Development of agri-environmental indicators for monitoring the integration of environmental concerns into the common agricultural policy - Hoofdinhoud

COUNCIL OF Brussels, 19 September 2006

THE EUROPEAN UNION

12973/06 ADD 1

STATIS 70 AGRI 287 ENV 469

COVER NOTE

from: Secretary-General of the European Commission,

signed by Mr Jordi AYET PUIGARNAU, Director

date of receipt: 18 September 2006 to: Mr Javier SOLANA, Secretary-General/High Representative

Subject: Development of agri-environmental indicators for monitoring the integration of environmental concerns into the common agricultural policy

Delegations will find attached Commission document SEC(2006) 1136.

________________________

Encl.: SEC(2006) 1136

COMMISSION OF THE EUROPEAN COMMUNITIES

Brussels, 15.9.2006 SEC(2006) 1136

COMMISSION STAFF WORKING DOCUMENT

accompanying the

COMMUNICATION FROM THE COMMISSION TO THE COUNCIL AND THE EUROPEAN PARLIAMENT

Development of agri-environmental indicators for monitoring the integration of environmental concerns into the common agricultural policy

{COM(2006) 508 final i}

EN EN

TABLE OF CONTENTS

• 1. 

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

• 2. 

  Progress with the development of agri-environmental indicators................................ 4

2.1. Scope and outputs of the IRENA operation................................................................. 4

2.2. The analytical framework for agri-environmental indicators ...................................... 5

2.3. Main IRENA results regarding indicator development ............................................... 7

2.3.1. Geographical level ....................................................................................................... 7

2.3.2. Time series ................................................................................................................... 8

• 3. 

  IRENA Indicator Report .............................................................................................. 8

3.1. Outline of the report ..................................................................................................... 8

3.2. Evaluation of the indicators ......................................................................................... 9

3.2.1. General trends in agriculture ........................................................................................ 9

3.2.2. Agricultural water use ................................................................................................ 10

3.2.3. Agricultural input use and state of water quality ....................................................... 10

3.2.4. Agricultural land use, farm management practices and soils..................................... 10

3.2.5. Climate change and air quality................................................................................... 10

3.2.6. Biodiversity and landscape ........................................................................................ 10

3.3. Conclusions and challenges for improving the indicators ......................................... 11

• 4. 

  IRENA Indicator-based Assessment Report .............................................................. 12

4.1. Outline of the report ................................................................................................... 12

4.2. Main findings on the usefulness of the indicators for assessing environmental integration .................................................................................................................. 13

4.3. Conclusions and recommendations............................................................................ 13

• 5. 

  IRENA Evaluation Report ......................................................................................... 13

5.1. Outline of the report ................................................................................................... 13

5.2. Practical recommendations ........................................................................................ 14

• 6. 

  Actions for future work on agri-environmental indicators ........................................ 14

6.1. Streamlining the IRENA indicator set and strengthening its policy relevance.......... 14

6.2. Consolidating the selected indicators, extending the coverage to the new Member

States and correcting existing weaknesses ................................................................. 15

6.2.1. Review of agricultural data sources ........................................................................... 16 6.2.1.1. Agricultural statistics ................................................................................................. 16

6.2.1.2. Farm Accountancy Data Network.............................................................................. 19

6.2.1.3. Land Use/Cover Area Frame Statistical Survey (LUCAS)........................................ 20

6.2.1.4. OECD/Eurostat Joint Questionnaire .......................................................................... 20

6.2.2. Review of environmental data sources ...................................................................... 21

6.2.2.1. CORINE Land Cover ................................................................................................. 21

6.2.2.2. EIONET Water........................................................................................................... 21

6.2.2.3. Pan-European Common Bird Monitoring Database .................................................. 21

6.2.3. Review of modelling approaches ............................................................................... 22

6.2.4. Review of administrative data sets............................................................................. 23

6.2.4.1. Common monitoring and evaluation framework for rural development programmes .................................................................................................................................... 23

6.2.4.2. Organic farming ......................................................................................................... 23

6.2.4.3. NATURA 2000 .......................................................................................................... 23

6.2.5. Other data sets ............................................................................................................ 24

6.2.6. New data sets.............................................................................................................. 24

6.3. Setting up a permanent and stable arrangement needed for the long-term functioning of the indicator system ............................................................................................... 24

ANNEX 1 ................................................................................................................................. 25

ANNEX 2 ................................................................................................................................. 31

ANNEX 3 ................................................................................................................................. 41 1. I NTRODUCTION

This document accompanies the Commission’s Communication “Development of agrienvironmental indicators for monitoring the integration of environmental concerns into the common agricultural policy” [COM(2006) 508 final i]. It reviews the progress achieved to date in

the development of agri-environmental indicators on the basis of the IRENA operation 1 and

presents the main findings of the IRENA reports in relation to indicator development. It also identifies key challenges and future actions.

• 2. 

  P ROGRESS WITH THE DEVELOPMENT OF AGRI - ENVIRONMENTAL INDICATORS

2.1. Scope and outputs of the IRENA operation

In January 2000, the Commission adopted the Communication “Indicators for the Integration of

Environmental Concerns into the Common Agricultural Policy” 2 , which identified a set of

35 agri-environmental indicators and presented an analytical framework for their development (see Figure 1). The Communication mentions the following reasons for developing agrienvironmental indicators:

– to understand the linkages between agricultural practices and the environment;

– to identify environmental issues related to agriculture;

– to help target measures that address agri-environmental issues;

– to help monitor and assess agri-environmental policies; and

– to provide contextual information for rural development.

In March 2001, the Commission published a second Communication entitled “Statistical

Information Needed for Indicators to Monitor the Integration of Environmental Concerns into

the CAP” 3 , which proposed definitions for each of the 35 indicators, and identified potential data

sources and information needed to make the indicators operational.

These two Commission Communications provided the conceptual input for the launch of the IRENA operation in September 2002.

The IRENA operation was based on a grant agreement between the European Commission and

the European Environment Agency (EEA) 4 . The operation was closely guided by a steering

group involving representatives of DG Agriculture and Rural Development, DG Environment, Eurostat, the Joint Research Centre, and the EEA. While the indicator work was developed in partnership, the EEA co-ordinated and managed the project and was responsible for providing the deliverables set out in the agreement.

The purpose of the IRENA operation was to develop and compile, for the EU-15, the set of 35 agri-environmental indicators identified in the Commission Communications COM(2000) 20 i and COM(2001) 144 i, at the appropriate geographical levels and, as far as possible, on the basis of existing data sources. The objectives also included regional analyses and an indicator-based

1 Indicator Reporting on the Integration of ENvironmental Concerns into Agriculture Policy. 2 COM(2000) 20 i. 3 COM(2001) 144 final i.

EN EN

assessment of the integration of environmental concerns into EU agricultural policy, as shaped by Agenda 2000.

The IRENA operation was finalised in December 2005. Its outputs are the following 5

• 1. 

  40 indicator fact sheets and their corresponding data sets (in the form of Excel files), accompanied by an information manual for the indicator data sets,

• 2. 

  an Indicator Report, which reviews the interactions between farming and the environment on the basis of the indicator results, and provides an assessment of the progress made in developing and compiling the set of agri-environmental indicators,

• 3. 

  an Indicator-based Assessment Report on the integration of environmental concerns into the CAP, based on the indicators and the agri-environmental analysis developed in the context of the Indicator Report, and

• 4. 

  an Evaluation Report, which reports on the working procedures and resources of the IRENA operation, assesses the suitability of the data sources used, and makes proposals for future indicator work and development.

2.2. The analytical framework for agri-environmental indicators

The agricultural DPSIR framework (Driving force – Pressure – State – Impact – Response; see Figure 1) is meant to capture the key ‘factors’ involved in the relationships between agriculture and the environment and to reflect the complex chain of causes and effects linking these factors. However, as with other models, the DPSIR model is a simplification of reality. Many of the interactions between agriculture and the environment are not (yet) sufficiently understood or are difficult to capture in a single framework. In addition, there are other, socio-economic, factors independent of the policy framework which can determine changes in farming systems and rural areas and can also significantly affect the environment.

Table 1 provides an explanation of the concepts behind the different domains/sub-domains of the DPSIR model and lists the indicators developed through the IRENA operation.

Figure 1: DPSIR framework for agriculture (from the IRENA Indicator Report)

5 All the IRENA outputs (with the exception of the Evaluation Report) can be found on the IRENA website:

http://webpubs.eea.europa.eu/content/irena/index.htm.

Table 1: The DPSIR framework and the IRENA indicators

Domain Sub-domain Explanation N° Indicator

Public policy Farming activities are strongly influenced by 1 Area under agri-environment support

agricultural and environmental policies and 2 Regional levels of good farming practice sensitive to input and product price signals.

s 3 Regional levels of environmental targets Moreover, changes in technology, farmers’

n se skills, and consumers’ and producers’ 4 Area under nature protection p o Market signals attitudes affect production methods and 5.1 Organic producer prices and market share R es agricultural practices. 5.2 Organic farm incomes

Technology and

skills 6 Farmers’ training levels

Input use A key characteristic of different farming 8 Mineral fertiliser consumption systems and determinant of farming intensity 9 Consumption of pesticides

is the use of inputs (fertilisers, pesticides,

energy and water). 10 Water use (intensity) 11 Energy use

es Land use

Land use changes as well as cropping and 12 Land use change livestock patterns indicate land use intensity

and trends in the agricultural sector. 13 Cropping/livestock patterns g f o rc

Farm management practices include, inter

in alia, rotation patterns, soil cover, tillage 14 Farm management practices

D ri

v methods and the handling of farm manure.

Trends Key trends in farming activities at an 15 Intensification/extensification aggregate (e.g. regional, national) level can

16 Specialisation/diversification be expressed in terms of intensification/extensification,

specialisation/diversification, and 17 Marginalisation

marginalisation.

Pollution Agriculture can lead to nutrient and pesticide 18 Gross nitrogen balance

residues in soil and water as well as to 18sub Atmospheric emissions of ammonia

ammonia and methane emissions. The use of

sewage sludge can improve soil fertility, but 19 Emissions of methane and nitrous oxide

needs to be carefully monitored from a 20 Pesticide soil contamination

pollution perspective. 21 Use of sewage sludge

Resource depletion Inappropriate use of water and soil leads to 22 Water abstraction

environmental pressures. Changes in land 23 Soil erosion

cover and genetic diversity can have similar

consequences. 24 Land cover change

25 Genetic diversity

EN EN

Preservation and Agriculture provides environmental benefits

enhancement of the via the management of high nature value 26 High nature value (farmland) areas

environment farmland and the production of renewable

energy sources. 27 Production of renewable energy (by

source)

Biodiversity The state of farmland birds provides a

measure of the state of the overall species 28 Population trends of farmland birds

diversity in farmed areas.

