The agricultural sector is facing significant challenges in managing and exchanging data across diverse systems and stakeholders. With the proliferation of sensors, weather data, farming machinery, and other digital tools, there is an increasing need for standardized methods to ensure that agricultural data can be exchanged seamlessly and with a clear, consistent meaning. The AgriDataValue project is at the forefront of tackling this issue, focusing on developing data models and enhancing semantic interoperability within the agriculture domain.

Data Models: The Backbone of Standardized Agricultural Data

In the context of the AgriDataValue project, data models play a crucial role. These models provide a structured and standardized way of organizing agricultural data, making it easier for stakeholders to understand, share, and use. By establishing clear frameworks for data representation, AgriDataValue helps ensure that agricultural data from various sources—ranging from IoT sensors and weather stations to farm management systems—can be efficiently integrated and analyzed (W3C, 2014).
The AgriDataValue platform serves as the central hub in the ecosystem, facilitating seamless data exchange and interoperability between various components. The diagram below illustrates the flow of data and interactions between the AgriDataValue platform and external data sources, such as IoT sensors, weather stations, and farm management systems, as well as the integration of standards like IDS, GAIA-X, AIM, and FIWARE. (Figure 1)

Semantic Interoperability: Ensuring Consistent Data Meaning

However, it is not enough just to organize data; we also need to ensure that the meaning of the data is preserved and understood across different systems and platforms. This is where semantic interoperability comes into play. Semantic interoperability ensures that data exchanged between different systems is not only compatible but also retains its intended meaning (International Data Spaces Association, 2020).
AgriDataValue achieves this by leveraging the International Data Spaces (IDS) standard and the GAIA-X Trust Framework, both of which provide robust guidelines for data exchange in the agriculture sector (GAIA-X, 2021). By aligning with the Agriculture Information Model (AIM) from the DEMETER project, AgriDataValue strengthens the way data is represented and exchanged within the sector (DEMETER Project, 2020).
The IDS framework and its reference architecture, IDS-RAM, facilitate the integration of various systems and ensure that data can be exchanged without ambiguity. AgriDataValue adheres to the IDS-RAM, which offers a comprehensive view of data structures, concepts, and vocabularies, allowing for consistent semantic interoperability across different platforms (International Data Spaces Association, 2020).

Integrating IDS and AIM for Sector-Specific Solutions

One of the key innovations of the AgriDataValue project is the integration of IDS Information Model and the Agriculture Information Model (AIM). The IDS model is domain-agnostic, providing a general framework that can be applied to a wide range of industries, while AIM is specifically designed to address the unique needs of the agriculture sector (DEMETER Project, 2020).
This integration allows AgriDataValue to provide both broad industry-wide data structures and domain-specific agricultural data elements. For example, while the IDS Information Model includes concepts such as data assets, contracts, and participants, the AIM brings in agricultural-specific elements like AgriParcel, Crop, Intervention, and Pest (W3C, 2012). This combination ensures that AgriDataValue can cater to the specific needs of the agricultural industry while remaining adaptable to cross-industry applications (FIWARE Foundation, 2020).
The Agriculture Information Model (AIM), developed under the DEMETER project, is a comprehensive framework designed to facilitate semantic interoperability in agriculture. The model is publicly accessible and available under the Creative Commons Attribution 4.0 License, which promotes open and transparent data exchange (DEMETER Project, 2020). AIM is structured into several modules, each addressing specific agricultural domains like crop management, animal husbandry, and intervention systems (W3C, 2014).
These modules are represented in standard OWL (Ontology Web Language) and RDF (Resource Description Framework) formats, ensuring compatibility with other data models and facilitating integration with various systems across the agriculture sector (W3C, 2012).

Enhancing Data Sovereignty and Security with GAIA-X

Another key element of the AgriDataValue project is its commitment to data sovereignty, security, and privacy. The GAIA-X Trust Framework plays a central role in this regard. By adhering to GAIA-X’s technical and organizational guidelines, AgriDataValue ensures that agricultural data is handled securely, preserving user privacy and complying with data protection regulations (GAIA-X, 2021).
This focus on trust and data security is essential for fostering confidence among data providers and consumers in the AgriDataValue ecosystem. The project implements mechanisms such as data anonymization and access control, ensuring that sensitive agricultural data is protected while still enabling meaningful insights and analysis (International Data Spaces Association, 2020).

