As part of the project, Synelixis installed leaf wetness sensors on existing SynField stations in NILEAS olive groves, targeting locations with a known history of olive anthracnose (gloeosporium). To date, a total of two SynField and six SynOdos devices have been installed across NILEA groves, equipped with sensors that collect real-time climatological and soil data. The recently installed leaf wetness sensors help identify and diagnose conditions that are favorable for the development of olive anthracnose (gloeosporium)

Disease development is favoured by the combination of prolonged leaf/fruit wetness, high relative humidity, and mild temperatures – conditions that are common in spring and autumn. Risk increases particularly during periods of rainfall, fog, or overnight dew, and becomes critical from autumn through harvest, especially when targeted spring plant protection has not been implemented, and when the ripening fruit is more susceptible due to olive fruit fly damage, leading to major yield losses ranging from 30% in mild epidemics to 70% in severe cases. This is why continuous monitoring of wetness duration at canopy level is essential for reliable forecasting.

Using sensor data (leaf wetness, meteorological and microclimatic variables etc), and integrating these into AI-based predictive models being developed within the AgriDataValue project, Synelixis’ digital tools translate measurements into risk indicators and send notifications/alerts when conditions converge towards an increased likelihood of infection. The aim is early warning, enabling growers and agronomists to make evidence-based plant protection decisions at the right time.

Climate stress is rapidly reshaping livestock production across Southern Europe, with Greece among the most exposed regions. Rising average temperatures, more frequent heatwaves, prolonged droughts and increasing water scarcity place significant pressure on animal health and welfare, particularly in sheep and goat systems that dominate Greek livestock farming. These systems are largely extensive or semi-extensive and therefore highly dependent on ambient environmental conditions rather than controlled indoor infrastructure.

            Animal welfare is a key determinant of meat quality and the credibility of origin, sustainability and ethical production claims. Climate-related welfare impacts are often poorly documented, as fragmented records fail to capture real-time environmental exposure. Data-driven monitoring addresses this gap by continuously collecting environmental and livestock-related parameters, enabling traceability, certification and compliance with evolving European standards. Within this framework, AgriDataValue supports a European agri-environmental data space that integrates farm-level sensing, climate information and advanced analytics. Pilot systems applied in cattle farming use these data to identify risk patterns linked to heat stress, animal aggregation, shared water or manure handling and increased farm traffic, enabling early and targeted biosecurity interventions.

            In Greece, recurrent outbreaks of sheeppox continue to pose a serious threat to meat-producing livestock systems, leading to movement restrictions, compulsory culling and significant economic losses, particularly in regions with dense small-ruminant populations. Although cattle are not susceptible to Capripoxvirus and do not develop sheep and goat pox, cattle farms can play an important preventive role at system level. This is especially relevant in Greece, where livestock systems often share infrastructure, resources and transport routes. During the twelve-month period preceding early 2025, official figures reported more than 2.400 confirmed cases and the culling of approximately 260.000 sheep and goats, representing around 2% of the national small-ruminant herd (https://www.reuters.com). These outbreaks are strongly linked to gaps in early detection, delayed reporting and limited real-time visibility of animal health and environmental stressors that may facilitate disease spread.

Data-driven pilot approaches developed within AgriDataValue, including pilots applied in cattle farming, combine environmental monitoring, animal-related data and interoperable data sharing. Although cattle are not susceptible to Capripoxvirus, data from cow pilots provide early warning signals linked to heat stress, animal aggregation, farm traffic and shared resources. By enabling early identification of abnormal patterns linked to heat stress, animal mobility and farm-level risk factors, such pilots support proactive disease surveillance, faster response mechanisms and targeted biosecurity measures. In this way, trusted data not only support animal welfare and stable meat quality but also contribute directly to preventing and mitigating infectious disease outbreaks in climate-stressed livestock systems.

Within the AgriDataValue project, wine pilots play a key role in validating the Agri-environmental Data Space, which is deployed across 23 pilots in nine European countries and spans a wide range of crops and production systems. Viticulture is represented through pilot activities in France and Italy, ensuring that high-value vineyard systems are fully integrated into the project’s smart farming and agri-climate monitoring framework.

