Data in the common agricultural policy : unrealised potential of big data for policy evaluations. Special report 16, 2022.
Date: 2022-11-25
Institutions: European Court of Auditors
Subjects: agricultural holding, agricultural performance, agricultural situation, big data, common agricultural policy, data collection, disclosure of information, information analysis, information technology applications, innovation, performance audit, report

EN 

2022 

16 

Special report  Data in the Common Agricultural 

Policy 
Unrealised potential of big data for policy 
evaluations 

 
Contents 

Executive summary 

Introduction 

The Common Agricultural Policy is a broad policy area 

Role of data in a policy cycle 

The EU’s data ambitions 

Audit scope and approach 

Observations 

Current data and tools partly deliver the information needed for 
well-informed policy-making at EU level 

DG AGRI collects mostly administrative data, and mainly uses conventional 
tools for data analysis 

Certain features of existing data and systems limit their use for policy 
analysis 

A lack of adequate data limits evaluation of CAP performance 

The Commission does not have enough evidence for its CAP policy needs 
assessment 

The Commission has various initiatives to make better use of 
existing data, but barriers remain 

The Commission is expanding data sources and encouraging data sharing in 
order to tackle data gaps and meet data needs for the CAP 

Specific actions in the context of CAP 2023-2027 focus on improving 
monitoring data 

Research initiatives explore the scope for modernising data and tools 

Member States have their own data initiatives for the CAP 

Some notable data gaps and challenges are yet to be addressed 

Conclusions and recommendations 

2 

Paragraph 

I-V 

01-10 

01-02 

03-05 

06-10 

11-13 

14-83 

14-53 

16-25 

26-36 

37-47 

48-53 

54-83 

57-66 

67-69 

70-72 

73-75 

76-83 

84-90 

 
 
Annex – Selected Commission data-related actions and 
ambitions 

3 

Acronyms and abbreviations 

Glossary 

Replies of the Commission 

Timeline 

Audit team 

 
 
 
 
4 

Executive summary 

I The Common Agricultural Policy (CAP) represents more than a third of the EU 
budget. The policy has numerous complex and inter-related objectives, ranging from 
living standards in the agricultural community to environmental and climate-related 
aspects and the development of rural areas. Using an evidence-based approach in 
policy decisions requires various data from different sources and subsequent analysis. 

II The objective of the audit was to assess whether the Commission is making good 
use of data and analytics for CAP policy analysis. This assessment is relevant both for 
the CAP starting in 2023 and for the post-2027 CAP. 

III First, we examined how the Commission has used the available data for policy 
analysis in recent years, and whether the available data are sufficient. Then, we 
examined what the Commission is doing to address data gaps, including the use of big 
data. 

IV We found that the Commission holds large amounts of data for CAP design, 
monitoring and evaluation. The Commission uses conventional tools such as 
spreadsheets to analyse the data it collects from the Member States. Current data and 
tools do not deliver certain significant elements (e.g. details of the environmental 
practices applied, and off-farm income) that are needed for well-informed policy-
making. The Commission has taken several legislative and other initiatives to make 
better use of existing data, but barriers to making the best use of collected data 
remain. Obstacles such as a lack of standardisation and limitations due to data 
aggregation reduce data availability and usability. 

V We recommend that the Commission should: 

o 

establish a framework for using disaggregated data from Member States; and 

o  make more use of and develop data sources to meet policy needs. 

 
 
 
5 

Introduction 

The Common Agricultural Policy is a broad policy area 

01 The Common Agricultural Policy (CAP) was launched in 1962 and represents more 
than a third of the EU budget: for the 2014-2020 period, agricultural expenditure 
totalled €408 billion. The broad Treaty objectives1 for the policy are specified further in 
the CAP regulations (see Figure 1). The policy seeks to impact not only agricultural 
production and farmers, but also environmental, climate-related and social aspects. 

Figure 1 – General objectives of the 2014-2020 and 2023-2027 CAP 
periods 

2014-2020*
Article 110(2)
of Regulation 
(EU) No 1306/2013

(a) viable food production, with 
a focus on agricultural 
income, agricultural 
productivity and price 
stability

(b) sustainable management of 

(c) balanced territorial 

natural resources and climate 
action, with a focus on 
greenhouse gas emissions, 
biodiversity, soil and water

development, with a 
focus on rural 
employment, growth and 
poverty in rural areas

2023-2027
Article 5 
of Regulation 
(EU) 2021/2115

(a) to foster a smart, 

competitive, resilient and 
diversified agricultural sector 
ensuring long-term food 
security

(c) to strengthen the socio-
economic fabric of rural 
areas

(b) to support and strengthen 
environmental protection, 
including biodiversity, and climate 
action and to contribute to 
achieving the environmental and 
climate-related objectives of the 
Union, including its commitments 
under the Paris Agreement

* The 2014-2020 policy period runs until 2022 due to delays in adopting the new CAP (2023-2027).

Source: ECA, based on Article 110(2) of Regulation (EU) No 1306/2013 and Article 5 of Regulation (EU) 
No 2021/2115. 

1  Article 39 of the Treaty on the Functioning of the European Union. 

 
 
 
                                                      
6 

02 Under the CAP, beneficiaries receive the largest share of subsidy on the basis of 
the land at their disposal. A further portion of subsidy may be paid as reimbursement 
of costs for undertaking specific activities, and to finance investments. EU law sets out 
the basis for most payments. Data on farms are created and collected through various 
means (Figure 2). 

Figure 2 – Examples of data created and collected that are relevant for 
the CAP 

Weather data

Data on land use 
from the land parcel 
identification system

Surveys (e.g. a farm’s 
economic performance)

Satellite data
(Copernicus)

Environmental data 
(e.g. on biodiversity, 
soil quality)

Data collected through 
precision agricultural 
machinery

Data on animal 
movement

Data from mobile phone 
apps (e.g. on fertilisers, 
plant diseases) 

Source: ECA. 

Administrative data 
(e.g. market prices 
and payments)

 
 
 
7 

Role of data in a policy cycle 

03 The Commission’s Better Regulation Guidelines call for an evidence-based 
approach, meaning that policy decisions need to be based on the best available 
evidence. The Commission’s definition of evidence is “data, information, and 
knowledge from multiple sources, including quantitative data such as statistics and 
measurements, qualitative data such as opinions, stakeholder input, conclusions of 
evaluations, as well as scientific and expert advice”2. A conventional policy cycle 
contains the various steps shown in Figure 3. An evidence-based policy needs relevant 
data at every stage of the cycle. 

Figure 3 – Data use in a policy cycle 

Source: ECA, based on Better Regulation Guidelines. 

2  Better Regulation Toolbox, 2021, p. 20. 

 
 
 
                                                      
8 

04 Globally, organisations are making increasing use of ‘big data’, enabling them to 
use data that have been captured in diverse ways. In the report, we consider ‘big data’ 
as data that are too complex or too large for traditional data-processing systems, and 
that require advanced tools and computing power. 

05 Agriculture is a sector where digital innovations and technologies are increasingly 
applied; Figure 4 presents examples of digital technologies in agriculture. The public 
sector can use many of these technologies. Technological advances can reduce the 
timeframe for policy formation, and increase the evidence base for policy decisions3. 
These improvements allow governments to adopt data-driven policies, in particular by 
making it possible to4: 

—  understand the environmental impacts of agriculture better, and formulate policy 

objectives that address those impacts holistically; 

—  design differentiated and targeted policies; and 

—  apply new data-driven monitoring systems. 

3  Höchtl, J., Parycek, P. & Schöllhammer, R., “Big data in the policy cycle: Policy decision 

making in the digital era”, Journal of Organizational Computing and Electronic Commerce, 
2016, 26(1-2), pp. 147-169. 

4  OECD: Digital Opportunities for Better Agricultural Policies, 2019, OECD Publishing, p. 13. 

 
 
                                                      
9 

Figure 4 – Digital technologies for agriculture 

Remote sensing, e.g. data 
acquired from satellites and 
drones

Data collection

In situ sensing, e.g. water and air quality sensors,
soil, biodiversity, crop monitors,
data from precision agricultural machinery

Online surveys /censuses, e.g. 
data collection portals

Capture of administrative data, 
e.g. payments’ data provided by 
authorities via IT systems

Secure and accessible data 
storage, e.g. cloud storage, 
confidential computing

Data analysis

GIS-based and sensor-based 
analytical tools, e.g. land use-
land cover mapping

Deep learning / AI, e.g. big data 
analysis algorithms, machine 
learning, predictive analytics

Data transfer 
and sharing

Data storage

Interoperability programs 
and applications

Data 
management

Digital communication 
technologies, e.g. digital data 
visualisation technologies

Source: ECA, based on Table 2.1 of the OECD’s “Digital Opportunities for Better Agricultural Policies”, 
OECD Publishing, Paris, 2019. 

The EU’s data ambitions 

06 The European Commission has issued several documents emphasising the need 
to improve and maximise the use of data for better policy-making, or impacting data 
sharing or tools in the EU (see Figure 5). 

 
 
 
Figure 5 – Main Commission data-related initiatives 

10 

Source: ECA, based on C(2016) 6626, DataStrategy@EC, C(2018) 7118, COM(2021) 37, COM 
(2018) 234/Directive (EU) 2019/1024, COM(2020) 66, COM(2020) 767, COM(2021) 118, COM(2021) 205 
and COM(2021) 206. 

07 The 2016 Communication from the Commission on “Data, information and 
knowledge management at the Commission” emphasised the need to improve 
information retrieval and delivery, and to maximise the use of data for better policy-
making. It stated that big data “has the potential to significantly increase the 
Commission’s capabilities by allowing early detection of trends and faster feedback in 
support of Better Regulation and evidence-based policy-making as well as to improve 
demonstration of results to all stakeholders”. The Commission planned to develop the 
necessary skills, tools and computing infrastructure to support a big data capability. It 
also highlighted that data needs and knowledge gaps need to be anticipated better to 
ensure that data are available, usable and useful for impact assessments, monitoring, 
reporting and evaluation5. The Commission’s internal data strategy (DataStrategy@EC) 
is the main tool used to put the Communication into practice. 

5  C(2016) 6626, Communication “Data, Information and Knowledge Management at the 

European Commission”. 

