Indicators for pan-European assessment and monitoring of soil ...

Environmental Science & Policy 7 (2004) 25–38

Indicators for pan-European assessment and monitoring of soil erosion by water A. Gobin a,d,∗ , R. Jones b , M. Kirkby c , P. Campling d , G. Govers a , C. Kosmas e , A.R. Gentile d a

Laboratory for Experimental Geomorphology, Institute for Earth Sciences, Katholieke Universiteit Leuven, Redingenstraat 16, 3000 Leuven, Belgium b European Soil Bureau, Institute for Environment and Sustainability, EU-DG Joint Research Centre, Ispra, Italy c School of Geography, University of Leeds, Leeds LS2 9JT, UK d European Environment Agency, Kongens Nytorv 9, 1050 Copenhagen K, Denmark e Agricultural University of Athens, Iera Odos 75, 118-55 Athena, Greece

Abstract Soil erosion forms a major threat to European soil resources. Although soil is a vital and largely non-renewable resource, it has not been the subject of comprehensive EU action so far. A thematic strategy for soil protection, which recognises soil erosion as one of the major threats, has currently been placed high on Europe’s political Agenda. Assessing and monitoring soil erosion is needed to evaluate the impact of, inter alia, agricultural and land use policies in Europe. The driving force–pressure–state–impact–response (DPSIR) policy framework, applied to soil erosion, is reviewed and suggestions for improvements are proposed. The agri-environmental indicators (AEIs) of soil erosion are discussed in relation to data availability, policy requirements and analytical soundness. We propose a reviewed framework and a set of soil erosion indicators that can be objectively calculated, validated against measurements or observations and evaluated by experts. © 2003 Published by Elsevier Ltd. Keywords: Soil erosion; Agri-environment; Indicator; Policy; DPSIR; Europe

1. Introduction The main problems for soils in the European Union are contamination and irreversible losses due to increasing erosion. It is envisaged that Europe’s soil resource will continue to deteriorate, probably as a result of changes in climate, land use and human activities in general. Soil erosion, in particular, is regarded as one of the major and most widespread forms of land degradation, and as such, poses severe limitations to sustainable agricultural land use. Erosion reduces on-farm soil productivity and contributes to water quality problems as it causes the accumulation of sediments and agrochemicals in waterways. Prolonged erosion causes irreversible soil loss over time, and reduces soil ecological functions such as biomass production and filtering capacity. The dynamic relationship between human activities and the environment requires that environmental processes such as erosion be monitored to evaluate the impact of, inter alia, agricultural and land use policies.

∗

Corresponding author. Tel.: +32-16326428; fax: +32-16326400. E-mail address: [email protected] (A. Gobin).

1462-9011/$ – see front matter © 2003 Published by Elsevier Ltd. doi:10.1016/j.envsci.2003.09.004

The degradation of the environment is an important concern for policy-makers. Many international organisations have recognised environmental problems and launched programmes to monitor progress in reaching sustainable development, as defined in Agenda 21 (UNCED, 1993). Sustainable development is to be supported through the provision of relevant, reliable, targeted and timely information to policy-makers and the general public. For this purpose international organisations have established a reporting system based on objective and measurable criteria with potential to compare between areas and monitor changes over time. Particularly environmental protection agencies are required to periodically assess the state of the environment, hence the search for ‘indicators’ that can quantify the condition and management of land resources and the pressures exerted upon the land. For environmental monitoring purposes, indicators have been defined as ‘parameters, or values derived from parameters, which provide information about the state of a phenomenon/environment/area with significance extending beyond that directly associated with a parameter value’ (OECD, 1993). OECD (1993, 1999) defines agri-environmental indicators (AEIs) as attributes of land units, which

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A. Gobin et al. / Environmental Science & Policy 7 (2004) 25–38

are policy-relevant, analytically sound and measurable. In addition to the above criteria, the EEA selects indicators having in mind the target audience, together with the most suitable level of aggregation and the availability of data needed to compile them. Headline indicators provide an overview of the situation at a high level of aggregation; while detailed indicators are needed to better understand underlying trends or existing links between policy measures and their effects. The challenge is to find an appropriate balance between simplification and completeness. In this paper, the emphasis is on headline indicators. In response to concerns about the degradation of soils in the EU, the 6th Environmental Action Programme (CEC, 2001a) established the objective to protect soils against erosion and pollution while the sustainable development strategy (CEC, 2001b) noted that soil loss and declining fertility are eroding the viability of agricultural land. Although soil is recognised to be a vital and largely non-renewable resource, it has not been the subject of comprehensive EU action so far. A thematic strategy for soil protection is currently being developed (CEC, 2002b). By mid 2004, environmental measures will be proposed to prevent soil contamination, a soil monitoring legislation will be proposed and recommendations for actions will be formulated. An adequate EU soil policy and soil protection legislation will provide a missing environmental pillar for sustainable development and enable national or local decision makers to better protect their soils within a harmonised European framework. However, there is no reporting mechanism in place to assess soil conditions, to gauge whether existing measures are leading to improvement of soil conditions or to estimate the level of implementation of existing legislation. Furthermore, the basic information available on the soils of Europe varies in quality and detail from country to country. This paper provides a summary overview on policyrelevant indicators for assessment and monitoring of soil erosion by water at the pan-European scale. A critical review is given of the driving force–pressure–state–impact–response

(DPSIR) policy framework based on agri-environmental indicators (EEA, 2000), applied to soil erosion. The headline indicators, used by the EEA to monitor and report on soil erosion, are discussed in relation to data availability, policy requirements and analytical soundness. The indicators are grouped into categories according to the framework. Options are presented for future development of headline indicators with particular reference to existing European data sources.

