Perspective Plan for Land Use Planning · Lack of erosion control measures and drainage facilities,...

REVISION OF PERSPECTIVE PLAN FOR LAND USE IN

TAMIL NADU

Final Report

Dr.N.Raveendaran

Dr.M.Chandrasekaran

Dr.R.Balasubramanian

Department of Agricultural Economics

Centre for Agricultural and Rural Development Studies

Tamil Nadu Agricultural University

Coimbatore – 3

Project Funded by

Tamil Nadu State Land Use Board

Chennai

1

Executive Summary

The competition for land is increasing in intensity due to increasing population pressure

on limited land resources. Land continues to be a major source of power and symbol of wealth.

In spite of the increasing competition for land and water resources in the state in the wake of

industrial development and urban expansion, scientific land use plan is not in place thus resulting

in the coexistence of both overuse and underuse of this precious natural asset. Unless proper

planning and management of land is done it will be difficult to achieve the sustainable

development of State economy. Land use capacity varies overtime with changing spatial

environmental and economic conditions. This necessitates the identification of land use capacity

for alternative choice of land allocation among the various uses. It is also necessary to delineate

the problem areas, which needs separate attention. Since the supply of land is fixed, use of land

for one purpose will be at the expense of the other and when huge investments are made on land

for one purpose it cannot be reverted back for other uses. Therefore, this study was undertaken to

prepare a realistic plan for land use in Tamil Nadu taking into consideration the growth in

various sectors of the economy which require different qualities of land. Based on analysis of

growth in various sectors in the past and also based on Markov chain analysis of probabilities of

shifts in land across different categories and uses, the land use plan has been suggested for the

state. Based on the econometric analysis of factors affecting land use under different categories,

the following suggestions are made to conserve the precious land resources for productive

purposes without affecting the growth in industrial development.

Enhancing agricultural productivity has been the major land-saving strategy that has

helped to meet the increasing demand for food and fodder amidst increasing pressure on land for

various other developmental purposes. Increasing productivity per unit of land is also helpful to

increase the productivity per unit of other resources such as water and labour. However, in recent

times there is deceleration in productivity growth in irrigated agriculture in several parts of the

state. Continued emphasis on increasing agricultural productivity especially in dryland areas and

marginal lands is essential to achieve production targets in the wake of shrinking land and water

resources. These are the areas with low or very low base yield levels and hence bear the potential

for substantial improvement in productivity. Future agricultural policies should focus more on

2

increasing net income per unit area of land rather than traditional focus on productivity alone.

This will enable farmers to invest in modern, land saving technologies in future.

Erosion and degradation of soils, loss of fertility due to mining of soil nutrients are taking

place at faster pace in some of the highly productive regions of the state. Lack of erosion control

measures and drainage facilities, inadequate investments in soil and moisture conservation,

imbalanced fertilizer use and inadequate use of micro-nutrients are some of the important factors

contributing for low productivity and poor soil health. Hence, massive investments are needed at

farm level and beyond to reverse these trends.

Development of rural infrastructure such as roads, markets, irrigation and social

infrastructure such as health and education are likely to increase farm income and labour

productivity in rural areas. Physical infrastructures are found to reduce the extent of fallow lands

and hence it will ensure effective utilization of land resources.

Enterprise diversification and precision farming technologies are the two important

means to achieve higher income and to reduce risk and cost of production which will in turn

enable the farmers to save and invest more in farming. This will also mitigate negative

environmental consequences of specialized, intensive agricultural practices. Specific policies are

required to reduce the diversion of fertile lands from agriculture to non-agricultural uses.

Demand for land for non-agricultural uses may be met only from marginal and low

productive lands. However, this is not possible as long as land allocation decisions are made

using market forces alone. Direct and indirect government intervention through regulation of

land use— preventing the diversion of fertile lands from agricultural to non-agricultural

purposes, landscape preservation act, permission to start new industrial units in least fertile lands,

etc.—are needed. Indirect regulatory policies such as tax concessions for setting up industries in

low productive regions will be helpful to reduce diversion of fertile and high-productive

agricultural lands for non-agricultural purposes.

Speculative activities in land market especially in the urban fringes of large towns and

cities should be prohibited by suitable amendments in existing rules governing the conversion of

agricultural lands for housing and other purposes. A comprehensive Agricultural Land

Preservation Act shall be passed by the Government to protect fertile farm lands.

1

Perspective Plan for Land Use Planning

I. Introduction

Since the dawn of civilization, land and water have been the basic elements of the life

support system on our planet. Great civilizations flourished where these resources were available

in plenty and they declined or perished with their depletion. The demand for land increases due

to economic development while the supply of land is fixed. In India land is the most threatened

resource, which is a critical input for agriculture. They have profound consequences on the

structure and functioning of landscapes. The intensification of agriculture and urban settlements

has severe consequences on the water cycle, nutrients and pollutants. More than 70 per cent of

the population of India derives livelihood and environmental security directly from natural

resources viz., soil, water, vegetation and forests. Escalating demographic pressure has reduced

per capita cultivated land from 0.48 ha in 1951 to 0.14 ha in 2000. Livelihood needs of rural

communities are expected to be realized from increased productivity without degrading qualities

of natural resources. In recent times the land resource has been subjected to a variety of pressures

and land degradation is a massive, global environmental problem. It is reported that degraded

lands worldwide include 5.8 million km2 degraded by deforestation - mainly for agricultural

production, 6.8 million km2 degraded by overgrazing, 1.37 million km

2 degraded for fuel wood,

5.5 million km2 degraded by agricultural mismanagement (as a result of wind and water erosion;

salinization and water logging; and soil nutrient loss), and 0.195 million km2

degraded by

industry and urbanization. By the year 2020, land degradation may pose a serious threat to food

production and rural livelihoods, particularly in poor and densely populated areas of the

developing world. In India, out of the total 328 million hectares of land, 173.6 million hectares

are considered to be affected by land degradation.

Tamil Nadu. the southern most state of the Indian peninsula, is spread over 1,30,058

Sq.Km; it lies between 80° 5" to 130° 35" N and 760° 15" to 800° 20" E and accounts for about

4 percent of the total area of the country. The topography of Tamil Nadu broadly consists of the

coastal plains in the east; uplands and hills as one proceeds westwards; the plains account for

more than half the area of the state, with a population of 6.21million (2001 census), shares about

6.8 per cent of the total population in India. However its share in total land area of the country is

2

only four per cent and its share in total water resources of the country is only three per cent. In

Tamil Nadu the per capita availability of land is 0.19 ha while the per capita net sown area is

only 0.10 ha.

The competition for land is increasing in intensity due to increasing population pressure

on limited land resources. Land continues to be a major source of power and symbol of wealth.

In spite of the increasing competition for land and water resources in the state in the wake of

industrial development and urban expansion, scientific land use plan is not in place thus resulting

in the coexistence of both overuse and underuse of this precious natural asset.

Rationale

The demand for land arises out of two basic needs, one as a consumption commodity for

housing, recreation, environmental preservation and asset creation and other as a factor of

production for use in a variety of agricultural, industrial and infrastructure production processes.

The transfer of good quality agricultural lands having high revenue grade to non-agricultural

purpose is indicative of growing demand in non-agriculture sector for lands, but with adverse

consequences to agricultural productivity and production, particularly in view of the rising

population and constancy of net cultivated area.

