Journal of Land and Rural Studies-2015-Palchoudhuri-1-28 (1)

Article

A New Socioeconomic Index for Modelling Land Use and Land Cover Change: A Case Study in Narmada River Basin, India

Yajnaseni Palchoudhuri1

Partha Sarathi Roy2

Vijay K. Srivastava3

AbstractHuman society has been utilising the natural resources from the dawn of its civilisation in varying intensity to improve their living standard. Over the course of time, the extraction of the amenities required for such developmental purpose, affects the resource use pattern and access. This has resulted in a change in the existing land use practices in the region. Thus, socio-economic setting of any region and the land use are interlinked and affect each other. This article presents a new socio-economic index (SEI) to quantify the socio-economic status of any river basin. UNDPs Human Development Index of 1990 has been used and modified to compute the index, in which various aspects of human life are considered and collected from National Survey Samples to reflect on the basins land use scenario. Results of the analysis are presented on Narmada River basin as a case study.

Keywordssocio-economic development, human development index, land use change, human drivers of change, population growth

Journal of Land and Rural Studies3(1) 128

2015 Centre for Rural Studies, LBSNAA

SAGE Publicationssagepub.in/home.nav

DOI: 10.1177/2321024914534051http://lrs.sagepub.com

1 CEPT University, Ahmedabad, India2 UCESS, Hyderabad Central University, Andhra Pradesh, India.3 National Remote Sensing Centre, Hyderabad, Andhra Pradesh, India.

Corresponding author:Yajnaseni Palchoudhuri, CEPT University, Ahmedabad, IndiaE-mail: [email protected], [email protected]

at Dehli University Library System on July 18, 2015lrs.sagepub.comDownloaded from

2 Journal of Land and Rural Studies 3(1)

Introduction

Development deals with improvement of quality of human life and consists of several parameters of the human environment. Some of its major parameters include: economic growth, education, health infrastructure, degree of modernisation, women empowerment, grass root participation in decision making in development level of nutrition, housing quality, distribution of goods and services and access to communication. These parameters are the indicators of the socio-economic development of a region which deals with improving the standard of life. Post-industrialisation, this development along with the rapid population growth has happened unevenly throughout the world. As a result, there is a gradual increase in the unsustainable utilisation of natural resources (Das, 1999).

Land is the major source of Earths natural resources. Human being has evolved themselves to harness these resources very effectively. The rapid depletion of these resources is best reflected in the spatial and temporal variation of land use classes. Thus, the population growth along with the socio-economic development of any region is often identified as the major cause behind its land use dynamics and the environmental change over time. To understand and assess the processes of land use change in developing countries like India, it is crucial to measure the growth in various aspects of socio economy and analyse them with respect to corresponding land use classes. The ISRO-Geosphere Biosphere Programme (IGBP) has undertaken a nationwide land use land-cover dynamics project (since 2007), studying the complex interaction between human and environment on a river basin level. In order to assess the human dimensions of land use change, an attempt is made to generate a robust socio-economic index which can be related with the temporal and spatial land use dynamics of the basin.

A socio-economic index measures the varying parameters of socio-economic development of a region (within any river basin). It identifies the homogeneous (in abstract) social groups that reflect the quality of human life. These parameters can be broadly grouped under the sub-heads of economy, literacy, health and infrastructure. The economy parameter constitutes of all the income based measures and employment source parameters, for example, GDP per capita, Purchasing power parity (PPP), growth of establishments providing employment, percentage of population earning income etc. It has been the key determinant of living standard, defining the extent to which a persons basic needs are met through his ability to consume. The literacy indicator includes factors like literacy rate, Enrolment ratio in the schools or access to education. The health parameters cover the health infrastructure and facilities accessed by the individuals, infant mortality rate and sex ratio. The infrastructural parameter explains the development of the basic facilities needed to sustain human life, for example, the drinking water facilities, access to market and source of employment through road connectivity. All of this information studying the varying aspects of social and economic growth of any region is state-wise collected and organised by the Census and National Survey Samples. It is essential to integrate these socio-economic parameters to generate a robust index in order to understand the status of socio-economic growth and