2. Soil erosion in Europe In Europe, soil erosion is caused mainly by water and, to a lesser extent, by wind. Rill- and inter-rill erosion affects the largest area, whereas gully erosion and landslides are relatively localised though often visually striking. Soil losses due to water erosion are high in southern Europe, and are becoming an ever-increasing problem in northern Europe (Van Lynden, 1995). According to the expert-based GLASOD map (Oldeman et al., 1991), the area of human-induced soil erosion by water in Europe, excluding Russia, is roughly estimated to be 114 million hectares (17% of total land area), of which 80% is topsoil loss and 20% terrain deformation. The loss of topsoil is often not conspicuous but nevertheless potentially very damaging. Soil erosion is a natural process, occurring over geological time, and most concerns about erosion are related to accelerated erosion, where the natural rate has been significantly increased by human activity. Soil erosion events are associated with the incidence of storms, that are patchy in both time and space, and site data must therefore be widespread and over long-duration. The majority of findings, however, are based on fragmented and often non-standardised measurements. Average soil erosion rates for larger regions are for both arithmetic and methodological reasons suspect (Boardman, 1998; Evans, 1995): an average rate overestimates the central tendency due to the right-skewed distribution of erosion

Table 1 Ranges of soil erosion rates in arable fields across Europe Location

Method

Range (average) (t ha−1 per year)

Period of observation (year)

Reference

Southern Sweden Southern Sweden England–Wales England Belgium Belgium Netherlands (Limburg) Northern France Austria Sardinia Italy Greece Portugal

Observation (935 fields) Plot measurements (six plots) Observation (1705 fields) Plot measurements (eight plots) Observation (86 fields) Plot measurements (12 plots) Plot measurements (12 plots) Observation (35 catchments) Plot measurements (nine plots) Plot measurements (10 plots) Plot measurements (49 plots) Plot measurements (18 plots) Plot measurements (16 plots)

0.0–9.5a (0.6) 0.0–10.7 (0.2) 0.1–5.5a (2.99) 0.0–19.4 (2.1) 0.0–35.0 (3.6) 0.1–20.0 (7.8) 0.1–3.4 (5.7) 0.0–13.2a (1.4) 0.0–30.1 (3.7) 0.2–9.1 (0.9) 0.2–83.3 (8.7) 0.7–4.6 (1.4) 0.0–35.8 (1.2)

3 3 5 3 4 6 4 3 5 3 6 4 16

Alström and Åkerman (1992) Alström and Åkerman (1992) Evans (1993) Quinton (1994) Govers (1991) Bollinne (1982) Kwaad (1994) Ludwig et al. (1995) Klik et al. (2002) Lucci and Della Lena (1994) Bazoffi et al. (1986) Kosmas et al. (1997) Roxo et al. (1996)

a

Bulk density estimated at 1.3 t m−3 (Evans, 1995).


events, and the establishment of soil erosion rates depends largely on the technique of observation or measurement. Ranges of erosion rates and reported means, although the median would be more appropriate but is rarely reported, are provided for different agri-environmental areas across Europe (Table 1). Bearing in mind the difficulties in comparing results from different observation and measurement techniques, reported rates for northern Europe are lower than for the Mediterranean areas (Table 1). Plot measurements tend to capture the temporal variation better, whereas field observations account better for spatial variability in the surveyed area. Apart from the method and period of observation/measurement, results should be interpreted against the characteristics of the agri-environment such as climate, topography, land use and land management. The influence of site-specific agri-environmental characteristics favours a spatial analysis and assessment of the soil erosion problem. Physical factors such as climate, topography, land cover and specific soil characteristics have important effects on the processes of soil erosion and soil formation. Soil formation is a slow process involving the breakdown of rock into small particles and the accumulation of organic matter. Compared to an estimated very slow soil formation rates of 0.05–0.5 mm year (Wakatsuki and Rasyidin, 1992), any soil loss of more than 1 t/ha year can be considered as irreversible within a time span of 50–100 years (EEA, 1999a). Soil can therefore be regarded as a non-renewable resource. These are compelling reasons for monitoring and improving the way soils are managed. 3. A European framework for the assessment and monitoring of soil erosion

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affect environmental quality, and as such the framework highlights the complex connection between the causes of environmental problems, their impacts and the society’s response to them (EEA, 2000). Eurostat uses the DPSIR framework for the organisation of environmental statistics. EEA together with its EIONET partners, which include the European Topic Centres (ETCs), is facilitating the process from national monitoring to European reporting. The framework consists of monitoring, data collection, information, assessment and reporting (MDIAR) (EEA, 2000). Data are integrated into indicators and assessed using the DPSIR approach. A soil reporting mechanism enables communication of the results obtained. The MDIAR chain concentrates on matching the best available environmental information with the best needed environmental and economic information. The set up of a European soil monitoring network would therefore permit harmonisation of national networks and enable full data comparability. This would be achieved through standardised database management procedures. The DPSIR framework has been applied to soil erosion in order to establish a set of policy-relevant soil erosion indicators (Düwel and Utermann, 1999; EEA, 2000; Gobin et al., 2003). This attempt identified intensification of agriculture as an important driving force related to soil erosion. Intensification of agriculture encourages unsustainable land use practices and deforestation, which in turn enhance the risk of soil erosion. These pressures may change the state of the soil resources, and result in soil loss. Soil loss is recognised to have both direct and indirect impacts on the environment, expressed in terms of on- and off-site effects, respectively. The responses at the European level include land use agri-environmental measures under the CAP rural development programme.

3.1. Application of the existing framework to the problem of soil erosion

3.2. Suggestions for an extended framework

A policy framework is needed that recognises the environmental importance of soil, takes account of problems arising from the competition between its concurrent uses, both ecological and socio-economic, and is aimed at maintaining soil’s multiple functions (EEA, 2000). The identification of areas that are vulnerable to soil erosion can be helpful for improving our knowledge about the extent of the areas affected and, ultimately, for developing measures to keep the problem under control. OECDs DSR-framework (driving force-state-response) has established a holistic systems approach to include cause–effect relationships (OECD, 1993, 1999). The OECD model has been extended by EEA to cover the causes (pressures) and the impacts on the environment (EEA, 1999b, 2000). The DPSIR framework shows a chain of cause–effects from driving forces (activities) to pressures, to changes on the state of environment, to impacts and responses (EEA, 1999b, 2000). DPSIR is based on the assumption that economic activities and society’s behaviour

The DPSIR framework is an integrated approach onto which further extensions and strategies of reporting can be built. The framework provides a basis for identifying the different factors influencing soil erosion, but it does not explicitly allow for the identification of actors in the DPSIR chain. On the assumption that the DPSIR framework is the best available framework to be applied to soil erosion, we have applied and revised it for soil erosion (Fig. 1). Possible driving forces are grouped according to human activities and physical phenomena that in turn result in potential pressures on the land. The most important pressures for soil erosion are land cover and precipitation. In this respect, population dynamics, tourism, agriculture and transport should be added to the list of driving forces. These may change the land cover, which is the major pressure indicator for soil erosion. However, land cover type and change should be evaluated against the background of physical factors that influence erosion, i.e. topography, soil and climate (Fig. 1).

