Transforming Agriculture: The Power of Data and Interoperability

#39, November 2023

Federica Rossi

Ri.Nova Soc. Coop.

Federica Rossi, Head of Precision Agriculture and Agri-Environment division at Ri.Nova Soc. Coop.  

Co-coordinator with Gian Luca Barchi of the Interoperability Working Group within the Thematic Partnership “Traceability & Big Data” in the Agri-Food Value Chain.

The digital revolution of Agriculture 4.0 lies in the increasing capacity to produce, transfer and analyse data. Indeed, digital technologies have unlocked the potential to record and process a greater volume of agri-food data, in order to achieve sustainability of production, both economically and environmentally. For instance, advances in sensor technology have empowered farmers to monitor specific parameters in real-time, while robotics have supported better automation of the processes. Additionally, computing power has become more accessible and affordable, which has also helped the creation of new decision-support tools for better agricultural management. Moreover, big data supports a high volume of real-time and historical data and Artificial Intelligence turns these data into added value and actionable knowledge. When accessed, shared and used among a range of stakeholders, these larger and more granular sets of agricultural data can enable benefits across many stages of the agri-food system.

However, Agriculture 4.0 introduces new challenges in terms of the adoption of data, knowledge and technologies, due to problems of interoperability of systems and also in terms of identifying the most suitable data sources to exploit and information models to use.

The usability, and thus the usefulness, of data generated by public and private sources depends essentially on their technical interoperability and quality. In this context, interoperability refers to the ability of two or more systems to exchange and use information, allowing different digital structures that are domain-specific to be connected into a larger workflow.

To enable a network of heterogeneous devices to be truly interconnected and interoperable, common data representation and exchange formats must be implemented, along with adequate communication protocols, to ensure that these devices can communicate effectively. In this scenario, big data analytics can play a key role in transforming data into added value of agri-food stakeholders, through its capacity to efficiently aggregate, process and visualise large and complex datasets, also in line with the European Commission’s Action Plan for Findable, Accessible, Interoperable and Reusable data (FAIR). The goals defined in the European Green Deal can be reached more easily through a combination of technologies that provide the basis for improved visibility, traceability, decision-making and involvement of the entire agri-food sector. To achieve this, the first step is the adoption of a common architecture to provide a convergence point for new solutions and thus promote their interoperability and adoption. Then the aspects of data governance, privacy and security are crucial, as the digitisation of the sector makes ever larger volumes of data available. 

Agriculture 4.0 and the interoperability of platforms can contribute to increasing the productivity and efficiency of agricultural and food systems, improving the quantity, quality and accessibility of agricultural products, adapting to climate change, reducing food losses and waste, optimising the use of natural resources in a sustainable way and, consequently, reducing environmental impacts in the years to come. Furthermore, Open Source interoperability represents an opportunity for the agricultural industry to embrace innovative models of collaboration and innovation in order to generate changes in the value chain and business models with greater emphasis on knowledge gathering, analysis and exchange.

In the framework of Agriculture 4.0, it is therefore clear that the theme of system interoperability is increasingly relevant and a priority. For this reason, in recent years, initiatives and projects in this direction have multiplied, including the number of data integration platforms for agriculture, most of which are directly aimed at supporting farms in making key decisions, using data from different sources in a harmonious way.

This is why Ri.Nova, as a cooperative and research centre, coordinates and implements projects and technological development activities to support and promote the competitiveness of companies in the agricultural and agri-food sector, through a team of specialists and a dense network of public and private partnerships in Italy and abroad. With a view to promoting innovation and competitiveness, Ri.Nova is now leading the Interoperability Working Group within the Thematic Partnership “Traceability&Big Data” in the Agri-Food Value Chain, to pave the way for seamless collaboration and data exchange between stakeholders in the agri-food sector.

Ri.Nova has also long been active in projects on Agriculture 4.0, in particular those on platform interoperability. Examples include the following projects:

  • AgriDataValue project - Smart Farm and Agri-environmental Big Data Space, funded under the EU Horizon Europe research and innovation Programme, introduces an innovative, open source, multi-technology Agri-Environment Data Space in Smart Farming (; 
  • Agro.Big.Data.Science project, co-financed by the Emilia-Romagna POR FESR 2014-2020, has developed a specialised hub for the agri-food sector, native Big Data, capable of processing complex data. A multifunctional platform that can be used as a territorial and business management dashboard for real-time planning and support to productivity and input management (  

In general, on the interoperability front, the European Union is actively working. For those who would like to learn more, we leave the following links to some active projects that we find interesting:


Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape - emerging trends, challenges and opportunities. In Agronomy (Vol. 11, Issue 4).
Bonneau, V., Copigneaux, B., Probst, L., & Pedersen, B. (2017). Industry 4.0 in agriculture: Focus on IoT aspects. Digital Transformation Monitor.
Duncan, E., Glaros, A., Ross, D. Z., & Nost, E. (2021). New but for whom? Discourses of innovation in precision agriculture. Agriculture and Human Values, 38(4).
Jouanjean, M., Casalini, F., Wiseman, L., & Gray, E. (2020). Issues around data governance in the digital transformation of agriculture: The farmers’ perspective. Food, Agriculture and Fisheries Papers, OECD Publishing, 146.
López-Morales, J. A., Martínez, J. A., & Skarmeta, A. F. (2020). Digital transformation of agriculture through the use of an interoperable platform. Sensors (Switzerland), 20(4).
Roussaki, I., Doolin, K., Skarmeta, A., Routis, G., Lopez-Morales, J. A., Claffey, E., Mora, M., & Martinez, J. A. (2023). Building an interoperable space for smart agriculture. Digital Communications and Networks, 9(1).