 
 
 
                                                      
11 

08 In November 2018, the Commission adopted the European Commission Digital 
Strategy for becoming a digitally transformed, user-focused and data-driven 
administration by 2022. It affirmed the direction set in the 2016 Communication. Of 
the nine actions listed in the strategy, we consider the following two to be the most 
relevant for the scope of our audit: 

— 

integrating new technologies into the Commission’s IT environment; and 

— 

facilitating the free flow of data relating to EU-wide policies between European 
public administrations. 

09 In February 2020, the Commission issued a Communication entitled “A European 
strategy for data”6 for the 2021-2027 period, the scope of which went far beyond the 
Commission itself. The strategy aims to realise the “vision for a genuine single market 
for data” through actions such as setting a governance framework for data access and 
use, and investing in data infrastructure and skills. The issues to be tackled include 
data availability, data interoperability and quality, data governance, data infrastructure 
and technologies (e.g. data-processing capacity and cloud infrastructure), and 
cybersecurity. 

10 The Commission 2020 proposal for a Data Governance Act7 aimed to facilitate the 
re-use of certain categories of protected public-sector data, increase trust in data 
intermediation services, and promote data altruism in the EU. 

6  COM(2020) 66. 

7  COM(2020) 767. 

 
 
 
                                                      
12 

Audit scope and approach 

11 Our audit aimed to assess whether the Commission makes good use of data and 
data analytics for policy analysis of the CAP. First, we examined how the Commission 
used the available data for policy analysis, and whether the data are sufficient. Then, 
we examined whether the Commission is addressing the data gaps, including the use 
of big data, and whether there were recent or ongoing EU research projects that could 
help to address those gaps and improve CAP policy analysis. 

12 Our audit scope encompassed policy design, monitoring during implementation, 
and evaluation. The audit covered the period from 2015 until February 2022. 
Assessment of CAP data governance is relevant, as our report could impact both the 
CAP starting in 2023 and the post-2027 CAP. Primary responsibility for the CAP lies 
with the Commission's Directorate-General for Agriculture and Rural Development 
(DG AGRI). 

13 As part of our audit work, we: 

o 

o 

o 

o 

o 

reviewed relevant data and documents, including scientific, strategic, 
legislative, policy and project documents; 

interviewed the staff of four Commission directorates-general (Agriculture 
and Rural Development, Eurostat, the Joint Research Centre (JRC), and 
Communications Networks, Content and Technology); 

held interviews with the agricultural umbrella organisation COPA-COGECA on 
the EU code of conduct on agricultural data sharing by contractual 
agreement, and with representatives of the Sen4CAP project; 

consulted all 27 EU Member States through a survey addressed to the 
ministry/department responsible for the CAP, and, based on the survey 
replies, had follow-up discussions with authorities in Belgium, Estonia, 
Germany, Ireland, the Netherlands, and Spain; 

conducted a desk review of three non-EU countries (the US, Australia and 
Japan) for benchmarking purposes. The audit team chose these countries 
based on a significant agricultural economy, innovative or digital initiatives 
for agricultural management, and the availability of public data; and 

o 

organised a panel discussion with scientific, policy and administrative 
experts.  

 
 
 
13 

Observations 

Current data and tools partly deliver the information needed 
for well-informed policy-making at EU level 

14 The Commission is required to assess the performance of the CAP in relation to 
its three general objectives8 (see Figure 1). The evidence the Commission gathers for 
policymaking should be proportionate and appropriate for informing policy options 
and addressing evaluation questions9. According to the Better Regulation Guidelines, 
the evaluations should also ensure that relevant evidence is available to support the 
preparation of new initiatives (the ‘evaluate first’ principle). 

15 We examined whether DG AGRI uses a sufficient range of data sources and data 
for CAP policy analysis, and whether it applies relevant analytical tools. We explored 
what type of data, IT systems and data analytics the Commission possesses and uses. 
To determine whether the data and tools are sufficient, we reviewed evaluations and 
policy preparation documents. 

DG AGRI collects mostly administrative data, and mainly uses 
conventional tools for data analysis 

16 To design, monitor and evaluate the CAP, DG AGRI possesses large volumes of 
mainly administrative data (e.g. market prices and payments, and farm accountancy 
data) that it mostly receives from the Member States, which collect the data in order 
to carry out the policy. EU agricultural statistics collected by Eurostat come from a 
variety of sources: surveys, administrative data, data from farms and other businesses, 
as well as farm-level data from agricultural censuses and samples10. 

17 DG AGRI follows the Commission’s internal data strategy. The Commission has a 
data inventory that indicates the ownership, accessibility, storage and re-usability of 
each data asset. This stock-taking exercise did not mention information on gaps or 
overlaps. 

8  Article 110 of the Regulation (EU) No 1306/2013. 

9  Better Regulation Toolbox, p. 20. 

10  Explanatory Memorandum of COM(2016) 786. 

 
 
                                                      
14 

18 DG AGRI’s data inventory as of February 2022 consisted of 57 data assets stored
in various IT systems and databases (for examples, see Figure 6). The databases mainly 
contain structured administrative data, and DG AGRI uses essentially statistical tools to 
process them. A number of documents that DG AGRI collects from Member States (e.g. 
annual implementation reports) include unstructured data, for which DG AGRI has no 
automated or semi-automated processing tools. 

Figure 6 – Examples of main IT systems and databases for CAP data 

ISAMM (Information 
System for Agricultural 
Market Management) –
Member States send via 
ISAMM to DG AGRI for 
example the indicators on 
direct support 

CATS/COMBO (Clearance 
Audit Trail System) –
database containing 
payment and control data

AGRIVIEW – system that 
includes a module which 
consolidates all data on 
CAP indicators for 
2014-2020. Values for the 
indicators are added into 
AGRIVIEW either by 
automatic transfer from 
other IT systems (e.g. 
CATS, ISAMM) or via an 
Excel template, where 
data are manually 
encoded for each 
indicator, country and year

d
a
o

l

c
i
t
a
m
o
t
u
A

S
e
m

i
-
a
u
t
o
m
a
t
e
d

SFC (System for Fund 
Management) – System 
for Member States to 
submit their programming 
documents and 
implementation reports to 
the Commission. Not a 
part of DG AGRI inventory, 
as it is used by several 
Commission departments

Agri-Food 
Data Portal

a
u
t
o
m
a
t
e
d

S
e
m

i

Other statistical and 
administrative data sources, 
e.g. FADN (Farm 
Accountancy Data Network)

Source: ECA. 

19 DG AGRI has an agreement with the JRC for data analysis and to explore ways of
making better use of existing data. On this basis, it uses some advanced methods in its 
CAP policy analysis (such as the IFM-CAP model, econometric models, and predictive 
analytics). The IFM-CAP model is the Individual Farm Model for Common Agricultural 
Policy Analysis, which aims to assess the impacts of the CAP on farm economics and its 
environmental effects. 

20 Our review of the four IT systems (ISAMM, CATS/COMBO, AGRIVIEW, and SFC)
and the FADN database feeding into the Agri-Food data portal (see Figure 6) 
established that DG AGRI mainly collects aggregated data. Of these, only CATS/COMBO 
contains disaggregated, farm-level, data. 

 
 
 
15 

21 DG AGRI publishes consolidated data on the Agri-Food Data Portal, which offers
information from many DG AGRI data assets and from Eurostat agricultural statistics, 
interactive visualisations, and dashboards. Users can consult time series, interactive 
maps, charts and tables, as well as download raw data for re-use and offline analysis. 
DG AGRI updates the portal continuously. We consider the portal to be a good practice 
for publicly available data, as it offers a single point of access to a large set of data 
about agri-food markets, analysis, CAP indicators, and EU financing. 

22 The key IT systems that the Commission and Member States use for the CAP
focus on descriptive and diagnostic analytics; very few are predictive or prescriptive 
(see Figure 7). 

Figure 7 – Four types of data analytics and their use 

e
u
a
V

l

How can we make it happen? 

What will happen? 

Prescriptive

Why did it happen?

Predictive

What happened?

Diagnostic 

Descriptive

Difficulty

Monitoring

Evaluation

Source: ECA, based on Gartner and Commission documents. 

Planning/policy design

16 

23 Based on interviews and Member States’ replies to our survey, we found several 
obstacles to the Commission and Member States using big data (see paragraph 04) and 
advanced analytics for CAP policy analysis, such as: 

(i)  differences in quality standards or requirements between different data sources; 

(ii)  confidentiality rules limiting the use of farm-level data; 

(iii)  limited data availability and data not being in the same or the right format; and 

(iv)  low data literacy and a lack of qualified staff. 

24 A lack of common references such as a unique identifier makes it difficult to 
combine farm-level data from different data sources for CAP analysis. A unique 
identifier or alternative data combination techniques would make it possible to link 
data from different data sources that relate to the same farm (see Box 1). 

Box 1 – An example where data combination techniques would be 
useful 

A unique identifier or other data combination technique could be useful for linking 
and combining farm-level data gathered in surveys by the FADN and soil samples 
from the Land Use and Coverage Area frame Survey (LUCAS). This would provide 
more information on the link between farming practices and the biophysical status 
of a land parcel, especially for the potential future collection of data, e.g. on crop-
specific soil management or crop rotation. 

25 Usually, DG AGRI manually assesses the textual information the Member States 
provide in their annual reports, and does not use big data techniques such as textual 
analytics or automated extractions. Our analysis shows that further automation is 
possible (see Box 2 for an example). 

Box 2 – Automation of data extraction for reporting 

Member States submit annual implementation reports to the Commission via a 
fund management system known as the SFC. These reports contain numerical and 
textual information, mainly in the national languages. 

 
 
17 

DG AGRI staff manually enter data from around 115 reports into an Excel table to 
analyse the information. We tested whether it was possible to use an automated 
tool for some of this work. For this purpose, we developed a robotic solution that 
logs into the SFC, and then navigates to and automatically extracts relevant data 
fields. This software performed automated data extraction from the SFC and an 
automated compilation of an Excel screening tool, which DG AGRI had previously 
produced manually. 

Certain features of existing data and systems limit their use for policy 
analysis 

26 We assessed the use and limitations of three very different data sources that the
Commission and Member States use widely (see Table 1). 

