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Vulnerability Assessment of Rural Communities to Environmental Changes in Mid-Hills of Himachal Pradesh in India

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Field survey was conducted during 2014 to study the vulnerability of rural communities to environmental changes in mid-hills of Himachal Pradesh in India. Integrated vulnerability analysis approach was employed based on indices constructed from carefully selected indicators for exposure, sensitivity and adaptive capacity. The household was selected as the main unit of analysis because major decisions about adaptation to environment-induced stresses and livelihood processes are taken at that level. The indicators were weighted using Principal Component Analysis (PCA). Those which got the highest weights included historical changes in climate (1.00), share of non natural resources based income (0.98) and physical assets (0.74) among the indicators of exposure, sensitivity and adaptive capacity, respectively. Inter-block analysis of the vulnerability index indicated that households located away from district headquarters have higher levels of biophysical and socioeconomic vulnerabilities compared to those near the district headquarters, due to higher reliance on natural resources which are now being impacted by ongoing environmental changes. Policy measures and development efforts should therefore aim towards addressing the high biophysical and socioeconomic vulnerabilities of the rural communities of the mountain of Himachal Pradesh and more emphasis should be laid on the enhancement of their adaptive capacity.
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Universal Journal of Environmental Research and Technology
All Rights Reserved Euresian Publication © 2015 eISSN 2249 0256
Available Online at: www.environmentaljournal.org
2015 Volume 5, Issue 1: 61-71
Open Access
Research Article
61
Ndungu et al.
Vulnerability Assessment of Rural Communities to Environmental Changes in Mid-Hills of
Himachal Pradesh in India
1
Ndungu Charles Kimani,
1
Bhardwaj S.K.,
2
Sharma D.P.,
3
Sharma R.,
4
Gupta R.K.,
5
Sharma B.
1
Department of Environmental Science
2
Department of Silviculture and Agroforestry
3
Department of Social Sciences
4
Department of Basic Sciences
1,2,3,4
Dr. Y S Parmar University of Horticulture and Forestry, Nauni (Solan) India
5
Himalayan Forest Research Institute, Panthaghati, Shimla (Himachal Pradesh)
Corresponding author: charlesndungu70@gmail.com
Abstract:
Field survey was conducted during 2014 to study the vulnerability of rural communities to environmental
changes in mid-hills of Himachal Pradesh in India. Integrated vulnerability analysis approach was employed
based on indices constructed from carefully selected indicators for exposure, sensitivity and adaptive
capacity. The household was selected as the main unit of analysis because major decisions about adaptation
to environment-induced stresses and livelihood processes are taken at that level. The indicators were
weighted using Principal Component Analysis (PCA). Those which got the highest weights included historical
changes in climate (1.00), share of non natural resources based income (0.98) and physical assets (0.74)
among the indicators of exposure, sensitivity and adaptive capacity, respectively. Inter-block analysis of the
vulnerability index indicated that households located away from district headquarters have higher levels of
biophysical and socio-economic vulnerabilities compared to those near the district headquarters, due to
higher reliance on natural resources which are now being impacted by ongoing environmental changes.
Policy measures and development efforts should therefore aim towards addressing the high biophysical and
socio-economic vulnerabilities of the rural communities of the mountain of Himachal Pradesh and more
emphasis should be laid on the enhancement of their adaptive capacity.
Keywords:
Adaptive capacity, biophysical vulnerability, climate change, natural resources, socio-economic
vulnerability
1.0 Introduction:
Resource use intensification coupled with
mountain specificities and concomitant
environmental changes have led to pronounced
vulnerability of rural communities inhabiting the
mountainous areas of North West Himalayas.
Inaccessibility, marginality, fragility and other
constraints in topography have been associated
with the difficulty of increasing mountain
agricultural productivity through intensification
and other plains-centric strategies (Jodha, 2000).
Over the years and due to mountain specificities,
urbanization and other developmental activities
have been concentrated in valleys and other
gentle sloped areas, leaving steeply sloped and
remote areas largely unaffected by anthropogenic
activities. However, increasing population pressure
and concomitant need to enhance livelihood
opportunities for people residing in far flung areas
in the mid hills region of Himachal Pradesh has in
the recent years led to mushrooming of
development activities to the fragile and
environmentally sensitive areas. Such
developmental activities have caused
environmental and natural resources degradation
in the area. The changing climate and weather
vagaries have also contributed towards the
degradation of natural resources and consequently
impacted socio-economic conditions of rural
people in the region.
Vulnerability has been defined by the United
National Development Programme (UNDP) as a
human condition or process resulting from
physical, social, economic and environmental
factors, which determine the likelihood and scale
of damage from the impact of a given hazard.