IoT technologies are transforming vineyard management by enabling continuous monitoring of environmental conditions such as temperature, soil moisture, and humidity. This real-time data empowers growers to optimize key agricultural practices, including irrigation, fertilization, and disease prevention, leading to improved efficiency and sustainability.

As part of the AgriDataValue pilot activities, SynField smart agriculture systems, developed by Synelixis, have been deployed in selected vineyards to deliver real-time environmental, climatological, and soil data. By continuously monitoring key parameters such as temperature, humidity, soil moisture, and leaf wetness, the system provides a clear and up-to-date picture of field conditions. These data-driven insights support optimized irrigation and more effective overall vineyard management, enabling informed decisions that optimize yield and overall vineyard performance. As a result, SynField contributes to higher product quality while promoting environmentally sustainable viticulture practices. Furthermore, within the AgriDataValue project, machine learning models are being developed to support the prediction of diseases. The ML models aim to identify risk periods and early warning indicators for disease development

The integration of IoT, ML and AI-driven analytics result in a more sustainable, efficient, and reliable wine supply chain. For vineyards participating in AgriDataValue, this technological ecosystem represents a significant step toward resilient farming practices and transparent business models, with broader relevance for other agri-food sectors.

AgriDataValue Vineyard Pilots in France and Italy

The vineyard pilots rely on in-situ IoT sensor installations provided by SynField smart agriculture systems. These installations collect real-time field data through a network of sensors deployed at pilot level. The captured data are integrated into the AgriDataValue platform, enabling secure data sharing, advanced analytics, and AI-based decision support tools for farm-level monitoring and management. Furthermore, AgriDataValue utilizes machine learning to predict Downy and Powdery Mildew outbreaks. By analyzing weather patterns and historical disease data, the models provide early warnings that allow for targeted interventions. This approach helps viticulturists optimize plant protection, minimize losses, and improve long-term sustainability.

Vineyards, Climate Risks, and Frost Monitoring

A central pillar of the AgriDataValue pilot activities is the mitigation of climate-related risks in viticulture. By integrating climate indicators with in-situ sensor networks and Earth Observation data, the project provides a high-resolution assessment of environmental variability. This data-driven framework is particularly vital for the early prediction of spring frost events. Such precision is critical, as frost can be catastrophic—destroying primary shoots, buds, and inflorescences, which represents the most yield-critical form of crop injury.

Vineyard Pilot in Saint-Émilion, France

The Saint-Émilion wine pilot represents a premium viticulture region with strict requirements for traceability, environmental monitoring, and regulatory compliance. Within the project, SynField smart agriculture systems were installed at multiple locations across vineyards cultivating Merlot varieties.

Each installation includes several SynField devices, specifically SynField X5 head nodes and SynOdos peripheral nodes, supporting a wide range of sensors for comprehensive data collection. Meteorological stations were deployed to monitor ambient temperature, relative humidity, wind speed, wind direction, and rainfall. In addition, soil sensors measuring soil moisture and soil temperature, leaf wetness sensors, and a pyranometer were installed, providing valuable insights into vineyard microclimatic and soil conditions.

Vineyard Pilot in Tebano, Italy

In Italy, SynField smart agriculture systems were installed as part of the AgriDataValue pilot activities in a vineyard located in Tebano (RA), within the Emilia-Romagna region of northeast Italy. The vineyard covers an area of seven hectares and is situated on flat terrain with clayey loam soil. Cultivation follows an integrated management approach, combining sustainable practices to maintain a balanced ecosystem. The vineyard cultivates Sangiovese and Trebbiano varieties, grafted onto KOBER 5BB rootstocks. Planting took place in 2021 using the Guyot training system, with row spacing of 2.6 meters and one meter between vines within the same row.

The SynField installation includes a SynField X3 head node, a meteorological station monitoring ambient temperature, relative humidity, wind speed, wind direction, and rainfall, as well as soil sensors measuring soil moisture and soil temperature. These systems continuously collect real-time environmental data, providing actionable insights that support precision vineyard management.

Data Integration and Benefits for Growers

The AgriDataValue pilots are designed to collect large and diverse datasets from multiple sources and operating environments. The deployment of SynField smart agriculture systems enables continuous monitoring of environmental, climatological, and soil conditions, supporting informed decision-making and more precise vineyard management. This data-driven approach enhances vineyard performance, contributes to improved grape and wine quality, and strengthens the long-term sustainability of vineyard operations.