The Road Ahead: Extending and Evolving the AgriDataValue Model

As the AgriDataValue project progresses, it remains focused on creating a flexible, extensible data model. This model is designed to evolve as new technologies, data sources, and user needs emerge within the agricultural sector (FIWARE Foundation, 2020).
In the coming stages, AgriDataValue plans to expand its semantic interoperability mechanisms by integrating other data models and frameworks. These include FIWARE AgriFood Data Model, which further enhances interoperability in the agri-food sector, and other industry-specific vocabularies that will continue to improve the integration of agricultural data (DEMETER Project, 2020).
The integration of such models will ensure that AgriDataValue can support the dynamic, evolving nature of agriculture and continue to provide value to stakeholders in the sector. Whether it’s enhancing precision farming, improving resource management, or supporting decision-making at the policy level, AgriDataValue is well-positioned to drive the future of agricultural data interoperability (Atzori et al., 2017).
The diagram below illustrates the flow of data from external sources such as IoT sensors, weather stations, and farm management systems, through data transformation and standardization, to the AgriDataValue platform, and finally to end-users like farmers and policymakers. This process ensures that agricultural data is standardized and integrated for actionable insights and effective decision-making. (Figure 2)

Conclusion

The AgriDataValue project stands as a key initiative in promoting data interoperability in the agricultural sector. By combining robust data models, semantic interoperability frameworks, and a commitment to data security, AgriDataValue is helping to pave the way for more efficient, transparent, and data-driven agricultural practices. As the project continues to grow, its impact on the sector will only become more profound, enabling farmers, policymakers, and businesses to make informed, data-driven decisions for a sustainable future (Stojanovic & Milinkovic, 2019).

References

1) DEMETER Project. (2020). Agriculture Information Model (AIM) Overview. Retrieved from https://www.demeter-h2020.eu
2) GAIA-X. (2021). GAIA-X Trust Framework. Retrieved from https://www.gaia-x.eu
3) FIWARE Foundation. (2020). FIWARE AgriFood Data Model. Retrieved from https://www.fiware.org
4) W3C. (2014). RDF 1.1 Primer. W3C Recommendation. Retrieved from https://www.w3.org/TR/rdf11-primer/
5) W3C. (2012). OWL 2 Web Ontology Language Document Overview. W3C Recommendation. Retrieved from https://www.w3.org/TR/owl2-overview/
6) Atzori, L., Iera, A., & Morabito, G. (2017). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.009
7) Stojanovic, J., & Milinkovic, D. (2019). Standards and Frameworks for Agricultural Data Integration. Sensors, 19(2), 289. https://doi.org/10.3390/s19020289
8) International Data Spaces Association. (2020). International Data Spaces Reference Architecture Model (IDS-RAM). Retrieved from https://www.internationaldataspaces.org

Quest for future-ready farms

The weather is getting wilder, the seasons less predictable, and farmers are feeling the heat—literally! Climate change is reality, it isn’t some distant threat; it’s here, reshaping our soils, crops, livestock and biodiversity. What does the future hold for my farm and other agri-environmental systems near and far, in the face of the changing climates? This is the pressing worry and question of farming communities in Europe and globally today.
One effective approach to addressing such an important question on the impact of climate change challenges in agriculture is co-designing measures that enhance resilience through both long-term mitigation as well as short-term adaptations.

Co-designing for resilience

In the AgriDataValue project, assessing the impact of climate change in the agriculture systems is one of the main addressed topics. The AgriDataValue Consortium is composed by a multi-stakeholder partnership that comprise agricultural practitioners (farmers, farming-companies, and cooperatives), CAP–agencies (Common Agricultural Policy) paying agencies, and technical technology partners working in the data-driven sectors, for a total of 30 partners from 14 European countries, and 23 pilots across nine EU countries. The adopted approach to address Climate change is to combine both the historical trends and the future projections of key climate change indicators, to arrive at the impact of climate change in biodiversity, soil health, cropping systems and animal husbandry. The ultimate target is to contribute to the sustainability of the European Agri-environmental systems (AES) today and in the future through developing adaptation and mitigation strategies.