Table 1 – Example of current use of data sources in various policy phases 

IACS 
Administrative farm-level 
data and spatial data 

Member States: some use, 
e.g. to estimate potential
number of applicants for
specific measures

Member States: for 
checking area- and animal-
related aid applications, 
and for checking and 
storing information. Data 
that Member States send 
to the Commission via 
CATS/COMBO are mostly 
based on information in 
IACS 

Member States: output 
and result indicators, e.g. 
number of hectares under 
a specific support scheme 

Member States and 
Commission: the indicators 
from monitoring are used 
as one data source for 
evaluation 

Policy 
planning/design 

Control and 
management 

Monitoring for 
performance 
reporting 

Evaluation 

Source: ECA. 

Copernicus 
Satellite data 

FADN 
Survey data 

Commission: various 
economic and some 
environmental analysis 
and modelling 

Not used 

Member States and 
Commission: limited 
use, except re-using 
monitoring and 
evaluation data 

Member States: 
‘Checks by Monitoring’ 
to replace on-the-spot 
checks 

Commission: context 
and impact indicators, 
e.g. land cover

Commission: context and 
impact indicators, e.g. 
farm net value added 

Commission: when 
using monitoring data 
for evaluations 

Commission: various 
economic and some 
environmental analysis 
and modelling 

18 

Integrated Administration and Control System 

27 The Commission has limited access to the Member States’ Integrated 
Administration and Control System (IACS), which is the main building block of the 
management of CAP payments in the Member States. For the 2014-2020 CAP, IACS 
consists of a number of digital and interconnected databases, in particular11: 

(i)  a system for identifying all agricultural plots in EU countries, known as the land 

parcel identification system (LPIS); 

(ii)  a system allowing farmers to indicate graphically the agricultural areas for which 

they are applying for aid (the geospatial aid application, or GSAA); 

(iii)  a system to record the identity of each beneficiary who submits an aid application 

or a payment claim; 

(iv)  an integrated control system to check aid applications, based on computerised 

cross-checks and physical on-farm controls. 

28 Member States use IACS for receiving aid applications, for administrative checks 
and other controls (e.g. on-the-spot-checks and checks by monitoring), and for making 
payments12. Member States can use different technical solutions for their IACS. A lack 
of standardisation, different data owners (i.e. not always the same type of authority), 
and independent IT developments create fragmentation, make it difficult to compare 
data, and limit how data can be shared or re-used. This reduces the possibilities for 
using advanced analytics or other big data techniques to assess the impact of EU 
funds13. The Commission has limited access to the 42 different Member State (national 
or regional) systems, which include detailed data on farms and businesses14. This 
makes it difficult, for example, to have detailed information on the distribution of EU 
funds. 

11  Article 68 of Regulation (EU) No 1306/2013. 

12  Articles 67-78 of Regulation (EU) No 1306/2013. 

13  Digitalisation of European reporting, monitoring and audit, EPRS. September 2021. 

14  NIVA roadmap for IACS transformation, p. 24. 

 
 
                                                      
19 

29 Based on our review of various EU-financed research projects15, we found that
the decentralised approach of IACS limits further integration and crosslinking of these 
data sources with other Commission data sources, mainly because of: 

(i)

(ii)

compatibility problems (different technical solutions) and a lack of
interoperability between data systems;

confidentiality rules not allowing farm data to be linked from various data sources
(e.g. IACS and the FADN); and

(iii)

low granularity of other databases, i.e. data with an insufficient level of detail,
and a lack of common identifiers to match IACS data.

30 To improve data sharing and availability, DG AGRI is encouraging the Member
States to share their geospatial non-personal IACS data through the common INSPIRE 
geoportal (see Figure 8), with the technical support of the JRC. The portal provides 
access to the download and view services for environmental geospatial data compiled 
by the Member States.  

Figure 8 – INSPIRE geoportal 

Member 
States’ 
geoportals

INSPIRE 
GEOPORTAL

Central European access point to the 
data provided by EU Member States 
and several EFTA countries under 
the INSPIRE Directive

Discovery 
service

View 
service

Download 
service

Transformation 
service

Other spatial 
data services

Registry 
service

Source: ECA, based on JRC. 

15  Deliverables of NIVA and IoF2020 projects, documents of ATLAS and DEMETER projects; 
Digitalisation of European reporting, monitoring and audit, EPRS. September 2021. 

20 

31 The extent to which data are shared via the INSPIRE geoportal varies by Member 
State. Examples of the number of metadata records for three selected themes are 
presented in Figure 9. Member States also publish some geospatial data through their 
independent national (or regional) geoportals. 

Figure 9 – Metadata records shared on the INSPIRE geoportal on three 
themes (by number and share of total records per topic) 

Number of records on the INSPIRE geoportal:

0 

< 10

>10

AT BE BG CY CZ DE DK EE EL ES FI FR HR HU IE IT LT LU LV MT NL PL PT RO SE SL SK

Habitats and 

1
biotopes  2

4
5 1

3
5
2 1 1

%
4 4 2 3 3 2 2 1 0

Soil 9 2

0

%
1 0

6
3
1 2

%
1 2 0

%
5 2 0

%
2 0

%
0

9
6
1

9
1
1

%
3 4 6 8 0

%
2 4 9 8 0

4

1 4 7 1 4 2 9 4 1 1 8

Atmospheric 
conditions

%
9 5 0

2
1 3 1

%
2 1 0

%
0

0
2

%
0

%
5 1 0

0
2

4
1 1

%
3 0

%
8 5 0

%
1 0

3 1

Habitats and biotopes

Soil

Atmospheric conditions

Top 5 Member States (≥2 %)
39 % Italy
35 % Germany
5 % Austria
3 % Bulgaria
2 % Romania
16 % All other

Top 5 Member States (≥2 %)
54 % Germany
27 % Italy
5 % Belgium
2 % Austria
2 % Portugal
10 % All other

Top 5 Member States (≥8 %)
17 % Italy
17 % Finland
12 % Luxembourg
10 % Germany
8 % Austria
36 % All other

Source: ECA, based on INSPIRE geoportal (as of 17.2.2022). 

Copernicus satellite data 

32 Copernicus satellite data meet the definition of big data (see paragraph 04). The 
Commission coordinates the ‘Checks by Monitoring’ (based on Copernicus data) 
approach, which is an example of automated monitoring of the CAP in the Member 
States. 

 
 
 
21 

33 Under ‘Checks by Monitoring’, continuous Copernicus satellite data streams are 
analysed in order to check whether specific land parcels comply with eligibility criteria. 
Since 2018, national authorities have been able to use Copernicus data to replace 
traditional field inspections. According to the Commission, in 2021 ‘Checks by 
Monitoring’ applied to 13.1 % of the area receiving direct payments. The target for 
2024 is 50 %16. In 2021, 10 Member States applied the process to at least one aid 
scheme on at least part of their territory, while in 2020, when we issued a special 
report covering ‘Checks by Monitoring’17 (see Box 3), the number was five. 

Box 3 – A recommendation from special report 04/2020 

In our special report 04/2020 on the use of new imaging technologies18, we 
recommended that the Commission should make better use of new technologies 
for monitoring environmental and climate requirements with a deadline of 
December 2021. The Commission accepted the recommendation. 

More specifically, we recommended using information from new technologies to 
provide better insights into the policy performance of the post-2020 CAP. By 
replacing the optional ‘Checks by Monitoring’ with a mandatory Area Monitoring 
System, the Commission encourages greater use of Copernicus satellite data for 
area-related interventions in the post-2020 CAP. The new system provides for 
automated processing of data from the Copernicus satellites and on-site photos. 

Farm Accountancy Data Network 

34 The main source of economic data is the FADN. The Commission and the Member 
States use the FADN widely for modelling, evaluations, and reporting. 

16  DG AGRI annual activity report, Annex 2, p. 25. 

17  Special report 04/2020 – Using new imaging technologies to monitor the Common 
Agricultural Policy: steady progress overall, but slower for climate and environment 
monitoring. 

18  Ibid., recommendation 2. 

 
 
                                                      
22 

35 Since 1965, the FADN has aimed to provide “objective and relevant information 
on incomes […] and on the business operation of holdings” for the CAP19. The FADN is 
the source of harmonised microeconomic data available to measure the impact of the 
CAP. It is based on national surveys, is voluntary for agricultural holdings, and covers 
EU agricultural holdings which are large enough to be considered commercial20. 

36 The exclusion of non-commercial and small farms makes the FADN less 
representative of CAP beneficiaries. In 2015, the survey included around 
83 000 holdings. While this is representative of around 90 % of the total utilised 
agricultural area and of total agricultural production21, it represents 4.7 million out of a 
total of 10.8 million holdings in the EU22. The FADN is not designed to be 
representative of CAP beneficiaries. According to the Commission, in 2019 the share of 
unrepresented beneficiaries of CAP direct payments varied from 5 % in the 
Netherlands to 78 % in Slovakia. 

A lack of adequate data limits evaluation of CAP performance 

37 Evaluations should use the best available evidence, drawn from a diverse and 
appropriate range of methods and sources (triangulation)23. Granular data make it 
easier to link policy objectives and results/impact24. According to the legislation, the 
information used to evaluate CAP performance should be based, as far as possible, on 
established data sources such as the FADN and Eurostat25. Good monitoring should 
generate factual time series data to improve the quality of future evaluation and 
impact assessment26. 

19  Council Regulation 79/65/EEC. 

20  Farm accountancy data network. 

21  Evaluation Helpdesk, “Best Use of FADN for the Assessment of RDP Effects on Fostering the 

Competitiveness in Agriculture”, 2021, p. 9. 

22  Commission, EU Farm Economics Overview based on 2015 (and 2016) FADN data, 2018, 

p. 5. 

23  Better Regulation Guidelines, pp. 6 and 26. 

24  Better Regulation Toolbox, p. 572. 

25  Article 110 of Regulation (EU) No 1306/2013. 

26  Better Regulation Guidelines, p. 45. 

 
 
                                                      
23 

38 We reviewed five Commission evaluations or evaluation support studies, covering 
at least one evaluation for each of the three general CAP objectives in Figure 1. We 
found that the evaluations made use of a variety of data collected for managing or 
monitoring the policy, e.g. the CAP indicators27, the FADN, CATS/COMBO, Eurostat 
statistics, and the Information System for Agricultural Market Management (ISAMM). 
These data are often complemented by external data (e.g. from the Organisation for 
Economic Co-operation and Development, the United Nations, and the Food and 
Agriculture Organisation), case studies, questionnaires, and interviews. 