Natural resources The state of key natural resources (soil 29 Soil quality

te quality, water quantity and quality) needs to 30 Nitrates/pesticides in water

S ta be monitored. 31 Ground water levels

Landscape Agriculture has a strong influence on the state of Europe’s landscapes through

cropping patterns, grazing of upland areas, 32 Landscape state

landscape elements such as hedgerows, etc.

Habitats and The share of agriculture in wider 33 Impact on habitats and biodiversity biodiversity environmental issues can be significant. Here 34.1 Agricultural share of GHG emissions

Natural resources the focus is on the global impact of p a ct

agriculture at a national or EU level. Its 34.2

Agricultural share of nitrate

Im impact on natural and landscape diversity is contamination

also important, but often spatially 34.3 Agricultural share of water use

Landscape diversity concentrated and scale-dependent. 35 Impact on landscape diversity

2.3. Main IRENA results regarding indicator development

The IRENA operation has largely achieved its objectives in terms of indicator development,

collection of data, and production of reports. A valuable effort has been made regarding the

conceptual development 6 of the indicators, the identification of appropriate data sources, and the

compilation of relevant data.

The indicators are based on data from a wide range of sources (e.g. agricultural and environmental databases, models, and administrative data) and collected at different geographical and time scales.

The table in Annex 1 provides the list of indicators and sub-indicators, their definition, the data sources used, the geographical reporting level, and the time series provided.

2.3.1. Geographical level

A key requirement for IRENA was the development of indicators at the appropriate geographical level, so as to reflect the regional diversity of environmental conditions (e.g. soils, climate) and types of agricultural production systems and structures (e.g. specialisations, production patterns, farming methods). The targeted geographical scale for reporting across the EU-15 was the

administrative regions NUTS 7 2 or 3. In order to achieve similarly sized regional units, the

NUTS levels used for the different Member States were:

– NUTS 2: Austria, Belgium, Germany, Greece, Luxembourg, Netherlands, Italy, Portugal and United Kingdom,

– NUTS 3: Denmark, Finland, France, Ireland, Spain and Sweden.

About one third of the indicators are based on data at the targeted regional level (NUTS 2

or 3). Nearly two-thirds of the indicators use national or sub-national level data (i.e. NUTS 0 and

6 To support this task, and in particular to improve the policy relevance and analytical soundness of certain

indicators, several IRENA expert meetings (with participation of researchers, Member State representatives, etc.) were organised.

EN EN

1), although data at a lower geographical level are provided for three of these indicators for some Member States. Several indicators of the state/impact domains were developed on the basis of modelled data or case studies.

Table 2: Spatial scale of IRENA indicators

NUTS level IRENA N° Number of indicators

NUTS 0 1, 2, 3, 5.1, 5.2, 8, 9, 11, 14.2, 18, 18sub, 19, 21, 25, 26, 27, 23 28, 30.1, 30.2, 33, 34.1, 34.2, 34.3

NUTS 0/1 15, 16, 17 (except for ES, FR and IT for which the reporting 3 level is NUTS 2)

NUTS 2/3 4, 6, 7, 10, 12, 13, 14.1, 14.3, 22, 24, 32, 35 (32 and 35 on a 12 case study basis only)

NUTS 2/3 based 20, 23, 29 3 on modelling

Case study 31, 32, 35 3

2.3.2. Time series

With regard to the temporal scale, the target of the IRENA operation was to cover the years between 1990 and 2000. This time period includes the MacSharry reform of the CAP in 1992 and precedes the implementation of the Agenda 2000 CAP reform.

Time series are provided for about half of the (sub-)indicators. Eighteen indicators cover the period between 1990 and 2000. The indicators with trends between 1990 and 2000 are often based on data from the 12 Member States that made up the EU in 1990 (EU-12). The indicators for which trends are not provided are those for which only single-year data are available (e.g. indicators No 25, genetic diversity, and 34.2, agricultural share of nitrate contamination), those based on case studies (e.g. No 31, ground water levels, No 33, impact on habitats and biodiversity) and indicators for which time series data are particularly difficult to obtain due to their complexity (e.g. No 23, soil erosion and No 29, soil quality).

Table 3: Temporal scale of IRENA indicators

Temporal scale IRENA N° Number of indicators

1990–2002 18sub, 19, 22, 28, 34.1 7

1990–2000 8, 10, 11, 12, 13, 15, 16, 17, 18, 24, 35 (also 1990–98, and 11

1996–2001)

Shorter time 1, 7, 9, 20, 21, 34.3, 30.1, 30.2, (the two last cover 1992–2002) 6

series

No time series 2, 3, 4, 5.1, 5.2, 6, 14.1, 14.2, 14.3, 23, 25, 26, 27, 29, 31, 32, 18 33, 34.2

• 3. 

  IRENA I NDICATOR R EPORT

3.1. Outline of the report

8

The Indicator Report (“Agriculture and environment in EU-15 – the IRENA indicator report”) reviews agri-environmental interactions on the basis of the indicator results and provides an

8 The report was jointly published by the Commission and the EEA in December 2005. It is available on the

web site http://reports.eea.europa.eu/eea_report_2005_6/en.

assessment of the progress made in the development and compilation of agri-environmental indicators during the IRENA operation.

Agri-environmental storylines, based on the DPSIR framework, are used to present the indicator results and the conclusions that can be drawn as regards the effect of farming on specific environmental areas: (a) agricultural water use and water resources; (b) input use and the state of water quality; (c) land use, farm management and soils; (d) climate change and air quality; and (e) biodiversity and landscape.

The storylines are developed in thematic chapters. They are introduced by a presentation of the general trends in EU-15 agriculture, which describes the main trends in farming and land use based on the indicators from the “driving force” and “response” domains.

Alongside the agri-environmental analysis, the Indicator Report carries out an evaluation of all IRENA indicators on the basis of the following criteria: policy-relevance, responsiveness, analytical soundness, data availability and measurability, ease of interpretation and cost

effectiveness 9 . The final chapter assesses the suitability of all data sources used and proposes

ways for improving the data sets. Additional recommendations for future agri-environmental indicator development are provided in the Evaluation Report.

The report builds on the 40 Indicator Fact Sheets (IFS). For each (sub-)indicator, the IFS presents the data and the assessment of the indicator (summarised at the beginning of the fact sheet in the form of key messages), the agri-environmental context and policy relevance, and a metadata section.

3.2. Evaluation of the indicators

Based on the pre-defined criteria, an evaluation framework was built to classify the indicators into three categories: ‘useful’, ‘potentially useful’ and ‘low potential’.

Out of the 42 (sub-)indicators, 11 were evaluated as being useful, 30 as being potentially useful, and only one was considered as having low potential. Data-related criteria (geographical coverage, availability of time series) and conceptual criteria (analytical soundness, data quality) had a significant influence on the evaluation results, which are summarised in the table in Annex 2.

The following main conclusions emerge from the evaluation of the indicators related to the different agri-environmental storylines.

3.2.1. General trends in agriculture

Five out of 13 of the indicators that show agricultural trends are in the ‘useful’ category, while

10

the rest are classified as ‘potentially useful’. In general, the indicators based on FSS 11 , FADN and CORINE land cover 12 have a higher score, because these data sources provide harmonised

regional information. However, an outstanding issue is the difficulty of linking indicators that are reported at different scales. This concerns, for example, national data on mineral fertiliser consumption (No 8), which are difficult to link with regional data on cropping and livestock patterns (No 13) and regional data on yields (No 15).

9 Criteria identified in COM(2001) 144 i.

10 Farm Structure Survey.

11 Farm Accountancy Data Network.

12 COoRdinate INformation on the Environment. It provides spatially referenced information on land cover

and land cover changes across Europe.

3.2.2. Agricultural water use

Six indicators are regarded as ‘potentially useful’ and one indicator has ‘low potential’ (No 31, groundwater levels). Data on trends in groundwater levels would be very useful, but EU-level data are not available and national level data sets are very expensive to acquire. Pressure, state/impact and response indicators are underpinned by low or medium quality data, and the links between the indicators are rather weak. Greater efforts are required to improve the indicators for monitoring the impact of agriculture on water resources. Modelling may have a role to play, whereby climatic information is combined with crop and land use data to determine water requirements from agriculture.

3.2.3. Agricultural input use and state of water quality

The three indicators classified as ‘useful’ are: mineral fertiliser consumption (No 8), cropping/livestock patterns (No 13) and area under organic farming (No 7). The other eight indicators are classified as ‘potentially useful’, including gross nitrogen balance (No 18) which is not available at regional level. In most cases, these indicators have not reached a level of development to be considered as ‘useful’, because of inadequate data availability, data measurability and analytical soundness. Information on the use and impact of pesticides is particularly difficult to obtain.

3.2.4. Agricultural land use, farm management practices and soils

Four indicators are classified as ‘useful’: the driving force indicators land use change (No 12), and cropping/livestock patterns (No 13), the pressure indicators land cover change (No 24) and the response indicator area under organic farming (No 7). The remaining indicators are evaluated as being ‘potentially useful’. These indicators have weaknesses regarding data availability, measurability and analytical soundness. Several indicators are based on modelling, and further efforts are needed to improve these models in order to achieve a greater degree of robustness and acceptability (e.g. No 23, soil erosion and No 29, soil quality). The indicators related to ‘farm management practices’ have the lowest score. Information about farm practices is highly relevant for several important indicators (e.g. gross nutrient balance, GHG emissions, soil erosion), but there is little harmonised information available at European level.

3.2.5. Climate change and air quality

Most of the indicators (six of the nine) used in this storyline fall into the ‘useful’ category. The indicators with the highest score are those related to emissions, such as atmospheric emissions of ammonia (No 18sub), emissions of methane and nitrous oxide (No 19), as well as the share of agriculture in GHG emissions (No 34.1). The response indicators (regional levels of environmental targets (No 3) and production of renewable energy (No 27)) are considered as ‘potentially useful’. To become useful, their measurability would need to be improved. The generally high evaluation of the indicators in this storyline is due to the fact that the pressure and state indicators are reported at national rather than regional level. Moreover, these indicators are developed on the basis of internationally harmonised procedures.

3.2.6. Biodiversity and landscape

Half of the indicators (eight out of 16) are classified as ‘useful’. These are the driving force indicators: land use (No 12), intensification/extensification (No 15), specialisation (No 16), cropping/livestock patterns (No 13) and land cover change (No 24), the state indicator on populations of farmland birds (No 28), and the response indicators area under nature protection (No 4), and area under organic farming (No 7).

The indicators that are considered as ‘potentially useful’ are marginalisation (No 17), genetic diversity (No 25), high nature value farmland areas (No 26), landscape state (No 32), impact on landscape diversity (No 35), area under agri-environment support and regional levels of good farming practice (No 2). These indicators, from the state, impact and response domains, suffer from a lack of regional data and inadequate time series. Some would also need further methodological development (e.g. No 26, No 32).

3.3. Conclusions and challenges for improving the indicators

A substantial amount of expertise concerning the technical feasibility of the indicators and their interpretation has been gained through the IRENA operation. A significant amount of information has been gathered on the state of and trends in the environmental conditions relating to agriculture and on the agricultural measures available to deliver environmental integration.