Palchoudhuri et al. 3

development of a region. So far, there has been no such positive effort to generate such an index comprising all the aspects of socio economy at a national level. United Nations Development Programs (UNDPs) Human Development Index (HDI), in 1990 is probably the first successful attempt of its kind, classifying the different groups of settlement of the nation according to their human development quotient. As founded by the Pakistani economist Dr Mahbub ul Haq, the Human Development Index has covered almost all aspect of economic, social, cultural, political and emotional well being (Noorbakhsh, 1998). The main components of HDI include a long and healthy life (health parameters), access to education (literacy parameter) and a decent standard of living (income/economy parameter) (HDR, 1990). The calculation of the final human development index value is based on the simple average of these three components. HDI being an aggregate index, it has not provided with the information regarding the contribution or relative importance of its different components. It also does not include the infrastructural indicator of socio-economic growth of a nation, in its calculation. It has not taken into account the development and the availability of the basic amenities like safe drinking water, road construction, access to local market and other household facilities, as an indicator to measure human well being. The availability and access to these basic requirements of living has immense impact on demographic and socio-economic growth of the region, which shapes the land use with time. Since HDI, does not consider the infrastructural parameter of socio-economic development, hence, cannot be used to study and assess the human dimensions of land use change. For a country like India, where the range of socio economic and cultural diversity being extremely high, the HDI value can hardly capture the true essence of growth in human development within the country or a state, that can be used in land use change analysis. These issues of Human development Index have been addressed and modified to generate an integrated socioeconomic index, in order to link the socio-economic growth of a region to its land use change scenario with time.

Human Dimensions of Land Use Change

Most of the land use and land cover changes (modifications and conversions) are triggered by human usage of land (Verburg et al., 1997). The term land use has its origin from the very decision as to how and for what purpose land resources are used by man. Land cover is in a constant state of flux since the time of evolution of Earth. There are certain well defined endogenic and exogenic forces of the Earth and its atmosphere that reforms the features on and within Earths surface and its surroundings at a regular pace. These physical parameters that drive the land cover of any region to change are termed as the direct drivers of change. Thus, the suitability of land for any particular land use/land cover type is foremost, defined by the attributes of physical factors such as soil, climate, topography etc. With the gradual change of these physical factors over time, there has always been a slow but steady change in land cover scenario.



But with the rise of human civilisation, these direct drivers or natural agents of change are affected indirectly by the human intervention to exploit the natural resources. Thus, the overall land cover change procedure is accelerated. These human interactions of land use change are the indirect drivers of change. They consist of the demographic, economic, socio-cultural and scientific and technological parameters of human dimensions, which alter the direct drivers of change more diffusely (Figure 1).

In recent times, land use and land-cover change is more influenced, as the increasing population needs more space and land to satisfy its demand for food, shelter and energy requirements. In order to meet this demand for better living, humans during the last 50 years have altered and influenced the natural ecosystem to an unprecedented extent (Adamowicz et al., 2005). Different region of the world has responded differently to these needs on the basis of their different socio-cultural and economic structure. The historical development of the region concerned, its cultural and political factors, trade and most importantly the suitability of the land (defined by the natural drivers) have been the major constraints reflected in this competition. This complex interaction among the socio-economic conditions and the suitability of land for the purpose produced the spatial variation of the land use classes.

Furthermore, global demands have always influenced the Economic factors like markets and policies, and have a direct impact on the decision making by land managers through the prices, taxes, subsidies, production and transportation

Relationship between bio-physical, socio-economicdrivers and land use/land cover system

Feed

back

Bio-physicaldrivers

Socio-economicdrivers

Technological change

Operationsequence Feedback

RegionalandglobalchangeBa

ckgr

ound

&

Indi

rect

effe

cts

Globally systemic changeSocialsystems

Landcover

Land usesystem

Landmanagers

Ecologicalsystems

Figure 1: Drivers of Land Use and Land Cover SystemSource: Briassoulis (2008).



