Publications in Journals

Soils provide crucial ecosystem services such as the provision of food, carbon sequestration and water purification. Soil is the largest terrestrial pool of carbon, hosts more than 25% of all biodiversity and provides 95–99% of food to 8 billion people. The European Union (EU) puts the concept of healthy soils at the core of the European Green Deal to achieve climate neutrality, zero pollution, sustainable food provision and a resilient environment.

Given the European Union's objective to become the first climate neutral continent by 2050, the European Commission adopted a series of communications for a greener Europe. In 2020, an ambitious package of measures were presented within the Biodiversity 2030, Farm to Fork and Chemicals Strategies, as well as the Circular Economy Action Plan and the European Climate Law, which included actions to protect soils (Montanarella and Panagos, 2021). In 2021, these were followed by the Fit for 55 package, the Zero Pollution Action Plan and the EU Soil Strategy for 2030. All these policies include provisions relevant to soils to achieve the ambitious objectives of the EU Green Deal.

10.1016/j.geodrs.2022.e00510

The European Soil Data Centre (ESDAC), hosted by the European Commission's Joint Research Centre (JRC), is the focal point for soil data, support to policy making and awareness raising for the European Union (EU). Established in 2006 to provide harmonised soil-related information for the EU Member States, and ESDAC currently hosts 88 datasets, 6000 maps, six atlases, 500 scientific publications, and a copious amount of soil-related material. Through its data repository publishing activity, ESDAC has licensed over 50,000 datasets during the past 15 years; 8500 of them in 2021 alone. It has published 140 monthly newsletters and is followed by more than 12,000 subscribed users, which receive regular updates. This article addresses the use, usability, and usefulness of ESDAC. About 75% of the ESDAC users come from academia and the research community while the remaining 25% includes public administration (at EU, national, regional, and local level) and the private sector. In addition, we provide some insights of the datasets evaluation and how they have been developed. The general ESDAC vision is to provide evidence underlying EU soil-relevant policies and to facilitate the access to relevant data for research. ESDAC is an integral part of the recently established European Union Soil Observatory (EUSO), with a target to have an even stronger role in supporting EU and regional policies.

10.1111/ejss.13315

Phosphorus (P) is an important nutrient for all plant growth and it has become a critical and often imbalanced element in modern agriculture. A proper crop fertilization is crucial for production, farmer profits, and also for ensuring sustainable agriculture. The European Commission has published the Farm to Fork (F2F) Strategy in May 2020, in which the reduction of the use of fertilizers by at least 20% is among one of the main objectives. Therefore, it is important to look for the optimal use of P in order to reduce its pollution effects but also ensure future agricultural production and food security. It is essential to estimate the P budget with the best available data at the highest possible spatial resolution. In this study, we focused on estimating the P removal from soils by crop harvest and removal of crop residues. Specifically, we attempted to estimate the P removal by taking into account the production area and productivity rates of 37 crops for 220 regions in the European Union (EU) and the UK. To estimate the P removal by crops, we included the P concentrations in plant tissues (%), the crop humidity rates, the crop residues production, and the removal rates of the crop residues. The total P removal was about 2.55 million tonnes (Mt) (± 0.23 Mt), with crop harvesting having the larger contribution (ca. 94%) compared to the crop residues removal. A Monte-Carlo analysis estimated a ± 9% uncertainty. In addition, we performed a projection of P removal from agricultural fields in 2030. By providing this picture, we aim to improve the current P balances in the EU and explore the feasibility of F2F objectives.

10.1007/s00003-022-01363-3

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.

