Presentation on data governance with an example of the tomato chain in Nigeria, given for the World Bank, March 2018

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, Coen van Wagenberg and others)
March 2018, Worldbank Washington DC, USA

2. 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

3. Wageningen University & Research
Wageningen University & Wageningen Research

4. 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

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
 A Case in Nigeria on Food Losses in the Tomato Chain

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. 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

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

11. 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

12. 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?

13. The battlefield of Big Data in Farming &
Food
Farming
Ag
Business
FoodVenture
Capitalists
Data
Start-up
Data
Start-up
Ag Tech
Tech
Companies
Tech
Start-up
Tech
Start-up
Retail
See: Wolfert et al., Agricultural Systems 153 (2017) 69–80
Processors

14. 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
 A Case in Nigeria on Food Losses in the Tomato Chain

15. 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

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. 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

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. 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

20.  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

21. 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
 A Case in Nigeria on Food Losses in the Tomato Chain

22. 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

23. 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

24. 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

25.  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 ?

26. 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).

27. Governance and data ethics issues
9
Code of Conduct

28. 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

29. Blockchain: A single shared layer of truth?
44
Blockchain

30. Blockchain for agrifood: Report
• BCT is a panacea to all problems
(Not necessarily outperforms
existing systems or offer added
value to existing businesses)
• You can put data on the
blockchain (A blockchain cannot
store as much data as people
would expect. In many use
cases, only references to
databases are stored)
• You can keep business as usual
with blockchain and make it
better (The mechanics and
social-economic implications of
the technology are still not well
understood among most
stakeholders. Most stakeholders
not ready for a paradigm shift)

31. Effects on government policy: towards
integrated national IT-policies ?
 Science & Innovation policy
 Regional policy
● Need for ICT infrastructure, Risk of rural exodus
● Some regions can become a big-datahub
 Agricultural policy
● Data sharing between government and business
● Worries on the future of the family farm
 Environmental policy
● Precision measurement: internalisation
 Competition policy: Monopolies in platforms ?

32. 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

33. 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
 A Case in Nigeria on Food Losses in the Tomato Chain

34. Reducing postharvest losses of tomatoes in
Nigeria
Coen van Wagenberg, Christine Plaisier,
Youri Dijkxhoorn, Diti Oudendag, Jim
Groot, Bart van Gogh, Auke Schripsema,
Han Soethoudt
15-1-2018
50

35. Reusable plastic crates instead of baskets?
51
 Postharvest losses: Baskets
and reusable plastic crates
simultaneously

36. Result South-West Nigeria December 2017
More Grade A remains
when using crates
Baskets: 65%
Crates: 85%
Less loss in weight
from farmer to retailer
Baskets: 11% loss
Crates: 5% loss
52
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
VC1 -
R1
VC1 -
R2
VC2 -
R1
VC2 -
R2
VC3 -
R1
VC3 -
R2
VC4 -
R1
VC4 -
R2
VC5 -
R1
VC5 -
R2
Loss (%)
Baskets Crates
-100
-50
0
50
100
150
VC1 -
R1
VC1 -
R2
VC2 -
R1
VC2 -
R2
VC3 -
R1
VC3 -
R2
VC4 -
R1
VC4 -
R2
VC5 -
R1
VC5 -
R2
∆ qual A (%)
Baskets Crates

37. Could ICT be of help in such a situation?
 Provide growers with more advise, weather data, market
data on smart phone ? (perhaps already done?)
 Trade platform to link growers and buyers to make trade
more efficient ?
 Chip crates like EuroPool does in Europe:
● Tracing of crates (and handle deposit money)?
● Tracing and tracking / food safety?
● Starts recording yields per field / benchmarking?
 Would better quality in crates lead to more export
options?
● Link to EuropGap etc (and e.g. Agriplace) ?
● Use Blockchain for data integrity / smart contracts?

38. IOF2020 ECOSYSTEM & COLLABORATION SPACE
WP1ProjectCoordination&
Management
Our generic approach & project structure
WP2 Trials/Use cases: Knowledge & App development
Lean multi-actor approach
3. EVALUATION
1. CO-DESIGN
2. IMPLEMENTATION
P1
P2
LARGE
SCALE
P3
WP3 IoT Integration WP4 Business Support
WP5 Ecosystem Development
IMPACTASSESSMENT

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