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Vulnerability to Agricultural Drought in Western Orissa: a Case Study of Representative Blocks§

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Vulnerability to Agricultural Drought in Western Orissa: a Case Study of Representative Blocks§ View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Papers in Economics Agricultural Economics Research Review Vol. 24 January-June 2011 pp 47-56 Vulnerability to Agricultural Drought in Western Orissa: A Case Study of Representative Blocks§ Mrutyunjay Swaina* and Mamata Swainb aAgro-Economic Research Centre, Sardar Patel University, Vallabh Vidyanagar-388 120, Gujarat bDepartment of Economics, Ravenshaw University, Cuttack- 753 003, Orissa Abstract The nature of vulnerability to agricultural drought in three study blocks of Bolangir district in western Orissa has been analysed. The indexing and vulnerability profile method have been used for assessing the nature of drought vulnerability, coping capacity and risk. The study has revealed that the three most influential biophysical factors of drought vulnerability are: rainfall variability, drought intensity and shortage of available waterholding capacity of soil and the three most influential socioeconomic factors are: low irrigation development, poor crop insurance coverage and smaller forest area. It is found that while drought risk varies widely across the study blocks and drought vulnerability and physical exposure to drought vary moderately, the coping capacity of study blocks differ marginally. However, the level of coping capacity has been found significantly lower than the level of drought risk and vulnerability in the study blocks. Key words: Drought, Drought vulnerability, Composite drought vulnerability index, Physical exposure index, Drought risk index JEL Classification: Q54, Q58, C43, O13 Introduction system are some of the key factors that determine the nature and extent of drought vulnerability in a region. Droughts produce a complex web of impacts that It is also influenced by the coping capacity of inhabitants span many sectors of the economy and reach well characterized by their resource endowments and beyond the area experiencing the physical drought. entitlements. Some exogenous factors like climate Agriculture being the major livelihood activity in the change do influence the level of risk and vulnerability rural areas, is severely affected by droughts. The nature of different livelihood groups in a region (UNDHA, and intensity of drought impacts vary from location to 1992; Blaikie et al., 1994). It is noteworthy that location, depending on relative influence of various increasing occurrence of climate-induced natural agro-climatic, geophysical and socio-economic factors. disasters (CINDs) like drought, flood, and cyclone is Rainfall variability, soil type, land topography, the major outcome of the intensification of climate groundwater availability and utilization, irrigation change (IPCC, 2001). Among these CINDs, drought coverage, economic strength and institutional support is considered by many to be the most complex but least understood phenomenon affecting more people than by any other hazard (Hagman, 1984). More than half * Author for correspondence, Email: [email protected] of the world population is susceptible to drought every § The paper is a part of PhD work (2009) of the first author year (Kogan, 1997). In the coming decades, the extent that was undertaken at NKC Centre for Development Stud- of drought risk and vulnerability is expected to increase, ies (ICSSR), Bhubaneswar. irrespective of the changes in drought exposure mainly 48 Agricultural Economics Research Review Vol. 24 January-June 2011 due to development pressure, population increase, and drought adaptability index (CDAI), physical exposure environmental degradation (ISDR, 2002). Keeping in index (PhyExpo) and drought risk index (DRI) for each view the changing circumstances, appropriate policy block under study. needs to be formulated for adapting to increasing A normalization procedure was adopted for vulnerability and a precautionary approach at different adjusting indicator values to take the values between 0 levels is essential. It is because the benefit of risk and 1 using formula (1): management is larger than the cost of repeated crisis management (Anderson, 1990; Dzeiegielewski, 2000), Vij = [(Xij – Min Xi)/ (Maxi – Mini) …(1) which in turn, brings much more stability to diversified where, Vij is the normalized value of drought livelihood systems and puts the rural economy on an vulnerability indicator, X is the value of ith drought upward trajectory. ij vulnerability indicator in the jth block, ‘Min X i’ and ‘Max The attention towards drought risk mitigation and Xi’ denote to the minimum and maximum value of the planning has dramatically increased in recent years due ith drought vulnerability indicator across blocks. The to unexpected rise in the magnitude of drought losses DVI and CDVI indices are given by: worldwide (Wilhite, 2002). One of the main aspects of Drought Vulnerability Index (DVI) = any drought mitigation and planning