Ind. Jn. of Agri. Econ. Vol.68, No.1, Jan.-March 2013 RESEARCH NOTE Analysis of Vulnerability Indices in Various Agro-Climatic Zones of

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 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, 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 depen- dency 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 CLIMATIC AGRICULTURAL AGRICULTURAL VULNERABILITY VULNERABILITY VULNERABILITY VULNERABILITY

• Density of • Variance in • Productivity of • Total workers population annual rainfall major crops • Cropping • Agricultural • Literacy rate • Variance in SW intensity labourer’s monsoon • Area under cultivation • Industrial workers • Variance in mean • Irrigation maximum and intensity • Cultivators minimum • Livestock temperatures population, etc • 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, 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. Functional No. Components Indicators Relationship (1) (2) (3) (4) (a) Density of population (persons per sq. km) ↑ 1. Demographic (2) (b) Literacy rate (per cent) ↓ (a) Variance of annual rainfall (mm2) ↑ (b) Variance of Southwest monsoon (mm2) ↑ 2. 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) ↓ 3. 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) ↓ 4. 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 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.

INDICATORS Districts 1 2 -- J -- K (1) (2) (3) (4) (5) (6) (7)

1 X11 X12 -- X1J -- X1K 2 ------I Xi1 Xi2 -- Xij -- XIk ------M XM1 XM2 -- XMj -- 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 − Min{X }] ij ij [Max{Xij} − Min{Xij}]

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 128 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

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 t development of i zone, is assumed to be a linear sum xij as

k yt = ∑ w x j=1 j ij k Where, w’s (0

w j = c / var xij

Where, c is a normalizing constant such that

−1 k c = ∑1/ varxij 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:

za−1(1− z)b−1 dx f(z) = , 0 < z < 1 and a, b > 0 B(a,b)

Where, B (a, b)is the beta function defined by

1 a−1 b−1 B (a, b) = ∫ x (1− x) 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. 130 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

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 (North agro-climatic zone) was found to be the most vulnerable and the district of Sabarkantha () 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 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

(per cent) Districts Demographic Climatic Agriculture Occupational Total (1) (2) (3) (4) (5) (6) 6.41 7.82 50.13 35.64 100.00 Amreli 4.34 2.96 59.04 33.66 100.00 Banaskantha 9.36 10.34 54.46 25.84 100.00 3.92 7.51 59.71 28.86 100.00 Jamnagar 3.03 11.28 56.46 29.23 100.00 Junagadh 4.86 7.49 50.67 36.98 100.00 7.35 14.84 50.22 27.59 100.00 4.73 5.93 58.24 31.10 100.00 Panchmahals 9.88 15.46 58.88 15.78 100.00 3.36 5.07 70.40 21.17 100.00 Sabarkantha 6.24 11.46 62.27 20.03 100.00 6.83 17.73 49.17 26.27 100.00 Surendranagar 4.42 7.36 70.01 18.21 100.00 7.24 10.01 60.80 21.95 100.00

TABLE 3. COMPONENT-WISE AND OVERALL VULNERABILITY INDICES FOR THE YEAR 1991 Over- Districts Demographic Rank Climatic Rank Agriculture Rank Occupational Rank all Rank (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Ahmedabad 0.0351 5 0.0428 8 0.2743 10 0.1950 1 0.5471 5 Amreli 0.0222 11 0.0151 14 0.3016 6 0.1720 4 0.5109 9 Banaskantha 0.0469 2 0.0519 5 0.2731 11 0.1295 9 0.5014 10 Bharuch 0.0187 12 0.0360 11 0.2861 8 0.1383 8 0.4791 12 Jamnagar 0.0177 13 0.0658 4 0.3294 4 0.1706 5 0.5835 1 Junagadh 0.0251 9 0.0387 9 0.2618 14 0.1911 2 0.5167 8 Kheda 0.0383 3 0.0775 3 0.2621 13 0.1440 7 0.5219 7 Mehsana 0.0270 8 0.0338 12 0.3322 3 0.1774 3 0.5704 2 Panchmahals 0.0543 1 0.0850 2 0.3237 5 0.0867 14 0.5497 3 Rajkot 0.0167 14 0.0251 13 0.3486 2 0.1048 10 0.4953 11 Sabarkantha 0.0277 7 0.0508 6 0.2762 9 0.0889 13 0.4436 14 Surat 0.0375 4 0.0974 1 0.2700 12 0.1442 6 0.5490 4 Surendranagar 0.0232 10 0.0386 10 0.3677 1 0.0957 12 0.5252 6 Vadodara 0.0346 6 0.0478 7 0.2904 7 0.1048 11 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 Moderately vulnerable Vulnerable Highly vulnerable Very highly vulnerable (1) (2) (3) (4) (5) Bharuch Banaskantha Surendranagar Panchmahals Jamnagar Vadodara Rajkot Kheda Surat Mehsana Sabarkantha Junagadh Ahmedabad 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 132 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

