Wastelands in Rural India : Policy Initiatives and Programmes

for their Development

G.K. CHADHA

National Bank for Agriculture and Rural Development

Mumbal

1996

Occasional Paper —2

Wastelands in Rural India : Policy Initiatives and Programmes

for their Development

G.K. CHADHA

National Bank for Agriculture and Rural Development Mumbai

1996

Published by National Bank for Agriculture and Rural Development, Department of Economic Analysis and Research, Jeevan Seva Complex (Annexe), S. V. Road, Santacruz (W), l^umbai - 400 054 and Printed at Karnatak Orion Press, Fort, tvlumbai - 400 001.

f

I

Acknowledgement

The present paper was commissioned by the National Bank for Agriculture and Rural Development, Bombay. The author is

grateful to them for showing keen interest in this area of crucial

importance to Rural India.

The paper is based on secondary data and other published

works/studies on the subject. The limitation of the work should

therefore be quite obvious.

All deficiencies and weakness belong to the author.

G.K. Chadha Jawaharlal Nehru University

New Delhi

Contents Page No.

1. The Wasteland Issue: An Overview 1

II. Land-use Pattern 7

1. National-Level Temporal Profile 7

2. State-Level Profile 13

3. Total Wasteland 16

4. Components of Wasteland 16

III. District-Level View of Wasteland 23

1. MoA Estimates: 1989-92 25

2. NRSA Estimates: 1986-88 43

3. Comparing MoA with NRSA Estimates 50

4. Major Categories of Wastelands 57

IV. Policies/Schemes for Wasteland DevelopmenI 63

1. NCIA Initiatives 63

2. NWDB Initiatives 65

3. DWD Schemes 66

4. MoAC Schemes 71

5. MoEF Schemes 72

6. Planning Commission Schemes 74

7. NABARD Initiatives 74

V. Looking Back, Looking Ahead 76

1. Operational Side of the Wasteland ?? Development Programme

2. Assessing Past" Performance 78

3. Looking Ahead 83

i) Approaching the Problem 84

ii) The Question of Technology 85

iii) Updating Information Base 85

iv) Working of Financial Institutions 86

v) Grassroots Adjustments 86

vi) Institutional Restructuring 87

References 89

I The Wasteland Issue : An Overview

Agriculture continues to be the main economic activity in rural areas of the developing world in spite of a steady diversification of their economic base during the preceding decades. Agriculture will continue to dominate rural economy of these areas for a long time to come. It is natural, therefore, that the availability of land to, and its use pattern in, agriculture would remain issues of extreme impor­tance to policy planners, especially because unlike other factors of production, land is not only a highly scarce but is also a non-repro­ducible means of production. In the particular context of Indian reali­ties, typically characterized by continuing population pressures, and ever declining land : man ratio, preponderance of small and frag­mented holdings, highly inequitous land distribution structure, etc., the significance of land-population balance rises all the more.Further, the limited capability of the urban-industrial sector to absorb the expand­ing rural labour force would keep a vast majority of work-seekers tagged to land, principally in agricultural activities, and to some ex­tent, in diverse non-farm jobs in and around the villages. From ev­ery conceivable angle, therefore, the most crucial and most vexatious issue in rural India would be of land availability. In a broad sense, the availability of land to agriculture by itself would set the future pattern of India's development, most ostensibly the farm-nonfarm link­ages, the rural-urban migration, the incidence of rural-urban poverty, and so on.

For populous countries such as China, India and Indonesia, the population-land balance is ipso facto the demand-supply balance for food. Broadly, this balance is conceived to operate in two ways. First, following the Ricardian School, the economy has some 'virgin' uncultivated land which it can gradually draw upon for expanding food production. The steadily increasing 'margin of cultivation' sus­tains the growing population and food requirements. On the other hand, Clark, Boserup and Simon do not foresee much possibility of adding on to 'area already under plough'. It is the improving technol­ogy of production that delivers more food and sustains expanding race of human beings. An increasing level of cropping intensity is, inter alia, an important off-shoot of improved technology. In fact, for many countries, expanding 'double cropped area' remains the only option for augmenting food/agricultural production, when all cultivable land has alredy come under plough.

1

It can be argued that the Ricardian option is at best a short-term cushion for most developing economies, most pointedly the populous ones among them. The extending "margin of cultivation' does cease to operate sooner or later. It is also a fact that the Ricardian option has worked as an easy way out with numerous de­veloping economies for expanding domestic food production and 'land augmenting' technological improvements have received scant public attention and resources. The African, especially the Sub-Sahara, ag­riculture is a typical example of this kind. For a majority of develop­ing economies, the land-use policy has been to draw upon both options simultaneously, with a relatively higher reliance on the easier Ricardian option for the first few decades and then switching over partially or completely to the other side when the first option ceases to exist or becomes much too costly to draw upon.

The problem of declining land: man ratio gets exasperated through the increasing incidence of wastelands. Both nature and man-induced factors are at work in pushing more and more of culti­vable area out of use for agricultural production, food supply and rural well-being. Inadequate property rights (most crucially access to land), poverty, population pressure and declining land-man ratio, inap­propriate government policies, and lack of access to markets, credit, and technologies appropriate for sustainable agricultural development, etc., are the more glaring among such factors. These are usually put under the rubric man-made factors. There is a natural tendency with the rural poor to "over exploit' land resources. This generally takes the form of overgrazing or deforestation or "unhealthy" cultiva­tion practices. For example, inappropriate cultivation practices can expose soil to water and wind erosion, water-logging,salinization or alkalinization, repeated tillage under economic stress can weaken soil structure, inadequate or imbalanced use of chemical fertilizers can remove soil nutrients and damage yield-raising capability of land, for­est clearing for cultivation or reckless grazing on hill slopes can throw land out-of-use as time passes and so on (Miglani 1988, pp. 4-9; Lutz-Pagiola-Reiche, 1994, p.273). Agricultural and pasture lands are affected the most, and the process of 'over-exploiting land' has the latent tendency of perpetuating itself as population pressure con­tinues to grow and cultivable land per head of rural population in general and of poor households in particular, continues to shrink (IFPRI, 1995, p.16).

Nature also contributes sizeably to the process of wasteland for­mation, in many different ways. For example, the continuously occur-

ring wind erosion tal<es away the top soil and impairs the texture of soil, water erosion also takes away the top soil and leads to gulley and ravine formation. Floods lead to sand deposition which affects fertility status of the soil and in certain cases, renders land unfit for cultivation (Singh, et al., 1988). Desert formation because of long spells of scanty rainfall (grossly inadequate to meet the minimum survival plant water requirement), snow covered areas during winter and glacial areas characterized by unassorted sediments, impeded natural drainage and poor soils, barren and rocky surface and steep sloping areas controlled by geological processes, landslides and snow avalanches, etc., are all manifestation of nature's handiwork in re­stricting man's access to cultivable land.

It is natural, therefore, to see that concerns are growing about the extent and rate of land degradation in the world and its effects on agricultural productivity and preservation of natural resources. In the past half-century, about 2 billion of the 8.7 billion hectares of agricultural land, permanent pastures, and forest and woodlands have been degraded. Two-thirds of the world's degraded lands are found in Asia and Africa, but human induced degradation is most severe in Africa, where 30 per cent of the agricultural land, pastures, for­ests and woodlands are degraded, followed by Asia (27 per cent) and Latin America (18 per cent). Of the total degraded lands in the world, about 40 per cent are mildly degraded which could be re­stored through good land husbandry measures, while another 45 per cent of moderately degraded land could be restored through signifi­cant on-farm investments. Restoring the remaining 15 per cent of severely degraded lands will be much more costly, involving major engineering investments. Worldwise, about 5 to 10 million hectares of land annually becomes unusable due to severe degradation (IFPRI, 1995, p. 16).

• The process of deforestation has been another area of deep concern. Petty farming and other poor households clear forest land, usually unauthorizedly, to meet immediate food needs. Such clearings accounted for roughly two-thirds of the deforestation in the 1980s. There is no reason to believe that such forest conversions will slow down unless very stringent measures are resorted to (Ibid, p. 17). In recent years, while Latin America had the largest area of forests cleared and converted to other uses, other areas with smaller forest endowments had higher rates of forest conversion and carry heavier risks of completely losing their forest assets. Rates of forest conver-

sion are most rapid, inter-alia, in continental Southeast Asia, averging about 1.5 per cent a year. Deforestation has serious conse­quences, ranging from increased soil and water degradation to greater food insecurity. The food insecurity effects apply all the more to the local poor people who depend on forest products for food, fibre, medicine and income (Ibid, 1995, P. 17).

It is widely believed that land degradation has catastrophic effects on agricultural productivity. Concrete evidence on the magni­tude of these effects is, however, hard to find. Nonetheless, crop productivity losses are reported to be far more significant and wide­spread in hilly areas, dry land cropping areas and irrigated areas under the spell of excess and unregulated use of water and so on {Ibid, p. 16).

The problem of land degradation needs to be tackled both at the individual farm household level and the institutional level. Acordingly, it requires conjunctive initiatives of private and public in­vestments, the former being usually supported by the latter. Private initiative and investment may concentrate on resource conservation and soil improvements (including new cropping patterns, crop rota­tions, farm techniques and practices, regulated irrigation timings and intensity, balanced use of chemical fertilizers, and so on). It is im­perative that incentives, such as partial coverage of costs, should be provided to farmers to undertake initiatives towards restoration of degraded lands; the underlying subsidy on land-improving on-farm in­vestment by individual farmers may better be interpreted as public investment for a social purpose. Such an incentive may spur private investment initiatives; at least the consciousness at the farm house­hold level is heightened and further degradation may stop.

From the above, it may not be concluded that all types of soil conservation initiatives at the farm household level are bound to suc­ceed or have a potential to succeed or throw up favourable benefit-cost ratios, and so on. "Returns to conservation depend on the spe­cific agro-ecological conditions faced, on the technologies used, and on the prices of inputs used and outputs produced" (Lutz-Pagiola-Reiche, 1994,pp.288-89). Moreover, all conservation proposals may not be economically viable to individual farmers, especially where 'very fragile soils' are involved and cropping patterns are "stubbornly likely to be profitable either when they are cheap and simple or when they allow farmers to adopt improved practices" {Ibid, p. 289).

On the other hand, the larger and capital intensive areas of land reclamation and other supportive actions necessarily falls in the do­main of state intervention and public investment. A two-pronged at­tack may be conceived. First, land reclamation schemes such as de-salinization of affected areas, reforestation, contour-bunding, flood control, drainage or developing crop varieties with higher salt toler­ance so that productivity on salinized lands goes up, etc, may be taken up with utmost urgency. Public investment in these areas of national importance must command high priority. In most of these areas neither market nor policy incentives are presently strong enough for farmers or other private agencies to undertake them. "Thus, either the government must make tlie investment or the land will be left to deteriorate further until incentives are right" (IFPRI, 1995, p.34).

Second, the basic causes which are usually responsible for 'un­scientific individual action inviting self-despair' must also be attended to, with equal vehemence. Reproductive health services and family planning, primary education especially among the females, primary health care and nutrition programmes are llie needed initiatives of one kind. No less important is to straighten out issues connected with land rights and security of land leases, employment-income-pov­erty levels since, in many cases, these are the factors that drive many a poor farmer to 'reckless exploitation' of land and water re­sources. Public policy intervention in these areas would clearly have a favourable impact on private initiatives towards land reclamation or soil conservation.

Going by her specific agrarian features, including declining land­man ratios, rural poverty and highly inequitous land distribution, in India too, the land degradation problem has assumed serious dimen­sions in recent years. 'Serious ecological and socio-economic' conse­quences of land degradation are echoed in many official documents and policy pronouncements, especially since the establishment of the National Wastelands Development Board in rnid-1980s (Govt, of In­dia, 1995, p.1. Govt, of India, 1994, pp 1-2). It is important to point out that there is no settled opinion about what constitutes wasteland. No wonder, therefore, estimates of wasteland differ widely, depending on how the word 'waste' is interpreted or played upon, and what potential uses are visualized for the wasteland as such or after it is reclaimed, and so on (Farmer, 1974; Miglani, Bt al. 1988; IFPRI, 1995; Chadha, 1982; Govt, of India, 1993; Govt, of India, 1995).

Again, it is not uncommon to mix up the terms 'wasteland' and "degraded land', and by implication, to tear off the time dimension of the reclaimability issue from the main debate (Govt, of India, 1995A, p.19). We have no desire to enter into the subtleties of individual definitions. It is not needed either since already "too much energy has been spent over the determination of exact extent of wastelands and of their exact locations" {Ibid, p. 19). This is all the more so since the country's readiness to deal with the wasteland problem is still at a rudimentary level. Accordingly, we fully endorse the view that "the need of the hour is not to get lost in such essentially pe­ripheral matters but to start work on the amelioration of sick lands in right earnest on the basis of the knowledge that is already avail­able ..." {Ibid, p.19). In our view, to say that 'nearly half of the total land area is suffering from degradation of some form or the other' is clearly an overestimate of the magnitude of the problem yet we endorse the fact that the problem is grave and that there is an ur­gent need to evolve integrated strategies for wasteland development (Govt, of India, 1995, p.1). A number of schemes are already in hand in at least three central government ministries, and some more may be in the offing. It is highly opportune that NABARD is involv­ing itself in this area of crucial national significance. The present paper is commissioned by NABARD to have a broad view of the problem, the ongoing policies and programmes to tackle the same and possibly to decide then the precise modus operandi of its inter­ventionist policy initiatives.

II Land-use Pattern in India:

National-Level Temporal Profile

Like most other developing economies, Indian agriculture has sus­tained itself on a mixture of 'land extensive' and 'land Intensive' pro­cesses of production. For the first two decades of its development effort since 1951, the Ricardian option of pushing up the 'margin of cultivation' was exploited to the fullest possible extent so that by the closing years of the 1960s, net sown area stood stretched pretty close to its ultimate limit. Nearly two-thirds of India's agricultural out­put during this period was contributed by 'sheer area expansions'. Thereafter, not much fresh area could be brought under plough so that yield and productivity expansions became the major contributor of farm output (Narain, 1977, p.8).

Let us look at the land use pattern a little more systematically. Table 2.1 gives a broad view of what has been happening at the national level for four decades since 1950-51. The steady expansion of 'net sown area' is clearly visible till 1970-71. Since 1970-71, it remained stubbornly constant. This confirms our contention that by the close of the sixties, we were left with very little possibility of bringing fresh area under cultivation. As a matter of fact, even ac­cording to the optimistic projections of the National Commission on Agriculture, net sown area would increase by just 0.15 per cent per year between 2000 and 2050 (Govt, of India. 1976. pp. 150-53). in brief, the option of adding to the net sown area is practically non­existent over large part of Indian agriculture and the expansion of multiple cropped area is now the only enduring option left for 'aug­menting land supply' and agricultural output (Chadha, 1982, p.31). Table 2.1 shows a much better record of cropping intensity during 1970-71/1990-91 than during 1950-51/1970-71 ;double-cropped area expanded by 11.87 million hectares during 1950-51/1970-71 com­pared with 18.24 million hectares during 1970-71/1990-91.

Table 2.1 Land-use Pattern in India since 1950-51

oo

Land-use Description

(Area: Miliion Hectares)

1950-51 1955-56 1960-61 1965-66 1970-71 1975-76 1980-81 1985-86 1990-91

1 8

1. Total Reported Area (According to Village Papers)

2. Area under Forests

3. Area not Available for Cultivation:

(a) Barren and Uncultivable Land;

(b) Land under Non-agricultural Uses

4. Permanent Pastures and Other Grazing Lands

5. Land under Misc. Tree Crops and Groves

6. Culturable Waste Land

7. Fallow Lands;

(a) Current Fallows

(b) Other Fallows

8. Net Sown Area

9. Gross Cropped Area 10. Cropping Intensity 11. Rural Population (Million Persons)

284.32 (100) 40.48

(14.24) 47.52

(16.71) 38.16

(13.42) 9.36

(3.29) 6.67

(2.35) 19.83 (6.98) 22.94 (8.07) 28.13 (9.89) 10.68 (3.76) 17.45 (6.14)

118.75 (41.77) 131.89 111.10 298.60

291.92 (100) 51,34

(17.59) 48.40 16.58) 36.30

(12.43) 12.10 (4.15) 11.47 (3.93) 5.88

(2.01) 21.54 (7.38) 24.12 (8.26) 11.58 (3.97) 12.54 (4.30)

129.16 (44.25) 147.32 114.05 327.9

298.46 (100) 54.05-

(18.11) 50.75

(17.00) 35.91

(12.03) 14.84 (4.97) 13.97 (4.68) 4,46

(1.49) 19.21 (6.44) 22.82 (7,65) 11,64 (3,90) 11,18 (3,75)

133,20 (44,62) 152,77 114,70 360,30

305,54 (100) 61,54 (20,14) 49,50

(16,20) 34,56

(11.32) 14,94 (4,89) 14,81 (4.85) 4.08

(1.33) 16.96 (5.55) 22.44 (7.34) 13.18 (4.31) 9.26 (3.03)

136.20 (44.58) 155.28 114,02 397,80

303,76 (100) 63,92

(21,04) 44,64

(14,70) 28,16 (9,27) 16,48 (5.43) 13.26 (4.36) 4.30

(1.42) 17.50 (5.76) 19.36 (6,37) 10,60 (3,49) 8,76 (2,88)

140,78 (46,35) 165.79 118,95 439,1

304,25 (100) 66,90

(21,98) 39,47

(12.99) 22.13 (7.28) 17.34 (5.70) 12.62 (4.15) 3.98

(1.31) 17.36 (5.71) 21.70 (7.13) 12.46 (4,10) 9,24 (3,03)

142,22 (46,74) 171,00 120,33 480,5

304,17 (100) 67,42

(22,16) 39,62

(13,03) 19,96 (6,56) 19,66 (6,46) 12,01 (3,95) 3,49

(1,15) 16,73 (5,50) 24,63 (8,10) 14.81 (4,87) 9,82

(3,23) 140,27 (46,12) 172,63 123,30 525.5

304.66 (100) 67.04 (22.00) 40.72

(13.37) 20.09 (6.59) 20.63 (6.77) 11.97 (3.93) 3.45

(1.13) 15.69 (5.15) 24.88-(8,18) 14,86 (4,88) 10,02 (3,29)

140,92 (46,25) 178,46 126,66 575,1

10

305,00 (100) 67,96

(22,28) 40.88 (13.40) 21.22 (6.96) 19.66 (6.45) 11.80 (3.87) 3.70

(1.21) 15.00 (4.92) 23.40 (7.67) 13.81 (4.53) 9.59

(3.14) 142.23 (46.63) 185.48 130.40 628.70

Source : 1. Govt. o1 India, Agricultural Statistics at a Glance, Ministry of Agriculture, Deparlmem of Agriculture and Cooperation, New Delhi, March 1994, pp.79-80 2. Govt, of Punjab, Statistical Abstract of Purtjab 1968, Economic and Statistical Organization, Chandigarh, March 1969, pp.48-49. 3. Govt, of India, Statistical Abstract INDIA 1992^ Ministry of Planning, Central Statistical Organization September 1994, pp.47-48. 4. G.K. Chadha, "Future of Land-use Pattern" in Golden Han/ests, A Patriot Survey of Agriculture, New Delhi, May 1982, p.29.

The area under forests as a proportion of total reported area increased sizeably from 14.24 per cent in 1950-51 to 21.04 per cent in 1970-71 whereafter it registered an extremely sluggish expansion, reaching 22.28 per cent only in 1990-91. Perhaps, the recent de­cades have witnessed a queer mixture of fresh afforestation initia­tives simultaneously with denuding of forest covers, partly through 'clandestine cuttings' by the rural people and partly through 'unwhole­some contracts' to forest lessees by state governments, and so on. On the whole, since the early 1970s, about 22.0 per cent of total area has been under forest cover, and it is not very pleasing in terms of the norms recommended by the National Forest Policy of 1988 (Govt, of India, 1995A, p. 65). All the same, the point of real concern is that all forest areas are not alike or equally healthy. It is believed that more than 40.0 per cent of total forest lands have 'poor tree cover' and can roughly be described as 'degraded forest lands' (Govt, of India, 1995A, p. 18).

'Areas not available for cultivation' consists of two components: (1) barren and uncultivable land, and (2) land put to non-agricultural uses. Absolutely barren and uncultivable land like mountains, deserts, etc. which cannot be brought under plough except at a cost that is difficult to imagine under the 'usual resource constraints of the devel­oping economies' or the ambit of the on-going reclamation technolo­gies, goes under (1). Land covered by buildings, roads, railways, waterbodies, etc., or otherwise appropriated for non-agricultural pur­poses, makes up (2) (Govt, of India, 1994. pp. 43. Govt, of Punjab. 1995, p. 139). Table 2.1 clearly shows that the barren and uncultivable land has witnessed a steady decline, nearly consistently from one plan to the other while land put to non-agricultural uses has been rising steadily over time. The latter may better be read along increasing area under forests noted above. It is understand­able that in" an expanding economy, more and more land is needed for building socio-economic infrastructure (industrial, residential and other buildings, roads, railways, irrigation systems, etc.), espe­cially during the phase of rising pace of urbanization or penetration of non-agricultural activities into the countryside (e.g. during the 1980s in India). It seems, a large part of 'expanding area under non-agricultural uses' has been corning through diversions from bar­ren and uncultivable land. In some sense, it is like activating land areas which are otherwise lying 'waste' and are non-reclaimable for agricultural purposes. If this has been happening through public in­vestment,- it must have exercised some check on agricultural lands

being diverted to non-agricultural uses besides promoting forest cover on the one hand and infrastructural expansion on the other.

