Modelling Individual Decisions to Support
the European Policies Related to Agriculture


Without a doubt, the impact of the COVID-19 outbreak on economies worldwide is devastating. Hardly any sector is not affected and the implementation of many research projects are also impaired. MIND STEP is no exception. 

Since the launch of the project in September 2019 we started defining a conceptual framework for the analysis of policies and global trends addressing the EU farming sector, for measuring their impact and for linking them to the corresponding modelling issues.  The stakeholder engagement process was initiated: a core stakeholder group was created with representatives from the scientific community, policy and farmers organisations. This group will be consulted in stakeholder workshops concerning the developments in the project. The 1st workshop was supposed to deal with the identification of key policy questions that shall be answered by the MIND STEP modelling system. It was planned for April in Brussels, had to be first rescheduled and due to the uncertainty of the COVID-19 situation, colleagues are now preparing individual interviews with the stakeholders. Beyond that, data requirements of the modelling teams were surveyed and a request for FADN data was put forward centrally. Due to the COVID-19 situation, however, some tasks concerning data acquisition are delayed as statistical offices are closed or understaffed.

Other ongoing activities in MIND STEP include a literature review on existing IDM models, internal discussions of a template model, and the preparation of a paper comparing farming system models and their modularity to contribute to protocol development. 
Regarding the development of models focusing on interactions between farmers and along agents of the supply chain, potential interfaces between the models FarmDyn and AgriPoliS are investigated to conceptualize a surrogate model. Further steps include the setup of the infrastructure to generate FarmDyn data and to develop the appropriate Machine Learning environment.