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

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, -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 taluk (Zone II).' Uttangarai block and 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 3 858.50 20.34 19 4 Dharmapuri 855.41 15.25 20 5 827.86 16.95 19 6 Denkanikottah 796.56 30.51 19 7 772.87 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 . 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'. If the original series consists of more or less instantaneous observations made at regular time intervals, a short wavelength variation can be misconstrued as a longer wavelength variation. The shortest wavelength that could be resolved in a spectrum is equal to twice the interval between successive observations. This limiting frequency is called 'Nyquist frequency'. It is often suggested to limit the analysis to less than two times the Nyquist. The effects of'aliasing' has to be borne in mind while choosing the sampling intervals and interpreting the peaks of the spectrum. In the present study, spectral decomposition of moisture indices was done with the help of Fast Fourier Transform (FFT) computer package available at the PATTERN OF DROUGHTS AND SURVIVAL STRATEGIES OF FARM HOUSEHOLDS 687

Climatology Division of the Indian Institute of Tropical Meteorology (IITM), Pune. Blackman and Tuicey algorithm was used to identify the drought cycles. The programme of Blackman and Tukey algorithm is as follows:

(i) Computation of auto-correlation coefficients for lags 0 to m time-series, where m < N. (ii) Harmonic analysis on auto-correlation coefficients yields raw spectral estimates. (iii) Smoothing of the raw estimates with Hanning weights gives smoothed spectrum, also called the Hanned spectrum. (iv) In the Hanned spectrum, various kinds of non-randomness will be revealed differently.

Significance Testsfor Power Spectrum

The ratio of the magnitude of any null continuum is distributed as chi-square divided by the number of df (WMO, 1966), i.e., / v, where df, v is given by v = (2N - m/2)/m. The computed spectrum is superimposed on the confidence limits. If none of the spectral estimates lies outside the limit, it implies that the computed spectrum belongs to a population, the spectrum of which approximates to the null continuum. If any spectral estimate exceeds the upper confidence limit, it indicates the presence of significant oscillations in the series. Similarly, if any spectral estimate falls below the lower confidence limit, a repetition of phenomena with these periods. is very rare. The period corresponding to any spectral estimate is given by the relation, P = 2(m+1)/L, where m is the maximum lag and L denotes the lag period of the spectral estimate in question. Simple percentage analysis was done to interpret the data relating to drought impact and survival mechanism.

III

RESULTS AND DISCUSSION Pattern ofDrought

The basic statistical parameters of In, relating to the eight taluks of Dharmapuri district, monsoonwise (Table 2) shows that the mean In,of both the monsoon seasons were negative for all taluks. The negative values imply that the taluks fall in the semi- arid and arid climatic types, the degree of aridity being more during Southwest(SW) monsoon ranging from (-) 89.3 to (-) 93.3 than in Northeast (NE) monsoon season where it ranged from (-) 57.6 to (-) 69.9. But the coefficient of variation was low in Southwest monsoon (32.8 to 78.7) as compared to Northeast monsoon (185.1 to 411.0). 688 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

TABLE 2. STATISTICAL PARAMETERS OF MOISTURE INDEX - TALUKWISE Mean moisture Coefficient Taluk Monsoon index Standard of variation (per cent) deviation (per cent) (1) (2) (3) (4) (5) SW -93.30 30.6 32.8 Uttangarai NE -69.98 176.1 251.9 SW -89.79 70.7 78.7 Dharmapuri NE -59.81 141.1 235.9 SW -91.07 47.2 51.8 Hosur NE -63.53 117.5 185.1 SW -89.81 57.3 63.8 Palacode NE -63.05 179.9 285.5 SW -89.30 64.4 Pennagaram 72.9 NE -59.96 146.6 244.7 SW -89.30 53.4 59.8 Denkanikottah NE -62.89 228.5 363.3 SW -91.85 45.6 Harur 49.7 NE -63.48 260.9 411.0 SW -91.43 68.9 75.4 Krishnagiri NE -57.62 226.1 392.6

The Mann-Kendall rank statistic for the 72 In, series (Table 3) brought out increased incidence of droughts in June series of Uttangarai; August series of Uttangarai, Hosur, Palacode and Denkanikottah; September series of Dharmapuri and Krishnagiri, besides Southwest monsoon series of Denkanikottah. In the case of Northeast monsoon, increased incidence of droughts was observed in Denkanikottah taluk alone. No definite trend was observed in the remaining 63 series.

