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D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
1
Building a European Framework
for the secure and trusted data space for agriculture
D2.1: Multi-stakeholder Governance
Scheme and Business Models for
Agricultural Data Spaces
Governance Scheme and Business Models of a Common European
Agricultural Data Space
Lead Authors:
Marlene Eisenträger, Inessa Seifert (VDI/VDE-IT) – Governance
Dimitris Fotakidis, Giannis Firogenis (FoodScaleHub) – Business Models
Contributors:
Maximilian Lindner (VDI/VDE-IT)
Marieke Rohde (VDI/VDE-IT)
Johanna Simon-Lehmstedt (VDI/VDE-IT)
Sebastian Straub (VDI/VDE-IT)
Benedict Wenzel (VDI/VDE-IT)
Can Atik (WR)
Marc-Jeroen Bogaardt (WR)
Thomas Gomez (ADH)
Review
Sjaak Wolfert (WR)
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
2
Document Information
Grant Agreement No.
101083401
Project Acronym
AgriDataSpace
Project Title
Building a European framework for the secure and trusted data
space for agriculture
Type of action
DIGITAL-CSA
Call
DIGITAL-2021-PREPACTS-DS-01
Start – ending date
1 October 2022 – 30 April 2024 | 19 months
Project Website
https://agridataspace-csa.eu/
Work Package
WP2: Building blocks for profitable and responsible data space in
agriculture
WP Leader
Stichting Wageningen Research (WR)
Deliverable type1 |
Dissemination level2
R | PU
Due Date
30 November 2023
Submission Date
29 March 2024
Lead Authors
Marlene Eisenträger, Inessa Seifert (VDI/VDE-IT) – Governance
Dimitris Fotakidis, Giannis Firogenis (FoodScaleHub) – Business
Models
Contributors
Maximilian Lindner (VDI/VDE-IT)
Marieke Rohde (VDI/VDE-IT)
Johanna Simon-Lehmstedt (VDI/VDE-IT)
Sebastian Straub (VDI/VDE-IT)
Benedict Wenzel (VDI/VDE-IT)
Marc-Jeroen Bogaardt (WR)
Can Atik (WR)
Review: Sjaak Wolfert (WR)
Disclaimer
Funded by the European Union. Views and opinions expressed, however, are those of the
author(s) only and do not necessarily reflect those of the European Union or European
Commission-EU. Neither the European Union nor the granting authority can be held
responsible for them.
Copyright message
This document contains unpublished original work unless clearly stated otherwise. Previously
published material and the work of others has been acknowledged by appropriate citation or
quotation, or both. Reproduction is authorised provided the source is acknowledged.
1
R: Document, report; DEM: Demonstrator, pilot, prototype, plan designs; DEC: Websites, patents filing, press & media actions,
videos, etc.; DATA: Data sets, microdata, etc.; DMP: Data management plan; ETHICS: Deliverables related to ethics issues;
SECURITY: Deliverables related to security issues; OTHER: Software, technical diagramme, algorithms, models, etc.
2
PU – Public, fully open, e.g. web (Deliverables flagged as public will be automatically published in CORDIS project’s page); SEN
– Sensitive, limited under the conditions of the Grant Agreement; Classified R-UE/EU-R – EU RESTRICTED under the
Commission Decision No2015/444; Classified C-UE/EU-C – EU CONFIDENTIAL under the Commission Decision No2015/444;
Classified S-UE/EU-S – EU SECRET under the Commission Decision No2015/444
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
3
Document Updates:
Additional feedback from the Stakeholder Meetups and Advisory Board Meetings (14.12.2023,
16.01.2024, 25.01.2024, 7.03.2024) and comments on the draft of this document from the
European Commission and the Advisory board is included in this revised version of the
deliverable.
The comments of the European Commission concerned the following questions:
Governance:
1. What are the tasks and the role of the member states in Common European Data Spaces
according to the DA and DGA?
2. Do DA and DGA mention model contracts with data sharing parties? Are the member
states involved in consultations regarding these model contracts, or providing support in
synergies between different public authorities: European Innovation Board etc.?
3. Can a public authority join CEADS as a Data Sharing Initiative and provide public data?
Business Models:
1. What are the main incentives for the key stakeholders to join the CEADS?
2. Which business scenarios are possible for the key stakeholders, when operating the
CEADS:
3. Will farmers be able to set up, invest and operate a CEADS?
4. Will land machinery providers be able to set up, invest and operate a CEADS?
5. Will public authorities be able to operate a CEADS and which services will they offer: e.g.
monitoring of food production and reporting from farmers? Are these services sufficient for
the CEADS?
6. You already provided business models for DSIs, are there more options that we can
discuss?
7. Which business models can scale?
8. Who will be ready to invest in CEADS?
9. We voted for DSIs who are the main stakeholders of the CEADS and are ready to invest.
What are the main benefits and value proposition for DSIs?
This document is the final revised version.
The questions concerning the business models and corresponding options for the governance
schemes are mainly addressed in Chapter 5; the role of the member states and the update of
the recommended governance structure is in Chapter 6.
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
4
AgriDataSpace Consortium
Participant organisation name
Short name
Country
1
AGDATAHUB
ADH
FR
2
FOODSCALE HUB GREECE ASSOCIATION FOR
ENTREPRENEURSHIP AND INNOVATION ASTIKI MI
KERDOSKOPIKI ETAIREIA
FSH
EL
3
INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ
AKADEMII NAUK
PSNC
PL
4
UNIVERSIDAD DE LLEIDA
UdL
ES
5
EIGEN VERMOGEN VAN HET INSTITUUT VOOR
LANDBOUW- EN VISSERIJONDERZOEK
EV ILVO
BE
6
FONDAZIONE BRUNO KESSLER
FBK
IT
7
VDI/VDE INNOVATION + TECHNIK GMBH
VDI/VDE-IT
DE
8
STIFTUNG FACHHOCHSCHULE OSNABRUECK
HSOS
DE
9
STICHTING WAGENINGEN RESEARCH
WR
NL
10
1001 LAKES OY
1001 Lakes
FI
11
ASOCIATIA NATIONALA A INDUSTRIILORDE MORARIT SI
PANIFICATIE DIN ROMANIA
ANAMOB
RO
12
COMITE EUROPEEN DES GROUPEMENTS DE
CONSTRUCTEURS DU MACHINISME AGRICOLE
CEMA
BE
13
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER
ANGEWANDTEN FORSCHUNG EV
Fraunhofer
DE
14
AGRICULTURAL INDUSTRY ELECTRONICS
FOUNDATION AEF
AEF
DE
15
FEDERATION NATIONALE DES SYNDICATS
D'EXPLOITANTS AGRICOLES
FNSEA
FR
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
5
Glossary of terms and abbreviations used
List of Abbreviations and Acronyms
CEDS
Common European Data Spaces
CEADS
Common European Agricultural Data Space
DSI
Data Sharing Initiative
DA
Data Act
DGA
Data Governance Act
OECD
Organisation for Economic Cooperation and Development
SLA
Service Level Agreement
GDPR
General Data Protection Regulation
PSD2
2nd Payment Services Directive
eIDAS
Electronic Identification, Authentication and Trust Services
ISO 27001
Information technology – Security techniques – Information security
management systems – Requirements
ISO 10303
Standard for the computer-interpretable representation and exchange
of product manufacturing information
eCi@ss
Global reference data standard for the classification and unambiguous
description of products and services
IDSA
Industrial Data Spaces Association
DKE
Deutsche Kommission Elektrotechnik Elektronik Informationstechnik in
DIN (Deutsches Normungsinstitut)
SME
Small and Medium Enterprise
DSSC
Data Spaces Support Centre
Sitra
Finish funding and innovation agency
SDBM/R
Service Dominant Business Model Radar
SaaS
Software as a Service
EDIC
European Digital Infrastructure Consortium
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
6
Executive Summary
With the call for preparation of a Common European Agricultural Data Space (CEADS), the
European Commission aims at leveraging business opportunities of data-driven value chains
in the agricultural sector and connect with data spaces in other sectors. In line with the current
digitalization efforts in agriculture, the landscape of so-called Data Sharing Initiatives (DSIs) in
Europe is rapidly evolving. Many regional, national and also international initiatives emerged,
to support the data sharing in the agricultural value chain and to provide data-based services.
Based on the guidelines for developing business models and data spaces from the literature,
legal frameworks, the Data Governance Act and Data Act, key insights from the interviews with
selected DSIs mapped in Work Package 1 and analysis of their web pages regarding business
models and service offerings, the main results of the deliverable are the following:
1. requirements for the business models and governance based on DA/DGA, and the
analysis of selected DSIs
2. recommendations for business, revenue models and services for the CEADS
3. suggestion of a business model for the CEADS
4. design principles and options for a multi-stakeholder governance scheme including
a. organisational governance
b. organisational structure of CEADS and onboarding of new members
c. data sharing governance
d. governance of services for DSI interoperation
5. international governance for cross-border data exchange
6. collaborations between Common Data Spaces
The core requirements for the CEADS are the following:
Governance:
• CEADS should not act as a central data intermediary. Each of the analysed DSIs
represents an own data ecosystem with a unique set of (key) stakeholders and
unique set of challenges that have to be addressed to successfully operate a DSI, be
it regionally specific regulation, technical interoperability, or gaining the trust of the
ecosystem stakeholders. A centralized entity will not be able to provide services with
a comparable quality and reliability and incorporate all locally important aspects.
• CEADS is a facilitator for DSI collaborations. Many interviewed DSIs already plan to
collaborate across borders and across parts of the same value-chains for a free flow
of data and a maximum of added value for all. There are already successful
collaborations ongoing and plans to extend these inter-DSI collaborations were
reported. There is a strong value proposition in a pan-European initiative to
coordinate and support DSI-collaborations and joint the individual efforts.
• CEADS should involve Member States, particularly data coordinators, who are
responsible for ensuring compliance, handling complaints, conducting regulatory
investigations and imposing sanctions for violations and resolve disputes.
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
7
• CEADS should grow its governance iteratively. A DSI’s governance grows alongside
its business model and its user base, following the changing needs of the DSI’s key
stakeholders. The same logic should apply to CEADS, as umbrella organisation.
The operating organisation should be credibly neutrally positioned in the market.
Representatives of all important sectors: e.g. from diary, livestock to agricultural
machinery should be able to have their voice heard in decision-making. Business
practices of the CEADS should be transparent to its members.
• CEADS should offer interoperability support for DSI’s data sharing governance. The
biggest challenges for inter DSI-collaboration lie in the interoperability of their
respective data exchange services. The most advanced inter DSI-collaborations
reported that they develop their services against the specifications of the Gaia-X
framework. However, even within this concise and well-accepted framework, there
are a lot of remaining issues for making DSI interoperability work in concrete cases,
e.g., when joining or combining data catalogues or service registries.
• CEADS’ operations should be independent of dominant market players and public
authorities. Even though the European commission will indirectly benefit from a free
flow of agricultural data across agricultural stakeholders and will also be able to
indirectly access this data as part of legally required documentation, a direct
involvement or direct data access would put CEADS perceived trustworthiness at risk
and should thus be avoided.
• CEADS should facilitate on-boarding of early-stage DSIs from an economic
perspective with best-practices from mature DSIs with training and tutoring.
Business models:
• CEADS should offer a hybrid business model, incorporating elements from a Data
Marketplace and Open Data Policy business models.
• CEADS should act as a trusted facilitator of cooperation, bringing together different
DSIs on a secure platform under a Multi-Stakeholder Governance scheme where the
various DSIs can connect and share data utilising interoperability mechanisms for
identity management and data catalogues.
• The CEADS should embrace an Open Data Policy, following a freemium revenue
model, allowing certain datasets to be freely shared to encourage the development of
innovative products and services but also provide mechanisms for increasing data
sharing between the DSIs, creating an extra revenue stream for them.
• CEADS business model should strike a balance between commercial viability through
increased data sharing and contributing to the wider ecosystem by encouraging open
data collaboration.
• CEADS should provide a shared and inclusive administrative and data governance.
The analyzed and interviewed DSIs are predominantly intermediaries according to the Data
Governance Act, and are therefore obliged to fulfil the strict requirements resulting from this
regulation. While the CEADS itself will not be a data intermediary, the services it will offer to
its member DSIs should adhere to the rules and principles laid down in the Data Governance
Act, as most or all of its participants will be obliged to follow these rules and regulations for
data intermediary services.
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
8
The following figure illustrates the initial organisational structure of the CEADS which is aligned
with the requirements from the DSIs, business model for CEADS, the guidelines provided by
Data Spaces Support Centre and with the current European legal framework.
Figure 1: Overview on the organisational structure of the CEADS
The Data Governance Act requires that data-intermediaries ensure non-discriminatory, fair and
transparent access to the data intermediary services. This principle of non-discriminatory, fair
and transparent access should likewise be implemented in CEADS’ organizational
governance:
• Participants: the CEADS should be open to any DSI or organization interested in its
services.
• Members of the General Assembly: at the beginning, only European DSIs found the
initial entity of the CEADS to set up rules and values of the European Union that would
attract and spread to international companies, DSIs and SMEs. Rules on access for
participants should be elaborated as part of the evolutionary process in the corresponding
working group, e.g., based on neutrality, openness and a focus on creating value for
farmers. DSIs that are publicly funded and operated by public authorities can be
shareholders of the CEADS.
• Management team: is responsible for the organisation and set up of the working groups,
support of the general assembly and the Advisory Board. In addition to the internal
organisation, the management team is responsible for the communication with the data
coordinators that are appointed by the Member States.
• The Advisory Board (as well as possible sub-units) should be led by representatives of
the General Assembly, but open to representatives of all members as well as
representatives of non-member organizations of the value-chain, from suppliers to farmers
and agricultural companies up to public sector, particular data coordinators. As part of the
evolutionary process, rules ensuring the equal representation of all stakeholders in the
agricultural sector should be implemented.
• The Working groups should be led by representatives of the shareholding DSIs. The
working groups should be open to representatives of other members or external
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
9
organizations (e.g. to machine manufacturers for a working group on handling machine
data or to representatives of standardization organizations or DSIs from other sectors) if it
suits the working group’s purpose. The initial topics of the Working groups include
interoperability recommendations for identity management, data catalogues, legal
contractual agreements and business models. A compatibility grid for the member DSIs
will allow for an efficient search and comparison of service offerings and corresponding
interoperability check that will facilitate data-sharing and provision of data-based services
between the member DSIs.
CEADS will contribute to create a single European market for data with a level playing field to
foster innovation, growth, and competitiveness as proposed in the Digital Market Act.
The services of data exchange, including the exposition of data products, the creation of a
contract between data provider and data user, obtaining farm’s permission to exchange its
data, the payment system, etc. are provided by DSIs and will be used to operate a single
“decentralized” data market.
The proposed Business Model and Multi-Stakeholder Governance Scheme are providing a
target for the development of a roadmap for the deployment and operation of the CEADS (Work
Package 4).
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
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Table of Contents
Executive Summary ........................................................................................ 6
1. Introduction ................................................................................................ 14
2. Developing Business Models and Multi-Stakeholder Governance
Scheme for a Common European Agricultural Data Space ...................... 18
2.1. Multi-stakeholder Business Models .................................................................18
2.1.1. Defining the Business Model Concept ................................................................. 18
2.1.2. Business Model’s Life Cycle ................................................................................ 19
2.1.3. Discovering the multi-sided and collaborative nature of Business Models ........... 20
2.1.4. Business and Revenue Models ........................................................................... 22
2.2. Multi-stakeholder Governance Scheme ..........................................................24
2.2.1. Terms and Scope regarding Governance ............................................................ 24
2.2.2. Governance Models and Guidelines .................................................................... 27
2.2.3. Legal framework: Data Governance Act and Data Act ......................................... 37
2.2.4. The role of Member States in promoting the CEADS in the context of current EU
data legislation .............................................................................................................. 42
2.2.5. Collaboration between Data Sharing Initiatives .................................................... 42
2.3. EU Code of Conduct for agricultural data sharing ...........................................44
3. Approach and Methodology ..................................................................... 45
3.1. Business Models .............................................................................................45
3.2. Analysis framework for governance schemes .................................................57
3.2.1. Example profile of a DSI (365FarmNet) ............................................................... 57
3.3. Analysed Data Sharing Initiatives ....................................................................61
4. Results of the Analysis of Business and Governance Models ............. 63
4.1. Synthesis of Agricultural Data Sharing Initiatives ............................................63
4.1.1. Results on the Governance for DSIs ................................................................... 63
4.1.2. Results on the Business Models for DSIs ............................................................ 69
5. Business Model for the CEADS ............................................................... 74
5.1. Co-design process ..........................................................................................74
5.1.1. First cycle of business models development ........................................................ 74
5.1.2. Second cycle of business models development ................................................... 79
5.2. Examination of the CEADS proposed services ...............................................82
5.3. Business models validation .............................................................................85
5.3.1. 1st Validation with stakeholders............................................................................ 86
5.3.2. 2nd Validation with stakeholders ........................................................................... 86
5.3.3. Alignment with DSCC .......................................................................................... 86
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
11
5.3.4. Validation with EU Member States ...................................................................... 87
5.4. Collaborative Business Models for the CEADS ...............................................87
5.4.1. Validated aspects of the CEADS Business Models ............................................. 87
5.4.2. Benchmarking business models .......................................................................... 88
5.4.3. Incentives to join the CEADS ............................................................................... 89
5.4.4. Business scenarios and models .......................................................................... 91
5.4.5. Benefits and value proposition for the DSIs ......................................................... 95
5.4.6. Business model proposition for the CEADS ......................................................... 97
6. Multi-Stakeholder Governance Scheme for CEADS ............................ 101
6.1. Principles for the Governance Scheme .........................................................101
6.1.1. Governing Principles and CEADS Mission, Ecosystem and Services ................ 101
6.1.2. Framework for governance building blocks ........................................................ 103
6.2. Organisational Governance ...........................................................................105
6.2.1. Organisational mode ......................................................................................... 106
6.2.2. Role of the Member States in the Data Governance Act .................................... 106
6.2.3. Role of the Member States in the Data Act ........................................................ 108
6.2.4. Organisational bodies ........................................................................................ 112
6.2.5. Participants ....................................................................................................... 114
6.2.6. Collaboration ..................................................................................................... 116
6.2.7. Onboarding ....................................................................................................... 116
6.3. Data Sharing Governance .............................................................................117
6.3.1. Data Sharing Governance Principles for CEADS ............................................... 117
6.3.2. Governance of Services for shared and inclusive administrative and data
governance ................................................................................................................. 119
6.3.3. International governance for cross-border data exchange – Collaborations
between Common Data Spaces .................................................................................. 120
6.4. Summary and Outlook ...................................................................................123
Literature ...................................................................................................... 124
Annex I: Clustering of the DSIs’ stakeholders .......................................... 127
Annex II: Results from the 1st Business Model Workshop ...................... 135
Annex III: Results from the 2nd Business Model Workshop .................... 144
Annex IV: Profiles of the Data Sharing Initiatives .................................... 147
IV.1. Overview of the Analysed Data Sharing Initiatives ......................... 147
IV.2. Business Models and Governance Schemes of the analysed DSIs
....................................................................................................................... 148
IV.2.1 Descriptions of Data Sharing Initiatives .....................................................148
IV.2.1.1. 365FarmNet GmbH ....................................................................................... 148
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
12
IV.2.1.2. Agdatahub ..................................................................................................... 153
IV.2.1.3. AgIN / AEF e.V. ............................................................................................. 158
IV.2.1.4. Agricolus ....................................................................................................... 162
IV.2.1.5. Agri-Gaia ....................................................................................................... 165
IV.2.1.6. Agrimetrics .................................................................................................... 168
IV.2.1.7. Agrirouter ...................................................................................................... 170
IV.2.1.8. AgroDataCube .............................................................................................. 174
IV.2.1.9. Altas .............................................................................................................. 176
IV.2.1.10. AVR Connect .............................................................................................. 178
IV.2.1.11. Cipher Trust Data Security Platform ............................................................ 181
IV.2.1.12. COGNAC .................................................................................................... 183
IV.2.1.13. DjustConnect .............................................................................................. 185
IV.2.1.14. Eden Library ................................................................................................ 190
IV.2.1.15. Hortivation Hub ........................................................................................... 193
IV.2.1.16. iDDEN ......................................................................................................... 196
IV.2.1.17. John Deere Operations Centre .................................................................... 201
IV.2.1.18. JoinData ...................................................................................................... 205
IV.2.1.19. ProAgrica .................................................................................................... 210
IV.2.1.20. ZEROW....................................................................................................... 212
List of Figures
Figure 1: Overview on the organisational structure of the CEADS ..........................................8
Figure 2: Approach to developing a Multi-Stakeholder Governance Scheme and Business
Models for a Common European Agricultural Data Space (CEADS) .................................... 15
Figure 3: Open DEI Soft Infrastructure ................................................................................. 29
Figure 4: Overview of Building Blocks (IDSA 2023) .............................................................. 31
Figure 5: Overview on the Organisational and Business Building Blocks defined by the Data
Spaces Support Centre from the Blueprint (Version 0.5 from October 2023, p.11) ............... 32
Figure 6: Key decision-making points for Data Space legal body (source DSSC) ................. 34
Figure 7: Methodological Action Plan ................................................................................... 46
Figure 8: Business model radar ............................................................................................ 47
Figure 9: Clustering of the DSIs’ stakeholders ...................................................................... 49
Figure 10: Value Chain Network example............................................................................. 53
Figure 11 The most useful Business Model options for CEADS from the first workshop ....... 76
Figure 12 The most useful Revenue Models for CEADS from the first workshop ................. 78
Figure 13 Data-sharing incentives the participants found to be able to motivate them to
participate in a Common Data Space ................................................................................... 79
Figure 14 Most useful Business Model options for CEADS from the second workshop ........ 81
Figure 15 Most useful Revenue Models for CEADS from the second workshop ................... 82
Figure 16 Services the participants would like to see from a Common Data Space .............. 83
Figure 17 CEADS services proposition ................................................................................. 85
Figure 18 CEADS Service Dominant Business Model Radar................................................ 99
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
13
Figure 19: Framework for the design of the CEADS governance scheme .......................... 105
Figure 20: Requirements on the Organisational Governance for the CEADS level ............. 105
Figure 21: Overview on various governance models for networks (Tijs van den Broek and
Anne Fleur van Veenstra 2015) .......................................................................................... 106
Figure 22: Overview on the organisational structure of the CEADS .................................... 116
Figure 23: 365FarmNet – Business model radar ................................................................ 152
Figure 24: AgDataHub – Business model radar .................................................................. 157
Figure 25: Agricolus – Business model radar ..................................................................... 165
Figure 26: Agrimetrics – Business model radar .................................................................. 170
Figure 27: Agrirouter – Business model radar .................................................................... 174
Figure 28: AgroDataCube – Business model radar ............................................................. 176
Figure 29: AVR Connect – Business model radar ............................................................... 180
Figure 30: Cipher Trust Data Security Platform – Business model radar ............................ 182
Figure 31: Djustconnect – Business model radar................................................................ 190
Figure 32: Eden Library – Business model radar ................................................................ 193
Figure 33: Hortivation Hub – Business model radar ............................................................ 196
Figure 34: IDDEN – Business model radar ......................................................................... 201
Figure 35: John Deere digital suite ..................................................................................... 204
Figure 36: John Deere Operations Centre – Business model radar .................................... 205
Figure 37: JoinData – Business model radar ...................................................................... 209
Figure 38: ProAgrica – Business model radar .................................................................... 212
List of Tables
Table 1: Overview of Organisational and Operational Building Blocks that support the
technical building blocks in creating interoperability and trust between all stakeholders ....... 30
Table 2: Overview and some main points regarding steering committee provisions as part of
the template for a governance model, given in the Sitra rulebook. ........................................ 35
Table 3: Customers’ Relation to Value Streams ................................................................... 50
Table 4: Partners’ Relation to Value Streams ....................................................................... 50
Table 5: Suppliers’ Relation to Value Streams ...................................................................... 51
Table 6: Operator(s)’ Relation to Value Streams .................................................................. 51
Table 7: Stakeholders categorisation .................................................................................... 52
Table 8: Overview on analysis of governance and business models for agricultural data
sharing initiatives .................................................................................................................. 61
Table 9: Participants in the 1st business model workshop ..................................................... 74
Table 10: Participants in the 2nd business model workshop .................................................. 80
Table 11: Benchmarking business models ........................................................................... 88
Table 12: Business models scalability potential .................................................................... 88
Table 13: Incentives to join the CEADS ................................................................................ 90
Table 14: Business scenarios per stakeholder category ....................................................... 91
Table 15: Actors and value propositions for various governance schemes ........................... 92
Table 16: Business models per governance scheme ............................................................ 94
Table 17: Structure of the CEADS Working Groups ........................................................... 120
Table 18: Overview on analysis of governance and business models for agricultural data
sharing initiatives ................................................................................................................ 147
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
14
1. Introduction
According to the European data strategy of February 2020 (European Commission 2020b) ,
data spaces will play a leading role in the economic exploitation of data for various industries.
It announced the creation of data spaces in ten strategic fields, including agriculture. The aim
is to unlock the value of data by removing barriers and digitally connecting objects in Europe.
The initiative for Common European Data Spaces (CEDS) accompanies the passing of new
regulations, which clarify how data can be used, shared and accessed in a fair and trustworthy
manner.
For agriculture, the importance of data cannot be overestimated, “moving into an era of digitally
enhanced farming, where data is generated during the various stages of agricultural production
and all related operations” (European Feed Manufacturers' Federation 2020). Digital farming
solutions will contribute to the required sustainable food production in context of climate
change.
With the call for preparation of a Common European Agricultural Data Space (CEADS), the
European Commission aims at leveraging business opportunities of data-driven value chains
in the agriculture sector. In line with the current digitalisation efforts in the agricultural industry,
the landscape of so-called Data Sharing Initiatives in Europe is rapidly evolving. Many regional,
national and also international initiatives emerged, to support the data sharing in the
agricultural value chain and to provide data-based services.
The objectives of the AgriDataSpace project are the following:
Objective 1: map the current landscape and take stock of ongoing data sharing
initiatives and design approaches in agriculture, including experience with the
European Code of Conduct
• Objective 2: analyse and assess current governance models and approaches in
this landscape and develop a multi-stakeholder governance scheme for the
CEADS
• Objective 3: analyse and assess current business models and approaches in this
landscape and explore potential business models for various stakeholder
relations, addressing both economic and environmental performance
• Objective 4: explore the evolving legislative framework and derive its implications for
the design approaches of the Agriculture Data Space and account for its shortcomings
by providing solutions and technical enablers for ethical tensions related to data
sovereignty
• Objective 5: develop a conceptual reference architecture for a common data space
framework in agriculture and a reference technology canvas for navigating through
heterogeneous repositories, ensuring backward compatibility with most prominent
existing initiatives
• Objective 6: engage stakeholders in various activities for evaluation and
validation in order to reach broad consensus and support for the development
of the Agriculture Data Space
• Objective 7: develop a roadmap that compiles all requirements and needed actions into
a comprehensive pathway towards implementation of the EU Agriculture Data Space
D2.1: Multi-stakeholder Governance Scheme and
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15
This document contains the results of Objectives 2 and 3: analysis of the governance and
business models based on the DSIs selected from Objective 1: mapping of the current
landscape. It highlights the design options for a multi-stakeholder governance scheme of the
CEADS. The resulting design options for governance describe the recommended target for
Objective 7: a Roadmap that will address the development and operation phases needed for
the implementation of the CEADS.
The consortium of the AgriDataSpace project involves important players of the data-driven
agricultural value chain, for example, Data Sharing Initiatives (Agdatahub, DjustConnect), a
Data Spaces technology provider (1001 Lakes) as well as industry associations (CEMA, AEF)
that represented and contributed with their views on the future of European data economy in
the agricultural sector. The involved scientific institutions and the VDI/VDE-IT funding and
innovation agency provided their expertise in the design and implementation of innovation
processes as well as in the analysis of ecosystems and the involvement of stakeholders.
Following Objective 6, the resulting concepts and recommendations for the multi-stakeholder
governance scheme, business models and services of CEADS were intensively assessed and
validated with all 15 partners of the AgriDataSpace consortium in various discussion panels.
External experts and stakeholders from the agricultural sector as well as representatives of the
European Member States provided additional feedback in webinars and workshops. The
feedback was incorporated into the design options, business models and services of CEADS.
Figure 2 illustrates the approach taken in this Deliverable 2.1.
Figure 2: Approach to developing a Multi-Stakeholder Governance Scheme and
Business Models for a Common European Agricultural Data Space (CEADS)
Interaction with Data Sharing Initiatives, Stakeholders and Member States
Literature
•Organisational
Governance
Data Sharing
Governance
•Collaboration of DSIs
•Hybrid business model
with elements from a
Data Marketplace and
Open Data Policy
•Analysis Framework
•Selection of DSIs
•Value Chain networks
and Business Models
•Synthesis of Business
Models and
Governance Schemes
of the selected DSIs
•Principals and Terms:
•Data Space Support
Centre Blueprint
•SITRA Rule Book
•Data Act and Data
Governance Act
•Business Models
•Value Chain networks
•Business Model Radar Approach to
Developing Business
Models and Gov.
Schemes for Data
Spaces
Analysis Framework
for Governance
Schemes and
Business models of
DSIs and Results
Design Options for
Multi-Stakeholder
Governance Schemes
Business Model
for the CEADS
Vision of a Common European Agricultural Data Space
Chapter 2
Chapter 3
Chapter 4
Chapter 6
Annex: Data Collection: Profiles of
Governance Schemes and Business
Models of DSIs
Chapter 5
D2.1: Multi-stakeholder Governance Scheme and
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At the beginning of the project, the AgriDataSpace Consortium developed a vision for the
Common European Agricultural Data Space, which is used for the common understanding of
the objectives and the expected results of the project among the consortium partners. The
resulting vision for CEADS is summarised as follows.
WHY = Purpose
We, the AgriDataSpace project, strongly believe that there are big opportunities for new
value creation and operational efficiencies for European agri-food stakeholders by exploiting
the available data.
HOW = Process
We strive for a network of interoperable data spaces (Data Sharing Initiatives) with business
models in the areas of data economy, responsible data sharing, digital inclusion, integrative
artificial intelligence and cross-sector integration. The decentralised approach will reduce
complexity and provide an easy entry point for the stakeholders of the value chain. The
interests of farmers will be the focus of this decentralised approach.
WHAT = Result
We will develop guidelines for a data space which can be easily adopted by companies. We
will define a set of procedures to explain and implement the legal, business, technical and
governance issues. We will identify and recommend requirements for an organisation (the
future CEADS) that will facilitate and monitor the adoption of these guidelines.
CEADS can be seen as an association of Data Sharing Initiatives. The main task of the data
space is to establish interoperability between existing solutions. To ensure the acceptance of
existing DSI/stakeholders, the future data space services must be complementary to the DSI
services to:
• Avoid competition between DSIs on the micro level
• Ensure a certain level of freedom of governance (business model, technological
solution, etc.) at DSI level
• Facilitate the exchange of data between DSIs
• Maintain national and federal specificities (legal context, organisation of farmers, user
support, etc.)
The AgriDataSpace project will make recommendation for the initial set of services that CEADS
should provide to ensure the interoperability of DSIs. The list of services is likely to evolve as
the data space is rolled out and specific requirements for standardisation and collaboration
emerge.
The initial set of expected services is the following:
• Shared and inclusive administrative and data governance
• Facilitating the access to Open Data.
• Identity interoperability, promoting seamless recognition and authentication between
different Data Space actors.
• Data catalogue interoperability, which ensures that diverse and disparate data sets
can be used together.
D2.1: Multi-stakeholder Governance Scheme and
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CEADS will contribute to create a single market for data with a level playing field to foster
innovation, growth, and competitiveness, as proposed in the Digital Market Act.
The services of data exchange, including the exposition of data products, the creation of a
contract between data provider and data user, obtaining the farmer’s permission to exchange
his data, the payment system, etc. will be provided by the DSIs and used to operate a single
“decentralised” data market.
The target audience for this document are company decision makers from the agricultural
sector, operators of Data Sharing Initiatives, policy decision makers (e.g., the European
Commission and Member States), who are shaping the European data economy using funding
and regulatory instruments, as well as data spaces specialists from industry and experts from
academia.
Starting with an introduction in this first chapter, Chapter 2 will describe our approach and
methodology, starting with terms and definitions from the literature on guidelines for developing
governance and business models for data spaces as well as legal aspects: Data Act and
Governance Act that need to be considered when implementing a common European data
space. Various DSIs, mapped in Work Package 1, are already evolving and providing
commercial data sharing services for farmers and agricultural machinery vendors and
suppliers. Following the idea of creating a network of interconnected DSIs, Chapters 3
describes the analytical framework for the analysis of their governance schemes and business
models, requirements and value proposition for their cooperation.
Based on the idea of creating a network of interconnected DSIs, Chapter 3 describes the
analytical framework for analysing their governance systems and business models as well as
the requirements and value proposition for their collaboration.
Chapter 4 outlines the synthesis of the business models adopted by the existing and
functioning DSIs and provides recommendations for business models and service offerings for
the Common European Agricultural Data Space. Annex I includes the profiles of the selected
and analysed DSIs for the interested reader. Chapter 5 pinpoints along with the stakeholder
interaction during the validation process a favorable business model for CEADS. Using the
results of the analysis and validation with internal and external stakeholders, Chapter 6
introduces the design options for multi-stakeholder governance for organizational governance,
data sharing governance and collaboration between the DSIs.
A comparison with multi-stakeholder schemes of other sectors is not the subject of the
deliverable and this project. It is aligned with the blueprint documents provided by the DSSC
and is focused on the service offerings, governance aspects and requirements for the
agricultural sector only.
D2.1: Multi-stakeholder Governance Scheme and
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2. Developing Business Models and Multi-
Stakeholder Governance Scheme for a Common
European Agricultural Data Space
2.1. Multi-stakeholder Business Models
The aim of this chapter is to provide a foundation of knowledge on multi-stakeholder business
modelling, and in particular to highlight the intricacies of service-oriented business models, as
well as the most prominent business and revenue models for DSIs. Section 2.1.1 provides a
broad definition of business models in general, while Section 2.1.2 gives an overview of the
life cycle of a business model. Section 2.1.3 focuses specifically on the multi-sided and
collaborative nature of business models, which is more relevant for the functioning of DSIs.
Finally, Section 2.1.4 takes a closer look at the business and revenue models most commonly
used in data-driven environments and more specifically in DSIs.
2.1.1. Defining the Business Model Concept
In the evolving landscape of digital transformation in agriculture, the concept of data spaces
has emerged as a pivotal force reshaping the way all stakeholders harness the power of
information. As businesses navigate the complexities of the data-driven era, a comprehensive
understanding of the underlying business models is paramount.
While there is a general understanding of the key components that make up a business model,
there is no universal agreement on a single, standardised definition or framework model (Zott,
Amit, and Massa, 2011). The concept of a business model is broad and can vary depending
on industry, company size, and specific business goals. Different experts, academics, and
practitioners may emphasise different aspects of a business model depending on their
perspectives and experience.
In order to define what constitutes a business model, we explored definitions from prominent
sources such as (Osterwalder et al. 2005), (Chesbrough und Rosenbloom 2002), (Johnson et
al. 2008), (Teece 2010), (Zott und Amit 2010). By drawing upon previous work, we can gain a
comprehensive understanding of the multifaceted nature of business models and the core
components and mechanisms that constitute them. Analysing these definitions will provide
valuable insights into the fundamental principles that underpin the concept of business models
and help shape our understanding of their role within the AgriDataSpace project. By exploring
a range of perspectives, we will identify common themes and divergent viewpoints, allowing
us to develop a cohesive and holistic understanding of the concept. This exploration will serve
as a vital foundation for subsequent analyses and decision-making processes, ensuring that
the development of a tailored business model for the Common European Agricultural Data
Space is rooted in a solid conceptual framework.
Some prominent definitions the business models concept that facilitated the establishment of
a solid conceptual foundation for subsequent analyses and decision-making processes are
presented as follows:
D2.1: Multi-stakeholder Governance Scheme and
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• "A business model is a conceptual tool that describes the logic and value creation
mechanisms through which a company aims to generate revenue and deliver value to
its customers." (Osterwalder et al. 2005);
• "A business model is the rationale of how an organisation creates, delivers, and
captures value, as well as how it sustains itself in the marketplace." (Chesbrough und
Rosenbloom 2002);
• "A business model defines the manner in which an organisation creates, delivers, and
captures value by aligning its resources, activities, and relationships with market
opportunities." (Johnson et al. 2008);
• "A business model is a representation of how an organisation creates, delivers, and
captures value. It encompasses the firm's value proposition, target market, revenue
streams, cost structure, and key activities and resources." (Teece 2010);
• "A business model is the architecture of the organisation as a system for creating and
capturing value. It defines the structure, components, and dynamics of the
organisation's value creation and value capture mechanisms." (Zott und Amit 2010).
For the purposes of this deliverable, and the duality of DSIs and CEADS business modelling
that it encompasses, we will consider the following combined definition:
A business model is a conceptual tool that represents how an organisation creates, delivers
and captures value by aligning its resources, activities, actors, structure and relationships with
market opportunities. These parameters are dynamically adjustable and defined depending on
whether the organisation operates at micro level (DSI) or macro level (CEADS).
In conclusion, the concept of business models plays a pivotal role in the AgriDataSpace
project. It provides a framework for understanding how organisations in the agricultural sector
create, deliver, and capture value in the context of data sharing initiatives. By exploring various
definitions and perspectives, we have gained insights into the fundamental principles
underlying business models and their significance in shaping the project's objectives. The
analysis of existing initiatives, organisational structures, and data sharing practices serves as
a stepping stone towards developing a tailored and effective business model for the Common
European Agricultural Data Space.
2.1.2. Business Model’s Life Cycle
The lifecycle of a business model typically consists of several stages, each with its own
characteristics and objectives. While the specific terminology or number of stages may vary
depending on different frameworks or perspectives, the following are commonly recognised
stages:
• Development: In this stage, the business model is conceptualised and designed. The
focus is on identifying the value proposition, target market, revenue streams, key
activities, resources, and partnerships required for the business to operate. It involves
conducting market research, validating assumptions, and refining the model.
• Introduction: The introduction stage marks the launch of the business model in the
market. It involves acquiring the first customers, generating initial revenue, and building
awareness about the value offered. This stage often requires significant effort to
establish market presence and overcome initial challenges.
D2.1: Multi-stakeholder Governance Scheme and
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• Growth: Once the business model gains initial traction, it enters the growth stage. Here,
the focus is on scaling operations, expanding the customer base, and increasing
market share. The organisation may need to invest in additional resources, optimise
processes, and build strong customer relationships to support the growth trajectory.
• Maturity: In the maturity stage, the business model has achieved a stable and
established position in the market. It has a significant customer base, a well-defined
value proposition, and optimised operations. The focus in this stage is on maintaining
profitability, strengthening the brand, and defending market share against competitors.
• Decline or Reinvention: Over time, market conditions, customer preferences, or
technological advancements may change, leading to a decline in the effectiveness or
relevance of the existing business model. At this stage, organisations face the choice
of either adapting and reinventing the model to address emerging challenges or
potentially facing decline or obsolescence.
The stages mentioned above are widely recognised in business literature and have been
discussed by various scholars and researchers. For example, (Chesbrough und Rosenbloom
2002) argue that business models evolve over time and undergo different stages, from
development to maturity. They emphasise the importance of continuous adaptation and
reinvention to sustain competitiveness. Similarly, (Zott und Amit 2010) propose an activity
system perspective for the business model design and highlight the importance of
understanding the dynamics and components of a business model throughout its lifecycle.
Regarding the scaling stage, it is typically associated with the growth stage of the business
model lifecycle. Scaling refers to the process of expanding the business's operations, customer
base, and revenue in a rapid and sustainable manner. Scaling involves increasing production
capacity, expanding into new markets, optimising distribution channels, and leveraging
economies of scale. The scaling stage is crucial for businesses to capitalise on their initial
success and achieve sustainable growth.
It's important to note that the stages of a business model's lifecycle are not always linear, and
organisations may iterate or move back and forth between stages, based on market dynamics
and strategic decisions. Additionally, the timeframe for each stage can vary significantly
depending on the industry, market conditions, and the organisation's ability to execute its
growth strategies effectively.
By understanding the different stages of the business model's lifecycle, organisations can
navigate the complexities of the AgriDataSpace project with a clear understanding of the
objectives and challenges at each stage. This knowledge will inform decision-making
processes and enable the development of a robust and adaptable business model for the
Common European Agricultural Data Space.
2.1.3. Discovering the multi-sided and collaborative nature of
Business Models
Data spaces are a strong and collaborative alternative to platforms, creating “multi-sided
business models” that rely on network effects, serving both “supply” and “demand” of data and
data-related services. Offering a useful collection of data resources and services attracts users
of data, and a large user base attracts additional data resources and services.
D2.1: Multi-stakeholder Governance Scheme and
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The multi-sided nature of business models refers to the existence of multiple interdependent
stakeholders or customer segments that a business model serves and creates value for. These
stakeholders can include different groups such as users, suppliers, developers, or other
complementary businesses. The concept recognises that the success of a business model
often relies on effectively managing the relationships, interactions, and value exchanges
between these multiple sides. Within the context of Data Sharing Initiatives, the multi-sided
nature of business models becomes particularly important due to the nature of data as a
valuable resource. Here are some key aspects regarding the importance and relation of the
multi-sided nature of business models in Data Sharing Initiatives:
• Data Providers and Data Consumers: Data Sharing Initiatives involve multiple parties,
including data providers who generate or possess valuable data and data consumers
who seek access to that data for various purposes such as analysis, insights, or product
development. The multi-sided nature of business models allows for the effective
connection and value exchange between these two sides.
• Value Creation: Multi-sided business models enable the creation of value through the
exchange and utilisation of data. Data providers can derive value by monetising their
data assets, while data consumers can gain insights, improve decision-making, or
enhance their products and services by leveraging the shared data. The platform or
intermediary (e.g., the Common European Agricultural Data Space), facilitating the
data sharing, plays a crucial role in creating value for both sides.
• Trust and Privacy: Data Sharing Initiatives require trust and a robust privacy framework
to address concerns regarding data security, ownership, and usage. Multi-sided
business models need to incorporate mechanisms to ensure transparency, consent,
and compliance with privacy regulations to foster trust between data providers and
consumers. This is particularly relevant in the context of sensitive or personal data.
• Network Effects: The multi-sided nature of data sharing platforms can lead to network
effects, where the value of the platform increases as more participants join and
contribute data. As the data ecosystem expands, it attracts more data providers and
consumers, creating a virtuous cycle of increased value and participation.
• Ecosystem Collaboration: Multi-sided business models encourage collaboration within
the data ecosystem. Data Sharing Initiatives often involve partnerships, data
exchanges, or data marketplaces where various stakeholders collaborate to unlock the
full potential of data. These collaborations can lead to innovation, co-creation, and the
development of new data-driven products and services.
• Revenue Models: The multi-sided nature of business models in Data Sharing Initiatives
allows for diverse revenue streams. Platforms or intermediaries (e.g., the Common
European Agricultural Data Space) can generate revenue through transaction fees,
subscriptions, data licensing, or value-added services. Different sides of the platform
may have distinct pricing or revenue-sharing models, enabling sustainable business
models.
• Scalability and Impact: Multi-sided business models offer scalability in Data Sharing
Initiatives. As the platform attracts more participants and expands its reach, the
potential impact of data sharing increases, allowing for broader insights, societal
benefits, and potential innovation across various industries and domains.
D2.1: Multi-stakeholder Governance Scheme and
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2.1.4. Business and Revenue Models
As the landscape of Data Sharing Initiatives continues to flourish, a diverse array of business
models emerges, each tailored to unlock the immense value residing within shared data
resources. Having delved into the significance of the multi-sided nature of business models
within the realm of Data Sharing Initiatives, it becomes evident that a diverse range of business
models is employed to facilitate the exchange, collaboration, and utilisation of valuable data.
In this section, we embark on a comprehensive exploration of the most influential and
commonly used business models in the context of Data Sharing Initiatives. Drawing upon
authoritative sources and industry insights, we shed light on the key frameworks that underpin
these initiatives, offering a glimpse into the dynamic landscape of data-driven business
models.
• Data monetisation: Unilateral approach under which companies make additional
revenues from the data they share with other companies. Data can also be monetised
through the provision of services.
• Data marketplaces: Trusted intermediaries that bring data suppliers and data users
together to exchange data in a secure online platform. These businesses generate
revenue from the data transactions that take place on the platform.
• Software as a service (SaaS): In this model, the data providers and analytics providers
get financially compensated for their data and services and the consumers pay a
subscription for accessing data and platform.
• Industrial data platforms: Collaborative and strategic approach to exchange data
among a restricted group of companies. They voluntarily join these closed, secure and
exclusive environments to foster the development of new products/services and/or to
improve their internal efficiency. Data may be shared for free, but fees may also be
considered.
• Technical enablers: Businesses specialised in and specifically dedicated to enabling
data sharing through a technical solution. Revenues are obtained from setting up,
using, and/or maintaining the solution (not from the data exchanged).
• Open data policy: Companies that opt to share data for free to foster the development
of new products and/or services.
The following are the most commonly used revenue or cooperation models:
• Free-to-all: Also known as open data (connected to the Open Data Policy business
model). Data Sharing Initiatives can leverage open-source models to create
communities where data contributors freely share their datasets, tools, and algorithms.
This approach promotes transparency, collaboration, and rapid development, driving
collective progress and enabling the democratisation of data-driven solutions.
• Freemium: The freemium model, popularised by companies like Spotify and LinkedIn,
combines free and premium offerings to attract and retain users. In the context of Data
Sharing Initiatives, this model allows organisations to offer basic data access and
services for free, while offering advanced features, premium data sets, or enhanced
support through paid subscriptions. Freemium models enable broader data
accessibility, driving user adoption and fostering long-term customer relationships.
• Licensing: Data is shared based on licensing agreements.
D2.1: Multi-stakeholder Governance Scheme and
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• Sponsorship/branded advertisement: Free access to data. The cost of the data is
subsidised by sponsors or advertisers.
• Demand-oriented: Cost of data depends on availability and complexity.
• Barter system: Data consumer gain access to data based on the exchange of data with
data provider (i.e. pay with data).
These are just a few of the prominent business and revenue models that have taken centre
stage within the vibrant landscape of Data Sharing Initiatives. The landscape of Data Sharing
Initiatives is adorned with diverse and dynamic business models that fuel the exchange,
utilisation, and commercialisation of data assets. From platform-based ecosystems to data
marketplaces, freemium offerings, and subscription models, these business models play a
pivotal role in shaping the success and sustainability of data-driven initiatives. By embracing
and adapting these models to their unique contexts, stakeholders can harness the
transformative power of data and forge ahead in the pursuit of innovation and value creation.
In recent years, there has been a noticeable trend of organisations transitioning from traditional
product-driven business models to service-driven ones. This shift is driven by evolving
customer demands, advancements in technology, and the recognition of the value that
services can bring into a data-driven economy. Data Sharing Initiatives (DSIs), which aim to
facilitate the exchange and utilisation of data, have also embraced this trend, predominantly
adopting service-driven business models. The reasons behind the shift from product-driven to
service-driven models and why DSIs have gravitated towards service-centric approaches are
multifaceted. The underlying motivations and advantages of this strategic shift are imprinted
below:
• Evolving Customer Preferences: The primary drivers behind the shift to service-driven
business models are evolving preferences and expectations of customers. In today's
digital age, customers seek customised on-demand solutions that address their specific
needs. They value convenience, flexibility, and outcomes rather than the ownership of
physical products. By adopting service-driven models, DSIs can cater to these evolving
customer preferences by offering data-related services, insights, and tailored solutions
that deliver tangible value.
