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Ind. Jn. of Agri. Econ.
Vol.68, No.1, Jan.-March 2013
RESEARCH NOTE
Analysis of Vulnerability Indices in Various
Agro-Climatic Zones of Gujarat
Deepa B. Hiremath and R.L. Shiyani*
I
INTRODUCTION
Climate and agriculture are inextricably linked. Climate change may affect
agriculture and consequently the livelihoods of people due to changes in temperature,
precipitation, soil moisture, soil fertility, the length of the growing season, an
increase in the probability of extreme events such as droughts, extreme heat waves,
heavy rainfall, cyclones, flooding of the coastal areas, erosion etc. A World Bank
report on the impact of climate change highlights the possibility of the declining
yields of major dryland crops in Andhra Pradesh, sugarcane yields in Maharashtra by
as much as 30 per cent and rice production in Orissa by 12 per cent. The brunt of such
environmental changes in India is expected to be very high due to greater dependence
on agriculture, limited natural resources, alarming increase in human and livestock
population, changing pattern in land use and socio-economic factors that pose a great
threat in meeting the food, fibre, fuel and fodder requirement.
In the developing countries like India, the small and marginal farmers, in
particular, are more vulnerable to both to the current and future climate change
impacts, given their high dependence on agriculture, strong reliance on ecosystem
and rapid population growth. Year to year variability in climate contributes to rural
poverty where the exposure is high and adaptive capacity is low. The effects of
climatic variability on farming is prominent as witnessed by delayed sowing, changes
in cropping patterns, higher evidence of pest and diseases, frequent and persistent
droughts, less availability of water in tanks and canals for irrigation, reduced profits
due to increased prices of inputs and wages as well as stagnation of output prices,
shift towards non-farm occupations, migration, asset disinvestment etc. Most climate
change models predict that the damages will adversely affect the small farmers,
especially in the rainfed areas. The existing models at best provide a broad-brush
approximation of the expected effects and hide the enormous variability in internal
*PG Scholar and Professor and Head, Department of Agricultural Economics, Junagadh Agricultural
University, Junagadh – 362 001 (Gujarat), respectively.
*This paper is a part of an M.Sc. (Agri.) thesis submitted to Junagadh Agricultural University, Junagadh by the
first author.
The authors are grateful to C.R. Ranganathan, Professor, Department of Mathematics, Tamil Nadu Agricultural
University, Coimbatore for his valuable help in carrying out the analysis for construction of vulnerability indices.
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
123
adaptation strategies. Many rural communities and traditional farming households,
despite weather fluctuations, seem able to cope with climatic extreme (Altieri, 2004).
Modernisation of agriculture in India over the past few decades, notwithstanding
the benefits, has brought a variety of economic, environmental, and social problems,
including food availability, ecosystem integrity and in many cases, disruption of rural
livelihoods. The push towards commercial agriculture and globalisation - with an
increasing emphasis on export crops and lately transgenic crops has increased dependency on purchased inputs (fertilisers and chemicals which the poor farmers cannot
afford) and, market dependence for output has proved to be unsustainable as it
damaged the environment, caused dramatic loss of biodiversity and associated
traditional knowledge, favoured the wealthier farmers, and left many poor farmers
deeper in debt.
Though several international level interventions have taken a step forward, there
is a need to carry out disaggregated analysis at the regional level, particularly, within
the state in order to fine-tune the hot spot areas that need immediate interventions.
Keeping this in view, and the fact that there exists a dearth of systematic literature
with reference to climate change in Gujarat, the present study aims to study the
relationship between climate change and the vulnerability of people living in different
districts of Gujarat.
Conceptualisation of Vulnerability to Climate Change
Vulnerability is understood as a function of three components—exposure,
sensitivity, and adaptive capacity – which are in turn, influenced by a range of
biophysical and socio-economic factors. The vulnerability profiles are based on the
assumption that exposure to climate change will influence sensitivity – either
positively or negatively – and that the Indian farmers will respond to these changes
provided that they have the capacity to adapt. Chamber (1983) defined that
vulnerability has two sides. One is the external side of risks, shocks to which an
individual or household is subject to climate change and an internal side which is
defenselessness, meaning a lack of means to cope without the damaging loss. Blaikie
et al. (1994) defined vulnerability as the characteristics of a person or a group in
terms of their capacity to anticipate, cope with, resist and recover from the impacts of
natural hazards and states that vulnerability be viewed along a continuum from
resilience to susceptibility. IPCC (2001) defined vulnerability as the degree to which
the system is susceptible to, or unable to cope with, the adverse effects of stresses
including climatic variability and extremes. Thus, vulnerability is a function of the
character, magnitude, and rate of change in stresses to which a system is exposed, its
sensitivity, its ability to adaptation or adaptive capacity. The sources and indicators of
vulnerability are well presented in Figure 1.
