Empirical Modelling of Determinants of Climate Change Adaptation by Farming Communities in Central Himalaya, India

Impact of climate change on farming system and adaptation of farming communities in the Himalaya is a key concern for policy planners. Inhabitants of this region have responded to climatic and socio-economic changes with wide range of coping and adaptation mechanism. In this paper, we developed an empirical modelling approach based on logistic regression model (19.3< χ²<28.04) to analyze two-step adaptation process and identify significant socio-economic and institutional factors that influence farmers’ perception and adaptation to climate change in central Himalaya. Household surveys were carried out to collect information from 193 households from three villages located in different agro-ecological settings in Kosi watershed in Uttarakhand state of India. The study suggests that perception of farmers to climate change is significantly influenced by age and education of head of the household and information on climate change. However, farmers’ adaptation to climate change is significantly determined by gender, household size, farm size, economic investments, dependency ratio, distance from market, irrigated land and livestock ownership. Further, the socio-economic factors play more vital role in farmers’ decision for adaptation than the institutional factors. Although climate change is perceived by most of the farmers (78%), yet due to various limiting factors, only 22% have responded to change through various adaptation measures. The determinants and adaptation strategies of farmers, living in different agro-ecological settings, found localized and varying according to the socio-economic and physiographic conditions. The study advocate for formulation of regional and local level climate change adaptation policy and programme considering farmers’ capacity to adapt and development of decision support system to identify and prioritize potential impacts and areas at risk to reduce vulnerability and build adaptive capacity.