Abstract
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.
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.
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•We predicted present and future SOC stocks using climate and land cover change scenarios.•The model produced two main outputs: present and future (2050) SOC stocks in Europe.•The results suggest an overall increase in SOC stocks by 2050 for selected Global Climate Models.•The extents of the increase in SOC stocks vary by different GCMs and their RCPs.
To give soils and soil degradation, which are among the most crucial threats to ecosystem stability, social and political visibility, small and large scale modelling and mapping of soil erosion is ...inevitable. The most widely used approaches during an 80year history of erosion modelling are Universal Soil Loss Equation (USLE)-type based algorithms which have been applied in 109 countries. Addressing soil erosion by water (excluding gully erosion and land sliding), we start this review with a statistical evaluation of nearly 2,000 publications). We discuss model developments which use USLE-type equations as basis or side modules, but we also address recent development of the single USLE parameters (R, K, LS, C, P). Importance, aim and limitations of model validation as well as a comparison of USLE-type models with other erosion assessment tools are discussed. Model comparisons demonstrate that the application of process-based physical models (e.g., WEPP or PESERA) does not necessarily result in lower uncertainties compared to more simple structured empirical models such as USLE-type algorithms. We identified four key areas for future research: (i) overcoming the principally different nature of modelled (gross) versus measured (net) erosion rates, in coupling on-site erosion risk to runoff patterns, and depositional regime, (ii) using the recent increase in spatial resolution of remote sensing data to develop process based models for large scale applications, (iii) strengthen and extend measurement and monitoring programs to build up validation data sets, and (iv) rigorous uncertainty assessment and the application of objective evaluation criteria to soil erosion modelling.
Most common author keywords used in USLE/RUSLE papers of the last 40 years excluding geographical terms. Size of keywords represents frequency of use. Display omitted
•Soil erosion modelling is crucially needed for soil monitoring and mapping.•Critical debate on USLE models requires discussion of model concepts and suitability.•USLE type modelling strives from purely empirical to more process based concepts.•Model comparisons illustrate that there is not one superior model.•Model concepts need to be chosen dependent on data availability and project needs.
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 500m) 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 0.032thahha−1MJ−1mm−1 with a standard deviation of 0.009thahha−1MJ−1mm−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.
•Soil erodibility in Europe is estimated at 0.032thahha−1MJ−1mm−1.•Stoniness has an important impact in Mediterranean countries.•High resolution (500m grid cell) dataset of K-factor is available for modelling.•Coarse fragments, permeability and soil structure were considered in K-factor.•K-factor map has very good correspondence with regional data in literature studies.
•LANDUM is a new model for estimating the C-factor in soil erosion modelling in Europe.•C-factor in arable lands combines crop composition and management practices.•Reduced tillage is the most ...efficient compared to plant residues and winter crop.•The 3 management practices reduce C-factor by 19.1% in European arable lands.•Fraction of vegetation cover is taken into account in C-factor for non-arable lands.
Land use and management influence the magnitude of soil loss. Among the different soil erosion risk factors, the cover-management factor (C-factor) is the one that policy makers and farmers can most readily influence in order to help reduce soil loss rates. The present study proposes a methodology for estimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE Land Cover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops and practices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data on management practices such as conservation tillage, plant residues and winter crop cover. The C-factor in non-arable lands was estimated by weighting the range of literature values found according to fractional vegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor in the EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor (0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651, respectively). Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reduce the C-factor by on average 19.1% in arable lands.
The methodology is designed to be a tool for policy makers to assess the effect of future land use and crop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arable land expansion) and the effect of policies (such as the Common Agricultural Policy and the push to grow more renewable energy crops) can potentially be quantified with the proposed model. The C-factor data and the statistical input data used are available from the European Soil Data Centre.
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
−
7
+
9.2
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.
Much research has been carried out on modelling soil erosion rates under different climatic and land use conditions. Although some studies have addressed the issue of reduced crop productivity due to ...soil erosion, few have focused on the economic loss in terms of agricultural production and gross domestic product (GDP). In this study, soil erosion modellers and economists come together to carry out an economic evaluation of soil erosion in the European Union (EU). The study combines biophysical and macroeconomic models to estimate the cost of agricultural productivity loss due to soil erosion by water in the EU. The soil erosion rates, derived from the RUSLE2015 model, are used to estimate the loss in crop productivity (physical change in the production of plants) and to model their impact on the agricultural sector per country. A computable general equilibrium model is then used to estimate the impact of crop productivity change on agricultural production and GDP. The 12 million hectares of agricultural areas in the EU that suffer from severe erosion are estimated to lose around 0.43% of their crop productivity annually. The annual cost of this loss in agricultural productivity is estimated at around €1.25 billion. The computable general equilibrium model estimates the cost in the agricultural sector to be close to €300 million and the loss in GDP to be about €155 million. Italy emerges as the country that suffers the highest economic impact, whereas the agricultural sector in most Northern and Central European countries is only marginally affected by soil erosion losses.
•RUSLE has been coupled with the MAGNET model to estimate the global economic impacts of soil erosion.•The estimated annual cost to global GDP is eight billion US dollars.•Global agri-food production ...is reduced by 33.7 million tonnes.•Abstracted water volumes are driven upwards by an estimated 48 billion cubic meters.•Soil erosion accelerates shifts in comparative advantage on world agri-food markets.
Employing a linkage between a biophysical and an economic model, this study estimates the economic impact of soil erosion by water on the world economy. The global biophysical model estimates soil erosion rates, which are converted into land productivity losses and subsequently inserted into a global market simulation model. The headline result is that soil erosion by water is estimated to incur a global annual cost of eight billion US dollars to global GDP. The concomitant impact on food security is to reduce global agri-food production by 33.7 million tonnes with accompanying rises in agri-food world prices of 0.4%–3.5%, depending on the food product category. Under pressure to use more marginal land, abstracted water volumes are driven upwards by an estimated 48 billion cubic meters. Finally, there is tentative evidence that soil erosion is accelerating the competitive shifts in comparative advantage on world agri-food markets.
•RUSLE2015 model estimates soil loss at 100 m resolution based on best available data.•The mean soil loss rate in European Union is estimated to 2.46 t/ha annually.•Policy interventions (CAP) reduced ...overall soil loss by 9.5% during last decade.•12.7% of European arable lands have soil loss >5 t/ha annually requiring protection.•Among all land uses, arable and sparse vegetation have the highest soil loss rates.
Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European Commission's Soil Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has proposed an approach to monitor soil erosion. This paper presents the application of a modified version of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover-Management, Topography, Support practices) are modelled with the most recently available pan-European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100m), peer-reviewed input layers. The mean soil loss rate in the European Union's erosion-prone lands (agricultural, forests and semi-natural areas) was found to be 2.46 t ha−1 yr−1, resulting in a total soil loss of 970 Mt annually.
A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land-use changes and support practices. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU's guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). The policy interventions (GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which currently have unsustainable soil loss rates of more than 5 t ha−1 yr−1, and to which policy measures should be targeted.
Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, ...worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr
of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.
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