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Fig. 1. The DPSIR framework applied to soil erosion (reviewed and modified from EEA, 2000).

The DPSIR assessment framework places emphasis on socio-economic related indicators, while physical indicators of pressure are not fully explored, nor explicitly resolved. Climate change is considered as a driving force but only in the sense that it relates to human activities. Important physical factors that influence soil erosion are topography, soil type, soil vulnerability and climatic factors (particularly rainfall). These factors are not separated from the identified pressure indicators (Fig. 1). On the other hand, they can be implicitly incorporated into indicators of state. The general DPSIR framework lends itself to a systems analysis approach and as such is very useful in describing the relationships between the origins and consequences of complex environmental problems. Obviously, the real world is more complex than can be expressed in simple causal relationships. Linkages between the different types of indicators are identified through the DPSIR chain. However, the linkages deserve further attention, not least to capture the dynamics of the system. Moreover, linkages within one type of indicator (e.g. pressure) are not explored, despite their importance. The assessment carried out through the DPSIR framework does not aim at understanding or analysing soil erosion as a process, but only provides information to support policy-makers’ actions so that the necessary measures can be defined and the effect of current measures assessed. However, it is necessary to recognise the huge difference between measured soil erosion, actual soil erosion risk and potential soil erosion risk. Indicators describing the driving forces and pressures may affect the risk of soil erosion, but they may not affect soil erosion in itself. A mechanism is therefore needed to jointly estimate the potential and actual risk, based on links between the identified driving force and pressure indicators, and on an estimation or measurement of what is actually happening.

It is widely recognised that a distinction ought to be made between on- and off-site impacts of soil erosion. This distinction, however, already applies at an earlier stage in the DPSIR chain, namely at the stage of state indicators. Soil erosion can be measured in terms of actual sediment loss per unit area (on-site) or in terms of sediment delivery into streams or rivers and subsequent silting up, of reservoirs and drainage systems (off-site).

4. Relevant data sources at pan-European scale This section describes the data that are available and uniform at a pan-European scale and that can be used to derive soil erosion indicators for European policy-makers. The design of effective indicators at a continental scale requires both conceptual and spatial aggregation (Niemeijer, 2002). Specific and local management interventions may require a larger set of detailed indicators to be developed at a higher resolution. The European statistical system (ESS), consisting of Eurostat and the appropriate bodies in member state administrations, ensures that the statistical needs of policy-makers are met. Data are geo-referenced and managed by Eurostat with the geographic information system of the European Commission (GISCO) (Eurostat, 2001). GISCO geo-referenced databases contain topographic and thematic layers at five different scales. Tools have been developed for standardised cartographic production and for advanced spatial analysis. 4.1. Hydrography The hydrography databases include rivers and lakes coverages at a 1:3 m scale and catchment boundaries at a 1:3 m scale. Recently, a more detailed catchment boundaries


database has been prepared at 1:1 m scale. The catchments have been derived from a hierarchical river network of scale 1:1 m, which was combined with a DEM of 1 km grid size. Climate data are provided from 5308 stations in 12 EU member states, collected by the monitoring agriculture with remote sensing (MARS) project (Vossen and Meyer-Roux, 1995). The two main climatic variables are precipitation (average, maximum 24 h rainfall, number of rain days, average snowfall, number of snowfall and snow cover days) and temperature (average, maximum, minimum, absolute monthly maximum and minimum, number of frost days). Other climate attributes include, relative humidity, vapour pressure, atmospheric pressure, bright sunshine, evapotranspiration, wind speed and cloud cover. There are more gaps in these records because of inconsistencies in the definitions and measurement procedures used in different countries, or because of the short or irregular periods for which stations have been maintained. Because of these gaps, climatic data have been interpolated on 50 km × 50 km grid cells covering Europe and Magreb and provide the basis for running the crop growth monitoring system (CGMS) (Van Der Goot, 1997). The monthly data have been recalculated from long term average daily data for the period 1975–1999 for the following parameters: absolute minimum temperature; average minimum temperature; absolute maximum temperature; sum of precipitation; sum of potential evaporation; and sum of global radiation. 4.2. Land resources The land cover database is derived from the CORINE land cover for the year 1990, and is distributed as grids of 100 and 250 m resolution. The minimum mapped unit for land cover is 25 ha, being based on visual interpretation of LANDSAT and SPOT multi-spectral data. There are three levels of classification, with the third level having 44 classes. The European land cover database is currently being updated by the joint ‘Image 2000 and CORINE land cover 2000’ project, using the necessary satellite coverage to create a multi-purpose spatial reference of Europe. The European Soil Database (Heineke et al., 1998) provides a harmonised and spatial coverage of soil types and descriptions, based on FAO nomenclature, at a resolution of 1:1 m scale (ca. 1 km × 1 km) in all European participating countries. The basic spatial units are the soil mapping units (SMU)—polygons representing areas of the same soil type, and these comprise soil typological units (STU)—indicating the main soil types. The database enables spatial data queries, data extraction and thematic mapping. A number of thematic interpretations have been made from the map, for example, on available water capacity (Jones et al., 2000) and land suitability (paper submitted to IUSS Bangkok. A digital elevation model exists as a pan-European raster coverage providing altitudes for 1 km × 1 km grid cells, effectively at a 1:3 m scale. This available from the EROS database in the USA (Gtopo30).

