Big Data is becoming a new asset in the agri-food sector including enterprise data from operational systems, sensor data, farm equipment data, etc. Recently, Big Data applications are being implemented, aiming at improving farm and chain performance. Many companies are refraining from sharing data because of the fear of governance issues such as data security, privacy and liability. Moreover, they are often in a deadlock or afraid to take the first step even though they expect to develop new business with data. To accelerate the development of Big Data applications, this paper analyses governance issues and introduces a set of guidelines for governance of data sharing in agri-food networks. A framework for analysis was derived from literature and used to identify lessons learned from recent projects or initiatives. From these results, a set of draft guidelines was developed. The framework and guidelines were evaluated in a workshop. The framework consists of factors that are related to governance on data sharing in networks. Internal factors are: efficiency, effectiveness, inclusiveness, legitimacy & accountability, credibility and transparency. External factors are: political, economic, social, technological, legal and environmental factors. For each of these factors, guidelines are provided in terms of: issues to be addressed, best practices and lessons learned from other projects and initiatives. It is concluded that the framework is complete in covering all relevant issues on governance in data sharing but the guidelines must be considered as a first set, which can be further improved and extended in the future. A wiki-type-of-website could help to upscale the guidelines at a global level. The guidelines could also be further refined accounting for different maturity levels of agri-food networks. The guidelines in this paper are considered to be a valuable step into the direction of solving governance issues in data sharing, which is expected to accelerate Big Data applications in the agri-food domain.

1. Governance of Data Sharing in Agri-Food
Networks: towards common Guidelines
Sjaak Wolfert, Marc-Jeroen Bogaardt, Lan Ge, Katrine Soma, Cor Verdouw
Forum on Food System Dynamics, 15 February 2017, Igls, Austria

2. Background and objective
 (Big) Data is an upcoming issue in Agri-Food
 Several projects/initiatives started/starting on sharing
data between several stakeholders
 Governance and business models are a main hurdle that
has to be taken, especially in the starting phase
Objective:
 Prepare a set of guidelines for governance of data
sharing in agri-food networks
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3. What is governance?
General:
 interactions between actors and/or organization entities
aiming at the realization of collective goals
Two inter-related processes (Soma et al., 2016; Termeer
et al., 2010):
 governing based on steering principles, on how to
influence a group of actors towards reaching collective
goals
 changing formal and informal institutional settings,
which provide shifts in incentives for governing
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4. Governance issues on data in agri-food
 Am I owning my own
tractor? (IPR on software)?
 Do I own my data? Who
has access?
 Does the government have
insight?
 Do certain companies get
much power in the market?
 Is there a lock-in situation?
Can I transport my data?
 Do I become a franchiser
carrying the risks and limited
returns?
Code of Conduct
See also: Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.-J.,
2017. Big Data in Smart Farming – A review.
Agricultural Systems 153, 69-80.
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5. Cloud DATA platform
The object system: projects/initiatives
 E.g. Smart Dairy Farming
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Farmer
Supplier C
Supplier A
Supplier B
Customer X
feed
sperm milk
milking
robot
data data datadatadata
data
data
data
data
data
data
data
data
Network
Administrative
Organization

6. DATA-FAIR:
Open Software
Ecosystem
Stakeholders
Platforms
Apps + services
Knowledge models
Governance
Business models
Data sharing
DATA-FAIR – value creation by data
sharing in agri-food business
Farmer
Open Architecture & Infrastructure
Event-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer
6

7. Approach
7
Scan literature
data-sharing (in
Agri-Food)
Scan past and
current projects
on data-sharing
Agri-Food
Workshops
(Final)
Guidelines
Scientific
Paper
Draft
Guidelines
Framework
Governance
Aspects
Literature
review
Current results:
This paper

8. DATA-SHARING
Framework for Governance of data sharing
based on literature, a.o. PESTLE framework
8
Governing possibilities
for data chain processes
(storage, transfer,
transformation, analytics,
marketing)
Institutional Setting
(formal rules, regulation &
control, perceptions, trust,
motivation, encouragement)
Stakeholder Network
External factors
Political
Economic
SocialTechnological
Legal
Environmental
Efficiency
Effectiveness
Inclusiveness
Legitimacy &
Accountability
Credibility
Transparency
Internal factors

9. DATA-SHARING
Framework for Governance of data sharing
based on literature, a.o. PESTLE framework
9
Governing possibilities
for data chain processes
Institutional Setting
Stakeholder Network
External factors
Political
Economic
SocialTechnological
Legal
Environmental
Efficiency
Effectiveness
Inclusiveness
Legitimacy &
Accountability
Credibility
Transparency
Internal factors
• Agricultural policies
• Restrictions on
cross-country
information flows
• Resource use
• Pollution
• Climate change
• Data access
• Digital divide
• Technological
developments
• Security
• Regulations on
privacy
• Public access
• Consumer rights
• Demand/supply
• Competition
• Globalization
• Cost reduction
• Profit increase
• Decision making
• Response time
• Participation:
voluntary or forced
• Enter/leave
• Who makes
decisions
• Members’ feeling
about decision-
making structure
• Trust/support in
management
• Ownership feeling
• Data Quality
• Quality of use
• Communication
• Organization of
data chain process
• Quality of
effectiveness

10. What are guidelines?
Issues that have to be addressed
● Steps to be taken
Best practices with pro’s and con’s
● Checklists
● If relevant, references to examples, templates,
etc.
Lessons learned from and references to other
projects and initiatives
10

11. Legal
Issues
 Formal contracts are needed at
data level, personal level and
product level.
 Be aware of impacts of
intellectual property rights.
 Prepare for liability in case of
data hacking.
 Do not make the legal contracts
too complicated; can be culture/
country dependent.
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Political
Environmental
SocialTechnological
Legal
Economic
Best practices
 Use a data code of practice
between stakeholders e.g.:
 New Zealand Farm Data Code of Practice
 BO-Akkerbouw: Gedragscode
Datagebruik Akkerbouw
 American Farm Bureau Federation:
Privacy and Security Principles for Farm
Data
 ...
Lessons learned:
 NZ: code is used for awareness
raising, not as a formal contract
 Micheal Sykuta (2016):
● Codes can also mystify issues on data
value, transparency, etc.
● Codes can obstruct new market entrants
and innovation
● Data transparency can influence
commodity markets

12. Conclusions and discussion
 Scope of the framework seems to be complete, but can be
further validated
 Guidelines are a first attempt and should be extended/refined
● For businesses: should not become too detailed or an
‘academic exercise’
● Setup a (post-graduate) course?
● WIKI-type of website – use power of the crowd
 Framework could account for different ‘maturity levels’
● focus more on start-up of networks (could be included in
factors e.g. ‘efficiency’)
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13. Relationship with Blockchains
 No 3rd party needed for Network Administrative
Organization  Distributed Automated Organization
● Higher transparency and credibility
● No current agri-food/ICT player is dominating
● Attractive/easy for small players to step in
(inclusiveness)
● Less personal
 Smart contracts: data is automatically exchanged
according to pre-set agreements and rules
 General: privacy and security can be better guaranteed
 ....more ideas are welcome
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14. Thank you for
your attention
Questions?
Discussion?
Contact:
sjaak.wolfert@wur.nl