(UNDP, 2004). Faulty agricultural practices in
severely constrained mountain areas have
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Ndungu et al.
degraded the natural resources, resulting in the
vulnerability of Himalayan mountain people
(ICIMOD, 2014). The changing climate situation in
rain fed agriculture system has further aggravated
the problem and consequently affected the
growth of the region. Moreover, population
pressure and deceasing per capita land holding has
forced the mountain people to use marginal lands
for agriculture and other developmental activities,
resulting in degradation of natural resources and
ultimately affecting the socio-economic conditions
(Jenny and Egal, 2002) In hilly areas factors such as
education, access to credit and information
technology, wealth status and transportation
facilities have also been reported to affect socio-
economic vulnerability(Adger and Kelly, 1999;
Fussel, 2007). On the other hand biophysical
vulnerability in hilly areas has been attributed to
relatively more undulating and steeply sloping
farmlands, steep and rugged terrain impeding road
construction and making transport difficult and
costly, low soil fertility due to land degradation by
soil erosion, diminishing water resources and
increasing trends of environmental hazards like
drought, floods, landslides, forest fires and hailing
events (Liverman, 1990; Hewitt, 1995).
Specific sources of vulnerabilities for communities
inhabiting mid-hills of Himachal Pradesh include
scattered and very limited land holding (0.013
ha/capita), environmental constraints (climate,
soils, slope, natural hazards), food insecurity, lack
of access to markets, education, health care,
dependence on one single economic factor, poor
communication facilities, inappropriate
governmental or industrial interventions; and
globalization. Many of these elements of
vulnerability are not well documented and there
are few studies that quantify the vulnerability of
mountain people to these different elements
(Huddleston et al., 2003).
Therefore, the present study focused on
household-level vulnerability assessment to
environmental changes in mid hills of North
Western Himalayan region of Himachal Pradesh in
India. It identifies some of the determining factors
for vulnerability based on certain household social,
economic and environmental (biophysical)
characteristics. The findings of this study can be
useful for targeting interventions, priority setting
and resource allocations at community level for
enhancing adaptive capacity of mountain people.
2.0 Materials and Methods:
2.1 Profile of the Study Area:
The study area consisted of mid-hills (800-1600 m
above mean sea level) regions falling in two
districts namely Kullu and Solan of Himachal
Pradesh in North Western Himalayas. The region
has mild temperate climate with annual average
precipitation amounting to 1150 mm. The soils
vary from sandy loam to loam in texture. The area
has a steep and rugged terrain which amplifies
biophysical and socioeconomic vulnerability of the
communities. Overall, the Mid Hill region occupies
about 33% of the geographical area and 53% of
the cultivated area of Himachal Pradesh State.
2.3 Research Design and Data Collection:
In order to collect primary data on various
indicators of vulnerability, a total of 275 farm
households were considered at the selected sites
of mid-hills in Solan and Kullu districts (Figure 1)
during the year 2014. The two districts were
stratified on the basis of development considering
distance from the district headquarters.
Consequently, two administrative blocks were
purposefully selected from each district, one near
the district headquarters and the other away from
it i.e. in remote areas. Kullu and Solan blocks from
Kullu and Solan districts, respectively formed study
sites near the district headquarters while Naggar
and Kandaghat blocks from Kullu and Solan
districts, respectively formed study sites away the
headquarters. Households falling within an
altitudinal range of 800 to 1600 m above mean sea
level (amsl) were randomly selected from study
area to constitute the sample and data relating to
exposure, sensitivity and adaptive capacity
collected from the household heads using a
pretested questionnaire. The household was
selected as the main unit of analysis because
major decisions about adaptation to environment-
induced stresses and livelihood processes are
taken at that level. Data were coded and analyzed
by using SPSS 16.
2.4 Selection of Vulnerability Indicators:
The process of construction of vulnerability index
progressed from selection of indicators,
assignments of weights to them and finally their
aggregation to form an index. Review of literature
supplemented with participant observation and
focus group discussions was used to select the
indicators for exposure, sensitivity and adaptive
capacity.
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Ndungu et al.
2.4.1 Exposure:
To capture the direct impact (stimulus) of
environmental change on households, two
parameters were considered, namely the historical
changes in climate variables and trend of
occurrence of extreme hazardous events.
Coefficient of variation (%age) of average annual
maximum temperature, average annual minimum
temperature and average annual precipitation for
the time period 1984- 2011 (Solan district) and
1991- 2005 (Kullu distict) represented the
historical climate changes. Computation of the
coefficient of variation for these parameters was
done at district level and extrapolated to house
hold level. The trend of occurrence of extreme
hazardous events such as floods, landslides,
droughts, snow events and hailstorms was
obtained from the household survey. It was
hypothesized that increasing trend of occurrence
of these extreme events and rate of change of
climate variables will increase the exposure of the
households to the impacts of environmental
change.
2.4.2 Sensitivity:
Sensitivity was indicated by impacts of
development projects and extreme events on land
and water resources and household income
structure. Higher share of natural resource based
income (composed of agriculture, livestock and
forest products) increase the sensitivity of the
household as these sources are more dependent
on climate; while higher share of non-natural
resource based remunerative income sources
(composed of salaried jobs, non-farm skilled jobs,
and remittances from abroad) reduce the
sensitivity. These three income sources are
categorized as remunerative sources because the
return from these sources is comparatively higher
than other sources of income.
Figure 1: Map of the study area showing selected
administrative blocks in mid-hills of Himachal
Pradesh
2.4.3 Adaptive Capacity:
Adaptive capacity of a household was taken to be
an emergent property of the five types of
livelihood assets viz. physical, human, natural,
financial, and social ( Ellis, 2000; DFID, 1999).
These indicators help in addressing shocks from
environmental and climate change through
minimization, pooling and redistribution of risks or
as buffer against extreme environmental events.
