Factors Affecting Distribution of Cattle in India

Transcription

1 Factors Affecting Distribution of Cattle in India Dr. E. Dayal Dr. Dayal is a visiting professor of the University of Minnesota. His normal position is Lecturer in Geography in the University of Wollongang, New South Wales, Australia. The problem of surplus cattle population in India has been widely discussed. The low yield of milk and the roaming cattle everywhere, even in the cities, give an idea of surplus cattle population, and have led to the general izations made about the irrational wastefulness of the large cattle numbers. However, the usefulness of cattle in India should not be judged only in terms of milk yield, because cattle contribute to the prosperity of the farmer in more important ways than by providing milk. The basic question of why the large cattle population is felt to be necessary must be examined in the light of various uses to which cattle are put, and other factors that affect the size of the cattle population. Some.such factors have been mentioned by earlier writers on the topic,', but no attempt has been made to test their ability to explain the size of the cattle population and their influence on the distribution pattern. Also, the problem has been dealt with only at the national level, even though the problem of cattle numbers is more a regional problem in that many decisions are affecting economic matters are taken at the regional state level. In this paper an attempt has been made to identify the factors that seem to affect the size and distribution of the cattle population, and through the application of simple and stepwise multiple regression of the statistical significance of selected factors has been tested. The Variables The selection of variables that appear related to the distribution of cattle in India has had to be based upon evidence drawn from 7

2 existing literature on the subject and some intuitive reasoning. Nine independent variables were selected to explain the spatial variations in cattle density, which is the dependent variable. The data for all variables was collected for the 20 states of Indi'a from the Statistical Abstracts of the Indian Union.' a A brief description of each variable is given below: Y Density of Cattle Units = Number of Cattle units/net Sown Area For the numerator in this index all bovine population, i.e., zebu cattle, buffaloes, and youngstock of both were converted into comparable standard units on the basis of food requirements, using a conversion factor developed by the Government of India (133.3 zebu cattle = 100 buffaloes, 33.3 zebu cattle = 100 zebu cattle youngstock, 50 zebu cattle = 100 buffalo youngstock).3 This approach gives comparable density figures and provides a fair idea of the pressure of cattle population on the agricultural land resources. X1 Density of Net Sown Area = Net Sown ArealGeograph ical Area This index indicates the importance of agricultural activities in a region. If cattle are mainly used as draft animals, one would expect a higher cattle density in the areas where the density of net sown area is high. X2 Rural Population Density = Rural Population/Net Sown Area There appears to be a symbiotic relationship between bovine and human populations in the rural areas of India, in which man benefits more from the presence of bovines, and therefore there is a good deal of accord in the distribution patters of human and cattle populations.' The bovines besides being used mainly as draft animals serve men in several small ways, and to that extent perform multiple functions in the rural economy. The spatial relationship between the cattle and rural population has been mentioned by Spate and Learmonth, who said "By and large the cattle being essentially working rather than food animals are where men are... '" X3 Density of Rice Cultivation = Rice Acreage/Net Sown Area Of all the field crops grown in India, field preparation is most elaborate for rice. As most of the work is done through draught animals, it appears quite sound to assume that there might be direct spatial relationship between cattle density and density of rice cultivation. X4 Density of Foodgrain Acreage = Area Under all Foodgrain Crops Net Sown Area Raj has mentioned that foodgrain acreage is spatially related to cattle density because draft requirement for foodgrain crops are more than for non-food crops." It is important to include this index in the analysis as foodgrain crops dominate the agricultural scene in India. X5 Intensity of Agriculture = Area Under Multiple Cropping Net Sown Area This index may be spatially related to cattle density, because in the area where agricultural intensity is 8

