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Understanding spatial and temporal patterns in land susceptibility to wind erosion is essential to design effective management strategies to control land degradation. The knowledge about the land surface susceptible to wind erosion in European contexts shows significant gaps. The lack of researches, particularly at the landscape to regional scales, prevents national and European institutions from taking actions aimed at an effective mitigating of land degradation. This study provides a preliminary pan-European assessment that delineates the spatial patterns of land susceptibility to wind erosion and lays the groundwork for future modelling activities. An Index of Land Susceptibility to Wind Erosion (ILSWE) was created by combining spatiotemporal variations of the most influential wind erosion factors (i.e. climatic erosivity, soil erodibility, vegetation cover and landscape roughness). The sensitivity of each input factor was ranked according to fuzzy logic techniques. State-of-the-art findings within the literature on soil erodibility and land susceptibility were used to evaluate the outcomes of the proposed modelling activity. Results show that the approach is suitable for integrating wind erosion information and environmental factors. Within the 34 European countries under investigation, moderate and high levels of land susceptibility to wind erosion were predicted, ranging from 25·8 to 13·0Mha, respectively (corresponding to 5·3 and 2·9% of total area). New insights into the geography of wind erosion susceptibility in Europe were obtained and provide a solid basis for further investigations into the spatial variability and susceptibility of land to wind erosion across Europe.

Various methods have been applied to generate Digital Elevation Models (DEM) but whatever method is used, DEM estimates will always be affected by errors. Comparing interpolated values with the actual elevation values, obtained with a high precision field survey, allows us to assess DEM accuracy. At every spot height location it is possible to subtract the actual values from the DEM values to obtain the error at that point. Using this error information, different types of statistics can be computed; however, Root Mean Squared Error (RMSE) is the standard measure of error used by surveyors around the world. Such a way of error reporting uses a single value for the whole DEM and makes several implicit and unacceptable assumptions about the error: it has no spatial distribution and is statistically stationary across a region. An alternative approach is proposed here to achieve an improved estimate of local error. It is based on error modelling using conditional stochastic simulations to produce alternative values of the data and so result in a probabilistic assessment of DEM accuracy. The study area is a doline of about 1.5 ha in size and is located in the Alps (North Italy) at a mean elevation of 1900 m above sea level. In this study, to test the accuracy of a previously generated DEM, elevation data were measured at 110 randomly distributed points using a laser distance system linked with an electronic theodolite. Five hundred simulations were generated using conditional and sequential Gaussian simulation algorithms. Statistical information was extracted from the set of simulated error images: 1) averaging the values for each pixel and producing the map of the ‘expected’ error in any considered location and that of standard deviation; 2) counting the number of times that each pixel exceeded the null value and converting the sum to a proportion, in order to produce the probability maps of overestimation and underestimation. Basic statistics and the histogram of errors showed them to be of approximately normal distribution with a small positive bias. The map of the expected values revealed a clear correlation of errors in the DEM to the slope of the land surface. The highest values were localised on the steepest areas in the northern half of the doline. The spatial distribution of errors was not random but showed a high probability for overestimation in the northern area, while an actual probability for underestimation was restricted to the southern area. Multiple DEM results generated by incorporating different images of the error field also allow us to derive a probable version of the products used for subsequent decision- making processes.

Specific oral tolerance induction to food (SOTI) is a new promising treatmentfor persistent IgE-mediatedfood allergy. Our paper reports a case of a 5-year-old girl with cow's milk allergy, who developed severe anaphylaxis after the ingestion of a croissant containing sheep's milk ricotta cheese, even though she had been previously desensitized to cow's milk through SOTI. The sheep's milk specific allergen causing the severe allergic reaction (a derivative of alpha-casein of 54,1kDa) was identified by SDS-PAGE and immunoblotting. We conclude that SOTI is a species-specific procedure and the induced tolerance to cow's milk doesn't necessarily provide protection against milk of other mammals. Therefore, children desensitized to cow's milk through SOTI should strictly avoid the intake of milk of other mammals, until tolerance to those kinds of milk is documented by an oral food challenge.

