Pattern of Droughts and Survival Strategies of Farm Households in a Drought-Prone District in Tamil Nadu

Ind. in. ofAgri. Econ. Vol. 56, No. 4, Oct.-Dec. 2001 Pattern of Droughts and Survival Strategies of Farm Households in a Drought-Prone District in Tamil Nadu L. Umamaheswari, S. Krishnamoorthy,P. Nasurudeen and Roop Kumar Kolli* INTRODUCTION Droughts, as a recurring feature of Indian agriculture, pose an imminent threat to sustainable agricultural development. A knowledge of the pattern of drought occurrence provides an understanding of the risky situation to which farmers in drought-prone areas are exposed. The probability of drought occurrence in various parts of the country have been identified based on deficiency in rainfall (Khanna, 1989; Kumar and Kumar, 1989; Patil, 1992; Dubashi, 1992), deviation of rainfall index (Singh et al., 1990) and aridity index (Ram Mohan, 1984). Spectral method has also been used to study the periodicities of drought using Palmer Drought Index (Rao et al., 1973) and fluctuations in rainfall (Raghavendra, 1974; I3hukanlal and Gupta, 1991; Bhukanlal etal., 1993). Spectral techniques are applied under various contexts in analysing time-series data. Earlier, spectral methods were used to test the existence of business cycles (Granger, 1966), movement of stock market prices (Granger, 1968; Kulkarni, 1978) and recently to study behaviour of agricultural commodities traded in the futures market (Cargill and Rausser, 1970; Weiss, 1970; Hunt, 1974; Chambers and Woolverton, 1982) and fluctuations in climatic variables. In the present study, time- series analysis of moisture indices was done to understand and identify the drought cycle at the taluk level through Power spectrum technique. Drought in agriculture alters cropping pattern (Muranjan, 1992), causes steep reduction in farm production, employment days, income level, household consumption (Pandey and Upadhyay, 1979; Uddin, 1984; Acharya, 1992) and reduces the calorie intake (Pinstrup-Andersen and Mauricio, 1985). These micro- level studies recommended soil and moisture conservation, irrigation facilities and *Assistant Professor, Department of Agricultural Economics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Pondicherry-609 603, Professor and Head (Retd.), Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore-641 003, Professor and Head, Department of Agricultural Economics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Pondicherry and Assistant Director, Climatology and Hydrometeorology Division, Indian Institute of Tropical Meteorology, Pune, respectively. The authors are thankful to G.B. Pant, Director, Indian Institute of Tropical Meteorology, Pune for providing the facilities for Power Spectrum analysis, to T.N. Balasubramanian, Professor and Head, Agricultural Meteorology Department and S.R. Subramaniam, Director (Retd.), Centre for Agricultural and Rural Development Studies (CARDS) for their guidance and to C. Ramasamy, Director, CARDS, Tamil Nadu Agricultural University, Coimbatore 641 003 for the moral support and encouragement provided throughout the study. 684 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS livestock and pasture development to lessen the severity of drought. Specific programmes suggested to minimise adverse consequences of drought in different regions include small-scale irrigation development (Klein and Kulshreshtha, 1989) and government rural works projects for creation of rural infrastructure (Bliven et al., 1994). Farm level analysis shows that drought causes a chain reaction of events in economic and social terms. Small farmers and marginal farmers are the worst affected people. However, the farm households with their ingenuity temporarily manage droughts through adjustments in production and consumption. The adaptive mechanism varies with the agro-climatic and resource characteristics of the area, knowledge of which would help in evolving location-specific drought coping measures. Studies on farmers adjustment mechanism against droughts is available in Jodha (1975, 1978, 1991); Jodha and Mascarenhas (1983); Walker and Jodha (1985). Against this backdrop, the present study was undertaken in a drought-prone district of Tamil Nadu State with the objectives of understanding the pattern of droughts, examining the consequences of 1990-91 drought on cropping pattern and employment of farm households and to know the drought survival strategies adopted to sustain household income. II DATA BASE AND METHODOLOGY Selection ofArea Dharmapuri, a drought-prone district in Tamil Nadu was purposively chosen. The district comprises eight taluks with 18 blocks, of which 12 blocks are drought-prone area blocks. Average annual rainfall, probability of drought occurrence and climatic index were taken as indicators of drought. Analysis of 60 years rainfall data ending 1994-95, talukwise (Table 1) revealed the frequency and intensity of drought to be the highest in Uttangarai taluk (Zone I) and lowest in Dharmapuri taluk (Zone II).' Uttangarai block and Nallampalli block respectively from the above taluks formed the universe for detailed study. TABLE 