3 high obviously more cattle will be in demand throughout the year for various agricultural and other operations. It is generally believed that intensity of cultivation is very high in rice areas, and that therefore there is considerable double cropping. This is not quite true in India. The intensive use of labour in rice farming is quite different from agricultural intensity as defined here. Thus, there is little spatial accord in the distribution of X3 and X5 in India. 7 X6 Density of Pasture and Grazing Land = Area Under Pasture and Grazing Geographical Area This index was intuitively selected as a variable, because common sense reasoning suggests that there will be higher density of cattle in the regions where there is more pasture and grazing land. X7 Density of Well Irrigated Area = Well Irrigated Area/Net Sown Area About nine million hectares of agricultural land is irrigated by wells in India, and cattle are the main source of power for drawing water from the wells. This index, therefore, appears a suitable independent variable for inclusion in the model. X8 Density of Hindus and Jains = Number of Hindus and Jains Total Population There is widespread bel ief that the large cattle population in India is the result o f Hindu doctrine of ahimsa.' The Hindus, who form the vast majority of the total population in most states, and Jains have strong objection to the killing of the cow and to eating beef. Therefore, it is assumed that the states in which the proportion of Hindus and Jains to the total population is high, will also have high cattle densities. X9 Availability of Domestic Fuel = Firewood Production/Rural Population There are about three million cattle in India which are decrepit and good for neither milk nor work, and which may be regarded useless. However, their existence has been justified on the ground that they provide dung which is the only source of fuel in some parts of India. According to Majumdar about 300,000,000 tons of dung is used annually as fuel.' The peasants like to keep even these useless animals if they have no other source of fuel, which is often the case in several areas in the Northern Plains of India. Such a line of argument suggests some negative correlation between fuel supply from alternative sources and cattle density. As firewood is the only other fuel used in villages, domestic fuel supply has been measured as firewood production per 100 rural population. THE ANALYSIS it is hypothesized that the distribution of cattle density in India (Y) is positively related to the distribution patterns of the density of net sown area (X1), rural population (X2), ric.e acreage (X3), all foodgrain acreage (X4), intensity of agriculture (X5), pasture and grazing land (X6), well irrigated area (X7), Hindus and Jains (X8), and negatively to firewood production (X9). The data on all these variables was squareroot transformed. 9

4 TABLE 1: CORRELATION MATRIX (Imput Variables) y X1 X2 X3 y * * 0.433* * X * 0.629* X " X X4 X5 X6 X7 X8 X9 X4 X5 X * 0.542* * " ' " 0.553" X7 X8 X " " " " " " * " " " *Significant at 99 percent level of confidence **Significant at 95 percent level of confiden ce o.-

5 TABLE 2 INDEPENDENT INCREASE PARTIAL OVERALL STEPS VARIABLES R R2 in R2 F-value F-value 1. X4 Percentage of Foodgrain Area * * * * 2. X5 Intensity of Agriculture * ** 3. X1 Percentage of Net Sown Area * * * 4. X8 Hindus and Jains as Percentage of Total Population * * * 5. X6 Percentage of Area Under Pasture * 6. X2 Density of Rural Population * 7. X3 Percentage of Rice Acreage * 8. X7 Well Irrigated Area as Percentage of Net Sown Area X9 Firewood Production per 100 of Rural Population **Significant at 99% level *Significant at 95% level I i 11

6 CATTLE DENSITY (By States, J Data) Okms 1000!! Per 100 Acre of Net Sown Area I~tttl >50'00 Fj}}] Ed o 15 ' * Data not available Fig. 1. Distribution of Cattle Un its in India. 12

7 RESIDUALS FROM MULTIPLE REGRESSION (Y-Yc)/Syc Okms 1000 L..', Standardized Values ~ 1 00 to to 0 99 o -0,99 to [3] -1,00 to -1,76 * Data not available Fig. 2. Pattern of residuals from regression 13

8 The results of simple correlation analyses show that the density of area under foodgrains is the most important variable in explaining the variation of cattle density. (Table 1). Next in order are the intensity of agriculture, density of net sown area, and density of rural population, which are all statistically significantly related to the distribution of cattle density. However, the association between net sown area and cattle density is inverse rather than direct as was postulated. A more careful examination seems to support the results of the correlation analysis. In regions where the area sown to crops relative to total area is large, the methods of farming are often extensive, because the pressure of rural population on cultivated land is relatively low, as for example in the southern section of the Punjab Plains, Gujarat, west central Deccan Plateau and Rajasthan. In such areas the demand for cattle for agricultural work will be less per acre of cultivated land because of the extensive nature of farming. This is also supported by the negative correlation between density of net sown area on the one hand and density of rural population and intensity of agriculture on the other (Table 1). Also, the overall negative relationship between cattle density and net sown area may be affected by some states where the proportion of the net sown area is small, because of the large extent of hilly and forrested tracts, leading to a higher density of cattle on net sown area although the total cattle population is small, as for example in Kashmir, Himachal Pradesh, Assam, Nagaland, Manipur, Tirpura, and Madhya Pradesh. Of the remaining five independent variables the direction of association for three, rice acreage, area under well irrigation, and firewood supply, are the same as were postulated, but the correlation coefficients are weak and statistically not significant. The relationship between cattle density and the percentage of Hindus and Jains is negative and not significant. This is an important result and shows clearly that the large cattle population in India does not reflect the Hindu attitude against cattle slaughter and the eating of beef, thus supporting the views of Harris'o and Raj. " For example the largest concentrations of non-hindu population are in the states of Kashmir, West Bengal, Assam, Tirpura, Manipur, and Kerala, but it is also in these states that there is a marked concentration of cattle population with the exception of the last. The relationship between cattle density and density of pasture and grazing land is also negative. This may be because in India animal raising is not an organized commercial enterprise. Cattle are not kept for milk or meat but for work, and are largely fed on crop residues. Also, the area which is cultivatable but not used for cultivation for some reason is classified as pasture and grazing land. Greater extents of such land are found in the areas which are less important for agriculture. Thus, the negative relationship brought out by the correlation analysis appears to be valid. But the Correlation coefficient is weak and not sign ificant. All the nine variables have been listed, and the results of simple correlation analyses substantiate 14

