REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL The use of new information and communication technologies to ensure better market transparency pursuant to Article 225(dc) of Regulation (EU) No 1308/2013 (“CMO Regulation”)
Date: 2024-12-12
Institutions: Directorate-General for Agriculture and Rural Development, European Commission
Subjects: agri-foodstuffs, agricultural market, common organisation of markets, data collection, dissemination of information, impact of information technology, information system, information technology

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 12.12.2024  COM(2024) 568 final  REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL The use of new information and communication technologies to ensure better market transparency pursuant to Article 225(dc) of Regulation (EU) No 1308/2013 (“CMO Regulation”)           
 
 
1.  Introduction 
In line with Article 225(dc) of Regulation (EU)  No 1308/2013  (the  CMO Regulation)1, this 
report to the European Parliament and the Council assesses the use of new information and 
communication  technologies  (ICTs)  and  reflects  on  whether  they  ensure  better  market 
transparency. 

Market transparency helps the market to operate more efficiently by facilitating the exchange 
of  market  signals  between  the  supply  and  the  demand  side.  More  information  on  prices, 
production volumes, stocks etc., along the different stages of the supply chain would generate 
numerous advantages, such as: 

(1) extending the information that is publicly available, particularly in the intermediate stages 
of supply chains, which would increase trust in the operation of markets; 

(2) helping to reduce asymmetries in bargaining power, in particular those affecting farmers, 
which  would  level  the  playing  field  between  participants  in  terms  of  their  access  to  supply 
chains and, more generally, to markets; 

(3)  enabling  a  better  design  of  relevant  economic  policies,  going  beyond  anti-competitive 
practices2. 

This  report  is  largely  based  on  the  findings  of  a  Commission  study  on  the  role  of  ICTs  in 
improving  market  transparency  (the  ICT  study)3.  The  study  describes  and  analyses  the 
potential of different ICTs to improve the existing EU systems for data on agri-food markets, 
as well as to assess the governance aspects that would make potential improvements possible. 

2.  The EU agri-food market information system’s contribution to market transparency 

The  EU  agri-food  market  information  system  based  on  the  CMO  Regulation  includes 
ISAMM  (the  Information  System  for  Agricultural  Market  Management  and  Monitoring, 
established  by  Commission  Implementing  Regulation  (EU)  2017/1185  (the  ISAMM 
Regulation))4, market observatories and the market section of the agri-food data portal. While 
ISAMM is used for data collection, both market  observatories and the agri-food data portal 
are used for data dissemination.  

This system also includes data from several other information sources, such as Eurostat (on 
production,  prices  and  trade)  and  DG  TAXUD’s  Customs  Surveillance  System  (on  daily 
imports and exports). 

2.1. Data collection - ISAMM 

ISAMM is one of the data collection IT support systems for the Common Agricultural Policy 
(CAP).  The  market  data  is  provided  by  Member  States  and  not  directly  by  individual 
undertakings. Around 4 650 communications per month are sent via ISAMM. Information is 

1 Regulation (EU) 1308/2013 of the European Parliament and of the Council of 17 December 2013 establishing 
a  common  organisation  of  the  markets  in  agriculture  and  repealing  Council  Regulations  (EEC)  No 922/72, 
(EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007, OJ L 347, 20.12.2013, p. 671. 
2 European Commission: Joint Research  Centre  and Ménard, C., Market transparency in food supply chain  – 
Goals, means, limits, Publications Office, 2018. https://data.europa.eu/doi/10.2760/285157. 
3 ‘Study on the role of information and communication technologies to improve market transparency’, upcoming 
publication. 
4 Commission Implementing Regulation (EU) 2017/1185 of 20 April 2017 laying down rules for the application 
of Regulations (EU) No 1307/2013 and (EU) No 1308/2013 of the European Parliament and of the Council as 
regards  notifications  to  the  Commission  of  information  and  documents  and  amending  and  repealing  several 
Commission Regulations. 

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submitted via standard forms, whose content depends on the data required and the frequency 
of reporting, which ranges from weekly to yearly. 

The data sent by Member States to the Commission is often entered manually into ISAMM 
by  Member  States  officials.  Occasionally,  data  files  are  uploaded  to  complete  individual 
forms, or xml files are submitted to complete multiple forms at once. Regarding the data that 
Member  States  collect  from  the  original  sources,  documents  are  mainly  submitted  by 
operators in electronic format, via online questionnaires or forms. 

