount of cropland under conservation agriculture constrained the ability of our study to comprehensively model the mitigating effects for all the 202 countries under observation. To date, 54 counties have provided statistics about their conservation agriculture practices to the FAO, which cover about 73% of the global cropland surface. In the conservation scenario, these countries experience 7% less soil erosion than in the baseline scenario, where no conservation practices are considered. Assuming the average conservation practices at continental level as representative for the remaining 27% of the global cropland, for 2012 a total annual average soil erosion of \(17_{ - 0.7}^{ + 1}\) Pg yr−1 is predicted. The confidence intervals refer to the variation between the conservation and baseline scenarios (superscript) and the conservation scenario assuming the maximum technical efficiency of the employed conservation practices (subscript).

Previous global estimates of soil erosion on agricultural land span across two orders of magnitude (23.7 and 120 Pg yr−1). The most cited estimate of global soil erosion in agricultural land by Pimentel et al.22 is equal to 73.5 Pg yr−1. Citations on global soil erosion estimates during the last three decades estimated around 75 Pg yr−1 of soil eroded in cropland19,23. Early RUSLE-based spatially distributed modelling approaches confirmed this value36,43. However, recent studies using methods that more closely link models to measured erosion values report smaller global erosion rates. By means of a combined plot data and RUSLE modelling extrapolation approach, Van Oost et al.13 estimated a soil erosion amount of 33.4 ± 4.4 Pg yr−1 for global agricultural land (pasture and cropland). More recently, Doetterl et al.37 constrained a simulation of a coarse resolution global application of the RUSLE model (ca. 10 × 10 km cell size) with the soil erosion data reported for the US cropland. This resulted in an estimated soil erosion by water in global cropland of 13.1 ± 6.6 Pg yr−1. Since RUSLE models do not include a description of gully and tillage erosion processes, and also do not represent other geomorphic processes such as landslides and river bank erosion, it is reasonable to assume that their estimates fall into the lower end of the 23.7 and 120 Pg yr−1 range of cited soil erosion estimates. The new cropland soil erosion estimate of \(17_{ - 0.7}^{ + 1}\) Pg yr−1 for the year 2012 that we present in this study is consistent with the recent estimate of Doetterl et al.37. The good correspondence of our results (without using constraining factors) with regional estimates (US and Europe) and Doetterl et al.’s37, supports the hypothesis that soil erosion due to sheet and rill processes is smaller than previously assumed in literature.

The estimates reported in this study rest on RUSLE, a deterministic and empirical-based model which was developed based on a statistical analysis of more than 10,000 plot-years of basic runoff and soil loss data44 in 49 US locations covering a large variety of landscape conditions. Although RUSLE-based models are derived from the most comprehensive set of measurements available45 including universally recognized factors that affect soil erosion by water29,33, they are predominantly built upon parameters that result from experiments conducted in the United States45. The application to a non-plot-level and in areas outside the range of the original estimates (e.g., tropics, subarctic and tundra) may substantially reduce the accuracy of the model45. The authors recognize that using an empirical-based prediction tool outside the original range of environmental variables could represent a legitimate concern46. Considering the proven capacity of RUSLE-based models to overcome their empirical origin47, the current lack of better performing models9,31, and the need for predicting the possible impacts of global change upon soil erosion27, the authors argue that at this stage the presented global RUSLE application represents a legitimate approach to narrow the current gap of knowledge and support the targeted soil conservation efforts aiming to mitigate soil erosion.

Given the quantitative and harmonized nature of the data set, there seemed to be no reasons to doubt the consistency between the estimates for the two time periods as well as the reliability of the resulting national trends. The difference obtained from the comparison of the estimates for the two time periods was driven by land use change and was unaffected by the predictive limits of the empirical soil erosion model. Validity, i.e., if the model accurately measures the amount of displaced soil, could have been an issue as the predictive capacity of both empirical and process-based physical models is still limited9,31,46, and because the model was run on a global scale based on a number of data-driven assumptions with soil erosion quantities estimated for each of the 2.9 billion cells of the global raster.

