The digitization of society makes data more important. This is enhanced by the Internet of Things (IoT) in which a variety of devices, sensors and the like are connected via the Internet together and deliver real-time data. Many companies see data as the way to generate new business, also in the agri-food sector. However, the added value is only created when multiple data sources are combined, aggregated and analyzed. The sharing of data between companies in the chain is therefore a critical success factor. Although several initiatives to share data have been initiated, this development seems to be stagnating. Now the agribusinesses are mainly developing their own proprietary platforms, resulting in a maze of platforms which makes it technically difficult to exchange data between different systems and devices. This causes rather more administrative burdens than they reduce. Exchange of data between platforms and applications is currently inhibited because of discussions about ownership of data, privacy, fear of concentration of power in the chain and the lack of clear business models where added value of data sharing is distributed among the various stakeholders. Now it seems that the benefit to the primary producers - the farmers - is minimal and the agri-business seems to benefit most. This creates resistance to sharing data that inhibits innovations that ultimately serve the interests of the farmer and the competitiveness of the whole agri-food sector. DATA-FAIR breaks this deadlock and will accelerate innovation by hosting several large-scale trials with companies and Wageningen Research in which applications are developed where data between various platforms is shared and value is created. In these trials, the farmer plays a central role as a main supplier and manager of data. He or she determines who may use which data and under what conditions. Digital permissions will play an important role enabled by a central register (e.g. AgriTrust). Also, special attention is paid to the development of attractive and transparent business models and good organizational embedding (governance) so that the results will continue to exist after the project. DATA-FAIR doesn’t create a new platform itself, but uses existing building blocks and will help improving these if necessary. Here one can think of open application interfaces (APIs) and standards to link platforms and databases. DATA-FAIR builds on experiences with existing data hubs such as EDI-Circle and AgriPlace.

1. DATA-FAIR - Value creation by data
sharing in agri-food business
Sjaak Wolfert – scientific project coordinator
23 February 2017, Wageningen

2. Internet of Food and Farm 2020:
• EU-H2020 budget: 30 M€
• Led by Wageningen University &
Research
• 5 trials: arable, dairy, fruits,
vegetables, meat
• 19 use cases demonstrating IoT
business cases
• 14 EU member states
• Project structure and approach
identical to DATA-FAIR

3. smart
sensing &
monitorin
g
smart
analysis &
planning
smart
control
Advancement of smart farming
(BIG)
DATA
exchange

4. Involving the entire supply chain network
and beyond
Smart Farming
Smart Logistics
tracking/& tracing
Source: Hisense.com
Domotics Health
Fitness/Well-being

5. Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
5
3. Smart, connected product
+
+
+
2. Smart Product
1. Product

6. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
6
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system

7. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
7
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system
Your company

8. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
8
5. System of systems
farm
management
system
farm
equipment
system
weather
data
system
irrigation
system
seed
optimizing
system
field
sensors
irrigation
nodes
irrigation
application
seed
optimization
application
farm
performance
database
seed
database
weather data
application
weather
forecasts
weather
maps
rain, humidity,
temperature sensors
farm
equipment
system
planters
tillers
combine
harvesters
4. Product system
Your company
Farmer: how many
platforms must I use?
Developer: on how many
platforms should I offer
my solution?
Platform owner: how
many connections do I
need to maintain?

9. Battlefield of IoT, Big Data and Farming
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
Cooperatives
Open Ag Data
Alliance
...
AgBusiness
Monsanto
Cargill
Dupont
...
Tech
Companies
Google
IBM
Oracle
...
Ag Tech
John Deere
Trimble
Precision planting
...
Tech
Start-upsFarm
Tech
Start-ups
Data
Start-upsVenture
Capital
Anterra
Founders Fund
Kleiner Perkins
...
Farm

10. Discussions
 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

11. Towards business/software ecosystems

12. DATA-FAIR:
Open Software
Ecosystem
Stakeholders
Platforms
Apps + services
Knowledge models
Governance
Business models
Data sharing
Overall vision
Farmer
Open Architecture & Infrastructure
Event-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer

13. DATA-FAIR Objectives – back on the
ground...
 Develop a few applications based on data-sharing - as a
proof of concept
 Get some experiences with data-sharing between
different organizations
● What are technical issues to be solved?
● What governance structure and business models do
we see/need?
● Who is willing to share data with whom?
 On te longer term: ‘blueprint’ for technical and
organistional infrastructure – the ‘glue’

14. Open Software and Data Ecosystem
WP1 Trials & App-development
Lean multi-actor approach
WP4 Ecosystem Development
WP3 Governance en
Business Modelling
WP2 IoT- en data-
integration
WP5ProjectCoordination&
Management
Project approach and structure
3. Evaluatie
1. Co-design en
voorbereiding 2. Implementatie
P1
P2
Large
scale
P3

15. Open Software and Data Ecosystem
WP1 Trials & App-ontwikkeling
Lean multi-actor approach
WP4 Ontwikkeling Ecosysteem
WP3 Governance en
Business Modelling
WP2 IoT- en data-
integratie
WP5ProjectCoördinatie&
Management
Project approach and structure
3. Evaluatie
1. Co-design en
voorbereiding 2. Implementatie
P1
P2
Large
scale
P3
Pull -push Pull -push
Pull -push
Minimum Viable Product:
a product with just enough
features to gather validated
learning about the product
and its continued
development

16. Eight trials
1. Farm-specific norms and sustainability data fo
benchmarking
2. Common farmer’s authorization system
3. Improving quality, transparancy and planning for
beef production
4. Big Data analyse service for benchmarking
5. Sustainability dashboard for farmers and auditors
for compliance data
6. Diminishing administrative burden and risk analysis
7. Data-sharing strategy for contractors
8. Financial data and sustainable soil management

17. Thank you for
your attention
Questions?
17