124
INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
VULNERABILITY
DEMOGRAPHIC
VULNERABILITY
CLIMATIC
VULNERABILITY
AGRICULTURAL
VULNERABILITY
AGRICULTURAL
VULNERABILITY
• Density of
population
• Variance in
annual rainfall
• Total workers
• Literacy rate
• Variance in SW
monsoon
• Productivity of
major crops
• Cropping
intensity
• Area under
cultivation
• Irrigation
intensity
• Livestock
population, etc
• Variance in mean
maximum and
minimum
temperatures
• Agricultural
labourer’s
• Industrial workers
• Cultivators
• Non- workers,
etc
Figure 1. Sources and Indicators of Vulnerability
II
METHODOLOGY AND ANALYTICAL FRAMEWORK
Vulnerability to climate change is a comprehensive multidimensional process
affected by a large number of related indicators. However, it will not be possible to
include all the sub-indicators and so only those indicators relevant to Gujarat state
were selected in the construction of vulnerability indices. Here, the important and
maximum possible available indicators were selected for the 1990s and 2000
decades. The data pertaining to various socio-economic indicators were collected and
compiled from different sources, viz., Directorate of Economics and Statistics,
Gandhinagar and Department of Agriculture and Co-operation, Gandhinagar.
Meteorological data were collected from the Meteorology Departments of Anand
Agricultural University, Anand and Junagadh Agricultural University, Junagadh.
There is a growing consensus in the scientific community to address the
vulnerability issues related to climate change particularly at the regional levels. This
would enable to fine-tune the hot spot areas that need immediate intervention. Thus,
the districts have been taken as the unit for computing vulnerability indices in the
present study. Keeping in view the availability of data, 14 districts representing
various agro-climatic zones of Gujarat were selected.
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
125
The list of selected indicators and their functional relationship identified with
climate change are shown in Table 1.
TABLE 1. FUNCTIONAL RELATIONSHIP OF INDICATORS WITH VULNERABILITY TO
CLIMATE CHANGE
Sr.
No.
(1)
1.
2.
3.
4.
Components
(2)
Indicators
(3)
(a) Density of population (persons per sq. km)
Demographic (2)
(b) Literacy rate (per cent)
(a) Variance of annual rainfall (mm2)
(b) Variance of Southwest monsoon (mm2)
Climatic (4)
(c) Variance of minimum temperature (o C2)
(c) Variance of maximum temperature (o C2)
(a) Total food grains (kg/ha)
(b) Productivity of kharif groundnut (kg/ha)
(c) Productivity of cotton (kg/ha)
(d) Productivity of kharif rice (kg/ha)
(e) Productivity of kharif bajra (kg/ha)
(f) Productivity of kharif maize (kg/ha)
(g) Productivity of sugarcane (qtl./ha)
Agricultural (14)
(h) Cropping intensity (per cent)
(i) Irrigation intensity (per cent)
(j) Forest area (per cent to geographic area)
(k) Total food crops (per cent)
(l) Total non-food crops (per cent)
(m) Net sown area (hectares)
(n) Livestock population (number per hectare of gross cropped
area)
(a) Total main workers (per hectare of net area sown)
(b) Number of cultivators (per hectare of net area sown)
(c) Agricultural labourers (per hectare of net sown area)
Occupational (6)
(d) Industrial workers (per hectare of net sown area)
(e) Marginal workers (per hectare of net sown area)
(f) Non-workers (per hectare of net sown area)
Note: Figures in parentheses indicate the number of indicators under each component.
Functional
Relationship
(4)
↑
↓
↑
↑
↑
↑
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
↓
Functional Relationship of Indicators with Vulnerability to Climate Change
The list of selected indicators is shown in Table 1. It is apparent from the table
that the vulnerability indices suggested many important hypotheses relating the
vulnerability of the districts to climate change with various key socio-economic,
climatic and agricultural indicators.