9 three hypotheses, modify one, and do not support five (Table 1). The proportion of the variation in cattle density accounted for by individual independent variables is generally small. Only the density of foodgrain acreage (X4) accounts for more than fifty percent of the variation in Y. It is however, inevitable that higher values of R' might be obtained if the variables were treated simultaneously in relation to cattle density. This appears closer to the real world situation, where several factors operate together to influence a situation. The five independent variables proved insignificant in the simple correlation analyses are retained in the multiple model, assuming that they might contribute significantly to the value of R2 when treated with other variables. Multiple Correlation and Regression Analysis The coefficient of multiple correlation based on all the nine independent variable (R = 0.865) is statistically highly significant, and accounts for 75 percent of the variation in cattle density. However, it is desirable to find out the minimum number of variables that can account for maximum variation. This has been achieved through the development of a stepwise multiple regression model. The computer programme (BMD02R) used in this analysis first picks up the independent variable most correlated with Y, and finds the first order linear regression equation. The next variable added, out of the remaining independent variables, is the one whose partial correlation coefficient with Y is the highest. This process continues until the regression equation is most satisfactory. At each step the significance of the last variable added is tested by a partial F-test, and an overall F-test indicates the significance of the multiple regression equation formed at that step." After the fourth step the new variable added ceases to make any significant contribution to the explanation of the variation in Y, but overall regression equation continues to be significant up to the seventh step (Table 2). Logically only those independent variables which make a significant contribution to the explanation of the variation in Y should be included in the refined multiple regression equation. Thus, the stepwise regression model yields four independent variables for inclusion in the refined multiple regression equation: Yc = x X X X8 Standard Errors of Coefficients (0.743) (0.644) (0.690) (0.669) R = R2 = The regression model provides a statistical description of the spatial difference between cattle density and a set of variables which describe the place characteristics. Density of foodgrain acreage (X4), intensity of agriculture (X5), density of net sown (X1) are most strongly associated with the density of cattle (Y). The variable added at the fourth step is the proportion of Hindus and Jains to the total population of Hindus and Jains to the total population (X8), which has a very low negative correlation with Y (Table 1), but it has a higher partial correlation with Y than any 15

10 of the remaining variables at the fourth step. This four variable regression model, based on the density of net sown area, density of foodgrain acreage, intensity of agriculture and proportion Hindus and Jains, explains two-thirds of the variation in cattle density, which is quite satisfactory when compared with the results generally achieved in spatial regressions. The only variable which is significantly correlated with Y and is not included in the refined multiple regression equation is the density of rural population. This is because rural population is also sign ificantly correlated with the density of foodgrain acreage (X4) and therefore its explanatory power has been considerably reduced by the inclusion of X4 at the first step. The remaining four variables, namely, density of rice acreage (X3), density of pasture and grazing land (X6), density of well irrigated area (X7), and firewood production (X9), which were proved insignificant in the simple correlation analysis also remain insignificant in the multiple regression model. A re-examination of the hypotheses associated with these variables suggests some explanations for the weak or insignificant relationship between them and cattle density. The weak relationship between firewood production and cattle density may be because cow dung is not used as domestic fuel all over India, particularly in the southern states and in the hilly districts even in north India. Harris appears to have made an over statement when he said that dung was the main source of domestic fuel in India. 13 The relationship between pastures and cattle is low because pastures and grazing lands are found more in the areas less important to agriculture. In such areas there is no need for keeping large number of cattle as they are mainly required for agricultural work. The cattle in India live on scrub, organic waste, like stalks of rice, wheat, maize, and other crops, and even consume large quantity of waste paper." The poor relationship between rice density and cattle is because rice alone perhaps does not adequately represent the cropping pattern even in some main rice areas. The relationship improves when total foodgrain acreage is taken. This is also supported by low intercorrelation between density of rice acreage and density of total foodgrain acreage. The Analysis of Residuals A comparison of the residual map with that showing the density of cattle in terms of standardized values shows that high negative residuals from regression occur in Punjab, Maharashtra, and Andhra Pradesh states, where the density of cattle units is low (Figures 1 and 2). Therefore, in these three states there is an over prediction, which means that the model over estimates the importance of selected independent variables in explaining cattle density in these areas. The high positive residuals occur in Kashmir, Mysore, and Madras states, where the cattle density is high to medium. In these three states the variables included in the model explain only a part of the actual density. In the two groups of states involved in over prediction or under prediction the cropping pattern is not adequately expressed by the density of all foodgrain acreage. In these states 16