39 For all three CAP objectives, the Commission and evaluators use counterfactual 
impact evaluation28. This requires data on control groups, i.e. entities that do not apply 
the policy. The FADN provides data on both groups, and can be useful for such 
analysis. A lack of counterfactual data limits estimates of the CAP’s contribution to 
climate mitigation, for example. According to the Commission, the CAP has been 
applied for too long and covers too large an area to allow for comparative data29, i.e. 
there is no scope for comparing the situation before and after, or with and without the 
policy. It is also difficult to use counterfactual methods for territorial development, as 
most regions receive CAP support. To address this issue, the JRC developed a 
quantitative analytical framework based on counterfactual impact evaluation methods 
to provide insight into the causal link between the policy and its results, taking into 
account the diverse range of measures deployed in rural areas30. 

27  For rural development, Annex IV of Implementing Regulation (EU) No 808/2014. 

28  Better Regulation Toolbox, Chapter VIII – Tool 68. 

29  SWD(2021) 115, p. 20. 

30  Dumangane, M. et al., An Evaluation of the CAP impact: a discrete policy mix analysis, 2021. 

 
 
                                                      
24 

Viable food production 

40 The main data sources for evaluating the viable food production objective are the 
FADN and the Economic Accounts for Agriculture (EAA) (see Table 2). The Commission 
established both specifically to provide data for CAP assessment. For example, to 
evaluate how CAP support impacts farmers’ incomes, the Commission uses Eurostat’s 
statistics on factor income (i.e. income derived from land, capital and labour), and the 
FADN31. 

Table 2 – Data for the ‘viable food production’ objective 

Key sources of evidence 
used 

Examples of data gaps and limitations identified by the 
evaluators or the Commission 

o 

o 

o 

FADN 

Eurostat: EAA and 
labour input 
statistics 

Payment data from 
CATS/COMBO 

o 

AGRIVIEW 

o 

o 

The FADN database does not represent non-
commercial and very small farms. 

FADN and CATS/COMBO data become available 
gradually within a period of two years since the 
base or claim year. 

o  Data organised by product at EU level on quantities 

marketed by producer organisations for fruit and 
vegetables are not available. 

o 

The aggregation of data makes it impossible to 
identify farmers producing peaches and nectarines, 
for example, from among farmers specialising in 
fruit. 

Source: ECA, based on evaluation and the evaluation support study on ‘viable food production’. 

41 It takes a year for Member States to collect and validate the FADN data, and a 
year for the Commission to verify and validate the FADN data from Member States. As 
a result, it takes at least two years before the data are available in the FADN database. 
When the Commission presented the legislative proposal for the post-2020 CAP in 
2018, only data from one year of the current CAP (those from the 2015 FADN survey) 
were available. This means that the Commission made its proposal before it had the 
latest FADN data about the performance and impacts of the current policy. 

31  Evaluation support study on ‘viable food production’, pp. 30-32. 

 
 
                                                      
25 

Sustainable management of natural resources and climate action 

42 For the CAP objective on natural resources and climate, a long period can elapse 
between applying a policy measure and witnessing its impact. In order to determine a 
causal link between a CAP measure and its results, various data have to be combined 
and external factors considered. Of the four components of the objective (see 
Figure 1), we examined biodiversity. Neither the Member States nor the Commission 
were able to provide good evidence of a causal link between standards of good 
agricultural and environmental conditions32 and biodiversity status33. Examples of 
evidence used and the limitations on assessing the biodiversity component are 
presented in Table 3. 

Table 3 – Data on the biodiversity component of the ‘sustainable 
management of natural resources’ objective 

Key sources of evidence used 

o 

o 

o 

o 

o 

CAP context, output, result and 
impact indicators 

Streamlining European Biodiversity 
Indicators (SEBI) 

Indicators of Sustainable Forest 
Management reported by Member 
States to Forest Europe 

The Commission’s agri-
environmental indicators 

FADN data at farm level on 
production, profitability, location 
(inside or outside a Natura 2000 
area) and uptake of CAP measures 

Examples of data gaps and limitations 
identified by the evaluators or the 
Commission 

o  Data on the uptake of landscape 

features under the agri-
environment-climate measures are 
not available. 

o  Monitoring data on the actual 

impacts of individual CAP measures 
are insufficient. 

o  No recent data for many of the 

statistical indicators. 

o  Data on the quantities of fertilisers 
and pesticides used on agricultural 
land in the EU are not available. 

Source: ECA, based on Evaluation support study of the impact of the CAP on habitats, landscapes, 
biodiversity. 

32  Annex II of Regulation (EU) No 1306/2013. 

33  Special report 13/2020 – Biodiversity on farmland: CAP contribution has not halted the 

decline, paragraphs 48-50. 

 
 
                                                      
26 

43 A 2019 evaluation concluded that an overall assessment of the policy impact on 
biodiversity was not possible due to the absence of suitable monitoring data34. Several 
of the Commission’s monitoring indicators are not regularly fed with data. For 
example, not all Member States collect and report data on the impact indicator for 
water abstraction in agriculture. 

44 Another limitation on evaluating the environmental objective is that no 
comprehensive data were available on the quantities of fertilisers and pesticides used 
in the EU on agricultural land. Since 2021, data on the quantities of pesticides used on 
agricultural land have been available, but for less than half of the Member States. The 
Commission and evaluators have used FADN data on spending on fertilisers and plant 
protection products per hectare as a proxy. 

45 Publicly available EU statistics on plant protection products relate to the amounts 
(kg) of active substances contained in plant protection products sold35. In special 
report 5/202036, we reported that grouping these active substances in the way 
required by EU legislation limits the information Eurostat can publish or even share 
with other Commission Directorates-General. Statistics compiled on the agricultural 
use of plant protection products under current EU legislation are not comparable, and 
Eurostat has not yet been able to publish EU-wide usage statistics. 

34  Evaluation of the impact of the CAP on habitats, landscapes, biodiversity, Executive 

summary, 2019. 

35  Special report 05/2020 – Sustainable use of plant protection products: limited progress in 

measuring and reducing risks. 

36  Ibid. 

 
 
                                                      
27 

Balanced territorial development 

46 In a 2021 evaluation37, the Commission and evaluators used CAP output 
indicators, payment data from CATS/COMBO, DG REGIO’s ARDECO database, and 
Eurostat’s regional database for the CAP’s third objective. The limited availability of 
complete, detailed and updated data on the socio-economic status of rural areas 
affected the robustness of the evaluation38. The contractors claimed that data were 
scarce for some of the core societal aspects, and, if available, were often not regularly 
updated, but produced on an ad hoc basis from specific research projects39. In some 
cases, the evaluators had applied proxy indicators. Overall, they cited the availability 
and quality of indicators and a lack of data on small regions as the main limitations on 
quantitative analyses. 

47 With the exception of the payment data in CATS/COMBO and the individual 
farms data in FADN, most of the data that the Commission collects from Member 
States are aggregated, yielding a single figure for an entire Member State or region. 
This limits the potential for the data to be re-used for further evaluation or policy 
design purposes. For some socio-economic aspects (e.g. social inclusion), data were 
only available at national level or at a low geographic resolution, which is not sufficient 
for analyses of territorial differentiation40. CAP monitoring data also lack details for 
more targeted analyses, e.g. information about the age or gender of beneficiaries41. 
These data are usually available in Member States’ databases, but are not accessible to 
the Commission. 

37  Evaluation on impact of the CAP on territorial development of rural areas. 

38  SWD(2021) 394. 

39  Evaluation support study on the impact of the CAP on territorial development of rural 

areas, 2020. 

40  Evaluation support study on the impact of the CAP on territorial development of rural 

areas, 2020. 

41  SWD(2021) 394 and Evaluation support study on the impact of the CAP on territorial 
development of rural areas: socioeconomic aspects; special report 10/2021 – Gender 
mainstreaming in the EU budget: time to turn words into action, paragraph 90. 

 
 
                                                      
28 

The Commission does not have enough evidence for its CAP policy needs 
assessment 

48 Under the Better Regulation Guidelines, the impact assessment accompanying a 
legislative proposal should start by verifying the existence of a problem42. It must set 
out the logical reasoning that links the problem with its underlying causes and the 
related objectives, as well as offer a range of policy options for tackling the problem. 

49 To examine data use at the policy design or planning stages, we reviewed the 
impact assessment accompanying the post-2020 CAP legislative proposal43 and various 
Commission documents supporting it. We identified weaknesses in the way relevant 
data were provided to support the description of the problem the policy addresses 
under the specific objective of ‘viable farm income’. We stated in our opinion on the 
post-2020 CAP legislative proposals that the data and arguments the Commission used 
to support the needs assessment for farmers’ incomes are insufficient44. The 
Commission does not have information on farmers’ or farming households’ incomes 
outside farming, and the averages mask a great variation in the income situation. In 
addition, in our 2021 report on gender mainstreaming, we highlighted that the 
unavailability of statistics on farmers’ household incomes and on disposable farm 
income broken down by sex is also a major data gap when looking at the effects of 
direct payments on gender equality45. 

42  Better Regulation Guidelines, p. 10. 

43  SWD(2018) 301. 

44  Opinion 07/2018, paragraph 2. 

45  Special report 10/2021 – Gender mainstreaming in the EU budget: time to turn words into 

action, paragraphs 89 and 90. 

 
 
                                                      
29 

50 In 2018, we recommended that “before making any proposal for the future 
design of the CAP, the Commission should assess the income position for all groups of 
farmers and analyse their income support need”, taking into account aspects such as 
income from food and other agricultural production, as well as from non-agricultural 
sources46. The Commission partially accepted the recommendation, adding that the 
policy is targeted at those farmers who are actively farming to earn their living. A 2015 
study47 on farm household incomes showed a major gap in information on CAP 
performance, as there was no EU statistical or monitoring system for assessing 
farmers’ total household incomes and comparing them with other groups in society. As 
of February 2022, the Commission has not made any progress in this area. 

