Publications in Journals

Soils encompass a huge diversity of organisms which mostly remains to be characterized due to a number of methodological and logistical issues. Nonetheless, remarkable progress has been made in recent years toward developing strategies to characterize and describe soil biodiversity, especially thanks to the development of molecular approaches relying on direct DNA extraction from the soil matrix.

Metabarcoding can be applied to DNA from any environment or organism, and is gaining increasing prominence in biodiversity studies. This approach is already commonly used to characterize soil microbial communities and its application is now being extended to other soil organisms, i.e. meso- and macro-fauna.

These developments offer unprecedented scientific and operational opportunities in order to better understand soil biodiversity distribution and dynamics, and to propose tools and strategies for biodiversity diagnosis. However, these opportunities also come with challenges that the scientific community must face. Such challenges are related to i) clarification of terminology, (ii) standardisation of methods and further methodological development for additional taxonomic groups, (iii) development of a common database, and (iv) ways to avoid waste of information and data derived from metabarcoding. In order to facilitate common application of metabarcoding in soil biodiversity assessment, we discuss these opportunities and challenges and propose solutions towards a more homogeneous framework.

https://www.sciencedirect.com/science/article/pii/S0038071714003617

The establishment of the range of soil biodiversity found within European soils is needed to guide EU policy development regarding the protection of soil. Such a base-line should be collated from a wide-ranging sampling campaign to ensure that soil biodiversity from the majority of soil types, land-use or management systems, and European climatic (bio-geographical zones) were included. This paper reports the design and testing of a method to achieve the large scale sampling associated with the establishment of such a baseline, carried out within the remit of the EcoFINDERS project, and outlines points to consider when such a task is undertaken.

Applying a GIS spatial selection process, a sampling campaign was undertaken by 13 EcoFINDERS partners across 11 countries providing data on the range of indicators of biodiversity and ecosystem functions including; micro and meso fauna biodiversity, extracellular enzyme activity, PLFA and community level physiological profiling (MicroResp™ and Biolog™). Physical, chemical and bio-geographical parameters of the 81 sites sampled were used to determine whether the model predicted a wide enough range of sites to allow assessment of the biodiversity indicators tested.

Discrimination between the major bio-geographical zones of Atlantic and Continental was possible for all land-use types. Boreal and Alpine zones only allowed discrimination in the most common land-use type for that area e.g. forestry and grassland sites, respectively, while the Mediterranean zone did not have enough sites sampled to draw conclusions across all land-use types. The method used allowed the inclusion of a range of land-uses in both the model prediction stage and the final sites sampled. The establishment of the range of soil biodiversity across Europe is possible, though a larger targeted campaign is recommended. The techniques applied within the EcoFINDERS sampling would be applicable to a larger campaign

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300342

Because of the increasing pressures exerted on soil, below-ground life is under threat. Knowledge-based rankings of potential threats to different components of soil biodiversity were developed in order to assess the spatial distribution of threats on a European scale. A list of 13 potential threats to soil biodiversity was proposed to experts with different backgrounds in order to assess the potential for three major components of soil biodiversity: soil microorganisms, fauna, and biological functions. This approach allowed us to obtain knowledge-based rankings of threats. These classifications formed the basis for the development of indices through an additive aggregation model that, along with ad-hoc proxies for each pressure, allowed us to preliminarily assess the spatial patterns of potential threats. Intensive exploitation was identified as the highest pressure. In contrast, the use of genetically modified organisms in agriculture was considered as the threat with least potential. The potential impact of climate change showed the highest uncertainty. Fourteen out of the 27 considered countries have more than 40% of their soils with moderate-high to high potential risk for all three components of soil biodiversity. Arable soils are the most exposed to pressures. Soils within the boreal biogeographic region showed the lowest risk potential. The majority of soils at risk are outside the boundaries of protected areas. First maps of risks to three components of soil biodiversity based on the current scientific knowledge were developed. Despite the intrinsic limits of knowledge-based assessments, a remarkable potential risk to soil biodiversity was observed. Guidelines to preliminarily identify and circumscribe soils potentially at risk are provided. This approach may be used in future research to assess threat at both local and global scale and identify areas of possible risk and, subsequently, design appropriate strategies for monitoring and protection of soil biota.

https://www.sciencedirect.com/science/article/pii/S004896971531247X

The selection of biological indicators for monitoring progress towards policy goals for soil quality should be without bias and in line with individual scenarios of need. Here we describe the prescription of a suite of appropriate indicators for potential application in such monitoring schemes across Europe. We applied a structured framework of assessment and ranking (viz. a ‘logical sieve’), building upon published data and a new survey taken from a wide section of the global soil biodiversity research and policy community.