However, several limitations have become apparent during the agri-environmental analysis based on indicators, and through the evaluation of the usefulness of the indicators for assessing environmental integration. These are:

– limits of the indicator-based approach itself for environmental reporting. The indicators give an insight into ‘real-life’ processes and their causal relationships, but they cannot fully represent them. More comprehensive information based on research and knowledge about the interactions between agriculture and the environment is required to interpret the indicator results;

– the DPSIR framework has revealed certain limits due to the insufficient development of key indicators in several domains (e.g. water resources) and the difficulty in reflecting the complex chain of causes and effects between the factors intervening at the interface of agriculture and the environment;

– deficiencies in the data sets in terms of harmonisation, data quality and/or geographical coverage have been identified as the most critical constraints. These deficiencies concern certain indicators related to agricultural driving forces and, even more so, the indicators in the water, soil and biodiversity domains, which are the most limited in terms of coverage, time series and reliability;

– the differences between indicators in terms of data reliability and spatial resolution limit the scope for performing the cross-referencing that is needed for a regional analysis. For instance, it is difficult to link the indicator on mineral fertiliser use, which is reported at national level, with the regional data on cropping and livestock patterns and yields;

– however, the regional breakdown of information for many IRENA indicators does allow some differentiation of environmental pressures/state across the EU-15. Thus, “association analysis” of the indicators can be carried out to assess aspects of integration (e.g. targeting of agri-environmental measures at agricultural areas under Natura 2000 as a proxy for integration);

– a number of the current indicators still require further methodological (conceptual and technical) development and/or more appropriate data (in terms of quality and/or geographical scale).

Following an in-depth analysis of the strengths and weaknesses of all the data sources used for producing the 42 (sub)-indicators, some of the challenges ahead for improving these indicators are:

– Availability of the relevant information at the required geographical level. The reporting scale is a critical issue for indicator development. The data sets for reporting at EU-level can be coarser than those for national or regional analysis. They make it possible to perform comparisons at EU level, because of the greater degree of harmonisation of data collection. However, the EU indicator data sets are, in the ideal case, aggregated from more local, spatial information. This allows a detailed analysis of agri-environmental issues which is not possible using EU-level data. The appropriate level of reporting will ultimately depend on the type of indicator.

– Precise spatial referencing of relevant data sets in a geographical information system (GIS) is a key element for improving regional environmental analysis. It also enables integration with other data sets.

– Further development and validation of models. Several IRENA indicators are underpinned by models (e.g. soil erosion risk). Modelling is an important approach for overcoming the lack of direct measurements, although it requires good input data. It also requires the gathering of field data to calibrate and validate the estimates. In addition, spatialisation methods (e.g. redistribution of agricultural data, reported at administrative level, to different geographical units) offer further opportunities to obtain the relevant information, although these techniques need further development and validation.

– Better use of administrative data. Administrative data can fill important gaps, but efforts should continue to improve such data sets so as to obtain greater added value, for example by adding geo-referencing information. The agri-environmental indicators may also benefit from the existing (e.g. Nitrates Directive) or future (e.g. Water Framework Directive) monitoring systems in the context of environmental policy.

– Integration of databases. There is a need to integrate the data sets used to develop indicators in order to achieve synergies, thus enabling common analytical objectives to be achieved more effectively. For example, the integration of LUCAS (ground observations) and CORINE Land Cover (satellite image interpretation) may lead to improvements in the validation of land cover information.

– Typology approach. The farm typology approach used for some driving force indicators (e.g. cropping/livestock patterns) could be further explored as a means of relating indicators to different types of farms, and to facilitate the interpretation of the indicator results.

• 4. 

  IRENA I NDICATOR - BASED A SSESSMENT R EPORT

4.1. Outline of the report

The Indicator-based Assessment Report (“Integration of environment into EU agriculture policy

– the IRENA indicator-based assessment report”) 13 builds on the indicator-based agrienvironmental

analysis developed in the Indicator Report.

The report provides an overview of the main agri-environmental policy issues in the EU, and of the national/regional implementation of the CAP measures that have the potential to meet the environmental integration objectives. It analyses the spatial (regional) targeting of several CAP measures (on the basis of “policy response” indicators) at two key environmental issues: the conservation of biodiversity, and nutrient management. The degree of targeting is used as a proxy measure for environmental integration. The analysis is complemented by policy examples

EN EN

from Member States to show positive experiences with the implementation of agrienvironmental instruments.

4.2. Main findings on the usefulness of the indicators for assessing environmental integration

– The IRENA indicators provide a useful basis of information for environmental analysis. The regional breakdown of information for many indicators allows some differentiation of relevant environmental driving forces/pressures/state across the EU-15. Thus, association analysis can be carried out between indicators for the purpose of assessing policy targeting. This produces some interesting results, for instance, in the area of biodiversity.

– However, indicators of pressure, state and policy response are not sufficiently underpinned by regional data to carry out a spatial targeting analysis. Moreover, the complexity of agrienvironmental processes and the lack of data or knowledge to substantiate (hypothetical) causal links limit the possibilities of drawing firm conclusions on environmental integration.

– The current set of indicators does not cover all relevant CAP policy instruments. The progress that has been made in integration would need to be reflected in the composition of any future indicator list.

4.3. Conclusions and recommendations

– The agri-environmental indicators appear to be more useful for agri-environmental analysis than for integration assessment.

– Some key state/pressure/policy response indicators would need to be developed at regional level to allow an assessment of the extent to which key CAP instruments are targeted at environmental problems.

– An indicator-based analysis alone is not sufficient to assess the environmental effect of policy integration efforts. The indicators allow an overview of agri-environmental issues at EU-15 level and of the extent to which these have been addressed by the available CAP measures. They also provide national/regional environmental contextual information, against which the specific local trends can be evaluated, which is the level at which the measures are implemented.

– The use of indicators has to be complemented by targeted monitoring and evaluations of the effectiveness of different agri-environmental policy measures at local and regional level (e.g. agri-environment schemes).

• 5. 

  IRENA E VALUATION R EPORT

5.1. Outline of the report

This report reviews the progress made in developing indicators and analyses the resources employed for this task, as well as the adequacy of resources employed in relation to the objectives. Some findings are drawn from structured interviews carried out with members of the IRENA steering group and representatives of Member States that have closely followed the operation. The report also includes a comparison of the IRENA indicator results with other indicator-reporting exercises at EU level (e.g. sustainable development) and at international level (OECD). An evaluation is conducted of the current weaknesses of the indicators and databases that would need to be addressed in the future, including a brief analysis of the main steps required for the different indicators to become operational. This builds on the individual evaluation sheets prepared for each indicator.

5.2. Practical recommendations

– Limited resources for data collection and analysis, both at national level and at EU level, as well as the need to extend the indicator-based reporting to the new and future Member States, make it necessary to select very carefully the set of indicators that can be maintained over the longer term. The experience gained under the IRENA operation as regards what is technically possible, and a careful evaluation of the policy relevance of the indicators, should be the guiding criteria in this regard.

– On the basis of the preparation of the Indicator Report and the Assessment Report, the EEA considers that some of the indicators developed are not absolutely necessary for environmental reporting, whereas other indicators could be added to respond to new policy information needs.

– The establishment of procedures that would allow the collection of the necessary data and the development of supporting methodological tools should be a priority task for future agrienvironmental indicator development at EU level. However, this will require a strong commitment by the Member States.

• 6. 

  A CTIONS FOR FUTURE WORK ON AGRI - ENVIRONMENTAL INDICATORS

Three major challenges can be identified for future agri-environmental indicator work:

• 1. 

  Streamlining the IRENA indicator set and strengthening its policy relevance.

• 2. 

  Consolidating the selected indicators, extending the coverage to the new Member States and correcting existing weaknesses.

• 3. 

  Setting up a permanent and stable arrangement needed for the long-term functioning of the indicator system.

This chapter elaborates on the actions needed in response to each challenge.

6.1. Streamlining the IRENA indicator set and strengthening its policy relevance

The technical experience gained under the IRENA operation (e.g. methodological issues, relevant trends), and the evaluation of the policy relevance and feasibility of the indicators are used as guiding criteria for the setting of priorities for future work on agri-environmental indicator development. In the light of the conceptual and technical limitations of certain indicators, a critical choice needs to be made regarding the list of indicators to be maintained and further developed.

The IRENA indicators can be grouped in three categories according to their level of development (see table in Annex 2):

A. Operational indicators, for which the concept and measurement are well-defined and for which data are available at national and, where appropriate, at regional level (e.g. area under organic farming).

B. Indicators that are well defined, but cannot realise their full information potential due to a lack of regional data (e.g. area under agri-environment support) or weaknesses in the modelling approaches on which they are based (e.g. soil erosion).

C. Indicators that need substantial improvements in order to become fully operational. These include indicators that have conceptual limitations or are not well defined (e.g. high nature value farmland area) and indicators where the quality of existing data needs to be improved, new data collection systems need to be set up (e.g. consumption of pesticides), or where modelling tools need to be further developed (e.g., soil quality).

Of the current 42 IRENA indicators and sub-indicators, 26 are proposed to be maintained, further developed and extended to the EU-25 (EU-27).

Some of these indicators are included in the C category and will need major conceptual and/or methodological development, and improved or new data collection systems or modelling tools. In addition, as a result of the work under IRENA and new needs emerging, it is proposed that two new indicators should be added. Annex 2 lists the set of indicators that are proposed to be developed and maintained in the future, and highlights the main requirements for their further development and improvement.

Nine IRENA indicators are considered not to have enough potential to be among those to be further developed in the next stage of the work (see Annex 3). With one exception, they were all evaluated as ‘potentially useful’ or ‘low potential’, and classified among the less developed (C category) indicators. All these indicators are among those that would need major investment in conceptual and/or methodological development and data collection.

Moreover, Indicator No 24 (land cover change) was evaluated as 'useful', but it is proposed to exclude it as an individual indicator, and include it instead as a measure of landscape change under the indicator landscape – state and diversity. The inclusion of three sub-indicators of indicator No 34, concerning the share of agriculture in GHG emissions, nitrate contamination, and water use, under the respective indicators (GHG emissions, nitrate concentrations in water, and water use) is also proposed.

However, while these are the indicators to be considered for the next stage of the work, in the long run the indicator list needs to have some flexibility so that it can be adapted to the evolving environmental and agricultural policy context, and emerging environmental issues.

6.2. Consolidating the selected indicators, extending the coverage to the new Member States and correcting existing weaknesses

Although the initial objective of the IRENA operation was to maximise the use of existing data sources, it was clear from the beginning that additional information would be needed, and that this need would have to be met mainly by extending the scope of the existing statistical or administrative data sets. New data collection systems should be set up only where the requirements cannot otherwise be met.