The density of population of the district was found to influence its demographic
vulnerability and consequently the overall vulnerability to climate change. It was
hypothesised to be positively related to the vulnerability to climate change, i.e., with
the increase in the number of persons per sq. km., the vulnerability to climate change
would increase due to its direct impact on global warming. This would be due to
increased pollution and Green House Gas (GHG) emissions as a result of greater use
of vehicles, enormous industrial carbon emissions, rapid use of non-renewable and
126
INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
other natural resources, greater use of non-biodegradable materials like polythene,
growing human settlements and their activities leading to faster destruction of natural
systems, deforestation, habitat destruction, extinctions, more exploitation of other
living forms and non-living systems like rivers. For the people there would be
increase in illness and diseases, shortage of natural resources such as water, land,
food, shortage of infrastructure such as medical facilities/medications etc. Moreover,
any occurrence of extreme events, viz., droughts, floods etc. is likely to be more
catastrophic for the people living in these districts (Patnaik and Narayanan, 2005).
The literacy rate, on the other hand, was hypothesised to have a negative functional
relationship with demographic vulnerability and thereby, on the overall vulnerability
to climate change. Literacy rate indicates the adaptability of the population to both
adverse impacts caused by shocks and the opportunities created. It also implies the
proportion of expenditure on education in total public expenditure which indicates
investment in human capital. It was seen that a high value of this variable implied
more literates in the region and so greater awareness to cope up with climate change
impacts (Palanisami et al., 2009).
Climatic vulnerability was assumed to be positively related to the indicators such
as variances in annual rainfall and Southwest monsoon as well as minimum and
maximum temperature variances. This indicated that any increase in the variability of
these climatic indicators would increase the vulnerability of the districts to climate
change. Glantz and Wigley (1986) studied the worldwide climate change and showed
that any change in climatic variables like temperature and precipitation could induce
vulnerability of food production in a major way. For instance, the climatic
abnormality during the 1970s caused relatively small fluctuations in the world cereal
supplies.
Yield is more uncertain with unfamiliar technology. Quite often the objective
risks are uncertain due to weather fluctuations, susceptibility to pests, uncertainty
regarding timely availability of crucial inputs etc. However, it could be seen that
higher yields of crops led to higher incomes of the farmers and thereby increasing
their risk bearing ability to various shocks. An increase in the livestock population
per gross cropped area also results in an increase in the farmer’s incomes through
various animal husbandry based activities, thereby its negative functional relationship
towards vulnerability.
Similarly, the percentage of total food crops and non-food crops, the cropping
and irrigation intensities and the net sown area in the district, each of these
comprising the agricultural indicators, were also hypothesised to have a negative
influence on the vulnerability to climate change.
The forest area was assumed to have a negative functional relationship with
climate change. Forest ecosystems capture and store carbon dioxide, making a major
contribution to the mitigation of climate change. However, when forests are
destroyed, over-harvested or burnt, they can become a source of CO2 emissions.
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
127
Thus, an increase in the percentage of forest cover would enable to reduce the
vulnerability to climate change.
Lastly, all the occupational indicators were hypothesised to have a negative
functional relationship to climate change as greater employment meant more secure
incomes which would in turn increase the risk bearing capacities of the people.
Arrangement of Data
For each component of vulnerability, the collected data were arranged in the form
of a rectangular matrix with rows representing districts and columns representing
indicators. Let there be M regions/districts and K indicators.
Let Xij be the value of the indicator ‘j’ corresponding to district ‘i’. The table with
M rows and K columns is as shown below.
Districts
(1)
1
2
-I
-M
1
(2)
X11
--Xi1
-XM1
2
(3)
X12
--Xi2
-XM2
INDICATORS
-J
(4)
(5)
-X1J
-----Xij
---XMj
-(6)
-------
K
(7)
X1K
--XIk
-XMK
Normalisation of Indicators Using Functional Relationship
Before doing this, the functional relationship between the indicators and
vulnerability was identified. Two types of functional relationship were observed:
vulnerability increases with increase (decrease) in the value of the indicator, i.e.,
positive and negative, respectively.