The top ten indicators included four indicators of biodiversity (three microbial and one meso-faunal) by various methods of measurement, and three indicators of ecological function (Multiple enzyme assay, Multiple substrate-induced respiration profiling, and ‘Functional genes by molecular biological means’). Within the techniques assessed, seven out of the top ten indicators made use of molecular methods.

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300585

Existing data sets on earthworm communities in Europe were collected, harmonized, collated, modelled and depicted on a soil biodiversity map. Digital Soil Mapping was applied using multiple regressions relating relatively low density earthworm community data to soil characteristics, land use, vegetation and climate factors (covariables) with a greater spatial resolution. Statistically significant relationships were used to build habitat–response models for maps depicting earthworm abundance and species diversity. While a good number of environmental predictors were significant in multiple regressions, geographical factors alone seem to be less relevant than climatic factors. Despite differing sampling protocols across the investigated European countries, land use and geological history were the most relevant factors determining the demography and diversity of the earthworms. Case studies from country-specific data sets (France, Germany, Ireland and The Netherlands) demonstrated the importance and efficiency of large databases for the detection of large spatial patterns that could be subsequently applied at smaller (local) scales.

https://www.sciencedirect.com/science/article/abs/pii/S0929139315300688

Over the last 2 decades, geospatial technologies such as Geographic Information System and spatial interpolation methods have facilitated the development of increasingly accurate spatially explicit assessments of soil erosion. Despite these advances, current modelling approaches rest on (a) an insufficient definition of the proportion of arable land that is exploited for crop production and (b) a neglect of the intra‐annual variability of soil cover conditions in arable land. To overcome these inaccuracies, this study introduces a novel spatio‐temporal approach to compute an enhanced cover‐management factor (C) for revised universal soil loss equation‐based models. It combines highly accurate agricultural parcel information contained in the Land Parcel Identification System with an object‐oriented Landsat imagery classification technique to assess spatial conditions and interannual variability of soil cover conditions at field scale. With its strong link to Land Parcel Identification System and Earth observation satellite data, the approach documents an unprecedented representation of farming operations. This opens the door for the transition from the currently used potential soil erosion risk assessments towards the assessment of the actual soil erosion risk. Testing this method in a medium‐size catchment located in the Swiss Plateau (Upper Enziwigger River Catchment), this study lays an important foundation for the application of the very same methods for large‐scale or even pan‐European applications. Soil loss rates modelled in this study were compared with the insights gained from emerging techniques to differentiate sediment source contribution through compound‐specific isotope analysis on river sediments. The presented technique is adaptable beyond revised universal soil loss equation‐type soil erosion models.

https://onlinelibrary.wiley.com/doi/abs/10.1002/ldr.2898

Soil erodibility, commonly expressed as the K‐factor in USLE‐type erosion models, is a crucial parameter for determining soil loss rates. However, a national soil erodibility map based on measured soil properties did so far not exist for Switzerland. As an EU non‐member state, Switzerland was not included in previous soil mapping programs such as the Land Use/Cover Area frame Survey (LUCAS). However, in 2015 Switzerland joined the LUCAS soil sampling program and extended the topsoil sampling to mountainous regions higher 1500 m asl for the first time in Europe. Based on this soil property dataset we developed a K‐factor map for Switzerland to close the gap in soil erodibility mapping in Central Europe. The K‐factor calculation is based on a nomograph that relates soil erodibility to data of soil texture, organic matter content, soil structure, and permeability. We used 160 Swiss LUCAS topsoil samples below 1500 m asl and added in an additional campaign 39 samples above 1500 m asl. In order to allow for a smooth interpolation in context of the neighboring regions, additional 1638 LUCAS samples of adjacent countries were considered. Point calculations of K‐factors were spatially interpolated by Cubist Regression and Multilevel B‐Splines. Environmental features (vegetation index, reflectance data, terrain, and location features) that explain the spatial distribution of soil erodibility were included as covariates. The Cubist Regression approach performed well with an RMSE of 0.0048 t ha h ha⁻¹ MJ⁻¹ mm⁻¹. Mean soil erodibility for Switzerland was calculated as 0.0327 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ with a standard deviation of 0.0044 t ha h ha⁻¹ MJ⁻¹ mm⁻¹. The incorporation of stone cover reduces soil erodibility by 8.2%. The proposed Swiss erodibility map based on measured soil data including mountain soils was compared to an extrapolated map without measured soil data, the latter overestimating erodibility in mountain regions (by 6.3%) and underestimating in valleys (by 2.5%). The K‐factor map is of high relevance not only for the soil erosion risk of Switzerland with a particular emphasis on the mountainous regions but also has an intrinsic value of its own for specific land use decisions, soil and land suitability and soil protection.