Countries and pilot agricultural Systems

Agricultural systems in Europe are highly diverse and complex, shaped by a wide range of plant and animal commodities, as well as the interactions between climate and management practices across various environments. Key components of these systems, considered for assessing climate change impacts, include cropping systems, livestock systems, soil health, and agro-biodiversity. These components encompass a variety of crops such as arable crops, vegetables, and trees found in vineyards and olive groves, as well as livestock and cross-cutting issues like soil quality and biodiversity. For instance, in Poland, the focus is on arable crops such as wheat, corn, rye, and oats, while in the Netherlands, the emphasis is on potatoes, onions, and sugar beets. Latvia centers on wheat and hard wheat, whereas Greece focuses on forage, vineyards, and olive trees. Belgium is known for its vegetables and livestock, Spain for its vegetables and vineyards, and France for its vineyards. Italy’s agricultural system assessment includes both vineyards and olive trees, while in Romania it is olive trees.

Climate change indicators and their projections

Indicators of climatic and meteorological conditions constitute a vital category of tools used to assess and understand climate characteristics and changes, as well as their impacts on our agri-environment and societies. These indicators capture a variety of parameters related to climate and weather conditions. Their utility lies in their ability to provide valuable information on long-term climate trends, seasonal variations, as well as extreme weather events. These data enable the assessment of climate impacts on ecosystems, natural resources, human activities, and infrastructure. Additionally, they serve as a basis for developing adaptation and mitigation strategies against climate change, as well as policies for sustainable environmental management. Some of the selected key indicators include metrics such as land cover classification, land surface temperature, evapotranspiration, soil composition, the normalized difference water index, and more. They serve as a baseline for understanding how agricultural systems have responded to environmental changes over time and set the foundation for future projections.

Projection of indicators is done building upon these historical indicators using CMIP6 models (1) for the years 2030, 2050, and 2070 under two scenarios: SSP2-4.5 (2) and SSP5-8.5. These projections include indicators like air temperature, soil water storage capacity, precipitation, drought frequency, and snowfall flux, covering both short-term (2025-2030) and long-term (2030-2070) periods. Short-term projections focus on specific metrics such as near-surface air temperature, wind speed, and humidity, while the long-term forecast encompasses a broader range of 23 indicators. These projections will be downscaled to 1km and 10 km resolutions to allow us to assess their impact on Pilot’s representing diverse cropping systems and livestock husbandry practices in landscapes harbouring different ecosystems and soils, the sub-systems are targeted for assessment of the effects of climate change and tailored recommended adaptation and mitigation strategies.

Mapping indicators, impacts and coping strategies

To effectively assess the impact of climate change on agricultural systems, a detailed mapping of candidate indicators to the affected agri-environment systems is being carried out. This mapping aligns historical trends and projection of indicators with possible expected impacts supported by literature and expert elicitations on specific AES such as cropping systems, livestock systems, soil health, and biodiversity. For examples, in scenarios of increased projected temperature, established research indicates that the impact on cropping systems might include challenges related to shifts in growing seasons, water availability, and crop productivity, leading to changes in crop types, planting schedules, and overall agricultural practices. Livestock systems will be affected by increased heat stress, altering feed availability and quality, and affecting animal health, growth rates, and reproduction. Soil health, a crucial foundation for sustainable agriculture, could degrade due to erosion, nutrient depletion, and changes in moisture retention, threatening long-term productivity. Agrobiodiversity, which supports the resilience of agricultural ecosystems, may decline as climate change alters habitats, reduces species diversity, and disrupts the balance of organisms essential for pest control, pollination, and nutrient cycling. Together, these impacts highlight the need for adaptive strategies to protect the sustainability and productivity of agricultural systems.

The major outcome of the conducted study – still being in progress – based on such projected impacts, is a set of recommendations for both short- and long-term actions. Taking the scenario of projected increased temperature as example indicator, the literature on climate adaptation and mitigation strategies offers variety of approaches. Short-term adaptation strategies may include adjusting planting schedules to account for shifts in growing seasons and implementing better water management practices, such as irrigation systems or rainwater harvesting. Farmers may also adopt minimum or zero tillage practices, which help maintain soil moisture, prevent erosion, and improve soil structure, particularly in regions facing drought or heavy rainfall. Additionally, diversifying crop varieties that are more heat- or drought-tolerant could help farmers cope with changing climatic conditions. Long-term mitigation strategies could involve adopting agroforestry practices, where trees are integrated into agricultural landscapes to sequester carbon, enhance biodiversity, and improve soil health. Sustainable land management practices that improve soil organic matter and reduce chemical fertilizer use will help mitigate climate change while making farms more resilient. Lastly, shifting to agroecological farming systems, such as intercropping or organic farming, could improve farm resilience to changing environmental conditions while maintaining sustainability.

