Climate-smart Agricultural Practices in Agro-climatic Zones of Meghalaya: A Social Network Analysis

Abstract

Aims: To understand the flow of information of Climate Smart Agricultural (CSA) practices among the farmers, the factors that impede this flow and the impact of the social network on adapting CSA practices. Place and Duration of Study: The study was conducted in hilly state, Meghalaya, India between August 2016 and April 2017. Methodology: A sample of 120 farmers was selected from agriculturally vulnerable villages to climate change by snowball sampling. The villages were selected from two Agro-climatic zones (ACZs) of the state, Meghalaya. UCINET software was utilised for analysing the Social Network Analysis (SNA) of the community with the performance index used to measure the impact in adopting CSA practices. Results: The network centralization index obtained in Tropical ACZ was relatively high (0.63) depicting a fragile social network as farmers relied on certain central actors for information and if these actors were to be removed, many farmers would be left isolated. However, in Sub-tropical ACZ, a low index (0.37) was attained implying that farmers had maximum connections in the network. Very low cohesion density measures (<0.05) was obtained in both the ACZs portraying a slow rate of diffusion of information on CSA in the farming community. Further, the homophily index of SNA indicated that the farmers tend to associate more with other farmers having similar socio-economic characteristics. The impact of the social networks in both of the ACZs were highest (68.30%) under low, and (63.30%) under the medium adoption levels of CSA practices in Tropical and Sub-tropical ACZs. Conclusion: Hence improving access to climate information is an important step to improve the livelihood of people in such variable conditions. With a better understanding of the social factors that influence the flow of knowledge and the adoption of CSA practices in the agricultural sector, researchers and policy makers could be able to identify and reduce barriers to technology diffusion and adoption.