https://onlinelibrary.wiley.com/doi/full/10.1002/jpln.201800128

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.

https://www.mdpi.com/2072-4292/12/9/1365

The EU Thematic Strategy for Soil Protection identified soil degradation caused by erosion as one of the major threats to European soils. A thorough literature review revealed important gaps in research on soil erosion processes in Europe. This is particularly true for wind erosion processes. The current state of the art in erosion research lacks knowledge about where and when wind erosion occurs in Europe, and the intensity of erosion that poses a threat to agricultural productivity. To gain a better understanding of the geographical distribution of wind erosion processes in Europe, we propose an integrated mapping approach to estimate soil susceptibility to wind erosion. The wind-erodible fraction of soil (EF) is one of the key parameters for estimating the susceptibility of soil to wind erosion. It was computed for 18,730 geo-referenced topsoil samples (from the Land Use/Land Cover Area frame statistical Survey (LUCAS) dataset). Our predication of the spatial distribution of the EF and a soil surface crust index drew on a series of related but independent covariates, using a digital soil mapping approach (Cubist-rule-based model to calculate the regression, and Multilevel B-Splines to spatially interpolate the Cubist residuals). The spatial interpolation showed a good performance with an overall R2 of 0.89 (in fitting). We observed the spatial patterns of the soils' susceptibility to wind erosion, in line with the state of the art in the literature. We used regional observations in Lower Saxony and Hungary to ensure the applicability of our approach. These regional control areas showed encouraging results, and indicated that the proposed map may be suitable for national and regional investigations of spatial variability and analyses of soil susceptibility to wind erosion.

https://www.sciencedirect.com/science/article/pii/S0016706114002389

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950–2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios.

https://www.sciencedirect.com/science/article/pii/S0048969716305095

In the European Union (EU), copper concentration in agricultural soil stems from anthropogenic activities and natural sources (soil and geology). This manuscript reports a statistical comparison of copper concentrations at different levels of administrative units, with a focus on agricultural areas. Anthropogenic sources of diffuse copper contamination include fungicidal treatments, liquid manure (mainly from pigs), sewage sludge, atmospheric deposition, mining activities, local industrial contamination and particles from car brakes. Sales of fungicides in the EU are around 158,000 tonnes annually, a large proportion of which are copper based and used extensively in vineyards and orchards. Around 10 million tonnes of sewage sludge is treated annually in the EU, and 40% of this (which has a high copper content) is used as fertilizer in agriculture. In the EU, 150 million pigs consume more than 6.2 million tonnes of copper through additives in their feed, and most of their liquid manure ends up in agricultural soil. These three sources (sales of fungicides, sewage sludge and copper consumption for pigs feed) depend much on local traditional farming practices. Recent research towards replacing copper spraying in vineyards and policy developments on applying sewage and controlling the feed given to pigs are expected to reduce copper accumulation in agricultural soil.