The normalisation was done using the formula:
[X ij − Min{X ij }]
[Max{X ij } − Min{X ij }]
Iyenger and Sudarshan’s Method for Construction of Vulnerability Index
Iyenger and Sudarshan (1982) developed a method to work out a composite index
from multivariate data and it was used to rank the districts in terms of their economic
performance. This method is statistically sound and well suited for the development
of composite index of vulnerability to climate change also. Hence, though
vulnerability indices were constructed using three methods, viz., Simple average
INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
128
method, Patnaik and Narayanan’s method and Iyenger and Sudarshan’s method, the
results of Sudarshan and Iyenger’s method were retained for the present study.
Additionally, Iyenger and Sudarshan’s method proved to be superior to both the
method of simple averages and the Patnaik and Narayanan’s method as it gave
weights to the indicators of vulnerability which were assumed to vary inversely with
their variance over the regions. On the contrary, the main drawback in the other two
methods was that they give equal importance for all indicators which may not
necessarily be correct.
In all, based on the availability of data, 26 indicators were used in the
construction of vulnerability indices for five different time periods, viz., 1991 and
2008 for the 14 selected districts of the state. Out of the 26 indicators, 2 indicators
are concerned with demographic vulnerability, 4 indicators are related to climatic
vulnerability, 14 indicators deal with agricultural vulnerability and the rest 6
indicators represented the occupational vulnerability component.
A brief discussion about the methodology is given below.
It is assumed that there are M regions/districts, K indicators of vulnerability and
xij, i= 1, 2, .…M ; j=1, 2, .…k are the normalised scores. The level or stage of
is assumed to be a linear sum xij as
development of it zone,
k
y t = ∑ w j x ij
j=1
Where, w’s (0<w<1 and ∑kj=1 w j = 1 ) are the weights. In Iyenger and Sudarshan’s
method, the weights are assumed to vary inversely as the variance over the regions in
the respective indicators of vulnerability. That is, the weight wj is determined by
w j = c / var xij
Where, c is a normalizing constant such that
k
−1
c = ∑ 1/ varx ij
j=1
The choice of the weights in this manner would ensure that large variation in any
one of the indicators would not unduly dominate the contribution of the rest of the
indicators and distort inter-regional comparisons. The vulnerability index so
computed lies between 0 and 1, with 1 indicating maximum vulnerability and 0
indicating no vulnerability at all.
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
129
For classificatory purposes, a simple ranking of the regions based on the indices
viz.,
would be enough. However, a meaningful characterisation of the different
stages of vulnerability, suitable fractile classification from an assumed probability
distribution is needed. A probability distribution which was suitable for this purpose
was the Beta distribution, which is generally skewed and takes values in the interval
(0, 1). This distribution has the probability density given by:
f(z) =
z a −1 (1 − z) b−1 dx
B(a, b)
, 0 < z < 1 and a, b > 0
Where, B (a, b)is the beta function defined by
1
B (a, b) = ∫ x a −1 (1 − x) b−1 dx
0
The two parameters a and b of the distribution can be estimated by using the
method by Iyenger and Sudarshan (1982).The beta distribution is skewed. Let (0,z1),
(z1,z2), (z2,z2), (z3,z4) and (z4,1) be the linear intervals such that each interval has the
same probability weight of 20 per cent.
These fractile intervals were used to characterise the various stages of
vulnerability as shown below:
1. Less vulnerable
if
0<
< z1;
2. Moderately vulnerable
if
z1<
< z2;
3. Vulnerable
if
z2<
< z3;
4. Highly vulnerable
if
z3<
< z4; and
5. Very highly vulnerable
if
z4<
< 1.
III
RESULTS AND DISCUSSION
The results pertaining to component–wise and overall vulnerability indices as
well as component-wise contributions to the overall vulnerability to climate change
for the year 1991 and 2008 are given in Tables 2 - 8. It is noticed that the ranks and
relative magnitude of the indices varied during the period 1991 and 2008. In general,
the variables pertaining to agricultural and occupational vulnerability were major
contributors in the overall vulnerability to climate change in these two periods. The
prime variables in the agricultural sector included the productivity of major crops,
total food grains, cropping intensity, irrigation intensity and percentage forest area.
INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
130
This shows the importance of these variables since agriculture in majority of the
districts is rainfed, climatic changes that alter temperature and precipitation patterns
may pose serious threats to agricultural production.