Landslides occur worldwide in response to a broad variety of natural predisposing conditions and triggering factors that include heavy rainfalls, earthquakes, and human activity. Landslides constitute a serious source of danger causing environmental damage and substantial human and financial losses. At a regional scale, landslide susceptibility zonation constitutes the first effective step to achieve a thorough risk assessment and management and contribute to public safety. For this reason, the predicted susceptibility maps must be carefully analysed and critically reviewed before disseminating the results. The tuning of statistical techniques and the independent validation of the results are already recognized as fundamental steps in any natural hazard study to assess model accuracy and predictive power. Validation also may permit to establish the degree of confidence in the model and to compare results from different models. For this reason, the spatial agreement among susceptibility maps, produced by different models, should also be tested, especially if these models have similar prediction power. This is usually a rather common occurrence as it may happen that two or more maps with similar predictive power may not have the same agreement in term of predicted spatial patterns. This study is aimed at assessing the degree of spatial agreement among different patterns of predicted values in susceptibility maps with almost similar success and prediction rate curves and areas under curves (AUC). A data-driven Bayesian method (Weights of Evidence modelling technique) is applied and the output maps reclassified to compare the predicted results. A relative classification, based on the proportion of area classified as susceptible, is performed. Maps are investigated by Kappa Statistic, Principal Component Analysis, and Distance Weighted Entropy procedures. The results show great differences within the output spatial patterns of the predicted maps and also within the highest predicted classes. Our approach is applied to an alpine environment (Italian Alps) where debris flows represent one of the most frequent and damaging processes. This topic is of great importance for achieving a reliable communication of the results to the final users.

Indirect assessment of landslide susceptibility provides planners and decision-makers with a practical and cost-effective way to zone areas susceptible to landsliding. This goal can be achieved by applying statistic models to estimate the spatial probability of slope instability within the investigated area. It is also of crucial importance to assess the accuracy of the model outcome. To this purpose, cross-validation techniques, based on independent samples, are usually adopted. However further attention has to be paid to the evaluation of the spatial variability of the predicted results. In this work, the assessment of the statistical relations between the landslides and the controlling geo-environmental factors was used to produce a series of landslide susceptibility maps. A quantitative data-driven model (Weights of Evidence modeling technique) was applied at a regional scale for a study area site in Central Italian Alps (Valtellina di Tirano). The landslides present within this area were identified from aerial photographs, field surveys and exiting inventories; consequently, two different landslide inventory maps at scale 1:10000, were produced for the study, each one characterized by a different level of accuracy. The goodness of fit and prediction capabilities of susceptibility maps was evaluated through the use of success-rate and prediction-rate curves. Using different landslide inventories, a series of different combinations of predisposing factors were utilized to produce different susceptibility maps, each one classified in 5 and 10 classes. Subsequently, Kappa Statistic, and Principal Component analysis were performed to measure the classification agreement among the maps produced by models using different combinations of support covariates. This analysis have shown that despite the substantially identical prediction rate of different models, the spatial agreement of these maps is non consistent, as their spatial pattern is considerably different. This result in problems connected with the use of success and prediction rates curves as an exhaustive measure of accuracy for spatial data.

Using the Weight of Evidence model (WofE), a groundwater vulnerability assessment to nitrate (NO3-) contamination has been performed considering both positive and negative evidences of contamination in the aquifer of the Province of Milan (Northern Italy). The WofE calculates the weighted relationship between hydrogeological-anthropogenic factors (explanatory variables) that influence the aquifer vulnerability and groundwater nitrate concentration in the wells used as training points (response variable). The use of the model requires to express the response variable as binary with the necessity to establish a threshold value of concentration which separates the data set in two subsets. The conventional approach is to use only the subsets containing wells with concentration higher than the threshold value as training points. In fact in groundwater vulnerability problems this subset represents the number and location of the events that is, where groundwater has been strongly impacted by contamination. A limit of this approach is that an entire subset, the one individuating areas where groundwater has been slightly or no impacted by contamination, is completely neglected. In this study the threshold value of concentration has been calculated by simple statistical analysis and both the subsets of data served as training points to run two different WofE models. This allows to avoid losing important information on experimental data and to better describe the aquifer vulnerability by directly considering the importance of factors which are related not only to high values of groundwater contamination but also to low values. The influence in the final outputs due to the use of the two different training point sets has been evaluated comparing the spatial distribution of the resulting vulnerability classes. For both models the obtained weighted relationships between the explanatory variables and response variable have also been investigated highlighting the main difference in the results.

