DemonstratIng Value of agri data sharIng for boostiNg data Economy in agriculture

Project Information

DIVINE

Grant agreement ID: 101060884

DOI

10.3030/101060884

Project closed

EC signature date 1 June 2022

Start date 1 October 2022

End date 31 January 2026

Funded under

• Food, Bioeconomy Natural Resources, Agriculture and Environment

Total cost

€ 3 954 797,50

EU contribution

€ 3 954 797,50

Coordinated by

EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON
Greece

Periodic Reporting for period 2 - DIVINE (DemonstratIng Value of agri data sharIng for boostiNg data Economy in agriculture)

Reporting period: 2024-04-01 to 2026-01-31

Summary of the context and overall objectives of the project

Agriculture is being managed more tightly than ever before and is generating more data than ever before, but the potential of a data economy in agriculture remains unexplored. However, the sharing of this data with others along the chain and its collective analysis needs more development and demonstration if more efficiencies are to be introduced and further value added to the agri-data economy. While some sharing is taking place on an ad-hoc basis, each new set of potential data sharers must start from scratch and work through the same issues common to all such arrangements. Several dimensions must be considered in policymaking if a fully functioning data economy in the agriculture domain is to emerge; DIVINE aims to build an agri-data ecosystem that incorporates existing common agri-data spaces while deploying industry-led pilots built on data sharing arrangements, to demonstrate the cost-benefit and added value in sharing agricultural data.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the second reporting period, the following main activities have been carries out and the following main achievements have been accomplished in DIVINE:

- Design and implementation of the second and third (final) release of the main Reference Archiecture software modules of DIVINE (including Agricultural Information Model+ (AIM+); interoperability support; Stakeholder Open Collaboration Space (SOCS); Agri-Data Management and Integration; Data Transparency, Protection, Trust, Sovereignty, Traceability & Usage Monitoring; Targeted agri data analytics, fusion and knowledge extraction; Agricultural domain benchmarking & KPI monitoring support; Transparent Decision Making Support for agri stakeholders; User Interfaces and Adaptive Dashboard Visualisations; etc.)
- Integration, testing and deployment of the second and third (final) release of the DIVINE technology integration tools.
- Integration, testing and deployment of the second and third (final) release of the DIVINE Agriculture Data Spaces Ecosystem (ADSE)
- Integration, testing and deployment of the second and third (final) release of the Knowledge extraction, decision support & benchmarking framwork of DIVINE
- Delivery of the initial and final release of the DIVINE validated agri data sharing policy recommendations
- Development & integration of final release of the DIVINE agri data sharing governance models, policies and regulations
- Delivery of the final release of the DIVINE agri data sharing policy framework adoption manual and guidelines
- Design, implementation and execution of the second round of the four pilots of DIVINE that validate all outputs listed above
- Delivery of pilot impact assessment, evaluation, and best practices for both pilot rounds
- Delivery of final release of the DIVINE Market Analysis, Business Plan and Exploitation activities
- Application for three patents based on the DIVINE outputs
- Implementation of extensive dissemination, collaboration and outreach activities, including the conduction of a large scale survey on data sharing and digital technologies in agriculture

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The DIVINE results that go beyond the current state of the art work lie in the following areas:

- Delivery of the DIVINE Reference Architecture that provides a reliable architectural framework for agricultural data spaces, addressing matters of data-space federation, interoperability, related policies, etc.
- Delivery, deployment and evaluation of the DIVINE agricultural data space ecosystem (ADSE) with facilities ensuring increased transparency in data sharing, data trust & sovereignty, data traceability & usage monitoring
- Delivery and usage of AIM+, a semantic agri-data model that supports semantic interoperability, while it integrates policies and regulations that concern data sharing and governance. The model and its components are involved and contribute to ongoing standardization efforts.
- Achievement of enhanced semantic interoperability and greater transparency on data sharing & usage
- Delivery of framework supporting the performance evaluation of the DIVINE facilities and the data sharing costs, benefits, risks, and added values using KPIs in four areas: (i) agronomic performance; (ii) economic performance, (iii) environmental sustainability and (iv) societal impact.
- A self-service dashboard framework (catering web and mobile devices) has been developed, capable of integrating a number of service front-ends, transformation operators and visualisation widgets, implementing proven AI-based decision making approaches providing actionable advices while users will be able to provide their feedback on the DIVINE services' performance.
- Delivery of innovative (AI-based) models involved in various agricultural operations such as the following: NDVI Super-resolution Model (Significantly improves the resolution of NDVI images, enabling more precise identification of terrain features and vegetation cover); Soil Organic Carbon Estimation Model (Estimates soil organic carbon levels using satellite imagery, a crucial indicator of soil fertility and water retention capacity); Vegetation Classification Model (Differentiates trees from other vegetation and assesses their health status, critical information for forest management and environmental conservation); NDVI Forecasting Model (Predicts NDVI values for grassland five days in advance, allowing for proactive irrigation scheduling, water management and grass clearing activities);
- Support for advanced Decision Making (via a system that integrates all data sources and model outputs to provide real-time decision support for various agricultural practices, namely grass weeding, irrigation, and soil health management), etc. In this respect, improvement in efficient farming practices towards sustainability is delivered; reduction of raw materials such as fertilisers and greenhouse gas emissions, especially carbon; delivery of near real time calculation of CAP relevant indicators based on agricultural practices recorded in the farm's calendar on a farm level and on a group of farms level; optimisation of resource usage and farming practices; etc.
- Delivery of stakeholder-validated policy framework for agri-data sharing and governance. Input collected from external stakeholders confirmed the relevance and applicability of DIVINE's policy-related findings, with the regulatory analysis and governance outputs receiving positive feedback from practitioners and policy-oriented actors.
- Contribution to future CAP discussions. Stakeholder interactions revealed convergent expectations around data governance and agricultural policy, positioning DIVINE as a potential reference framework and working example for future Common Agricultural Policy (CAP) discussions at EU level.
- Application for three patents within DIVINE: (1) Seed Selection Support for Intelligent Next Sow Decisioning (UCD/SETU) [This patent is focused on selecting seeds that best address the local conditions and historical performance of a specific farm, based on a specialised seed data repository.], (2) Yield Predictor Tuning for Seed Selection Support (UCD/SETU) [This patent describes a management system for crop planting to support highly localised yield predictions.] and (3) A Computer Implemented Method for Generating Cloud-Free Satellite Imagery and Extracting Data Therefrom, Data Processing System and Computer Program (VICOM) [This patent overcomes remote sensing surveillance challenges. Specifically, it enables the generation of cloud-free satellite imagery and the subsequent extraction of high-quality data.]