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