Permanent pastures and other grazing lands have witnessed a steady increase between 1950-51 and 1964-65 whereafter a gradual decline occurred. Land under miscellaneous tree crops and groves has witnessed an unmistakable decline. The Indian situation clearly shows that 'trees have been cut' and 'groves have been cleared', ostensibly to meet the immediate fuel and fodder needs of the rural poor as also to satisfy the commercial lust of forest lessees, espe­cially in forest-thick, hilly regions. The land cover under tree crops and groves is an important component of 'agricultural health' of rural India and should not receive a casual attention, as in the past.

A redeeming feature of Table 2.1 is a consistent decline in culturable waste land. A decline of this kind may be explained by reclamation of water-logged or marshy or salt-affected lands, by a decreasing incidence of shifting cultivation, and through a reduction of degraded land under pastures and plantation crops, and so on. In spite of the declining trend, nearly 15.00 million hectares are still lying as the culturable wasteland in 1990-91. This constituted a neat 5.0 per cent of total reported area and as much as 10.5 per cent of net sown area. This is clearly the area on wfiich most attention must be fixed if wasteland development for augmenting agricultural land mass is to succeed. Regional variations, especially those at the district-level, must be thrown bare so that the stark gross-roots reali­ties facing certain depressed parts of rural India come up more focussedly for policy interventions. Depending on what the secondary data permit, this is done later in the present study.

As regards fallow lands, current fallows have generally gone up while old fallows have gone down. The decline in old fallows practi­cally stopped around the time that the green revolution arrived in In­dia. If some of the old fallows were activated back into the cultivation cycle, after the green revolution ensued in mid-1960s, it must have kept a check on 'total culturable waste land'. In any case, in a land-scarce rural economy, such as ours, old fallows must be kept under constant vigilance so that climatological stresses do not automatically throw some "old fallows' into the pool of 'culturable waste land'.

It may be worthwhile to look at the incremental expansion/con­traction of area under various land-use categories, decade by de-

10

cade. Table 2.2 looks at this phenomenon in two alternate ways, i.e. by neutralizing the change in total reported area, and by letting the increase in total reported area reflect itself in change under each land-use heading. Both ways, the relative changes show the same tendencies, as indeed they should, except that the expansions get mellowed down, and contractions get slightly played up when adjust­ment for neutralizing the change in reported area is made. We will refer mainly to adjusted figures. A few striking features need to be underlined.

First, during the 1950s, area under forests and net sown area expanded by as much as 11.01 and 8.14 million hectares respec­tively. It is clear that since a part of these two expansions was con­tributed by the steep decline occurring in 'land area under miscella­neous tree crops and groves' whose significance to the total eco­nomic base of rural India was not fully grasped in the first decade of India's planned development era. Some incremental land under plough and forests came through a big decline in 'other fallows' also. Again, thanks to the initial spurt in all-round infrastructural de­velopment, the additional land going to non-agricultural uses was also as big as 4.78 million hectares. Happily, culturable waste land and barren and unculturable land also declined respectively by 4.64 and 3.95 million hectares during the 1950s. In total terms, the 1950s reflected all features of a growing rural economy whose con­cerns for forest cover as also for expanding food production through additional acreage under plough were accommodated, happily by a decline in waste land and unhappily through a steep decline in 'land under miscellaneous tree crops and groves'. The development vision of the time was not thus an unmixed blessing.

11

Table 2.2 Allocation of Incremental Area among Various Land Uses

(Area: Million Hectares)

Land-use Description 1950-51 1960-61 1970-71 1980-81 1950-51 1970-71 1950-51 to to to to to to to

1960-61 1970-71 1980 81 1990-91

S

0,83

1970-71

6

19,44

1990-91

7

1990-91

1 2 3 4

1990-91

S

0,83

1970-71

6

19,44

1990-91

7 8

1. Total Reported Area 14.14 5.3 0.41

1990-91

S

0,83

1970-71

6

19,44 1.24 20.67

(0.00) (0.00) (0.00) (0,00) (0.00) (0.00) (0.00)

2. Area under Forests 13.57 9.87 3,5 0,54 23.44 4.04 27.48 (11.01) (8.75) (3.41) (0,36) (18,35) (3.76) (22.87)

3. Area not Available for 3.23 -6.11 5.02 1.26 2.88 -3.76 -6.64

Cultivation (0.83) (6.89) (-5.07) (1,15) (-5,74) (-3.93) (-9.41)

(a) Barren and -2.25 -7,75 8.2 1,26 -10 -6.94 -16.94

Uncultivable Land (-3.95) (8,25) (8,23) (1,20) ( 1 1,80) (-7.03) (-18.38)

(b) Land under Non- 5.48 1,64 3.18 0 712 3.18 10.3

Agricultural Uses (4.78) (1,35) (3,15) (0,05) (6 07) (3.10) (8.97)

4. Permanent Pastures and 7.3 0,71 1,25 •0,21 6,59 -1.46 5.13

Other Grazing Lands (6.64) (0,94) (1,27) (-0,24) (5 74) (-1.51) (4.33)

5. Land under Misc. Tree -15.37 -0 16 -0.81 0.21 1553 -0.6 -16.13

Crops and Groves (-15.58) (-0,24) (-0,81) (0,20) (-15,81) (-0.62) (-16.38)

6. Culturable Waste Land -3.73 -1,71 -0,77 -1.73 -5.44 -2.5 -7.94

(-4.64) (-202) (-0,79) (-1.77) ( 6.56) (-2.56) (-8.96)

7. Fallow Lands : -5.31 -3.46 5.27 1.23 8 77 4,04 -4,73

(-6.39) (-3.80) (5.24) (-1.29) (-10,01) (3.94) (-6,32)

(a) Current Fallows 0.96 -1.04 4.21 1 0,08 3,21 3.13

(0.41) (-1,22) (4 19) ( 1.04) (0.76) (3 15) (2.19)

(b) Other Fallows -6.27 -2.42 1 06 , 0 23 8 6 9 0.83 -7.86

(-6.80) (-2.58) (1.05) (-0,25) (9,25) (0.79) (-8,51)

8. Net Sown Area 14.45 7 58 0.51 1.96 22 03 1,45 23.48 (8,14) (5 12) (-0,70) (1,57) (13.02) (0.87) (13.84)

9. Area Sown More than 6.43 5,96 7,11 1061 12.39 17.72 30.11 Once

10. Net Irrigated Area 3.81 6.44 7.62 8.71 1025 16.33 26.58

11. Gross Irrigated Area 5.42 10.21 11.59 12 15 63 23.59 39.22

Source: The same as in Table 2,1

12

Second, during the 1960s, the same thrust prevailed except that the draft on 'land under miscellaneous tree crops and groves' was hardly a source to contribute to the expanding 'net sown area' as also to the area under forests.

Third, during the 1970s, increment to 'nel sown area' was hardly in evidence, and additional area under forests was also of a modest magnitude (3.41 million hectares against 8.75 million hectares during the 1960s and 11.01 million hectares during the 1950s). 'Barren and unculturable land' declined sizeably by as much as 8.23 million hect­ares which went partly to 'area under forests' and partly to 'land under non-agricultural uses'.

Fourth, during the 1980s, all increments and decrements were of marginal magnitudes. It seems, the possibility of switch-over from one land-use category to the other had exhausted itself nearly completely during the preceding three decades, and a nearly stable land-use pattern was in operation during the 1980s. The marginal increase in 'area under forests' as well as 'net sown area' and a negligible de­cline in 'old fallow', 'permanent pastures and other grazing lands', 'land under non-agricultural uses', etc. all point to the reality of a stable land-use pattern.

Lastly, an overall view of the preceding four decades reconfirms that (i) 'area under forests', 'net sown area' and 'land under non-agricultural uses' witnessed significant expansion, and (ii) 'barren and unculturable land', 'culturable waste land', 'land under miscellaneous tree crops and groves', 'old fallows', etc. declined sizeably. Among the latter, the steep decline in 'land under miscellaneous tree crops and groves' has been a source of anxiety largely because the dis­tress among the lower strata of the rural society is usually held re­sponsible for 'reckless felling of trees'. On the other hand, the size­able decline in 'barren and unculturable land' as well as 'culturable waste land' has been a healthy development since additional forest cover and 'net sown area' are directly contributed by these sources, respectively.

State-Level Profile

Let us look at the state-level changes. Table 2.3 sets out a few important details, for the period 1960-61 to 1990-91. With the excep­tion of Bihar, and to a much smaller extent. Gujarat,'net sown area'

13

increased, by varying magnitude, in each slate. Tlio increase in 'net sown area' seems to have been directly conlributcd by declining lev­els of 'culturable waste land' practically in each state, except in Gujarat. The formidable decline in Andhra Pradesh, Madhya Pradesh, Rajasthan, Uttar Pradesh, etc., needs to be noted in particular. It also emerges clearly that the decline in 'land under permanent pas­tures and other grazing lands' and /or in 'land under miscellaneous tree crops and groves' paved some way for expansion in 'net sown area'. As a matter of fact, decline in 'cultuiable waste land' seems to have moved, hand in hand, with decline in 'land under permanent pastures and other grazing land' and/or 'land under miscellaneous tree crops and groves'. The coefficient of concurrent deviation be­tween CWL (culturable waste land) and TUL (total uncultivated land) (columns 3 and 4, respectively) is as high as 0.95. In other words, the very distress which prompts people to encroach upon 'land un­der permanent pastures and other grazing land' and/or resort to unimaginative cutting of trees^ or clearing groves, etc. goads them to clear some of the 'cultuj:aDle waste land' for cultivation and allied uses. Reclamation of 'eCilturable waste land' by state authorities is a more likely possibility/but that, in no way, reflects a non-distress situ­ation at the grass-roots level.

14

Table 2.3 Changes in Land-use by State and Use Category

1960-61/1990-91

(Area: Thousand Hectares)

Absolute Change in Area

State N.S.A C.W.L T.U.L T.W.L. F.L. F.A. N-a.L

1 2 3 4 5

-1113

6 7 8

Andhra Pradesh 238 -847 -1239

5

-1113 509 421 477

Assam* 480 -80 -110 -341 -114 -96 146

Bihar -330 -531 -336 •361 582 -809 722

Gujarat -108 1165 925 -754 332 966 699

Haryana 174 -129 -174 -192 -18 170 260

Himachal Pradesh 39 -36 -81 31 -2 -1749 12

Jammu-Kashmir 74 -24 -75 5 -16 -386 8

Karnataka 153 -210 -900 -335 399 365 378

Kerala 323 -48 -262 -101 •58 25 92

Madhya Pradesh 3454 -1635 -3031 -1854 -610 499 465

Maharashtra 63 95 167 17 -562 0 411

Orissa 316 -250 17 -1282 -453 1871 -623

Punjab 468 -175 -194 -264 -201 187 -160

Rajasthan 3265 -1273 -1040 -3641 -1385 2335 395

Tamil Nadu 418 -416 -667 -1 1 42 711 289 525

Uttar Pradesh 111 -606 -694 -2163 535 1368 534

West Bengal 102 -352 -481 -1180 64 -11 469

Note: 1, T.U.L = Total Uncultivated Land (C.W.L -f Land under Permanent Pas­tures and other Grazing Lands + Land under Misc. Tree Crops and Groves); T.W.L = Total Waste Land (C.W.L 4 Barren and Uncultivable Land); F.L. = Fallow Lands;

F.A. = Forest Area; N-a.L = Land under Non-agricultural Uses

2. * is for the period 1970-71 to 1990-91.

Source: Govt, of India, Statistical Abstract, India, tor various years. Govt, of Punjab, Statistical Abstract of f\injab, lor various years. Govt, of India, Indian Agricultural Statistics, Volume I : Summary Tables, Ministry of Food and Agriculture, 1965, pp. 22-25.

15

Total Wasteland

Table 2.3 also shows a sizeable decline in the total of wasteland (TWL, column 5) in each state. The magnitude of the decline in Andhra Pradesh, Madhya Pradesh, Orissa, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal, etc. need to be underlined in particular. Again, in most of the states, the decline in 'culturable waste land' has been accompanied by a decline in 'barren and unculturable land' in as much as the decline in total waste land (TWL) is higher than the decline in one of its components, viz. 'culturable waste land', in most of the states. The coefficient of concurrent deviation between CWL and TWL is as high as 0.80.

The fallow lands throw a mixed picture; they increased in some states and decreased in others. The 'area under forests', however, increased in most of the states, except in hilly states of Himachal Pradesh, Jammu-Kashmir, Assam, and parts of Bihar and West Ben­gal. The declining forest cover in these states is indeed a matter of great national concern; in no way, a sizeable expansion of 'area under forests in some states (e.g. Rajasthan, Uttar Pradesh, Orissa, Gujarat, Andhra Pradesh, etc.) could be a compensation for the de­clining 'forest cover' in many others. The environmental cost of 'un­scientific' or 'unregulated' deforestation does have a specific locale although, in the ultimate analysis, it is the whole nation that bears the consequences of such happenings. The political economy of for­est management is, after all, a national affair.

It is fairly evident from Table 2.3 that culturable wasteland as well as barren and unculturable land witnessed a decline, in varying degree, practically in all states of India, between 1960-61, and 1990-91. The absolute decline is indeed welcome but it may be purpose­ful to see the position in relation to total area or area already under plough or the specific type(s) of land use to which the decline might have been assigned, and so on. Table 2.4 is an exercise of this kind.

Components of Wastelands

In Table 2.4, we deal differently with the two types of wasteland. In the first instance, both culturable wasteland (CWL) and barren and unculturable land (BUL) are seen in relation to total reported area (TRA) (cols. 3 and 6), only to gauge their temporal behaviour

16

in a comparable way. But then, the potential end-use differs between the two. While a decline in CWL may add to area already under plough (net sown area = NSA) or to potentially ploughable area (NSA + Fallows), the decline in BUL cannot, given the limitations of the known land reclamation technologies, serve the same purpose; for all practical purposes, additions to cultivable land is ruled out through this source. The declining levels of BUL may promote some afforestation effort, including social forestry through public sector sup­port, or may pave the way for some kind of non-agricultural uses, and so on. It is, therefore, in the fitness of things that the changing levels of CWL and BUL may be seen in relation to land-use cat­egories to which they can possibly be assigned. NSA alone or in combination with fallows is the natural choice for CWL while forest area and land under non-agricultural uses are the legitimate end-uses for any part of reclaimed land against the existing stock of BUL. Table 2.4 may thus be interpreted in this light.

It is evident that CWL as a proportion of total reported area or net sown area or potentially ploughable area (cols. 3,4 and 5) has witnessed a significant decline, in all states except Gujarat, during the three decades since 1960-61. In spite of the decline, in certain states, CWL still constitutes a fairly big proportion of NSA. In other words, a fairly high proportion of cultivable area is still lost to agriculture, and viewed in terms of ever-declining land: man ratios, inequitable land distribution, absence of buoyant avenues of non-ag­ricultural employment and earnings, etc., this loss of land to agricul­ture is a loss of the most precious national resource. The high inci­dence of this, loss is quite conspicuous in Gujarat, Rajasthan, Himachal Pradesh, Jammu-Kashmir, Madhya Pradesh, Orissa and so on. At the national level, in 1990-91, net sown area could be aug­mented by as much as 11.0 per cent if CWL yvere liquidated. An additional accrual af land under plough to tfiis extent speaks clearly for the latent loss of agricultural] output that Indian economy is suf­fering even at the present stage mi its development.

17

Table 2.4 Culturable Wasteland (CWL) and Barren and Unculturable

Land (BUL) in Major States : 1960-61/1990-91

C.W.L as a BUL as a

Year

% age of % age of

State Year T.F^.A N.S.A N.S.W+ T.R.A Forest Fallows Area -i-

4

15.09

5

11.51

6

8.64

LNAU

1 2 3

5.96

4

15.09

5

11.51

6

8.64

7

Andhra Pradesh 1960-61

3

5.96

4

15.09

5

11.51

6

8.64 30.73 1990-91 2.84 7.08 5 2 4 7.64 24.44

Assam* 1960-61 2.36 8.39 7.33 23.08 63.27 1990-91 1.32 3.84 3.61 19.63 7.35

Bihar 1960-61 5.21 11.24 8 84 4.88 16.42 1990-91 2.15 4.83 3.55 5.86 7.35

Gujarat 1960-61 4.26 8.13 7.52 25.54 344.50 1990-91 10.25 20.77 18.57 14.17 89.11

Haryana 1960-61 3.42 4.41 4.18 4.11 145.97 1990-91 0.48 0.59 0.56 2.22 19.80

Himachal Pradesh* 1970-71 5.71 29.60 26.57 4.22 3.94 1990-91 3.71 21.43 19.44 5.46 14.94

Jammu-Kashmir 1960-61 3.33 24.66 20.88 5.46 7.79 1990-91 3.06 18.88 16.55 6.55 9.71

Karnataka 1960-61 3.49 6.41 5.67 4.91 26.22 1990-91 2.34 4.30 3.68 4.19 18.72

Kerala 1960-61 3.71 7.43 6,96 3.91 11.97 1990-91 2.45 4.23 4 36 1.49 4.21

Madhya Pradesh 1960-61 7.34 19.96 17,56 5.24 14.57 1990-91 3.56 8.07 7.47 4.69 12.44

Maharashtra 1960-61 3.03 5.22 4.60 5.84 29.38 1990-91 3.34 5.73 5.19 5.58 26.33

Orissa 1960-61 5.48 14.14 12.50 9.91 32.45 1990-91 3.84 9.47 9.00 3.21 8.02

Punjab 1960-61 4.18 5.60 5.17 4.15 36.16 1990-91 0.70 0.83 0.81 1.65 13.72

Rajasthan 1960-61 20.21 52.17 37.30 15.23 269.93 1990-91 16.25 33.99 27.67 8.15 72.58

Tamil Nadu 1960-61 5.42 11.77 9 30 7.26 29.90 1990-91 2.23 5.20 3,68 3.91 12.81

Uttar Pradesh 1960-61 5.56 9,54 8.81 8.78 45.40 1990-91 3.47 5.98 5 3 7 3.48 13.60

West Bengal 1960-61 5.06 8.24 7,70 1.37 5.35 1990-91 1.20 1.99 1.83 2.11 6.88

INDIA 1960-61 6.39 14.34 12.26 12.02 51.02 1990-91 4.92 10.56 9.06 6.45 22.04

Note: T.R.A.=

Source: 1.

Total Reporting Area; N.S.A = Net Sown Area; L.N.AU. = Land under Non-agricultural Uses

Govt, of India, Indian Agricultural Statistics, Vol.1, 1961-62. Summary Tables, Dept. of Economics and Statistics, Ministry of Food, Agriculture, Community Development and Cooperation, 1965, pp. 23-25 Govt of Punjab, Statistical Abstract of Punjab, 1973, pp, 56-57, Govt of Punjab, Statistical Abstract of Punjab 1994, pp 144-45.

18

A similar picture holds for EJUL. In 1960 61, in most states, the barren and unculturable land constituted a fairly high proportion of forest area and land under non-agricultural uses put together. The strikingly high proportions for the desert regions, e.g. Gujarat and Rajasthan, need to be noted in particular. But then the BUL declined sizeably in each state, and in relation to forest area and land under non-agricultural uses, the decline was rather formidable in many states. This development is particularly noteworthy for the desert re­gions of Rajasthan and Gujarat, although the progress was quite impressive for many other states as well (e.g. Assam, Haryana, Orissa, Punjab, Uttar Pradesh, etc.). Tlie two exceptions are the hill, and forest-thick states of Himachal Pradesh and Jammu and Kashmir where forest-cover itself fell from 2788 and 2776 thousand hectares in 1970-71 respectively to 1039 and 2747 thousand hectares in 1990-91.

It may be useful to look at the full temporal profile of OWL and BUL. Table 2.5 gives this profile for all major states. There is no denying the fact that the culturable wastoland as also the barren and unculturable wasteland have been declining steadily and in vary­ing proportions, in individual states.

In sum. Tables 2.4 and 2.5 point to the formidable national and state-level efforts towards wasteland development during the three decades between 1960-61 and 1990-91. Yet, much more needs to be done. A fairly high proportion of net sown area could be augmented if only we succeed in reducing culturable wasteland. Likewise, area under forests or land area needed for building non-agricultural assets including socio-economic infrastructure, could be augmented if only we are able to r(?(Juce !:)arren and unculturable land. Naturally, individual, household levul (Mfort needs to be com­bined with state-supported wasteland development programmes. Re­source crunch is a real limitation, and prioritization is inescapable. After all, land reclamation technologies, both for attacking CWL as well as BUL, are highly capital intensive, slow and, in some cases, uncertain in terms of final outcome. Tfie best choice, therefore, is to identify the more seriously afflicted areas, and concentrate our effort on them on a priority basis, somewhat reminiscent of the selective area agricultural development approacli ol tlie late sixties. It is evi­dent, in this context, that we cannot operate willi state-level data since all parts of a given state may not at all be suflering from the wasteland malaise, or suffering equally soveiely, and so on We must come down to district-level picture. This is what we do now in Chapter III.

19

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22

Ill District-level View of Wasteland

As pointed out earlier in Chapter I, tfie form 'wasteland' lends itself to varying interpretations, and accordingly, estimates of waste­land differ widely from source to source. In its most far-stretched in­terpretation, the term can be taken to mean all rural land unoccu­pied or unused by human beings. On tfie other extreme, only the 'culturable wasteland', which is clearly the total of sucfi land area as can possible be brought under plough aflot some land reclamation operation is gone through, is taken as the estimalo for wasteland. In between the two extreme views, many olher porcoplions can be vi­sualized.