10.1016/j.jhydrol.2022.127865

Since soil biodiversity sustains above-ground life, the European Union (EU) has recently announced its new Soil Strategy to better protect soil ecosystems as part of the Biodiversity Strategy for 2030. Also, the EU's Farm to Fork Strategy and the Zero Pollution Action Plan aim for soil protection. However, the status of soil biodiversity protection has not been comprehensively assessed. Therefore, we explored regulatory, incentive-based and knowledge-based instruments and strategic policy documents at the EU and national levels to determine whether they adequately protect soil biodiversity. Our review of 507 literature references concluded that only eight EU member states explicitly address threats to soil biodiversity in 14 regulatory instruments while 13 countries mainly focus on implicit threats to soil biodiversity, whereas six countries do not consider soil biodiversity. At the EU level, current directives and regulations only tackle individual threats to soil biodiversity. An EU-wide, legally binding protection could ensure a standardised minimum level of soil biodiversity protection while preventing surging costs of not acting. The EU Soil Health Law foreseen for 2023 could couple land management practices beneficial for soil biodiversity with incentive-based instruments. Simultaneously, models should be designed to predict soil biodiversity, considering soil biodiversity's spatial and temporal heterogeneity.

10.1016/j.biocon.2022.109475

The European Green Deal puts a healthy environment at the core of policy-making initiatives in the European Union (EU). Soil, due to its nature, is a central actor to be considered when developing and implementing actions in many areas, from biodiversity and agriculture to climate and pollution. New means for monitoring the impact of decisions taken in these sectors are needed. The Land Use and Coverage Area frame Survey (LUCAS modules of Soil Biodiversity and Pesticides provide both policy-makers and the research community with tools in this direction. The first component corresponds to the largest analysis of soil biodiversity across the EU through molecular biology techniques. The second component is the most extensive harmonised pan-European assessment of pesticide residues in agricultural soils. Specific features, together with policy and research potential of these instruments for achieving Green Deals targets under the Biodiversity 2030 Strategy, the Farm to Fork Strategy and the Zero Pollution Action Plan, are presented. All generated data are made available to the public through the EU Soil Observatory and European Soil Data Centre.

10.1111/ejss.13299

This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages. Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture.

10.3390/land11122287

Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm-2 h-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively. RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected).

10.1371/journal.pone.0272161
 

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.

10.1038/s41597-022-01489-x

Cover management and support practices largely control the magnitude and variability of soil erosion. Although soil erosion models account for their importance (particularly by C- and P-factors in the Revised Universal Soil Loss Equation), obtaining spatially explicit quantitative field data on these factors remains challenging. Hence, also our insight into the effects of soil conservation measures at larger spatial scales remains limited. We analyzed the variation in C- and P-factors caused by human activities and climatic variables by reviewing 255 published articles reporting measured or calculated C- and P-factor values. We found a wide variation in both factor values across climatic zones, land use or cover types, and support practices. The average C-factor values decreased from arid (0.26) to humid (0.15) climates, whereas the average P-factor values increased (from 0.33 to 0.47, respectively). Thus, support practices reduce soil loss more effectively in drylands and drought-prone areas. The global average C-factor varies by one order of magnitude from cropland (0.34) to forest (0.03). Among the major crops, the average C-factor was highest for maize (0.42) followed by potato (0.40), among the major orchard crops, it was highest for olive (0.31), followed by vineyards (0.26). The P-factor ranged from 0.62 for contouring in cropland plots to 0.19 for trenches in uncultivated land. The C-factor results indicate that cultivated lands requiring intensive site preparation and weeding are most vulnerable to soil loss by sheet and rill erosion. The low P-factor for trenches, reduced tillage cultivation, and terraces suggests that significantly decreased soil loss is possible by implementing more efficient management practices. These results improve our understanding of the variation in C- and P-factors and support large-scale integrated catchment management interventions by applying soil erosion models where it is difficult to empirically determine the impact of particular land use or cover types and support practices: the datasets compiled in this study can support further modeling and land management attempts in different countries and geographic regions.

10.1016/j.iswcr.2021.12.002

Soil erosion is time compressed into a number of episodic erosive rainfall events with an associated potential to detach and transport soil particles (rainfall erosivity), each possessing unique spatial and temporal characteristics. Rainfall erosivity events in Europe follow extreme value distributions in which a limited number of rainstorms dominate the long-term budget of available erosive energy. To combat soil erosion in Europe in a targeted manor, timely erosion mitigation measures should derive from dynamic model simulations that incorporate spatially and temporally distributed estimations of rainfall erosivity. Rain gauge measurements from singular points are typically used to quantify rainfall erosivity, however the spatial uniqueness of rainfall presents a key limitation to dynamically model rainfall across broad spatial scales with a limited number of point measurements. Discretised gridded precipitation datasets with a widespread (e.g. continental) spatial coverage potentially offer an opportunity to adequately replicate the dynamics of rainfall erosivity events, however their performance remains poorly tested in the pan-European context.