A prerequisite for the maintenance of the selected indicators is the consolidation of the required data sources. This section looks at the main actions that the Commission, in co-operation with the Member States, needs to undertake in order to support the improvement of existing data and to start the collection of new information. These actions are presented by data source and indicator.

The work carried out during the IRENA operation should be transformed into a continuous process of updating and maintaining the indicators and, at the same time, developing them at the proper geographical level and extending them to the new Member States. The necessary data should be made available as soon as possible by the new Member States.

At the same time, the indicators that are not yet fully operational and the new indicators should be further developed in collaboration between the Commission services, the EEA and national authorities. The table in Annex 2 names the lead-service(s) for each indicator.

It is important that co-ordination with other EU and international indicator initiatives is ensured. There is scope for improving the synergies between other EU activities (e.g. sustainable development indicators, Streamlining European 2010 Biodiversity Indicators – SEBI 2010, EEA Indicators Core set) and international indicator activities (e.g. OECD agri-environmental indicators, indicators under the Convention on Biological Diversity). Stronger ties could also be developed with other initiatives on developing EU-wide data sets, such as the Global Monitoring for Environment and Security (GMES) and the Infrastructure for Spatial Information in Europe (INSPIRE), as well as with international initiatives such as the Global Earth Observation System of Systems (GEOSS).

6.2.1. Review of agricultural data sources

6.2.1.1. Agricultural statistics

The present system of agricultural statistics focuses on information to support policy making mainly in relation to economic and production issues. The IRENA operation has identified the information related to the interactions between agriculture and the environment, and how the statistical system needs to be adapted to produce these data. This can partly be done by adapting the present statistical tools. However, in some cases the information required might be better collected by setting up new surveys. Considering that Member States have developed different data collection strategies, they should be given the choice as to how they want to set up the new statistical tools, except when there are clear constraints to this option.

When reviewing the usefulness of agricultural statistics (and other data sources) some key requirements of environmental analysis need to be taken into account. The first is the importance of linking spatially the environmental impacts from agriculture. This means that the georeferencing of agricultural statistics is very important for their use in environmental analysis. The second principle arises from the need to be able to link different agricultural data sources with each other, e.g. farm structure surveys with farm management data and the geo-physical context of a given farm. Data on individual farms should, therefore, be collected using specific identification numbers (while adhering to data protection principles).

The Farm Structure Survey (FSS) is the backbone of European agricultural statistics. The survey is fully harmonised between the Member States and, since the individual data are sent to Eurostat for processing, it is very flexible in terms of the possibility of extracting data. The FSS is constantly being reviewed with a view to adapting the survey to new policy needs. In the context of agri-environment indicator development, this calls for an evaluation of the usefulness of individual variables from an environmental perspective.

The main purpose of FSS is to follow structural trends in agriculture and this might lead to limitations on the use of certain variables for agri-environmental analysis. An example is the Utilised Agricultural Area (UAA), as FSS censuses only include holdings above certain thresholds and do not include common grazing land that is not allotted to individual holdings. Consequently, when the aim is to compare overall crop or livestock production in the EU, additional data sources will be useful. Moreover, even if the FSS is quite an efficient tool for collecting and analysing data on the individual farm level, it also has some limitations in terms of the type and the amount of information that can be included. In this respect, only variables that are easy to define and that the farmers can easily respond to should be included. In addition, there is also a limit to the number of variables that the survey can support without a reduction in quality.

The extent of the need for potential new surveys will depend on the new list of characteristics for the FSS from 2010 onwards, which is currently under discussion, and the development of other surveys. The Commission is suggesting setting up a separate survey on production methods that would be linked to the FSS. In the following, the indicators for which the agricultural statistics can provide data are listed.

Indicator Action

Farmers’ training The present data, showing the percentage of farmers having levels and use of only practical experience, basic or full agricultural training, are environmental not sufficiently targeted to draw conclusions regarding their advisory services. environmental knowledge and attitudes.

(IRENA 6) If possible, the indicator should focus on environmental

training. This information could therefore be collected either through the FSS or by means of specific surveys.

The indicator can also cover the use of environmental advisory services. The relevant information could be better obtained though administrative monitoring data for rural development programmes.

Area under organic Regional data have been obtained from FSS. In future, it will farming be important for agricultural statistics and administrative data (IRENA 7) to be harmonised (see also 6.2.4).

Water use Variables on irrigable area, irrigable crops and irrigation (IRENA 10) techniques used, are currently included in the FSS. Future FSS questionnaires or other, specific, surveys on irrigation should continue to cover these variables.

It is proposed to call the indicator “Irrigation” so as to reflect its content more accurately.

Cropping/Livestock Data are available from different sources, of which the FSS patterns seems to be the most useful.

(IRENA 13) A new specific survey on production methods would be able to

provide valuable data to further develop the information potential of this indicator, which consists of 2 sub-indicators.

Farm management Farm practices can include several indicators that are relevant practices from an agri-environmental perspective. These various (IRENA 14) indicators could be covered by the FSS, but together they form a complex that is interdependent and too broad to be covered with only a few new questions. A complementary FSS survey linked to the FSS on production methods would make it possible to improve the indicator.

Farm management Data on spring and winter cereals are collected in crop practices: soil cover production statistics, but not data on cover crops in winter. In (IRENA 14.1) addition, regional data/coefficients on seeding and harvesting

dates, as well as information about the correlation of rainy

weather conditions and plant stand, are needed.

Farm management The tillage practices designed to manage the soil in a way that practices: tillage alters its natural composition, structure and biodiversity as little practices as possible, known as “conservation agriculture”, include direct (IRENA 14.2) sowing, non-tillage or minimum tillage.

The information about the use of different tillage methods

should be developed to a significant extent.

Farm management The source of the data is the FSS. Additional information about practices: manure certain topics, i.e. whether the storage containers are covered or storage not, would be needed and it could be gathered via a (IRENA 14.3) complementary survey on production methods.

Intensification The possibility of extending animal and crop statistics to the (IRENA 15) regional level could be investigated, in order to provide

improved data on yields.

Specialisation The FSS and the Community typology of farms can be used for (IRENA 16) distinguishing between specialised and non-specialised farms.

No changes or additional data are necessary.

Marginalisation The indicator needs to be further developed. A possible (IRENA 17) solution would be to use data from the FSS to try to identify

regions where there is a danger of land abandonment.

Gross nitrogen Data on cropping area, livestock type and numbers, and balance nitrogen-fixing crops (legumes and pulses) are used in (IRENA 18.1) combination with coefficients to calculate gross nutrient

balances. The FSS provides numerous parameters for the calculation of GNB, but further information is needed in the form of different coefficients and other base data. There might be a need for specific actions to create the base data. The development of the indicator needs to be continued, in order

also to be able to estimate regional GNB.

Risk of pollution by The FSS can provide several parameters for the calculation of phosphorus gross phosphorus balances, but further information is needed in (New) the form of different coefficients and other base data. There

might be a need for specific actions to create the base data. Development of the indicator in collaboration with the OECD

has already begun.

Production of The inclusion of information on areas devoted to renewable renewable energy energy in either the FSS or the crop production statistics should (IRENA 27) be examined. Data on other types of renewable energy (like

wind energy) will be difficult to collect in agricultural statistics.

Data on supported areas for renewable energy production can be obtained from administrative data in the context of the

implementation of the CAP.

These data could be supplemented with information concerning:

(a) the CO 2 benefits

(b) the contribution of energy crops to improved rotation systems and to the viability of farms in high nature value areas.

6.2.1.2. Farm Accountancy Data Network

The Farm Accountancy Data Network (FADN), which is an instrument for evaluating the income of agricultural holdings and the impacts of the Common Agricultural Policy, was one of the data sources used in the IRENA operation. Derived from national surveys, the FADN is a harmonised micro-economic database which combines data on farm structure, input use, and economic variables. As regards input use, it does not record the volumes of inputs used but the total value of expenditure on certain inputs (fertilisers, pesticides, feedingstuffs, energy, water, etc.) purchased by the holding (considered as a whole).

In the context of the ongoing upgrading of the FADN, it is planned to analyse the possibility of improving and extending the coverage of the FADN in order to respond to the growing demand for agri-environmental analyses. The improvements may concern the following indicators and data in particular:

Indicator Action

Mineral fertiliser At present, the consumption of mineral fertilisers is mainly consumption estimated on the basis of sales figures.

(IRENA 8) The proposal for a complementary FSS survey on

production methods should also cover the use of mineral fertilisers.

Alternatively, the inclusion of variables on farm level fertiliser consumption (N, P) in the EU FADN farm return could be considered. Several Member States already collect this information in their national farm returns.

Gross nitrogen balance Regional fertiliser application rates per crop are necessary (IRENA 18.1) and risk in order to estimate regional gross nutrient and phosphorus of pollution by balances. phosphorus (New)

Water abstraction The possibility of collecting farm level data on volumes of (IRENA 22) water for irrigation is being considered. However, a survey carried out by DG AGRI on the availability of (quantitative) data on use of inputs has shown that there are currently few possibilities of collecting such data, as volumes of irrigation water are not consistently recorded at farm level.

Energy use At present, information on energy use is available on a (IRENA 11 national level only. FADN provides data on energy expenditure. Volume data should be obtained; this could be done by including the relevant variables in the FADN.

Intensification

(IRENA 15) The farm typology approach has been used to illustrate trends in the share of agricultural area managed by lowinput,

medium-input or high-input farm types (based on the average expenditure on inputs per hectare). A framework for enabling comparison of FADN input cost data between Member States could be developed.

6.2.1.3. Land Use/Cover Area Frame Statistical Survey (LUCAS)

The LUCAS survey has been used only in a limited way in IRENA due to its pilot character and the low sampling density. Its main contribution is information on landscape features, which is used as a parameter for the landscape state indicator (IRENA No 32). Farm practice data were explored for inclusion in the farm management practices indicator (No 14), but the data were not of sufficient quality to be included. LUCAS would be a useful complementary tool, as it provides geo-referenced land use and land cover data which can help in validating CORINE Land Cover. The usefulness of LUCAS will be further improved with the new methodology recently introduced, leading to a higher sampling density and accuracy.

The possibilities of using the LUCAS survey should be explored, in particular for the following two indicators:

Indicator Action

Land use change Validation and improvement of the existing CORINE Land (IRENA 12) Cover inventory.

Landscape change Transect data provide the number of agriculturally-linked (IRENA 32) linear elements per square kilometre for case study areas selected to illustrate the diversity of landscapes across Europe. The possibilities for further use should be explored.

6.2.1.4. OECD/Eurostat Joint Questionnaire

Information from the OECD/Eurostat Joint Questionnaire has been used to underpin the three IRENA indicators mentioned below. Although it is an annual survey, some Member States do not provide the information.

Indicator Action

Water abstraction Continuation of co-operation with OECD on the joint (IRENA 22) ESTAT/OECD questionnaire is necessary. These data could partly be replaced if FADN gave reliable farm level data on volumes of water use for irrigation at regional level.