Unless proper planning and management of land is done it will be difficult to achieve the

sustainable development of State economy. Land use capacity varies overtime with changing

spatial environmental and economic conditions. This necessitates the identification of land use

capacity for alternative choice of land allocation among the various uses. It is also necessary to

delineate the problem areas, which needs separate attention. Since the supply of land is fixed, use

of land for one purpose will be at the expense of the other and when huge investments are made

on land for one purpose it cannot be reverted back for other uses.

Land use planning is the important process through which the economic growth and the

environmental objectives are defined. Lack of effective land use planning and practices is one of

the major factors responsible for natural disasters, and low agricultural productivity, contributing

to unsustainable overall socio–economic development. There is an immediate need for

appropriate land use planning, for undertaking protective measures to economize the use of land.

Hence, there is need for a proper land use policy to monitor and regulate the use of available

land. A careful analysis of changes taking place in land use pattern from time to time is thus

essential to identify the challenges confronting the scientific use of land.

3

Several studies have been undertaken to examine land use, the State Planning

Commission has now suggested to take up a study on preparing a perspective plan for land use.

This Research study has therefore been developed with the following objectives.

Objectives

The general objective of the study is to prepare a perspective plan for land use in Tamil

Nadu. The specific objectives are;

1. to analyze the changes in area under different classes of lands in all the districts of Tamil

Nadu, and

2. to estimate the demand for land from agricultural and non-agricultural sectors

independently at sub sector level.

4

II. Methodology

The study was primarily based upon the secondary data. The study historically traced the

trend for the period of 1960 to 2003-04 (pre- and post-Green Revolution periods and post-

liberalization period). Secondary data on land use pattern, relating to agriculture – area under

major crops, area of crops irrigated, net return from crops, soil classification, growth of housing,

transport, warehousing and storage, educational institutions, health, industries, recreation, etc.,

were collected and analyzed.

The approach was first viewed at the needs of the above sectors/sub-sectors for the next

15 years, including population growth rates to allocate land based on standards established. A

thorough review of studies sponsored by State Planning Commission on land use and other

agencies was made as a prelude to further analysis.

The study covers (i) agriculture including animal husbandry, community cattle grazing

needs, need for fallowing of intensely cultivated lands, ways and means to discourage long

period of fallowing rendering land difficult to cultivate, (ii) industry and service sectors, (iii)

housing and other domestic requirements, (iv) forest cover, (v) transport infrastructure and (vi)

the requirements of health, education, recreation, etc. The study also covers the degraded lands,

their causes and strategies/methods to reclaim/ use.

Data Analysis

District wise compound growth rate and decadal changes were worked out for different

land use categories, area, production, and productivity for major crops and for population.

Growth trends were worked out for different land use categories, population, livestock and

infrastructures at district level to project the demand for the year 2020. Markov chain model was

used to find out the shift in different land use categories. The study is based on the analysis of

historical trends in land use pattern, crop pattern and infrastructure development using time-

series data at state level and at district level. Data on land used for agricultural and non-

agricultural purposes such as industrial sector, housing, roads and other minor uses are also being

collected. All these data pertain to the period 1960 to 2003-04. The land use for agriculture is

disaggregated into crop-wise land use pattern (cropping pattern). The time series data for the

period from 1960 to 2003-04 were further classified into green revolution and post green

revolution periods and pre- and post-liberalization period for the purpose of comparative analysis

and projection of future trends in land requirement.

5

The analytical approach is follows: In the first stage we intend to look at the needs of the

above sectors/sub-sectors in the next 15 years, taking into consideration the population growth

rate and other demand-side factors which will be growing in their magnitude. In the second

stage, the remaining land will be optimally allocated to other categories of land use based on

standards established. A thorough review of studies on land use already sponsored by State

Planning Commission on land use and other studies conducted in India and abroad was

undertaken to firm up the methodology for this study.

The Markov Probability Model

Any sequence of trials (experiments) that can be subjected to probabilistic analysis is

called a stochastic process. For a stochastic process it is assumed that the movements

(transitions) of objects from one state (possible outcome) to another are governed by a

probabilistic mechanism or system. A finite Markov process is a stochastic process whereby the

outcome of a given trial t(t=1,2… T) depends only on the outcome of the preceding trials (t-1)

and this dependence is the same at all stage in the sequence of trials. Consistent with this

definition, let

Si represent the r states or possible outcomes; i 1,2, …., r,

Wit represent the probability that state Si occurs on trials t or the proportion observed in

trial t in alternative outcome state i of a multinomial population based on a sample of size n, i.e.,

Pr (Sit),

Pij represent the transitional probability which denotes the probability that if for any time

t the process is in state Sij it moves on the next trial to stage Si, ie., Pro (Sj, t+1/Sji) = Pij.

P = (Pij) represent the transitional probability matrix which denotes the transitional

probability for every pair of states (i,j=1,2 …, r) and has the following properties

0 Pij 1 ……… (1)

and Pij = 1, for i = 1,2 ……, r ……… (2)

j

Given this set of notations and definitions for a first order Markov chain, the probability

of a particular sequence Si on trial t and Sj on trial t+1 may be represented by

6

Pr (Sij t, Sj t+1) = Pr (Sij) Pr (Sj t+1/Sit) = Wit Pit ……………. (3)

and the probability of being in state j at trial t+1 may be represented by

Pr (Sj, t+1) = Wit Pij or Wj, t+1 = Wit Pij ………….. (4)

The data for the study are the proportion of area under six groups of crops, ie., rice,

coarse cereals, pulses, oilseeds, vegetables and fruits and other crops. These proportions change

from year to year as a result of the factors like weather, technology, price and other institutional

changes. It is reasonable to assume that the combined influence of these individually systematic

forces approximates to a stochastic process and the propensity of farmers to move from one crop

state to another differs according to the crop state involved. If these assumptions are acceptable,

then the process of cropping pattern change may be described in the form of a matrix P of first

order transition probabilities. The element Pij of the matrix indicates the probability of a farmer

in crop state i in one period will move to crop state j during the following period.

Estimation of Transition Matrix

Equation (4) can be a basis for specifying the statistical model for estimating the

transition probabilities. If errors are incorporated in Equation (4) to account for the difference

between the actual and estimated occurrence of Wj t+1, the sample observations may be assumed

to be generated by the following linear statistical model

Wit = Wt t-1 Pij + Uji ………….. (5)

j

or in matrix form it can be written as :

Yj = Xj Pj + Uj …………… (6)

Where Yj is a (T x 1) vector of observations reflecting the proportion in cropping pattern j in

time t, Xi is a (T x R) matrix of realised values of the proportion in cropping pattern i in r-1, Pj is

a (Rx1) vector of unknown transition parameters to be estimated and Uj is a vector of random

disturbances.

Markov chain model has been used to analyze the pattern of spatial shift in different land

use categories over the last 44 years (1960 to 2004). The transition probabilities from Markov

7

chain analysis were used to forecast the likely trends in land required for different uses. The

Markov chain procedure analyzes a pair of land use images and outputs a transition probability

matrix, which is a matrix that records the probability that each land use category will change to

every other category. This analysis establishes the extent of expected land use change from each

existing category to each other category in the next time period.

Econometric model of factors affecting land use changes

Econometric analysis of factors affecting the change in various land use categories has

been carried out to identify the key variable affecting the change in land put to various uses. The

demand for land for various developmental activities such as roads, schools, residential buildings

and industries were worked out based on the past trends in these activities and the unit area

required for these activities.

