PowerPoint Presentation

[Audio] Welcome to Module 1, where we will build a shared understanding of what digital transformation actually means in an agricultural context..

[Audio] Let's begin with Module 1: Understanding Digital Transformation in Agriculture. Our first task is to get precise about what we actually mean by digital transformation, because the term gets used loosely, and that looseness causes real confusion in business planning. Digital transformation is not simply the act of buying new technology: installing a sensor, adopting an app, or automating a spreadsheet. Those are ingredients, not the outcome. At its core, digital transformation is the use of digital technologies to fundamentally change how an organisation creates, delivers, and captures value. Klerkx, Jakku and Labarthe (2019), in their review of the social science literature on digital agriculture, make exactly this point: digitalisation in agriculture reaches beyond individual farm production practices into value chains, business models, and institutional arrangements. In other words, real digital transformation touches four things at once: the technology itself, the processes built around it, the people and skills required to use it, and, critically for us today, the business model that determines how the organisation actually earns money from it. If any one of these four is missing, what you have is digitisation or automation, not transformation. Keep this distinction in mind, because it is the difference between a farm that owns a drone and a farm that has built a new service business around the data that drone collects..

[Audio] It helps to think of this as three stages on a continuum, and agriculture gives us very clean examples of each. The first stage is digitisation: simply converting analogue information into digital form. A farmer who used to record field observations on paper and now types them into a spreadsheet has digitised. Nothing about the underlying process has changed, only the format of the record. The second stage is digitalisation: using digital technology to change or improve an existing process. If that same farmer installs soil moisture sensors and uses the data to adjust irrigation timing, the process of irrigation itself has improved: decisions are now data-driven rather than based on habit or guesswork. This is where most on-farm technology adoption sits today, and it delivers real efficiency gains. But the third stage, digital transformation, is different in kind, not just degree. This is where the digital capability itself becomes the basis of a new business model. If that farmer, having built expertise in sensor-based irrigation management, starts offering irrigation scheduling as a paid advisory service to neighbouring farms, the business has been transformed: it now sells a digital service in addition to, or instead of, purely agricultural produce. That is the leap we are focused on in this seminar, not stage two, but stage three..

[Audio] It is useful to place this moment in a longer historical arc, often referred to as the shift from Agriculture 1.0 to Agriculture 4.0. Agriculture 1.0 describes farming based on manual labour and animal power, largely unchanged for centuries. Agriculture 2.0 corresponds to the mechanisation era and the Green Revolution: tractors, synthetic fertilisers, and chemical inputs dramatically increased yields from the mid-twentieth century onward. Agriculture 3.0 is precision agriculture: GPS guidance, yield mapping, and variable-rate application of inputs, which began scaling from the 1990s and 2000s. And Agriculture 4.0, the phase we are in now, is characterised by the integration of the Internet of Things, big data analytics, artificial intelligence, and robotics, not just at the level of the individual field, but across the entire food supply chain. Wolfert, Ge, Verdouw and Bogaardt (2017), in their widely cited review of big data in smart farming, describe this shift precisely: they show that the scope of big data applications in agriculture now extends well beyond primary production, influencing and reshaping the entire food supply chain, from input suppliers through to processors, distributors, and consumers. That last point matters for us: Agriculture 4.0 is not just more automation on the farm. It is connectivity across the whole chain, and every point of connectivity is a potential point of new value creation, which is exactly where business opportunity lives..

[Audio] Why is this happening now, and not ten years ago? Wolfert and colleagues (2017) offer a helpful framework: they distinguish between push factors and pull factors driving big data and smart farming adoption. Push factors sit on the technology supply side, the enabling conditions that make digital agriculture possible. These include the maturing of the Internet of Things, global navigation satellite systems, satellite imagery, advanced remote sensing, and robotics, combined with the falling cost of sensors, connectivity, and data storage. Ten years ago, many of these tools were prohibitively expensive for anyone outside large-scale commercial operations. Today, the same capability is available at a fraction of the cost. Pull factors sit on the demand side, the business and policy pressures creating appetite for these tools. These include the constant business drive for efficiency and better decision-making, the need to manage increasingly complex regulatory and administrative processes, broader public priorities around food security and environmental sustainability, and, very significantly for market opportunity, rising demand from consumers and retailers for more information about how food is produced. When falling technology costs meet rising demand for data-driven decisions and transparency, you get exactly the kind of accelerating adoption curve we are seeing across the agri-food sector today. Understanding which of these factors matters most in your own market is a useful diagnostic question, and one we will return to in Module 5..