• Value Co-Creation and Collaboration: Service-driven business models emphasise the
co-creation of value through collaboration between multiple stakeholders. In the context
of DSIs, where data sharing and collaboration are paramount, service-driven models
enable the integration of diverse data sources, expertise, and capabilities. By offering
services that facilitate data sharing, analytics, and insights, DSIs can foster
collaboration among participants, unlocking new value creation opportunities and
driving innovation. Service-driven models encourage a collective approach to problem-
solving, promoting partnerships and ecosystem development.
• Monetisation of Data Assets: Data has become a valuable asset for organisations
across industries. Service-driven business models provide opportunities for DSIs to
monetise their data assets by offering data-related services and solutions. Instead of
relying solely on the sale of data products, DSIs can generate revenue through
subscription-based services, data analytics, consulting, or value-added offerings. This
shift allows DSIs to capitalise on the inherent value of data and create sustainable
business models that can adapt to evolving market dynamics.
D2.1: Multi-stakeholder Governance Scheme and
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• Flexibility and Scalability: Service-driven business models offer inherent flexibility and
scalability, making them well-suited for DSIs. The dynamic nature of Data Sharing
Initiatives requires adaptable and scalable models that meet the different needs
participants, data types, and evolving market conditions. Services can be tailored,
expanded, or modified to meet changing demands, providing DSIs with the agility to
adapt and grow. Service-driven models also enable the incorporation of emerging
technologies, such as artificial intelligence and cloud computing, to enhance data
processing, analysis, and delivery capabilities.
The above analysis provides an insight into the most important and most commonly used
business models of Data Sharing Initiatives, although it should be noted that this overview
offers merely a glimpse into the diverse landscape. An in-depth analysis of Data Sharing
Initiatives and their business models will follow in Chapter 4, providing a comprehensive
understanding of their intricacies and nuances. We will explore these business models, unravel
their intricacies and shed light on their implications for value creation and collaboration in the
realm of data sharing.
2.2. Multi-stakeholder Governance Scheme
This section starts with the basic terms and definitions that include the relevant stakeholders,
their roles and interactions. The subsequent sections provide a summary of guidelines for
developing governance schemes for data spaces from literature. Following these guidelines,
the relevant legal aspects (Data Governance Act and Data Act) outline the legal context, in
which the CEADS is going to operate. The resulting design options for governance schemes
as well recommendation for the organisational structure of CEADS are derived from the
outlined guidelines, legal framework as well as analysis of the interviews with DSIs.
2.2.1. Terms and Scope regarding Governance
Modern agriculture generates and collects a wide range of data on the farm. Specific examples
include data on the use of fertilisers and water, the use of seeds, health data for livestock and
soil data. It is about data on the immediate location and assets of the farms (barns, fields with
crop, machinery and livestock). The data can be generated and managed by the farmer himself
or by an “agent” acting for the farmer (e.g. contractor or milking robot). In these cases, the
holders of the data rights are often involved in the storage of the data.
The data is used by farmers or their partners (e.g. contractors) on the farm, but it can also be
used outside the farm. In the latter case, it is usually processed or combined with other data
and information generated elsewhere, and is used by third parties. These can be private
companies such as farm input providers, service providers, banks, insurance companies,
agricultural consultants, scientists and farmers’ associations as well as the government.
Data sharing is required for data utilisation beyond the farm. Therefore, the sharing of farm
data is therefore a business-to-business (B2B) process: A data holder passes on farm data to
a third party (data user) with the farmer's consent (see also Section 2.2.3.1 on the Data Act).
According to the Data Governance Act, data sharing is “the provision of data by a data subject
or a data holder to a data user for the purpose of the joint or individual use of such data, based
on voluntary agreements or Union or national law, directly or through an intermediary, for
example under open or commercial licences subject to a fee or free of charge” (DGA Art. 2(8))
D2.1: Multi-stakeholder Governance Scheme and
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A data space is an infrastructure that enables this data sharing / exchange between data
holder and data (re)user. The Data Space Support Centre defines a data space as “a
distributed system defined by a governance framework that enables trustworthy data
transactions between participants while supporting trust and data sovereignty. A data space is
implemented by one or more infrastructures and supports one or more use cases.” (Data
Spaces Support Centre 2023) This Deliverable will follow this understanding, as leading
initiatives such as Gaia-X defines data space similarly as a “federated, open infrastructure for
sovereign data sharing based on common policies, rules, and standards.” (gaia-x Hub
Germany 2022).
However, data spaces do not cover all relevant initiatives for trading and exchanging data. To
take this into account, especially for the conducted analysis of the agricultural landscape, the
term Data Sharing Initiative (abbreviation: DSI) is applied in the AgriDataSpace project. DSIs
are aiming at “providing data access for use by others, subject to applicable technical, financial,
legal, or organisational use requirements” (Organisation for Economic Co-Operation and
Development 2021). With this understanding, data spaces are a specific type of Data Sharing
Initiatives, but the term is also applicable to other types such as data platforms, internal access
systems and open, multi-sided platforms according to the data collected in Work Package 1.
Examples for a DSI:
A DSI can be temporal, for example a research project or a consortium, or a permanent profit
or no-profit organisation. Public authorities, who offer open public data and provide technical
infrastructure for its delivery and access, can also be seen as a DSI. According to D 1.1.
“Understanding and mapping of the data sharing”, many of the mapped DSIs are still in their
infancy and exist as temporal concepts, pilots and sandboxes. WP1 classified the mapped
DSIs as follows:
• Internal access system: these are e.g. Enterprise Resource Planning or Human Resource
Management systems with the access to the data that is granted within one organisation. For
an HRM system for example the organisation has access to all the data of its employees.
Examples: Les AgroAnalytics (Arvalis) FR, Ekylibre IT, Octopize FR, Rede Rural Nacional PL
• Data platform with restricted unilateral or multi-lateral external access: for ERP systems
it is possible that some of the clients or input suppliers have access to some data (multilateral
external access), that are needed to facilitate certain transactions. For example, it is possible
that the farmer’s Farm Management System gets access to data on how a crop was treated
with pesticides (external access).
Examples: WALLeSmart BE, Eden Library GR, I4DATA SP, Bodempaspoort BE, NumAlim FR,
MAPEO BE, GeoPard DE
• Open multi-sided platform: a digital platform that acts as an intermediary to connect two or
more mutually dependent groups of users (e.g. sellers and buyers) with shared economic
objectives. To be successful, multi-sided platforms must attract as many users as possible to
make it useful and valuable.
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Examples: DjustConnect BE, AgDataHub FR, AgGateWay Europe EU, EU-FarmBook platform
BE
Open data common: the social practice of governing data by a community of users that self-
governs the resource through institutions that it creates. It can be a set of legal tools and
licenses to help publish, provide and use open data. Open means anyone can freely access,
use, modify and share for any purpose (subject, at most, to requirements that preserve
provenance and openness).
Examples: agrirouter DE, Agri DISCRETE, European Spatial Data Infrastructure, Platform
Linked Data Nederland
As for the type of the mapped DSIs, most are data platforms with restricted unilateral and multi-
lateral external access. These initiatives have a potential to evolve towards data sharing in a
broader ecosystem, with multiple providers and users of datasets.
The aim of task 2.1 was to develop a multi-stakeholder governance scheme of the CEADS.
In general, the term governance is defined as the action or manner in which something is
regulated. In this context, the governance system is the system of regulation and coordination.
In the field of business, governance refers to the control and management of an organisation.
As the purpose and understanding of data spaces and their governance is still evolving, we
refer to the concept of governance, which was summarised succinctly in the ZEROW project:
“In simple terms, governance is the answer to the question: Who decides what and how, and
how these decisions are enforced. Governance of data space helps to support the key
principles of a data space such as data sovereignty and a level playing field for partners
involved by indicating, monitoring and mandating how aspects such as data exchange,
participation and use of the data space, decision making rights and responsibilities, and rules
and principles are configured.” (Šestak und Copot 2023) In short, governance is the
coordination of the necessary activities of the organisations involved / participating in this data
exchange infrastructure (data space). Therefore, the aim of the task was to derive and describe
the scheme (plan or arrangement) of the organisational structure and rules for the future
Common European Agricultural Data Space.
The governance scheme is further specified by the prefix “multi-stakeholder”. This depicts the
requirement to involve the various relevant groups of interest for the CEADS in the governance
scheme. The CEADS could bring together multiple, interested groups of actors to participate
in dialogue, decision-making and implementation of solutions to common problems. A focus of
the task therefore lays on the organisational structures and rules for collaboration of the
relevant stakeholder groups and define their roles with regards to the CEADS.
Therefore, the relevant stakeholders are key for the design of the governance scheme. The
main focus lays on agriculture and actors of the value chain in this sector. In the proposal of
the AgriDataSpace project, the following stakeholder groups were named: farmers, advisors
and their associations, technology providers and data intermediaries, agribusiness, society,
policy makers, academia and scientific communities and professionals.
The relevant stakeholders also include the Data Sharing Initiatives for agriculture – an overview
and broad analysis of DSIs is given by Work Package 1 (Objective 1). In addition, the
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relationship with other CEDS (cross-sectoral collaboration) and other international data
spaces, especially in agriculture, will be taken into account.
2.2.2. Governance Models and Guidelines
As there are many isolated DSIs in the agriculture domain with different governance
platforms, the realisation of a CEADS in terms of a network of interoperable DSIs is primarily
a coordination challenge in which standards and design principles must be agreed upon and
which are accepted by all participants and stakeholders. Creating a soft infrastructure
specifying common functional, legal, operational and technical aspects, is therefore an
important task.
The following subsections focus on the review of some existing proposals and approaches
for governance models and guidelines.
2.2.2.1. Guidelines for Governance of Data Sharing in Agri-Food Networks
Wolfert et al. (2017) distinguishes governance into two interrelated processes, in the sense
that they form a cyclic process. Firstly, the formal and informal institutional settings such
as formal regulation, informal rules. Secondly, the steering principles that influence the
behaviour of the stakeholders in the network towards realising the common goal. These two
processes can be used for analysing (big) data governance.
Next to these two processes, six internal factors play a role in the governance of a network
of participating organisations that share data with each other:
• Efficiency, referring to the business plans for the network of collaborating organisations.
• Effectiveness, referring to the time spent on data capture, storage, transfer,
transformation, and analysis of data, and data management. For example, how much
time is spent for a data re-user to receive the requested data set?
• Inclusiveness, referring to several aspects: is inclusion (participation) forced or
voluntary? Who takes part in the decision-making on investments of the data sharing
infrastructure, its services, its finance, etc.? Is it easy to join and leave the network of
collaborating organisations at any time?
• Accountability and legitimacy have to do with the feelings that participants – farmers,
data collectors, data holders, data re-users, third parties – have towards the people in
charge of the network. Do participants support, trust and comply with the decisions
made by the body that is governing and managing the network?
• Credibility refers to the quality of data and the quality of businesses which are making
(re-)use of data.
• Transparency refers to qualities that must be openly communicated, must be
transparent.
There are also external factors that influence the success of data-driven initiatives such as
Data Sharing Initiatives. To identify and classify external factors, the PESTLE approach to data
governance was applied: Political, Economic, Social, Technical, Legal, and Environmental
factors (Wolfert at al., 2017: 5).
For each of these internal and external factors, guidelines have been drafted which can be
used to assess whether the governance scheme(s) for the European network of collaborating
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Data Sharing Organisations will work well or not. Finally, the following conditions are also
important to take into account when designing a governance scheme for the European
Agricultural Data Space (Wolfert et al. 2017, S. 6–8):
• Start with sharing data in a closed experimental setting to showcase the value of data
for participants. But do not start an initiative without a clear business case for all
participants.
• Make clear arrangements about the distribution of costs and benefits (part of the
business model).
• Make the initiative appealing to suppliers of capital as well as to agricultural and
technology stakeholders.
• Do not promise improvements that are not proven yet (expectation management).
• Try not to limit access to data and do not share data with a third party without secured
consent (authorisation) and guaranteed data quality.
2.2.2.2. Governance principles according to Open DEI
Agriculture represents one of four key fields of the EU strategy for digital transformation, next
to manufacturing, energy and healthcare. As such, this industry domain is also subject to the
EU-funded OPEN DEI project. The project “OPEN DEI Aligning Reference Architectures, Open
Platforms and Large-Scale Pilots in Digitising European Industry” fosters the creation of
common data platforms based on a unified architecture and an established standard.
In their position paper on design principles for data spaces from April 2021, the project
underlines the importance of data spaces allowing for sovereign sharing of data in creating the
future data economy.
The paper, written in collaboration with more than 40 data spaces and industrial domain
experts, shows the first approach to define principles for data spaces, agreements on the
building blocks for a soft infrastructure and governance for data spaces (Nagel und Lycklama
2021).
To enable data economy through data spaces, a soft infrastructure consisting of rules and
agreements of a legal-functional, technical and operational nature must be made available to
all stakeholders. The soft infrastructure can be seen as the basis for a data space and for
cooperation between data spaces. Two main types of blocks that build this infrastructure can
be defined independently of the domain: technical building blocks on the one hand and
governance building blocks on the other (see figure below).
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Figure 3: Open DEI Soft Infrastructure
The focus in this section and in the present document lies on the governance aspects.
Therefore, the technical building blocks are not discussed here. The category “governance”
contains building blocks such as:
• overarching cooperation agreements
• continuity model
• business agreements
• operational agreements
• organisational agreements
where (citation):
Business agreements comprise service level agreements (SLAs), data usage and access
control policies as well as accounting and pricing/billing/payments schemes, which data
service providers may specify in connection with their offerings to govern their interaction
with data consumers.
Such agreements specify the terms and conditions that regulate the sharing and exchange
of data between parties. To do so, smart contracts can be used that connect legal and
organisational agreements to technically enforceable and measurable agreements.
Operational agreements regulate policies that need to be enforced during data space
operation. For example, they comprise terms and conditions dealing with the ever-growing
importance of compliance with mandatory regulations like General Data Protection
Regulation (GDPR) or the 2nd Payment Services Directive (PSD2) in the finance sector.
Organisational agreements comprise terms and conditions regarding governance bodies
and procedures established for data space.
The above overarching cooperation among data space stakeholders must be enforced through
legal frameworks and via technical building blocks (for legal frameworks refer to 2.2.3). They
are the prerequisite to ensure data sovereignty in data sharing and exchange.
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Besides providing support and enable data usage policies, organisational and operational
agreements also allow for creating “interoperability” and “trust” between all stakeholders of a
data space and in between data spaces. To do so and to support the technical building blocks
in this respect, the agreements should address and contain the following sub-blocks
connecting the digital and physical world.
Table 1: Overview of Organisational and Operational Building Blocks that support the
technical building blocks in creating interoperability and trust between all stakeholders
Organisational/Operational
Building Block
Role and Scope
Example
Unique Identifiers
Identification of legal
entities, natural persons, or
matters in terms of a unique
identifier and other
information about entities.
Tax identification numbers,
legal entity identifiers.
Authorisation Registries
Verification and validation of
digital identities and their
mapping to real-world
objects.
eIDAS-qualified seals
provide a mechanism to
verify and validate identities.
The providing (national)
registry implements the
policy as defined by the
eIDAS regulation.
Trusted Parties
Provide neutral evidence on
specified facts based on
predefined criteria.
A trusted party is an
independent and accredited
evaluator of a certification
scheme (e.g. ISO 27001).
Domain Data Standard
Provides the syntax and
semantics for data exchange
and data sharing on different
levels.
In manufacturing data
spaces, a combination of
different standards is used to
describe the syntax and
semantics of data
transactions (e.g. ISO 10303,
Asset Administration Shell,
eCi@ss).
Regarding data space administration, the continuity model is an important governance building
block. It is a model describing the processes for the management of changes, versions, and
releases for standards and agreements (IDSA 2023).
There are different ways of integrating the above-mentioned blocks into a data space,
depending on the underlying domain-specific or technical requirements. The synthesis of
building blocks enables the development of a data space solution, as shown in Figure 4.
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Figure 4: Overview of Building Blocks (IDSA 2023)
Next to the above-described generic data space building blocks, additional building blocks for
a soft infrastructure may be defined and added by the data space stakeholders to allow for
domain-specific features and functions.
Regarding the agriculture domain, the experts recommend to closely monitor embryonic
initiatives, such as API-AGRO, DjustConnect and DKE-agrirouter to learn and benefit from
their practices and solutions. A more detailed work on currently existing agriculture DSIs is
given in section 4.
As the supply chain in agriculture is very complex and contains aspects reaching from
manufacturing over energy logistics to waste management, data sharing between many
different stakeholders must be conducted under fair and transparent rules. Regarding this topic
of public-private governance, according to Open DEI “The EU Code of Conduct on agricultural
data sharing can be seen as a best practice to be followed by other domains wishing to create
thriving and balanced data spaces.“
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“The code of conduct for data sharing in the agricultural sector specifies the conditions for
defining the soft infrastructure based on contractual agreements and guidance on fair and
transparent use of data“ (European Feed Manufacturers' Federation 2020).
With respect to a decentralised soft infrastructure, interoperability in digital agriculture is an
important requirement due the heterogeneous set of machines and entities that are being
involved into the exchange and collection of (high-value) datasets. Therefore, an agreement
on a set of data and system interoperability mechanisms and standards is needed, especially
with respect to the suppliers of farm management systems.
The participants must also agree on functional reference architectures in order to achieve data
sovereignty. Especially with respect to SMEs, a human-centric approach to privacy and data
usage control should be chosen.
Generally, the organisational structure of the individual data space’s governance entities
should be similar to the overall governance structure for European data spaces. The
overarching governance context for European data spaces is formulated in the Data
Governance Act.
2.2.2.3. Data Space Support Centre
The Data Space Support Centre (DSSC) is an organisation founded by the European
Commission in the Digital Europe Programme. Its mission is to coordinate the coherent
development of the common European data spaces for a fast start and scale-up. The DSSC
explores and supports the needs of data space initiatives and provides common requirements,
best practices and tools in a co-creation process. It supports the work of the European Data
Innovation Board by identifying the common requirements on CEDS. The DSSC provides a
platform for knowledge exchange to support the deployment of data spaces and creates a
network of stakeholders, working closely with the funded data space projects. This includes
the hosting of topic-specific working and exchange groups e.g., on governance. The discussion
results are one of the main sources for the definition of building blocks for their blueprint, which
was published in the Version 0.5 in October 2023 (Data Spaces Support Centre 2023).
The following building blocks were defined by the DSSC for the organisation and business
aspects of data spaces, based on the previous work from Open DEI:
Figure 5: Overview on the Organisational and Business Building Blocks defined by the
Data Spaces Support Centre from the Blueprint (Version 0.5 from October 2023, p.11)
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The implementation of these organisational and business building blocks requires “well-
defined purpose, objectives, values and design principles for the data space to provide a basis
for coherent actions and decisions” (DSSC Blueprint V05, p. 12). In the category of data space
governance, the DSSC differentiates between the Organisational Governance and the Data
Sharing Governance.
Organisational Governance covers guidelines for “setting up the data space governance
authority” (Data Spaces Support Centre 2023). The aims are to define key decision points,
options for governance and transparent rules and roles. Important design aspects are the
scope of the data space, its position in the ecosystem, the openness for participants and their
support, and the organisational principles. The recommendation of the DSSC is to “aim to
promote collaborative, multi-stakeholder governance for effective data space operation” (Data
Spaces Support Centre 2023).
Data Sharing Governance is about the common rules for effective and reliable data sharing
processes in the CEDS, “to organise data transactions within a data space” (Data Spaces
Support Centre 2023) and how they are facilitated as part of the functionality of the data space.
This covers aspects like rules and standards for security and interoperability, which “are
essential for building trust between data space participants” (Data Spaces Support Centre
2023). Data sharing governance also covers the rule enforcement strategy, which could affect
the efforts for participants to join and be active in the data space, but is also needed to ensure
reliability and trustworthiness of a CEDS.
The building blocks of the DSSC focus on the intra data space governance, including the
involvement of participants. The inter data space governance, so the regulations and
structures for collaboration between CEDS and other (European) data spaces, is currently not
part of the defined building blocks.
On the 11th of March, DSSC published a new version for the governance building blocks that
covers options for the legal form of a Common European Data Space (see Figure 6):
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Figure 6: Key decision-making points for Data Space legal body (source DSSC
3
)
The outlined key decision-making points are essential for the establishment and operation of
a data space and its governance framework. The decision-making points depend on the
options for an organisational form (or legal form) that have impact on the corresponding
business models with a value proposition for the key stakeholders of a Common European
Data Space (see Chapter 5 for details). The elaboration of pro- and contra-options and the
summary of the relevant legal forms for the CEADS are outlined in Chapter 6.
2.2.2.4. Sitra Rulebook
Further proposals and ideas with regard to governance approaches for building trust between
participants of data spaces can be found in terms of so-called rulebooks, e.g. the one provided
by IDSA, the International Data Space Association e. V. and the one provided by Sitra, an
independent public foundation operating under the supervision of the Finnish Parliament. Both
3
https://dssc.eu/space/BVE/357074549/Organisational+Form+and+Governance+Authority
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organisations are part of the Data Spaces Support Centre (DSSC), described under subsection
2.2.3 above and as such share a common commitment – namely, to help building a
decentralised European data economy and the guiding principle of data sovereignty (Sitra
2023).
In the following, some main aspects regarding governance are being reviewed from the
rulebook approach provided by Sitra as it also builds the basis for the recommendations given
by the IDSA rulebook. Generally, the Sitra “rulebook for a fair data economy”, describes the
roles, responsibilities, processes, and governance for decentralised networks with many
competing parties such as the agriculture domain.
One of the main points of the rulebook approach is, that there are no point-to-point contracts
between the different parties. Instead, the rulebook can be used as a constitution for data
sharing networks, which is set up and then signed by all respective parties/stakeholders. In
their work, the authors Olli Pitkänen and Juhani Luoma-Kyyn define a governance model for
data spaces as follows:
“Governance Model” means an appendix to the Constitutive Agreement that includes a
network-specific description of the rules and procedures of accession, i.e. who may be
admitted to the Network and how, applicable decision-making mechanisms, and further
governance provisions regarding the administration of the Network.”
The “governance model” template provided in the Sitra rulebook, part 2, comprises a general
provision and provisions on the steering committee. Regarding the general provisions it reads
as follows:
“The Data Network is established by the Constitutive Agreement, which is signed by the
Members of the Network. This Appendix [governance model] includes a description of the
Governance Model of the Data Network. The purpose of the Governance Model is to define
the procedures and mandates for managing the Data Network and any related changes during
the life cycle of the Data Network. The Constitutive Agreement must include […] a List of
Members that also sets out the Parties to the Constitutive Agreement and the contact details
of their representatives. The List of Members must be updated upon the accession of new
Parties and the termination of incumbent Parties as well as when any contact details are
changed.”
Furthermore, the template for a governance model contains a detailed proposal on the steering
committee and its provisions, as being depicted in Table 1. More details can be found in the
Sitra rulebook, part 2, p. 50 ff.
Table 2: Overview and some main points regarding steering committee provisions as
part of the template for a governance model, given in the Sitra rulebook.
Steering Committee Provisions
Some main points
General
Ultimate decision-making body of the data
network
• Facilitate collaboration
• Organise administration
• Deciding on matters connected with
severe financial or risk impact
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Primary Functions
• Ensure the coordination of and any
decision-making related to the data
network’s business or to its legal,
technical or ethical matters
Composition, Meetings and Organisation
• Each Party appoints one
representative to serve on the
Steering Group which will select a
chairperson
• Chair must convene ordinary meeting
at least once every three months
• Secretary coordinates matters
related to the duties of the Steering
Committee
Meeting Agenda
• At each meeting, the topical issues
affecting the data network will be
reviewed
Quorum and Decisions
• Steering Committee strives to work
on the basis of achieving a
consensus
• Chair will have the casting vote
• New parties may join the network by
signing an accession agreement
Subcommittees
• Steering Committee may authorise a
subcommittee
• Steering Committee will appoint the
chairs of the subcommittees
• All subcommittees must operate
under a full consensus
Invited Attendees
• Steering Committee representatives
may invite necessary and appropriate
persons
• Chair is entitled to decide whether the
attendance of the relevant invitee is
necessary and appropriate
Conflicts
• The parties must strive to resolve any
conflict with respect to the data
network/ constitutive agreement in
good faith at the Steering Committee
Next to the governance model template that covers the organisational aspects of governance
in a data network, part 2 of the rulebook also contains a range of control questions with respect
to data governance, which are given in the following.
• What are the data governance principles and responsibilities in the data network?
• What are the data storage and availability principles in the data network?
• What are the data life cycle management principles?
• How is change management of different aspects handled (data, structures, systems,
interfaces, governance-related)?
• How are changes managed and communicated to different parts of the data
infrastructure and related operations?
• What are the mechanisms for archiving and deleting data?
• What systems and processes exist to manage the last steps of data life cycle?
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• Who is responsible for data beyond its life cycle?
• Are there shared responsibilities?
• How is this responsibility transferred?
The Sitra rulebook has already been used and thus validated in practice along two case
studies: 1, Premium Grain Chain and 4, DIH AGRIFOOD. In this context, the following issues
and problems were identified that may arise from the regulatory concept for the agrifood sector:
• Identify and realise real business cases for data sharing
• Inability to adequately identify different actors, their roles and responsibilities
• Lack of technical and semantic interoperability
• Lack of data quality
• Cultural and attitudinal problems; not understanding the benefits from data sharing
• Risks related to losing control of data and trade secrets, infringing others' rights, and
data protection
• Inability to define success and show value for all entities in a data ecosystem
• Support adherence to the agrifood data sharing Code of Conduct and respect the rights
of all participants, including and especially individual farmers
• Engage with and build trust among especially individual farmers but also other actors
in the agrifood data value chain
• Take advantage of the opportunities opened by new business actors (“data
intermediation service providers” in the Data Governance Act) and data sharing
obligations (especially for connected devices like farm equipment manufacturers in the
Data Act)
• Remain compliant with the relevant national and EU legislation
• Architect technical implementations of data sharing that are secure, efficient, and fit for
purpose
2.2.3. Legal framework: Data Governance Act and Data Act
To achieve the overall goal of a single market for data, the EU has taken various measures to
enhance the access to and availability of data (European Commission 2020a). Central to these
measures, as outlined in the EU Data Strategy, is the expansion of the legal framework beyond
the existing regulations on personal data (General Data Protection Regulation, GDPR),
cybersecurity and open-data. Considering the central role of data in modern agriculture, from
precision farming to supply chain management, the EU's approach is particularly relevant to
DSI in the agricultural sector.
A cornerstone of this approach is ensuring that data can be shared through trusted entities.
The Data Governance Act (DGA), therefore, sets out specific rules for data intermediaries, like
data marketplaces, aiming for a model based on their neutrality and transparency. The aim is
to ensure that both individuals and companies retain control over their data (European
Commission 2023).
While the Data Governance Act sets standards for how data is shared, the Data Act aims to
improve the overall conditions for data accessibility, especially for data generated by
connected products. It focuses on removing barriers to data access for all sectors, including
agriculture, while ensuring balanced control for data creators (European Commission
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28.06.2023). Moreover, it emphasises the need for interoperability between data spaces,
facilitating easier data exchange across the EU.
Both acts will influence the organisational structure and governance of DSI. Furthermore, they
will serve as a legal framework for the CEADS. The key provisions of both acts are described
in the following chapters.
2.2.3.1. Data Governance Act
The Data Governance Act (DGA) establishes a harmonised framework for data governance
across the EU, aiming to foster trust and enable easier data sharing across different sectors
and borders. At the very centre of the DGA is a notification and supervisory framework for the
provision of data intermediation services. These services act as facilitators for trustworthy data
sharing, linking data holders to data users through various mechanisms, be they technical,
legal or other.
Chapter III of the DGA sets out a number of requirements for providers of data intermediation
services. These requirements focus on transparency and compliance with data protection
rules. Additionally, the DGA underlines the importance of neutrality. It mandates a clear
separation between the processes of data provision, intermediation, and usage, ensuring that
data holders maintain greater authority over their data (European Commission 2023).
The following requirements may influence the organisation and governance of data spaces
and other data sharing initiatives and are therefore relevant to CEADS.
Scope
The provisions of Chapter III of the DGA apply only to "data intermediation services". These
services are aimed at facilitating business relationships to enable the sharing of data between
a wide range of data holders and data users. The term 'intermediation' is interpreted broadly
to include all forms of assistance in establishing a business relationship for the purpose of
using data, whether legal, technical or otherwise. It covers not only services that technically
execute a transaction, but also explicitly includes intermediation at an organisational level (e.g.
matchmaking services). Such intermediary services act as a broker between data holders and
data users, facilitating the connection and exchange of data, including assisting data subjects
in exercising their rights in relation to personal data.
In this scenario, it is not essential for the service to technically interact with the data exchanged
between data holders and data users. However, it is important to note that the definition in Art.
2 (11) DGA contains some exclusions for certain types of services. For example, services that
collect, modify or enhance data in order to significantly increase its value, before licensing its
use to data users, are not classified as data intermediation services, because they do not
promote a direct commercial relationship between the original data holders and the data users.
Instead, they add a value layer to the data before it reaches the data users. Moreover, services
focused on mediating copyright-protected content, services utilised solely by one data holder
for their own data usage, or services used by a closed group of legal entities (like suppliers or
customers bound by contract), are also beyond the scope of data intermediation services. The
same exclusion applies to data sharing services provided by public sector bodies or cloud
storage applications that do not aim to establish commercial relationships. It is important to
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stress that only those entities that qualify as data intermediary services are required to comply
with the provisions of the DGA.
Requirements applicable to data intermediation services
If providers qualify as data intermediary services, they must fulfil the conditions set out in Art.
12 DGA and are subject to the notification procedure (Art. 11 DGA). The conditions set out in
Art. 12 DGA may affect DSI at the organisational level and as well as at the level of data
governance.
Organisational Governance
A centrepiece of Art. 12 DGA concerns the neutrality of data intermediary services. It
mandates that providers should solely use the data to make it available to data users.
Therefore, there must be a strict separation between the intermediation, provision and use of
data. The intermediary service must therefore be unbundled from other services and provided
by a separate legal entity (Art. 12 (1) DGA). In order to maintain neutrality, the commercial
conditions, including pricing, should not be influenced by the use of other services provided by
the data intermediary (Art. 12(a) DGA). At organisational level, the provider must also ensure
that access to the service is fair, transparent and non-discriminatory for data holders and data
users with regard to prices and terms of service (Art. 12(b) DGA). In addition, measures must
be taken to prevent fraudulent practices and to ensure continuity of service in the event of
insolvency. Finally, Art. 12 DGA stipulates that the provider must immediately inform the data
holders in the event of unauthorised disclosure, access or use of the shared data.
Data sharing governance
Article 12 of the DGA sets out conditions that influence the data sharing governance of a
provider offering data intermediation services. This includes a strict purpose limitation of the
processed data, which is also part of the aforementioned neutrality of the provider. The
(meta)data collected by the provider shall only be used for the development of the data
intermediary service, which may include the use of data for fraud detection or cybersecurity
(Art. 12(c) DGA). When data is exchanged, it should remain in its original format unless it is
necessary to change it to improve compatibility between different sectors, if requested by a
data user or if required by law (Art. 12(d) DGA). In cases where format conversion is necessary,
data holders should be able to opt out, unless required by law otherwise. Providers may offer
additional tools, such as temporary storage, data conversion or anonymisation tools, to
facilitate data exchange, but only if requested or agreed to by data holders (Art. 12(e) DGA).
In addition, data should be exchanged in the format received, unless conversion is necessary
to improve interoperability within and between sectors, requested by the data user or required
by Union law (Art. 12(d) DGA).
Governance of collaboration
At the level of collaboration governance, Art. 12(i) DGA also includes a requirement for
technical interoperability. It states that the provider should take measures to ensure
interoperability with other data intermediation services by means of open standards commonly
used in the sector in which it operates. This requirement is necessary to avoid lock-in effects
(recital 2) and is in line with the requirement that data format changes are prohibited unless
conversion is necessary to enhance interoperability between and across sectors (Art. 12(d)
DGA).
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2.2.3.2. Data Act
The Data Act establishes a comprehensive governance framework for data sharing,
addressing the challenges of accessing and utilising the vast amounts of data generated by
machines and products. The Act primarily governs manufacturers placing products on the EU
market, particularly technologically embedded products that generate and transmit data,
extending also to providers of essential related services. The Data Act has not yet come into
force. It is expected to be passed before the end of 2023.
Data access by design
The Data Act ensures that users, whether individuals or businesses, have the right to access
and use the data generated by these products or associated services, including the freedom
to share it with third parties acting in a commercial or professional capacity. With an focus on
'accessibility by design', the Act requires manufacturers to design products that allow easy and
secure access to data, and requires a high level of transparency, particularly before contracts
are concluded, about the nature, extent and accessibility of the data and the arrangements for
sharing it with third parties.
Data access rights
Article 4 DA establishes the right of the user to access all data generated by the use of the
product, including both user-initiated data and passively generated data such as standby mode
or environmental data such as room temperature, but excluding derived data obtained through
analysis by the data holder. This right is exercised towards the data holder, e.g. the
manufacturer of the product, who is obliged to provide the user with the generated data
promptly, free of charge, securely and in a user-friendly, machine-readable format upon a
simple electronic request. At the request of a user, the data holder is obliged to make the data
generated from the use of the product available to third parties, as provided for in Article 5 of
the Data Act. In this scenario, the same provisions apply as for access to the data by the user
in Article 4(1) DA. In particular, the data should be provided promptly, securely, in a structured,
widely accepted, machine-readable format, on an ongoing basis and in real time.
Implications for the governance of DSI and the CEADS
The definition of the term data holder is very broad. It includes any natural or legal person who
has the right or the obligation to make available certain data or who can enable access to the
data by controlling the technical design of the product (Art. 2(6) DA). In other words, the data
holder is not necessarily the manufacturer, but could also be an entity within a DSI acting in
the interests of a manufacturer and effectively "holding" the data. If this is the case, the
requirements within the Data Act could also affect the governance of the CEADS (the extent
of the effects also depend on various design decisions, e.g. on the service portfolio of the
CEADS). As a result, some of the obligations set out in the Data Act will need to be considered
in relation to governance issues. This includes the provision on accessibility by design, but
also the obligation to respond to requests for access to data. In addition, the horizontal
obligations under Chapter III of the DA must be taken into account. These include e.g., a
number of technical and contractual obligations.
Rules on the interoperability requirements for data processing services, smart
contracts and European data spaces
Another specific objective of the Data Act concerns the development of interoperability
standards for data to be re-used between sectors. Therefore, Article 28 DA outlines the
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essential requirements for interoperability aimed at operators of data spaces to facilitate
seamless data sharing, data mechanisms, and services. These obligations encompass
providing a thorough description of the dataset content, use restrictions, licenses, data
collection methodology, data quality, and uncertainty to enable the recipient to easily find,
access, and use the data. Additionally, operators are required to describe data structures, data
formats, vocabularies, classification schemes, taxonomies, and code lists in a publicly
available and consistent manner to maintain a standardised understanding and handling of
data. They must also provide sufficient description of the technical means to access the data,
such as application programming interfaces, along with their terms of use and quality of service
to enable automatic access and data transmission between parties, potentially in real-time or
in a machine-readable format. Lastly, they are required to provide means to enable the
interoperability of smart contracts within their services and activities, facilitating a structured
and automated way of executing agreements and transactions based on pre-defined
conditions, thereby fostering a conducive environment for data interchange and collaborative
innovation.
The Article also empowers the Commission to adopt delegated acts to further specify these
essential requirements and suggests that operators adhering to harmonised standards
published in the Official Journal of the European Union are presumed to be in conformity with
the stipulated requirements. It also provides a framework for the Commission to request
European standardisation organisations to draft harmonised standards and to adopt common
specifications or guidelines to ensure conformity with the essential requirements. This extends
to adopting guidelines for interoperability specifications for common European data spaces,
covering architectural models, technical standards, and legal arrangements between parties
to foster data sharing, such as rights to access and technical translation of consent or
permission. Through these measures, Article 28 aims to establish a framework for
interoperability within and across various data spaces in the EU.
Implications for CEDS
Article 28 of the Data Act introduces several provisions that are likely to affect the governance
of the CEDS, including the CEADS. This article introduces requirements for standardisation
and harmonisation in data spaces across the European Union. It focuses on interoperability
aspects, such as data formats, structures, and access methodologies, aiming to facilitate data
exchange and usage across various sectors and entities.
The act also places an emphasis on transparency and accessibility. It requires detailed
descriptions of dataset content, data collection methodologies, and use restrictions. This
approach is designed to make data more accessible and in a structured and machine-readable
format, which could ease the process of data sharing and usage within CEDS.
In addition, Article 28 addresses the need for both technical and legal frameworks in the
governance of CEDS. It covers technical aspects including data formats and APIs as well as
legal aspects like use restrictions and licenses. This dual approach is intended to provide a
balanced framework for managing both the technical and legal aspects of data governance
within CEDS.
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2.2.4. The role of Member States in promoting the CEADS in the
context of current EU data legislation
The aim of CEADS is to develop a secure and trusted data space to enable the agricultural sector to
share and access data in order to improve the economic and environmental performance of the sector.
When considering the governance of CEADS in the context of the European Union's Digital Strategy, it
is necessary to recognise the regulatory framework provided by both the Data Governance Act and the
Data Act. The Data Governance Act (DGA) provides a regulatory framework for data intermediation
services and aims to increase trust in data sharing by ensuring neutrality and transparency of data
intermediaries. It is also relevant to the CEADS as it sets out a framework for how data is shared
between data owners and data users within DSIs. The Data Act (DA) is important for the CEADS as it
sets out rules for the fair access and use of data generated by networked products. Within the framework
established by the DGA and the DA, the role of EU Member States is to implement and enforce these
provisions. Member States' responsibilities include ensuring compliance and providing guidance to
stakeholders within their jurisdictions.
2.2.4.1. Conclusions
The DGA provides a blueprint for the governance of DSIs. However the requirements of DGA,
especially in Art. 12, only apply if the provider is a data intermediary service in the meaning of
Art. 2(10). In this context, the characteristic of establishing commercial relationships for the
purposes of data sharing between an undetermined number of data subjects and data holders
on the one hand and data users on the other, through technical, legal or other means is
particularly important. If the purpose of the CEADS is not to establish those kind of business
relationships, then the requirements of the DGA do not have to be complied with. In addition,
the provisions of the Data Act may also effect the governance of existing DSI and in
perspective also shape the governance of the CEADS. This concerns not only the guarantee
of data access rights, but above all the requirements for interoperability as outlined in Art. 28
DA.
CEADS will need to cooperate with the Member States, in order to ensure compliance, handle
complaints, conduct regulatory investigations and impose sanctions for violations and resolve
disputes.
2.2.5. Collaboration between Data Sharing Initiatives
This section presents insights from literature on collaboration between organisations that
regionally or nationally coordinate data sharing between organisations.
We focus on the governance of collaboration between organisations that coordinate Data
Sharing Initiatives in ecosystems of suppliers, partners, competitors, and customers. To start
with, data governance in ecosystems concerns arranged institutions and structures to ensure
that individuals behave in line with the collective goal(s) and that conflicts between them are
prevented or resolved, and the effective and fair use of collective resources within the inter-
organisational collaboration (Tijs van den Broek and Anne Fleur van Veenstra 2015).
The governance structure must provide control mechanisms to allow those collaborations in
data ecosystems. These control mechanisms include three dimensions: configuration,
structure and type of mechanism. The first one, configuration, has to do with the positioning of
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the governing actor or actors in the ecosystem. We can choose between a centralised,
decentralised, and self-organising configuration. Secondly, the structure describes
overarching governance arrangements for control and incentives. This relates to the question:
how can governance be organised in an inter-organisational and even cross-border data
sharing collaboration between organisations that govern data sharing initiatives? In general,
four types of governance can be identified: Market, Bazaar, Hierarchy, and Network (Dominik
Lis and Boris Otto 2020; Tijs van den Broek and Anne Fleur van Veenstra 2015). These four
types can be adapted to interpret inter-organisational data sharing collaborations in
ecosystems (Dominik Lis and Boris Otto 2020). When organisations involved want to retain
control over commercially sensitive data, setting up a purely commercially viable model for
cross-organisational data sharing seems to be difficult. This means that the market type is less
or not suitable for the European Agricultural Data Space. And concerning the network
governance type, (Provan und Kenis 2007) have proposed three different modes of
governance of networks: shared governance, lead organisation governance, and network
administrative organisation governance. Thirdly, the mechanism distinguishes between formal
regulations and informal mechanisms. It allows the enforcement of the control mechanisms.
Formal regulations provide rules that must be obeyed by all the organisations involved. We
can find informal mechanisms in the social norms. These can be used complementary to
support formal regulations. (Dominik Lis and Boris Otto 2020)
Besides data transactions in buyer-supplier relationships – i.e. the hierarchy governance type
– we notice that businesses in the agri and food sectors have also started to share data within
an ecosystem of partners through data platforms. The regulation of data access and use is
very important for the continued existence of these platforms and also for the perception of the
benefits, technological and organisational readiness, user-friendliness and security (Prieelle et
al. 2022), which result in the following requirements:
• Rules for the allocation of data ownership, access to data and access to the data
platform.
• Rules on data may be used and how the use and history of data can be verified.
• Potential benefits of data sharing: what is in it for your company? Money, services,
knowledge, relationships both now and in the future.
• Available technological means, i.e. appropriate infrastructure and technology in each
company for data sharing.
• Ease of use of the data platform for users.
• Security of the data platform: protection of the data on the platform.
Before data sharing between parties within a data ecosystem can and will occur, the parties
must first reach sufficient mutual trust to establish agreements and governance policies. Firstly,
data sharing depends on the collaborative or competitive nature of the network. Farmers do
not want to share confidential information if they feel they will lose their bargaining power. Also,
a powerful actor in the data ecosystem may use its power to force companies to share data.
Thus, the power structure of the agricultural data ecosystem is crucial for creating trust in the
ecosystem, which depends on three factors: (1) the data ownership, which describes who
controls and may use the data, (2) customer ownership, i.e. the establishment of direct
relationships with end users of data, and (3) data capacity, i.e. how intelligence is distributed
across different players in the ecosystem, based on algorithms and skills. Furthermore, data
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sharing can occur after a certain level of trust has been established to reach data sharing
agreements (D’Hauwers und Walravens 2022).
2.3. EU Code of Conduct for agricultural data sharing
Governance of collaboration for data sharing is about arrangements and structures to ensure
that participating organisations behave in line with the collective goal(s). With regard to
behaviour, the control mechanisms, and in particular formal regulations play an important role.
This includes governmental (legal) regulations (like the Free Flow Regulation, GDPR, Data
Act, Data Governance Act), but also self-regulation in the form of codes of conduct. A Europe-
wide code for data exchange in agriculture in Europe has only been in place since 2018. This
EU Code of Conduct supports the exchange of agricultural data between farmers, agricultural
advisors and AgTech providers. Although this code is a voluntary initiative without obligations,
it is seen as the basis for rights and obligations for platforms and companies.
Outside of Europe, in New Zealand, a code of conduct for agricultural data was published in
2014. And in 2016, the American Farm Bureau Federation's Privacy and Security Principles
were published in the USA. This code is primarily aimed at agricultural technology providers
(ATPs) and does not contain any guidelines for implementing its principles in practice. In 2020,
the Farm Data Code of Australia was drafted, which prioritises the needs of farmers.
What does such a code as the EU Code of Conduct for agricultural data sharing mean for the
governance of inter-organisational data sharing collaboration? Well, the governing body or
group of organisations responsible for the inter-organisational, cross-border data sharing
collaboration will have to take into account how the rules laid down in this EU Code, are
currently applied in the farm data sharing practice. The governing body (or group of
organisations) will have to decide, together with the participants of the collaboration in the data
ecosystem, whether and how to follow the rules of the EU Code or whether to adjust the rules
due to new insights or other points of view.
This can still be a difficult process, as research has shown that stakeholders in EU countries
currently have different views on the EU Code. There appear to be four predominant views
(Ryan et al., 2023; D1.2 Systematic assessment of the experiences with the Code of Conduct,
to be published): Abandoning the EU Code; aligning the terminology of the EU Code with the
Data Act; substantially updating the EU Code based on relevant EU legislation; focusing on
sector-specific allocation of data rights in the absence of binding rules.
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3. Approach and Methodology
The aim of this chapter is to guide the reader through the methodology used to define the
CEADS business model. It starts by presenting a comprehensive roadmap of the Action Plan
and, in the following chapters, its main modules (The Service Dominant Business Model Radar,
Stakeholder Clustering, Value Chain Network Analysis and Criteria for further analysis of
selected DSIs).
3.1. Business Models
AgriDataSpace coordinates the preparatory actions for the Common European Agricultural
Data Space and business models are one of its building blocks. When speaking about
business models in the context of data spaces, we need to make a clear distinction between:
• The business model of the data space as an infrastructure that can support multiple
use cases;
• The business models of the individual data space participants engaged in one or more
data space use cases.
Both the data space itself and its participants are parts of data ecosystems. The notion of
ecosystems in business has been borrowed from biology, where the natural ecosystems reach
equilibrium without central planning. Similarly, the data space participants have their own
business models, which are not centrally coordinated. Still, the overall data ecosystem
supported by the data spaces should be able to find equilibrium.
In order to analyse all the elements of the business model building block, we conducted a
systematic identification and evaluation of various Business Models employed by a
representative sample of Data Sharing Initiatives (DSIs). Through a meticulous analysis of the
advantages and disadvantages associated with these models, the overarching goal is to derive
a novel and robust Business Model specifically tailored for the Common European Agricultural
Data Space.
This endeavour aims to bolster the performance, sustainability, and widespread acceptance of
the data space within the dynamic digital market, thereby augmenting its overall efficacy and
influence. However, having in mind that Business Models constitute a living tool that interacts
with both the endogenous and exogenous environment, in order for the development of this
Business Model for the Common European Agricultural Data Space to be achieved, a series
of steps need to precede.
Therefore, the action plan adapted and implemented was based on the steps showcased in
Figure 7.
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Figure 7: Methodological Action Plan
3.1.1.1. The Service Dominant Business Model Radar
In an era marked by a shift from conventional product-centric business models towards
service-driven paradigms, the business landscape is witnessing a transformative wave of
innovation and value creation. This remarkable transition is driven by a heightened recognition
of the intrinsic worth of services in meeting evolving customer needs, stimulating continuous
innovation, and fostering enduring competitive advantages. Against this backdrop, the Service
Dominant Business Model Radar (SDBM/R)[1] emerges as a pivotal and indispensable tool for
organisations aspiring to forge new frontiers in business model design and orchestration.
Within the realm of the Common European Agricultural Data Space, the decision to embrace
the Service Dominant Business Model Radar is substantiated by an earlier conducted and
meticulously crafted value chain network analysis. This insightful analysis unveiled the intricate
web of interdependencies and opportunities within the agricultural data ecosystem, laying bare
the fundamental categories of value creation. By harnessing the deep-rooted insights gleaned
from the value chain network analysis, the Service Dominant Business Model Radar assumes
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a commanding role, unravelling a tapestry of service-driven opportunities and illuminating
pathways to an extraordinary and dynamic business model for the Common European
Agricultural Data Space. In this awe-inspiring quest, the Service Dominant Business Model
Radar becomes the compass that navigates the uncharted waters of service-centric
innovation, propelling the agricultural data landscape towards unparalleled horizons of
prosperity and sustainable growth.
A business model may take an informal scenario as a basis for inspiration, which is refined
during the design process into a description of a customer service scenario. This scenario
offers a brief description of the high-level operation of the future solution. In the context of a
service-dominant business model, the focus is on identifying the added value of the service to
the customer or user, as well as the functions and capabilities required by each party
participating in the model, including organisations, institutions, companies, and customers. The
expected costs and benefits are also considered in this evaluation.
