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Perception to Adaptation of Climate Change in Nepal: An Empirical Analysis Using Multivariate Probit Model

Department of Agricultural Economics, Kyungpook National Univeristy, 80 Daehakro, Daegu, South Korea
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Received: 17 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
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Published: 6 March 2019
DOI: 10.3390/sci1010012.v1
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Abstract

This study assessed farmers’ perception of climate change, estimated the determinants of, and evaluated the relationship among adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering 10 sample districts considering five agro-ecological zones and a vulnerability index. Four adaptation choices (change in planting date, crop variety, crop type and investment in irrigation) were deemed as outcome variables and socioeconomic, demographic, institutional, farm-level and perceptions variables were deployed as explanatory variables. Their marginal effects were determined for three climatic variables—temperature, precipitation and drought. Age, gender and education of head of household, credit access, farm area, rain-fed farming and tenure, are found to be more influential compared to other factors. All four adaptation-options are found to be complimentary to each other. Importantly, the intensity of impact of dependent variables in different models, and for available adaptation-options, are found to be unequal. Therefore, policy options and support facilities should be devised according to climatic variables and adaptation options to achieve superior results.
Keywords: adaptation; perception; climate change; Nepal; multivariate probit adaptation; perception; climate change; Nepal; multivariate probit
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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GC, A.; Yeo, J.-H. Perception to Adaptation of Climate Change in Nepal: An Empirical Analysis Using Multivariate Probit Model. Sci 2019, 1, 12.

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