presentation for the USDA Nifa Data Summit in Chicago on ICT in agriculture and research planning.

1. Data Unleashed: Visualizing the
Future of Agriculture, Health, and
Future Generations
Krijn Poppe Wageningen Economic Research
Based on work with WUR team and others
October 2016 NIFA Data Summit Chicago

2. DATA
CAPTURING
TOOLS FOR
BETTER
CONTROL

3. 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

4. Content: line of reasoning
 ICT developments – why now?
 Effects on management and business models
 Effects on markets
 Effects on chain organisation
 Governance: centralisation or not?
 Implications for government policy
 Implications for public research

5. Prescriptive Agriculture
Predictive Maintenance
5
IoT in Smart Farming
cloud-based event
and data
management
smart sensing
& monitoring
smart analysis
& planning
smart control

6. Virtual Box
Location A Location B
Location
& State
update
Location &
State
update Location
& State
update
IoT in Agri-Food Supply Chains
6Drones, Big Data and

7. IoT and the consumer
7Drones, Big Data and Agriculture
Source: Hisense.com
Smart Farming
Smart Logistics
tracking/& tracing
Domotics Health Fitness/Well-being

8. 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

9. 4 grand challenges: tomorrow’s business
Transport
Input industries
Farmer
Food processor Retail / consumerSoftware
provider
Logistic solu-
tion providers
Collaboration and Data Exchange is needed!
Food & nutrition
security
Climate
change
Healthy diet
for a healthy
life
Environmental
issues

10. 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

11. Effect of ICT on management

12. Effect 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

13. Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
13
3. Smart, connected product
+
+
+
2. Smart Product
1. Product

14. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
14
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?

15. Dynamic landscape of Big Data & Farming
15
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

16. USA Start ups in different activities

17. Farm data harvesting initiatives

18. 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!

19. The USA battleground: Monsanto (et al.)
19
PRESCRIPTIVE
FARMING
based on
VARIABLE RATE
APPLICATION

20. USA: Farmers Business Network
20
Farmers’ owned,
investment by Google
Ventures
Summer 2015:
FBN has aggregated data from 7
million acres of farm land across
17 states, and they’re growing
30% month over month. The
platform is currently
able to assess the performance
of 500 seeds and 16 different
crops.
Costs farmer $ 500 / year.

21. USA: Farm Mobile
21
“Farmers believe their trust has been violated”:
their data go to multinationals, that announce
big future income from big data, while they have
pay for everything.
Farmers collect
‘crop stories’
and decide where
they travel (and get a
few cents per item?)
(Anterra Capital invests)

22. Discussions among (US) farmers:
22
Code of Conduct
• Do I own my tractor? (IPR on software)
• Do I own my own data? Who has access to my data?
• Does the government have access?
• Do companies gain market power on future markets ?
• Is there a lock inn – can I take my data with me?
• Do I become a franchiser with the risks but not the
returns?

23.  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

24. FIspace: an eco-system of apps to push
data
FARMER SCANS PESTICIDES PACKAGE IN THE FIELD
APP CONNECTS BASF FOR E-INSTRUCTION,
CROP AND SOIL SPECIFIC
APP ASK METEO FOR 24 hour WEATHER FORECAST
BASF SENDS INSTRUCTION TO SPRAYING MACHINE
ON WATER / PESTICIDE RATIO >> Machine adjusts
APP CHECKS ADVISE WITH GOV.AGENCY
FARMER CAN SHARE DATA WITH GOVERNMENT,
SGS-AUDITOR GLOBAL GAP AND PUBLIC
CAN I USE
MY CURRENT
SERVICE ?
CAN I USE
MY FMS ?
DOES IT
WORK WITH
BAYER /
DEERE
DOES IT
WORK WITH
BRC /
ISAcert
Can we link apps / services in a clever way ?
Leading to a market for services (apps and
data)?
Can this market be European (not MS), so
that development costs of services (apps and
data) are shared ?

25. Towards highly integrated solutions
Platforms in the cloud of input suppliers and food processors:
• What is the scope (connect only machinery or also with chemical
companies and accountants ?)
• Reduce costs of linking individually with many other platforms and
software packages (especially in chains that are not integrated)
• Is it possible to use apps with their own business model, so that the
platform does not have to pay all their costs? >> can (non-strategic)
apps be available on several platforms?
• How to prevent that farmers complain to have to pay for basis apps
(e.g. weather service) more than once?
MyJohnDeere.com Farmers
Biz architect
bundles apps
in a platform
...
80 Accelerator
companies
Apps

26. Towards highly integrated solutions
Highly Integrated Service Solutions
• Event-driven
• Configurable
• Customizable
• Service model
Data (Standardisation) Services
AdaptEPCIS
MyJohnDeere.com
Data Standards
to connect
Business
Collaboration
Services -
Based on Open
Source Software
Farmers
Biz architect
bundles apps
in a platform
...
80 Accelerator
companies
Apps
Modules:
Single SignOn
Biz Collab.
Event Proces.
System-Data
integration
App repository