This study is aimed at assessing different spatial patterns of predicted values of landslide susceptibility maps with almost similar success and prediction rate curves. Our approach is applied to an alpine environment (Italian Central Alps) where debris flows represent a frequent damaging phenomenon. The Weights of Evidence modelling technique (a data driven Bayesian method) was applied using ArcSDM (Arc Spatial Data Modeler) an ArcGIS extension. The output prediction maps were reclassified in the same way to compare the predicted results: a relative classification, based on the proportion of the area classified as susceptible, was made. The thresholds among different susceptibility classes were put at each 10% of the study area, classified decreasingly from the highest to the lowest susceptibility values. After applying Kappa Statistics, Cluster Analysys, and Principal Component Analysis (PCA), we analysed the spatial variability of the predicted maps, and also within the highest susceptibility classes.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensivelyreviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the re-gions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv)how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To per-form this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. Theresulting database, named‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 indi-vidual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluatedand transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insightsinto the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to sup-port the upcoming country-based United Nations global soil-erosion assessment in addition to helping to informsoil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is anopen-source database available to the entire user-community to develop research, rectify errors, andmakefutureexpansions

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Healthy soil is the foundation underpinning global agriculture and food security. Soil erosion is currently the most serious threat to soil health, leading to yield decline, ecosystem degradation and economic impacts. Here, we provide high-resolution (ca. 100 × 100 m) global estimates of soil displacement by water erosion obtained using the Revised-Universal-Soil-Loss-Equation-based Global Soil Erosion Modelling (GloSEM) platform under present (2019) and future (2070) climate scenarios (i.e. Shared Socioeconomic Pathway [SSP]1–Representative Concentration Pathway [RCP]2.6, SSP2–RCP4.5 and SSP5–RCP8.5). GloSEM is the first global modelling platform to take into account regional farming systems, the mitigation effects of conservation agriculture (CA), and climate change projections. We provide a set of data, maps and descriptive statistics to support researchers and decision-makers in exploring the extent and geography of soil erosion, identifying probable hotspots, and exploring (with stakeholders) appropriate actions for mitigating impacts. In this regard, we have also provided an Excel spreadsheet that can provide useful insights into the potential mitigating effects of present and future alternative CA scenarios at the country level.

The erosive force of rainfall (rainfall erosivity) is a major driver of soil, nutrient losses worldwide and an important input for soil erosion assessments models. Here, we present a comprehensive set of future erosivity projections at a 30 arc-second (∼1 km2) spatial scale using 19 downscaled General Circulation Models (GCMs) simulating three Representative Concentration Pathways (RCPs) for the periods 2041–2060 and 2061–2080. The future rainfall erosivity projections were obtained based on a Gaussian Process Regression (GPR) approach relating rainfall depth to rainfall erosivity through a series of (bio)climatic covariates. Compared to the 2010 Global Rainfall erosivity baseline, we estimate a potential average increase in global rainfall erosivity between 26.2 and 28.8% for 2050 and 27–34.3% for 2070. Therefore, climate change and the consequential increase in rainfall erosivity is the main driver of the projected + 30–66% increase in soil erosion rates by 2070. Our results were successfully compared with 20 regional studies addressing the rainfall erosivity projections. We release the whole dataset of future rainfall erosivity projections composed of 102 simulation scenarios, with the aim to support further research activities on soil erosion, soil conservation and climate change communities. We expect these datasets to address the needs of both the Earth system modeling community and policy makers. In addition, we introduce a modeling approach to estimate future erosivity and make further assessments at global and continental scales.

The European Commission's Thematic Strategy for Soil Protection (COM(2012)46) identified soil erosion as an important threat to European Union's (EU) soil resources. Gully erosion is an important but hitherto poorly understood component of this threat. Here we present the results of an unprecedented attempt to monitor the occurrence of gully erosion across the EU and UK. We integrate a soil erosion module into the 2018 LUCAS Topsoil Survey, which was conducted to monitor the soil health status across the EU and to support actions to prevent soil degradation. We discuss and explore opportunities to further improve this method. The 2018 LUCAS Topsoil Survey consisted of soil sampling (0–20 cm depth) and erosion observations conducted in ca. 10% (n = 24,759) of the 238,077 Land Use/Cover Area frame Survey (LUCAS) 2018 in-field survey sites. Gully erosion channels were detected for ca. 1% (211 sites) of the visited LUCAS Topsoil sites. Commission (false positives, 2.5%) and omission errors (false negatives, 5.6%) were found to be low and at a level that could not compromise the representativeness of the gully erosion survey. Overall, the findings indicate that the tested 2018 LUCAS Topsoil in-field gully erosion monitoring system is effective for detecting the incidence of gully erosion. The morphogenesis of the mapped gullies suggests that the approach is an effective tool to map permanent gullies, whereas it appears less effective to detect short-lived forms like ephemeral gullies. Spatial patterns emerging from the LUCAS Topsoil field observations provide new insights on typical gully formation sites across the EU and UK. This can help to design further targeted research activities. An extension of this approach to all LUCAS sites of 2022 would significantly enhance our understanding of the geographical distribution of gully erosion processes across the EU. Repeated every three years, LUCAS soil erosion surveys would contribute to assess the state of gully erosion in the EU over time. It will also enable monitoring and eventually predicting the dynamics of gully erosion. Data collected were part of the publicly available Gully Erosion LUCAS visual assessment (GE-LUCAS v1.0) inventory.

