On 25–26 February 2026, the Joint Research Centre (JRC) organised the 2nd FIRE Workshop in Ispra, Italy, bringing together representatives from the European Commission, Member States and partner organisations to discuss the implementation of regulatory elements in agricultural monitoring. The workshop provided an important forum for exchanging experience on the Area Monitoring System (AMS), land parcel identification system (LPIS), and new technologies supporting the implementation of the Common Agricultural Policy (CAP). Tomas Orlickas, the Deputy Director of the National Paying Agency, Lithuania (NPA), familiarised the audience with the innovative AMS solutions and cutting-edge approaches by sharing the insights from NPA‘s participation in the international projects.

The workshop opened with remarks from JRC and DG AGRI representatives, setting the context for two days of discussion on both current challenges and future opportunities. A key message of the event was that effective agricultural monitoring increasingly depends not only on technical capacity, but also on cooperation between institutions, countries and research networks.

The discussions focused on AMS implementation challenges and possible solutions, reviewing survey results and highlighting current difficulties in monitoring. Presentations covered practical approaches such as the Dutch experience, outlier detection in Sentinel time series, and crop detection models designed to improve AMS performance. Further presentations explored the use of LPIS and GSA data, AI-supported updates of aerial imagery, and continuous updates of the Land Parcel Identification System with geotagged information. The day ended with a forward-looking session on the next CAP period and innovative monitoring tools.

Tomas Orlickas, Deputy Director of the National Paying Agency (Lithuania), presented “What’s Next after AMS? Strategy of Lithuania post-2027”, focusing on future developments in agricultural monitoring systems. He highlighted the growing role of international projects within this domain, showcasing Horizon Europe (HE), Horizon 2020 projects and other EU initiatives, as as well as national and EU-funded platforms that support data-driven agriculture. His presentation demonstrated how these collaborative projects contribute to advancing monitoring capabilities, including climate and environmental assessment, biodiversity tracking, and carbon accounting systems, thereby reinforcing the importance of international cooperation in shaping the next generation of agricultural monitoring tools. In the presentation the Horizon Europe project AgriDataValue was defined as an example of a “platform of platforms”, demonstrating how multiple data sources and digital tools can be integrated into a unified ecosystem. By combining satellite data, sensor information and farm-level inputs, the project supports smarter decision-making in agriculture, enhances environmental monitoring and promotes more efficient and sustainable farming practices. The project benefits farmers by providing integrated data tools that improve decision-making, optimise resource use, and enhance productivity while reducing environmental impact.

During the workshop the discussions also covered support to candidate countries and LPIS creation, followed by rapid-fire presentations on tools related to crop phenology, farming practices, vegetation dynamics, parcel viewing, image occurrence estimates and farm fragmentation analysis. Drone technology was another important topic, with presentations on the Czech use case and JRC field tests showing how drones can complement satellite-based monitoring with more detailed and flexible observations.

The AgriDataValue project proudly took part in the 31st Agrotica Expo, the International Fair for Agricultural Machinery, Equipment & Supplies, held in Thessaloniki from 12–15 March 2026.

Over the course of four days, the exhibition attracted a large number of visitors, transforming Thessaloniki into a dynamic hub for knowledge exchange, innovation, and collaboration within the agricultural sector.

AgriDataValue was represented by SYNELIXIS, welcoming attendees at Hall 2, Stand 15. Throughout the event, visitors had the opportunity to discover ADV’s forward-looking approach to smart agriculture. Particular interest was shown in how the project integrates advanced sensing technologies, AI-powered decision support systems, and agroecological practices to help farmers adapt to evolving climate challenges.

As emphasized during the exhibition, AgriDataValue goes beyond conventional farming methods by promoting data-driven, precise, and sustainable agricultural practices, ultimately enhancing both efficiency and effectiveness.

The strong engagement with stakeholders throughout the event highlighted the critical role of collaboration, knowledge exchange, and the adoption of innovation in tackling today’s agricultural challenges.

Participating in events like Agrotica offers valuable opportunities to increase the visibility of ADV’s work, strengthen ties with the farming community, and support the transition toward more resilient and sustainable agricultural systems.

AgriDataValue proudly participated in the Verde.Tec Exhibition, one of Greece’s leading events dedicated to environmental technologies and sustainable development. The exhibition took place from February 27–28 and March 1, 2026, at the Mediterranean Exhibition Center (MEC) in Paiania, Attica.

Verde.Tec brings together pioneering companies, research institutions, public authorities, and innovation-driven initiatives that showcase cutting-edge solutions in the fields of environmental protection, circular economy, smart cities, and sustainable infrastructure. The event serves as a key meeting point for stakeholders shaping the green and digital transition in Greece.

During the exhibition, AgriDataValue presented its vision and ongoing work toward its goals. Visitors had the opportunity to learn more about the project’s approach to the development of scalable digital solutions that support sustainable farming and informed decision-making.

Participation in Verde.Tec 2026 provided an excellent platform for networking, knowledge exchange, and strengthening collaboration with industry leaders and technology providers active in environmental and smart-city ecosystems. Through its presence at the exhibition, AgriDataValue further reinforced its commitment to contributing to Europe’s green transition by promoting innovation at the intersection of agriculture, data, and sustainability.