In the year 1991, the district of Jamnagar (North Saurashtra agro-climatic zone)
was found to be the most vulnerable and the district of Sabarkantha (North Gujarat)
was the least vulnerable. The values of the vulnerability indices varied from 0.4436
(Sabarkantha) to 0.5835 (Jamnagar) during the period (Table 2). The agricultural
sector played a significant role in ranking Jamnagar district at the first position by
contributing to the tune 56.46 per cent, followed by occupational (29.23 per cent),
climatic (11.28 per cent), and demographic factors (3.03 per cent) (Table 3).
TABLE 2. COMPONENT-WISE CONTRIBUTIONS TO THE OVERALL VULNERABILITY TO
CLIMATE CHANGE FOR THE YEAR 1991
Districts
(1)
Ahmedabad
Amreli
Banaskantha
Bharuch
Jamnagar
Junagadh
Kheda
Mehsana
Panchmahals
Rajkot
Sabarkantha
Surat
Surendranagar
Vadodara
Demographic
(2)
6.41
4.34
9.36
3.92
3.03
4.86
7.35
4.73
9.88
3.36
6.24
6.83
4.42
7.24
Climatic
(3)
7.82
2.96
10.34
7.51
11.28
7.49
14.84
5.93
15.46
5.07
11.46
17.73
7.36
10.01
Agriculture
(4)
50.13
59.04
54.46
59.71
56.46
50.67
50.22
58.24
58.88
70.40
62.27
49.17
70.01
60.80
Occupational
(5)
35.64
33.66
25.84
28.86
29.23
36.98
27.59
31.10
15.78
21.17
20.03
26.27
18.21
21.95
(per cent)
Total
(6)
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
TABLE 3. COMPONENT-WISE AND OVERALL VULNERABILITY INDICES FOR THE YEAR 1991
Districts
Demographic
(1)
(2)
Ahmedabad
0.0351
Amreli
0.0222
Banaskantha
0.0469
Bharuch
0.0187
Jamnagar
0.0177
Junagadh
0.0251
Kheda
0.0383
Mehsana
0.0270
Panchmahals
0.0543
Rajkot
0.0167
Sabarkantha
0.0277
Surat
0.0375
Surendranagar
0.0232
Vadodara
0.0346
Rank
(3)
5
11
2
12
13
9
3
8
1
14
7
4
10
6
Climatic
(4)
0.0428
0.0151
0.0519
0.0360
0.0658
0.0387
0.0775
0.0338
0.0850
0.0251
0.0508
0.0974
0.0386
0.0478
Rank
(5)
8
14
5
11
4
9
3
12
2
13
6
1
10
7
Agriculture
(6)
0.2743
0.3016
0.2731
0.2861
0.3294
0.2618
0.2621
0.3322
0.3237
0.3486
0.2762
0.2700
0.3677
0.2904
Rank Occupational
(7)
(8)
10
0.1950
6
0.1720
11
0.1295
8
0.1383
4
0.1706
14
0.1911
13
0.1440
3
0.1774
5
0.0867
2
0.1048
9
0.0889
12
0.1442
1
0.0957
7
0.1048
Rank
(9)
1
4
9
8
5
2
7
3
14
10
13
6
12
11
Overall
Rank
(10) (11)
0.5471
5
0.5109
9
0.5014 10
0.4791 12
0.5835
1
0.5167
8
0.5219
7
0.5704
2
0.5497
3
0.4953 11
0.4436 14
0.5490
4
0.5252
6
0.4777 13
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
131
TABLE 4. CLASSIFICATION OF SELECTED DISTRICTS UNDER DIFFERENT DEGREES OF
VULNERABILITY FOR THE YEAR 1991
Less vulnerable
(1)
Moderately vulnerable
(2)
Vulnerable
(3)
Highly vulnerable
(4)
Very highly vulnerable
(5)
Bharuch
Banaskantha
Surendranagar
Panchmahals
Jamnagar
Vadodara
Rajkot
Kheda
Surat
Mehsana
Junagadh
Ahmedabad
Sabarkantha
Amreli
Figure 1. Ranking of the Districts Based on Vulnerability Indices to Climate
Change for the Year 1991
In the year 2008, the district of Amreli (North Saurashtra Agro-climatic Zone)
was the most vulnerable district to climate change. The districts of Ahmedabad and
Surendranagar stood at the second and third position, respectively According to Modi
(2009), the need to deal with the water scarcity in the parched lands of Saurashtra
region by creating a sustainable network of micro irrigation and recharge structures;
was a challenge that could only be handled by mass participation supplemented by
financial and technical support provided by the government. In the dryland areas,
there is a call for strategies unique to their system that takes into account their
uncertain dynamics. Strategies such as rainwater harvesting, livestock development
and techniques to enhance dryland agriculture can help overcome many of these
constraints. Policies for promotion of efficient irrigation systems (eg. drip etc.) must
be implemented. As a part of water management strategies, there is a need to deepen
wells, utilise water supply system properly, construct check-dams and focus on
integrated watershed management and rainwater harvesting.
