Final version – June 2018


In 2030 agri-food systems and businesses will produce healthy nutritious foods for all, through input-efficient methods and an environment that fosters collaborative networks that constantly seek to improve their economic, environmental, technological and social performance for all players.


Further, food systems will respond in an agile way to local, regional, and global needs, and contribute to achieving a wide range of objectives as framed in the Sustainable Development Goals. These objectives include achieving food security, mitigating global warming, ensuring good health and preserving biodiversity.


To be effective, these food systems need to be inclusive, resilient and knowledge intensive. Agri-food systems and businesses create and use knowledge on food production, environmental effects, food processing, distribution, nutritional values, production costs and the likely health benefits. They must be supported by open science-based knowledge systems to stimulate further innovation and accelerate impact.


Delivering on this approach requires a perspective that looks beyond individual value chains, crops, livestock or farm types. In the ‘open-science-based food knowledge cloud’, researchers should be able to:

To realize this Vision 2030, the transition of the research approach towards a systemic, integrated, multidisciplinary and global approach needs to be accelerated. As part of this transition, agriculture and food systems research must embrace digitization, transparency and cooperation. 


The agri-food sector is very diverse. It relies on complex knowledge – both theoretical and experimental. For policy makers, planners and food producers to progress with the new thinking described here, they will need to take a view that integrates multidisciplinary, multi-scale, multi-actor and geo-location-based approaches. This will benefit from integrating a range of agricultural data, models and analytical and visualization tools, of which there are many to choose from.


This increase in available tools is driven by the emergence of systems thinking, coupled with the exponential growth of digital resources and global use of the Internet. Other key factors include: automated data collection (robots, Unmanned Aerial Vehicles, connected sensors, etc.); new tools in the “omics” fields  and emerging new information and data sources (e.g. Internet of Things, crowdsourcing, text and data mining approaches).  These trends are coupled to the fact that natural and societal phenomena are increasingly being described by massive data at different scales, from various sources and with different temporal-spatial resolutions. The ability to share, access and integrate heterogeneous data is a key to addressing climate change impacts on food security; to providing healthy and nutritious for food all; for developing sustainable food value chains; and supporting local agricultural adaptations and rural development. To best address these challenges in an agile approach, the research and innovation sectors need easy access to digital knowledge, data resources and technologies. This will be achieved with resources such as interoperable data, connected sensors, data sharing and exploration methods, modelling and coding frameworks, intensive simulation environments, or social networks – all fuelled by shared information exchange standards and wide access to high-speed internet connections.


To make this happen, it is critical that the industry and the community of research and development agencies has a robust and open e-science framework and related facilities or e-infrastructures that operate following common standards. This will enable ethical, responsible and secure sharing of the ‘engine’ of this new approach – data and information, computing and storage resources, codes and data-mining algorithms, models and ontologies. To make the system work effectively, partners need to have relevant expertise, and access to examples and best practices and an ethical framework that underpins an open science approach for agriculture.

In this light, ensuring sustainable and nutritious regional and global food systems requires that we commit to ‘Open Science’. This requires that research and development institutions, and every stakeholder in the innovation process fully embrace a digital transition for each phase of the knowledge production cycle for innovation. This extends from research planning and design to collection, analysis and simulation of data and the dissemination of knowledge and the underlying data.


This will enable increased collaboration and efficient, ethical and secure data flow between all partners from R&D, farming, supply chains and consumers. The support of policy makers is vital to create an environment for open science that will speed the pace of development and innovation.


Agri-food science and innovation will benefit significantly from such a shared knowledge ecosystem. This shared knowledge will be produced and used by diverse users including academic researchers but also farmers, the industry, extension services and citizens. A shared global data space will help build the infrastructures that will open useful information to all these stakeholders and propel the agri-food sector forward.


Embracing digitization, transparency and collaboration for this ambitious endeavour will generate significant benefits:

    Universal knowledge access and promotion of a knowledge sharing culture at community level has to be assured. While different levels of data openness and use need to be acknowledged, the FAIR principles are always an effective guide. Diversity has to be recognized and enforced. Data, information and knowledge today exists as an unconnected ecosystem, whose power will be greatly magnified if similar efforts are linked in a commons approach, that allows for various degrees of openness, depending on the context – ranging from sharing the subject matter or title of information, to limited openness and full open access, with embargo where necessary. Regulations will be needed to avoid the establishment of data monopolies and implementation of these regulations needs to be supported.

     The need to enable researchers to be able to easily explore, integrate and simulate their own data and data that have been produced by others – in or outside their community – that are complementary in terms of objects of study, scales and disciplines has to be emphasized. There is also a need to regulate database management rates depending on if data are used by academic scientists, private firms or the non-profit sector. This is most important for potentially valuable data produced by NGOs, who cannot afford the cost of ongoing database management. Only if these links are made, will knowledge production be able to be accelerated.

      The power of data produced by farmers and by land observation, which includes (but is not limited to) precision agriculture needs to be harnessed. Farms need to become laboratories linked to scientific research. As such, Farmers need to be recognized as partners in participatory research and not only as data providers.

      Data and information management start with research planning, data production – in the lab, on the field or at the observational level. A strong collaboration with equipment producers to introduce common data sharing principles and standards is of importance.

   Interoperability across data sources and agreement on the adopting of ‘good sense’ standards, without reinventing wheels is a “leitmotive”. Standards need to be;  open and shared; inter and cross-disciplinary; and co-defined with communities to ensure their adoption.

   It has to be built on existing digital infrastructures within the field and with generic (i.e. technological) infrastructures. Specific infrastructure and services to express  communities’ needs and requirement need to be developed

   Distributed efforts and flexible governance for long-term empowerment by and sustainability across the agri-food community is necessary. Appropriate business models for data sharing and related services, especially for ‘common goods’ such as those supporting semantic interoperability for shared information and data discovery will be developed.

    The scope needs to be beyond a European community, to build a global network. The importance of Big Agricultural Sciences in the Global South has to be understood. European Initiatives need to be integrated with those of G20 and G77 countries.

     Machine-readable means for encoding licenses for data and information (to support value-chain legal interoperability) and for encoding provenance of data and information (to enable attribution and quality assessments for any part of the value-chain) will be adopted.

     Approaches to improve the semantic interoperability of data that will facilitate and improve reasoning on data and information will be strongly encouraged

     Cornerstone is the  development of the necessary skills and capacity so that all partners and stakeholders in this endeavour can achieve this Vision 2030


Zane Sime (The Council of the Baltic Sea States)
Baltic TRAM Project
Baltic TRAM Reflections and Suggestions to the e-ROSA Vision Paper is included to the attached file



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