Behavioural, Ecological and Socio-economic
Tools for Modelling Agricultural Policy
BESTMAP will
- Design and develop a new Policy Impact Assessment Model (PIAM) framework, relying on modern socio-economic, behavioural and biophysical approaches that captures the environmental, social and economic variability of individual farms and EU regions
- Operationalize the framework by using co-design workshops, existing georeferenced datasets and the experience of EU institutions, national, regional and local decision-makers, expert personnel and other researchers
- Demonstrate the approach in five regional case studies covering heterogeneous agricultural, socio-economic and political backgrounds
- Synthesise case studies results and scale up the approach at a national, EU and global levels
- Disseminate the outcomes and build capacity for improved policy impact assessment modelling in EU and Member States institutions
Case studies
BESTMAP will demonstrate the new approach in five regional case studies regions which hold diverse agricultural, socio-economic and political backgrounds:
Structure
WP 1: Monitor and ensure the progress of the project, manage risks and mitigation, coordinate the research and data management
Lead: University of Leeds (UNIVLEEDS)
Work package 1 will ensure proper progress of the entire project. It will establish guidelines and protocols as well as data management plan.
WP 2: Create the framework and methodology of the new BESTMAP-PIAM
Lead: Centre for Research on Ecology and Forestry Applications (CREAF)
Work package 2 will co-design and co-develop the framework, policy scenarios, indicators and dissemination tools. It will create the conceptual framework of the new Policy Impact Assessment Model (BESTMAP-PIAM).
WP 3: Collect existing and new geospatial, social and behavioural data on a case study and European levels
Lead: Technische Universität Dresden (TUD)
Work package 3 will collect geospatial, social and behavioural data at Case Study and EU levels. It will collect new empirical data via interviews and questionnaires, producing a typology of Farm System Archetypes. Biophysical models will help identify the impacts of farmer’s decisions on the environment, climate system and rural economy.
WP 4: Build Agent-Based Models of farmers decision making
Lead: Palacký University Olomouc (UPOL)
Work package 4 will set up and implement Agent-Based Models for each case study, capturing the decision making process of farmers, including social learning and habitual behaviour associated with adopting agri-environmental schemes.
WP 5: Upscaling to national, EU and global level
Lead: United Kingdom Research and Innovation (UKRI)
Work package 5 will analyse the representativeness of the case studies in the EU context, and demonstrate the BESTMAP-PIAM farmwork at national, EU and global levels.
WP 6: Dissemination, Communication and Knowledge Transfer
Lead: BioSense Institute - Research and Development Institute for Information Technologies in Biosystems (BIOS)
Work package 6 will raise public and stakeholders awareness to the project and build technical online tools and capacity to use the project outputs.
WP 7: Ethics requirements
Lead: University of Leeds (UNIVLEEDS)
Work package 7 will ensure high ethical standards and compliance with GDPR requirements throughout the project.