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UC 35 — MethData

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Key Information
Title
Methane Mitigation through Cattle Breeding
Acronym
MethData
Coordinator
Aarhus University (Denmark)University
Duration
23 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Sectors
Livestock/animal productionDairy
Data Types
Machine sensor dataFarm Management Information Systems (FMIS) dataTest and experimental facilities (TEF research) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.6 Boost data re-usability through quality control
  • 3.1.2.8 Context-based curation & curated small data
  • 3.1.2.9 Granularity through smart systems & edge compute
  • 3.1.2.10 Error processing & quantifying methods
  • 3.1.2.11 Models to increase data granularity

3.1.3 Data marketplaces and cooperatives in agriculture

  • 3.1.3.2 "Pay as you go" system for services
  • 3.1.3.3 Reward mechanisms for data sharing
Themes
Livestock & animal health
Partners (1)
  • Aarhus UniversityP1
    Coordinator

    DenmarkUniversity

#OrganisationCountryType
P1Aarhus UniversityCoordinatorDenmarkUniversity
Models & Macro data
Statistics Registers data
  • 3.1.3.5 User-adapted data payment services

    • 3.1.4 Applications of AI techniques

    • 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

    3.2.1 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

    3.2.2 Farm modelling systems

    • 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

    3.2.3 Assessment of farm performance

    • 3.2.3.1 Thematic areas for farm metrics
    • 3.2.3.2 Ambitious farm performance targets
    • 3.2.3.4 Obstacles in on-farm data collection

    3.2.4 Data-based solutions for addressing environmental challenges

    • 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

    3.2.5 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

    3.3 Data-based solutions for policy-making

    • 3.3.3 Common-approach indicators across MS
    • 3.3.4 Monitor agri-environmental conditions & GAEC
    • 3.3.8 Supplement Member States' FaST services

    4.1 Public-private synergies (R&I activities)

    • 4.1.1.3 Stock-take EU/national R&I projects (umbrella)
    • 4.1.1.5 Develop reusable data-based solutions
    • 4.1.1.6 Scalable B2G data-sharing solutions

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

    • 4.2.4.8 Frameworks for re-use of publicly-funded data
    • 4.2.4.9 Data brokerage services
    • 4.2.4.11 Test data altruism under DGA

    4.3 Uptake & innovation management (R&I activities)

    • 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