[Audio] We touched on this briefly at the start, but it is worth returning to with more precision now that we have some conceptual grounding. According to MarketsandMarkets (2024), the global digital agriculture market, spanning hardware, software, and services, was valued at approximately 24.2 billion US dollars in 2024 and is projected to reach roughly 39.8 billion dollars by 2029, a compound annual growth rate of around 10.4 percent. It is worth pausing on that segmentation: hardware, software, and services. This tells us the opportunity is not only about equipment manufacturers selling sensors and drones. A significant and growing share of this market is software and services, precisely the kind of value that farms, cooperatives, and agri-food SMEs can capture directly, not just purchase from outside suppliers. At the European policy level, this growth is being actively supported. As part of the 2019 Declaration of cooperation, EU Member States committed to establishing a Europe-wide innovation infrastructure for a smart agri-food sector and creating a European dataspace for smart agri-food applications (European Commission, 2019), essentially shared digital infrastructure designed to make data easier to access, combine, and build new services on top of. For a business owner, that combination, a growing market plus active public investment in shared infrastructure, is a genuine signal worth paying attention to..

[Audio] One reason digital transformation creates so much business opportunity is that it does not just affect one stage of the value chain; it touches all of them, and each stage represents a different kind of opportunity. At the inputs and advisory stage, digital platforms now offer precision recommendations on seed variety, fertiliser rates, and crop protection, as well as digital marketplaces connecting farmers directly with suppliers. At the on-farm production stage, the stage most people think of first, sensors, robotics, and farm management software support real-time decisions. But it is the later stages that are often underexploited by smaller businesses, and that is where I want to draw your attention. At the post-harvest and logistics stage, Schmidt, Butturi and Sellitto (2023) studied an engineering solutions provider that built an entire new line of business around digital post-harvest services, integrating monitoring technology with quality and storage management for grain producers, turning what used to be a purely physical service into a combined digital-and-physical offering. At the distribution and retail stage, e-commerce and direct-to-consumer digital platforms are allowing producers to bypass traditional intermediaries entirely. And at the consumer-facing end, growing demand for transparency and provenance information is turning traceability data itself into something with commercial value: a certification, a story, a premium. The lesson here: do not only look at your own production process for opportunity. Look at every point where your business touches data, information, or a transaction, anywhere along this chain..

[Audio] Before we close this module, let's directly address four misconceptions that come up regularly, because they tend to stop good businesses from even starting the conversation. Myth one: digital transformation is only for large farms with big budgets. This is not accurate. The falling cost of sensors and connectivity we discussed earlier, combined with the rise of shared and subscription-based digital services, means many tools are now accessible to smaller operations, and in fact smaller, more agile businesses can sometimes adapt their business models faster than larger ones. Myth two: it is just about buying equipment. As we established earlier in this module, technology adoption without business model change is digitalisation, not transformation; the equipment is a means, not the outcome. Myth three: it happens all at once, in one big investment. In practice, and we will see this in the case studies in Module 4, successful digital transformation is almost always incremental: a pilot, a lesson learned, a second step, a scale-up. Klerkx, Jakku and Labarthe (2019) note that the adoption and adaptation of digital technologies on farms is itself an active area of ongoing social science research, precisely because this process is gradual, context-dependent, and shaped by farmer identity and existing skills, not a single switch that gets flipped. And myth four: it replaces people. This one has a grain of truth. Digital transformation does change what skills are needed and can reduce the need for certain manual tasks. But it does not eliminate the need for human expertise and judgement; if anything, it increases the value of people who can interpret data and make good decisions with it. Getting these four myths out of the way now means we can spend the rest of this seminar focused on realistic, practical opportunity, rather than misplaced fear or hype..

[Audio] Let's briefly consolidate what we have covered in Module 1 before we move on. We defined digital transformation as something broader than buying new technology; it requires technology, process change, people and skills, and, most importantly for this seminar, a change in the business model itself. We placed today's moment in context as Agriculture 4.0: a phase defined by connectivity and data flowing across the entire food value chain, not just within the boundaries of a single farm. We looked at why this is accelerating now, using Wolfert and colleagues' (2017) push-and-pull framework: falling technology costs on one side, and rising demand for efficiency, transparency, and sustainability on the other. We mapped how this opportunity touches every stage of the value chain, from inputs through to the end consumer. And we cleared away four common myths that often stop businesses from engaging with this topic seriously. With that foundation in place, we are ready to get more concrete. In Module 2, we are going to open up the technology toolbox itself: what the Internet of Things, big data, artificial intelligence, robotics, drones, and blockchain actually do in an agricultural context, in plain language, so that when we move into business models in Module 3, you have a clear picture of the raw materials you have to work with..

References. European Commission. (2019, April 9). EU Member States join forces on digitalisation for European agriculture and rural areas. Shaping Europe's Digital Future. https://digital-strategy.ec.europa.eu/en/node/238 Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS – Wageningen Journal of Life Sciences, 90, 100315. https://doi.org/10.1016/j.njas.2019.100315 MarketsandMarkets. (2024). Digital agriculture market by offering, technology, operation, type and region – Global forecast to 2029 [Market research report]. https://www.marketsandmarkets.com/Market-Reports/digital-agriculture-market-235909745.html Schmidt, D., Butturi, M. A., & Sellitto, M. A. (2023). Opportunities of digital transformation in post-harvest activities: A single case study of an engineering solutions provider. AgriEngineering, 5(3), 1226–1242. https://doi.org/10.3390/agriengineering5030078 Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023.

[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..