Lecture given at Cornell University, NY, USA

1. Data Governance in Agriculture and Food
Krijn Poppe Wageningen Economic Research
Based on work with WUR team (Sjaak Wolfert, Cor Verdouw, Lan Ge, Marc
Jeroen Bogaardt, Jan Willem Kruize and others)
October 2017 Cornell University, NY, USA

2. Wageningen University & Research
Wageningen University & Wageningen Research

3. Wageningen University & Research
 Academic research & education, and applied research
 5,800 employees (5,100 fte)
 >10,000 students (>125 countries)
 Several locations
 Turnover about € 650 million
 Number 1 Agricultural University for the 4th year in a row
(National Taiwan Ranking)
To explore the potential of nature to improve the quality of life

5. Krijn J. Poppe
 Economist
 Research Manager at
Wageningen Economic Research
 Member of the Council for the
Environment and Infrastructure
(foto: Fred Ernst)
 Member Advisory Committee Province of South-Holland on the
quality of the Living Environment
 Board member of SKAL – Dutch organic certification body
 Fellow EAAE. Former Secretary General of the EAAE, now involved
in managing its publications (ERAE, EuroChoices)
 Former Chief Science Officer Ministry of Agriculture

6. Content of the presentation
 What is happening: disruptive ict trends leading to data
capturing
 Why does that happen now: long wave theory
 New players challenge food chains
 Effects on management
 Effects on markets
 Platforms: examples and their business model
 Effects on business models: value of data
 Effects on industry / chain organisation
 Effects for government policy

7. Disruptive ICT Trends:
 Mobile/Cloud Computing – smart phones, wearables,
incl. sensors
 Internet of Things – everything gets connected in the
internet (virtualisation, M2M, autonomous devices)
 Location-based monitoring - satellite and remote sensing
technology, geo information, drones, etc.
 Social media - Facebook, Twitter, Wiki, etc.
 Block Chain – Tracing & Tracking, Contracts.
Big Data - Web of Data, Linked Open Data, Big data
algorithms
High Potential for unprecedented innovations!
everywhere
anything
anywhere
everybody

8. DATA
CAPTURING
TOOLS FOR
BETTER
CONTROL

9. Prescriptive Agriculture
Predictive Maintenance
9
IoT in Smart Farming
cloud-based event
and data
management
smart sensing
& monitoring
smart analysis
& planning
smart control

10. Virtual Box
Location A Location B
Location
& State
update
Location &
State
update Location
& State
update
IoT in Agri-Food Supply Chains
10Drones, Big Data and

11. IoT and the consumer: food and health
Smart Farming
Smart Logistics
tracking & tracing
Domotics Health
Fitness/Well-being

12. tijd
Mate van verspreiding
van technologische revolutie
Installatie periode
Volgende
golf
Uitrol periode
Draai-
punt
INDRINGER
EXTASE
SYNERGIE
RIJPHEID
Door-
braak
Werkeloosheid
Stilstand oude bedrijfstakken
Kapitaal zoekt nieuwe techniek
Financiele bubble
Onevenwichtigheden
Polarisatie arm en rijk
Gouden eeuw
Coherente groei
Toenemende externalities
Techniek bereikt grenzen
Marktverzadiging
Teleurstelling en gemakzucht
Institutionele
innovatie
Naar Perez, 2002
Crash
2008
1929
1893
1847
1797
time
Degree of diffusion of the
technological revoluton
Installation period
Next
wave
Deployment
period
Turning
point
IRRUPTION
FRENZY
SYNERGY
MATURITY
Big Bang
Unemployment
Decline of old industries
Capital searches new techniques
Financial bubble
Decoupling in the system
Polarisation poor and rich
Golden age
Coherent growth
Increasing externalities
Last products & industries
Market saturation
Disappointment vs
complacency
Crash
2008
1929
1893
1847
1797
Institutional
innovation
Based on Perez, 2002
The opportunity for green growth
1971 chip ICT
1908 car, oil, mass production
1875 steel
1829 steam, railways
1771 water, textiles

13. Food chain: 2 weak spots – opportunity?
Input industriesFarmerFood processorConsumer Retail
• Public health issues –
obesity, Diabetes-2 etc.
• Climate change asks for
changes in diet
• Strong structural change
• Environmental costs
need to be internalised
• Climate change (GHG)
strengthens this
Is it coincidence that these 2 are the weakest groups?
Are these issues business opportunities and does ICT help?

