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.
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.
Figure 1: Trust stack for agricultural data spaces – from infrastructure to governance and ecosystem participants
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].
Figure 2: Secure data-sharing journey – from data provider to consumer with policies, protection and audit trail
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].
Figure 3: Concept of a transparency dashboard – data flows, policy decisions and connector status at a glance
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
[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.
[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.
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.
Almaviva presented the AgriDataValue in this significant Agri-food Festival. The event, held online from 10 to 13 November, was organized by Food Hub Srl Società Benefit and brought together over 50 speakers for more than 30 hours of content focused on innovation in the agri-food sector. The event explored key themes such as digitalization, sustainability, new production and processing technologies, and nutrition, offering participants access to live sessions and recorded materials within virtual rooms. Supported by institutions like CNR and Banco Alimentare, the festival aimed to bridge research and industry while fostering networking and technology transfer across the agro-food community.
The State of the Climate in Europe 2022 report, issued by the World Meteorological Organization (WMO), found that Europe is warming twice as fast as the global average due to climate change (WMO 2023). In particular, the Mediterranean has been identified as a “hotspot” particularly sensitive to the effects of global climate change due to rising temperatures and decreasing precipitation (Giorgi 2006; Giorgi and Lionello 2008; Lionello et al. 2012). As Europe’s agriculture faces escalating climate shocks, through prolonged droughts and heatwaves to erratic rainfall and soil degradation: decision-makers must transition from reactive crisis management to anticipatory, data-driven resilience.
Leveraging EO, climate indicators, and federated agricultural data spaces such as those developed under AgriDataValue offers a transformative opportunity to modernize agricultural planning and climate adaptation. This blog post intends to elaborate the reasons to establish an EU-wide policy framework enabling the operational integration of EO-based climate intelligence into agricultural risk management (RM), the Common Agricultural Policy (CAP), and National Adaptation Strategies.
Context and strategic imperative
Agriculture accounts for over 10% of EU CO2 emissions. Yet, it remains one of the most climate-vulnerable sectors (EEA, 2024). Between 1980 and 2023, extreme weather events cost Europe’s agriculture over €487 billion in losses (European Parliament, 2023).
These challenges are expected to intensify: the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6 WGII, 2023) notes that “Risk of water scarcity will become high at 1.5°C and very high at 3°C GWL in Southern Europe (high confidence).” Therefore, Southern Europe is a climate hotspot where water stress, heatwaves, and land degradation will threaten productivity and food security.
Figure 1: Consecutive dry days and annual harvested rain-fed area
EO and data-driven models already demonstrate their potential. Projects such as AgriDataValue, DestinE, and EO4AGRI have shown that integrating climate indicators (temperature anomalies, evapotranspiration, soil moisture, drought indices) into agricultural planning can support early warning, adaptive irrigation, and yield prediction. However, these innovations remain fragmented due to lack of standardization, governance frameworks, and incentives for data sharing.
The opportunity: from datasets to decision support systems (DSS)
Europe has made strategic investments in digital and space infrastructures, such as Copernicus, GALILEO, and the upcoming European Green Deal Data Space.
AgriDataValue builds upon this momentum through its federated Agri-Environmental Data Space, connecting satellite, the Internet of Things (IoT), and in-situ data without centralizing ownership. This model aligns with the EU Data Strategy (COM/2020/66), Data Act (Reg. 2023/2854), and the Data Governance Act (Reg. 2022/868), all promoting trustworthy, interoperable data ecosystems that preserve data sovereignty.
By operationalizing climate and EO data through the AgriDataValue framework, policymakers can:
– Enable evidence-based CAP implementation, particularly for eco-schemes and drought monitoring.
– Support climate-informed insurance and credit instruments, reducing fiscal pressure from disaster compensations.
– Strengthen multi-hazard early warning systems, as urged by the UNDRR Sendai Framework (2015–2030) and the United Nations Early Warnings for All Initiative (2023).
1.Governance fragmentation: Data ownership remains unclear across EO providers, private platforms, and farmers. National adaptation and CAP agencies often lack coherent governance mechanisms for integrating climate intelligence.
2. Uneven access and incentives: Smallholders and cooperatives rarely have access to tailored EO analytics. Without clear economic incentives, many refrain from sharing or using data (OECD, Data Governance in Agri-Food, 2022).
3. Lack of harmonized climate indicators: The absence of an EU-wide taxonomy for agri-climate indicators limits interoperability and comparability across regions. FAO’s Agro-Environmental Indicators and Eurostat’s Agri-Environmental Data Framework remain underutilized for operational planning.
Policy recommendations
A. Establish an EU framework for climate-informed agriculture: Create a dedicated “EU Climate Intelligence for Agriculture” initiative under the Green Deal Data Space and the Common European Agricultural Data Space (CEADS).
It should provide:
– A standardized set of climate indicators for agricultural decision-making, aligned with FAO, WMO, and EEA methodologies.
– Open-access geospatial tools derived from Copernicus Climate Change Service (C3S) and AgriDataValue platforms.
– Integration of climate services into CAP Strategic Plans and National Adaptation Plans.
B. Incentivize data sharing and use through CAP instruments: Use eco-schemes and conditionality payments to reward farms that integrate EO-based risk assessments or adopt predictive drought management tools. Encourage data cooperatives and public–private partnerships (PPPs) to ensure equitable access to analytics and training (OECD, Enhancing Resilience in Agriculture, 2021).
C. Embed governance and ethics-by-design: Adopt governance models inspired by AgriDataValue’s federated architecture, ensuring trust, traceability, and consent management under the EU AI Act (2024) and GDPR. Encourage Member States to establish national climate data coordinators to ensure local interoperability within CEADS.