Share of agriculture in Co-operation with OECD on the joint ESTAT/OECD nitrate contamination questionnaire must continue. (IRENA 34.2) and Share of agriculture in water use (IRENA 34.3).

6.2.2. Review of environmental data sources

6.2.2.1. CORINE Land Cover

The CORINE Land Cover (CLC) programme provides spatially referenced information on land cover and land cover changes across Europe during the past decade. CLC works on the principle of identifying land cover classes for polygons of a minimum size of 25 ha. This means that it cannot provide land cover information for each individual land parcel, but only information that is representative for a wider area. Due to the spatial referencing of polygons it increases the possibilities for environmental analysis, especially when combined with other data sets.

Indicator Action

Land use change Validation and improvement of the existing CLC inventory (IRENA 12) on the basis of national data and ground surveys is needed.

Landscape change CLC has been used for analysing patch density on a case (IRENA 32) study basis. Land cover change aspects could be integrated in this indicator.

6.2.2.2. EIONET Water

EIONET 14 Water is a monitoring network designed for collecting data on the status and trends of

water resources in terms of quality and quantity, and for analysing how this reflects pressures on the environment. Currently, EIONET Water does not include enough monitoring stations to provide reliable regional analyses. Also, the monitoring stations included are not designed to monitor non-point sources of pollution from agriculture. Instead, stations are positioned to monitor major industrial and sewage recycling plants. Therefore, considerable work is needed in order to meet the monitoring requirements of pollution from agriculture. Efforts are currently being made to geo-reference monitoring stations to the new catchments database developed by the JRC. In future, the network will be adapted to meet the reporting needs of the Water Framework Directive.

Indicator Action

Nitrates in water Encourage Member States to increase and harmonise (IRENA 30.1) and transmission of national monitoring data to EIONET Water. Pesticides in water

(IRENA 30.2) Data reported by Member States under the Nitrates Directive could be used as part of a monitoring system to measure

pollution from agriculture.

6.2.2.3. Pan-European Common Bird Monitoring Database

The Pan-European Common Bird Monitoring Database is maintained by the Royal Society for the Protection of Birds (RSPB), the European Bird Census Council (EBCC), and BirdLife International. Survey methods and data compilation follow tested and widely recognised approaches in the biological monitoring field. Data gathering is largely carried out by volunteer ornithologists, who are trained to achieve maximum standardisation and data quality.

EN EN

Indicator Action

Population trends of Continuation of cooperation with data providers is needed in farmland birds order to secure, consolidate and harmonise the existing data (IRENA 28) set. The existing data should be extended and transparency increased. Trends should be established for different groups of

birds (steppe, meadow, etc).

6.2.3. Review of modelling approaches

Modelling approaches are adopted for indicators where surveyed environmental data are not available. Models can be very useful tools for environmental analysis as long as the required input data are available and of sufficient quality. The following indicators are developed on the basis of modelling approaches:

Indicator Action

Gross nitrogen balance The methodology for calculating GNB is well developed for (IRENA 18.1) national data. However, to create regional balances, there is a

need to develop regional data/coefficients.

Risk of pollution by The indicator has to be developed, although work on phosphorus phosphorus balances has already been initiated in

(New) collaboration with the OECD.

Soil erosion A new pan-European risk assessment of soil erosion by water (IRENA 24) will be carried out (JRC). A new pan-European risk

assessment of soil erosion by wind will also be developed.

Soil quality A new definition and assessment of soil quality will be carried

(IRENA 29) out a as part of the Thematic Strategy on Soil Protection.

Pesticide risk A research project on Harmonised Pesticide Risk Indicators (New) (HAIR), which is financed by the Commission and involves the JRC, aims to provide a harmonised European approach for indicators of pesticide risk. This project is expected to make a useful contribution to the development of the pesticide risk

indicator.

Atmospheric ammonia The data are based on official national data submissions emissions reported by Member States under the UNECE/EMEP (IRENA 18.2) Convention on Long-Range Transboundary Atmospheric Pollution. Estimates of emissions could be improved with more accurate data on the size of different emission sources (including the contribution of agriculture to air pollution), as

well as with improved emission coefficients.

Emissions of methane The data come from the official annual national submissions and nitrous oxide of total and sectoral greenhouse gas (GHG) emission data to (IRENA 19) United Nations Framework Convention on Climate Change (UNFCCC), the EU Monitoring Mechanism and EIONET.

The data are compiled for the EU by the EEA.

Emission estimates could be improved with better emission

coefficients.

High Nature Value Continuation of co-operation between the EEA, the JRC and Farmland the Member States is needed. DG AGRI has launched a study (IRENA 26) on HNVF, which could also contribute to the development of

this indicator.

6.2.4. Review of administrative data sets

6.2.4.1. Common monitoring and evaluation framework for rural development programmes

The databases on the monitoring of rural development programmes are managed by DG AGRI.

Indicator Action

Area under agri The reporting by Member States within the rural development environment support monitoring framework should be further standardised, and

(IRENA 1) more appropriate and clearly identifiable categories of agrienvironmental commitments should be developed.

The geo-referenced data on the uptake of agri-environment schemes that the Member States have to provide (from 2005) through the Integrated Administration and Control System

(IACS) will allow better spatial reporting.

Use of environmental The number of farms that use environmental advisory farm advisory services services has been proposed as an output indicator for the (New sub-indicator of monitoring and evaluation of future rural development

IRENA 6) programmes.

6.2.4.2. Organic farming

Indicator Action

Area under organic Data are supplied by Member States to DG Agriculture and farming Rural Development, using the administrative data from the (IRENA 7) organic farming questionnaire (database OFIS). Reporting should be made compulsory and the collection time delays minimised to follow the dynamic development of the sector

more closely.

The proposal for a new regulation on organic farming includes an article on the statistical information to be collected. These data are to be collected under the responsibility of Eurostat, as it is essential that the data be

harmonised with other agricultural statistics.

6.2.4.3. NATURA 2000

The Habitats Directive component of the Natura 2000 database is managed by the European Topic Centre on Nature Protection and Biodiversity of the EEA. There is no common protocol for collecting the data, and different approaches have therefore been adopted by Member States in filling out the standard data form.

Indicator Action

Area under nature Consolidation and more standardised reporting procedures

protection within Natura 2000 monitoring would be needed.

(IRENA 4)

6.2.5. Other data sets

Indicator Action

Genetic diversity The information on the risk status of livestock breeds is (IRENA 25) obtained from the FAO’s Domestic Animal Diversity Information System (DAD-IS). A common definition of the risk status for all countries is available. However, data are limited and difficult to assess. There is a need to assess trends

in the genetic diversity of crops and livestock.

6.2.6. New data sets

Indicator Action

Pesticides consumption A proposal for a Regulation concerning statistics on plant (IRENA 9) protection products, under which would require statistics on the sale and use of plant protection products to be collected on

a mandatory basis, is being prepared by the Commission.

6.3. Setting up a permanent and stable arrangement needed for the long-term functioning of the indicator system

Defining the relevant indicators is only a part of the work required to build the information system for monitoring environmental integration. The results of the IRENA operation suggest that, in order to arrive at an indicator system that is durable in the long-run, it is necessary to develop a stable and continuous process of systematic data collection and management, indicator compilation, and improvement of models, methods and concepts underpinning the indicators.

This requires the setting up of an organisational structure (in terms of partners involved) with well-defined management arrangements (in relation to the division of tasks) and procedures.

The establishment of this permanent and stable arrangement should be a priority task for future indicator development at EU level, in particular for the systematic collection of necessary data on an appropriate geographical scale and the development of supporting methodological tools.

It is clear that improving data quality and availability, and maintaining and updating the indicator data base, will require the full involvement and commitment of the Member States. This concerns in particular the collection of geo-referenced data or data at appropriate territorial level, which has been identified as the main weakness of the current indicators. This entails that Eurostat will be called upon to play a pivotal role in the management of the future information system, based on close co-operation with the Statistical Offices of the Member States and the Ministries of Agriculture and Environment, and in collaboration with other European bodies, such as the EEA. The future development of certain agri-environmental indicators will benefit from the involvement of other Commission services, such as the Joint Research Centre, and particularly from the contribution that they can give to the improvement of models, methods and concepts related to certain indicators.

ANNEX 1

15

Development of agri-environmental indicators under the IRENA operation

Domain/

Sub-domain No IRENA Indicator

Headline indicator and

sub-indicators Data sources Spatial scale Temporal scale

Trends in the agricultural area enrolled in agri-environmental measures Common indicators for monitoring of NUTS 0 (some RDP

and share of the total agricultural area. implementation of RDP, DG AGRI. programming 1998, 2002 regions)

1 Area under agri 1) European Agriculture Guarantee environment support 1) Trends in agri-environment expenditure per hectare of utilised and Guidance Fund (EAGGF), DG

agricultural area (UAA) AGRI. NUTS 0 1) 2000–2003.

• 2) 

  the endangered breeds under agri-environment measures. 2) Common indicators for monitoring of 2) 2001

  implementation of RDP, DG AGRI. Range and type of relevant categories of farming practices covered by the codes of good farming practices defined by regions in their Rural

Development Programmes. National/regional codes of Good NUTS 0 level, except

Responses – 2 Regional levels of good 1) The ‘regulatory’ (requirements based on legislation) or ‘advisory’ Farming Practices included in Rural Belgium (2 = NUTS Current status in

Public Policy farming practice approach (based on recommendations) taken by Member States in Development Programmes (RDPs) 1) and Italy (1 = 2004 preparing their code of GFP. (period 2000–2006) NUTS 2 region)

• 2) 

  The range of GFP requirements being verifiable standards (subject to control).

3 Regional levels of Commission and national policy Current status in environmental targets Environmental targets set at Member State level relevant to agriculture. documents NUTS 0 2004

Data received

NUTS 0 between 1997

Proportion of Natura 2000 sites covered by targeted habitats that depend Database of sites proposed under the and March 2005 4 Area under nature

protection on a continuation of extensive farming practices. Habitat Directive as NATURA 2000 areas Data received

NUTS 2 and 3 between 1997 and July 2004

5.1 Organic producer prices Organic producer prices and market share (to indicate levels of consumer

Research project OMIaRD (Organic

Responses – and market share demand for organic products and market signals to organic producers).

Marketing Initiatives and Rural NUTS 0 2000, 2001

development)

Market signals Organic farm incomes compared to similar conventional farms (to indicate

Partial coverage

5.2 Organic farm incomes combined impacts of prices, agri-environmental support payments and FADN NUTS 0 2000, Complete other factors on financial viability of organic holdings). coverage 2001

15 The acronyms used are: CLC (CORINE Land Cover), ECPA (European Crop Protection Association), EFMA (European Fertiliser Manufacturers Association), FSS (Farm Structure Survey), FADN (Farm

Accountancy Data Network), RDP (Rural Development Programme), SIRENE (section of the Eurostat-New Cronos database with information on energy use in agriculture).