To be sure, all 'wasteland' cannot be reclaimed given the ambit of the known or ongoing reclamation tecfinologies, both in India and abroad. Nor can the cost considerations be set aside; perhaps for some reclamation operations, the anticipated cost involves excessively severe burden on the stale exchequer and those cannot, therefore, be taken up for many years to come. And then, it must also be recognized that reclaiming different typos ol wasteland' would serve different purposes. Otearly, reclamation of 'culturable wasteland' can potentially add to frie total stock of land under plough or land under tree crops and groves. On the other hand, a reclamation success, far more costly and time-consuming especially from India's point of view, with 'barren and unculturable land' would not augment the stock of ploughable land area; perhaps, this type of reclaimed land can be used for increasing forest cover or sparing more land for diverse types of non-agricultural activities or for adding to permanent pastures and other grazing lands, and so on. Moreover, reclamation of 'barren and unculturable land' is a possiibilify available only In the long run while 'culturable wasteland' can bo managed even in the short-run. In sum, therefore, clubbing 'culturable wasteland' with 'bar­ren and unculturable land' to arrive at ttie so-called total of waste­land, literally obfuscates the issue of wasteland development; policy perspectives must differentiate between the more real and short-run possibilities of managing 'culturable wasteland' and the less likely and distant possibility of cutting into 'barren and unculturable land'. The present study takes care to keep the two possibilities apart.

For building a state as well as district level profile on wasteland, the land-use classification data, published t)y the Ministry of Agricul-

2:3

ture (MoA), is the best to draw upon. The classification, available on year-to-year basis, 'is according to village papers and is based on returns of area prepared by village staff for revenue purposes in the districts' (Govt, of Punjab, 1995, pp. 138-39). According to this clas­sification, the total area reported in village papers, is split into the following four major categories:

i) Land not available for cultivation, comprising area under forests, r&nd put to non-agricultural uses and barren and uncultivable land;

ii) other uncultivated land, consisting of permanent pastures and other grazing lands, land under miscellaneous tree crops and groves, and culturable wasteland;

iii) fallow land (not included in (ii) above) comprising current fal­lows and other fallows; and

iv) net sown area.

The revenue records at the village-level have their own shortcom­ings, and cannot always be relied upon especially in areas of land holding structure, cropping patterns, common village lands and so on. For our purpose, however, wastelands can clearly be delineated from other land uses firstly under 'barren and uncultivable land' and sec­ondly under 'culturable wasteland'. It would be rather presumptuous to say that the two types of wastelands, delineated above, are free of all blemishes. Perhaps, inter-penetration between 'old fallows' and 'culturable wasteland' is a real possibility. Such a possibility cannot be totally ruled out in the case of 'permanent pastures' and 'land under tree crops and groves', at least for some districts and for some points in time. Again, perhaps for reasons of improvement in measurement techniques, 'barren and unculturable land' may sud­denly drop and its counterpart, viz. 'culturable wasteland' may sud­denly go up. This seems to have happened in Gujarat, for example, where a sudden break in the trend (from 1960-61 to 1969-70) oc­curs in 1970-71, for both categories of wastelands; 'barren and unculturable land' suddenly tell from 4327 thousand hectares in 1969-70 to 3089 thousand hectares in 1970-71, just as 'culturable wasteland' suddenly went up from 498 thousand hectares in 1969-70 to 1966 thousand hectares in 1970-71. Such breaks are discernible for Bihar in 1965-66, Jammu-Kashmir in 1965-66 and 1966-67, for

24

I^aharashtra irs 1970-71, for Orissa in 1969-70 and so on. Fortu­nately, no such break or sudden reversibility is noticed during the 1970s, 1980s, and 1990-92, in any state, and that assures us of the underlying stability in metfiodology of wasteland mea­surement. What is included under each category of land-use is however, a different matter. In particular, the scientific rationale for classifying land as 'wasteland' is not spelt out in official publications; perhaps the 'eye' of the village-level revenue official is the supermost rationale apart from the usual set of guidelines for dealing with dif­ferent land categories. This is precisely the reason that alternate estimates of wasteland, prepared by the National Remote Sensing Agency (NRSA) under the Department of Space, Government of In­dia, differ widely from those prepared by us. More of this follows later in this chapter.

IVIoA Estimates

So, we begin with MoA estimates of wasteland given under two ht adings: 'culturable wasteland' which can make more land available for crop cultivation or, in some cases, add to land under miscella-nec us tree crops and groves, and 'barren and unculturable vvaste-lanc!' which can pep up afforestation activities or make more land avai ^able for non-agricultural uses, and in some cases, add to per-man> ^nt pastuies and other grazing lands. To play it safe, we go by the li itest available estimates averaged over 1989-90 to 1991-92, and for a limited purpose, also those averaged over 1985-86 to 1987-88. For s*)me states, (e.g. Assam), the 1989-92 data are not available; for ttne^Ti, the riearest available information was used.

As many as 400 odd districts are included under MoA estimates. No msfor state is left out. The 60 odd districts which could not be inc lud^ for this set of estimates posed unmanageable data prob­lems. F<)r example, 'culturable wasteland' is clubbed with 'fallow lands' in some regions/districts; district-level data are just not avail­able for some eastern states; old data are constantly repeated for some areas, and so on. It was considered advisable to keep such 'problem districts' out. Jn any case, the 400 odd districts included in our MoA estimates would account for most of the national-level area under each category of land-use, including the two kinds of waste­lands; after all, the total geographical area of the excluded districts is a very small proportion of the corresponding national area.

25

Finally, no attempt is made here to see the changing levels of wasteland over time; only the latest available picture is presented. Accordingly, the number of districts and their related details are gen­erally in accordance with the 1991 population census descriptions.

As pointed out earlier, it is no use seeing the total of wasteland as a proportion of total geographical area of the district, as done by some other studies notably the 1986-88 NRSA estimates (Govt, of India, 1993, pp. 9-18). It is no use seeing it as a proportion of total area reported in local revenue records either. The more advisable procedure is to split the total of wasteland into two parts : (i) waste­land which can potentially be made culturable, through public and private initiatives, and (ii) wasteland which cannot come under culti­vation and can instead be used for non-cultivation purposes notably afforestation and non-agricultural activities, and so on. Clearly, 'culturable wasteland' (CWL) falls under category (i) while 'barren and unculturable wasteland' (BUL) falls under (ii).

Table 3.1 Culturable Wasteland (CWL) Barren and Unculturable Land

(BUL) and Total Wasteland (TWL) In Districts of India : AVERAGE for 1989-92

District CWL CWL1 CWL2 GWL3 BUL BUL1 BUL2 TWL

ANDHRA PRADESH

Srikakulam 10163 3.10 282 2.79 62793 43.28 42.58 72956 Visakhpatnam 13821 4.22 3.78 3.56 168676 30.16 29.93 182497 East Godavari 24987 5.68 4 9 8 4.90 83445 19.18 18.01 108432 West Godavari 37778 8.47 7.75 7.63 46676 26.43 23.37 84453 Vizianagaram 4911 1.49 1 41 1.38 78341 42.03 40.93 83252 Krishna 42858 8.56 7.59 7.51 64992 35.69 32.36 107850 Guntur 52395 8.64 7.83 7.37 38813 12 73 11.74 91209 Prakasam 78531 12.97 9.69 9.57 158387 27.06 24.11 236918 Ncllore 61585 19.37 12.68 12.25 188956 4236 33.59 250542 Kurnool 83341 9.30 7.23 7.22 99375 23.71 23.49 182716 Anantpur 70357 7.17 5.50 5.46 174297 48.97 45.91 244655 Cuddapah 65669 16.99 12.78 12.33 243920 36.16 35.06 309589 Ctiittoor 48399 10.07 7.76 7.44 166038 27.98 26.18 214437 Hyderabad 0 0.00 0.00 0.00 720 3.58 3,57 720 NIzamabad 9299 3.34 2.05 2.03 55038 22.20 19.40 64336 Mcdak 26124 6.34 3.97 3.91 56235 35.65 27.82 82359 Mahabubnagar 14005 1.55 1.01 1.00 29731 7.88 7.32 43736 Rangareddy 27809 9.22 5.86 5.76 40550 27.01 20.03 68359 Nalgonda 23260 3.96 2.27 2.26 90592 49.23 32.67 113851 Warangal 21986 5.02 3.04 3.01 56512 13,30 11.88 78499 Ktiammam 19119 4.07 3.52 3.40 78175 8.94 8,49 97294 Karimnagar 19995 4.56 2.86 2.83 83300 25.84 21,98 103295 Adilabad 23097 4.01 3.10 3.06 38907 5.14 4.84 62005

ANDHRA PRADESH 779321 7.05 5 2 4 5.15 2105468 24.54 22,35 2884789

26

BIHAR

Patna 1216 0.59 0.51 0.01 13475 21.14 20.91 14691 Nalanda 638 0.35 0.34 0.00 1218 2.70 2.70 1856 Gaya 4380 1.92 1.34 0.01 27664 21.36 21.01 32044 Nawada 2110 1.70 1.51 0.02 11304 12.02 11.87 13414 Aurangabad 1464 0.74 0.58 0.01 16440 28.51 28.13 17904 Shahabad 1137 0.36 0.33 0.00 9183 19.25 19.09 10320 Rohtas 4777 1.19 1.09 0.01 36241 14.37 14.34 41018 Saran 324 0.18 0,15 0.00 18086 70.32 69.46 18410 Siwan 288 0.16 0.16 0.00 8882 33.58 33.35 9170 Gopalganj 1637 1.05 1.02 0.01 5577 19.60 19.44 7214 Champaran East 708 0.23 0.21 0.00 8206 12.16 11.98 8914 Champaran West 4974 1.94 1.77 0.02 3025 1.76 1.74 7999 Madhepura 65 0.05 0.05 0.00 3953 13.37 13.36 4018 Godda 7467 9.03 4.71 0.05 9710 21.15 18 24 17177 Deoghar 16947 39.62 20.14 0.18 12446 100.00 87.10 29393 Sahebganj 14995 21.10 11.13 0.11 27190 100.00 92.43 42185 Lohardaga 4891 9.60 5.77 0.06 9401 17.55 17.45 14292 Muzaffarpur 240 0.11 0.10 0.00 5301 9.52 9.47 5541 Vaishali 355 0.30 0.27 0.00 24265 69.39 68.57 24620 Sitamarhi 224 0.13 0,12 0.00 2237 4.01 3.96 2461 Darbhanga 429 0.29 0.25 0.00 1977 4.07 4.00 2406 Madhubani 476 0.21 0.19 0.00 2382 3.05 2.98 2858 Samastipur 446 0.23 0.22 0.00 4520 7.80 7.79 4966 Munger 13250 5.05 3.73 0.04 48415 22.88 22.71 61665 Begusarai 619 0.50 0.47 0.00 18133 50.61 50.50 18752 Bhagalpur 14608 5.43 4.39 0.04 65810 47.80 46.59 80418 Saharsa 4512 1.85 1.62 0.02 34000 46.41 45,24 38512 Purnea 2356 0.79 0.65 0.01 17932 26,16 25.78 20288 K^ ihar 819 0.51 0.41 0.00 22289 37.07 36.90 23108 RanoNi 27406 10.36 5.81 0.06 39519 18.52 18.39 66925 Hazari ;Bagh 21672 11.52 5.88 0.06 88821 14.59 14.32 110493 Gifidih 27011 21.05 9.07 0.09 53135 18.71 17.70 80146 iRalamu 28332 12.83 5.24 0.05 83634 13.77 13.63 111966 Dhanbad 17495 27.62 13.03 0.13 42837 42.90 4 2 3 8 60332 Gumla 40810 14.43 7,00 0.07 59466 27.60 27.40 100276 Singhbhum East 49002 26.02 14.07 0.14 66112 19.04 18.79 115114 SInghbhum West 41221 17.41 12.99 0.13 71112 19.88 19.60 112333 Kishanganj 1817 1.58 1.33 0.01 11395 33.59 32.97 13212 Araria 1066 0.59 0.50 0.00 5087 12.59 12.41 6153

BIHAR 387792 5.03 3.70 0.04 1015630 20.15 19.66 1403422

GUJRAT

Ahmedabad 22667 4.38 3.51 0.04 71833 91,01 64.27 94500

Banaskantha 24267 3.06 2.74 0.03 35733 16,41 12.43 60000 Vadodara 9400 1.74 1.70 0 0 2 28133 18,31 15.03 37533 Broach 39767 9.38 8,86 0.09 21267 8.64 7.86 61033 Bulsar • 19900 6.50 6,43 0.06 15800 9.78 9.33 35700 Gadhinagar 1500 3.21 3.12 0.03 233 1.87 1.51 1733 Kaira 2400 0.47 0.45 0.00 32667 33.98 26.42 35067 Mehsana 17700 2.73 2.41 0.02 11400 13 70 8.24 29100 Panchmahals 13233 2.83 2.52 0.03 37833 13.36 12.18 51067 Sabarkantha 14067 3.33 2.95 0.03 36100 21 46 17 83 50167 Surat 22800 5.44 5.19 0.05 61000 27.16 24.52 83800 Amreli 9767 1.97 1.88 0.02 17033 21.55 13.67 26800 Bhavnagar 35000 5.59 5.24 0.05 101033 98.06 57.93 136033

27

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Balaghat 25117 9.12 8.26 8.25 28952 5.60 5.12 54070 Chhindwara 21458 4.41 3.84 3.84 48058 9.63 8.64 69516 Narslmhapur 20507 7.05 6.75 6.75 1027 0.65 0.55 21534 Seoni 31685 8.67 7.27 7.27 5256 1.43 1.32 36941 Mandia 33359 7.82 6.02 6.02 58265 9.10 8.57 91624 Sagar 12160 2.35 2.24 2.24 16483 4.89 3.64 28642 Damoh 30349 10.69 9.95 9.92 54789 18.54 16.23 85138 Panna 69320 31.02 26.75 26.75 57043 18.76 17.97 126362 Chhattarpur 102764 29.53 23.85 23.83 103723 78.36 46.02 206487 Tikamgarh 21906 9.21 7.92 7.91 49220 47.50 31.56 71126 Rewa 10635 2.97 2.57 2.56 41204 33.94 25.63 51338 Sidhi 57135 16.28 13.56 13.56 56398 11.18 11.18 113533 Satna 47344 13.27 11.96 11.83 84185 44.24 39.95 131529 Shahdol 62296 13.63 10.36 10,34 39271 6.13 5.76 101567 Indore 4883 1.88 1.84 1,84 2892 3.58 2.62 7776 Ratlam 27311 8.62 8.48 8,47 42661 68.60 44.80 69973 Ujjain 15019 3.23 3.18 3,18 6767 15.33 5.76 21786 Mandsaur 41760 7.73 7.64 7,64 378434 180.96 143.83 420194 Dewas 3420 0.95 0.94 0,94 14055 5.95 4.42 17475 Dhar 21101 4.21 4.14 4,14 51389 28.28 21.68 72490 Jhabua 18132 5.08 4.92 4,92 82165 49.84 39.69 100298 Morena 62679 15.61 14.54 14,54 208443 52.42 44.73 271122 Bhind 10317 3.09 3.00 3,00 27152 68.59 42.66 37469 Gwalior 25289 9.86 9.13 9,13 53813 37.40 32.47 79102 Shivpuri 111846 29.84 25.80 25,08 51684 14.73 12.67 163530 Guna 92471 15.11 14.50 14,50 100230 47.82 37.45 192701 Datia 13408 10.29 9.67 9,66 3105 7,43 6.45 16513 Bhopal 6889 4.36 4.25 4,23 3697 5,73 3.80 10587 Sehore 13813 3.75 3.70 3,70 6893 3,36 2.63 20706 Raisen 18240 4,35 4.28 4.28 3591 0.97 0.90 21831 Vidisha 13586 2.61 2.58 2.57 11104 7.92 6.24 24690 BetuI 45940 11.22 9.79 9.79 25461 5.77 5,45 71401 Rajgarh 23952 5.87 5.76 5.76 29918 60.04 21,62 53870 Shahjapur 22389 5.20 5.14 5.14 48409 103.16 43.37 70798 Hoshangabad 36917 8.13 7.71 7.66 36148 9.27 8.16 73065 Khargone (W.Nimar] 30420 4.80 4.70 4.70 59996 11.64 9.82 90416 Khandwa (E.Nimar) 1545 0.35 0.33 0.33 14499 2.57 2.29 16044

MADHYA PRADESH 1582674 8.15 7.48 7.44 2102695 12.67 10.87 3685368

MAHARASHTRA

Thane 40800 15.24 14.10 0.14 81500 18.99 15.79 122300 Raigarh (Kolaba) 38100 20.59 15.55 0.14 128400 61.06 51.24 166500 Ratnagiri 147500 63.30 36.94 0.36 230300 1218.52 713.00 377800 Nasik 19600 2.17 1.99 0.02 181300 51.27 48.05 200900 Dhule 5300 0.72 0,70 0.01 56000 9.80 9.04 61300 Jalgaon 6100 0.74 0.73 0.01 80800 44.86 33.82 86900 Ahmednagar 12900 1.13 0.98 0.01 140000 74.63 61.14 152900 Pune 38300 3.78 3.62 0.04 157200 68.71 50.96 195500 Satara 40600 6.99 6.34 0.06 119400 68.62 47.49 160000 Sangli 6300 1.07 0.92 0.01 42900 45.35 38.00 49200 Solapur 38900 3.48 3.09 0.03 60400 143.47 48.83 99300 Kojhapur 47800 11.83 10.47 0.10 42900 23.51 19.12 90700 Aurangabad 15200 2.14 1.92 0.02 21900 16.77 12.52 37100 Parbhani 36900 4.36 3.92 0.04 17700 27.40 18.17 54600 Beed 48200 5.86 5.16 0.05 25500 37.06 23.65 73700 Nanded 32100 4.43 4.05 0.04 12000 9.97 6.27 44100

30

Osmanabad 43100 7.56 6.39 0.06 3200 32.65 11.72 46300 Buldana 14500 2.11 2.02 0.02 26200 16.25 12.72 40700 Akola 9200 1.14 1,10 0.01 22600 18.59 12.31 31800 Amravati 12300 1.70 1.64 0.02 18900 5.11 4.36 31200 Yeotmal 25100 2.95 2.77 0.03 39200 12.74 10.67 64300 Wardha 14300 3.67 3.21 0.03 6000 5.25 3.87 20300 Nag pur 16100 2.93 2.74 0.03 13100 4.97 3.57 29200 Bhandra 26600 7.37 7.06 0.07 17700 4.92 3.55 44300 Chandrapur 35400 7.47 7.15 0.07 27800 5.91 5.30 63200 Sindhudurg 168200 155.60 99.47 0.93 99900 182,63 182.30. 268100 Jalna 25500 4.11 3.77 0.04 7600 27.84 14.10 33ioa Latur 41500 8.36 7.53 0 0 7 15200 71.70 32.76 56700 Hadchiroli 19400 10.16 9.16 0.09 13800 1.19 1.11 33200

MAHARASTRA 1028500 5.73 5.20 0.05 1716700 26.33 21.35 2745200

MEGHALAYA

East Khasi Hills 135424 329.75 196.32 116.72 51958 24.17 24.17 187382 West Khasi Hills 110004 433.76 119,70 83.35 56625 25.04 25.04 166629 Jalntia Hills 121152 372.50 206.60 159.29 15176 8.99 8.99 136327 East Garo Hills 44697 155.18 75.46 54.28 5030 3.92 3.92 49727 West Garo Hills 81256 108,61 53,91 46.48 13015 4.56 4.56 94270

MEGHALAYA 492532 243.14 114.68 84.74 141802 13.86 13.86 634334

NAGALAND

Kohima 41302 113.91 56.41 36.51 0 0.00 0.00 41302 Phek 8222 26.94 13.99 11.21 0 0.00 0.00 8222 Wokha 11910 47.87 21.40 16.94 0 0.00 0.00 11910 Zunheboto 9087 48.26 21.28 16 43 0 0.00 0.00 9087 Mokokehung 5753 23.50 10.53 8.46 0 0.00 0.00 5753 Tuensang 13267 38.42 17.28 12.72 0 0.00 0.00 13267 Mon 6803 33.11 12.55 11.58 0 0.00 0.00 6803

NAGALAND 96344 51.14 23.25 17.79 0 0.00 0.00 96344

ORISSA

Balasore 20000 4.37 4.25 4.07 24000 31.17 23.30 44000 Bolangir 32000 6,83 6.44 5.77 33333 16.29 12.69 65333 Cuttak 35000 5.14 4.96 4 7 8 10000 3.65 3.20 45000 Dhenkanal 6000 1.32 1.28 1,22 10000 1.85 1.71 16000 Ganjam 5000 1.04 1.03 0.98 5000 0.75 0.71 10000 Kalahandi 6000 1.07 1.04 1.02 3000 0.56 0.53 9000 Keonjhar . 47333 15.42 14.81 10.86 27000 9.54 8.41 74333 Koraput 196000 25.79 22.82 17.93 205000 19.56 16.97 401000 Mayurbhanj 6000 1.39 1.34 1.30 18000 3.42 3.24 24000 Phulbani 55000 20.81 17.74 14.59 54000 9.29 8.74 109000 Purl 33567 7.40 6.98 6.62 43000 10.89 9.33 76667 Sambalpur 106333 15.93 15.26 13.02 54000 7.98 7.00 160333 Sundargarh 27000 8.20 7.67 6.14 11000 2.56 2.20 38000

ORISSA 562000 8.89 8.42 7.46 499000 8.02 7.18 1061000

PUNJAB

Hoshiarpur 774 0.30 0.30 0.30 10024 8.42 8.33 10799 Jalandhar 5089 1.71 1.68 1.68 11607 52.42 52.42 16696 Ludhiana 0 0,00 0.00 0.00 42 0.08 0.08 42