2.5 Construction of Vulnerability Index:
The study followed integrated assessment
approach in assessing household level community
vulnerability to environmental and climate change,
in which vulnerability was operationalized as a
function of exposure, sensitivity and adaptive
capacity following the IPCC definition (Füssel,
2007). The integrated approach combines both
biophysical and socio-economic vulnerabilities to
arrive at final vulnerability index. In this approach
biophysical vulnerability is a function of exposure
and sensitivity while adaptive capacity is analogous
to socio-economic vulnerability and the overall
community/ household vulnerability is
conceptualised as a net effect of the two types of
vulnerabilities. The framework was first proposed
by Madu (2012) and later adopted by Tesso et al.
(2012) in Ethiopia and Opiyo et al. (2014) in Kenya.
To begin with, the indicators were normalized to
bring them within a comparable range using the
following formula;
(1)
where is the normalised score of the jth
variable in the ith household, is the indicator
score being normalised and is the mean and
standard deviation of the indicator score.
The next step involved assignment of weights to
the indicators using principal component analysis
(PCA) in SPSS. The weights assigned for each
indicator varied between -1 and +1, sign of the
indicators denoting the direction of relationship
with other indicators used to construct the
respective index. The magnitude of the weights
describes the contribution of each indicator to the
value of the index. PCA was run separately for the
indicators of exposure, sensitivity and adaptive
capacity. Stepwise PCA was run for the indicators
of exposure and adaptive capacity, and overall
indices calculated using the weights (loadings)
obtained from second step PCA.
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Ndungu et al.
The normalized variables were then multiplied
with the assigned weights to construct the indices
for exposure, sensitivity, and adaptive capacity,
each separately. The overall equation looked as
follows;
(2)
where, ‘I’ is the respective index value, ‘b’ is the
loadings from first component from PCA (PCA1)
taken as weights for respective indicators, ‘a’ is the
indicator value, ‘x’ is the mean indicator value, and
‘s’ is the standard deviation of the indicators.
Finally, vulnerability index for each household was
calculated as: V = E + S – AC, where, V is the
vulnerability index, E the exposure index, S is the
sensitivity index and AC is the adaptive capacity
index for respective household. The study
assumed a linear relationship between the three
components of vulnerability.
The overall vulnerability index facilitates inter-
household comparison within the study
administrative blocks and inter-administrative
block comparison as well. Higher value of the
vulnerability index indicates higher vulnerability.
However negative value of the index does not
imply that the household is not vulnerable at all.
This index gives a comparative ranking of the
sampled households and/or selected sites. Tests of
analysis of variance (ANOVA) were conducted to
compare the means among the four study sites
and four vulnerability quartiles.
3.0 Results and Discussion:
3.1 Exposure Indicators:
The weights obtained from PCA analysis for the
indicators of natural disasters (Table 1) ranged
from 0.90 (drought) to -0.04 (snow events). The
weights for drought, floods, landslides and hail
events were positive indicating a positive
relationship with the overall environmental hazard
composite score and consequently the exposure
index. The weight for snow events was negative
indicating thereby a negative relationship with
overall environmental hazards composite score
and consequently the exposure index. This implies
that in mid-hills of Himachal Pradesh droughts,
floods, landslides and hailing events have
increased the exposure of mountain people
whereas occurrence of snow events proved to
have reduced the exposure probably due to its
better infiltration in the soil thereby recharging
ground water and reducing runoff. Similar findings
by Vedwan and Rhodes (2001) while working in
Himachal Pradesh, indicated that increase of
floods lead to destruction of property and
subsequent exposure of the people to hazards.
Moreover, occurrence of snow events is
accompanied by low temperatures which realise
the chilling requirements of temperate crops such
as apples. Conversely, plummeting snow events
could affect production of temperate crops as also
reported by Vedwan (2006) in other studies.
Drought was weighted highest followed by
landslides, floods, hail and snow events. In
Himachal Pradesh, 80% of agriculture is rainfed
and therefore, increase in drought events may
enhance exposure of the people to vulnerability
and hence management of drought need to be
considered on priority for the upliftment of
mountain people. Similar studies by Vishwa et al.
(2013), Vedwan (2006) and Vedwan and Rhoades
(2001) showed that incidents of meteorological,
hydrometeorological and agricultural droughts
lead to massive crop failures thereby increasing
the exposure of the farmers.
Examination of mean values for the environmental
hazards across the study sites revealed that study
sites away from the district headquarters (Naggar
and Kandaghat blocks) comparatively recorded
significantly higher mean values than study sites
near the district headquarters (Kullu and Solan
blocks) for all the hazards except the snow events
whose mean values were not significantly different
across the study sites. This may probably be due to
the fact that Kandaghat and Naggar areas being
inaccessible, marginal and fragile with steep slopes
and swallow soils with poor water retention
capacity are prone to drought and surface runoff.
Similar studies by Hallegatte and Przyluski (2010)
indicated that people inhabiting far flung remote
areas and with low income perceive and
experience high incidence of environmental
hazards. On the other hand Kullu and Solan district
headquarters are located relatively on less steep
slopes characterised by low incidents of
environmental hazards. Major commercial and
administrative centres at these locations also
attract huge investments from governments and
private investors and these enhance the
development of adaptive structures and facilities
in these areas. Therefore, people living in these
areas perceive less apparent risks and threats from
the physical environment.