Responses – The level of agricultural training of managers of agricultural holdings. FSS NUTS 2 and 3 1990–2000

Technology skills 6 Farmers’ training levels Training in agri-environmental issues. Common indicators for monitoring of

implementation of RDP, DG AGRI. NUTS 0 2001

Organic farming questionnaire on

Responses – Area under organic Trends in organic farming area and in the share of organic farming area in Regulation (EEC) No 2092/91 i 1998– NUTS 0 1998 to 2002.

Attitudes 7 farming the total utilised agricultural area (UAA). 2002, DG AGRI

FSS (for regional share) NUTS 2 and 3 2000

Mineral fertiliser consumption is indicated by the evolution of the Most recent consumption of nitrogenous (N) and phosphate (P 2002 2 O 3 ) mineral fertilisers FAOSTAT NUTS 0

8 Mineral fertiliser over time. Trend consumption 1990– 2001

Fertiliser application rates for selected crops. EFMA NUTS 0 Most recent 1999/2000

The consumption of pesticides (here plant protection products, excluding biocides and disinfectant products) is indicated by:

9 Consumption of ECPA (use data),

Use: 1992–99;

pesticides (a) Used/sold quantities of different pesticide categories;

(b) Application rates of different pesticide categories Member States (sales data)

NUTS 0 Sales: 1992–2002

(insecticides/herbicides/others). Driving forces –

Input use a) Trends in irrigable area (area covered with irrigation infrastructure)

NUTS 2 and 3 (Only Most recent

and b) trends in total areas (and by crops) irrigated at least once a year FSS Greece, France, 2000

(actual area irrigated). Spain reported b) in Trend

10 Water use (intensity) 1990–2000 1990–2000

Most recent

Trends in the share of irrigable area in total UAA. FSS NUTS 2 and 3 2000 Trend

1990–2000

Energy use is indicated by the annual use of energy at farm level by fuel Trend

type (GJ/ha). FADN, SIRENE , FSS NUTS 0 (and 1) 1990–2000

11 Energy use

Estimate of energy used to produce mineral fertilisers for agricultural use FAOSTAT for fertiliser use, ‘energy

(GJ/ha). content’ based on industry data (the NUTS 0

Trend

Netherlands) 1990–2000

Area of land use change from agriculture to artificial surfaces between

12 Land use change 1990 and 2000. CLC 1990 and 2000 NUTS 2 and 3 1990–2000

Sector share of land converted from agriculture to artificial surfaces. CLC 1990 and 2000 NUTS 2 and 3 1990–2000 Driving forces –

Land use Cropping patterns: trends in the share of the utilised agricultural area FSS: NUTS 2 and 3

occupied by the major agricultural land uses (arable, permanent

13 Cropping/livestock patterns grassland and permanent crops). Livestock patterns: trends in the share

FSS, FADN FADN: NUTS 0 1990–2000

of major livestock types (cattle, sheep and pigs). and 1

Trends types of farms particularly relevant for environment (typology).

Driving forces – 1) Cropping method: soil cover. FSS NUTS 2 and 3 2000

Farm 14 Farm management practices 2) Cropping methods: tillage methods. PAIS II project (2005) NUTS 0 only 2003–2004

management 3) Type and capacity of storage for farm manure and slurry. FSS NUTS 2 and 3 2000

• a) 

  Trends in the share of agricultural area managed by low-input,

medium-input or high-input farm types (based on the average FADN FADN: NUTS 0 expenditure on inputs per hectare). and 1

1990 and 2000

15 Intensification/ extensification

• b) 

  Livestock stocking densities per selected types of farm. FSS, FADN. FSS: NUTS 2 and 3 FADN: NUTS 0 and 1 1990 and 2000

Driving forces – c) Trends in yields of milk and cereals.

FADN NUTS 0 and 1 1990, 1997, 2000

Trends Specialisation: trends in the share of the agricultural area managed by

16 Specialisation/ specialised types of farm.

FADN NUTS 0 and 1 1990 and 2000

diversification Diversification: share of agri-environment payments in gross farm

income. FADN NUTS 0 and 1 1990 and 2000

Share of holdings with low Farm Net Value Added per Annual Work Unit 17 Marginalisation in combination with a high share of holdings with farmers close to retiring FADN NUTS 0 and 1 1990 and 2000

age.

OECD website and EEA calculations

18 Gross nitrogen balance Gross soil surface balance for nitrogen. on the basis of Eurostat’s ZPA1 data NUTS 0 1990 and 2000

set or FSS

Officially reported 2004 national total This indicator shows the annual atmospheric emissions of ammonia and sectoral emissions to

18b Atmospheric emissions of ammonia (NH (NH 3 ) in the EU-15 for 1990–2002, and the contribution that agriculture UNECE/EMEP (Convention on Long NUTS 0 1990 to 2002.

3 ) made to total atmospheric emissions of ammonia in 2002. Range Transboundary Atmospheric

Pollution)

Official national total, sectoral

Emissions of methane Aggregated annual emissions from agriculture of methane (CH 4 ) and emissions, livestock and mineral

Pressures – 19 (CH 4 ) and nitrous oxide nitrous oxide (N 2 O). Emissions are shown relative to 1990 baseline fertiliser consumption data reported NUTS 0 1990 to 2002 Pollution (N 2 O). levels expressed as CO 2 equivalents. to UNFCCC and under the EU

Monitoring Mechanism and EIONET

Calculation of the total PPP quantity

20 Pesticide soil The indicator uses a model to calculate the potential average annual

present in a specific NUTS 2 region is

contamination content of herbicides in soils. based on EUROSTAT pesticide NUTS 2 and 3 1993 to 1999 statistical data (2002) and FSS

(1997, 2000)

Data submitted by Member States to the European Commission in the

21 Use of sewage sludge Use of sewage sludge in agriculture context of the requirements under the NUTS 0 1995 to 2000

standardised reporting directive (91/692/EEC)

Water abstraction by agriculture is indicated by the annual water

allocation rates for irrigation. Joint OECD/Eurostat questionnaire NUTS 0 1990 to 2000 22 Water abstraction

Regional water abstraction rates for agriculture Joint OECD/Eurostat questionnaire, FSS NUTS 2 and 3 2000

PESERA model using CLC (Land

Pressures – 23 Soil erosion Annual risk of soil erosion by water. use), GTOPO30 (Relief), MARS

Resource database (Meteorology), European

NUTS 2 and 3 2003

depletion Soil Database

Net land cover changes for arable land and permanent crop and pasture

between 1990 and 2000. CORINE Land Cover NUTS 2 and 3 1990 and 2000 24 Land cover change

Changes in net arable and permanent crop and pastureland cover

between 1990 and 2001. CORINE Land Cover NUTS 2 and 3 1990 and 2000

25 Genetic diversity Distribution of risk status of national livestock breeds in agriculture. FAO’s Domestic Animal Diversity Information System (DAD-IS) NUTS 0 July 2003.

26 High nature value This indicator shows the share of the Utilised Agricultural Area that is (farmland) areas estimated to be High Nature Value farmland. CORINE Land Cover and FADN NUTS 0 1990

Pressures –

Benefits FSS; European Biodiesel Board;

27 Production of renewable Land use devoted to energy/biomass crops, and primary energy EurObserv’ER; Fachverband Biogas; energy (by source) produced from crops and by-products. SKstat: International Energy Agency; NUTS 0 2003

FAOSTAT

Pan-European Common Bird Population trends of up to 24 selected bird species that are common and Monitoring project

characteristic of European farmland landscapes. (RSPB/EBCC/BIRDLIFE NUTS 0 1990–2001

State – International)

Biodiversity 28 Population trends of farmland birds

BIRDLIFE, EBCC (2000): European

Share of farmland birds with declining populations. Bird Populations – Estimates and trends. BIRDLIFE Conservation NUTS 0 1990–2002

series No 10.

Soil: European Soil Database, CORINE Land Cover, Global

29 Soil quality Topsoil (0–30cm) organic carbon content. Historical Climatology Network – NUTS 2 and 3 (2000)

GHCN, Pedo-transfer model to calculate organic carbon content

30.1 Nitrates in water Annual trends in the concentrations of nitrates (mg/l N) in ground and

State – surface water bodies.

EUROWATERNET NUTS 0 1992–2001

Natural resources EUROWATERNET

DK: NERI (2004); GEUS (2004); Ministry of Environment (2003)

30.2 Pesticides in water Annual trends in the concentrations ( g/l) of selected pesticide compounds in ground and surface waters. UK: Environment Agency (2004) NUTS 0 1992–2001

AU: UBA Vienna (2005) FI: FEI (2001

31 Ground water levels Trends of groundwater levels. Ministry of Environment of Spain Case study (Spain) 1978–1998

State – The diversity of agricultural landscapes across Europe is shown by CLC (patch density)

CLC 1990 and

Landscape 32 Landscape state analysing selected landscape parameters with strong links to agricultural NUTS 2 and 3 (for

2000

land use. These parameters have been calculated for selected regional FSS (crop distribution) case studies) FSS 1990 and

case study areas representative of different European landscapes LUCAS (linear elements) 2000

• 1) 

  Share of Important Bird Areas (IBA) in the EU-15 affected by IBA programme of Birdlife

Impact – Impact on habitats and agricultural intensification and/or abandonment International NUTS 0 2004

Biodiversity 33 biodiversity 2) Population trends of agriculture-related butterfly species in Prime Survey of Prime butterfly areas by

Butterfly Areas Butterfly Conservation International. NUTS 0 2003

Official national total, sectoral

34.1 Share of agriculture in Contribution of the agricultural sector to total EU-15 emissions of the

emissions, livestock and mineral

GHG emissions greenhouse gases CO fertiliser consumption data reported NUTS 0

1990 to 2002 2 , CH 4 , and N 2 O.

Impact – to UNFCCC and under the EU Monitoring Mechanism and EIONET

Natural

resources 34.2

Share of agriculture in

nitrate contamination Nitrogen emissions to water by economic sector. OECD website and UBA, 2001 NUTS 0

1990 and 1998

Joint OECD/Eurostat questionnaire

34.3 Share of agriculture in water use Share of agriculture in water use from surface and ground waters. FSS (variable Irrigable area – area NUTS 0 1990 and 1998

covered with irrigation infrastructure)

Trends of indices of overall agricultural diversity. This indicator presents CLC (change number of agricultural the evolution of some of the parameters calculated in IRENA 32. The classes and patch density) NUTS 2 and 3 for 1990 and 2000

changes of the crop type distribution (e.g., arable, grasslands) and patch case studies

density are shown for the selected landscape types. FSS (change in crop areas) Impact –

Landscape 35

Impact on landscape

diversity UK Countryside survey

Swedish Countryside Survey –

Changes in total linear landscape features (km). NUTS 0 Monitoring landscape features, (UK, Sweden) 1990 to 1998

biodiversity, and cultural heritage (LiM project)

ANNEX 2

Agri-environmental indicators to be maintained/updated and, where appropriate, further developed/added

IRENA OPERATION PROPOSED SET OF AGRI-ENVIRONMENTAL INDICATORS

t

DOMAIN/ e n Main

Sub-domain e s s f

No Indicator ln p

m

fu e

l o No Title and measurement Main limitations / Improvements needed lead for

e lo the

U s

e L e v indicator

d e

v

RESPONSES 1 Area under agri Potentially B Improve and further standardise the reporting AGRI/

Public policy environment support useful [13-15] ►

1 Agri-environmental commitments

  Area under AE commitments concerning AE commitments by Member States in ESTAT

(per category) the context of the annual reports on the implementation of rural development programmes.

