Protecting public values in
a digitized food and health system
Session IV Digital agenda: towards integration of novel technologies with social innovations
Petra Verhoef Paris FoodNexus Visioning Summit
29 & 30 May 2018
2
Food safety and quality
Nutrition and Health
Robust and sustainable food system
Vision of FoodNexus “Transformed EU system that is sustainable, consumer-centered and fully integrated, transparent, climate resilient, and resource efficient, and thus competitive globally”
Summit key question: What are the most urgent problems facing the food industry in the next 10 years, and how can we tackle them? Rephrased for workshop:
• How can digitization enable food industry to meet its challenges? • How can we – at the same time - ensure public values are safeguarded?
3
Three challenges for the food industry
FOOD INDUSTRY Consumer Close - yet digitized - relationship & services, personalized to needs of consumer
2
Science Knowledge ecosystem that advances innovation and accepts industry as a trusted, valuable partner
3
Supply chain Sustainable, fair*, trusted ingredients from a reliable system
1
* Referring to social responsibility
4
Science
Consumer
Examples of digital technologies that can help meet challenges:
Supply chain
Persuation via app/algoritm
Internet of Things Blockchain
Digital platforms
Big data Omics technology
Linda Kool, Jelte Timmer, Lambèr Royakkers and Rinie van Est, 2017
5
Rathenau Report Urgent upgrade: Protect public values in our digitized society
Public values Privacy
Autonomy
Safety and security
Balance of power
Human dignity
Justice
6
Example: the case of personalized nutrition services
Habits & preferences
Value Issues Privacy Can these platforms track consumers in daily life?
Is asking about mood invading ‘mental privacy’?
Autonomy Is consumer losing autonomy? Is this technical paternalism? (“we know what is good for you”)
Safety and security Are data safe, in particular sensitive biometric data? Will the food choices made be safe?
Balance of power Who sets the standards for what is good? Can consumers communicate on this with platform?
Human dignity With shoppings delivered on door mat, what does it mean for social interactions?
Justice Will a profile of unhealthy behavior or high level of disease risk factors be linked to a consumer forever?
Biometric health data
Your nutrition solution
https://meetzipongo.com/
Politics and Government
Policy develop-
ment Policy
implemen-tation
Agenda setting
Scientific community
Society, incl. (food) industry
Rights institutions
Gaps in ‘governance system’ of digitization:
Scientists flagging up right issues and
providing solutions
Responsible behavior of industry
New frameworks to protect public values 1. Translating emerging societal and ethical issues
into policy & political debate
2. Safeguarding fundamental and human rights in the digital society
3. Strengthening supervisory bodies
4. Defining new responsibilities for companies that develop (or use) digital products and services.
5. Strengthening civil society, augmenting the public’s knowledge and skills
Opposing voices from civil society
Urgent upgrade, Kool et al. 2017
8
Conclusion / inspiration for workshop
Government, industry, science and civil society must take action together to strengthen the governance landscape in the digital transformation Install governance frameworks, with ethical guidelines Increase citizen digital literacy (e.g. on data sharing, persuasive technologies) For (food) industry, trust is a key issue, and might be ensured by: Explore greater uptake of block chain-based technology for safe, fair food chain Give transparency of the digital technology used (e.g. algoritms) Build in ‘ethics by design’ (protecting values like autonomy, balance of power),
next to ‘privacy by design’ and ‘data protection by design’ in services or products
Thank you!
Digitising Food and Agriculture / ICT in AGRI-FOOD Systems FoodNexus Visioning Summit 29- 30 May 2018 in Paris
• Niels Gøtke • Danish Agency for Science and Higher
Education
• Coordinator of ICT-AGRI ERA-NET
ict-agri.eu
Future Internet
Internet of Things
Big Data
Precision Agriculture
Sustainable Intensification
More for Less Smart Applications
Agriculture 4.0
Cloud computing
Drones
Sensors
Satellites
Many players and initiatives
• DG CNCT, DG AGRI, DG RTD
• EIP AGRI
• ESA
• EIT FOOD
• ESIF and RDP
• ICT-AGRI, SmartAGRIFood, FIspace,IOF2020, E-ROSA..
• International Bioeconomy Forum
• National initiatives ( NL, UK, DK, US. NZ….)
History – Precision farming
• Focus on precision farming starts around 1990 (national initiatives, FP 3….)
• SCAR Committee, SCAR foresights
• SCAR working group (CWG) set up in 2006 on ICT and robotics in agriculture
• Cross Thematic ERA-NET (ICT-ENV-AGRI) in 2009
European Research Area - NETwork
Information and Communication Technology and Robotics for Sustainable Agriculture
ICT-AGRI-1 2009 – 2014
ICT-AGRI-2 2014 – 2017
ICT-AGRI-3 2019-
Strategic Research
Agenda
In December 2012, the ERA-NET ICT-AGRI 1 published a Strategic Research
Agenda (SRA).