31

Ferozepur 12688 2.51 2.42 2.41 245 0.56 0.53 12933 Amritsar 3129 0.72 0.72 0.72 4181 6.70 6.70 7310 Gurdaspur 4961 1.76 1.76 1.76 11816 23.48 23.48 16777 Kapurthala 1865 1.27 1.20 1.20 2680 49.62 42.98 4545 Bathinda 658 0.13 0.13 0.13 1220 2.65 2.61 1878 Patiala 0 0.00 0.00 0.00 8940 15.82 15.82 8940 Sangrur 899 0.20 0.19 0.19 1285 2.79 2.78 2184 Ropar 5754 4.95 4.43 4.43 16690 28.83 27,77 22444 Faridkot 0 0.00 0.00 0.00 8038 20.04 20.04 8038

PUNJAB 35818 0.85 0.83 0.83 76769 12.75 12.59 112587

RAJASTHAN

Ajmer 70059 17.74 13.96 13.96 98206 107.93 57.03 168265 Alwar 12560 2.53 2.32 2.32 112729 150.91 114.10 125289 Banswara 18605 8.30 6.97 6.96 81756 73.99 58.85 100360 Barmer 270555 18.20 12.80 12.79 127800 132.78 42.00 398355 Bharatpur 3639 0.94 0.89 0.89 28813 51.89 44.67 32452 Bhilwara 180688 50.14 39.65 39.62 165135 139.10 69.25 345823 Bikaner 1059363 125.66 82.32 82.19 40935 15.86 11.58 1100298 Bundj 37841 16.14 13.12 13.11 58369 33.88 29.76 96210 Chittorgarh 195449 51.77 46.29 46.27 145250 75.07 53.27 340700 Churu 24064 1.80 1.57 1.57 736 0.94 0.59 24800 Dholpur 14464 10.15 8.71 8.66 G9805 228.00 140.99 84269 Dungarpur 25497 20.28 15.79 15.55 79862 104.36 68.70 105360 Ganganagar 79550 5.52 4.49 4.48 5637 3.01 2.81 85187 Jaipur 58660 7.20 5.97 5.96 95261 61.26 36.46 153921 Jaisalmer 2908225 1279,51 812.89 809.51 363699 358.38 174.05 3271924 Jalore 25552 4.00 3.04 3.03 88172 153.92 87.49 113725 Jhalawar 66719 22.31 19.98 19.90 38882 27.87 20.32 105601 Jhunjhunu 6061 1.40 1.29 1.29 17042 29.85 17.39 23104 Jodhpur 71264 6.05 3.89 3,89 145856 170.54 70.31 217120 Kota 60548 10.98 9.41 9,40 89702 23.92 20.99 150249 Nagaur 15183 1.21 1.00 1,00 62382 65.63 37.00 77565 Pali 42590 7.05 5.22 5.22 149095 110.89 66.30 191685 S.Madhopur 29587 6.20 5.30 5.29 108066 36.43 30.38 137653 Sikar 11096 2.14 1.81 1.81 21572 25.74 16.82 32668 Sirohi 11276 7.13 5.19 5.19 79330 44.93 37.78 90606 Tonk 44801 9.95 8.65 8 64 34533 58.00 28.60 79334 Udaipur 241938 67.25 52.62 52.53 482600 84.32 66.03 724538

RAJASTHAN 5585297 35.30 27.80 27.77 2787546 70.87 48.34 8372844

TAMIL NADU

Chengalpattu 19499 6.47 4.42 4.17 29451 12.11 10.97 48950 South Arcot 26811 4.73 3.84 3.59 89695 40.41 39.42 116506 North Arcot 7848 3.62 2,53 2.51 26359 10.94 10.77 34207 Salem 19426 4.45 3.75 3.70 67851 28.44 26.97 87277 Dharmapuri 16667 4.04 3.59 3.53 44707 10.78 10.45 61373 Coimbatore 2371 0.73 0.49 0.49 10851 4.50 4.46 13223 Periyar 966 0.30 0.19 0.19 7337 2.45 2.43 8303 TIruchirapalli 34517 6.33 4.47 4,38 32165 13.87 12.91 66682 Thanjavur 19265 3.72 3.30 3.24 33310 19.53 19.14 52575 Madural 9027 2.96 2.29 2.28 34952 14.80 14.77 43979 Ramanathapuram 5764 2.73 1.79 1.77 4961 5.78 5.72 10725 Tirunelveli 52861 34.25 14.54 14.17 25840 11.79 11.24 78701 Nilgiris 2857 4.00 3.18 3.12 2711 1.78 1.73 5568

32

Kanyakumari 208 0.25 0.24 0.24 3336 4,36 4.35 3543 Pudukottai 14295 9.53 5.27 4.93 9863 6,73 6.51 24158 Dindigul-Anna 7639 2.96 2.10 2.04 36597 18,28 17.67 44237 Kamrajhar 4890 2.54 1.55 1,54 4467 4,57 4,52 9357 Pas.Muthuram.Thevar 12994 10.08 5.33 5.20 4077 3,12 3.09 17071 Chidambarnar 19866 8.59 6.37 5.94 19944 25.06 23,56 39810 Tiruvannamalai 12568 5.40 3.69 3.59 21813 8.97 8,85 34381

TAMIL NADU 294093 5.20 3.73 3.63 510163 12.79 12,41 804256

JTTAR PRADESH

Dehradun 12818 24.32 21.53 20.09 1684 0.71 0,71 14502 Saharanpur 2440 0.89 0.87 0.87 3440 3.21 3,20 5880 Muzaffarnagar 3964 1.23 1.19 1.18 8282 12.07 11,96 12246 Meerut 4514 1.45 1.39 1.39 6151 10,97 10,90 10665 Bulandsahar 11406 3.34 3.18 3.16 14413 29,99 29,07 25818 Aligarh 9830 2.51 2.36 2,35 27504 62.75 59,16 37333 Mathura 7102 2.31 2.17 2.16 6105 18.29 17,36 13206 Agra 6402 2.26 2.07 2.06 11935 17.51 17,22 18336 Mainpuri 11659 6.95 5.33 5.28 19934 109.16 100.33 31593 Etah 38976 12.80 11.13 11.04 10109 25.21 24,56 49085 Bareilly 3782 1.15 1.10 1.09 12564 28.48 28,21 16346 Bijnor 4505 1.32 1.22 1.21 8919 9.13 9,07 13424 Budaun 5999 1.50 1.36 1.33 13881 28.76 28,11 19880 Moradabad 7023 1.44 1.38 1.36 12209 20.62 20,35 19232 Ghaziabad 7671 4.16 3.80 3.78 8854 22.51 22,22 16524 Lalitpur 68083 55.94 42.69 42.31 17791 18,10 17,07 85873 Shahjahanpur 6637 1.89 1.73 1.71 9993 19.84 19,35 16630 Pilibhit 6570 3.00 2.89 2.86 4569 4.20 4,19 11139 Rampur 1004 0.52 0.51 0.51 6736 22.20 22,19 7740 Farrukhabad 18542 6.53 5.65 5.50 22811 49.08 45,95 41353 Etawah 10262 3.55 3.17 3.15 24139 32.48 31,54 34400 Kanpur 26120 4.54 4.05 3.97 71556 77.40 72,81 97675 Fatehpur 13010 4.38 3.85 3.80 12903 25.94 24,60 25913 Allahabad 27287 5.78 4.93 4.80 31868 31,63 30,94 59155 Jhansi 50352 16.49 14.66 14.57 31041 43,59 43,11 81893 Jalaun 5426 1.56 1.45 1.44 14090 24,05 24.01 19516 Hamirpur 23961 4.59 4.14 4.13 22174 25,09 24.75 46134 Banda 33936 6.60 5.88 5.79 37133 30.15 30.04 71069 Varanasi 29359 9.21 8.37 8.22 1)496 8.57 8.55 40855 MIrzapur 35670 11.98 9.51 9.17 31955 7.93 7.91 67625 Jaunpur 8585 2.96 2.57 2 5 3 8024 18.81 18.17 16609 Ghazipur 5261 1.99 1.87 1.85 6874 19,48 18.89 12135 Ballla 2614 1.18 1.09 1.06 13951 39.06 38,84 16564 Gorakhpur 4512 1.27 1.20 1 13 4602 5,16 5,14 9114 Sidarthnagar 3628 1.44 1.40 1.38 2552 8,07 7,83 6180 Harldwar 2729 2.26 2,16 2.14 2280 4,30 4.29 5009 Firozabad 4810 2.78 2.52 2.50 12721 49,23 47,97 17531 Deoria 6161 1.46 1.38 1.36 12318 18,91 18,83 18479 BastI 11452 2.55 2.40 2.34 6114 8,83 8,68 17565 Azamgarh 8516 2.80 2.46 2.41 9555 19,69 18.99 18071 Nainital 28464 13.75 13.12 12.23 4449 1,02 1.01 32913 Lucknow 11048 7.37 5.92 5.76 10713 30,56 27.87 21761 Unnao 17250 6.04 4.82 4.73 17468 31,45 29.54 34718 Rae-Bareli 22584 8.24 6,73 6.33 24620 48,68 45.17 47204 Sitapur 9859 2.37 2.04 2.01 7821 12,49 12.36 17680 Hardoi 23215 5.78 4.79 4.67 20577 39,03 35.61 43792 Kheri 9166 1.98 1.81 1.78 6125 2 5 8 2.57 15291

33

Faizabad 12289 4.18 3.63 3.47 9230 14.37 13.92 21519 Gonda 11570 2.30 2.09 2.04 8336 5.66 5.60 19905 Bahraich 8541 1.87 1.76 1.72 8115 4.67 4.65 16656 Sultanpur 14278 5.02 4.10 4.01 16649 33.45 32.00 30927 Pratapgarh 9010 4.07 3.18 2.99 10796 26.97 26.42 19806 Bara-BankI 12918 4.50 3.72 3.61 10860 16.93 16.40 23778 Almora 58699 51.15 47.71 34.86 31396 7.70 6.72 90095 Garhwal 43493 46.36 39.03 25 32 33550 7.19 6.59 77043 Tehri-Garhwal 72216 101.92 91.14 91.12 12122 2.97 2.95 84338 Pithoragarh 52009 64.42 55.35 38.09 25017 7.25 5.91 77025 Chamoli 32771 65.76 63.63 37.99 162081 29.72 28.59 194852 Uttar Kashi 8929 28.94 25.73 21.16 19776 2.76 2.71 28705 Sonbhadra 33465 18.56 14.56 14.05 33262 8.83 8.82 66727 Mau 2953 2.26 2.11 2.06 2815 12.67 12.55 5768

UTTAR PRADESH 1045153 6.05 5.42 5.27 1032218 13.59 13.08 2077371

WEST BENGAL

24-Parganas 2262 0.36 0.34 0.33 6970 0.90 0.90 9232 Nadia 3853 1.25 1.20 1.19 174 0,27 0.27 4027 Murshidabad 6010 1.45 1.45 1.44 828 0.73 0.73 6838 Barddhaman 19784 4.40 4.09 4,08 4180 2.19 2.19 23964 Birbhum 10544 3.31 3.09 3.07 4917 5.36 5,31 15461 Bankura 18603 4.88 4.35 4.34 6494 2.81 2.80 25097 Medinipur 13422 1.57 l!48 1.46 20294 4.98 4.97 33716 Hugh 8598 3.89 3.76 3.73 1023 1.37 1.37 9621 Haora 293 0.36 0.30 0.30 298 0.65 0.65 591 Jalpaiguri 3595 1.20 1.19 1.17 13642 4.67 4.67 17237 Darjeeling 821 0.61 0.56 0.55 111552 178.38 173.71 112373 Maldah 2891 1.13 0.99 0.98 1327 2.20 2.19 4218 West Dinajpur 2730 0.64 0.59 0.59 1410 2.13 2.12 4140 Kochbihar 3030 1.19 1.18 1.15 5309 7.73 7.70 8339 Puruliya 15622 4.93 3.73 3.72 8520 4.77 4.75 24142

WEST BENGAL 112055 2.10 1.94 1.93 186938 6.87 6.85 298993

OTHER STATES & UTs

Goa, Daman & DIu 87545 62.92 62.92 0.62 15782 12.55 12.41 103327 Manipur 0 0.00 0.00 0.00 1418600 225.60 225.60 1418600 Tripura 697 0.26 0.25 0.00 0 697 Arunachal Pradesh 0 0.00 0.00 0.00 48129 0.92 0.92 48129 A & N Islands 4270 11.62 10.40 0.06 1931 0.27 0.27 6201 Dadra, Nagar Hawal 237 0.97 0.96 0.01 84 0.37 0.35 321 Pondicherry 1783 6.65 5.65 0.05 80 0.57 0.57 1863

Note: 1. In Nagaland districts, the high values of CWL1 (and CWL2/CWL3) and zero-values for BUL1 (and BUL2) are primarily because in MoA published data, separate figures for 'culturat)le wasteland and 'barren and unculturable land' are not given; the total for the two is given under the former. Hence the result. The result essentially refelects the position for the total wasteland instead of its two components. The reverse seems to hold for Manipur, Tripura and Arunachal Pradesh.

Source: 1. Government of India, Indian Agricultural Statistics, Ministry of Agriculture and Rural Development.

Vol.ll-Districtwise for various years.

2. Data for 1991-92 and 1990-91 obtained personally from the Ministry of Agriculture, Economic Adviser's office.

34

So as to see the benefits that CWL and BUL can bestow on land endowment of the district, both are expressed in relation to the landuse categories to which they can possibly be assigned. The possible uses of CWL are: net sown area (NSA), fallow lands (FL) and land devoted to miscellaneous tree crops and groves, perhaps for the whole lot of districts in India in that very order. Similarly, the possible uses for BUL could be ; area under forests (FL), area un­der non-agricultural uses (NAU) and permanent pastures and other grazing lands. It is difficult to say that the same order of Importance would operate for all districts but it is pretty sure that increasing area under forests and expansion of non-agricultural activities would assume much higher significance than increasing pastures and graz­ing lands, and so on. All these ideas are articulated in our 1989-92 district-level estimates of eulturabte (GWL) as well as unculturable (BUL) wasteland, based on MoA data and as set out in Table 3.1.

To set the record straight, absolute figures are given for 'culturable wasteland' (CWL), 'barren and unculturable wasteland' (BUL) and for 'total wasteland' (TWL) in Table 3.1, so that the seri­ously affected districts more really catch the eye. Nevertheless, the comparable picture is presented in terms of three versions of culturable wasteland and two of unculturable wasteland. They are as follows:

Culturable Wasteland

1) CWL1 = Culturable wasteland as a percentage of net sown area;

2) CWL2 = Culturable wasteland as a percentage of total of

i) net sown area and

ii) fallow lands;

3) CWL4 = Culturable wasteland as a percentage of the total of

i) net sown area,

ii) fallow lands, and

iii) land under miscellaneous tree crops and groves.

35

Unculturable Wasteland

1) BUL1 = Barren and unculturable land as a percentage of the total of area under

i) forests and ii) non-agricultural uses;

2) BUL2 = Barren and unculturable land as a percentage of the total or area under

i) forests, ii) non-agricultural uses, and iii) permanent pastures and other grazing lands.

For reasons outlined above, among the three versions of CWL, CWL1 is of extreme significance. Similarly for BUL, BUL1 is far more important than BUL2. Most of our discussion on Table 3.1 naturally, therefore, revolves around CWL1 and BULL Let us first examine the picture on culturable wasteland (CWL).

It is evident that in quite a large number of districts, culturable wasteland constitutes a fairly big proportion of net sown area. In other words, wasteland area that can possibly become cultivable makes a fairly big proportion of what is already under cultivation. For example, this proportion is about as high as 20.0 percent or more in districts Deoghar, Giridih, Dhanbad, Singhbumn, (East) and Sahebganj of Bihar; Kutch of Gujarat; Leh, Kargil, Doda, Udhampur, Jammu, Kathua, Rajouri and Poonch of JammDakshu-Kashmir; Kangra, Kinnaur, Lahaul-Spiti, Sirmaur, Hamirpur, Solan and Una of Himachal Pradesh; Daksh in Kannad of Karnataka; Bastar, Panna, Chhattarpur and Shivpur of Madhya Pradesh; Raigarh, Ratnagiri and Sindhudurg of Maharashtra; all districts of Meghalaya and Nagaland(see note 1 below Table 3.1); Koraput and Phulbani of Orissa; Bhilwara, Bikaner, Chittorgarh, Dungarpur, Jaiselmer, Jhalawar and Udaipur of Rajasthan; Tirunelveli of Tamil Nadu; Dehradun, Lalitpur, Almora, Garhwal, Tehri-Garhwal, Pittoragarh, Chamoli, Uttar Kashi and Sonbhadra of Uttar Pradesh; in Goa, Daman and Diu, and so on. The total number of districts suffering from such a high level of wasteland malaise is around 60. If the list is extended to those districts where culturable wasteland makes 10.0 per cent or more of net sown area, their number goes up to 110, nearly 30.0 per cent of the 400 odd districts, considered by us. By all reckon-

36

ing, this is a high proportion. The statewise description of such dis­tricts is given in Table 3.2.

Table 3.2 clearly shows that about three-fourth of the total culturable wasteland in India is to be found in these 110 districts, it may be conceded that the problem may be equally serious among some of the 60 odd districts that could not be included in our MoA estimates, primarily due to lack of data availability. Their inclusion would push up the number of affected districts but the proportion of culturable wasteland would not change much since the total geo­graphical area of the excluded districts makes a negligible proportion of India's geographical area. In other words, the remaining 70 per­cent of districts together have only about 25.0 percent of India's culturable wastelands. All policy attention must necessarily be fixed on tliese districts.

Secondly, among some of the states, the affected districts suffer a very high degree of wasteland malaise. For example, all the dis­tricts of Meghalaya and Nagaland suffer heavily (for Nagaland, see note 1 below Table 3.1); most of the districts in Himachal Pradesh and Jammu-Kashmir suffer the same fate; 86.0 per cent of culturable wasteland in 18 districts of Gujarat is located in 2 districts alone; nearly 93.0 per cent of the culturable wasteland in 27 districts of Rajasthan is concentrated in 13 districts; 14 of the 45 districts in Madhya Pradesh have more than 60.0 per cent; 11 of the 39 dis­tricts in Bihar have about three fourth of the state's culturable waste­land, and so on. The problem is thus highly localized in some dis­tricts of some states.

Thirdly, in the conventional green-revolution states, Punjab, Haryana, Western Uttar Pradesh, etc. The problem of wasteland is of a very low order. For example, the only visible spots are Rohtak and Sirsa in Haryana, Ferozepur in Punjab, Ghaziabad, Mainpuri, Farrukhabad, etc. in western Uttar Pradesh. But then, in none of these districts, culturable wasteland makes more than 5.0 per cent of net sown area. It seems, lands are kept in fine trim in those regions just as is the case with the other pre-requisites of the high production technology prevalent in those areas for nearly three de­cades now. Perhaps, the green revolution prosperity leaves no room for 'reckless exploitation of land' or 'extending the extensive margin of cultivation', and so on.

37

Table 3.2 Districts Where Culturable Wasteland (CWL) was 10.0 Percent

or More of their Net Sown Area : 1989-92

Share of CWL in Affected Districts

State No. of Names of Affected in terms of Share in Such Districts State's India's Districts Net Sown

Area Culturable Waste­

Districts Net Sown Area Net Culturable

Culturable Waste­

Sown Area

Wasteland land

1 2 3 4 5 6

Andhra Pradesh 4(23) Prakasam, Nellore, Chiltor 230 32.62 1.70

Assam' 1(10) Lakhimpur 0.88 22.44 0.16

Bihar 11(39) Deoghar, Sahebganj 3.76 74.33 1.93

Gujarat

Haryana

Himachal Pradesh

Jammu-Kashmir

Karnataka

Kerala

Madhya Pradesh

Maharashtra

Meghalaya

Nagaland*

2(18)

(16)

10(13)

11(14)

3(19)

Ranchi, Hazaribagh, Giridih, Palamu, Dhanbad, Gumla, Singhbhum (E), Singhbhum (W)

Broach, Kutch 18.06

Bilaspur, Chamba, Kinnaur, 19 85 Lahaul-Spiti, Shimla, Sirmaur, Hamitpur, Solan, Una

Baramula, Srinagar, Kargil, 17.48 Leh, Doda, Udhampur Jammu, Kathua, Rajouri Poonch

Mandya, Dakshin Kannada, Tumkur

1.67

Jaintia Hills, East Garo Hills, West Garo Hills

7(7) Kohima, Phek, Wokh Zunheboto, Mokokechung, Tuensang, Mon

51 14

86.12

93.93

93.09

39.60

100.00

11.26

0.77

0.85

1.18

3(14) Palakkad, Idukki, 2.87 65.47 0.43

14(45) Bastar, Jabalpur, Panna, Chhattarpur, Sidhi, Satna, Shahdol, Morena, Datia, Betul.

5.00 60.58 6.39

6(29) Thane, Raigarh, Kolhapur, Sindhudurg, Gadchiroli

3.09 53.93 3.70

5(5) East Khasi Hills, 243.14 100.00 3.28

0.62

38

Orissa

Punjab

Rajasthan

Tamil Nadu

Uttar Pradesh

4(13)

(12)

13(27)

3(20)

13(61)

Keonjhar, Koraput, Phulbani, Sambalpur

6.40

Ajmer, Barmer, Bun 32.71 Bhilwara, Bikanet, Chittorgaih, Dholpur, Dungarput, Jaisalmer Jhalawar, Kota, Tonk, Udaipur

Tirunelveli, Puduk Pasumpon Muthuramalinga Thevar

142

Dehradun, Nainital, Gaihwal, 3.21 Tehri-Garwal, Chamoli, Pithoragarh, Uttar Kashi, Etah, Sonbhadra, Lalitpur, Jhansi, Mirzapur

72.00

92.67

27.25

53.00

2.70

34.51

0.50

3.70

West Bengal

Total

(15)

110(400) 74.00

Note: 1. In col.2, the figure in parenthesis is the total number of districts considerd by us for OWL analysis.