The data in Table 2 indicated that weights for
indicators of exposure ranged from 1.0 (historical
changes in climate) to 0.14 (natural disasters
composite score). All the weights were positive as
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Ndungu et al.
hypothesized, thus affecting exposure positively.
The absolute values of the weights indicate that
temperature and rainfall trends contribute more
to the exposure index compared to the composite
environmental hazards. Both minimum and
maximum temperature coefficients show a slow
increasing trend for all the study sites, with Solan
and Kandaghat blocks registering significantly
higher mean values in maximum temperature than
Kullu and Naggar blocks. The situation was
reversed for minimum temperature whereby Kullu
and Solan registered significantly higher mean
values than Solan and Kandaghat. Precipitation
also exhibited an increasing trend, the rate for
Solan and Kandaghat being significantly higher
compared to the other two blocks. The increase in
biophysical vulnerability due to climate change of
people inhabiting hilly areas has also been noticed
by Negi et al. (2012) and Vishwa et al. (2013) while
working in Himachal Pradesh. The trend for
environmental hazards over the last ten years is
highest for Kandaghat, followed by Solan, Naggar,
and Kullu regions.
3.2 Sensitivity Indicators:
The weights for indicators of sensitivity are
presented in Table 3 and ranged from 0.98 (share
of natural based income) to 0.10 (number of
livestock killed by extreme events). All the
indicators had a positive relationship with
sensitivity index except trend in availability of
water resources and share of non natural resource
based income which had a negative relationship.
The absolute values for the weights indicate that
share of natural resources based income and share
of non natural resources based income contribute
more to the sensitivity index than the other
indicators. However, the share of non natural
resources based income decrease the overall
household sensitivity as shown by negative sign of
the weight, while higher share of natural resource
based income makes the household more sensitive
to environmental change.
Table 1: Weights and mean values of natural disaster indicators in mid-hills of Solan and Kullu districts of
Himachal Pradesh
Indicators Weight Aggregate
(n=275)
Kullu
(n=81)
Naggar
(n=63)
Solan
(n=72)
Kandaghat
(n=59)
P-
Value
Frequency of
floods
0.68 2.56 (0.52) 2.43
(0.57)
2.68
(0.47)
2.53
(0.503)
2.63 (0.49) 0.02**
Frequency of
drought
0.90 2.55 (0.53) 2.36
(0.60)
2.68
(0.47)
2.53
(0.50)
2.69 (0.46) 0.00***
Frequency of
landslides
0.88 2.51 (0.54) 2.33
(0.59)
2.63
(0.49)
2.49
(0.53)
2.66 (0.48) 0.00***
Frequency of hail
events
0.79 2.54 (0.54) 2.42
(0.50)
2.59
(0.64)
2.53
(0.50)
2.68 (0.51) 0.04**
Frequency of
Snow events
-0.04 1.06 (0.26) 1.07
(0.26)
1.05
(2.15)
1.08
(0.31)
1.03 (0.18) 0.66
Note: Figures in parenthesis indicate standard deviation
***,
**indicates significant at 1% and 5% level of significance, respectively
Table 2: Weights and mean values of exposure indicators in mid-hills of Solan and Kullu districts of
Himachal Pradesh
Indicators Weight Aggregate
(n=275)
Kullu
(n=81)
Naggar
(n=63)
Solan
(n=72)
Kandaghat
(n=59)
P-
Value
Trend in Maximum
Temperature
1.00 4.55 (0.50) 4.07
(0.00)
4.07
(0.00)
5.08
(0.00)
5.08 (0.00) 0.00***
Trend in Minimum
Temperature
1.00 9.14 (1.97) 11.02
(0.00)
11.02
(0.00)
7.08
(0.00)
7.08 (0.00) 0.00***
Trend in Rainfall 1.00 4.48 (0.01) 4.46
(0.00)
4.46
(0.00)
4.51
(0.00)
4.51 (0.00) 0.00***
Environmental
disaster composite
score
0.14 8.20 (1.43) 7.69
(1.51)
8.56
(1.26)
8.13
(1.39)
8.62 (1.34) 0.00***
Note: Figures in parenthesis indicate standard deviation
***indicates significant at 1% level of significance
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Ndungu et al.
Higher share of natural resource based income
(composed of agriculture, livestock, forest, honey
and handicrafts) increase the sensitivity of the
household as these sources are more dependent
on climate; while higher share of non-natural
resource based remunerative income sources
(composed of salaried jobs, non-farm skilled jobs,
and remittances from abroad) reduce the
sensitivity. These three income sources are
categorized as remunerative sources because the
return from these sources is comparatively higher
than other sources of income. Similar studies
conducted by Collier et al. (2008) indicated that off
farm income is stable, reliable and less climate
sensitive. Further, Davis et al. (2007) has reported
contribution of off farm income towards reducing
sensitivity of the rural people. The negative sign of
the weight of trend in water resources shows
movement in the opposite direction compared to
the other indicators. Decreasing trend in water
resources will have positive effect to sensitivity
index while increasing trend will have a negative
effect. Number of livestock killed and physical
property destroyed by extreme events contributed
least to the sensitivity index as shown by their
respective weights.