The Service-Dominant Business Model Radar serves as a conceptual tool that can guide the
design of business models. It is designed with a network-centric approach, allowing for the
composition of service design in multi-party business networks. The radar defines how the
actors in the business ecosystem participate in value co-creation and outlines the distribution
of cost and benefits among them. Notably, the Service-Dominant Business Model Radar has
been successfully applied to represent the business models underlying various mobility
solutions.
Figure 8: Business model radar
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In a nutshell, the interactions and the value gained and offered, within all actors identified in
the SDBM/R, are explained below:
• The first concentric layer framing the value-in-use contains the actor value propositions,
which represent the part of the central value-in-use contributed by a single actor. The
co-production activity defines the activities that each actor performs in the business for
achieving the co-creation of value, i.e. its actor value proposition. The effects of this
activity can be observed by the customer.
• The third frame, actor cost/benefits, defines the financial (monetary) and nonfinancial
expenses/gains of the co-creation actors. Finally, each ‘pie slice’ of the radar
represents a co-creation actor, including the focal organisation, core and enriching
partners, and the customer. We placed the labels of the actors in the fourth frame. The
focal organisation is often the party that initiates the setup of the business model and
participates actively in the solution. The customer is always one of the parties
contributing to the production of the value-in-use. A core partner contributes actively to
the essentials of the solution, while an enriching partner enhances solution’s added
value-in-use. SDBM/R accommodates an arbitrary number of actors, according to the
network-centric character of SD business.
• All parties – including the customer – collaborate in such a way that each of them has
a clear interest in the business model. Collaboration is the basis for mutual ethical
benefit in the sense of SD logic. In concrete terms, this means that a business model
is designed in such a way that it brings benefits to all parties, but also incurs costs for
all parties. These benefits and costs can be of financial or nonfinancial nature. This
calls for bidirectional collaboration between actors rather than an outsourcing
relationship, which implies a client/server relationship with typically opposite interests.
• A business model defines a concrete value-in-use for a concrete customer segment,
and specifies its realisation, i.e. the way the customer experiences the creation and
delivery of this value-in-use. Therefore, a business model may take an informal
scenario as a basis for inspiration, which is refined during the design process into a
description of a customer experience (Bitner et al. 2008). The customer experience
offers a brief description for the high-level operation and future realisation of the
business model.
Having analysed all components of the SDBM/R and their interrelations, for the creation of a
business model design using the SDBM/R, the following design steps need to be implemented:
1. Identifying and agreeing on the co-created value-in-use and the targeted customer (or
customer-segment). The value-in-use is the added value of a solution for the customer,
who also contributes to its creation.
2. Determining the components of the value-in-use (actor value propositions) and
associated actors (roles). One actor is the focal organisation, often taking the role of
orchestrator. The number of actors is arbitrary, but it is a good practice to focus on the
core actors at the initial stages of the design to reflect only the essence of the model.
3. Determining the costs and benefits for each actor. These can be of a financial or a non-
financial character. A cost item of an actor typically relates to a benefit, often with
another actor(s). An optional practice at this step is to define the cost/benefit flow
among actors. This flow also provides an input for the customer journey and cost-
benefit analysis.
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4. Determining for each actor, the high-level activities that realise the actor-value
proposition. These activities become a part of the customer journey and will be mapped
-at a later stage- to (sequences of) tasks in use case descriptions/ business processes
executed by the actors in the network.
3.1.1.2. Stakeholder Clustering
The methodology, which was adapted, included the analysis of the stakeholders (both data
consumers and data providers) of 64 DSIs mapped within the WP1 survey, followed by their
clustering into a set of stakeholders (Farmers and Agricultural Producers, Technology and
Data Providers, Data Intermediaries and Service Providers, Government and Regulatory
Bodies, Financial and Insurance Services, Research and Academic Institutions, Business and
Industry Stakeholders, Multi-actor Collaborations), paving the way for the creation of the value
chain network.
This clustering approach allows us to uncover synergies, facilitate effective collaboration, and
unlock the full potential of data-driven innovations in the agricultural domain. Through a well-
structured value chain network, we can harness the collective capabilities of stakeholders,
promote sustainable data sharing practices, and drive positive impact across the entire
agricultural ecosystem.
The methodology adapted towards the mapping and identification of the stakeholders, based
on which the development of the main stakeholder’s categories was built upon included the
identification of the stakeholders involved at DSI level, followed by a accumulation of all
stakeholders from all DSIs, in order to create the main categories, which represent all
stakeholders. In Figure 2 the eight categories of stakeholders are presented, whereas in the
Annex I the full table of the analysis can provide extra information on the process.
Figure 9: Clustering of the DSIs’ stakeholders
With the completion of stakeholder’s categorisation, their further clustering according to their
roles and stakes (customer, partner, supplier, operator) took place, as well as their
relationships and interconnection with the value streams of the Common European Agricultural
Data Space (data, money, services).
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3.1.1.3. Value Chain Network Analysis
After the creation of the set of stakeholders, these were further distributed to the categories
covering the actors of the value creation network, according to their relation and
interconnection with the value streams. The actors of the value creation network consist of four
categories: customer, partner, supplier, and operator, whereas the components of data, money
and services constitute the value streams.
The actors in the value creation network and the value streams are interconnected and
mutually dependent within the value chain network. The suppliers provide the data, which is
accessed and utilised by partners and operators, who in turn offer services or products to
customers. The value streams (data, money, services) flow between these actors, creating a
collaborative ecosystem for value creation in the context of DSIs. In other words, the value
streams (data, money, services) flow through and connect these actors in the value chain
network, enabling the creation, exchange, and utilisation of value within the DSIs. Each actor's
role and contribution determine how the value streams are leveraged, combined, and
transformed to deliver value to the customer and other stakeholders involved in the data
sharing initiatives.
Customers: Customers are the end users or beneficiaries of the value created through the
Common European Agricultural Data Space. They derive value from the insights, services, or
solutions generated from the shared data. Customers include farmers and agricultural
producers, government and regulatory bodies, business and industry stakeholders, multi-actor
collaborations, research and academic institutions.
Table 3: Customers’ Relation to Value Streams
Customers’ Relation to Value Streams
Data
Customers access and utilise the shared data to gain insights,
make informed decisions, or conduct research.
Money
Customers may contribute financial resources to access or
acquire the shared data or derived services.
Services
Customers may receive specialised services, such as data
analytics, customised solutions, or research findings, based on
the shared data.
Partners: Partners refer to entities or organisations that collaborate and participate in the value
chain network to support the Common European Agricultural Data Space. Partners can include
technology and data providers, financial and insurance services, data intermediaries and
service providers, government and regulatory bodies.
Table 4: Partners’ Relation to Value Streams
Partners’ Relation to Value Streams
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Data
Partners contribute their data to the shared pool, enriching the
available dataset for analysis and insights.
Money
Partners gain financial capitals from the provision of their
offered services.
Services
Partners may provide specialised services or expertise to
optimise data collection, integration, analysis, or other value-
added services, such as legal and governmental clarifications
and guidelines.
Suppliers: Suppliers encompass entities or organisations that contribute to the data and
services provided within the Common European Agricultural Data Space. They include farmers
and agricultural producers, technology and data providers, DSIs.
Table 5: Suppliers’ Relation to Value Streams
Suppliers’ Relation to Value Streams
Data
Suppliers provide data resources that form the foundation of
the shared dataset.
Money
Suppliers may receive financial compensation for their data
contributions or services rendered.
Services
Suppliers may offer specialised services, technology
solutions, or expertise to support data sharing, integration, or
analysis.
Operators: Operators play a crucial role in facilitating and managing the value creation
processes within the Common European Agricultural Data Space. They provide the
technological infrastructure, platforms, tools, and services to enable secure and efficient data
sharing among stakeholders.
Table 6: Operator(s)’ Relation to Value Streams
Operator(s)’ Relation to Value Streams
Data
Operator(s) manage the data sharing platform, ensuring data
security, privacy, and access control.
Money
Operator(s) may charge fees for platform usage, data storage,
or specific services provided to stakeholders.
Services
Operator(s) offer services such as data integration, analytics,
visualisation, or data management to enhance the value
derived from the shared data.
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With the actors and value streams having been analysed, the actors of the value creation
network of DSIs for the Common European Agricultural Data Space, considering the set of
stakeholders presented above is as follows:
Table 7: Stakeholders categorisation
Actors of Value Creation Network
Stakeholders Categorisation
Customer
Farmers and Agricultural Producers
Government and Regulatory Bodies
Business and Industry Stakeholders
Multi-actor Collaborations
Research and Academic Institutions
Partners
Technology and Data Providers
Financial and Insurance Services
Data Intermediaries and Service Providers
Government and Regulatory Bodies
Suppliers
Farmers and Agricultural Producers
Technology and Data Providers
Operator(s)
Research and Academic Institutions
Multi-actor Collaborations
Technology and Data Providers
Business and Industry Stakeholders
The clustering of the key stakeholders' categories into the actor of value creation were
presented and validated during the first internal consultation workshop with the AgriDataSpace
consortium (July 2023). Moreover, in order to validate the analysis of the DSIs, we asked the
consortium for their perspective on the value chain network. Different approaches were
presented (based on three working groups) and a consolidated result of this interactive
workshop is the following network:
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Figure 10: Value Chain Network example
The importance of the development of this value chain network resulting from the analysis of
the 64 DSIs lies in the fact that this network will form the basis on which the business model
for the Common European Agricultural Data Space will be built. Certain groups of
stakeholders, such as farmers, have a dual role (e.g. as customer and supplier) within the
value chain network for the Common European Agricultural Data Space.
Regional Differences
Aiming at creating a Common European Agricultural Data Space, embedded into the evolving
legislative framework and agreed ethical standards, especially on data sovereignty, including
representatives (legal experts) for possible regional differences is of utmost importance. The
countries operate differently in terms of Data Sharing Initiatives (DSIs). These differences can
arise due to varying regulatory frameworks, cultural factors, technological capabilities, and the
specific needs and priorities of each region. More specifically, the parameters which have to
be considered are the following:
• Regulatory Frameworks: Different countries may have distinct legal and regulatory
frameworks governing data protection, privacy, and sharing. These regulations can
influence the approach and requirements for data sharing initiatives. For example,
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some countries may have stricter data protection laws that impact how data can be
shared and accessed.
• Cultural Factors: Cultural factors can also play a role in data sharing initiatives.
Attitudes towards data privacy, information ownership, and collaboration can vary
across countries and impact the willingness of organisations and individuals to
participate in data sharing initiatives.
• Technological Capabilities: The level of technological infrastructure and capabilities
can differ between countries or regions. This can affect the feasibility and scalability of
data sharing initiatives. Countries with more advanced technological infrastructure may
have an easier time implementing large-scale data sharing initiatives.
• Needs and Priorities: The specific needs, priorities, and challenges faced by different
regions can shape the focus and scope of data sharing initiatives. For example,
agricultural practices, environmental conditions, or market dynamics may vary between
countries, necessitating customised approaches to data sharing in the agricultural
sector. It's important to consider these regional differences when designing and
implementing data sharing initiatives, as they can impact the success and adoption of
such initiatives. Collaboration and knowledge exchange between different regions can
also be valuable to learn from best practices and address challenges collectively.
Navigating regional regulatory differences is crucial for DSIs, requiring identification of key
legal considerations. Involving legal experts in data protection and privacy ensures
compliance, particularly vital for Data Sharing Initiatives within the EU, as seen in the WP1
survey of identified DSIs.
When operating Data Sharing Initiatives within the European Union (EU), as is the case of the
identified DSIs within the WP1 survey, several legal aspects should be considered due to their
impact on business relationships, such as:
• Data Ownership and Intellectual Property Rights: Clear agreements must address data
ownership and intellectual property rights in data sharing initiatives. This involves
determining who owns the data, usage conditions, and addressing intellectual property
aspects like copyrights or patents. Organisations should define ownership, establish
licensing arrangements when necessary, and ensure clarity to prevent disputes over
data usage and ownership.
• Data Protection and GDPR Compliance: Ensuring compliance with data protection and
privacy laws, such as the GDPR, is crucial in data sharing. This involves adhering to
legal requirements on data collection, consent, storage, security, and the rights of data
subjects. For any DSI operating within the EU, GDPR compliance is essential,
encompassing strict rules on processing and protecting personal data. DSIs must
establish legal grounds for processing, obtain valid consent, implement data protection
measures, and adhere to data subject rights.
• Data Sharing Agreements: Establishing clear contractual agreements is essential for
data sharing initiatives. These agreements, including data sharing agreements, must
outline the purpose, data access, usage rights, obligations, dispute resolution, and
termination conditions. They define the rights, responsibilities, and obligations of each
party involved, covering aspects like data ownership, permitted use, confidentiality,
security measures, breach notification, and liability. Alignment with applicable data
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protection laws is crucial, and careful drafting is necessary to protect the interests of all
involved parties.
• Confidentiality and Non-Disclosure: Data sharing initiatives often involve sharing
sensitive or proprietary information. Legal agreements, such as confidentiality or non-
disclosure agreements, can be used to protect the confidentiality of shared data and
prevent unauthorised disclosure or misuse.
• Liability and Indemnification: Data sharing initiatives should define the liability of
participating parties regarding the accuracy, security, and compliance of shared data.
Agreements should outline the responsibilities of each party and include provisions for
indemnification in case of data breaches, non-compliance, or other legal issues.
• Security and Cybersecurity: DSIs involve the exchange and sharing of data, making
data security and cybersecurity critical aspects to consider. Organisations should
implement appropriate technical and organisational measures to ensure the security of
shared data, protect against unauthorised access or breaches, and safeguard the
integrity and confidentiality of the data.
• Sector-Specific Regulations: Depending on the nature of the data being shared, DSIs
may need to comply with sector-specific regulations. For example, if the data includes
sensitive agricultural information or genetically modified organism (GMO) data,
additional regulations may apply that govern their collection, storage, and sharing. It's
important to consult legal experts with expertise in data protection, privacy, and relevant
sector-specific regulations to ensure compliance and mitigate legal risks within your
specific DSI operating within the EU.
Identifying these key legal considerations is a fundamental step in the creation of the
stakeholders and hence the value chain network, as the involvement of legal representatives
and experts specialised in data protection and privacy is more than important to ensure that
the European agricultural data space is operated in accordance with the relevant legal
requirements and to mitigate potential legal risks.
3.1.1.4. Criteria for further analysis of selected DSIs
The selection of data sharing initiatives (DSIs) is a crucial step in the development of new
business models that rely on collaborative data exchange. In order to identify the most suitable
DSIs for further analysis, several criteria have been applied. These criteria ensure that the
selected initiatives are diverse, representative, and cover a range of different business models
and revenue streams:
• Are already in the mapping of the Holistic Analysis Framework.
• Cover both public and private ownership.
• Include new and mature DSIs.
• Include national and international DSIs.
• Cover all European regions.
Firstly, the DSIs chosen for analysis are already in the mapping of WP1 (Holistic Analysis
Framework). This criterion ensures that the analysis builds on existing knowledge and
information and avoids duplication of effort. This means that they have already been identified
as relevant and important for the project and have undergone some initial evaluation. This
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criterion provides a starting point for the selection process and ensures that the chosen
initiatives are already considered as viable options.
The second criterion applied in the selection of DSIs is to cover both public and private
ownership. This criterion ensures that the analysis captures the full spectrum of data sharing
initiatives, including both types of ownership, allowing for a broader understanding of how
different ownership models impact the success of data sharing initiatives. This will provide
valuable insights into the advantages and disadvantages of each ownership type.
Moreover, an additional criterion of significant importance, constitutes the business model
sustainability. The business model of the selected DSIs should be financially sustainable and
scalable. This will ensure that the European Data Space for Agriculture will be built upon
financial sustainable and scalable business models, contributing this way to its overall long-
term operation, growth and expansion, increasing the added value for the stakeholders.
The maturity of the initiative is also a critical criterion for the selection of the DSIs, and as so
initiatives that are already established and operational are being considered, since the aim of
the AgriDataSpace project is to identify best practices and opportunities for improvement in
existing initiatives, rather than to explore nascent or theoretical initiatives. The analysis focuses
on initiatives that are already in operation or have reached an advanced stage of development.
The rationale behind this choice is that mature initiatives have more significant impacts, and
their experiences can provide valuable insights into the challenges and opportunities of data
sharing. The sources used to identify the maturity of the DSIs include the initiatives’ websites,
reports, and articles.
An additional criterion that poses of great importance, is the selection of DSIs, including both
national and international initiatives. This ensures that the analysis captures the full
spectrum of data sharing initiatives in Europe and beyond, which will provide insights into how
different legal frameworks and cultural norms impact the success of data sharing initiatives.
This will also help identify best practices and common themes that can be adopted across
different regions.
Finally, we also focused on the geographical diversity. The aim of the chosen DSIs is to
cover a range of initiatives from different regions of Europe, including Central Europe
(Germany), Eastern Europe (Greece), Northern Europe (UK), Southern Europe (Italy), and
Western Europe (France, Netherlands, Belgium) and in different status of development. This
ensures that the selected initiatives are deriving from a broad and representative sample of
the whole of Europe and not just a specific region. This criterion is essential because it
safeguards that the analysis is comprehensive and provides a broad understanding of data
sharing initiatives across Europe. However, this criterion was balanced against the need to
focus on initiatives that were most relevant and mature, meaning that some regions may be
underrepresented in the final selection. This criterion was based on an analysis of the
geographic distribution of existing data sharing initiatives in Europe.
In conclusion, the selection of the 15 data sharing initiatives is a critical step in the development
of new business models that rely on collaborative data exchange. The criteria outlined above
ensure that the selected initiatives are diverse, representative, and cover a range of different
business models and revenue streams, deriving from rigorous and comprehensive research.
The analysis of these initiatives will provide valuable insights into the strengths and
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weaknesses of different models, legal frameworks, and cultural norms. These insights will be
used as a starting point for the development of new business models for the European Data
Space in Agriculture creating this way of value for all stakeholders involved.
3.2. Analysis framework for governance schemes
Based on the input from Work Package 1: “Understanding and mapping of the data sharing
landscape”, we identified a selection of Data Sharing Initiatives. The selection criteria
encompass the geographical distribution, maturity of the business models and also readiness
to cooperate with AgriDataSpace project (we only could consider the DSIs who answered our
interview requests). The analysis framework incorporates organisational and data sharing
governance as well as legal aspects outlined in chapter 2.
For the analysis of the selected DSIs, we employed desk research exploring the internet web
pages of the DSIs as well as semi-structured interviews. The interviews were recorded and
transcribed. The interview guidelines include questions regarding general information about,
e.g. legal form as well as organisational and data sharing governance. The following table
illustrates an example for the profile of 365FarmNet that follows the applied analysis
framework. Annex IV includes all profiles of the analysed DSIs.
3.2.1. Example profile of a DSI (365FarmNet)
General information
Name
• 365FarmNet GmbH
• 365 refers to the intended
consistent availability
• Website: https://www.365farmnet.com/
Legal form
• Limited liability company
• Under German law („Gesellschaft mit beschränkter Haftung“)
Geography
• The free basic version is available worldwide.
• The availability of paid modules depends on the country.
• For example, the module on fertilization is available in Austria,
Poland, Germany, Switzerland and France, as it has to take the
national and regional fertilization ordinances into account.
Sector
• Agriculture: Farm management for crops and livestock
Scope
• 365FarmNet is an information and work platform on which the
agricultural operating processes are linked together intelligently.
• Its aim is to network and transparently document operations
throughout the whole agricultural production process to improve
planning and efficiency, helping the farmer to focus on the work.
• The DataConnect initiative is a collaboration between John Deere,
CLAAS, CNH and 365Farmnet: a direct cloud-to-cloud-solution for
automated transfer of machine data between the clouds into the
chosen Farm Management Information System (365FarmNet).
• Exemplary use case of “precision farming”: Using applications
maps for the field to plan operations such as harvesting, being able
to involve contractors and use a “colorful” fleet of agricultural
machinery (from various manufacturers).
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Business approach
• Business model freemium for the software platform 365FarmNet:
Free basic version and payment for various modules with country-
specific availability and variations.
• The DataConnect functionalities and other modules from partners
are provided via the software platform 365FarmNet.
• The prices for modules depend on the farmed hectares and the
module.
• For modules from partner companies: 365FarmNet GmbH and the
partner company share the revenue (collected by 365FarmNet).
Lifecycle phase
• Started the initiative in 2013 as an agricultural pioneer in cloud data
management and mobile applications.
• 365FarmNet with modules (e.g. DataConnect) is in operation
• Currently scaling regarding the paid modules, features and
international availability
Organizational Governance
Participants
• Farmers are the main group of customers
• Involved companies: Partners can provide modules, data or other
services for the 365FarmNet platform; there are 20 partners
named on the website (e.g. BASF, AGRARMONITOR).
• Partners for the DataConnect initiative: manufacturers of
agricultural equipment (John Deere, CLAAS, CNH)
Organizational mode
• Central, commercial organization
Governing bodies
• 365FarmNet GmbH is owned by CLAAS (subsidiary company),
they hold decision rights via the advisory board.
• 365FarmNet GmbH has a board of directors with a managing
director and the management team with departments: a lean
commercial structure for decision making and providing the basic
platform.
• The collaboration with partners is mainly about product / services
to provide farmers with a variety of modules on the open platform.
• Customers provide feedback and are in the development focus,
but not part of the governance system of the 365FarmNet GmbH.
Collaboration
• Partners conclude bilateral agreements with farmers using their
services (e.g. modules, data) to regulate the financial and data
sharing aspects.
• 365FarmNet is the platform provider and the intermediary between
the farmer and other service providers (modules, data).
• Decision-making process at the 365FarmNet GmbH: Feedback is
appreciated, but no formal involvement of farmers or partners.
Onboarding
• New partners:
o setup of partner contract with 365FarmNet,
o support for development of module with alignment of data
formats
o setup of the offer on the platform
o customers have to agree to the contractual terms (incl. data
sharing regulations) before using the partner’s applications on
the platform
• New customer (farmer):
o free usage and setup of 365FarmNet basic features
o setup of farm data and data import via provided APIs
o chosen paid modules: setup of contractual agreements with the
providers
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Data Sharing Governance
Data characteristics
• The main source of data is the agricultural machinery.
• Integrated services, e.g. weather forecast data provision, data
exchange with contractors, management of market data for profit
management
Roles regarding data
sharing
• Each farmer has data sovereignty for their data on the platform.
• The application of modules is based on the available data of each
farmer in the 365FarmNet software.
• The access to farmers’ data for partners of 365FarmNet depend
on the bilateral contracts between farmer and the partner
company.
• 365FarmNet is the platform provider, it does not use the data for
purposes except the provided basic features of the platform.
Technical foundation
• Online system with mobile and offline accessibility via cloud, e.g.
for documentation on premise (field / stable)
• 365FarmNet runs on all standard browsers and is not dependent
on the operating system.
• Open platform concept with basic functionalities and additional
modules from cooperation with partner companies.
• Modular system for adaptation to the farmers requirements
• Access rights feature for security and collaboration support
• Data transfer via ISO-XML format
Data transactions
• All data is collected and stored on the platform, e.g. via transfer of
data from agricultural machinery to the 365FarmNet cloud system.
• 365FarmNet provides the needed access on farm data for the
data-based services and partner companies: data transfers from
farmers to services providers are a service of 365FarmNet for
application of data-based services (modules).
• No data transactions solely for data sharing with third parties, but
farmers are enabled to share data themselves for other
applications via exchange formats.
Legitimate purpose
• Farmers manage their data.
• Contracts regulate the legitimate purpose of any data usage.
• 365FarmNet follows strict guidelines on data security, privacy and
business secrets.
Risk and change
management
• Management via contracts: Partner contracts between service
providers and 365FarmNet, which also supports trust through clear
regulations.
• 365FarmNet is not involved in the contracts between service
providers and farmers (e.g. regarding misused data).
• Clear statements on farmers owning their data by company
leaders to facilitate trust as a cloud pioneering company.
Governance of Collaborations with other DSIs
Current state
• Platform concept: 365FarmNet is highly interconnected with other
companies.
• Agrirouter is one of the partners of 365FarmNet.
• 365FarmNet GmbH is a member of the AEF e.V. and the
DataConnect initiative is a basis for the AgIN project of AEF e.V.
Future plans
• Open for cooperation via cloud (no peer-to-peer data exchange)
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Key Insights on Governance
Profit-oriented business company: The legal form of a GmbH (limited liability company) was
chosen in particular for legal reasons to minimize risk and to balance costs and profits.
Additionally, it suits the fact that 365FarmNet sees itself as a software provider and IT
company. Its establishment as a startup formally serves to separate 365FarmNet's business
field from CLAAS' main agricultural machinery business. It also underlines the manufacturer
independence of the 365FarmNet applications.
Establishing trust: The initiative for 365FarmNet arose in the early phase of cloud
establishment in the agricultural sector. As a pioneer in mobile and cloud applications, the
company had to deal with open scepticism in the industry, regarding data security in cloud
storage among other things. 365FarmNet earned its trust over time, initially supported by
statements from well-known CLAAS company representatives. An inference of trust due to the
parent company CLAAS as an established machine manufacturer can also be assumed. At
the same time, the focus on a cross-company solution for “colourful” fleets corresponds to a
high level of customer orientation and justifies the spin-off.
Acting like a data trustee: Many of 365FarmNet's basic principles correspond to the
specifications for data trustees. 365FarmNet initiated them as trust-building measures in the
early phase of cloud use. Central to this is ensuring data sovereignty. There is a high level of
sensitivity here with statements such as "data belongs to the farmer". As a platform provider,
365FarmNet manages the customer data provisionally and does not access it contentwise. In
addition, the strict consent solution is used for data transfers. Customers must conclude
contracts with each software solution provider in order to use paid modules and agree prior to
data transfers.
Requirements to and role of CEADS: The interview partner is missing structure in the current
discussion on data sharing, e.g. regarding pricing and costs and concrete business initiatives
with long-term perspective. A startup on agricultural data sharing will not be profitable in his
mind. On the other hand, the agricultural community is highly networked, raising the question
on the role and provided services of the future CEADS. The ecosystem needs to know, what
added value the CEADS will provide them with. The interview partner doesn’t see a need for
a new institution for central data storage, which the agricultural companies would have to pay
for. He refers to decreasing pricing for cloud storage and companies being able to set up their
own clouds nowadays.
Key Insights on the Agricultural Sector:
Regulations and subsidies: For farmer’s work, regulations such as the Fertilizer Ordinance are
central, as they lead to a lot of effort for reports and applications and significantly influence
their work processes, yields, subsidies and topics such as plant protection. 365FarmNet faces
the challenge and high efforts of including the federated system of regionally individual
specifications and structures in the software modules. This reduces the availability of the
software solution to a few countries and, in the view of the interview partner, also results in
unnecessary effort. Here he calls for political will to unify and standardize the regulations at
the European level.
Demand for networking and standardization of machine and farm data: The initiative
DataConnect is one step 365FarmNet took in this direction, in cooperation with selected
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machine manufacturers. They deliberately decided not to aim for market-wide standardization
in this first step. DataConnect is now being further developed in the AgIN project with the
participation of the many members of the AEF e.V.. The aim is for agricultural machinery to be
able to exchange data with each other and with clouds independently of manufacturers, and
for their formats to be standardized internationally accordingly. In addition, the interviewee also
calls for political efforts to standardize data management on farm data such as area and soil
quality across Europe and to make it available centrally. Currently, there are many data silos.
The key insights from the interviews create a baseline for the requirements on business models
(see previous section) and a multi-stakeholder governance scheme for CEADS. The resulting
design options for multi-stakeholder schemes are based on the guidelines from literature (see
chapter 2) and combined with the key insights from the interviews. The regulatory requirements
coming from DGA and DA frame the final purpose and services of CEADS in the legal context.
3.3. Analysed Data Sharing Initiatives
The following Table 8 depicts which data sharing initiatives were analysed in Task 2.1 on
governance and in Task 2.2 on business models. The referenced chapters summarise the
results of the conducted data collection and DSI-specific insights from the analysis. The
resulting DSI profiles of the governance schemes and business models only partially overlap,
which is due to the quality of information publically available on the websites of the
corresponding organisations. The governance schemes are based on individual interviews and
provide in-depth insights into the organisational structures that are usually not available on the
internet. The information on service offerings and respective business models is usually
provided online. Together with our partner FoodScaleHub, responsible for analysing business
models, we joined our forces and compiled a selection of 20 DSIs whose quality of information
provides insights and observations for the final recommendations for the CEADS.
Table 8: Overview on analysis of governance and business models for agricultural data
sharing initiatives
Chapter
Data Sharing Initiative
Governance
Analysis
Business
Model
Analysis
Geographical
Coverage
4.1.1
365FarmNet GmbH
yes
yes
Austria
Poland
Germany
Switzerland
France
4.1.2
Agdatahub
yes
yes
Mainly France
but not excl.
4.1.3
AgIN / AEF e.V.
yes
no
Worldwide
4.1.4
Agricolus
yes
yes
Italy
4.1.5
Agri-Gaia
yes
no
Germany
4.1.6
Agrimetrics
no
yes
UK
4.1.7
Agrirouter e.V.
yes
yes
International
4.1.8
AgroDataCube
no
yes
Netherlands
4.1.9
Altas
yes
no
International
(EU)
4.1.10
AVR Connect
no
yes
Belgium
4.1.11
Cipher Trust Data Security
Platform
no
yes
-
D2.1: Multi-stakeholder Governance Scheme and
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4.1.12
COGNAC
yes
no
Germany
4.1.13
DjustConnect
yes
yes
Flanders,
neighbouring
regions, coop.
agreements
France,
Finland
4.1.14
Eden Library
no
yes
Greece
4.1.15
Hortivation Hub
yes
yes
Netherlands,
expansion to
west and
central EU
4.1.16
iDDEN GmbH
yes
yes
Worldwide
4.1.17
John Deere Operations
Centre
no
yes
Germany
4.1.18
JoinData
yes
yes
Netherlands
Belgium
4.1.19
ProAgrica
no
yes
Worldwide
4.1.20
ZEROW
yes
no
International
(EU)
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4. Results of the Analysis of Business and
Governance Models
To highlight key insights, a subsequent text points out links between business and governance
aspects and perceived success factors, interesting statements from the interviewed person as
well as overarching remarks, e.g. on the agricultural sector, other DSIs and a future CEADS.
4.1. Synthesis of Agricultural Data Sharing Initiatives
The agricultural sector consists of a large variety of sub-sectors and value-chains, which rely
on a variety of different technical systems. It also involves a variety of diverse stakeholders,
with diverse needs that are addressed by digital services that require sharing data across
companies. Each of the DSIs interviewed focuses on the data-related needs of a specific
stakeholder group with respect to the development or deployment of specific data-driven
services that require data-sharing. The interviews have provided a broad overview of this
variety and of common and useful choices for governance schemes and business models.
This section summarises the key insights gained from the interviews and the requirements for
CEADS derived from these insights. Section Results on the Governance for DSIs 4.1.1 starts
with a summary of key insights on DSI organisational and data governance, before CEADS
requirements are derived from these insights. A corresponding summary of key insights on
DSI business models and resulting requirements for CEADS follows in Section 4.1.1.4.
4.1.1. Results on the Governance for DSIs
4.1.1.1. Key insights on Organisational Governance
For the organisational governance, a trustworthy and credibly neutral stakeholder as
platform operator as well as transparency about business practices is widely considered a
prerequisite for DSI success.
Neutrality can be achieved with a variety of legal forms:
• Limited liability companies are the most common legal form for DSI operators,
especially for DSIs that are already operational or scaling. In DSIs with private
ownership, credibility as a neutral operator can be achieved with a suitable structure of
company members (e.g. including public bodies or a range of ecosystem
representatives or associations) and company statutes that ensure a neutral position
in the market.
• Some DSIs are operated by professional associations for ecosystem stakeholders,
which are by nature of the operating organisation’s governance already credible as
neutral stakeholders, as equal membership is open to all players in the market.
• A foundation and a public-private agro-tech research institute are also among the DSI
operators. They are credible as neutral operators as in both cases, the organisational
mission is supporting farming companies, the track record proves this commitment to
the mission statement and these platforms are non-profit.
• Four DSIs are the results of research projects and do not have an operator outside the
project team yet.
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In the agricultural sector, farm data is not usually stored on the premises of a farm, but in cloud
systems (platforms) of, on the one hand, farming technology providers (farm information
management systems, farming machinery providers, farm sensor system providers, …) and,
on the other hand, the farm’s business partners (suppliers and customers). DSIs help to share
and integrate this data to support the development and deployment of data-driven services.
For the majority of DSIs and applications, this leads to the distinction of three technically
different kinds of stakeholders involved:
• Data providers are mostly the technology providers and business partners of farms
that store farm-related data. Providers of non-farm data (e.g. weather data providers or
public data providers) sometimes also participate in agriculture DSIs as data providers,
but most interview partners saw them as less central and also less challenging to admit,
because data rights ownership is simple (just one rights holder) in these cases.
• Data rights holders are farmers and farmer organisations, even if data providers store
and transmit farm data. That implies that there are multiple data right holders, and thus
have to give their permission to the sharing of farm-related data. In the interviews and
this report, farmers are therefore referred to as data right holders.
• Data consumers are digital service providers. In some cases, these are dedicated
data-driven service companies (e.g. start-ups). In many cases, however, the data
consumers are from the same stakeholder group as the data providers (i.e. farm
suppliers or customers), who want to combine their data with those of other data
providers in order to develop data-driven services for themselves, for the farmers or for
third parties.
Given that, depending on the use-case, the same stakeholder can act as data provider for one
use-case and as data consumer for another use case, there is usually no formal distinction
between these roles on the level of organisational governance, i.e. there tends to be one kind
of membership for the providers and consumers of farm data (pricing, terms and
conditions).
Concerning the involvement of data right holders (farms), different models are implemented:
• In most DSIs, data right holders (farms) are admitted as a second type of active
participants on the platform with different terms and agreements (frequently without
fees).
• In some DSIs (especially early-stage), there is just one type of membership for all
stakeholders
• In one DSI, data right holders (farms) are not platform participants, but are indirectly
involved by using the services of the platform participants.
Concerning the inclusiveness of the DSIs, different practices are pursued depending on the
DSI’s scope and business model. DSIs whose main function is that of a data-intermediary for
the entire agricultural sector usually have inclusive statutes and business practices, as the
value of their DSI grows with the number of stakeholders, data-sets and services registered
on the platform. DSIs that focus on making specific services available to a specific user group,
on the other hand, tend to be more selective and focus on partnering with the stakeholders
necessary for the implementation of their core service.
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Decentralised decision-making is only practiced in some DSIs that are under development in
research projects. Central decision-making by the operating institution is practiced in the vast
majority of DSIs. Mechanisms to coordinate development and operations with ecosystem
representatives are widely implemented, firstly, for feedback and improvement of service
quality and secondly to be transparent and establish trust within the ecosystem. This tends to
happen on three levels:
• Firstly, due to the usually neutral position of the operating organisation in the market
(cf. beginning of this section), the governance of the operating organisation most of
the time already implements a decision-making process that ensures consensus
among important ecosystem stakeholders in DSI governance, e.g., by corporate or
association law.
• Additionally, most DSIs have set up advisory boards, steering committees, ethics
committees and user groups to ensure stakeholder involvement or have plans to do
so. However, these bodies typically have a consulting role.
• Early stage DSIs sometimes rely on informal methods for stakeholder involvement
(typically, the partners for pilot use-cases serve as representatives).
4.1.1.2. Key Insights on Data Sharing Governance
The majority of the interviewed DSIs function as data-intermediaries and orchestrate
decentral data-sharing between data providers and data-consumers, without collecting data.
Transparency on these practices is described as essential to gain ecosystem trust.
• The orchestration of federated data-sharing in line with Gaia-X and/or IDSA
specifications is the most commonly adopted approach, but DSIs differ in the extent to
which the required services, e.g. a data catalogue, a service registry and specific
identity management, are already implemented.
• Few DSIs pursue a related decentral approach (orchestrating decentralised data
exchange and providing interfaces), but do not develop against Gaia-X specifications
(yet).
• One DSI temporarily stores data on their platform (maximum: 24h) as part of their data
exchange service, which allows them to ensure the successful transfer and to perform
data quality checks.
Two DSIs offer a suite of selected data-based services for farmers and no data exchange
services. They centrally collect data from selected partners for service development and
therefore are DSIs only in a very wide sense of the term. In some research projects there is no
working technical infrastructure for data sharing (yet).
Different solutions were implemented to obtain the permissions of the data right holders
(farms):
• Some DSIs obtain the farm’s permission for data exchange indirectly: The farm
consents to data transfers when agreeing to use a data-driven service (“data against
service”). This approach is typically adopted for use cases, in which farm
representatives have little interest in or understanding of the data-driven technology
and want solutions to work without hassle (e.g. when a smart tractor and a smart
implement exchange data for robust operation). Indirect consent can even be obtained
for an entire DSI, if the purposes of the DSI are sufficiently limited and all data-providers
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(platform operators) include corresponding clauses in their service’s terms and
agreements.
• Other DSIs require the explicit consent of a farm to any transmission of data related
to their farm (e.g. via a dashboard). In these DSIs, membership for farmers is typically
free and there is a strong focus on dashboard usability to smooth the process of
obtaining permission. Explicit consent is typically chosen if a DSI has a strong focus on
data sovereignty for farms or if a DSI cannot obtain implicit consent because it includes
use-cases where the farmer is not a direct user/beneficiary of all services that rely on
their farm-related data.
In order to be perceived as reliable and trustworthy and to comply with current legislation, DSIs
have to ensure the quality and trustworthiness of the services that they offer to their participants
by implementing suitable risk management processes. Data mediated by the platform should
only be used for legitimate purposes, which is usually ensured by limiting the purposes of
data usage and transmission to developing and/or using a given data-driven service in the
DSI’s terms and agreements with data-providers and data-consumers. In the hypothetical case
of suspected misuse, most DSIs would support possible action by a harmed party by providing
activity logs and by removing participants that do not adhere to terms and agreements from
the DSI and de-publishing its data, but would not take legal action themselves to counter this
misconduct. As data-intermediaries, the DSIs consider such legal action outside the realm of
their responsibility. One DSI is setting up an ethics committee for dispute management. On the
other hand, the reputation of a DSI is related to the quality of the data and services it offers. In
the interviews, the DSI’s approach to quality management depended on their role in the data-
value-chain. DSIs that act as data-intermediaries for the entire sector usually do not have a
strong focus on quality management, as decentral data-sharing makes an explicit data quality
management impossible because the DSIs only transfer meta-data and because data quality
requirements may differ from data consumer to data consumer. On the other hand, DSIs with
a focus on providing data-driven services for a specific application area or sector vertical
tend to have a stronger focus on quality management and may even implement service
quality control, which includes data quality control as part of their value proposition. Such
different approaches to quality management and other risk management can be a challenge
for implementing platform collaborations.
4.1.1.3. Key insight on the Governance of Collaborations with other DSIs
Many DSIs reported active collaboration with and contribution to existing standardisation
initiatives. Some of the interviewed DSIs were members or partners of other DSIs, mostly, if
they focused on different parts of the same value-chains (i.e. cross-regional use-cases or
cross-sector-vertical use-cases). Partnering with complementary organisations (e.g. a
blockchain organisation for traceability applications) that are not DSIs was also mentioned.
DSIs that act as data-intermediaries for the entire agriculture sector, typically focus on farms
in a specific region, as this allows them to build up trust within the regional community and
focus on addressing the regional stakeholders’ needs. Such regional data intermediaries have
a strong interest in cooperating with DSIs focussing on farms in other regions, as they expect
strong synergy effects from joining forces. Especially mature (operating or scaling) DSIs are
keen to collaborate with other DSIs, because they are aware of the immense effort
involved with getting individual services off the ground due to technical interoperability issues.
They therefore see value for the ecosystem as a whole in joining forces to provide more
services to more users and realise vertical use cases across borders and sectors.
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ODSIs that offer services for closed value chains that are fully represented in their initiatives
are less interested in cooperation between DSIs as they cannot derive any significant benefit
from it. DSIs at an early stage (research projects) also have less activity and interest in cross-
DSI collaboration.
The implementation of the Gaia-X and IDSA specifications was described by several DSIs as
a step towards preparing for future cross-DSI collaboration. Given that the different DSIs have
made different choices for their organisational and data exchange governance as well as their
business model, the incompatibility of the different DSI terms and conditions was often
mentioned as a challenge for the implementation of concrete collaborations. Supporting
interoperability, not only on a technical but also on a legal and economic level, was often
mentioned as a useful contribution of CEADS.
4.1.1.4. Requirements for CEADS
From these interviews we derive the following requirements for CEADS, which in parts directly
reflect the perspectives of the interview partners and in other parts result from our interpretation
of the reported state of affairs.
CEADS should not act as a central data intermediary: There are many DSIs that fulfil the role
of data-intermediary for the agricultural sector in Europe. Some focus on specific regions and
on applications relevant to these regions, others operate internationally but focus on a narrow
range of technical applications. Each of these DSIs represents an own data ecosystem with a
unique set of (key) stakeholders and unique set of challenges that have to be addressed to
successfully operate the DSI, be it regionally fragmented regulation, technical interoperability,
or gaining the trust of the ecosystem stakeholders. All DSIs focus on making data exchange
possible for individual, reliably functioning data-driven services that are of specific economic
relevance to their respective ecosystem. A possible central European data intermediary could
likely not be implemented because it would have to address all these unique challenges and
needs at once to be attractive to the entire sector, which is not feasible. Moreover, such an
initiative would be perceived as competition by most of the existing initiatives that have working
business models, so it would be hard to attract participants. Enabling the free exchange of
data across the agricultural sector in Europe requires parallelised efforts in parallel DSIs, so
CEADS should not aspire to be a data-intermediary itself.
CEADS should support DSI collaborations: Not all DSIs collaborate with each other. There are
a few DSIs that represent closed data ecosystems that do not need to collaborate with other
DSIs to operate successfully and hence do not pursue any collaborations, as any substantial
collaboration requires effort. DSIs that represent open data ecosystems are, however, more
common, and these DSIs plan to collaborate across borders and across parts of the same
value-chains for a free flow of data and a maximum of added value for all. There are already
successful collaborations ongoing and plans to extend these inter-DSI collaborations were
reported. The interview partners representing DSIs in open data-ecosystems saw a value in a
pan-European initiative to coordinate and support DSI-collaborations and joint efforts, which
should be CEADS’ mission.
CEADS should grow its governance iteratively: A DSI’s governance grows alongside its
business model and its user base, following the changing needs of the DSI’s key stakeholders:
Even DSIs that nowadays are well-established in their respective markets with working
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business models and a large user base once started off as research projects or informal
working groups. The same logic applies to CEADS, as umbrella organisation: It is advisable to
keep CEADS’ governance lean initially and impossible to foresee the most pressing future
needs of its members (i.e. the DSIs), which CEADS’ future governance should support.
CEADS should follow common organisational governance decisions. Even though there is a
lot of variety in governance decisions across the interviewed DSIs, there are some
commonalities among all of them or among the vast majority. CEADS long-term organisational
governance should reflect these choices and the underlying values.
• The operating organisation should have a credible, neutral position in the market.
• Representatives of all important ecosystem sub-sectors should have a say in the DSI’s
decision-making process.
• Business practices should be transparent.
CEADS should offer interoperability support for DSI’s data sharing governance: The biggest
challenges for inter-DSI-collaboration lie in the interoperability of their respective data
exchange services. Given that DSIs have made different choices for their data sharing
governance and business models, shared permission management (e.g. across DSIs with
direct and indirect consent or in DSIs with different service terms and conditions) as well as
identity management or data and service quality management may vary. Gaia-X is an
important initiative to address such interoperability challenges and its framework is adopted by
all mature DSIs that emphasise inter-DSI collaboration. The most advanced inter-DSI-
collaborations reported also develop their services based on the specifications of the Gaia-X
framework. However, even within the framework, there are still many open issues to make DSI
interoperability work in concrete cases, e.g. when joining or combining data catalogues or
service registries. CEADS should install working groups to jointly progress on such issues,
which include technical as well as legal and economical interoperability challenges.
CEADS operations should be independent of the public administration: Even though the
European Commission will indirectly benefit from a free flow of agricultural data across
agricultural stakeholders and will also be able to indirectly access this data as part of legally
required documentation, a direct involvement or direct data access would put CEADS
perceived trustworthiness at risk and should thus be avoided. DSI success relies on voluntary
and active participation.
CEADS should represent the interests of DSIs in public discourse in Europe: Fragmented
regulation and diverse technical standards are among the biggest challenges for a free data
economy in the agricultural sector in Europe. The European Commission can play an active
role in establishing legal and in selected cases also technical standards (one interviewee
mentioned USB C phone chargers as an example) across Europe. A useful role for CEADS is
to represent the interests of DSIs on the European level and lobby for unified standards, among
other things.
CEADS should support early-stage DSIs from an economic perspective: Many DSIs are
technically driven in the early stages. Despite core technologies with high economic potential,
they are at risk of not tailoring their services sufficiently to the market’s most pressing needs
or of failing to address the difficult challenges associated with ecosystem build-up and thus of
failing to progress beyond the pilot phase despite evident economical potential. CEADS should
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support promising DSIs in the early stages with training and tutoring from an experienced,
economical perspective.
4.1.2. Results on the Business Models for DSIs
4.1.2.1. Key insights on DSI Domain of Engagement and Offered Services
While all DSIs are involved in the wider agricultural sector, certain trends and niches can be
identified:
• 8 of the 15 DSIs cover a wider range of agricultural and farming activities. Of these,
three have services that include livestock-specific services, such as herd management
and animal health services. One has developed services related to land use
management, including water catchment, soil, weather services and natural capital.
Another one offers additional services related to food processing and two focus their
domains of engagement on providing services that are more focused on data safety,
security and privacy. The services offered by these DSIs are mainly centred around
dashboards and APIs that provide their users with a one-stop-shop of raw or
aggregated and visualised targeted information. Many of these services include and
support services such as enhanced connectivity between platform users and relevant
data rights holders, automated and optimised for efficiency, and centralised access to
field equipment or data without the need to be on-site.
• 4 of the 15 DSIs provide services specifically related to crop production and
management, including one DSI which is focused on the protection of specialty crops.
Three DSIs have developed platforms that allow sharing and centralised access to
data relevant to crop production through visualisations, maps, pre-analysed data or
data in raw format. One of these DSIs provides large amounts of annotated agri-food
data, mainly in image format, which can be used to train relevant AI solutions.
• 1 out of 15 DSIs has developed data sharing services that address the needs of
greenhouse management in the horticultural sector by sharing data on the optimal
positioning of objects inside greenhouses using mutually usable formats (CGO -
Common Greenhouse Ontology).
• 1 of the 15 DSIs provides services that share data related to the food chain, through
a platform that facilitates sharing and access to relevant data.
• 1 out of 15 DSIs provides services that make data related to livestock and dairy
production available to relevant stakeholders through the development and
application of tools that enable data sharing among dairy stakeholders.
4.1.2.2. Key insights on DSI Actors and their Co-Production Activities
The usual actors involved in the DSI business model as defined by the SDBM/R are the
customers/end users, the orchestrators and the core partners.
The orchestrators are usually the DSIs themselves or, in two cases, overarching
organisations, and their co-production activities focus on facilitating the secure and
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centralised sharing of data between data consumers and data providers. Many DSIs focus on
adding value to the services they provide by developing data schemas and enabling data
consolidation by integrating disparate data from different sources, transforming, manipulating
or adding metadata to the data to optimise use and framework integration, and making public
datasets accessible to the tailored needs of agricultural end-users.