2. The figures for Nagaland are not for OWL alone; they are for CWL and BUL put together. See note 1 under table 3.1.

Source: 1. Columns 1 to 3 compiled from data in Table 3.1.

2. Columns 4 to 6 based on:

(i) Govt, of India, Agricultural Statistics at a Glance, Ministry of Agriculture, 1994.

(ii) Govt, of India, Indian Agricultural Statistics, Vol-ll^ Districtwise, for various years. Ministry of Agriculture.

(iii) Govt, of Punjab, Statistical Abstract of Punjab. 1994, Economic & Statistical Or­ganization, Chandigarh.

Lastly, in many states (e.g. Andhra Pradesh. Karnataka, Kerala, Maharashtra, Tamil Nadu, Uttar Pradesh, etc.), the share of culturable wasteland in the affected districts to the net sown area at the state level (Col.4, Table 3.2) is very small. In other words, even if the reclamation of culturable wasteland is a cent percent success in the affected districts, the addition to state's net sown area would be extremely limited. The choices for policy makers are, therefore, quite difficult. This could possibly be a reason for 'routine attention' going to these more severely affected districts like it goes to their less-seriously affected counterparts. After all,'political demonstrability' of

39

major economic programmes is as much inescapable as the 'avail­ability of funds'! But then, the affected districts in Gujarat, Himachal Pradesh, Jammu-Kashmir, Meghalaya, Nagaland and Rajasthan, do have the 'demonstrability potential'; fairly big additions to each state's net sown area could accrue if culturable waste could somehow be liquidated. What stands in the way? Perhaps, hostile physical terrain poses the problem. Or the task is too big to fit into individual state coffers?

Let us revert to Table 3.1 Two alternative estimates of CWL are CWL2 and CWL3. Under the former, fallow lands are clubbed with net sown area and then culturable wasteland is seen as a propor­tion of the two. Under CWL3, land under miscellaneous tree crops and groves is added to the total of culturable wasteland and fallows. For obvious reasons, all percentages go down under CWL2 com­pared with CWL1, and further down under dWL3 compared with CWL2, because of the increasing value of the'd^Vibminator as we move from CWL1 to CWL2 and further on to CWL3.

The relative picture under CWL2 and CWL3 is the same as un­der CWL1. It is essential, however,to stress that the extent of culturable wasteland is so small in some districts that in the event of its reclamation and distribution, it would hardly add any area to the total of the three uses put together: net sown area -i- fallows + land under miscellaneous tree crops, etc. The point is highly dramatized, for example, in the case of districts Deoghar, Sahebganj, Lohardagga, Ranchi, Hazaribagh, Giridih, Palamu, Dhanbad, Gumla, Singhbhum (E) and Singhbhum (W); GWL4 becomes nearly zero compared with fairly high values of CWL2. The moral: the re­claimed land need not be spread thinly among all possible uses; perhaps, each district/area demands its own specific prioritization.

We now turn to 'barren and unculturable land'. BUL1 shows it as a percentage of area under forests and land under non-agricul­tural uses put together. On the basis of BULl, the states that are most seriously affected are Gujarat and Rajasthan, less seriously af­fected are Andhra Pradesh, and mildly affected are Jammu-Kashmir, Kerala, Madhya Pradesh, Orissa, Punjab, Tamil Nadu and West Ben­gal. There are, however, considerable inter-district variations within each state. For example, within Bihar, BUL1 has a value as high as 100.0 for Deoghar and Sahebganj and as low as 1.76 only for

40

Champaran West; within Gujarat, it is 479.0 for Kutch and only 1.87 for Gandhinagar; in Jammu-Kashmir, it varies from 10.32 for Udhampur to 894.82 for Leh; in Madhya Pradesh, the highest value is 180.96 for Mandsaur and a mere 0.65 for Narsimhapur; in Maharashtra, Ratnagiri is the worst sufferer with a value of 1218.52 and Gadchiroli suffers the least with a value of 1.19; in Rajasthan, Jaiselmer tops the list with a value of 358.38 and Churu suffers practically no loss of this kind (BUL1 := 0.94); in Uttar Pradesh, BUL1 is the highest at 109.16 for Mainpuri and lowest at 0.71 for Dehradun, and so on.

Let us look at the districts which suffer from this handicap rather seriously. Somewhat arbitrarily yet to dramatize the widespread na­ture of the problem, we pick up ail those districts where BUL is 25.0 per cent or more of the total of the two types of land-uses: (!) area under forests, and (ii) land put to non-agricultural uses. These, as argued earlier, are the two major uses to which any land re­claimed from BUL may be assigned. A total of 156 districts (out of 383 considered on the basis of MoA data) suffer from this level of unculturable wasteland. The problem seems to be more seriously manifest in Andhra Pradesh,Gujarat, Jarnmu-Kashmir, Madhya Pradesh, Maharashtra, Rajasthan, etc; not only that a majority of their districts suffer from this malaise but a very substantial propor­tion of India's 'barren and unculturable land is located in these dis­tricts. The vulnerable position of Rajasthan and, to a slightly lesser extent, of Gujarat need to be underlined in particular. In the former state, 23 of its 27 districts are suffering from 25.0 per cent or higher level of this handicap and as much as 16.3 per cent of India's 'barren and unculturable land' is located in these 23 districts. Likewise in Gujarat only 8 of its 18 districts together have nearly 14-15 per cent of India's BUL.

To remind ourselves, we have discussed so far the total of BUL in each district (Table 3.1) or each bunch of districts (Table 3.3). We have no idea about the internal components of BUL in such districts. In the MoA data used by us, the finer split of BUL into area occupied by mountains or snow-covered or glacial areas, desertic or sandy or coastal areas, barren rocky or stony area, min­ing or industrial wastelands, etc. is not given; every category of 'unculturable wasteland' is lumped under 'barren and unculturable land'. It need hardly be emphasized that the relative position of dif­ferent components of BUL varies markedly from state to state, and

41

Table 3.3 Districts Where 'Barren and Unculturable Land' (BUL) was 25.0 Per cent or More of the Total of Their Area under Forests and

Nonagricultural Uses: Average for 1989-92

BUL in Affected Districts

state No. of such

Names of Affected Districts

as a percentage of state No. of

such Names of Affected Districts State's India's

Districts BUL BUL 2 3 4 5

Andhra Pradesh 14(23) Srikakulam, Visakhapatnam, Krishna, Vizinagaram, Nellore, Anantpur, Medak, Cuddapah, Nalgonda, West Godavari, Prakasam, Chittor, Rangareddy, Karimnagar

77.10 10.00

Bihar

Gujarat

14(39) Saran, Siwan, Deoghar, Sahebganj, Vaishali, Begusarai, Bhagaipur, Saharsa, Katihar, Dhanbad, Gumla, Kishanganj, Aurangabad, Purnea

8(18) Ahmedabad, Kaira, Bhavnagar, Jamnagar, Kutch, Rajkot, Surendranagar, Surat

37.30

91.10

2.30

14.70

Haryana 5(16) Jind, Mohindergarh, Sonepat, Bhiwani, Rewari

55.30 0.40

Hinfiacha! Prad&sh 6(13) Bilaspur, KInnaur, Kuliu, Hamirpur, Solan, Una

55.00 0,60

Jammu-Kashmir 11(14) Baramula, Kupwara, Srinagar, Anantnag, Phulwana, Leh, Kargil, Jammu, Kathua, Rajouri, Poonch

75.30 1.40

Karnataka 10(19) Bellary, Bidar, Bijapur, Bangalore, Gulbarga, Kolar, Raichur, Tumkur, Hassan, Mandya

58.00 2.90

Kerala 1(14) Kasargod 20.60 0.10

Madhya Pradesh 16(45) Jabalpur, Dhar, Chhattarpur, 71.70 9.30 Tikamgarh, Rewa, Satna, Ratlam, Mandsaur, Jhabua, Morena, Bhind, Gwalior, Guna, Rajgarh, Shahjapur, Raigarh

Maharashtra 15(29) Raigarh, Ratnagiri, Nasik, Jalgaon, Ahmednagar, Pune, Satara, Sangli,

76.30 8.10

Meghalaya 3(12) East and West Khasi Hills 76.60 0.70

Punjab 2(5) Jalandhar, Kapurthala, Ropar

42

40.40 0.20

Rajasthan

Orissa

Tamil Nadu

23(27) Ajmer, Alwar, Banswara, Barnier. 95.20 16.30 Bharatpur, Bhilwara, Bundi, Ghlttorgarh, Dholpur, Dungarpur, Jaipur, Jaisalmer, Jalore, Jhunjhunu, Jodhpur, Nagaur, Pali, S.Madhopur, Sikar, Sirohi, Tonk, Udaipur, Jhalawar

1(13) Balasore 5.00 0.20

3(20) South Arcot, Salem, Chidambarnar 3500 1.10

Uttar Pradesh 23(61) Bulandshahr, Aligarh, Mainpuri, Etah, 62.20 Farrukhabad, Etawah, Kanpur, Fatehpur, Allahabad, Jhansi, Banda, Budaun, Bareilly, Ballia, Firozabad, Lucknow, Hamirpur, Unnao, Rae Bareli, Hardoi, Sultanpur, Chamoli, Partapgarh

4.00

West Bengal

Total

1(15) Darjeeling

156(383) —

59.70 0.90

73.00

Note: 1. In Col, 2, the figure in parenthesis is the total number of districts considered by us for BUL analysis.

2. All the 383 districts cover nearly 83.0 percent of India' BUL while the 156 seriously affected districts cover about 73.0 per cent of BUL in the 383 districts.

Source: The same as in Table 3.2

within many states, from district to district. For example, in certain districts, rocl<y and mountain area may be the biggest component of BUL; in desertic districts, it may simply be sandy-area; in some dis­tricts, degraded forest land may dominate over other components of BUL while in some others, mining/industrial wastelands may be more dominant than others, and so on. With MoA data in hand, no such dissections can be done.

NRSA Estimates

We have an alternative set of estimates provided by the National Remote Sensing Agency (NRSA), Hyderabad, in respect of 146 odd districts, covering the average of 1986-87 and 1987-88, and spread practically over all the states. It is essential to understand the meth­odological differences between our estimates and those of the NRSA, before we think of dissecting the individual components of

43

BUL (as also of CWL). In particular, we must see to what extent the two sets of estimates are, or can be made, comparable. Table 3.4 looks at both sets of estimates, in respect of 143 districts which figure in NRSA estimates. About 3 districts that figure in NRSA es­timates are not included in Table 3.4 since MoA data for mid-1980s are not available for them. At this stage a word of clarification about the NRSA estimates is in order.

First, these estimates were based on satellite remote sensing techniques and G.I.S against the MoA figures compiled through vil­lage records. Although more time consuming, the NRSA estimates should naturally be more scientific in content, and largely free of human bias or recording distortions although it is believed that their quality would have increased further if, in addition to the use of satellite imageries, the NRSA had conducted 'ground test surveys'. Second, the NRSA estimates give as many as 13 categories of wastelands against only 2 given by MoA data. Conceptually NRSA estimates are more satisfying especially from the perspective of ef­fective policy interventions. Third, the NRSA estimates express the total of wasteland (CWL -i- BUL in MoA terminology) as a percent­age of total geographical area of each district. This percentage, in our view.serves a limited purpose. To say the least, it fails to differ­entiate between that portion of wasteland (CWL) which can possibly be added on to culturable area against that (BUL) which can poten­tially increase area under forests and/or land put to non-agricultural uses. From the perspectives of declining land: man ratio and increas­ing stress on land as a means of agricultural production and rural sustenance, this distinction is of great operational significance. More­over, if reclamation programmes have to be viewed from a time perspective, the distinction between CWL and BUL gains still more significance. Lastly, the NRSA estimates are somewhat fuzzy when a distinction is to be made between 'culturable wasteland' and 'fallow lands'.

An attempt is made to put the 13 NRSA categories of wasteland into two broad categories, CWL and BUL, so that some degree of comparability is gained between the NRSA estimates and MoA esti­mates. Five NRSA categories were lumped to make the total of culturable wasteland (CWL, col.2. Table 3.4):

1) waterlogged and marshy land;

2) land affected by salinity and alkalinity;

44

3) shifting cultivation area;

4) degraded pastures and grazing land and

5) degraded land under plantation crops. Accordingly, the remain­ing eight NRSA wasteland categories were put together to make the total of 'uncultivable wasteland', roughly corresponding to BUL of our MoA data based perception (BUL, Col.3, Table 3.4). Needless to emphasize, the above exercise lends only a rough comparability between the two sets of data which, in no case, negates the possibility of a vast divergence between what comes out of the local revenue records and that captured through statellite imageries. Nevertlieless.a comparison is worth making.

Table 3.4 brings out the divergence between the NRSA and the MoA estimates rather glaringly, in none of the 143 districts, waste­land estimates from the two sources are identical. Let us confine our attention to estimates of total wasteland (TWL), and keep aside the far more glaring divergences in respect of CWL and BUL. To gain more visual clarity, the MoA estimates are seen in relation to NRSA estimates and the extent of underestimation or overestimation of the former is worked out for each district. This is set out in column 8 of Table 3.4 (-t-ve sign for under- estimation against -ve sign for over-estimation).

In as many as 112 of the 143 districts included in Table 3.4, the MoA estimates are lower than those of the NRSA; the reverse is true only for 31 districts. In plain terms, in a preponderant majority of districts, MoA data suggest a much lower magnitude of the wasteland problem. One need not unnecessarily deride the MoA estimates just because they give relatively lower figures for wasteland. By the same token, nobody can claim that the NRSA estimates are cent percent accurate. After all, there could be big divergences between what the satellite imageries throw up from above and what actually exists on ground, at least for a few land-use categories (e.g. lands involving slopes, fallows getting mixed up with culturable wasteland' etc.). Although the NRSA procedure is to reconfirm the satellite images through 'ground truth surveys' yet the coverage of such 'ground surveys' is limited and cent per cent reconfirmations are almost an impossible proposition. For sloppy ter­rains in particular, the degree of accuracy is somewhat lower. Never-

45

Table 3.4 Comparison Between two sets of Wasteland Estimates

Absolute Figures in Hectares

state 1986-88 NRSA 1985-88 MoA Relative Estimates Estimates Underesti­

CWL BUL TWL CWL BUL TWL mation of MoA Esti-

mates (7/4)

1 2 3 4 5 6 7 8

ANDHRA PRADESH

Anantpur 12202 311143 323345 85990 189553 275543 15.0 Chittoor 3139 361810 364949 51740 168718 220458 40.0 Cuddapah 793 458966 459759 56669 251781 308450 33.0 Karimnagar 80 145754 146929 20809 84409 105218 28.0 Kurnool 7173 380741 387914 93170 99374 192544 50.0 Mahbubnagar 1116 248496 249612 14406 116451 130857 48.0 Medak 0 113760 113760 26242 65567 91809 20.0 Nalgonda 7504 165413 172917 22915 89912 112827 35.0 Nellore 130904 360912 491816 61410 196485 257895 48.0 Nizamabad 8822 70480 79302 12977 59047 72024 10.0 Prakasam 94106 284199 378305 96216 131334 227550 40.0 Rangareddy 0 146113 146113 27974 47136 75110 49.0 Vizianagaram 206 238522 238728 7798 84420 92218 61,0 Vistiakhapatnam 776 76251 77027 14169 176854 191023 -148.0

ASSAM

Karbi Anglong 545113 37606 582721 SNA SNA 633251 -8.7 N.C.Hills 259560 25688 285248 SNA SNA 404696 -41.9

BIHAR

Aurangabad 460 13414 13874 1877 16440 18317 -32.0 Dhanbad 12865 28265 41130 19407 42162 61569 -50.0 Muzaffarpur 8422 3316 11738 348 5634 5982 49.0 Nawada 125 22499 22624 2638 11304 13942 38.0 Palamu 3108 256767 259875 28127 83634 111761 57.0 Rohtas 2210 156620 158830 2666 36241 38907 76.0 Singhbhum 6155 332601 338756 78213 113520 191733 43.0 Si wan 6258 225 6483 174 8882 9056 -39.7

GUJRAT

Ahmedabad 91794 18161 109955 23500 71450 94950 14,0 Bhavnagar 58278 137899 196177 32950 101350 134300 31.5 Gandhinagar 164 2525 2689 1100 300 1400 48.0 Jamnagar 130 288226 288358 36800 161900 198700 31.0 Junagarh 0 314114 314114 11650 31000 42650 86.0 Kheda 17037 35661 52698 2550 31550 34100 35.0 Mehsana 20562 41325 61887 19400 10850 30250 51.0 Panchmahals 0 135601 135601 21050 40550 61600 54.6 Raikot 11982 187664 199646 12400 113150 125550 37.0 Surendranagar 79487 181888 261375 33500 131950 165450 37.0

46

1 2 3 4 5 6 7 8

HARYANA

Gurgaon 4272 31737 36009 0 7003 7003 80.6 Hisar 24237 7473 31710 0 49184 49184 -55.1 Jind 14428 494 14922 0 16087 16087 -7.8 Karnal 19567 388 19955 1217 12999 14216 29.0 Sirsa 7770 2094 9864 31 19632 19663 -99.3

JAMMU & KASHMIR

Udhampur 13 693547 693560 19344 26463 45807 93.3

HIMACHAL PRADESH

Chamba 245694 94301 339995 5314 4459 9773 97.0 Hamlrpur 8385 8022 16407 12105 11841 23946 -46.0 Kangra 9710 138944 148654 48805 52165 100970 32.0

KARNATAKA

Bangalore 4818 148007 152825 13214 58947 72161 53.0 Belgaum 216 144723 144939 12733 44341 57074 61.0 Bellary 100 164195 164295 29974 60333 90307 45.0 Chlttradurga 87 195585 195672 23983 30599 54582 72.0 Hassan 0 57135 57135 22095 31567 53662 6.0 Kolar 7916 88913 96829 17079 64050 81129 16.0 Raichur 0 94583 94583 13479 36947 50425 47.0 Tumkur 680 276359 277039 66874 67539 134413 51.0

KERALA

Cannore 0 6980 6980 6603 11564 18167 -160.3 Idukkl 180 38233 38413 32590 18611 51201 -47.1 Kasargod 0.00 9813 9813 21065 14058 35124 -257.9 Mallapuram 0.00 12367 12367 14064 7187 21251 -71.8 Palghat 0.00 28356 28356 23976 12983 36959 -30.3 Wayanad 0.00 5184 5184 5633 2066 7699 -48.5

MAHARASHTRA

Ahmednagar 5896 380062 385958 12967 151133 164100 57,5 Dhule 4558 300859 305417 5633 58500 64133 79.0 Jalgaon 2196 123146 125341 8433 75333 83767 33.2 Kolhapur 0 39812 39812 50500 35333 85833 -115.6 Nasik 1962 347573 349535 31867 153167 185033 47.0 Pune 69925 413275 483200 34067 156300 190367 60.6 Ratnagiri 66196 279196 345394 151033 260333 411367 -19.1 Sangll 3022 102827 105849 31400 37633 69033 35.0 Satara 10671 167593 178264 45633 87700 133333 25.0 Thane 76415 165537 241952 46900 74633 121533 50.0

ORISSA

Bolangir 752 127090 127842 36000 30000 66000 48.0 Cuttack 9293 67560 76853 50333 10000 60333 21.5 Dhenkanal 123704 29837 153541 6000 10000 16000 89.6

47

1 2 3 4 5 6 7 8

Ganjam 9845 421239 431084 5000 3000 8000 98.1 Kalahandi 1589 122558 124147 5667 3000 8667 93.0 Sundergarh 2054 112746 175185 27000 11000 38000 78.3 Puri 28064 147121 114800 28333 43000 71333 37.9

PUNJAB

Bathinda 2753 149 2902 121 1123 1244 57.0 Kapurthala 6097 440 6537 8471 2200 10672 -63.3 Sangrur 783 20725 21508 672 1169 1841 91.4

MADHYA PRADESH

Bhind 14436 115822 130258 9347 29692 39039 70.0 Datia 4512 57850 62362 13623 14040 27664 56.6 Dewas 4584 103609 108192 3742 14223 17965 83.4 Durg 4475 127327 131802 23488 8790 32278 75.5 Guna 16824 369202 386026 98036 102920 200956 48.0 Indore 1335 58288 59623 5517 2802 8319 86.0 Mandsaur 5356 276503 281859 43944 99440 143384 49.0 Narsingpur 13101 125740 138841 23316 1027 24343 82.5 Raipur 13401 230713 244114 45122 21925 67047 72.5 Raisen 7148 76307 83455 20811 2956 23768 71.5 Rajgarh 150 181801 181951 28661 29928 58589 67.8 Rajnandgaon 7583 118273 125856 19936 23689 43625 55.3 Ratlam 2275 155717 157992 31274 45703 76976 51.3 Rewa 3475 76475 80050 9617 39958 49575 38.1 Shajapur 275 216904 217179 29650 51539 81189 62.6 Tikamgarh 1788 208415 210203 26829 47750 74579 64.5 Ujjain 625 109332 109957 14197 8148 22345 79.7 Vidisha 17640 95621 113261 14914 10946 25859 77.2