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4.3. Quasi socio-economic data layers The degree of urbanisation can be derived from population census data from 1981 to 1991 at a 1:1 m scale. The coverage has three density classes: densely-populated area (contiguous set of local areas with a population density greater than 500 inhabitants per km2 , and a minimum total population of 50,000 inhabitants); intermediate area (contiguous set of local areas, not belonging to the densely-populated area, with a population density greater than 100 inhabitants per km2 , and a minimum total population of 50,000 inhabitants or adjacent to a densely-populated area); and, thinly-populated area (contiguous set of local areas belonging neither to a densely-populated nor to an intermediate area). The less favoured areas, originally created at DG-AGRI, are part of the structural funds programme, which represent areas defined as regions where economic activities, from the agricultural point of view, are difficult to pursue. The criteria, developed through consultation with the member states, include mountainous regions, areas in danger of de-population, and areas with specific handicaps (for example, desertification, marsh lands, salinisation). The coverage is provided at a 1:3 m scale. The Structural Funds is made up of five datasets at a 1:1 m scale, which indicate the areas of the EU eligible for support from Structural Funds during five consecutive periods. The integrated administration and control system (IACS) is a tool used by the commission and member states to carry out checks on payments granted to farmers for particular crops and livestock (Willems et al., 2001). In a few member states, the IACSs are established in geographical information systems. 4.4. Land use and management The nomenclature of territorial units for statistics (NUTS) serves as a base map of regional boundaries covering the entire EU territory. The nomenclature subdivides the EU economic territory into six administrative levels, from country (level 0), through regional (levels 1–3) to local (level 4 and 5) level. At present, three versions (V5–V7) for three scale ranges (1, 3 and 10 m) are maintained at GISCO. The NUTS provide the means to spatially present agricultural statistical survey and census data. The farm structure survey (FSS), farm accountancy data network (FADN) and agricultural statistics data cover all member states and include information of crop type and area, farm size, farming income, crop yields, livestock type and number at the NUTS 2 and 3 levels (Fig. 2). Trends in livestock numbers and composition, crop areas and farm produce can be related to the corresponding product prices at the NUTS 2 level. The latest available datasets are from 1997 for FSS and from 1998 for FADN. The land use/cover area frame statistical survey (LUCAS) project provides harmonised and bi-yearly updated

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Fig. 2. Difference in size between NUTS2 and NUTS3 databases. NUTS 2 data are collected on a bi-annual basis using representative samples, whereas the NUTS 3 data is census information collected on a decades basis.

European statistics on land use and land cover, including non-agricultural uses and environmental information such as noise and natural hazards at EU 15 level (Bruyas et al., 2002). A systematic area sampling method of 10,000 sampling segments (transects), 100,000 fields and 5000 farmers’ interviews represents an extension of a pure land cover/land use information system towards a multi-purpose and multi-user agri-environmental monitoring system. At the field level, observations on soil erosion are made. The 2001 pilot survey will be repeated in 2003. The rural development programme and other administrative sources may provide information relevant to agri-environmental indicators, but information tends to be fragmentised.

5. Review of existing indicators for soil erosion The DPSIR and other frameworks are analytical tools for the definition of policy-relevant indicators to describe pressures, changes in state and impacts or responses by society to these changes, within the context of policy. In this section, the headline indicators, used by the EEA (Düwel and Utermann, 1999; EEA, 2000) to assess and monitor soil erosion (Table 2), are discussed in relation to data availability (and quality), policy requirements and analytical soundness.

5.1. Indicators of driving force and pressure According to the EEA (2000), the main driving force that causes soil erosion is the intensification of agriculture. This is a complex indicator that is related to different pressure indicators. The corresponding pressures are often cost-effective but unsustainable land use practices, e.g. the use of heavy machinery, enlarged fields, overgrazing and other intensive land use practices. The following headline indicators are used as components of the complex indicator ‘intensification of agriculture’: net profit, farm size, field size, crop yield, consumption of fertilisers, and the number of grazing animals (Table 2). The above indicators of driving force and pressure (Table 2) are available in the farm structure survey and farm accountancy data network, but represent only averages on a large area basis, related to the NUTS level (Fig. 2). Overall, the indicators would benefit from a concise effort of spatialising or disaggregating the agricultural statistical data to the maximum possible. Where there are difficulties in obtaining crop yield data, forecasts are taken from the crop growth monitoring system (Supit and Van Der Goot, 1996) that underpins the monitoring agriculture with remote sensing project. Fertiliser consumption may be obtained through the FSS, LUCAS or FADN. However, data should be expanded to cover quantities and type of fertiliser purchased. Fertiliser applications vary for different crops making it impossible to predict their consumption without

Table 2 EEA list of indicators for soil erosion (Düwel and Uterman, 1999; Gentile, 1999; EEA, 2000) tested according to the OECD criteria Typea

Unit

Policy relevance and utility

Analytical soundness

Measurability

Comments

Representative

Easy to interpret

Comparable

Scientific

Data available

Data quality

Updated

Score

Not always

No

Yes for CAP

Yes

Yes

Yes

Yes

D D/P D/P D/P

Agricultural intensification (compound index) Net profit and trend Farm size and trend Field size and trend Crop yield and trend

Euro Ha Ha t/ha

Yes Yes Yes Yes

No Yes In part No

Yes Sometimes Yes Yes

Yes Yes Yes Yes

FADN FSS FSS FSS

Yes Yes Yes Yes

2–3 2–3 2–3 2–3

P

Fertiliser use and trend

t/ha

Yes

No

Limited

?

FSS

Yes

2–3 years

P

Grazing stocking rate and trend Extent of area affected by soil erosion Area affected by soil erosion Magnitude (sediment delivery) Removal of sediment

No/ha

Yes

No

Yes

Yes

FSS

Yes

2–3 years

Percentage of area km2

Yes

No

?

No

No

?

In part

No

t

Yes

No

?

In part

No

Euro

No

No

No

Rarely available Rarely available Rarely available ?