  Share of area under AE The Common monitoring and evaluation framework commitments / total UAA is meant to include more detailed categories of AE

  Area under AE commitments commitments

within Natura 2000 sites

  Share of agricultural holdings with agri-environmental

commitments/total number of agricultural holdings

  Share of total expenditure for AE payments/ total rural

development expenditure

  AE payments/ UAA

2 Regional level of Potentially

good farming practice useful [9-10] C

3 Regional levels of Potentially

environmental targets useful [11] C

4 Area under nature Useful [17] A 2 Agricultural areas under Natura EEA/

protection ► 2000 AGRI

  UAA (ha) under Natura 2000

  Area of habitat types dependent on extensive agriculture under Natura 2000

  UAA under Natura 2000 / total UAA

  Share of Natura 2000 payments/total rural development expenditure

RESPONSES 6 Farmers’ training Potentially A/B The IRENA indicator refers to the level of AGRI/

Technology levels useful [13] ►

3 Farmers’ training levels and use of environmental advisory agricultural training of managers of agricultural ESTAT

skills services holdings, based on FFS data. This indicator is

  Share of farmers having deemed to be not sufficiently targeted to draw practical experience, basic conclusions regarding the environmental knowledge training, and full agricultural of farmers. If possible, the indicator should focus on

training environmental training. This information could be collected either through the FFS or specific surveys.

  Number (share) of farmers

having made use of Moreover, it would be relevant to develop a subenvironmental farm advisory indicator on the use of environmental farm advisory

services per year services. Existing and future farm advice and training will be essential for providing the necessary

information to farmers to better comply with crosscompliance rules and improve their environmental farm management. As regards the use of environmental advisory services, the relevant information can be provided by the annual reports on the implementation of rural development programmes

RESPONSES 5.1 Organic producer

Market signals prices and market Potentially and attitudes share useful [13]

C

5.2 Organic farm incomes Potentially useful [13] C

7 Area under organic Useful [18] A There are two possible sources for data on organic ESTAT

farming ►

4 Area under organic farming

  Area under organic farming farming: the FSS and the data sent to DG AGRI within the administrative framework set up under

  Share of areas under organic Regulation 2092/91 i. The proposal for a new farming/total UAA regulation on organic farming includes an article on the statistical information to be collected. It is important that the data are harmonised with other agricultural statistics.

DRIVING 8 Mineral fertiliser Potentially B Regional fertiliser application rates should be ESTAT/

FORCES consumption useful/ Useful ►

5 Mineral fertiliser consumption obtained. They are also necessary for estimating AGRI

Input use [14-15] Absolute volumes and application rate by crop of: regional gross nutrient balances, via existing or new

surveys.

  N (nitrogen), The Commission is currently preparing a proposal

  P (Phosphorus) for a complementary FSS survey on production methods, which is meant to cover, inter alia, the use of mineral fertilisers.

Alternatively, the incorporation of variables on farm level fertiliser consumption (by crop type) into the FADN surveys should be considered.

9 Consumption of Potentially C The indicator needs better data, particularly at ESTAT

pesticides useful [12-14] ►

6 Consumption of pesticides

  Used/sold quantities of different regional level, in order to be fully operational.

pesticide categories; Currently, there is a lack of comparability between use and sales data.

  Application rates of different

pesticide categories The Commission is currently preparing a legal framework to collect statistics on the sale and use of

plant protection products on a mandatory basis, to overcome the existing data deficiencies at national level.

In order to overcome the existing data deficiencies at regional level, ideally, specific periodical surveys on the use of plant protection products should be organised.

10 Water use (intensity) Potentially A 7 Irrigation The FSS variables related to irrigation areas were ESTAT

useful [16] ► Irrigated areas; complemented in the 2003 survey by data on the

type of irrigation equipment or techniques being Irrigated crops; used (surface, sprinkler, rain gun, drip).

  Irrigated area/total UAA Either the information on type of irrigation equipment

  Share of irrigated area according should be kept in future FSS questionnaires or to irrigation systems. other, specific surveys on irrigation should be set up.

The Commission is currently preparing a proposal for a complementary FSS survey on production methods. This is meant to cover, inter alia, the irrigation methods employed and the source of irrigation water used.

11 Energy use Potentially B Energy use Existing surveys need to be consolidated and ESTAT/

useful [13] ►

8

  Annual use of energy at farm harmonised. EEA

level by fuel type (GJ/ha)

DRIVING 12 Land use change Useful [15-17] B ► 9 Land use change There are some important differences between the EEA/

FORCES Land use change from estimates with CORINE Land Cover (CLC) and JRC/ Land use agricultural land to artificial national surveys. ESTAT

surfaces (ha) Validation and improvement of the existing CLC

  Percentage of the total inventory on the basis of national data and ground agricultural area that has surveys (e.g. LUCAS).

changed compared to a reference period

13 Cropping/livestock Useful [17-19] B The policy relevance of this indicator could be ESTAT/

patterns ►

10 Cropping/livestock patterns

Two sub-indicators: improved by targeting the "farming systems" AGRI associated to certain "crop types" or "livestock

• 1. 

  Title “Cropping patterns” types".

  Area occupied by the major In this respect, one aspect to be further explored agricultural land types (e.g. could be the farm typology approach for further arable crops, permanent differentiation of the analysis, focusing on the share grassland and permanent crops) of agricultural area managed by different farm types.

  Share of agricultural land As regards "cropping patterns", it would also be types/total UAA useful to know more about the "rotation systems"

• 2. 

  Title “Livestock patterns” that are associated with the "base" arable crops (i.e. the arable crops occupying the highest percentage

  Number of major livestock types of the UAA in a region). Moreover, concerning (e. g. cattle, sheep, pigs, and "livestock patterns", it would be useful to know more poultry) about the main characteristics of the grazing

  Share of major livestock types livestock production systems, and particularly concerning the grazing systems (housing vs.

  Stocking rate (LU/UAA) grazing) and the feeding systems (pasture/meadow

  Grazing stocking rate (grazing vs. maize silage vs. concentrates) used.

LU/grasslands and forage crops) DRIVING 14 Farm management Potentially B/C The soil cover estimates need to be improved. The ESTAT/

FORCES practices useful/useful ►

11 Farm management practices

Farm [8/16] Three sub-indicators:

following issues could be considered: seeding date and date of harvest, as well as information about the AGRI

management 1. Title “Soil cover” coincidence of rainy weather conditions and plant

  Share of the year where the stand. Regional coefficients may be created per type arable area is covered by plants of crop.

or plant residues The data on the use of different tillage methods

• 2. 

  Title "“Tillage practices” should be built up significantly. The indicator can be extended to different farming methods following

  Arable areas under conservation accepted guidelines, where the aim is to use tillage sustainable farming practices.

• 3. 

  Title “Manure storage" Data on manure storage are collected through the

  Type of storage for farm manure FSS. They also provide input to the indicator related and slurry. to ammonia emissions.

The policy relevance of the indicator could be improved by targeting further topics related to the way farmers manage their holdings, such as "agricultural practices with limited input" (e.g. integrated pest management), adoption of "antierosion measures" (e.g. terraces, contour farming), use of "fertiliser plans", etc.

DRIVING 15 Intensification/ Useful [15] A 12 Intensification/extensification The farm typology approach could be further ESTAT/

FORCES extensification ► Intensification (e.g. share of low, explored. A framework for enabling comparison of AGRI

Trends medium, high-input farms (based FADN input cost data between Member States on average input should be developed.

expenditure/UAA). The possibility of extending the animal and crop

  Milk yield statistics to a regional level could be investigated to give improved data on yields.

  Cereal yield FADN input data would benefit from better

harmonisation.

16 Specialisation/ Useful [15] A The indicator should focus exclusively on ESTAT

diversification (spec.) ►

13 Specialisation specialisation, which has the strongest link with the

C Share of the agricultural area environment.

(diver.) (ha) managed by specialised farm types

17 Marginalisation Potentially C Risk of land abandonment The indicator needs conceptual and technical JRC/

useful [13] ►

14 development to be relevant to agri-environmental AGRI analysis. A modelling approach combining socioeconomic data with an assessment of the risk of farm abandonment resulting from geographic conditions could de developed.

An assessment could be undertaken of the relevance and possibility of including data on land in receipt of direct payments, and so covered by obligatory standards of good agricultural and environmental condition (GAEC), but which is not actually being used for farming purposes.

PRESSURES 18 Gross nitrogen Potentially B ESTAT/

Pollution balance useful [14] ►

15 Gross nitrogen balance The model underpinning the gross nitrogen balance

  Potential surplus of nitrogen on is well-developed. However, this indicator needs to EEA

agricultural land (kg N/ha/year). be developed at the regional level. To this end, the availability of data on the use of nitrogen fertilisers at

regional level needs to be improved. A further necessary improvement concerns the "Livestock excretion rates", i.e. the coefficients (kg N/animal/year) to be applied to different livestock categories to estimate the nitrogen input from livestock manure.

The Commission is currently preparing a proposal for a complementary FSS survey on production methods, which is meant to cover, inter alia, the use of mineral fertilisers. Alternatively, the inclusion of variables on farm level fertiliser consumption into the FADN surveys should be considered.

NEW 16 Risk of pollution by phosphorus For the development of this indicator, two associated ESTAT/

Two associated sub-indicators: aspects need to be analysed: EEA

• a. 

  Phosphorus balance. a. Potential surpluses of phosphorus. In this respect, ground work on phosphorus balance is already in

  Potential surplus of phosphorus place through collaboration with OECD. on agricultural land (kg However, the availability of data on the use of P/ha/year). phosphorus fertilisers at regional level needs to

• b. 

  Vulnerability to phosphorus be improved. The Commission is currently

leaching/run-off preparing a proposal for a complementary FSS survey on production methods, which is meant to

cover, inter alia, the use of mineral fertilisers. Alternatively, the insertion of variables on farm level fertiliser consumption into the FADN surveys should be considered.

• b. 

  Vulnerability of the area concerned to phosphorus leaching/run-off. In this respect, a recent study by DG ENV “Addressing phosphorus related problems in farm practice” could be helpful. The study proposes vulnerability classes, based on propensity for soil erosion and P sorption capacity. The methodology proposed in that study needs to be validated and, if appropriate, improved.