11 TABLE 3 WORKING CAPACITY OF DRAFT ANIMALS IN SELECTED AREAS Area ploughed Percentage of per animal per farmers who felt day (8 hours a shortage of Regions working day) Dominant draft animals in (District) (Acres) _ Crops the peak season Punjab (Karnal) 1.41 wheat, gram 56.6 Western U.P wheat, bajra 87.5 (Delhi and Meerut) gram Eastern U.P. (Varanasi and Allahabad) 0.54 wheat, rice 86.6 and millets Bihar (Chapra) 0.32 rice, maize 62.7 Assam (Nowgong) 0.25 rice, jute 53.3 Source: Compiled by the author, and is based on field survey. 17

12 commercial crops like tobacco, cotton, groundnuts, and coconuts are of much importance in the cropping pattern. In the remaining fourteen states the density of cattle is well predicted by the model as the values of the standardized residuals for these states are less than one (Figure 2). They form a large contiguous area, spread over almost the entire Northern Plains the Northern Slopes of the Decca~ Plateau, and the Assam Valley. These areas are the most important agricultural areas of the country, where there is a high concentration of foodgrains acreage, double cropping, and net sown area. The model has taken good account of the influence of these independent variables in explaining cattle density over this large area. The pattern of residuals indicates no obvious factor, in addition to those already included in the model that could improve predictablility. ' It is important to recognize the limitations of the regression model which operates in a deterministi~ framework, and is therefore descriptive and not analytic. However, it does give some idea of the relative importance of some of the principal factors that seem to affect the distribution of cattle density in India. CONCLUSION The spatial association between cattle density and a set of factors has been tested using multiple correlation and regression analysis at the state scale. The analysis seems to suggest that the distribution of cattle in India is fairly strongly related to four environmental factors, the proportion of net sown area agricultural intensity, proportion of crop area sown to all foodgrains and proportion of Hindus and Jain~ to total population, which were distilled frorll an original nine. The pattern of cattle density shows a high concentration of cattle in agriculturally important areas, where a majority of the above four variables is also well represented, as for example the Northern plains of India, the Assam Valley, and some coastal areas. Low densities of cattle occur in the areas that are less important for agriculture such as Rajasthan, Gujarat, and much of western Deccan Plateau, where the values of the independent variable are also generally low. The more important agricultural areas in India largely produce foodgrains to feed the large local populations. Since foodgrain production requires more elaborate and more frequent cultivation, there is greater demand for draft animals, and farmers have to keep more work animals than their counterparts in less important agricultural areas and in areas where commercial crops dominate. Information collected in a field survey of some selected villages in North India in 1975, for the purpose of checking the resulta of this analysis, shows that over much of the Northern plains, where there is largest concentration of cattle, there is often a shortage of cattle in the peak farming season (Table 3). Thus, it may be concluded that in the areas where cattle density is high there is really no surplus, and that cattle are kept because there is a need, that is, for economic rather than any sentimental reasons. The only way to reduce the cattle numbers is to bring about a change in agricultural technology. Perhaps partial mechanization of some 18

13 agricultural operations, such as ploughing with tractors, which will not seriously affect the rural employment situation, might help reduce the numbers and improve the quality of cattle. References 1. Kumarappa, J. D. (ed.) The Cow in Our Economy. Bhargava Press, Varanasi, India, Government of India, Central Statistical Organization, Statisticat Abstracts of the tndlan Union, Manager of Publications, New Deihl, 1967 and Government of India, Cabinet Secretariat, Tables and Notes on Animal Husbandry, National Sample Survey, Report No. 65, New Deih l, HarriS, M., " Cultural Ecology of India's Sacred Cattle," Current Anthropology, 7, 1966, pp Spate, O. H. K. and Learrnonth, A. T. A., India and Pakistan, Methuen, London, 1967, p Raj, K; N., " Investment In Livestock In Agrarian Economies," Indian Economic Review, 4 (New Series), 1969, pp Spate and Learrnonth, op. cit., pp. 235 and 245 (cf. Figures 8.3 and 8.8). 8. Harris, M., op. cit., p Majumdar, N. A., "Cow Dung as Manure," Economic Weekly, 12, 1969, pp Harris, M., op. cit., p Raj, K. N., op. cit., p Draper, N. R. and Smith, H., Applied Regression Analysis, John Wiley and Sons, New York, 1967, pp Harris. M., op. cit., p Perelman, M. A., " Sacred Cows," American Journal 01 Agricultural EconomiCS, 54, 1972, pp