The customers/end users are usually traditional agri-food stakeholders such as farmers,
suppliers, growers, government agencies, producer organisations and equipment
manufacturers. In a few cases (6 out of 15), the business plan of the DSIs includes the
integration of third-party service providers, such as API and application developers and
research institutes, in the role of customers/end users. The co-production activities of
customers/end users involve both the provision of rich (active or passive) data to the DSI and
the consumption of data to build capacity and increase efficiency or to transform it into new
services. Their benefits from the shared data are manifold and range from simplified
administrative procedures to increased production efficiency, risk management and optimised
resource utilisation.
The core partners of the DSIs (for 14 out of the 15 DSIs) bring, in most cases, their own
technical expertise. They include developers, technology providers, academic institutions,
government agencies and data networks that contribute to the DSIs' platform development,
enable more efficient data flows, improve user access to the platform and provide data analysis
services, such as building machine learning models and decision algorithms.
11 of the 15 DSIs have additional participating actors in the form of enrichment partners
who contribute to improving one or more aspects of their operations (providing additional data,
improving specific technological processes, covering specific equipment needs or providing
their own services in addition to the DSI's data sharing for a more comprehensive experience).
3 of the 15 DSIs have other actors in addition to the enriching actors, who play specific niche
roles.
4.1.2.3. Key insights on DSI Value Propositions
The overall DSI value propositions fall into the following main categories:
• Improved data accessibility: The most immediate value derived from DSIs is that of
immediate and efficient access to datasets with tailored characteristics, defined
specifications, in formats and structures that allow interoperability and efficient
communication. In the same direction, DSIs can also act as incubators for improving
standardisation and uniformity in the agricultural data space.
• Increasing the value of data: In addition to acting as hubs for data exchange, DSIs
can increase the value of data by:
o Integrating diverse data: Data consolidation through the inclusion of disparate
data, such as weather, geological and soil data, enriches the information
available and provides a more comprehensive view of the issue by enabling
more nuanced and detailed insights.
o Predictive analytics: Access to large historical datasets allows DSIs to build
robust predictive models and AI solutions, making data-driven decisions much
more reliable and enabling risk mitigation.
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o Visualisation: By creating visual applications such as interactive dashboards,
DSIs can provide stakeholders with a much more immediate and easily
monitored bird's eye view of relevant indicators and metrics, enabling
unencumbered day-to-day decision making.
• Data security and privacy: Providing a neutral intermediary that enables access to
agricultural data while maintaining the privacy and security of all parties involved can
be critical to the peace of mind and operational success of both data providers and
consumers. DSIs play a critical role in ensuring both the safety (ensuring that data
remains intact and accessible, protecting against events such as hardware failures or
power outages) and security (maintaining data integrity, protecting against malicious
attacks and preventing unauthorised access) of shared data. Many of the DSIs go the
extra mile to make data transactions fully traceable and transparent.
• Innovation and research: The existence of centralised, clean and curated datasets
provides third-party developers with the necessary ammunition to power new services
that use AI and machine learning models to automate daily operations, mitigate risk,
increase production and efficiency, etc.
• Encouraging collaboration and networking: Beyond its undeniable practical value,
data sharing can also bring together agricultural stakeholders. By enabling easier and
more immediate exploration of the various available dataset portfolios used by different
actors in the agricultural ecosystem, new collaborations can be born, leading to a peak
in mutual scalability and enhancing relevant economies of scale.
4.1.2.4. Key insights on the Main Pain Points the DSIs Address
In terms of the main pain points that the DSIs aim to address, there is a convergence across
the DSIs on the currently fragmented and complex data collection processes, disparate data
sets or inefficient communication channels between stakeholders speaking different data
languages, lack of centralisation and automation interfaces. Some of the DSIs are more
explicitly committed to focusing on unnecessarily complicated authorisation management
and data access processes, trying to tailor their services to existing challenges in the day-to-
day needs of agricultural stakeholders. Others recognise the most prevalent problems as a
lack of standardisation and interoperability protocols and aim to create a smoother data sharing
landscape.
3 of the DSIs express their mission in terms of contributing to addressing broader
issues. 2 of them by improving the overall economic competitiveness of the agri-food industry,
not only in terms of quantity, but also in terms of food standards and context, as well as food
safety and security. The other is to address the increasingly pervasive challenges facing the
agricultural economy, such as climate change, increased administrative costs and changing
legislation.
2 of the DSIs explicitly try to build on the lack of centralised interfaces from which users they
can access all relevant information about their equipment, including machine health and repair
metrics.
4.1.2.5. Key insights on DSI Business Models
The breakdown of the business models used by the 15 analysed DSIs is as follows:
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• SaaS 33%
• Technical Enabler 27%
• Data Marketplace 20%
• Industrial Data Platform 13%
• Data Monetisation 7%
• Open Data Policy 0%
SaaS was the most popular business model, with one in three DSIs charging a subscription
for their services and/or data. This model is well suited to the scalability of the data sharing
industry, as SaaS applications can more easily adapt to the growing or changing needs of
users. Two of the DSIs used hybrid business models, combining SaaS with the Data
Marketplace and Industrial Data Platform business models. This allows them to combine the
strengths of each business model by offering enhanced services on subscription in one case
and a basic service that can be enhanced on subscription in the other. The Technical Enabler
business model is the second most popular business model, not too far behind SaaS. This is
followed by the Data Marketplace business model, which is used by DSIs that act as
intermediaries to enable data providers and users to trade data, providing them with security
and efficiency in their transactions. This allows them to build on existing trends in the data
economy and provides them with great flexibility and scalability as well as the existence of a
self-balancing data ecosystem that allows data to be monetised through supply and demand.
Given that the demand for diverse agricultural data often comes from within the industry itself,
it is not surprising that the Industrial Data Platform business model is also used by two DSIs.
These DSIs focus on maintaining and providing a neutral, trusted and interoperable solution
that facilitates the growing needs of industry stakeholders. Finally, one DSI used the Data
Monetisation business plan, opting to generate additional revenue from data originally
collected for its own services. Interestingly, no DSI in our sample used an Open Data Policy
business model, which supports free data sharing, suggesting that data is seen as a
competitive advantage and a monetisable resource.
4.1.2.6. Key insights on DSI Revenue Models
The breakdown of the 15 DSIs based on the Revenue Model they use is as follows:
• Freemium 47%
• Licensing 40%
• Barter system 13%
• Free-to-all 0%
• Sponsorship/branded advertisement 0%
• Demand-oriented 0%
The most common revenue model is the Freemium model, used by almost half of the DSIs.
Considering that the sharing, use and monetisation of data in the agricultural sector is a
relatively new service, the Freemium revenue model helps to increase the scalability of the
DSI and to facilitate the introduction of the service to new users. Free access to at least some
of the data offered allows for increased accessibility, more immediate user engagement and
retention (as it allows users to be introduced to the service without daunting initial fees), and
more marketing opportunities. This is followed by the Licensing revenue model, used by 40%
of DSIs that offer users access to data or their services (as intermediaries offering secure and
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confidential data transactions) on the basis of a licensing agreement. Compared to the
Freemium revenue model, the Licensing model tends to depend on targeting a less broad user
base. While freemium models attract users with freely available data and aim to offer enough
value for them to migrate to a paid model, licensing aims to offer a product that is sufficiently
tailored to the needs of users who will pay a licence fee in advance to access it. One DSI uses
a hybrid revenue model, offering data availability in some components, while others can be
upgraded with either a subscription model or a licence. The third most popular revenue model
is the Barter System model and is only used by two of the 15 DSIs, the same DSIs that use
the Industrial Data Platform business model. These DSIs pay and are paid with data, thus
promoting the growth of the data economy and mutually created value. One of them describes
itself as a digital Fastlane for data-driven growth.
It is noteworthy that the Free-to-all, Sponsorship/branded advertisement and Demand-
oriented revenue models were not used by any of the DSIs. This may indicate that the nascent
agricultural data sharing ecosystem has not yet gained enough momentum to make these
models viable or that the limited range of potential end users makes the use of demand-driven
revenue models daunting.
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5. Business Model for the CEADS
5.1. Co-design process
This chapter details the co-development of the CEADS business models through stakeholder
consultation. We present the outcomes of two workshops that engaged diverse European
actors in co-designing business and revenue models, assessing value propositions, and
determining cost-benefit distributions, laying the groundwork for the collaborative framework
of CEADS.
5.1.1. First cycle of business models development
AgriDataSpace has conducted the 1st external online consultation workshop (September
2023) with the title: Co-designing collaborative business models for the Common European
Agricultural Data Space.
An interactive and collaborative approach was followed aiming at achieving consensus on the
development of Collaborative Business Models. During the workshop, stakeholders were
asked to provide their insights and feedback on the Service Dominant Business Model Radar,
which will be implemented for the collaborative Business Models of the CEADS, where all
parties collaborate for mutual ethical benefit, with business models designed to bring financial
and non-financial benefits to each actor.
The fundamental aspects of the SDBM/R that were validated within the workshop are the
following:
• Components of SDBM/R:
o Value-in-use and Actor Value Propositions
o Co-Production Activities
o Actor Cost/Benefits
o Identification of Co-Creation Actors
• Design Steps for SDBM/R:
o Identify co-created value-in-use and target customer segment.
o Determine actor value propositions and roles.
o Assess costs and benefits for each actor.
o Identify high-level activities to realize the actor-value proposition.
In total, 165 people were invited representing actors from all the stakeholders’ categories
across Europe, 101 attended the workshop and 45 responded to a series of question (42
live during the workshop and 3 through an online questionnaire) as follows:
Table 9: Participants in the 1st business model workshop
Stakeholder Category
Participants
Farmers and Agricultural Producers
5
Technology and Data Providers
5
Data Intermediaries and Service Providers
5
Government and Regulatory Bodies
2
Financial and Insurance Services
0
Research and Academic Institutions
15
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Business and Industry Stakeholders
4
Multi-actor Collaborations
8
Other
1
Total
45
All the results from the workshop are presented as an annex, while the highlights are explained
as follows.
Participants’ responses to the individual value proposition of each data space participant
within the CEADS showed a wide variance between the available options:
• “Data-driven innovation” and “Sustainable Agricultural Practices” gathered the
most responses with 17 and 16 responses respectively. Almost all stakeholder
categories were represented in both options.
• This was followed by “Improved Decision-Making Tools” and “Enhanced Data Access”
with 13 and 11 responses each, again with representation from most stakeholder
categories in both options.
• “Cost Savings” attracted the lowest number of responses with 7 respondents.
• In terms of preference trends, stakeholders in the “Research and Academic
Institutions” category responded that “Data-Driven Innovation” and “Enhanced Data
Access” were more valuable to them. Stakeholders in the “Technology and Data
Providers” category indicated that “Data-Driven Innovation” was more valuable to them,
while “Business and Industry Stakeholders” perceived more value in “Cost Savings”.
When asked about the distribution of costs and benefits among the data space participants
in the CEADS the responses followed clear trends:
• A large majority of the respondents agreed that the costs and benefits should be
proportionate to the contribution of the participants. This option was chosen by at
least 1 participant from each stakeholder category. This option received 25 responses,
more than 60% of the total.
• The second most popular option was “Other” with 8 responses, indicating that the other
options available were of limited appeal to the respondents. It is worth noting that this
option was chosen by the same number of “Multi-actor Collaboration” respondents as
the “proportionate” option (both received 3 answers) and by more “Farmers and
Agricultural Producers” than the most popular option (“Other” received 2 answers
contrary to 1 answer for the “proportionate” split).
• Interestingly, only research and academic institutions voted for an equal
distribution of costs and benefits.
Regarding what the collective value proposition would be for the CEADS as a whole, the
responses were spread across a large number of available options:
• “Enhanced Decision-Making for the Agricultural Sector”, “Sustainable
Agricultural Practices Promotion”, “Accelerated Data-Driven Innovations” and
“Holistic Agricultural Data Ecosystems” received the largest number of responses
with 18, 16, 16 and 14 responses respectively. “Enhanced Decision-Making for the
Agricultural Sector” and “Sustainable Agricultural Practices Promotion” were selected
by all stakeholder categories, while the other two options were selected by almost
all stakeholder categories, as they were not selected by stakeholders from the
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“Government and Regulatory Bodies” and “Business and Industry Stakeholders”
categories.
• The “Enhanced Data Collaboration” and “Policy making” options were chosen by 8
respondents, which is a respectable but much smaller number of respondents.
“Enhanced Data Collaboration” was chosen by almost all stakeholder categories, while
“Policy Making” was chosen by stakeholders from 5 categories. Notably, none of the
stakeholders from the “Farmers and Agricultural Producers” and “Business and
Industry Stakeholders” categories chose either of these options.
• Overall, it can be observed that the options that were more closely related to value
creation at the individual level (“Enhanced Decision-Making for the Agricultural
Sector”, “Holistic Agricultural Data Ecosystems” and “Accelerated Data-Driven
Innovations”) attracted on average a much higher number of responses from
stakeholders from a larger number of categories, while the options that were more
related to community-wide value creation or enhanced collaborations (“Sustainable
Agricultural Practices Promotion”, Enhanced Data Collaboration” and “Policy Making”)
attracted much fewer responses.
When asked which Business Model within a Common Data Space would be most useful to
them there was a wide range of the responses but with clear ranking of preferences:
Figure 11 The most useful Business Model options for CEADS from the first workshop
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• The “SaaS” Business Model received the most responses (16), with almost all
stakeholder categories represented. It was closely followed by the “Data
marketplaces” Business Model (14 responses from almost all stakeholder
categories), the “Open data policy” Business Model (13 responses from
stakeholders from almost all stakeholder categories) and the “Data monetization”
Business Model (12 responses from almost all stakeholder categories).
• Interestingly, while the five stakeholder categories were the same for the four most
popular choices, the sixth was different for some of them. “SaaS” and “Data
monetization” were preferred by “Farmers and Agricultural Producers”, “Data
marketplace” by “Business and Industry Stakeholders” and “Open data policy” by
“Government and Regulatory Bodies”.
• The “Industrial data platforms” and “Technical enablers” Business Models were chosen
by a much smaller number of respondents from 5 and 4 categories respectively.
• The “Other” option was chosen by only 2 respondents showing that the available
options broadly covered the needs of the stakeholders in all categories.
The responses on the Business Models are also shown graphically in Figure 11.
When asked which Revenue Models would be most useful for a Common Data Space there
were some defined highlights in the respondents’ preferences:
• The “Freemium” revenue model was the most popular answer, receiving 14
responses from almost all stakeholder categories, closely followed by the “Demand-
oriented” revenue model, which received 13 responses, again from almost all
stakeholder categories. The two most popular responses had stakeholders from only 4
categories in common – “Multi-actor Collaborations”, “Business and Industry
Stakeholders”, “Research and Academic Institutions”, and “Data Intermediaries and
Service Providers”.
• Both the “Licensing” and “Barter system” Revenue Models received 11 responses and
were represented by almost all stakeholder categories, showing that although they
received a lower total number of responses, the interest expressed was broad.
• Among the 4 leading revenue models stakeholders from the “Farmers and Agricultural
Producers” category chose more traditional Revenue Models (“Licensing” and
“Demand-oriented”), while stakeholders from the “Government and Regulatory Bodies”
category chose more agile models (“Freemium” and “Barter System” models).
• Apart from “Other”, the “Sponsorship/ branded advertisement” Revenue Model was
by far the least preferred option available, as it was chosen only by 3 stakeholders
from the “Research and Academic Institutes” category.
The responses on the Revenue Models are also shown graphically in Figure 12.
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Figure 12 The most useful Revenue Models for CEADS from the first workshop
Respondents’ answers to the question of which data-sharing incentives or rewards they
thought would motivate them or their organization to actively participate in a Common Data
Space follow the following trends:
• “Access to valuable data” received by far the most responses from all the stakeholder
categories, garnering a positive vote from 32 out of the 45 possible responses. It is
also worth noting that compared to other possible options “Access to valuable data”
attracted the highest percentage of answers from stakeholders in the “Research and
Academic Institutions” category.
• The “Enhanced data analytics tools”, “Recognition and reputation within the data
system”, “Regulatory compliance support” and “Financial incentives” categories also
collected a considerable number of responses with 19, 17, 15 and 12 responses
respectively. Each of these was selected by stakeholders from all or almost all
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categories, showing that each stakeholder category perceives several data-sharing
incentives or rewards as potential motivators for joining a Common Data Space.
• Only 4 respondents indicated that “Other” incentives would motivate them or their
organizations to join a Common Data Space meaning that the options available
covered a large part of the variability of possible reasons.
The data-sharing incentives or rewards the participants found to be able to motivate them to
join a Common Data Space are also show graphically in Figure 13.
Figure 13 Data-sharing incentives the participants found to be able to motivate them to
participate in a Common Data Space
In terms of the potential economic, social, and environmental benefits that respondents
see arising from a Common Data space, there was a wide range of responses across all
possible benefits, ranging from 17 responses for “Facilitated Rural Development” to 32
responses for “Greater Innovation and Research Opportunities”, with “Improved Food
Security”, “Enhanced Agricultural Productivity”, “Strengthened European Agricultural
Competitiveness” and “Reduced Environmental Impact” in between.
5.1.2. Second cycle of business models development
After analysing the results from the initial external workshop, AgriDataSpace has conducted
the 2nd external Consultation Workshop with the title: Co-design multi-stakeholder governance
schemes and collaborative business models for the CEADS. The workshop was organized in
person during the Synergy Days event in Thessaloniki (4-5 October 2023). The Synergy Days
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is one of the most important conferences connecting the digital innovators of the European
agri-food sector.
During the workshop, stakeholders were asked to provide their insights and feedback on their
perspective for the business models, the revenue models and the services of the Common
European Agriculture Data Space.
In total, 24 people attended the workshop responded to a series of question, representing
actors from almost all the stakeholders’ categories across Europe as follows:
Table 10: Participants in the 2nd business model workshop
Stakeholder Category
Participants
Farmers and Agricultural Producers
1
Technology and Data Providers
3
Data Intermediaries and Service Providers
5
Government and Regulatory Bodies
4
Financial and Insurance Services
1
Research and Academic Institutions
7
Business and Industry Stakeholders
1
Multi-actor Collaborations
0
Other
2
Total
24
All the results from the workshop are presented the annex, while the highlights are explained
as follows.
Participants’ responses to which of the available Business Model options would be most
useful to them were spread widely across available options:
• The “Open data policy” Business Model attracted the largest number of
responses (15) from almost all stakeholder categories. More than half of the responses
came from “Research and Academic Institutions” (33%) and “Government and
Regulatory Bodies” stakeholders.
• This was followed by the “Data marketplace” business model with 11 responses from
stakeholders in 5 categories.
• Finally, the “SaaS” Business Model gathered 9 responses from stakeholders in 4
categories, more than half of which came from stakeholders in the “Data Intermediaries
and Service Providers” category.
• Only 2 respondents answered “Other”, indicating that the participating stakeholders
were largely satisfied with the available Business Model options.
The Business Models are also shown graphically in Figure 14.
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Figure 14 Most useful Business Model options for CEADS from the second workshop
Respondents showed clear trends in their answers as to which of the available Revenue
Model options would be most useful for a Common Data Space:
• The “Freemium” Revenue Model received 16 responses, making it the most
popular option across almost all stakeholder categories. This Revenue Model attracted
high percentages of respondents from the “Research and Academic Institutes”,
“Government and Regulatory Bodies” and “Technology and Data Providers”
categories.
• This was followed by the “Demand oriented” and “Licensing” Revenue Models,
each of which received 10 responses. Interestingly, most of the responses for the
“Demand oriented model came from “Research and Academic Institutions”, while most
of the responses for the “Licensing” revenue model came from “Data Intermediaries
and Service Providers”.
• The “Barter System” Revenue Model received only 2 responses, both from
“Research and Academic Institutions” stakeholders.
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• The category “Other” received only 2 responses as well, indicating that the available
options were sufficient for the participants.
The Revenue Models are also shown graphically in Figure 15.
Figure 15 Most useful Revenue Models for CEADS from the second workshop
5.2. Examination of the CEADS proposed services
During both workshop sessions, the participating stakeholders were asked what services a
Common Data Space could potentially provide to enhance value creation and
stakeholder collaboration. During the first workshop they were also asked what their
willingness to pay for services offered by a Common European Data Space would be.
The participants’ responses regarding the services they would like to see from a Common
Data Space during the first workshop showed a wide variation.
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Figure 16 Services the participants would like to see from a Common Data Space
• “Data Standardization and Governance Frameworks” attracted the largest number
of responses, 28 out of a possible 45, with responses from stakeholders in all
categories. This service received a significant number of responses from all
participating stakeholder categories, except for the “Business and Industry
Stakeholders” which voted for it in a lower percentage compared to the other available
services.
• “Regulatory Compliance and Ethical Data Practices Oversight”, “Quality Control and
Data Verification Services”, Access to Data / API Catalogues”, “Consultation and
Advisory Services”, “Intermediary Services to Connect with Other DSIs”, “Data
Exchange and Marketplace Facilitation” and “Access to Open Data” all garnered
responses within a very small range of 13 to 18 responses. Of these, “Consultation and
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Advisory Services” was selected by the smallest number of stakeholder categories (5
categories) but received high percentage of answers from stakeholders in the “Multi-
actor Collaborations” and “Research and Academic Institutions” categories. The
remaining benefits received responses from stakeholders in almost all categories.
• “Data Aggregation” is the only available service that gathered a small number of
responses (only 4) and appears to be of relatively low value to the vast majority of the
respondents.
The participants’ responses regarding the services they would like to see from a Common Data
Space are also graphically presented in Figure 16.
The participants’ willingness to pay for services offered by a Common Data Space is similar
to a negatively skewed normal distribution meaning that:
• 0 participants responded that they would be “Very willing” to pay for the service.
• 10 participants responded that they would be “Moderately willing” to pay, with the
largest percentage (40%) being in the “Research and Academics Institutions” category.
• 12 participants indicated that their willingness to pay for Common Data Space
services was “neutral” and this is the option with the largest number of
responses. This option also gathered the largest number of stakeholders from different
categories as well, as it was selected by almost all stakeholder categories.
• 7 respondents answered that they would be “Slightly willing” to pay and only 6 that
they were “Not willing at all” to pay for the services.
During the second workshop the participants’ responses regarding what services a Common
Data Space could provide them to enhance value creation and stakeholder collaboration
were also spread across the – more limited based on the results of the first workshop –
available options:
• “Data Standardization and Governance Framework” and “Access to Open Data”
received the largest number of responses (18 and 16 respectively). Interestingly both
services were selected by stakeholders in the same categories and by roughly the
same percentage of stakeholders in each category. Most of them belonged to the
“Research and Academic Institutions”, “Government and Regulatory Bodies” and “Data
Intermediaries and Service Providers” categories.
• This was followed by “Data Exchange and Marketplace Facilitation” services which
received 15 responses, from almost all stakeholder categories.
• “Intermediary services to connect other DSIs” collected the smallest number of
responses among the available services, but still reached the considerable number of
12 responses.
• It is worth noting that although there were small differences in the categories of the
responding stakeholders all 4 available services were selected by a similar percentage
of each participating stakeholder category. This combined with the small number of
stakeholders who answered “Other” (only 2), shows that the participating stakeholders
consider all these services to have the potential to enhance value creation and
stakeholder collaboration.
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Considering the results of the workshops, to create a field of broad acceptance among
existing stakeholders, future Data Space services must be organically synergistic with existing
stakeholders’ services. While the list of services will be a constantly evolving task that will
grow together with the participants involved and Europe's societal and technological progress,
the potential minimum expected services can be identified as follows:
• Shared and inclusive administrative and data governance
• Facilitating the access to Open Data.
• Identity interoperability, promoting seamless recognition and authentication between
different Data Space actors.
• Data catalogue interoperability, which ensures that diverse and disparate data sets
can be used together.
The diagram in Figure 17 outlines the structure of the proposed services. Central to the
CEADS is the concept of shared governance and the creation of an environment where DSIs
can freely exchange data while maintaining their operational and technological autonomy. It
emphasizes levels of data sharing with a clear focus on open data (paid services and even
licenced data can be explored in the scaling stage of the CEADS) and aims to support an
ecosystem where different levels of service can co-exist and complement each other. The
focus on the interoperability of identity and data catalogues reflects a commitment to creating
a seamless, inclusive data market that respects regional specificities and drives innovation,
growth and competitiveness in the agricultural sector.
Figure 17 CEADS services proposition
5.3. Business models validation
Validation of the proposed minimum services and related business models is critical while
designing a common data space that meets the diverse needs of stakeholders. This process,
involving presentations and feedback sessions with different stakeholders, is essential to refine
and confirm the relevance and adaptability of the business models. Collaboration and iterative
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feedback are necessary mechanisms to ensure that the data space is inclusive, interoperable
and aligned with the evolving needs of the sector. This section highlights the importance of
these validations in developing a business model that fosters innovation and collaboration
within the agricultural data landscape.
5.3.1. 1st Validation with stakeholders
On 14 December 2023, the AgriDataSpace 1st Stakeholder Meetup convened fourteen (14)
experts from six (6) stakeholder groups to focus on the development of the Common
European Agricultural Data Space. The discussions critically evaluated governance, business
models, regulatory frameworks and the necessary technical foundations. A strong consensus
emerged on the need to demonstrate clear, tangible use cases before introducing pricing
models, reflecting a wider reluctance to incur costs without clear value. The meeting underlined
the likely need for public sector funding, particularly from the European Commission, in the
early stages, given the likely reluctance of the private sector to invest early on. The
dialogue advocated a balanced, inclusive ecosystem that benefits a wide range of
stakeholders, emphasising the creation of a competitive yet collaborative data space.
Regulatory considerations, in particular the forthcoming Data Governance Act, were
highlighted as critical to aligning the business model with the objectives of the European data
space. This feedback was instrumental in validating the selected business model, emphasising
the importance of an approach that prioritises inclusivity, public-private synergies and
regulatory alignment to ensure the sustainability and effectiveness of the digital agriculture
transformation initiative.
5.3.2. 2nd Validation with stakeholders
The 2nd Stakeholder Meetup for AgriDataSpace, held on 25 January 2024, built on previous
discussions and convened sixteen (16) experts from seven (7) stakeholder groups,
focusing on gathering feedback for the proposed CEADS roadmap, but also reintroduced the
content of the 1st Stakeholder Meetup to gather additional feedback. This session facilitated
critical feedback from a wide range of stakeholders, emphasising the adaptability, inclusivity
and scalability of the business models in response to the evolving needs of the agricultural
sector. Through interactive discussions, stakeholders helped to shape a data space business
model that balances technological innovation, compliance with EU regulations and the diverse
interests of the agricultural community, ensuring a pathway towards a collaborative and
sustainable agricultural data ecosystem.
5.3.3. Alignment with DSCC
At the 6th Business Thematic Group meeting of the Data Spaces Support Centre on 11
December, the presentation of the AgriDataSpace "Proposal for multi-stakeholder business
models" received constructive feedback, providing significant validation of the proposed
approach. Participants emphasised the critical importance of data catalogue interoperability,
recognising it as a cornerstone for the successful implementation of the business models
across different sectors. The discussion underlined a general consensus on the need to
address the different needs of stakeholders within the Common European Agricultural Data
Space, highlighting in particular the nuanced preferences for open data integration between
entities dealing with public and private data. The dialogue around these distinctions and
the proposed freemium model, which aims to balance open and licenced data sharing, further
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confirmed the relevance and adaptability of the proposal. Feedback from the meeting
demonstrated stakeholder support for an inclusive and interoperable ecosystem, reinforcing
the potential of the proposal to facilitate a collaborative, dynamic agricultural data space that
is responsive to the diverse contributions and needs of all stakeholders.
5.3.4. Validation with EU Member States
The CEADS workshops with EU Member States emphasised the critical feedback on the
inclusion of adaptable and scalable business models. It was pointed out that the involvement
of private actors is essential to ensure that each participant benefits from CEADS. With an
emphasis on regulatory compatibility and synergy, these sessions aimed to promote a
consistent approach to data sharing that takes into account the different agricultural and
regulatory contexts across Europe. This collaboration with Member States highlighted the
importance of developing a data space that not only supports innovation and sustainability in
the agricultural sector, but also ensures a balanced integration of interests between public
and private stakeholders, driving collective progress towards a comprehensive and
beneficial digital agricultural ecosystem.
5.4. Collaborative Business Models for the CEADS
The collaborative and multi-sided business model of CEADS is unique because it requires
aligned activities from different organisations. Together, these organisations aim to establish
a platform that connects supply and demand while avoiding too much power and control
residing with one party. Although related, the data space business model is distinguished from
the business model of the individual organisations affiliated with the data space.
5.4.1. Validated aspects of the CEADS Business Models
Through the iterative processes that were analysed above, the following aspects of the CEADS
business models have been validated:
1. Inclusivity and engagement
Prioritises the involvement of a wide range of stakeholders to ensure broad benefits
and meet the diverse needs of the farming community.
2. Regulatory compliance
Aligns with EU regulations, including the Data Governance Act, to ensure legal and
operational sustainability.
3. Public-private funding model
Emphasises early public sector support and subsequent private investment based on
demonstrated value.
4. Technological Adaptability
Focuses on innovation and interoperability, facilitating seamless data integration
across disparate sources.
5. Scalability
The business model is designed to evolve with the changing needs of the sector,
maintaining its effectiveness and relevance.
6. Balanced data sharing
Supports both open and commercial data sharing, considering stakeholder preferences
for public and private data integration.
7. Clear value before pricing
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The need to demonstrate tangible benefits to stakeholders before introducing pricing
models.
5.4.2. Benchmarking business models
In the development of the framework of the Common European Agricultural Data Space,
different business models have been considered to identify the most effective strategy for data
use and sharing within the sector. Each model presents unique opportunities and challenges
that affect the way data is accessed, shared and monetised. This section provides a
comparative analysis of these models, highlighting their advantages and disadvantages.
By examining potential direct monetisation opportunities, scalability and challenges such as
cost barriers and dependency issues, this overview aims to provide insight into the viability and
implications of each approach for stakeholders in the agricultural data landscape. The aim is
to facilitate an informed decision-making process that balances innovation, accessibility and
sustainability in the development of the data space.
Table 11: Benchmarking business models
Business Model
Pros +
Cons -
Data
monetisation
• Creates direct monetization
opportunities from shared data.
• Encourages data sharing
among companies.
• May limit sharing due to cost
barriers.
• Potential issues with data
privacy and control.
Data
Marketplaces
• Facilitates secure data
exchange.
• Can ensure data quality and
reliability.
• Opens new revenue channels.
• Transaction fees may reduce
profitability.
• Requires trust in
intermediary.
Software as a
Service (SaaS)
• Predictable revenue through
subscriptions.
• Scalable and flexible access to
data and analytics.
• Ongoing costs for users.
• Dependency on a single
provider’s platform.
Industrial Data
Platforms
• Fosters innovation through
collaborative data sharing.
• Can improve efficiency and
product development.
• Limited to members, reducing
broader industry impact.
• Coordination and governance
challenges.
Technical
Enablers
• Specialized solutions can
enhance data sharing
capabilities.
• Potentially high demand for
enabling technologies.
• Business model dependent
on continued need for
technical solutions.
• May not capture value of data
itself.
Open Data
Policy
• Promotes innovation and
accessibility.
• Can lead to unexpected new
products/services.
• No direct revenue from data
sharing.
• Relies on indirect
monetization strategies.
The development of the Common European Agricultural Data Space involves leveraging
various business models to optimize data use, sharing, and monetization within the agriculture
sector. A breakdown of how each model can scale and the factors contributing to its scalability
is presented in the following table:
Table 12: Business models scalability potential
Business Model
Scalability
Reasoning
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Data
monetisation
Moderate
While data monetization has significant potential due to
the growing value of agricultural data, it faces challenges
related to data privacy, ownership rights, and the need for
standardization. These issues can limit the speed and
extent of scalability, though the high demand for
actionable insights in agriculture supports a positive
growth trajectory.
Data
Marketplaces
Moderate
to High
Data marketplaces offer a scalable platform for data
exchange due to their ability to aggregate data from
various sources and facilitate transactions between
numerous buyers and sellers. Their scalability is
somewhat dependent on overcoming regulatory and
standardization hurdles, but the underlying demand for
diverse datasets in agriculture supports strong growth
potential.
Software as a
Service (SaaS)
High
SaaS models benefit from cloud infrastructure, allowing
rapid scaling to meet the needs of users across different
regions and scales of operation. The recurring revenue
model and the ability to continuously update and improve
services also support scalability. Challenges include
differentiation in a competitive market and ensuring data
security and privacy.
Industrial Data
Platforms
Moderate
These platforms' scalability is contingent on their ability to
integrate data from diverse sources and provide valuable
analytics and insights. While there is high potential,
scalability is moderated by the complexity of agricultural
systems, the need for interoperability among different data
sources, and the investment required to develop and
maintain such platforms.
Technical
Enablers
Low to
Moderate
The scalability of technical enablers like IoT devices and
sensors is influenced by technological advancements and
cost reductions. However, the adoption rates in agriculture
can be limited by infrastructure challenges, the need for
technical expertise, and initial setup costs, making
scalability more gradual.
Open Data
Policy
High
Open data policies inherently support scalability by
removing barriers to data access and encouraging
innovation and collaboration across the agricultural sector.
The main challenges lie in ensuring data quality,
protecting sensitive information, and fostering a culture of
data sharing. Despite these challenges, the potential for
widespread impact is significant.
The scalability of each business model in the context of the Common European Agricultural
Data Space is influenced by technological advances, market dynamics, regulatory
environments, and the collaborative efforts of stakeholders. The most effective strategies will
likely combine elements of multiple models to leverage their strengths while mitigating their
limitations, aiming for a balanced approach that promotes innovation, accessibility, and
sustainability.
5.4.3. Incentives to join the CEADS
The business model development for the CEADS is complex because of its unique properties.
On the one hand, it must be attractive to use cases in which both providers and users of data
create value. On the other hand, it must also be attractive for providers of services needed to
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operate the data space or add value to use cases. It requires the alignment of the value
propositions of multiple organisations jointly offering the data space and organisations in use
cases using the data space. A successful business model requires multiple organisations to
participate to align their value propositions. This section explores the perceived incentives and
potential barriers of each stakeholder group for participating in the CEADS.
Table 13: Incentives to join the CEADS
Stakeholder
Participation Incentives
Potential Barriers
Farmers
• Access to advanced analytics to
optimise yields.
• Improved decision-making with real-
time data.
• Enable sustainable practices and
certification.
• Concerns about privacy
and control.
• Costs associated with
upgrading digital
infrastructure.
Technology
and Data
Providers
• Expand market reach through data-
driven product improvements.
• Collaborate with a wide range of
agricultural stakeholders.
• Driving innovation with access to
comprehensive data sets.
• Compliance with
interoperable standards.
• Competition in a
growing digital
agriculture marketplace.
Data
Intermediaries
& Service
Providers
• Central role in facilitating data
exchange and monetisation.
• Opportunities to develop and offer
new services.
• Enhanced network effects through a
large user base.
• The challenges of
compliance and data
governance.
• Building trust with data
providers and users.
Public
authorities
• Informed policymaking through data
insights.
• Monitoring and promoting sustainable
farming practices.
• Supporting the digital transformation
of agriculture.
• Balancing access to
data with privacy and
security concerns.
• Managing public-private
partnerships.
Financial and
Insurance
Services
• Data-driven risk assessment and
product customisation.
• Improved decision making for loans,
insurance and investments.
• Enable innovative financial products.
• Data reliability and
standardisation issues.
• Privacy and ethical
handling of sensitive
financial data.
Multi-actor
Collaborations
/ Data Sharing
Initiatives
• Synergies through cross-sectoral
data sharing and projects.
• Increased innovation through multi-
stakeholder input.
• Strengthening the agricultural data
ecosystem.
• Coordinating and
aligning the objectives of
different stakeholders.
• Managing complex
collaborative
agreements.
Research &
Academic
Institutions
• Access to diverse and rich datasets
for research.
• Potential for multidisciplinary study
and innovation.
• Data management and
ethical considerations.
• Ensuring academic
freedom when
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• Collaboration with industry for
practical applications.
collaborating with
industry.
Business and
Industry
Stakeholders
• Monetisation and Enhanced
Audience for Proprietary Datasets
• Insight into market trends and
consumer preferences.
• Competitive advantage through early
adoption of data-driven strategies.
• Opportunities to improve
sustainability and efficiency.
• Investing in data
analytics capabilities.
• Navigating a rapidly
evolving digital farming
landscape.
The anticipated incentives for participation in the CEADS are significant across all stakeholder
categories and offer unique opportunities for progress and collaboration within the agricultural
sector.
5.4.4. Business scenarios and models
The Common European Agricultural Data Space represents a significant initiative aimed at
integrating and leveraging agricultural data across various stakeholders to enhance efficiency,
sustainability, and innovation in the sector. The setup, investment, and operation of such a
data space require careful consideration of the roles, capabilities, and investment readiness of
each stakeholder group. In the following table the potential of the key stakeholder to contribute
to the CEADS, together with their investment readiness, is evaluated in Table 14:
Table 14: Business scenarios per stakeholder category
Stakeholder
Category
Investment
Readiness
Analysis
Farmers
Low to
Moderate
Farmers, especially those from small to medium-sized
farms, may face challenges in terms of the initial
investment and technical expertise required to actively
set up, invest and operate a CEADS. However, with the
right support mechanisms, such as subsidies, training, and
accessible technologies, farmers can benefit significantly
from actively participating in CEADS by gaining insights that
improve productivity and sustainability. The success for
farmers largely depends on making the technology
affordable and user-friendly and providing them with
clear benefits from data sharing.
Business and
Industry
Stakeholders
(e.g.
machinery
providers)
High
Machinery providers are well-positioned to invest in and
operate within the CEADS due to their technical expertise
and financial resources. These stakeholders can benefit
from and contribute to the data space by enhancing their
products with data-driven features and services, offering
insights into equipment performance, and facilitating
precision agriculture practices. They can also play a
crucial role in standardizing data formats and ensuring
interoperability among different equipment and platforms.
Public
authorities /
EU Member
States
Moderate
to High
Public authorities have a strong interest in the successful
implementation of the CEADS, as it aligns with broader
goals of sustainability, food security, and rural
development. They can operate CEADS-related services,
such as monitoring food production, environmental impact
assessments, and providing anonymized aggregated data
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for research and policy-making. The provision of these
services is crucial but may need to be supplemented
with additional services to ensure the data space's
comprehensiveness and attractiveness to other
stakeholders.
Data Sharing
Initiatives
High
Data sharing initiatives, particularly those with a focus on
open data or collaborative data exchange platforms, are
inherently aligned with the goals of CEADS. These entities
can provide the necessary know-how, governance
models, and community-building efforts to encourage
participation and ensure the data space's functionality.
Their success in setting up, investing, and operating within
CEADS will depend on their ability to foster trust among
participants, ensure data security and privacy, and
demonstrate clear value to all stakeholders.
Research &
Academic
Institutions
Moderate
to High
Invest in research and development within CEADS by
utilising the access to funding through grants, partnerships,
and governmental support. Main challenge is operational,
as their focus tends to be more on innovation and
knowledge creation than on day-to-day operations.
However, their contributions can be invaluable in terms of
developing new technologies, analysing data for
insights, and training the next generation of
agricultural data scientists and professionals.
Overall, while each stakeholder group has varying levels of investment readiness, the
successful operation of the CEADS will require collaborative efforts to align their value
propositions. This involves addressing the specific challenges and needs of each group
through targeted support mechanisms, incentives, and regulatory frameworks that encourage
participation and investment. Public-private partnerships, as well as cross-sector collaboration,
will be key to leveraging the strengths of each stakeholder and ensuring the CEADS can deliver
on its promise to transform European agriculture.
The actors, their value proposition and the value in-use for the 3 main governance schemes
derived from the investment readiness of the key stakeholders are presented in Table 15,
while in Table 16 their most relevant business models and relevant pros and cons are
identified.
Table 15: Actors and value propositions for various governance schemes
Governance
Scheme
Co-created value
Actors
Value Proposition
Private
initiative
Acceleration of
technological
innovation and
market
responsiveness
through agile
development and
commercialization of
data-driven
solutions. This
model emphasizes
profitability,
Customer/End
User: Farmers
Direct beneficiaries of tailored
agricultural solutions, enhancing
productivity and sustainability.
Orchestrator:
Technology
Providers
Drive the development and
deployment of innovative
agricultural technologies, setting
standards for interoperability and
user experience.
Core Partner: DSIs
Facilitate data sharing and analytics
services, providing the
infrastructure necessary for data-
driven decision-making.
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competitive
advantage, and the
efficient delivery of
tailored services to
meet specific market
demands.
Enriching Partner:
Research
institutions/ service
providers
Contribute cutting-edge research
and development, ensuring that the
latest scientific advancements
inform technology and service
offerings.
Other Actors:
Public Sector
Regulatory oversight and potential
for subsidies or incentives to
support the adoption of innovative
agricultural practices.
Public-
Private
Partnership
Optimisation of
resource allocation
and maximization of
innovation
capabilities through
collaborative data
sharing
Customer/End
User: DSIs
Facilitate widespread data sharing
and integration
Orchestrator:
Public Sector/
Member States
Compliance monitoring that fosters
an environment of trust and
collaboration.
Core Partner:
Technology
Providers
Key role in providing the
technological foundation for
CEADS, ensuring interoperability,
security, and the seamless
integration of different data sources
and services.
Enriching Partner:
Research
institutions/ service
providers
Inject cutting-edge research,
innovation, and specialized
services into the ecosystem,
enhancing its capabilities.
Other Actors:
Farmers
Access to integrated data services
and innovative agricultural
technologies, leading to improved
decision-making, productivity, and
sustainability.
Public
Governance
Ensuring equitable
access to
agricultural data and
fostering a
transparent,
accountable
ecosystem that
prioritizes public
welfare and
sustainable
development. This
approach focuses
on creating public
value by leveraging
data for societal
benefits, including
improved food
security,
environmental
sustainability, and
support for rural
communities.
Customer/End
User: Farmers
Gain access to data and tools
necessary for improving agricultural
practices, with an emphasis on
sustainability and community
welfare.
Orchestrator:
Public Sector/
Member States
Oversees the entire ecosystem,
ensuring equitable access to data
and services, fostering
transparency, and maintaining
public trust.
Core Partner: DSIs
Key players in managing the data
sharing framework, ensuring data is
accessible, secure, and useful for
public and private sector needs.
Enriching Partner:
Technology
Providers
Offer the technological backbone
for the initiative, ensuring that
infrastructure is robust, scalable,
and capable of supporting diverse
public services.
Other Actors:
Research
Institutions/Service
Providers
Provide insights and innovations
that can be translated into public
services and policies, enhancing
the overall value of the agricultural
data ecosystem.
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Table 16: Business models per governance scheme
Governance
Scheme
Private initiative
Public-Private Partnership
Public Governance
Relevant
Business
Models
Data Monetisation: This model aligns
with the profit-oriented goals of private
initiatives, leveraging data as a valuable
asset that can be sold or licensed to
generate revenue.
Industrial Data Platforms / Software as
a Service (SaaS): Private initiatives can
effectively develop and manage these
platforms, focusing on integrating data
from various sources to offer value-added
services and cloud-based solutions.
Data Marketplaces: PPPs can facilitate the
creation of data marketplaces that ensure a
fair, transparent, and secure exchange of
data, balancing commercial interests with
public goods.
Open Data Policy: PPPs are uniquely
positioned to advocate and implement open
data policies, promoting accessibility while
ensuring data privacy and security
standards are met.
Open Data Policy: This model is central to public
governance, emphasizing free access to
agricultural data to spur innovation, research,
and informed decision-making across the sector.
Software as a Service (SaaS): While more
commonly associated with private initiatives,
SaaS can also be relevant under public
governance, especially when services are
provided to enhance public goods (e.g., disease
tracking, weather information services) and are
funded or subsidized by government bodies.
Key Aspects
Driven by private entities, focusing on
innovation, efficiency, and profitability.
Prioritizes scalability, market needs, and
competitive advantages.
Combines public oversight and private
sector efficiency, aims to balance public
interest with innovation, and addresses both
market and regulatory requirements.
Governed by public authorities, focuses on
public welfare, transparency, accountability, and
ensuring equitable access to data for all
stakeholders.
Pros +
• Dataset curation.
• Efficiency in policy implementation.
• Fast-track standardization.
• Promotes innovation and diverse datasets
and services.
• Fosters a culture of data sharing and
collaboration.
• Increased resilience and stakeholder
empowerment.
• Promotes Uniform standards and open Data,
enhances research opportunities.
• Streamlined integration with other DSIs/Data
Spaces.
• Equitable access and accountability.
Cons -
• Less prioritised environmental
sustainability.
• May limit open data sharing.
• Limited stakeholder input.
• Exclusion of smaller players / Monopoly
building.
• User privacy.
• Consensus building is more time
consuming.
• Complicated interoperability.
• Risk of fragmentation.
• Limited Technical expertise & Innovation.
• Limited flexibility / bureaucratic delays.
• Dependency on prioritised policies.
• Risk of over-regulation.
• Expensive interoperability support.
Conclusion
Efficient and innovative but falls short in
inclusivity and public value. It's driven by
commercial interests, which may not align
with broader community objectives.
Offers adaptability and innovation by
leveraging diverse stakeholder expertise.
It's best for meeting specific regional needs,
ensuring spread benefits of the CEADS and
encouraging collaborative progress, despite
coordination challenges.
Offers standardisation and reliability but lacks
the Federated model's flexibility and innovation
pace. Good for uniform data practices, yet not
as adaptable to fast changes or diverse needs.
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5.4.5. Benefits and value proposition for the DSIs
Data Sharing Initiatives emerge as the best choice for setting up, investing in, and operating
the CEADS in a public-private governance scheme for several compelling reasons while many
of them have already expressed their interest to participate but also to invest resources in the
CEADS (i.e. the consortium members Agdatahub and EV ILVO for the DjustConnect platform).
These initiatives are inherently designed to manage data ecosystems, which aligns closely
with the objectives and challenges of creating a unified agricultural data space. Their suitability
stems from a combination of their foundational goals, expertise, and the nature of the
agricultural data landscape:
Alignment with CEADS Objectives: The majority of the Data Sharing Initiatives are
fundamentally committed to the principles of open access, interoperability, and secure data
sharing. Their core mission supports the creation of a platform that facilitates the exchange
and utilization of agricultural data, making them naturally aligned with the goals of CEADS.
Their involvement ensures that the data space is built on a foundation that prioritizes the
seamless exchange of information across borders and sectors within the European Union.
Expertise in Data Management and Governance: These initiatives typically possess
significant expertise in handling complex data management challenges, including data
standardization, privacy, security, and user access controls. Their experience in developing
governance models that balance openness with the need for data protection is crucial for the
success of CEADS, where diverse data types from different sources must be harmonized and
made accessible in a secure manner.
Experience with Stakeholder Engagement: Data Sharing Initiatives have a track record of
successfully engaging multiple stakeholders, fostering collaboration, and building consensus
among diverse groups, including farmers, agribusinesses, tech companies, and public
authorities. This experience is invaluable for CEADS, which requires the alignment of various
stakeholders' interests and needs to ensure widespread adoption and participation.
Facilitation of Innovation and Value Creation: By promoting the open exchange and
interoperability of data, these initiatives can drive innovation, research, and development within
the agricultural sector. They are adept at creating ecosystems where data can be easily shared
and used to develop new solutions, services, and technologies that address key challenges in
agriculture, enhancing sustainability, productivity, and profitability.
Scalability and Sustainability: Data Sharing Initiatives are equipped to handle the scalability
challenges of CEADS, given their experience in managing growing data volumes and user
bases. They can implement scalable infrastructure and adopt evolving technologies to
accommodate the expanding needs of the agricultural sector. Moreover, their focus on
sustainability ensures that the data space remains viable and relevant over the long term.
Regulatory Compliance and Ethical Standards: These initiatives are well-versed in
navigating the regulatory landscape, ensuring that data sharing practices comply with existing
laws and ethical standards, such as the General Data Protection Regulation (GDPR) in the
EU. This expertise is critical for CEADS, ensuring it operates within legal boundaries and
maintains high ethical standards, particularly concerning data privacy and security.
