Potential for data and IT to improve resliience in the food supply chain.

1. The potential for data and IT
technology to improve
resilience in the food supply
chain
Krijn Poppe

2. Krijn J. Poppe
 Business economist, Erasmus University Rotterdam 1981
 Former Chief Policy Analyst Wageningen Economic
Research
 Rli – Council for the Environment & Infrastructure: member of
the council
 Fellow and former Secretary-General of the EAAE
 Involved in its journal management (ERAE, EuroChoices,
Q Open)
 Member South Holland Council for Environmental Advise
 Chair of the Agro-Expert Council Flevoland
 Member of the Governing Board of SKAL (Dutch Organic
Control Body)
 Co-owner of a family arable farm.

3. Content of the presentation
Resilience of the food chain
ICT and the platform economy: some examples
A framework for analysis (with the milking robot as
an example)
Current bottlenecks: platforms for data exchange
Policy recommendations

4. Resilience Food chain: 2 weak spots
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 / market failure?
Or system failure and lack of transformative capacity?

5. Institutional economics: regulating mechanisms
5
Culture (religion,
customs, norms)
Formal rules
(laws)
Governance
(contracts, market
organisation)
Resource
allocation
(incentives, outcomes)
Formal rules
(laws)
Culture (religion,
customs, norms)
Governance
(contracts, market
organisation)
Resource
allocation
(incentives, outcomes)
© Williamson, 2000

6. Unchanged
Unchanged
Larger
Healthier and
more sustainable
Role of the State
Behaviour
Business
as Usual
Government
Control
Regional
Communities
Green
High Tech
Transformation
Dutch Food
System:
post-
Covid-19
scenarios

7. 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

8. Disruptive ICT Trends:
 1980s: Personal Computer, 1990s: Internet, 2000s: mobile phones, 2010s:
social media
More to come:
 Mobile/Cloud Computing, 5G – smart phones, wearables, incl. sensors
 Location-based monitoring - satellite and remote sensing technology,
geo information, drones, etc.
 Internet of Things – everything gets connected in the internet
(virtualisation, M2M, autonomous devices)
 Digital Twins – run a real time digital replica to control a process
 Block Chain – Tracing & Tracking, Contracts.
 Big Data - Web of Data, Linked Open Data, Big data algorithms
Resulting in a platform economy
With a high potential for unprecedented innovations!
everywhere
anything
anywhere
everybody

9. Services
Platforms
Data
Physical objects
© Rli, 2021
IoT
AI
big data
sensors

10. 10
IoT in Smart Farming
cloud-based event
and data
management
smart sensing
& monitoring
smart analysis
& planning
smart control
© Sjaak Wolfert et al., WUR

11. Robots in market gardening could
lead to strip farming on ecological
principles
And IoT platforms
are available in
dairy farming

12. 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
© Cor
Verdouw
et al.,
WUR

13. Improved data management: digital farm dashboard with
kpi’s (© EU project MEF4CAP >> Farm Sustainability Data Network)

14. “Citizen health cycle”
Citizens,
Patient groups
Employers
Health and Food industry
(Local) government
Farmers in short supply
chains (marketing plans)
Citizen Data Platform (© Food, Nutrition and Health RI)
Data
Analytics
Smartwatch
(iWatch)
Data needs
and research
questions
Datawarehouse
Food Consumption
Activity / Lifestyle
Health
Fitness / Sport plan
Menu / Diet Advise
Health Treatment plan
Other (external) data
sources
(smart training data,
retail loyalty cards
Digital Patient Dossier
social media, other apps)
Smartphone
(FoodProfiler)
Citizen is owner
and provides
accces
Behavorial Insights
Policy Advise
Policy makers
and industry
Research (output)
CDP
Governance

15. A framework for analysis in research and policy
1. Identify trigger for analysis
•Effect on animal welfare, landscape, energy use
2. Determine societal relevance:
chances, problems
•Lower food prices, better competivity, effect on animal welfare etc.
3. Which patterns of change could happen?
•Increase in scale: more globalisation, larger farms.
•Entanglement; dairy becomes entangled with IT, issues of cybersecurity
•Diffused relationship production-consumption (prosumers): no? (cf. airbnb, etsy)
•Ownership: platform as a service, decisions move from the farm to the robot company
•Business model: efficiency of scale / market milk from the individual cow ? / data analysis as business
© Rli, 2017

16. 16
Culture
Formal
rules
Gover-
nance
Resource
allocation
• The milking robot is a next step in the industrialisation of farming with
the farmer as a dependent franchise. This could conflict with the view
of the independent farmer and the farm as steward of the countryside
• More control of the production process could support current
legislation on animal welfare laws, nitrate directive implementation
etc. Are new rules on data ownership necessary (prevent lock in)?
• Industry organisation could change. Decisions move from farm to
algorithms created in the city. Robot company can become a breeding
company too. Farmers become more dependent on a platform, will
their dairy company / vet / feed supplier/farm advisor have access to
the data? Is the 3rd party data added to the platform?
• Economies of scale lead to lower cost price of food. Energy use (and
CO2 emissions?) increase. Better planning of milk flow in dairies?
Data adds value but is not priced.
4. Zoom in: analyse the effect on regulating mechanisms:

17. Chain governance changes, ICT is a driver
(©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. 18
• Datafication: the mechanisms that organise the access to data by
farmers, platform owner, third parties including the government.
• The effects of a change in power relations In the food chain: is the
resilience increased (either by strengthening the position of the
farmer or moving the decision making to a stronger link in the
chain – that is also easier to regulate)
• The effects of a change in power relations for the rural economy
and sustainability management: does the local government
become dependent on a foreign platform supplier in case of
regulating a local farm (e.g. Uber makes it hard to regulate the
local taxi-market)
5. Zoom out: Reflect on broader context
and relation to other trends

19. ICT bottlenecks in Dutch farming:
connectivity – compatability
Services: some available (e.g. VRA) but not much intelligence
or advanced user-interaction (e.g. vrirtual reality, digital twins)
embedded yet.
Platforms: More than 25 in Dutch arable farming, mainly
developped in the interest of the owner, not the farmer
Data: it is impossible for farmers to bring their data together in
an easy to use dashboard. Data governance unclear and
reluctance with farmers to share
European farms typically use machines from different vendors
(AEMs) and use contractors
© C. Kempenaar et al., WUR

20. There seem to be 2 types of platforms
Agricultural Technology Platform Data Sharing Platform for Sustainability Mgt
Operated by one providor (John
Deere, Claes, Lely Dairy Robot)
Farm management information system /
certification / accounting
Users: large farmers, contractors
Data linked to business secrects, IPR
Relevant for all farmers to deal wit “red
tape” and to run sustainabiliy programs
Data in family farms linked to privacy
Problems:
• Industrialisation, increase scale
• Farmer becomes franchiser
• Vendor lock in
• Lack of competition
Farm oriented dashboard hardly exist (are
created for the interest of others)
Why do FMIS / Accountancy not scale up or
ERPs not scale down
• A business model problem?
• How to mix public and private data?
Government reaction:
• Promote start ups
• Support frontier research like AI
• Regulate algorithms (sustainability)
• Regulate competition (e.g. data
portability / number of vendors)
Government reaction:
• Create a dashboard with data locker for
farmers as infrastructure (utility) governed
as a data trust/cooperative
• Oblige the use of UBL in paper work

21. Platform economy
requires
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
• Biggest issue: data
governance: do we need
data trusts / cooperatives ?
law & regulation
innovation
geographic
cluster
horizontal
fulfillment
Vertical

22. Recommendations for policy makers
Support innovation on new services.
Base policies on smart metering.
Platforms: Support farmers to create ABCDEF Data Trusts.
Regulate algorithms to include sustainability objectives
Data: Support development data standards.
Open data policy in government
Oblige food chain partners to use UBL on invoices
Oblige farmers and others to make (environmental) data open
ICT Infrastructure: 5G, nearby networks (Lora).
Government:
Develop knowledge
to understand what
is going on (HRM)

23. In conclusion: does it make us more resilient?
Input industriesFarmerFood processorConsumer Retail
• For a resilient society we need to accept that transitions are part of life
• Given the outcome in resource allocation: change the governance
• New technologies help in bringing that change.
• Digital technologies could make farming more smart, more efficient,
more sustainable. And support consumers in food-lifestyle-health issues.
• More attention to platforms and data governance are needed, to make it
work and understand the effects
• The longer term effects of the platform economy on the (organisation of
the) food chain and its resilience are unclear: use the research
framework.

24. references
Lutz Goedde, Joshua Katz, Alexandre Menard, and Julien Revellat:Agriculture’s connected
future: How technology can yield new growth. McKinsey & Company, October 2020
Kempenaar, C. et al: Management samenvatting eindrapport fase 1 PL4.0, July 2020
[https://topsectoragrifood.nl/project/af-18101-precisielandbouw-4-0-op-naar-data-gedreven-landbouw/]
Perez, C. (2002) Technological Revolutions and Financial Capital: The Dynamics of Bubbles and
Golden Ages. Edgar Elgar, London
Krijn J. Poppe, Sjaak Wolfert, Cor Verdouw and Tim Verwaart: Information and Communication
Technology as a Driver for Change in Agri-food Chains in: EuroChoices vol 12. Nr. 1, 2013 pages
60–65
Krijn Poppe, Sjaak Wolfert, Cor Verdouw and Alan Renwick: A European perspective on the
economics of big data in: Farm Policy Journal, Vol. 12, no. 1, autumn quarter 2015 p 11-19.
Krijn J. Poppe, Marc-Jeroen Bogaardt and Tamme van der Wal: The economics and governance of
digitalisation and precision agriculture. Paper for European Parliament’s Science and Technology
Options Assessment Panel, 2016.

25. references
Poppe, K.J. (2020) Covid-19 will change the Agri-FoodSystem – but how? in: EuroChoices (in
press).
Rli, Technologie op waarde schatten, The Hague 2017
Rli, Digitaal Duurzaam, The Hague, forthcoming 2021
R. Mansell and W.E. Steinmueller: Platform Economics, Elgar, Cheltenham, 2020
Peter H. Feindt: Challenges and opportunities of digitalisation in the agricultural value chain – A
governance perspective. Keynote at the EurAgri conference Digital Transformation of the
Agricultural Value Chain – Opportunities, Challenges and the Role of Science. Humboldt-
Universität zu Berlin, December 2. 2020

26. krijn.poppe@wur.nl
www.wur.nl
kjpoppe@hccnet.nl
Thanks for
your attention