AgData — EU Partnership on Agriculture of Data

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UC 68 — SmartField

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Key Information
Title
SmartField
Acronym
SmartField
Coordinator
Københavns Universitet / University of Copenhagen (Denmark)University
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Sectors
Arable cropVegetablesLivestock/animal production
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.3 Schemes for data interoperability
  • 3.1.2.4 Reference data sets & non-discriminatory data
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.6 Boost data re-usability through quality control
  • 3.1.2.7 Multi-layer geospatial data tool with API
  • 3.1.2.8 Context-based curation & curated small data
  • Models to increase data granularity
Themes
Crop production & monitoring
Partners (1)
  • Københavns Universitet / University of CopenhagenP1
    Coordinator

    DenmarkUniversity

#OrganisationCountryType
P1Københavns Universitet / University of CopenhagenCoordinatorDenmarkUniversity
Dairy
Other
Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
Public administration data
Other
3.1.2.11
  • 3.1.2.12 Solutions for private/public interests
  • 3.1.2.13 Procedures to aggregate sensitive data
  • 3.1.2.14 Monitor functionality & product evolution

    • 3.1.3 Data marketplaces and cooperatives in agriculture

    • 3.1.3.1 Service Cloud & network of data-hubs
    • 3.1.3.2 "Pay as you go" system for services
    • 3.1.3.3 Reward mechanisms for data sharing
    • 3.1.3.4 Discoverability & composability of services
    • 3.1.3.5 User-adapted data payment services

    3.1.4 Applications of AI techniques

    • 3.1.4.1 Identify key reference/training data sets
    • 3.1.4.2 Capitalize historical satellite data
    • 3.1.4.3 Privacy law solutions for satellite imagery
    • 3.1.4.4 AI handling heterogeneous & fuzzy information
    • 3.1.4.5 Data governance for farming data ownership
    • 3.1.4.6 Digital twins of farms & environments
    • 3.1.4.7 Strengthen AI uptake; trust in AI

    3.2.1 Enhancing functionality of and generating input for DSS including FMIS

    • 3.2.1.1 Data layers & algorithms for FMIS services
    • 3.2.1.2 Extrapolate farm-generated sensor data
    • 3.2.1.3 New satellite imagery & ground sensors for DSS
    • 3.2.1.4 Take stock of existing FMIS & their uptake
    • 3.2.1.5 Interoperability & switchability for FMIS
    • 3.2.1.6 Multi-criteria simulation modules
    • 3.2.1.7 Increase profit of DSS/FMIS use
    • 3.2.1.8 Business models demonstrating ROI

    3.2.2 Farm modelling systems

    • 3.2.2.1 Take stock of existing modelling approaches
    • 3.2.2.2 Novel forecasting & prediction methodologies
    • 3.2.2.3 Whole-farm & landscape environmental impact
    • 3.2.2.4 Farm modelling for optimal practice
    • 3.2.2.5 Farm modelling for agri-environmental measures

    3.2.3 Assessment of farm performance

    • 3.2.3.1 Thematic areas for farm metrics
    • 3.2.3.2 Ambitious farm performance targets
    • 3.2.3.3 Displaying performance & MCDA trade-offs
    • 3.2.3.4 Obstacles in on-farm data collection
    • 3.2.3.5 Long-term funding strategy for indicators

    3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.1 Assess needs for environmental decision support
    • 3.2.4.2 Prescription maps for precision cropping
    • 3.2.4.3 Bridge crop/pasture yield gaps
    • 3.2.4.4 Areas for biodiversity & pollinator conservation
    • 3.2.4.5 Continuous soil & water sensor monitoring
    • 3.2.4.6 Robots/UGV + IoT for pest & weed detection
    • 3.2.4.7 Long-term experiments for soil health & C

    3.2.5 Strategies and technologies for climate change adaptation

    • 3.2.5.1 Resilient livestock & cropping systems
    • 3.2.5.2 Lessons from other biogeographic regions
    • 3.2.5.3 High-throughput phenotyping infrastructures
    • 3.2.5.4 Transformational DSS for resilient agriculture

    3.3 Data-based solutions for policy-making

    • 3.3.1 Identify data needs for policy monitoring
    • 3.3.2 Take stock of existing indicators & approaches
    • 3.3.3 Common-approach indicators across MS
    • 3.3.4 Monitor agri-environmental conditions & GAEC
    • 3.3.5 Extend Area Monitoring System (AMS)
    • 3.3.6 Methodologies to monitor compliance
    • 3.3.7 Proposals for future CAP design
    • 3.3.8 Supplement Member States' FaST services
    • 3.3.9 New satellites, drones & ground sensors for policy
    • 3.3.10 Europe-wide upscaling of (precision) farming data

    4.1 Public-private synergies (R&I activities)

    • 4.1.1.1 Establish governance structures
    • 4.1.1.2 Map data needs in public & private domains
    • 4.1.1.3 Stock-take EU/national R&I projects (umbrella)
    • 4.1.1.4 Moderate innovation ecosystem for umbrella effect
    • 4.1.1.5 Develop reusable data-based solutions
    • 4.1.1.6 Scalable B2G data-sharing solutions
    • 4.1.1.7 Europe-wide data layers from public data

    4.2 Data governance, standards and security (R&I activities)

    • 4.2.4.1 Stock-take existing data ecosystems
    • 4.2.4.2 Privacy-preserving handling of personal data
    • 4.2.4.3 Harmonised access to public-sector data for research
    • 4.2.4.4 Legal interpretation services
    • 4.2.4.5 Highlight Common European Agriculture Data Space
    • 4.2.4.6 Feedback on data-sharing policy instruments
    • 4.2.4.7 Increase trust in agricultural data sharing
    • 4.2.4.8 Frameworks for re-use of publicly-funded data
    • 4.2.4.9 Data brokerage services
    • 4.2.4.10 Trusted intermediaries under DGA
    • 4.2.4.11 Test data altruism under DGA
    • 4.2.4.12 Privacy-preserving analytics (MPC)

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.1 Evidence of the value of data technologies
    • 4.3.1.2 Communicate value to end-users
    • 4.3.1.3 User-friendly data platforms
    • 4.3.1.4 Two-way interactive e-platform & knowledge hub
    • 4.3.1.5 Capacity building & data literacy
    • 4.3.1.6 Training in advanced digital skills (DEP)
    • 4.3.1.7 Persuasive technologies for sustainable practices
    • 4.3.1.8 National mirror groups for policy uptake
    • 4.3.1.9 Connect actors for climate-adaptation innovation
    • 4.3.1.10 Business & governance models for data flows
    • 4.3.1.11 Promote open science
    • 4.3.1.12 Communication, brokerage & events