51 Every three to four years, Eurostat receives from Member States ‘Farm Structure 
Survey’ data on other gainful activities on farms. The survey data indicate whether the 
other gainful activities are a main or a secondary activity of the holder-manager, but 
not the share or range of income from it. The latest data published on the Eurostat 
website are for 201648. 

52 The current standard list of FADN variables does not include information on off-
farm income, as the survey is about farms and not about farmers. Income tax data in 
the national tax authorities’ registries alone are insufficient to provide those data, 
because they do not contain information on farm characteristics and contain 
agricultural incomes of also those persons whose main activity is not farming49. 

46  Special report 10/2018 – Basic Payment Scheme for farmers – operationally on track, but 

limited impact on simplification, targeting and the convergence of aid levels, 
recommendation 3. 

47  Hill, B. & Dylan Bradley, B. (2015), “Comparison of farmers’ incomes in the EU Member 

States”. Study prepared for the European Parliament. 

48  Dataset on other gainful activities (ef_oga_main). 

49  Hansen, H. and Forstner, B. (2021), “A differentiated look at the economic situation of 
German farmers”, presentation at 27th meeting of the OECD Network for Farm Level 
Analysis. 

 
 
                                                      
30 

53 Some Member States (e.g. Ireland and the Netherlands) collect data on off-farm 
income using national FADN surveys, which could fill one of the data gaps relating to 
farmers’ real incomes. The Irish authorities regularly publish data indirectly on off-farm 
income, including ‘the presence of off-farm employment’, ‘the days and hours worked 
off-farm’ and ‘the sector worked in’. 

The Commission has various initiatives to make better use of 
existing data, but barriers remain 

54 The Commission should take further initiatives to address existing weaknesses 
and improve data collection and processing in order to evaluate the CAP and to 
support the development of future policy. These initiatives should be put into practice 
in line with the defined timetable and outputs. The Commission needs to adapt and 
strengthen existing data sources for the new CAP. It should also explore and mobilise 
new data sources to reduce the burden on farmers and administrations, while at the 
same time improving the policy evidence base50. 

55 In its internal data strategy action plan, the Commission has set itself objectives 
to guarantee access to data that are relevant for decision-making and functioning 
across the organisation, and to foster the use of modern data analytics technologies to 
identify patterns and trends more quickly and more effectively. 

56 We examined which initiatives the Commission has taken to make better use of 
available data and new technologies in order to address the data gaps and challenges 
identified above. In addition, we looked at EU-financed research projects and Member 
States’ initiatives that could contribute to CAP policy analysis and fill some of the gaps. 

50  SWD(2018) 301, p. 51. 

 
 
                                                      
31 

The Commission is expanding data sources and encouraging data sharing 
in order to tackle data gaps and meet data needs for the CAP 

57 The Commission’s internal data strategy states that “internal and external data 
sources need to be exploited as much as possible to generate evidence supporting 
decisions”. The costs and administrative burden of additional data collection for policy 
monitoring have to be commensurate with data needs. According to the Better 
Regulation Toolbox51, not all data gaps have to be filled. 

58 The Commission started to put its data strategy into practice in 2018. Activities 
range from creating a data inventory (see paragraphs 17-18) to data governance rules, 
data analytics, and training and skills. At the end of 2020, DG AGRI set up a board and a 
working group to implement the strategy. Since January 2022, it has had a specific 
‘Data Governance’ unit to improve coordination of data management. 

59 The Commission has initiated several actions that could contribute to better 
policy analysis by improving data infrastructure and data use for the CAP (e.g. digital 
solutions, e-tools, algorithms, and good practices). See Annex for examples. 

60 A 2016 Eurostat evaluation of agricultural statistics52 concluded that agricultural, 
forestry, land use and environmental statistics are not sufficiently harmonised and 
coherent. The reasons for this include the fact that legislation has been developed in 
silos, but also that different definitions and concepts exist in various agricultural areas. 
To address this issue, the Commission introduced two new regulations and amended 
one existing regulation (see Figure 10). 

51  Better Regulation Toolbox, p. 363. 

52  SWD(2017) 96, Evaluation accompanying the document “Strategy for Agricultural Statistics 

2020 and beyond and subsequent potential legislative scenarios”. 

 
 
                                                      
Figure 10 – Legal framework of the European agricultural statistics 
system 

32 

Integrated farm statistics 
(IFS) regulation

•Covers data on farm
structure, orchards and
vineyards
•Involves farm micro-data
transmission to Eurostat
•Adopted as Regulation
(EU) No 2018/1091

Regulation on economic 
accounts for agriculture 
(EAA)

•Includes data on the value
of output, intermediate
consumption, subsidies and
taxes, rent and interest,
capital formation, etc.
•Aggregated data at
national (compulsory under
Regulation) and NUTS2
level (voluntary
transmission)
•Existing regulation was
amended to include
regional economic
accounts at NUTS2 level
•Adopted as Regulation
(EU) 2022/590

Framework regulation on 
statistics on agricultural 
input and output (SAIO)

•Covers data on agricultural
inputs (e.g. plant protection
products, nutrients, price of
fertilisers) and outputs
(crop and animal
production, and agricultural
prices)
•Data are collected from
farms, administrative
sources, intermediaries (e.g.
dairies), wholesale entities
and market organisations,
and often take account of
expert estimates
•Only aggregated data
•Commission proposal
COM(2021) 37
•Target: to be in place by
2022
•Current status: not yet
adopted

Source: ECA, based on Regulation (EU) 2018/1091, COM(2021) 37, Regulation (EU) 2022/590. 

61 In 2019, Eurostat issued a call for proposals to set up a network of national
statistical institutes interested in developing methods to modernise agricultural 
statistics. One of the two priorities concerned activities that “exploit the use of new 
data sources for agricultural statistics (e.g. big data, satellite imagery, geo-referenced 
information, precision farming), including access, confidentiality and quality 
assessment aspects”. The call did not receive any applications. According to the 
Commission, Member States said one reason for this was that national statistical 
offices had insufficient resources to set up and co-ordinate such a network. 

62 Other initiatives to tackle data gaps fall into two broad categories: data sharing
from Member States or stakeholders, and adding new variables to existing data 
sources. 

33 

63 Under the ‘DG AGRI process for IACS data sharing under INSPIRE’ project,
DG AGRI, in collaboration with the JRC, the Directorate-General for Environment and 
the Directorate-General for Climate Action, is building a framework and support 
procedures for sharing non-personal IACS spatial data across the EU. The objective is 
to ensure that spatial IACS data are easily locatable, efficiently accessible (via a single 
entry point), and effectively re-usable in a coherent policy environment (see 
Figure 11). 

Figure 11 – Three interlinked goals addressed in the IACS data 
exploration procedure 

Ensure discoverability, efficient access 
and effective re-use of spatial IACS 
data in a coherent policy environment 

‘DG AGRI process for IACS data 
sharing under INSPIRE’ project

INSPIRE 
GEOPORTAL

IACS (LPIS/GSAA), data 
on land claimed for 
financial support

Other services, similar 
information for areas 
not subsidised or fully 
covered by the CAP 

Other spatial and non-
spatial databases

Enriched data sets

CAP Green Architecture

European Green Deal

Link IACS to other data 
sources

Assess how a “linked dataset” 
can contribute to other 
initiatives

Respond to new policy 
needs

Source: ECA, based on Joint Technical Report: IACS data exploration and integration, EC, 2021, p. 7. 

34 

64 In the European Strategy for Data53, the Commission acknowledges the 
importance of data sharing for improving the availability of data. In the strategy, the 
Commission announced its plan to establish nine EU-wide sectoral common data 
spaces, including a “Common European Green Deal data space” and a “Common 
European agricultural data space”. The latter aims to make it easier to share, process 
and analyse production data, open data, and possibly other public data (e.g. soil 
data)54. 

65 The strategy lists two specific preparatory activities for the agricultural data 
space: to take stock of experiences with the ‘stakeholder code of conduct on 
agricultural data sharing’55, and to take stock of existing agricultural data spaces in 
2020 and early 2021. The Commission currently plans to carry out these activities as 
part of the Digital Europe Work Programme for 2021-2022, which it approved in 
November 2021. According to the Commission, the data space will be included in the 
2023-2024 work programme, with a possible prototype in 2024 and a further roll-out 
of the data space in the following years. 

66 Under the Farm to Fork strategy56, the Commission intends to convert the FADN 
into a Farm Sustainability Data Network (FSDN), with a view to collecting farm-level 
data on the Farm to Fork and Biodiversity Strategy targets, and other sustainability 
indicators. The Commission published a roadmap in June 2021, and plans to present a 
proposal for a regulation in the second quarter of 202257. 

53  COM(2020) 66. 

54  C(2021) 7914, Annex to the Commission Implementing Decision on the financing of the 

Digital Europe Programme and the adoption of the multiannual work programme for 2021-
2022, p. 54. 

55  EU Code of conduct on agricultural data sharing by contractual agreement. 

56  COM(2020) 381. 

57  Roadmap: Conversion of the FADN to a Farm Sustainability Data Network (FSDN). 

 
 
                                                      
35 

Specific actions in the context of CAP 2023-2027 focus on improving 
monitoring data 

67 Except for the change from the FADN to the FSDN, the Commission plans no 
significant changes in the core IT systems presented in Figure 6. However, it is working 
to increase the functionality of ARACHNE, which is a data-mining tool that Member 
States use on a voluntary basis in their administrative controls. The tool is useful, for 
example, for identifying projects or beneficiaries that might be susceptible to risks of 
fraud or conflicts of interest, although non-compulsory use might limit its benefits. The 
effectiveness of the tool’s data analytics depends on data input. This means that the 
more quality data are uploaded, the more accurate, comprehensive and informative 
the system’s outputs are. 

68 DG AGRI uses new technologies and satellite data to improve monitoring 
indicators. For example, it introduced a new impact indicator for monitoring landscape 
features for the 2023-2027 period. The 2014-2020 CAP did not include an impact 
indicator on landscapes; this weakened assessment of the impact of the CAP on 
habitats, landscapes and biodiversity (see Table 3). For the new indicator (the share of 
agricultural land covered with landscape features), the Commission will use data from 
the Copernicus Land Monitoring Service, which contains information on linear 
hedgerows and scrubs, tree rows, and isolated patches of trees. 