Overall, AgriDataValue illustrates how the integration of digital technologies can deliver tangible value to viticulture by enabling smarter, more resilient, and more sustainable practices. By combining real-time field data, advanced analytics, and secure data-sharing mechanisms, the project empowers growers to better understand their vineyards, manage climate-related risks, optimize yields, and reduce environmental impact. At the same time, increased traceability and transparency across the wine supply chain build trust and add value to premium wine products. By linking on-farm decision support with data-driven innovation across the value chain, AgriDataValue establishes a strong foundation for the future of sustainable and competitive vineyard management.

As dairy cows only produce milk after calving, the calving process is an important part of the daily farm life. It is a critical stage for both mother and calf, and farmers are doing everything they can to make sure calving goes as smoothly as possible. Farmers check on their cows regularly in the period before calving, but the actual onset of the calving process can vary depending on the cow and circumstances. Sometimes cows might calve a few weeks early or in the middle of the night, making it more difficult for the farmer to keep track of the process and to be standby in case of potential problems. Therefore, smart farming technology that can help the farmer by indicating the approaching onset of the calving process facilitates the farmers’ work and allows for better follow-up and earlier intervention when necessary. As the moment of calving approaches, cow behaviour and activity change as the cow prepares for this important moment. As such, cow activity data can provide information on the approaching birth, and thus be useful to monitor the onset of calving.

For this use case on calving monitoring, we join forces with our Latvian ZSA colleagues who collect cow activity data using pedometers in the Vecauce dairy barn of the Latvia University of Life Sciences and Technologies. Our ILVO Research Dairy Farm is located 10 km from Ghent, Belgium, and is used to conduct research on animal husbandry and welfare, animal nutrition and emissions, and precision livestock farming.  At ILVO, activity data are collected using neck collars that record cows’ motion. Measuring similar aspects of cow behaviour, these sensors result in very similar data. Therefore, data of both farms are joined in an effort to improve machine learning models towards a tool to help farmers detecting the onset of calving. Favouring all cow, calf and farmer, such technology can support farm management and help improving farm sustainability in the future through the AgriDataValue project.

The AgriDataValue project was presented at the 32nd International Geographical Union – Commission on the Sustainability of Rural Systems (IGU-CSRS) Annual Colloquium, held in December 2025 in the Philippines under the theme “New Ruralities: Contestations and Iterations on Rural Spatialities”.

Researchers from the University of Lodz contributed to the conference within a panel session dedicated to Technology & Digitalisation, a core thematic strand of the colloquium programme. The session focused on how digital tools, data infrastructures, and technological innovation are reshaping rural systems, governance structures, and socio-economic relations in contemporary rural spaces. Papers presented within the panel addressed diverse dimensions of digitalisation, including smart village strategies, digital food heritage, storytelling, and the role of technology in shaping local development pathways.

The University of Lodz presentation focused on digitalisation as a locally embedded process, highlighting experiences from Poland that illustrate how data, digital tools, and community-driven strategies can support rural development initiatives. In line with the AgriDataValue project, the contribution emphasised the importance of data integration, accessibility, and practical usability for local actors, rather than technology adoption as an end in itself. The presentation situated digital solutions within broader socio-economic and institutional contexts, stressing that their effectiveness depends on governance structures, stakeholder engagement, and alignment with local development needs.

Together, the panel papers demonstrated that digitalisation contributes to “new ruralities” not through uniform technological models, but through context-specific configurations shaped by culture, knowledge, and local capacities. Participation in the IGU-CSRS colloquium provided a valuable opportunity to embed AgriDataValue within an international scientific debate on rural sustainability and digital transformation.

Europe’s agricultural sector is entering a period of significant transition as digital technologies become part of everyday farm management and value-chain operations. This shift, originally driven by innovation, is now shaped by a developing regulatory and governance environment. New instruments such as the Data Act, the AI Act, the NIS2 Directive and the Cyber Resilience Act introduce clear rules on data access, system transparency and cybersecurity. Alongside these regulations, efforts such as the emerging Common European Agriculture Data Space (CEADS) aim to bring greater coherence to how agricultural data initiatives align across Europe by introducing shared governance and interoperability principles. Within AgriDataValue, the political and techno-socio-economic radar has been tracking these developments to understand their practical implications considering increasing operational demands.

