Rural Development Policies and Indicators - uni-bonn.de · PDF file5 indicators from the CAPRI-RD perspective, including the underlying databases, the modelling modules and the modelling

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Common Agricultural Policy Regionalised Impact – The Rural Development Dimension Collaborative project - Small to medium-scale focused research project under the Seventh Framework Programme Project No.: 226195

WP2.1 Databases – Rural Development Policies and Indicators

Deliverable: D2.1.3 – Report on additional Databases to model/define RD indicators

L Schroeder2, JM Terres1, A Gocht2, C Bulgheroni1, C Capitani1, ML Paracchini1

1- JRC-IES 2- VTI

March-2012

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Content

Abbreviations and Acronyms .................................................................................................................................. 3

Introduction ............................................................................................................................................................. 4

CMEF baseline indicators in the CAPRI-RD model ................................................................................................... 5

CMEF impact indicators and their potential implementation into CAPRI-RD ......................................................... 9

Description of additionally required databases .................................................................................................... 10

Farm Structure Survey (FSS) .............................................................................................................................. 10

Farm Accountancy data network (FADN) ......................................................................................................... 11

Farmland Bird index (FBI): regression model coefficients ................................................................................ 11

High Nature Value farmland index: stocking density thresholds ...................................................................... 12

JRC Revised Universal Soil Loss Equation factors .............................................................................................. 12

Summary and conclusions ..................................................................................................................................... 14

References ............................................................................................................................................................. 14

Annex ..................................................................................................................................................................... 16

Tables and Figures

Table 1: CMEF baseline indicators (Horizontal and Axis 1: competitiveness) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level .......................................................... 6

Table 2: CMEF baseline indicators (Axis 2: Environment) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level .......................................................................................................... 7

Table 3: CMEF baseline indicators (Axis 3: Quality of live) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level ................................................................................................... 8

Table 4: CMEF baseline indicators (Axis 1-3) not implementable into CAPRI-RD ................................................... 8

Table 5: CMEF impact indicators, their measurement, related baseline indicators and potential for implementation into CAPRI-RD ............................................................................................................................... 9

Table 6: Country overview of Common Bird Monitoring Schemes in Europe ...................................................... 16

Fig. 1. Soil erodibility factor in Europe (t ha h ha-1 MJ-1 mm-1) .......................................................................... 18

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Abbreviations and Acronyms

A Potential long term average annual rate of erosion AgroSAM Social Accounting Matrices for EU27 with a Disaggregated Agricultural Sector C Crop/vegetation cover factor CAP Common Agricultural Policy CAPREG Regionalisation program of the Member State database in CAPRI CAPRI Common Agricultural Policy Regionalised Impact Modelling System CAPRI-DynaSpat Common Agricultural Policy Regionalised Impact - The Dynamic and Spatial Dimension CAPRI-RD Common Agricultural Policy Regionalised Impact - The Rural Development Dimension CGEregEU+ Computational General Equilibrium model CMEF Common Monitoring and Evaluation Framework COCO Complete and Consistent Member State data base DG AGRI Directorate-General for Agriculture and Rural Development DNDC Biogeochemistry model DeNitrification and DeComposition EAA Economic Account of Agriculture EC European Commission ECA&D European Climate Assessment and Dataset EENRD European Evaluation Network for Rural Development EU European Union EU27 European Union 27 Member States EUROSTAT Statistical office of the European Union FADN Farm Accountancy Data Network FP7 Framework Programme 7 FBI Farmland Bird Index FSS Farm Structure Survey GHG greenhouse gases GVA/AWU Gross value added / Annual working unit ha Hectare HNV High Nature Value HSMU Homogeneous Soil Mapping Units IES Institute for Environment and Sustainability JRC Joint Research Centre K Soil erodibility factor km Kilometre LEADER Liaison entre actions de développement de l'économie rurale LS Slope length gradient factor MARS Monitoring Agricultural ResourceS MS EU Member state(s) N Nitrogen NASA National Aeronautics and Space Administration NECR New Energy Crops NGA National Geospatial-Intelligence Agency NUTS Nomenclature des Unités Territoriales Statistiques P Management practices PRIMES Energy system model R Rainfalrun-off factor RD Rural Development RUSLE Revised Universal Soil Loss Equation SGDBE European Soil Database SRTM Shuttle Radar Topography Mission UAA Utilized Agricultural Area

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Introduction In order to monitor and assess Rural Development (RD) programmes, the European Commission established

the Common Monitoring and Evaluation Framework (CMEF) for the programming period 2007-2013. This

framework includes certain indicators dedicated to assessing the main thematic axes of the Council Regulation

EC (1698)2005 which reflect the core objectives of the Strategic Guidelines for Rural Development.