The empirical SURE model

FOR_GA = a + b0 TIME + b1 ROADS + b2 TNPOPDEN

BUCL_GA = a + b3 TIME + b4 NSA + b5 LPNAU + b6 TNPOPDEN+b7 URB_TOTP

NAU_GA = a + b8 TIME + b9 ROADS+ b10 URB_TOTP

CW_GA = a + b11 TIME + b12 NSA + b13 TNPOPDEN + b14 GIA_GCA

PPAGL_GA = a + b15 TIME + b16 NSA + b17 RF + b18 TNPOPDEN + b19 URB_TOTP

MTC_GA = a + b20 TIME + b21 NSA + b22 LPNAU + b23 TNPOPDEN

CFAL_GA = a + b24 RF + b25 ROADS + b26 LPNAU+ b27 URB_TOTP

OFAL_GA = a + b28 TIME + b29 RF + b30 + b31 ROADS + b32LPNAU

+ b33GIA_GCA + b34URB_TOTP + b35AGLAB_TW

NSA_GA = a + b36TIME + b37RF + b38ROADS + b39TNPOPDEN

+ b40URB_TOTP + b41GIA_GCA + b42 AGLAB_TW

where,

FOR_GA = Share of forest lands to total geographical area

BUCL_GA = Share of barren and uncultivable land to total geographical area

NAU_GA = Share of land put to non-agricultural uses to total geographical area

CW_GA = Share of cultivable wastes to total geographical area

PPAGL_GA = Share of permanent pastures and grazing lands to total geographical area

MTC_GA = Share of land under miscellaneous tree crops to total geographical area

CFAL_GA = Share of current fallows to total geographical area

OFAL_GA = Share of other fallows to total geographical area

NSA_GA = Share of net sown area to total geographical area

TIME = Trend variable from 1 to 40 (for the period 1960- 61 to 1999-2000)

RF = Rainfall in mm

ROADS = Road density (km/sq.km of geographical area)

TNPOPDEN = Population density

NSA = Net sown area

8

LPNAU = Area under land put to non-agricultural uses

URB_TOTP = Share of urban population to total population

GIA_GCA = Share of gross irrigated area to total geographical area

AGLAB_TW= Share of agricultural labour population to total workforce

9

III. Results and Discussion

State-level and district-level secondary data on land use pattern, cropping pattern, area of

crops irrigated, net return from crops, soil classification, transport, etc., for the period of 1960 to

2003-04, are classified into pre- and post-green revolution periods and pre- and post-

liberalization periods.

Nature of the Secondary data collected:

o Trends in population and urbanization

o Land use pattern

o Cropping pattern

o Road infrastructure

The data collected on land use pertain to the following aspects: (i) Agriculture including

horticulture and tree crops, grazing lands and fallow lands, (ii) Industrial sector, (iii) housing and

other domestic requirements, (iv) forest cover, (v) transport infrastructure and (vi) land required

for public uses such as health , education , recreation, etc.(vii) waste lands and barren and

uncultivable lands.

All these data have been computerized. Growth rates have been estimated for all the land use

categories so as to predict the future trends in land use.

Salient findings

The analysis of growth rates in various land use categories over the last 44 years (1960 to

2004) indicates the following trends (Table 1):

Out of the 13 composite districts (which exist right from the beginning of the data series),

the area under forests has increased in six districts, while it has declined in seven districts. The

area under forests in the state as a whole has increased marginally at the rate of 0.35 per cent per

annum. Barren and uncultivable lands which normally remain unused for any productive

economic purpose has declined in all but two districts. Consequently the area under this category

of land has declined in the state as a whole at the rate of 1.83 per cent per annum. Land put to

non-agricultural uses has registered a moderate progress of about one per cent per annum with

eight districts showing positive growth and the remaining five districts showing negative growth.

The area under culturable wastes has declined significantly in the state as a whole at the

rate of 1.86 per cent per annum. Four districts viz., South Arcot, North Arcot, Salem and

10

Coimbatore districts have registered a positive growth rate in area under this category of land.

Permanent pastures and other grazing lands have registered the largest negative growth (-2.87%)

among all land use categories in the state, with only Salem and Pudukottai districts showing

positive growth. Area under miscellaneous tree crops and groves has recorded a marginal decline

of about 0.25 per cent per annum with only three districts viz., Pudukkottai, Ramathapuram and

Tirunelveli recording positive growth.

Both current fallows and other fallows have increased in the state, with other fallows

showing the higher growth rate of 2.25 per cent per annum. One of the most disturbing trends is

the growth in land under other fallows in all districts except the composite South Arcot and

North Arcot districts. Both the net sown area and gross cropped area have decreased by about

0.50 per cent in the state as a whole. Gross cropped area has shown a negative trend in all but

two districts viz., Salem and The Nilgiris.

11

Table 1. Classification of districts based on growth trends in various land use

categories (1960-2004)

Land use Categories Districts registering

positive growth

Districts registering negative growth

Forests (0.35) Chengalpattu (0.7)

Salem (0.38)

Madurai (1.61)

Ramnathapuram (0.29)

The Nilgris (0.88)

Kanniyakumari (0.29)

South Arcot (-2.21),

North Arcot (-0.04)

Coimbatore (-0.09), Trichy (-0.77)

Pudukkottai (-0.09),

Thanjavur (-0.48),

Thiruneveli (-0.03)

Barren land (-1.83) Pudukkottai (0.04)

Thanjavur (0.39)

Chengalpat (-2.06)

South Arcot (-2.19)

North Arcot (-1.56)

Salem (-1.27), Coimbatore (-2.68))

Trichy (-2.28), Madurai (-0.7)

Ramnathapuram (-5.23)

Tirunelveli (-1.37)

Nilgris (-7.47), Kanyakumari (-5.42)

Land put to non

agricultural Use

(1.04)

Chengalpat (1.21)

South Arcot (0.52)

North Arcot (1.25)

Coimbatore (2.69)

Madurai (1.25)


Tirunelveli (0.96)

Kanyakumari (2.3)

Salem (-0.1)

Trichy (-0.69)

Pudukkottai (-0.11)

Thanjavur (-0.48)

The Nilgris (-0.8)

Culturable waste

(-1.86)

South Arcot (1.54)

North Arcot (2.75)

Salem (0.54)

Coimbatore (7.76)

Chengalput (-3.73), Trichy (-1.04)

Pudukkottai (-1.56),

Thanjavur (-1.39), Madurai (-2.01),


Tirunelveli (-0.58),

The Nilgiris (-6.65)

Permanent pastures

(-2.87)

Salem (2.12)

Pudukkottai (0.7)

Chengalpat (-1.93)

South Arcot (-1.37)

North Arcot (-3.03)

Coimbatore (-7.43)

Trichy (-2.93), Thanjavur (-1.79)

Madurai (-6.59)


Tirunelveli (-3.65),

The Nilgris (-1.79)

Miscellaneous tree

crops (-0.25)

Pudukkottai (5.01)


Tirunelveli (1.4)

Chengalpat (-2.83), South Arcot (-1.2)

North Arcot (-0.46), Salem (-1.00)

Coimbatore (-0.8), Trichy (-1.37),

Thanjavur (-2.34), Madurai (2.72), The

Nilgris (-3.9)

12

Current Fallows

(0.38)

South Arcot (2.77)

North Arcot (2.21)

Salem (0.02)

Coimbatore (0.47)

Trichy (3.45)

Thanjavur (0.23)

Nilgris (0.95)

Chengalpat (-2.78), Madurai (-0.94)


Tirunelveli (-2.16)

Kanniyakumari (-1.65)

Other Fallows (2.25) Chengalpat (2.64)

