Farmers’ social networks and adoption of disaster risk reduction measures

An experimental study in Uganda

Bibliographic Data

ID22025586
AuthorsRodgers Mutyebere (0000-0002-4169-7612, Mountains of the Moon University, corresponding author), Ronald Twongyirwe (0000-0003-3379-7709, Mbarara University of Science and Technology), John Sekajugo (0000-0002-8904-0898, Mountains of the Moon University), Clovis Kabaseke (Mountains of the Moon University), Mercy Gloria Ashepet (0000-0002-2997-3493, KU Leuven), Grace Kagoro‐rugunda (0000-0002-4498-9135, Mbarara University of Science and Technology), Grace Kagoro-Rugunda, Matthieu Kervyn (0000-0002-4966-3468, Vrije Universiteit Brussel), Liesbet Vranken (0000-0003-1170-1139, KU Leuven)
Year2026
Volume136
Pages106070
Publication date2026-04-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2026.106070
OpenAlexW7131777673
LanguageEN
References cited37

Action against the severe impacts of climate change requires strengthening the local community’s resilience to hazards such as landslides and flash floods. Therefore, improving farmers’ knowledge to adopt farm-based Disaster Risk Reduction (DRR) measures is key. In addition, mountainous areas limit accessibility by external, less-facilitated government extension workers mandated for information transfer. Other informal, local social networks can play a role to promote DRR measures’ adoption, but their effectiveness remains unclear. Using an experimental study involving 533 rural households, we investigated the effectiveness of different channels of information transfer: we tested whether a network of Citizen Scientists (CS) - an example of local social networks, can promote adoption of tree planting and diversion channels as DRR measures in the disaster-prone Western Uganda region. We also examined whether the CS network was more effective, than conventional outreach formal extension workers in influencing farmers’ behaviour. Analyzing the treatment effect using the Analysis of Covariance, findings indicate that knowledge transfer through CS – either in one-on-one or group sessions - is more effective in enhancing tree adoption than transfer through external agents. For diversion channels, only group sessions facilitated by CS significantly increased their adoption but not the sessions by external agents or CS-facilitated one-on-one sessions. Therefore, social networks promise a bright future for knowledge transfer but may not be a ‘silver bullet’ to information access problems. Long-term impact of knowledge transfer, adoption by others in the farmers’ networks, and integrating social networks like CS into the formal extension programs should be investigated

Disaster risk reduction · Natural disaster · Occupational safety and health · Poison control · Social risk · Agricultural risk and resilience · Climate change impacts on agriculture · Disaster Management and Resilience · Human Factors and Ergonomics

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