This study builds upon the comprehensive Rainfall Erosivity Database at European Scale (REDES) archive of over 300,000 events from 1181 gauge stations to develop a two-step modelling process: 1) firstly, optimal monthly models were fitted and evaluated between gauge-recorded rainfall depth and rainfall erosivity (EI30) across European climatic regions to develop a European-scale parameter surface, 2) secondly, three datasets (EMO-5 (6-hr), E-OBS (24-hr), UERRA MESCAN-SURFEX (24hr)) were directly evaluated via a grid-to-point analysis based on their ability to simulate the station-specific event rainfall erosivity timeseries at a random selection of 32 locations. EMO-5 (Nash-Sutcliffe model efficiency mean = 0.24) outperformed other tested gridded datasets, showing the capability to adequately replicate the event number, timing, and their average magnitude. A higher model performance in Northern compared with Southern European climatic regions, in which characteristically higher and spatially-complex event rainfall erosivity magnitudes are found, was symptomatic of a poor ability of grid-based simulations to replicate the magnitudes of events in the outer extents of the frequency-magnitude spectrum. The absence of a clear global systematic predictive bias amongst simulated locations suggests the need for future upscaling of this analysis to the entire European REDES dataset to fully understand and correct for the method-derived bias in a climate region-specific way.

10.1016/j.catena.2022.106157

The cover management factor (C-factor) calculation requires the assessment of the intra‐annual spatiotemporal variability of biomass cover, owed to the natural growth cycle of vegetation and the impact of agriculture on land cover. However, this is frequently omitted, and the vegetation conditions are approximated by assigning constant values to static classified Land Use/Land Cover (LULC) maps, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC). Using as test site the Sperchios River catchment, Central Greece, this study introduces a new approach to estimate C-factor in a spatiotemporally exhaustive manner. The goal is to increase estimation accuracy in erosion modelling applications. The C-factor computations are performed on monthly scale, based on LULC maps that portray the basin’s agricultural areas in unprecedented detail. The methodology involves the use of a biophysical index, namely Fraction of Vegetation Cover (Fcover) and empirical literature data on crop types. Fcover was developed from Sentinel-2 (S2) imagery in 10-m analysis. Such analysis (compared to the 300-m one provided by the EU) is a major improvement towards a more precise estimation of C-factor. The study identified the monthly C-factor fluctuation at basin scale, and the most susceptible months seasons at localities in terms of land cover/soil loss potential. The higher C-factor values were acquired in October and the lower in May. Mean annual (numerical) C-factor complies with the value of July. All monthly values are significantly higher – almost double – than the mean annual stationary one. The revealed patterns would not have been detected in a lower temporal (e.g., annual) resolution without the incorporation of vegetation density seasonality. The study shows high reproducibility and upscaling potential, as the utilized datasets are available in all European Union (EU) Member States, having similar structure, thus they can be harmonized towards a unified continental approach.