In part

Yes

Method dependent Method dependent Difficult to measure No

No

No

Euro Euro

Yes Yes

No No

Yes Yes

Yes Yes

IACS, SFP No

In part In part

No No

Extent not known, expensive to measure Extent not known, expensive to measure Measurement difficult, source difficult to establish Comprehensive measurements not possible Usually piecemeal Usually piecemeal

Percentage of area

Yes

Yes

Yes

Yes

Rarely available

In part

No

Usually piecemeal

S S S/I I R R

Prevention (agriculture) Prevention (forest fire, natural hazard) Erosion control (code of good farming practice)

R a

years years years years

Complex not directly linked; only comparable for countries under CAP Not relevant to soil erosion Not linked directly Data partially available Data for actual and estimated yields; not linked directly Economic criterion, link variable


D

EEA indicator

D: driving force, P: pressure, S: state, I: impact, R: response.

31

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knowing the spatial distribution of crops and local agricultural practices. Adjustments that account for losses (e.g. 10–15%) and applications outside general agriculture such as in market and domestic gardens (e.g. 10%) are not taken into consideration. Moreover, fertiliser applications do not directly relate to soil erosion. In certain farming systems fertiliser use may increase when using appropriate soil conservation measures such as terracing. In this case reduced soil erosion coincides with increased fertiliser use. The Common Agricultural Policy (CAP) has provided a high level of support and protection for European agriculture. When the CAP price support system led to an enormous intensification of production methods and negative effects on the environment became apparent, the European Commission launched several initiatives towards sustainable agriculture such as environmental directives and agri-environmental measures under ‘Agenda 2000’ (CEC, 1998, 1999, 2002a). The principal justification for a more integrated common rural policy is the perceived non-market benefits that (agricultural) land management and the delivery of environmental standards offer. For regions under the CAP, the relationship between intensified agriculture and environmental problems is therefore not always straightforward. Since CAP support, farmers’ strategies of production maximisation have made it difficult to demonstrate direct links between actual soil erosion and either crop yields or stock numbers. Intensive land use can be effectively combined with soil conservation measures such as terracing and the use of cover crops. There is historical evidence that overgrazing causes land degradation (Evans, 1998) and that soil erosion causes reduction in crop yields and a decline in pasture areas (Arnalds et al., 2001). In addition to crop yield, crop type, crop rotations, crop calendars, crop management and area devoted to a particular crop should be considered as indicators of pressure. Indicators of pressure should always be related to the underlying physical factors that influence soil erosion. It is essential therefore that the intensity of agriculture should never be evaluated alone in relation to erosion. Nor should the values for any of the composite indicators to measure agricultural intensification, be used independently. Ultimately, the factors that relate directly to soil erosion, are precipitation, topography, soil properties, land cover type, timing and duration of vegetative cover, and land management. 5.2. Indicators of state The EEA uses two key indicators: ‘percentage of area affected by soil erosion per defined region’, ‘extent (km2 ) to which the total area is affected by soil erosion’ and the magnitude of total soil loss by soil erosion due to water, measured in tonnes per ha per annum (Table 1). Policy-makers need to know the area affected by soil erosion at a regional scale, which requires a regional assessment method. EEA use the potential and actual soil erosion risk. The potential risk calculations are based on climatic, topographic and

edaphic conditions whereas the actual risk takes into account present land cover and land use. The comparison of the potential with actual soil erosion risk could be considered as a measured risk due to land use changes. Methods based on questionnaire surveys (GLASOD map; Oldeman et al., 1991) or erosion measurement sites (Hot Spots map; Turner et al., 2001) are inadequate on their own. Estimates of the area affected by actual soil erosion at regional and national levels are not readily available, because standard measurements and monitoring campaigns are difficult and usually expensive to maintain. Soil erosion often takes place over long periods before the true extent is appreciated and long-term accurate data are scarce. The temporal and spatial patchiness of soil erosion makes interpolations between limited available data scientifically not justified. Differences between expert assessments and measurement methods reduce the comparability between the limited data available even further. Soil erosion risk maps are available using scoring methods based on a simplified universal soil loss equation (USLE) approach: CORINE maps for southern Europe at 1:3 m scale (CORINE, 1992), European Soil Bureau maps at 1 km × 1 km resolution (Van Der Knijff et al., 2000) and RIVM maps at 50 km×50 km resolution for Europe (RIVM, 1992). Methods based on scoring factors have the immediate benefit of accessing distributed data that are available at a European scale. A risk rather than the actual occurrence of erosion is not strictly a state indicator. On the other hand, actual levels of erosion are difficult to measure, so estimates, based on the available physical evidence, are necessary for policy and management purposes. Another indicator of state that relates closely to impact is the sediment delivery ratio (t m−3 per year). The EuroWaterNet provides data on river sediments (Nixon et al., 1998), which of course should be related to the catchment area. However, sediment loads can rarely be traced back to the exact source, be it the surrounding land, riverbanks or channel. As an indicator for soil erosion, sediment delivery data are rarely accurate enough to be an independent indicator. 5.3. Indicators of impact On-site impacts (not presented in Table 2) include loss of soil fertility, changes in soil functions, changes in crop yields and desertification. On-site impacts in Europe are mostly compensated for by technical advances. Decline in crop yields is suggested as an indicator for measuring on-site impacts, since yield data are readily available from Eurostat. In drier Mediterranean environments, loss of productivity is often caused by reduced soil water storage capacity, mostly as a result from loss in soil depth. Yield reductions due to loss of nutrients in the topsoil are masked by the application of fertilisers in Europe. However, the increase in fertiliser consumption cannot readily be used as a suitable indicator for loss of nutrients, not least because the data present averages on a large area basis (Fig. 2).