Several dynamics stand out:

• A clearer legal foundation for data governance: The Data Act and its model contractual templates help standardise how agricultural data is shared, protected and used. Roles such as data holder, data user and data recipient are becoming more consistent across the sector.
• Rising expectations for trustworthy AI: The AI Act sets enforceable requirements for documentation, transparency and human oversight. This affects decision support tools used for agronomy, environmental monitoring and compliance.
• Cybersecurity as a core requirement: The Cyber Resilience Act and NIS2 make cybersecurity a legal obligation. This increases responsibility for farms, service providers and platforms operators that handle agricultural data.
• Environmental regulations that rely on accurate data: Instruments such as the EU Deforestation Regulation require geolocation accuracy and verifiable data links to Earth Observation sources. This raises the importance of consistent and high-quality data infrastructures.
• Social and organisational challenges that remain central: Concerns about fairness, ownership and skills continue to affect adoption. Many farmers need clearer benefits and more support before engaging fully with digital tools. Cooperative data models and human centric governance remain important in building trust.

These trends are shaping the conditions in which digital agriculture will evolve. To summarise this landscape, AgriDataValue has prepared an updated SWOT analysis that captures the main strengths, weaknesses, opportunities and threats influencing adoption and value creation. The figure below presents these insights in a consolidated form and supports the project’s ongoing exploitation and sustainability planning.

As digital agriculture becomes more regulated, more interconnected and more dependent on reliable data, the sector must balance innovation with responsibility and efficiency with fairness. AgriDataValue will continue monitoring these developments and will translate them into practical solutions that support a trustworthy and inclusive agricultural data ecosystem.

Stay connected for future updates as this landscape continues to evolve.

Introduction

In today’s digital landscape, increasing trust in the use of digital assets is a fundamental goal, essential for their widespread adoption and utilization. In this context, organizations—and not only them—are increasingly relying on data and Artificial Intelligence (AI) models to make decisions in more critical sectors such as healthcare, finance, industrial and agricultural planning. Data is the essential fuel powering modern AI systems, and there is a growing demand for large volumes of “quality” data—data that accurately represents the reality being modeled and is free from internal biases.

This dependency raises a crucial question: how can we ensure that an AI model and the data it relies on are authentic? One technology that can help address this challenge, and has steadily matured over the years, is Blockchain.

Why Blockchain Matters

Blockchain is a distributed ledger that stores information in a way that makes it immutable and verifiable, without requiring a centralized certification authority. Each piece of information (or set of information) is recorded in a “block,” which contains the data along with a cryptographic link, a fingerprint of the previous block, creating an ever-growing structure inherently resistant to tampering. Once recorded, data remains permanently and immutably on the Blockchain. This immutability, combined with decentralization—where no central authority controls the system, makes Blockchain a reliable foundation for digital integrity.

Blockchain-Based Notarization

Within the platform developed by the AgriDataValue project, a Blockchain and a dedicated component called CHAINTRACK have been implemented to leverage these capabilities and offer advanced functionalities to users. One of these is Blockchain Notarization—a process that certifies that a digital asset exists in a specific form at a precise moment in time. This is achieved by computing a cryptographic hash of the asset—a kind of digital fingerprint—and recording it on the Blockchain. The hash does not reveal the asset’s content but allows anyone to verify its authenticity later. If the asset changes, its fingerprint (hash) changes as well, and any comparison with the hash stored on the Blockchain immediately reveals any alteration. Blockchain thus acts as a digital notary, guaranteeing authenticity without the need for centralized authority.

Notarizing an AI Model

The concept of notarization has been applied by the AgriDataValue consortium to AI model governance. These models are increasingly used in critical systems, making integrity assurance more important than ever. Within the AgriDataValue platform, once an AI model is created, trained and registered in a particular storage repository, the system automatically computes its hash and records it on the Blockchain using CHAINTRACK’s functionalities. This creates an immutable audit trail. Later, to verify that an AI model is authentic, one simply computes its hash and uses another CHAINTRACK feature to check if it exists on the Blockchain. The key benefits of this approach include:

• Real-time authenticity verification of AI models, enabling the blocking of unauthorized modifications and reducing security risks.
• Intellectual property protection, providing undeniable proof of ownership.
• Regulatory compliance, such as with the European AI Act, which requires transparency and traceability.

