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 to improve farm and chain performance in agri-food networks. Still, many companies are refraining from sharing data because of fear of governance issues such as data insecurity, or lack of privacy or liability, among others. To overcome such barriers for developments with Big Data, this paper aims at: 1) analysing governance issues in agri-food networks, and 2) introducing a set of guidelines for data-sharing. Based on a literature review, a framework for analysing agri-food networks was developed, with internal governance factors (efficiency, effectiveness, inclusiveness, legitimacy & accountability, credibility and transparency) and external governance factors (political, economic, social, technological, legal and environmental factors). The framework contributes to development of a set of draft guidelines. Accordingly, for each factor, the guidelines address issues, best practices and lessons learned from other projects and initiatives. The approach developed in this paper creates a baseline for possible future developments of Big data in terms of 1) upscaling of the guidelines at a global level, 2) refining and fine-tuning of the guidelines for context specific agri-food networks, and 3) contributing to solving governance challenges in data sharing. In the future, the relevance of Big Data in the agri-food domain is expected to increase, and so are the contributions of this approach.

1. Guidelines for Governance of Data Sharing in
Agri-Food Networks
Sjaak Wolfert, Marc-Jeroen Bogaardt, Lan Ge, Katrine Soma, Cor Verdouw
Tri-conference on Precision Agriculture, 16 Oct. 2017, Hamilton, New Zealand

2.  (Big) Data is an upcoming issue in Agri-Food
 Several projects/initiatives started/starting on sharing data between
several stakeholders
 Issues on 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 and business models of
data sharing in agri-food networks
Background and objective
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3. The object system: projects/initiatives
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Cloud DATA platform
Farmer
Supplier C
Supplier A
Supplier B
Customer X
feed
sperm milk
milking
robot
data
data
data
datadata
data
data
data
data
data
data
data
data
Network
Administrative
Organization

4. 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
What is governance?
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5. 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 et al., 2017. Big Data in Smart
Farming – A review. Agricultural Systems 153, 69-80.

6. Open Software
EcosystemData sharing
DATA-FAIR - value creation by data sharing
Farmer
Open Architecture & Infrastructure
Event-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer

7. Approach
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
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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

9.  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
 ...?
What are guidelines?
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10. An example: Legal (external factor)
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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
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.

11. Main outcomes of the workshop
Do’s
 Start in closed experimentation
environment to showcase the
(unexpected) value of big data
 Make clear arrangements for the
distribution of costs and benefits
 Make it appealing to capital
suppliers as well to agricultural and
technology stakeholders
Don’ts
 promise improvements not proven
 start initiative without clear
business case for all participants
 limit access to data: without open
data no successful big data project
 share data with a 3rd party without
secured consent and guaranteed
data quality
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12.  Scope of framework seems 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’)
Conclusions and discussion
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13.  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
Relationship with Blockchains
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14. Thank you for your
attention
Questions?
Discussion?
Contact:
sjaak.wolfert@wur.nl
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