Table 3: Weights and mean values of sensitivity indicators in mid-hills of Himachal Pradesh
Indicators Weight Total
(n=275)
Kullu
(n=81)
Naggar
(n=63)
Solan
(n=72)
Kandaghat
(n=59)
P-Value
Physical Property destroyed
by extreme events
0.16 0.66
(0.47)
0.63
(0.47)
0.71
(0.46)
0.58
(0.50)
0.75 (0.44) 0.174
Number of livestock killed by
extreme events in the last 10
years
0.10 0.13
(0.34)
0.10
(0.30)
0.14
(0.35)
0.08
(0.28)
0.22 (0.42) 0.94*
Trend in availability of water
resources
-0.31 1.25
(0.44)
1.37
(0.49)
1.17
(0.38)
1.32
(0.50)
1.08 (0.28) 0.00***
Percentage of land destroyed
by extreme events in the last
ten years
0.21 9.75
(13.03)
4.46
(10.52)
13.33
(14.50)
7.36
(10.24)
16.10
(14.30)
0.00***
Share of natural resources
based income
0.98 58.24
(34.33)
38.98
(29.10)
81.68
(24.57)
46.23
(33.10)
74.28
(29.29)
0.00***
Share of non natural resource
based income
-0.98 41.77
(34.33)
61.02
(29.10)
18.32
(24.57)
53.77
(33.10)
25.72
(29.29)
0.00***
Note: Figures in parenthesis indicate standard deviation
***,
*indicates significant at 1% and 10% level of significance, respectively
As expected areas located away from the district
headquarters registered significantly lower mean
values compared to those which fall near them for
all the indicators except physical property
destruction by extreme events which was found to
be non significant. Increasing trends of number of
livestock killed by extreme events, decreasing
trends in water resources, higher percentage of
land destroyed by extreme events and high
dependence on natural based incomes all underlie
higher sensitivity to impacts of environmental
change in Kandaghat and Naggar blocks compared
to Kullu and Solan blocks.
3.3 Adaptive Capacity Indicators:
The mean average values of the indicators for
adaptive capacity revealed that Kullu block had
comparatively higher asset possession, with
figures ranging from 65.04 (irrigated land), 79.94
(share of more productive land) and 9.58 (monthly
savings) among others while Kandaghat had the
least asset possession, except ownership of
number of bullocks (1.24) (Table 4). Examination of
the weights for the five groups of indicators for
adaptive capacity revealed that among the
physical assets distance to the market had the
highest influence (-0.93) followed by distance to
the nearest motorable road (-0.91), percentage of
irrigated land (0.69) and the type of the house
(0.74) (Figure 2). Distances to the nearest natural
produce market and the nearest motorable road
influenced the adaptive capacity negatively as
indicated by the negative sign of their weights.
Among the human assets, education level of the
household head got the highest weight (0.82)
followed by number of people with salaried
employment (0.63) and number of people with
vocational skills (0.06). All had a positive influence
on the adaptive capacity. Under natural assets
category both percentages of productive (0.97)
and unproductive land (-0.97) had the highest
impact on adaptive capacity while the number of
bullocks owned had the least (0.45). Percentage of
unproductive land influenced the adaptive
Universal Journal of Environmental Research and Technology
67
Ndungu et al.
capacity negatively as indicated by the negative
sign of the weight. For financial assets, both
monthly income and savings had equal influence
(0.99) and the same picture is replicated under
social assets category whereby the number of
CBOs that the household had membership had
equal weight with the household access to credit
(0.76).
Second-step PCA shows that physical assets are
the most important determinants of overall
adaptive capacity followed by human and social
assets. Physical assets are very important because
they enhance extraction and utilisation of natural
assets. For example, without proper roads and
transport services inputs such as fertiliser and
planting materials may not be easily available for
farming and this may result in a decrease in
agricultural yield, and it is even more difficult and
expensive to transport produce to the market.
Moreover, a higher percentage of irrigated land
will lessen dependence on rain fed agriculture
which is becoming more unpredictable with the
advent of environmental climate change. The
index values for adaptive capacity and its
components indicated that Kullu block fared the
best in three of the asset categories (physical,
financial and natural) and second best in social and
human assets, thereby scoring the highest in
overall adaptive capacity (Figure 3). The mean
values of individual indicators in Table 4 indicated
that Kullu’s households ranked first in terms of
possession of the best house type, are the nearest
to the road and market for natural produce, have
comparatively higher percentage of irrigated land,
highest number of people with vocational training,
highest monthly income, highest savings and
highest share of productive land. Kandaghat stands
the last in terms of all the asset categories (except
number of bullocks owned) and thus had the least
adaptive capacity. Solan ranked the second and
Naggar third in terms of adaptive capacity index.
The higher adaptive captive capacity of the
households near the district headquarters can be
explained by easy access to infrastructure and
services e.g. better roads linkages, access to credit
and market facilities. The present trend of the
study may also be ascribed to higher income and
access to technology of the households which
might have increased the adaptive capacity (Kim et
al., 2012; Burton et al. 2000). Moreover, wealthier
farmers are more interested to adapt by changing
planting practices, using irrigation and altering the
amount of land farmed (Uddin et al., 2014).