2.2 Data dissemination - Agri-food data portal and market observatories 
The markets section of the agri-food data portal5 covers a wide range of products. Most of the 
sector-specific  subsections  include:  (i)  datasets  covering  prices,  production  and  trade;  (ii) 
dashboards compiling key recent data for each sector; and (iii) a short-term outlook, featuring 
estimates for production, consumption and trade for the months ahead. 

The portal  supports  the  work  of market  observatories for  seven  agricultural  products  (milk, 
meat, sugar, arable crops, fruit and vegetables, wine and olive oil), as well as of the Fertiliser 
Market Observatory and the recently created EU agri-food chain Observatory. These market 
observatories collect information from their members, and publish information, analyses and 
minutes of board meetings on their respective web pages6. The role and working practices of 
agricultural market observatories have been described in a recent report7. 

3.  Assessment of the current agri-food market information system under the CMO 

3.1 The  CMO  agri-food  information  system  ensures  overall  market  transparency 

despite some weaknesses 

Regarding data quality, the current  system  generally provides  a  comprehensive compilation 
of data, that is deemed reliable overall by actors in the EU food supply chain8. It puts in place 
a common knowledge base to underpin economic and market analyses, which is useful when 
making  decisions  on  production  and  investment.  It  also  provides  the  necessary  data  for 
policymaking  and  further  research.  Agri-food  information  systems  in  the  main  non-EU 
countries do not significantly differ from the EU system. 

However, several margins for improvement have been identified: 

Timing and frequency of updates 

While  the  data  in  the  CMO  agri-food  market  information  system  is  updated  frequently 
(weekly,  monthly,  etc.),  the  data  entered  by  the  sources  are  sometimes  delayed  by  quality 
validation  requirements.  In  addition,  delays  in  publication  can  be  caused  by  administrative 
steps  that  are  not  automated,  such  as  data  collection  by  Member  States  from  the  relevant 
economic actors, transmission to the Commission and final dissemination. As EU-level data 
collection and dissemination relies on submissions from each Member State, some of which 
can  be  held  up  by  the  factors  mentioned  above,  EU  data  is  sometimes  published  later  than 
that of certain non-EU countries. 

Data reliability and occurrence of material errors 

5 https://agridata.ec.europa.eu/extensions/DataPortal/agricultural_markets.html. 
6 https://agriculture.ec.europa.eu/data-and-analysis/markets/overviews/market-observatories_en. 
7 Report from the Commission to the European Parliament and the Council on Union Market Observatories, 
COM(2023) 679 final of 31.10.2023. 
8 According to stakeholder interviews and surveys carried out for the ICT study. 

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Despite  the  overall  reliability  of  the  CMO  agri-food  market  information  system  and  its 
internal quality control procedures, material errors are likely to occur when human handling 
of data is required at different stages in the process. 

The  comparability  of  data  between  Member  States  can  be  uncertain  due  to  the  different 
methodologies  used9  (for  example,  one  Member  State  may  only  collect  data  from  the  most 
representative operators in the market, while another may collect them from a wider sample 
of  operators),  and  to  specific  national  characteristics  (market  structure,  different  product 
characteristics, etc.). 

Data accessibility and user-friendliness 

The vast majority of data users positively assess the overall usefulness of dissemination tools 
and platforms for agri-food information. Nevertheless, they point to a lack of clarity about the 
availability of data and about the meta-data (context, methodology of collection, processing). 
Users also expressed a need for more flexible features for the extraction/download of data. 

Non-compliance with data submission obligations 

Although most Member State authorities notify on time, some do not submit information to 
the  Commission  within  the  legal  deadlines,  which  leads  to  incomplete  data  and  delays. 
Certain Member States have not fulfilled all of their data collection obligations. The extent of 
non-compliance varies between Member States and notification types, and is more significant 
for the new data series requested as of 2021. 