TABLE 3. MANN-KENDALL RANK STATISTICS FOR THE TIME-SERIES OF MOISTURE INDEX

Mann-Kendall rank statistics

Taluk June July August September South- October November December North- West East (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Uttangarai -0.229* -0.083 0.263* 0.012 -0.062 -0.113 -0.080 -0.155 -0.137 Dharmapuri -0.076 70.1 1 3 -0.113 0.238* 0.038 0.032 0.037 0.125 0.021 Hosur -0.028 -0.167 -0.275* 0.009 0.107 -0.101 -0.026 -0.087 0.063 Palacode -0.153 -0.125 -0.189* 0.071 0.142 -0.113 -0.066 -0.101 0.108 Denkanikottah -0.139 -0.169 -0.389* 0.034 -0.278* -0.167 0.017 0.004 -0.352* Pennagram -0.144 -0.007 -0.116 0.038 0.163 0.016 0.070 0.017 -0.152 Harur 0.077 0.078 -0.073 0.110 -0.008 0.008 0.014 0.016 0.021 Krishnagiri 0.008 0.010 -0.105 0.271* 0.149 -0.070 0.006 0.037 0.152

*Denotes significance at 5 per cent level.

The spectral analysis (Table 4) revealed the presence of significant cycles in 45 series. There existed cycles of 10 year and 13.3 year in Northeast monsoon of Dharmapuri; cycles of 6.7 year period in October series of Uttangarai; 5-6.7 year cycles in June series of Dharmapuri and July series of Denkanikottah and Krishnagiri PATTERN OF DROUGHTS AND SURVIVAL STRATEGIES OF FARM HOUSEHOLDS 689

and 6-8 year cycles in August seies of Harur. Of the remaining series, spectral peaks were recorded in 39 series of various taluks around 2-4.4 year cycles, i.e., they exhibited Quasi-Biennial Oscillations(QBO) at 5 per cent level of significance. A comprehensive review of periodicity in Indian rainfall data is available in Dhar et al. (1982). They noted the existence of two spectral peaks around 2-2.44 year and 3.66-4.44 year cycles in the Northeast monsoon rainfall of Tamil Nadu. Sarker and Thapliyal (1988, p. 133) pointed out the presence of few significant cycles with periods ranging from quasi-biennial to 15 year in the monsoon rainfall of some regions in . Therefore, the significant cycles observed in the moisture index series in the various taluks of Dharmapuri district is genuine and not due to any random fluctuations.

TABLE 4. RESULTS OF SPECTRAL ANALYSIS

Significant cycles in years

Taluk June July August September South- October November December North- West East (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Uttangarai 2.35 3.64 2.86 3.64 6.67 2.11 2.22 3.33 3.33 2.11 Dharmapuri 6.67 3.08 2.35 4.00 3.64 13.33 5.71 2.86 2.22 10.00 Hosur 4.00 3.64 3.64 2.86 4.00 2.22 3.33 Palacode 2.22 3.33 3.33 3.33 3.08 Denkanikottah 6.67 4.44 3.64 2.86 2.67 2.67 5.70 2.35 2.00 Pennagaram 3.33 3.64 3.33 3.64 3.33 3.33 3.08 Harur 8.00 2.86 2.86 3.64 3.33 6.67 2.35 3.33 Krishnagiri 6.67 3.33 2.35 2.11 3.64 2.11 5.71 2.00 3.33 2.00 Impact of1990 -91 Drought

