Farmers develop their activity supporting the complexity of interrelated nature and human systems, being in a favourable position to provide first-hand observations and a deeper understanding of climate change manifestation, relevance, effects, and actions. Likewise, irrigation districts managers can collect farmers’ experiences and evaluate their adaptive capacity to promote effective risk management.
The BECLICK project(A Roadmap on Climate Change Farmers’ Behaviour by Modelling Social-Learning)aims to reinforce risk management on climate change by delving into farmers’ and irrigation districts’ behaviour regarding climate change awareness, perceived impacts and adaptations measures and barriers. BECLICK takes a systemic and stakeholder-centred approach and conducts an evidence-based analysis of different case studies in Europe, the United States, and Asia to enrich the results obtained in the MODFABE project (a H2020-MSCA-IF-2018 project initially focused on the Lombardy region, Italy).
Insights collected from qualitative-social data are analysed to identify risk preferences patterns to be incorporated to the ABNexus, an Agent-Based Model (ABM)which simulate individual decisions by integrating social and physical environments, dealing with heterogeneity, and studying the drivers and rules underpinning behavioural patterns. The project addresses a twofold question in today’s climate change adaptation research:
- Could social-learning from farmers’ climate change adaptation capacity provide new social scenarios able to increase model robustness when addressing decision-making processes?
- Could behaviour modelling help farmers and irrigation districts to promote actions and anticipate decisions to better adapt to climate change and become less vulnerable?
The project of three-years duration is led by Dr. Sandra Ricart (IP) and Prof. Andrea Castelletti (Project Advisor), and involves researchers from the Environmental Intelligence Lab and the support of Prof. Claudio Gandolfi, from the Dept. of Agricultural and Environmental Sciences, Università degli Studi di Milano. The project is funded by the Italian Ministry of Education, Universities and Research (MUR).