This first version focused on the global challenges in agriculture, with proposals for
addressing those challenges, and a discussion of how ICT and robotics could contribute
to their resolution or mitigation.
The conclusion of this report defined the focus of calls for transnational European
research projects in ICT and agriculture, both within the ICT-AGRI project as well as
influencing other funders.
Strategic Research &
Innovation Agenda
6 years later, the use of new technologies in agriculture has grown immensely in significance
and there is widespread expectation that we are on the cusp of a “digital revolution” in the agri-food
sector, which is expected to revolutionise the primary sector, dissolve the boundaries between the
agriculture and food systems, create new markets for data, etc. And for many of these new
technologies and markets this will also require new global policies to be created. In this SRIA, we aim
to review the main current and future challenges for sustainable agriculture as well as the key goals.
In addition, we describe the state of the ICT and robotics art and trends as well as the current and
future challenges of ICT and robotics adoption in agri-food systems.
A reference for research and projects priorities for the next 10 years
Strategic Research &
Innovation Agenda
Calls for transnational projects
2010 Integrated ICT and automation for sustainable agriculture (7)
2012 ICT and automation for a greener agriculture (8)
2014 Applications for smart agriculture (with SmartAgriFood) (9)
2015 Enabling Precision Farming (8)
2017 Farm Management Systems for Precision Farming
Funded and managed by National Funding Agencies
ICT-AGRI-3 ERA-NET Cofund
• Proposal for “ICT-enabled agri-food systems” in H2020 SC2 Work Programme 2019
• Core challenges of the agrifood sector ▫ Food and nutrition security
▫ Climate change and environmental impact
▫ Social, economic and environmental sustainability
▫ New business and ecosystems.
Outline- Trends • Trends in hardware
▫ Trend 1: More sensors and UAVs (unmanned aerial, vehicles, satellites, planes…)
▫ Trend 2: More robotics ▫ Trend 3: More network connectivity
• Trends in software ▫ Trend 4: Big Data ▫ Trend 5: Open/FAIR data ▫ Trend 6: Apps everywhere ▫ Trend 7: farm to fork integration/standards
• Trends in the ecosystem ▫ Trend 8: Explosion of start-ups ▫ Trend 9: Consolidation and market dominance
Trend 1: Sensors and UAV • Precision Agriculture has gone from using
GPS (only) as a data source to many sensors: ▫ Remote sensing via satellite – Copernicus ▫ Proximal sensing on farm machinery, in the
ground, on plants ▫ Growing use UAVs to complement satellite data,
using hyperspectral cameras
• Much more agri machinery with sensors and actuators (up to 80% of new machinery)
• Major drop in prices • Challenges: Low uptake (e.g. 35% of new
spreaders have precision weighing); insufficient use of data standards; difficulty of integration with FMIS
• Major focus of PA research is robotics: ▫ dairy primarily (e.g. https://www.lely.com/ ), ▫ arable crops (e.g. http://www.handsfreehectare.com/
and https://www.deepfield-robotics.com/en/ ) ▫ horticulture (greenhouses) (e.g. http://www.sweeper-
robot.eu/ )
• Parallel to development of autonomous cars (different challenges)
• Major area for deep learning (AI) and application of Big Data methods
• Most research occurring outside agrifood (e.g. proximity sensors, image processing etc.)
Trend 2: More robotics
Trend 3: More network
connectivity • Rural areas (in EC and globally) suffer poor
connectivity (only 28% of rural population have broadband): network essential for PA .
• Major support from EC (e.g. Rural Summit 2017) • Essential for Smart Farming/Internet of
Things/Big Data scenarios • Commercial initiatives – Wide Area Networks for
IoT: ▫ LoRA ▫ Sigfox ▫ … challenged by growth of 5G
• Needed for long range monitoring of agricultural land, with low energy consumption
Trend 4: Big Data • Poster child in US: Climate Corp $1Bn
purchase by Monsanto • In EU, major development is
Copernicus Open Data ▫ Very very large data sets e.g. ERA5
climate data is 900 Tb (terrabytes) ▫ Apps already appearing e.g. evaluation
of wine using soil and meteorlogical data (http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Fine_wine_app_wins_top_prize_at_App_Camp)
• PA is a major area for Big Data with growing flow of data from sensors e.g. integration of field map, satellite, drone, seed drilling etc. data may involve 9000 data sets/over 3Gb
• Challenge: need for greater processing power
Trend 5: Open/FAIR Data • Making data Open (freely available) or FAIR (easy
available/accessible) major development • Data is new oil/Data is infrastructure • Types of Open Data for PA:
▫ Satellite data (e.g. Copernicus) ▫ Soil and Plot data (e.g.
