Digitalisation of agriculture: development and evaluation of a model-based requirements engineering process

[Context and Motivation] The requirements elicitation process for socio-technical systems requires the involvement of diverse stakeholders with different backgrounds and skills. In these contexts, ef- fective communication between business analysts and stakeholders can be supported by model-based requirements engineering (MoDRE) strategies, which leverage diagrammatic notations as a means for information exchange. [Question/Problem] Several diagrams and approaches exist to facilitate MoDRE. However, empirical evidence on their applicability to real-world contexts with a relevant social com- ponent, and going through a process of digitalisation, is limited. Furthermore, existing approaches do not evaluate the impact that the deployment of a novel digital system has on the process and its actors. [Principal idea/Results] The research outlined in this paper aims to evaluate the joint usage of typical requirements engineer notations, namely i*, class diagrams, and business process models in the elicitation of requirements for socially-intensive systems that are going through a transformative digitalisation process. We apply these notations to represent the system-as-is, and the system-to-be, with the goal of also evaluating the impact of digitalisation. We focus on living labs (LL, i.e., networks of stakeholders participating in a socio-technical system) belonging to the agriculture domain, and provide a preliminary application on a farm that is introducing an AI-based irrigation system. [Contribution] The results show that effective communication with non-technical stakeholders is feasible with the envisioned approach. Although multiple iterations are required, agronomists and farmers are able to provide constructive feedback on the basis of the models. Furthermore, impacts in terms of additional/removed tasks and actors can be effectively characterised through business process models. As part of our overall project, we will refine the method, and then apply it in 20 living labs in the EU.