https://www.mdpi.com/2071-1050/10/7/2380

Soil is a multifunctional, non-renewable natural resource for Europe as clearly expressed in the European Union (EU) Thematic Strategy for Soil Protection (COM (2006)231). Soil carries out multiple functions, including the support of food production. Urban development and its associated land take poses a major threat to soil and could have significant effects on agricultural production. This paper aims to evaluate the potential productivity losses in European agriculture due to land-take processes between 1990 and 2006. Agricultural land take was calculated using CORINE Land Cover maps of 1990, 2000 and 2006. For 21 of the 27 EU member states, agricultural land take was computed to be 752,973 ha for 1990–2000 and 436,095 ha for 2000–2006, representing 70.8% and 53.5%, respectively, of the total EU land take for these periods. The impact of this land take on the production capabilities of the agricultural sector for the period 1990–2006 for 19 of the 21 states was estimated to be equivalent to a loss of more than six million tonnes of wheat. The paper demonstrates that Europe's intense urbanisation has a direct impact on its capability to produce food.

https://www.tandfonline.com/doi/abs/10.1080/09640568.2014.899490 

The utilization of crop residues in the production of second-generation biofuels has the potential to boost the bioenergy sector without affecting food commodity prices. However, policies leading to large-scale biomass removal should carefully balance the consequences, both environmental and in terms of emissions, on soil organic carbon (SOC) stocks depletion. Using a recently developed simulation platform, SOC changes were estimated at European level (EU + candidate and potential candidate countries) under two scenarios of low (R30) and high (R90) maize stover removal for cellulosic ethanol production (i.e. 30 and 90 % of stover removal, respectively). Additionally, mitigation practices for SOC preservation, namely the introduction of a ryegrass cover crop (R90_C) and biodigestate return to soil (R90_B), were explored under the highest rate of stover removal. The results showed that 15.3 to 50.6 Mt year⁻¹ of stover (dry matter) would be potentially available for ethanol production under the lower and high removal rates considered. However, large-scale exploitation of maize residues will lead to a SOC depletion corresponding to 39.7–135.4 Mt CO₂ eq. by 2020 (under R30 and R90, respectively) with greater losses in the long term. In particular, every tonne of C residue converted to bioethanol was predicted to have an additional impact on SOC loss almost ranging from 0.2 to 0.5 CO₂ eq. ha⁻¹ year⁻¹, considering a continuous biofuel scenario by 2050. The mitigation practices evaluated could more than halve SOC losses compared to R90, but not totally offsetting the negative soil C balance. There is a pressing need to design policies at EU level for optimum maize biofuel cultivations that will preserve the current SOC stock or even generate C credits.

https://link.springer.com/article/10.1007/s12155-014-9529-2

This study aimed to assess the effects of forest management on the occurrence of accelerated soil erosion by water. The study site is located in a mountainous area of the Italian Central Apennines. Here, forest harvesting is a widespread forestry activity and is mainly performed on the moderate to steep slopes of the highlands. Through modeling operations based on data on soil properties and direct monitoring of changes in the post-forest-harvesting soil surface level at the hillslope scale, we show that the observed site became prone to soil erosion after human intervention. Indeed, the measured mean soil erosion rate of 49 t ha^− 1 yr^− 1 for the harvested watershed is about 21 times higher than the rate measured in its neighboring undisturbed forested watershed (2.3 t ha^− 1 yr^− 1). The erosive response is greatly aggravated by exposing the just-harvested forest, with very limited herbaceous plant cover, to the aggressive attack of the heaviest annual rainfall without adopting any conservation practices. The erosivity of the storms during the first four months of field measurements was 1571 MJ mm h^− 1 ha^− 1 in total (i.e., from September to December 2008). At the end of the experiment (16 months), 18.8%, 26.1% and 55.1% of the erosion monitoring sites in the harvested watershed recorded variations equal or greater than 0–5, 5–10 and > 10 mm, respectively. This study also provides a quantification of Italian forestland surfaces with the same pedo-lithological characteristics exploited for wood supply. Within a period of ten years (2002–2011), about 9891 ha of coppice forest changes were identified and their potential soil erosion rates modeled.

