data governance in agriculture
TRANSCRIPT
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
Wageningen University & Research
Wageningen University & Wageningen Research
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
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
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
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
DATA CAPTURING TOOLS FOR
BETTER CONTROL
Prescriptive AgriculturePredictive Maintenance
9
IoT in Smart Farming
cloud-based event and data
management
smart sensing& monitoring
smart analysis & planning
smart control
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 Agriculture
IoT and the consumer: food and health
Smart Farming
Smart Logistics
tracking & tracing
Domotics Health
Fitness/Well-being
tijd
Mate van verspreiding
van technologische revolutie
Installatie periode
Volgende
golf
Uitrol periode
Draai-
punt
INDRINGER
EXTASE
SYNERGIE
RIJPHEID
Door-
braak
WerkeloosheidStilstand oude bedrijfstakken
Kapitaal zoekt nieuwe techniek
Financiele bubbleOnevenwichtighedenPolarisatie arm en rijk
Gouden eeuwCoherente groei
Toenemende externalities
Techniek bereikt grenzenMarktverzadiging
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 bubbleDecoupling in the system
Polarisation poor and rich
Golden age
Coherent growth
Increasing externalities
Last products & industries
Market saturation
Disappointment vscomplacency
Crash
2008
1929
1893
1847
1797
Institutional
innovation
Based on Perez, 2002
The opportunity for green growth
1971 chip ICT1908 car, oil, mass production1875 steel1829 steam, railways1771 water, textiles
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?
Dynamic landscape of Big Data & Farming
14
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
AgBusinessMonsanto
Cargill
Dupont...
ICT Companies
GoogleIBM
Oracle
...
Ag TechJohn Deere
Trimble
Precision planting...
ICT
Start-upsFarm
Ag software
Companies
AgTech
Start-upsVenture Capital
Founders Fund
Kleiner PerkinsAnterra
...
Farm
Ag Start-ups in the USA
15
USA Start ups in different activities
Farm data harvesting initiatives
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
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
Effect of ICT on management
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 by3 °C
Event Management
I am too warm: I lowerthe cooling of my truck
X by 2 °C.
I don’t want tostand besidesthat banana!
I am thirsty!
I am warm!
Optimalisation
Autonomy
• Products change: the tractor withICT – from product to service
• New products: smart phones, apps, drones: should markets becreated 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-lineauctions, 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)
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)
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
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!
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
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
Value Chain Integration: FieldScripts
PRESCRIPTIVE FARMING
based on PRECISION AGRICTULTURE
Farmers Business Network – value net creation
• Owned by farmers• Funded by Google
Ventures
2015:7 million acres groundAssessment of 500 seeds and 16 crops
Costs for farmer: $ 500 / year.
Agriplace –compliance in food safety etc. made easy
Two platform examples from our work
Donate to (citizen) research
RICHFIELDS: manage yourfood, lifestyle, health data and donate data toresearch infrastructure
audit
FMIS
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
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
Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
32
3. Smart, connected product
+
+
+
2. Smart Product
1. Product
Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
33
5. System of systems
farmmanagement
system
farm equipment
system
weather data
system
irrigation system
seed optimizing
system
fieldsensors
irrigation nodes
irrigation application
seedoptimizationapplication
farmperformance
database
seeddatabase
weather dataapplication
weatherforecasts
weathermaps
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?
Disruptive scenario: Toshiba’s lettuce growing
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
Chain organisation changes (©Gereffi et al., 2005)
inputs
End p
roduct
PRICE
Shops
Complete Integration
Lead company
Leadcompany
Turnkeysupplier
Relationalsupplier
Market Modular Relational Captive Hierarchy
Low Degree of explicit coordination and power asymmetry High
Leadcompany
Farmers
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 ?
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).
Governance and data ethics issues
39
Code of Conduct
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 withauthorisations
Best situation for the farmer is that (s)he has oneportal for all authorizations (like a password manager)
Question: who is going to manage this portal?
40
DataFAIR: AgriTrust authorization register
Digital data flows in Dutch Agriculture
41
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
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
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)
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
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
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