10.1016/j.catena.2022.106149

Promoting sustainable soil management is a possible option for achieving net-zero greenhouse gas emissions in the future. Several efforts in this area exist, and the application of spatially explicit models to anticipate the effect of possible actions on soils at a regional scale is widespread. Currently, models can simulate the impacts of changes on land cover, land management, and the climate on the soil carbon stocks. However, existing modeling tools do not incorporate the lateral transport and deposition of soil material, carbon, and nutrients caused by soil erosion. The absence of these fluxes may lead to an oversimplified representation of the processes, which hinders, for example, a further understanding of how erosion has been affecting the soil carbon pools and nutrients through time. The sediment transport during deposition and the sediment loss to rivers create dependence among the simulation units, forming a cumulative effect through the territory. If, on the one hand, such a characteristic implies that calculations must be made for large geographic areas corresponding to hydrological units, on the other hand, it also can make models computationally expensive, given that erosion and redeposition processes must be modeled at high resolution and over long timescales. In this sense, the present work has a three-fold objective. First, we provide the development details to represent in matrix form a spatially explicit process-based model coupling sediment, carbon, and erosion, transport, and deposition (ETD) processes of soil material in hillslopes and valley bottoms (i.e., the CE-DYNAM model). Second, we illustrate how the model can be calibrated and validated for Europe, where high-resolution datasets of the factors affecting erosion are available. Third, we presented the results for a depositional site, which is highly affected by incoming lateral fluxes from upstream lands. Our results showed that the benefits brought by the matrix approach to CE-DYNAM enabled the before-precluded possibility of applying it on a continental scale. The calibration and validation procedures indicated (i) a close match between the erosion rates calculated and previous works in the literature at local and national scales, (ii) the physical consistency of the parameters obtained from the data, and (iii) the capacity of the model in predicting sediment discharge to rivers in locations observed and unobserved during its calibration (model efficiency (ME) =0.603, R2=0.666; and ME =0.152, R2=0.438, respectively). The prediction of the carbon dynamics on a depositional site illustrated the model's ability to simulate the nonlinear impact of ETD fluxes on the carbon cycle. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models such as ORCHIDEE. We also hope that the patterns obtained in this work can guide future ETD models at a European scale.

10.5194/gmd-15-7835-2022

Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

10.5194/hess-26-1907-2022

10.1016/j.catena.2021.105957

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.

10.1016/j.iswcr.2021.09.002

Climate change is associated with elevated temperatures, more intense rainfalls and longer and hotter droughts. These changes add stress to soil and water resources, which form the foundation for a productive resilient agriculture. Crop management practices often stress soil and water resources leading to loss of soil organic carbon, increased soil erosion and degraded water quality. However, selected management systems can improve soil and water quality or limit their degradation, even in light of anticipated climate change stressors. This chapter identifies approaches to increase the adoption of recognized favorable practices in different countries or regions. Three primary approaches seem to exist: (1) incentive-based with no or minor regulatory component; (2) regulatory-dominated with government exercising authority over producer practice options; and (3) long-term planning addressing spatial and temporal land management elements and adoption of those plans with a combination of government support and regulatory authority.

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We present the developments on soil erosion modelling at European scale to respond to the policy needs. The Joint Research Centre (JRC) of the European Commission has developed the European Soil Erosion Modelling Platform (EUSEMP) to support the agro-environmental policies in the European Union (EU). The major component of EUSEMP is an hybrid soil erosion model named RUSLE2015 (RUSLE-based) to estimate soil loss by water erosion. The model runs with updated input layers (years: 2000, 2010 and 2016) and provides baselines for evaluating the current status of agricultural soils in the EU, evaluating the impact of agri-environmental policies on land management and making projections of soil loss by water erosion in 2050. In addition, EUSEMP includes also the Revised Wind Erosion Equation (RWEQ) to access wind erosion in EU arable lands and another component to access soil loss due to harvesting crops. Another component of EUSEMP is the sediment delivery model WaTEM/SEDEM to estimate net soil loss. Finally, the erosion models are coupled with the biogeochemical CENTURY model to estimate soil organic carbon losses by water erosion. EUSEMP targets to integrate the sediments transfer datasets with soil pollution data (e.g. mercury, copper) and nutrient losses (e.g. phoshorus).

DOI: 10.1007/978-981-16-7916-2_21

We present the version 1.2 of the recently published [Nature communications 8, 2013 (2017)] RUSLE-based Global Soil Erosion Modelling platform (GloSEM). Unlike version 1.1, effects of permanent crops, managed pasture and temporary disturbed forest loss are spatially defined based on a probabilistic land by using allocation approach and their implications for soil erosion are assessed in the advanced version 1.2. For 2012, we estimated an annual total soil erosion of 38.9 Pg/a. This constitutes an increase of ca. 8% compared to the previous version (35.9 Pg/a) which is due to an increase of soil erosion mainly related to the new areas classified as managed pasture and to a lesser extent to permanent crop and forest disturbances.