Consequently, EU member states shall use the CMEF tools to report on the implementation of the RD policy in

their respective regions/country.

The different CMEF indicators are related to each level of policy objectives (overall, specific, operational) and

are divided into common baseline, output, result and impact indicators for each of the three thematic axes.

These axes are: AXIS 1: Improving the competitiveness of the agricultural and forestry sector; AXIS 2: Improving

the environment and the countryside through land management; and AXIS 3: Improving the quality of life in

rural areas and encouraging diversification of economic activity. A fourth horizontal and methodological axis is

assigned to the mainstreaming of the LEADER approach. A list of all CMEF indicators is included in Annex VIII of

Commission Regulation (1974)2006.

One of the proposed objectives of the CAPRI-RD project is to develop operational interfaces to simulate CMEF

indicators or their proxies. Within CAPRI-RD deliverable 5.1 (TERRES et al., 2010), a systematic review of the

CMEF indicators was carried out and their potential usefulness for the CAPRI modeling system was discussed.

The overall concept behind the CAPRI-RD project is to link the partial equilibrium model CAPRI, covering the

agricultural sector in Europe and worldwide, to the Computational General Equilibrium model CGEregEU+,

modeling all sectors in the EU. The aim of the final combined models is to assess developments in rural areas

and the impact of the European Common Agricultural Policy (CAP) on the regional and farm type level through

the combined model (CAPRI-CGEregEU+). This allows a large subset of CMEF indicators (related to

competiveness, environment, diversification of the rural economy) to be calculated. The aim of this deliverable

is to provide a discussion on which CMEF-indicators can be calculated with CAPRI-CGEregEU+ and for which

additional data sources would further be needed to extend the current list of CAPRI-related CMEF indicators.

In quantitative models, indicator calculation can be done at different modelling steps (or time frames):

The first step is the base year. The base year for CAPRI RD is the year 2004. The latest available statistics at

that date are used to feed the model. At this stage, the advantage exists that other data sources can be

employed to further refine or complement the indicators calculation and the model calibration, for

example the number of farms in a certain region or farm type with 'other gainful activities' could be

reported using the Farm Structure Survey (FSS).

Then in a second step, the CAPRI component of the combined models describes the agricultural sector in

the baseline, which is some point in time in the future simulated under the hypothesis of current policy

continuation, and is based on different information sources such as 'Prospects for Agricultural Markets and

Income in the EU' published by DG AGRI, historical trends, and expert information. The baseline relies,

hence, on a combination of projections values of historical trends and expert information, rather than a

scenario analysis with the base year model.

To account for this conceptual difference in indicator calculation, the deliverable distinguishes between base

year indicators and baseline/scenarios indicators.

A first inventory provides information on which CMEF baseline indicators are currently available in the CAPRI-

RD model and which additional data sources would be required. Tables 1-3 provide a review of CMEF baseline

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indicators from the CAPRI-RD perspective, including the underlying databases, the modelling modules and the

modelling level (base year, baseline/scenario).

After this identification of data sources and modeling possibilities, the appropriateness of additionally required

data and modelling platforms will be described and then analysed regarding criteria such as:

availability

spatial resolution (regional)

regularity of updating

EU coverage

responsible institution

data access regulations

A short section on CMEF impact indicators and a concluding summary will complete this deliverable.

CMEF baseline indicators in the CAPRI-RD model In the following section, the results of the data assessments regarding the potential of implementing CMEF

baseline indicators into the CAPRI-RD model will be described. The tables are split up according to the main

thematic axes of the Council Regulation EC(1698)2005. The section concludes with a table listing all of the

CMEF baseline indicators which cannot be implemented.

For instance, data regarding the CMEF baseline indicator 6, 'Labour productivity in agriculture', can be provided

by the current version of CAPRI model, it can be modeled as base year and also ex-ante modeling is possible.

On the other hand, indicator 4, 'Training and education in agriculture', is not yet implemented in the model, a

potential data source could be the Farm Structure Survey (FSS) but computation would hereby only be possible

as base year indicator.

Regarding the following tables, it should be noted that CAPRI is based on other, different, data bases. Hence,

when we refer to CAPRI, we refer to i) the database at MS level called COCO, integrating data from the EAA

farm balances, crop areas and herd sizes, and an engineering animal flow model for fattening and dairy animal

activities (BRITZ and WITZKE 2008) and ii) the data base CAPREG, breaking down the COCO national database to

NUTS2 and Farm type level farm balances. Databases not yet integrated but foreseen for use are given in the

column 'other data bases'. Those are discussed in more detail below.

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Table 1: CMEF baseline indicators (Horizontal and Axis 1: competitiveness) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level

Data base (modelling module) Modelling level Already available in the model?

AXIS CMEF baseline indicator

CAPRI / CGEregEU+ Other databases

Base year Indic.