Data gaps that need to be addressed 

The  system  is  less  exhaustive  for  certain  type  of  data.  For  stocks,  despite  the  recent 
notification obligation  for cereals, oilseed and oilseed products,  data  seem  to  be difficult to 
collect  as  operators  consider  such  information  subject  to  business  confidentiality.  This 
hampers  the  usefulness  of  such  data  to  inform  on  the  EU’s  food  security  status.  For  trade, 
data on intra-EU trade and re-direction of international flows from key trade hubs within the 
EU is not sufficiently incorporated in the dissemination part of the system. 

3.2. New challenges for the CMO agri-food information system 

Data on upstream and downstream sectors 

Although  some agricultural  inputs are  already covered by the agri-food  market  information 
system  (such  as  fertilisers,  which  have  their  own  market  observatory,  and  seeds  with  data 
collected  on  area,  production  and  stocks),  data  on  the  upstream  stages  of  the  supply  chain 
(input supply) as well as on the downstream stages (retail distribution, consumption) are less 
developed than those concerning production, and to a lesser extent processing, in this system. 

Since  there  are  some  other  public  and  several  private  systems  that  collect  data  on  the 
upstream and downstream stages, any extension of the CMO agri-market information system 
to  up- and downstream  sectors  should be  assessed as regards its  added  value. Any possible 
duplication of data collection and dissemination should be avoided. 

9 Article 9 of the ISAMM Regulation provides for such methodological flexibility. 

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Data in times of crisis 

It  became  clear  during  the  COVID-19  pandemic  that  in  times  of  crisis,  normal  reporting 
delays  are  too  long,  and  frequent  dissemination  of  timely  transparent  and  evidence-based 
information  and  data  is  essential  to  maintain  the  trust  and  confidence  of  actors  in  the  food 
supply chain. 

There  is  currently  no  data  stream  specific  to  a  crisis  situation  in  the  agri-food  market 
information  system.  The  European  Food  Security  Crisis  preparedness  and  response 
Mechanism (EFSCM) provides a dashboard and a platform to exchange views but does not 
cater  for  market  data  collection.  Its  Food Alert  System10  section  compiles  data  from  other 
early warning systems on weather-related events, animal diseases, energy costs, freight costs, 
etc. This mechanism bases itself on qualitative information provided voluntarily by economic 
operators rather than on data. 

Incorporating  rapid  alert  systems,  based  on  automated,  real-time  monitoring  of  critical 
availability,  accessibility  and  affordability  of  agri-food  products,  and  possibly  inputs,  at 
different  levels  of the supply  chain,  would  represent  a  significant  change  to  the CMO agri-
food market information system. 

4.  Improving agri-food market transparency through a better use of ICTs 

Developing  ICTs  would  provide  opportunities  to  address  some  of  the  shortcomings  of  the 
CMO  agri-food  information  system  and  therefore  further  improve  its  capacity  to  deliver 
market transparency. 

4.1. Optimising the existing agri-food information system 

The  use  of  specific  technological  solutions  can  make  data  collection  and  processing  more 
efficient. Notably by using already available data to the maximum extent possible, avoiding 
duplication of efforts by all actors involved and saving resources. 

The  CMO  agri-food  market  information  system  can  be  optimised  in  several  ways.  For 
example,  by  importing  data  already  available  in  other  information  systems.  This  could  be 
done by interconnecting  the Commission  information  systems  with other  systems  to  enable 
interoperability  between  all  systems  concerned.  This  requires  technological,  organisational, 
and possibly also legislative measures alongside the Interoperable Europe Act, which entered 
into force on 11 April 2024 with the aim of strengthening the cross-border interoperability of 
IT systems and public sector cooperation across the EU. 

According  to  the  stakeholders  consulted,  six  main  innovative  solutions  could  improve  the 
quality of the information provided by the agri-food market information system: 

1. 

The  use  of  ICTs  could  improve  the  current  processes  or  enable  new  automated 
systems  to  be used  for data collection  and  management (processing, storage and 
validation of data). Examples include: 

o  Putting  in  place  a  machine-to-machine  (M2M)  interface  so  that  information 
can  flow  directly  from  Member  States’  systems  to  the  Commission  (via 
ISAMM). This would contribute to both the timeliness and quality of the data 
collected  and  would  also  help  reduce  the  administrative  burden  and  costs 

10 https://agridata.ec.europa.eu/extensions/DataPortal/food-alert-system.html. 