Figure 2: Structure of aggregate adaptive capacity index, composite sub-indices, and component indicators
Note: Figures in parenthesis are the loadings obtained from first principal component taken as weights for the
respective indicators
Adaptive capacity
Physical Asset (0.74)
Human Asset (0.54)
Natural Assets
(0.49)
Financial Assets
(0.46)
Social Assets (0.53)
House type (0.28) Distance road (-0.91)
Distance market (-0.93) Irrigated land (0.69)
Education level (0.82) Salary (0.63)
-
(0.06)
Bullock (0.45)
Productive land (0.97) Unproductive land (-0.97)
Monthly income (0.99)
Savings (0.99)
CBO (0.76)
Access to credit (0.76)
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Ndungu et al.
Table 4: Mean values of indicators of adaptive capacity in mid-hills of Himachal Pradesh
Indicators Total
(n=275)
Kullu
(n=81)
Naggar
(n=63)
Solan
(n=72)
Kandaghat (n=59) P- Value
Type of house 3.89
(0.32)
3.93
(0.26)
3.89 (0.31) 3.90
(0.30)
3.81 (0.39) 0.21*
Walking distance to the nearest
motorable road
17.63
(12.61)
8.15
(4.90)
28.00
(11.21)
10.29
(5.63)
28.54 (10.73) 0.00***
Walking distance to the nearest
agricultural produce market
58.82
(43.20)
21.01
(10.23)
101.16
(28.40)
29.76
(10.00)
100.98 (29.81) 0.00***
Irrigated land 54.10
(35.13)
65.04
(29.68)
43.12
(35.98)
60.80
(30.01)
42.65 (40.51) 0.00***
Education qualification of the
household head
12.31
(4.65)
11.89
(5.05)
13.00(4.44) 12.06
(4.29)
12.44 (4.65) 0.51
Number of persons in the household
having salaried employment
1.01
(0.82)
1.10
(0.72)
0.83 (0.77) 1.33
(0.92)
0.71 (0.74) 0.00***
Number of persons in the household
with vocational training
0.30
(0.81)
0.41
(1.20)
0.19 (0.47) 0.39
(0.68)
0.17 (0.46) 0.17
Have bullock 0.63
(0.88)
0.17
(0.5)2
1.05
(0.97)
0.28
(0.61)
1.24 (0.88) 0.00***
Share of more productive land 73.28
(18.33)
79.94
(17.65)
65.57
(14.58)
76.81
(19.28)
68.09
(17.49)
0.00***
Share of less productive land 27.11
(17.86)
21.24
(16.78)
34.07
(14.35)
24.09
(19.43)
31.43
(17.43 )
0.00***
Gross monthly household income 33.41
(20.18)
38.20
(21.83)
30.56
(18.82)
36.32
(19.48)
26.34 (17.94) 0.00***
Monthly household savings 8.35
(5.24)
9.58
(5.60)
7.60 (4.76) 9.11
(4.97)
6.54 (5.03) 0.00***
Membership to CBOs 0.81
(1.02)
0.91
(1.10)
0.60 (0.79) 1.01
(1.20)
0.66 (0.82) 0.06
Access to credit from credit &
savings societies
0.67
(0.47)
0.73
(0.45)
0.59 (0.50) 0.78
(0.42)
0.56 (0.50) 0.02*
Note: Figures in parenthesis indicate standard deviation, ***,
*indicates significant at 1% and 10% level of significance, respectively
Figure 3: Index scores for adaptive capacity and it’s components in mid-hills of Himachal Pradesh
Universal Journal of Environmental Research and Technology
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Ndungu et al.
Figure 4: Index scores for vulnerability and it’s components for the study sites in mid-hills of Himachal Pradesh
Figure 5: Index scores for vulnerability and it’s components by vulnerability quartiles
3.5 Vulnerability Index:
The order of vulnerability index was Kandaghat
Naggar Solan Kullu (Figure 4). The results
indicated that among the selected sites, Kandaghat
block of Solan district has highest vulnerability
index to environmental changes whereas Kullu was
the least vulnerable. The highest exposure coupled
with lowest adaptive capacity in Kandaghat block
made it the most vulnerable. Naggar on the other
hand, despite having a lower value of exposure
index ranks the second most vulnerable study site
owing to its highest sensitivity index and lower
adaptive capacity index. The lowest sensitivity
index and highest adaptive capacity index makes
Kullu to emerge as the least vulnerable study site.
Solan was second best both in sensitivity and
adaptive capacity indices and it was in the second
position overall in vulnerability index. Further
examination of the results revealed that study
sites near the district head quarters (Kullu and
Solan) were less vulnerable compared to the study
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70
Ndungu et al.
sites located away from the district head quarters
(Kandaghat and Naggar). This is because
households located away from the district head
quarters experience more social economic and
biophysical vulnerability. High social economic
vulnerability is caused by households operating on
less diversified livelihoods, low off farm
engagement, low access to credit and markets,
small landholding, low holding of perennial crops
and small or no area under irrigation among
others. Similar studies by Ellis and Freeman (2004)
found that households which diversify their
livelihood activities in the form of nonfarm
business activities such as trade, transport, shop
keeping and brick making among others are better
off economically and hence less vulnerable.