14. Dynamic landscape of Big Data & Farming
14
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
AgBusiness
Monsanto
Cargill
Dupont
...
ICT
Companies
Google
IBM
Oracle
...
Ag Tech
John Deere
Trimble
Precision planting
...
ICT
Start-upsFarm
Ag software
Companies
AgTech
Start-upsVenture
Capital
Founders Fund
Kleiner Perkins
Anterra
...
Farm

15. Ag Start-ups in the USA
15
USA Start ups in different activities

16. Farm data harvesting initiatives

17. Content of the presentation
 What is happening: disruptive ict trends leading to data
capturing
 Why does that happen now: long wave theory
 New players challenge food chains
 Effects on management
 Effects on markets
 Platforms: examples and their business model
 Effects on business models: value of data
 Effects on industry / chain organisation
 Effects for government policy

18. Issues at several institutional levels
 Data ethics, privacy
thinking, on-line and wiki
culture. Libertarian
‘californisation’
 Data “ownership”, right to
be forgotten, Open data
cyber security laws etc.
 Platforms (nested
markets), contract design
(liability !), open source
bus. models
 Value of data and
information

19. Effect of ICT on management

20. Towards smart autonomous objects
Source: Deloitte (2014), IT Trends en Innovatie Survey
Tracking &
Tracing
Monitoring
I am thirsty: water
me within 1 hour!
I am product X at
locatie L of Z
My vaselife is
optimal at a
temperature of
4,3 °C.
I am too warm:
lower the
temperature by
3 °C
Event
Management
I am too warm: I lower
the cooling of my truck
X by 2 °C.
I don’t want to
stand besides
that banana!
I am thirsty!
I am warm!
Optimalisation
Autonomy

21. • Products change: the tractor with
ICT – from product to service
• New products: smart phones,
apps, drones: should markets be
created or regulated ?
New entrants:
• Designers on Etsy
• Landlords on AirBnb
• Drivers on Uber
New entrants:
• Direct international
sales by website
• Long tail: buyers for
rare products
• Due to ICT new options
to fine tune regulation /
monitor behaviour
• Regulation can be out of
date
• New types of pricing and contracts: on-line
auctions, dynamic pricing, risk profiling etc.
• Shorter supply chains (intermediaries as
travel agencies and book shops disappear)
• Strong network effects in on-line platforms
(rents and monopolies)

22. Effects on markets
22
• In terms of suppliers, buyers, market organisation
and market regulation (government involvement)
Make an analytical difference between:
• current (old) product markets (e.g. social effects old
producers, is gov. using up to date ICT for auditing)
• market for new products/services (milking robots,
milk with credence attributes like ‘nature-friendly’)
• new ict/data markets (e.g. platforms)

23. There is a need for
software ecosystems
for ABCDEFs:
Agri-Business
Collaboration & Data
Exchange Facilities
• Large organisations have
gone digital, with ERP
systems
• But between organisations
(especially with SMEs) data
exchange and
interoperability is still poor
• ABCDEF platforms help
law & regulation
innovation
geographic
cluster
horizontal
fulfillment
Vertical

24. Platforms as central nodes in network
economy: some agricultural examples
• Fieldscripts (Monsanto)
• Farm Business Network (start-up with Google Ventures)
• Farm Mobile (start-up with venture capitalist): strong
emphasis on data ownership
• Agriplace (start up by a Dutch NGO with a sustainability
compliance objective)
• DISH RI – Richfields (consumer data on food, lifestyle
and health)
• FIspace (recently completed EU project ready for
commercialisation via a Linux-like Open Source model)
Note the different business models / governance
structures!

25. Effects on business models: how to earn
money with data?
 basic data sales (commercial equivalent of open data;
new example: Farm Mobile)
 product innovation (heavy investments by machinery
industry, e.g. John Deere, Lely’s milking robots)
 commodity swap: data for data (e.g. between farmers
and (food) manufacturers to increase service-component)
 value chain integration (e.g. Monsanto’s Fieldscript)
 value net creation (pool data from the same consumer:
e.g. AgriPlace)
See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into
profit“, 2014

26. Basic data sales
How does it work?
- A ‘box’ collects all data
- Data is stored in a cloud
- Data is being marketed/invested
- Farmer gets a share of profit
“Farmers think their trust is violated”
Their data goes to multinationals that promise
high future yields based on big data,
while farmers have to pay for everything

27. Value Chain Integration: FieldScripts
PRESCRIPTIVE FARMING
based on PRECISION
AGRICTULTURE

28. Farmers Business Network – value net creation
• Owned by farmers
• Funded by Google
Ventures
2015:
7 million acres ground
Assessment of 500 seeds and
16 crops
Costs for farmer: $ 500 / year.

29.  Agriplace –
compliance in
food safety etc.
made easy
Two platform examples from our work
Donate to (citizen)
research
 RICHFIELDS:
manage your
food, lifestyle,
health data and
donate data to
research
infrastructure
audit
FMIS

30. Content of the presentation
 What is happening: disruptive ict trends leading to data
capturing
 Why does that happen now: long wave theory
 New players challenge food chains
 Effects on management
 Effects on markets
 Platforms: examples and their business model
 Effects on business models: value of data
 Effects on industry / chain organisation
 Effects for government policy

31. Effects on Chain organisation
31
ICT lowers transaction costs
• In social media (Facebook etc.): the world is flat
with spiky metropolises
• In ‘sharing’ platforms (peer-to-peer like AirBnb,
Uber, crowd funding): creates new suppliers
(reduce overcapacity) and users. Long tail effects.
• In chain organisation: centralisation to grab
advantages of data aggregation or more markets?
• Platforms: centralisation via data management

32. Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
32
3. Smart, connected product
+
+
+
2. Smart Product
1. Product

33. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
33
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
Is this
‘mono-equipment
system’ reality?
How to cope with
changes in industry
boundries?
How many
platforms should
users and
developers enter?