Off-site impacts are mostly associated with the deposition of sediments, e.g. damage to roads and filling of dams, or they are related to the effects that erosion has on other media, e.g. reduction in water quality. In general, off-site impacts can often be expressed in economic terms. The EEA indicator relates to ‘expenditures for removals of sediment deposits in built up areas (traffic routes, houses)’ (Table 2). Data on remedial measures are rarely available at the national level, let alone at the European level. However, there are subsidies provided by the EU for remedial works via CAP, which are reported at NUTS2 level. Remedial measures usually follow major floods and should be linked to flood monitoring and forecasting systems. 5.4. Indicators of response Responses to soil erosion include conservation practices, mitigation strategies and general prevention programmes. All are important in reducing or eliminating soil erosion but they are usually only adopted after soil erosion has been identified as a significant problem. Indicators of response are the expenditure for local agricultural programmes to enforce sustainable farm management systems, such as the set-aside of arable land and the percentage of area under erosion control management (Table 2). Figures on agricultural support and agri-environmental measures (Agenda, 2000) are recorded by the FADN. Conservation practices, in other parts of the world, have considerably reduced soil loss through erosion. Practices for soil conservation include contouring, terracing, strip cultivation, and subsurface drainage (Renard et al., 1997). Other measures involve adoption of minimum tillage systems, planting cover crops, and changing fundamentally the land use system, e.g. conversion from arable to pasture. Many of these practices increase plant cover and therefore directly reduce erosion. Another indicator used by the EEA is the expenditures for policy instruments to safeguard natural resources, which may mitigate soil erosion (Table 2). These policy programmes include forest fire protection (e.g. implementation of fire prevention systems), European soil desertification programme (e.g. building of holding reservoirs) and the Common Agricultural Policy Reform, including agri-environmental measures under the implementation of the rural development plans. Data and information on conservation practices are stored centrally in Europe. 5.5. Conclusion Following the DPSIR framework, a set of soil erosion indicators used by the EEA was reviewed in this section. The pressure indicators link to the driving force ‘agricultural intensification’ and all have in common that they are complex and not directly linked to the phenomenon of soil erosion. The data for the identified indicators of state and impact are not readily available or are difficult

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and expensive to measure. Indicators of response are prevention and control measures, which are rarely in place at present. 6. Recommendations for indicators In this section, recommendations are formulated that integrate the latest developments in both research and policy into a set of newly proposed indicators. 6.1. Indicators of driving force Driving force or pressure indicators should be evaluated against a background of physical factors in order to understand the complexity of accelerated erosion (Fig. 1). Potentially adverse effects might occur only in areas of medium and high erosion risk, and are often mitigated by the adoption of conservation techniques. Physical factors that influence erosion rates are topography, soils, climate and land cover. Land cover is in turn influenced by the socio-economic environment and as such by anthropogenic activities, notably land use and management. The underlying factors that influence soil erosion should be explicitly formulated and linked with the indicators of state and impact. All factors that influence the underlying physical factors should be included as driving forces. Climate change, land use and management, agro-ecosystem management and human population dynamics are identified as the most important driving forces (Table 3). 6.2. Indicators of pressure The revised DPSIR framework (Fig. 1) presents land cover type/change, the agro-ecosystem, population density, natural hazards and precipitation as the most important pressure indicators of soil erosion, as they are seen to be directly influencing the degree of soil erosion (Table 3). Land cover type and changes, including forest fires and deforestation, can be detected by combining the reference land cover database, CORINE land cover, with vegetation changes indices such as NDVI derived from earth observation to capture seasonal variations. Land cover type and changes should be compared with the data collected in the LUCAS project, particularly in zones identified as high risk to soil erosion. The updated CORINE land cover database ‘CLC2000’ will enable the detection of major land cover changes across Europe. Of particular interest to soil erosion is the area of bare land or exposed soil surface. Depending on the particular type of land use and management, including intensity, land resources are subject to a given degree of stress. Land use and management, should therefore be monitored as an important factor that influences soil erosion. Land use change data of particular interest are changes in arable land and grassland (Table 3). Set-aside

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Table 3 Proposed indicators to monitor the environmental problem soil erosion Type

Proposed indicator

D D D D P P P P P P P P S S

Land use and management Population dynamics Natural hazards Climate change Land cover, type and changes Area of bare land or exposed soil surface Land use, type and changes Agro-ecosystem Number of grazing animals Area of set-aside Precipitation Population density Gully density Percentage of area affected to soil erosion per region Extent of total soil loss by soil erosion Sediment delivery ratio of selected rivers Soil depth and water storage capacity Biomass production Sediment deposition Conservation practices

S S/I S/I I I R R R

Restrictions Policy (direct: soils directive; indirect: directives, CAP, Agenda 2000, EAP)

Unit

Data source

% and km2 % and km2 % and km2 km2 No. ha−1 % and km2 l m−2 No. km−2 km km−2 % and km2

CORINE LC 2000, Image 2000, LUCAS, IACS GISCO Remotely sensed data MARS interpolated Precipitation Database CORINE LC 2000, Image 2000, LUCAS, IACS CORINE LC 2000, Image 2000, LUCAS, IACS CORINE LC 2000, Image 2000, LUCAS, IACS LUCAS, IISA (global) FSS FSS, FADN MARS interpolated Precipitation Database GISCO To be done, verified with LUCAS Based on modelled risk analysis (PESERA)

t ha−1 t m−3 mm and mm m−1 kg m−2 t km−2 and Euro ha and Euro Euro and ha Euro

land, number of grazing animals and general ecosystem characterisation provide a further indication of land management. Human population density in a certain region has an important effect on land cover, land use and general land management. Precipitation regimes influence soil erosion directly and indirectly, through their effect on land cover, and should therefore be included as important pressure indicators. These regimes can be detected using the GISCO Climate coverages and the monitoring agriculture by remote sensing (MARS) meteorological database (Vossen and Meyer-Roux, 1995). The combination of precipitation regimes with other physical factors such as topography (e.g. aspect) should be further analysed. 6.3. Indicators of state In order to formulate a European soil protection policy, soil erosion indicators of state should provide a picture of both the extent and the severity of the problem at the pan-European scale. There is a huge difference between measured erosion, actual erosion risk and potential erosion risk. Indicators describing the driving forces and pressures may affect the risk of soil erosion, but they may not affect soil erosion in itself at present. A mechanism is therefore needed to jointly estimate the potential and actual risk, based on links between the identified driving force and pressure indicators, and on an estimation or measurement of what is actually happening.