Any Digital Asset

The same notarization principle has been generalized within AgriDataValue to apply to any critical digital resource where authenticity verification is important. Examples include commercial agreements between partners and data governance policies. More broadly, legal contracts, intellectual property documents, product certifications, and compliance reports can all be protected in the same way.

Going further

We conclude this article with a potential extension of the current use of AI model notarization within AgriDataValue. One possible step in this direction is to extend notarization to the entire AI development lifecycle. This means notarizing not only the final model but also its architecture, all training and testing data, and performance and reliability scores achieved after training. Model, data, performance—all rendered immutable and verifiable through Blockchain notarization.

Having complete traceability of the entire AI production process significantly enhances reliability, compliance, and auditability. Certifying that a model was trained with specific data also reduces risks related to data integrity and potential bias introduced by poisoned training datasets. Finally, intellectual property protection is strengthened, safeguarding both models and data assets.

In general, extending notarization to every phase of AI development builds a solid framework of trust and accountability, a crucial step toward broader adoption of Responsible AI principles, fostering trust in AI usage and driving its diffusion and competitiveness.

The 58th Conference of EU Paying Agencies Directors took place in Copenhagen, Denmark, on 19–21 November 2025, bringing together the EU Paying Agencies‘ senior representatives to discuss innovation, simplification, data governance, and the evolving role of AI in implementing the Common Agricultural Policy (CAP) as well as the future direction of the CAP development, with a strong focus on interoperability, AI applications, and green transition. The representative of the Lithuanian National Paying Agency (NPA) – Deputy Director Tomas Orlickas – took part in the event and familiarised the audience with the NPA achievements in carrying out the multiple objectives of the CAP in Lithuania. 

The sessions of the 58th Conference of EU Paying Agencies’ Directorsfocused on several key themes, including:
• -The use of AI in the administration of support schemes;
• -Administrative simplification and improved institutional efficiency;
• -Modernisation of processes and advancing the green transition in the agricultural sector.

Over three days the delegates engaged in high-level presentations, thematic workshops, pitches and exchanges on best practices – particularly on AI applications, administrative simplification, aerial monitoring, and performance audit findings.

During the conference a key intervention was made by Tomas Orlickas, Deputy Director of the National Paying Agency (NPA) of Lithuania: “Research and development activities for more effective implementation of Lithuania’s CAP Strategic Plan” In his presentation, Tomas Orlickas highlighted Lithuania’s comprehensive  contribution to the EU-funded research, development, and innovation initiatives designed to strengthen the implementation of the CAP Strategic Plan and support the transition towards a more sustainable, modern, and data-driven agricultural sector. The Deputy Director presented Lithuania’s ongoing research and innovation initiatives aimed at improving the efficiency, accuracy, and strategic coherence of the country’s CAP Strategic Plan implementation.

The Deputy  Director also pointed out that the NPA’s Research & Development activities are closely aligned with EU priorities for digital transformation, environmental performance, transparency, and administrative efficiency. Tomas Orlickas outlined the importance of environmental monitoring, modelling of agricultural practices and tools that help measure biodiversity, carbon impacts and land-use changes, that are essential for delivering the green architecture of CAP. The showcased key initiatives included a number of projects with a focus on AgriDataValue being among them.  

The Horizon Europe AgriDataValue project focuses on accelerating agriculture’s digital transformation and improving agri-environmental monitoring by developing a decentralized smart-farming data ecosystem. The project’s work includes:

• -Harnessing big data to enhance productivity, strengthen environmental performance, and support fair and stable income for farmers.
• -Creating tools for area monitoring, sensor-based data collection, and observation of climate and land-use changes (e.g., soil moisture, erosion, crop damage, yield estimations), as well as automated crop and object classification from geotagged images.
• -Connecting with, or aligning to existing satellite and remote-sensing infrastructures such as Sentinel, along with other Horizon initiatives.

The project will establish a pan-European, open-source agricultural data space that advances smart farming and environmental monitoring through a novel “platform of platforms.” Key expected outcomes include a fully operational federated platform enabling secure data sharing, new data-driven business models, trustworthy AI solutions using federated machine learning, and extensive pilot trials across nine EU countries to validate the tools in practice. Ultimately, the project will provide farmers with access to a far broader and richer set of data resources, supporting a faster and more seamless shift toward smart agriculture.