During the drought year, the gross cropped area (Table 5) declined by 19.1 per cent and 22.1 per cent respectively over the normal year in Zone I and Zone II. There has been a shift in the area under paddy and groundnut to sorghum, samai, cumbu and horsegram in Zone I and to sorghum, samai, horsegram, Bengal gram and tomato in Zone II. The on-farm and non-farm activities (Table 6) were the major sources of employment during the normal year. In the event of a drought, there was a change in the occupational structure of small farms from agricultural labour to non-agricultural labour in the form of migration, gathering and activities like rope and mat making. 690 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

TABLE 5. CROPPING PATTERN IN SAMPLE FARMS

Percentage area under crops to gross cropped area

Zone I Zone II Crops Normal year Drought year Normal year Drought year Small Large Small Large Small Large Small Large farms farms farms farms farms farms farms farms (2) (3) (4) (5) (6) (7) (8) (9) Irrigated crops Paddy 8.6 23.5 2.8 8.7 20.0 15.8 12.1 13.1 Ragi 3.3 8.0 1.0 2.3 1.5 1.8 Groundnut 12.9 18.9 1.7 4.2 20.0 11.5 14.6 9.6 Others 2.6 6.2 0.6 17.4 18.0 20.1 6.5 10.9 Rainfed crops Sorghum 14.6 3.9 13.3 8.7 2.0 2.9 11.1 5.4 Ragi 9.9 2.0 3.9 1.6 5.0 3.1 2.5 3.1 Cumbu 0.1 6.5 10.5 3.9 5.0 2.3 1.0 Samai 0.1 19.9 1.9 2.0 2.9 26.1 7.9 Horsegram 13.3 1.3 10.5 9.7 7.0 7.5 12.1 10.9 Bengal gram 1.3 1.1 0.9 0.5 1.2 5.0 3.1 Groundnut 23.3 20.9 12.2 13.5 12.5 9.2 2.0 8.3 Others 13.3 13.5 23.5 21.5 7.5 21.2 5.5 25.9 Gross cropped 30.1 30.7 18.1 31.1 20.0 34.9 19.9 22.9 area (ha) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0)

TABLE 6. EMPLOYMENT PATTERN OF HOUSEHOLDS PER PERSON (man-days per year)

Normal year Drought year Per cent and farm Region On Off- Non- Total On Off- Non- Total change size-class farm farm farm farm farm farm (1) (2) . (3) (4) (5) (6) (7) (8) (9) (10) Zone I Small farms 117 44 17 178 46 . 23 67 136 -23.60 Large farms 125 56 12 193 101 30 24 155 -19.69 Zone II Small farms 129 46 11 186 78 31 39 148 -20.43 Large farms 158 44 28 230 138 36 17 191 -16.96 -

The average number of man-days employed per person reduced by 23.60 per cent and 20.43 per cent respectively in the small farms of Zone I and Zone II over the normal year and in the large farms the decline was 19.69 per cent and 16.96 per cent respectively.

Drought Survival Strategies

(a) Income diversification

The major strategy adopted was to diversify household income. In the normal year, crop, livestock and wage earnings were the major sources of income (Table 7). During the drought year, in small farms of Zone I, transfers that included sale of PATTERN OF DROUGHTS AND SURVIVAL STRATEGIES OF FARM HOUSEHOLDS 691

livestock and other movable assets, help from relatives, borrowings, etc., formed the largest proportion of household income. Migration in search of jobs to places like to work in construction sector at wages Rs. 50-65 per day and quarries in Andhra Pradesh at the rate of Rs. 40-70 per day was the next important source. The small farmers also resorted to cutting and selling of trees. In Zone II, crop sector was the major income source, followed by transfers and migration in small farms. In the large farms, transfers constituted the largest share of household income in both the zones. On an average, the income of households declined by 53.3 per cent and by 45.95 per cent against the normal year in Zone I and Zone II respectively. It could be concluded that there existed differences in the degree of income diversification across zones, and farm size-classes.