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related to data collection and validation. To enable full M2M communication, 
both semantic and technical interoperability challenges need to be tackled11. 

o  Automating  the  data  collection  processes  from  operators  to  the  Member 
States. This could include integrating the Member States’ data collection and 
validation systems within the Commission’s system (ISAMM). 

o  Carrying  out  automated  checks  on  data,  according  to  pre-defined  parameters 
that would help ensure the reliability of data validation more systematically. 

2. 

The use of advanced  ICT tools, including  artificial intelligence (AI) tools, could 
improve publication and sharing of the collected data, by: 

links 

o  Ensuring better data accessibility and visualisation, for example by including 
direct 
on 
sources, 
collection/elaboration methods, using alternative extraction systems, providing 
more  formats  that  allow  editing,  and  creating  a  mobile  app  version  of  the 
website. All of these measures would help improve data user experience. 

information 

providing 

original 

to 

o  Providing dissemination materials tailored to the needs of key user typologies, 

thus raising awareness of the system and the available resources. 

o  Helping reduce risks of market manipulation due to ‘time sensitive’ data, that 
actors  have  access  to  before  others  and  that  can  be  used  to  take  business 
beneficial decisions (e.g. information on low stocks could incite some actors to 
advance their purchases)12. 

The role of artificial intelligence in agri-food market data collection and dissemination 
Artificial  intelligence  (AI)13  is  a  broad  concept  that  encompasses  applications  of  machine 
learning  (ML),  deep  learning  (DL),  and  generative  AI  (Gen  AI)14.  Deep  learning  is  a 
subsection of machine learning and forms the models for Gen AI. 

The  current  use  of  AI  in  agri-food  market  information  systems,  operated  by  the 
Commission,  and in  general,  is  rather limited, although  there is  awareness  of  its  potential. 
Elsewhere,  the  Agricultural  Marketing  Service  (AMS)  of  the  USDA  has  held  preliminary 
discussions on whether to incorporate AI into the system for data validation. 

One of the key AI applications being developed is the use of machine learning to assess web 
scraped  data  (e.g.  by  training  the  tool  to  recognise  items  and  classify  them  according  to 
product definitions). However, quality controls by humans may nonetheless be necessary to 
fine-tune the dataset extracted and refined with these tools. 

Examples of cutting-edge AI technology being used in other projects related to agri-market 

11 Such challenges are being addressed for example in the interoperability study carried out by the Digital 
Europe programme and the plant variety databases (Crop code list). 
12 See also Regulation (EU) No 596/2014 (Market Abuse Regulation). 
13 AI is regulated in the EU by ‘The AI Act’ (Regulation (EU) 2024/1689 laying down harmonised rules on 
artificial intelligence) which provides AI developers and deployers with clear requirements and obligations on 
specific uses of AI. 
14 See explanations of the differences between AI, ML, DL and Gen AI at: https://synoptek.com/insights/it-
blogs/data-insights/ai-ml-dl-and-generative-ai-face-off-a-comparative-analysis/ or 
https://www.sumologic.com/blog/machine-learning-deep-learning/. 

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data include: 

•  ongoing  FAO  projects  exploring  opportunities  and  challenges  in  the  use  of  AI  by 
different divisions to improve the provision and integration of remote sensing data; 

•  data  validation  and  analysis  in  the  World  Food  Programme’s  ‘Data  Bridges’ 
application which aims to quickly identify emerging food security issues, including 
early warning indicators such as the Alert for price spikes (ALPS). 

Beyond  market  data  provision,  some  private  providers  are  experimenting  with  AI 
applications  to  provide  forecasts  based  on  available  data.  For  example,  the  Swiss  Federal 
Office for Agriculture (FOAG) is carrying out AI-based forecasts for milk prices for internal 
use. 

The advancement of generative AI and the availability of user-friendly AI tools will allow 
more organisations  to develop  or use AI-based tools.  More specifically, applying AI to  an 
agri-food market information system can be a viable option for data collection, cleaning and 
analysis automation, for creating summaries and visualisations and for developing predictive 
models for crop yields, prices and demand forecasts. 

3. 

4. 

5. 