cost, capital flows, investments, trade and technology. The ever increasing market based demands for various land use classes thus lead to an increase of land under market crops and a reduction in subsistence cropland, and intensification of agricultural practices (Lambin et al., 2003). Hence, both rise and decline of a regional population have a large impact on its land use. Many times, land use has changed because of weak policies and institutional enforcement, for example, the widespread illegal logging in Indonesia because of corruption and the devolving of forest management responsibilities to the district level. On the other hand, strong and effective land use policies have resulted in the proper restoration of land. Some of policies that have influenced land use change are state policies to attain self-sufficiency in food, taxation, credits, subsidies; price control in agricultural inputs and outputs; decentralisation; infrastructure support; investments in monitoring natural resources; land consolidation; nationalisation; and international environmental agreements (Lambin et al., 2003). The various sectors of land use change as mentioned earlier are strongly interlinked within the various levels of humanenvironment systems. The human intervention and the natural system of change, follows a vicious cycle, both changing and acting as feedback to change with time.

Socio-economic Driver of Change

Society has utilised the natural resources in many ways in order to improve life for every common man. As the population rises with time, the demand for a better life has increased, magnifying the pressure on natural resources. This has lead to a change in land use practices in the region, for example, increase in built up area, increase in crop land area, change in water bodies, decrease in forest area, barren land and waste land etc. The socio-economic drivers of the land use change are the factors, which drives the change in land use and land cover scenario, at any region over the period of time. These drivers comprise of the different aspects of demography, society, economy, political and institutional factors and processes such as population change, industrial structure and change, technological change, the family, market, various public sector bodies, and the related policies, rules, community organisation and norms.

The main aim of the research lies in the development of an integrated socio-economic entity and to analyse, how the demographic change and socio-economic development of a nation can affect its land use scenario over time. This analysis would help in modelling and projecting future land use changes, enabling the land planners and policy makers for a better land management.

The study was undertaken with following objectives:

l To develop integrated Socio-economic index (SEI) which can act as drivers as land use and land cover change; and

l To determine relationship of socio-economic and demographic drivers with land use dynamics in river basin.



Study Area

Narmada River Basin

The basin encompasses catchment area of the river Narmada and lies between 20 28 0.97 N to 24 4242.6 N latitude and 68 63.7 E to 81 4616.18 E longitude. It extends over an area of 98,796 Km2. which is nearly 3 per cent of the total geographical area of the country and is home to 21 million people, nearly 80 per cent of whom live in villages. The basin lies in the states of Madhya Pradesh, Chhatisgarh, Gujarat, Maharashtra and Rajasthan. Table 1 gives the number of districts and taluks of each district in each state which falls in Narmada River basin.

Physiographically, the basin can be divided into hilly and plain regions. The hilly regions are forested. The plain regions are located between the hilly tracts and in the lower reaches. This region is fertile thus it is well suited for cultivation. The climate of the basin is humid and tropical. In the cold weather, the mean annual temperature varies from 17.5 to 20C and in the hot weather from 30 to 32.5C. In the upper hilly areas, the annual rainfall is, in general, ranges between 1,400 and 1,650 mm. In the upper plains, the annual rainfall decreases from 1,400 to less than 1,000 mm. In the lower plains the annual rainfall decreases rapidly from 1,000 mm at the eastern and to less than 650 mm representing the most arid part of the Narmada basin.

Agriculture and forest are the main land cover in the basin. The legal forest covers an area of about 32 per cent of the basin while 45 per cent area is under agriculture. Most of the legal forests are highly degraded and the actual dense forest as per Forest Survey of India is about 15 per cent of the basins geographic area. The dense forest is located in the hilly regions of the upper basin dominated by tropical moist tree species. The farmers in the Narmada basin mostly cultivate small plots located on terrains and are traditionally driven by desire to produce for sustenance rather than profit. Table 1 and Figure 2 show the distribution of districts and taluks covered by the basin boundary and the location map of the Narmada basin containing boundary of each district falling within the basin, respectively.

Table 1: Distribution of Taluks in Each District of the State Covered in Narmada River Basin

S. No.Name of the State

Number of Districts in Each State Located in Narmada River Basin

Number of Taluks in Each District in Each State Located

in Narmada River Basin

1 Madhya Pradesh 19 67

2 Chhatisgarh 02 05

3 Gujarat 20 175

4 Maharashtra 07 55

5 Rajasthan 06 29

Total 05 54 331

Source: All India Soil & Landuse Survey (AISLUS) Atlas.