Salem (0.02)

Coimbatore (0.47)

Trichy (3.45)

Pudukkottai (5.75)

Thanjavur (2.03)

Madurai (2.62)


Tirunelveli (0.8)

The Nilgris (0.16)


South Arcot (0.91)

North Arcot (0.55)

Net sown area

(-0.45)

South Arcot (0.26)

North Arcot (1.23)

Salem (0.02)

The Nilgris (1.29)


Chengalpat (-.62), Coimbatore (-0.44)

Trichy (-1.15), Pudukkottai (-1.18)

Thanjavur (-0.68), Madurai (-0.29)

Ramnathapuram (-0.77),

Tirunelveli (-2.48)

Gross cropped area

(-0.52)

Salem (0.12)

The Nilgris (1.20)

Chengalpat (-1.05), South Arcot (-0.13)

North Arcot (-1.07),

Coimbatore (-0.93), Trichy (-1.44)

Pudukkottai (-0.28), Thanjavur (-2.3)

Madurai (-0.57)


Tirunelveli (-1.86)

Kanniyakumari (-0.93)

Note: All the districts are composite districts as in existence during 1960s.Figures in parentheses in

column (1) indicate growth rate of respective land use categories in Tamil Nadu state and those in column

(2) and (3) indicate growth rates in the respective districts.

13

Table 2. District-wise Compound Growth Rate for Land use Pattern (1960-2004)

Districts Year Forests

Barren

land

Land put

to NAU

Culturable

waste

Permanent

pastures

Misc.tree

crops

Current

fallows

Other

fallows

Net area

sown

Total

Cropped

area

Chengalpattu

1960-1990 1.14 -2.26 1.05 -4.76 -2.82 -3.27 3.32 -1.19 -0.52 -1.00

1990-2004 0.02 -6.13 -4.11 -1.08 0.44 2.99 -8.12 -3.04 -7.57 -1.57

1960-2004 0.77 -2.06 1.21 -3.73 -1.93 -2.83 -2.78 2.64 -0.62 -1.05

South Arcot

1960-1990 -0.18 -2.6 1.82 -1.22 -1.61 -1.8 3.74 -1.42 -0.1 -0.13

1990-2004 -100 -2.2 1.55 -1.4 1.68 -6.29 3.92 2.63 -1.93 -1.44

1960-2004 -2.21 -2.19 0.52 1.54 -1.37 -1.2 2.77 -0.91 0.26 -0.13

North Arcot

1960-1990 -0.01 -1.86 1.26 -3.94 -4.34 -1.28 4.61 0.44 -0.78 -0.82

1990-2004 -100 -0.12 0.4 -0.6 0.39 -0.18 2.69 5.34 -1.84 -4.42

1960-2004 -0.04 -1.56 1.25 -2.75 -3.03 -0.46 2.21 -0.55 1.23 -1.07

Salem

1960-1990 0.26 -1.24 -0.73 0.84 -0.12 3.32 3.32 0.39 1.14 -0.08

1990-2004 -0.05 -0.09 2.01 -2.71 0.26 0.06 4.4 3.2 -1.18 -2.77

1960-2004 0.38 -1.27 -0.1 -0.54 2.12 -1 0.02 0.02 0.02 0.12

Coimbatore

1960-1990 -0.1 -4.1 3.83 -10.38 -7.03 0.71 1.75 -1.15 -0.38 -0.68

1990-2004 0.01 -0.82 0.9 2.02 -6.58 0.49 0.17 -66.4 -0.74 -2.41

1960-2004 -0.09 -2.68 2.69 -7.76 -7.43 -0.8 1.371 0.47 -0.44 -0.93

Tiruchirapalli

1960-1990 -1.02 -3.1 -1.5 -5.1 -3.41 -3.35 1.89 -0.9 -1.33 -1.51

1990-2004 -0.35 -0.33 0.52 9.22 -0.45 3.49 2.00 -3.14 -1.94 -2.8

1960-2004 -0.77 -2.28 -0.69 -1.04 -2.93 -1.37 -1 3.45 -1.15 -1.44

Pudukkottai

1972-1990 -0.41 0.03 -0.58 -2 0.66 -3.97 4.07 3.94 -0.81 -0.7

1990-2004 0.04 -1.00 0.22 -1.94 -0.03 4.57 -11.68 9.04 -0.75 2.43

1972-2004 -0.09 0.04 -0.11 -1.56 0.7 5.01 -2.97 5.75 -1.18 -0.28

Thanjavur

1960-1990 -0.3 0.55 -1.19 -2.94 -2.41 -5.78 1.89 -1.34 -0.59 0.02

1990-2004 -2.71 0.3 -9.95 -9.86 -9.98 1.21 9.18 1.58 -0.96 -0.28

1960-2004 -0.48 0.39 -0.48 -1.39 -1.79 -2.34 0.23 2.03 -0.68 -2.3

14

Madurai

1960-1990 2.41 -0.43 1.24 -2.95 -11.56 -3.06 0.9 -0.89 -0.11 -0.41

1990-2004 -0.21 -0.48 10.24 0.96 -0.93 0.0977 -77.5 -3.809 -1.71 -2.34

1960-2004 1.61 -0.7 1.25 -2.04 -6.59 -2.72 -0.94 2.62 -0.29 -0.57

Ramanathapuram

1960-1990 -0.09 -7.81 3 -6.22 -5.87 -4.54 0.6 5.2 -0.54 -0.64

1990-2004 -0.97 0.19 0.42 0.4 -2.72 13.32 -3.81 3.41 -2.33 -3.48

1960-2004 0.29 -5.23 1.78 -3.97 -3.71 1.72 -0.72 4.81 -0.77 -1.47

Thirunelveli

1960-1990 0.15 -2.48 1.85 -2.98 -3.33 0.69 0.8 -0.46 -2.79 -0.13

1990-2004 -1 -3.98 -4.86 -2.17 -8.45 -13.49 -5.25 -1.81 -9.4 -1.40

1960-2004 -0.03 -1.37 0.96 -0.58 -3.65 1.4 -2.16 0.8 -2.48 -1.86

The Nilgris

1960-1990 1.38 -10.23 -1.29 -6.78 -1.44 -6.85 3.93 0.3 1.28 1.29

1990-2004 -0.04 1.79 0.83 -1.35 0.48 3.95 -6.9 -4.93 -9.9 -9.9

1960-2004 0.88 -7.41 -0.8 -6.65 -1.79 -3.09 0.95 0.16 1.29 1.2

Kanniyakumari

1960-1990 0.58 -8.24 4.08 -11.99 -1.00 10.76 0.77 -1.65 0.35 -0.63

1990-2004 0.65 -0.01 -0.95 -100 -12.09 2.35 6.14 -3.39 -0.61 -2.51

1960-2004 0.29 -5.42 2.3 -100 -100 5.89 -1.65 1.28 0.23 -0.93

Tamil Nadu

1960-1990 0.45 -2.56 1.42 -3.65 -4.02 -1.97 2.33 0.67 -0.3 -0.34

1990-2004 -0.09 -0.65 0.29 2.12 -0.23 1.62 0.51 2.74 -1.72 -2.29

1960-2004 0.35 -1.83 1.04 -1.86 -2.87 -0.25 0.38 2.25 -0.45 -0.57

15

Factors driving land use changes in Tamil Nadu

The major factors driving land use changes are the growth in human and livestock

population, changes in cropping pattern, growth in area and productivity of agricultural crops,

demand for land for non-agricultural purposes such as industries, housing, roads and other

development infrastructure such as educational institutions, health and other rural and urban

amenities. Besides these direct land using factors, the indirect factors such as relative prices in

agriculture and non-agriculture sectors, income, and industrial and agricultural policies also have

significant influence on land use changes. The changing structure of agriculture in terms of crop

pattern, land holding pattern, irrigation facilities, and labour availability are some of the factors

that determine land use within agriculture. For example, improvement in productivity of crops is

land-augmenting since it leads to less land requirement to produce a given amount of crop

output. The data presented in the following tables indicate that area under major crops especially

food crops such as paddy, sorghum, cumbu, and ragi have recorded negative growth during 1960

to 2004. Area under traditional food crops has shown dramatic down trend throughout this

period. Area under cotton which is a traditional, commercial crop has been declining

continuously throughout the last four decades. On the contrary, area under coconut, sugarcane,

maize, fruits and vegetables has been showing continuous increase during the period 1960-2004.