As observed by Auerswald et al.48, a validation sensu strictu of USLE-based modelling at regional or larger scales is not feasible due to the lack of long-term field-scale measurements. Therefore, a cross-comparison of the modelling results to gain insights on the validity of the modelling predictions was performed. This operation shows that the modelling results are consistent with both empirical observations and other regional soil erosion assessments. The analysis at meta-data level confirms that our estimates fall in the range of measured data collected by Montgomery8, as well that the global model can describe the magnitude of soil erosion incurring between the different land cover types. Adapting the figure he created (Fig. 6), we superimposed the results from our global analysis for different land covers, to which we added data on native forests and data from other meta-analysis studies (Supplementary Note 5). In addition, an exiguous deviation was observed from the comparison between our estimates and the ones provided by independent studies of the US Department of Agriculture (USDA) for cropland in the United States30 (4.5%) and by the Joint Research Centre (JRC) of the European Commission for cropland of the 28 EU countries(1.1%). The good agreement between our estimates and the ones provided by independent studies give confidence that the quantitative estimates achieved through the global model are reliable and valid to a level close to these higher resolution regional assessments (Supplementary Note 5). Further insights supporting the validity of the global model were achieved comparing the spatial patterns of the estimates with the ones reported by previous UN funded global assessments based on expert judgement (GLASOD)5 and remote sensing data (GLADIS) (Supplementary Fig. 4 and Supplementary Note 5). On the basis of a sensitivity analysis (Supplementary Note 3), the authors observed that the soil erosion predictions of the global RUSLE-based model were most sensitive to the cover-management factor (C-factor) (Supplementary Figs. 5 and 6). This supports the hypothesis that a thorough definition in the C-factor of the land uses and their changes, the extent, types and the spatial distribution of the global croplands and cropping systems are the key to improve RUSLE-based global assessments. In addition, the sensitivity analysis allows us to define the influence of the input parameters on the global RUSLE predictions and spatially map their effects on the model output (Supplementary Fig. 7).

Fig. 6 Comparison of measured and modelled erosion rates. Representation of soil erosion rates measured on agricultural fields under conventional agriculture (n = 779), geologic erosion rates measured on alpine terrain (n = 44), soil-mantled landscapes (n = 1456), low gradient continental cratons (n = 218), grassland and scrublands (n = 63), native forests (n = 46) and averages of our predictions (indicated by an asterisk). Large parts of the measured data come from the study of Montgomery8 integrated with data from other meta-analysis studies. The vertical red line indicates average value of soil erosion. The red dots refer to averages soil erosion rates modelled for two country highly susceptible to water erosion (Haiti and Rwanda) Full size image

The uncertainty of the spatial predictions was estimated using a Markov Chain Monte Carlo (MCMC) approach (Supplementary Note 4). The map of uncertainty is presented in Supplementary Fig. 8 as the standard deviation of the MCMC simulated values. The map gives an outline of the geographical distribution of the prediction variance, and it can be used to compare the potential error in different areas of the world. The error of the model estimates associated with the input data assessed with a MCMC approach is about 8 Pg yr−1 for the whole world. Accounting for uncertainties in the soil erosion rates, we estimated an annual average potential soil erosion amount of \(35_{ - 2.4}^{ + 5.6}\) and \(35.9_{ - 2.4}^{ + 5.6}\) Pg yr−1 for the 2001 and 2012 baseline scenarios, respectively.

It should be noted that the positive results highlighted by the cross-check analysis do not imply that the global model captures reality by 100%. The authors recognise that the modelling based on data-driven assumptions has its limitations, and there is a need for field monitoring and local scale process-based modelling. However, in light of the useful insights gained from the operations of the model performance evaluation, we argue that the presented RUSLE-based global approach constitutes a powerful assessment tool for identifying hotspots and areas of concern at the global scale. It provides the basis for a more strategic approach in directing new monitoring/modelling efforts and informing decision making for the development of policy. Moreover, in the proposed new form, the global scale RUSLE-based assessment is brought to a new level, as for the first time it links to the key parameters required to assess the effects of global change and support conservation planning and land management. Hereinafter, we therefore discuss the implications of our global modelling from a multidisciplinary perspective linking the findings of our map to GDP measures to identify potential pressures on food production systems, risks of increased food and feed prices due to phosphorous shortages, the global soil organic carbon (SOC) pool that forms the basis for emission levels and climate change analyses, the economic costs of soil erosion and the overall implications for policy decision-making and sustainable development goals.