RAJASTHAN

Ajmer 98773 234963 333736 84466 100071 184537 44.7 Bhilwara 136026 242912 378938 211160 170565 381725 -0.7 Bundi 19761 217158 236919 38719 63252 101971 57.0 Churu 127992 895986 1024978 32642 728 33370 96,7 Dungarpur 2114 102185 104299 26972 79975 106947 -25 Jhalawar 35009 158109 193118 66533 40443 107076 44.6 Jodhpur 143717 554381 698098 72110 146548 218658 68.7 Kota 68073 341150 409223 58461 97674 156136 61.8 Pali 153340 176133 329473 46337 154607 200945 39.0 S.Madhopur 33100 320281 353381 37636 110025 147661 58.2 Tonk 89578 61368 151446 52699 38075 90774 40.0 Udaipur 7937 334647 342584 247499 453404 700903 -104.5

TAMIL NADU

Dharampuri 3596 167599 171195 18709 63384 82093 52.0 Madurai 3484 205627 209111 10137 35365 45502 78.2 N.Arcot 8892 226929 235821 21069 48116 69185 70.7 Peryar 20875 199196 220071 2923 7324 10252 95.3 R.KPuram 3037 63683 66720 4446 4961 9407 85.9 Salem 12972 137677 150649 21339 68949 90288 40.1 Tiruchirapailj 845 138105 138950 30919 34374 65294 53.0

48

1 2 3 4 5 6 7 8

UTTAR PRADESH

Agra 2512 38838 41350 663) 14753 21385 48.3 Aligarh 48527 0 48527 9583 31400 40983 15.5 Allahabad 31089 43588 74677 28125 35597 63723 14.7 Azamgarh 16980 405 17385 11905 13829 25734 -48.0 Bulandshahr 32421 1700 34121 10909 14815 25724 24.6 Etah 43403 464 43867 39058 11447 50506 -15.1 Etawah 22989 27937 50926 10525 25301 35826 29.7 Fatehpur 19162 10240 32299 12066 13774 25839 20.0 Farrukhabad 26393 5906 29402 19858 22452 42310 -43.9 Ghazipur 11110 391 11502 5572 6638 12210 -6.2

Hamirpur 1455 46150 47605 31053 23382 54435 -14.3

Jalaun 379 33873 34252 6585 14855 21440 37.4

Jaunpur 19661 5670 25331 9103 8011 17114 32.4

Jhansi 670 93088 93758 54489 31113 85602 8.7 Kanpur 53677 12867 66544 18405 50951 69356 -4.2

Lalitpur 0 125970 125970 131100 18805 149906 -19.0

Lucknow 27138 397 27535 11302 10718 22020 20.0

Mainpuri 50333 4591 54925 16164 35552 51716 58 Pratapgarh 59588 3985 63575 9505 12108 21613 66,0

Rae Bareli 56098 1825 57923 23166 25256 48422 16.4

Sultanpur 40575 9735 50310 16336 17911 34247 31.9

Unnao 66821 1221 68042 19006 20173 39179 42.4

Varanasi 4658 19192 23850 5858 11490 17348 27.3

WEST BENGAL

Bankura 113 63324 63437 24490 6494 30984 51.2

Medinipur 6677 52763 59440 24708 20294 45002 24.3

Puruliya 3480 84859 88339 33991 8520 42511 51.9

NAGALAND

Kohima 172840 3759 176599 SNA SNA 32742 81.5

Mokokchung 108785 0 108785 SNA SNA 5850 94.6

Zunheboto 84070 0 84070 SNA SNA 9283 89.0

Note: 1. SNA= Separately not Available

2. For Assam, tvloA data(Cols. 5,6 and 7) are for 1980-82

3. The MoA estimates are for 1985-88 while NRSA estimates are averaged over 1986-88, the relative deviations(col.8) are not different, even marginally, even if we drop 1985-86 from IvIoA estimates

Source: 1. For NSRA Estimates (cols.2 through 4), Govt, of India, Wasteland Atlas of India, Volume-I, National Remote Sensing Agency, Dept. of Space, Hyderabad, 1993.

2. For MoA Estimates, Table 3.1.

49

theless, all said and done, the greater reliability and wider acceptabil­ity of the NRSA estimates can hardly be under doubt. In that case,a clear message emanating from Table 3.4 is ttiat the methodology or guidelines followed by the village-level revenue officials for delineating 'wastelands' need to be updated. The least that needs to be done is to guard against the permeating of 'human bias' at the local level; some cross-checks are absolutely essential. It is indeed a serious matter that the extent of underestimation of MoA figures is more than 50.0 per cent in a very large number of districts; for some, it goes even beyond 100.0 per cent. The overestimation of MoA fig­ures, although not so glaring and not so wide-spread, do also point towards methodological faults.

Comparing MoA with NRSA Estimates

Table 3.5 looks at the NRSA and MoA estimates in relative terms rather than in absolute deviations as in Table 3.4. The gen­eral conclusion of Table 3.4 - that in a preponderant majority of dis­tricts, the MoA estimates are lower than their NRSA counterparts -must still be intact. After all, we are dividing both NRSA and MoA total wasteland area by the same figure, for deriving columns 6 and 10 or columns 7 and 11. What is important to note is that the rela­tive ranking or districts between the two estimates, whether according to values of TWL or TWL1, is similar for a vast majority of districts. In other words, within each major state, a district shown as critically afflicted by wasteland malaise through NRSA estimates (Col. 6 or 7) shows the same tendency under the MoA estimates (Col. 10 or 11). Obviously, identical ranking cannot be expected along the whole continuum of TWL/TWL1 values, yet for a wide range of districts, it does not vary between one estimate and the other. As a matter of fact, rank correlation coefficients, computed for states which have about 10 or more districts in Table 3.5,are fairly high in many cases. For example, it is 0.75 between cols.6 and 10, (r6,10 = 0.75) and 0.72 between cols. 7 and 11 (r7,11 = 0.72) in respect of Andhra Pradesh; r6,10 = r7,11 = 0.62 for Maharashtra; r6,10 = 0.80 and r7,11 = 0.81 for Madhya Pradesh; r6,10 - 0.80 and r7,11 = 0.73 for Uttar Pradesh, and so on. For Rajasthan the ranking is topsy turvy between the NRSA and MoA estimates; r6,10 = 0.19 and r7,11 = -0.12.

Generally, in each state, the relative underestimation of the MoA estimates is discernible for an overwhelming majority of districts, yet

50

Table 3.5 Wasteland Estimates for 143 Districts of India by National Remote Sensing Agency (NRSA)

CJI

State District 1986-88

Estimates

Modified 1986-88 NRSA Estimates MOA 1985-88 Estimates District 1986-88

Estimates CWL BUL TWL TWL1 CWL BUL TWL TWL1

1 2 3 4 5 6 7 8 9 10 11

Andhra Pradesh Anantpur 16.90 1.50 89.50 27.70 16.90 10.50 53.40 23.50 14.40 Chittoor 24.10 0.73 117.30 49.50 24.30 12.10 28.40 21.60 14.70 Cuddapah 29.90 0.21 109.10 57.90 29.90 15.20 37.30 29.50 20.10 Karimnagar 12.40 0.02 93.30 25.60 12.40 5.00 26.10 14.30 8.90 Kurnool 22.00 0.82 91.70 30.20 22.00 10.70 23.90 15.00 10.90 Mahbubnagar 13.50 0.15 59.90 21.40 13.50 1.90 28.10 11.20 7.10 Medak 12.00 0.00 86.90 21.00 72.00 6.40 50,10 16.90 9.60 Nalgonda 12.20 1.26 92.80 22.40 12.20 3.90 51.30 14.70 7.90 Nellore 37.60 40.89 120.90 79.50 37.40 19.20 65.80 41.70 19.60 Nizamabad 10.00 3.21 29.40 15.40 9.80 4.70 24.70 14.00 8.90 Prakasam 21:50 16.50 79.60 40.80 22.10 16.90 36.80 24.50 13.30 Rangareddy 19.50 0.00 98.80 32.90 19.40 950 31,90 16.90 10.00 Vizianagaram 21.40 0.27 41.20 16.30 12.20 2.70 45.70 19.50 14.60 VIshakhapatnam 11.80 0.10 93.70 45.80 21.00 5.30 32.20 32.40 1680

Assam Karbi Anglong 56.00 431.00 14.00 146.00 56.40 SNA SNA 158,30 61.30 N. Cachar Hills 58.30 1239.00 41.00 338.00 58.30 SNA SNA 480.00 82.80

Bihar Aurangabad 4.20 0.24 22.80 5.50 4.20 1.00 28.00 7,30 5.50 Dhanbad 13.70 20.45 29.40 25.90 13.80 30.80 43.90 38 70 20.60 Muzaffarpur 3.70 3.91 5.90 4.30 3.70 0.20 10.00 2.20 1.90 Nawada 9.10 0.10 26.00 10.70 9.10 2.10 13.10 6.60 5.60 Palamu 20.30 1.42 42.70 31.70 20.40 12.80 13.90 13.60 8.80 Rohtas 22.00 0.56 61.60 24.50 21.70 0.70 14.30 6.00 5.30 Singhbhum 25.20 1.72 65.80 39.20 25.00 21.80 22.40 22.20 14.10 Slwan 2.90 3.64 0.80 3.30 2.90 0.10 32.40 4.50 4.00

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a few exceptions need to be underlined. For example, in all the 6 districts of Kerala (figuring in Table 3.4), the story goes the other way round; NRSA estimates are lower than MoA estimates. Is it that the revenue department machinery in this state is more 'scientifically-attuned'? Is it that the guidelines for wasteland measurement are 'more liberal'? But then, the MoA underestimation manifests itself in each individual district of Gujarat, Karnataka, Orissa, Madhya Pradesh, Tamil Nadu, West Bengal and so on. Except for Visakhapatnam, MoA underestimation is discernible in all other dis­tricts of Andhra Pradesh as well.

In brief, the" need for more reliable estimates of wasteland is fairly obvious. Based on 1986-88 position, the NRSA has identified 237 districts as seriously affected. A district-by-district blow-up of wastelands is already published for 146 districts. Detailed district wise information for the remaining 91 districts must be put up as expedi­tiously as possible, in the mean time, another round of satellite sur­vey may better be initiaJted; perhaps, in the 10 years that have gone by since the first NRSA survey, many new districts might have got affected or the situation might have worsened in some and so on. On the other hand, the MoA annual routine of putting out 'waste­land' statistics, as a part of land-use classification, must improve; the states have a more decisive role to play in this regard. It seems, no change, much less an improvement, in declaring land as 'wasteland' has taken place for years together; in fact, land-use classification is hardly taken seriously at the state or lower levels.

The glaring deviations between the NRSA and MoA estimates prompts us to look at them in relation to total area. Unlike the NRSA procedure of measuring 'total wasteland' as a percentage of total geographical area of a district, we measure total wasteland (as in Table 3.4) as a percentage of the total of four items:

i) net sown area,

ii) fallows;

iii) forest area, and

iv) land under non-agricultural uses. As already pointed out, these are the four uses to which the total wasteland can ultimately be assigned, (i) and (ii) for 'culturable wasteland' and (iii) and

56

(v) for 'barren and unculturable wastelands'. These percentages (TWL) are set out in columns 6 and 10 of Table 3.5. To add to our confidence, we divide total wasteland by total reporting area also (TWL1, cols. 7 and 10, Table 3.5), to see if a better accord emerges between the NRSA and MoA estimates.

A consolation that emerges from Table 3.5 is that if attention is to be fixed, first and foremost, on the most seriously affected dis­tricts, within each state, it generally does not matter whether we choose to be guided by the NRSA or MoA estimates. The absolute figures for wasteland, including those for its two components CWL and BUL, do differ significantly between the two sets of estimates but relative rankings, most surely for the more seriously affected dis­tricts, do not deviate much. The policy intervention focus need not, therefore, get fuzzed. The exact magnitude of wasteland to be tack­led, say, in a seriously affected district, may look formidable through the NRSA prism and less frightening from the MoA vision, yet the fact remains that the district is suffering the loss of land more than any other. Expediency demands that the battle between absolute deviations and relative ranking should not hold up policy interventions and remedial initiatives. Perhaps, the reality is somewhere between the NRSA and MoA estimates; neither is one totally useless nor is the other totally acceptable. So we must pose faith in both sets of estimates. Since we are not sure how much we ought to tilt in favour of NRSA estimates against their MoA counterparts, or vice-versa, for each district, any exercise in locating the mid-values would be highly arbitrary. We are thus carried back to Table 3.2 which give us a nearly exhaustive list of the 110 seriously affected districts from the point of view of culturable wasteland, to Table 3.3 which identifies 156 seriously affected districts from the viewpoint of barren and unculturable land, and to Table 3.4 which portray the picture of 146 seriously affected districts from the viewpoint of total wasteland. Let each table be drawn upon depending on the perception and the mould of policy intervention.

Major Categories of Wastelands

But then, a specific policy intervention calls for precise identifica­tion of wasteland type for each district. It is here that the NRSA es­timates are most handy while MoA estimates serve no purpose. Let us, therefore, go into the 13 wasteland categories provided by the NRSA estimates. A careful perusal of the districtwise NRSA esti-

57

mates convinces us ttiat out of thie 13 wasteland categories, only 5-6 are more pervasive, and are clearly discernible in most of the dis­tricts. Accordingly, Table 3.6 summarizes the districtwise blow-up of NRSA estimates into a few major categories. An effort is made to report all those districts in each state which are seriously affected by each specified wasteland category.

It is clear that the nature of wasteland varies from district to dis­trict, although many districts in big states commonly suffer from the same type of wasteland malaise. It is equally clearly discernible that some types of wasteland occur, in a big way, practically in each state. For example, degraded notified forest area and upland with/ without scrub are the most common categories; shifting cultivation is the most common problem in hill areas; salinity/alkalinity affected lands and waterlogged/marshy lands are located in the green revolu­tion areas; sandy/desertic areas are the 'preserve' of Rajasthan while gullied/ravinous lands are located in Madhya Pradesh and parts of Uttar Pradesh, and so on. In is clear that the policy initiatives needed to develop wasteland must necessarily vary from area to area. Perhaps, even for the same category of wasteland, say, in two separate regions, the same prescription may not work.

In any case, it is fairly evident that the most commonly occurring problem is degraded forest land. No wonder, therefore, it has en­gaged the maximum of policy attention during the past two decades or so, and one may not be surprised to discover many programmes of reforestation or forest preservation coming up in recent years. The overbearing attention is thus quite understandable. Chapter IV looking as it does into the policy initiatives of the past confirms this fact.

58

Table 3.6 Major Categories/Components of Wasteland: 1986-88 NRSA Estimates

state No. of Dists. Covered Category Description

Major Categories of Wasteland Discovered

Critically Affected Districts age Share of all Affected

Districts State's

Wasteland 5

Andhra Pradesh 14

Assam

Bihar

Gujarat

2

9

10

(1) Upland with/without Scrub

(2) Degraded Notified Forest Area

(3) Barren Rocky Waste Area

(1) Shifting Cultivation Area

(1) Degraded Notified Forest Area

(2) Upland with/without Scrub

(1) Upland with/without Scrub

Chittor, Cuddapah, Karimnagar, 41.4 Mahbubnagar, Medak, Nalgonda, Nellore, Nizamabad, Prakasham, Rangareddy, Visakhapatnam, Vizinagaram

Anantpur, Chittor, Cuddapah, Karimnagar 34.0 Kurnool, Mahbubnagar, Medak, Nalgonda, Prakasham, Rangareddy, Visakhapatnam, Vizianagaram

Anantpur, Kurnool 13.4

Karbi Anglong, North Cachar Hills 92.7

Aurangabad, Nawada, Palamu, Rohtas, 75.4 Singhbhum

Dhanbad 8.4

Bhavnagar, Jamnagar, Junagarh, Mehsana, 61.8 Rajkot, Surendranagar

(2) Salinity/Alkalinity Affected Area

Haryana

Himachal Pradesh 3

CD o Jammu & Kashmir 1

Karnataka

Kerala

(3

(1

(2

(1

(2

(3

(4

(1

(2

Degraded Notified Forest Area

Degraded Pastures/Grazing Land

Upland with/without Scrub

Degraded Land under Plantations

Snow-covered and/or Glacial Area

Steep Sloping Area

Degraded Pastures/Grazing Land

Barren Rocky Waste Land

Snow-covered and/or Glacial Area

Upland with/without Scrub

Degraded Notified Forest Land

Barren Rocky Waste Land

Degraded Notified Forest Land

Upland with/without Scrub

Steep sloping Area

Ahmedabad, Bhavnagar, Kheda, Mehsana, 15.2' Surendranagar

Junagarh, Panchmahals, 10.6

Hissar, Jind, Karnal, Sirsa, 45.6

Gurgaon 21.5

Chamba 48.6

Chamba, Kangra 27.5

Chamba, Kangra, Hamirpur 11.3

Hamirpur 3.5

Udhampur 60.2

Udhampur 20.8

Banglore, Belgaum, Bellary, Chitradurga, 43.0 Hassan, Raichur, Tumkur

Banglore, Belgaum, Bellary, Chitradurga, 42.4 Hassan, Raichur, Tumkur, Kolar

Bangalore, Tumkur 10.6

Iddukki, Palghat, Wayanad 42.6

Cannanore, Kasargod, Malapuram, Palghat 31.2

Iddukki, Malapuram, Palghat 14.4

1

Madhiya Pradesh 18

C5)

Maharashtra 10

Manipur 4

Nagaland 3

Orissa 7

Punjab

Rajasthan 12

(1) Upland with/without Scrub

(2) Gullled/Ravinous Land

(3) Degraded Notified Forest Lan^J

(1) Upland with/without Scrub

(2) Degraded Notified Forest Land

(1) Shifting Cultivation Area

(2) Waterlogged and Marshy Area

(1) Shifting Cultivation Area

(1) Degraded Notified Forest Land

(2) Upland with/without Scrub

(3) Barren Rocky Waste Land

(4) Shifting Cultivation Area

(1) Sandy Land

(2) Waterlogged and Marshy Area

(1) Sandy/Desertic Land

Datia, Dewas, Durg, Guna, Indore, 49.3 Narsingpur, Raipur, Rajgarh, Rajnandgaon Ratlam, Rewa, Shajapur, Tikamgarh, Ujjain, Vidisha, Mandsaur

Bhind, Datia, Dewas, Guna, Indore, 20.5 Rajgarh, Shajapur, Ujjain, Narsingpur

Dewas, Guna, Mandsaur, Raisen, 19.6 Ratlam, Tikamgarh, Vidisha, Rajnandgaon

Ahmednagar, Jalgaon, Nasik, Pune, 46.4 Ratnagiri, Sangli, Satara, Thane

Ahmednagar, Dhule, Jalgaon, Kolhapur, 28.2 Sangli, Satara, Thane, Nasik

Bishnupur, Imphal, Senapati, Thoubal 87.8

Bishnupur, Imphal, Thoubal 12.2

Kohima, Mokokchung, Zunheboto 99.0

Bolangir, Cuttak, Ganjam, Kalahandi, Puri Sundargarh,

54.2

Bolangir, Kalahandi, Sundargarh 15.5

Ganjam, Puri 11.5

Dhenkanal 10.5

Sangrur 50.3

Bathinda, Kapurthala 28.2

Churu, Jodhpur 26.9

4

Tamil Nadu

Uttar Pradesh 23

West Bengal 3

(2) Upland with/without Scrub

(3) Degraded Notified Forest Land

(4) Degraded Pastures/Grazing Land

(1) Degraded Notified Forest Land

(2) Upland with/without S&ub

(1) Salinity/Alkalinity Affected Land

(2) Upland with/without Scrub

(3) Gullied/Ravinous Land

(4) Waterlogged and Marshy Land

(1) Upland with/without Scrub

(2) Degraded Notified Forest Land

Ajmer, Bhilwara, Bundi, Dungarpur, 24.7 Jhalawar, Jodhpur, Pali, Udaipur

Bundi, Jhalawar. Kota, Pali, 16.7 Sawai Madhopur

Ajmer, Bilwara, Jodhpur, Pali, Tonk 16.4

Dharampuri, Madurai, North Arcot, Salem, 48.4 Periyar, Tiruchirappalli

Dharampuri, Madurai, North Arcot, Salem, 33.9 Periyar, Ramnathapuram, Tiruchirappalli

Aligarh, Allahabad, Azamgarh, Etawah 46.4 Bulandshahr, Etah, Farrukhabad, Unnao, Fatehpur, Ghazipur, Jaunpur, Kanpur, LiJcknow, Mainpuri, Pratapgarh, Rae Bareli, Unnao

Hamirpur, Jhansi, Lalitpur, Agra, Varanasi 19.0

Agra, Etawah, Fatehpur, Hamirpur, Jalaun 14.6

Aligarh, Etah, Unnao 10.1

Bankura, Medinipur, Purulia 40:4

Bankura, Medinipur 28.2

Source : Govt, of India, Wasteland Atlas of India, Vol. i 1993, NatiShal Remote Sensing Agency and Dept. of Wasteland De­velopment, Ministry of Rural Development, pp 10-20.