The presentation was followed by a Q&A session, allowing other Paying Agencies to explore how similar innovation-focused approaches could strengthen CAP implementation across the EU.

Overall, the conference underscored a shared commitment among EU Paying Agencies to modernisation, interoperability and innovation, setting the stage for enhanced collaboration.

Across Europe, initiatives around the Common European Agricultural Data Space are pushing for a future in which agricultural data can move securely between farmers, cooperatives, service providers and public authorities, under clear rules and shared governance frameworks [1][2]. AgriDataValue (ADV) contributes to this vision by developing a “platform of platforms” for smart farming and agri-environmental monitoring, where security and transparency are not just requirements on paper but are designed into the architecture and exercised in pilots [3][4].

The starting point is simple: without trust, there is no data sharing. Agricultural data is often commercially and personally sensitive, from yield maps and input usage to compliance and environmental indicators. Actors will only share this information if they know who can access it, for which purpose and under which guarantees. This is why the latest AgriDataValue reference architecture treats security, transparency and accountability as cross-cutting concerns that influence every component and interface, from IoT gateways and edge nodes to cloud services and data-space connectors.

Security controls in practice

In practice, human users and services authenticate against a trusted identity provider, and internal as well as external communication uses token-based authorisation, following patterns that are widely adopted in secure web and API architectures [5][6]. Access to datasets and services is governed by a combination of role-based and attribute-based access control, so that permissions can take into account not only who is asking, but also properties of the data, such as whether it contains personal information or commercially sensitive indicators, and the applicable legal or contractual constraints. These choices are aligned with data-space concepts such as data sovereignty and usage control, where policies describe not just whether data can be accessed, but also under which obligations and for which purposes [5][6].

At the technical level, the platform enforces end-to-end protection. Traffic between components is secured via TLS, while sensitive data at rest is encrypted using well-known algorithms. API gateways validate requests, apply rate-limiting and record security-relevant events. Logging and monitoring are treated as built-in capabilities: authorised stakeholders can see, at a metadata level, which connectors are active, which policies have been evaluated and whether access requests have been granted or denied, without exposing the underlying data itself. This combination of secure communication, strong identity and fine-grained authorisation turns high-level trust requirements into concrete controls that can be tested in pilots [3][4].

Transparency and accountability

Transparency is the other side of the trust equation. For many stakeholders, the important questions are not only whether data is protected, but also what happens to it once it enters the platform and whether it is possible to demonstrate that agreed policies have been followed. To address this, the AgriDataValue architecture emphasises location transparency with compliance guarantees: users and applications interact with platform services without needing to know where the data is physically stored or processed, while the system still respects data-residency rules and GDPR obligations [1][2].

Data-flow visibility is enabled through dedicated monitoring and auditing capabilities. Within their authorised scope, data providers can inspect how their data assets are routed between edge nodes, cloud components and external connectors. Logs record key events along the data-sharing journey, from publication and policy attachment to access evaluation and delivery to consumers. This supports accountability and helps participants demonstrate compliance with governance rules defined at project or ecosystem level. In pilots, these features are exercised with real IoT streams, Earth observation products and farm management data, showing how trust mechanisms behave under realistic conditions rather than only in synthetic test cases [3][4].

Alignment with European frameworks for data spaces

The design choices in AgriDataValue build on established European reference models and frameworks for data spaces. The International Data Spaces Reference Architecture Model (IDS-RAM) provides a detailed blueprint for trusted data exchange, including identity management, secure connectors and usage-control enforcement [5]. Gaia-X, through its Trust Framework, defines baseline criteria and evidence for participants in federated European data ecosystems, focusing on transparency, controllability and interoperability [6]. In parallel, the European strategy for data and the Data Governance Act set out the principles and regulatory context for sectoral data spaces such as agriculture [1][2].

The AgriDataValue architecture adopts and adapts these ideas when defining its interfaces, policy models and governance functions, so that individual platform instances can plug into the emerging agricultural data-space landscape rather than forming isolated silos. Concepts such as usage policies, verifiable identities and auditable connectors are therefore not only referenced in documentation, but also reflected in the APIs and deployment models that are exercised across the project’s pilots [3][4][6].