40 Pags.- 10 Figs.- 2 Tabls.- Suppl. Informat. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697, To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions., Jae E. Yang and Pasquale Borrelli are funded by the EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea (Grant No. 2019002820004). Diana Vieira is funded by national funds (OE), through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen - DL57/2016 (CDL-CTTRI-97-ARH/2018 - REF.191-97-ARH/2018), and acknowledges CESAM financial support of through (UIDP/50017/2020+UIDB/50017/2020). Walter Chen is funded by the Ministry of Science and Technology (Taiwan) Research Project (Grant Number MOST 109-2121-M-027-001). Nejc Bezak and Matjaž Mikoš would like to acknowledge the support of the Slovenian Research Agency through grant P2-0180.

Mercury (Hg) is one of the most dangerous pollutants worldwide. In the European Union (EU), we recently estimated the Hg distribution in topsoil using 21,591 samples and a series of geo-physical inputs. In this manuscript, we investigate the impact of mining activities, chrol-alkali industries and other diffuse pollution sources as primary anthropogenic sources of Hg hotspots in the EU. Based on Hg measured soil samples, we modelled the Hg pool in EU topsoils, which totals about 44.8 Gg, with an average density of 103 g ha−1. As a following step, we coupled the estimated Hg stocks in topsoil with the pan-European assessment of soil loss due to water erosion and sediment distribution. In the European Union and UK, we estimated that about 43 Mg Hg yr−1 are displaced by water erosion and c. a. 6 Mg Hg yr−1 are transferred with sediments to river basins and eventually released to coastal Oceans. The Mediterranean Sea receives almost half (2.94 Mg yr−1) of the Hg fluxes to coastal oceans and it records the highest quantity of Hg sediments. This is the result of elevated soil Hg concentration and high erosion rates in the catchments draining into the Mediterranean Sea. This work contributes to new knowledge in support of the policy development in the EU on the Zero Pollution Action Plan and the Sustainable Development Goal (SDGs) 3.9 and 14.1, which both have as an objective to reduce soil pollution by 2030., Highlights • Hg pool in EU topsoils totals about 44.8 Gg, with an average density of 103 g ha−1. • Coupling Hg stocks in topsoil with soil losses and sediment distribution in Europe. • Hg displaced by water erosion is c.a. 43 Mg yr−1 and c.a. 6 Mg Hg yr−1 reach the rivers. • Agricultural lands contributes to more than 85% of Hg losses. • The Mediterranean Sea receives almost half of the Hg fluxes followed by Black Sea.

Changes in future soil erosion rates are driven by climatic conditions, land use patterns, socio-economic development, farmers’ choices, and importantly modified by agro-environmental policies. This study simulates the impact of expected climatic and land use change projections on future rates of soil erosion by water (sheet and rill processes) in 2050 within the agricultural areas of the European Union and the UK, compared to a current representative baseline (2016). We used the Revised Universal Soil Loss Equation (RUSLE) adjusted at continental scale with projections of future rainfall erosivity and land use change. Future rainfall erosivity is predicted using an average composite of 19 Global Climate Models (GCMs) from the Coupled Model Inter-comparison Projects (CMIP5) WorldClim dataset across three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Concerning future land use change and crop dynamics, we used the projections provided by the Common Agricultural Policy Regional Impact Analysis (CAPRI) model. Depending on the RCP scenario, we estimate a +13 %-22.5 % increase in the mean soil erosion rate in the EU and UK, rising from an estimated 3.07 t ha−1 yr−1 (2016) to between 3.46 t ha−1 yr−1 (RCP2.6 scenario) and 3.76 t ha−1 yr−1 (RCP8.5 scenario). Here, we disentangle the impact of land use change and climate change in relation to future soil losses. Projected land use change in the EU and UK indicates an overall increase of pasture coverage in place of croplands. This land use change is estimated to reduce soil erosion rates (-3%). In contrast, the increases in future rainfall erosivity (+15.7 %–25.5 %) will force important increases of soil erosion requiring further targeted intervention measures. Given that agro-environmental policies will be the most effective mechanisms to offset this future increase in soil erosion rates, this study proposes soil conservation instruments foreseen in the EU Common Agricultural Policy (CAP) to run policy scenarios. A targeted application of cover crops in soil erosion hotspots combined with limited soil disturbance measures can partially or completely mitigate the effect of climate change on soil losses. Effective mitigation of future soil losses requires policy measures for soil conservation on at least 50 % of agricultural land with erosion rates above 5 t ha−1 yr−1.

