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Research Article Open access CC BY 4.0

Socio-economic Impact of Climate Change, Adaptation and Determinants of Willingness to Pay for Crop Insurance in Central Agro-climatic Zone of Afghanistan

Meraj Sarwary, S. Senthilnathan, A. Vidhyavathi, S. Kokilavani

Current Journal of Applied Science and Technology · pp. 83–92 · Published 25 Jun 2020

10.9734/cjast/2020/v39i1630739

Abstract

Afghanistan is frequently affected by drought and other climate change related hazards due to increased temperature and reduced precipitation over most parts of the country. The present study was undertaken to analyze the climate change preparedness measures for adaptation and willingness to pay for crop insurance to mitigate the climate change impacts. This study was conducted in central agro-climatic zone of Afghanistan to collect the primary data through the random sampling technique with a total sample of 105 respondents from the seven provinces located in this zone. Likert type responses were used to assess the climate change adaptation and binary log it regression model was used to identify the factors affecting the willingness to pay for crop insurance to mitigate the climate change impacts on agriculture. The results of the study indicated that changing cropping pattern (74%) followed by receiving technical information from experts (70%) and keeping the land as fallow (60%) were rated as very high to high preparedness measures to mitigate the climate change impacts. Logit regression model revealed that the education and technical information received from experts were positively significant and their marginal effects implied that the probabilities of willing to pay for crop insurance were increased by 1.3 and 15.9 percent, respectively. The establishment of crop insurance agencies at province level may help reducing the impact of climate change and increasing the income and livelihood of the farmers.

Climate change adaptation willingness to pay crop insurance binary logit model.

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Climate Change and Cereal Crops Productivity in Afghanistan: Evidence Based on Panel Regression Model

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