The agricultural and occupational indicators were the greatest contributors
towards vulnerability, which accounted for 52.61 per cent and 32.07 per cent,
respectively (Table 6). Since the agricultural sector was found to have the greatest
INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
132
Table 5. COMPONENT-WISE CONTRIBUTIONS TO THE OVERALL VULNERABILITY TO
CLIMATE CHANGE FOR THE YEAR 2008
(per cent)
Districts
(1)
Ahmedabad
Amreli
Banaskantha
Bharuch
Jamnagar
Junagadh
Kheda
Mehsana
Panchmahals
Rajkot
Sabarkantha
Surat
Surendranagar
Vadodara
Demographic
(2)
6.23
3.72
9.40
2.32
3.97
6.00
6.82
4.61
10.43
2.82
5.40
8.06
4.99
7.42
Climatic
(3)
13.39
11.60
10.88
5.21
5.31
24.68
12.27
13.80
12.36
11.92
15.89
11.26
9.00
14.24
Agriculture
(4)
55.03
52.61
46.82
61.62
51.31
34.05
57.43
52.73
60.98
49.98
51.26
59.24
50.36
52.62
Occupational
(5)
25.35
32.07
32.90
30.85
39.41
35.27
23.48
28.86
16.23
35.28
27.45
21.44
35.65
25.72
Total
(6)
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
TABLE 6. COMPONENT–WISE AND OVERALL VULNERABILITY INDICES FOR THE YEAR 2008
Districts
(1)
Ahmedabad
Amreli
Banaskantha
Bharuch
Jamnagar
Junagadh
Kheda
Mehsana
Panchmahals
Rajkot
Sabarkantha
Surat
Surendranagar
Vadodara
Demographic
(2)
0.0355
0.0213
0.0486
0.0122
0.0189
0.0258
0.0317
0.0238
0.0428
0.0149
0.0276
0.0388
0.0268
0.0338
Rank
(3)
4
11
1
14
12
9
6
10
2
13
7
3
8
5
Climatic
(4)
0.0762
0.0664
0.0562
0.0272
0.0253
0.1061
0.0570
0.0711
0.0507
0.0630
0.0814
0.0542
0.0482
0.0650
Rank
(5)
3
5
9
13
14
1
8
4
11
7
2
10
12
6
Agriculture
(6)
0.3131
0.3014
0.2418
0.3223
0.2447
0.1464
0.2668
0.2715
0.2499
0.2639
0.2626
0.2853
0.2699
0.2400
Rank
(7)
2
3
12
1
11
14
7
5
10
8
9
4
6
13
Occupational
(8)
0.1442
0.1837
0.1699
0.1614
0.1879
0.1516
0.1091
0.1485
0.0665
0.1863
0.1406
0.1032
0.1910
0.1174
Rank
(9)
9
4
5
6
2
7
12
8
14
3
10
13
1
11
Overall
(10)
0.5690
0.5729
0.5165
0.5231
0.4768
0.4299
0.4646
0.5148
0.4099
0.5281
0.5123
0.4816
0.5359
0.4562
Rank
(11)
2
1
6
5
10
13
11
7
14
4
8
9
3
12
bearing towards the overall vulnerability to climate change, there is a need to shift
focus towards investments in adaptation research capacity: particularly, in the
development of climate proof crops (drought resistant and heat tolerant varieties) as
well as redeploying the existing improved crop varieties that can cope with a wide
range of climatic conditions. An improvement in the agronomic practices of different
crops such as revising planting dates, plant densities and crop sequences can help
cope with the delayed rainy seasons, longer dry spells and earlier plant maturity.