Road map to resilience

The initial results of the climate change impact assessment on agriculture systems that we produced so far, are starting to provide valuable insights into how the systems can prepare for a changing climate, with specific strategies tailored to the needs of farmers across Europe. By using climate projections, historical data, and expert insights, we can develop adaptation and mitigation strategies that are both practical and scalable. As we move forward, the integration of climate indicators into decision-making tools will help farmers, policymakers, and researchers make informed choices. This collaboration will ensure that farmers across Europe are ready for the challenges ahead, building climate-resilient agricultural systems that can thrive in the face of uncertainty. Through continuous monitoring and the active engagement of farming communities, both the short- and long-term strategies would remain effective to mitigate and enable the systems adapt in the face of evolving climate challenges while ensuring environmental sustainability.

(1) The Coupled Model Intercomparison Project (CMIP) is a global scientific initiative designed to improve the understanding of climate change by comparing different climate models. It provides a framework to simulate past, present, and future climate conditions based on various factors such as greenhouse gas emissions and land use changes. The latest phase, CMIP6, integrates data from multiple climate models worldwide to generate future projections, helping scientists and policymakers assess potential climate impacts.

(2) Within CMIP6, Shared Socioeconomic Pathways (SSP) are scenarios that describe different possible futures based on socioeconomic developments and greenhouse gas emissions. These pathways help researchers explore how human activities might influence climate change. SSP2-4.5 represents a future where moderate efforts are made to reduce emissions, leading to a balanced climate response, while SSP5-8.5 assumes high emissions with minimal climate policies, resulting in more extreme warming and environmental changes. These projections provide valuable insights for developing climate adaptation and mitigation strategies.

Agriculture is rapidly becoming digitalised, with stakeholders increasingly relying on technologies such as IoT sensors, farm management systems, remote sensing, and precision farming equipment. Within this digital transformation, dataspaces have emerged as crucial frameworks enabling secure, structured, and regulated data exchange among stakeholders such as farmers, agribusinesses, technology providers, policymakers, and researchers. By facilitating transparent collaboration, dataspaces enhance the potential for innovation and collective value creation, allowing stakeholders to integrate fragmented agricultural data and drive significant productivity and sustainability improvements across the sector.

However, despite enabling robust data exchange, agricultural dataspaces currently lack structured methodologies to assess the value of the data being shared and exchanged. Although the infrastructure for secure and interoperable data sharing is growing, the assessment and transparent quantification of data’s inherent value remain missing. This gap prevents data producers and consumers from fully recognising, quantifying, and realising the economic potential of their data assets. Data producers may underestimate their contributions, while consumers may struggle to accurately assess the benefits or justify the costs associated with data access.

Acknowledging the significance of this issue, the AgriDataValue project will conduct targeted stakeholder interviews within its partners to understand how agricultural stakeholders currently perceive, interpret, and articulate the value of their data. These stakeholder insights will provide critical input and context to support and inform ongoing research efforts in this domain. Specifically, these insights will help researchers better understand stakeholder perspectives, identify gaps, and inform the development of robust, actionable data value assessment methodologies tailored to agricultural contexts.
Incorporating effective data value assessment methodologies into agricultural dataspaces can enhance interactions between data producers and data consumers. By evidencing data value, stakeholders can better engage in data-sharing practices, improve trust, and benefit from fairer and more productive collaboration. Furthermore, robust assessment frameworks would stimulate greater participation in agricultural dataspaces, contributing to the growth and sustainability of a thriving agricultural data economy.

On Wednesday, February 26, Vicky Inglezou, Project Manager of the NILEAS Producers Group, presented the AgriDataValue Project at the informative Workshop on Carbon Farming, entitled: “Prospects & Challenges of Carbon Farming”, within the framework of the Erasmus+ Carbon Farming HUB program.

Held in Lesvos Chamber (Mytilene), Ms. Inglezou highlighted the AgriDataValue Project’s mission to enhance agricultural digital transformation in Europe. The project aims to create a digital platform that integrates real-time data from ground sensors and agrometeorological stations with multispectral images from satellites and drones, supporting machine learning models and smart agriculture applications.

