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The Vision and the Grand Challenges of the Agri-Food Community

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Sander Janssen's presentation at the eROSA Workshop “Towards Open Science in Agriculture & Food”, a side event to High Level conference on FOOD 2030, Plovdiv, Bulgaria (13/6/2018)

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The Vision and the Grand Challenges of the Agri-Food Community

  1. 1. The Vision and the Grand Challenges of the Agri-Food Community Sander Janssen (WUR), Odile Hologne (INRA), Panagiotis Zervas (AgroKnow) and many others
  2. 2. 2 Link to Roadmap document Corresponds to Section 2 on Vision Section 3 on Grand Challenges Feedback and insights welcome through open consultation
  3. 3. 3 Food System at a turning point Multiple challenges Feeding the 9 billion Climate change Unhealthy food patterns Planetary boundaries Overall challenge = Interconnectedness!
  4. 4. 4 Three trends/developments Adoption of a systems perspective: More complicated in short term New genetic techniques Also/especially for non-commodity crops/breeds Digital Agriculture (or Data Revolution in Agriculture)
  5. 5. 5 Policy frameworks • SDGs • COP21, etc • Europe2020 • FOOD2030 • EOSC • Other: IPBES, etc…
  6. 6. 6 Food system in three components Smart farming, food security & the environment Gene-based approaches from omics to landscape Food Safety, Nutrition & Health
  7. 7. 7 Societal Scientific • Disruptive changes in food production without damage to the less favoured • Inclusive approach, using local communities • Towards new business models – agriculture as a service • Support non-intensive farming (smallholder, organic etc.) • Fair & sustainable process for farmers • Balance between supply and (qualitative) demand, e.g. nutrition • Responsible ownership of data • Improving the data value chain • Using more timely and more localized data and knowledge • To be able to serve local stakeholders and provide more precise and localized advice • E-capacity building for intermediaries, NGO’s, farmers • Opening and sharing data • Sharing of e-infrastructure (hardware, software, data repositories etc.) Smart farming, food security & the environment
  8. 8. 8 Smart farming, food security & the environment Obstacles Expectations • Knowledge gap between current scientific working practice and Open Science (reg. ICT’s, capacity, IPR, licensing models etc.) • Lack of incentives to practice OS • Lack of advocacy and education for Open Science • Lack of sharing and re-use culture • Issue of trust around big data analytics (e.g. privacy & commercial issues) • Lack of understanding of business models • Uncertainty around ownership • Uncertainty around provenance, traceability, transparency • Lack of standards & interoperability • E-infrastructures to not only support agricultural production but also the environment, livelihoods • More respect for and protection of privacy (e.g. of farmers) • Grip on data sharing and data protection • Better valorisation opportunities (monetizing, citation etc.) • More collaborative research • Easier to work on broader, cross-domain and cross-community use cases • Better access to better data and data integration tools • Improved capacity to work with e-infrastructures • “reverse science”, using data analytics as the input for new research
  9. 9. 9 Example of case study Global Agricultural monitoring and early warning systems Impact: Better predictions of famines, drought and agricultural production allows for an earlier policy and disaster relieve response. Beneficiaries: farmers, rural population Users: GEOGLAM, policy makers at national and international level, FAO, UNWFP, development banks, insurance companies Role of Science: innovation in the development and validation of methods and tools required in the fields of data acquisition, data analytics, modelling and decision support integrating agronomic, climate, soil and weather data Road to open science: Improving the availability of research infrastructures (HPC, storage, grid), Improving the availability and access to data and the capacity to work with Remote Sensing data and other data sources; Development and testing of big data analytics solutions for geospatial data.
  10. 10. 10 Cross cutting issues Scientific challenge: design methods for better targeting of farmers/consumers/value chain actors, while at the same time improving efficiency, lowering environmental burdens, improving health Overall, for the development of Open Science for food systems, we need to Share, Connect and Collaborate
  11. 11. 11 Share Across use cases, efforts required in data curation and data rescue  getting data available Beyond data: share analytics, models and the scientific process Smarter interoperability platforms: needs to be easy, not challenging
