AgData — EU Partnership on Agriculture of Data

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UC 57 — DAKIS

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Key Information
Title
DAKIS (Digital Agricultural Knowledge & Information System)
Acronym
DAKIS
Coordinator
Leibniz Centre for Agricultural Landscape Research (ZALF) (Germany)Research Institute
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Decision Support Systems & FMIS Integration
Sectors
Arable cropOther
Data Types
Machine sensor dataEarth observation dataFarm Management Information Systems (FMIS) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.1 Data acquisition & re-use framework
  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.7 Multi-layer geospatial data tool with API

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
  • 3.2.1.6 Multi-criteria simulation modules
  • 3.2.1.8 Business models demonstrating ROI

3.2.4 Data-based solutions for addressing environmental challenges

Themes
AI / ML / Decision Support
Partners (1)
  • Leibniz Centre for Agricultural Landscape Research (ZALF)P1
    Coordinator

    GermanyResearch Institute

#OrganisationCountryType
P1Leibniz Centre for Agricultural Landscape Research (ZALF)CoordinatorGermanyResearch Institute

Introduction: DAKIS (Digital Agricultural Knowledge & Information System) develops a scalable decision support system (DSS) for the site-specific planning, implementation and valorisation of agri- environmental and climate measures (AECM) by integrating ecosystem service (ESS), biodiversity and management data across field and landscape scales. Methodologies: DAKIS combines geodata, ESS and biodiversity models, remote sensing, AI-based monitoring and economic models within interoperable software pipelines connected to FMIS and advisory systems. Site-specific AECM placement considers factors such as erosion risk, field geometry, machinery waylines, wind direction, precipitation and habitat connectivity. Output: The system generates spatially explicit ESS, risk and potential maps together with optimized recommendations for AECM placement, such as beetle banks for soil erosion reduction or hedgerows/tree strips for wind erosion protection, while accounting for operational farm management constraints, as well as a cost-benefit ratio dashboard based on CAP as well as regional incentive policies. We can also provides insights into first implementations from model to field Upscaling Goal within AgData Our goal within AgData is the upscaling of DAKIS approaches to other European regions by adapting validated ESS and AECM workflows to different environmental and agricultural contexts across EU countries. What We Need Harmonized EU-wide datasets and information on AECM implementation, management requirements and monitoring approaches from other European countries to support transferability and cross-regional scaling. We are also looking for Agroforestry system datasets related to establishment costs, maintenance costs, workforce costs etc, as well as geospatial and yield information of such systems. Another aspect is the costs of establishing Hedges/flower strips on fields.

Source: Annual AgData Use Case Summit 2026 booklet.

Test and experimental facilities (TEF research) data
Models & Macro data
Statistics Registers data
Public administration data
Other
  • 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

3.2.5 Strategies and technologies for climate change adaptation

  • 3.2.5.1 Resilient livestock & cropping systems
  • 3.2.5.4 Transformational DSS for resilient agriculture

3.3 Data-based solutions for policy-making

  • 3.3.3 Common-approach indicators across MS
  • 3.3.4 Monitor agri-environmental conditions & GAEC
  • 3.3.7 Proposals for future CAP design
  • 3.3.9 New satellites, drones & ground sensors for policy
  • 3.3.10 Europe-wide upscaling of (precision) farming data