69 For the 2023-2027 CAP, the Commission will define a new framework, including 
an implementing act, to receive data on individual transactions for monitoring, 
evaluation, and policy-design. According to the Commission, by collecting the 
individual data on the application/claim, and information on the beneficiary and its 
farm/business, it will try to solve the issue of data disaggregation. 

Research initiatives explore the scope for modernising data and tools 

70 Under Horizon 2020 (H2020), the Commission finances research and innovation 
projects. We identified a number of recent or ongoing H2020 and other research 
projects that could contribute to improving the data infrastructure and data use (e.g. 
digital solutions, e-tools, and algorithms) that are needed to provide better data for 
the CAP (see Figure 12). Some projects (such as NIVA and Sen4CAP) have already 
produced relevant results, which could be useful for future developments. 

 
 
Figure 12 – Examples of research projects with a policy analysis element 

36 

Source: ECA, based on data in the European Commission’s CORDIS database. 

71 The NIVA (New IACS Vision in Action) project tackles some of IACS’s limitations
(see paragraphs 28-29), especially by reducing the administrative burden and using the 
potential of data. The project’s objective is to modernise IACS by making efficient use 
of digital solutions and e-tools, thereby creating reliable methodologies and 
harmonised data sets for monitoring agricultural performance. 

37 

72 Another EU-financed project, FLINT (Farm-Level Indicators for New Topics in 
policy evaluation), addressed the gap between data needs for policy evaluation and 
available agricultural statistics58. This project is potentially relevant for the planned 
revision of the FADN, because the project covered sustainability indicators and used 
the FADN as a framework. The project proposed 33 topics or indicators relating to 
environmental, social, economic and innovation aspects to be collected in the future59. 
In its roadmap60, the Commission stated that the conversion to the FSDN will build on 
the FLINT project. However, in February 2022, it was too early to assess this claim. 

Member States have their own data initiatives for the CAP 

73 Our survey of all 27 Member States found that a majority of them recognise the 
added value of advanced analytics, with most selecting faster decision-making, 
predictive and cross domain analysis, cost reduction, and more effective 
communication with farmers and stakeholders, from a provided list of possible 
elements. 

74 In their replies to our survey, more than half of the Member States suggested the 
following measures as priorities to support their use of big data: more Commission 
funding for IT tools and data analytics projects (67 %), additional guidelines/manuals 
(56 %), and support for the development of new methodologies or standardisation 
(52 %). Fewer Member States chose support for analytical technologies (48 %), data 
access solutions (41 %), and research support and common research projects (48 %). 

75 Our survey and follow-up interviews showed differences between Member States 
on how they incorporate new data sources and advanced data analytics techniques. 
Box 4 and Box 5 include examples of Member State actions. 

58  Poppe, K., Vrolijk, H., Dolman, M., and Silvis, H., 2016, FLINT – Farm-level Indicators for New 

Topics in policy evaluation: an introduction. Studies in Agricultural Economics, 118, 
pp. 116-122. 

59  Final report summary of FLINT project. 

60  Roadmap: Conversion of the FADN to a Farm Sustainability Data Network (FSDN). 

 
 
                                                      
38 

Box 4 – Examples of combining data sources with modern analytics 

Spain 

o  One Spanish region (Castile and Leon) has been an advanced user of the 

‘Checks by Monitoring’ approach since 2019. Its monitoring methodology is 
based on processing and analysis by artificial intelligence of the images 
provided by the Copernicus Sentinel satellites. Using specific indexes and 
markers, and subsequently applying a series of rules, allows the authorities to 
reach a conclusion about the eligibility of declared areas. 

o 

o 

The Spanish authorities apply automatic photo-interpretation using ‘Deep 
Learning’ classification techniques, for example a Random Forest algorithm 
for crop classification. They also use it to assess the indicative risk of land 
abandonment. 

Advanced analytical tools for forecasting harvests by means of machine 
learning allow the authorities to assess the presence of agricultural activity 
and to predict market behaviour. 

Source: ECA and the Spanish authorities. 

Box 5 – An attempt to link various databases 

The Estonian authorities initiated an agricultural big data programme, aiming “to 
create more value-added in the agricultural sector by providing data-driven tools 
for farmers”. The intention is to establish an electronic system (tool) for 
agricultural big data, which should link existing data to relevant analytical models 
and practical applications. 

For policy analysis, the big data system could facilitate data collection on farm-
level agronomic performance. 

A feasibility study concluded that: 

—  no extensive changes are needed in the legal system, but the regulations on 
agricultural data processing should be modified, and a common framework 
established; 

— 

it is relevant and possible to include 83 % of the 41 databases analysed in the 
Big Data System, but only 10 % of the databases could be included without 
further development; 

 
 
39 

—  potential services to the Ministry of Rural Affairs could include: 

o  monitoring trends in the economic performance of agricultural holdings; 

o 

providing an overview of the use of fertilisers and plant protection 
products (a digital fieldbook is a prerequisite). 

As of February 2022, the system had not yet started to be developed. The plan is 
to develop an electronic system, including an electronic field book and possibly 
some other e-tools, e.g. a humus balance calculator, and plant protection 
recommendations. 

Source: ECA, Long-Term Knowledge Transfer Program on Agricultural Big Data, and the Estonia 
authorities. 

Some notable data gaps and challenges are yet to be addressed 

76 The Commission acknowledges that cross-linking existing data sources is a key 
challenge in ensuring there are appropriate data to evaluate the CAP61. It is working on 
re-using IACS data and expanding the FADN, but has not initiated specific actions to fill 
the data gap regarding farmers’ incomes outside farming (‘off-farm income’), or to 
combine various data sources of disaggregated data to increase the value of data that 
have already been collected. 

77 The Commission has expressed the need for a common unique identifier for 
agricultural holdings (farms) that would make it possible to link farm-level data from 
various data sources (e.g. administrative registers, and surveys)62. The identifier would 
have to take account of different Member State systems and complex farm structures 
with different combinations and locations. This requires a common definition of a 
farm, and such a definition has an impact on financial indicators like farm income63. A 
unique identifier could help to increase data accessibility and provide more reliable 
information on policy impact. As of February 2022, there has been no progress on such 
an identifier. 

61  SWD(2018) 301, Part I, p. 51. 

62  See, for example, Strategy for agricultural statistics for 2020 and beyond, pp. 8, 12 and 

16-17. 

63  Poppe, K. J. and Vrolijk, H.C.J. (2019), How to measure farm income in the era of complex 

farms, Paper prepared for presentation at the 171st EAAE Seminar. 

 
 
                                                      
40 

78 Farm-level data from management applications and systems are a new and rich 
source of information. There are many commercial solutions offering a variety of 
services for digital record keeping, field monitoring and labour tracking, and many 
aspects of farm operations can be enhanced by such applications (see example in 
Box 6). The Commission does not know how many farmers use farm management 
software, but the Integrated Farm Statistics campaign64 planned for 2023 could help to 
provide knowledge of the use of management information systems and precision 
farming equipment. 

Box 6 – An example of collecting data at farm level 

Akkerweb in the Netherlands is a good example of an application for collecting 
data from farms. The structure of the platform supports several applications, and 
uses data from individual farms and production. Farmers can choose which 
applications to use, and have the opportunity to link to other systems. 

Currently, Akkerweb helps farmers to take decisions based on public information 
and their own farm data. In the future, the Dutch authorities plan to improve data 
sharing between public administration data sources and private data platforms. 

Source: ECA and the Dutch authorities. 

79 Using a digital field book, where farmers register their activities, would be a step 
forward for digitising farms and improving the monitoring of consumption and impact 
as regards pesticides, fertilisers, water and soil. The Commission’s proposed FaST 
(Farm Sustainability Tool for Nutrients) platform is a tool with flexible architecture that 
provides modern analytics and interoperability with many data sources. FaST builds on 
several data sources, which are either connected (live sources) or imported (static 
sources) on the platform. In order to provide farmers with access to their own data, 
FaST connects to the regional/national IACS (or equivalent farm registry), where the 
farmers’ data are stored. 

64  Commission Implementing Regulation (EU) 2021/2286. 

 
 
                                                      
41 

80 Precision farming data can be a valuable data source65. Examples of such data 
include sensor and machinery data on soil humidity and nutrients, and location-specific 
data on the use of pesticides. The above-mentioned NIVA project explores the 
possibilities for an electronic farm registry that can be linked to IACS. It also aims to 
integrate machine/precision farming data into IACS. However, there are obstacles to 
doing so, such as the diverse nature of farm machines and a lack of standardisation. 

81 Accessing individual data for policy analysis can be difficult, and there is no legal 
or technical framework for using commercial information for policy analysis. According 
to one study66, farmers are reluctant to share data for reasons such as the risk of data 
being shared for other purposes, a lack of clarity about what ‘personal data’ means, 
and general ‘resistance to modern data platform technologies’. Under the 2023-2027 
CAP, farm advisory services for farmers are required to cover digital technologies67. 

82 Figure 13 below summarises the main data-related challenges the Commission is 
facing, and our assessment of the extent to which these have been addressed. 

65  Punt, T. and Snijkers, G., Exploring precision farming data: a valuable new data source? A 
first orientation, 2020. Paper presented at the 2019 UNECE Workshop on Statistical Data 
Collection ‘New sources and New technologies’. 

66  Internet of Food and Farm 2020: Policy Recommendations from IoF2020. 

67  Article 15 of Regulation (EU) 2021/2115. 

 
 
                                                      
42 

Figure 13 – Assessment of the extent to which the initiatives address the 
challenges 

Challenges: 

Assessment: 

Data not collected: 
Insufficient data to support 
evaluations and impact 
assessment (Table 2 and 
Table 3, paragraph 49) 

Positive moves on setting a framework for more open, available and 
adequate data (paragraphs 60, 64, 66). More is left to be done in areas 
such as farmers’ incomes, territorial development and environmental 
indicators. 
Assessment: Significant progress required 

Data not accessible: The 
Commission has limited 
access to Member States’ 
IACS (paragraph 28) 

Under the INSPIRE initiative, the Member States share some non-personal 
spatial data. This is expected to increase data sharing (paragraphs 30, 63). 
Assessment: It is too early to assess whether the actions will address the 
challenge 

Excessive aggregation: The 
Commission does not have 
enough disaggregated 
monitoring data 
(paragraph 47) 

The Commission plans to specify in an implementing act the beneficiary-
level data that Member States have to report for the CAP (paragraph 69). 
Additionally, H2020 projects are exploring the needs for farm-level data 
(Figure 12). 
Assessment: It is too early to assess whether the actions will address the 
challenge 

Restrictions on combining 
data: Data from different 
datasets cannot be easily 
combined (paragraphs 23, 
24, 29) 

The data portal is the main tool for publishing consolidated data on the 
CAP. It is continuously upgraded with new datasets (paragraph 21). 
However, there has been no progress on introducing a common identifier 
or similar technique to link different datasets (paragraph 77). 
Assessment: Not yet addressed 

Exploring big data use: 
Limited use of big data and 
big data analytics 
(paragraphs 22, 25) 

Source: ECA. 