Based on modelled risk analysis (PESERA) EuroWaterNet LUCAS, European Soil Database Agricultural yield (FSS, FADN), NPP (vegetation, NOAA) Sediment removal LUCAS, FADN (agri-environmental measures and code of good farming practices) FADN, Natura 2000 Eurostat

6.3.1. Measured and observed erosion rates A database of actual soil erosion measurements, such as collected for the Hot Spots Map (Turner et al., 2001), should be expanded. In conjunction with soil erosion measurements and observations, data on climate, topography, soil and land use should be carefully documented for each observation or measurement. This requires the set up of a comprehensive database, including meta-data on, inter alia, the erosion type, scale of measurement/observation and study period. A programme to monitor soil erosion across different agro-ecological regions and under different land uses should be set up. Measuring campaigns may lead to new insights and therefore to both better mapping, modelling results and risk assessments. Questionnaire-based approaches provide quick results for creating awareness, but should be avoided in the future whilst not rejecting field observations and measurements of soil erosion. Field observations are invaluable as soil erosion indicators of state. However, the impossibility of making truly objective comparisons between and often within areas calls for a standardised approach to record and particularly map the observations. The LUCAS project records observations related to land degradation at locations that are selected on the basis of a rigorous statistical sampling strategy. Temporal aspects, however, are not incorporated in the sampling strategy. In areas where soil erosion causes prominent features in the landscape, they can be mapped (Arnalds et al., 2001). In high risk zones, the length of active gullies could be identified using high resolution imagery or aerial photographs (Watson and Evans, 1991).


6.3.2. Actual and potential risk The temporal and spatial patchiness of soil erosion favours a risk analysis approach in order to make comparisons between regions across Europe and to complement field measurements and observations. The widespread availability of GIS data for controlling key variables enables a spatial modelling basis for assessments of soil erosion. Factorial models are useful for identifying the extremes of low and high erosion, but less satisfactory in identifying the gradation between the extremes. There are also difficulties about combining different factor ratings into a single scale, about the individual weightings and about the assumed linearity and statistical independence of the separate factors. A process modelling approach is therefore recommended in case the full spectrum of soil erosion has to be assessed. The difficulties associated with a process modelling approach should not be under-estimated and a suitable model should (a) represent the state-of-the art in current understanding of soil erosion; (b) respond explicitly and rationally to changes in climate and land use; (c) combine sufficient simplicity for application at a regional scale; and (d) relate coarse scale forecasts to measured erosion rate data so that explicit validation can be made with field monitoring data in order to make full use of experimental sites. The process modelling method has the advantage of producing an indicator of state with the possibility for analysing different scenarios, which in turn enables the formulation of soil conservation policies. The model results would provide an appropriate state indicator including time series for use by policy-makers. The pan-European soil erosion risk assessment (PESERA) project has adopted a physically-based modelling approach that provides regional and quantitative forecasts of soil erosion and plant growth (Kirkby et al., 2000; Gobin and Govers, 2003). Therefore it has the potential to respond explicitly and rationally to changes in climate or land use, offering great promise for scenario analysis and impact assessment. Set against this advantage, the model can only incorporate the impact of past erosion where this is measured and thus requires numerous good datasets for testing. The model simplifies the set of processes operating and may therefore not be appropriate under particular local circumstances. The PESERA model is currently being calibrated and validated at different resolutions and across different agro-ecological zones (Gobin and Govers, 2002, 2003). Crucial to any modelling effort is validation. The PESERA project has adopted a unique approach to calibrating and validating the model. Calibration is only realised at the very high resolution taking measurements from plot and field scale experiments. On the basis of a long-term dataset of soil erosion measurements at the plot scale, the concepts of the model are tested in various agro-ecological environments. The overland flow runoff generation and sediment transport equations are calibrated on the measurements, and runoff threshold and soil erodibility are examined in relation to different soil textures, land uses or to seasonal

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variation in rainfall regimes. Due to the spatial and temporal variability of soil erosion processes, direct field measurement is not always feasible for larger spatial entities such as catchments. At the catchment scale, model predictions are confronted with alternative but indirect validation methods such as monitored sediment loads in rivers and measurements of sediment deposition in lakes and reservoirs. At the regional scale, PESERA maps are compared with national or regional expert maps using correspondence analysis. Erosion literature commonly identifies ‘tolerable’ rates of soil erosion, but these rates usually exceed the rates that can be balanced by weathering of new soil from parent materials, and can be considered acceptable only from an economic or political viewpoint. Tolerable rates of soil loss could be estimated for areas of high erosion risk but should be evaluated carefully by experts. 6.4. Indicators of impact The impact that soil erosion has on European soil resources is difficult to capture and quantify in indicators, particularly since effects of soil erosion can be masked for many years. The most immediate impact is loss of nutrients and organic matter, which in turn reduces productivity and alters the soil ecological function. General biomass production, crop yields and natural vegetation cover can be monitored in conjunction with fertiliser use. In drier regions of Europe, reduced soil depth and subsequent effects on water storage capacity are important impacts and should be monitored. However, the majority of these indicators only make sense at farm level or in areas where high risks have been identified. The LUCAS project could record the data. Off-site impacts due to accelerated erosion are associated with the deposition of sediments and with sediment-bound nutrients or agrochemicals. The deposition of sediments could be monitored using records of sediment removal after floods. Water quality and in-stream biota could be monitored, but should be linked to non-point source pollution studies and indicators. 6.5. Indicators of response 6.5.1. Conservation practices In addition to expenditures on conservation practices, area and percentage of farmland should be closely monitored in relation to soil erosion (Table 3). The area of farmland covered by the agri-environmental programmes (classified by type of activity and land cover) and related payments are good indicators of response. Under the new mid-term review, the commission proposes a continuation of the historical set-aside obligation (10% requirement) of arable land on a long-term-oriented (10 years), non-rotational basis (CEC, 2002a). Other land use