Moreover, high levels of social economic
vulnerability due to poor livelihood options of
communities living away in remote mountainous
areas have also been reported in Ethiopia, Kenya
and India, respectively by Tesso et al. (2012),
Opiyo, et al. (2014) and O’Brien (2004).
On the other hand biophysical vulnerability is
exacerbated by relatively more undulating and
steeply sloping farmlands, steep and rugged
terrain impeding road construction and making
transport difficult and costly, low soil fertility due
to land degradation by soil erosion, diminishing
water resources and increasing trends of
environmental hazards like drought, floods,
landslides, forest fires and hailing events. All these
factors lead to deterioration of agroecosystmes
thereby compromising their ability to provide
ecosystem services leading to farmers’
vulnerability as also reported by Callo-Concha and
Ewert (2014) in other studies. Moreover,
households in these far flung mountainous areas
depend more on natural resources as source of
their livelihoods which are becoming more
susceptible to environmental climate change and
consequently exacerbating vulnerability. Similar
results of pronounced biophysical vulnerability of
communities inhabiting remote areas
characterised by low developments were found by
Deressa et al. (2008) and IPCC (2014). Inter-
household analysis of vulnerability revealed that
indices for exposure and sensitivity were highest
for the first quartile (most vulnerable) and least for
the last quartile (least vulnerable) as expected
(Figure 5). Similarly, adaptive capacity followed the
expected order, with the value being lowest for
the first quartile and consecutively higher for the
subsequent quartiles. This shows that irrespective
of the locations, households with lower adaptive
capacity are faced with higher exposure and higher
sensitivity to climate change and extreme events.
Poorer households are thus vulnerable anywhere
irrespective of their locations.
4.0 Conclusion and Policy Implications:
The study revealed that mountain people of mid-
hills of Himachal Pradesh face social economic and
biophysical vulnerabilities which are mediated by
environmental change and amplified by the
mountain specificities. Exposure of a locality to
impacts of environmental change which constitute
long term changes in climate variables and
occurrences of environmental hazards is the most
important component determining the overall
vulnerability of the people of mid-hills. Out of the
three components of vulnerability, adaptive
capacity is the component having direct policy
implications. Improving the adaptive capacity also
has indirect implications on improving the
sensitivity of the community. Therefore adaptive
capacity of the mountain people need to be
enhanced by creating facilities such as irrigation,
infrastructure for community development and
options for non farm income.
5.0 Acknowledgements:
The authors are grateful to Indian Council of
Cultural Relations (ICCR) for funding PhD studies
for the first author. The facilities provided by the
Department of Environmental Science, Dr. Y.S
Parmar University of Horticulture and Forestry,
Nauni- Solan (HP) India, are highly appreciated.
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... The natural resource-based livelihood income of households or social groups from agriculture, livestock, and forest products increases the sensitivity as these sources are more dependent on climate. On the other hand, non-natural resource-based livelihood income sources that constituted salaried jobs, Fig. 4 Monthly average rainfall, maximum, minimum, and mean temperature of South Wollo administrative zone non-farm jobs, and remittances reduce the sensitivity (Ndungu et al. 2015). With the scope of this study, we have weighed and mapped the sensitivity of the local community using the biophysical (topography/slope, soil types, and their erodibility) and socioeconomic attributes such as accessibility travel time and population pressure/ density. ...
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Thesis
Full-text available
Η παρούσα διδακτορική εργασία έχει ως τομέα μελέτης την Ολοκληρωμένη Διαχείριση των Υδατικών Πόρων (ΟΔΥΠ), βάσει των αρχών της οποίας δημιουργήθηκε ένα εργαλείο (δείκτης) για την αναγνώριση των ξηρασιών και των τρωτών περιοχών, καθώς και τη σύνδεση της τρωτότητας στηv ξηρασία και την ερημοποίηση. Η Ελλάδα παρουσιάζει συχνά φαινόμενα ξηρασίας, τυχαία και περιοδικά. Αυτό οφείλεται στη χωροχρονική κατανομή των κατακρημνισμάτων, δηλαδή στη διαδοχή των υγρών (Οκτώβριος-Μάρτιος) και των ξηρών περιόδων (Απρίλιος-Σεπτέμβριος). Αν δεν σημειωθούν βροχοπτώσεις κατά τη διάρκεια των υγρών περιόδων τότε ενδέχεται να δημιουργηθούν προβλήματα στη διαθεσιμότητα των υδατικών πόρων κατά τη διάρκεια των ξηρών περιόδων. Για την αντιμετώπιση αυτού του προβλήματος απαιτείται τακτική παρατήρηση και επεξεργασία των μετεωρολογικών δεδομένων, αλλά και ταυτόχρονη εφαρμογή του προληπτικού σχεδιασμού. Ένας τρόπος πρόληψης είναι ο υπολογισμός απλών και σύνθετων δεικτών ξηρασίας μιας περιοχής ή μιας χώρας