The increasing population places greater pressure on global food production systems. The global spatial coverage of modelled soil erosion enables us to explore the relationship between the average soil erosion in croplands and the GDP of each country based on World Bank figures. The results presented in Fig. 7 also show latitude according to size. Clearly, the wealthy countries in temperate latitudes have the least erosion with poorest tropical countries being the most susceptible to high levels of soil erosion. The countries that can least afford soil protection measures are the most vulnerable. This emphasises the importance of soil protection in the sustainable development goals if there is any hope of intensifying agriculture in these countries to meet the food needs of the populations.

Fig. 7 Soil erosion in cropland areas on a country basis vs. the log of the respective countries GDP according to the World Bank. The size of the circle represents the latitude indicating the higher latitude, wealthier countries are the least impacted by soil erosion, either through more favourable climatic conditions, or soil erosion prevention measures Full size image

Along with the loss of fertile soil through erosion as quantified in this study goes the imminent threat of limited nutrient resources. In 2009, clean phosphorous reserves were predicted to run out in only 20–50 years49,50. Even though this prediction was revised only 2 years later with previously overlooked phosphorous reserves found in Morocco and Western Sarah51, the finite nature of resources could become a source of political tension especially in developing-world countries where farmers cannot afford phosphate fertilizers even at today’s non-monopoly prices. With continuously rising demand due to the increasing world population and thus higher demand of food in general and livestock products in particular, fertilizer prices are likely to increase. This may encourage companies to explore new reserves from lower grade rock which is subject to a higher cadmium pollution and exacerbate the conflict with the less and least developed countries due to food and feed shortages. Both developments could impede sustainable soil use and the application of soil conservation management practices even further. The former development may increase the cadmium pollution of soils and potentially restrict soil usage. The latter, in turn, could lead to an even more intensive land use with negative effects on soil erosion rates. No substitutes exist for phosphates (with the exception of organic farming using manure which is limited by livestock availability in many countries). In this regard, one promising but not yet widely discussed approach could be to protect phosphorous by reducing soil erosion rates. Cordell et al.50 estimated that around 36% of the total phosphorus fertilizer applied to arable land was directly lost due to erosion. Our global soil erosion assessment highlights the areas where agricultural management based on sustainable farming practices with low soil erosion and high phosphorous recycling rates could be most effectively applied to help keep global food and feed prices at reasonable levels.

The prediction of the global soil organic carbon (SOC) pool by Earth-system models is still one of the main sources of uncertainty, undermining the confidence in the carbon (C) budget and its future projections52,53. Poor representation of different mechanisms driving SOC turnover and low accuracy of soil data inputs are among the primary causes of this uncertainty. Land-C-atmosphere feedbacks may not be properly disentangled as long as relevant missing processes are not implemented. Among those, soil erosion is certainly a key process as it displaces consistent amounts of C as lateral fluxes, then subjects it to different environmental conditions that control its stabilisation and release. The consequences of neglecting this component by large-scale modelling and inventories are still uncertain, so that erosion is estimated to induce a carbon sink or source up to 1 and −1 Pg C yr−1 globally54, respectively. In this respect, the new global soil erosion assessment presented in this paper has the potential to become a reference input for integrating later C fluxes into large-scale model frameworks of different complexity40. Combining the global soil erosion with a recent SOC map (Supplementary Methods), we estimated a gross SOC displacement by soil water erosion on the order of 2.5 Pg C yr−1. Thirty-six percent came from agricultural land, although this covers only 11% of the total area investigated. Geographically (Supplementary Fig. 9), the regions across the Tibetan plateau and China were extensively exposed to high rates of SOC displacement, as also the north-eastern part of Siberia.