As some of these crops are more water-intensive than the traditional food crops, the increase in

area under crops such as sugarcane and coconut might have led to the decline in area under other

crops, possibly due to the diversion of more water to these water-intensive crops from other

crops. Increasing scarcity of labour is yet another factor responsible for decrease in cultivated

area. As a consequence of these factors the area under net sown area has been declining albeit at

a moderate rate during the entire period from 1960 to 2004.

Table 3. Compound Growth Rate for Area of Major Crops in Tamil Nadu (1960-2004)

Period Paddy Cholam Cumbu Ragi Maize

Other

cereals

Total

Cereals

Total

Pulses Sugarcane Cotton Groundnut

1960-1990 -0.78 -0.55 -1.87 -4.23 7.11 -15.77 -1.23 1.86 4.20 -1.50 0.27

1990-2004 -1.84 -3.66 -5.62 0.64 11.78 -23.46 -2.16 -0.50 0.68 -6.62 -5.61

1960-2004 -0.93 -2.11 -3.28 -3.25 7.25 -14.89 -1.57 1.34 3.41 -2.51 -0.32

Period Coconut

Spices &

Condiments

Sugar

crops

Total

Fruits

Total

Vegetables Oilseeds

Fodder

Crops

Total

foodcrops

Total

Nonfood

crops

Net area

cultivated

1960-1990 4.25 1.04 3.10 2.08 2.47 0.54 3.12 -0.50 0.57 -0.31

1990-2004 5.39 0.68 0.34 2.68 1.56 -3.70 0.88 -1.42 -2.74 -0.45

1960-2004 4.70 0.70 2.68 2.58 2.57 0.19 3.31 -0.77 0.24 -0.43

16

Table 4. Compound Growth Rate for Production of Major Crops in Tamil Nadu

(1960-2004)


Other

cereals

Total

cereals

Total

pulses Sugarcane Cotton Groundnut

1960-1990 0.99 0.00 1.42 -0.40 8.74 6.61 4.62 8.53 7.91 0.41 0.48

1990-2004 -1.33 -5.71 -6.11 -2.77 10.25 -7.12 -2.95 -1.83 -1.36 -9.40 -2.67

1960-2004 0.59 -2.06 -1.08 -1.10 -91.69 -2.07 2.11 4.89 5.03 -1.42 0.53

Table 5. Compound Growth Rate for Productivity of Major crops in Tamil Nadu

(1960-2004)


Other

cereals

Total

Cereals

Total

Pulses Sugarcane Cotton Groundnut

1960-1990 2.50 1.77 3.50 2.38 3.01 9.60 4.63 6.60 3.29 3.87 0.54

1990-2004 1.96 -3.59 -0.15 1.09 3.07 15.24 -0.24 18.24 -4.16 -1.79 1.45

1960-2004 2.24 0.08 1.97 1.61 -3.64 12.14 3.32 5.90 1.63 -0.24 1.68

Table 6. Decadal changes in population (%)

Districts 1971-81 1981-91 1991-2001

Total Rural Urban Total Rural Urban Total Rural Urban

Kancheepuram 14.89 11.47 21.92 13.34 11.76 16.38 10.07 -5.48 29.97

North Arcot 22.14 14.12 47.27 18.86 13.91 21.00 13.54 0.64 18.14

South Arcot 14.92 12.54 22.89 12.92 12.10 15.56 10.51 2.01 30.03

Salem 13.90 12.35 22.23 13.87 13.81 14.18 6.62 -1.59 34.81

Coimbatore 44.98 48.37 32.76 14.01 14.16 13.45 13.61 -0.41 42.66

Madurai 14.73 -59.61 22.16 12.01 48.25 17.32 14.26 -20.94 39.22

Thiruchirapalli 13.29 9.62 19.43 12.94 12.12 14.36 6.56 -14.33 28.85

Thanjavur 11.63 8.80 19.64 12.70 12.14 14.27 -4.24 -3.67 30.06

Pudukkottai 14.73 11.14 17.64 8.12 87.06 21.30 6.71 67.93 9.36

Ramnad 18.11 17.03 25.18 12.83 71.27 85.21 8.62 58.27 16.58

Thoothukudi 14.25 11.73 20.75 11.93 10.04 16.41 7.30 0.84 19.68

Tirunelveli 0.00 0.00 0.00 0.00 0.00 0.00 7.01 5.26 9.40

Nilgris 10.44 7.06 17.56 -42.85 -36.71 -56.06 10.69 -13.98 39.09

Kanniyakumari 21.61 19.40 20.99 11.27 9.66 12.90 7.14 -15.22 22.35

State 67.21 13.56 16.75 -113.92 11.45 9.10 4.16 -128.27 75.15

17

Table 7. Trends in population and urbanization in Tamil Nadu

(population in million)

Year Total

Population

Urban

population

Share of Urban

Population (%)

1901 19.3 2.70 13.99

1911 20.9 3.10 14.84

1921 21.6 3.37 15.59

1931 23.5 4.15 17.66

1941 26.3 5.09 19.36

1951 30.1 7.33 24.35

1961 33.7 8.99 26.68

1971 41.2 12.47 30.26

1981 48.4 15.95 32.95

1991 55.8 19.06 34.15

2001 62.1 27.20 43.80

Average annual

growth rate

2.22 9.07 2.13

Urbanization and related economic variables

The process of urbanization is governed both by economic factors such as the growth in

agricultural and non-agricultural sectors, wage rates, and the changing structure of labour force

in rural and urban areas and the non-economic factors such as historical premises, demographic

pattern and sociological parameters. However, an analysis of the salient indicators of

urbanization such as the ratio of urban to total population and share of land put to non-

agricultural uses to total geographical area vis-à-vis the related economic variables is considered

useful so as to delineate the linkages between these variables and to draw implications for

devising appropriate land use policies. The data presented in Table 8 provides an overview of

trends in urbanization and related economic and demographic variables in Tamil Nadu over the

last four decades. The area of land put to non-agricultural uses has shown the highest compound

growth rate during 1970s, while the rate of urbanization as shown by the ratio of urban

population to total population has shown the highest growth rate during the latest decade

(1990s). The growth rate in both the net state domestic product from agricultural sector and the

18

per capita agricultural net state domestic product were the highest during the 1990s even though

the share of agricultural net state domestic product to the state’s net state domestic product has

shown the maximum deceleration during this decade. The sharp fall in the share of agricultural

sector in the net state domestic product, however, did not accompany with a corresponding

decline in population directly depending on agricultural sector as indicated by the negative

growth rates in the percentage of agricultural labour and farmers to total workforce.

