Presentation at Studium Generale of Van Hall Larenstein Applied University in Leeuwarden.

1. Socio-economic impact of Big Data and
Smart Farming
Sjaak Wolfert – Sr. Scientist Infomanagement & ICT in Agri-Food
Credits: Lan Ge, Cor Verdouw & Marc-Jeroen Bogaardt
Studium Generale, Van Hall Larenstein, Leeuwarden, 11 May 2016

2. Take-home messages
 Agri-Food chains become more technology/data-driven
● Can cause major shifts in roles and power relations among
different players in agri-food chain networks
● Infrastructure and software development are key issues
 Significant socio-economic impacts; two scenarios:
1. Strong integrated supply chain
• farmer becomes franchiser/contractor
• limited freedom in doing business
2. Open collaboration network
• Farmer empowered through easier switch
between suppliers
• Options for direct sales to consumers
Reality somewhere in between?
F
F

3. Disruptive ICT Trends
 Mobile/Cloud Computing – smart phones, wearables,
incl. sensors
 Location-based monitoring - satellite and remote sensing
technology, geo information, drones, etc.
 Social media - Facebook, Twitter, Wiki, etc.
 Internet of Things – everything gets connected in the
internet (virtualisation, M2M, autonomous devices)
Big Data - Web of Data, Linked Open Data
High Potential for unprecedented innovations!
everywhere
anything
anywhere
everybody
everything

4. smart sensing
& monitoring
smart analysis
& planning
smart control
Closing the cyber-physical management cycle
BIG
DATA

5. Big Data involves the whole supply chain
network and beyond
5
Source: Hisense.com
Smart Farming
Smart Logistics
tracking/& tracing
Domotics Health Fitness/Well-being

6. Challenges of Big Data in Smart Farming
 Big data is more about identifying the right questions
instead of finding the right answers
 The importance of analytics (intelligence)
● ‘Actionable data’
● Integration of various data sources (intelligent
processing).
● Linking ‘small data’ systems to the application of
big data
 Addressing societal issues
● Privacy and data ownership
● Supply chain organization
● Business models – sharing costs and revenues

7. Redefining Industry Boundaries (1/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
7
3. Smart, connected product
+
+
+
2. Smart Product
1. Product

8. Redefining Industry Boundaries (2/2)
(according to Porter and Heppelmann, Harvard Business Review, 2014)
8
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
Your company
How many platforms should
users and developers enter?
How many interfaces to
maintain?

9. Battlefield of Big Data & Smart Farming
Farm
Farm
Farm
Farm
Data
Start-ups
Farming
Cooperatives
Open Ag Data
Alliance
...
AgBusiness
Monsanto
Cargill
Dupont
...
Tech
Companies
Google
IBM
Oracle
...
Ag Tech
John Deere
Trimble
Precision planting
...
Tech
Start-upsFarm
Tech
Start-ups
Data
Start-upsVenture
Capital
Anterra
Founders Fund
Kleiner Perkins
...
Farm

10. The USA battleground: Monsanto (et al.)
10
PRESCRIPTIVE
FARMING
based on
VARIABLE RATE
APPLICATION

11. Dairy Software Ecosystem
Data-driven dairy application development
Genotypic
cow data
Roughage
intake
Medicines
Milk
production
Animal
monitoring
Logistics
Dairy
products/
process
Consumer
use
Open Data Infrastructure
(privacy, security, trust)
Application
Services &
Components
Platform
Actors
Open
Software
Organization
Domain
Knowledge/
Models
Concentra
tes intake
...? ...?

12. New Business Models based on Big Data
See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014
 Basic data sales
● commercial equivalent of open data (e.g. FarmMobile)
 Product innovation
● use data to improve your product (machinery industry, e.g. John
Deere, Lely’s milking robots)
 Commodity swap
● data for data (e.g. between farmers and (food) processors to
increase service component)
 Value chain integration
● use data to control the whole chain (e.g. Monsanto’s Fieldscript)
 Value net creation
● pool data from the same consumer (e.g. AgriPlace)

13. Take-home messages
 Agri-Food chains become more technology/data-driven
● Can cause major shifts in roles and power relations among
different players in agri-food chain networks
● Infrastructure and software development are key issues
 Significant socio-economic impacts; two scenarios:
1. Strong integrated supply chain
• farmer becomes franchiser/contractor
• limited freedom in doing business
2. Open collaboration network
• Farmer empowered through easier switch
between suppliers
• Options for direct sales to consumers
Reality somewhere in between?
F
F

14. Thank you for
your attention
Questions?
More information
sjaak.wolfert@wur.nl
nl.linkedin.com/in/sjaakwolfert/
Twitter: @sjaakwolfert
http://www.slideshare.net/SjaakWolfert