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UC 44 — DSS-IPM

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Key Information
Title
Decision Support Systems and data-based solutions for Integrated Pest Management
Acronym
DSS-IPM
Coordinator
Julius Kühn Institute (Germany)Research Institute
Duration
24 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Health, Pests, Diseases & Protection
Sectors
Arable cropFruits
Data Types
Earth observation dataFarm Management Information Systems (FMIS) dataTest and experimental facilities (TEF research) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.7 Multi-layer geospatial data tool with API

3.1.3 Data marketplaces and cooperatives in agriculture

  • 3.1.3.4 Discoverability & composability of services

3.2.1 Enhancing functionality of and generating input for DSS including FMIS

  • 3.2.1.1 Data layers & algorithms for FMIS services
  • 3.2.1.3 New satellite imagery & ground sensors for DSS
  • 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

3.2.2 Farm modelling systems

  • 3.2.2.2 Novel forecasting & prediction methodologies
Themes
AI / ML / Decision SupportPlant protection & pest/disease
Partners (1)
  • Julius Kühn InstituteP1
    Coordinator

    GermanyResearch Institute

#OrganisationCountryType
P1Julius Kühn InstituteCoordinatorGermanyResearch Institute

Timm.Waldau@julius-kuehn.de; Burkhard.Golla@julius-kuehn.de Introduction Integrated Pest Management (IPM) is a key component of sustainable agriculture and climate resilience in the European Union (2009/128/EC). Increasing digitisation in agriculture enables Decision Support Systems (DSS) to support IPM implementation through data-driven recommendations. This Use Case explores opportunities to enhance existing DSS solutions and extend their applicability across regions and member states within the AgData partnership. The work builds on ongoing activities related to pest forecasting, biodiversity monitoring, EO, and AI- supported analytics. Methodologies The Use Case combines EO data, field monitoring, pest and phenology models, biodiversity indicators, and FMIS-compatible data flows to support IPM-related DSS. Existing tools and services will be assessed for interoperability and transferability. Weather, landscape, and ecological infrastructure data will be integrated into harmonised web services and user-oriented digital platforms. Output Expected outputs include improved DSS components for pest and disease risk forecasting, interoperable data services, EO-derived indicators, and demonstrator tools supporting farmers, advisors, researchers, and policy stakeholders. Recommendations for cross-border adaptation and implementation will also be developed. Impact The Use Case supports sustainable and climate-resilient agriculture by improving access to data- driven DSS and strengthening biodiversity-oriented farm management. It contributes to reducing pesticide dependency and supports the objectives of the European Green Deal, Farm to Fork Strategy, and AgData SRIA priorities. What I can offer • Expertise in EO-supported pest and disease modelling • Access to existing DSS tools, datasets, and monitoring infrastructures • Experience with interoperable geospatial web services and FMIS integration • Collaboration on testing and validating transferable DSS solutions What I need • Collaboration with partners interested in testing DSS solutions in new regions • Exchange of datasets, methodologies, and best practices • Support for harmonisation and interoperability of agricultural data services • Feedback from end users and stakeholders to improve usability and transferability

Source: Annual AgData Use Case Summit 2026 booklet.

Models & Macro data
Statistics Registers data
Public administration data
  • 3.2.2.3 Whole-farm & landscape environmental impact
  • 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

    3.2.4 Data-based solutions for addressing environmental challenges

    • 3.2.4.4 Areas for biodiversity & pollinator conservation
    • 3.2.4.5 Continuous soil & water sensor monitoring

    4.3 Uptake & innovation management (R&I activities)

    • 4.3.1.3 User-friendly data platforms
    • 4.3.1.11 Promote open science