Social network analysis of climate change adaptation communication in Makueni county
Climate change is already negatively affecting communities who depend on rain fed agriculture for their livelihoods. Adaptation through the adoption of appropriate agricultural technologies is crucial for the survival of the affected communities. The awareness among farmers of the existence of the phenomenon is perhaps the first step in the adaptation process. Awareness occurs through information sharing both horizontally and vertically channels. The growing of drought resistant, drought avoiding (early maturing) crop varieties, and irrigation are among innovations adopted by crop farmers adapting to climate change. The adoption of these techniques occurs within a social setting and is initiated by awareness of the existence of the technique through the process of information exchange. These actors in the information exchange process play different roles which determine the flow of such information and resultant communication patterns. An understanding of how this information flows through a social system is crucial in the development of agricultural communication approaches. There is limited literature on the study of information flow in agricultural field using a methodology that integrates communication, statistics and graph theory. Social Network Analysis (SNA) enhances the understanding of how information flows through social system in the context of climate change adaptation. SNA is a relatively new methodology that has gained currency due to its ability to combine graph theory, statistics and computer programmes to produce visual socio-grams and indices that assign values to relationships in a network. This study used social network analysis (SNA) using NodeXL vii version 220.127.116.11 computer programme to generate socio-grams that showed the patterns of information flow. Correlation and regression analyses were used to show if there were any significant relationship among the study variables. Sakai sub-location in Makueni County, Kenya is an area that is already experiencing climate change. The Kenyan government in collaboration with Centre for Science and Technology Innovation (CSTI) agency has stepped in to assist the farmers to adapt to climate change through introduction of appropriate agricultural techniques. The aim was to support the farmers in Sakai sub-location to adapt to climate change phenomenon. A social network analysis was carried out among farmers in five villages in Sakai sub-location in Makueni County in Kenya. The villages were namely:-Nthongoni, Kiteani, Kathamba, Linga and Muiu to assess the effects of social network structures on climate change adaptation communication. Questionnaires were administered to 165 farmers and this yielded 485 nodes and 747 edges. The socio-grams showed dense clustering of actors at the centre that are well but weakly connected to the peripheral actors who most likely acted as links to neighbouring villages. Low eigenvector centrality of 0.002 implies a lower number of opinion leaders who influence information flow but the high average in-degree and out-degree of 1.5 shows a structure which supports flow of information with every farmer at least being a source of information for two farmers and as well as being a receiver from another two farmers. The existence of both weak and strong ties as shown by the wide variation in the clustering co-efficient (maximum 0.5 and average of 0.025) and visual observation of the socio-grams show structural features which supports information flow. viii The results of the study show that homophily in this social network enhanced horizontal flow of climate change adaptation information within groups and showed that the structure of a social network affects how information on climate change flows through the social system. A correlation analysis shows that there is a statistically significant relation at 0.05 co-efficient between age (0.238), education level (0.624), size of the farm (0.509) and group membership (0.173) access to information on climate change. A probit regression analysis showed that even though some variables such as household marital status and land size positively affect information flow on climate change there is no variable statistically which influences information flow on climate change and adaptation strategies. This study shows that SNA can be used to study agricultural communications. The socio-grams can be used to identify opinion leaders and map out information flow and therefore inform the best agricultural extension approach to be used in creating awareness on climate change adaptation.