Baseline/ Scenario Indic.

Horizontal 1-3 Economic development, Employment rate, Unemployment

CGEregEU+ (quantity of labour demanded (xlab))

Yes Yes Yes

1 Competitiv- eness

5 Age structure in agriculture

FSS, FADN Yes No No

6 Labour productivity in agriculture

CAPRI (GVA/AWU) CGEregEU+ (labour prod. (alab))

Yes Yes Yes

7 Gross fixed capital formation in agriculture

CGEregEU+ (agrosam)

Yes Yes Yes

8 Employment development of primary sector

CGEregEU+ (quantity of labour demanded (xlab))

Yes Yes Yes

9 Economic development of primary sector

CGEregEU+ (industry output (xtot))

Yes Yes Yes

10 Labour productivity in food industry

CGEregEU+ (labour prod. (alab))

Yes Yes Yes

11 Gross fixed capital formation in food industry

CGEregEU+ (industry output (xtot))

Yes Yes Yes

12 Employment development in food industry

CGEregEU+ (quantity of labour demanded (xlab))

Yes Yes Yes

13 Economic development of food industry

CGEregEU+ (industry output (xtot))

Yes Yes Yes

14 Labour productivity in forestry

CGEregEU+ (industry output (xtot))

Yes Yes Yes

15 Gross fixed capital formation in forestry

CGEregEU+ (agrosam)

Yes Yes Yes

Tables 1 and 3 indicate that for almost all CMEF horizontal-, Axis 1 and 3 baseline indicators, the CGEregEU+

model serves as sufficient data source. In the CGEregEU+ model all markets are assumed to clear out, via prices

to balance demand and supply. However, the model CGEregEU+ allows for unemployment in the labour

market, assuming that this market is not cleared. Unemployment is calculated for each region (NUTS2). The

model also allows the development of quantity of labour Xlab (i,r) to be recovered by industry and region.

Employment development of primary sector and employment development in the food industry can be

evaluated for the corresponding sector.

The indicator 'Labour productivity in agriculture' and 'food industry' and 'forestry' is approximated by factor-

augmenting technical change (alab). 'Gross fixed capital formation in agriculture', 'food industry' and 'forestry'

can be approximated using the information from the Agrosams in CGEregEU+. The Economic development of

the primary sector and in the food industry is approximated by the industry's total output (xtot) in the

corresponding sectors.

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The age structure in agriculture is not implemented in CAPRI yet. Although FSS and FADN, as potential data

sources, are already extensively used within the model, the necessary information is not available in the

current version. The reason is that both databases are, at the necessary regional resolution of NUTS2 and farm

types, not public domain. Hence, to implement the variable 'age structure', applications must be submitted to

FSS/FADN.

Table 2: CMEF baseline indicators (Axis 2: Environment) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level

Data base (modelling module) Modelling level Already available in the model?

AXIS CMEF baseline indicator

CAPRI / CGEregEU+ Other databases

Base year Indic.

Baseline/ Scenario Indic.

2 Environment

17 Biodiversity: Population of farmland birds

CAPRI HSMU (crop share, livestock density, N input)

FBI regression coeff.

Yes Yes No

18 Biodiversity: High Nature Value farmland

CAPRI HSMU (crop share, livestock density, N input)

HNV thresholds coeff.

Yes Yes Yes (partially)

20 Water Quality: Gross Nutrient Balance

CAPRI-DNDC Yes Yes Yes

21 Water quality: pollution by nitrates

CAPRI-DNDC Yes Yes Yes

22 Areas at risk of soil erosion

CAPRI HSMU (crop share, soil type, climate, slope)

JRC RUSLE factors

Yes Yes No

25 UAA devoted to renewable energy

CAPRI (NECR, PRIMES projections)

Yes Baseline: Yes; Scenario: No

Yes

26 Gas emissions from agriculture

CAPRI Yes Yes Yes

In the case of Axis 2 (Environment), the link between the economic models and the Axis 2 baseline indicators

cannot be established directly, but some relationships can be found between output variables of the model

and some biophysical parameters affecting the indicators. These relationships have already been developed for

some of the CMEF Axis 2 indicators, both at the regional and at homogenous soil mapping units (HSMU) scale

for almost all EU27 countries1. The indicators are calculated from variables which can be estimated and

simulated either directly by the CAPRI model or through the link with the biogeochemistry model

DeNitrification and DeComposition (DNDC) to simulate greenhouse gas (GHG) fluxes. For indicator 21 (Water

quality: pollution by nitrates), only proxies are available in the form of leaching and run-off estimates as by-

products of the CAPRI-DNDC linkages. While the linkage for the nitrogen cycle and the GHG emissions

indicators is already operational in the current CAPRI system through the existing link with the DNDC model,

the development of a new linkage is still to be implemented for the two biodiversity and the soil erosion

related indicators. However the concept of how to link to existing CAPRI variables is finalised. An example is the

High Nature Value (HNV) Farmland indicator (see CMEF Baseline Indicator 18), which is under implementation

in CAPRI. The indicator is currently calculated as a proxy, using variables derived from the CAPRI-Dynaspat

model outputs, namely crop share, N input and livestock density.