With AMS, the Commission will encourage the use of Copernicus Sentinel 
satellite data for area-related interventions (Box 3). 
Sen4CAP produced open-source algorithms and advanced tools to make it 
easier for Member States to use Copernicus data for CAP monitoring and 
analysis (Figure 12). 
Assessment: Significant progress required  

83 In our desk review of comparable practices outside the EU, we looked at three
non-EU countries: Australia, Japan and the US. These countries present information in 
the public domain on integrating modern data techniques into agriculture; Box 7 
presents some of their initiatives.  

 
43 

Box 7 – Examples of practices outside the EU 

Australia 

The FLAD-BLADE database68 predicts agricultural production at farm level, based 
on prevailing climate conditions (e.g. rainfall and temperature), commodity prices 
and farm characteristics (e.g. location and size). The database can generate farm-
level information on production and financial outcomes for essentially every farm 
in Australia69. 

In addition, Australia’s Commonwealth Scientific and Industrial Research 
Organisation has explored the use of confidential computing to improve access to 
farm-level data for policy or research, while maintaining data confidentiality and 
security. Confidential Computing enables a new, low-friction method for 
exploratory linkage and analysis of data sources. This approach may allow the 
discovery of new connections between data sources, while maintaining data 
confidentiality70. 

Japan 

The Japanese authorities have set up an Agricultural Data Collaboration Platform 
(WAGRI)71. The platform co-ordinates, shares and supplies agriculture-related 
data. It includes public data, such as the position and size of agricultural land, and 
meteorological information. Future development plans include consolidating data 
held by farmers, agricultural machinery manufacturers, ICT vendors and others, 
and using big data to optimise agricultural production management. 

US 

Crop-CASMA (Crop Condition and Soil Moisture Analytics) is a web-based 
geospatial application where remotely-sensed geospatial index data can be used 
to assess US crop vegetation conditions and soil moisture condition72. 

68  Agricultural Data Integration Project. 

69  Hughes, N. et al. (2020), The Agricultural Data Integration Project, ABARES research report, 

Canberra. 

70  Digital Opportunities for Better Agricultural Policies, 2019, OECD. 

71  WAGRI webpage. 

72  Crop-CASMA User's Guide. 

 
 
 
 
                                                      
44 

Conclusions and recommendations 

84 We examined whether the Commission makes good use of data and data 
analytics for analysis of the Common Agricultural Policy (CAP). The CAP has many 
complex, inter-related objectives. Determining whether policy instruments are 
relevant, and whether they efficiently address those objectives, requires data and 
information from a range of sources, both internal and external. 

85 We found that although the Commission is using a significant amount of data on 
economic, environmental, climate and social aspects, in some areas current data and 
tools do not deliver certain significant elements of the information that are needed for 
well-informed policy-making (paragraphs 16-53). The Commission has taken several 
initiatives to make better use of existing data (paragraphs 57-69), but in addition to 
delays in the availability of data (paragraph 41), barriers remain (paragraphs 76-81). 

86 The main data barriers, by stages of data gathering and processing, are: 

—  data not being collected: for example on farming inputs (e.g. the quantity of 

chemical and non-chemical pesticide applied, the quantity of mineral/organic 
fertiliser applied and to which crop), and farming practices with an environmental 
impact (see Table 3, paragraphs 42-45); 

—  data not being accessible: farm-level information is owned, managed and stored 
in Member States’ local Integrated Administration and Control System (IACS) to 
which the Commission has limited access (see paragraphs 27-29); 

—  excessive aggregation: the Commission receives mostly aggregated data from the 

Member States, thus limiting the extent to which it can extract value from them 
(see Table 2, paragraph 47); 

— 

restrictions on combining data sources, e.g. due to the lack of a common 
identifier (see paragraph 24). 

87 As a result, the Commission has partial knowledge of the baseline or policy 
impact in areas such as farmers’ off-farm incomes, environmental 
information/practices, and socio-economic development. These gaps in data 
availability affect evidence quality in some evaluations (see paragraphs 39-47) and 
impact assessments (see paragraphs 48-53). 

 
 
45 

Recommendation 1 – Establish a framework for using 
disaggregated data from IACS 

The Commission should establish a technical and administrative framework for sharing 
and re-using disaggregated data from IACS (beyond those needed for annual 
performance reports) in order to monitor, evaluate and ultimately design policy. This 
should respect principles of efficiency, and so minimise the administrative burden and 
costs on beneficiaries and Member State authorities. 

Timeframe: 2024 

Recommendation 2 – Make more use of and develop data 
sources to meet policy needs 

The Commission should address the data gaps identified in evaluations of the 
2014-2020 CAP and the impact assessment of the post-2020 CAP by: 

(a)  making more use of existing data sources (e.g. administrative data and statistical 
surveys, and Copernicus data), considering new data sources, or combining 
existing ones; 

(b)  examining the possibility of using proxies or indirect data sources when the use of 

direct sources is not feasible for assessing key indicators or aspects; and 

(c)  assessing the possibility of scaling up the use of farm-machinery data. 

Timeframe: 2025 

88 At the Commission, the Directorate-General for Agriculture and Rural 
Development has an agreement with the Joint Research Centre for data analysis and 
for exploring how to make better use of existing data. Based on this agreement, the 
Commission uses advanced quantitative analysis and models for CAP policy analysis. 
However, the Directorate-General for Agriculture and Rural Development does not use 
big data techniques for textual analytics, text mining or automated extraction itself. 
There are potential benefits in replacing manual and time-consuming procedures with 
automated tools (see paragraphs 19, 25 and Box 2). 

 
 
46 

89 Across the EU, there are several initiatives, sometimes funded by the EU under 
Horizon 2020 or other programmes, that explore the possibilities for modernising data 
and IT tools for designing, monitoring and evaluating the CAP. Some of these initiatives 
have already produced deliverables in the field of interoperability and new and more 
comprehensive indicators. The projects are at various stages, and may address similar 
issues from different angles (paragraphs 70-75). The Commission has not yet identified 
the elements that could be put into practice for the CAP. 

90 There is therefore a significant scope for the Commission to incorporate cost-
effective advanced analytics and related tools into existing IT systems and/or other IT 
solutions for automated information processing (e.g. replacing manual or non-
reproducible procedures) and make better use of data assets (e.g. increasing data 
processing outcomes) for policy analysis. 

This report was adopted by Chamber I, headed by Ms Joëlle Elvinger, Member of the 
Court of Auditors, in Luxembourg on 18 May 2022. 

For the Court of Auditors 

Klaus-Heiner Lehne 
President 

 
 
 
 
 
 
 
 
 
Annex – Selected Commission data-related actions and 
ambitions 

 - completed 

- too early to assess or delay is less than a year 

- delay more than a year 

Topic/ challenge 

Source 
document 

Ambition/action 

Objective/purpose 

Deadline 

State of implementation 

Modernisation of 
European 
agricultural 
statistics 

Strategy for 
agricultural 
statistics for 2020 
and beyond 

New framework 
regulation for Integrated 
Farm Statistics enters into 
force in 2018 at the latest. 

Framework regulation on 
Statistics on Agricultural 
Input/Output (SAIO) in 
place by 2022. 

Launch legislative 
procedures for the 
delegated/implementing 
acts on the SAIO 
framework regulation. 

To ensure that the series of 
European farm structure surveys 
continues, thus ensuring 
consistent time series, while 
fulfilling new and emerging needs 
for data at farm level. 

To harmonise and better integrate 
statistics on inputs into agriculture 
and outputs from it (e.g. crops and 
animals, pesticides, nutrients, 
agricultural prices); to take 
account of new data needs; to 
make collected data easier to 
compare. 

Specifying data sets for SAIO. 

2018 

2022 

2021 

Regulation (EU) 
2018/1091 entered in 

force in August 2018. 

The Commission 

adopted proposal 
(COM(2021) 37) in February 
2021, currently undergoing 
the legislative process. 

Adoption of 
implementing acts 

possible after the co-
legislators adopt the main 
legal act. Estimated adoption 
of the main legal act: 2022. 

Current timeframe for 
adoption of 
implementing 
regulations is 2022-
2023. 

47 

Next steps, including 
timing 

Agricultural census 
was carried out in 
2020 and the next 
data collection is in 
2023. 

The Commission will 
launch legislative 
procedures for 
implementing and 
delegated acts under 
the framework 
regulation. 

 
 
Topic/ challenge 

Source 
document 

Ambition/action 

Objective/purpose 

Deadline 

State of implementation 

Technologies for 
data analytics 

Communication: 
Coordinated Plan 
on Artificial 
Intelligence 
(COM(2018) 795) 

and 2021 review 
(COM(2021) 205) 

Data 
sharing/making 
data open 

Communication: 
A European 
strategy for data 
(COM/2020/66) 

Amending Regulation 
138/2004 on Economic 
Accounts for Agriculture 

Commission and Member 
States seek to establish 
world-leading testing and 
experimentation sites for 
AI-powered products and 
services throughout 
Europe  

Inclusion of regional economic 
accounts (NUTS 2) 

2021 

To optimise investment and avoid 
duplication or competing efforts, a 
limited number of AI specialised 
large-scale reference sites should 
be developed and opened to all 
stakeholders across Europe. 

2020 

Preparatory action for the 
agricultural data space. 

The Commission will take 
stock of experiences 
gained with the 
stakeholder code of 
conduct on agricultural 
data sharing by 
contractual agreement, 
also on the basis of the 
current market for digital 
farm solutions and their 
requirements in terms of 
data availability and use. 