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Furthermore, Data Sharing Initiatives can significantly benefit from setting up, investing in,
and operating the CEADS through several avenues:
Strengthened Ecosystem and Network: Participation in CEADS allows Data Sharing
Initiatives to expand their network and ecosystem, connecting with a wider range of
stakeholders within the agricultural sector, including farmers, agribusinesses, tech companies,
and public authorities. This expanded network fosters collaborations, partnerships, and
knowledge exchange, enhancing the initiatives' impact and reach.
Increased Data Availability and Quality: By being at the heart of CEADS, these initiatives
gain access to a vast and diverse pool of agricultural data, contributing to richer datasets. This
increased availability and quality of data can improve the accuracy and utility of analytics and
insights offered by the initiatives, driving innovation and supporting informed decision-making
across the sector.
Enhanced Visibility and Influence: Operating CEADS positions Data Sharing Initiatives as
key players in the digital transformation of agriculture in Europe. This enhances their visibility
and influence, not only among stakeholders within the agriculture sector but also in policy-
making circles. Such a position allows them to advocate more effectively for open data policies,
interoperability standards, and other causes aligned with their mission.
Opportunities for Innovation and Service Development: Access to a comprehensive and
interoperable data ecosystem opens up new opportunities for innovation and the development
of new services and applications. Data Sharing Initiatives can leverage this rich data
environment to create value-added services for different stakeholders, such as predictive
analytics for crop yield, pest management solutions, and precision farming tools, among
others.
Sustainability and Financial Viability: Involvement in CEADS can also contribute to the
financial sustainability of Data Sharing Initiatives. By providing valuable services and
leveraging the data space, they can explore new revenue streams, such as subscription
models, data analysis services, and consultancy. Furthermore, their pivotal role in CEADS can
attract funding opportunities, including grants from public institutions and investments from
private entities interested in agricultural innovation.
Leadership in Data Governance and Standards: Data Sharing Initiatives can lead the way
in establishing data governance frameworks and interoperability standards within CEADS. This
leadership role not only cements their expertise and authority in data management but also
ensures that the data space adheres to high standards of data quality, privacy, and security,
benefiting the entire agricultural ecosystem.
Facilitation of Research and Development: Being at the forefront of CEADS enables these
initiatives to facilitate and participate in cutting-edge research and development activities. They
can collaborate with academic institutions, research organizations, and innovation hubs to
drive advancements in agricultural sciences, technologies, and practices, contributing to the
sector's long-term resilience and competitiveness.
In summary, Data Sharing Initiatives stand out as the most suitable entities to be the main
actor of the CEADS due to their alignment with the data space's objectives, expertise in data
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management, stakeholder engagement capabilities, and commitment to innovation and
sustainability. Their role is crucial in ensuring that CEADS achieves its goal of fostering a more
connected, efficient, and innovative agricultural sector across Europe.
5.4.6. Business model proposition for the CEADS
In the development of the Business Model for the Common European Agricultural Data Space,
the following critical sources of insights have played a pivotal role:
● The Holistic Analysis Framework: Review of the results deriving from the Holistic
Analysis Framework conducted within WP1 (mapping the data sharing landscape) in
terms of the 64 DSIs business aspects.
● The Value Chain Network Analysis: The Value Chain Network Analysis has served
as a foundational element in identifying the key actors involved in creating and
delivering value within the agricultural data ecosystem. Through this analysis, the
network structure and customer segmentation of the Common European Agricultural
Data Space have been identified, shedding light on the stakeholders with whom
interactions will take place, and from whom value will be gained or provided. This
comprehensive understanding of the network dynamics and the specific needs and
expectations of different customer segments will enable the Common European
Agricultural Data Space to tailor its services and value propositions effectively, thus
establishing strong and meaningful connections within the ecosystem.
● The analysis of 15 Data Sharing Initiatives as well as their business models using
the Service Dominant Business Model Radar: The Service Dominant Business
Model Radar has emerged as a powerful analytical tool for dissecting and
comprehending the intricate components of the business models employed by the 15
identified DSIs. By applying this radar, we have gained profound insights into the value
propositions, customer segments, revenue streams, partnerships, and other critical
elements that underpin their respective business models. This rigorous analysis has
provided a rich foundation of knowledge and insights, which will guide the development
of the Business Model for the Common European Agricultural Data Space.
● Business models co-development with external stakeholders: Aiming at achieving
consensus on the development of Collaborative Business Models, AgriDataSpace has
conducted two external consultation workshops (online in September and physical in
October 2023). The results of the workshops provided insights on the business models,
the revenue models and the services of the Common European Agriculture Data
Space.
● Business models validation with external stakeholders: To validate the findings of
the co-development workshops, AgriDataSpace has conducted two external
consultation workshops, complemented with a meeting with the EU Member States and
a working session with the DSSC.
● Business models comparison: In assessing business models suitable for the
CEADS, our comparison highlights clear advantages that are relevant to the needs of
the ecosystem. Data monetisation models, while offering opportunities for direct
revenue from data sharing, may inadvertently constrain sharing through cost barriers
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and raise concerns about privacy and control. On the other hand, Data Marketplaces
can secure data exchange, maintaining data quality, and providing new revenue
streams, although their profitability can be impacted by transaction fees and reliance
on intermediary trust. Software as a Service models promise predictable revenue
through subscription models and offer scalable data analytics solutions, but they also
introduce ongoing costs for users and dependencies on single platform ecosystems.
Industrial data platforms advocate innovation and improved efficiency by facilitating
collaborative data sharing, but their benefits are typically limited to their members and
present unique coordination and governance challenges. Technical enablers are
instrumental in expanding data sharing capabilities, relying on sustained demand for
specific technological solutions without necessarily capturing the broader value of the
data shared. Conversely, Open Data Policies are critical to fostering innovation and
ensuring accessibility, potentially catalysing the development of novel products and
services without direct revenue from data sharing, and thus relying on indirect
monetisation strategies.
● Business scenarios based on the stakeholder participation and governance
schemes: In order to identify the potential of each stakeholder to contribute to the
CEADS and their investment readiness a comparison was made, while the possible
business models based on the governance schemes were analysed
Based on the results of the analyses outlined in the previous chapters, a hybrid business
model, incorporating elements from the Data Marketplace and Open Data Policy business
models, seems to be the most viable one. The CEADS, adopting a public-private
governance scheme- will act as a trusted facilitator of cooperation, bringing together different
Data Sharing Initiatives on a secure and trusted data space where they can connect and share
data utilising interoperability mechanisms.
The CEADS could embrace a freemium revenue model, allowing certain datasets to be freely
shared to encourage the development of innovative products and services but also provide
mechanisms for increasing data sharing between the DSIs, creating an extra revenue stream
for them. The model aims to strike a balance between commercial viability through increased
data sharing and contributing to the wider ecosystem by encouraging open data collaboration.
Expenses associated with establishing the technical and organisational frameworks of a data
space should be borne by the beneficiaries of the service in the long term. Commercial
providers of data spaces typically institute membership or transaction fees, a practice that may
also apply to non-profit organisations with diverse legal structures. Nevertheless, in the realm
of agriculture, public funding can serve as a valuable instrument (e.g. EDIC)
4
, particularly
in the initial phases of creating a common data space. As the practical viability of the data
space concept is not yet fully confirmed, and potential participants may harbour reservations,
public funding can play a role in alleviating financial risks for private stakeholders and “big
players” who may express interest. As such, the selection of the CEADS Business Model can
heavily influence its impact and the potential it has for fostering field-defining innovations.
The next and final step is the creation of the service-driven business model radar for
the Common European Agricultural Data Space.
4
European Digital Infrastructure Consortium (EDIC):https://ec.europa.eu/newsroom/dae/redirection/document/98557
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Figure 18 CEADS Service Dominant Business Model Radar
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The results of this conceptual proposal, shown in Figure 18, are subject to continuous
refinement in the evolving landscape of agricultural data sharing. The co-created value of the
CEADS lies in optimising resource allocation and maximising innovation through collaborative
data sharing. Stakeholders, including data sharing initiatives as customers/end users, the public
sector (e.g. Member States through competent authorities) as the orchestrator of the framework,
and technology providers as possible core partners, aim to collectively improve decision-making,
resource efficiency and innovative solutions. Research institutions, data intermediaries, service
providers and farmers further enrich this collaborative model, promoting efficiency, sustainability,
and resilience in the European agricultural sector.
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6. Multi-Stakeholder Governance Scheme for CEADS
As outlined in the previous sections, the governance of CEADS should be designed in an
evolutionary process, alongside the business models and in response to the foreseeably changing
needs of its stakeholders. This chapter presents the elements that make up a suitable Multi-
Stakeholder Governance Scheme and the general requirements they have to fulfil. Additionally it
presents options for possible concrete implementations of governance schemes and outlines their
drawbacks and benefits, which are derived from the analysis of DSIs as well as the literature
review.
The chapter starts with general principles and requirements for CEADS’ governance Scheme
(Section 6.1), before it addresses details of the implementation on the level of Organisational
Governance (Section 6.2), Data Sharing Governance (Section 6.3) and Governance of
Collaboration (Section 6.3.3 ). The chapter concludes with an Outlook in Section 6.4.
6.1. Principles for the Governance Scheme
This section outlines the assumptions, definitions and requirements that guide and frame the
recommendations for a Multi-Stakeholder Governance scheme for CEADS. First, five principles
that pertain to CEADS mission and purpose as well as conditions given by the regulatory
framework and the sector’s structure principles are defined in Sect. 5.1.1.. Sect. 5.1.2. Finally, the
conceptual framework for the Multi-Stakeholder governance scheme is outlined.
6.1.1. Governing Principles and CEADS Mission, Ecosystem and
Services
One of the main steps in the internal validation and alignment process of the AgriDataSpace
project was the vision workshop conducted in work package 4 on roadmapping. As a result, it was
decided that the CEADS mission is to “strive[s] for a network of interoperable data sharing
initiatives”, rather than filling the role of a central data-intermediary. This assessment also meets
the corresponding CEADS requirements derived from the interviews, as some interview partners
explicitly advised against a “data space of data spaces”, i.e. a monolithic organisation which is
providing the data sharing and data marketplace functionalities for the whole agricultural sector in
Europe (Chapter 4). Existing DSIs in Europe serve as data intermediaries for specific subgroups
of stakeholders and have either a regional scope or focus on a specific sector vertical (e.g. iDDEN)
or technological application area (e.g. AEF AgIN) or both (e.g. Hortivation). Within their focus
areas, the DSIs involve individual actors from specific value-chains in the build-up of the DSI, the
development of specific use-cases or services and the ecosystem’s community. In the vison
workshop it was furthermore decided that the interests of farmers will be “the centre of gravity of
this decentralised approach“ (Chapter 1). CEADS will thus be a European umbrella organisation
to connect individual DSIs, which themselves function as access points to different stakeholder
groups including public sector in different agricultural value-chains, most importantly farmers, but
also their business partners in different regions.
• Agricultural DSIs are the target stakeholders for the CEADS and should be directly
involved in CEADS, whose aim it is to support their interoperation and collaboration to
enable data sharing across DSIs in Europe and to thus allow offering more and more
powerful data-driven services to different stakeholders in agricultural value-chains.
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• Individual actors (companies) of the agricultural value chains are primarily involved in
individual DSIs and can thus indirectly participate in the CEADS via the respective DSIs.
• Regional or sector-specific DSIs are mediating organisations that serve as interface
between CEADS and local actors (in particular: farmers). Member DSIs thus represent
regions and/or sector verticals, entering their specific requirements, needs and conditions
into CEADs decision processes and communicate the benefits and processes on the
CEADS level back to their user base.
• The CEADS focus lays on the agricultural sector. Value-chains of the agrifood supply-
chain extend into a range of related sectors and areas (e.g. food and retail), yet CEADS
focuses on farmers of agricultural sector-verticals relevant in Europe as central
stakeholders.
It is essential to define the purpose of the CEADS as a basis for the design of the governance
scheme. The feedback from the validation of business models depicts the ecosystem‘s viewpoint
on desirable service offerings of the CEADS. The following four services were rated as most
relevant (Chapter 5):
• Shared and inclusive administrative and data governance
• Facilitating the access to Open Data.
• Identity interoperability, promoting seamless recognition and authentication between
different Data Space actors.
• Data catalogue interoperability, which ensures that diverse and disparate data sets can
be used together.
This result is in agreement with the results of the vision workshop as well as several internal and
external validation rounds, i.e. that the main purposes of the CEADS is in connecting agricultural
DSIs within Europe (“collaboration support”). In addition, interoperability support was frequently
mentioned as a desirable service by CEADS in the interviews (see Section 4.2). The central
mission of CEADS should thus be the provision of services aimed to support a federated
European data market based on the connection of individual DSIs:
The primary selection criteria for assumed service offerings of the CEADS are:
• The CEADS should support the networking of regional, national and other already existing
European DSIs and foster the build-up of the European data sharing landscape.
• The services of CEADS are targeted at DSIs and therefore should not be in competition
to their respective service offerings.
The status quo of the agricultural DSI landscape was analysed broadly in Work Package 1,
followed by in depth investigations of governance and business models in Work Package 2 (see
chapter 5). One of the conclusions is that many of the existing DSIs are still in the build-up phase,
piloting or scaling, or are even conducting research projects without a business model, revenue
streams and establishment in the ecosystem or a clear market perspective. To assert themselves
in the dynamic market, the growing DSIs have to be able to act flexibly and to adapt their
governance and business models to the dynamic market situation and its requirements, conditions
and opportunities. This report therefore proposes the “Minimal Change”-principle for CEADS:
“Minimal Change”-principle: CEADS aims to avoid imposing additional changes or
requirements on existing DSIs with existing or maturing governance schemes and business
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models, as such interventions would negatively affect the effective build-up of the ecosystem
from the bottom-up and put unnecessary stress on the sector.
The DSSC states that a “governance in data spaces needs to adapt as the data space evolves“
(Data Spaces Support Centre 2023). This means that the governance scheme for CEADS should
not be decided ex ante but should rather be designed alongside the economic and technical
implementation of CEADS and undergo continued revision in its operation phase. CEADS’
governance scheme should adapt to changing requirements of central stakeholders as well as
changing regulatory requirements. CEADS has to take the diversity of participating DSIs into
consideration when developing and defining its own governance structure, as there is variability
in the extent to which extent is a DSI profit-oriented, which sector vertical it focuses on, how open
its business philosophy is, which roles it distinguishes, etc.. CEADS will have to try and cater for
most or all of these DSI instantiations to fulfil its role as central European network of DSIs.
“Form follows function”-principle: In order to guarantee that a Multi-Stakeholder
Governance scheme for CEADS is suited to its members’ needs, it is important to focus on
the (potential) added value of CEADS’ services, their clear communication to evolving data
ecosystem and especially to already existing DSIs as key actors for the CEADS. In order to
not diminish potential benefits for these crucial DSIs that should be attracted in the early
stages as active contributors, unnecessarily complicated concepts or governance schemes
should be avoided – especially during the build-up phase.
The same goes for the business model, which is why business model and governance should be
co-developed and aligned, not just in their vision but also in implementation details.
The analysed DSIs are predominantly intermediaries according to the Data Governance Act and
are thus obligated to fulfil the strict requirements resulting from this regulation (Section 2.2.3).
While the CEADS itself will likely not be a data intermediary, the services it will offer to its member
DSIs should adhere to the rules and principles laid down in the Data Governance Act, as most or
all of its participants will be obligated to follow these rules and regulations for data intermediary
services.
“Compatibility”-principle: The design of the governance scheme for CEADS aims for
compatibility with the data intermediary role, in order to
• enable DSIs that act as data-intermediaries to be members in the CEADS without
contractions to their own governance and business model;
• reserve the possibility for CEADS to adapt its services in the future and become a data
intermediary, if this is deemed useful and desirable by its participants at any point.
6.1.2. Framework for governance building blocks
The recommendations for a multi-stakeholder governance scheme for the European agricultural
sector are formulated within a framework, which consists of several building blocks. This
framework was derived from the analysis of the literature, such as the DSSC building blocks and
of the data sharing initiatives. It comprises of two levels, three layers and three sources for
requirements.
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Levels of governance
Our framework distinguishes the following two levels of multi-stakeholder governance.
• Data Sharing Initiative Level (DSI level): This level refers to the governance of individual
DSIs with their regional or sector-specific business, applications and networks. They are
directly linked to their respective stakeholder groups from the agricultural or agri-food
value chain and include farmers, equipment manufacturers, suppliers and consumers, etc.
• Common Data Space Level (CEADS level): The on-going projects addresses a
governance for a network of agricultural data-spaces (each of which is on the DSI level)
across Europe, and its relation to data spaces in other related sectors as well as other
parts of the world.
Layers of governance
The DSSC differentiates two central building blocks for the Intra Data Space Governance, which
are also two of the three layers of the governance framework for the CEADS:
• Organisational governance consists of rules, roles, processes and structures of the
operating organisation, its organisational bodies, its position in the ecosystem and the
involvement of participants and other stakeholders.
• Data Sharing governance consists of rules, roles, processes and structures for effective
and reliable data sharing across the members of the CEADS.
To point out the importance of designing and supporting collaborations for the governance
scheme, the following layer was added:
• Shared, inclusive administrative and data governance
These layers apply to both levels of governance (DSI level and CEADS level): The analysis of
governance schemes in existing DSIs (Section 4.1) presents how the layers of governance are
implemented on the DSI level and reveals the range of possible implementations. The following
Sections 5.2-5.3 describe recommendations and design options for these levels of governance
on the CEADS level. It should be noted that the concept of Data Sharing governance does not
apply to CEADS in the strict sense, as CEADS role will be that of a network of interoperable DSIs
rather than that of a data space in its own right (Section 6.3).
Sources of Requirements for Governance
As part of the research in Work Package 2, three main sources of specific requirements for the
design of CEADS’ governance were identified:
• The Data Governance Act, as it regulates the activity of data intermediaries like the
individual DSIs that are the main stakeholders of CEADS.
• The Data Act, as it removes barriers for wide data sharing and ensures fair and equal
access to data assets.
• The demands of DSIs and their central stakeholders to ensure that CEADS is suited
to and beneficial for the market and the existing landscape of DSIs.
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Figure 19: Framework for the design of the CEADS governance scheme
6.2. Organisational Governance
The organisational governance of CEADS should specify the following processes and properties:
• Organisational mode: What form of organisation can the CEADS have and what are its
core characteristics regarding the degree of neutrality, openness and centrality? What is
the scope of the organisation setting the boundaries for the governance?
• Organisational bodies: In which bodies can the CEADS organisation be structured and
what are the roles, rights and obligations according the CEADS regulations?
• Participants: Which stakeholder groups should be involved in the CEADS, in which roles
and with which rights and obligations? How can they access the provided services? How
is the CEADS protected against involving fraudulent actors?
• Collaboration: How do the internal processes of the CEADS (organisational bodies and
participants) work, with focus on decision-making and communication?
• Onboarding: How can participants of the CEADS start their involvement and how are they
enabled to use the offerings of the CEADS?
The requirements for the organisational governance of the CEADS are derived from the Data
Governance Act to fulfil the goal of compatibility with the data intermediary role stated in the
principles. Central aspects are the neutrality and separation of the data intermediary service from
other offerings (e.g. data-based services), open access to the service for participants and safety
against the involvement of fraudulent actors:
Figure 20: Requirements on the Organisational Governance for the CEADS level
CEADS level
DSI level
Organisational Governance Data Sharing Governance Governance of Collaborations
Requirements for CEADS derived from
Data Spaces Support Centre (Blueprint)
Data Governance Act
Data Act
Interviews with DSIs
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6.2.1. Organisational mode
Organisational DSI governance describes the structures that coordinate the necessary activities
of the organisations involved with or participating in DSI. The following figure presents three
options for organisational governance in a network of participants:
Figure 21: Overview on various governance models for networks (Tijs van den Broek and
Anne Fleur van Veenstra 2015)
The options from left to right are:
1. There is no specific organisation for governance, the involved organisations govern the
activities of the network communally, either in an organised (meeting and representation
structure) or in an unorganised process. This is called shared participant governance.
2. One of the participating organisations is (acts as) leading organisation and takes care of
the coordination. This is called lead organisation governance.
3. The participating organisations have decided to specifically set up a separate
administrative entity to govern the data sharing between all the involved organisations.
This is called a network administrative organisation.
Based on the vision workshop, the CEADS is a specific organisation in the network of DSIs. Along
with the assumed service portfolio from the previous section, governance model option 3 network
administrative organisation (NAO) was chosen as the governance model that CEADS should
implement.
• The governance scheme for CEADS applies the model of network administrative
organisation based on the DSI network.
• The evolution of the governance model and its alignment with (the evolution of) the
business model.
The organisation behind the CEADS needs to have the necessary degree of neutrality in order to
guarantee a sufficient level of trust within the existing data sharing landscape of the agricultural
sector. Therefore no single stakeholder group or even single actors within the sector should be
given privileged access to the organisation itself and its decision-making system.
6.2.2. Role of the Member States in the Data Governance Act
The implementation of the DGA creates responsibilities for EU Member States that affect the governance
and operational aspects of the CEADS. These responsibilities include the establishment of competent
authorities, the monitoring of compliance, the development of operational standards, the management of
complaints and the establishment of sanction regimes for breaches. In particular, Article 13 DGA empowers
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Member States to supervise data brokers by designating competent authorities, while Article 34 DGA allows
for the formulation of sanctions for infringements of the DGA. In addition, the Participation in the European
Data Innovation Board enables Member States to contribute to EU-wide data governance policies, aiming
at harmonised data sharing practices. The following paragraphs detail these rules.
6.2.2.1. Competent authorities for data intermediation services
Article 13 DGA provides Member States with the authority to regulate data intermediation services. This
can be relevant in the context of Common European Data Spaces. By designating competent authorities to
oversee notification procedures, Member States ensure that data intermediaries comply with legal
standards, potentially supporting secure and transparent data sharing between data subjects. In addition,
this regulation imposes coordination with the European Commission and certain requirements to be met,
emphasising the role of Member States in establishing a consistent and standardised supervisory
framework across the EU. Furthermore Article 13 of the DGA mandates cooperation between these
authorities and other regulatory bodies, such as data protection and cybersecurity authorities, to ensure a
comprehensive approach to data governance. This collaborative effort aims to achieve regulatory
consistency, which impacts the operational environment of data intermediation services.
6.2.2.2. Monitoring of compliance
Article 14 of the DGA gives EU Member States the power to monitor and enforce the compliance of data
intermediaries and provides a structured approach to the regulation of these entities. Member States can
initiate monitoring on the basis of external requests or their own assessments, and have the power to
request information necessary to verify compliance. In cases of non-compliance, they are authorised to
notify providers, request rectification and, where appropriate, impose penalties, suspend services or
demand cessation of activities. This article also focuses on cooperation between Member States in dealing
with cross-border data services to ensure consistent enforcement across the EU. Through these measures,
Member States may influence the integrity and trustworthiness of data sharing ecosystems, which could
also influence the effectiveness of the CEADS.
6.2.2.3. Requirements relating to competent authorities
Article 26 of the DGA sets out the responsibilities and operational standards for competent authorities
overseeing data intermediaries. Key points include the requirement for these authorities to be legally distinct
and functionally independent from the entities they regulate, to ensure impartiality and to avoid conflicts of
interest. Member States have the flexibility to establish new or use existing authorities for these tasks,
emphasising their role in integrating these responsibilities into their national frameworks. The authorities
must operate in a transparent, accountable and non-discriminatory manner, ensuring fair competition.
6.2.2.4. Member States' Role in Overseeing Complaints and Judicial Remedies
Articles 27 and 28 of the DGA empower Member States to oversee complaints and judicial remedies related
to data intermediation services. These provisions allow individuals and entities to submit complaints to the
competent national authorities, thereby ensuring accountability and compliance with the DGA. Member
States influence the enforcement of regulations by handling complaints and facilitating access to justice for
disputing legally binding decisions. This oversight mechanism reinforces the objectives of the DGA within
the jurisdiction of each Member State.
6.2.2.5. European Data Innovation Board
The establishment of the European Data Innovation Board under Article 29 of the DGA aims to achieve
cooperation and coherence in the European Union's digital and data strategy, with potential implications for
CEADS. The Board is formed by representatives from all Member States, including the competent
authorities for data intermediation services, as well as key EU bodies and stakeholders from different
sectors, and is tasked with advising the European Commission on several aspects of data governance and
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policy. Member States influence the development and implementation of policies and practices through
their participation in the European Data Innovation Board. It covers several thematic areas, including
- uniform practices for the application of requirements for data intermediation service providers,
- formulating guidelines to protect commercially sensitive non-personal data,
- establishing cybersecurity guidelines for data exchange and storage,
- advising on cross-sector standards and enhancing interoperability across European data spaces
Through the European Data Innovation Board, Member States can help shape the regulatory environment
for data sharing and governance, addressing issues such as entry barriers, ensuring fair competition and
promoting non-discriminatory access to data spaces.
6.2.2.6. Penalties and sanctions
Article 34 of the DGA empowers EU Member States to establish penalty regimes for certain infringements,
such as the unauthorised transfer of non-personal data to third countries, failure to comply with notification
requirements and failure to comply with conditions for data brokering services. States must ensure that
sanctions are effective, proportionate and dissuasive and take into account the recommendations of the
European Data Innovation Board. This gives Member States a central role in enforcing the law, allowing
them to tailor penalties to their national context, while contributing to a consistent EU-wide approach to data
governance.
6.2.3. Role of the Member States in the Data Act
The Data Act sets out rules for access to and use of data across different sectors. Within this framework,
the potential implications for CEADS include the possibility of increased data sharing and use within the
agricultural sector. The success of this regulation will also depend on how EU Member States implement
and enforce the provisions of the Act. Within the framework of the Data Act, EU Member States assume
specific roles, which are outlined in the following paragraph.
6.2.3.1. Dispute resolution
Article 10 of the DA introduces a dispute resolution mechanism that gives users, data holders and data
recipients access to certified dispute resolution bodies. These bodies are designed to deal with disputes
relating to the conditions of data sharing, including those relating to fair, reasonable and non-discriminatory
terms of data provision and transparency. Member States play a role in this framework by accrediting these
dispute resolution bodies and ensuring that they meet certain criteria, such as impartiality, expertise,
accessibility, and the ability to provide prompt, efficient, and cost-effective decisions. For CEADS, this article
potentially provides a structured way to address conflicts that arise within the agricultural data sharing
ecosystem. The certification and oversight of dispute resolution bodies by Member States may influence
how effectively disputes are resolved within the CEADS, potentially affecting the flow and use of agricultural
data.
6.2.3.2. Oversight and data coordinators
Article 37 of the Data Act requires EU Member States to establish competent authorities and, where
appropriate, data coordinators. These authorities are responsible for ensuring compliance, handling
complaints, conducting regulatory investigations and imposing sanctions for violations. The designation of
data coordinators is intended in particular to improve cooperation between different authorities within a
Member State and to ensure a consistent approach to the application of the Act. The provisions of Article
37 DA potentially affect the CEADS by establishing a framework for data governance and compliance within
the EU. The designation of competent authorities and data coordinators by Member States is important as
it directly influences the enforcement of data sharing and use practices within the agricultural sector. The
potential impact on CEADS also includes improved data interoperability and accessibility, as these
authorities ensure that data sharing complies with the law.
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6.2.3.3. Interoperability and Standardization
Article 33 DA provides a framework for harmonised standards to ensure interoperability between different
data spaces. It states that participants in data spaces offering data or data services shall be presumed to
comply with the essential requirements for interoperability if they conform to harmonised standards
published in the Official Journal of the European Union. The European Commission is responsible for
mandating European standardisation organisations to develop these standards and ensure that they meet
the defined essential requirements for data sharing and processing. Member States can influence these
standards through their participation in the regulatory process. They can contribute to the development of
delegated acts, which are adopted by the Commission to further specify essential requirements, and
participate in discussions that shape the content and direction of these acts. Although the direct mandate
to standardisation organisations lies with the Commission, Member States can influence the standardisation
process indirectly through national bodies or by participating in European standardisation committees. In
addition, Member States have a role in the establishment of common specifications, in particular where
harmonised standards are not available or do not meet the requirements. This is achieved through the
adoption of implementing acts by the Commission, which can be shaped by contributions from Member
States to address specific national or sectoral needs. In addition, Member States have the possibility to
criticise these specifications if they consider that they do not fully meet the essential requirements for
interoperability.
CEADS: Options for the organisation and its legal form
Using an existing organisation as basis for the CEADS can basically be a possible option but
represents more the example of a leading organisation (comparable to option 2 in Figure 21
including the described negative connotations for the perceived neutrality of the CEADS. Other
existing organisations such as farmers associations, are also more likely to lack the perceived
neutrality due to their specific customer group or provided services. Hence, the founding of a new
organisation appears to be the most suitable option for the CEADS and applies to the rules of
the DGA stating that the provider of data intermediation services must operate through a separate
legal entity. The choice of the founding actor(s) directly influences the perceived neutrality within
the establishing landscape of DSI within the agricultural sector. Therefore a concentration of
influence through the establishment of the organisation behind CEADS by one single public or
private actor has to be avoided. Nevertheless actors from the public or private sector can and
maybe should be embedded in the evolutionary process of building the CEADS – as longs as
there are clear limitations for the influence of single players within the decision-making-process
of the underlying organisation. In order to foster the emerging landscape of DSIs within the
agricultural sector the most suitable option for the CEADS appears to be the founding of the
underlying organisation by already existing public and/or private DSIs as a shared
sponsorship/ownership model with initial financial assistance from the public sector.
Since the CEADS should act as a multi-stakeholder operator for the agricultural sector, its
legitimacy and acceptance among possible participants of the emerging data sharing landscape
can be fostered by making existing Data Sharing Initiatives (DSIs) members of the underlying
organisation. Ideally the DSIs being directly involved in the evolutionary process of developing the
CEADS cover the bandwidth of the field of application within the agricultural sector as broad as
possible.
To the start of the development process of forming the CEADS the EU Code of conduct can act
as a basis for the decision if a DSI is suitable for being a member. Nonetheless specific
requirements for joining the CEADS organisation as a member but also for integrating other
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stakeholder groups into its decision-making system need to be detailed in the evolutionary
process.
Choosing the best legal form for an organisation focused on organizing data sharing within the
European agricultural sector based on a diversified ownerships depends on various factors,
including the specific goals, structure, and preferences of the organisation. Different legal forms
ensuring divided ownership / membership are possible and have unique advantages and
disadvantages. The three most suitable options are listed down below:
1. Non-profit Organisation, Association or Foundation: Nonprofit organisations are often
formed for purposes that benefit the public or a specific community, and they may have a
mission aligned with promoting data sharing and collaboration in the agricultural sector. It
is advantageous that non-profits enjoy certain tax benefits, may be eligible for grants and
donations, and are often perceived as mission-driven entities with a higher degree of
perceived neutrality within the relevant stakeholder group. On the other hand nonprofits
must comply with specific regulations governing their tax-exempt status, and their activities
are generally restricted to furthering their relatively fixes stated mission. While eligible for
grants and donations, non-profits may also face challenges in generating enough revenue
through traditional business activities in the long run.
2. Limited Liability Company (LLC) or other type of private company: An LLC (or other
type) is a flexible form that combines characteristics of a corporation and a partnership. It
provides limited liability to its members, flexibility in management and a taxation structure
best suitable for generating revenues. If an LLC is chosen as legal form for CEADS
administration, it would be advisable to set it up in a way that guarantees the perceived
neutrality of CEADS, such as getting a non-profit/not-for-profit status, a representative
member structure (across regions and sectors) or a company statute that guarantees
neutrality.
3. Cooperative: Cooperatives are owned and operated by their members, who share in the
profits and benefits. In the context of data sharing, a cooperative structure could involve
collaborative efforts among various stakeholders in the agricultural sector. Members have
a direct stake in the organisation, fostering a sense of ownership and collaboration.
Furthermore trust within the organisation is strengthened by the more consensus-oriented
decision making processes but further development can be also slowed down by it.
Therefore it requires careful governance and decision-making processes to accommodate
the diverse interests of members.
Additionally all of the three options can be set up as Public-Private Partnership (PPP) which allows
collaboration between public and private entities, enabling the organisation to leverage resources,
expertise, and funding from both sectors. Furthermore this offers flexibility in terms of governance
structure and funding models, accommodating the diverse needs of public and private
stakeholders. Furthermore Public-private collaborations can gain support from government
agencies, potentially opening avenues for funding and regulatory cooperation. But establishing
and managing a PPP involves negotiating roles, responsibilities and funding contributions from
both the public and private partners. Balancing the interests and priorities of public and private
stakeholders requires careful consideration and effective governance and it usually takes more
time to be put into action. Also, a close affiliation with the public administration can have negative
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impacts on the perceived independence of an organisation, which might impact negatively on the
acceptance of the organisation in the market. Hence, choosing a PPP-variant of the options
mentioned above is only advisable if the actors form the agricultural sector – especially the
existing DSIs – see a clear benefit to it and the actors from the public sector are well defined and
ready to act quickly.
Revenue Streams / Finances
The ways to finance an organisation focused on enabling data sharing between existing initiatives
in the agricultural sector depend on its legal structure, mission, and objectives.
Therefore the alignment of governance and business model development is a crucial part of
setting up the CEADS organisation should be both serve the needs of the users. Here are several
financing options commonly used by organisations, particularly those in the nonprofit or for-profit
social enterprise space:
1. Grants and Donations: Nonprofit organisations often rely on grants from foundations,
government agencies, and philanthropic organisations. Some social enterprises also qualify for
grants, especially those aligned with social impact goals.
2. Membership Fees: Charging membership fees can provide a steady source of revenue.
Members contribute financially to support the organisation's mission and gain certain benefits,
such as access to data-sharing platforms and exclusive events.
3. Corporate Partnerships / Sponsorships: Sponsorships or partnerships with corporations that
have an interest in supporting agricultural data sharing can be an option. This could include
technology companies, agribusinesses, or other organisations with a stake in the sector.
Nonetheless this will probably cause problems when it comes to the perceived neutrality of the
CEADS.
4. Revenue for CEADS Services: It is also possible to offer services related to data sharing,
analytics or agricultural technology for the participants of the emerging data ecosystem within the
agricultural sector to generate income. But every offered service requires a detailed analysis to
identify possible risks for the trustworthiness and legitimacy of CEADS.
Goal of cost coverage
The charging model should be not for profit, but strive for cost coverage, as DSIs with this principle
will be participants or even members. High profits and margins won’t be tolerated, as they might
be in conflict with the principles of participating DSIs and will be a hurdle for participation. A low
participation would e.g. hinder the development of interoperability guidelines and the
attractiveness of the emerging data ecosystem of CEADS as a whole.
The build-up phase of the CEADS organisation will probably require an initial funding by the public
sector. To ensure neutrality, it should not come directly from single agricultural companies and
should rely in the long run not heavily on public funding solely.
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6.2.4. Organisational bodies
Independently from the selection of its legal form, the organisation should possess different bodies
ensuring trust and neutrality of the CEADS, an effective but also legitimate decision making
process as well as the representation of the different stakeholder groups within the agricultural
sector. On the one hand there has to be a central decision making body in the form of General
Assembly of all members. On the other hand there should be an advisory board. Due to the
complexity of the technical and organisational challenges when it comes to bringing together the
variety of existing and emerging DSIs within the agricultural sector we also come to the conclusion
that it makes sense to establish a management team, working groups assisting the advisory
board and preparing decisions for the general assembly.
The Management Team is responsible for the organisation and set up of the working groups,
support of the general assembly and invitation and management of participants of the advisory
board. In addition to the internal organisation, the management team is responsible for the
communication with the data coordinators that are appointed by the Member States.
General Assembly: Designing a committee within the organisation responsible for setting up
rules for data sharing and deciding on new members involves careful consideration of its structure,
responsibilities, and decision-making processes. Its main purposes are to establish and enforce
policies for data sharing within the CEADS and between engaged DSIs and make decisions
regarding the admission of new members, as well as setting up procedures for risk management,
change management and monitoring, which have to be adapted based on the members
experience and processes for the CEADS. As already stated it should only consist of the members
of the organisations being DSIs. Its main functions and responsibilities can be summarised as
followed:
• Policy Development: Develop and update policies related to data sharing, ensuring
alignment with legal requirements and industry best practices considering the ethical
implications and societal impacts of data sharing in the agricultural sector.
• Membership Evaluation: Review applications for ownership/membership, assessing
alignment with the organisation's mission, commitment to data sharing principles, and
adherence to ethical standards. Establish criteria for membership, considering factors
such as expertise, contribution potential, and willingness to comply with data-sharing
policies.
• Conflict Resolution: Address conflicts related to data sharing and
ownership/membership issues, promoting fair and transparent resolution processes.
Develop mechanisms for handling disputes and appeals related to data sharing and
membership decisions.
• Education and Awareness: Conduct awareness campaigns to educate members and
stakeholders about the importance of responsible data sharing. Provide training on data
governance, security, and privacy.
The chosen mode for decision-making within the body should focus on Consensus Building rather
than simple majority decisions. Consensus-based decision-making ensures broad agreement on
data-sharing policies and membership decisions and facilitates open discussions considering
diverse perspectives. Nevertheless a voting mechanism for situations where consensus is not
reached has to be established as well. It is important that the thresholds required for policy
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changes and membership approvals are designed in a way which is still inclusive. Therefore a
qualified majority voting seems to be a suitable solutions. However the specific criteria for a
qualified majority may vary in different phases of the evolutionary development process of CEADS
and may also change over time. But criteria such as the involvement of different sub sectors within
the agricultural sector or the geographical coverage of EU member states should be taken into
consideration. The criteria such as the size of the single DSIs (either by amount of active
participants or by usable data) on the other hand should not play an important role in order to
ensure a fair decision making process.
Open questions regarding the specificities of the voting system and the financial commitment of
the participating DSIs, should be decided with the interested parties in the implementation project,
taking the aim of neutrality into account. It is typical for DSIs with several members (e.g. iDDEN)
to have one vote per organisation, and similar distributed financial shares accordingly.
Ensuring transparency in decision-making processes by documenting discussions, decisions, and
the rationale behind them is a key factor here. This includes communicating decisions to the
organisation's members, stakeholders within the agricultural sector in general as well others DSIs
as possible future members. The meeting of the committee should be conducted on a regular
basis to frequently discuss and decide on ongoing data-sharing issues, policy updates, and
membership applications. In order to make sure that new perspectives are heard within the
evolutionary process of the development of CEADS it can be helpful to implement term limits for
committee members and establish a rotation system to ensure continuity while allowing for the
infusion of new ideas.
This committee structure aims to balance expertise, inclusivity, and transparency in decision-
making related to data sharing and membership within the organisation. Regular reviews and
adaptations to the committee's structure and processes will be essential to ensure its
effectiveness over time.
Advisory Board
Before formal voting takes place, initiating a consultative process where stakeholders across the
agricultural sector are invited to express their opinions and concerns on proposed data-sharing
policies or new membership applications is vital for the trustworthiness and legitimacy of the
CEADS.
In order to guarantee a broad involvement of stakeholders from the agricultural sector besides
existing DSI it seems suitable for the CEADS to build op an advisory board. The Board can
prepare decision about rules for data sharing, the involvement of new members or technical
standardisation but has to have limited to no decision rights, to ensure neutrality of the whole
CEADS. Therefore its main goal is advising the members and generating input regarding the
agricultural sector and needs of specific stakeholder groups on the CEADS offerings. JoinData as
one an analyzed DSI for example has several advisory boards. Among others there is the Advisory
Board “Data” and the Advisory board “farmers”.
In addition, the data coordinators should invite to participate in Advisory Board and General
Assembly meetings to inform its members about the activities of the European Data Innovation
Board.
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Working Groups
The working groups are the most active part of the CEADS, since they actively shape the service
offerings for the members such as interoperability support regarding legal, data sharing and
business models, provision of open data, support of DSI development and market access and
DSI collaboration support.
6.2.5. Participants
The Data Governance Act requires that data-intermediaries ensure non-discriminatory, fair and
transparent access to the data intermediary services. This principle of non-discriminatory, fair and
transparent access should likewise be implemented in CEADS’ organisational governance.
Open and voluntary access
We recommend the following restrictions for access to specific roles:
• Membership: Membership should be open to any DSI or organisation interested in the
services provided by CEADS.
• Members: At the beginning, only European DSIs founded the initial entity of the CEADS.
Rules on membership should be worked out as part of the evolutionary process in the
corresponding working group, e.g. based on neutrality, openness and a focus on creating
value for farms (cf. part on transparent rules below).
• The Advisory Board (as well as possible sub-units) should be headed by representatives of
members, but open to representatives of all members as well as representatives of non-
member organisations of the value-chain, from suppliers to farmers and agricultural
companies up to public sector. As part of the evolutionary process, rules ensuring the equal
representation of all stakeholders in the agricultural Sector should be implemented.
• The Working groups should be headed by representatives of the shareholding DSIs. The
working groups should be open to representatives of other members or external organisations
(e.g. to machine manufacturers for a working group on handling machine data or to
representatives of standardisation organisations or DSIs from other sectors) if it suits the
working group’s purpose.
Transparent Rules for Participation
Specific rules for each role have to be defined in detail. This should be part of the evolutionary
build-up process for main positions.
The key stakeholders for CEADS are existing and future DSIs that provide data-intermediary
services to agricultural stakeholders in different regions of Europe, which raises the questions:
• What is considered agricultural?
• What is considered a DSI/data broker?
• What is considered European?
Among the DSIs that were analysed for this document, there are some clear examples of regional
data-intermediaries that follow the guiding principles laid down in Section 6.1, which we refer to
as “stereotypical agricultural DSIs”. Examples are AgDataHub (France), DJustConnect (Flanders)
or JoinData (Netherlands), whose core services are data intermediary services for farmers in a
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certain European region and their (international) business partners, sometimes focusing on a
specific sector-vertical. These stereotypical agricultural DSIs could be the central members of
CEADS.
Additional to the stereotypical agricultural DSIs, there are edge cases, e.g., DSIs that do not have
a European focus (e.g., AEF) or that are only indirectly associated to farms (e.g. food trade DSIs),
but which might be equally relevant to the implementation of data-driven services for the
agricultural sector in Europe. Also, technical solutions such as cloud platforms or service providers
(examples are the interviewed John Deere operation center, Italian Agricolus or German
FarmNet) might be used for data-sharing in certain agricultural communities, even if they do not
implement the DGA’s principles of openness and inclusivity and if it is arguable whether they are
DSIs or data intermediaries at all. Such edge cases should likewise be admitted as members and
considered as relevant partners for the implementation of CEADS’ services.
The rules of access to certain governance bodies will then have to be defined based on the guiding
principles but following the demands of the central stakeholders and might restrict specific roles
to specific types of DSI (e.g., not-for-profit, Gaia-X compliant, farmers as central user groups…)
In defining terms for membership in CEADS and its governance bodies, CEADS has to find
a balance between ensuring equal representation of regions and sector verticals and
implementing the guiding principles laid down in Section 6.1.
• Initially, all stereotypical agricultural DSIs in Europe should be sought out and
involved in the setup of CEADS as central stakeholders and prospective members
of CEADS.
• Membership rules should initially be inclusive.
• The eventual terms can only be worked out following the demands of central
stakeholders and the guiding principles in the evolutionary governance building
process.
Examination of potential participants
Identification and verification of the legitimacy of actors on entry is key. In the technological design,
concrete guidelines are needed.
Protective measures against access by fraudulent actors
The DGA asks DSIs to implement protective measures against access by fraudulent actors,
which should be supported by CEADS as well as matched by equivalent practices on the DSI-
network level by CEADS. CEADS should have robust procedures in place to prevent fraudulent
or abusive practices concerning parties seeking access through its data intermediation services.
A dedicated working group should prepare information for the General Assembly regarding
fraudulent behaviour or conflicts and their resolutions.
CEADS should have a working group on aligning fraud prevention activities on the DSI levels,
by means of
• de-publishing of data across DSI
• blacklisting of end users across DSIs
The same working group should be in charge of mediation in case of (suspected) misconduct by
one of the member DSIs.
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If specific cases cannot be internally resolved, the management team communicates fraudulent
behaviour or conflicts to data coordinators to find appropriate resolution.
6.2.6. Collaboration
The following Figure 6 summarises the recommended organisational structure of CEADS and the
envisioned collaboration of the bodies and functions of CEADS.
Figure 22: Overview on the organisational structure of the CEADS
The data organisational governance of the CEADS covers the following topics to design the
guidelines for collaborations of the CEADS with DSIs:
• Shared and inclusive administrative and data governance, in particularly collaboration
agreements between CEADS and DSIs in various roles (e.g. participants, member) and
facilitation of cross-border administration formalities
• Topics and structure of the working groups
• Concrete ways and actions to connect the CEADS with the value chain actors, e.g. active
involvement in the Advisory Board
• Integration of DSI services in the CEADS
• Involvement of public bodies, especially data coordinators, with the CEADS and the DSIs
The development process for reaching this structure, implementing its details and further adapting
the organisation with the changing needs and conditions in the agricultural sector is part of the
roadmaps for the deployment and development of the CEADS (Work Package 4).
6.2.7. Onboarding
Measures creating an inviting environment for (future) Participants
Furthermore, the governance scheme for the CEADS should address to which degree it is part of
the mission of the CEADS to actively engage new members/owner but also participants for the
mentioned bodies from other stakeholders from the agricultural sector. On the one hand, it is
crucial to get new players onboard in order to increase the attractiveness of the emerging data
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ecosystem as a whole and also integrate new perspectives and domains. On the other hand, this
can lead to legitimacy issues and a lack of trust within the group of existing members/owners or
participants. Therefore setting the rules for the CEADS in this regard should be acknowledged
with its due attention. Possible design element of a Collaboration Framework can be summarised
as followed:
• Memoranda of Understanding (MOUs): Establish MOUs or collaboration agreements
with key partners to formalise relationships and responsibilities.
• Develop Policies: Establish clear data governance policies that address ownership,
privacy, security, and ethical considerations.
• Data Quality Standards: Define standards for data quality and integrity to ensure
reliability.
• Open Membership: Design an open membership framework as far as possible to
encourage participation from various stakeholders.
• Membership Criteria: Establish criteria for membership, considering factors such as
expertise, contribution potential, and commitment to data sharing principles.
• Communication and Outreach: Develop a communication plan to raise awareness about
the organisation's mission and activities and Conduct outreach programs, workshops, and
awareness campaigns to engage with potential members and stakeholders.
• Technical Support and Training: Provide technical support to help members integrate
with the organisation's data sharing framework and offer training programs to ensure that
members understand and adhere to data sharing protocols.
A requirement derived from the Data Governance Act is the provision of independent commercial
terms for all. The commercial terms of the CEADS including pricing for the provision of its services
should be fair, open and transparent, in line with the commercial terms of its member DSIs.
6.3. Data Sharing Governance
As argued previously, CEADS should not act as a data-intermediary itself. Instead, its mission
should be to foster and support collaboration across DSIs and provide a framework for DSI
interoperation, so as to enable data-driven value generation across regions and sector-verticals
with their own respective established DSIs. Therefore, CEADS does not require a data sharing
governance of its own. Instead, it requires appropriate structures and processes to support shared
and inclusive administrative and data governance across DSIs and the implementation of across-
DSI use-cases (see WP3). A governance for the development and offering of these processes
will be outlined in this Section 5.3. Section 5.3.1. and summarises principles and requirements
that should guide the development of CEADS services for DSI interoperation. Section 5.3.2 gives
a brief overview of topics that are challenging for DSI interoperation and outlines a preliminary
structure for possible working groups. Section 5.3.3. dives a bit deeper into the topic of transferring
data across DSIs that operate in different regions to illustrate the concrete impact on the
governance of DSI-interoperation. Section 5.3.4. addresses the possible provision of open data
as part of CEADS’ services.