IV Policies/Schemes for Wasteland Development

The concern for wasteland development is discernible, in varying form and content, in the long list of policies and programmes pur­sued in India for well over three decades now. From the constitution of the Wasteland Survey and Reclamation Committee way back in June 1959 to the National Land-use and Wastelands Development Council in May 1985, and to the ongoing efforts of the Wasteland Development Department of the Ministry of Rural Development, it is a long chain of ideas and gestures that has engaged the attention of policy makers in India besides direct and indirect involvement of NGOs and other agencies at home and abroad (Singh - Burra, 1993, p.12). Consequently, therefore, over time, clearer perceptions of wasteland as a problem and the strategies needed to develop them have evolved as a result of better diagnosis and rising con­sciousness of public-spirited agencies/people. Not surprisingly, in re­cent years, one discovers a bit of impatience in shifting over from one wasteland development programme to the other, even while 'overwhelming successes are reported' from one at the top of the other. A brief review of the past policy initiatives is essential, there­fore, to grasp the nuance and content of the ongoing programmes and the policy thrusts needed for the future. To put the record straight, we give below a chronological description of the policy changes initiated, articulated and supported at the level of central government. In other words, the changing policy regimes at the state level are not discussed in this paper.Nonetheless, policy changes at the national level are ultimately implemented at the state, district and lower levels and we hope to gain some insights, albeit sketchy, about what has been happening to various national programmes/poli­cies at state level and below.

NCIA Initiatives

The real beginning ensued with the 1976 Report of the National Commission on Indian Agriculture (NCIA). The NCIA made a distinc­tion between wastelands that needed to be reclaimed (more impor­tantly shrub and bush infested lands, ravines, waterlogged and sa­line/alkali affected lands, riverine lands and less importantly coastal sandy land, stony and gravelly lands and lateritic soil with thin soil cover) and those that needed to be conserved, put to permanent agriculture or afforested (e.g. chos/khads, lands on steep slopes,

63

and lands affected by shifting and cultivation) (NCIA, 1976, pp. 277-312). A number of suggestions were offered (1) for reclamation of various categories of wastelands, (2) iTistitutiona< siipport for research and finance, (3) complementary action to ward against reoccurrence of 'land deterioration {Ibid, pp. 284-341). A few suggestions such as (1) provision of subsidy to small/marginal farmers for reclaiming alkali affected lands {pp. 293-94), (2) intensification of research in multiple directions most ostensibly for evolving rational land-use patterns, watershed development and affonestafion (pp. 311, 330-31, 333-334), (3) the utilization of wastelands for attaining self-sufficiency in fodder, fuel, and small timber (pp. 322-23), (4) the inescapable need for providing necessary inputs (viz. agricultural implements, current inputs and credit) to the landless agricultural labourers along with the allot­ment of government ownes wasteland (pp. 318-20), (5) the urgency for enacting consolidation of wastelands, (6) rocky, stony and hilly areas with skeletal soil, being suitable neither for agriculture nor for­estry, being better earmarked for recreation purposes,nature reserves or other non-agricultural purposes (pp. 333), (7) the multiple rehabili­tation package for the tribal people engaged in Jhum cultivation (pp. 334-36), (8) the respective areas of operation of financial and devel­opment institutions, and of iindividual farmers/allottees, in reclamation endeavours (pp. 338-39) and (9) that economic benefits alone should not form the sole criterion for the reclamation of revenue lands such as those in Gujarat, Rajasthan, Madhya Prades;h and Uttar Pradesh (pp. 306-07, 327), clearly formed the basis for subsequent wasteland development policies/programmes. Incidentally, the NCIA did not visualize any special role for women in the wasteland develop­ment programme.

The NCIA report made its impact in many areas; afforestation was the most striking issue to engage government attention and re­sources. Social forestry was visualised as a powerful means for meeting the fodder, fuel and small timber requirements of village people besides increasing the total forest cover. Actual progress re­mained rather tardy till the revised 20-Point Programme emerged in 1980. Social forestry now engaged prime attention. The imple­menting responsibility, however, rested exclusively with the Forest Department; local panchayats/bodies stood aloof and consequently, survival, protection, maintenance, etc. of newly planted saplings suf­fered due to lack of 'local, intimate care'. Ironically, eucalyptus was chosen for a majority of plantations which resulted in increased sup­ply of raw material to industry instead of mitigating the shortage of fodder, fuelwood and small timber (Govt, of India, 1991, p.4).

64

the quality of work is, on the whole, satisfactory while the involve­ment of the people varies from very high to just satisfactory levels (Ibid, p. 7).

The Scheme is currently in operation in 125 odd districts, spread over 23 states/U.Ts (GOI, 1995A, p. 52). The greening of Deolali Hills, a highly successful venture, falls under the purview of the IWDP initiatives.

In 1994-95, a new scheme called Technology Development and Extension (TDE) Scheme was chipped off the IWDP umbrella for the development of non-forest wastelands. The main activities of the TDE Scheme are:

i) to increase the productivity of private/community wastelands in non-forest areas, through the adoption of agro-forestry models (e.g. agri-silvi culture, silvi pasture, silvi- and agri-silvi-horticul-ture, homestead planting, etc.) developed by the ICAR;

ii) planting of trees, shrubs, grasses, legumes, etc. having nitrogen fixing ability;

iii) to undertake land-based economic activities (other than those at (i) above) such as horticulture, pisciculture, piggery, duckery, etc;

iv) to conserve and increase water regime through such measures as gully-plugging, check dams, terracing, bunding, trenching, etc.;

v) to explore new and innovative techniques like use of VAM, tis­sue culture seedlings, vegetative propagation, etc. to increase productivity of wastelands (GOI, 1995, p.27)

For projects based on lands owned by government and govern­ment agencies including universities, non-profit agencies or village panchayats (so that usufruct flows to the community), 100% central grant is to be admissible. For projects based on land belonging to private farmers/corporate sector, 60% of the project cost would be met by central grant. Agricultural universities, government agencies, reputed NGOs, public sector undertakings, etc. are to be the imple­menting agencies. They will take up pilot projects to develop/demon­strate proven tethriologies and to disseminate appropriate technolo-

67

gies, and so on. Training of the prospective beneficiaries (farmers) and trainers is an integral part of the scheme {Ibid, p. 29). Currently, 57 projects are being implemented under TDE scheme (GOI, 1995A, p. 53).

Investment Promotion Scheme (I.P.S) is another new scheme, based on bold ideas and extensive coverage. The scheme aims at promoting development of non-forest wastelands on a large scale, through the involvement of financial institutions, corporate bodies in­cluding user-industries and other intending entrepreneurs. The inten­tion is to (1) put wastelands under a sustainable use and ensure its enhanced productivity, thereby maintaining ecological conservation, and (2) ensure that benefits of the scheme accrue substantially to the rural poor in terms of expansion of employment and income lev­els (GOI, 1995, pp. 19-24).

The extensive coverage of the scheme manifests itself in the fact that the non-forest wasteland to be taken up for development may belong to individual farmers or the village community as a whole or institutions or government agencies, and so on. Not only that, forest wastelands adjacent to non-forest wastelands close to habitation may also be taken up in conjunction with non-forest wasteland area. However, no relaxation of Land Reform Rules, Land Ceiling Laws, etc. will be involved under this scheme {Ibid, pp. 20).

The scheme is based on a triangular funding pattern: bank loan, central assistance and promoter's own money. The central assistance would not ordinarily exceed 25.0% of the total project cost which, in the case of Scheduled Castes/Tribes beneficiaries, can go upto 50.0%. At least 50.0 percent of the project cost must be financed by financial institutions who would invariably feel assured about and testify to the economic viability of the project. Among such institu­tions, NABARD is designated to play a pivotal role. A wide range of institutions/agencies are eligible for taking up projects under this scheme and for getting central assistance. Central and State Government undertakings, Cooperative Institutions, Public Trusts and Societies registered under the Companies Act, individual entrepre­neurs, etc. are all eligible to take up such projects. Interestingly, the case for central assistance is to be decided only after the concerned financial institution is convinced of the economic soundness of the project and has actually sanctioned the loan component of the project cost {Ibid, pp. 20-21).

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The NABARD, while appraising a project for bank loan, will en­sure that the promoter is using good quality seedling of grafted plants for planting. Though the species for plantation would have to be site-specific yet due emphasis would be given to vegetal propa­gation and wasteland development. The commercial viability of the project is necessary for attracting institutional finance. Accordingly, the species chosen would have to demonstrate their commercial capabili­ties. The list of recommended species is, therefore, fairly long: Jo Jo Ba, Acacia Holosaricea, Poplar, Kadam, Eucalyptus, Casurina, Jatropha, Salvidera, Cashew, Tea, Red oil Palm, medicinal plants and herbs, etc. {Ibid, pp. 22).

The forgoing discussion clearly shows that NABARD is to play a leading role from beginning to the end. For example, NABARD is to help in the formulation of project proposals; it has to act as a financier under the prescribed guidelines; it has to be a go-between the project agency and NWDB; it has to oversee the progress of the project work in many different ways (financial propriety, time spread of expenditure, physical work in progress, periodic reporting, etc.); it has to keep itself associated with advisory/review committees for each such project so that a watchful scrutiny is exercised from a very close angle, and so on (Ibid, pp. 21).

The I.P. Scheme is yet to pick up in a big way.

Yet another special scheme was mooted in June 1994 for the development of inaccessible and highly degraded ravines in Morena District of Madhya Pradesh. The wasteland development is entrusted to a taskforce of 300 ex-servicemen. About 390 hectares of ravines would be developed every year. Besides ensuring resettlement of ex-servicemen, the scheme would restore ecological balance and create sustainable assets for the community/state, in ecologically fragile parts of the district. A triangular involvement of NWDB, state govern­ment and Morena Wasteland Development Force (WDF) is envis­aged with their respective spheres of activities and involvement. A 3-year Action Plan is visualized for each reclamation effort. The scheme is fairly ambitious in form and content, and one hopes it comes off well in due course (GOI, 1995, pp. 37-45).

Following Hanumantha Rao Committee recommendations, all de­graded lands situated within a mini watershed were decided to be treated simultaneously for soil and water conservation, irrespective of

69

whether such lands belong to the forest department or to private individuals or to local communities (GOI, 1995A, p.12). One of the most important implications of the Rao Committee Report is that a fair degree of co-ordination would be ensured amongst the Ministry of Agriculture (dealing with erosion-prone agricultural lands alone), Ministry of Environment and Forests (concerned only with forest wastelands) and Ministry of Rural Areas and Employment (respon­sible for non-forest wasteland development alone) (GOI, 1995A, p. 12). The common guidelines are inspired by outstanding examples of successes of area development projects under watershed basis (Govt, of India, 1'§95A, p.55). Common guidelines have been evolved for all area development schemes of the three Ministries. Among the more important of these schemes are the IWDP, OPAP, DDP, JRY and EAS, which expressly germane to our study, are also to be governed by these common guidelines. These guidelines are now to be implemented at the district level by the Zila Parishad/DRDA. The actual implementation of a project would be done by a Project Implementation Agency - PIA (e.g. a government department, univer­sity or a cooperative body) which, in turn would be assisted by a Watershed Development Team (WOT). Each PIA would be respon­sible for development of at least 10 Watershed Development Asso­ciations (WDA) and each WDA would have its own Watershed Com­mittee (WC). There is thus a high degree of decentralized functioning under the new guidelines, with specific respoosteiifies/activities being assigned to WC, WOA, WDT, PIA, Zila Parishad/DRSA, state/central governments, under each wasteland development project. Hopefully, the decentralized system works well (GOI, 1995, pp. 48).

The strength of these guidelines lies in the decentralisation of decision making. In ideal conditions, the watershed development plan should move up from the bottom so that the wisdom and aspiration of the people directly depending on the watershed are taken care of. In the proposed arrangement, the plan would be approved by the WDA, the ZP/DRDA would have the final say in the choice of PIA, the WC would be free to choose various project activities and so on (G.O.I, 1995A, P.56).

Another strength of the guidelines is a flexible approach in the release of funds, the area to be covered in each watershed as well as choice of components. A strong technical support is built in the scheme through the presence of WDT while large scale training programme for policy makers, implementers at the district level and below shall lend technical robustness to the scheme.

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MoAC Schemes

Department of Agriculture and Cooperation has a number of schemes directly and indirectly connected with development of waste­land. A synoptic view of these schemes is as follows:

1) Scheme of soil conservation in the catchment of River Val­ley Projects which aims at controlling pre-mature siltation of reservoirs through soil and water conservation measures, cover­ing badly degraded lands on watershed basis. Watersheds hav­ing high sediment production potential are being currently cov­ered in 418 watersheds. The impact on land productivity and soil moisture regime has been quite encouraging.

2) Integraded Watershed Management in the Catchment of Flood Prone Rivers is a centrally sponsored scheme. Its ob­jective is to moderate the run-off by increasing the in-situ con­servation of water and thereby reducing flood hazard. At present, the scheme covers 234 watersheds spread over 10 catchments of 8 states.

3) Reclamation of Special Problem Areas and Improvement of Productivity, another centrally sponsored scheme launched dur­ing the 7th plan, is being continued in Punjab, Haryana and Uttar Pradesh. The scheme aims at improving the physical con­ditions and productivity of the alkali soils. The major compo­nents include assured irrigation, on-farm improvements such as land-levelling, bunding and ploughing, community drainage sys­tem, etc.

In 1993-94, with EEC assistance, the scheme has been ex­tended to Bihar and some Other parts of Uttar Pradesh.

4) Scheme of Watershed Management for Shifting Cultivation Areas in North-Eastern India, aims at total development of shifting cultivation area on watershed basis, by increasing the jhum cycle. It is a 100.0 per cent centrally assisted scheme spread over the north-eastern states.

5) National Watershed Development Project for Rainfed Areas aims at maximizing crop production system through rain water conservation on lands having agricultural production potential.

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The works are undertaken on watershed basis. Currently, the project is going on in 2500 watersheds in 115 agro-climatic zones.

A number of foreign-assisted schemes are also in operation. EEC assisted Integrated Watershed Management in the Ravinous Areas, Indo-Dutch North Bengal Terai Development Project, Indo-Ger-man Programme on Watershed Management, World-Bank Aided Projects on Integraded Watershed Development in Hills and Plains, etc. are examples of such schemes.

The MoAc is thus a major organization connected with wasteland development through a variety of schemes. Some of its schemes are large and some comparatively small. "There is always some confu­sion at the field-level when such a wide variety of schemes are being implemented. Since all these schemes are directed towards the watershed principle, perhaps there is an imperative need to bring all of these schemes under one umbrella ..." (GOI, 1995A, pp. 62-63).

MoEF Forest Schemes

The improvement of degraded forest lands has been largely un­der the state sector. Nonetheless, private initiatives and people's participation have also revealed their increasing presence in recent years. In 1994-95, some international aid projects were also commis­sioned. Briefly, the Ministry of Environment and Forests (MoEF) regu­lates the following schemes:

1) Integrated Afforestation and Eco Development Project, cover­ing about 60,000 ha of afforestation annually;

2) Fuelwood and Fodder Projects which cover about 70,000 ha annually;

3) Non-timber Forest Schemes which cover about 20,000 ha an­nually;

4) Grant-in-Aid Scheme to voluntary agencies for developing close relationship between forest officials and local communities;

5) Aerial Seeding Scheme which covers afforestation over 25,000 ha annually;

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6) Eco-Task Force of ex-servicemen who are deployed in remote and difficult areas to undertake restoration of degraded eco-sys-tem through afforestation, conservation and water resource man­agement techniques (GOI, 1995A, p.64).

In addition, financial assistance is provided to research institutes, government and semi-government organizations for the purpose of administering environmental protection techniques, integrated eco-de-velopment of degraded areas and conservation of natural resources.

In the state sector, there are 14 ongoing projects with external assistance in 10 states (Andhra Pradesh, Himachal Pradesh, Haryana, Karnataka, Madhya Pradesh, Maharashtra, Orissa, Rajasthan, Tamil Nadu and West Bengal). Another 8 projects are in the pipeline covering the states of Bihar, Gujarat, Kerala and Uttar Pradesh and some additional projects in Himachal Pradesh, Karnataka,. Orissa and Tamil Nadu {Ibid, p.64).

Two significant features of MoEF forest development policies need to be highlighted. First, in recent years, the MoEF have launched Forest Protection Committees (FPC) which involve government-people partnership in the protection and management of degraded forests. About 12,000 FPCs are new looking after about 1.80 million hect­ares of forest lands. The partnership is doing particularly well in West Bengal, Orissa and Madhya Pradesh. Second, the MoEF have recently launched a scheme to involve the corporate sector in the development of degraded forest lands by allowing them access through Forest Development Corporations. The first charge on forest produce should be to the rural communities living in/around the for­ests.

Other Schemes

Department of Poverty Alleviation and Rural Employment (DPARE) administers a few schemes which, In essence, lead to wasteland development. Some of these schemes are:

1) Drought Prone Area Programme (DPAP) under which de­graded areas due to soil erosion, water and moisture stress, etc. get treated. Started wayback in 1973-74, the scheme is currently in operation in 627 blocks spread over 96 districts of 13 states. The funding is shared on 50:50 basis between cen­tral and state governments.

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2) Desert Development Programme (DDP) was launched basically to restore eco-system of desert areas affected by extreme tem­peratures, poor rainfall, low humidity and high wind velocity combined with dust storms and recurring droughts. The scheme was started in 1977-78 with special emphasis on shelter belt plantation, pasture development, soil moisture conservation and water resource development. This covers both hot desert areas for sand dune stabilization and cold desert areas for vegetative propagation and moisture conservation.

The other schemes of DPARE such as Employment Assur­ance Scheme dealing, inter alia, with horticulture and watershed de­velopment especially directed to marginal/small farmers, Jawahar Rozghar Yogna aiming to create community assets particularly for SC/STs and freed bonded labour etc. do have an indirect (and meak) association with wasteland development programmes.

Planning Commission Schemes

Two schemes need to be noted.

1) Hill Area Development Programme (HADP) is being imple­mented for certain designated hill areas with a view to ensure ecologically sustainable socio-economic development of the hills.

2) Western Ghats Development Programme (WGDP) covers 163 talukas of the Western Ghats which have been recognized as ecologically fragile and degraded. Eco-restoration, eco-develop-ment and eco-conservation are the underlying objectives of the scheme.

NABARD Initiatives

NABARD is playing an important role in promoting a few major wasteland development schemes. For nearly a decade now, it has been providing refinance facilities to financial institutions against the loans advanced by them to individual or group of farmers, corporate bodies, user-industries, etc. for undertaking wasteland development activities, under Farm-Forestry or Agro-Forestry Schemes. Besides tree plantation by individual farmers, a tie-up with wood-based indus­tries also attracts support by NABARD. Examples of such tie-ups are ITC-Bhadrachalam Paperboards in Andhra Pradesh, Straw Products

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Ltd. in Orissa under Farm Forestry and WIMCO-Kadam Plantation Projects for Assam and West Bengal, under Agro-Forestry.

NABARD approves farm forestry/agro-forestry/watershed develop­ment schemes for wasteland development if the proposed projects are technically feasible and financially viable and are directed to pro­duce firewood, pulpwood, fruit, small timber, etc. for the benefit of the society in general rather than a few individuals.

In spite of the best efforts of NABARD, the refinance has not picked up well. Perhaps, with the introduction of new common guide­lines for all area development schemes on watershed basis, NABARD's refinancing and other activities will go up soon.

The forgoing discussion of the wasteland development policies clearly shows that the perception of the problem and the approach to tackle it have undergone a remarkable change since the late 1970s. In the earlier years, the solutions appeared easy: identify a plot of wasteland and mobilize people to plant some trees. Since the late 1980s, the solutions appeared more complex. Policy makers no longer talk about developing plots of lands, planting trees, and gen­erating income or subsistence. Their vision now is far too wide and they discuss both protection and plantation measures. The range of remedies goes from establishing the tree, grass or bush cover on degraded grazing and forest lands, to stabilizing sand dunes and watersheds by establishing vegetative cover, to agro-forestry or tree crop production on fields and field boundaries, to management of common property resources, and so on (Chen, 1993, pp. 46-47). As a matter of fact, a long range of wasteland development technolo­gies is now available, partly through government initiative and sup­port and partly through research and development effort of a number of institutes, spread over different parts of India (GOI, 1991, p.73, GOI, nd). It is also a fact that the central and state governments have been activating financial institutions, including the apex agencies such as NABARD, to play a more active role in wasteland develop­ment programmes, especially those mooted through people's Institu­tions at the grass-roots level. Perhaps, the initiatives and policy inter­ventions of the past 3-4 years would show a more dramatic impact during the next few years. From this, it does not follow that all that was conceived and done in the past leaves no scope for second thinking and improvement. There is much scope for improvement. Some ideas are attempted by us in the concluding Chapter V.

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V Looking Back, Looking Aliead

In Chapter IV, we had a temporal view of numerous policy initia­tives, more pointedly at the level of the central government and development agencies associated with wasteland development programmes. It is clear that our understanding of the problem has significantly improved, that the crucial role of the local communities, caste-class configurations, migration especially male out-migration, topography of villages is becoming more and more manifest, that the wasteland afflicts the existence of the poor all the more precariously, that private investment and development effort can prove highly pro­ductive under specified conditions, that multi-agency approach is in­escapable for certain wasteland development projects, and so on (Burra, 1993, p.245; G.O.I, 1995A. pp. 32-36, 52-69). The review of policy strands in Chapter IV readily convinces us that wasteland is a two-edged problem. While it should be a national priority to develop as much of the land already 'declared waste' as possible, it should be equally essential to prevent fresh land going to the pool of 'wasteland' (GOI, 1995A, p.4). In other words, both 'conserving' and 'converting' are important for ensuring a sustainable process of agri­cultural development and rural well-being.

To look at various policy initiatives, programmes and schemes, as we did in Chapter IV, is one way of understanding the national or regional level efforts towards wasteland development. The operational side must also be peeped into. Going by secondary data and pub­lished reports, we take a very broad view of how the problem has been approached from an operational angle so that the gaps and deficiencies with the existing policies, programmes and projects be­come evident. We must admit, we cover some ground but leave much uncovered.