1 For Malta, Cyprus, Norway, the Western Balkan countries and Turkey such indicators are available only at the national scale, therefore

for these countries the calculation of the indicators at a higher resolution level is related to the availability of more accurate datasets

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Table 3: CMEF baseline indicators (Axis 3: Quality of live) already available or to be implemented into CAPRI-RD, underlying data sources and modelling level

Data base (modelling module) Modelling level Already available in the model?

AXIS CMEF baseline indicator

CAPRI / CGEregEU+ Other databases

Baseyear Indic.

Baseline/ Scenario Indic.

3 Quality of life

28 Employment development of non-agricultural sector

CGERegEU+ (quantity of labour demanded (xlab))

Yes Yes Yes

29 Economic development of non-agricultural sector

CGERegEU+ (industry output (xtot))

Yes Yes Yes

33 Development of services sector

CGERegEU+ (industry output (xtot))

Yes Yes Yes

34 Net migration

CGERegEU+ (change of labour persons moving to net migration rate)

Yes Yes Yes

Table 4: CMEF baseline indicators (Axis 1-3) not implementable into CAPRI-RD

AXIS CMEF baseline indicator 1 Competitiv- eness

4 Training and education in agriculture 16 Importance of semi-subsistence farming in New Member States

2 Environment

18 Biodiversity: High Nature Value forestry

19 Biodiversity: Tree Species Composition

21 Water quality: pollution by pesticides 23 Organic Farming 24 Production of renewable energy from agriculture and forestry

3 Quality of life

27 Farmers with other gainful activity

30 Self-employment development

31 Tourism infrastructure in rural areas 32 Internet take-up in rural areas 35 Life-long learning in rural areas

36 Development of Local Action Groups

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CMEF impact indicators and their potential implementation into CAPRI-RD Since CMEF impact indicators are the ones to be used for evaluation of RD policy in the end, we will give a

short overview of the seven impact indicators, their measurement, link to the CMEF baseline indicators and

potential implementation into CAPRI-RD based on the findings from previous sections. Most of the CMEF

impact indicators are closely linked to baseline indicators, since impact measurement consists of the difference

between the base year and the baseline (or scenario) year of the baseline indicator values.

Table 5: CMEF impact indicators, their measurement, related baseline indicators and potential for implementation into CAPRI-RD

CMEF Impact Indicator

Measurement

Related CMEF Baseline Indicator

(Minimum requirement)

B.line Ind. already in CAPRI-RD?

1 Economic growth

Net additional value added expressed in PPS standards (NAGVA-PPS)

1 Economic Development

Yes

9 Economic Development of Primary Sector

Yes

13 Economic Development of Food Industry

Yes

29 Economic Development of Non-Agricultural Sector

Yes

2 Employment creation

Net additional Full Time Equivalent jobs created

2 Employment Rate

Yes

3 Unemployment Rate

Yes

8 Employment Development of Primary Sector

Yes

12 Employment Development in Food Industry

Yes

28 Employment Development of Non-Agricultural Sector

Yes

3 Labour productivity

Change in Gross Value Added per full Time Equivalent (GVA/FTE)

6 Labour productivity in agriculture

Yes

10 Labour productivity in food industry

Yes

14 Labour productivity in forestry

Yes

4 Reversing Biodiversity Decline

Change in trend in Farmland Bird species population

17 Biodiversity: Population of farmland birds

No (but implementable)

5 Maintenance of HNV areas

Changes in HNV areas 18 Biodiversity: High Nature Value farmland and forestry

Yes (partially)

6 Improvement in water quality

Changes in Gross Nutrient Balance

20 Water quality: Gross Nutrient Balances (Nitrogen and/or Phosphorus)

Yes

7 Contribution to combating climate change

Increase in production of renewable energy

24 Climate change: Production of renewable energy from agriculture and forestry

No (and not implementable)

25 Climate change: UAA devoted to renewable energy

Yes (but not for scenario)

26 Climate change: GHG emissions from agriculture

Yes

Sources: EENRD, 2010; DG AGRI, 2006

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Table 5 shows that each of the seven impact indicators is related to at least one baseline indicator as a

minimum requirement for definition. For the majority of those related baseline indicators no additional data

source would be needed to implement them into the CAPRI-RD model. Only to calculate 'Biodiversity:

Population of farmland birds' an additional data source, the FBI, would need to be added. Due to a lacking data

source, it is not possible to implement 'Climate change: Production of renewable energy from agriculture and

forestry' into the model. Nevertheless, 'GHG emissions from agriculture' could be calculated and hence

contribute to implementing indicator 7.