The growth trends over the entire 40 year period between 1960 and 2000 reveal that the

share of agricultural labour to total workforce in the state has increased almost one per cent per

annum while the percentage of farmers to total workforce has declined at a compound negative

growth rate of about 1.90 per cent per annum. Though both the total and per capital net state

domestic product from agricultural sector have shown a phenomenal growth over the entire

period, there was a significant acceleration in the growth rates of these parameters in the last two

decades than the first two decades of the forty year period. The rate of urbanization has been the

highest in the latest decade (1990s) together with the sharp fall in the share of agricultural sector

to the net state domestic product of the state. These factors together with the negative growth

rates in both the share of agricultural labour and farmers indicate a net out-migration from rural

to urban areas at a faster rate in the more recent period which is primarily due to the industrial

boom in the recent times. The growth rate of land put to non-agricultural uses has been 1.15

percent per annum which is marginally higher as compared to the rate of growth (1.00 percent)

in the share of urban population to total population.

19

Table 8. Annual Compound Growth Rates in Indicators of Urbanization and

Related Factors in Tamil Nadu

Variables 1960s 1970s 1980s 1990s 1960-2000

Land put to non-agricultural uses 0.96 2.12 0.45 0.85 1.15

Net sown area -0.03 -0.03 0.33 -0.49 -0.28

Ratio of urban population to total

population 1.28 0.87 0.36 2.55 1.00

Population density 2.03 1.62 1.44 1.07 1.53

Literacy rate 2.23 1.82 1.42 1.77 1.73

Agricultural labour as a percentage

to total labour force 5.11 -0.18 0.88 -0.5 0.96

Number of farmers as a percentage

to total labour force -2.92 -1.27 -1.59 -2.67 -1.88

Agricultural GDP at constant prices 6.02 5.96 11.01 13.03 9.58

Share of Agricultural GDP in Net

State Domestic Product -1.13 -3.66 -1.98 -5.81 -2.66

Agricultural GDP per capita 3.99 4.33 9.58 11.84 8.03

The various factors driving changes in different land use categories were analysed using

econometric techniques (Seemingly Unrelated Regression Model). This analysis is based on the

data on land use categories and other economic variables affecting land use changes such as

infrastructure (roads and irrigation), urbanization, population density and overall economic

growth. The key results from this analysis are as follows:

The area under forests is negatively affected by the trend variable and road density. The

decline in barren and uncultivable lands could be attributed to the increase in net sown

area as well as the land put to non-agricultural uses.

The land put to non-agricultural uses seems to have a positive relationship with only the

trend variable indicating that the overall economic growth is responsible for the increase

in area under this category of land use rather than the growth in any particular sector.

20

Cultivable wastes are found to be absorbed largely by net sown area. The other factors

that make this land category available for productive purposes are the population density

and the ratio of gross irrigated area to total cultivated area. This seems plausible as the

population pressure leads to expansion of the cultivated land frontier into uncultivated

lands and increasing population density increases the pressure on wastelands. The trend

variable, which is a broad proxy for overall economic growth is also found to have play a

strong role in reducing the area under cultivable wastes.

Population density in the state, rate of urbanization and the trend variable are found to

have significant negative effect on the area under permanent pastures and other grazing

lands. Thus the decline in area under permanent pastures and grazing lands could be

attributed to the increasing population pressure and rising levels of urbanization. This

result has a very important policy conclusion since most of the pasture and grazing lands

is held as common property resource which becomes the first casualty of any

developmental activity—private or public.

The area under miscellaneous tree crops and groves is found to decrease with increase in

land put to non-agricultural uses and population density as also the trend variable. This

result, as the previous one, is once again the consequence of the fact that most of the

lands under miscellaneous tree crops and groves are held as common property and hence

they are vulnerable to encroachment by both the public and private interests.

Rainfall, road density and the extent of urbanization (defined as the percentage of urban

population to total population in the state) reduce the extent of fallow lands while the

land put to non-agricultural uses tend to increase the area under current fallows.

Land put to non-agricultural uses, irrigation facilities and the availability of labour

(agricultural labour population) for agriculture are found to reduce the extent of other

fallows, while urbanization and trend variable increase the area under other fallows.

Rainfall and irrigation facilities (defined as percentage of gross irrigated area to gross

cropped area) are the only variables that have increased the area under net sown area in

the state.

Transition probability of land use

The results of Markov Chain Analysis provide the probabilities of retention of a

particular land use category within its class and also its possible shift to other categories. The

21

diagonal elements (the numbers in boldface) indicate the retention probabilities. For example, in

Table 9, the probability value of 0.44 in the first cell of first row indicate that the probability of

forest lands remaining under the same category was only 0.44 during the period 1960-70, while

the value of 0.41 in third cell of third row indicate that the probability of land put to non-

agricultural uses to remain in the same category is 0.41. The value of 0.70 in the last cell of last

row indicate the probability of the land under net sown area being retained under the same

category was only 0.70. This indicates that there is higher stability in land under net sown area

and there was very high instability in barren and uncultivable lands and fallow lands.

Table 9. Transitional Probability Index (1960-70)

Forest

Barren &

uncultivable

land

Non-agri.

use

Cultivable

waste

Permanent

pasture & tree

crops

Fallow

lands

Net sown

area

Forest 0.44 0.00 0.40 0.00 0.00 0.00 0.15

Barren &

uncultivable

land 0.06 0.01 0.00 0.00 0.00 0.93 0.00

Non-

agricultural

use 0.52 0.00 0.41 0.00 0.00 0.06 0.00

Cultivable

waste 0.00 0.03 0.00 0.80 0.16 0.00 0.00

Permanent

pasture

&.tree crops 0.00 0.00 0.00 0.20 0.59 0.00 0.19

Fallow

lands 0.12 0.00 0.01 0.00 0.00 0.00 0.87

Net sown

area 0.02 0.14 0.00 0.00 0.02 0.10 0.70

22

The data presented in Table 10 reveal that the retention probability of land under net

sown area has decreased from 0.70 during 1960s to 0.49 during 1970s, thus showing a significant

reduction in the stability of land under net sown area during the 1970s. The retention probability

of land under forests and land under permanent pastures and miscellaneous trees has also

declined sharply during 1970s as compared to the previous decade. On the other hand the

retention probability of fallow lands has increased significantly during 1970s as compared to

1960s thus causing concern for sustaining the gross cropped area of the state.


Forest

Barren &

unculti-

vable land

Non-

agri. use

Culturable

waste

Permanent

pasture &

tree crops

Fallow

lands

Net

sown

area

Forest 0.18 0.00 0.00 0.00 0.00 0.00 0.81

Barren &

uncultivable land 0.00 0.30 0.00 0.26 0.43 0.00 0.00

Land put to non-

agricultural use 0.00 0.00 0.40 0.00 0.00 0.08 0.50

Culturable waste 0.00 0.34 0.00 0.00 0.03 0.00 0.61

Permanent pasture &

misc.tree crops 0.00 0.32 0.00 0.31 0.00 0.00 0.35

Fallow lands 0.18 0.00 0.14 0.00 0.00 0.49 0.17

Net sown area 0.21 0.03 0.11 0.01 0.01 0.11 0.49

The transition probabilities for land use categories during 1980s are presented in Table

11. The decline in retention probability of land under net sown area witnessed during the

previous decade (1970-80) has been reversed during the 1980s with the probability of retention

showing a marginal increase from 0.49 to 0.55 during the 1980s. The retention probability of

forests has increased sharply from 0.18 during 1970s to 0.77 during 1980s and the retention

probability of land put to non-agricultural uses has also increased during the 1980s as compared

to its position during the previous decade. The retention probability of fallow lands has shown a

sharp decline from 0.49 during 1970s to zero value during 1980s thus indicating a higher

instability in fallow lands during the 1980s.