Fig

ure

2: L

ocat

ion

Map

of t

he N

arm

ada

Riv

er B

asin

Sou

rce:

All

Indi

a So

il &

Lan

duse

Sur

vey

(AIS

LUS)

Atla

s.



Table 2: Selected Socio Economic Parameters for Narmada River Basin

Human Development Factors (UNDP,1990)

Modified HDI (UNDP)

Socio-economic Factors (Narmada basin) Sources

Income Economy Number of establishments India Statistics website, State Reports pdfs, Census of India website

Number of working population

Education Education Literacy rates (%)

Health Health Medical facilities (number)

Sex Ratio(per 1000 males)

Infrastructure Drinking water facility (population having drinking water)

Road length (km)

Source: Data analysis by authors.

Methodology

United Nations Development Program in 1990 has identified parameters that has defined the socio-economic conditions of a nation and developed indices indicating the human development and assessing the longevity of human life and their survival strategy. Some of these parameters can be classified into (i) long and healthy lifelife expectancy and medical facilities; (ii) knowledgeliteracy; (iii) safe and decent livingdrinking water facility; (iv) gender equalitysex ratio; (v) economynumber of establishment and total number of working population in these establishments and (vi) infrastructureroad and transportation facility.

l Selection of Socio-economic parameters On the basis of the UNDPS Human Development Programme, the effort

has been made to look for those parameters, which explain the three basic aspects of Socio Economy of the basin, that is, income, education and health. Since Narmada River basin covers, parts of five different states, as a result the economy within the basin will vary widely.

The selection criterion has been decided on the basis of the level of analysis, the availability of data at that scale of analysis uniform for all states within the basin, and the optimum significance of the selected ones with respect to the context.

In view of the earlier said criterion, the various socio-economic parameters of Narmada River basin has been selected which are as follows (Table 2):

Selected parameters for Narmada SEI: Health facility: District-wise health facility collected including the number

of hospitals, dispensaries, community health centres and primary health centres;

Literacy rate: District-wise number of literates in percentage was recorded;



Drinking water facility: District-wise number of population benefited by drinking water facilities and measured with respect to number of population for the 3 time frames;

Sex ratio: District-wise ratio of number of female population per thousand male populations was recorded for the 3 time frames;

Number of establishments: Recorded district-wise number of both agricultural and non-agricultural establishments that provide employment to the inhabitants of the region;

Working population: District-wise number of population employed in the above mentioned establishments; and

Road: Road connectivity is an important aspect of development as it connects the villages to taluks to districts head quarters to different taluks and district, enabling local populations to move from one place to other for their needs. District wise total road length was recorded for the period of the present study.

Data for all 5 states, within the Narmada River basin, have been collected from different sources at district level to ensure a greater variability of socio economy prevailing there.

l Development of An Integrated Socio-Economic index (SEI) for the basin at district level

The selected parameters for socio-economic development of Narmada River basin at district level have been collected from various earlier mentioned Government sources for three census years of 1981, 1991 and 2001 at an interval of 10 years.

The following procedure has been followed for computing an integrated socio-economic index for the basin at the district level of analysis:

Calculation of the selected parameters for the year 1985, 1995 and 2005, on the basis of the collected input data for the census years of 1981,1991 and 2001 using linear progression.

Organisation of the three time frame data (1985, 1995 and 2005) for all the five states covering Narmada basin in tabular format.

Calculation of SUB INDEX for each socio-economic parameter using the formula:

Sub-index (For any parameter) = (Actual valueMinimum value)/(Maximum valueMinimum value)

Where, Minimum value = 0 Maximum value = Maximum value of the parameter within the set of

districts located within a river basin. Actual value = the value that correspond to a particular district. Calculation of Socio-Economic Index (SEI) from the SUB INDICES

values of the selected parameters, using the linear integration approach:

SEI = ([{(SI-1*a-1)} + {(SI-2* a-2)} + {(SI-7 * a-7)}]/ (sum of the weights))*100



Where, SI-1 to SI-7 = Sub index (1 to 7), a -1 to a -7 = Weightage of each parameter derived from the correlation analysis amongst the parameters as shown in Table 3.

l Organising SEI for three time period in Narmada River basin at taluk level

The organised district level socio-economic data for Narmada River basin (Table 4) is appended to the district boundary of the basin for its spatial representation, using the join function in arcmap. Figures 3 and 4 illustrate the visual representation of the output district-wise SEI map in thematic and vector format, respectively.

