This document discusses how emerging technologies are enabling unprecedented data capture in the agriculture and food sectors. Key points include: - Disruptive ICT trends like mobile/cloud, IoT, sensors, and social media are allowing more data to be generated and shared across supply chains. - This data can power applications like predictive maintenance, prescriptive agriculture, and tracking/tracing that give new insights and efficiencies. - However, challenges also exist around data governance, privacy, ownership, and ensuring the benefits are shared widely. Platforms and policies are needed to facilitate collaboration and data exchange while building trust. - If developed responsibly, these technologies could help address issues like sustainability, public health,

1. Attention to data creates growth
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)
November 2017 Global Food Summit, Berlin

2. Content of the presentation
 What is happening: disruptive ict trends leading to data
capturing
 New players challenge food chains
 Why does that happen now: long wave theory
 Institutional change is happening
 Changes in the organisation of the food chain
 How to organise data exchange
 Next steps for business and governments

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. Big Data – the ‘official’ definition: 5 V’s
 Volume – vast amounts of data
 Velocity – different types, unstructured
 Veracity – speed of generation / transfer
 Veriaty – messiness / trustworthiness
 Value – generated by artificial intelligence
● Symbolic reasoning
● Connections modeled on the basis of
the brain's neurons
● Evolutionary algorithms that test
variation
● Bayesian inference
● Sytems that learn by analogy

5. DATA
CAPTURING
TOOLS FOR
BETTER
CONTROL

6. Prescriptive Agriculture
Predictive Maintenance
6
IoT in Smart Farming
cloud-based event
and data
management
smart sensing
& monitoring
smart analysis
& planning
smart control

7. Virtual Box
Location A Location B
Location
& State
update
Location &
State
update Location
& State
update
IoT in Agri-Food Supply Chains (digital twins)
7Drones, Big Data and

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

9. 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

10. Trends in the food chain
0

11. Dynamic landscape of Big Data & Farming
11
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-ups
Farm
Ag software
Companies
AgTech
Start-upsVenture
Capital
Founders Fund
Kleiner Perkins
Anterra
...
Farm

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. The end of the expert >> citizen science?
 Post-modernism: “science is just another opinion”
 Distrust of experts; and of elites / the powerful
 But also search for ‘gurus’ (e.g. in food consumption),
strong dogma’s.
 Commercial and competitive influences in research
(funding, need to be in the media, publications and citations as yardstick)
 Media looking for new business models (advertising goes
online)
 Single issue NGO’s, with their own ‘business model’
 Politicians exploit fear instead of reducing it; too
focussed on next vote? Quality issues in civil service?
One answer: citizen science, (digital) commons ????

14. 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?
Which institutional innovations are needed?

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

16. • 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)

17. 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

18. 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

19.  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 ?

20. 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).

21. Data gets value by combining them
Property rights on data needs to be designed
Privacy disappears, de-anonimisation with big data
techniques (profiling) becomes too easy.
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)
Governance of this portal: public, non-profit, profit?
21
DataFAIR: AgriTrust authorization register
to build trust in data exchange

22. Sustainability: Incentivise farmers
0
20
40
60
80
100
Cost price
per 100
kg milk
Income per
Family
Labour unit
solvability
(%)
Energy use
per euro output
Water use per euro output
Pesticide use
per hectare
Grazing days
Education
Surplus of
Phosphate per
hectare
Surplus of
Nitrogen per
hectare
PEOPLE
PROFIT
<< PLANET >>

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. Next steps for the agri-food sector
Climate change - a new narrative for change
● Food Policy to connect farming and consumers
● Digitalisation to support change
● Sustainability monitoring with data exchange in
the whole food chain (see the FLINT project)
● Including food, lifestyle and health data at
consumer level to support healthy lifestyles
Governments should care for ict infrastructure
including utilities like essential platforms
Replace privacy thinking by data “ownership”
(consent) thinking. Farmers and consumers should
show more ownership, companies should share.

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