Cross-sectional study maps climate adaptation networks among smallholder farmers, revealing low institutional participation and highlighting key drivers of local knowledge sharing.
Collaborative learning networks play a significant role in strengthening resilience of smallholder farmers to climate change. However, limited research exists on the nature of collaborative leaning networks on climate change adaptation practices. Therefore, this study intends to bridge this gap by mapping the existing collaborative learning networks on climate change adaptation practices. The study conducted a cross-sectional survey, using semi-structured questionnaire to collect data from 265 randomly selected smallholder farming households coupled with key informant interviews and focus group discussions. Using descriptive statistics, the study computed frequencies and percentages of actors involved in collaborative learning for climate change adaptation. The study used social network analysis (SNA) to map and visualize the existing collaborative learning networks (both formal and informal) and centrality measures to determine the key and influential actors in the networks on climate change adaptation practices. The study revealed five collaborative learning networks on; water and pasture management, soil and water conservation, improved animal breeds, indigenous pest and diseases management, and indigenous animal breeding. The results revealed that academic institutions were not participating in collaborative learning with smallholder farmers. Religious leaders were found to be at 0.4%, while local leaders and other researchers accounted for 0.8% participation in collaborative learning for climate change adaptation. Factors shaping collaborative learning networks included; NGO intervention, access to extension services, relational trust and shared agricultural challenges. Therefore, the study provides opportunities for collective efforts to scale intervention and platforms that would otherwise facilitate knowledge co-creation and exchange at the local level.
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Kekirunga et al. (2026) studied this question.
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