6.3.1. Data Sharing Governance Principles for CEADS
All data-related CEADS services will focus on collaboration support
• Shared, inclusive administrative and data governance
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• Facilitating access to Open Data
Even if membership for CEADS should be inclusive (Section 6.1), its goal is to support initiatives
that follow the guiding principles outlined in Section 6.1 and to encourage existing and new DSIs
to implement services, technologies and business models that adhere to European regulation and
the guiding principles. Eventually, these principles may be translated into terms for participation
in CEADS. However, this should be critically evaluated as part of the evolutionary process of
governance development. Initially, the governance of CEADS should be more inclusive to grant
existing DSIs a transition period, at least.
DSIs have to implement the following functions and principles that result from the Data
Governance Act if they act as data intermediaries in Europe
• Neutrality with respect to pricing and service purposes
• Restriction of data usage to data intermediary services
• Non-discriminatory, fair and transparent access to data intermediary services
• Security against fraud and unauthorised access
• Data availability to authorised parties
• DSGVO compliant handling of personal data
• Documentation of intermediary activities
The Data Act will require companies that collect data via IoT devices to:
• make the data accessible by design and to provide access for the users of those IoT
devices on request
• ensure portability of data so the stored data can be transferred to a third party on request
by the users.
These requirements will therefore be part of the collaboration agreements of the CEADS with the
specific DSIs, who want to provide services via the CEADS to the agricultural ecosystem including
providers of IoT technology.
European Code of Conduct
All data-based services from CEADS are following the Code of Conduct and Data Sovereignty
principles. Personal data sovereignty is highly valued in European policy as well as in the
agricultural sector, where farm-related data assets are frequently considered as sensitive. Acting
in farmers’ interests is part of CEADS mission statement. Therefore, data is usually not offered
openly within DSIs and not centrally stored/collected for data protection. Usage control processes
and mechanisms to ensure legitimate use (be it by explicit approval of specific data transmissions
or by service-against-data agreements) should thus be implemented by agricultural DSIs.
The specifications of the European initiative Gaia-X for the secure and sovereign exchange of
data between members are already adopted by some of the existing DSIs (e.g. AEF AgIN,
AgDataHub, DjustConnect), and the use of this framework or compatible technical
implementations that support personal data sovereignty should be encouraged.
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6.3.2. Governance of Services for shared and inclusive administrative
and data governance
Even if the encouragement or requirement to implement certain principles or standards is
practiced, the existing and future DSI landscape will remain technically, legally and economically
heterogeneous, which poses challenges for the interoperation between CEADS member DSIs.
Both the feedback from the validation of business models and the interviews identified
interoperation support as central value that CEADS could provide to its member DSIs.
This section gives an overview of existing challenges for DSI interoperation and outlines a
possible structure of working groups to develop the required services. The subsequent section
then dives deeper into one of these challenges (international governance for cross-border data
exchange).
The analysis of DSIs (Section 4.1) revealed that DSIs currently have little or no experience in
inter-DSI collaboration (bi- or trilateral), let alone in DSI networks, even though many (but not all)
DSIs expressed interest to cooperate with other DSIs that were relevant to the value chains of
their central users. Those DSIs that were already active in collaboration, reported a range of
challenges that derived from differences in the technical, legal and economical implementation of
the DSIs. Those differences included differences in:
• technical choices in the implementation of infrastructure
• technical standards
• implementations of permission management (e.g. direct or indirect consent to a data
transmission by a farmer)
• definition of roles
• terms and conditions, and business models (e.g., for-profit vs. not for profit data
intermediation services, different pricing models)
• systems for the identification of organisations or assets (such as fields or animals) across
regions
• procedures or rules for ensuring the quality of data or services offered, especially in cross-
border data sharing scenarios
• rules for ensuring the legitimate purpose
• regional administrative or regulatory requirements for specific sectors.
In order to support the technical, legal and economical interoperability of DSIs, CEADS should
develop interoperation services and provide them to its members, including:
• templates for contractual agreements (to deal with certain discrepancies)
• best practices
• a framework for interoperation that categorises common and valid DSI design options and
assesses their interoperability (compatibility grid)
• technical interoperability tools, such as interfaces, connectors, data models, etc.
Some of these challenges concern the DSI as a whole, whereas others concern specific data-
driven services/use-cases, which only concern specific sector verticals (such as arable farming,
dairy farming, food trade). This leads us to suggest the following structure for the organisation of
working groups and resulting service offerings:
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Table 17: Structure of the CEADS Working Groups
DSI level Working Groups
Identity and
Permission
management
Quality
management
Organisation
identity
DSI business
models
DSI technical
infrastructure
DSI legal terms
Sector-specific Working Groups
Data +
catalogue
interoperability
Regulatory
framework
Asset identity
…
Sector vertical 1
Sector vertical 2
Sector vertical 3
Sector vertical 4
As with previous topics that CEADS has to address, whether and to which extent the results of
these working groups will determine not just CEADS services but might also be reflected in
CEADS governance itself, is to be determined by the affected stakeholders, i.e. agricultural DSIs,
during the process of making interoperation work. However, one topic that is particularly
interesting in the European agricultural sector and that likely will be addressed in the CEADS’
governance scheme is the data transfer across national borders. Therefore, the following section
dives a bit deeper into this issue.
6.3.3. International governance for cross-border data exchange –
Collaborations between Common Data Spaces
The European common agricultural data space (CEADS) can be considered as a socio-
technological infrastructure or network of national, regional and local farm data spaces that
already exist or will evolve in the EU member states. In order to connect these existing and
upcoming national and local farm data spaces into a common farm data space on the European
level, the individual governing bodies responsible for the functioning of those separated national
and local fam data spaces, will have to collaborate to ensure cross-border farm data sharing and
in that way establish the single European agricultural data space (the CEADS).
The next subsection is about how cross-border farm data exchange between organisations
(business-to-business) based in different Member States can be organised and governed: what
form of international collaboration is needed and what issues should be addressed in that
collaboration? And the last subsection 5.3.3.3 indicates briefly how organisations from different
sectors can govern/manage/organise to achieve (inter)national cross-sectoral data sharing.
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6.3.3.1. Cross-border farm data exchange
Every country in the EU has one or more national DSIs or national farm data highways, or some
that are currently being developed. By national, we mean that a large number of farmers and data
holders in the country use such an electronic farm data sharing infrastructure. An infrastructure or
highway for the international exchange of farm data can be implemented if one or more national
information highways for farms in one country are connected to one or more national information
highways for farms in other countries. Consider, for example, connecting the digital highway of
Agdatahub in France with that of JoinData in the Netherlands, that of DJustConnect in Belgium
and Agrirouter in Germany. Agdatahub has stated that it is willing to collaborate with DSIs in other
countries. And DJustConnect stated it wants to collaborate with DSIs in Wallonia (Belgium),
France, Netherlands and Germany, and other European countries.
Content of collaboration agreements
For the collaboration to make, implement and operate a cross-border B2B data sharing
connection, the two parties involved will have to make agreements with each other about how
they will arrange and organise the important issues and obstacles. The following issues and
obstacles are not exhaustive and are partly based on statements made by interviewed
stakeholders and partly on knowledge and experience of project consortium members:
1. How does one deal with different views on managing risks of sharing sensitive
commercial data with third parties in different countries?
Lack of interoperability between different datasets and information systems: different
data formats. Data interoperability has been raised by the German AgIN. And according
to Agrirouter, formats need to be standardised internationally. Also political efforts can
stimulate to standardise data management on farm data across Europe.
2. Not only the technical interoperability, but Agdatahub also sees difficulties on the level of
legal and economical interoperability. Agdatahub suggests a “compatibility grid” as a
possible collaboration support service from a CEADS and points to the “EDIC framework”
as a promising approach.
3. Lack of standardisation (compatible standards) of data and metadata. In country X it has
been agreed to use a certain format / standard with which data are being transferred to
data re-users, compared to country Y where a completely different format / standard is
being used.
4. How to deal with it, when governing bodies of national digital highways have different
security standards (some apply a higher security level and others a lower security
standard)?
5. Lack of incentives to farmers to share their data to unknown third parties in other
countries.
6. Lack of trust in the data receiver (data re-user). When farmers suspect that the farm data
will be misused, they are often less than eager or willing to share their data.
7. How well organised is the control of compliance with the rules agreed by the
companies in other countries that re-use the farm data?
8. To what extent do the same agreements apply in each country with regard to
accountability if something goes wrong with the data transfer and the quality of the data
supplied? How far does the responsibility of the governing body extend, i.e. can one
governing body hold another national governing body liable, or does that governing body
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pass that responsibility on to the data holder who provided and exchanged the data to data
re-users located in another country?
9. Does every national data sharing initiative strive for the same norms of reliability in the
data sharing – e.g. due to being out of operation for maintenance – and for duration of
data exchange? To whom should a data re-user in country X contact if access to the
national data highway in country Y does not work? Can he then contact the governing
body of his national highway or does he have to contact the governing body of that foreign
farm data highway?
10. Are there differences in the terms or conditions to data access and re-use imposed by
governing bodies of national DSIs? Are some conditions more unfair than those of other
national data highways?
11. From the perspective of the data re-user in each country: does a data re-user have to go
to an online portal of the national data sharing infrastructure in his own country or can he
go directly to the online portal in the other country (where he can register and read and
accept the general conditions of the data sharing)? Or will there be a separate European
online portal where every data re-user can log in and request data? A kind of one-stop-
shop where a catalogue containing the metadata of farm datasets that all involved
data holders in different European countries have stored and are available, after informed
consent of the farmers concerned? This means that an ATP will only have to deal with one
organisation instead of many more. And in which languages is it presented?
12. We have noticed that the current EU Code of conduct on agricultural data sharing is
viewed differently between countries in the EU (see section 2.3). Some are in favour of
abandoning the EU code, others suggest to align the terminology of the EU code with the
Data Act. And others favour updating the EU code substantially based on the relevant EU
legislation, while also some state to focus on the sector-specific allocation of data rights in
the absence of binding rules. These different views can have consequences for
differences in the data contracts between farmers and ATPs and others among
countries, which can hinder cross-border farm data sharing. Agreements must therefore
also be made about this.
13. Payments for the re-use of farm data by a third party located in another EU country must
be arranged. The data holder in the Netherlands can charge a fee to the data re-user for
transferring the data (data portability) to a data re-user in another EU country if that data
is a commercial entity.
6.3.3.2. Cross-sectoral data sharing
Finally, we also need to address the issue of possible connections for data sharing between the
(European) agricultural data space(s) with data spaces of other economic sectors in society:
(inter)national cross-sectoral data sharing. This is the least elaborated topic in our project. How
the DSIs view cross-sectoral data sharing seems to be too far from them at the moment.
As far as we know, there are not many practical examples of the governance of (inter)national
data sharing between the agricultural sector and other economic sectors. An interesting example
is the European research infrastructure for Food, Nutrition and Health. This FNH-RI is a European
research infrastructure for healthy and sustainable nutrition, involving more than 150 institutes
from 24 EU countries (see: https://fnhri.eu/).
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An important point to take into account is to realise that organisations and businesses from
different economic sectors may have different and even conflicting goals for data sharing and
addressing societal issues (Susha et al. 2018). This can be both a source of value and conflict
(Klievink et al., 2018) which hinders cross-sectoral data sharing at national or international level.
For answering the question, how organisations from different sectors can manage to achieve data
sharing, (Susha et al. 2023) rely on (Selsky und Parker 2005) who proposed three models of
partnerships:
1. The first model assumes that organisations seek collaboration because they lack certain
competencies to meet their organisational needs. This model is aimed at gaining access
to the necessary resources – data – and thus fulfilling the self-interest of the organisation.
2. The second model assumes that certain social issues transcend the scope of a single
organisation, making it necessary to collaborate with other organisations. In this model,
the social issue comes first and profit for the organisation is secondary. This model is
based on corporate social responsibility.
3. The third model stems from the realisation that traditional industry solutions cannot
effectively address certain challenges and therefore need to be improved by learning and
borrowing – data, knowledge – from organisations in other industries. This model
emphasises the changing and blurred roles and functions between sectors.
The kind of drivers and challenges involved determines which partnership model is most
appropriate. By looking at the specific drivers and the challenges to be faced, the involved
organisations and governing bodies of national agricultural data highways can determine the key
terms of the collaboration and the mechanisms to be used to achieve their partnership goal –
namely cross-sector data sharing.
6.4. Summary and Outlook
Chapter 6 sets out the basic design elements of governance that need to be taken into account
when forming the CEADS and its underlying organisation. In addition, various design options for
each element were presented and their advantages and disadvantages weighed up. The
proposals and recommendations are based on features of existing frameworks for the
establishment of data spaces and the organisations behind them, such as the DSSC blueprint,
but also on the analyses of existing agricultural DSIs and the partially implemented validation
process.
The most important recommendations for the design of a multi-stakeholder governance scheme
for the Common European Agricultural Data Space (CEADS) are summarised below:
Choosing an evolutionary implementation process is key:
In line with the DSSC Blueprint, which clearly states that "governance in data spaces must adapt
as the data space evolves" (Data Spaces Support Centre 2023), the governance scheme for
CEADS is not a fixed model that is defined and only followed in the implementation and
operational phases. Rather, it must change over time to keep pace with changing conditions,
participants and regulatory requirements. Therefore, the previous chapters do not contain fixed
design decisions for the organisation behind the CEADS, but rather a structured collection of the
most important design elements with specific design options.
The Form has to follow the Function of CEADS:
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To ensure this evolutionary approach to the establishment of a multi-stakeholder governance
system for CEADS, it is important to focus on the (potential) added value and its clear
communication to the (future) participants of the evolving data ecosystem and in particular the
existing DSIs as key stakeholders for CEADS. In order not to diminish the potential benefits for
the DSI by overcomplicating the concept, the complexity of the governance scheme should be
minimised – especially in the set-up phase.
Existing DSIs as members and decision makers is a baseline:
The involvement of existing DSIs as main members and thus decision-makers is important to
ensure a pragmatic set-up process of CEADS, which must focus on the specific needs of the
different types of stakeholders within the agricultural landscape and guarantee a fundamental
trust in the neutrality of CEADS. On the other hand, the involvement of relevant, even dominant,
industry actors through supporting structures such as an advisory board ensures that all voices –
from farmers, farmers' organisations, public bodies, but also machinery manufacturers or
providers of data-driven services – are heard without individual groups of stakeholders gaining
too much influence. It is advisable to continuously increase the number of participating DSIs as
members and other stakeholders. However, to start the evolutionary implementation process of
the organisation behind CEADS, it may be helpful to start with a smaller number of committed
DSIs, as long as there is transparent communication within the landscape of existing DSIs in the
European agricultural sectors with a fixed deadline for confirming participation.
Next Steps
The next step involves the definition of actions that are needed for the initiation, implementation,
deployment and operation phase for the CEADS. Work Package 4 covers the description of these
steps, including dependencies and risks, enablers and also synergies with other initiatives that
will facilitate the growth of the emerging landscape of DSIs in the agricultural sector.
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Annex I: Clustering of the DSIs’ stakeholders
Nr
Organisation
Name
DSI Name
Data
Consumers
Data Providers
Stakeholders per DSI
1
Maschinenfabrik
Bernard Krone
GmbH & Co. KG
Agrirouter
Farmer
Farmer /
Machines
Farmer / Machines
2
ATB Institute for
Applied Systems
Technology
Bremen GmbH
Data4Food2030
- Case Study 5 -
DIRECT
Cooperatives,
Traders,
Retailers
Farmers,
Logistics
provider
Cooperatives, Traders,
Retailers, Farmers,
Logistics provider
3
ILVO
DjustConnect
Farm
management
system
Government,
Service/product
providers,
Laboratories/Ana
lytics
Farm management
system, Government,
Service/product providers,
Laboratories/Analytics
4
University College
Dublin
DIVINE
Arable Farmers,
Advisors/Agron
omists,
Arable Farmers
Arable Farmers,
Advisors/Agronomists
5
AEF / AGCO
GmbH
AEF Agricultural
Interoperability
Network (AgIN)
Agricultural
OEMs, FMIS,
Other Software
Providers, Data
Hubs
Agricultural
OEMs
Agricultural OEMs, FMIS,
Other Software Providers,
Data Hubs
6
DunavNET
DEMETER
Farmers,
Agriculture
consultants
Farmers,
Solutions/service
providers
Farmers, Agriculture
consultants,
Solutions/service
providers
7
ITC
DADS - DIH
AGRIFOOD
DATA SPACE
Software
companies,
Farmers
Farmers,
Agricultural
advisory board,
Retailers
Software companies,
Farmers, Agricultural
advisory board, Retailers
8
Elevéo
WALLeSmart
n/a
n/a
n/a
9
South East
Technological
University (SETU)
AgriDISCRETE
Academics/
researchers,
policy-makers,
agri processors
Individual
farmers, groups
of farmers, agri-
processors,
policy-makers,
academics/resea
rchers
Individual farmers, groups
of farmers, agri-
processors, policy-
makers,
academics/researchers
10
Institute of Plant
Breeding and
Genetic
Resources, HAO-
DEMETER
DEMETER
Agri
Consultancy
businesses,
Farmers /
Groups of
Farmers,
IoT sensors,
Weather forecast
services,
Farmers /
Groups of
Farmers
Agri Consultancy
businesses, Farmers /
Groups of Farmers, IoT
sensors, Weather forecast
services
11
Agoria
Gaia-X for
Belgium
Agriculture WG,
led bij Agoria
n/a
n/a
n/a
12
1001 Lakes
Sitra Rulebook
for agri data
sharing
Farmer,
Equipment
manufacturer,
Retail supply
chain, Public
sector, Data
intermediary
Farmer,
Equipment
manufacturer,
Retail supply
chain, Public
sector, Data
intermediary
Farmer, Equipment
manufacturer, Retail
supply chain, Public
sector, Data intermediary
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13
Politecnico di
Milano
Observatory on
Smart AgriFood
Private
Companies
Private
companies
Private Companies
14
Federatia
Nationala ProAgro
No DSI yet
Confidential
Farmers,
solution and
service providers
Farmers, solution and
service providers
15
WUR
THESIS, or The
Sustainability
Insight System
developed by
The
Sustainability
Consortium
(TSC)
Retailers
Suppliers
(manufacturers/s
uppliers),
Farmers
Retailers, Suppliers
(manufacturers/suppliers),
Farmers
16
Agricultural
University of
Athens
Eden Library
Researchers,
Technology
providers
Farmers,
Agronomists,
Researchers, Technology
providers, Farmers,
Agronomists
17
VistaMilk/Teagasc
VistaMilk
Research Data
Portal
Researchers
Researchers,
Industry partners
Researchers, Industry
partners
18
Farm Connect
Farm Connect
Main DSS
editors, Start-up
FMS
Main DSS editors, Start-
up, FMS
19
AnySolution
I4DATA
Farmers,
Cooperatives,
Denomination of
origin
Farmers,
Cooperatives,
Denomination of
origin
Farmers, Cooperatives,
Denomination of origin
20
Institute of Soil
Science and Plant
Cultivation - State
Research Institute
Aqua da Vida
Living Lab
R&D,
Government
Farmers, R&D
Farmers, R&D,
Government
21
AVR
AVR Connect
Farmers
Farmers
Farmers
22
Vlaams
Datanutsbedrijf
Vlaams
Datanutsbedrijf
regional
government,
local
government,
private sector,
telco's
Private sector,
regional
government,
local government
Private sector, regional
government, local
government, private
sector, telco's
23
Fraunhofer IAIS
ATLAS -
Agricultural
Interoperability
and Analysis
System
Digital
agricultural
solution
providers
Digital
agricultural
solution
providers
Digital agricultural solution
providers
24
Wielkopolski
Agricultural
Advisory Center in
Poznań
Econominal
calculator and
ControlBee
n/a
n/a
n/a
25
UGent
EU-FarmBook
platform
Practitioners:
farmers,
foresters,
advisors,
teachers,
researchers;
multi-actor
project partners,
operational
groups
educators,
trainers,
students,
policymakers on
Multi-actor
project
coordinators,
Operational
groups, thematic
networks,
Regional,
national
policymakers
/AKIS actors
Practitioners: farmers,
foresters, advisors,
teachers, researchers;
multi-actor project
partners, operational
groups educators,
trainers, students,
policymakers on regional,
national and European
level, journalists, Multi-
actor project coordinators,
Operational groups,
thematic networks,
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
129
Diffusion : Public
regional,
national and
European level,
journalists
Regional, national
policymakers /AKIS actors
26
iDDEN GmbH
International
Dairy Data
Exchange
Network
Dairy data
organizations,
dairy farms
n/a
Dairy data organizations,
dairy farms
27
AgroConnect
Association with
members to
improve
interoperability.
AgroConnect-
members
Input providers
for farmers,
FMIS suppliers,
laboratories,
breeding
organizations,
meat processing
industry
AgroConnect-members,
input providers for
farmers, FMIS suppliers,
laboratories, breeding
organizations, meat
processing industry
28
ILVO
DjustConnect
Companies
Companies,
Government,
Research
n/a
29
DKE-Data GmbH
& Co. KG
agrirouter
FMIS Providers,
Ag-Software
Provider,
Machine
Manufacturer,
Input
Companies,
Food Processor
companies
FMIS Providers,
Ag-Software
Provider,
Machine
Manufacturer,
Input
Companies,
Food Processor
companies
FMIS Providers, Ag-
Software Provider,
Machine Manufacturer,
Input Companies, Food
Processor companies
30
FNSEA
Data Agri
n/a
n/a
n/a
31
Agdatahub
Agdatahub
Research/techni
cal institute,
Farm advisors,
Service
providers
Cooperatives,
Research/techni
cal institute,
Public bodies,
Service
providers
Cooperatives,
Research/technical
institute, Public bodies,
Service providers
32
FAST - FIEA Agri
Services
Technologies
Agata Consent
n/a
n/a
n/a
33
Ifip
PigLink
Farmer
organization,
feed
manufacturer,
equipment
manufacturer,
inter-
professional
association
Farmers, inter-
professional
association
Farmers, farmer
organization, feed
manufacturer, equipment
manufacturer, inter-
professional association
34
Chambres
d'agriculture
France
Sièges des
Chambres
d'Agriculture
départementales
et régionales
n/a
n/a
n/a
35
DGDAR (General
Directorate of
Agriculture and
Rural
Development)
Rede Rural
Nacional
(National Rural
Network)
Individual
farmers,
associations of
farmers and/or
producers,
Local Action
Groups,
Operational
groups,
Researchers /
Research Teams
/ Collabs,
Competence
Centers
Individual farmers,
associations of farmers
and/or producers, Local
Action Groups,
Operational groups,
Researchers / Research
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
130
Diffusion : Public
Operational
groups,
Researchers /
Research
Teams / Collabs
Teams / Collabs,
Competence Centers
36
Care4Growing
Care4Growing
Growers,
Coöperations,
Research
organizations,
Lab's, Advisors
Legislation data
source,
Research
organizations,
Growers,
Advisors
Growers, Coöperations,
Research organizations,
Lab's, Advisors,
Legislation data source
37
Wageningen Food
& Biobased
Research
Not yet decided
n/a
n/a
n/a
38
OKP4
OKP4
Cooperatives,
Logistics,
Accounting firm
n/a
Cooperatives, Logistics,
Accounting firm
39
Departement
Landbouw en
Visserij
Bodempaspoort/
Soil Passport
Farmers,
government,
mandated
advisors
Public data,
laboratories,
farmers,
government data
Farmers, government,
mandated advisors,
laboratories, Public Data
40
BioScope
FieldScout
Farmers/grower
s, agricultural
contractors,
research
Satellite data
providers, drone
data providers,
AI supplier, data
integrator
Farmers/growers,
agricultural contractors,
research, satellite data
providers, drone data
providers, AI supplier,
data integrator
41
WIRELESSINFO
AGRIHUB CZ
Farms
GeoData
providers
Farms, GeoData providers
42
University of
Applied Sciences
Osnabrück
Agri-Gaia
Αgriculture
machine
manufactures,
AI development
companies,
research
organizations
Research
organizations
Agriculture machine
manufactures, AI
development companies,
research organizations
43
Wielkopolska
Agriculture
Advisory Center in
Poznan
eDWIN
farmers, public
institutions,
private software
developers for
agriculture,
policy makers
public
institutions,
farmers
Farmers, public
institutions, private
software developers for
agriculture, policy makers
44
German
Federation for
Plant Innovation
BreedFides
Plant Breeding
Companies,
Scientific
Institutions
Public Data,
Scientific
Institutions, Plant
Breeding
Companies
Plant Breeding
Companies, Scientific
Institutions, Public Data
45
LoginEKO
LoginEKO
Farming
Software
n/a
n/a
n/a
46
Tetra Tech
Enabling Crop
Analytics at
Scale (ECAAS)
n/a
n/a
n/a
47
agmadata GmbH
Farm & Food
Hub
n/a
n/a
n/a
48
Unova / Hubwatch
(for supply chain
specific it is
Hubwatch, for
general purpose
Supply chain
stakeholders,
retailers,
manufacturers,
Supply chain
stakeholders,
retailers,
manufacturers,
Supply chain
stakeholders, retailers,
manufacturers, producers,
slaughterhouses
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
131
Diffusion : Public
data sharing
between
stakeholders it is
Unova)
producers,
slaughterhouse
s
producers,
slaughterhouses
49
Josephinum
Research
TerraZo
Farmers, 3rd
party
companies
ESA Images,
Field
Boundaries,
Farm
Management
systems
Farmers, 3rd party
companies, ESA Images,
Field Boundaries, Farm
Management systems
50
BD PORC
BD PORC - PIG
CONNECT
Slaughterhouse
s, Producers
organization,
Veterinary
services
(DDPP),
Ministry of
Agriculture
Farmers,
Slaughterhouses
, Producers
organization,
Ministry of
Agriculture,
TRACES UE
Slaughterhouses,
Producers organization,
Veterinary services
(DDPP), Ministry of
Agriculture, Farmers,
TRACES UE
51
German Research
Center for
Artificial
Intelligence -
DFKI
GEOBOX
Service
providers, e.g.
Labs,
Consulting
services,
currently
demonstrators
only,
governmental
institutions,
statistics
Farm, state data
sources, 3 states
active, further
progress
ongoing
Labs, Consulting services,
currently demonstrators
only, governmental
institutions, statistics,
Farm, state data sources,
3 states active, further
progress ongoing
52
Bundesministeriu
m für Land- und
Forstwirtschaft,
Regionen und
Wasserwirtschaft
INSPIRE Agrar
Atlas
Farmers /
publicly
available
Government
(national and
regional)
Farmers / publicly
available, government
(national and regional)
53
Vantage
Agrometius
taakkaart
Farmers,
contractors
Field sensors,
other FMIS
Farmers, contractors, field
sensors, other FMIS
54
CNH Industrial
Belgium N.V.
PLM Connect /
AFS Connect
n/a
n/a
n/a
55
Cooperativas
Agroalimentarias
de España
Sistema de
Información
Geográfica
Cooperativa de
las
Explotaciones
(SIGCEX)
Cooperatives,
Federations
Cooperatives -
farmers
Cooperatives,
Federations, Farmers
56
South East
Technological
University
Agri DISCRETE
n/a
n/a
n/a
57
Gradiant
DIXITEGA
(DIXITALIZACIÓ
N DA CADEA
DE VALOR DA
IXP TERNERA
GALLEGA)
Spanish
Farmers
Spanish meat
and cereal
market, Manager
of Galician Veal
GPI
Spanish farmers, Spanish
meat and cereal market,
Manager of Galician Veal
GPI
58
Gradiant
GC4SHEEP -
Plataforma
Cloud de Datos
Federados con
Capa de
SELECTION
AND GENETIC
IMPROVEMEN
T CENTER,
Cattle breeders
SELECTION
AND GENETIC
IMPROVEMENT
CENTERS
Selection and genetic
improvement center,
Cattle breeders
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
132
Diffusion : Public
Inteligencia
Artificial para la
mejora Genética
y Reproductiva
del Ovino
Lechero
Nacional
59
DIH DATAlife
NEXTGENDATA
Public
administration,
Companies
Public
administration,
Companies in
the agrifood
sector,
Technological
providers
Public administration,
Companies in the agrifood
sector, Technological
providers
60
DataSpace
Europe Oy
Tritom
n/a
n/a
n/a
61
AGDATAHUB
NUMAGRI
farmers, storage
organization,
crushing factory,
animal feed
processing
factory, breeder
farmers, storage
organization, crushing
factory, animal feed
processing factory,
breeder
62
Agdatahub
NumAlim
SMEs and other
Panelist,
industries
SMEs, Panelist, industries
63
TNO
Zero-W -
Dataspace
Farmers,
Foodbanks,
Service
Providers
Farmers,
Foodbanks,
Service
Providers
Farmers, Foodbanks,
Service Providers
64
Co.Di.Pr.A. -
Condifesa Trento
CRM - Portale
del Socio
farmers
farmers,
insurance
companies,
public
administration
farmers, insurance
companies, public
administration
Clustering Results
Farmers and Agricultural Producers
Individual farmers
Cooperatives
Groups of farmers
Dairy farms
Cattle breeders
Growers
Producers’ organizations
Foresters
Technology and Data Providers
IoT sensor providers
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
133
Diffusion : Public
Weather forecast services
Equipment manufacturers
Data hubs
Software companies
Technology providers
AI development companies
Satellite data providers
Drone data providers
Field sensor providers
GeoData providers
Data Intermediaries and Service Providers
Data intermediaries
Farm management system providers
Laboratory and analytics services
Solutions/service/product providers
Data integrators
Government and Regulatory Bodies
Government agencies
Policy-makers
Regional and local government bodies
Public sector organizations
Ministry of Agriculture
Governmental institutions
Legal Experts
Statistics offices
Financial and Insurance Services
Insurance companies
Accounting firms
Research and Academic Institutions
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
134
Diffusion : Public
Universities and research institutions
Research/technical institutes
Competence centers
Business and Industry Stakeholders
Traders
Retailers
Logistics providers
Input providers for farmers
FMIS (Farm Management Information System) suppliers and providers
Ag-Software providers
Machine manufacturers
Food processor companies
Mandated advisors
Agricultural contractors
Supply chain stakeholders
Manufacturers
Small and Medium-sized Enterprises (SMEs)
Multi-actor Collaborations
Multi-actor project partners
Thematic networks
Inter-professional associations
Local Action Groups
Agricultural advisory boards
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
135
Diffusion : Public
Annex II: Results from the 1st Business Model
Workshop
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
136
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
137
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
138
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
139
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
140
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
141
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
142
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
143
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
144
Diffusion : Public
Annex III: Results from the 2nd Business Model
Workshop
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
145
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
146
Diffusion : Public
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
147
Diffusion : Public
Annex IV: Profiles of the Data Sharing Initiatives
IV.1. Overview of the Analysed Data Sharing Initiatives
The following Table 18 provides an overview about the data sharing initiatives that were analysed
in Objective 2 on governance schemes and in Objective 3 on business models.
Table 18: Overview on analysis of governance and business models for agricultural data
sharing initiatives
Data Sharing Initiative
Governance
Analysis
Business Model
Analysis
Geographical
Coverage
365FarmNet GmbH
yes
yes
Austria
Poland
Germany
Switzerland
France
Agdatahub
yes
yes
mainly France
but not excl.
AgIN / AEF e.V.
yes
no
worldwide
Agricolus
yes
yes
Italy
Agri-Gaia
yes
no
Germany
Agrimetrics
no
yes
UK
Agrirouter e.V.
yes
yes
international
AgroDataCube
no
yes
Netherlands
Altas
yes
no
International
(EU)
AVR Connect
no
yes
Belgium
Cipher Trust Data Security
Platform
no
yes
worldwide
COGNAC
yes
no
Germany
DjustConnect
yes
yes
Flanders,
neighbouring
regions, coop.
agreements
France, Finland
Eden Library
no
yes
Greece
Hortivation Hub
yes
yes
Netherlands,
expansion to
west and central
EU
iDDEN GmbH
yes
yes
worldwide
John Deere Operations Centre
no
yes
worldwide
JoinData
yes
yes
Netherlands
Belgium
ProAgrica
no
yes
worldwide
ZEROW
yes
no
International
(EU)
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Diffusion : Public
IV.2. Business Models and Governance Schemes of
the analysed DSIs
This section contains the profiles of the Data Sharing Initiatives (DSIs) that have been interview
in the scope of AgriDataSpace project. The profiles provide the data for the analysis of the
governance schemes and business models of the DSIs and the design options for the resulting
multi-stakeholder governance scheme of CEADS.
IV.2.1 Descriptions of Data Sharing Initiatives
Each profile summarizes the scope of the DSI under investigation and gives insights on the
technical background, as the governance is highly interlinked with the DSI’s business and
applications. To highlight key insights, a subsequent text points out links between governance
aspects and perceived success factors, interesting statements from the interviewed person, as
well as overarching remarks e.g., on the agricultural sector, other DSIs and a future CEADS.
IV.2.1.1. 365FarmNet GmbH
IV.2.1.1.1. Governance
General information
Name
• 365FarmNet GmbH
• 365 refers to the intended
consistent availability
• Website: https://www.365farmnet.com/
Legal form
• Limited liability company
• Under German law („Gesellschaft mit beschränkter Haftung“)
Geography
• The free basic version is available worldwide.
• The availability of paid modules depends on the country.
• For example, the module on fertilization is available in Austria,
Poland, Germany, Switzerland and France, as it has to take the
national and regional fertilization ordinances into account.
Sector
• Agriculture: Farm management for crops and livestock
Scope
• 365FarmNet is an information and work platform on which the
agricultural operating processes are linked together intelligently.
• Its aim is to network and transparently document operations
throughout the whole agricultural production process to improve
planning and efficiency, helping the farmer to focus on the work.
• The DataConnect initiative is a collaboration between John
Deere, CLAAS, CNH and 365Farmnet: a direct cloud-to-cloud-
solution for automated transfer of machine data between the
clouds into the chosen Farm Management Information System
(365FarmNet).
• Exemplary use case of “precision farming”: Using applications
maps for the field to plan operations such as harvesting, being
able to involve contractors and use a “colorful” fleet of agricultural
machinery (from various manufacturers).
D2.1: Multi-stakeholder Governance Scheme and
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Business approach
• Business model freemium for the software platform 365FarmNet:
Free basic version and payment for various modules with
country-specific availability and variations.
• The DataConnect functionalities and other modules from partners
are provided via the software platform 365FarmNet.
• The prices for modules depend on the farmed hectares and the
module.
• For modules from partner companies: 365FarmNet GmbH and
the partner company share the revenue (collected by
365FarmNet).
Lifecycle phase
• Started the initiative in 2013 as an agricultural pioneer in cloud
data management and mobile applications.
• 365FarmNet with modules (e.g. DataConnect) is in operation
• Currently scaling regarding the paid modules, features and
international availability
Organizational Governance
Participants
• Farmers are the main group of customers
• Involved companies: Partners can provide modules, data or other
services for the 365FarmNet platform; there are 20 partners
named on the website (e.g. BASF, AGRARMONITOR).
• Partners for the DataConnect initiative: manufacturers of
agricultural equipment (John Deere, CLAAS, CNH)
Organizational mode
• Central, commercial organization
Governing bodies
• 365FarmNet GmbH is owned by CLAAS (subsidiary company),
they hold decision rights via the advisory board.
• 365FarmNet GmbH has a board of directors with a managing
director and the management team with departments: a lean
commercial structure for decision making and providing the basic
platform.
• The collaboration with partners is mainly about product / services
to provide farmers with a variety of modules on the open platform.
• Customers provide feedback and are in the development focus,
but not part of the governance system of the 365FarmNet GmbH.
Collaboration
• Partners conclude bilateral agreements with farmers using their
services (e.g. modules, data) to regulate the financial and data
sharing aspects.
• 365FarmNet is the platform provider and the intermediary
between the farmer and other service providers (modules, data).
• Decision-making process at the 365FarmNet GmbH: Feedback is
appreciated, but no formal involvement of farmers or partners.
Onboarding
• New partners:
o setup of partner contract with 365FarmNet,
o support for development of module with alignment of data
formats
o setup of the offer on the platform
o customers have to agree to the contractual terms (incl. data
sharing regulations) before using the partner’s applications on
the platform
• New customer (farmer):
o free usage and setup of 365FarmNet basic features
o setup of farm data and data import via provided APIs
D2.1: Multi-stakeholder Governance Scheme and
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Diffusion : Public
o chosen paid modules: setup of contractual agreements with the
providers
Data Sharing Governance
Data characteristics
• The main source of data is the agricultural machinery.
• Integrated services, e.g. weather forecast data provision, data
exchange with contractors, management of market data for profit
management
Roles regarding data
sharing
• Each farmer has data sovereignty for their data on the platform.
• The application of modules is based on the available data of each
farmer in the 365FarmNet software.
• The access to farmers’ data for partners of 365FarmNet depend
on the bilateral contracts between farmer and the partner
company.
• 365FarmNet is the platform provider, it does not use the data for
purposes except the provided basic features of the platform.
Technical foundation
• Online system with mobile and offline accessibility via cloud, e.g.
for documentation on premise (field / stable)
• 365FarmNet runs on all standard browsers and is not dependent
on the operating system.
• Open platform concept with basic functionalities and additional
modules from cooperation with partner companies.
• Modular system for adaptation to the farmers requirements
• Access rights feature for security and collaboration support
• Data transfer via ISO-XML format
Data transactions
• All data is collected and stored on the platform, e.g. via transfer of
data from agricultural machinery to the 365FarmNet cloud
system.
• 365FarmNet provides the needed access on farm data for the
data-based services and partner companies: data transfers from
farmers to services providers are a service of 365FarmNet for
application of data-based services (modules).
• No data transactions solely for data sharing with third parties, but
farmers are enabled to share data themselves for other
applications via exchange formats.
Legitimate purpose
• Farmers manage their data.
• Contracts regulate the legitimate purpose of any data usage.
• 365FarmNet follows strict guidelines on data security, privacy and
business secrets.
Risk and change
management
• Management via contracts: Partner contracts between service
providers and 365FarmNet, which also supports trust through
clear regulations.
• 365FarmNet is not involved in the contracts between service
providers and farmers (e.g. regarding misused data).
• Clear statements on farmers owning their data by company
leaders to facilitate trust as a cloud pioneering company.
Governance of Collaborations with other DSIs
Current state
• Platform concept: 365FarmNet is highly interconnected with other
companies.
• Agrirouter is one of the partners of 365FarmNet.
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• 365FarmNet GmbH is a member of the AEF e.V. and the
DataConnect initiative is a basis for the AgIN project of AEF e.V.
Future plans
• Open for cooperation via cloud (no peer-to-peer data exchange)
Key Insights on Governance
Profit-oriented business company: The legal form of a GmbH (limited liability company) was
chosen in particular for legal reasons to minimize risk and to balance costs and profits.
Additionally, it suits the fact that 365FarmNet sees itself as a software provider and IT company.
Its establishment as a startup formally serves to separate 365FarmNet's business field from
CLAAS' main agricultural machinery business. It also underlines the manufacturer independence
of the 365FarmNet applications.
Establishing trust: The initiative for 365FarmNet arose in the early phase of cloud establishment
in the agricultural sector. As a pioneer in mobile and cloud applications, the company had to deal
with open scepticism in the industry, regarding data security in cloud storage among other things.
365FarmNet earned its trust over time, initially supported by statements from well-known CLAAS
company representatives. An inference of trust due to the parent company CLAAS as an
established machine manufacturer can also be assumed. At the same time, the focus on a cross-
company solution for “colourful” fleets corresponds to a high level of customer orientation and
justifies the spin-off.
Acting like a data trustee: Many of 365FarmNet's basic principles correspond to the specifications
for data trustees. 365FarmNet initiated them as trust-building measures in the early phase of cloud
use. Central to this is ensuring data sovereignty. There is a high level of sensitivity here with
statements such as "data belongs to the farmer". As a platform provider, 365FarmNet manages
the customer data provisionally and does not access it contentwise. In addition, the strict consent
solution is used for data transfers. Customers must conclude contracts with each software solution
provider in order to use paid modules and agree prior to data transfers.
Requirements to and role of CEADS: The interview partner is missing structure in the current
discussion on data sharing, e.g. regarding pricing and costs and concrete business initiatives with
long-term perspective. A startup on agricultural data sharing will not be profitable in his mind. On
the other hand, the agricultural community is highly networked, raising the question on the role
and provided services of the future CEADS. The ecosystem needs to know, what added value the
CEADS will provide them with. The interview partner doesn’t see a need for a new institution for
central data storage, which the agricultural companies would have to pay for. He refers to
decreasing pricing for cloud storage and companies being able to set up their own clouds
nowadays.
Key Insights on the Agricultural Sector:
Regulations and subsidies: For farmer’s work, regulations such as the Fertilizer Ordinance are
central, as they lead to a lot of effort for reports and applications and significantly influence their
work processes, yields, subsidies and topics such as plant protection. 365FarmNet faces the
challenge and high efforts of including the federated system of regionally individual specifications
and structures in the software modules. This reduces the availability of the software solution to a
few countries and, in the view of the interview partner, also results in unnecessary effort. Here he
calls for political will to unify and standardize the regulations at the European level.
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Demand for networking and standardization of machine and farm data: The initiative DataConnect
is one step 365FarmNet took in this direction, in cooperation with selected machine
manufacturers. They deliberately decided not to aim for market-wide standardization in this first
step. DataConnect is now being further developed in the AgIN project with the participation of the
many members of the AEF e.V. (see chapter 0). The aim is for agricultural machinery to be able
to exchange data with each other and with clouds independently of manufacturers, and for their
formats to be standardized internationally accordingly. In addition, the interviewee also calls for
political efforts to standardize data management on farm data such as area and soil quality across
Europe and to make it available centrally. Currently, there are many data silos.
IV.2.1.1.2. Business Model
Co-created value-in-use
Integration of data from service and product providers of the agricultural sector in a consolidated
data set which is user accessible.
Roles in value co-creation (co-creation activities)
● Customer/End User - Farmers
● Orchestrator - 365Farm (private technology provider)
● Core partner - Technology providers
● Enriching partner - Agriculture equipment providers
● Actor - Food processors
Figure 23: 365FarmNet – Business model radar
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IV.2.1.2. Agdatahub
IV.2.1.2.1. Governance
General information
Name
• Agdatahub
• https://agdatahub.eu/en/
Legal form
• Joint venture
• Limited liability company (French)
Geography
• The geographical focus is on France, but the services are
also offered to other European stakeholders.
Sector
• Agdatahub is limited to the agricultural sector, but not
specialized on specific verticals.
• Use-cases are offered, e.g., for poultry production, for (goat)
dairy production, vegetable oil production and animal feed
production.
Scope
• Agdatahub is a data intermediary in the sense of the Data
Governance Act, providing services and infrastructure to
enable the transfer of agricultural data (e.g., from providers
of farming equipment, Farm Management Information
Systems) from all sectors to providers of data-based
services.
• Data collection for a blockchain-based traceability service
(e.g., to provide proof of the carbon footprint of agricultural
goods) constitute one important family of use-cases.
• Farm optimization services (e.g., precision farming, supply-
chain optimization, services to better deal with weather or
health crises) constitute a second important family of use-
cases.
Business approach
• Agdatahub is a private enterprise (joint venture).
• Agdatahub offer services for the secure exchange and
trading of data, including the identity and permission
management.
• Agdatahub and its predecessors chose the legal form of
private company in 2017 to avoid having to make all the
data public in the light of a pending French regulatory
initiative.
• The pricing model is layered, depends on the size and
revenue of the participating organization as well as data
usage (Free “discovery package”, custom “on demand”
package and “all-inclusive” package).
Lifecycle phase
• Scaling: Currently tens of use-cases are live.
• Agdatahub and its predecessors have a long (almost 10
year) experience.
Organisational Governance
Participants
• The company’s stakeholders include public bodies (Caisse
des Dépôts, Imprimerie nationale) as well as important
stakeholders of the French and European agricultural and
agro-technology sectors.
• Agdatahub does not distinguish the roles of data-provider
and data-consumer (both are paying customers).
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• Farmers join for free and solely manage data from their
farm and consent to data exchange.
Organizational mode
• Hierarchical (service offered by a private enterprise)
Governing bodies
• The Agdatahub team is in charge of day-to-day operations.
• Like any private company, the Agdatahub team reports to a
board of directors, which is chaired by a farmer.
• The market perspective is represented by the platform user
club and the use-case user club.
• A steering committee of different internal Agdatahub
representatives is headed by a directors’ board member. It
is in charge of coordinating the plans for product
development (“product road-map”).
• An ethics committee (soon to be installed, headed by a
directors’ board member) will be in charge of dispute
management.
Collaboration
• Operative decision-making by the internal team
• The steering committee develops ideas for new features,
validates them with the user club every six months to then
propose appropriate plans to the board of directors, who
then decide on the roadmap (R&D budget and plans).
Onboarding
• Platform users (data providers and data consumers) have to
pay license fees, have to confirm their identity with a unique
identifier (SIREN number) and have to agree to the general
terms and conditions and the general terms of use.
• With the future consent management (currently in
development) farmers will use the system free of charge
and give their permission on data sharing.
Data Sharing Governance
Data characteristics
• Very diverse data from genetics to weather to supply-
chains.
Roles regarding data
sharing
• Partners offer subscriptions to data or consume offered data
• Farmers (data owners) can give their permission for data
sharing
Technical foundation
• The facilitation of efficient and secure data exchange and
trading is the core value proposition of Agdatahub (Data
intermediary).
• Agdatahub adheres to European data standards, where
standards are already established (e.g., in the food sector).
• An identity management stipulates trust.
• A number of historical interoperability initiatives are part of
Agdatahub, notably NumAgri, which is a large French data
interoperability initiative (NumAgri support is a revenue
stream for the DSI).
Data transactions
• Different modes of data sharing are supported
(open/closed).
• Individual consent by farmers is also supported as one
possible feature.
• However, also non-sensitive data (e.g., weather data) is
also offered on the platform.
Legitimate purpose
• Data usage control (general terms and conditions)
• Unpublishing data in case of misuse.
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• Ethics committee and verification of users/identities
(partially not yet implemented)
Risk and change
management
• A steering committee is in place to manage changes.
• An ethics committee is in place to manage risks.
Governance of Collaborations with other DSIs
Current state
• There are collaborations with standardization (e.g.,
NumAgri) and blockchain/traceability organizations (e.g.,
CrystalChain). Agdatahub have concrete plans to
collaborate with ILVO DJustConnect. Agdatahub has
chosen to collaborate with technology partners who have
already implemented Gaia-X standard: Dawex with its Data
Exchange technology, and Orange Business with its trusted
hosting & its clearing house available in beta mode.
Future plans
• Agdatahub are planning to collaborate widely on the
interoperation of DSIs. These implementations will be done
based on the Gaia-X data clearing houses, on data
catalogues, and on Gaia-X conformity of participants'
identities in the near future. Partners are other platforms from
agricultural institutes in Belgium or agricultural machinery in
Germany. Agdatahub thus wishes for a “compatibility grid” as
a possible collaboration support service from a CEADS and
point to the “EDIC framework” as a promising approach.
Key Insights:
Established for-profit DSI: Agdatahub is one of the most mature data-intermediaries in Europe
and has been a private limited liability company for more than five years. Even though the
company’s origins go back to publicly funded research and the company’s stakeholders include
public bodies, the DSI’s agenda and activities are first and foremost profit-oriented, focusing on
areas of application such as environmental traceability and supply-chain optimisation that are
economically relevant to large corporate partners. Thus, Agdatahub has a relatively clear and
promising future in the European market. Especially DSIs that aspire to fulfil a similar role as
agricultural data-intermediary in other countries or areas, will likely look towards Agdatahub for
inspiration on how to set up their DSI governance, among other things.