In her presentation on olive cultivation, she emphasized smart tools that can manage risks and tackle climate challenges. She highlighted NILEAS’ three pilot use cases: disease forecasting, anti-frost control, and pest management for olive fruit flies. She concluded with the benefits of digital transformation in agriculture, including environmental impact, contribution to climate change mitigation, biodiversity preservation, improved food security, access to nutritious food, affordability, fair economic returns for producers, and promotion of fair trade.

When we talk about interoperability in data spaces, it’s easy to think of it within one sector at a time, such as agriculture, manufacturing, healthcare, or logistics. But in real life, industries don’t operate in silos. A farm doesn’t just grow food. It relies on weather data, transport networks, energy providers, and financial services to function.
This is why interoperability must be planned from a domain-agnostic angle from the very beginning of design and implementation. If data-sharing models are developed with only a single sector in mind, we might overlook opportunities to connect agriculture with its natural partners. In reality, industries like transport, energy, insurance, and finance constantly interact with agriculture. Ensuring that data is structured in a way that makes sense across domains can help strengthen these collaborations. By thinking from a cross-domain perspective, we can build on existing interoperability efforts to make agricultural data more accessible and usable beyond its immediate ecosystem.

An Experiment in Cross-Sector Interoperability: ADV Data Model

Agriculture already has robust semantic interoperability frameworks that ensure different platforms, systems, and organizations within the sector can exchange data smoothly. But the challenge we are addressing now is how to extend these mechanisms beyond agriculture.
The experiment we are working on in the AgriDataValue project is called the ADV Data Model. This model does not aim to replace existing agricultural models but rather enrich them by adding domain-agnostic capabilities on top of already comprehensive agricultural interoperability mechanisms. We are exploring how agriculture can seamlessly integrate with external data spaces, allowing agricultural data to be more easily understood, accessed, and used by industries like logistics, energy, and finance without requiring complex custom mappings or conversions.
Since interoperability must be tested in real-world applications, the ADV Data Model is still under development. After the validation process is complete, the model is planned to be made available as open-source, ensuring that stakeholders across industries can reuse, adapt, and contribute to its evolution.

Why This Matters for the Future of Agriculture?

At the end of the day, an agricultural organization should be able to share its data with stakeholders from other industries without barriers. A farming cooperative should be able to exchange sustainability data with financial institutions to access green financing programs. A smart irrigation system should be able to adjust water usage based on real-time energy grid prices without manual intervention. A food distributor should be able to align with logistics networks using standardized, interoperable data.
This experiment is a step toward making these cross-sector interactions smoother, faster, and more efficient. By enhancing existing agriculture-focused semantic models with domain-agnostic interoperability, the ADV Data Model is being designed as a flexible and open solution for a more connected and collaborative future. The goal is to ensure that agricultural data can move seamlessly across industries, just like business and supply chains do in the real world.

On Thursday, January 23, Vicky Inglezou, Project Manager of the NILEAS Producers’ Group, delivered an impactful presentation on the AgriDataValue Project (HorizonEurope) at the event themed “Agricultural Production and Community Resilience in Magnesia.” This significant initiative is part of the NKUA Project “Governance of Just Socio-Technical Transitions (Go-JuST)” under the National Recovery and Resilience Plan “Greece 2.0,” which collaborates with the Hellenic Foundation for Research and Innovation (HFRI).
Held in Volos (Region of Thessaly), Ms. Inglezou showcased the AgriDataValue Project’s mission to drive developments and strengthen the agricultural digital transformation at European level. The aim of the project is to create a digital platform that will combine and utilize data collected in real time from ground sensors and agro-meteorological stations, with multispectral images from satellites and drones, in order to support the creation of machine learning models and high-value-added smart agriculture and livestock applications.

In her engaging discussion on olive cultivation, she highlighted how smart tools and applications can mitigate management risks and address the urgent challenges of the climate crisis. She specifically mentioned the three use cases in which NILEAS participates as a pilot in AgriDataValue (olive tree disease forecast/detection, anti-frost control, and pest control on olive fruit fly). She concluded by outlining the potential benefits of digital transformation in agriculture, including: a)positive environmental and climate impacts, b)contribution to climate change mitigation, c)reversal of biodiversity loss, d)enhanced food security, nutrition, and public health, e)access to safe and nutritious food, f)affordability of healthy food for everyone, g)fair economic returns for all stakeholders, especially primary sector producers and h)promotion of competitiveness and fair trade.