Q3/Q4 2020 

 Agreement reached, but 

not yet adopted 

The Agri-food AI Testing 
and Experimentation Facility 
for Agri-Food is included in 
the Digital Europe 
Programme’s Work 
Programme for 2021-2022. 
The call was launched in Q1 
2022. 

(Note: there is a general 
delay in implementing the 
Digital Europe Programme) 

Deadline not met. The 
body that will do this is 

yet to be set up.  

The call for the Coordinated 
and Support Action (the 
“preparatory action” was 
launched at the end of 2021 
and will close on February 
2022, with the subsequent 
evaluation of proposals and 
contracting and launch of the 
project expected in the 
course of 2022. 

48 

Next steps, including 
timing 

The results of the 
Coordinated and 
Support Action will 
inform the roll-out of 
the implementation 
action subsequently 
funded under the 
second work 
programme of the 
Digital Europe 
Programme. 

 
 
 
 
Topic/ challenge 

Source 
document 

Ambition/action 

Objective/purpose 

Deadline 

State of implementation 

49 

Next steps, including 
timing 

The results of the 
Coordinated and 
Support Action will 
inform the roll-out of 
the implementation 
action subsequently 
funded under the 
second work 
programme of the 
Digital Europe 
Programme. 

Preparatory action for the 
agricultural data space. 

(Note: there is a general 
delay in implementing the 
Digital Europe Programme) 

 Deadline not met. The 
body that will start doing this 
is yet to be established. 

Q4 2020/ 
Q1 2021 

(Note: there is a general 
delay in implementing the 
Digital Europe Programme) 

To open up key public-sector 
reference datasets for innovation, 
and make them available across 
the EU for free, in machine-
readable format and through 
standardised Application 
Programming Interfaces (APIs). 

To enhance the sustainability 
performance and competitiveness 
of the agricultural sector by 
processing and analysing 
production and other data, 
allowing for precise and tailored 
application of production 
approaches at farm level. 

As of February 2022, 
the draft act is still 

Public consultation in 
2022 

Q1 2021 

being discussed at the 
Commission 

 Too early to assess. 

2024 

The Commission will take 
stock of agricultural data 
spaces in current use, 
including those funded 
under the Horizon 2020 
programme, with 
stakeholders and MS 
organisations and then 
take a decision on an EU 
approach. 

Start a procedure to 
adopt an implementing 
act on high-value data 
sets. 

Set up an agricultural data 
space. 

Coordinated Plan 
on Artificial 
Intelligence 2021 
Review 

 
 
 
Topic/ challenge 

Source 
document 

Ambition/action 

Objective/purpose 

Deadline 

State of implementation 

To improve data collection and 
analysis on rural areas further 

Set up a Rural 
Observatory at the 
Commission to bring 
together all data it has 
collected on rural areas, 
including official statistics. 

 Too early to assess.  

The observatory will 

be created under Knowledge 
Centre for Territorial Policies.  

2022 

Decreasing 
fragmentation 
and possible 
administrative 
burden 

Communication: 
A long-term 
Vision for the 
EU's Rural Areas - 
Towards 
stronger, 
connected, 
resilient and 
prosperous rural 
areas by 2040 
(COM(2021) 345) 

50 

Next steps, including 
timing 

The first dashboards 
of the rural data 
platform are 
tentatively scheduled 
for the end of 2022. 

Analysis of links 
between CAP 
Reform and 
Green Deal 
(SWD(2020) 93) 

The Commission will 
propose legislation to 
convert the FADN into the 
Farm Sustainability Data 
Network. 

Also to collect data on ‘Farm to 
Fork’ targets and other 
sustainability indicators, in full 
compliance with data protection 
rules. 

No specific 
deadline 

No specific deadline. 
The Commission plans 

to present a legislative 
proposal in Q2 of 2022. 

Use adequate 
data for policy 
analysis 

Impact 
Assessment 
accompanying 
the post-2020 
CAP legislative 
proposals 
(SWD(2018) 301) 

New sources of data such 
as satellite monitoring 
(Copernicus), big data 
solutions, and 
cooperation with specific 
data providers should be 
put to better use. 

To reduce the burden on farmers 
and administrations, while at the 
same time improving the policy 
evidence base. 

No specific 
deadline 

Not applicable – an 
ongoing process with 
no specified end. 

The Area Monitoring 
System (AMS) will be 

introduced into the post-
2020 CAP. The AMS will make 
use of Copernicus Sentinel 
data and other data sources 
of at least equivalent value, 
such as geotagged photos, 
ortho-rectified and/or very 
high spatial resolution 
imagery. 

 
 
 
51 

Next steps, including 
timing 

The assessment for 
the AGRIVIEW system 
has started, and will 
be followed with the 
FADN. 

Topic/ challenge 

Source 
document 

Ambition/action 

Objective/purpose 

Deadline 

State of implementation 

Information 
management in 
DG AGRI 

DG AGRI Data 
Management 
Work 
Programme 
2021-2022 

To implement corporate data 
strategy. 

To promote and enable data 
sharing 

January 
2021 – 
December 
2024 

January 
2021 – 
December 
2022 

 Too early to assess. 
Assessment of ISAMM 

data policies has been 
finalised. 

 Too early to assess.  

DG AGRI has a multi-
annual plan for the portal. 

 Too early to assess. 

Country data in the form of 
analytical factsheets have 
been published. 

March 2021 
– December 
2022 

Implementation of 
corporate data 
governance principles for 
key DG AGRI data assets. 

Extend DG AGRI data 
dissemination through the 
Agri-Food data portal. 

Promote and enable data 
sharing and analytics in 
DG AGRI: 

— 

Country data 
knowledge 
portal/dashboard 

— 

Thematic 
dashboards 

Q = quarter 
Source: ECA, based on Commission documents and interviews. 

 
 
 
 
 
 
52 

Acronyms and abbreviations 

AMS: Area Monitoring System 

ATLAS: Agricultural Interoperability and Analysis System 

CATS: Clearance of Accounts Audit Trail System 

CROP-CASMA: Crop Condition and Soil Moisture Analytics 

FADN: Farm Accountancy Data Network 

FaST: Farm Sustainability Tool for Nutrients 

FSDN: Farm Sustainability Data Network 

GSAA: Geo-Spatial Aid Application 

IACS: Integrated Administration and Control System 

IFM-CAP: Individual Farm Model for the Common Agricultural Policy 

IFS: Integrated Farm Statistics 

ISAMM: Information System for Agricultural Market Management 

LPIS: Land Parcel Identification System 

LUCAS: Land Use/Land Cover Area Frame Survey 

MEF4CAP: Monitoring and Evaluation Frameworks for the Common Agricultural Policy 

NIVA: New IACS Vision in Action 

SAIO: Statistics on Agricultural Input and Output 

SEN4CAP: Sentinels for Common Agricultural Policy 

SFC: System for Fund Management 

 
 
 
53 

Glossary 

Advanced analytics: Use of high-tech methods such as predictive modelling and 
machine learning to analyse big data. 

Big data: Data sets of increasing volume, velocity and variety: the three Vs. Big data is 
often largely unstructured. 

Data: Concrete, objective facts, measurements or observations that need to be 
processed to generate information. 

Data analysis: The process of collecting, modelling, and examining data to extract 
insights that support decision-making. 

Data analytics: Science of analysing data using systematic computational methods to 
produce insights. 

Data asset: An IT system, application or database owned by an entity. 

Data gap: Any data that are required for a specific purpose but are unavailable. 

Data re-usability: The ease with which data collected for one purpose can be used for 
another. 

Database: Structured set of data stored electronically and available for consultation 
and extraction. 

Deep learning: Artificial intelligence technique that entails training a software system 
using millions of examples. 

Interoperability: Ability of a system to communicate and work with other systems, 
including by exchanging data. 

Land Use/Cover Area frame Survey (LUCAS): A regular, harmonised survey carried out 
on the spot across all EU Member States to gather information on how land is used 
and what is growing on it, including an analysis of underlying soil. 

Spatial data: Data referring to a specific location or geographical area and its natural 
or constructed features. 

Structured data: Standardised quantitative information that follows a predefined data 
structure, making it easy to analyse. 

 
 
Unstructured data: Information stored in its original format without pre-defined 
categorisation or organisation, often making it more complex to analyse. It can include 
both quantitative and qualitative information, such as images, text, dates, emails, or 
numbers. 

54 

 
 
55 

Replies of the Commission 

https://www.eca.europa.eu/en/Pages/DocItem.aspx?did=61415 

Timeline 

https://www.eca.europa.eu/en/Pages/DocItem.aspx?did=61415 

 
 
 
 
 
 
 
 
 
 
56 

Audit team 

The ECA’s special reports set out the results of its audits of EU policies and 
programmes, or of management-related topics from specific budgetary areas. The ECA 
selects and designs these audit tasks to be of maximum impact by considering the risks 
to performance or compliance, the level of income or spending involved, forthcoming 
developments and political and public interest. 

This performance audit was carried out by Audit Chamber I Sustainable use of natural 
resources, headed by ECA Member Joëlle Elvinger. The audit was led by ECA Member 
Joëlle Elvinger, supported by Liia Laanes, Head of Task; Dimitrios Maniopoulos, Deputy 
Head of Task; Ildikó Preiss, Head of Private Office; Paolo Pesce and Charlotta Törneling, 
Private Office Attachés; Emmanuel Rauch, Principal Manager; Claudia Albanese, 
Auditor and data scientist; Marika Meisenzahl, Auditor and graphic design; 
Michał  Szwed, Auditor. Mark Smith provided linguistic support. 

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Using an evidence-based approach in policy decisions requires 
various data from different sources and subsequent analysis. We 
assessed whether the Commission is making good use of data and 
data analytics for policy design, monitoring and evaluation of the 
Common Agricultural Policy, which represent more than a third of 
the EU budget. We found that the Commission has taken several 
initiatives to make better use of existing data. However, barriers to 
making the best use of collected data remain. Obstacles such as a 
lack of standardisation and limitations due to data aggregation 
reduce data availability and usability. We make a number of 
recommendations, including improving the use of disaggregated 
data from Member States. 

ECA special report pursuant to Article 287(4), second subparagraph, 
TFEU.