PRESSURES NEW 17 Pesticide risk The indicator on "Consumption of pesticides" does ESTAT/

Pollution Index of risk of damage from not allow an assessment of the potential increase in JRC/

pesticide toxicity and exposure environmental risk associated with higher pesticide ENV/ sales or use volumes. The new indicator is meant to AGRI

address this issue.

The conceptual and, where appropriate, modelling framework underpinning this indicator needs to be developed. A specific research project financed by the Commission and with the involvement of the JRC on Harmonised Pesticide Risk Indicators (HAIR) aims to provide a harmonised European approach for indicators of the overall risk of pesticides. This project is expected to make a useful contribution.

The Commission is currently preparing a legal framework to collect statistics on sales and usage of plant protection products on a mandatory basis, to overcome the existing data deficiencies at national level.

In order to overcome the existing data deficiencies at regional level, ideally, specific periodical surveys on the use of plant protection products should be organised.

18b Atmospheric Useful [18] B EEA/

emissions of ►

18 Ammonia emissions Estimates of emissions are based on a model, which could be improved with more accurate data on the JRC

ammonia (NH 3 ) Emissions of NH 3 in tonnes (T) size of different emission sources (including the

  Share of agriculture in total contribution of agriculture to air pollution) as well as ammonia emissions with improved emission coefficients.

  Distance to NEC targets

19 Emissions of methane Useful [18] A Estimates of emissions are based on a model, which EEA/

(CH 4 ) and nitrous ►

19 Greenhouse gas emissions could be improved thanks to the availability of better JRC

oxide (N ktonnes CO 2 equivalents 2 O) emission coefficients for methane and nitrous oxide.

  Share of agriculture in GHG Cooperation with Member States is needed to emissions develop country-specific emission factors instead of the IPCC standard values.

An assessment could be undertaken of the possibility of including data on stocks of soil and plant carbon.

20 Pesticide soil Potentially C contamination useful [10]

21 Use of sewage sludge Potentially C useful [12]

PRESSURES 22 Water abstraction Potentially C Co-operation with OECD on the joint ESTAT/OECD ESTAT

Resource useful [11] ►

20 Water abstraction

depletion Water use for irrigation

questionnaire should continue. Data need to be

(m3/year) improved either as part of FSS or through specific surveys. The reporting mechanism should be

  Share of agriculture in water use improved and Member States should be asked to provide an explanation of the data provided (droughts, increase in irrigation area, new reservoirs etc…).

Availability of regional data should be improved.

23 Soil erosion Potentially B Soil erosion The existing models for water erosion can be further JRC

useful [13] ►

21

  Estimated soil loss by water developed and calibrated through empirical data. A

erosion (T/ha/year) new approach combining empirical data and modelling can be developed making use of land use

  Estimated soil loss by wind data from LUCAS in combination with land cover erosion (T/ha/year) data from CORINE land cover. Wind erosion should be added.

A new pan-European risk assessment of soil erosion by water will be carried out. A new pan-European risk assessment of soil erosion by wind will be developed.

24 Land cover change Useful [15-16] B

25 Genetic diversity Potentially C

useful [12] ►

22 Genetic diversity Data on genetic diversity are limited and difficult to EEA/

  Number and range of crop interpret. The data compiled by FAO should be AGRI varieties and livestock breeds. improved in cooperation with Member States.

  Share in production of main crop varieties registered and certified for marketing.

  Number of breeds per total livestock population for different types of livestock

  Distribution of risk status of national livestock breeds in agriculture

PRESSURES 26 High nature value Potentially C 23 High nature value farmland Using the current data it is not possible to assess EEA/

Benefits (farmland) areas useful [12] ► Estimated area HNFV trends in HNV farmland for individual Member JRC/

States, but the data do provide an overall estimate AGRI Estimated area HNFV/total UAA of the share of such areas. The methodology needs

to be refined on the basis of CLC, FADN and biodiversity data, such as Natura 2000.

Continuation of the co-operation between EEA, the JRC and involvement of Member States.

DG AGRI has launched a study on HNVF, which could also contribute to developing this indicator.

27 Production of Potentially B Production of renewable energy Consolidate diverse sources of information ESTAT/

renewable energy (by useful [14] ►

24 AGRI

source) Production of primary energy

concerning crops (oilseed crops, starch/sugar crops,

from crops and by-products grasses, etc.), short rotation forestry and by(Ktoe)

products (livestock manure, cereal straws, etc.) used for the production of energy (biodiesel, ethanol,

  Area of energy crops (biodiesel biogas, heat, electricity, etc.). crops, ethanol crops and short

rotation forestry) Further aspects that could be added are: supported areas for renewable (a) The potential CO2 benefits. In this respect,

energy production consistency needs to be ensured with the values used in work on biofuels and other forms of

renewable energy.

(b) The potential contribution of energy crops to improved rotation systems and to the viability of farms in high nature value areas.

STATE/ 28 Population trends of Potentially B ESTAT/

IMPACT farmland birds useful/ Useful ►

25 Population trends of farmland Continue cooperation with data providers to birds consolidate and extend existing data set and EEA

Biodiversity [11-15] Farmland bird population index increase transparency. and habitats Explore the relevance and possibility of calculating

trends for different groups of birds (steppe, meadow, etc), which could allow a more detailed assessment of the effect of key agricultural land use trends on bird species by habitat and facilitate more targeted policy action where necessary.

Explore the possibility of developing regionalised biodiversity indexes and of covering habitat state/impact aspects with the (sub-)indicators related to "Agricultural areas under Natura 2000" and "High Nature Value Farmland".

33 Impact on habitats Potentially C and biodiversity useful [13]

STATE/ 29 Soil quality Potentially C 26 Soil quality The definition and assessment of soil quality needs JRC

IMPACT useful [13] ►

Natural Humus content (%) in the topsoil

to be in line with the Thematic Strategy on the Protection of Soil.

resources Moreover, existing models need to be validated

through ground calibration; use of LUCAS should be considered.

30.1 Nitrates in water Potentially B Increase and harmonise transmission of national EEA/

useful [13] ►

27.1 Water quality – Nitrate pollution

  Nitrate concentration in water monitoring data to EIONET Water. JRC

bodies Further explore the possibility of using data reported

  Share of agriculture in total by Member States under the Nitrates Directive as

nitrate pollution part of a monitoring system to measure pollution from agriculture.

30.2 Pesticides in water Potentially B

useful [13] ►

27.2 Water quality – Pesticide Increase and harmonise transmission of national EEA pollution monitoring data to EIONET Water.

In the future, data could be provided by the monitoring system under the Water Framework Directive.

31 Ground water levels Low potential C [6]

34.1 Share of agriculture in Useful [19] B GHG emissions

34.2 Share of agriculture in Potentially C nitrate contamination useful [12]

34.3 Share of agriculture in Potentially C water use useful [9]

STATE/ 32 Landscape state Potentially C 28 Landscape – State and diversity It is very difficult to capture all the different Europe JRC/

IMPACT useful [12] ► wide landscape features by means of landscape EEA/

Landscape Typology of farmed landscapes metrics and parameters; some of them are difficult to AGRI

35 Impact on landscape Potentially C Changes/ landscape type communicate.

diversity useful [12] ► Land-cover change Work should continue on developing parameters of

landscape change (likely to be based on a case study approach).

DG AGRI has launched a study on traditional agricultural landscapes.

ANNEX 3

IRENA indicators to be retained as sub-indicators or considered not to have the potential for further development

IRENA operation e s

s f e n t

DOMAIN/ ln e l o p m 16

Comments/ main steps required to improve indicators

Sub-domain e fu L e v

No Indicator U s v

e lo d e

2 Regional levels of Potentially C No longer policy relevant. In the new rural development regulation for the period 2007–2013, the Good Farming

good farming useful Practices as baseline for support of certain rural development measures have been replaced by the cross practice [9-10] compliance requirements that, as from 2005, also apply to the beneficiaries of direct payments under the first pillar (market and income policy) of the CAP.

RESPONSES Public policy

3 Regional levels of Potentially C This indicator has proven to be difficult to develop and is considered not to be relevant enough for environmental

environmental useful reporting. Therefore, it is recommended not to continue the indicator in the future. targets [11] To be replaced by an indicator on "agri-environmental commitments". This would also incorporate the IRENA indicator "Area under agri-environment support".

5.1 Organic producer Potentially C These indicators are difficult to develop due to the lack of harmonised data. The indicator on "Area under organic

RESPONSES prices/ market share useful [13] farming" is deemed to cover appropriately the matter "organic farming" in relation to both market signals and

Market signals 5.2 Agricultural income Potentially attitudes. C

of organic farmers useful [13]

16 Diversification C The share of agri-environment payments in gross farm income was used as an indicator of diversification. However, this is deemed not to be a good indicator of "provision of environmental services", as a result of the farming activity.

DRIVING FORCES Moreover, "diversification" may also refer to other issues, such as combination of different agricultural activities

Trends (i.e. mixed farming) and pluri-activity (i.e. combination of agricultural and non-agricultural activities). The environmental implications of these issues are difficult to assess, however.

In this context, it is proposed not to maintain "share of agri-environment payments in gross farm income" as a specific sub-indicator. In any case, the issue of diversification (in terms of combined production of different commodity outputs in the same holding) remains indirectly covered by the indicator "Intensification".

EN 41 EN

20 Pesticide soil Potentially C Due to its complexity, it is proposed not to maintain the indicator.

PRESSURES contamination useful [10]

Pollution 21 Use of sewage Potentially C On the basis of the current data sets the indicator is deemed not to be sufficiently useful for agri-environmental

sludge useful [12] reporting.

PRESSURES 24 Land cover change Useful B It is proposed to be included as a measurement of landscape change under the indicator "landscape – state and Resource depletion [15-16] diversity".

31 Ground water levels Low C In principle, the only possibilities to get the data needed for computing the indicator would be purchasing STATE potential [6] commercial hydrological data or establishing specific regional surveys in cooperation with Member States. Natural resources However, the possibilities to obtain harmonised data at EU level in the coming years appear remote. In this

context, it is proposed not to maintain the indicator.

STATE 33 Impact on habitats Potentially C This aspect can be covered by indicators related to "Agricultural areas under Natura 2000", "High Nature Value Biodiversity and biodiversity useful [13] (farmland) areas" and "Population trends of farmland birds".

34.1 Share of agriculture Useful [19] B The share can be a sub-indicator of the indicator "Emission of greenhouse gasses". in GHG emissions

IMPACT 34.2 Share of agriculture Potentially C The share can be a sub-indicator of the indicator 'Water quality – Nitrate pollution".

Natural resources in nitrate useful [12] contamination

34.3 Share of agriculture Potentially C The share can be a sub-indicator of the indicator "Water abstraction". in water use useful [9]

Met de EU Monitor volgt u alle Europese dossiers die voor u van belang zijn en bent u op de hoogte van alles wat er speelt in die dossiers. Helaas kunnen wij geen nieuwe gebruikers aansluiten, deze dienst zal over enige tijd de werkzaamheden staken.

De EU Monitor is ook beschikbaar in het Engels.