Description of additionally required databases For the implementation of four indicators additional data sources would be needed. The description of these

additionally required databases will be carried out in the following section.

Farm Structure Survey (FSS) The basic Farm Structure Survey (agricultural census), is carried out by all European Union Member States

every 10 years with intermediate sample surveys carried out three times between the basic surveys.

The Member States collect information from individual agricultural holdings and forward the data to Eurostat.

The information collected in the FSS covers land use, livestock numbers, rural development, management and

farm labour input (also demographic information). The survey data can be aggregated to different geographical

levels (member states, regions, and for basic surveys also district levels) and can be arranged by various other

criteria.

The basic unit underlying the FSS is the agricultural holding: a technical-economic unit, under single

management, engaged in agricultural production. The FSS covers all agricultural holdings with a utilised

agricultural area of at least one hectare (ha) and also those holdings with a UAA of less than 1 ha if their market

production exceeds certain natural thresholds.

Regional resolution: NUTS 2 / Farm type

Years: different years from 2000 onwards

Coverage EU: EU-27

Responsible Institution: EUROSTAT

Data access regulation: Public domain

Link: http://epp.eurostat.ec.europa.eu/portal/page/portal/agriculture/data/database

Related CMEF indicator: Age structure in agriculture (No. 5) 1

Related domain path for indicator 5: EUROFARM domain/ Special interest topics/ Rural development/ Other

activities on the holding: Number of farms, agricultural area and economic size (ESU) by age of holder and sex

of holder. The domain EUROFARM (ef) contains information (statistical tables) on the structure of agricultural

holdings collected through agricultural structure surveys. Link:

http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/EN/ef_esms.htm#access_doc

1 Indicator 5 'Age structure in agriculture' will not be implemented in CAPRI-RD because too many other data would

additionally be required (e.g. life tables, statistics on vocational disability). The FSS data will nevertheless be requested.

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Farm Accountancy data network (FADN) The Farm Accountancy Data Network (FADN) is an annual survey carried out by the member states of the

European Union, it is an instrument for evaluating the income of agricultural holdings and the impacts of the

Common Agricultural Policy.

The EU services responsible for the operation of the FADN collect accountancy data from a sample of the

agricultural holdings in the European Union every year. Derived from national surveys, the FADN is the only

source of microeconomic data that is harmonised in all countries. Holdings are selected to take part in the

survey on the basis of sampling plans established at the level of each region in the Union. The minimum

economic size for farms to be integrated in the survey differs in every member state due to different farm

structures. The methodology applied aims to provide representative data along three dimensions: region,

economic size and type of farming.

Regional resolution: FADN regions (combination of NUTS1 and NUTS2)

Years: All

Coverage EU: varying according to the adhesion date of each MS

Responsible Institution: DG AGRI

Data access regulation: The legislation establishing FADN is Council Regulation 79/65/EEC of 15 June 1965. This

legislation has since been modified and expanded. For the list of relevant regulations see http://eur-

lex.europa.eu/en/legis/latest/chap0330.htm

Link: http://ec.europa.eu/agriculture/rica/

Related CMEF Indicator: Age structure in agriculture (No. 5) 1

Related FADN-path for indicator 5: Code C01YR (= Description: Year of birth (last 2 digits); Related rica-table: C;

Serial Number: 01; column: YR)

Farmland Bird index (FBI): regression model coefficients Farmland Bird Index (FBI) cannot be directly derived from the outputs of CAPRI model. However relationships

may be established between some outputs and the FBI. Candidate variables affecting FBI trends are: crop

shares, yields, nutrient input and nitrogen balance. The JRC has currently commissioned a study to test building

a regression model relating these variables with the FBI (results available early 2012).

The modelling is based on a (multi)regression through the use of mixed models and deviance analyses. So far,

the spatial unit is the NUTS2 level with CAPRI data from 2000 to 2007. Tests are also done at finer spatial

resolution but having only one year of crop and input spatialised (des-aggregated) data clearly limit the

exercise.

The study focuses on France and possibly on Sweden and gives information on the feasibility of calculation of

the FBI in CAPRI scenarios.

The analysis of bird data for EU coverage will depend on results of the feasibility study in France commissioned

by the JRC. In case of a positive outcome, it will be necessary to establish such a multi-regression for each

country or each European agri-environmental zone.

The regression coefficients derived from the model for the independent variables will be then used in the

CAPRI model in equations to forecast changes in the FBI according to policy scenarios (for France only).