TABLE 7. COMPOSITION OF HOUSEHOLD INCOME IN ZONES I AND II (Rs./year)

Zone I Zone II Source Normal year Drought year Normal year Drought year

Small Large Small Large Small Large Small Large farms farms farms farms farms farms farms farms (1) (2) (3) (4) (5) (6) (7) (8) (9)

Crop 7,341.6 14,409.9 1,508.7 2,902.7 17,010.6 23,187.8 7,067.7 7,760.6 (36.2) (56.8) (18.3) (22.1) (50.3) (56.9) (38.9) (35.0) Livestock 5,727.9 6,942.5 248.0 3,161.7 7,300.0 14,002.4 1,691.9 4,095.2 (28.2) (27.4) (3.0) (24.2) (21.6) (34.3) (9.3) (18.5) Agricultural 5,276.5 116.6 560.6 90.3 6,480.2 57.1 897.5 labour (26.0) (0.5) (6.8) (0.7) (19.2) (0.1) (4.9) Non-agricultural 454.8 524.9 450.0 527.2 571.2 1,211.4 146.7 928.0 jobs (2.1) (2.1) (5.5) (4.0) (1.7) (3.0) (0.8) (4.2) Migration 1,785.0 1,046.5 1,976.0 1,096.3 (21.7) (8.0) (10.9) (5.0) Petty trade 289.3 230.7 154.7 87.7 219.3 175.3 49.2 70.9 (1.4) (0.9) (1.9) (0.7) (0.6) (0.4) (0.3) (0.3) Relief works 204.4 48.4 - 194.3 50.9 (2.5) (0.4) (1.1) (0.2) Transfers 1,217.4 3,131.5 2,332.6 5,229.9 2,227.5 2,153.6 6,146.3 8,146.1 (6.0) (12.4) (28.3) (39.9) (6.6) (5.3) (33.8) (36.8) Gathering 986.8 (11.9) Total house- 20,307.4 25,356.2 8,230.7 13,094.4 33,808.9 40,787.8 18,169.5 22,148.1 hold income (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) (100.0) Per capita Income at 1993- 94 prices 5,076.9 6,339.1 2,057.7 3,273.6 6,761.8 8,157.6 3,633.9 4,429.6 Figures in parentheses indicate percentage of each source to respective total household income.

(b) Migration

Migration is a traditional drought coping strategy of households. The incidence of migration was more among small farms and among the regions in Zone I (Table 8). More than 60 per cent of the migrants in the region temporarily migrated in search of 692 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

employment during the months of September, October, March, April and May in the drought year.

TABLE 8. MIGRATION DURING DROUGHT IN VARIOUS REGIONS

Zone I Zone II Details Small farms Large farms Small farms Large farms (1) (2) (3) (4) (5) Incidence of migration Number migrated 8 2 2 1 Per cent migrated 16.67 11.11 9.52 7.14 Reasons for migration (per cent) Employment 77.27 57.14 60.00 100.00 Grazing 14.29 Others 22.73 28.57 40.00

(c) Asset disposal

The major assets disposed off were livestock (Table 9), followed by home and farm assets in the sample. This reduced the farmers' capacity for investment and in turn their income and hence were forced to borrow money. The small farmers borrowed mostly from the large farmers and the large farmers from the traders mainly to meet their consumption expenditure.

TABLE 9. CHANGES IN ASSET POSITION OF HOUSEHOLDS

Per cent change in per household value of assets during drought year

Details Zone I Zone II Small farms Large farms Small farms Large farms (1) (2) (3) (4) (5) Farm assets -6.62 -9.89 -9.35 - -10.13 .Livestock assets -60.96 -41.01 -24.89 -21.60 Home assets -19.85 -16.73 -11.64 -12.39 Note: Value of assets are expressed in terms of 1990-91prices. (d) Adjustments in consumption

The households resorted to curtailing current commitments as a drought management device by reducing or altering the consumption pattern. The consumption expenditure (Table 10) during drought year reduced by 17.45 per cent and by 15.14 per cent respectively in Zone I and Zone II. The decline in expenditure on total food items was the smallest, while the cut-down on items like protective foods, socio-religious ceremonies, clothing, education, medicine, etc., was significant in all the categories. Interaction with the respondents revealed that a majority of the households did not celebrate religious festivals. But the villagers performed special PATTERN OF DROUGHTS AND SURVIVAL STRATEGIES OF FARM HOUSEHOLDS 693

rituals to the rain goddess Jakkamma during January/February as a village ceremony with the belief that the goddess would avert the drought and bring relief to them.