They  could  enable  to  improve  the  collection  and  processing  of  data  from  the 
sectors  indirectly  related  to  agricultural  supply  chains,  for  instance  logistics  and 
transportation  (such  as  real-time  monitoring  of  seaborne  shipments).  The 
technologies behind this type of solution are already being developed and used by 
some private data providers15 and international organisations16. 

They  could  enable  to  improve  the  collection  and  processing  of  big  data  directly 
from  farms  thanks  to  the  use  of  agri-tech  solutions  by  professional  farmers, 
including  for  precision  agriculture.  To  improve  on-site  collection  of  farm-level 
data, more work is needed on data intermediaries and the potential of digital farm 
passports/wallets.  Increasing  market  data  collection  and  further  data  sharing 
between  farmers  could  also  be  beneficial.  Developments  in  the  field  should  be 
closely monitored as they could open up new possibilities for data collection. For 
example, as  announced in the European Strategy for Data,  a Common European 
Agricultural Data Space is under development contributing to the aim of creating 
a  single  market  for  data  while  a  European  Digital  Infrastructure  Consortium  is 
being  formed  that  will  foster  the  set-up  and  use  of  so-called  digital  farm 
passports/wallets in agriculture. 

The  ability  of  ICTs  to  collect  data  on  up-  and  downstream  sectors  could  be 
boosted  by  expanding  the  practice  of  web-scraping17.  Automatic  scraping  of 
market  information  (often  by  using  AI)  from  various  online  sources,  is  an 
increasingly  used  cost-effective  method  of  collecting  data.  Especially  in 
downstream  stages,  such  as  consumption  and  retail  prices,  which  have  larger 

15 For example, supply chain data solutions for suppliers, buyers and service providers in the fresh produce 
industry. 
16  For example, for real-time tracking of vessels to monitor grain and wheat maritime trade and food security: 
https://globaltradedata.wto.org/real-time-data. 
17 Web-scraping is already used by the Commission in the European Market Observatory for Fisheries and 
Aquaculture Products (EUMOFA), which has a custom-made price scraper instrument for retail prices: 
https://eumofa.eu/. 

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online  exposure,  web-scraping  enables  the  automation  of  data  collection 
processes,  allowing  for  real-time  or  near-real-time  monitoring  of  market 
information  from  diverse  sources,  covering  a  wide  range  of  online  sources, 
including  websites,  social  media  platforms  and  online  marketplaces.  However, 
challenges  associated  with  web-scraping  include  difficulties  in  validating  data 
(only a very structured and advanced system of checks can ensure the reliability 
and  representativeness  of  data  collected)  and  legal  issues  (data  privacy,  for 
example).  For  the  upstream  sectors,  web-scraping  is  less  promising,  given  that 
reliable and representative input prices are less available online. 

6. 

On  data  at  times  of  crisis,  the  use  of  ICTs  could  enhance  real-time  data 
capabilities,  establish  dedicated  communication  channels  and  likely  improve 
predictive  analytics.  Such  data  could  be  critical  during  crises,  in  particular  if 
supported by  ad hoc  new data  collection streams and  integrated into  a dedicated 
rapid  alert  system  that  combines  data  from  a  range  of  other  IT  systems,  both 
public and private, and has trigger functions integrated. Such triggers could cover 
data  indicators  relevant  to  measure  food  availability/affordability  metrics,  for 
example critical level of stocks, transport time or delays at the border, etc. Having 
such a rapid alert system could allow business operators, and  also consumers, to 
prepare for possible supply-chain disruptions. 

4.2. Conditions for the successful development of technological solutions 

The  ICT  solutions  identified  above  may  only  be  useful  if  their  implementation  is  feasible, 
which depends on the following conditions: 

1.  A large-scale redesign of the agri-food data collection and validation systems would 
require  a  close  coordination  between  the  Commission  and  all  Member  States  and 
considerable  investment  in  technology.  This  is  particularly  relevant  in  the  case  of 
ISAMM  as  the  data  collection,  aggregation  and  validation  is  entirely  carried  out  by 
the Member States, and  therefore  relies on their commitment. This is  an area where 
the harmonisation of IT systems brings the most value added by automating processes 
and  making  them  more  effective.  This  redesign  of  data  collection  and  validation 
systems  should  not  be  done  in  isolation  and  should  not  create  parallel  or  duplicate 
systems.  It  should  tie-in  with  the  development  of  data  governance  and  data  sharing 
mechanisms in the agri-food sector, considering the benefits for greater data sharing 
in the sector and with other sectors. 