http://www.groenmonitor.nl/) ▫ Research data sets (crop models) ▫ Commercial open data (e.g. Syngenta Good Growth
Plan)
• Major international support: GODAN project • Challenges: Huge variability in acceptance, need to
change culture, data governance rules
Trend 6: Apps everywhere (FMIS) • Precision Agriculture dominated by software …
▫ Migration from desktop (2000s) to ▫ Smartphone and tablet
• Farm Management Information Systems ▫ Integrate multiple data sources, multiple services ▫ Growing ecosystem of Software as a Service (SaaS) ▫ E.g. 365FarmNet, Trible Farm
• Plenty of standalone apps too (e.g. Virtual Vet, Wunderground)
• Challenges: Lack of standards for data integration/interoperability
Trend 7: Development of Data
Standards/Farm to Fork • Many available data standards
▫ For agricultural research data: AGROVOC/GACS, cf. http://vest.agrisemantics.org/
▫ For on farm PA: ISOBUS for machinery, AgGateway for FMIS
▫ For post farm: UN/CEFACT XML, GS1 EPCIS, EFSA, Schema.org
• Major roadblock is lack of data standards for sensors
• The promise of IoT and Big Data depends on greater uptake of data standards
Trend 8: Explosion of start-ups • As a result of:
▫ EC investment in cascading research projects (SmartAgrifood 2, FINISH etc.)
▫ Major VC start-up capital ▫ Growth of agrifood hackathons (cf. farmhack.nl)
• Many data focussed start ups for all agrifood sectors including PA
• Examples: https://gamaya.com/ (crop monitoring), http://www.agrivi.com/ (farm management), Farmeron (dairy management), Cropti, Agricolus (farm management), http://smartvineyard.com/ (Vineyard Management) …. (cf. https://angel.co/europe/agriculture/jobs to see activity)
• Tendency to consolidation now …, plus major danger of being overwhelmed by US capital (e.g. Fameron)
Conclusion • Broadband access crucial if you shall benefit from PA (new sensors,
gps systems in new machines)
• Focus on competences as farming becomes high tech (skills higher education)
• Trust (Consumers do not trust big food companies. Demand is about more than price, taste, safety and access. Consumer preferences are also about health, sustainabily, local production..)
• Data ownership / open data. Different structures in different countries. How can data be used for smart regulation?
• Many examples of digitalisation has failed. We must learn from these cases. Think about cybersecurity
Conclusion
• Remember that farming is business and farmers are economic agents
• Connect initiatives in Europe and work smart together (IOF; ESA; ICT-AGRI; EIP; EIT FOOD…..)
• Connect to countries and initiatives outside Europe (International Bioeconomy Forum / IBF, Africa…)
Bynavn, xx.xx.xxxx - Navn og efternavn - Ændres via Indsæt>Sidehoved & Sidefod
Side 22
More information from our website:
ict-agri.eu
Thank you for your attention
• Niels Gøtke
• Danish Agency for Science and Higher Education
Digitising Food and Agriculture / ICT in AGRI-FOOD Systems FoodNexus Visioning Summit 29- 30 May 2018 in Paris
http://ict-agri.eu/node/38607
PRIVATE DATA AS ASSET FOR MY OWN HEALTH, RESEARCH AND INDUSTRY
CHANCES FOR THE FOOD INDUSTRY
Jildau Bouwman
https://humanstudies.tno.nl/nrc/
PERSONAL DATA VALORIZATION AS DISRUPTOR
However … Is this value normal or high? What to do if it is too high? => Personal Advice is needed based on science!
Consumer empowerment
IS THIS FOOD HEALTHY FOR ME??
I don`t need to look to the advertisements, health claims, suggestions, package,
I don`t need consumer protection, I am empowered
I scan a product and the App tells me if this is the right product for me, based on my preferences:
Cheap / Healthy / Biological / Sustainable / Allergy AND IT MAY SUGGEST AN ATERNATIVE ….
DIGITAL NEEDS
New Ideas for Habit 2.0
• Individual data • Individual digital interaction • Privacy solutions • Data management • Study data • Knowledge • Modeling/analysis • Personal advice
DATA MANAGEMENT (FAIR)
7 | Digital health technologies
FINDABLE Machine and person findable
• Has a persistent identifier • Standardized API • User friendly interface
ACCESSIBLE For who, what, when is the data accessible (machine re • Legal Conditions (eg. CA) • Embargo • Ethical (consent) INTEROPERABLE • Format • Terminology (ontologies) REUSABLE • Minimal metadata • License • If all other points are well implemented
CONNECT ALL DATA VIA FAIR PRINCIPLES
Models
Data (individual, study) Knowledge Information
Apps
Measuring health
systems change in health economy
Health Data Cooperative as legal entity that valorizes my own health data.
Doctors
Hospitals
Research Health Service Providers Retail
Infant formula
producers Schools & daycare
Farmers market
Government Developers
food industry Education
www.mdog.nl
The real value of MY health data: how can this data work for me?
Holland Health Data Coöperatie
citizens
citizens
HEALTH VS SICK CARE
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