Why look at global societal and technological trends for the data economy?

First of all, the trends often have a significant impact on the data economy and, implicitly, on the data economy for food systems. Thus, the exploration of these trends might help understand how some of the changes could influence and shape future requirements and data value propositions for food systems and see how changes in the data economy affect and impact the data economy for food systems. In this article, our partner organisation Lisbon Council presents the mega-trends, classified as follows:

  • Technological trends. Technology developments are important for the data economy; therefore, it is also important to identify the technologies relevant to food systems (and implicitly for data economy for food systems) to understand the changes, challenges and opportunities they bring.
  • Societal and environmental trends. Societal expectations from the food systems can define future developments in the field. And, any future development needs to consider the environmental and societal implications too.
  • Policy and regulatory developments. Policy and regulations can make or break the future developments of the data economy for food systems. Too much burden on stakeholders will hamper the adoption and use of new technologies or processes by food system actors. A lack of coherence in regulations across different areas (e.g., between data and food regulations) could also lead to ethical and cybersecurity risks with potentially harmful results for customers and stakeholders. Therefore, balance in the regulatory environment is crucial.

How can we identify the impact of these mega-trends for the data economy for food systems?

When we consider the technological trends, we look at the new developments that can be found in
both hardware and software domains. The hardware development enhances the potential of
machines for better performances (e.g., advanced sensors, high-band 5G and 6G, low-power wide-
area networks, etc.) and supports more data generation. At the same time, software developments
tap into the data availability and provide better solutions for food system actors (e.g., big data
analytics, artificial intelligence, cloud and edge computing, data spaces, etc.) to improve their overall
performance. For the food system, the impact expected considers the improvement of the
digitalisation of the agri-food systems, which could contribute to a further increase in the volume of the
available data from the food system. For example, combining the sensors’ data as well as other open
data available (e.g., cadastral data, weather data, etc.), and AI or big data analytics applications can
provide relevant information for food system stakeholders and enable potential solutions to more
efficient processes. In this sense, precision agriculture uses data and applications to help agriculture
production become more efficient but also provides new products and services for the agri-food
sectors. At the same time, data and applications can improve food logistics and delivery by ensuring
better delivery processes of fresh produce, reducing food loss and waste and providing more
opportunities for new services for the agri-food businesses.

However, all changes can raise some societal and environmental challenges. A high volume of data could be more susceptible to cybersecurity and privacy risks. New technologies could be expensive, therefore the need for high up-front investments might not be for everyone. Multiple sources of data raise interoperability issues as data might not always be compatible. Thus, digital transition has some important challenges too (not only benefits).

Another example is the use of big data analytics to provide accurate crop predictions based on sophisticated computer algorithms to analyse weather and crop data. At the same time, the technology could also help design chemically engineered seeds and facilitate the use of drones for data gathering which could allow for agricultural automation. For agriculture, the use of big data analytics will enable process optimisation through capabilities like productivity forecasting and driverless tractor applications. In retail, using machine learning (ML) to analyse huge sets of purchasing data, discern patterns, and give shoppers customised recommendations is expected to boost sales. On the benefits side, the use of new technologies contributes to saving costs, increasing revenue for businesses, and providing opportunities for new developments in the area. On the challenges side, the new technologies often come with a high up-front investment in both talent and resources; it increases the cybersecurity and privacy concerns in data usage and might generate interoperability issues. And, often, it implies a higher level of ethical and regulatory compliance for users and providers.

When we consider the societal trends that could raise important challenges for the agri-food sectors, we look at economic globalisation, demographic change, migration and urbanisation. In addition, climate change, natural resources scarcity and the rise in energy consumption increase the pressure on the food system. The potential impacts of these trends on the food system concern more the re-adaptation of a way of working in the agri-food sectors: the development of agroecology (i.e., regenerative agriculture or agriculture in synergy with nature) and of food innovations to relieve environmental pressure. Agroecology will use new ways for agricultural production in synergy with nature and will make use of technology to ensure food production efficiency, reduce food loss and waste and track the flow of feed and food more effectively. The green transition aims to ensure more sustainable farming and livestock production and processes, provide healthier products and reduce food loss and waste. The healthier diets will help the population address some of the health issues related to food. It will encourage the consumption of locally sourced foods and contribute to a better integration of the communities. It will also help reduce food loss and waste through a better and more efficient approach to food consumption and sourcing.

If we look at the urbanisation trend, we notice that in 2021, the urban areas represented 2% of the total land and were associated with increased productivity (70% of GDP). Why is the population willing to move to the city? Because urbanisation provides better opportunities for people – more and better jobs, services and education. Thus, in 2021, the urban areas (cities and towns) hosted approx. 75% of the EU population. But at the same time, urbanisation brings several challenges, such as environmental degradation, public health, housing shortages, congestion and increased inequalities. For example, in 2021, the urban area accounted for 60% of energy consumption and 70% of greenhouse gas emissions. Moreover, they produced 70% of global waste. And, while the last trends show cities are shrinking, the suburbs are growing. What does this mean for the agri-food systems? For agri-food systems, these developments translate into a shrinking availability of land for agricultural activities, an increasing need for food and a rise in food prices (which would impact food availability in the long-term development).

Policy and regulatory developments play a significant role in shaping the future of technology and society. The policies and regulations can make or break the success of transitioning towards sustainable food systems.

The data economy already has several regulations concerning data use and re-use within society (e.g., the Digital Services Act, the Digital Market Act, the Data Governance Act, the Data Act and the AI Act) and will probably not stop here. On the other hand, to transition to more sustainable food systems, several acts in the food domain were also developed (e.g., the Sustainable food systems framework, the EU labelling framework, the Directive on Green Claims, etc.). Moreover, tools such as Product Environmental Footprint (PEF) and Life-Cycle-Assessment (LCA) are often used to assess the sustainability of products and services in the food systems. All these regulations impact both the data economy and agri-food systems and will influence the future developments of the Sectoral data spaces (e.g., AgriData Space). But, at the same time, they reflect also on individual freedoms and democratic values, on intellectual property protection and innovation, and on promoting FAIR principles in data sharing and trust. What policy and regulations need to do is to ensure synergies between policies and digitalisation that could enhance the development of sustainable food systems.

For example, let’s look at how food sustainability labelling works. First, we consider the data economy regulations and policies to establish the context for access to data, data sharing and data use. Second, the agri-food policies and regulations define the needs and requirements for the development of sustainable food systems. In this context, AgriData Space will become the facilitator of the ethical and responsible access and use of data in the agri-food system. In this context, stakeholders would be able to assess the sustainability of products and/or operations by accessing various sources of data (e.g., primary data, such as data from farmers and supply chain partners, and secondary data, such as open data related to agri-food systems – weather data, cadastral data, etc.) and combining them with new business models. It will help assess the environmental impact of food, enable higher transparency of the food impact and support the transition towards a more sustainable food system.

Thus, there is an intricate relationship between technological, societal, and regulatory trends in shaping the future of data economies, particularly when it comes to food systems. Trends bring both benefits and challenges for agri-food systems and it is important to find the right balance between the two to be able to shift towards a sustainable food system, considering at the same time the environmental impacts, ethical data use, and transparency in food production processes.