From RiSe: “Digitalization of agriculture is taking the next step after precision farming by linking different systems with IoT (Internet of Things) and analysing data with AI (artificial intelligence). This is often referred to as Smart farming or Farming 4.0. Connection and sharing of data is a key factor for success.”

From the EU (not a definition, but the opening sentence on their page about digitalisation of agriculture): “From artificial intelligence (AI) and robotics to the Internet of Things (IoT) and 5G, the latest technologies can offer invaluable support for farmers and agribusinesses.”

From the OECD: “Digitalisation is the defining technological transformation of this era, and, as in other sectors, it will have important impacts on agriculture. Digitalisation refers to the adoption of information communication technologies, including the Internet, mobile technologies and devices, as well as data analytics, to improve the generation, collection, exchange, aggregation, combination, analysis, access, searchability and presentation of digital content, including for the development of services and applications.”

So, IoT, AI, Big Data, Models, Robots, etc. It’s real-time decisions support. With some large cross-over with precision agriculture.


From the OECD: “… Common barriers to adoption include costs (up-front investment and recurring maintenance expenses), relevance and limited use cases, user-friendliness, high operator skill requirements, mistrust of algorithms, and technological risk. National governments have an important role in addressing bottlenecks to adoption, such as by ensuring better information about costs and benefits of various technologies (including intangible benefits such as quality of life improvements); investing in human capital; ensuring appropriate incentives for innovation; serving as knowledge brokers and facilitators of data-sharing to spur inclusive, secure and representative data ecosystems; and promoting competitive markets.”

A common issue that has come up in NBR projects, and is noted in the OCED list of challenges, is getting the machines to talk easily with each other. In particular, standadisation of metrics and nomeculature. There are some companies and organisations working on this issue:

  • AgGateway: working to improve interoperable of digital systems in agriculture, largely through data standardisation (a good podcast about them)
  • Agrirouter
  • agronod (focus on reporting within the Swedish agriculture sector)


A digital rating on a data collection process is basically a summary of how automated it is. At the lower end is a data collection process that uses pens and paper and lots of people and time. At the higher end is data collection that is basically instantaneous and has no human interaction in the actual instance (of course a human (?) has built the system).

A key goal of digitalisation of agriculture is to increase digital ratings.


Digitalisation of Agriculture is one of NBRs five focus areas for the strategy period 2023-27. As stated in the strategy, this focus area has the specific aim of contributing to the development of decision support systems useful for the sugar beet growers in our area. This can be models to monitor pests, and crop management models in relation to, for example, harvest date, mechanical damage, quality parameters, and storage losses.


My work in Digitalisation of Agriculture is all through NBR. This includes the role of monitoring this field within NBR and wider world. I don’t have any particular responsibility, I don’t preclude other colleagues from working in this space, and don’t decide what projects NBR undertakes in this area. I do try to keep things catalogued and suggest what areas we should put resources to.


NBR904: Rymdstyrelsen Skörde Prognos project [Swedish Space Agency Yield Prognosis project]

NBR905: Nordic Beet Yield Challenge

NBR918: Bolter prediction