The presentation provided a clear overview of the project’s objectives and its transformative impact on the agricultural data landscape. The event was attended by over 100 participants, including representatives from local authorities, farmers, advisors, and members of the scientific community.

In the ongoing transition from conventional to smart farming and precision agriculture, AgriDataValue’s WP1 aims to provide comprehensive insights on how farmers across Europe are engaging with these technologies. For this purpose, a survey destined to Europe’s farmers through the AgriDataValue pilot sites and network was built. The aim of the survey was to gather information on the status of awareness and knowledge on smart farming and precision agriculture, perceived benefits, barriers/concerns, implementation considerations and future outlooks. A set of 12 questions was developed and added to the EU Survey platform in 24 languages (https://ec.europa.eu/eusurvey/runner/AgridataValueSurvey). Available languages are: Bulgarian, Czech, Danish, German, Greek, English, Estonian, Finnish, French, Irish, Croatian, Hungarian, Italian, Lithuanian, Latvian, Maltese, Dutch, Polish, Portuguese, Romanian, Spanish, Slovak, Slovene and Swedish. Overall, 444 contributions were collected. The findings of this survey are presented in what follows.

1. Sources of Information: Education Matters
   The survey reveals distinct preferences in information sources based on educational background. Farmers with a Master’s degree or higher, predominantly rely on online resources (133 respondents) and agricultural publications (126 respondents). This group values reviewed, authoritative sources and structured learning opportunities like conferences and seminars (92 respondents). In contrast, those with a Technical/Agricultural diploma or Bachelor’s degree adopt a more balanced approach, incorporating different corpora including online resources (112 respondents), publications (82 respondents), and practical sources like extension services (55 respondents) and social media (54 respondents).
   Farmers with Secondary or Technical education show a strong reliance on online resources (65 respondents) and agricultural events (64 respondents), emphasizing community knowledge and informal networks. Those with Primary education heavily depend on peer networks (13 respondents), with limited use of online resources and formal channels, highlighting potential accessibility constraints or educational background-related challenges.
2. Perceived Benefits: Experience Shapes Priorities
   The survey highlights that farmers with varying years of experience perceive the benefits of smart farming differently. Climate change adaptation and mitigation are top priorities for farmers with 25, 20, 15, 10, and 5 years of experience, reflecting a universal need for resilience and a general awareness on climate change and its impacts. Cost savings and enhanced environmental sustainability are also highly valued across all experience levels, indicating a broad recognition of smart farming’s potential to improve efficiency and sustainability.
   Experienced farmers (30 years) particularly value data-driven decision-making and improved resource management, suggesting that these technologies complement their extensive field knowledge. Less experienced farmers (5-10 years) are keen on yield improvement and labour reduction, aligning with their need to establish productive and efficient operations somewhat under an economic/profit point of view.
3. Barriers to Adoption: Financial and Technical Hurdles
   Despite the promising benefits, according to responders, several barriers hinder the widespread adoption of smart farming. High initial investment costs are the most significant barrier, cited by both familiar (218 respondents) and unfamiliar (133 respondents) participants, hence underlining again the weight of economic considerations. Limited access to financing options and lack of technical knowledge are also major concerns, highlighting the need for financial support and educational initiatives.
   Farmers familiar with smart farming are more aware of detailed challenges, such as lack of institutional support and technology customization limitations. In contrast, those unfamiliar with the concept show a relatively lower perception of barriers, indicating a potential gap in awareness and understanding. The latter can be considered as the easiest barrier as education and knowledge spreading can contribute to bridging these gaps.
