PowerPoint Presentation

[Audio] Welcome to Module 7 of the seminar series Digital Value Chains and Business Ecosystems in Agriculture. This module is called Skills, education and the role of higher education. In the earlier modules we looked at the technologies, the data flows and the business models that are reshaping agricultural value chains. We discussed platforms, sensors, traceability, and the way different actors come together in ecosystems. In this module we turn to the question that sits underneath all of those topics: do the people who need to use, manage, govern and improve these systems actually have the skills to do so? Technology does not create value on its own. Value appears when a farmer, an adviser, a cooperative manager, a processor or a policy officer understands what a tool can do, trusts it, and knows how to fit it into a real decision. Over the next twenty-odd minutes we will look at the evidence on skills in European agriculture, at which skills are needed, at how people actually learn, and at the particular responsibility and opportunity that universities and other higher education institutions have in this picture. Let us begin with what you should take away from this module..

[Audio] By the end of this module you should be able to do five things. First, describe the skills challenge in European agriculture using official statistics rather than impressions. Second, distinguish the different families of skills that digital value chains require, because digital skills are only one part of the story. Third, explain how formal education, non-formal training and informal learning on the farm and in the firm fit together, instead of competing with each other. Fourth, identify what higher education contributes to an agricultural ecosystem beyond the classic role of teaching students. And fifth, propose concrete actions that you, in your own organisation, project or region, could take to strengthen skills. Notice that the last outcome is practical. This is not a module about education policy in the . It is a module about how skills become a design element of every digital value chain and every business ecosystem you work with, whether you are a researcher, a company, an advisory body, a cooperative or a public authority. Keep your own context in mind as we move through the slides, and note down where each idea could apply to you..

[Audio] Why do we give skills a whole module? Because in many digital agriculture projects, the technology is not the bottleneck. Sensors, satellite data, decision support tools and traceability platforms already exist. What limits their impact is whether people can choose the right tool, set it up, interpret the output, and act on it with confidence. Digital tools also change roles. A farmer becomes a data producer as well as a food producer. An adviser becomes an interpreter of data as well as an agronomist. A cooperative becomes a data steward for its members. A processor or retailer asks for digital evidence of sustainability and quality. Each of these shifts requires new competences, and where those competences are missing, value tends to move to whoever has them, which is often a technology provider rather than the primary producer. That is why skills shape the distribution of value in an ecosystem, not only its efficiency. There is also a second pressure. Agriculture is going through a digital transition and a green transition at the same time. Farmers and their partners are asked to adopt precision tools while also meeting new environmental, climate and reporting expectations. Doing both requires people who can combine technical knowledge, data literacy and business judgement. So skills are best understood as infrastructure for the whole ecosystem..

[Audio] Let us look at the evidence. Eurostat's 2020 Farm Structure Survey is the most recent full census of farm managers in the European Union, and its numbers are striking. Around seventy-two per cent of farm managers, 72.3 to be precise, have only practical experience. Another 17.5 per cent have basic agricultural training, which includes completed agricultural apprenticeships and courses at agricultural colleges. And just 10.2 per cent have full agricultural training, meaning at least two years of full-time study after compulsory education at an agricultural college, university or other institution of higher education. Practical experience is enormously valuable, and I want to be clear that this is not a criticism of farmers. But it does tell us that the formal education system reaches only a minority of the people who take daily decisions on European farms. The age picture matters too. Only 11.9 per cent of farm managers were under forty years old. The encouraging news is that younger farm managers are better trained: 21.4 per cent of them have full agricultural training, compared with 3.6 per cent of farm managers aged sixty-five or over. So generational renewal and skills renewal are closely linked. As the next generation takes over, the skills profile of the sector can change, but only if entry routes, training and advisory support are in place..

[Audio] The European average hides very large differences between countries. In the Netherlands, 62.6 per cent of farm managers have full agricultural training. In Luxembourg it is 53.1 per cent, in France 38.4 per cent, and in Czechia 35.8 per cent. At the other end, Romania and Greece each record only 0.7 per cent of farm managers with full agricultural training, and the overwhelming majority, 94.5 per cent in Romania and 94.1 per cent in Greece, rely on practical experience alone. Greece also has one of the lowest shares of young farm managers in the Union: 7.2 per cent are under forty, against the European average of 11.9 per cent. I show Greece here because it is the context many of us work in, but also because it illustrates a general lesson. If you design a digital value chain or a business ecosystem for the Netherlands, you can assume a very different baseline of formal agricultural knowledge than if you design it for a region of Greece, Romania or elsewhere. Skills strategies must therefore start from a local diagnosis. Copying a training programme from one country to another without adapting it to the existing skills base, to the farm structure and to the language, is a common reason why programmes fail. Remember this when we talk about advisory services and higher education later in the module..