2.  The cost of setting up various IT systems with different scopes and operability must 
be assessed. The current report does not include any assessment of the costs, as there 
are  too  many  variables  for  such  a  comprehensive  analysis.  Most  stakeholders  and 
experts  have not  been in the position  to provide  cost  estimates for setting  up  the  IT 
solutions mentioned and only limited data on past setting up costs is available18. 

18 For example, the setting-up costs of the US system of high-frequency mandatory reporting of market data in 
the livestock sector, based on the 1999 Livestock Mandatory Reporting Act (LMRA), were estimated at around 
USD 2 million (1999). According to the US Agricultural Marketing Service, the operational costs of LMRA 
cover 15-20 reporters and auditors, plus USD 1 million a year in IT support. 

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3.  Data  systems  must  be  designed  with  the  user  in  mind.  The  reasons  a  user  has  for 
searching  and  using  agri-food  market  data  will  influence  their  perception  of  its 
timeliness  and  coverage.  Although  the  level  of  detail  and  frequency  of  the  data 
provided  by  the  current  systems  is  generally  enough  for  long-term  market  analysis, 
academic  research  and  policymaking,  it  tends  to  be  insufficient  for  day-to-day 
business  decision-making  or  for  complex  systems  for  making  financial  market 
transactions. 

4.  Regarding  data  submission,  business  confidentiality  data  de-anonymisation  is  a 
concern  for  private  operators.  Data  on  stocks,  marketed  volumes  and  prices  are 
considered  as  particularly  sensitive  by  business  operators  as  data  breaches  can 
compromise  their  competitive  position.  Reaching  a  balance  between  improving 
market  transparency  and  protecting  operators’  confidentiality  could  entail:  (i)  legal 
and  IT  protections  for  confidentiality;  (ii)  strengthened  dialogue  and  cooperation 
between  supply-chain  stakeholders  and  the  Commission;  (iii)  involving  non-
governmental  institutions  like  public  research  institutes;  and  (iv)  using  a  ‘privacy-
enhancing technology’ model (a depersonalised system that uses inputs and outputs to 
generate  useful  information,  without  any  knowledge  of  its  internal  workings)  in  the 
data crunching process. 

5.  Finally,  EU  legislation  on  agri-food  market  information  systems  and  on  the  use  of 
ICTs needs to be effective, up to date (for example, on confidentiality, personal data 
protection, use of AI, etc.), and applied and enforced consistently across the EU. 

5. Conclusions and further debate 

This report presents, for the purpose of a policy debate, the main features of the CMO agri-
food  information  system,  some  of  the  challenges  it  faces  and  some  potential  ICT-based 
solutions that could contribute to addressing those challenges. 

Existing  IT  technology  could  help  improve  several  aspects  of  the  EU  agri-food  market 
information.  For  example,  ICTs  could  help  expand  the  scope  of  the  current  system  by 
automating  data  collection  and  dissemination  and  extending  it  to  sectors  and  stages  not 
currently covered. Moreover, further integration of the EU and national agri-food information 
systems through machine-to-machine interaction would also contribute to the development of 
a  single  agricultural  data  space  in  Europe.  Such  integration  would,  however,  require 
considerable  financial  and  administrative  investment.  Finally,  ICTs  could  help  with  the 
collection and processing of market data during times of crisis, when data must be frequently 
updated and provided with minimum delay.  

As  cutting-edge  ICTs  or  elements  of  them  are  already  being  used  in  the  private  sector, 
fostering  public-private  partnerships  to  develop  agri-food  market  data  solutions  warrants 
further exploration. This would help improve the quality and accessibility of data in a cost-
efficient way. 

Thanks  to  technological  advances,  tomorrow’s  ICTs  have  a  huge  potential  to  improve  the 
efficiency  of  the  agri-food  market  information  system.  AI  can  help  to  automate  the  data 
processing workflow, making it faster, more accurate and more efficient. AI algorithms and 
machine learning can manage large volumes of data, making sense of complex patterns that 
humans might  not  identify. While still under-used in  the field  of  agri-food market  data, the 
role of AI will certainly grow in the future. 

8