On Friday, 27 February, AgriDataValue successfully organized the 1st Liaison Webinar of Sister Projects, bringing together seven European projects in a highly productive and engaging online meeting. The webinar marked an important step toward strengthening collaboration and fostering synergies among projects working on agricultural data, digital innovation, and interoperability across Europe.

The session kicked off with a dynamic lightning overview round, where each participating project delivered a concise presentation outlining its core objectives, recent progress, and key achievements, setting the stage for a focused and engaging exchange. Alongside AgriDataValue, the sister projects AGRARIAN, CEADS, CODECS, ScaleAgData, 4Growth, and OpenAgri contributed to the exchange, highlighting the complementary nature of their approaches and their shared ambition to advance data-driven agriculture.

AgriDataValue was represented by its Project Coordinator, Theodore Zahariadis, Professor and Head of the Department of Agricultural Development, Agri-Food & Natural Resources at the National and Kapodistrian University of Athens and CTO at Synelixis. The webinar also featured distinguished speakers from across Europe, including Miguel Cachão (Winegrowers’ Association of the Setúbal Peninsula, Portugal), Capwell Forbang Echo (ILVO, Belgium), Gianluca Brunori (University of Pisa, Italy), Tuna Coppens (ILVO – Research Institute for Agriculture, Fisheries and Food, Belgium), Dáire Boyle (Evenflow Consulting LTD, Belgium), and Felipe Arruda Pontes (Maastricht University)

Following the project presentations, participants engaged in a constructive discussion focusing on interoperability approaches, standards and vocabularies, APIs, governance models, and FAIR data practices. The dialogue highlighted common technical and organizational challenges, while also identifying opportunities for alignment and joint development efforts.

The webinar concluded with an agreement on next steps and follow-up collaboration opportunities, reinforcing the importance of structured cooperation among sister projects. Through initiatives like this, AgriDataValue continues to promote knowledge exchange and contribute to building a more connected, interoperable, and sustainable European agri-food data ecosystem.

Phytosanitary treatments, to achieve maximum effectiveness, must be applied at specific phenological stages of the trees.
The following table provides a brief summary of the relationship between the phenological moments when treatments should be applied in stone and pome fruit trees, along with the degree‑days (°D) required for Anarsia lineatella and Grapholita molesta, calculated from the following biofix points:

• Anarsia lineatella: first male captured consistently in traps.
• Grapholita molesta: first consistent peak in traps.

For Anarsia, the first treatment is determined mainly by the crop’s phenology, since the overwintering larvae attack tender shoots at the beginning of the cycle, and this moment is not well described by degree‑days. However, later generations do show a good thermal correlation: the first generation usually coincides with approximately 150–180 °D, the second with 350–450 °D, and the third with 650–750 °D, which allows for more precise scheduling of interventions throughout the season.

For the oriental fruit moth (Grapholita molesta), development depends much more on thermal models than on phenology, making degree‑days an essential tool for correctly timing treatments. Key thresholds begin at 90–120 °D, when damage to young shoots typically starts, and exceed 350 °D when the risk of significant fruit damage increases. These thermal milestones make it possible to anticipate generational development and adjust interventions more effectively.

The availability of models capable of predicting when specific phenological stages will be reached, and how the pests will develop, is therefore essential for carrying out effective phytosanitary treatments, contributing to a reduction in the use of such products and, consequently, to the sustainability of agricultural operations.

Traditional models for phenology prediction (GDD, Winkler, Richardson, etc.) and disease‑risk models are based on calibrating formulas that link temperature to the development of plants and pests. While calibration can be done relatively easily for a field with a local weather station, it becomes much more complex when, as in AgriDataValue Pilot 12: Non‑Citrus Fruit Trees, the goal is to model an entire region (Aragón, in northeastern Spain). In such cases, manual calibration is practically impossible. This is where the capabilities of Big Data and Artificial Intelligence (AI)—particularly Machine Learning (ML)—become crucial: these technologies can perform an enormous number of calculations in a time frame unthinkable for humans, and they can learn from their errors.

Furthermore, these techniques allow for the use of many more data sources: ML‑based models can incorporate additional variables. This enables the models to find relationships that, whether due to limited computational capacity or lack of knowledge, are not considered in current models based primarily on temperature—although temperature is known to be the most influential variable, it is not the only one.

For the development of the ML models, data from  49 public meteorological stations located near the selected fruit orchards are being used. Field observations—phenology and pest presence—are obtained from Red FARA, an application of the Plant Health and Certification Center of the Government of Aragón, responsible for issuing regional phytosanitary advisories, which for woody crops cover 160.000 ha. Additionally, although their relevance is limited, images from Copernicus Sentinel‑2 are included. This restricts the time period considered: data from 2016 to 2025 have been used to train the initial models.

These models represent, to some extent, a general model for Aragón that can be specialized for specific zones or orchards within the region. This will allow the consortium to test the Federated Learning capabilities of the AgriDataValue data space.

The results of the models are made available as services to be used by the CSCV, more than 2000 users, and by anyone interested in applying them in Aragón. In addition, the dataset on which they are based will be published as an outcome of the AgriDataValue project.

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.