From administrative point of view, lands in rural India fall under one or the other of the following four categories:

1) private land, usually owned by individuals/households, comprising agricultural crop fields, groves and plantations, or fallow;

2) forest land, usually owned and controlled by state forest depart­ment;

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3) revenue land, under the control of state revenue department, a fairly big proportion of which is 'encroached upon', usually by local people'

4) panchayat land, under the control of village panchayat and vari­ously described as village common land, is used predominantly as grazing lands, usually open to certain social groups of the village.

It should be obvious by now that the problem of wasteland may exist, in varying form and content, with each of the four land cat­egories. For understandable reasons, the most affected are the for­est lands followed by other common lands; an estimate suggests that not less than 40.0 per cent of the total forest cover in our country falls under the category 'degraded forest lands' (GO!, 1995A, p.18). Most of the earlier policy initiatives (e.g. 1976 NCIA policy recommendations) rested nearly exclusively with development/improve­ment of degraded forest lands. No wonder, therefore, social forestry has been the most enduring policy intervention in India, has been a subject of intense debate, has lent rich experience of fostering gov­ernment-people, people-people and people-industry partnership and so on. But then, in recent years, much has happened on the side of non-forest wastelands. The Indian reality shows a criss-cross of policy initiatives and on-ground action between non-forest and forest lands.

In operational terms, five broad approaches or arrangements to the development of wastelands have been at work in India, espe­cially in response to policy initiatives of the 1980s:

1) Private management of private land under which tree plantation or agro-forestry on small blocks of private land or on strips of private land has grown under the ruberic of farm forestry. This generated an implicit bias against the landless households be­sides making a negative impact on labour-demand since tree crops are less labour-intensive than annual crops (Chen, 1993, p. 47).

2) Private management of revenue lands under which temporary ownership or usufruct rights to revenue wastelands (and occa­sionally degraded forest lands) are extended to individual households or groups of households. Tree Patta Scheme, usu-

77

ally targetted to Scheduled Castes/Tribes and women, West Bengal Group Farm Forestry directed to groups of poor land­less families, strip plantations planted on revenue lands along roads, canals and railways, with usufruct rights to individual households etc, are examples of this approach.

3) Group management of common lands under which 'community' woodlots are established on common lands through the involve­ment of the whole village community. The effort was much less successful compared with farm forestry. Recently, Tree Growers' Cooperatives, organized by the NDDB, have shown better re­sults than the old, sterotype village woodlots.

4) Government management of common lands under which the state governments take upon themselves the right and respon­sibility to manage forest lands for the public good. Here, 'com­munity' woodlots are established by the forest department on village or revenue grasslands, usually without consulting the lo­cal people. The government-people partnership could not emerge in many cases; on the contrary, misgivings, especially when 'open grazing lands' were 'recklessly' converted into pro­tected woodlots, emerged and persisted (Ibid, p. 51).

5) Joint management of common lands under which villages and settlements on the fringe of forest areas are organized, under the umbrella of Forest Protection Committee (FPC), to watch against unauthorized encroachments. One representative of each household is put on the FPC. Each cooperant member is en­titled to 100 per cent share of intermediate yields from non-tim­ber forest products and a cash equivalent of 25 per cent share of the final timber harvests. Besides, the FPC members are guaranteed employment in any local forest management opera­tion.

The FPCs are in operation in West Bengal, Gujarat, Madhya Pradesh and Orissa and are good examples of government-people partnerships, in forest management and wasteland devel­opment.

ASSESSING PAST PERFORMANCE

By all reckoning, the past performance has been slow and indequate. One can point out gaps both at the policy level as well

78

as in implementation. Quite frankly, development of these lands has never been a high priority Item both for the central and state governments, as candidly admitted by the High-level commit­tee on Wastelands Development (GOI, 1995A, p.1). Over the years, the real emphasis has been on arable lands and the immediate need for enhancing 'food security' has prevented state governments from taking a very serious view of the wastelands problem. Paucity of funds, more aggravated as it has become in recent years, has also stood in the way {Ibid, p.38). Lack of coordination between the forest department which is the implementing agency in most states for a number of wasteland development schemes and other departments such as agriculture, horticulture, soil conservation, minor irrigation and rural development, has been another big road block {Ibid, p. 57 ). Not only that, one comes by contradictory policy stances, say, between central and state governments, and more glar­ingly, between one government department and the other. For ex­ample, while the Forest Department in the state insists that a coop­erative be registered before land is granted, the Registrar of Coop­erative Societies insists that land be granted before the cooperative is registered (Singh-Burra, 1993, pp. 15-16). Likewise, a recent amendment (in Section 2-iii) of the Forest Conservation Act bans the assignment or lease of forest land to the people. All this runs con­trary to (Section 28 of) the Indian Forest Act, especially as it nulli­fies the entire concept of village forests. In pursuance of the powers given to the states , under the amended section, some states had actually transferred forest areas to village bodies for protection and management. The contradictions are thus of government's own mak­ing.

The amended Conservation Act also prohibits planting of horticul­ture crops, oil-bearing plants, palms and medicinal herbs on forest lands unless prior permission of the central government is obtained (Saxena, 1993, p. 305). This has two clear implications. One, it gives rise to a suspicion that all usufruct-based trees, which attract people to the forest areas, like ber, mahua, neem, karanj, jamun tamarind and several medicinal herbs, would no more be permitted. The tendency to replace such livelihood trees by commercial trees (e.g. teak, eucalyptus, pine, etc.) gets reinforced. Two, in all fairness, the new laws are not being implemented rigidly, as usufruct schemes are being promoted by central government itself. Yet, the potential power for harassing the people is immense, and in some areas, more 'conservation-oriented' forest officials may wake up to see their

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way through (Ibid p. 305). Finally, the scheme of making forest pro­duce available to the people through government depots, as envis­aged in the new Forest Policy, amounts to banning the entry of the people to forest lands. Its consequences for the poor and the forest-dwellers can be easily imagined. Most seriously, people living inside forests would now have to trudge long distances to reach govern­ment depots to buy their requirement which so far they got free. Throwing them again to the mercy of petty officials shows that gov­ernment has still some faith left In the tottering public distribution system.

it is not surprising, therefore, to see that under such contradictory policy stances, lack of clarity and commitment and weak bargaining position of the local poor, wasteland development programmes did not register the ready enthusiasm of the people at large. For ex­ample, first, in the matter of granting usufruct rights for the planted trees to the people, in most cases, no agreements were executed even simple lette. s were not given. The 'fear' of the forest officials are more real than imaginery (Saxena, 1993, p. 300).

Second, discrepancies existed from district to district between what the beneficiaries were told about the prospective benefits and what was actually done to accommodate local needs. For example, some district forest officers permitted inter-crop cultivation of legumes while others did not, although the state government scheme specifi­cally provided for it (Ibid, p. 300).

Third, again in many cases, species were selected, such as eucalyptus and acacia auriculliforniis, which did not produce any in­termediate goods of substantial benefit to the poor. On the other hand, people were so poor that women were forced to use leaves of these trees for cooking though the fumes harmed their eyes. This also removed litter from the ground, thus increasing the possibility of soil erosion. Their sense of identification with the planted trees would have been stronger had more socially useful trees been planted. The result of all this was that the beneficiaries saw themselves only as wage-employees working for less than minimum wages. The part of the deal concerning a share in the final produce meant little to them in operational terms {Ibid, p. 300).

Like in most other government programmes and schemes, performance in this area too is measured in terms of 'expenditure

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targets'. One does not come across any precise and progressively updated estimates of 'wasteland developed' while the financial out­flows are religiously mentioned under each scheme (GOI, 1994, pp. 5, 13, 27-34; GOI, 1995, pp. 57-67). The weaknesses that charac­terize our delivery systems in rural areas are pretty open now, a la the plethora of poverty eradication programmes and it must be frankly admitted that a great deal of public investment made in this field turns out to be infructuous. The feedbacks are not very com­mon to come by and whatever feedbacks are available show de­pressing outcomes in many cases. The gaps between financial out­flows and physical achievements especially for remotely controlled centrally sponsored schemes are indeed big and in the area of wasteland development, not accurately known for many schemes.

Let us quote a precise case to substantiate this point. A volun­tary agency in Orissa was forced to go in for fodder and fuel vari­eties because those were the terms of the grant from the funding agency. The local tribal community did not have problems of either fuel or fodder but were interested in growing fruit trees and bamboo for the monetary benefit they would bring. In contrast, another group in Orissa - PREM - consciously tried to respond to their perception of what tribal needs were and in the particular case, these were thought to be related to better income-earning opportunities rather than fuel and fodder. Hence they went on to plant fruit trees (Burra, 1993, p. 278).

Much of the money routed through voluntary agencies achieves little primarily because feedbacks are not forthcoming. And those who do a good job face 'official hassles' of diverse types, most typi­cally rigid official norms and people do not raise their voice since they are at the receiving end. After all, there is a big difference between the beneficiaries being 'treated' as wage-earners and usu­fruct-holders and so on. People's participation has been only a belief in the past, except for a handful of schemes mounted on watershed basis. Thanks to Hanumantha Rao Committee recommendation, the prominance being attached to all area development schemes, should make some difference in the coming years.

The flow of institutional finance has had its own share of constraints, especially under the Investment Promotion Scheme (IPS) for which the NABARD is expected to play a leading role. By its very design, the IPS involves 2-3 'cooperating parties' and a few

81

pre-requisites which it is not that easy to fulfil in many cases. A few glaring constraints ne&d to be underlined in particular:

1) In some cases, tripartite partnersliip is conceived among the prospective tree growers, a user-industry and the financial insti­tution. The tree growers are expected to sell their produce to the user-industry through whose partnership the whole project is conceived and acquires a commercially-viable status. The tree growers have the pre-emptive option to sell elsewhere but re­turn the borrowed sum to the financial agency/user-industry, and so on. The borrowed money is not coming back in some cases. The whole partnership scheme collapses. So does the faith of the lending agency. Recent complaints by WIMCO to the NWDB echo such 'wilful lapses'. Who these defaulting tree-growers are is an open secret !

2) In many cases, the tripartite arrangement can take oil ortf when public land is made available t^ fte slaJe gmmrmmmii lor ttree plantation on a commercial seal©.. A inegrel toy fiiie state goveTrareent is quite commonly reported. The scheme fails half way Ihreugh.

3) The state land cefling laws are rigidly enforced when big projects are conceived of by user-industry. The contradiction is quite apparent. The user-industry cannot 'own' or 'operate' land beyond the prescribed limit because the law does not peri^it. The economies of scale cannot be de.nved. on the other hand, because volume of operation remains limited.

4) The prospects for attracting institutional finance are genemif low in under-banked districts. By any objective reckorin^, m fty districts in '\n0m, m^^mWy those in tribal or fo'rest ®r hilly re­gions, are under-toankM. And somewhat paradoxie^Ily, thesfe are the areas wh^re the incidence of wa::iteland i^ relatively higher, fh^ potential for launchirag mm4Vff\enX promotion schemes m lairly high but the yaiod-rp,'alities and the facilitat-Inq infr^^^Ktures are not conducive. • a

5) Sometimes, the modus operandi or the lending agencies includ­ing the rural banks, is unhelpf'Ji. For a repayment default by a few households, the whole village is declared a defaulter. The credit agency wants to p'ay safe, especially because, under the

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law, it has no legal sanction for attachment of property or as­sets. The credit flow thus gets stifled.

6) Quite honestly, wasteland development schemes, especially those for which gestations cannot be accurately foreseen by the lenders, are not in the high priority sector.

Another serious criticism of wasteland development policy (espe­cially for forest development programmes) is the general exclusion of women from such activities. There is a near concensus that "given both their knowledge of the natural resource base and their orientations, women's involvement in designing wasteland projects and in choosing species for plantation is crucial for the welfare of the household" (Singh-Burra, 1993, p. 13). Ovenwhelming evidence of successes registered by women's initiatives, especially through coop­eratives and people's samitis, in such projects is now available to support their case. The Bhurkura Women Samiti in Bankura district of West Bengal, the Forest Protection Committees in districts East Midnapore, Purulia and Bankura of West Bengal, the Dasholi Gram Swarajya Mandal's Chipko Movement In Chamoli district of Uttar Pradesh etc. are a few examples to support the view that women's involvement in some types of wasteland development projects could be highly rewarding (ILO, nd; Viegas-Menon, 1993, pp. 179-209). In particular in areas characterized by high male out-migration (e.g. large parts of Rajasthan and the hills of Uttar Pradesh), greater participation of women is inescapable. "It is not only the arithmetic of gender presence that accounts for increased participation of women but also the fact that in the absence of their men, women are forced to play a greater role in decision-making ..." (Burra, 1993, p. 281). The future policy thrust must, therefore look more and more to women participation in the interest of economizing the cost, better performance and more effective relieving of the poverty-stress.

LOOKING AHEAD

It is clear that much needs to be done to plug in policy deficien-ri$ cies, implementation weaknesses and for raising the conscionsness

of the people at large. To put it bluntly, same hard thinking and some unconventional doing are needed. In what follows, without pre­tending to cover much ground, we ventuie to give below a few sug­gestions for improving the wasteland development programme in India.

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Approaching the Problem

1) It is essential to look at the wasteland problem in conjunction witfi agricultural development plans. Each state may be asked to prepare 10-15 year perspective plan for proper water and land-use as also for wasteland development. A village or a cluster of villages having regard to the mini-watershed principle should be the unit of planning and such plans should form the basis for agricultural plans at the state and national levels.

While taking due care of degraded lands is essential, it is equally essential to look after the non-degraded lands as well. The past experience clearly shows that our good forests are subjected to illegal and unscientific exploitation and on agricul­tural lands, due to overuse of irrigation, chemical fertilizers and pesticides, productivity gets threatened. In one word, 'prevention' and 'cure' must go side by side (GOI, 1995A, pp. 10, 37). Let us learn from our own past and check unbridled pace of re­source depletion, land and water alike.

2) Following the Hanumantha Rao Committee recommendation, the mini-watershed as the overarching principle of area development sfiould be adopted by all departments. All the land in the mini-watershed regime, whether private, forest or panchayat, should be taken up for development, in a systematic way from ridge to valley {Ibid, p. 37).

3) The past approach of treating non-forest wastelands in isolation of forest wastelands has not worked well. It is clear by now that 'non-forest and forest wastelands are almost invariably so juxtaposed on the ground that it is not possible to treat them separately, if the complete watershed approach is to be fol­lowed {Ibid, p. 42).

4) We should not feel shy in inviting private investment effort to­wards development of public/common wastelands. The idea of forging people-industry-government partnership reflects a robust common sense besides freeing government from its 'involvement from beginning to the end'. The problem is too serious to be tackled by government alone {Ibid, p. 51). Perhaps, some un­conventional steps such as handing over degraded public wastelands to private corporate sector for its development may

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be indicated in some cases. We should not hesitate to do so, provided overall socio-economic safeguards are not thrown to the winds.

There is a need to specify the respective role and involvement of each public institution, begining with people's group at the village-level and ending with government at the central level. The involvement of NGOs and women groups can be highly productive in many cases. But then, the working rapport be­tween government and NGOs can be strengthened if project approvals are decentralized, say, to district level and such agencies are treated as partners in development {Ibid, p. 49).

5) Public wasteland must first of all be offered to village commu­nity for development. User-groups should be the second choice followed by the NGOs as the third choice and so on. The moot point is that the 'maximum good of the maximum number of people' may be promoted, depending upon the local condi­tions. No single rigid rule can work under all circumstances.

The Question of Technology

The question of wasteland development technologies has attracted only a peripheral attention. A few technologies are already at work, some others are ready on the shelf and a few others are still to be finalized. It would be presumptous to believe that we have techno­logical answers for each category of wasteland. Nor is it necessary to have such answers in one go. The available technologies are evolved by various regional research institutes, presumably guided by local land degradation problems. A common national-level thread is missing; Prioritarization at national-level has hardly ever been a point of focus. The National Remote Sensing Agency gives us as many as 13 categories of wastelands. We have no technology for tackling many of them. Technology development is thus to be an area of high priority.

Updating Information Base

There is likewise the need for updating ourselves about the true magnitude of the wasteland problem. The need for a second round NRSA satellite survey, perhaps an extended and improved version of the 1986-88 attempt for 237 districts, is obvious. It is a pity that

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detailed information/mapping is still not available for more than 146 districts although the survey w as conducted nearly a decade back.

There is also the need for straightening out land-use classifica­tion at the village-level so that more accurate ideas on wastelands come forth through revenue records. It is commonly conceded that village-level revenue records, as maintained in their present form and content, do not give scientific estimates of wastelands. The ideal situation would be to evolve scientific guidelines for the local-level revenue officials which may be checked for their observance from time to time. The ongoing attitude of sparing only a casual attention for such records must be given up.

Working of Financial Institutions

The financial institutions must also change their mindset while dealing with wasteland development proposals. This is indeed the area where the NABARD's leadership is immediately called for. Its role must go beyond working out or prescribing norms for project cost components. Its subsidiary role as a modest go-between has been responsible for keeping its main refinance activity at a fairly low level. For example, nursery raising could also be financed by it. Similarly, growing stock on leased-in lands could be accepted by fi­nancial institutions as collateral security for advancing loans. Further, NABARD finances can possibly focus on a much wider variety of in­vestments on private lands. In short, NABARD may better institute a research inquiry at its own level, especially in the matter of commer­cial success of species, e.g Jo Jo Ba, Poplar, Kadam, Eucalyptus etc, to decide the extended canvas of its operation. Also, the poten­tial for extending its range of operation can emerge from a careful study of the most-seriously affected districts. The moot point is that the financial institutions themselves should breathe a 'liberated air' and let their prospective clientele 'enjoy' the same.

Grassroots Adjustments

1) The past experiences clearly show that the most successful projects are those which did not have to deal with inter-caste rivalries and tensions. "When people belong to the same social class, there is much less dependence upon each other in a hierarchical patron-client relationship. One could hardly expect equal participation in a project, where the target group con-

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sisted both of large landowners and the landless (Burra, 1993, p. 280). In the highly stratified village society in India, the poor and landless can be emboldened to articulate themselves through their own homogeneous group kinship. Socio-political mobilization among homogeneous caste/class groups is thus an

• indispensable first step towards the success of rural develop­ment programmes, including the development of wastelands.

2) Usually, the wasteland development schemes involve long ges­tation periods, from the point of view of individual farmers. It may seem desirable to supplement wasteland development schemes with some other income-generating schemes such as

^ dairying, goat rearing, tassar cocoon-rearing, fruit processing and so on. Admittedly, many more schemes can be thought of, depending upon local resources, skills and market situations {Ibid, p. 279). The integration of such schemes into a project

• on wasteland development is likely to nurture and sustain inter­est in wasteland development itself. Involvement of women in such supplementary schemes would be yet another step in the right direction.

> 3) Imaginative efforts are needed to link wasteland development with the marketing and if possible processing, of fruit and other products grown on that land. It is obvious once again that in­dustry-agriculture linkages for some specific wasteland develop­ment could be highly beneficial to both sides.

4) Often conflicts arise over sharing the usufruct when common lands are developed. When entertaining proposals tor develop­ment of public wastelands through community or group partici­pation, the funding agency must ensure that sufficient safe­guards, say a written concensus, are built in against such eventualities. It is almost certain that in areas where develop-

1^ ment of common wasteland is yet to take place, the possibility of such disputes is not foreseen. Such areas should learn from their predecessors which have already undergone tremendous

I transformations of production capabilities (Burra, 1993, p. 278).

Institutional Restructuring

As we noticed in Chapter IV, we have no nodal authority to look after the wide variety of degraded and wastelands; individual Ministries/Departments pursue specific land development schemes,

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often with no coordinated view of the national effort. Sometimes, in­ter-departmental conflicts arise regarding the status, ownership and ^ use of land. In sum, the work for wasteland development is piece­meal, diffused and not sharply focussed (GOI, 1995A, p. 38). The consolidated experience of the past few years suggests that all affor­estation, wasteland development, soil and water conservation and • related schemes must be put under the charge of a suitably struc­tured and strong nodal authority at the centre, with its counterpart in each state and district. This authority should coordinate linkage of different scheme and see that a common approach to development of degraded and wastelands is adopted {Ibid, p. 38).

In the opinion of the High Level Committee (HLC) on Wastelands ^ Development, this nodal authority at the central level may be called the Central Landuse Authority (CLA). The CLA should have the Minister of Rural Areas and Employment as its Chairman and 3-4 non-official experts. Secretaries of all concerned Ministries/Depart- ' ments, etc as its members. The CLA should have wide functions, encompassing practically all aspects connected with wasteland devel­opment which, at the moment, lie scattered at 4-5 points in central government hierarchy (Ibid, p. 39). Understandably,, the two dormant bodies (Landuse and Wasteland Development Council and Board) * would get wound up, or suitably subsumed in the CLA.

A similar State Landuse Authority (SLA) would perform the same set of functions at the state level, in particular, each SLA should prepare a Master Plan on mini-watershed basis of 12-15 years for degraded and wasteland development. A time-bound agenda should be set for effective implementation {Ibid, p. 40).

The Zila Pahshad would play a much bigger role under the new dispensation. Besides approving the watershed development plans, it shall receive funds directly from the Central/State governments, shall release funds to panchayat/watershed development committees, shall JH exercise administrative/financial control over PIAS, WDTs, village-level WDCs, and so on. Then, the Gram Panchayat would act as the watchdog for ail watershed development schemes {Ibid, p. 41). ^

Finally, the watershed development strategy is soon bound to overtake the rural landscape in India. With this, the questions of accountability and transparency are adequately answered. Feedbacks may also flow in. In sum, the partnership between public money and people's performance may show better results.

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