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More information on the FBI is provided in the Annex.

Regional resolution: NUTS2

Years: set of regression coefficients will be established using CAPRI outputs for the 2000-2007 period

Coverage EU: France (+ Sweden)

Responsible Institution: JRC-IES-(MARS- Action AGRIENV)

Data access regulation: data will be publicly available

Related CMEF Indicator: Biodiversity: Population of farmland birds (No. 17)

High Nature Value farmland index: stocking density thresholds The High Nature Value (HNV) farmland index is currently under development in the CAPRI model as 'likelihood

of HNV' index (PARACCHINI and BRITZ, 2010). The index is calculated for the HSMUs units.

Currently, the HNV index already implemented in CAPRI covers arable and permanent crops. As grasslands

need to be included as well (they are the predominant land-use for HNV), it is proposed to use the stocking

density as a proxy of farming intensity for grasslands.

Therefore an additional dataset has to be inserted into the CAPRI model including two threshold values per

HSMU, identified for environmental areas in Europe: a minimum stocking density below which shrub

encroachment may start, and a maximum grazing density above which the carrying capacity of the land in

relation to biodiversity presence may be exceeded.

Regional resolution: HSMU, Environmental zones (http://pan.cultland.org/cultbase/?document_id=152)

Years: 2004 (only available as-aggregated dataset)

Coverage EU: EU27+

Responsible Institution: JRC-IES-(MARS- Action AGRIENV)

Data access regulation: data will be publicly available

Related CMEF Indicator: Biodiversity: High Nature Value farmland (No. 18)

JRC Revised Universal Soil Loss Equation factors As we aim at complete pan-European coverage for the soil erosion indicator, the erosion model and the input

datasets to be used have to be easily available and to provide harmonised EU information.

Consequently, the Revised Universal Soil Loss Equation (RUSLE) was chosen as empirical model that calculates

soil loss due to sheet and rill erosion. The model considers seven main factors controlling soil erosion: the

erosivity of the eroding agents (water), the erodibility of the soil, the slope steepness and the slope length of

the land, the land cover, the stoniness and the human practices designed to control erosion.

The RUSLE predicts the potential long term average annual rate of erosion (A) on a field slope based on rainfall

pattern (R), soil type (K), slope length (LS), crop system (C), and management practices (P), according to the

formula

A = R x K x LS x C x P

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The index is calculated for the HSMUs units.

Therefore two additional datasets are required: (i) dataset including physical factors and (ii) dataset for crop

cover and land management factors.

(i) Physical Factors. The modified version of the RUSLE index for Europe is currently being developed at the Soil

Action of EC Joint Research Centre (BOSCO et al., 2011), which also includes a Stoniness Correction factor. The

results of this study are utilised to integrate the indicator into CAPRI, in particular with regard to harmonised

information layers on Rainfall pattern (R), Erodibility (K) and Slope length (LS). These parameters cannot be

directly modelled in CAPRI, and future climate scenarios are currently not available in the model. Therefore

these parameters are included in the model as a constant physical parameter, obtained by multiplying R, LS

and K factors (at the spatial resolution of 1 km2) and averaging the result per HSMU.

(ii) Crop cover (C factor) and land management (P factor). This second dataset contains the C factor associated

to every crop. The C factor is obtained from data reported in literature. The average was calculated when more

than one value was found for the same crop referring to different agricultural practices and environmental

zones in Europe. Information on P factor for every modelled crop was not available, therefore was set to 1.

A lookup table of C factors linked to every crop activity modelled in CAPRI can be included into the system.

Through the crop share available for each reference unit (HSMU), a dynamic indicator on the risk of soil erosion

can be implemented, which dynamics is given by the crop areas simulated by CAPRI and des-aggregated on the

HSMU.

Soil erosion can be calculated in the CAPRI model at HSMU level by multiplying the constant value at HSMU

level of R * LS * K * P by the values of C, which vary according to crop shares and therefore to scenarios.

Regional resolution:

For C factor: Table with C factor per crop

For physical factors (R, LS, K): Calculated value for each HSMU

Years: 2011

Coverage EU: EU27

Responsible Institution: EC-JRC-IES

Data access regulation: data available to CAPRI-RD partners, table with C factor per crop can be made publicly

available

Link: http://eusoils.jrc.ec.europa.eu/esdb_archive/ESDBv2/fr_intro.htm

Related CMEF Indicator: Areas at risk of soil erosion (No. 22)

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Summary and conclusions The CMEF contains 36 baseline indicators linked to the wider objectives of the RD programme and used as

reference against which the programmes' impact is to be assessed ('Baseline indicators related to objectives').

Implementing these indicators into CAPRI-RD is one possibility to put this intention into practise and covering

the whole EU 27 at a regional solution.