D. Link climate intelligence with disaster risk reduction and finance: Integrate AgriDataValue’s outputs into the EU Civil Protection Mechanism and European Drought Observatory.
Support parametric insurance models and climate-linked financing tools through the European Investment Bank (EIB) and European Innovation Council (EIC). Promote synergies with the United Nations Framework Convention for Climate Change (UNFCCC) Koronivia Joint Work on Agriculture (2022) and OECD-FAO Joint Working Party on Agriculture and Climate (2023-2032).
Implementation Roadmap (2025–2030)
Conclusion
The transition to a climate-informed agricultural system is not merely technical: it is institutional, economic, and political. Initiatives like AgriDataValue demonstrate that federated data architectures and EO-based indicators can form the backbone of a resilient, equitableagricultural future. Nevertheless, realizing this potential requires policy alignment, governance innovation, and targeted incentives. By acting now, Europe can turn data into foresight. Foresight into prevention. And prevention into sustainable resilience. In doing so, the European Union would ensure its farmers not only survive climate change but thrive through it.
Giorgi, F., & Lionello, P. (2008). Climate change projections for the Mediterranean region. Global and Planetary Change, 63(2–3), 90–104. Link: https://doi.org/10.1016/j.gloplacha.2007.09.005
OECD. (2020). Strengthening Agricultural Resilience in the Face of Multiple Risks. Link
One of the biggest challenges in modern agriculture is data. Everyone has it, but it’s locked away in silos. Farmers are (rightfully) protective of it, and building one giant, central database for all of Europe is just not practical or secure. So, how do we build a connected, data-driven agricultural ecosystem without forcing everyone into a single “mothership”? And how do we convince farmers—the ones creating the most valuable data—to participate? This is exactly what we’ve tackled with the AgriDataValue (ADV) platform architecture. It’s built on two core ideas that change the game.
1. Growing Sideways: A “Platform of Platforms”
First, we knew a single, monolithic platform wouldn’t work. The ADV platform is designed as a multi-instance, “platform-of-platforms.” Instead of one big system, imagine a network of independent ones. A regional farmers’ co-op in France can run its own ADV platform instance. A national agricultural ministry in Greece can run another. Each instance is a complete, self-contained hub. But here’s the magic: they are all designed to securely talk to each other. This is what we call “horizontal expansion.” When a new organization wants to join, they just plug in their own instance. The whole ecosystem grows organically, region by region, without a single point of failure. It’s scalable, resilient, and respects that data is often best managed locally.
2. Solving the “Why Share?” Problem
Okay, so we can connect the platforms. But why would a farmer, who is busy running a business, want to share their data? This is the “data provider blocking problem,” and our answer is simple: incentives. The ADV platform isn’t just about storing data; it’s about valuing it. We’ve built in a Marketplace concept that directly addresses the “What’s in it for me?” question. A farmer or data provider can choose to upload a dataset (like soil moisture levels or drone imagery). They can then negotiate its value and set clear sharing policies. This turns data from a passive byproduct into a potential asset. By making this process transparent—using a secure, shared ledger to record agreements and transactions—we build trust. The farmer knows exactly who is using their data and that they are being compensated for it. This motivation is the key to unlocking the data silos and fueling a new generation of agricultural apps and insights. By combining a scalable, federated architecture with a fair and motivating marketplace, the AgriDataValue platform aims to create a data economy that truly benefits everyone.
Multi-instance concept – vision
From upload to the ADV Platform instance to publication
The concept of AgriDataValue project is shaped around CAP objectives, thereby enhancing farmers’ contribution to the implementation of the CAP itself.
The activities of ADV pilots are aligned with the CAP objectives in several fields like climate change mitigation, efficient resource management and animal welfare.
Focusing on ADV italian pilots, Pilot #15 and Pilot #17 managed by Ri.Nova in the region of Emilia-Romagna, it was implemented AI-powered disease prediction systems in order to favour a reduction in pesticide use, enabling optimisation of the use of resources and limitation of losses in agricultural products. Both pilots contribute to improving disease prediction, favouring data-driven decision-making for pest and disease management and reducing the use of pesticides.
The activities carried out under Pilot #15 and Pilot #17 comply with CAP objective 5 – Effective natural resource management, which aims at fostering sustainable development and efficient management of natural resources such as water, soil and air by reducing chemical dependency.
Specifically it complies with CAP result indicator R.24 – Sustainable and reduced use of pesticides which goal is increasing the share of utilised agricultural area (UAA) under supported specific commitments which lead to a sustainable use of pesticides, thereby reducing risks and environmental impacts such as pesticides leakage.
Within the Italian CAP Strategic Plan framework, the Region Emilia-Romagna has also activated the measure SRA 19 – Reduction in the use of plant protection products according to which farmers receive a subsidy per hectare when implementing one or more of the following actions:
Action 1 – 50% reduction in the drift of plant protection products
Action 2 – Reduction in the use of plant protection products containing active substances identified as the most dangerous
Action 3 – Adoption of advanced crop protection strategies based on biotechnological and biological methods
Pilot #15 and Pilot #17 are playing a pivotal role by steering agriculture towards more sustainable and productive practices, leveraging digital technologies for more efficient resource management and greater protection of the environment and soil.
We are pleased to share our latest publication, “Sustainability Challenges in the Bovine Sector and the Implementation of Waste Management Policies within the EU Framework.”
The paper examines the environmental impact of the bovine industry and explores how EU policies, circular economy strategies, and smart farming technologies can transform waste into valuable resources. It highlights the need to align science, policy, and practice to advance sustainable, low-impact livestock production and support Europe’s climate goals.