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changes include the reversion of arable land to extensive grassland and changes between crops and rotations. The reintroduction of environmentally beneficial arable rotations and use of perennial ley is a feature of several programmes addressing intensive arable production. Encouragements are in place to maintain landscape features such as terraces, hedgerows, farm woodlands, earth banks, and ponds. In addition, measures designed to retain small and irregular field sizes are found in several programmes. Codes of good agricultural practice exist, at national or regional level, in all member states. Additionally, some regions have established specific codes of good agricultural practice that go beyond the requirements of community legislation. The number of farms complying with regional standards of good farming practice is a good indicator for conservation practices. However, it is still necessary to evaluate the codes for good farming practice against local agri-environmental problems. The area and percentage of farmland subject to restrictions imposed by nature conservation can be monitored using Natura 2000, a European network of areas, proposed under the Birds Directive and the Habitats Directive. The type of restriction and programme will have to be related to soil conservation within this context. 6.5.2. Mitigation strategies The expenditures related to implementation of different policy measures provide a good indication of mitigation strategies. Due to the soil’s multi-functional role, a broad range of different policy instruments influence soil protection and will have to be included in this response indicator. In the near future, the European Soil Directive (CEC, 2002b) should provide a thematic strategy to protect the European soil resources. A number of community directives positively influence soil conservation. General land use policy plays an important role in soil protection when decisions on allocation and use of land are concerned. The Strategic Environmental Assessment Directive and Environmental Impact Assessment Directive require an environmental assessment for projects and plans including land use. Both the Birds Directive and Habitats Directive define a number of terrestrial habitats that depend on specific soil characteristics. The Nitrates Directive aims at preventing the contamination of surface water by excessive nutrients from soils, and is linked to good farming practices and additional action programmes in vulnerable regions. The Water Framework Directive aims at safeguarding ecological, qualitative and quantitative functions of water, and focuses on remedial actions within river basin management plans. The CAP mid-term review (CEC, 2002a) foresees an increased level of integration of environmental measures. The CAP maintains and/or introduces a set of soil conservation measures such as long-term non-rotational set-aside, minimum tillage, grassland strips, winter covers, use of compost and the maintenance of terraces. Sus-

tainable development measures such as erosion and flood prevention fall under the regional and agricultural structural fund programmes. All measures may include support in return for agri-environmental commitments, general mandatory environmental requirements or specific environmental requirements constitute conditions for direct payments.

7. Conclusions Soil erosion is one of the major and most widespread forms of land degradation in Europe. International organisations and environmental protection agencies are required to periodically assess environmental processes such as soil erosion and establish a reporting system that is based on objective and measurable criteria. Effective monitoring of and reporting on soil erosion can only take place when the underlying biophysical and socio-economic factors that influence (accelerated) soil erosion are taken into account. A major problem with soil erosion is the temporal and spatial scale of reporting and the extent to which the phenomenon occurs. Although problems of both spatial and temporal patchiness are well recognised, a more integrated approach of reporting seems recommendable. Soil erosion indicators should be quantitative, objectively calculated, validated against measurements and evaluated by experts. Indicators developed in such a manner should combine strategies of modelling, measuring, field validation and expert evaluation. Policy-makers need to know the area affected by soil erosion and an estimate of the magnitude at a regional and even continental scale in order to formulate suitable remediation measures and mitigation strategies. Factor- and model-based approaches offer the advantages of repeatability and transparency. However, the results need to be validated against measurements and evaluated by experts so that the regional methods can be adapted to reflect the reality. This calls for an integrated approach requiring a good communication between modellers, GIS specialists and field experts. The PESERA project has adopted a methodology of cross-scale integration between physically-based forecasts of erosion risk and measured or observed soil erosion. A programme to monitor soil erosion across different agro-ecological regions and under different land uses should underpin mapping exercises and regional soil erosion risk assessment methods. A regional model that estimates the risk of soil erosion should be combined with periodical monitoring of actual soil erosion in selected test areas. The regional soil erosion model should express the links between the different biophysical and socio-economic factors, i.e. be process-based; establish various spatial and temporal resolution linkages; and, provide a nested strategy of focussing on environmentally sensitive areas which may require remedial measures to be taken.


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Anne Gobin obtained a PhD from the Katholieke Universiteit Leuven on participatory and spatial modelling methods for land resources analysis. She is currently on secondment to the European Environment Agency. She participated in several international projects on land resources analysis and agri-environmental modelling, including PESERA and environmental risk for sustainable agriculture (ENRISK). Robert Jones is a senior research scientist working with the European Soil Bureau (ESB, 1998; EI; DG-JRC), where he is primarily involved with developing the European Soil Database for several environmental applications. Before then, he was principal research scientist, at Cranfield University’s Silsoe site responsible for the development of land information systems. Mike Kirkby is emeritus professor at the University of Leeds. His research focuses on the understanding and modelling of landscape processes and their impact on landscape form. He has been involved with several European projects including Mediterranean desertification and land use (MEDALUS) and PESERA, and has over 60 publications on geomorphological and hydrological modelling. Paul Campling obtained a PhD from the Katholieke Universiteit Leuven on landform analysis for rainfall-runoff modelling. He has participated in several international projects on environmental modelling and on developing agri-environmental indicators. He coordinates the indicator reporting on the integration of environmental concerns into agriculture (IRENA) project at the European Environment Agency. Gerard Govers is professor and head of the Geography–Geology Department at the Katholieke Universiteit Leuven. He has over 90 publications on overland flow hydraulics and on the contribution of various components to soil erosion processes. He participated in several national and European research projects, including a European soil erosion model (EUROSEM), and coordinated tillage erosion (TERON) and PESERA. Costas Kosmas is an associate professor at the Agricultural University of Athens. He has over 22 years experience in land resources and the environment mainly in agricultural ecosystems. He has participated in 20 educational and research EU and Greek projects related to land degradation, desertification and environmental protection including CORINE, MEDALUS, and TERON. Anna Rita Gentile is project manager for soils and contaminated sites at the European Environment Agency. She supports policy development in the field of soil protection for the European Commission and is involved in the European thematic strategy on soil protection. She is collaborating with the UNCCD in the development of the DIS-MED project.