Impact of Social Networks on Farmers' Choice of Technology

Social Network Analysis (SNA) is a powerful tool for understanding the dynamics of social networks and their influence on the adoption of technology among farmers. By examining the interactions and relationships within a network, SNA can reveal how knowledge, information, and innovations are shared among its members. This article explores the impact of social networks on farmers' choice of technology, with a focus on the Sericulture Innovation System (SIS) in Karnataka, India.

Understanding Social Network Analysis

SNA is based on the premise that network ties play a crucial role in the dissemination of knowledge and innovations. The strength of these ties is determined by the number, frequency, and duration of interactions between actors, as well as the reciprocal services exchanged. In the context of farming communities, these ties can significantly influence the adoption of new technologies. The relationships between actors in a network are often represented by binary ties, which indicate the direction of information flow and can elicit an adoption response. The strength and direction of these ties are essential for understanding how innovations spread within a community.

The Sericulture Innovation System under investigation encompasses five villages in Sreerangapattana Taluk, Mandya District, Karnataka. This network includes 358 sericulture farmers, 30 Chawky Rearing Centres (CRC), five Chawky agents, 11 input suppliers, eight mulberry nurseries, eight mountage rental agencies, three cooperative societies, six markets, and eight government extension agencies. 

Measures of Network Cohesion

The network's cohesion, or interconnectedness, is a critical factor in its effectiveness. Cohesion is defined as the desire of individuals to maintain their connection with a particular group. It is important for achieving common tasks and is measured by various metrics, including average degree, network diameter, graph density, modularity, connected components, clustering coefficient, and path length.

The SIS network has an average of 9.7 links per node, a network diameter of 6, and an average path length of 2.6. However, only 5% of the potential links between farmers are present, indicating underutilization of the network's potential. This suggests that there is ample scope for promoting linkages between actors to enhance the network's effectiveness.

The network's cohesion can be further analyzed by examining the centrality measures of its actors. Centrality measures identify the most influential actors in a network and can provide insights into the flow of information and innovations.

Centrality Measures and Key Actors

Centrality measures include in-degree centrality, out-degree centrality, betweenness centrality, and eigenvector centrality. These measures help identify the most prominent actors, or "key players," in the network.

In-degree centrality measures the popularity of an actor, based on the number of times they are mentioned by others in the network. In the SIS, markets, mountage rental services, CRCs, Chawky agents, and extension agents exhibit high in-degree centrality, indicating their frequent approach by other actors. Among farmers, those who adopt new technologies (adopters) are more frequently approached for technical information.

Out-degree centrality measures the potential of actors to introduce innovations. Adopter-farmers are shown to be the best disseminators of innovations, aligning with previous research. This suggests that adopter-farmers play a crucial role in spreading new technologies within the network.

Betweenness centrality highlights the role of actors as intermediaries in information dissemination. Farmers who have adopted new technologies exhibit the highest betweenness centrality, accounting for 46.3% of all ties in the SIS. This implies that these farmers are key intermediaries in the network and play a significant role in the dissemination of innovations.

Eigenvector centrality considers the influence of an actor's connections. In the SIS, markets and rental services have high eigenvector centrality, indicating their importance in the network. However, caution is advised in interpreting these results, as high eigenvector values may arise from the inevitability of these organizations to farmers.

The Role of Farmers in Technology Adoption

Farmers, especially those who adopt new technologies, play a decisive role in the dissemination of knowledge within the SIS. The analysis reveals that opinion leaders in the network are predominantly non-adopters, suggesting that their influence may not encourage further adoption. This highlights the need for targeted extension interventions to convert these opinion leaders into adopters and advocates of new technologies.

The centrality measures of the top ten actors in the network indicate that farmers are the sole players in terms of closeness and betweenness centrality. This underscores the strong influence of farmers over other actors in the network as opinion makers. Among the most influential players are the farmer-cum-Chawky agent (F27), Market no.1 (M1), Extension agent no. 4 (E4), CRC no. 5 (C5), and Chawky agent no.1 (A1). 

Policy Implications

The SNA of the SIS reveals certain policy implications. First, there is a need to promote linkages between actors to fully exploit the network's potential. Second, extension policies should focus on creating favorable opinions among adopters and utilizing their influence to spread innovations. Third, targeted interventions are required to convert strong opinion leaders into adopters.

One particularly striking finding is the leadership roles played by the government extension officer (E4) and the farmer-cum-Chawky agent (F27). The SNA indicates that the village community has polarized into two groups under their respective influence. While these two leaders are personally well-connected, their personal relationship has not resulted in networking between the two groups they represent. 

Figure shows polarization of the village community into two separate networks around two different influencers. The green bubbles represent the members connected with extension officer E-4 as central node and the pink bubbles those, connected with farmer F-27 as central node (click to enlarge the picture)

This calls for policy intervention at two levels:

1. Opening Channels of Communication: There is a need to create platforms for interaction between the two groups. This can be achieved through group discussions, focus group discussions, demonstration programs, and off-farm training programs. By facilitating communication between the groups, the favourable impact of new technologies can be shared more effectively.

2. Targeted Extension Interventions: Given the strength of SNA in identifying key players, extension policies should focus on converting strong opinion leaders into adopters. Recognizing and educating these leaders can help them become strong advocates of new technologies, thereby enhancing the overall effectiveness of the network.

Conclusion

SNA provides valuable insights into the impact of social networks on farmers' choice of technology. In the SIS, farmers who adopt new technologies play a crucial role in disseminating knowledge and innovations. However, the network's potential is underutilized, and opinion leaders are predominantly non-adopters. By promoting linkages and targeting interventions, policymakers can enhance the adoption of new technologies and improve the overall effectiveness of the network.

The findings from the SNA of the SIS highlight the importance of social networks in the adoption of technology among farmers. By understanding the dynamics of these networks, policymakers can design more effective extension programs and interventions to promote the adoption of new technologies. This, in turn, can lead to increased productivity and sustainability in farming communities.

In summary, the impact of social networks on farmers' choice of technology is significant. The relationships and interactions within a network can greatly influence the dissemination of knowledge and innovations. By leveraging the insights gained from SNA, policymakers can create targeted interventions to enhance the adoption of new technologies and improve the overall effectiveness of agricultural innovation systems.