  12. 12. 12 Connect Be explicit about adopting standards Use existing ones, do not develop new ones Recommendations are needed Establish & advocate ‘best practices’ of open science Deliver impact-stories: what does open science achieve? Learning resources for capacity building
  13. 13. 13 Collaborate System of systems: Organize absorption capacity for smaller projects/initiatives to join Certify good practices Innovation incubator: scaling up useful examples Infra should be as ‘invisible as possible’ Advocate for user centric perspective of EOSC
  14. 14. CONSORTIUM WWW.EROSA.AGINFRA.EU Thank you for your attention! @H2020_eROSA
  15. 15. 15 Objectives Identify societal impacts & research challenges that benefit from an open science e-infrastructure in agri-food Identify common challenges in ICT & data that could be tackled with an e-infrastructure approach Engage a broad community of scientists with a diverse background to ensemble transformative use cases
  16. 16. 16 Societal Scientific • Developing efficient plant and cattle breeding to provide genetic solutions to the disruptive changes in food production • Breeding to support non-intensive farming (smallholder, organic etc.) • Speed-up the control of new invasive species (pests) • Providing genetic solutions adapted to the end-user needs (farmers, consumer, etc) • Helping the development of plant participatory breedings • Helping the up-scaling : from omics to population • For plant breeding, easy the extrapolation of results from lab to field(S) • Improving the characterisation of the environment components of phenotyping systems. • Develop model-assisted breeding • Providing an alternative to GMOs? • Opening and sharing data • Sharing of e-infrastructure (hardware, software, data repositories etc.) Gene-based approaches from omics to landscape
  17. 17. 17 Gene-based approaches from omics to landscape Obstacles Expectations • Available skills to take profit of the open-science approach • Shared and adopted international standards • Starting from problems: having a actual and efficient user involvement • Integrate a large diversity (type of data, cultural differences between omics and higher-scales communities, IT skills,… • Having actual interoperable systems • Involvement of private companies (which business model, which IP?) • Available innovation platforms • Different levels of progress between the plant, microbiome, and animal communities • Knowledge gap between current scientific working practice and Open Science (reg. ICT’s, capacity, IPR, licensing models etc.) • Better understanding of positive and negative impacts of openness and sharing • Easier to work on broader, cross-domain and cross- community use cases • E-infrastructures to not only favour data exchanges and analysis, but also models and training • The FAIRification should be transparent • Better valorisation opportunities (monetizing, citation etc.) • Higher virtualisation of the IT system: web services, cloud => interoperability, scaling up, traceability, security, etc • Demonstrating cases of linked data use and analytics.
  18. 18. 18 Societal Scientific • Personalised nutrition and health advice: advice consumers based on specific characteristics • Fast and targeted responses, preferably ex-ante, to food and health risks • Supply chain efficiency across the actors in the value chain • Tracking and tracing: transparency across value chain • Reducing food waste • Inclusive and cost-effective health insurance • How to connect food intake to health outcomes? (and to agricultural production)? • How to provide estimate & predicts risks as occurring in the value chain? What are appropriate responses? • What are the impacts of changing diets in terms of food-fuel, protein transition in relation to the environment, social conditions and farming? • What is optimal transparency for a supply chain? What do consumers want/need to know? Food Safety, Nutrition & Health
  19. 19. 19 Food Safety, Nutrition & Health Obstacles Expectations • Purchasing power in the value chain buys data access • Data = power = money • Lack of mechanisms of benefit sharing across the supply chain • Lack of public infrastructures that work along the supply chain • Legal validity and governance issues • Dissemination of scientific outcomes: raising sensitivity around risks and benefits • Lack of standardized vocabularies, lack of standardization. • Weaknesses in data curation and data rescue • Better understanding of positive and negative impacts of openness and sharing • Urgently need data sharing arrangements • Need for a broader innovation approach than the current step in the supply chain • Demonstrating cases of linked data use and analytics. • Collaborative models with the different actors in the supply chain

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