AgData — EU Partnership on Agriculture of Data

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UC 55 — VariVisie

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Key Information
Title
VariVisie
Acronym
VariVisie
Coordinator
VITO (Belgium)Research Institute
Duration
23 months
Budget
Not shown at your access level. Hidden
Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Earth Observation & Remote Sensing Products
Sectors
Arable cropFruitsVegetables
Data Types
Machine sensor dataEarth observation dataModels & Macro data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.12 Solutions for private/public interests

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

  • 3.2.1.2 Extrapolate farm-generated sensor data
  • 3.2.1.3 New satellite imagery & ground sensors for DSS

3.2.2 Farm modelling systems

  • 3.2.2.4 Farm modelling for optimal practice

3.2.4 Data-based solutions for addressing environmental challenges

  • 3.2.4.2 Prescription maps for precision cropping
Themes
Crop production & monitoring
Partners (1)
  • VITOP1
    Coordinator

    BelgiumResearch Institute

#OrganisationCountryType
P1VITOCoordinatorBelgiumResearch Institute

Introduction Variations in yield potential within and between fields, combined with largely uniform management, lead to inefficient input use and limit optimal yields. VariVisie addresses this challenge by identifying and managing field variability through data-driven insights, enabling targeted interventions and long- term soil improvement to enhance productivity, resilience, and sustainability. Methodologies • Screening fields on variability, using satellite imagery, weather, and soil data, and identification of precision agriculture opportunities • Diagnostics: combining variability maps with agronomical knowledge and farmer input to train a diagnostic AI tool to identify the cause(s) of variability • Management strategy: field trials and development of tailored remediation measures to improve yield potential • Implementation in WatchITgrow and end user training Output • Improved variability maps (API) • Variability detection protocol & diagnostic AI tool • Proposed remediation measures for selected fields • Tools and maps accessible via the WatchITgrow platform in Belgium Impact • Increased crop yields and resource efficiency while reducing environmental impact • Improved soil health and long-term productivity • Enhanced resilience to climate extremes (drought, flooding) What I can offer • VariVisie methodologies and tools to generate similar outputs for your region/country: variability maps, diagnostic AI tool, integration in WatchITgrow What I need • Opportunities to scale through follow-up projects and local partnerships • Access to local (public) data for variability screening • Engagement of local farmers and advisors to provide farm-level data to train the diagnostic AI tool / identify remediation measures

Source: Annual AgData Use Case Summit 2026 booklet.