Also, technologies for minimising soil disturbance such as reduced tillage,
conservation agriculture and crop rotation must be adopted. So far as the livestock
sector is concerned, measures relating to utilisation of fodder banks, control of
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
133
livestock population and improvement in the livestock productivity, organising of
cattle camps and conservation of fodder must be undertaken.
The district of Panchmahals on the other hand, exhibited least vulnerability,
followed by Junagadh and Vadodara districts yet again due to agricultural and
occupational indicators. The values of vulnerability indices varied from 0.05 to 0.06
per cent during this period. Junagadh district ranked last indicating that it was the
least vulnerable so far as agricultural vulnerability was concerned. (Table 5) The
reasons for such a positive scenario for Junagadh district can be ascribed to the higher
productivity of major crops like groundnut and cotton, high cropping as well as
irrigation intensity, vast areas of grazing and permanent pastures along with greater
livestock population and greater forest cover in the district. The Agricultural
University played a greater role in the transfer of technology to the farmer’s door for
enhancing crop productivity. Similarly, adoption of water harvesting technologies on
a mass scale also led to change in the agricultural scenario in the district.
The aforementioned period-wise results reveal that the agricultural sector was the
principal contributor to the overall vulnerability to climate change which is in line
with the studies which show that as a part of the problem, agriculture contributes
nearly 14 per cent of the annual green house gas (GHG) emissions, compared with
about 13 per cent by transportation (considered the principal culprit along with
deforestation (19 per cent)). The principal agricultural sources of GHG’s include
methane emissions from irrigated rice fields and livestock, nitrous oxide emissions
from fertilised fields, energy use for pumping irrigation supplies and soil and land
management practices. However, it can be a part of the solution by mitigating GHG
emissions through better crop management, carbon sequestration, soil and land use
management and biomass production (Rao and Joshi, 2009).
The occupational indicators were found to be the second largest contributors
towards vulnerability. The studies reveal that a regional economy that offers only
limited employment alternatives for workers dislocated by the changing profitability
of farming and other climatically sensitive sectors were relatively more vulnerable
than those that were economically diverse.
TABLE 7. CLASSIFICATION OF SELECTED DISTRICTS UNDER DIFFERENT DEGREES OF
VULNERABILITY FOR THE YEAR 2008
Less vulnerable
(1)
Vadodara
Junagadh
Panchmahals
Moderately vulnerable
(2)
Surat
Jamnagar
Kheda
Vulnerable
(3)
-------------------------------
Highly vulnerable
(4)
Surendranagar
Rajkot
Bharuch
Banaskantha
Mehsana
Sabarkantha
Very highly vulnerable
(5)
Amreli
Ahmedabad
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INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
Figure 2. Ranking of the Districts Based on Vulnerability Indices to Climate
Change for the Year 2008
To provide a meaningful characterisation of the different degrees of vulnerability,
a suitable classification of the districts was made using beta distribution. The relative
share of population and area of the state that would be vulnerable by different degrees
were also computed. The results presented in Table 8 reveal that in the year 1991, the
districts of Jamnagar and Mehsana were classified as “very highly vulnerable”
districts. These districts together constituted about 10.90 per cent of the total
population of the state and 11.81 per cent of the total area. The districts of Bharuch,
Vadodara and Sabarkantha were placed in the category of “less vulnerable” districts.
These districts jointly comprised 12.36 per cent and 15.49 per cent of the state’s area
and population, respectively. The districts placed under “vulnerable” category
collectively occupied the maximum proportion of the state’s area, i.e., 17.88 per cent
while the districts placed in the “highly vulnerable category” constituted the
maximum proportion of the state’s population, i.e., 27.01 per cent.