TABLE 10. EXPENDITURE PATTERN OF SAMPLE HOUSEHOLDS (at 1993-94 prices)

Percentage decline in consumption expenditure due to drought Details Zone I Zone II

Small farms Large farms Small farms Large farms (1) (2) (3) (4) (5) Fruits, vegetables, milk, etc. 18.92 19.90 14.10 20.28 Cereals, pulses, oils, sugar, etc. 20.08 13.79 15.65 11.32 Total food expenses 19.89 15.03 15.49 12.90 Socio-religious functions 27.04 24.96 17.53 16.78 Other non-food expenses 30.60 27.01 26.34 21.74 Total non-food expenses 30.15 26.68 25.11 21.13 Total consumption expenses 22.37 17.45 17.58 15.14

IV

CONCLUSIONS AND POLICY IMPLICATIONS

Droughts are a recurring feature (majority of the series have a cycle ranging from 2.0-4.4 years) in the study area warranting advanced contingent crop and employment planning. Satellite surveillance could be useful for timely and efficient monitoring of drought conditions. Soil moisture monitoring and watershed approach for manage- ment of drought-prone areas is another aspect for which research and development efforts have to be strengthened in future. Farmers tried to compensate for their reduced income during drought through non-crop sources. Livestock was the major asset sold during the drought period due to difficulty in maintenance. Development of community grazing land in the villages will help solve this problem. Gathering is another source of livelihood for small farms. Inter-face forestry with regulatory mechanism for -cutting of trees by public at nominal charges is to be encouraged. This will also help in the supply of fodder at nominal charges to the farmers for livestock maintenance during drought. Migratory income was another major income source particularly for small farmers and so off-farm activities like animal husbandry, sericulture, poultry and agro-based industries like fruit and vegetable processing, rope and mat making, etc., need to be encouraged. The small farm households are greatly affected by drought. In this context, the Small Farmer's Agri-Business Consortiums have to be strengthened covering a number of activities and involvement of small farmers to provide better income stability. Received March 2001. Revision accepted November 2001. 694 INDIAN JOURNAL OF AGRICULTURAL ECONOMICS

NOTES

I. The probability of drought occurrence indicates the frequency of drought years based on deficiency in annual rainfall from normal rainfall of respective taluks. Climatic index (CI) indicates the intensity of drought. A lower CI means,higher the intensity of drought. The computation of CI is as follows: If a, b, c are respectively the number of months where the ratio P/PE is equal to or less than 0.5, between 0.5 to 1.0 and greater than 1.0, then the CI = a + 2b + 3c. Superimposition of the climatic values on soil map enables to identify drought-prone area in the state. P denotes normal monthly rainfall and PE the potential evaporation. Among the eight taluks, the frequency and intensity of drought was the highest in Uttangarai taluk and was the lowest in . 2. Thornthwaite's Moisture Index(I) = (P/PET) - 1 x 100, where P = precipitation in mm (month) PET = potential evapo-transpiration in mm (month). Based on in,the climate of a region could be delineated as humid/semi-arid/arid.

Moisture Climatic types 1m

A Pre-humid > 100 B Humid 80 - 100 B Humid 60 - 80 B Humid 40 - 60 B Humid 20 - 40 C Moist sub-humid 0 -20 C Dry sub-humid - 33.3 to 0 . D Semi-arid - 66.7 to - 33.3 E Arid <-66.7

3. Drought has been defined differently by various organisations and research workers. The Indian Meteorological Department considers rainfall deficit between 25 to 50 per cent of the normal as moderate drought and above 50 per cent as severe drought. This concept is widely used. 4. The survey was conducted during 1992-93 to know the impact of 1990-91 drought on households and also during 1994-95 to know the farming situation in a normal year (1993-94). Adequate care was taken in the framing of interview schedule by pre-testing and data collection through cross-checks to capture the impact of drought on economic position of households.

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