Context of the case study
In the Aegean region of Turkey, where traditional agriculture is in decline and is gradually being replaced by reverse immigration from the cities, this case study will provide an opportunity for field research, observation and promotion of the adoption of smart agriculture technology and permaculture by two contrasting groups: (a) middle-class corporate white collar retirees who opt for an ecological life in rural areas; (b) traditional family farmers with low educational, social and cultural capital.
Objectives
Build a “new generation rural model” that raises the life standards of the farmers, provides them with qualified knowledge and allows them to increase their productivity with the use of smart IoT devices dedicated to precision agriculture.
Co-design with them new processes and materials for a more effective adoption of smart farming technologies and build transition scenarios and pathways using participatory agent-based modeling.
1. Activity 1: Training stakeholders, essentially farmers, to the use of sensors and Low-Power\Wide Area (LPWA) Wireless Sensor Network infrastructure developed by BU’s engineering team, in order to create awareness about smart farming technologies and exchange information for applying them locally.
Activity 2: Co-design and cooperative prototyping within physical and virtual “makerspaces”, by considering, in a typical action research and citizen science approach, participants as active makers. Provide the infrastructure, P2P networking between scientists, engineers and citizens, but leave the latter ones customize the technology, develop modular products, and assemble sensors themselves in a grassroots innovation perspective. Build an online platform acting as a knowledge repository for smart farming agroecology, with forums and learning hubs for developing apps, sensors, instruments dedicated to smart farming.
Activity 3: Modeling. Have BU’s qualitative researchers and TABIT observe these activities and generate data by means of observations, interviews and focus groups. These data will be used in particular for designing agent-based models that simulate the dynamics of technology adoption among farmers of the two groups (a) and (b), its impact at a larger scale on the transition towards sustainable practices, and the disruptions that may be foreseen by the introduction of new innovations.
Implementation
Expected methodological outcomes: usage of agent-based models for agricultural transition scenario design; participatory monitoring using low-cost innovative sensors.