Mature Governance Structure: Agdatahub’s statutes foresee a Steering Committee (for product
development), which is already implemented and an Ethics Committee (for conflict resolution),
which is currently being set up. There are defined processes and rules on how these two bodies,
both of which have an advisory role, are constituted and involved in company decision-making.
Additionally, the company’s user groups are organised in two “user-clubs” (one for use-case users
and one for platform users), which are consulted by the Steering and Ethics Committees.
Data Governance Act: Agdatahub sees itself as a data-intermediary in the sense of the Data
Governance Act and has taken the necessary steps to comply with the regulatory demands, such
as registering as a data-intermediary with the European Commission and ensuring the open and
transparent data access to all market stakeholders.
Concerns about interoperability: Agdatahub is already collaborating with standardisation
initiatives and DSIs. In particular, Agdatahub aims to set up collaborations with DSIs that have a
similar role as data-intermediaries in other European areas, such as the Flemish DJustConnect,
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the Finnish Tritom and the Dutch JoinData. Apart from technical interoperability, the interview
partners stressed that the economic and legal interoperability might be challenging when
implementing such collaborations, especially, because some other data-intermediaries are run by
public bodies or professional associations with a certain agenda, which might have incompatible
business models or legal terms. Agdatahub has chosen to collaborate with technology partners
who have already implemented the de facto Gaia-X standard: Dawex with its Data Exchange
technology, and Orange Business with its trusted hosting and its clearinghouse available in beta
mode. These implementations will be based on the Gaia-X data clearinghouses to demonstrate
the interoperability of Agdatahub's data offering catalogue and the Gaia-X compliance of
participant identities with other platforms in the near future. Agdatahub sees great value in a
coordinated European framework to support technical, legal and economic interoperability, which
might include a “compatibility grid” for existing DSIs and their services.
IV.2.1.2.2. Business Model Elements
Co-created value-in-use
Data sharing has profitable and efficient results for all engaged parties. Farmers use this secure
data platform to be validated as a data provider, while food processors and researchers - data
consumers in general - access specific data to support their business. It is a means of
universalizing data from the production stage and ensuring that it follows the exact routes to
maximise usability, through the agricultural network of France/ Europe.
Roles in value co-creation (co-creation activities)
● Customer/End User - Farmers, cooperatives, traders, Agri-Tech start-ups, agro-suppliers,
technical institutes, public bodies/ local authorities
● Orchestrator - Agdatahub
● Core partner - Technology providers (e.g. Dawex)
● Enriching partners - Orange Business Services, IN Groupe
● Actor - Food processors
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Figure 24: AgDataHub – Business model radar
Established for-profit DSI. Agdatahub is one of the most mature data-intermediaries in Europe
and has been a private limited liability company for more than five years. Even though the
company’s origins go back to publicly funded research and the company’s stakeholders include
public bodies, the DSI’s agenda and activities are first and foremost profit-oriented, focusing on
areas of application such as environmental traceability and supply-chain optimization that are
economically relevant to large corporate partners. Agdatahub thus has a relatively clear and
promising future in the European market. Especially DSIs that aspire to fulfill a similar role as
agricultural data-intermediary in other countries or areas will likely look towards Agdatahub for
inspiration on how to set up their DSI governance, among other things.
Mature Governance Structure. Agdatahub’s company statute foresees a Steering committee (for
product development), which is already implemented and an Ethics committee (for conflict
resolution), which is currently being set up. There are defined processes and rules on how these
two bodies, both of which have an advisory role, are constituted and involved in company
decision-making. Additionally, the company’s user groups are organized in two “user-clubs” (one
for use-case users and one for platform users), which are consulted by the Steering and Ethics
committees.
Data Governance Act. Agdatahub see themselves as data-intermediary in the sense of the Data
Governance Act and have taken the necessary steps to comply with the regulatory demands,
such as registering their company as data-intermediary with the European Commission and
ensuring the open and transparent data access to all market stakeholders.
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Concerns about interoperability. Agdatahub are already collaborating with standardization
initiatives and DSIs. In particular, Agdatahub aims to set up collaborations with DSIs that have a
similar role as data-intermediaries in other European areas, such as the Flemish DJustConnect,
the Finnish Tritom and the Dutch JoinData. Apart from technical interoperability, the interview
partners stressed that the economic and legal interoperability might be challenging when
implementing such collaborations, especially because some other data-intermediaries are run by
public bodies or professional associations with a certain agenda, which might have incompatible
business models or legal terms. Agdatahub has chosen to collaborate with technology partners
who have already implemented the de facto Gaia-X standard: Dawex with its Data Exchange
technology, and Orange Business with its trusted hosting & its clearing house available in beta
mode. These implementations will be done based on the Gaia-X data clearing houses to
demonstrate the interoperability of Agdatahub's data offerings’ catalogue, as well as the Gaia-X
conformity of participants' identities in the near future, with other platforms. Agdatahub sees a lot
of value in a coordinated European framework for supporting technical, legal and economic
interoperability, which might include a “compatibility grid” of existing DSIs and their services.
IV.2.1.3. AgIN / AEF e.V.
IV.2.1.3.1. Governance
DSI’s governance profile
General information
Name
• AEF Interoperability Network [AgIN]
• Listed under https://www.aef-online.org/about-
us/teams.html#/Projects
Legal form
• Association (AEF e.V. is an “eingetragener Verein” in
Germany) [AEF]
• Members are technology providers (Agricultural machinery
industry, software providers, academic partners and
associations).
Geography
• Focused on farming equipment and software worldwide
• Members are technology providers worldwide that are relevant
for the agricultural market. AEF has specialized staff for the
american and other international stakeholders.
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Sector
• Focused on agriculture in the strict sense (i.e., fields, not
livestock)
Scope
• AEF works to improve cross-manufacturer compatibility of
electronic and electric components in agricultural equipment,
and to establish transparency about compatibility issues.
• Implementing international electronic standards is therefore a
cornerstone of the work.
• The focus is on enabling services that require data-sharing
between different AEF members because farmers combine
machinery by different OEMs and providers.
• AgIN facilitates the exchange and integration of data from
different data providers (i.e., machinery providers) to allow
members to distribute specific quality-controlled data-driven
services.
Business approach
• Lots of AEF members are working together within the AgIN
project team, including all important OEMs (AEF has 24 OEM
members), some of which support the platform development
with dedicated staff.
• AEF is a reputable stakeholder in the area of farm machinery
interoperability.
• Farmers are only indirect users of AgIN’s developments (by
using the equipment and clouds of different manufacturers,
including services that involve data exchange).
Lifecycle phase
• Piloting
Organizational Governance
Participants
• Any provider of technology for agriculture can join AEF as
an equal member and can equally benefit from its results
(such as guidelines and a legal framework).
• Effectively, the market power determines the influence of
different members (i.e., large OEMs have a strong
influence) AgIN provides guidelines, legal framework and
compatibility for all members independently of size.
Organizational mode
• Hierarchical organization – AEF is the sole developer,
operator and decision-maker. AEF is the association that
fosters Ag companies to collaborate in common needs.
• The AEF has a long history as initiative for the (technical)
interoperability of farming equipment that is open to the
whole sector and are therefore perceived as a trustworthy
intermediary by the stakeholders.
Governing bodies
• The AEF will be the sole governance body and will manage
the AgIN network autonomously.
• The AgIN project group identifies the participants’ needs
and organizes development and maintenance activities to
address those needs.
• It consists of employees of powerful AEF members (funded
by the respective member companies).
• A steering committee (also by AEF) oversees their work, the
documentation of these meetings is accessible to all AEF
members.
Collaboration
• Operational decision-making takes place directly within the
project group and is coordinated with the AEF Steering
Committee.
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Onboarding
• Farmers get indirect access to AgIN by using the OEM
clouds.
• Technology providers have to be AEF members to join the
network.
• AEF members also have to sign an agreement with AEF to
participate (by providing and/or consuming data), which
specifies, among other things, the strict limitation of the
purpose for using and combining data to the deployment of
a specific service to a specific customer.
Data Sharing Governance
Data characteristics
• The data about farms and farming activity remains
collected, stored and shared by the different technology
providers (e.g., machinery providers), the network is
providing users the possibility to move use case relevant
data from one provider’s platform to another via the
network.
Roles regarding data
sharing
• Farmers (data owners) agree to the transfer and integration
of data when accessing a service that is enabled by the
network.
• The network provides templates for additional bilateral
agreements between data owners/providers on the one
hand and data consumers on the other hand.
• The network itself enables data exchange and service
delivery, performs quality control of services (including
quality of the underlying data), but does not access to the
data itself.
Technical foundation
• The network development focuses on data interoperability
(connectors, harmonization of heterogeneous data-sets
from different technology providers).
• A service registry, data catalogue service, conformance
tests and simulators are provided to the network.
• The AEF is an active and long-standing contributor of
standards to international standardization initiatives.
• This activity is now extended from the level of equipment
communication to the level data-driven digital services,
which involves providing connectors and data models and
standards for the integration of farm-related data from
different providers.
Data transactions
• The AEF manages bilateral data transactions that take
place directly between the technology providers involved
(purpose and permission management, data and service
catalogues).
Legitimate purpose
• The legitimate purpose of the data sharing is limited to the
delivery of a given data-driven service by a network
member to a farmer.
• Every service is quality controlled by conformance test
services (AEF certified label), which includes quality control
of the underlying data.
• Every member (data provider or consumer) signs an
agreement with the AEF that limits the legitimate purposes.
• Additional agreements between data owners, data providers
and data consumers are possible and AEF provides
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templates for those agreements (e.g., for co-innovation
partnerships).
• The affected stakeholder is responsible for taking action in
the case of suspected misuse, not the network.
Risk and change
management
• Services provided within the network will support versioning.
Governance of Collaborations with other DSIs
Current state
• LoIs have been signed with a number of different
associations (primarily, standardization/interoperability
organizations, but also DSIs).
Future plans
• Compliance with IDSA/Gaia-X (e.g., development of
federated catalogue) is implemented to prepare for
collaboration with other European DSIs on a technical level.
• Because they offer quality control services, AgIN explores
options for aligning the service and data quality management
between different DSIs.
Farmers are indirect participants. The AgIN network’s services concern a very narrow, technical
scope of applications, i.e., ensuring the interoperability of data collected and stored within the
systems of different manufacturers of agricultural equipment and the integrity of services based
on this data. Farmers are not concerned with such technical issues associated with the use of
their equipment, which is why the network’s governance does not foresee an active decision-
making role for individual farmers. The farmers’ consent to any transmission of data related to
their farms across machinery providers is given when they agree to using a service that requires
the transmission of data from one technical provider to another.
Quality control. AEF is an established provider of interoperability services. With the AgIN
network, AEF extend this work from the realm of electrical component compatibility and machine
communication to the realm of data-driven digital services, enabling the exchange of data
between systems from different machinery providers. The services listed in the AgIN network
thus provide the nuts and bolts for the development of possible services for the “multi-colored
fleet” that are built on top of these interoperability services, which therefore have to function very
reliably. Strict quality control is essential to stipulate trust in the network and services offered
within the network are therefore conformance tested (AEF certified label). Aligning quality
standards is an issue for AgIN's future collaboration with other DSIs.
Legal framework. Legal compliance when sharing data and distributing data-driven services is
an important concern for the participants of the AgIN network. Even though the AgIN network
itself does not collect, transfer, store or process data, it provides a legal framework for doing so,
consisting of an agreement between AEF members and the AEF when joining the network
(defining, among other things, the legitimate purpose of using data transfers over the network),
as well as additional contract templates to agree on the bilateral data sharing between AEF
members. This framework will be updated whenever important legislative changes (such as the
Data Act) come into effect.
Inter-DSI collaboration. As experienced providers of interoperability services, AEF is aware that
it is extremely costly and laborious to provide data-sharing services for a given application area.
Even their limited application area (agricultural machinery interoperability) is challenging enough
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to require an own network and staff to make data-sharing possible. Given that the agricultural
sector is diverse, i.e., it involves many stakeholders, sector verticals, value-chains and technical
systems, the interview partners were adamant that a possible single European agricultural DSI
that centrally manages the transmission of data relevant to the agricultural sector and that
combines the functionality of existing and future data-sharing initiatives could not be feasibly
implemented and that any such effort would likely fail to address the needs of all stakeholders to
an equal extent. They see their DSI as one building block among many that act in parallel, that
are driven by different market demands and that make different contributions to facilitating data-
sharing in the agricultural sector. AEF see it as important that different DSIs collaborate on
topics and activities concerning data interoperability (coordinated contributions to
standardization initiatives) as well as on data and service quality management and legal and
permission interoperability.
IV.2.1.4. Agricolus
IV.2.1.4.1. Governance
General information
Name
• Agricolus S.r.L.
• Website: https://www.agricolus.com/
Legal form
• Limited liability company under Italian law (“Società a
responsabilità limitata”)
Geography
• Italy
Sector
• Agriculture, primarily farming
Scope
• Data Sharing / Data Collecting as a Basis for Agricolus ensuring
optimal decisions for farmers within the farming process
Business approach
• Collecting data from farmers / farmers associations and
additionally pooling open data to ensure the best performance of
farmers by providing a DSS (Decision Support System) for
different types of crops.
• Hence, it is a platform with all the relevant information made
available for single farmers and their associations. This aims to
support them to apply and develop their business approach
successfully.
• Other services of Agricolus are focused on the traceability of
processes within the supply chain, the connection of agricultural
machinery of different manufacturers and for the enhanced
management of farms with a focus on different types for crops.
Lifecycle phase
• Scaling phase
Organizational Governance
Participants
• Farmers, associations, public bodies
Organizational mode
• Centrality is high due to the fact that all the data is used by
Agricolus to further develop its decision support system.
Governing bodies
• Board of the company
Collaboration
• No information available yet
• Question: Is there any specific set of rules or a specific mode of
communication for the collaboration with the farmers and/or the
farmers associations?
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Onboarding
• Agricolus Academy consisting of the Professional Academy and
the Educational Academy.
• The Professional Academy trains agronomists, agricultural
experts and agrotechnicians willing to introduce the Agritech tools
in their daily work. It provides different levels of certification
according to the type of course, for which credits are recognized
by the Order of reference. For students of Agricultural and
Forestry Universities, Agricultural Institutes, and Higher Technical
Institutes, the Educational Academy has been created to provide
a basic technical training.
Data Sharing Governance
Data characteristics
• Data from satellite pictures
• Mobile Data of the farmers
• Data from the Machinery of farmers
• Weather Data
• Data of farmers associations
Roles regarding data
sharing
• There is no actual data sharing between farmers as such but
more a collecting of data of farmer / farmers associations to
enhance the DSS of Agricolus,
• Data is shared between farmers and Agricolus or between
associations and Agricolus and distributors of technical means
(see https://agritrack.eu/en/agriconnect/)
Technical foundation
• Agricolus has joined the “Bronze” program of the Esri Partner
Network. The Esri Partner Network is a rich ecosystem of
organizations that work together to amplify the “Science of
Where”. Partners deliver solutions, content, and services using
the Esri Geospatial Cloud.
• Agricolus uses Fiware’s technology and it is involved in the Smart
AgriFood Committee of the organization. The FIWARE platform
provides a rather simple yet powerful set of APIs (Application
Programming Interfaces) that ease the development of Smart
Applications in multiple vertical sectors. The specifications of
these APIs are public and royalty-free. Besides, an open source
reference implementation of each of the FIWARE components is
publicly available so that multiple FIWARE providers can emerge
faster in the market with a low-cost proposition.
Data transactions
• Agricolus provides different services for the digitization of
agronomic processes (AgriConnect), the digital management of
farms (Agri Services including the DSS and tools that enable
customers to trace farming produce (Agri ValueChain).
• The necessary data transactions are managed by a customized
technical infrastructure based on existing standards (such das
ESRI / FIWARE).
Legitimate purpose
• No need for establishing a sort of legitimate purpose for data
sharing requests since there is no actual sharing of data between
farmers.
Governance of Collaborations with other DSIs
Current state
• Agricolus is a member of GODAN – Global Open Data for
Agriculture and Nutrition. GODAN supports the proactive sharing
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of open data to make information about agriculture and nutrition
available, accessible and usable to deal with the urgent challenge
of ensuring world food security. It is a rapidly growing group,
currently with over 547 partners from national governments, non-
governmental, international and private sector organizations that
have committed to a joint Statement of Purpose.
Future plans
• None
Key Insights on Governance
- Value added: Farmers are not really interested in Data Sharing in itself but rather in the
positive effects for their own work by sharing their data. The same is true for other actors
within the agricultural sector such as farmers associations. Therefore a DSI has to
always take into account the needs of its (possible) participants and take into
consideration which data from which sources is relevant for ensuring a real value added
for the stakeholders.
- Usability & Onboarding: Farmers need technical solutions which are easy to implement
and to use. In addition to that “onboarding”-services for stakeholders are helpful
instrument to strengthen the growth of the emerging data eco system.
- Focus on a specific use case: A clear Focus on a specific use case within the
agricultural sector (in this case farming and giving participating farmers an advantage for
growing their plants by sharing the data with Agricolus and receiving recommendations
of the DSS) is a key factor for building up a functioning data ecosystem.
- Applying existing technical solutions without forgetting the specificities of the chosen use
case: Using existing technical models, interfaces or standards (in the case of Agricolus
ESRI, FIWARE) is a good starting point and makes it easier for a DSI to get started but
the specificities of different use cases need to be taken into account by setting up
customized tools and interfaces in order to take the needs of the involved stakeholder
seriously. Furthermore the tools in use should be chosen in accordance to the basic
governance principles of the DSI and foster the implement set of rules for the usage of
the data.
IV.2.1.4.2. Business Model Elements
Co-created value-in-use
Integration of data from actors of the agrifood chain in a consolidated data set which is user-
accessible.
Roles in value co-creation (co-creation activities)
● Customer/End User- Farmers
● Orchestrator- Agricolus (private technology provider)
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● Core partner- Technology providers
● Enriching partner- Agriculture equipment providers
Figure 25: Agricolus – Business model radar
IV.2.1.5. Agri-Gaia
IV.2.1.5.1. Governance
General information
Name
• Agri-Gaia
• Website: https://www.agri-gaia.de/
Legal form
• No decision made
• Options include association or company with limited liability
(under German law).
Geography
• Germany
Sector
• Agri-food domain, focusing on AI development and
application
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• Use cases: Semantic Environment perception of
autonomous agriculture machinery, Recognition of potato
quality and size upon delivery (among others).
Scope
• AI development and data sharing in the agri-food domain.
Business approach
• Decentralized Ecosystem of AI development platforms
• Business and revenue model is under development. Most
probably open source community + commercial support
Lifecycle phase
• Piloting
Organisational Governance
Participants
• Agriculture Machine manufacturers
• Food Processing Companies
• Agri-Food Technology Companies
• AI service provider
• Data service providers
• Data intermediaries
Organizational mode
• Decentralized AI platforms connected as technical
backbone, interoperable with an optional central
marketplace
• current organization as research project
Governing bodies
• Project structure with work packages and steering
committee
• There are no decisions on the operator for the platform yet
and its future role is not specified yet
Collaboration
• Central data space architecture with decentralized AI
platform using a data space connector.
• Roles of DSI Participants: Data providers, data services, AI
service provider.
• Peer-to-peer data exchange
• Provision of decentralized spaces for collaborative AI
development
• The decision making process in the organization is not
specified yet.
Onboarding
• Registration on data platform for associated partners.
• No standardized onboarding process for other stakeholders
yet, due to research character of the project.
• Project pursues non-discriminatory participation of the DSI
when technical requirements match.
Data Sharing Governance
Data characteristics
• Very diverse data from data streams or batches
• Content includes sensor data (images, 3D, etc.), audio data,
satellite data and weather data.
• Data is mainly used for training and testing AI models.
Roles regarding data
sharing
• Data providers (not primarily farmers, but possibility to
connect them via the platform of the cooperating project
NaLamKI)
• Intermediaries
• Data services
• AI service provider
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Technical foundation
• Eclipse Data Space Connector for data exchange and
connecting with other platforms
• Open Source Software on the decentralized platforms
• Marketplace doesn't store any data, it only exchanges
metadata.
• Peer-to-Peer data sharing via the connectors of the
platform. That ensures that the marketplace has no
knowledge of the content of the data.
• Testing and data quality assessment of data sets that are
offered on the market place
• Standards and ontologies for AI models
Data transactions
• Exchange on peer-to-peer basis plus a centralized
marketplace to make data assets visible to the entire DSI.
Legitimate purpose
• Ensuring control through transparency of data processing
• Policy enforcement as part of the Eclipse Data Space
Connector not yet integrated
• Contractual legal regulations must be integrated between
the parties in the terms of use.
Risk and change
management
Not specified
Governance of Collaborations with other DSIs
Current state
No specific plans for cooperation agreement with other DSIs
Future plans
No specific plans but future cooperation is conceivable with other
projects like Agrifood-TEF-Project and NaLamKI (research project)
for data exchange.
Diversity of stakeholders: it is essential to acknowledge the diverse stakeholders involved in
the agricultural sector. These include data producers, agricultural machinery companies,
contractors, and public sector entities. Their roles and contributions are fundamental to the
successful governance of data sharing initiatives in agriculture.
Standards and alignment with existing Systems: New standards should not be introduced.
The governance should focus on using and integrating the existing standards and formats that
have already been adopted by companies. Therefore it's important to consider the long history
and variety of data exchange initiatives already in existence. A vast array of data formats and
platforms are already in use, and thus starting from scratch would not be practical. Efforts
should be directed towards understanding and integrating these existing structures. This would
ensure compatibility and ease of adoption. In addition recognizing that existing data spaces and
Farm Management Information Systems (FMIS) would face challenges adapting to a new
system, it is critical to integrate with and align closely with these existing systems and initiatives.
Focus on Unification: The main objective should be to gradually bring existing initiatives closer
together, creating a unified approach that integrates seamlessly with the present landscape,
rather than pursuing solely advanced technical solutions.
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Standard Contracts: There should be an effort to prepare a standard set of contracts to help
guide transactions within the EADS, even if these are not immediately actionable. This
preparation will provide a clear framework for future engagements.
Incremental Adoption: An incremental approach should be adopted for the implementation of
the EADS. The focus should not be on immediately achieving a high-end technical solution but
rather on taking progressive steps towards it.
IV.2.1.6. Agrimetrics
(1) DSI basic information
Main goal of the DSI
The main goal of the DSI is to provide data to farmers, students and researchers working on agri-
food research, commercial actors of the agri-food sector seeking to gain knowledge in order to
improve their products etc. The data is provided consolidated, aggregated from disparate sources,
and standardised, so that it can be processed by the end-user.
Domain of engagement
Linked agri-food data on:
● Water catchment
● Natural Capital
● Crops
● Landuse
● Satellite imagery
● Soil
● Weather
Country of the DSI owner
United Kingdom
DSI owner identification
The Platform is owned by Agrimetrics Ltd, which is a private company limited by guarantee without
share capital. Agrimetrics is one of four (4) Agri-Tech Centres, a collaboration between the UK
government, academia and industry. The degree of openness is distinctly one attributed to
marketplaces and the range of the data set should be considered to be intra-industry in scope.
Offerings of the DSI
The DSI offers raw and optimised data sharing. For optimization of the data sets developer tools
are brought into the fold, as an offer of service, that the end-user/ customer can employ to process
the data sets.
Business model
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Data Marketplaces: Agrimetrics acts as an intermediary between the data providers and the end-
user (who is also contributing to the sharing of data). To elaborate, Agrimetrics’ marketplace offers
to the end-user/ customer the option of monetizing on the sharing of his/her data through the
Agrimetrics’ marketplace, amongst its subscription-based services.
Revenue model
Agrimetrics’ revenue model employs the software as a service option. Pricing is based on
subscription services and tiers of expanded access (e.g. access to new data sets) are then
available to the end-user based on subscriptions. There exists the option of creating a free
account, which provides access to limited data sets with a core functionality and also provides
access to the marketplace.
Type and size of data and data applications
The main types data revolves around the following thematics:
● food data
● farming data
● environmental data
● satellite imaging
● field level insights
There is no specific data available regarding the size of the SDI, however given that Agrimetrics
is the result of a collaboration between state actors and academia access to data sets from these
sources is implied. As such, large data sets are insinuated.
Data acquisition models
Marketplaces: Agrimetrics is both an intermediary and aggregator of data whilst, at the same
time, it operates the marketplace.
Main pain points
Agri-Food is increasingly being digitised through the introduction of all kinds of smart devices
and software in the cyber-physical management cycle of corporate decision-making at any level
of the supply chain, from farm to consumer, generating a huge amount of data. Agrimetrics’
marketplace is a solution aiming at consolidating relevant data of the agrifood sector, mainly
targeted towards farmers and land users.
(2) Business Model Elements
Co-created value-in-use
Roles in value co-creation (co-creation activities)
● Customer/End User: Farmers, food producers, land users, Academia (students &
researchers)
● Orchestrator: Agrimetrics - SDI operator
● Core partner: state universities and research institutions
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● Enriching partner: technology providers of data processing services
● Actor 1: Equipment manufacturers of agri-food related machinery
● Actor 2: Processed foods manufacturers
● Actor 3: Companies engaged in geo-data services
Figure 26: Agrimetrics – Business model radar
IV.2.1.7. Agrirouter
IV.2.1.7.1. Governance
General information
Name
• DKE-Data GmbH & Co. KG
(company)
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• Website: www.agrirouter.com
Legal form
• GmbH & Co. KG
Geography
• International application
Sector
• Agriculture, primarily cultivation of fields with agricultural
machinery
• Networking for data exchange between the machines and
software, e.g. farm management information systems (FMIS)
Scope
• Manufacturer-neutral solution for the exchange of data between
machinery and agricultural software from different manufacturers
(e.g. FMIS - farm management information systems)
Business approach
• Manufacturer-neutral, non-discriminatory R&D joint venture
• Operates as a non-profit company on a cost center basis
• Each “Qualified Agribusiness Market Participant” along the entire
Agricultural Value Chain can join DKE-Data on a non-
discriminatory basis as:
o Association member
o Business-Partner
o Shareholder
• Financial commitment is calculated based on a fair “Cost
Contribution Model”.
• One vote principle (irrespective of its financial commitment) for
Business Partner and Shareholder
• Data exchange with personal data sovereignty
• Members can provide applications for the users of Agrirouter,
which are usually famers and contractors.
• Typical use cases: data transfer on task data, telemetry data,
GPS positions, documents, images and videos from your
agricultural software to your machines or the other way round
Lifecycle phase
• Founded in 2016 as R&D joint venture by 16 Shareholders
• Current state: in operation
• Current no. of customers according to the website: 3.969 (Sept.
2023)
Organizational Governance
Participants
• Equipment manufacturers, e.g. manufacturers of agricultural
machinery, and software companies: members of the
organization implement the Agrirouter API for their products.
• Farmers and contractors: use the Agrirouter API for data
exchange between the Agrirouter -ready software applications,
which support the work processes.
Organizational mode
• Central organization, integrating the members into the API
development
Governing bodies
• Management by DKE-Data GmbH & Co. KG
• Typical consortium members: manufacturers of farming
equipment, providers of agricultural software
• Customers (farmers and contractors) are not part of the initiative.
Collaboration
• One vote principle (irrespective of its financial commitment) for
Business Partner and Shareholder
• Shareholders, business partners and organization members
provide applications for the Agrirouter solution via the API for
data exchange (provision of data from their application and
integration of data from other software into their systems).
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Onboarding
• In addition to the website, Agrirouter is promoted at public events
of the DKE to interested parties
• Step 1: Becoming member (e.g. via the website)
• Step 2: Implementation of the API with support of the
organization
• Step 3: Practical usage
Data Sharing Governance
Data characteristics
• Shared data is mainly collected by the farm machinery on the
field to be processed in agricultural software applications, but
also vice versa.
• List of data types: task data, telemetry data, GPS positions,
documents, images and videos
Roles regarding data
sharing
• Farmers are enabled to keep data sovereignty and handle the
data independently in their chosen software according to their
needs.
• Shareholders, business partners and organization members
provide the data sharing capabilities in their respective software
for machinery or agricultural software applications.
• In order to use the API the end-users have to be willing to also
share the needed data (no freeloading by only consuming data)
Technical foundation
• Standardized API with development and support by Agrirouter
• Members have reduced effort for API development, SDKs for
Agrirouter are available for several programming languages on
GitHub.
• Additional services provided such as remote support with team
viewer
• The Agrirouter application is hosted on AWS (Amazon Web
Services) in Frankfurt.
Data transactions
• Agrirouter provides the API and a so-called data highway for data
transfer between two applications.
• Agrirouter has the principle not to store or view any transferred
data.
• Shareholders, business partners and organization members do
not access the shared data, only the needed meta-data for the
transfer process, which is handled and stored for a short term
(process time).
• The use of the Agrirouter is free of charge for the end-users (see
section on business approach).
Legitimate purpose
• No need to address this, as the data access is strictly limited and
the farmer keeps their data sovereignty.
Risk and change
management
• Structured and participatory development process for the
standardized API in the organization
Governance of Collaborations with other DSIs
Current state
• DKE is part of other initiatives, e.g. the AgIN project at AEF e.V.
• Other collaborations are unknown.
Future plans
• Plans for future collaborations are unknown.
Key Insights on Governance
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Data Intermediary: Agrirouter does not explicitly characterize itself as a data intermediary
according to the data governance act, but the collected information and characteristics are
compatible with this role.
Core service: The main idea of Agrirouter is easy to understand for both the providers of the
software applications and the end users (farmers). Agrirouter strictly focuses on its core product
of data transfer for agricultural machinery. The transfer is realized without data access for DKE,
even if one could certainly get beneficial added value from the data. The straight-forward
governance regulations reflect this concentration on one core service, e.g. in high transparency
and the openness of the organization to all relevant actor groups from the agricultural value
chain.
Neutrality and openness as design principles and central values: DKE-Data GmbH & Co. KG is
a manufacturer-neutral, non-discriminatory R&D joint venture founded by 16 shareholders. All
interested actors can formally become members of the organization - even farmers and
contractors, although it’s not necessary for their usage of Agrirouter services (see next section).
At the same time, openness in terms of content and equal treatment of members is a core
value. All members, which use the Agrirouter API in their software, are required to release data
themselves. They can’t only consume. In addition, all members can contribute to the standard
and have a vote in decisions. The fee structure also enables all agricultural companies to
participate in principle. DKE-Data acts as a non-profit company on a cost center basis, so
mainly aiming for cost coverage via the member fee structure, and the impression of neutrality
and trustworthiness is supported by this.
Indirect involvement of farmers: The farmer as end user is not required to be part of the
Agrirouter association in order to benefit from the service. The end user is also not charged
directly and specifically for Agrirouter expenses. Farmers are the customers of software
applications or product-software-bundles using the Agrirouter services, provided by the
association’s members as integrated software feature.
Remarks on the agricultural sector: Agrirouter specifically addresses the problem of so-called
colorful fleets, so the lack of interoperability and data exchange between machinery and
software systems of various machine manufacturers. Currently, the focus of Agrirouter lays on
field cultivation; indoor livestock equipment offers potential for expanding the area of application.
IV.2.1.7.2. Business Model Elements
(2) Business Model Elements
Co-created value-in-use
Data sharing enables farmers to have direct access to automate the farming management
process, while software providers are advertised and linked to customers.
Roles in value co-creation (co-creation activities)
● Customer/End User: Farmers and contractors
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● Orchestrator: Agrirouter
● Core partner: SAP (Creator of the platform)
● Enriching partner: Software providers
Figure 27: Agrirouter – Business model radar
IV.2.1.8. AgroDataCube
(1) DSI basic information
Main goal of the DSI
AgroData Cube offers Data Exchange Services via a portal. The goal of the Data Exchange
Services project is to create a prototype of a system that makes relevant datasets from and for
the Wageningen research community accessible and presents them online. The AgroDataCube
aims at building on common agro-semantic standards.
Domain of engagement:
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AgroDataCube provides a large collection of both open data and derived data about crop
registrations, soil, weather conditions, etc. for use in agri-food applications.
Country of the DSI owner: Netherlands
DSI owner identification:
Ownership: Wageningen Environmental Research (WENR) provides the web site and API as a
service to the public. Open data has been collected from, among others, the Dutch government,
Rijkswaterstaat, KNMI and Wageningen University and Research. Commercial products like
GeoNetwork, CKAN, Docker, OpenShift have been used to collect data.
Offerings of the DSI: Optimised data sharing.
Business model: Technical enablers
Data is offered according to the FAIR data publishing principle. Open data has been collected
from, among others, the Dutch government, Rijkswaterstaat, KNMI and Wageningen University
and Research. The DSI allows the user to make specific queries to the open data databases.
Revenue model: Freemium/ Limited free access as long as a token is supplied upon email
registration. Data in the AgroDataCube is free of charge following the Creative Commons BY-
NC-SA licence.
Type and size of data and data applications
Data include:
● Crop registration datasets from the Netherlands Enterprise Agency.
● The Current Height File Netherlands (AHN), a digital file containing height data for the
whole of the Netherlands from Rijkswaterstaat, an organisation of the Ministry of
Infrastructure and Water Management.
● Information about and from the KNMI (the Royal Netherlands Meteorological Institute)
weather stations.
● Information about soil conditions from the BOFEK 2012 datasets and the Dutch soil map
1:50.000 (2014), created and provided by WUR.
● NDVI (Normalised Difference Vegetation Index).
● More details about a specific crop code or soil code returned by another request.
● Information about administrative boundaries of provinces, municipalities, and postal code
areas, based on 2015 data.
Data acquisition models: Intermediaries
Open Data has been collected from the Dutch government and Rijkswaterstaat (PDOK), KNMI,
and Wageningen University and Research.
Main pain points: Great diversity of the data
To make full use of the possibilities of datasets, it is necessary to present the data in such a way
that it can be easily accessed, tailored to the needs of the user.
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(2) Business Model Elements
Co-created value-in-use
The user is allowed to make specific pre-defined queries in the connected databases.
Roles in value co-creation (co-creation activities)
● Customer/End User: various stakeholders in agri-food applications
● Orchestrator: Wageningen Environmental Research
● Core partner: Dutch government, Rijkswaterstaat (PDOK), KNMI, and Wageningen
University and Research
● Enriching partner: various stakeholders in agri-food applications e.g., growers who provide
information to the Dutch government about crops cultivated in specific fields.
Figure 28: AgroDataCube – Business model radar
IV.2.1.9. Altas
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General information
Name
• Atlas (Agricultural Interoperability and
Analysis System)
• www.atlas-h2020.eu
Legal form
• Research project
• Planned: Association
Geography
• International application
Sector
• Agriculture
• Connection of data from different services & devices
(agricultural machinery, FMIS, etc.)
Scope
• Establishing the interoperability of software systems from
different providers
• Objective: End users can link data generated by the systems of
different providers with each other; data can thus be exchanged
independently of the provider.
Business approach
• Idea: Marketplace of services
• Interoperable software is offered as a service to link data from
agricultural software systems.
• Idea: Funding through subsidies and consulting services for the
development of Atlas-compliant services
Lifecycle phase
• Piloting
• Current status: Research project, end of July 2023 (starting
point: EU call for proposals under Horizon 2020)
• Functioning solution has been developed.
Organizational Governance
Participants
• Software providers (anyone who offers software for their
product)
• Manufacturers of agricultural services
Organizational mode
• Idea: centralized organization, non-profit
Governing bodies
• Current status: Foundation of association and committee
structure not yet conclusively discussed
• Goal: lean structures for the standardization processes
• Idea: three pillars (standardization, onboarding and business
development)
Collaboration
• Open to all providers who offer systems for agriculture
• "Atlas service registry": Overview of the services and
information on how to connect to the services
• "Atlas participant portal": Registration of service providers
• In development: Catalogue for Atlas-conformer software (here
touchpoint to farmers)
• Idea: Members influence the development of
standards/adaptations of existing standards
Onboarding
• Verification of the entities (e.g. tax number, contact person,
etc.)
• Validation of the service via connection to test farm
management system; testing of data, interfaces and
interoperability
• Atlas standards must be implemented; software must enable
connection to all other Atlas systems
Data Sharing Governance
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Data characteristics
• No information
Roles regarding data
sharing
• No data exchange takes place via Atlas, only the linking of the
systems
Technical foundation
• "Service templates": Standards that have been defined for the
services in Atlas (readable description of services)
• Requirement-based, if the service is used--> identification of the
data formats e.g. geopackage + pictorial API descriptions
• "Atlas Equipment Centre": interface to the vendor specific
portals (for managing agricultural machinery)
• Verification of providers, quality of interfaces and data
• Unique identification of providers via user ID
• Linking of services via OAUF 2 protocol
• Encrypted communication via HTTPCS
Data transactions
• Data exchange does not take place via Atlas, only linking of
services
• The farmer decides which software systems are linked with
each other.
Legitimate purpose
• Not relevant (responsibility lies with the service providers).
Risk and change
management
• Question of availability in the event of increased demand (e.g.
harvest season, application season)
• Required: good infrastructure and operator for the system
(currently hosted by Fraunhofer Institute Germany)
Governance of Collaborations with other DSIs
Current state
• Research project completed
• Functioning solution has been developed
• Idea: Atlas is to be further operated by various partners (no
further information available)
Future plans
• Vision: Atlas as infrastructure for establishing a digital ecosystem
in agriculture
IV.2.1.10. AVR Connect
(1) DSI basic information
Main goal of the DSI
AVR Connect is an online reporting tool for all the (on and off the field) machine data, which
provides a further linked access to other relevant platforms for additional field and yield data. The
main goal of this DSI is to provide the farmer with the important information about his field and
resources aiming for reducing the cultivation costs and contributing to a sustainable food
production.
Domain of engagement
The AVR Connect platform is geared towards the “crop production” domain, and more specifically
it focuses on the effective use of on-field machines and resources.
Country of the DSI owner
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Belgium
DSI owner identification
The AVR Connect platform is owned and provided by AVR, a company that manufactures and
sells professional farming machinery and equipment. This digital tool processes and visualises
the data collected by sensors that are integrated into the AVR cloud-connected farming
equipment. The tool is offered to the customers/farmers as a digital add-on service, supporting
supplementary the AVR equipment purchase. The platform refers only to farmers who have
purchased the appropriate relevant AVR equipment and its services are limited only in the
agriculture sector.
Offerings of the DSI
The platform offers a variety of services. It maily offers data visualisation such as integrated tiles
measurements, overview and history of machine settings, overview of current positions and
performance of all the on-fields machines and more. However, there is the possibility to extract
raw data and exchange with other users.
Business model
For this DSI, it seems that we have a hubrid business model. The original AVR Connect platform
(i.e. the tool that refers only to machinery data) follows a “Software as a service” business model.
The software is provided to existing customers of AVR products, however there is nor financial
fee for accessing the platform, neither financial compensation for data sharing. The financial fee
comes if the user wants to combine field and weather data with the machine ones, by linking the
AVR Connect platform with some other third-party digital platforms, used as extensions, which
require a subscription fee. Some of these platforms provide the option to the user to exchange
“his own” data with external data (such as weather and satellite data) for free, an approach related
to “Industrial data platforms” business model.
Revenue model
The revenue model of the AVR Connect platform can be characterised as Freemium. The
machine data collected by the respective at-field AVR equipment and the relevant data services
are freely accessible to the users/farmers. However, some extensions of the tool that offer more
information and data related to the field or the yield are available at a cost. This happens, because
these extra services are provided with collaborator third-party platforms that require registration
fees.
Type and size of data and data applications
Collected data is both machinery and parcel and their size is proportional to the field and the
resources. Collected data during harvesting can be current fuel consumption of the used
equipment, yield, number of plants per square meter, machine status and settings and remote
software updates. In addition, these on-field data can be exchanged with already existed data
(coming from other collaborator third-party platform) with crop growth, weather and soil data, IoT
data and algorithms.
Data acquisition models
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Data is acquired by the sensors that are integrated to specific products/machines of the AVR
portfolio. The sensors collect both machine and agronomic data. However, as already mentioned,
other additional data is available through linked third party DSIs. The sources of these data can
be satellites, contour maps, types of soil etc, however it is not clear if this data is private or public
and the method that is used for their acquisition.
Main pain points
The AVR Connect platform offers a completed overview of the resources, the field and the yield
to the farmer effortlessly, without even going to the field.
(2) Business Model Elements
Co-created value-in-use
The user has full access to data provided by sensors on his own field, however in an easy-to-
understand way through reports and visualised elements.
Roles in value co-creation (co-creation activities)
● Customer/End User: Farmers who have purchased specific AVR machines and use the
platform
● Orchestrator: AVR
● Enriching partners: John Deere Operations Center, Dacom Cloudfarm, WatchItGrow
Figure 29: AVR Connect – Business model radar
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IV.2.1.11. Cipher Trust Data Security Platform
(1) DSI basic information
Main goal of the DSI
CipherTrust Data Security Platform unifies data discovery, classification, data protection, and
unprecedented granular access controls with centralized key management. This results in less
resources dedicated to data security operations, ubiquitous compliance controls, and significantly
reduced risk across your business.
Domain of engagement
Data control & protection, data security
Country of the DSI owner
Thales has offices all over the world including America, Europe, Middle East, Africa and Asia and
Pacific region.
DSI owner identification
CipherTrust Data Security Platform is owned by Thales Accelerate Partner Network. Thales
provides Data Protection solutions through several partner-type programs in order to meet the
challenges of cloud, mobility, authentication, encryption, and crypto-management. These
programs are widely addressed to resellers, Managed Service Providers (MSP), Technology
providers, Original Equipment Manufacturers (OEM), Advisory providers and Cloud providers.
Interested partners can become members of the Network by either learning and integrating the
Thales solutions to their products/ services or by selling them. Applications are made through
Thales website and are reviewed and approved by the Network.
Offerings of the DSI
CipherTrust Data Security Platform is an integrated suite of data-centric security products and
solutions that unify data discovery, protection and control in one platform. It offers data security,
acceleration of time to compliance, and secure cloud migrations.
Business model
Technical enabler
Revenue model
Freemium: free access to data services. Access to specific services is available at a cost upon
request.
Type and size of data and data applications
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CipherTrust Data Security platform is built on a modern micro-services architecture, is designed
for the cloud, includes Data Discovery and Classification, and fuses together the best capabilities
from the Vormetric Data Security Platform and KeySecure and connector products.
Main pain points
Inconsistency in data security practices and lack of data visibility. The need for protection and
control of sensitive data is profound.
(2) Business Model Elements
Co-created value-in-use
Roles in value co-creation (co-creation activities)
● Customers/End Users: Stakeholders from various industries that face the need to protect
the data they possess (e.g., financial services providers, telecommunications providers
etc.)
● Orchestrator: Thales
● Core partner: Developers
Figure 30: Cipher Trust Data Security Platform – Business model radar
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IV.2.1.12. COGNAC
IV.2.1.12.1. Governance
General information
Name
• Cognitive Agriculture (COGNAC)
• Website:
https://www.iese.fraunhofer.de/de/projekt/cognitive-
agriculture.html
Legal form
• Research project
• No legal form has been specified.
Geography
• Germany
Sector
• Various stakeholders across the agricultural supply chain
Scope
• Agricultural Data Space: it allows for the multivalent use and
linking of complex agricultural data in secure data spaces,
supporting cognitive services.
• Sensors for automated recording, integration, and
interpretation of high-resolution measurement data to aid
information-based decision-making
• Automation concepts for field robotics for plant-specific
fieldwork using robotic platform equipped with specific
sensor systems
Business approach
• Agriculture-specific Digital Ecosystem
• Enabling and optimizing planning and work processes in
agriculture to make them more efficient and economical.
Lifecycle phase
• Research project completed
Organizational Governance
Participants
• Agricultural machinery manufacturer, farmers, service
provider
Organizational mode
• Decentralized data space consisting of IoT, data processing
systems/platforms and data hubs offering digital twins of
agricultural assets (logical centralization of data)
Governing bodies
• Governing structure depends highly on use case structure.
• Absence of a central entity
• Tendency to peer-to-peer data sharing
• Stakeholder create responsibilities and determine rules for
bilateral data sharing
Collaboration
• A peer-to-peer approach, where every stakeholder
communicates directly with each other, rather than relying
on centralized intermediaries
• The approach was to identify use cases and see how they
align with other stakeholders' use cases and data/service
offerings.
• The primary focus was to see if there is a match in
application cases and data/service offerings between
different stakeholders.
Onboarding
• No standardised onboarding process
• Manual onboarding of stakeholders for each use case
Data Sharing Governance
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Data characteristics
• Data is managed on a set of data hosting platform (data
hubs / digital twins) that is based on the reference
architecture model of „International Data Space“.
• "Digital twin" is a foundational concept in the COGNAC
project
• Agricultural physical asset can have representations via
„digital twins“.
• All related data to an asset is stored in one place, making it
interoperable and easily accessible.
• Relevant data: Manufacturers' Data, Service Providers'
Data, Sensor Data, Agricultural Activities Planning Data,
Automation and Robotics Data
Roles regarding data
sharing
• Data sharing via individual decision making of the
stakeholders
• Data owner (farmer) received notification and grants or
denies request
• The platform primarily provides technical components for
data sharing.
Technical foundation
• Agriculture Data Space as infrastructure for multiple actors
• Data hub platforms as essential building blocks
• The project focuses largely on a peer-to-peer-approach,
meaning every participant could communicate directly with
any other participant.
Data transactions
• Data usage control through transparency
• Logging of all transactions, allowing participants to trace
who has consumed their data.
o Every participant gets an overview of information
about who and how their data has been processed.
Legitimate purpose
• Data usage decisions are made individually by the data
subject (i.e. farmer).
• Data usage control through technical measures (i.e.
transparency dashboard)
Risk and change
management
Not specified
Governance of Collaborations with other DSIs
Current state
No specific plans for cooperation with other DSIs
Future plans
No specific plans for future cooperation with other DSIs
Key Insights on Governance
Transparency: Transparency stands out as a paramount value in the governance of
agriculture-related data sharing initiatives. It is imperative that all processes and decisions within
the EADS are conducted in an open manner, ensuring that stakeholders are informed and
involved in the evolution of the project.
Data quality: Credibility is also crucial, particularly in relation to the quality of data. The
reliability of data is essential, as the success of various agricultural operations, such as those
utilizing GPS technology, depends heavily on the accuracy of the information provided. Ensuring
high data quality is non-negotiable for the success of the EADS.
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Ensuring trust: Understanding the diversity of trust levels among different entities is important.
Trust in companies or government bodies varies significantly among individuals. This variance
necessitates a tailored approach to governance that considers these differences and works to
establish trust through consistent, reliable practices.
Importance of Regional Focus: The role of regional focus within Data Space Initiates (DSIs)
has been underlined. Some initiatives deliberately concentrate on specific regions and sectors
to align closely with the trust levels and needs of local stakeholders, suggesting that a one-size-
fits-all approach may not be suitable for the nuanced agricultural landscape.
Aligning with Use Cases: Moreover, it is important to align with specific use cases with the
data and service offerings available. The central aim should be to ensure that connections and
integrations serve a clear purpose and offer tangible benefits, rather than pursuing connections
for their own sake.
Interoperability: Interoperability is another critical aspect. It is vital for ensuring that various
systems, tools, and datasets within agriculture can work together efficiently. The ability to
integrate diverse data sources smoothly is a key factor in avoiding inefficiencies and potential
errors. The governance model should promote and support experimentation with various
conversion interfaces and mechanisms to foster seamless data management, as evidenced by
the work within projects such as COGNAC.
IV.2.1.13. DjustConnect
IV.2.1.13.1. Governance
General information
Name
• DjustConnect (pronunciation: “Just
connect”)
• https://www.djustconnect.be/en
Legal form
• Project/public service operated by the regional Flemish
research institute ILVO (public-private partnership), without
a separate legal form
Geography
• Central target group: Farmers in Flanders (Belgium)
• Their business partners (also abroad) are also within the
scope.