[Audio] Skills in agriculture do not exist in isolation. They sit within a European policy framework that has become quite explicit. The Digital Decade policy programme sets two headline targets for 2030: that at least eighty per cent of adults have basic digital skills, and that the European Union has twenty million employed ICT specialists. When the Council discussed these targets in 2022, it noted that only about fifty-four per cent of the adult population had basic digital skills at that time, and the Commission's own factsheet put the number of employed ICT specialists at about nine million in 2021. So the distance to the targets is substantial. Two further instruments are worth knowing. The Digital Education Action Plan for 2021 to 2027 sets out the Union's approach to adapting education and training systems to the digital age. And in 2023 the Council adopted two recommendations, one on the enabling factors for successful digital education and training, and one on improving the provision of digital skills, from basic to advanced and specialist, including artificial intelligence. For describing what digital competence actually means, the common reference is the Digital Competence Framework, known as DigComp, now in version 2.2. It organises competence into five areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem solving. We will use that logic in the next slides..

[Audio] If we want to design training, we first need a map of the skills involved. I suggest five families. The first is basic digital competence: finding and managing information, communicating and collaborating online, creating content, staying safe, and solving everyday technical problems. This is the foundation, and the Digital Decade targets are built on it. The second family is data and technical skills: understanding how sensors and satellite imagery produce data, how to clean, store and interpret it, how systems talk to each other through interoperability, and how to use artificial intelligence tools critically, knowing both their power and their limits. The third family is agronomic and sustainability knowledge. This one is often forgotten in digital discussions, yet without it nobody can tell whether a recommendation from an algorithm is sensible in a particular field, climate or market. The fourth family is business, ecosystem and governance skills: understanding how platforms create and distribute value, how data sharing agreements and contracts work, how to protect your rights over farm data, and how to comply with reporting requirements. The fifth family is human skills: collaboration across disciplines, communication, critical thinking, and the willingness to keep learning. Digital value chains reward people who can work across these five families, even if they specialise in only one or two..

[Audio] Skills needs differ by role, so let us walk along the value chain. Farmers and farm workers need practical competence to use digital tools in daily work, to understand what happens to their data, and to use decision support without surrendering their own judgement. Advisers and trainers need to go one step further: they must interpret data from many farms, facilitate peer learning, and translate between what technology can do and what a specific farm needs. Cooperatives and agri-food small and medium enterprises need skills in data stewardship, digital marketing and analytics, because they often sit at the centre of the ecosystem and aggregate information from many members. Input suppliers, processors and retailers need competences in traceability, interoperability and in producing credible sustainability evidence for customers and regulators. Technology providers need something that is often underestimated: deep agricultural domain knowledge and user-centred design skills. Many digital tools fail because they were designed without understanding how work is really organised on a farm. Finally, public bodies and researchers need skills in data governance, evaluation and knowledge transfer. The key message of this slide is that upskilling only farmers is not enough. An ecosystem is as capable as its least prepared link, and some of the largest gaps sit between the actors rather than within them..

[Audio] Let us now turn from what skills are needed to how people acquire them. It helps to distinguish three modes of learning. Formal learning leads to recognised qualifications, such as an apprenticeship, a diploma or a university degree. Non-formal learning is organised but does not necessarily lead to a formal qualification: a course offered by an advisory service, a workshop at a cooperative, a webinar series, or a vocational training measure. Informal learning happens through doing, through conversation with neighbours and colleagues, through visiting a demonstration farm, through trial and error. Given what we saw in the Eurostat data, where most farm managers rely on practical experience, informal learning is clearly central in agriculture, and any serious skills strategy has to respect that. At the same time, informal learning alone struggles with fast-moving, data-intensive technologies, where good practice is not always visible by looking over the fence. The most effective programmes therefore combine the three modes. A farmer might attend a short course, test a tool on a pilot plot with an adviser's support, share results at a field day, and later have that learning recognised in a micro-credential. Higher education institutions can contribute to all three modes, which is a theme we will return to shortly..