For 20 of the CMEF baseline indicators required data sources for their calculation in CAPRI-RD are (partly)

already available in the current model. These are mainly indicators from Axis 1 and 3 with an economical

background for which the CGEregEU+ can serve as source, but also indicators from Axis 2 which (partly) can be

calculated using the HSMU, DNDC, NECR and PRIMES components of the current model.

Additional data sources would mainly be needed for the implementation of environmental indicators:

'population of farmland birds', 'High Nature Value farmland', 'areas at risk of soil erosion', but also for 'age

structure in agriculture' (the latter will not be considered for implementation into the CAPRI-RD model because

too many other data would additionally be required). These 4 additionally required data sources were

identified and described in this deliverable. They are public domain or available to CAPRI-RD partners and

cover the EU 27 or more (apart from FBI). The temporal coverage is different for all databases.

13 CMEF baseline indicators can (partly) not be implemented into CAPRI-RD due to missing data. These are

indicators from all three Axes. However, a relatively great proportion is assignable to Axis 3 'Quality of life'.

Regarding the CMEF impact indicators it can be summarized that the majority of the related baseline indicators

can be implemented in the model without the need for additional data sources.

In conclusion, it can be said that CAPRI-RD already covers a majority of the CMEF indicators. However, a

number of new indicators are to be added and, especially for the environmental Axis, further required data

sources were identified.

References Bosco, C., De Rigo, D., Dewitte, O., Montanarella, L. (2011): Towards a reproducible Pan-European soil erosion

risk assessment – RUSLE. Geophysical Research Abstract Vol. 13 EGU2011 – 3351

Britz, W. and Witzke, P. (2008): CAPRI model documentation 2008: Version 1; http://www.capri-

model.org/docs/capri_documentation.pdf

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Rural Development (EAFRD)

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assessing the impact of the RD Programmes in the context of multiple intervening factors. Available at

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Annex Farmland Bird Index

The Farmland Bird Index itself is calculated from the Pan European Common Bird Monitoring Scheme (PECBMS)

dataset. The PECBMS is a partnership involving the European Bird Census Council, the Royal Society for the

Protection of Birds, BirdLife International, and Statistics Netherlands. In order to deliver policy relevant

biodiversity indicators to decision makers in Europe, it collates national data in a harmonized way from a

network of expert ornithologists. The project depends on cooperation with national monitoring.

´PECBMS Europe´ is EU 27 + Norway and Switzerland and consists of those countries which already deliver their

data to PECBMS or are supposed to do so in the near future: Austria, Belgium, Bulgaria, Czech Republic,

Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Netherlands, Norway, Poland,

Portugal, Republic of Ireland, Romania, Slovakia, Spain, Sweden, Switzerland, United Kingdom. Greece has a

pilot scheme.

National monitoring schemes produce population yearly indices and trends. The index gives bird numbers in

percentages relative to a base year. Trend values express the overall population change over a period of years.

The coordinators of the monitoring schemes produce national species indices using specific statistical software,

namely TRIM (Trends and Indices for Monitoring data, VAN STRIEN et al., 2001) and BirdSTATs.

The test made to relate CAPRI outputs to FBI is made for France (only data available) and is based on a

sampling scheme with about 2000 points where birds’ presence is recorded.

Table 6: Country overview of Common Bird Monitoring Schemes in Europe

Country Scheme Name status start end Number of

species

Austria Monitoring der Brutvögel Österreichs ongoing 1998 60-65

Belarus National Scheme of Environmental Monitoring in Belarus

pilot 2007 ?

Belgium-Flanders

Common Breeding Birds in Flanders pilot 2007 ?

Belgium-Wallonia

Common Bird Monitoring Scheme ongoing 1990 ?

Belgium-Brussels

Common Bird Monitoring Scheme ongoing 1992 ?

Bulgaria Common Bird Monitoring Scheme ongoing 2004 30

Croatia Common Bird Monitoring Scheme planned ?

Cyprus Cyprus Common Bird Census pilot 2005 ?

Cyprus Western Cyprus Common Bird Census ongoing 2003 ?

Czech Republic

Breeding Bird Census Programme ongoing 1981 100

Denmark Point count census of breeding and wintering birds

ongoing 1976 100

Estonia Point Count Project ongoing 1983 45

Finland Annual monitoring of breeding birds in Finland

ongoing 1981 100

Finland Summer bird atlas of breeding birds finished 2000 2005 ?

France Temporal Survey of Common Birds finished 1989 2001 ?

France New Temporal Survey of Common Birds

ongoing 2001 150

Germany DDA monitoring program for common breeding birds

ongoing 1989 100-150

Germany DDA Mon breeding birds in the wider countryside monitoring program of common

ongoing 2004 100-130

Greece Hellenic Common Breeding Bird Monitoring Scheme (HCBBMS)

pilot 2006 ?