• Collaboration with DSIs in other regions, e.g., Wallonia
(Belgium), France and Finland in order to collaborate and
contribute to a common Agricultural data space
Sector
• DjustConnect offers its services to the entire agri-food
sector and currently has use-cases in different subsectors:
dairy farming, arable farming, goats, potato, and wine.
Scope
• DjustConnect is a neutral data-sharing platform for all
stakeholders in the agri-food sector.
Business approach
• Data exchange with personal data sovereignty
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• DjustConnect’s focus is on setting up an open, transparent
and reliable data ecosystem to support all players,
especially farmers and horticulturists, in gaining value from
sharing data.
• Use-cases usually are initiated by data consumers (service
providers), who formulate their demands and offers, then
ILVO goes out to identify suitable data sources and try to
convince the data provider to provide their data. The
farmers (data owners) are provided with a dashboard on
which they can evaluate the data request/offer and decide if
they want to give their consent.
• Membership is
o free for farmers
o data providers pay 1500 Euro annually
o data consumers pay 2000 or more depending on the
extent of their data need.
• Inclusivity: Any organization that is able to accept data
sharing rules and agreements can join, also the advisory
board will soon be opening up.
Lifecycle phase
• The platform is operational with a slow but sustainable
growth.
• They scale up on a use-case driven basis, company by
company and farmer by farmer to ensure added value for
each user.
• The central target group consists of 23 000 Belgian farmers.
Organizational Governance
Participants
• Farmers are the central participants.
• The platform is open to every player or stakeholder in the
agri-food chain, e.g., farm suppliers (machinery, farm
management systems, contractors, feed suppliers, etc.) and
farm customers (food processors, retail). Every stakeholder
can provide data through the platform (be data provider)
and consume data through the platform (be data consumer)
to offer digital services to other stakeholders (usually
farmers).
• The Flemish Department of Agriculture and Fisheries also
provides data from farms in Flanders in order to promote
digital innovation, to contribute to a sustainable agri-food
sector and to increase the adoption of DjustConnect.
• A collaboration with the Digital Flanders Agency exists to
reuse (data sharing) technology, promote digitalization
across domains and contribute to the “Flemish DataSpace”.
Organizational mode
• The DSI is currently centrally hosted and managed by ILVO
• ILVO is commonly perceived as trustworthy and neutral due
to their public status and mission statement as well as track
record of supporting farmers in Flanders with science,
technology and innovation.
Governing bodies
• The DjustConnect Project team manages the data-space on
a day-to-day basis.
• They are accountable to the ILVO management.
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• An advisory board consisting of farmers associations that
represent entire sub-sectors (“verticals”) – currently ~10 –
advises the project team.
Collaboration
• The decision-making on the level of the organization is lean.
But the team is accountable to the ILVO management.
• Given the early stage of DjustConnect and the small size of
the Belgian ecosystem, collaboration between the platform
team and individual stakeholders currently occurs largely
informally and on a use-case by use-case basis.
Onboarding
• All clients (data providers and data consumers) sign an
agreement/contract with DjustConnect that specifies the
rights and duties of each role. The contract includes a data-
sharing agreement and the acceptation of the privacy
policy.
• Also farmers sign a contract with clauses relevant for their
use-cases.
Data Sharing Governance
Data characteristics
• Farm data collected by farm machinery, by contractors, by
customers or by farmers themselves (e.g. animals or animal
products, fields, harvests, etc.)
• both static and dynamic data is possible
Roles regarding data
sharing
• At least three roles are involved with a data transfer:
o data consumer (digital service provider or
supplier/costumer of the farmer)
o data providers (an organization that hosts data of
farmers)
o data owner (farmer)
• In some specific cases, the data right holder is the data
provider (non-farm-related data) and permission by farmers
is not necessary.
Technical foundation
• DjustConnect has a data catalogue where connectors/APIs
are listed.
• They also manage identity management, consent and
authorizations. Transparency about the data sharing is
given to each user of the platform.
• Data is not stored in the platform, nor opened or collected
through data sharing.
• The development of interfaces between data providers and
data consumers (interoperability support) is within the scope
of the DSI.
• The deployment of services and payments for the data itself
takes place without direct involvement of the platform.
Data transactions
• The DSI manages data transactions (initiation, as well as
purpose and permission management).
• Any data transaction requires the explicit permission of both
the data provider (e.g., farming machinery supplier) and the
data owner (farmer) that can be revoked at any point in time
(with a three months notice on the side of data providers).
Legitimate purpose
• With the agreement, it is ensured that data is only shared
for the legitimate purpose of providing a specific service.
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• Data owner, data provider and data consumer are free to
negotiate any possible additional terms (e.g. availability,
data quality, payment for data access).
• A clearing house lists all data requests, permissions etc.
including the purpose of data sharing.
• The platform will support any possible investigation on mis-
use by providing relevant meta-data on transactions and
has the contractual right to audit.
• As a good reputation is key to business success in the
sector, it is expected that fear of reputation-loss prevents
data misuse.
Risk and change
management
• No specific information was provided
Governance of Collaborations with other DSI
Current state
• DjustConnect has started collaborating with other regional
DSIs on implementing cross-border use-cases and see this
as an important pathway future developments (e.g., on
sustainability and climate impact, but also on making their
regular services available for farms in border regions that
collaborate across borders, and ensuring that international
companies can work with the DSI)
• Current collaborations on consent and identity management
as well as technical interoperability are under way with
wallonian WALLeSmart, French AgDataHub and Finnish
Tritom.
• DJustConnect follows the activities of the organizations
behind the most commonly used data standards like GS1,
AGGateway, ISO, OSLO etc. to optimize the interoperability
with other DSIs
Future plans
• DjustConnect wants to collaborate with all relevant and
interoperable platforms and to become an active part of a
possible CEADS that enables cross-border and cross-
sectoral data sharing in Europe.
Credibility and Trust: DjustConnect see farmers’ sovereignty over data as essential and
therefore request their permission for any data transmission that concerns their farm (via a
Dashboard). As a publically funded research institute with a track record in supporting Flemish
farmers in innovation and technology endeavors, ILVO is a credibly neutral stakeholder as the
central platform provider. A profit-oriented operator with an own commercial self-interest might
experience more mistrust as central DSI platform operator.
Regional eco-systems: DjustConnect’s primary target group are Belgian famers, which is a
relatively small core target group, even if the Belgian agri-food chain and ecosystem also
include large international players as part of the value-chains. The regional focus has several
advantages: Firstly, the DSI can tailor its services to the target groups’ regional needs (e.g.,
provide services that support compliance with Flemish regulation) and build up trust. Secondly,
the regional scope implies that reputation loss has a high cost for participants, which provides
another level of protection against misconduct. Thirdly, governance and operations can be kept
comparably lean in the early stage of the DSI with just ILVO (management and project team)
and an advisory board of agricultural representatives as governing bodies, because a lot of
consultation with the stakeholders occurs informally. The platform developers are adamant that
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independent regional DSIs that maintain close relationships with the regional community and
pursue use-cases specifically relevant to the respective regions and tailored to their needs are
essential units of an overarching European Data Sharing Initiative for the agri-food sector.
Public support of standardization: Competing standards and inhomogeneous data assets across
agriculture companies (no data interoperability) is probably the biggest challenge for data-
sharing in the agricultural sector and substantially slows down the establishment of DSIs across
all regions and most sector verticals. The interview partner mentioned that technology providers
with a short-sighted business strategy (e.g., start-ups that become scale-ups) often contribute to
the uncontrolled proliferation of existing standards to secure their market share in the short term
and suggested that the public administration (European Commission) might consider regulating
standards more often (as they did for USB-C phone chargers).
Collaboration with government: The operating research institute ILVO is a Flemish public private
partnership. The link to the public administration has an influence on building up the eco-
system: on the one hand, it gives ILVO credibility as neutral player in the market, and the
government participates as data provider to DjustConnect with public open data and generally
supports the initiative. On the other hand, DjustConnect’s independence from the public
administration is essential for gaining the trust of the ecosystem stakeholders. For DSI
acceptance, it is important that the public administration does not have direct or privileged
access to the data shared using the platform. The public administration only indirectly benefits
from the platform, e.g., because the platform enables services that support farmers in fulfilling
their documentation requirements and because it strengthens the regional agricultural sector
and allows Flemish stakeholders to participate in and profit from the European data economy.
Inter-DSI collaboration: The interview partner stressed that he experienced his involvement in
setting up a DSI instructive because he was not aware of the immense effort (“grind”)
associated with implementing transferable solutions for specific use-cases, growing an
ecosystem and providing reliable infrastructure to support the ecosystem. Concerning the idea
of a possible central European DSI for the entire European agricultural sector, he expressed
doubts that such an endeavor could be successful. In their experience, DSI representatives with
practical experience of implementing DSIs tend to be open to collaboration because data-
sharing across stakeholders in Europe can only become possible if a multitude of DSIs that
focus on their respective target group and its needs work in parallel. DjustConnect already
collaborate with other DSIs that provide similar data intermediation services for agricultural
stakeholders in other regions and plan to extend these activities. They see the areas of
technical interoperability and of shared permission/consent management as particularly
important areas for such inter-DSI collaboration.
IV.2.1.13.2. Business Model Elements
Co-created value-in-use
A high-performance, safe, transparent, neutral platform to exchange data in a smooth, regulated
manner and facilitate the development of more accurate and smarter applications.
Roles in value co-creation (co-creation activities)
● Customer/End User: agricultural business (farmers & horticulturists), government,
suppliers, buyers, producer organisations or agricultural cooperations, API developers
(data providers)
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● Orchestrator: DjustConnect
● Core partner: app/tool developers (data receivers)
Figure 31: Djustconnect – Business model radar
IV.2.1.14. Eden Library
(1) DSI basic information
Main goal of the DSI
Focus on exhaustive annotations and integrate quality control frameworks in order to produce i)
problem-specific datasets, ii) rich in metadata, iii) clustered based on multiple criteria and iv)
standardised in ways that facilitate researchers, tech industry and agri food business experts to
use them in various contexts based on their needs.
Domain of engagement: Specialty crops protection
Country of the DSI owner: Greece
DSI owner identification:
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Ownership: the datasets (images & notebooks) and device (Eden LIbrary Viewer, a novel camera-
based system for sustainable crop protection and effortless plant monitoring) are owned by the
company (EdenCore Technologies PC).
Degree of openness to new participants: At the moment, contribution by others is not possible,
because the company wants to maintain certain quality standards for the images, thus symptoms
are examined at field-level by experts. In the future, community members or third parties e.g.
growers, agronomists, other research universities specialising in crop protection management,
public authorities that study pests or diseases on crops, agrochemical companies, crop vendors,
etc. could contribute images. In the site, a form is available to be filled by anyone wanting to
contribute their own data.
Industry scope: the Eden Library Viewer (device) is targeted to professionals operating in the field
e.g., growers, agronomists. The datasets and notebooks are targeted to agritech companies and
AI practitioners that can utilise the datasets to train new tools. The notebooks are targeted to the
deep learning community as training tools.
Offerings of the DSI
Services ranging from ready-to-use datasets for AI projects, to annotations fitted to each
customer, and a camera-based pest and enemy detection device.
Eden Library includes a wide range of agrifood datasets (178) such as images of plant pests,
plant diseases, weeds, and healthy plants. The user can choose between a variety of plants,
enemies, image acquisition scenarios and export annotations. All images after being collected
are checked and categorised by expert agronomists (e.g. entomologists and phytopathologists),
and then annotated (the process of adding metadata for the description of an object of interest),
which are then used to “train” machine learning models to detect, recognize, and interpret objects
of interest. The quality of the annotations is checked using Eden Library’s own proprietary quality
assurance tool. Images that fail this quality control test are sent back to be annotated again. All
images and annotations are checked by domain experts before they are uploaded to the
database.
Along with the images, there is access to related notebooks (36) where the solution to train
machine learning models is described. Users from the deep learning society can give their own
solutions to improve the readability of the explained techniques.
The Eden Library Viewer is a camera-based pest and enemy detection device that can be used
in field operations.
Business model: Data monetisation
EdenCore Technologies PC aims to make an additional revenue from data sharing with other
companies. EdenCore originally collects the data to train its own device (Eden Library Viewer).
Revenue model: Freemium
For each dataset, there is a specified price or number of credits required for purchase.
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Every premium dataset comes with a specific price, considering the added value it delivers to the
user. For each premium dataset, a "request price" button is available (this is stated in the FAQ
section but I did not find it).
There is no limit in the number of datasets labelled as free which one can download. However,
beyond 10 downloads the user is requested to give information about how the datasets are going
to be used. Once the user replies, an additional 10 downloads are granted for free.
Type and size of data and data applications
Eden Library includes a wide range of agrifood datasets such as: plant pests, plant diseases,
weeds, and healthy plants. The images were acquired using various styles (Proximal, UAV upon
request) and various sensors (RGB, thermal, multispectral & hyperspectral upon request). For
each dataset, the number of images and MB are stated.
Data acquisition models
The datasets are created by EdenCore Technologies PC. Contribution from others may be an
option in the future.
Main pain points
High quality data is a prerequisite for effective AI tools.
(2) Business Model Elements
Co-created value-in-use
If we assume the end user is the grower who will use AI enabled tools for better crop protection
management, his contribution of images will help train these new tools and make them more
effective.
Roles in value co-creation (co-creation activities)
● Customer/End User: growers
● Orchestrator: EdenCore Technologies PC
● Core partner: Contributors of images such as growers, agronomists, other research
universities specialising in crop protection management, public authorities that study pests
or diseases on crops, agrochemical companies, crop vendors, etc.
● Enriching partner: researchers, tech industry and agri food business experts can use the
datasets in various contexts based on their needs and their approaches to enhance crop
management methods. Other experts (or individuals who want to train) in deep learning
techniques can create better algorithms to analyse the images having access to the
notebooks.
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Figure 32: Eden Library – Business model radar
IV.2.1.15. Hortivation Hub
IV.2.1.1.15. Governance
General Information
Name
• Hortivation Hub
• Website: https://www.hortivation.nl/en/
Legal form
• Foundation
Geography
• Focus on the Netherlands (since the focused greenhouse farming
contributes a big share to the national agricultural sector,
including also greenhouse building companies and technology
providers) but other actors from western / central Europe are also
included.
Sector
• Greenhouse technology and farming
Scope
• Data Sharing for planning, building and running greenhouses to
ensure efficiency and high quality within the different processes
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• Making it easier for all actors participating in the process by
enabling them to share data which are relevant for the next steps
in the process
Business approach
• Data Exchange for the participating actors
• Data Driven Decision Making and management of greenhouses
Lifecycle phase
• Until now prototyping
• Pilot phase starting in 2024
Organizational Governance
Participants
• Planers
• Suppliers of materials
• Building companies for greenhouses
• Farmers using the greenhouse
Organizational mode
• Very decentralized approach, the data sharing is “only” facilitated
by the Hortivation Hub and no data is stored by the Hub itself or
used for any purposes of its own.
• In Addition to that, being a foundation is beneficial for the
perceived neutrality of Hortivation Hub as a data intermediary.
Due to the fact that it does not need to make profits and the hub
is funded by the participants and all actions are organized on a
pre-competitive level, the trust in the foundation is high.
Governing bodies
• The main governing body is a board which makes the central
decisions. It is a board of seven CEO's of some of the partners
and companies involved within the foundation. The members of
the board are selected by the board itself.
Collaboration
• In accordance to the general agreement, every member of the
hub has to sign every data sharing transaction, which has also to
be approved by the data rights holder.
Onboarding
• For being part of Hortivation Hub you have to sign a general
agreement with the foundation.
• In assistance to that Hortivation plans to offer an onboarding
process for new members in order to make it easier (technically
or organizationally) for new actors to join.
Data Sharing Governance
Data characteristics
• Very different types of data, which are necessary to build
greenhouses effectively and efficiently in accordance to the
plants which are to be grown there.
• Examples: Planning data, data of suppliers for raw materials,
hard- and software, machinery, weather data, sensor data
Roles regarding data
sharing
• A basic principle is that the companies who use the data hub are
in the “driver seats”.
• Every participating actor in the hub can be data owner or data
user as long as they sign the general agreement with Hortivation
hub.
Technical foundation
• The data hub is not a big database, but it facilitates data traffic
between connected parties and systems; an example of this data
traffic is the exchange of information about the greenhouse
design between CASTA and SIOM (two existing planning tools
for greenhouses).
• But external systems and platforms can also be linked, so that
each party has access to numerous other parties and systems
with one link.
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• To understand and use each other's data, a common language is
used: the Common Greenhouse Ontology (CGO). By translating
data to this CGO - by means of connectors - the receiving party
can receive the data and easily read it into its own applications.
Data transactions
• See section on roles regarding data sharing
Legitimate purpose
• See section on roles regarding data sharing
Risk and change
management
• The interoperability of various systems is challenging. The
common greenhouse ontology (CGO) aims to tackle this issue.
Governance of Collaborations with other DSIs
Current state
• Not planned yet
Future plans
• Not planned yet
Key Insights on Governance
- Focus on a specific use case: A key success factor is the focus on a specific use case or
a specific branch of the agricultural sector (in this case planning, building and running
greenhouses) in order make necessary stakeholders and the needed types of data more
clear for all involved stakeholders of the growing data ecosystem.
- Knowledge about the landscape within the specific use case: Knowing and actively
involving important players of the specific use case / specific branch in which the data
sharing initiative is located encourages the legitimacy of the data intermediary (in this
example the Hortivation hub).
- Usage of existing technical solutions: Building up on existing technical solutions and
strengthening interoperability between them (without being exclusive to new systems) is
very important in order to scale the ecosystem faster.
IV.2.1.15.2. Business Model Elements
Co-created value-in-use
Automatization of the operational and construction processes in the Greenhouse horticulture
sector.
[Hortivation Hub makes strategic innovations available to the sector quickly and, together with the
companies, to guarantee the top position of the Netherlands in the field of 'integrated growing
systems'. The data can be released via the Hortivation Hub to suppliers and other parties involved.
The Hortivation Hub is a major step in digitization and facilitates collaboration in the sector. The
simulations of the performance of greenhouses are also combined with practical data so that
further product optimization can take place.]
Roles in value co-creation (co-creation activities)
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● Customer/End User: Growers, Suppliers, Builders
● Orchestrator: Hortivation Hub
● Core partner: Growers, Suppliers, Builders
● Enriching partner: ICT Companies
Figure 33: Hortivation Hub – Business model radar
IV.2.1.16. iDDEN
IV.2.1.16.1. Governance
General information
Name
• The International Dairy Data Exchange
Network (iDDEN)
• iDDEN GmbH
• Website: https://www.idden.org/
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Legal form
• Limited liability company with several shareholders
• Under German law („Gesellschaft mit beschränkter Haftung“)
Geography
• International focus beyond Europe
• Involved farmer-owned organizations from Australia, Austria,
Belgium, Canada, Denmark, Finland, Germany, Iceland,
Luxembourg, the Netherlands, Norway, Sweden and the United
States
• iDDEN further operates in other countries through partnerships
with dairy data organizations.
Sector
• Dairy farming (including data from 20 Mio. Cows from 200.000
dairy herds collected by 35 their recording organizations)
Scope
• iDDEN provides the iDDEN Hub: data exchange services that
seamlessly integrate on-farm dairy equipment, devices, and
software with national dairy information systems and databases.
• Typical use case: Dairy farmers want to integrate their data on
their cows, e.g. from machinery, such as feeding robots and
milking robots from various manufacturers and from sensors on
the cow’s health, and to reduce manual effort for data exchange
with partners (e.g. veterinarians) and for data handling.
Business approach
• Data exchange with data sovereignty for farmers
• Exclusivity for the application area: usage of iDDEN services is
open to all actors in the dairy industry, e.g. milking equipment
manufacturers, dairy farmers and milk recording organizations.
• Companies pay fixed yearly fees, the amount is depending on the
size of the company (at least 10.000 EUR).
• Farmers pay a small usage fee per shared data set via the
cooperative they are involved in.
• Low pricing for the provided services. This targets the availability
of sufficient financial freedom to make the necessary
improvements/investments instead of profits.
Lifecycle phase
• iDDEN GmbH was founded in 2020, based on previous
networking and standardization activities of 3-4 years in ICAR
ADE (animal data exchange working group).
• Current state: Operation phase, striving for break-even-point
Organizational Governance
Participants
• Dairy farmers: Members of the cooperatives (shareholders of
iDDEN) and main users of the data pipelines
• Milking equipment manufacturers (OEMs and smaller ones) are
mainly involved as users of the iDDEN data exchange service.
• Milk recording organizations and other farm service providers are
also either part of the shareholding cooperatives and / or users.
• Other stakeholder groups, e.g. pharma industry, veterinaries, who
want to exchange data with dairy farmers.
• Partnership and shareholder positions are restricted, usage is
available for all actors from the dairy industry (if they meet the
requirements and are willing to pay a fee).
Organizational mode
• Centralized international organization, built on an existing
network
• iDDEN is based on national cooperatives, utilizing the trustful
relationship of diary famers and their cooperatives as well as the
local networks and contact points (national or even regional)
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• Support on the national and regional level including on-site
services for the farmers (e.g. setup of data sharing authorizations
and obligations).
Governing bodies
• The seven shareholders of iDDEN GmbH are cooperatives,
which have to represent dairy farmers with at least one million
cows and are required to buy their share of the iDDEN GmbH
and its assets.
• Some cooperatives have formed partnerships to fulfill the
requirements, so that the current seven shareholders comprise a
total of 35 organizations.
• Strategic partners: collaboration agreements with milking
equipment manufacturers to enable fast and cost-effective data
exchange (currently six partners, Oct. 2023).
• Two of the three major manufacturers of milking equipment with
high shares of the international market are strategic partners.
Collaboration
• Each shareholder has one vote in the decision-making process.
• Therefore, each shareholder provides the same financial
contribution to the iDDEN company.
• Shareholder have monthly video calls and one or two yearly
meetings of shareholders in person.
• Interested stakeholders have to become customers to participate
in the data exchange and pay fees for this.
• Communication is done in collaboration with the shareholding
cooperatives.
Onboarding
• Onboarding process for farmers is done by the local
cooperatives.
• Farmers can receive personal support from the shareholder or
customer organizations with the setup of the software and
definition of rules for data sharing (who gets which data).
• iDDEN supports companies with the API implementation and
setup of their applications to comply with the standard (a lot of
effort also due to the different national standards).
• iDDEN labels users of the ICAR ADE data standard “iDDEN
ready” (certification service).
Data Sharing Governance
Data characteristics
• Animal data from milk cows and related to dairy farming e.g.,
health information on cows, amount of milk per cow
• The content of the shared data is not relevant for the iDDEN Hub.
• Standardized data format (International Committee for Animal
Recording animal data exchange, abbr. ICAR ADE)
Roles regarding data
sharing
• Farmers retain control of their data through authorized access.
• Companies, such as milk recording organizations, provides the
standardized APIs in their software applications with support from
iDDEN.
• Harmonization and standardization of dairy data is the key
argument for companies to become an iDDEN customer, to
reduce the effort for implementation of the new standards.
• Cooperatives support farmers to setup the fitting rules and
controls for data sharing.
Technical foundation
• iDDEN implements the ICAR ADE standard for dairy farming and
has ongoing implementation processes for improvement.
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• Data exchange with both farm‐based and cloud-based software,
providing the needed support, tools and collaboration network.
• iDDEN provides the open, unified standard for the data exchange
and implements the technical solutions to apply it in the data
exchange process (iDDEN Hub infrastructure).
• The iDDEN system also comprises authentication systems to
protect the data exchange and data sovereignty of users: each
customer of iDDEN receives a specific iDDEN ID to ensure clear
identification and safe data transfer.
Data transactions
• Agreements for data exchange are setup bilaterally between the
contract partners (such as farmer and dairy data organization,
milk recording organization or milking equipment manufacturer).
• Even if a company is already customer of iDDEN and pays fees,
it still has the responsibility to create the conditions for data
exchange (find partners and set up contracts).
• Farmers do the settings for the data transactions according to the
agreements.
• iDDEN is only responsible for providing the technical
infrastructure according to the agreed standards (e.g. ICAR
ADE).
• Transferred data is only stored for a short time (max. 24h) in the
data hub to ensure a safe transfer process and prevent data loss
effects due to connection problems.
• Data transactions are logged for documentation purposes (meta
data e.g. which partners did exchange how much data).
Legitimate purpose
• Focus on data exchange without analysis of the content
• Farmers have data sovereignty: they define the legitimate
purposes for the exchanged data as well as which data is
provided to whom
Risk and change
management
• Technical change management: ongoing improvement of the
ICAR ADE standards, implementation and involvement of
shareholders in current standardization processes
• The focus on data exchange without data-based services is
inherent risk management, as companies and farmers are both
sensitive towards unauthorized data access and data analysis.
Governance of Collaborations with other DSIs
Current state
• No current collaborations with other DSIs
Future plans
• No plans for collaborations with other DSIs stated
Key Insights on Governance
Focus on core business and sector: iDDEN focuses on data exchange support and providing
the needed infrastructure as core business in the dairy farming sector. This goes hand in hand
with a clearly defined scope of application on dairy farming and this sector’s needs. The
concentration on this is possible, as the cooperatives are taking over the local processes
(onboarding, information provision and support), financial flows, data collection and interface to
the farmer. The DSI service builds on the international ICAR ADE standard, which helps further
focusing and is a means for trust with involved participants. They have reduces efforts and cost
for implementation of the new standard, resulting in benefits for the whole ecosystem.
Basis network of cooperatives results in leap of faith and rapid international establishment: The
iDDEN network is still in the build-up phase, but already established on the international market.
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This is due to the integration and active utilization of the resources and networks of the
cooperatives. They provide a trustworthy basis for the iDDEN GmbH, allowing many
stakeholders and customers to use its data transfer services. Without this basis, a startup
company would definitely struggle to cover the international dairy market and to gain the trust of
farmers.
Similarities to the CEADS situation: iDDEN is an international active DSI built on a network of
cooperatives. These organizations are the direct contact for farmers and local stakeholders.
This network structure shows similarities to the initial situation for the CEADS based on the
network of DSIs, as characterized also in the CEAD. Therefore, the governance structure of
iDDEN could be particularly interesting for the design of the CEADS.
Indirect involvement of farmers: The data-providing farmers, who want to reduce data silos and
the resulting manual transfer effort, were the point of origin for the iDDEN initaitve. However, the
involvement of farmers in the DSI’s governance is minimal and indirect; the direct stakeholders
are companies, especially software providers. The financial contribution of the farmers depends
on their usage behaviour (low cost rate per data set / cow) and thus directly correlate to their
personal benefit. The financial side is taken over by the cooperatives, which further simplifies
the application for farmers. The iDDEN business model for companies, on the other hand, is
based on costs for participation.
Clear regulations for balance between neutrality and openness: In iDDEN, it is particularly
apparent that the regulations provide a clearly defined framework for the openness and
neutrality of the organization. The specifications for shareholders, such as the restriction to
cooperatives and size specifications, result in a high degree of neutrality for the iDDEN
management. In addition, they are established actors from the existing agricultural landscape,
who are perceived as trustworthy. The neutrality and trust-building is also supported by the facts
that the pricing model of iDDEN covers costs instead of making profit and the service offering is
a pure data transfer, without processing and gaining insights into the data (complementary
offers from cooperatives are possible).
On the aspect of openness, the governance structure enables a strong involvement of dairy
equipment manufacturers through the role of strategic partners, as they are important, but not
neutral actors in the market. All other players in the dairy industry can also participate in iDDEN
as customers.
Clear separation of tasks between iDDEN and involved actors: iDDEN depends on their
shareholders to cover a lot of processes. The cooperatives and their regional locations are the
direct point of contact to the farmers and act as their personal contact on the ground. They know
the local needs and conditions. The financial aspects with the farmer also go through the
cooperatives and the authorization management for data sharing is done through existing tools.
There are several levels for the contractual arrangements: Each actor involved has a contract
with iDDEN, but for data exchange, additional bilateral contracts between the parties are
required.
IV.2.1.16.2. Business Model Elements
Co-created value-in-use
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Farmers can retain control of their own data and reduce costs related to data transfer, while they
get real-time valuable information from other stakeholders.
Roles in value co-creation (co-creation activities)
● Customer/End User: Milk recording organizations (farms and milking equipment
manufacturers)
● Orchestrator: iDDEN
● Core partner: iDDEN technology support company, called Mtech
Figure 34: IDDEN – Business model radar
IV.2.1.17. John Deere Operations Centre
(1) DSI basic information
Main goal of the DSI:
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Agricultural/ Construction and other applications’ machines and operations at their peak
performance.
Domain of engagement:
● Agriculture
● Landscaping and Grounds Care
● Lawn and Garden
● Construction
● Forestry
● Compact Equipment
Country of the DSI owner: International
DSI owner identification:
● Ownership: The DSI is owned by John Deere operations centre
● Degree of openness to new participants: Widely opened
● Industry scope: Lawn & Garden, Electric, Agriculture, Construction, Landscaping &
Grounds Care, Golf & Sports Turf, Forestry & Logging, Engines & Drivetrain, Electronics,
Government & Military Sales, Attachments, Accessories, & Implements, Rental Sales
Offerings of the DSI: Data applications - the primary function is to contribute to the automation
of processes and operations with regard to end users’ equipment.
Business model: Software as a service - John Deere operations centre uses the end customers’
data to provide them with digital solutions for their business
Revenue model: Freemium/ Licensing| Data availability in some components, while other
components are expandable with a subscription model or licence.
→ Note included in the terms and conditions:
Some tools, features, and software available on our websites, web
apps, and mobile apps may not be usable or accessible to you or
through your organization unless you purchase and maintain a
separate licence (“Premium Features”). Not all Tools and Premium
Features are available in all markets or compatible with all our
services and products. Access to some Tools and Premium Features
is restricted to John Deere dealers and distributors. Please contact
your John Deere dealer or review our websites to learn about the
Tools and Premium Features that may be available to you.
Type and size of data and data applications:
Data related to the equipment used for various operations / Data size info
Data requested include: type of crop, field, location, variety, product etc
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Suite of tools: Agrian Prescription Creator, Crop Planner, Maintenance of equipment, Work
planner for the equipment, Analysis and report on completed work across multiple fields, Field
analyser, Machine Analyser, Machine Reports, Bulk Boundary Creator, Files to send to the
machines, Data Manager, Software manager, Remote display access report.
● Crop protection recommendations
● Location data of machinery
● Optimization of equipment according to particular characteristics
● Field data management
● Access rights sharing
● Stock management
● Financial management
● Crop view and seed planning
Data acquisition models: Intermediaries – integrators, aggregators and marketplaces
End user connects with other companies when he creates an account. Creating connections
allows the user to bring his data together and help him make the right decisions with information
from the sources of his trust. John Deere platform enables them to understand who has access
to their data and to help them manage them. This is part of the company’s ongoing commitment
to safeguard the users’ information.
Note:→If you have allowed a partner to access your data, the partnered organisation may connect
your data with software outside of John Deere. When this occurs, your organisation will receive a
notification letting you know data is now accessible by the software. You can manage what data
is available to the software company in Team Manager. You may also manage in Team Manager
which partnerships have permission to allow data access to an external software solution.
Main pain points: John Deere’s digital suite of tools provides accessibility to the users’ equipment
data in one place—whenever they need it, wherever they are.
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→Quickly and easily manage equipment health, access support, and ensure your machine is
operating at its peak performance
Figure 35: John Deere digital suite
(2) Business Model Elements
Co-created value-in-use:
To offer automation to the end users’ equipment management
Roles in value co-creation (co-creation activities):
● Customer/End User: Ag Retailer, Ag service provider, Dairy and Livestock producer,
Farmer/ Producer, Software developer, Constructor (Earth & Moving, Quarry & Aggregate,
Roadbuilding, Underground Utilities), Forester, Landscape contractor, Golf Course
administrator, Property Owner (Commercial property owner or Homeowner)
● Orchestrator: John Deere operation centre
● Core partner: Partnered organisations (ABACO Farmer, ABAX Smart Connect, Adapt-N
etc.)
● Enriching partner: Technology providers (most likely OKTA:
https://www.okta.com/integrations/john-deere-service-advisor/ )
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Figure 36: John Deere Operations Centre – Business model radar
IV.2.1.18. JoinData
IV.2.1.18.1. Governance
General information
Name
• Name: JoinData
• Website: https://join-data.nl/
Legal form
• Cooperative association (with 9 member-owners)
• Statutes
• Non-profit
• Registered at the Chamber of Commerce
Geography
• Netherlands and Belgium (read: https://join-
data.nl/nieuws/joindata-breidt-uit-naar-belgie/).
Sector
• Dairy farming, arable farming, livestock
• Farm input supply (breeding, feed, agricultural machinery)
• Food processing industry (dairy, meat)
Scope
• Digital infrastructure facilitating the safe exchange of data
between organizations and businesses in and related to the
dairy, arable and pig-farming sectors.
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• JoinData does not store the data and does not edit the data.
JoinData only ensures that data is accessed and safely
distributed, after the farmer has given an authorization. JoinData
is not in charge of the data.
Business approach
• Farmers pay a fixed annual fee of 50 euros (excl. VAT) for the
use of the digital infrastructure and the MyJoinData authorization
system (portal).
• Data re-users also have to pay a fixed annual fee of 50 euros
(excl. VAT) and a variable fee of 0.14 eurocent per data-
message transported to them.
• Data holders do not have to pay a fixed annual fee. This may
change in the future.
• Every organization can join if it signs and complies with the terms
and conditions set by JoinData.
Lifecycle phase
• Started in 2011 as a pilot (research) project called Smart Dairy
Farming, with public and private financing.
• In 2016 three farm cooperatives continued their collaboration and
decided to end the project and establish a foundation under the
name “Smart Dairy Farming”.
• In 2017 two other organizations (LTO and accountancy
organization) joined. The foundation became a cooperative
association. The goal was to accelerate the data sharing and
use.
• In 2018 the name was changed to JoinData. More organizations
joined: Rabobank, Cosun, and AVEBE.
• Currently, in 2023, in total 9 organizations are member-owner of
the digital data sharing infrastructure.
Organizational Governance
Participants
• The General Assembly of the association is composed by the
following members members:
o CRV - breeding company
o Agrifirm - feed company
o LTO - Dutch farmers association
o Rabobank
o POV - pig farmers organization
o Cosun - agricultural cooperative sugar beets
o AVEBE - agricultural cooperative potatoes
o VION - international meat processing company
o NZO - Dutch dairy industry association
o Flynth - big accountancy firm
• The agricultural robot manufacture Lely is as well in the advisory
board data.
• 4 Dutch dairy farmers are in the farmers’ advisory board and in
the audit committee:
o NAV - Dutch Arable Farmers Union
o LTO - Dutch Farmers Organization
o NMV - Dutch dairy farming lobby organization
o POV
Organizational mode
• Central organization with a General Assembly of 9 members as
the highest authority (i.e. decision making body).
Governing bodies
• General Assembly: Policy-making, strategic (long term) planning,
investment decision making. Meets twice a year.
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• Supervisory Board: Independent people i.e. not related to the
members.
• Daily management by company of the cooperative association.
The company consists of director supported by staff.
Collaboration
• General Assembly: decisions are met by voting.
• Supervisory Board supervises the daily management.
• Audit committee monitors and controls the authorization process
and the data exchanges; ensures that the position of farmers is
strengthened.
• Advisory Board Data: advices daily management on trends
regarding data.
• Advisory Board Farmers: represents the voice of farmers,
advises the daily management.
Onboarding
• The General Assembly decides whether an organization may
become a member of the association.
• Participation is voluntary. But farmers feel forced to participate
by some of their suppliers and/or customers.
• Organizations who want to make use of the digital data highway
and to get potential access to farmers data stored by data
holders, must first sign an agreement with the company of the
association JoinData. This counts for roles: farmers, data re-
users, data-holders, third parties.
• You have to agree and comply with the terms and conditions set
by JoinData.
• Then the technical connection is made (API).
• Then data flow takes place (after authorization of the farmer).
Data Sharing Governance
Data characteristics
• 13 categories of farm data that are exchanged. Data about:
sustainability, fertility of the livestock, financial management,
livestock health, herd registration and its locations, farm
identification, key figures for the farm, laboratory tests, logistics,
specific dairy-related (e.g. milk quality), parcels, slaughter data,
nutrition of livestock.
Roles regarding data
sharing
• Farmers are in control of re-use of data i.e. farmers decide
whether farm data, stored at servers of data holders, can be
shared with organizations and business who wants to re-use that
data.
• Farmers are asked for data authorizations (permission or ending)
by farmers through MyJoinData dashboard.
Technical foundation
• Access with a standardized electronic identification for logging in
to JoinData. Assurance (security) level EH2+.
• The data delivery via APIs is the responsibility of JoinData.
Data transactions
• Farmers are requested by data re-users.
• Farmers authorize their data holders.
• At the beginning of 2022, more than 7,700 participants were
logged in to MyJoinData.
• In total 260 data suppliers were connected to JoinData in 2022.
And there were 70 parties that reused data provided by the
participating data holders, after authorization of farmers.
• In 2022, there are almost 490,000 active authorization given by
farmers in JoinData. And nearly 10 million data messages were
sent along the digital infrastructure of JoinData.
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• Also more than 15,000 authorizations were revoked in 2022.
Legitimate purpose
• Each farmer is free to decide whether if they participate and use
JoinData. And JoinData does not oblige the farmers to give an
authorization.
• In practice, many agricultural input suppliers and processing
industry, with which farmers do business, force the farmers to
participate in JoinData. The dairy industry for example does that
in the case of the Kringloopwijzer.
Risk and change
management
• JoinData has an ISO 27001 certification which means that the
data flows (data traffic) through the online infrastructure is
guaranteed as safe. ISO 27001 is a globally recognized standard
in the field of information security.
• JoinData states it is not responsible for the services of the
application providers, based on the data they received through
the digital highway.
• JoinData aims for its digital data highway to be available for
98.8% of a full year outside of maintenance.
Governance of Collaborations with other DSIs
Current state
• Contacts have been made with DJustConnect (Belgium) and
Agrirouter (Germany).
• Considering setting up a pilot with Agrirouter to explore potential
business case(s).
Future plans
• The goal is to become the national digital data highway for all
agricultural sectors (including arable, poultry, horticulture, etc.).
• Also in countries outside the Netherlands; besides Belgium, also
connects to Germany.
Key Insights on Governance
Data Intermediary: JoinData does not explicitly characterize itself as a data intermediary
according to the data governance act, but the collected information and characteristics are
compatible with this role.
Trust: Due to its characteristics (organizational and legal) JoinData can be seen as a trusted
data sharing infrastructure (or platform) and there will come many trusted platforms.
Trust is currently regulated at national or regional level. Then there is no longer any need to try
to regulate it at European level with e.g. the European Code of Conduct for farm data sharing.
That code is too general for that and can be interpreted in different ways which does not create
trust among farmers and others to share data.
Data sovereignty: The governance of JoinData is decided and executed by its 9 members which
are mainly agricultural cooperatives. And data sovereignty is very important to these agricultural
cooperatives.
Cross border data sharing: It will still be a challenge to get national DSIs to work together when
they apply different principles, governance and business models.
Data holders are also data re-users: Many 260 data holders who are affiliated with JoinData, are
often also data re-users of other data that they want to re-use to improve their services for
example. They therefore combine multiple roles. They also see themselves fulfilling multiple
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roles. This raises the question, what this means for their rights and obligations regarding data
sharing from the perspective of the upcoming Data Act.
JoinData continues to extend collaborations with other stakeholders: Like in April 2023, when
JoinData and GS1 – which develops standards for electronic communication between
businesses – signed a letter of intent to create a single source for organizational and location
data of agricultural companies, such as stables, plots, etc.. JoinData and GS1 want this data to
be exchanged more efficient and to be centrally recorded based on the GS1 standards. By
linking locations and organizations with products, the product can be identified from country to
customer (see: https://join-data.nl/nieuws/gs1-nederland-en-joindata-lanceren-samenwerking-
voor-locatieregister/ ).
IV.2.1.18.2. Business Model Elements
Co-created value-in-use
Data from separate suppliers can easily be shared and managed with the application providers.
This way, farmers gain actual insights and are able to make smarter decisions.
Roles in value co-creation (co-creation activities)
● Customer/End User: farmers, suppliers, government, accountant, customers
● Orchestrator: JoinData Platform
● Core partner: Developer(s) who can work with agricultural data via JoinData
Figure 37: JoinData – Business model radar
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IV.2.1.19. ProAgrica
(1) DSI basic information
Main goal of the DSI: Superior products and services connect and empower industry participants
to address their key needs around trading, productivity and compliance. Proagrica solutions are
built around the key competences of data connectivity and data analytics delivering seamless
supply chain management, customer insight and engagement, essential for businesses looking
to improve their value offering and expand in the modern marketplace. Through the use of a
central platform the entire value chain can benefit from data sharing efficiently and at a lower cost
compared to one-to-one links.
Domain of engagement: Agriculture and animal health industries
Country of the DSI owner: Germany (However, it is a global provider of data-driven support
DSI owner identification: Proagrica is part of RELX Group and is a digital supply chain platform
that electronically connects agricultural trading partners, automating and streamlining the order-
to-cash process.
Offerings of the DSI: Raw farm data turned to information and analytics for professional and
business customers. Stakeholders can easily gain access to large numbers of retail partners,
removing the need for costly and time-consuming point to point EDI connections, and reducing
manual order processing.
Business model: Technical Enablers
Revenue model: Licensing: In order to use the central hub, companies must also enter into an
agreement with Proagrica. The big advantage of a central platform is that a company only needs
to make one connection with the platform to exchange messages with all other companies that
are connected to the platform. The central platform is efficient and saves costs compared to one-
to-one links that otherwise have to be made between individual companies. It is possible to link
an ERP system to the central hub, but companies without an ERP system can also use a web
portal. It is contractually guaranteed that only the chain parties involved in a transaction have
access to the relevant data. Commercially sensitive information is protected to the highest
standards and privacy is assured.
Type and size of data and data applications
Data relating to crop input, animal health market, trading, productivity and compliance,weather
stations, soil sensors, in cab information systems .
Data acquisition models - Intermediaries - integrators, aggregators and marketplaces or
Public procurement of data
● Proagrica has completed the acquisition of CDMS (Crop Data Management Systems), a
provider of compliance data and solutions to support agronomic recommendations and
crop production decisions. The acquisition of CDMS reinforces Proagrica’s ability to
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support the agriculture industry, enhancing Proagrica’s customer solutions and
engagement with the broader set of downstream and speciality crop partners within the
agricultural supply chain.
● RELX Group, a global provider of information and analytics for professional customers
across industries
Main pain points: Proagrica needed a massively scalable data refinery environment for ingesting
and transforming structured and unstructured big data from diverse sources.
(2) Business Model Elements
Co-created value-in-use
Superior products and services connect and empower industry participants to address their key
needs around trading, productivity and compliance.
Proagrica’s solutions are built around the key competences of data connectivity and data analytics
delivering seamless supply chain management, customer insight and engagement, essential for
businesses looking to improve their value offering and expand in the modern marketplace.
Proagrica's global network is focussed solely on agriculture and animal health and now spans
every sector of the marketplace, giving businesses the opportunity to connect once for a seamless
electronic link to customers and supply chain partners. In its basic form, connectivity increases
productivity by reducing inefficiency. For example, businesses relying on manual operations
devote extensive resources towards maintaining their ERP (Enterprise Resource Planning)
system, without taking into account the loss of productivity caused by human error or oversight.
The more exciting vision is the opportunity for businesses to have a single view of the customer,
through finance, CRM and logistics creating an incredibly powerful single source of truth. The
subsequent opportunities for productivity gains across the whole supply chain are immeasurable
as inefficiencies are removed and resources are focussed upon the activities that create the
greatest added value.
Roles in value co-creation (co-creation activities)
● Customer/End User: businesses in the agricultural and animal health markets (Input
manufacturers, wholesale and distribution, AG retailers, agronomists, farmers/growers,
food processors, pharmaceutical corporate vet groups)
● Orchestrator: Central platform
● Core partner: Data Networks
● Enriching partner: Data scientists
● Actor: Developers
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Figure 38: ProAgrica – Business model radar
IV.2.1.20. ZEROW
• triangulation interview – shorter profile, longer / broader key insights
IV.2.1.20.1. Governance
General information
Name
• ZEROW
• Website: https://www.zerow-project.eu/
Legal form
• Research Project
• Planned: Association
Geography
• EU (Living Labs in different EU Member states, such as
Slovenia, Romania, Lithuania, Spain and the Netherlands)
Sector
• Food trade and supply-chain including different agricultural
goods but also, e.g., packaging, manufacturers of
equipment and food banks.
Scope
• Sustainability optimization, food waste minimization along
the supply chain in the agri-food-sector
• Not one specific area but 11 different living labs in different
areas, for example:
o Farm-to-Fork Flow Monitoring & Assessment
o Mobile food valorization as a service
o Ugly food identification etc.
o Food banks
• Data Sharing Initiative, in the sense that the project is aimed
at reducing food loss and food waste.
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Business approach
• It is an open question, whether there will be one data space
or several. This decision will follow the needs of the problem
to solve.
• Challenge: all use-cases are R&D projects themselves, so
data-sharing is a secondary concern.
• A business oriented approach to governing is practiced and
developed in the project (“design approach”, business
model canvas and governance canvas together).
• Their research is a “what if this worked” thing, preparing for
a scaling of the applications.
Lifecycle phase
• Exploration phase
Organizational Governance
Participants
• Large ecosystem: Farmers, suppliers, retailers, end-users,
food banks, packaging industry, etc.
Organizational mode
• Numerous organizational modes for different use-cases is
being considered
Governing bodies
• Existing bodies in the internal DSI’s organization and their
roles
• Different tasks that have to be accomplished by a
governance have been outlined:
o Decision-making
o Risk management
o Monitoring
Collaboration
• Multi-stakeholder governance is to be preferred in many
ways (as it stipulates trust).
• The lifecycle phase is crucial when setting up a governance
(different structures for different lifecycles).
Onboarding
• No information available
Key Insights (according to the interview):
o For Individual DSI Governance:
▪ should not be prescriptive – e.g., implementation of democratic values should be
discussed as an option, but it should be assessed whether it fits the users’ needs
▪ the project developed “minimal viable governance” in order to get going faster;
▪ The lifecycle matters on how to stipulate trust
• Initially, there are mostly human factors (decision making, trustworthiness of
the DSI, necessary effort) the most relevant;
• once a platform has a significant amount of users, people trust the verdict of
the others and just agree to the same agreement;
• it is important that people can join and leave freely
• In many cases, a multi-actor governance will be more credible.
▪ Also governance will be standardized when DSIs become more common-place:
“there are fifty other DSIs run like that, it’s a good way”
▪ DSIs should be organized in a “business-like” way to be financially self-reliant
(business model development alongside governance development from the set-off is
key)
D2.1: Multi-stakeholder Governance Scheme and
Business Models for Agricultural Data Spaces
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Diffusion : Public
▪ agricultural sector specifics
• peculiarity: very private data, more distrust, very diverse state of digital
transformation within the sector
• farmers are under a lot of pressure to oblige regulatory demands, which
makes them secretive (decision-making not always rationally business
driven):
• EDIH should become active in stipulating digital transformation including
participation in suitable DSI
• getting inspired by I4.0 (manufacturing) where machinery providers are the
drivers of data-spaces, not the end-users (more skeptical stakeholders should
consent, more tech-savvy stakeholders (technology providers) should take
the lead in shaping the DSI)
•
o For collaboration between DSIs:
▪ There should not be a “dataspace of dataspaces”;
▪ for meta-data spaces and their governance, the same principles apply as for
individual ones (no prescriptive approach);
▪ once the value is there, the governance should be designed to fit the users’ needs,
and again, the establishing phase is critical for shaping the governance.
▪ use-case specific: for a successful collaboration between different DSIs there must
be a use-case specific added value for the participants