[Audio] Skills are not acquired once. Technologies, regulations and markets change, so learning has to continue across a working life. One European response is the approach to micro-credentials. In June 2022 the Council adopted a Recommendation on a European approach to micro-credentials for lifelong learning and employability. A micro-credential records the learning outcomes of a short, focused learning experience, and the Recommendation provides a common definition and standard elements for describing them, together with principles on quality, transparency, recognition and portability. They can be linked to the European Qualifications Framework and, in higher education, to ECTS credits. For agriculture this is promising, because many people cannot leave their farm or business for a full degree programme, but they can complete a short module on, say, farm data management, precision application or sustainability reporting. Those modules can be stacked over time. The Commission has also said it will support the recognition of digital skills certifications, including through a planned European Digital Skills Certificate. One caution: micro-credentials only help if employers, advisers, funding bodies and farmers trust them. So the design question is not just how to create more courses, but how to make learning visible, comparable and valued in the ecosystem..

[Audio] In agriculture, one of the most important bridges between knowledge and practice is the advisory system. The European Union uses the concept of the Agricultural Knowledge and Innovation System, or AKIS, to describe the combined organisation of people, organisations and institutions that generate, share and use knowledge in agriculture. Under the Common Agricultural Policy, the regulation governing the strategic plans, Regulation 2021/2115, requires Member States to support farm advisory services and to strengthen cooperation within their knowledge and innovation systems. Advisers are the knowledge brokers of this system. They connect researchers, companies and public bodies with farms, and they translate general knowledge into local decisions. Peer learning is another proven method: demonstration farms, field days and innovation groups let farmers see results in conditions similar to their own, which builds trust. Here is a point that is easy to miss. If advisers themselves are not trained to work with data and digital tools, then adding digital tools to farms simply moves the bottleneck. Training the trainers and the advisers is therefore among the highest-leverage investments in a skills strategy. And this is also where higher education can help, as a partner in continuous professional development for advisers and not only as a supplier of graduates..

[Audio] We now come to the central question of this module: what is the role of higher education? I suggest four contributions, which go well beyond the classic picture of a university as a place that produces graduates. The first contribution is teaching. Universities and agricultural colleges educate the future agronomists, engineers, data scientists, managers and policy makers who will work in the sector. The challenge is to educate profiles that combine agronomic, digital and business understanding. The second is research. Higher education institutions generate, test and validate the knowledge and tools on which digital agriculture depends, and they can provide independent evidence about what works, for whom and under what conditions. The third is innovation and knowledge transfer: through collaboration with companies, advisory services and farmers, universities help convert research results into practice, and they can support start-ups and spin-offs. The fourth is convening and lifelong learning. A university can act as a neutral, trusted platform that brings together actors who do not normally meet, such as farmers, technology firms, public authorities and civil society, and it can offer flexible learning opportunities to people already in work. In a business ecosystem, this fourth role is particularly valuable, because ecosystems rely on trust and shared understanding between actors with different interests..

[Audio] If higher education is to deliver on these contributions, curricula have to change. The first principle is interdisciplinarity. A student of agronomy should learn enough data analysis to question a model, and a student of computer science or business should learn enough about crops, soils, animals and food systems to understand the problem they are solving. The Council Recommendation on digital skills explicitly asks Member States to strengthen digital skills for all students in higher education, across levels and disciplines, which supports exactly this approach. The second principle is learning through real problems. Challenge-based and project-based learning, in which students work with farms, cooperatives, agri-food companies or advisory services, develop the competences that employers describe as hardest to find: communication, teamwork, critical thinking and the ability to work with incomplete data. The third principle is work-based learning: internships, placements and theses co-supervised by industry. The fourth is flexibility. Many of the people who most need new skills are already working, so universities need modular, part-time and online options, combined with micro-credentials and recognition of prior learning. None of this is easy. It requires staff time, cooperation between faculties, and a willingness to involve non-academic partners in programme design, but the payoff is graduates and professionals who can operate across the whole value chain..