1. FREQUENCY AND INTENSITY OF DROUGHT Indicators of drought Taluk Average annual rainfall Probability of Climatic (mm) drought occurrence index (1) (2) (3) (4) 1 Krishnagiri 888.23 25.42 19 2 Palacode 868.61 25.42 19 3 Pennagaram 858.50 20.34 19 4 Dharmapuri 855.41 15.25 20 5 Harur 827.86 16.95 19 6 Denkanikottah 796.56 30.51 19 7 Hosur 772.87 28.81 19 8 Uttangarai 721.32 32.20 18 PATTERN OF DROUGHTS AND SURVIVAL STRATEGIES OF FARM HOUSEHOLDS 685 Sample Design To study the impact of drought and survival mechanism of farmers, a multi-stage random sampling approach was adopted. Two villages were randomly selected from each of the chosen blocks. In the second stage, the cultivators were randomly drawn in probability proportion to their size in the respective villages. Taking 20 per cent of the total population, the sample size worked out to 138. The sample was post- stratified into small farins(<2 ha) and large farms(>2 ha) to know the differential impact of drought among farm size-classes. Data Collection Secondary data on potential evapo-transpiration (PET- Penmans method, 1948)2 and monthwise rainfall for the taluks during 1934-1994, to study the drought pattern were collected from district Collectorate and Public Works Department. The year 1990-91 was a moderate drought year.' Primary data was gathered to know the impact of 1990-91 drought and mode of survival of farm households. For compara- tive analysis, the data were collected for the normal year 1993-94.4 Model to Study the Pattern ofDrought Thointhwaite's Moisture Index (I)n was taken as a proxy for drought index and the In,were computed from the monthly rainfall and PET. The time-series of moisture indices relating to the individual months from June to December and the seasons of South-West and North-East monsoon for 60 years ending 1994-95 were analysed for trends and periodicity, besides seasonal variability. Variability in I,, The coefficient of variation was worked out to know the extent of variability in the seasonal mean moisture index for all the taluks of the district. Trend Analysis To know the monthwise incidence of droughts, Mann-Kendall's rank statistic was applied. The Mann-Kendall rank statistic .method is quite robust and departure from the Gaussian normal frequency distribution will not be of much serious concern (WMO, 1966). The statistic is computed from T = 4 Eni / N (N-1) where ni is the number of values larger than the i-th value in the series subsequent to its position in the series of N values. Its expected value in a random series is zero and its variance is given by c32 T =(4N+10) / 9N(N-1). 686 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS The ratio oft to the standard deviation a T is an indication of trend in the data. For no trend in the data series T ar should be within the limits of± 1.96 at 5 per cent level of significance. Periodicities The time-series of moisture index were subjected to Power spectrum analysis to know the cyclical pattern of droughts in various taluks of Dharmapuri district. The plot of amplitude against frequencies is called power spectrum and spectral analysis is, in essence, an analysis of the variance of a time-series in terms of frequency. The units of measurement are frequency and density plotted on X and Y axis respectively. Frequency (1\1-') indicates the number of cycles per unit of time, while period (N) denotes the time required for one complete cycle. Density measures the relative contribution of each of the frequency bands to the overall variance of the entire •series. Practical considerations in the use of spectral analysis are (i) continuous observations of reasonable length is required. Granger and Hatanaka (1964) suggested n 100 and for satisfactory application n = 200 is considered a desirable minimum. It is the ratio of nim and not merely 'n' that determines the degrees of freedom. However, for proper determination of cycles, data of at least seven times the length of the largest cycle one wishes to study has to be considered. (ii) The cut-off points or the number of lags used has to be carefully decided. It essentially represents the number of frequency bands for which the spectrum is estimated. The larger the 'n', the more points at which the spectrum is estimated, and easier to localise the period of cycles. However, the sample variance increases as bandwidth decreases. For best results (William and Panofsky, 1956), it should be one-third of the total length of the period. (iii) The presence of trend in the time-series poses problems in examining the cyclical component due to very high power concentration at lower frequency and leakage from this point obscures the other components. Therefore, stationarity of economic time-series is a pre-requisite. There are various kinds of linear transformations applied for removal of trend, usually referred to as 'pre-whitening filter'. Here, pre-whitening of the series of monthly moisture indices was done by dividing the actual value by average value. (iv) Another problem in the use of spectral analysis is that of 'aliasing'.

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