23


Forest

Barren &

unculti-

vable land

Non-

agri. use

Culturable

waste

Permanent

pasture &

tree crops

Fallow

lands

Net

sown

area

Forest 0.77 0.00 0.22 0.00 0.00 0.00 0.00

Barren & uncultivable

land 0.16 0.00 0.16 0.03 0.04 0.60 0.00

Land put to non-



Permanent pasture &

tree crops 0.00 0.47 0.00 0.08 0.20 0.00 0.23

Fallows lands 0.05 0.05 0.03 0.03 0.00 0.00 0.83

Net sown area 0.00 0.05 0.04 0.01 0.01 0.34 0.55


Forest

Barren &

unculti-

vable land

Non-agri.

use

Culturable

waste

Permanent

pasture &

tree crops

Fallow

lands

Net sown

area

Forest 0.79 0.00 0.0.12 0.00 0.00 0.09 0.00

Barren &

uncultivable land 0.00 0.55 0.00 0.00 0.00 0.00 0.44

Land put to non-



Permanent pasture &

tree crops 0.00 0.00 0.00 0.00 0.12 0.00 0.88

Fallow lands 0.23 0.00 0.31 0.00 0.00 0.08 0.36

Net sown area 0.09 0.04 0.00 0.05 0.06 0.00 0.76

The data presented in Table 12 indicate that the net sown area has recorded the retention

probability of 0.76 in this decade showing a relatively high stability in net sown area during the

period 1990-2004. The steady state (retention) probability of land put to non-agricultural uses

has also recorded the highest value of 0.95 during this period as most other land use categories

such as forests, barren and uncultivable land and culturable wastes have also recorded the highest

retention probabilities in their respective categories. The higher steady-state probabilities in most

of the land use categories during the latest 15 years after a period of wide fluctuations indicate

24

probably the achievement of economic stability in various sectors due to the achievement of

increasing stability in various economic activities.

The results presented in Table 13 indicate the long-term transitional probabilities in

various land use categories for the period from 1960 to 2004. These values indicate moderate

retention probabilities for land use categories such as fallow lands, permanent pastures and

miscellaneous tree crops and net sown area. There is a 66 percent probability of land under net

sown area being retained under the same category, while the fallow lands have a retention

probability of 31 per cent. Forest lands are likely to be retained in the same category with a

probability value of 0.69 while the permanent pastures and miscellaneous tree crops have shown

very high instability probably due to the increasing pressure on these land use categories as most

of these lands are held as common property or open access resource thus making them prone to

encroachments and privatization as demand for land is increasing over a period of time.


Forest

Barren

&

unculti-

vable

land

Non-agri.

use

Culturable

waste

Permanent

pasture &

misc. tree

crops

Fallow

lands

Net

sown

area

Forest 0.69 0.00 0.18 0.00 0.00 0.13 0.00


land 0.00 0.55 0.00 0.00 0.00 0.00 0.44

Land put to non-



Permanent pasture &

misc.tree crops 0.00 0.00 0.00 0.00 0.09 0.00 0.91

Fallow lands 0.23 0.00 0.29 0.00 0.00 0.31 0.16

Net sown area 0.18 0.04 0.00 0.05 0.06 0.00 0.66

Projection of demand for land for various purposes

Population

To know the demand for land for agricultural and non-agricultural activities in the state,

the demand for various activities has been projected for the year 2020.The methodology

followed in projecting the requirement is discussed below. The area required for agricultural

purpose during 2020 is calculated based on the projected population. The annual growth rate of

25

population between 1991 and 2001 was 1.3 percent. Considering the efforts taken by the

Government of Tamil Nadu on family welfare, the population is assumed to grow at the rate of

1.3 per cent per annum between 2001 and 2010 and at 1.2 per cent per annum between 2010 and

2020. The projected population by 2020 on the above basis is 77.96 million.

Crop production

The area required for the agricultural crops by 2020 is projected based on the per capita

requirements as per the recommendations of the Indian Council of Medical Research. From the

quantity projected, the area is arrived by dividing the same by the average productivity of the

respective crops during the last five years. In the case of fruits an additional 30 per cent has been

added to take into consideration the post harvest loss.

Forest

The state has a forest cover of 16.5 per cent as against 33 percent suggested for

ecological balance. The discussion with the state Forest Department showed that it would not be

possible to achieve the prescribed norm. However as per the recommendations of the

Government of India, the state government has planned to bring in one per cent of the

geographical area under tree cover per year. The demand for land for afforestation purpose has

been worked out based on this.

Non-agricultural uses

The area required for industries were worked out, considering the industrial growth rate

for the past 10 years. This worked out to 1.42 per cent for industries. The growth rate recorded

for Small-scale industries the last 10 year is 12.72 percent. Using this growth rate, the total

number of industries in 2020 is projected. The area required is calculated by using a land area of

4000 m2 for factories and 40m2 for small-scale industries, which is the norm, adopted by Town

and Country Planning Organization, New Delhi. The land required for schools was projected

using the Past 50 year’s growth rate in schools. This worked out to 0.4 percent per year. The rate

required for the projected number of schools in 2020 was arrived at using the norm of 4.0 ha per

school as fixed by the Directorate of Town and Country Planning. The demand for residential

units by 2020 was projected using a growth rate of 0.77 per cent per year. The area required for

housing is arrived using the norms of 5 cents per dwelling unit. The area required for roads

during 2020 was arrived at using an annual growth rate of 6.75 percent for National Highway,

9.04 percent for State Highways, 1.42 percent for other districts roads and 2.95 per cent for

26

panchayat and Municipal roads. The land required for the road is worked out using a norm of 30

mts for national highway and State highways, 24 mts for major district roads, and 12 mts for

other districts roads, Panchayat and Municipal roads. This was worked out since there is no

respective plan for road development. However a discussion with the officials of Highways and

Rural Works Department showed that the length of National Highway would be 3850 Km

whereas it will be 7000 km for State Highways and 1000 km for ring Roads and Bypass roads.

Due to increasing traffic in future it has been estimated that the width of national highways will

be 30m, whereas it will be 20 m for state highways and 60 m for ring roads. The total road length

(highways) would be 60,000 Km. The total length of village roads ha been estimated to be about

60,000 km. The land required for roads was estimated on the above basis.