[Audio] Beyond courses and degrees, higher education institutions can act as active members of a business ecosystem. Many universities operate experimental farms, greenhouses, laboratories and field sites. These can function as living labs and testbeds, where companies and farmers try new tools under realistic but monitored conditions, and where students and staff learn alongside them. This turns infrastructure that already exists into a shared learning and validation space. Universities can also connect to the wider support landscape. The Digital Europe Programme funds the European Digital Innovation Hubs, whose mission is to help organisations, including small and medium enterprises, test and adopt digital technologies and develop related skills. A university can be a partner in such a hub, or can refer ecosystem actors to it. A further contribution is trust. Digital agriculture raises questions about data ownership, privacy, algorithmic bias and fair sharing of value. A university can offer independent evaluation, ethical review and data governance expertise that individual companies or public bodies may lack, and can help communities develop shared rules. Finally, universities can model good practice by sharing open data, open educational resources and open tools where possible, reducing the cost of participation for smaller actors in the ecosystem..

[Audio] Skills strategies need funding and structure, and the European Union offers several instruments that can be combined. Erasmus+ supports learning mobility and cooperation between education institutions and the world of work, including partnerships between higher education, vocational education and business. The European Universities initiative encourages deeper, long-term cooperation among universities across borders. Horizon Europe funds research and innovation, and within it the Marie Skłodowska-Curie Actions support the training and mobility of researchers, including doctoral networks and staff exchanges, which are particularly interesting for our topic because they encourage researchers to move between academia, industry and other sectors. That cross-sectoral experience builds exactly the kind of boundary-spanning skills that digital value chains need. The Digital Europe Programme supports the development of advanced digital skills and funds the digital innovation hubs I mentioned. And the Common Agricultural Policy, through its strategic plans, supports advisory services, training and knowledge exchange for farmers and rural businesses. The practical lesson is to think in terms of combinations. A regional skills initiative might use Erasmus+ to design a curriculum with industry, Horizon Europe to generate new knowledge and train researchers, CAP resources to deliver training to farmers, and a digital innovation hub to help companies test tools. Each instrument has its own rules, but together they can cover the whole learning pathway..

[Audio] What stands in the way of progress, and what helps? The barriers are well known. Many farm managers have no formal agricultural training, so they may not see classroom learning as relevant. The workforce is ageing. Rural connectivity can still be uneven. Training costs money and, even more, time, particularly in the busy seasons. Materials may not exist in the local language. And many people have had poor experiences with technologies that promised more than they delivered, so trust is often low. There are also inclusion gaps. Eurostat data show that about 31.6 per cent of EU farm managers were women in 2020, and the Council has pointed out that only about one in five ICT specialists are women. Skills strategies that ignore these gaps risk reproducing them. On the enabling side, the evidence from practice points to a consistent set of ingredients. Peer learning, where farmers learn from other farmers, works well. Trusted intermediaries, such as advisers, cooperatives and local universities, help build confidence. People adopt skills when they see visible, local benefits, such as saved time, lower input costs or better access to a market. Modular offers fit around the working calendar. And recognition, through certificates or micro-credentials, gives people a reason to invest in learning. The design principle is simple: short, practical, local and trusted..

[Audio] How do we know whether a skills initiative works? Many programmes report only participation: how many people attended, how many hours were delivered, how many certificates were issued. Those numbers are useful, but they describe activity, not change. A stronger evaluation framework distinguishes four levels. The first is inputs and outputs: participants, hours, courses and credentials. The second is outcomes: did participants actually adopt tools or change practices, and did the quality of their decisions improve? The third is impact: effects on productivity, resource efficiency, income, environmental performance and inclusion, ideally compared against a baseline. The fourth is ecosystem change: new partnerships, new data-sharing agreements, new jobs or start-ups, and stronger links between education and industry. To measure this, you need to collect baseline information before the programme starts, and follow up after six or twelve months, because skills often take time to show their effect. You also need to be transparent. If a module did not lead to adoption, that is valuable information, and it can lead to a redesign. This applies to any funded project or initiative, where accountability to funders and participants means reporting results that can be verified, including the difficult ones..

