Rural institutions, social networks, and self-organized adaptation to climate change

Published: 23-07-2020| Version 1 | DOI: 10.17632/prs95bn8wj.1
Harry Fischer


The data was collected from the Takoli Panchayat in the lower Kullu Valley of Himachal Pradesh, India in 2011. Our analysis includes 273 households, which comprises all households that engage in agriculture in Takoli. The data identified 46 crops. We divided these crops into five types: vegetables, fruit, seeds, food grains, and ‘others’; each represents a distinct combination of labor requirements, inputs, market risk, and climate risks. We then calculated the proportion of land that each farmer has devoted to each of the five main crop types across one agricultural year. This was calculated as the total land of each crop type under cultivation divided by the gross cropped area for each farmer. Based on these variables, we undertook hierarchical cluster analysis using wards linkage, from which we derived 5 “agricultural portfolios”. This included three portfolios that specialize in fruit, vegetables, and food grains and two that are diversified across crop types with an emphasis on either food grains or vegetables. Households were also asked to identify the other households in Takoli they interact with on any issue regarding agricultural production. We used this data to create a bi-directional social network (using igraph package in R). We then calculated several measures of network centrality to capture different aspects of a household’s integration within the broader network: degree, betweenness, and eigenvector centrality. Within the full sample of 273 households, 4 households have no linkages within the network (they have a degree of 0). As such, they have no possible value for betweenness or eigenvector centrality (n=269). We also asked if households interacted with the six institutions of interest in our study: the Agricultural Department, Horticulture Department, the Bajaura Field Research Station (linked to state agricultural universities), Regional Banks, the Block Development Office (the lowest level of the development bureaucracy), and the Panchayat (local governmental unit). To calculate the network distance between households and each institution included in our study, we created a second social network that includes both households and institutions as ‘nodes’. We then calculated the additive inverse of the network distance to each of the institutions: a distance of ‘-1’ indicates that a household interacts directly with a given institution, a distance of ‘-2’ indicates that a household interacts with a household that interacts with the institution, and so on. Households with no network relationships can have no network distance (n=269). However, one household reports a direct interaction with the agricultural department and thus has a network position with regard to this institution (n=270). Please see detailed description in the publication SI.