Insights from research on trusted agri-data spaces

Academic work on smart farming and agricultural data spaces reinforces the importance of combining security, transparency and governance. Studies on the ethics of smart farming underline that unclear ownership, opaque data flows and weak usage policies can seriously undermine farmers’ willingness to share data and adopt digital tools [7]. Analyses of blockchain and distributed-ledger applications in agri-food supply chains show that traceability and verifiable logging can improve trust, but only when they are embedded in broader governance frameworks and aligned with real stakeholder needs [8].

These insights are reflected in AgriDataValue’s approach: policies are made explicit and, where possible, machine-readable; key events in the data life cycle are traceable; and technical controls are designed to be usable in day-to-day operations rather than remaining as abstract architectural patterns. In this way, requirements coming from policy documents, reference architectures and research are translated into mechanisms that can actually support data sharing in concrete agricultural scenarios.

Conclusions

By combining requirements from European policy, architectural guidance from IDS and Gaia-X and lessons learned from pilots and research, AgriDataValue offers a concrete example of how “trusted agricultural data spaces” can move from concept to implementation. Security mechanisms such as strong identity management, encryption and fine-grained access control, together with transparency features like monitoring, auditing and policy-aware data flows, make it possible to answer key questions about who can access which data, under which rules, with which guarantees and how this can be demonstrated over time.

As the Common European Agricultural Data Space evolves, these building blocks can be reused, extended and federated. The result is an ecosystem in which data sharing in agriculture is not only technically feasible, but also trustworthy for all participants, supporting innovation while respecting data sovereignty, legal obligations and the legitimate expectations of farmers and other stakeholders.

References

[1] European strategy for data
Communication “A European strategy for data”, COM(2020) 66 final, 2020 – EUR-Lex

• Link: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0066 Eur-Lex+1

[2] Data Governance Act
Proposal for a Regulation on European data governance (Data Governance Act), COM(2020) 767 final – EUR-Lex

• Link: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020PC0767 Eur-Lex+1

[3] AgriDataValue project
Smart Farm and Agri-environmental Big Data Space – CORDIS project page

• Link: https://cordis.europa.eu/project/id/101086461 CORDIS+1

[4] AgriDataSpace / CEADS context
AgriDataSpace – “Building a European framework for the secure and trusted data space for agriculture”

• Link: https://agridataspace-csa.eu/ AgriDataSpace+1

Dacă vrei explicit CEADS în lista de referințe:
CEADS – Common European Agricultural Data Space project site

• Link: https://ceads.eu/ CEADS Project

[5] IDS Reference Architecture Model 4.0
International Data Spaces Association – IDS-RAM 4.0 (PDF)

• Link direct PDF: https://internationaldataspaces.org/wp-content/uploads/dlm_uploads/IDSA-Tech-Talk-IDS-RAM.pdf International Data Spaces+1

[6] Gaia-X Trust Framework
Gaia-X Trust Framework (latest public spec)

• Link (intro / spec PDF): https://gaia-x.eu/wp-content/uploads/2024/05/An-Introduction-to-the-Gaia-X-Trust-Framework_2024-V4.pdf gaia-x.eu+1

[7] Ethics of smart farming
van der Burg, S., Bogaardt, M.-J., Wolfert, S. (2019), “Ethics of smart farming: current questions and directions for responsible innovation towards the future”, NJAS – Wageningen Journal of Life Sciences.

• Publisher link: https://www.sciencedirect.com/science/article/pii/S1573521418301490 ScienceDirect+1

[8] Blockchain in agriculture & food chains
Kamilaris, A., Fonts, A., Prenafeta-Boldú, F.X. (2019), “The rise of blockchain technology in agriculture and food supply chains”, Trends in Food Science & Technology.

• Publisher link: https://www.sciencedirect.com/science/article/pii/S0924224418303686

Project partners convened in a two-day hybrid meeting to discuss key project matters and review ongoing progress. The meeting was hosted by Sinergise on 25–26 November 2025 in Ljubljana, Slovenia.

Over the course of two days, the plenary included several technical sessions from all active work packages. These sessions facilitated productive discussions among consortium members, enabling them to assess the current status of the project and address development challenges. Partners presented the results achieved to date and outlined the next steps toward the successful completion and delivery of the AgriDataValue project.