hand, the state has faced droughts or moisture stress for 22 years, during the period
from 1950 to 2013 (Government of Odisha 2013). Even under normal conditions,
agricultural production in Odisha is marked by low productivity, but its simultaneous susceptibility to climate change and droughts lead to wide fluctuations in
output (Arora et al. 2015). Moreover, this state is one of the most underdeveloped
states of India featured by higher poverty rate and lower growth rate. Under such
conditions of lower social development with ever changing climatic conditions pose
already marginalised remote regions of Odisha to higher drought risk.
5 Sampling and Data Collection
For the collection of primary data from the field, multistage random sampling
procedure was employed for this research.
Balangir district was selected from the list of drought-prone districts of Odisha
state. After the initial selection, two randomly selected blocks from Balangir were
Patnagarh and Puintala (Fig. 1). At third stage, one panchayat from each selected
block was randomly selected namely Tamian grama panchayat from the former and
Mahimunda grama panchayat from the later. In the next step, two villages from
each panchayat were selected and finally 55 households from each village were
interviewed using face-to-face survey mode with the help of a pre-designed and
pre-tested questionnaire. The selected villages were Aintalunga and Bagbahali
(from Tamian) and Bilaikani and Sirabahal (from Mahimunda). As a result, the total
sample size for this study was 220 households.
6 Analytical Framework
For measuring the vulnerability of rural households, the Climate Vulnerability
Index (CVI) developed by Pandey and Jha (2011) was used. Major domains and
sub domains were modified for this study which included only the sub domains that
are relevant to rural communities of India. Vulnerability is a function of three
dimensions, i.e. adaptive capacity, sensitivity and exposure (IPCC 2007) and it can
be divided into six major domains, i.e. Demographic (Dem), Social (Soc),
Economic (Eco), Physical (Phy), Drought (Dro) and Climate variability (CV).
Table 1 shows the organisation of these six domains in the IPCC framework. On
the other hand, Table 2 presents the explanation about the sub domains.
The primary data collected from the study area were transferred into appropriate
measurement units like counts, ratios, percentages and indices for each sub domain.
In few cases, sub domains were case-specific and/or qualitative in nature. Since
each of the sub domains was measured in different scale, each measure was standardised into an index using Eq. 1.
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413
from 1950 to 2013 (Government of Odisha 2013). Even under normal conditions,
agricultural production in Odisha is marked by low productivity, but its simultaneous susceptibility to climate change and droughts lead to wide fluctuations in
output (Arora et al. 2015). Moreover, this state is one of the most underdeveloped
states of India featured by higher poverty rate and lower growth rate. Under such
conditions of lower social development with ever changing climatic conditions pose
already marginalised remote regions of Odisha to higher drought risk.
5 Sampling and Data Collection
For the collection of primary data from the field, multistage random sampling
procedure was employed for this research.
Balangir district was selected from the list of drought-prone districts of Odisha
state. After the initial selection, two randomly selected blocks from Balangir were
Patnagarh and Puintala (Fig. 1). At third stage, one panchayat from each selected
block was randomly selected namely Tamian grama panchayat from the former and
Mahimunda grama panchayat from the later. In the next step, two villages from
each panchayat were selected and finally 55 households from each village were
interviewed using face-to-face survey mode with the help of a pre-designed and
pre-tested questionnaire. The selected villages were Aintalunga and Bagbahali
(from Tamian) and Bilaikani and Sirabahal (from Mahimunda). As a result, the total
sample size for this study was 220 households.
6 Analytical Framework
For measuring the vulnerability of rural households, the Climate Vulnerability
Index (CVI) developed by Pandey and Jha (2011) was used. Major domains and
sub domains were modified for this study which included only the sub domains that
are relevant to rural communities of India. Vulnerability is a function of three
dimensions, i.e. adaptive capacity, sensitivity and exposure (IPCC 2007) and it can
be divided into six major domains, i.e. Demographic (Dem), Social (Soc),
Economic (Eco), Physical (Phy), Drought (Dro) and Climate variability (CV).
Table 1 shows the organisation of these six domains in the IPCC framework. On
the other hand, Table 2 presents the explanation about the sub domains.
The primary data collected from the study area were transferred into appropriate
measurement units like counts, ratios, percentages and indices for each sub domain.
In few cases, sub domains were case-specific and/or qualitative in nature. Since
each of the sub domains was measured in different scale, each measure was standardised into an index using Eq. 1.
Analysing Vulnerability to Climate Change in India …
413