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

Food, Bioeconomy & Natural ResourcesHORIZON-RIAID: 101060884
EC Contribution
€39,548
Consortium Size
15 orgs
Start Year
2022
Summary

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. The reasons for this are varied, and include technical interoperability, business relationships between stakeholders, and social acceptability issues around data ownership and market transparency.Individual stakeholders make use of the data they generate at their own particular stage in the agri-food supply chain. 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. Equally, the lack of data sharing precedents in agriculture inhibits data owners from taking a more exploratory view of the world.Several dimensions must be considered in policy-making if a fully functioning data economy in the agriculture domain is to emerge. Such a multi-disciplinary approach is at the core of the DIVINE consortium, which encompasses technical (agriculture and ICT), markets, and social sciences expertise. It will 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 agri data. DIVINE will assess its ecosystem at the level of policy impacts, the uptake of digital technologies, and economic and environmental performance.DIVINE will promote its ecosystem and its assessments to technology providers, policy-makers, farm representatives, and various other agri-data stakeholders. It will take the first real concrete steps towards mature data markets in European and global agriculture.

Consortium (15)

Project Results (11)

Source: CORDIS, the EU research results database.

Publications (10)
A Reference Architecture for Agricultural Data Spaces: Case Study from DIVINE Project
2025 International Wireless Communications and Mobile Computing (IWCMC)· 2025DOI
Soumya Kanti Datta, Tomaz Bokan, Lara Resman
ADAPTATION OF DIFFUSION MODELS FOR REMOTE SENSING IMAGERY
2024 IEEE International Geoscience and Remote Sensing Symposium proceedings· 2024DOI
A. Ettari, A. Nappa, M. Quartulli, I. Azpiroz, G. Longo
Addressing Agricultural Data Management Challenges with the Enhanced TRUE Connector
Book of short papers - SDS 2024· 2024
Sergio Comella, Delia Milazzo, Mattia Giuseppe Marzano, Giulia Antonucci, Susanna Bonura, Angelo Marguglio
CredSSI: Enhancing Security and Privacy with Self-Sovereign Identities Approach
2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)· 2024DOI
José Álvaro Fernández Carrasco, Lucía Muñoz-Solanas, Lander Segurola Gil, Daniel Paredes-García
Deep Learning models to estimate High Resolution NDVI for multiple augmentation factors
2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)· 2024DOI
M. Zabala, I. Azpiroz, P. Gonzalez, M. Maiza
Open-source Tools and Supports to Advance Data Interoperability in the Agriculture Domain
2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)· 2024DOI
Kieran Sullivan, John McLaughlin, Christine O'Meara, Kevin McDonnell, Conor Kehoe
PIXEL-LEVEL QUALITY INDICATOR FOR IMAGE DATA ANNOTATION
2024 IEEE International Geoscience and Remote Sensing Symposium proceedings· 2024DOI
M. De La Fuente, P. Gonzalez , I. Azpiroz, M. Maiza, N. Barrena, M. Quartulli
Plant Disease Identification Using Machine Learning Algorithms on Single-Board Computers in IoT Environments
Electronics· 2024DOI
George Routis, Marios Michailidis, Ioanna Roussaki
Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture
Ecological Informatics· 2024DOI
A. Nappaa, M. Quartullia, I. Azpiroza, S. Marchib, D. Guidottib, M. Staianoc, R. Sicilianoc
Smart Farming data and IoT in support of agricultural policy monitoring
2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)· 2024DOI
Nikos Kalatzis, Marios Paraskevopoulos, George Routis, Ioanna Roussaki
Other Results (1)
Periodic Reporting for period 1 - DIVINE (DemonstratIng Value of agri data sharIng for boostiNg data Economy in agriculture)