How economics, labour shortages and new business models are shaping the adoption of autonomous field robotics
1. From Machine Ownership to Robotics as a Service
Field robots are no longer just a technological vision. Autonomous systems are already being used in agriculture, particularly for tasks such as mechanical weed control, seeding and harvesting. Labour shortages, CO₂ emissions requirements and the cost of fossil fuels are further accelerating electrification and the trend towards autonomy. The key question is therefore no longer simply whether a robot can perform a task, but whether it can do so economically.
A recent study by Michels et al., based on interviews with 58 German arable farmers, distinguishes between two approaches: owning a robot and using a robot as a service. Under the service model, the provider takes care of tasks such as transportation, setup, monitoring and technical operation, while the farmer pays for the service provided, for example on a per-hectare basis. [1]
This fundamentally changes the economic logic.
· Ownership: “Can I afford a robot and keep it sufficiently utilised?”
· Service model: “Is the robotic service per hectare attractive for my farm?”
This lower entry barrier could make robotics attractive to farms where purchasing their own machine would not be economically viable because of farm size or insufficient utilisation.
2. The Key Metric Is Productive Hectares
When autonomous machines are discussed, the focus is often on purchase prices and technical performance. From an economic perspective, however, a crucial question is how many hectares a system can actually work productively.
A recent cost analysis by Jorissen and Recke shows that field size, transport distances and field access points have a significant impact on the operating costs of autonomous field robots. [2]
The simulated total costs averaged around €73/ha, of which approximately €60/ha were attributable to the actual field operation. Field size was the most important influencing factor, explaining 58% of the cost variance, followed by transport distance at 11%.
The practical conclusion is that the economics of a robot depend not only on its technical performance, but also heavily on field size, spatial distribution and logistics. This is precisely where service models could offer an advantage: a provider can operate a robot across multiple farms and fields, potentially increasing utilisation and spreading logistical and capital costs across a larger area.
3. Labour Shortages Change the Equation
In addition to economic viability, another factor is becoming increasingly important: the availability of labour.
The study by Michels et al. identifies different decision-making logics for ownership and service use. For ownership, perceived benefits and digital capabilities are particularly relevant. For the attractiveness of a service model, the combination of labour pressure and a lower perceived minimum farm size is particularly important. [1]
This means that a robot is not only an investment decision. It can also be a response to a labour problem. This is economically relevant because the alternative is not always another machine. Sometimes the alternative is simply not having enough people to perform the work.
Other recent research also shows that the expected reduction in labour requirements does not automatically materialise. Spykman, Lowenberg-DeBoer and Gandorfer found that, in the small-scale cropping systems they investigated, conventional tractors were still more efficient than the field robot studied. Logistics and support activities remained important factors. [3]
Robotics therefore needs to achieve more than technical autonomy. It also needs to reduce organisational complexity and workload.
4. Technical Autonomy Does Not Yet Equal Economic Autonomy
Current research clearly shows that technical capability and economic viability are two different questions.
An analysis by Jorissen and Recke, published in August 2026, examined autonomous field operations in maize production and reached a cautious conclusion. Depending on the supervision scenario, the net costs of robotic field operations corresponded to approximately 28% to 45% of the economic output of maize production. The contribution of the automation benefits to total revenues, by contrast, was at most around 3%. [4]
This highlights a fundamental challenge:
A robot can operate autonomously and still fail to deliver a convincing economic case.
Economic viability depends on factors such as crop type, field size, labour costs, robot utilisation, logistics and technical performance. [2][4]
There is therefore no single answer to the question of the “economics of field robots”. There are economically viable and economically unviable use cases.
5. The Competition Could Be Between Business Models
When these findings are considered together, an interesting perspective emerges.
Traditional machine sales are only one possible business model. Other options include leasing, rental, contractor-based services and Robotics-as-a-Service.
A recent conceptual study on Agriculture 4.0 describes a broader shift from hardware-centred business models towards data-, service- and platform-oriented models. These include recurring revenues and increasingly outcome-oriented services. [5]
This development is particularly relevant for field robotics. A contractor could operate several robots and sell their capacity to numerous farms. This would allow investment, technical expertise and utilisation to be pooled across multiple customers.
However, this creates a new challenge: availability.
For mechanical weed control, for example, the optimal operating window can be very short. Transport distances, field structure and regional customer density therefore become economically relevant factors. [1][2] The same applies to crops such as asparagus, where harvesting is concentrated in a relatively short period of around ten weeks.
The question is therefore no longer simply: “Which robot is better?”
It is: "Which business model can bring robotics to the field most efficiently?”
The existing studies do not yet demonstrate which business model will ultimately prevail in the market. They do, however, show that ownership and service use involve different requirements and should therefore be considered separately. For some farms, owning a robot may make economic sense. For others, purchasing robotic field capacity on a per-hectare basis could be the more attractive option.
The winner in the field robotics market may therefore not necessarily be the company that sells the most robots. It could be the company that delivers robotics as an agricultural service in the most reliable, economical and scalable way.
Sources:
[1] Michels, M., Leege, F.-L., Widekind-Buschulte, C. & Mußhoff, O. (2026): Own or outsource? Configurational pathways to field robot adoption among German arable farmers. Smart Agricultural Technology 15, 102494. DOI: 10.1016/j.atech.2026.102494.
[2] Jorissen, T. & Recke, G. (2026): Structural cost modeling and sensitivity analysis of autonomous field robot operations. Smart Agricultural Technology 13, 101913. DOI: 10.1016/j.atech.2026.101913.
[3] Spykman, O., Lowenberg-DeBoer, J. & Gandorfer, M. (2026): Crop robots as potential enablers of economical and biodiversity-smart small-scale farming. Precision Agriculture 27, 80. DOI: 10.1007/s11119-026-10367-0.
[4] Jorissen, T. & Recke, G. (2026): Ökonomische Relevanz autonomer Feldroboter im Maisanbau. Agricultural engineering.eu, 81(3). DOI: 10.15150/ae.2026.3365. Veröffentlicht am 28.08.2026.
[5] Technology in Society (2026): AI- and robotics-driven reconfiguration of a traditional industry: Business model innovation in agriculture 4.0. Technology in Society 88, 103440. DOI: 10.1016/j.techsoc.2026.103440.