strategy is the m 1/ m K V × 100 ‘vulnerability assessment’ (Wilhelmi et al., 2002), which ()∑i1= iij requires identification of who and what are most …(2) vulnerable and why. Composite Drought Vulnerability Index (CDVI) = This paper has assessed the nature and n 1/n W DVI × 100 determinants of drought risk and vulnerability ()∑i1= ii experienced by selected blocks of drought-prone …(3) Bolangir district in western Orissa. It has examined where, K is the weight attached to ith normalized the relative influence of different socio-economic and i drought vulnerability indicator with the value of i varying biophysical factors on the levels of drought vulnerability from 1 to m. The scalar m is the number of drought in the study blocks. The major events or processes vulnerability indicators considered for a particular DVI. that increase the extent of drought risk and vulnerability The value of m is different for different DVIs. In in the study region have also been studied. addition, n is the number of DVIs considered for Data and Methodology computing the CDVI. The weights attached to vulnerability indicators were decided in consultation with The Bolangir district was deliberately chosen for agricultural experts and key informants. Three types the study. At the second stage, three blocks Saintala of CDVI were constructed: (i) as simple average, (ii) (most vulnerable), Patnagarh (moderately vulnerable), with equal weights, and (iii) with unequal weights. The and Titlagarh (least vulnerable) were selected on the blocks were ranked by the type of CDVI that ranked basis of degree of drought vulnerability. Following Bolangir district around middle of its blocks. Suitable UNEP (2001), drought vulnerability was taken as the cut-off points were assigned for classifying blocks into composite of conditions and exposure to adverse different vulnerability zones. processes that increase the susceptibility level of populations and their habitations to drought. A composite The risk associated with drought episodes was drought vulnerability index (CDVI) was constructed considered as the product of drought hazard and drought using indexing and vulnerability profile method. Nineteen vulnerability, relative (i.e., divided by) to a variable that key drought vulnerability factors were taken into proxies coping capacity based on an indexing procedure consideration for deriving the CDVI; of these 19 factors, termed as composite drought adaptability index (CDAI) six were biophysical factors and thirteen were socio- and was calculated using the same procedure as was economic factors, as given in Table 1. Data on different followed for deriving CDVI. The physical exposure to biophysical and socio-economic indicators of drought drought (PhyExpo) was taken as the product of adaptability, physical exposure, and drought risk were probability of drought occurrence and the extent of used to generate aggregate indices such as composite human population exposed. At the block level, the Swain & Swain : Vulnerability to Agricultural Drought in Western Orissa 49 Table 1. Indicators of drought vulnerability used for constructing CDVI Sl Indicators Proxy for indicator Data Study blocks of Bolangir Bolangir Orissa No. period Saintala Patnagarh Titlagarh district state (most (moderately (least vulnerable) vulnerable) vulnerable) Bio-physical indicators of drought vulnerability 1 Drought Drought frequency (%) 1986- 61.1 66.7 44.4 56.8 38.9 frequency 2003 2 Drought Decrease in rainfall from 1986- 40.3 43.7 17.5 27.6 11.0 intensity long-term normal in drought 2003 years (%) 3 Rainfall Average annual rainfall 1986- 40.6 40.5 31.6 27.7 17.7 variability (CV %) 2003 4 Soil Available waterholding 1998 4 1 2 1 1 capacity of soil (Rank*) 5 Land Land slope (%) 1998 3.2 4.2 2.4 3.3 2.0 topography 6 Ground Decline in post monsoon 1998- 25.2 13.3 22.3 21.9 9.9 water table water level in drought year 2002 compared to normal (%) Socio-economic indicators of drought vulnerability 7 Irrigation Area without any irrigation 2001 96.4 93.8 86.8 94.4 59.9 potential (%) 8 Unirrigated area to total 1991 95 94.3 87.3 96 80.2 cultivable area (%) 9 Major crop Paddy area variability (CV %) 1986- 17.1 5.6 6.0 16.3 2.7 production 2003 10 Paddy yield variability (CV %) 1986- 35.4 44.7 36.2 41.0 15.6 2003 11 Poverty Households below poverty 1997 81.0 64.0 55.0 61.0 47.2 line (%) 12 Social factors Landless and marginal 2001 47.7 50.3 58.8 48.8 35.0 labourers to total main workers (%) 13 People illiterate (%) 1991 64.2 62.3 72.5 61.4 59.2 14 People living in rural area (%) 2001 100 100 97.6 88.5 85.0 15 Population density ( per sq km) 2001 206.5 168.2 292.7 203.3 236.0 16 Land-use Geographical area not 2001 92.7 87.1 97.6 93.3 62.7 pattern covered under forest (%) 17 Barren uncultivable and 2001 13.5 24.3 12.6 16.7 5.4 other fallows (%) 18 Institutional Farmers not covered under 2001 97.4 97.2 98.3 96.2 93.7 factors crop insurance (%) 19 People not benefited by 1991 96.9 97.0 98.3 97.6 91.0 IRDP (%) Notes: (1) CV stands for co-efficient of variation; IRDP stands for Integrated Rural Development Programme.
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