Soil erosion is a major global soil degradation threat to land, freshwater, and oceans. Wind and water are the major drivers, with water erosion over land being the focus of this work; excluding gullying and river bank erosion. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity, is important both for policy makers engaged in land use decision-making and for earth-system modelers seeking to reduce uncertainty on global predictions. Here we predict future rates of erosion by modeling change in potential global soil erosion by water using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. Global predictions rely on a high spatial resolution Revised Universal Soil Loss Equation (RUSLE)-based semiempirical modeling approach (GloSEM). The baseline model (2015) predicts global potential soil erosion rates of 43+9.2−7 Pg yr−1, with current conservation agriculture (CA) practices estimated to reduce this by ∼5%. Our future scenarios suggest that socioeconomic developments impacting land use will either decrease (SSP1-RCP2.6–10%) or increase (SSP2-RCP4.5 +2%, SSP5-RCP8.5 +10%) water erosion by 2070. Climate projections, for all global dynamics scenarios, indicate a trend, moving toward a more vigorous hydrological cycle, which could increase global water erosion (+30 to +66%). Accepting some degrees of uncertainty, our findings provide insights into how possible future socioeconomic development will affect soil erosion by water using a globally consistent approach. This preliminary evidence seeks to inform efforts such as those of the United Nations to assess global soil erosion and inform decision makers developing national strategies for soil conservation., Proceedings of the National Academy of Sciences of the United States of America, 117 (36), ISSN:0027-8424, ISSN:1091-6490

Soil phosphorus (P) loss from agricultural systems will limit food and feed production in the future. Here, we combine spatially distributed global soil erosion estimates (only considering sheet and rill erosion by water) with spatially distributed global P content for cropland soils to assess global soil P loss. The world’s soils are currently being depleted in P in spite of high chemical fertilizer input. Africa (not being able to afford the high costs of chemical fertilizer) as well as South America (due to non-efficient organic P management) and Eastern Europe (for a combination of the two previous reasons) have the highest P depletion rates. In a future world, with an assumed absolute shortage of mineral P fertilizer, agricultural soils worldwide will be depleted by between 4–19 kg ha−1 yr−1, with average losses of P due to erosion by water contributing over 50% of total P losses., Phosphorus is an essential nutrient critical for agriculture, but because it is non-renewable its future availability is threatened. Here the authors show that across the globe most nations have net losses of phosphorus, with soil erosion as the major route of loss in Europe, Africa and South America.

Soil erosion is one of the eight threats in the Soil Thematic Strategy, the main policy instrument dedicated to soil protection in the European Union (EU). During the last decade, soil erosion indicators have been included in monitoring the performance of the Common Agricultural Policy (CAP) and the progress towards the Sustainable Development Goals (SDGs). This study comes five years after the assessment of soil loss by water erosion in the EU [Environmental science & policy 54, 438–447 (2015)], where a soil erosion modelling baseline for 2010 was developed. Here, we present an update of the EU assessment of soil loss by water erosion for the year 2016. The estimated long-term average erosion rate decreased by 0.4% between 2010 and 2016. This small decrease of soil loss was due to a limited increase of applied soil conservation practices and land cover change observed at the EU level. The modelling results suggest that, currently, ca. 25% of the EU land has erosion rates higher than the recommended sustainable threshold (2 t ha−1 yr−1) and more than 6% of agricultural lands suffer from severe erosion (11 t ha−1 yr−1). The results suggest that a more incisive set of measures of soil conservation is needed to mitigate soil erosion across the EU. However, targeted measures are recommendable at regional and national level as soil erosion trends are diverse between countries which show heterogeneous application of conservation practices.

This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO3), C:N ratio, nitrogen (N), phosphorus (P) and potassium (K). The LUCAS survey collected harmonised data on changes in land cover and the state of land use for the European Union (EU). Among the 270,000 land use and cover observations selected for field visit, approximately 20,000 soil samples were collected in 24 EU Member States in 2009 together with more than 2000 samples from Bulgaria and Romania in 2012. The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. GPR was selected for its capacity to assess model uncertainty and the possibility of adding prior knowledge in the form of covariance functions to the model. The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union., Highlights • The LUCAS harmonised soil survey comprising 20,000 observations was used in this study. • Soil chemical properties were mapped over the extent of Europe using Gaussian process regression (GPR). • GPR with Matérn kernel modelled soil properties with high accuracy. • GPR allows the production of uncertainty maps as it allows the estimation of prediction variance. • These maps complete the series of topsoil properties for Europe based on LUCAS.