Table 5. COMPONENT-WISE CONTRIBUTIONS TO THE OVERALL VULNERABILITY TO CLIMATE CHANGE FOR THE YEAR 2008 (per cent)

Districts Demographic Climatic Agriculture Occupational Total (1) (2) (3) (4) (5) (6) Ahmedabad 6.23 13.39 55.03 25.35 100.00 Amreli 3.72 11.60 52.61 32.07 100.00 Banaskantha 9.40 10.88 46.82 32.90 100.00 Bharuch 2.32 5.21 61.62 30.85 100.00 Jamnagar 3.97 5.31 51.31 39.41 100.00 Junagadh 6.00 24.68 34.05 35.27 100.00 Kheda 6.82 12.27 57.43 23.48 100.00 Mehsana 4.61 13.80 52.73 28.86 100.00 Panchmahals 10.43 12.36 60.98 16.23 100.00 Rajkot 2.82 11.92 49.98 35.28 100.00 Sabarkantha 5.40 15.89 51.26 27.45 100.00 Surat 8.06 11.26 59.24 21.44 100.00 Surendranagar 4.99 9.00 50.36 35.65 100.00 Vadodara 7.42 14.24 52.62 25.72 100.00

TABLE 6. COMPONENT–WISE AND OVERALL VULNERABILITY INDICES FOR THE YEAR 2008

Agri- Occu- Over- Districts Demographic Rank Climatic Rank culture Rank pational Rank all Rank (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Ahmedabad 0.0355 4 0.0762 3 0.3131 2 0.1442 9 0.5690 2 Amreli 0.0213 11 0.0664 5 0.3014 3 0.1837 4 0.5729 1 Banaskantha 0.0486 1 0.0562 9 0.2418 12 0.1699 5 0.5165 6 Bharuch 0.0122 14 0.0272 13 0.3223 1 0.1614 6 0.5231 5 Jamnagar 0.0189 12 0.0253 14 0.2447 11 0.1879 2 0.4768 10 Junagadh 0.0258 9 0.1061 1 0.1464 14 0.1516 7 0.4299 13 Kheda 0.0317 6 0.0570 8 0.2668 7 0.1091 12 0.4646 11 Mehsana 0.0238 10 0.0711 4 0.2715 5 0.1485 8 0.5148 7 Panchmahals 0.0428 2 0.0507 11 0.2499 10 0.0665 14 0.4099 14 Rajkot 0.0149 13 0.0630 7 0.2639 8 0.1863 3 0.5281 4 Sabarkantha 0.0276 7 0.0814 2 0.2626 9 0.1406 10 0.5123 8 Surat 0.0388 3 0.0542 10 0.2853 4 0.1032 13 0.4816 9 Surendranagar 0.0268 8 0.0482 12 0.2699 6 0.1910 1 0.5359 3 Vadodara 0.0338 5 0.0650 6 0.2400 13 0.1174 11 0.4562 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 Moderately vulnerable Vulnerable Highly vulnerable Very highly vulnerable (1) (2) (3) (4) (5) Vadodara Surat ------Surendranagar Amreli Junagadh Jamnagar ------Rajkot Ahmedabad Panchmahals Kheda ------Bharuch ------Banaskantha ------Mehsana ------Sabarkantha

134 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 136 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.

REFERENCES

Altieri, M.A. (2004), “Linking Ecologists and Traditional Farmers in the Search for Sustainable Agriculture”, Frontiers in Ecology and the Environment, Vol.2, Pp. 35-42. Blaikie, P., T. Cannon, I. David and B. Wisner (1994), At Risk: Natural Hazards, People’s Hazards, People’s Vulnerability and Disasters, Routledge, London. Chamber, R. (1983), Rural Development: Putting the Last First, Essex, Longman. Glantz, M.H. and T. M. L. Wigley (1986), “Climatic Variations and their Effects on Water Resources”, Resources and Water Development. IPCC (2001), Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge. Iyenger, N. S. and P. Sudarshan (1982), “A Method of Classifying Regions from Multivariate Data”, Economic and Political Weekly, Vol. 17, No.51, Special Article, December 18, Pp.48-52. Modi, Narendra (2009), “Convenient Action: Gujarat’s Responses to Challenges of Climate Change”, p.3. ANALYSIS OF VULNERABILITY INDICES IN VARIOUS AGRO-CLIMATIC ZONES 137

Palanisami, K., C.R. Ranganathan, S. Senthilnathan, S. Govindaraj and S. Ajjan (2009), Assessment of Vulnerability to Climate Change for the Different Districts and Agro- Climatic Zones of Tamil Nadu”, CARDS Series 42/2009, Coimbatore. Patnaik, U. and K. Narayanan (2005), “Vulnerability and Climate Change: An Analysis of Eastern Coastal Districts of India”, Human Security and Climate Change: An International Workshop of India, Asker. Rao, N.H. and P.K. Joshi (2009), “Agriculture can be a Part of Climate Change Mitigation Strategy”, Financial Express, December 10.