για την έγκαιρη αναγνώριση του σχετικού προβλήματος. Σε αυτό το πλαίσιο, παρουσιάζεται η ανάπτυξη και η δημιουργία ενός σύνθετου δείκτη που αποτελείται από έξι υπο-δείκτες και συγκεκριμένα από τους υπο-δείκτες κατηγοριοποιημένα SPI6 (cSPI6 και cSPI12), Ζήτηση (Demand), Εφοδιασμός (Supply), Επιπτώσεις (Impacts) και Υποδομές στους Υδατικούς πόρους (Infrastructure), έτσι οι υπο-δείκτες του Standardized Drought Vulnerability Index (SDVI) σχετίζονται με όλες τις πτυχές μιας ξηρασίας (μετεωρολογική, αγροτική, υδρολογική και κοινωνικοοικονομική). Συνυπολογίζοντας όλα τα παραπάνω, εκτιμάται η τρωτότητα σε ένα σημείο ή μια περιοχή. Έπειτα, εξετάστηκε η σχέση των υπο-δεικτών και αναπτύχθηκαν συντελεστές βαρύτητας με στατιστικές και εμπειρικές μεθόδους. Ο σύνθετος δείκτης εφαρμόστηκες σε διάφορες συνθήκες (σενάρια, πραγματικές και υποθετικές τιμές) και διαφορετικές περιοχές (Ελλάδα και Νοτιοανατολική Ευρώπη με τη χρήση δεδομένων από 324 μετεωρολογικούς σταθμούς και περίοδο τουλάχιστον 30 ετών). Η αρχική υπόθεση του SDVI με ίδια βάρη και η μέθοδος των κύριων συνιστωσών δείχνουν ότι προσεγγίζουν καλύτερα την τρωτότητα. Στην συνέχεια, εξετάζεται εάν υπάρχει σύνδεση των δύο διαδικασιών τρωτότητας στην ξηρασία και τρωτότητας στην ερημοποίηση. Για τη συσχέτιση αυτή χρειάστηκε να γίνει όμοια κατηγοριοποίηση και των σύνθετων δεικτών σε 3 κλάσεις (χαμηλή, μέτρια και υψηλή). Με βάση αυτή την παραδοχή, εμφανίστηκε μία σχέση μεταξύ τους, της οποίας η εφαρμογή θα ήταν δόκιμο να εξεταστεί και σε άλλες περιοχές. Τέλος, αναλύθηκαν οι δείκτες και των δύο διαδικασιών και δημιουργήθηκε ένας σύνθετος δείκτης που εμφανίζει συνδυαστικά την υδατική και εδαφική υποβάθμιση μιας περιοχής έχοντας, επίσης, τη δυνατότητα να τις εμφανίσει και ξεχωριστά. Οι δείκτες που περιλαμβάνονται στην τελική εξίσωση είναι αυτοί της Ξηρότητας (Aridity), της Ζήτησης (Drought Demand), των Επιπτώσεων από την ξηρασία (Drought Impacts), της Αντοχής στην ξηρασία (Drought Resistance), των Υποδομών στους υδατικούς πόρους (Infrastructure), της Έντασης Χρήσης Γης (Land use intensity), του Μητρικού Υλικού (Parent Material), της Φυτοκάλυψης (Plant Cover), των Βροχοπτώσεων (Rainfall), της Κλίσης (Slope) και της Σύστασης του Εδάφους (Soil Texture). ENG The present work refers to the implementation of the integrated water resources management methodology for the development of a tool in the form of an index for the recognition of droughts and pertinent vulnerable areas. Such an attempt is further expanded in an effort to produce the connection of vulnerability to drought with that to desertification. Greece is sensitive to drought phenomena (random and periodical). This is a consequence of predominant spatial and temporal distribution of precipitation in the area, expressed with the existing climatic succession of wet (October-March) and dry periods (April-September). Specifically, lower precipitation values during the wet periods could cause serious issues in the water resources availability. Regular observation and processing of meteorological data as well as concurrent application of contingency planning are necessary in order to deal with such issues. Early recognition and possible prevention could be achieved through the estimation of simple and composite drought indices of an area. In this context, the development of a composite index was presented, that included six indicators, namely categorized SPI (cSPI6 and cSPI12), Demand, Supply, Impacts and Infrastructures. The Standardized Drought Vulnerability Index is attempting to relate with all the aspects of drought (meteorological, agricultural, hydrological and socio-economic) and provide an estimation procedure. Thereafter, the relation between the indicators was examined and weights with statistical and empirical methods were developed. The composite index was implemented in various conditions (existing, real and artificial generated data for scenarios testing) and in different areas (Greece and South Eastern Europe with data from 324 meteorological stations for a period of more than 30 years). The initial hypothesis of equal weighting and the method of principal components expressed the vulnerability estimation more effectively. Furthermore, it was analyzed whether there was a correlation between drought and desertification vulnerability. To associate these attempts, a similar classification of these composite indices into three classes (low, medium and high) was necessary. Based on this assumption a relationship between drought and desertification vulnerability was surfaced. Finally, the indicators of both procedures were analyzed and a composite index was created, which shows the water resources and soil degradation of a region. The indicators that have occurred in the final equation are Αridity, Water Demand, Drought Impacts, Drought Resistance, Water Resources Infrastructure, Land Use Intensity, Parent Material, Rainfall, Slope and Soil Texture. All in all, it would be appropriate for further research to implement this methodology in other regions worldwide.
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