34. Disruptive scenario: Toshiba’s lettuce growing

35. Programmability: Low High
Asset specifity: Low High Low High
Contribution
partners
separable
High spot long-t. spot joint
market contract mrkt venture
Low coope- coop./ inside vertical
ration vertical contract owner-
© Boehlje ownership ship
Organisational arrangements in the food
chain are changing

36. Chain organisation changes (©Gereffi et al., 2005)
inputsEndproduct
PRICE
Shops
Complete
Integration
Lead
company
Lead
company
Turnkey
supplier
Relational
supplier
Market Modular Relational Captive Hierarchy
Low Degree of explicit coordination and power asymmetry High
Lead
company
Farmers

37.  Agri-Food chains become more
technology/data-driven
 Probably causes major shifts in
roles and power relations among
different players in agri-food chain
networks
 Governance and Business Models
are key issues
 There is a need for a facilitating
open infrastructure (scenario 2)
Two extreme scenarios:
1. Strong integrated supply chain
2. Open collaboration network
Reality somewhere in between!
2 Scenarios, with significant impacts ?

38. Governance issues
2 Scenario’s to explore the future:
 HighTech: strong influence new technology owned by
multinationals. Driverless tractors, contract farming and a
rural exodus. US of Europe. Rich society with inequality.
Sustainability issues solved. Bio-boom scenario.
 Self-organisation: Europe of regions where new ICT
technologies with disruptive business models lead to self-
organisation, bottom-up democracy, short-supply chains,
multi-functional agriculture. European institutions are
weak, regions and cities rule. Inequalities between
regions, depending on endowments.
(Based on EU SCAR AKIS-3 report that also included a Collapse scenario:
Big climate change effects, mass-migration and political turbulence leads to a
collapse of institutions and European integration).

39. Governance and data ethics issues
9
Code of Conduct

40. Data gets value by combining them
Property rights on data needs to be designed
Where do my data travel ?
Need to exercise data property rights with
authorisations
Best situation for the farmer is that (s)he has one
portal for all authorizations (like a password
manager)
Question: who is going to manage this portal?
40
DataFAIR:AgriTrust authorization register

41. Digital data flows in Dutch Agriculture
41

42. Functionalities
Use of AgriTrust as webpart
Customize permission settings via an APP
Synchronization with other registers
Registration use receivers for cost accounting
Monitoring of the use (time, etc.)
Actively approaching users for additional
authorizations
M2M connections to authorized machines (precision
agriculture).
Several parties allow bundling access.
AgriTrust

43. Benefits
One portal for the farmer where all his
authorizations are shown
Can manage from here, revoke, modify etc.
Farmer can also authorize consultant to see his
permissions
Authorization Register is picked up by the "sector“,
it does not need to be invented at 100 places
Agri Trust is an independent cooperative trusted
partner controlled by the concurrent users of the
system.
Also suitable in the SME sector.
43
AgriTrust

44. Effects on government policy
Agricultural policy
● Data sharing between government and business
● Worries on the future of the family farm
Environmental policy
● Precision measurement: internalisation
Regional policy
● Risk of rural exodus, need for ICT infrastructure
● Some regions can become a big-datahub
Competition policy
● Monopolies in platforms ?
Science & Innovation policy: enablers (+ next sheet)

45. Much is invested by multinational companies – Why should
government intervene and plan research in ICT?
• Public objectives like food security, employment, regional development are
not automatically guaranteed by the market
• Many SME (also in food and machinery industry) that underinvest in
knowledge as IPR cannot easily be protected: quickly copied in the market.
Pooling of funds make sense.
• There could be systemic bottlenecks in collaboration agriculture with
ICT-sector or logistics.
• There is a need for common pool investments (Standards, infrastructure
like ABCDEFs = Agri-Business Collaboration and Data Exchange Facility.
• There are (negative) external effects of ICT that needs attention: privacy,
data ownership, power balance, effects on small farms, remote regions…
• There are (negative) external effects in agriculture that can be solved
by ICT more attractively than by regulation (environment,
food safety, animal welfare, etc.)
• Government is user of ICT: simplification issue CAP; E-science

46. What does this mean for the AKIS ?
Big Data and other ICT developments will not only influence
agriculture but also science, research and development and
innovation processes in the AKIS.
This goes much deeper than open access and linked open data sets
in science. Where the past is characterised by doing research on
data from one experimental farm or only a sample of farms (like in
the FADN / ARMS) that results into one set of advice for everybody,
the future is characterised by doing research on data of all farms,
in real time, that results in individually customised advice for
individual farms. That blurs borders in AKIS between research and
advice and advisors will need continuous training on these
developments.
(c) EU SCAR AKIS Towards the future – a foresight paper, 2015

47. Thanks for your
attention
and we welcome
collaboration in
your projects !!
krijn.poppe@wur.nl
www.wur.nl