https://www.sciencedirect.com/science/article/pii/S0169555X13004273

A new soil erosion model, namely G2, was applied in the island of Crete with a focus on agricultural land uses, including potential grazing lands. The G2 model was developed within the Geoland2 project as an agro-environmental service in the framework of the Global Monitoring for Environment and Security (GMES, now Copernicus) initiative. The G2 model takes advantage of the empirical background of the Universal Soil Loss Equation (USLE) and the Gavrilovic model, together with readily available time series of vegetation layers and 10-min rainfall intensity data to produce monthly time-step erosion risk maps at 300 m cell size. The innovations of the G2 model include the implementation of land-use influence parameters based on empirical data and the introduction of a corrective term in the estimation of the topographic influence factor. The mean annual erosion rate in Crete was found to be 8.123 t ha⁻¹. The season from October to January (the rainy season in Crete) was found to be the most critical, accounting for 80% of the annual erosion in the island. Seasonal erosion figures proved to be crucial for the identification of erosion hotspots and of risky land uses. In Crete, high annual erosion figures were detected in natural grasslands and shrublands (14.023 t ha⁻¹), mainly due to the intensification of livestock grazing during the past decades. The G2 model allows for the integrated spatio-temporal monitoring of soil erosion per land-use type based on moderate data input requirements and existing datasets.

https://www.sciencedirect.com/science/article/pii/S0303243413001116

Arbuscular Mycorrhizal Fungi (AMF) are well known for their ecological importance and their positive influence on plants. The genetics and phylogeny of this group of fungi have long been debated. Nuclear markers are the main tools used for phylogenetic analyses, but they have sometimes proved difficult to use because of their extreme variability. Therefore, the attention of researchers has been moving towards other genomic markers, in particular those from the mitochondrial DNA. In this study, 46 sequences of different AMF isolates belonging to two main clades Gigasporaceae and Glomeraceae have been obtained from the mitochondrial gene coding for the Cytochrome c Oxidase I (COI), representing the largest dataset to date of AMF COI sequences. A very low level of divergence was recorded in the COI sequences from the Gigasporaceae, which could reflect either a slow rate of evolution or a more recent evolutionary divergence of this group. On the other hand, the COI sequence divergence between Gigasporaceae and Glomeraceae was high, with synonymous divergence reaching saturated levels. This work also showed the difficulty in developing valuable mitochondrial markers able to effectively distinguish all Glomeromycota species, especially those belonging to Gigasporaceae, yet it represents a first step towards the development of a full mtDNA-based dataset which can be used for further phylogenetic investigations of this fungal phylum.

https://www.sciencedirect.com/science/article/pii/S1055790314000633

In this article, an extensive inventory in the literature of water erosion modelling from a geospatial point of view is conducted. Concepts of scale, spatiality and complexity are explored and clarified in a theoretical background. Use of Geographic Information Systems (GIS) is pointed out as facilitating data mixing and model rescaling and thus increasing complexity in data-method relations. Spatial scale, temporal scale and spatial methodologies are addressed as the most determining geospatial properties underlying water erosion modelling. Setting these properties as classification criteria, 82 water erosion models are identified and classified into eight categories. As a result, a complete overview of water erosion models becomes available in a single table. The biggest share of the models is found in the category of the mechanistic pathway-type event-based models for watershed to landscape scales. In parallel, geospatial innovations that could be considered as milestones in water erosion modelling are highlighted and discussed. An alphabetical list of all models is also listed in the Appendix. For manipulating scale efficiently, two promising spatial theories are suggested for further exploitation in the future such as hierarchy theory and fractals theory. Regarding erosion applications, uncertainty analysis within GIS is considered to be necessary for further improving performance of erosion models.

https://www.tandfonline.com/doi/abs/10.1080/17538947.2012.671380

There has been the recognition that trans-national binding legal agreements related to soils are very difficult to achieve, given the sensitivities related to national sovereignty in relation to land and soils. However, the Soil Thematic Strategy played an important role to raise the awareness of soil importance, integrate soils in different policy areas (agriculture, climate change, SDGs), develop research findings and finally increase our know how on European Soils. The Soil Thematic Strategy continue to be the main policy instrument dedicated to foster soil protection in the European Union (EU). The EU consider the importance of soils and land degradation taking into account global challenges such as the sustainable production intensification, food security, climate change and escalating population growth. During the last decade both the 7th Framework Programme for Research (FP7) and the HORIZON2020 financed research and innovation projects for advancing soil protection and better understanding of soil management in EU.