TABLE 8. PROPORTION OF AREA AND POPPULATION VULNERABLE TO CLIMATE
CHANGE IN GUJARAT DURING 1991 AND 2008
(per cent)
1991
2008
Degrees of Vulnerability
Area
Population
Area
Population
(1)
(2)
(3)
(4)
(5)
Less vulnerable
12.36
15.49
11.03
16.02
Moderately vulnerable
12.20
11.32
13.26
17.61
Vulnerable
17.88
20.09
----Highly vulnerable
12.92
27.01
25.89
24.63
Very highly vulnerable
11.81
10.90
7.90
14.23
Finally, in 2008, Amreli and Ahmedabad districts were placed in the category of
“very highly vulnerable” districts. Together they comprised 7.90 per cent and 14.23
per cent of the state’s area and population, respectively. It may be inferred that the
ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES
135
higher vulnerability of these districts was because they were relatively thickly
populated. The districts of Vadodara, Junagadh and Panchmahals were “less
vulnerable” districts. These districts collectively covered 11.03 per cent and 16.02 per
cent of the state’s area and population, respectively. It could be clearly noticed that
there was a shifting of districts from one level of vulnerability to another over a
period of time.
IV
CONCLUSION AND POLICY IMPLICATIONS
Gujarat state is one of the fastest growing economies in our country. It is rapidly
expanding its production and consumption activities. Thus, the state not only
contributes to climate change but is equally vulnerable to its impacts. There is a
pressing need to balance this development by simultaneously acting upon climate
change and other issues which are putting tremendous pressure on the environment’s
carrying capacity.
The results of vulnerability indices analysis for the selected districts revealed that
the variables pertaining to agricultural vulnerability were the major contributors in
the overall vulnerability to climate change during the periods 1991 and 2008. Since
the agricultural sector was found to have the greatest bearing there is a need to shift
focus towards investments in adaptation research capacity: particularly, in the
development of climate proof crops (drought resistant and heat tolerant varieties) that
can cope with wide range of climatic conditions. An improvement in the agronomic
practices of different crops such as revising planting dates, plant densities and crop
sequences can help cope with the delayed rainy seasons, longer dry spells and earlier
plant maturity. Also, technologies for minimising soil disturbance such as reduced
tillage, conservation agriculture and crop rotation must be adopted. In order to
enhance the resilience of the agriculture sector new strategies must be built around
'green' agricultural technologies, such as adaptive plant breeding, forecasting of pests,
rainwater harvesting and fertiliser microdosing. So far as the livestock sector is
concerned, measures relating to utilisation of fodder banks, control of livestock
population and improvement in the livestock productivity, organising of cattle camps
and conservation of fodder must be undertaken.
Further in the year 1991, the district of Jamnagar (North Saurashtra agro-climatic
zone) was the most vulnerable and the district of Sabarkantha (North Gujarat) was the
least vulnerable. The agricultural sector played a significant role in ranking Jamnagar
district at the first position. In the year 2008, the district of Amreli (North Saurashtra
agro-climatic zone) was the most vulnerable district and the district of Panchmahals
was the least vulnerable to climate change. The agricultural indicators were the
greatest contributors towards vulnerability. Next to the agricultural indicators, the
occupational indicators were found to be the second largest contributors. Since the
occupational indicators were the second largest contributors towards overall
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INDIAN JOURNAL OF AGRICULTURAL ECONOMICS
vulnerability, thus, to reduce the climate change impact, the policy makers must focus
on generating better employment opportunities including income diversification
options for the people in the regions where the incidences of out-migration are high.
The dependence on agriculture should to be reduced, by encouraging other non-farm
sources of income. Since the worst sufferers of climate change impacts are the rural
communities, (who depend mainly on agriculture for their livelihoods), it is important
to focus on the impacts of climate change on livelihoods, and re-establish the links
among poverty, livelihood and environment. However, focusing on the communities
only is not enough, and so long as the community initiatives do not become part of
the government policies, it is difficult to sustain the efforts. A unique way of
vulnerability reduction is through enhancing the capacities of local people and
communities. Livelihood security should be the first and the foremost priority, where
the improvement of lifestyle is desired through income generation in different
options: agriculture, aquaculture, fishing, animal husbandry. In addition, some of the
important suggestions and policy options that were evolved from the study were that
a specific component of climate change may be added while making investments
particularly on dryland agriculture. In research priority setting, besides the criteria of
poverty, equity, export competitiveness and sustainability, one more objective criteria
of climate change should also be given due weightage. This would take care of
sustainability aspect too. Apart from this, predicted impacts should be introduced into
development planning in the future, including land use planning and necessary
remedial measures should be included to reduce vulnerability in disaster reduction
strategies.
Thus, the state of Gujarat requires a development strategy that integrates climate
change policies with sustainable development strategies to effectively combat climate
change issues.
Received July 2011.
Revision accepted February 2013.
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