[Audio] Let me turn the evidence into recommendations for different actors. Higher education institutions should co-design curricula and short courses with farmers, advisers, companies and public bodies, offer open modular formats, and develop ways to recognise prior learning and practical experience. Advisory services and cooperatives should invest in training the trainers, build peer-learning networks and treat digital competence as a core professional skill for every adviser. Companies and technology providers should see user training as part of product design, not an afterthought. They should co-develop tools with users, offer clear explanations of how data is used, and support farmers' data literacy and data rights, since trust is a commercial asset as well as an ethical obligation. Public authorities should fund skills within knowledge and innovation systems and CAP strategic plans, support recognition of micro-credentials, and work on connectivity, because training cannot be effective where the network does not reach. Finally, researchers and project consortia should build training, cross-sectoral secondments and knowledge transfer into their proposals from the start, with realistic budgets and indicators, and publish their training materials openly so that others can reuse them. These recommendations all point in the same direction: skills are co-produced by the ecosystem, so the responsibility for them is shared as well..

[Audio] Let me summarise the main messages of this module. First, skills are infrastructure for digital value chains. Without them, even excellent technologies deliver little, and the value they create flows to those who already have the capabilities. Second, the starting point is low and uneven. Across the European Union only about ten per cent of farm managers have full agricultural training, and the differences between countries are large, with Greece and Romania at less than one per cent compared with more than sixty per cent in the Netherlands. A good strategy begins with a local diagnosis. Third, digital agriculture needs five families of skills: basic digital competence, data and technical skills, agronomic and sustainability knowledge, business and governance skills, and human skills such as collaboration and critical thinking. Fourth, people learn in formal, non-formal and informal ways, throughout their lives, and micro-credentials and advisory systems can connect these modes. Fifth, higher education has four contributions to make: teaching, research, innovation and knowledge transfer, and convening ecosystems and providing lifelong learning. And sixth, skills are co-produced by the whole ecosystem: farmers, advisers, companies, public bodies and universities each hold part of the solution, and each depends on the others..

[Audio] To close, I would like to invite you to reflect on four questions, either on your own or in the discussion forum of the seminar. First, which skills gap limits value creation most in the value chain or region you work in? Be specific: is it data literacy among producers, digital competence among advisers, agronomic knowledge among technology developers, or something else? Second, who is the trusted intermediary in your ecosystem, and is that actor equipped with the skills and resources to play the role? Third, what could a university or college near you offer within the next twelve months, for example a short course, a living lab, a student project or a co-supervised thesis? And fourth, which of the European instruments we discussed, Erasmus+, Horizon Europe, the Digital Europe Programme or the Common Agricultural Policy, could you combine to support that offer? I encourage you to write down short, concrete answers, because the exercise of naming actors, gaps and actions is often the first step towards a funded project or a partnership. Thank you for your attention, and I look forward to continuing the discussion with you in the next part of the seminar series..

References. Eurostat, Statistics Explained: Farmers and the agricultural labour force – statistics (Farm Structure Survey 2020). ec.europa.eu/eurostat/statistics-explained/index.php/Farmers_and_the_agricultural_labour_force_-_statistics Eurostat, Statistics Explained: Glossary: Farmers training level (definitions of practical, basic and full agricultural training). ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Farmers_training_level European Parliament resolution (2023), document 52023IP0376, published in OJ C, 2024/2658. eur-lex.europa.eu/eli/C/2024/2658/oj Council of the EU (18 November 2022), Digital skills for the Digital Decade, doc. ST 14868/2022. data.consilium.europa.eu/doc/document/ST-14868-2022-INIT/en/pdf European Commission, Digital Education Action Plan: factsheet on digital skills (April 2023). education.ec.europa.eu/sites/default/files/2023-04/DEAP-Factsheet-skills-180423_en.pdf European Commission, Council adopts two recommendations on digital education and skills (Nov 2023). education.ec.europa.eu/news/european-council-adopts-two-recommendations-on-digital-education-and-skills Decision (EU) 2022/2481 establishing the Digital Decade Policy Programme 2030 (14 December 2022). Council Recommendation of 16 June 2022 on a European approach to micro-credentials for lifelong learning and employability. Regulation (EU) 2021/2115 (CAP Strategic Plans Regulation). Vuorikari, R., Kluzer, S., Punie, Y. (2022), DigComp 2.2: The Digital Competence Framework for Citizens. European Commission, Joint Research Centre..

[Audio] Thank you!. thank you!. TALLHEDA has received funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101136578. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them..