https://www.sciencedirect.com/science/article/pii/S2468584417300533

The overall aim of this research was to investigate the spatial patterns of the soil erosion risk. We focused on accelerated soil erosion processes in an Italian central Apennine intermountain watershed using modeling techniques implemented in a GIS environment. Our thorough literature review revealed a gap in research on soil erosion processes in such forested, intermountain watersheds. To gain a better understanding of the soil erosion processes in such landscapes, we proposed an integrated modeling approach applying a RUSLE model and a Turbidity Unit Index. The model outcomes were validated through measurements of lake sediment deposition. Our findings indicate a potential high soil erosion risk. With 1.33 M t^− 1 yr^− 1 of annual sediment yield, corresponding to an area-specific sediment yield of 32.35 t ha^− 1 yr^− 1, the Turano drainage basin belongs to the Italian basins with the highest sediment discharge. The outcomes of the RUSLE model showed that, despite the diverse forms of forests that cover about 62% of the drainage basin area, sizable plots of the investigated area are prone to soil erosion. The validation of the model outcomes revealed that the TU Index model performed significantly better than the RUSLE model with regard to sediment yield prediction. Accordingly, we found that even though rill and interrill processes reach very alarming values (RUSLE), they are not the dominant sediment source within the Turano watershed. Other geomorphological processes contributing to the watershed sediment yield – for instance, megarill, gully, bank and channel erosion and re-entrainment of landslide sediments – were very active in the study area. If both models are used in a combined approach, the amount of river load (TU Index) as well as the relative spatial distribution of rill and interrill erosion processes (RUSLE) can be described with sufficient precision.

https://www.sciencedirect.com/science/article/pii/S0341816213002518

This study presents a thorough approach, based on the application of multi-spectral remote sensing Landsat imagery, to determine human-induced forest cover change in Italy during the decade 2002–2011. A total of 785.6 ×1 0⁴ ha of forestland was mapped using the main forest classes described within the CORINE land cover 2006 database (3.11 – broad-leaved forest; 3.12 – coniferous forest; 3.13 – mixed forest). The approach employs multi-temporal Landsat imagery to determine large-scale spatiotemporal variations in forest cover with a high degree of precision. The semi-automated procedure is based on Normalized Difference Vegetation Index (NDVI) pixel-oriented image differencing technique. The results were validated and rectified as a result of on-screen visual interpretation, whereby all the false-positive forest changes that were incorrectly mapped during the automatic procedure were identified and removed. The derived high-resolution data of forest cover change show that 317,535 ha (4.04% of the total forest area in Italy) were harvested during the period under review. The 125,272 individual clear-cut areas identified are mainly located within protected areas of the European Natura 2000 network. The outcome of this study is a publicly accessible database that can encourage further studies in the framework of international biodiversity and soil protection conventions (http://eusoils.jrc.ec.europa.eu/library/themes/erosion/italy/). The methodology can contribute to the monitoring of human-induced forest changes in support of the Kyoto Protocol.

https://www.sciencedirect.com/science/article/pii/S014362281400006X

The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha^− 1 MJ^− 1 mm^− 1 with a standard deviation of 0.009 t ha h ha^− 1 MJ^− 1 mm^− 1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.

https://www.sciencedirect.com/science/article/pii/S0048969714001727

Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout the country. The almost 30-ha sampling surfaces were examined with visual interpretation of multi-temporal imagery from the online Google Earth for the period 2002–2019. In parallel, rill and sheet erosion signs, land uses, and presence of terraces and other anti-erosion features, were recorded within every sample. One hundred fifty-three ephemeral gullies were identified in total, inside 22 examined agricultural surfaces. The mean length of the gullies was 55.6 m, with an average slope degree of 9.7%. Vineyards showed the largest proportion of gullies followed by olive groves and arable land, while pastures exhibited limited presence of gullies. Spatial clusters of high gully severity were observed in the north and east of the country. In 77% of the surfaces with gullies, there were no terraces, although most of these surfaces were situated in slopes higher than 8%. It was the first time to use visual interpretation with Google Earth image time-series on a country scale producing a gully erosion inventory. Soil conservation practices such as contour farming and terraces could mitigate the risk of gully erosion in agricultural areas