4. Detailed Insights: Benefits and Barriers by Farming Type
   The survey also provides a detailed breakdown of perceived benefits and barriers based on the type of farming practiced:
   o Crop Farming: Farmers engaged in crop farming see significant benefits in climate change adaptation (124 respondents), cost savings (184 respondents), and enhanced pest and disease control (147 respondents). However, they also face barriers such as high initial investment costs (224 respondents) and lack of technical knowledge (123 respondents).
   o Mixed Farming: Mixed farmers value improved resource management (85 respondents) and increased crop yield (85 respondents). Barriers include high initial investment costs (92 respondents) and limited access to financing (67 respondents).
   o Livestock Farming: Livestock farmers perceive benefits in enhanced pest and disease control (9 respondents) and improved resource management (14 respondents). Barriers include high initial investment costs (17 respondents) and lack of technical knowledge (7 respondents).
   o Viticulture: Farmers in viticulture see benefits in improved data-driven decision-making (9 respondents) and enhanced environmental sustainability (6 respondents). Barriers include high initial investment costs (8 respondents) and lack of reliable internet connectivity (3 respondents).
   o Other Niche Farming: These farmers value increased crop yield and productivity (6 respondents) and improved resource management (2 respondents). Barriers include high initial investment costs and limited availability of suitable technologies.
5. Influencing Factors: What Drives Adoption?
   Financial incentives and subsidies are the strongest motivators for adopting smart farming, especially among those facing high investment costs. The availability of affordable and reliable technologies, access to training and technical support, and demonstrated success stories from other farmers also play crucial roles in influencing adoption decisions.
   Interestingly, participants who prioritize data security and privacy are more cautious, reflecting a need for reassurance about the safety of their information particularly for large-scale industrial farmers. This aspect highlights the importance of addressing data concerns to foster trust and encourage adoption.
6. Gender and Education: Shifting Dynamics
   The survey also sheds light on gender representation and educational attainment in agriculture. While male participants dominate across all age groups, female representation is highest among the youngest farmers (18-25 years), suggesting a positive trend towards gender inclusivity particularly for upcoming farmers. Education levels vary, with a notable presence of advanced degrees among male participants, while female participants are more represented in technical and secondary education according to the survey’s responders.
7. Pathways to Progress: Overcoming Barriers and Enhancing Adoption
   To fully harness the potential of smart farming, it is essential to address the identified barriers and leverage the factors that drive adoption. Some recommendations are proposed below:
   o Financial Support and Incentives: Governments and institutions should provide more financial incentives, subsidies, and accessible financing options to lower the initial investment barrier. This support can encourage more farmers to adopt smart farming technologies.
   o Training and Technical Support: Establishing comprehensive training programs and technical support services can help bridge the knowledge gap. These initiatives should focus on practical, hands-on training to build confidence and competence in using smart farming tools.
   o Success Stories and Demonstrations: Sharing success stories and conducting demonstrations can showcase the tangible benefits of smart farming. Peer-to-peer learning and examples from successful initiatives like AgriDataValue can inspire and motivate farmers to embrace new technologies.
   o Policy and Institutional Support: Developing favorable policies and strengthening institutional support can create an enabling environment for smart farming. This includes ensuring access to reliable internet connectivity and addressing concerns about data security and privacy.
   o Inclusive and Accessible Solutions: Tailoring smart farming solutions to be inclusive and accessible for farmers with varying educational backgrounds and experience levels is crucial. This approach can help ensure that all farmers, regardless of their starting point, can benefit from these technologies.