DOI: 10.1007/978-981-16-7916-2_1

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha−1 year−1. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha−1, while it was only 7% in soils that had > 40 Mg C ha−1. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha−1. For soils with less than 40 Mg C ha−1, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha−1 and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y−1) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha−1 and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates.

DOI: 10.1111/ejss.13338

The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. The detection and monitoring of salinity are critical to the sustainability of soil management. The current study compared the performance of machine learning models to produce spatial maps of electrical conductivity (EC) (as a proxy for salinity) in an alluvial irrigation plain. The current study area is located in the Isparta province (100 km2), land cover is mainly irrigated, and the dominant soils are Inceptisols, Mollisols, and Vertisols. Digital soil mapping (DSM) methodology was used, referring to the increase in the digital representation of soil formation factors with today’s technological advances. Plant and soil-based indices produced from the Sentinel 2A satellite image, topographic indices derived from the digital elevation model (DEM), and CORINE land cover classes were used as predictors. The support vector regression (SVR) algorithm revealed the best relationships in the study area. Considering the estimates of different algorithms, according to the FAO salinity classification, a minimum of 12.36% and a maximum of 20.19% of the study area can be classified as slightly saline. The low spatial dependence between model residuals limited the success of hybrid methods. The land irrigated cover played a significant role in predicting the current level of EC.

DOI: 10.3390/land11122148

The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root-mean-square error  = 0.58 t ha−1) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

10.1002/agj2.21279

Back in the 1930s, the aphorism “publish or perish” first appeared in an academic context. Today, this phrase is becoming a harsh reality in several academic environments, and scientists are giving increasing attention to publishing and disseminating their scientific work. Soil erosion modelers make no exception. With the introduction of the bibliometric field, the evaluation of the impact of a piece of scientific work becomes more articulated. The post-publication impact of the research became an important aspect too. In this study, we analyse the outreach and the impact of the literature on soil erosion modelling using the altmetric database, i.e., Altmetric. In our analysis, we use only a small fraction (around 15%) of Global Applications of Soil Erosion Modelling Tracker (GASEMT) papers because only 257 papers out of 1697 had an Altmetric Score (AS) larger than 0. We observed that media and policy documents mentioned more frequently literature dealing with global-scale assessments and future projection studies than local-scale ones. Papers that are frequently cited by researchers do not necessarily also yield high media and policy outreach. The GASEMT papers that had an AS larger than 0 were, on average, mentioned by one policy document and five Twitter users and had 100 Mendeley readers. Only around 5% and 9% of papers with AS > 0 appeared in news articles and blogs, respectively. However, this percentage was around 45% for Twitter and policy mentions. The top GASEMT paper’s upper bound was around 1 million Twitter followers, while this number was around 10,000 for the 10th ranked GASEMT paper. The exponentially increasing trend for erosion modelling papers having an AS has been confirmed, as during the last 3 years (2014–2017), we estimated that the number of entries had doubled compared to 2011–2014 and quadrupled if we compare it with 2008–2011.

DOI: 10.3390/su14031342

The importance of soils to society has gained increasing recognition over the past decade, with the potential to contribute to most of the United Nations’ Sustainable Development Goals (SDGs). With unprecedented and growing demands for food, water and energy, there is an urgent need for a global effort to address the challenges of climate change and land degradation, whilst protecting soil as a natural resource. In this paper, we identify the contribution of soil science over the past decade to addressing gaps in our knowledge regarding major environmental challenges: climate change, food security, water security, urban development, and ecosystem functioning and biodiversity. Continuing to address knowledge gaps in soil science is essential for the achievement of the SDGs. However, with limited time and budget, it is also pertinent to identify effective methods of working that ensure the research carried out leads to real-world impact. Here, we suggest three strategies for the next decade of soil science, comprising a greater implementation of research into policy, interdisciplinary partnerships to evaluate function trade-offs and synergies between soils and other environmental domains, and integrating monitoring and modelling methods to ensure soil-based policies can withstand the uncertainties of the future.

10.1111/ejss.13145