https://www.mdpi.com/2073-4441/12/2/603

This study presents the updated version of the recently published LANDUM model [Land Use Policy 48, 38–50 (2015)]. LANDUM is integrated into the 100 m resolution RUSLE-based pan-European soil erosion risk modelling platform of the European Commission. It estimates the effects of local land use and management practices on the magnitude of soil erosion across each NUTS2 region of the European Union. This is done based on a spatially explicit estimation of the so-called cover-management factor of (R)USLE family models which is also known as C-factor. In this updated version, the data on soil conservation measures (i.e., reduced tillage, cover crops and plant residues) reported in the latest EU Farm Structure Survey (2016) were integrated and elaborated in LANDUM in order to estimate the changes of the C-factor in Europe between 2010 and 2016. For 2016, a C-factor of 0.2316 for the arable land of the 28 Member States of the European Union was estimated. This implies an overall decrease of C-factor of ca. -0.84 % compared to the 2010 survey. The change in C-factor from 2010 to 2016 could be an indication for the effectiveness of Common Agricultural Policy (CAP) soil conservation measures in reducing soil erosion in Europe, especially key CAP policies such as Good Agricultural and Environmental Conditions and Greening.

https://www.sciencedirect.com/science/article/pii/S026483771931172X

Soil degradation due to erosion is connected to two serious environmental impacts: (i) on-site soil loss and (ii) off-site effects of sediment transfer through the landscape. The potential impact of soil erosion processes on biogeochemical cycles has received increasing attention in the last two decades. Properly designed modelling assumptions on effective soil loss are a key pre-requisite to improve our understanding of the magnitude of nutrients that are mobilized through soil erosion and the resultant effects. The aim of this study is to quantify the potential spatial displacement and transport of soil sediments due to water erosion at European scale. We computed long-term averages of annual soil loss and deposition rates by means of the extensively tested spatially distributed WaTEM/SEDEM model. Our findings indicate that soil loss from Europe in the riverine systems is about 15% of the estimated gross on-site erosion. The estimated sediment yield totals 0.164 ± 0.013 Pg yr⁻¹ (which corresponds to 4.62 ± 0.37 Mg ha⁻¹ yr⁻¹ in the erosion area). The greatest amount of gross on-site erosion as well as soil loss to rivers occurs in the agricultural land (93.5%). By contrast, forestland and other semi-natural vegetation areas experience an overall surplus of sediments which is driven by a re-deposition of sediments eroded from agricultural land. Combining the predicted soil loss rates with the European soil organic carbon (SOC) stock, we estimate a SOC displacement by water erosion of 14.5 Tg yr⁻¹. The SOC potentially transferred to the riverine system equals to 2.2 Tg yr⁻¹ (~15%). Integrated sediment delivery-biogeochemical models need to answer the question on how carbon mineralization during detachment and transport might be balanced or even off-set by carbon sequestration due to dynamic replacement and sediment burial.

https://www.sciencedirect.com/science/article/pii/S0013935117308137

Over the last two decades or so, there has been many research carried out to understand the mechanics and spatial distribution of soil loss by water erosion and to a lesser extent of wind, piping and tillage erosion. The acquired knowledge helped the development of prediction tools useful to support decision-makers in both ex-ante and ex-post policy evaluation. In Europe, recent studies have modelled water, wind and tillage erosion at continental scale and shed new light on their geography. However, to acquire a comprehensive picture of soil erosion threats more processes need to be addressed and made visible to decision-makers. Since 1986, a small number of studies have pointed to an additional significant soil degradation process occurring when harvesting root and tuber crops. Field observations and measurements have shown that considerable amounts of soil can be removed from the field due to soil sticking to the harvested roots and the export of soil clods during the crop harvest. This study aims to scale up the findings of past studies, carried out at plot, regional, and national level, in order to obtain some preliminary insights into the magnitude of soil loss from cropland due to sugar beets and potatoes harvesting in Europe. We address this issue at European Union (EU) scale taking into account long-term (1975–2016) crop statistics of sugar beet and potato aggregated at regional and country levels.

During the period 2000–2016, sugar beets and potatoes covered in average ca. 4.2 million ha (3.81%) of the EU-28 arable land estimated at 110 million ha. The total Soil Loss by Crop Harvesting (SLCH) is estimated at ca. 14.7 million tons yr⁻¹ in the EU-28. We estimate that ca. 65% of the total SLCH is due to harvesting of sugar beets and the rest as a result of potatoes harvesting.

https://www.sciencedirect.com/science/article/pii/S0048969719304887