Conclusion: Pathways to Progress

The AgridataValue Survey underscores the transformative potential of smart farming and precision agriculture. By addressing financial and technical barriers, providing robust support systems, and fostering an inclusive environment, the full potential of these technologies can be unlocked. As AgriDataValue moves forward, leveraging insights and findings to shape policies and initiatives that support farmers in their journey towards a sustainable and efficient agricultural future.

The AgriDataValue project was presented at the 13th International Exhibition for Livestock and Poultry, Zootechnia 2025, which was held from January 30 to February 2 at the Thessaloniki International Exhibition Centre (HELEXPO). Dr. Theodore Zahariadis, the project’s coordinator, presented AgriDataValue’s innovative contributions to smart farming and agri-environmental monitoring, along with utilization of AI/ML in livestock production.

Established in 1999 and held biennially, Zootechnia (zootechnia-expo.gr) stands as Greece’s premier specialized exhibition dedicated to the livestock and poultry sectors. The event serves as a central platform for showcasing advancements in productive animal species, state-of-the-art machinery, innovative supplies, and sector-specific services. Its primary objective is to foster a dynamic marketplace that encourages collaboration among specialized enterprises, public and private sector stakeholders, professionals, and the general public.

During the exhibition, Dr. Zahariadis engaged in comprehensive discussions with representatives from the Greek Ministry of Rural Development and Food, as well as livestock and poultry producers from Greece and the broader Balkan region. He presented key findings from the AgriDataValue project, emphasizing the development of an innovative, intelligent, and multi-technology platform designed to enhance smart farming capabilities and agri-environmental monitoring. This platform aims to address challenges such as over-irrigation, excessive use of synthetic fertilizers and pesticides, and suboptimal livestock production, thereby promoting sustainable farming practices and environmental protection (agridatavalue.eu). Funded by the European Union under the Horizon Europe research and innovation program, the AgriDataValue project, seeks to establish itself as a game-changer in the agricultural sector. By leveraging advanced big data and data-space technologies, combined with agricultural knowledge and new business models, the project aims to strengthen smart farming capacities, enhance competitiveness, and ensure fair income for farmers.

In parallel to the Zootechnia 2025 Exhibition, Dr. Theodore Zahariadis, Coordinator of the AgriDataValue project participated in a strategic meeting organized by the National Union of Agricultural Cooperatives of Greece (ETHEAS). The focus of the meeting was the advancement of training and certification programs for agricultural cooperatives, aiming to enhance their capacity to provide advisory services to their members and associated agricultural organizations within their regions.
The assembly was organized over by Mr. Moschos Korasidis, General Director of ETHEAS. The meeting was attended by the President of ETHEAS, Mr. Pavlos Satolias, members of the ETHEAS Board of Directors, the Head of the Special Management Service of the Rural Development Program of Greece, Mr. Nikolaos Manetas and representatives of livestock cooperatives. During the discussions, Dr. Zahariadis emphasized the critical importance of adopting Circular Economy principles within the livestock sector and presented insights of the AgriDataValue project’scontributions, particularly concerning the smart farming and assessment of zoonotic disease transmission risks among small ruminants, such as goats and sheep.

The AgriDataValue project was presented by the project coordinator, Dr. Theodore Zahariadis, at the 2nd Multi-Disciplinary International Conference on “India and Greece: History, Society, Science & Entrepreneurship.” The event took place in Thessaloniki, Greece, from December 2 to 6, 2024.

The conference focused on key thematic areas, including:
• Culture, Civilization & Tourism
• History, International Relations, Diplomacy & Geopolitics
• Informatics, Technology & Innovation
• Business, Engineering, Agriculture & Environment

Dr. Zahariadis introduced the AgriDataValue platform, emphasizing its innovative approach to data-driven agriculture. He highlighted the critical role of in-situ data collection in training AI models, demonstrating how precision agriculture can benefit from advanced technological solutions.
The participation of AgriDataValue in such a prestigious conference underscores the growing recognition of smart agricultural solutions in global discussions on technology and innovation.

Agenzia Provinciale per i Pagamenti (APPAG) is the Common Agricultural Policy (CAP) Paying Agency for the Autonomous Province of Trento. APPAG is in charge of managing and controlling expenditure financed by the European Agricultural Guarantee Fund (EAGF) and the European Agricultural Fund for Rural Development (EAFRD). APPAG authorises and checks payments in order to establish the amount to be paid to farmers in accordance with European law. It is also responsible for administrative and on-site checks on the operations performed by farmers within the framework of CAP financing.

APPAG manages different types of agricultural data related to the Autonomous Province of Trento, i.e. land use, graphic cultivation plans, the Provincial livestock database and the Provincial graphic pasture land register. This data originates from orthophotos provided by the national Agricultural Funding Agency (Agenzia per le erogazioni in Agricoltura – AGEA), APPAG’s periodical on site inspections and farmers’ self-declarations.

In AgriDataValue and within the framework of WP4, APPAG will contribute valuable data to train the projects climate models. This initiative focuses on the Autonomous Province of Trento, which will act as a small-scale pilot region. The goal is to predict the impacts of climate change on local agriculture and specific cultivation areas. By analyzing this data, we aim to develop comprehensive guidelines for local farmers. These guidelines will help farmers understand future agricultural trends and implement targeted mitigation measures to adapt to changing climate conditions. This proactive approach will ensure that the agricultural sector in Trentino remains resilient and sustainable in the face of climate change.

In addition, with the application of climate models, APPAG could identify the possible impact of climate change on CAP 2023-2027 performance indicators at European and/or national level. CAP’s performance is measured by a wide set of indicators provided by the European Commission and Member States, some of which are closely influenced by climatic conditions. Identifying potential barriers posed by climate change on the achievement of these goals can help in adopting some mitigation measures and intensifying efforts on specific aspects of the CAP.