| dc.description.abstract | 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
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version 1.0.1.245 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.
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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. | en_US |