Several on-site effects of soil erosion which occur directly at the site where the soil is removed have been cited in the literature12. Increasingly, scientists also mention offsite effects of soil erosion in the surrounding areas55,56. Given these on- and offsite effects, soil erosion assessments are highly relevant from an economic point of view because erosion is associated with an unequal distribution of economic costs. It degrades soil and thereby reduces soil productivity and yields of the land due to the loss of fertility and water storage capacity22,57. Land users in economies which can afford it, therefore have an incentive to use fertilizers or water management practices which unfold immediate effects to compensate the negative effects of soil erosion on their short-term yields. In economies that cannot afford these measures, the price of erosion is paid for by reduced food or forest production22. The on-site costs of soil erosion are thus internalized by pricing them directly into the economic decision making of the land user. As long as cash flows remain positive, land users, by themselves, thus hardly have an incentive to adopt land conservation practices to contain soil erosion because the costs of adopting these technologies are high and occur immediately (reducing early cash flows), while the benefits spread out over a longer time horizon55 and are discounted more heavily. From an economic perspective, the high resolution global soil erosion assessment could help to estimate the global costs of these on-site effects especially from a long-term land value perspective. More precisely, the monetary costs of fertilizers, additional water management, etc. or productivity losses in the poorer countries could be contrasted with the cost of soil conservation practices to reduce soil loss in the first place in order to derive appropriate political regulations for sustainable on-site land management. Soil erosion, however, also affects the surrounding areas with off-site costs that are external to the future cash flow calculus of the land user. Here the price mechanism breaks down in that the offsite costs caused by sedimentation, siltation, eutrophication, flooding, etc. are not internalized into the land user’s investment calculus but are borne by society, i.e., the tax payer. While in the short-term, the exploitation of soil resources may be economically sound for land users, this may not be the case from a socio-economic perspective because the society bears the offsite costs (e.g., cleaning of public waterway infrastructure, prevention of dam bursts) while consumers may be confronted with higher prices. In the case of off-site effects, the global assessment of soil erosion is especially beneficial as it provides the long-awaited basis for an economic assessment of the off-site costs of soil erosion on a global scale. An economic assessment, similar to Pimentel et al.22 but based on a global assessment with high resolution soil erosion information could assign a value to on- and off-site costs of soil erosion, locally as well as globally, in order to calculate the net costs of soil erosion. Placing a value on the on- and off-site effects may help decision-makers to internalize the off-site costs into the investment calculus of the land user, e.g., in the form of obligatory soil conservation practices in areas with high off-site costs otherwise borne by society. This is important because fertile soils on the planet are limited and essentially non-renewable, at least on human time scales. It is therefore of crucial importance to protect available soil resources from further degradation if society wants to maintain this precious natural resource for future generations. In this sense preserving soil quality and achieving a land degradation neutral world have been explicitly recognized in the recently approved sustainable development goals (SDG). Goal 2 explicitly mentions the relevance of maintaining soil quality for achieving food security while Goal 15 calls for a land degradation neutral world by 2030. These goals can only be achieved if we are able to limit current soil erosion rates by applying sustainable soil management practices especially in the areas mostly affected by erosion processes. Moreover, the results of this study can also be relevant for Goal 13 aiming at taking action to combat climate change and its impacts12,13 and Goal 6 to ensure availability and sustainable management of water. The recently endorsed FAO Voluntary Guidelines for Sustainable Soil Management58 provide the necessary guidance to National governments on the way forward in order to achieve such ambitious goals by 2030. The insights of our high resolution global modelling approach can provide a solid starting point to support decision-making in both ex-ante and ex-post policy evaluation, while scientifically, it can enable better estimates of the global SOC pool including the effects of land use change and conservation agriculture. Our findings can also provide the basis to test the possible effects of the four per mil initiative proposed by the French authorities during the COP21 in Paris.