27. Value proposition
Platforms solve the issue of connecting individually with a lot of business
partners to exchange data : connect easily to apps (and data
services in apps) based on EDI-standards or let farmers / end-users
make the connection
App-developers Develop one app for different platforms
Reach a European / Global market
Governments
(and industry
organisations)
See above for your government platform (paying agency, public
advisory service etc.)
Promote innovation by a competitive market for apps with new
services
Prevent lock-inn situations for farmers and unbalanced power
relations in the information exchange in food chains
Farmers Not a direct FIspace client. Platforms using FIspace inside provide
you more choice
Software writers
in platforms and
app-companies
Helps you to be part of an open source community that cares for
sustainable food production with up to date ICT – be recognized by
your peers

28. FIspace
App Store
80 Accelerator
companies
Configure &
Use Systems
First Commercial MVP by ... ?
App developer Business Configurator End User
Advertiser
Access fee
Use Fee Use Fee
Access fee (e.g.
CargoSwApp)
Pay for app use (e.g.
Spraying Advice)
Sponsored app
FIspace Foundation
MVP – open source
My JohnDeere
365 Farmnet
Akkerweb
Dacom/CROP-R
Datalab Pantheon
ICT company Service model ?

29. Towards highly integrated solutions
Highly Integrated Service Solutions
• Event-driven
• Configurable
• Customizable
• Service model
Data (Standardisation) Services
AdaptEPCIS
MyJohnDeere.com
Data Standards
to connect
Business
Collaboration
Services -
Based on Open
Source Software
Farmers
Biz architect
bundles apps
in a platform
...
80 Accelerator
companies
Apps
Modules:
Single SignOn
Biz Collab.
Event Proces.
System-Data
integration
App repository
Is this commercially feasible?
Or is it too much a common pool
investment in a market where
everybody wants to grab a stake, over-
estimates the value of its own data and
finds it easier to builds its own website
?

30. Effects on markets
30
• 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)

31. • 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)

32. Effects on Chain organisation
32
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

33. 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

34. 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

35. 2 Scenarios, with significant impacts ?
1. Scenario CAPTIVE PRODUCT CHAINS:
● Farmer becomes part of one integrated supply chain as a
franchiser/contractor with limited freedom
● one platform for potato breeder, machinery company, chemical
company, farmers and french fries processor.
● Weak integration with service providers, government ?
2. Scenario OPEN NETWORK COLLABORATION:
• Market for services, apps and data
• Common, open platform(s) are needed
• Higher upfront, common investment ??
• Business model of such a platform more difficult?
• More empowerment of farmers and cooperatives?
F
F

36. 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).

37. 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

38. 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)

39. 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

40. 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

41. Different objectives,
methods, and public roles

42. What is going on in the European Union cs.
• EU SCAR AKIS Towards the future – a foresight paper, 2015
• ERA-NET ICT AGRI: strategic research agenda
• Future Internet PPP
• Smart AgriFood
• Fispace
• Accelerator projects: Finish, SmartAgrifood2, Fractals
• H2020: Internet-of-Farm &Food-2020: Internet of
Things (30 mln.)
• European Innovation Partnership: seminar data driven
data models (Sofia) + benchmarking
• DISH-RI en RICHFIELDS: consumer data on food,
lifestyle and health
• Plus several other projects in H2020 where ict is an
important work package (e.g. Valerie)

43. FI-PPP Programme Architecture
90 M€ 80 M€ 130 M€
Accelerators

44. Building blocks for the Future
Internet
44
Linked with the
Whitehouse’
Global City
Challenge

45. IoF2020: Overall concept
(2017-2020, 30M€ funding)

46. Elements for an Agri-ICT research strategy
• Promote data-exchange (reduce administrative
burdens, create value via combination, aggregation)
• Standardisation for interoperability; AgGateway, UN/CEFACT
• Platform(s) for data exchange
• Open data by government
• Promote innovation with new services
• Especially ict-start ups, connect them with farmers and
companies (e.g. FIware 3 stage approach)
• Internet of Things
• Big Data (use of social media data, machine learning etc.) ?
• Advisory service: “just” another player in data
exchange + update own software: go real time
• Research: support all this + real time agronomic
models.

47. Don’t forget:
• The interstates made the cars flowing, changed our way of
living more (specialisation, suburbs etc.) than the car itself.
• We need utilities in rural areas: 3G/4G/5G, but also ABCDEF
platforms to combine and aggregate data for value creation
and to create markets for apps
• It raises issues of data governance (business model, data
ownership, organisation model) as (vendor)platforms are
only linked to one part of the farm and can be natural
monopolies with lock-in effects
• Solutions (market-based or otherwise) are contingent on
situation and institutional environment

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