Hungary Monitoring of our common birds (MMM) ongoing 1999 100

Hungary Point counts of passerines finished 1988 1998 ?

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Ireland Countryside Bird Survey (CBS) ongoing 1998 55

Italy MITO2000 (Monitoraggio ITaliano Ornitologico)

ongoing 2000 75

Latvia Monitoring of birds and habitats in agricultural lands

finished 1995 2006 ?

Latvia Breeding Bird Counts finished 1983 1994 ?

Latvia Latvian Breeding Bird Monitoring scheme

ongoing 2005 60

Lithuania Monitoring of breeding birds suspended

1991 20

Luxembourg Common bird monitoring program finished 2002 2003 ?

Macedonia Common bird Monitoring Scheme - Macedonia

pilot 2007 ?

Netherlands BMP - Common breeding species project

ongoing 1984 113

Norway Norwegian breeding bird census ongoing 1995 58

Norway New Norwegian breeding bird census ongoing 2005 ?

Poland Monitoring Pospolitych Ptakow Legowych (MPPL)

ongoing 2000 178

Portugal Censo de Aves Comuns (CAC) ongoing 2004 60

Romania Common Bird Monitoring (CBM) in Romanian

pilot 2006 ?

Russia Bird population monitoring ? 1973 ? ?

Slovakia Monitoring of breeding bird populations in Slovakia

ongoing 1994 ?

Slovenia Slovenian monitoring of common birds of agricultural landscape

pilot 2007 ?

Spain Common Breeding Bird Monitoring Scheme (“SACRE”)

ongoing 1996 100

Spain Catalan Common Bird Survey (SOCC) ongoing 2002 100

Sweden Swedish Breeding Bird Survey ongoing 1975 120

Sweden Swedish Breeding Bird Census finished 1969 ? ?

Sweden Swedish Breeding Bird Survey ongoing 1996 80

Switzerland Monitoring of abundant breeding birds ongoing 1999 75

Turkey Common Bird Monitoring (CBM) in Turkey

pilot 2007 ?

UK Breeding Bird Survey ongoing 1994 70

UK Common Birds Census finished 1962 2000 ?

UK Waterways Bird Survey ongoing 1974 24

UK Waterways Breeding Bird Survey ongoing 1998 70

Ukraine Counts of birds in Western Ukraine ongoing 1980 50

Some countries have more than one scheme in place. Names of schemes given in italics are indicative only,

there are no exact titles known to us or established yet (Modified from Klvaňová and Voříšek, 2008)

Soil erosion – JRC Revised Universal Soil Loss Equation factors

The original datasets from which the physical factors are derived are the following:

Rainfall pattern: R factor from E-OBS dataset.

The intensity of precipitation is one of the main factors affecting soil water erosion processes. R is a

measure of the precipitation’s erosivity and indicates the climatic influence on the erosion phenomenon

through the mixed effect of rainfall action and superficial runoff, both laminar and rill.

Due to the difficulty in obtaining precipitation data with adequate temporal resolution over large areas, the

R factor has been calculated using a series of simplified equations available in scientific literature. In the

present application, a climatic-based ensemble model to estimate erosivity from multiple available

empirical equations has been created for the pan-European maps. The R factor has been computed using

18

the E-OBS database as data source (HAYLOCK, 2008). E-OBS is based on the largest available pan-European

precipitation data set, and its interpolation methods were chosen after careful evaluation of a number of

alternatives.

E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in

Europe based on ECA&D information. The full dataset covers the period 1950-2010. It was originally

developed as part of the ENSEMBLES project (EU-FP6) and is now maintained and elaborated as part of the

EURO4M project (EU-FP7)

Length of slope: LS factor from the DEM SRTM.

The Shuttle Radar Topography Mission (SRTM) is an international project spearheaded by the National

Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA).

SRTM obtained elevation data on a near-global scale to generate the most complete high-resolution digital

topographic database of Earth. SRTM consisted of a specially modified radar system that flew on board the

Space Shuttle Endeavour during an 11-day mission in February of 2000.

Erodibility: K factor from the SGDBE Soil Geographical Database of Eurasia at scale 1:1,000,000

The soil erodibility factor represents the effects of soil properties and soil profile characteristics on soil loss

(RENARD et al., 1997). The K factor is affected by many different soil properties and therefore the

quantification the natural susceptibility of soils is difficult. For this reason, K is usually estimated using the

soil-erodibility nomograph (WISCHMEIER and SMITH, 1978). The European Soil Database (SGDBE) at

1:1.000.000 scale has been used for the calculation (see also HEINEKE et al., 1998).

Fig. 1. Soil erodibility factor in Europe (t ha h ha-1 MJ-1 mm-1)