Table 14. Growth of industries in Tamil Nadu (in numbers)

1985 1990 1995 2000 2003

Food Products 9095 15066 22695 35002 39805

Beverages, tobacco and tobacco

products

695 1233 1844 2680 2818

Cotton Textiles 4605 7916 16680 23653 24752

Wool, Silk and Synthetic Fibre

Textiles

996 1455 1854 2670 2844

Jute, hemp and Mesta products 72 150 144 319 1558

Hosiery and Readymade garments 3785 8207 34548 78964 116093

Wood and wood Products 2368 4180 8420 16421 19601

Paper and Paper Products 5616 9247 14147 20519 26124

Leather and Fur Products 1368 2568 6312 10022 11753

Rubber and Plastic products 3383 5908 8606 13173 15382

Chemicals and Chemical Products 8493 11729 13774 16853 18374

Non-Metallic Mineral Products 3789 6060 8534 11576 12747

Basic Metals and alloy Industries 7975 2847 3302 5067 6105

Metal products and Parts 1999 12290 15692 20640 22399

machinery and Parts except electrical

Machinery

7734 12600 18148 26054 29764

electrical Machinery and Apparatus 1827 4032 7938 10975 12545

Transport Equipment and Parts 1592 3228 7267 10094 12139

Other Manufacturing Industries 3068 4942 17452 50257 74102

Total 68460 113658 207357 354939 448905

27

Table 15. Projected demand for land for non-agricultural uses under different scenarios

(Lakh ha)

S.No Uses Total area required

Low

growth

scenario I

Medium

growth

scenario II

High growth

scenario III

1 Housing 4.6 5.75 6.90

2 Schools 2.2 2.75 3.30

3 Roads 4.76 5.95 7.14

4 Industries 5.4 6.75 8.10

Total 16.96 21.20 25.44

Land Use Plan for the year 2025

Based on the short-run (1990-2004) and long-run (1960-2004) retention probabilities

from Markov chain analysis which are summarized in Table 16, it is possible to develop two

kinds of land use scenarios. In developing these scenarios, various land use categories have been

classified into three categories: a) Land use categories which are to be allocated a certain

minimum land area for maintaining ecological stability. Forests fall under this category; b) The

traditional land use categories supporting low-value economic activities such as barren and

uncultivable lands, culturable wastes, permanent pasture lands and land under miscellaneous tree

crops are classified as declining land use categories which are declining in their economic

importance and hence are available for redistribution for economic uses such as crop production

(net sown area) or non-agricultural uses; and c) Economically important land use categories such

as net sown area and land put to non-agricultural uses. Based on this classification the short-run

and long-run steady-state probabilities have been used along with the base-case land use scenario

(average land use pattern for the triennium ending 2003-04) to simulate the possible land use

scenario during 2025 which are presented in Table 17 along with the land allocation pattern

developed by the Committee headed by Prof.R.K.Sivanappan during 2003.

28

Table 16. Short-run and long-run steady-state probabilities

Land use category

Steady-state probability

Short-run Long-run

Forest 0.49 0.69


land 0.55 0.55

Land put to non-

agricultural use 0.95 0.24

Culturable waste 0.64 0.14

Permanent pasture &

miscellaneous tree

crops 0.12 0.09

Fallow lands 0.08 0.31

Net sown area 0.76 0.66

Table 17. Land use plan

Land use category Base case

(Average for the

triennium ending

2003-04)

Land use

plan for 2020

(Sivanappan

et al 2003)

Suggested land use

scenario (Present study)

Based on

short-run

transition

probabilities

Based on

Long-run

transition

probabilities

Forest 21.32 30.00 18.97 26.70

Barren & uncultivable land 4.77 4.95 4.20 4.36

Land put to non-

agricultural use

19.98 25.00 32.32 32.87

Culturable waste 3.87 1.25 2.63 0.85

Permanent pasture &

miscellaneous tree crops

3.89 5.45 3.89 5.45

Fallow lands 24.35 3.47 18.75 2.67

Net sown area 51.72 60.00 49.13 57.00

Total geographical area 129.90 130.12 129.90 129.90

The results presented in Table 17 reveal that in the long run the maximum forest area that

could be preserved for ecological balance works out to about 26.70 lakh ha, while the lower

bound for this category is about 19 lakh ha which is slightly less than the current area under

forests. Barren and uncultivable lands are likely to stabilize around 4.25 lakh ha, culturable

wastes are likely to decline to less than one lakh ha. Permanent pastures and grazing lands are

likely to remain constant ranging from 3.9 to 5.45 lakh ha. Fallow lands are the land use

29

categories that is likely to show a wide fluctuation with the area under this category ranging from

2.67 lakh ha to 18.75 lakh ha. Net sown area is likely to stabilize between 50 lakh ha and 57 lakh

ha, even though the earlier projections peg it at 60 lakh ha (Sivanappan, 2003).

30

IV. Conclusions and Recommendations

The projected demand for land for agricultural and nonagricultural uses by 2020 is higher

than the existing geographical area of the state. To minimize the demand for land for different

uses the following strategies needs to be considered.

Enhancing agricultural productivity has been the major land-saving strategy that has

helped to meet the increasing demand for food and fodder amidst increasing pressure on

land for various other developmental purposes. Increasing productivity per unit of land is

also helpful to increase the productivity per unit of other resources such as water and

labour. However, in recent times there is deceleration in productivity growth in irrigated

agriculture in several parts of the state. Continued emphasis on increasing agricultural

productivity especially in dryland areas and marginal lands is essential to achieve

production targets in the wake of shrinking land and water resources. These are the areas

with low or very low base yield levels and hence bear the potential for substantial

improvement in productivity.

Future agricultural policies should focus more on increasing net income per unit area of

land rather than traditional focus on productivity alone. This will enable farmers to invest

in modern, land saving technologies in future.

Erosion and degradation of soils, loss of fertility due to mining of soil nutrients are taking

place at faster pace in some of the highly productive regions of the state. Lack of erosion

control measures and drainage facilities, inadequate investments in soil and moisture

conservation, imbalanced fertilizer use and inadequate use of micro-nutrients are some of

the important factors contributing for low productivity and poor soil health. Hence,

massive investments are needed at farm level and beyond to reverse these trends.

Development of rural infrastructure such as roads, markets, irrigation and social

infrastructure such as health and education are likely to increase farm income and labour

productivity in rural areas. Physical infrastructures are found to reduce the extent of

fallow lands and hence it will ensure effective utilization of land resources.

Enterprise diversification and precision farming technologies are the two important

means to achieve higher income and to reduce risk and cost of production which will in

31

turn enable the farmers to save and invest more in farming. This will also mitigate

negative environmental consequences of specialized, intensive agricultural practices.

Heavy industries requiring large areas of land should be located in areas with low

irrigation facility and low agricultural productivity.

Large scale migration from rural to urban areas in search of employment is responsible

for increasing urbanization and deterioration of urban environments as well as the nearby

rural ecosystems. Therefore, long-term policies are necessary to mitigate the increasing

demand for land for various non-agricultural uses. Specific policies are required to reduce

the diversion of fertile lands from agriculture to non-agricultural uses. Some of these

policy instruments are:

o Decentralized industrialization

o Large-scale employment generation in rural areas

o Provision of urban amenities in rural areas to reduce the large-scale out-migration from

rural areas.

Identification of the extent and type of wastelands and development of appropriate

strategies for use

Demand for land for non-agricultural uses may be met only from marginal and low

productive lands. However, this is not possible as long as land allocation decisions are

made using market forces alone. Direct and indirect government intervention through

regulation of land use— preventing the diversion of fertile lands from agricultural to non-

agricultural purposes, landscape preservation act, permission to start new industrial units

in least fertile lands, etc.—are needed. Indirect regulatory policies such as tax

concessions for setting up industries in low productive regions will be helpful to reduce

diversion of fertile and high-productive agricultural lands for non-agricultural purposes.

Speculative activities in land market especially in the urban fringes of large towns and

cities should be prohibited by suitable amendments in existing rules governing the

conversion of agricultural lands for housing and other purposes. A comprehensive

Agricultural Land Preservation Act shall be passed by the Government to protect fertile

farm lands.

Perspective Plan for Land Use Planning · Lack of erosion control measures and drainage facilities,...

Documents

Transcript of Perspective Plan for Land Use Planning · Lack of erosion control measures and drainage facilities,...