Conversion of the vector layer of Narmada district having three time appended SEI values from polygon to point feature data, using polygon to point conversion in arcmap as shown in Figure 5.

Using the Spatial Interpolation technique, TIN in Arcmap, the district-wise socio-economic data are converted into raster format, separately for each year.

The output raster TIN (Figure 6) is further analysed for the spatial redistribution of SEI data at taluk level of analysis, using the Narmada taluk Boundary.

The Output raster SEI (Figure 7) for three time period is used as an input raster, of the SEI value source, in the zonal statistics table computation, where a zone represent one taluk in the basin. The zonal statistic table has been used to extract the mean value for SEI, within a taluk area (zone) and append the same with the taluk boundary layer, on the basis of taluk name as shown in Figure 8.

l Organisation of Population Data at taluk level of analysis Taluk-wise population data are collected at each Census year of 1981, 1991

and 2001 from the Census of India website and has been used as the base data for the calculation of population data of 1985, 1995 and 2005, using the following exponential formula of growth (Census of India):

Input data: Population of 1981, 1991 and 2001, Population Decadal Growth Rate (19811991, 19912001 and

20012011 Formula: (for example) Population of 1982 = Population of 1981* log(e) {power(Growth

rate 19811991/year)} Population of 1983 = Population of 1982 * log(e) {power(Growth

rate 19811991/year)} Population of 1985 = Population of 1984* log(e) {power(Growth

rate 19811991/year)} [where log(e) has a constant value of 2.17828]

In a similar procedure, population for 1995 and 2005 is also calculated from population 1991 and 2001 and their respective growth rates. Table 5 shows the


Tab

le 3

: Cal

cula

tion

of W

eigh

tage

for

the

Nar

mad

a So

cio-

econ

omic

Par

amet

ers-

2005

Esta

blis

hmen

tW

okin

g Po

pula

tion

Lite

racy

R

ate

Hea

lth

Faci

lity

Sex

Rat

ioD

rink

ing

Wat

er F

acili

tyR

oad

Leng

th

Esta

blis

hmen

t1

Wok

ing

popu

latio

n0.

941

Lite

racy

rat

e0.

340.

291

Hea

lth fa

cilit

y0.

340.

380.

191

Sex

ratio

0.56

0.60

0.40

0.10

1

Dri

nkin

g w

ater

faci

lity

0.80

0.86

0.29

0.63

0.52

1

Roa

d le

ngth

0.40

0.39

0.18

0.58

0.14

0.68

1

Tot

al3.

392.

511.

061.

310.

670.

681

10.6

2

Wei

ghta

ge0.

320.

240.

100.

120.

060.

060.

094

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.


Tab

le 4

: Com

puta

tion

of S

EI fo

r N

arm

ada

Riv

er B

asin

in 2

005

DIS

T_1

Tot

al

Esta

blis

hmen

t (N

umbe

r)

Fina

l Su

b In

dex

(1)

Tot

al

Wor

king

Pe

rson

s (N

umbe

r)

Fina

l Su

b In

dex

(2)

Lite

racy

R

ate

(%)

Fina

l Su

b In

dex

(3)

Tot

al

Hea

lth

Faci

lity

(Num

ber)

Fina

l Su

b In

dex

(4)

SEX

R

atio

(p

er

1000

)

Fina

l Su

b In

dex

(5)

Popu

latio

n H

avin

g Fa

cilit

y of

Wat

er

(Num

ber)

Fina

l Su

b In

dex

(6)

Leng

th

of R

oads

(k

ms)

Fina

l Su

b In

dex

(7)

SEI_

20

05

(%)

Ahm

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3700

08.0

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00

9951

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81.8

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79

8917

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50

1651

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49

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89

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87

0.68

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0 0.

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2196

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19

78.2

9 0.

88

204.

00

0.29

91

9.40

0.

89

1391

599.

49

0.25

27

38.0

0 0.

20

34.1

1 G

andh

inag

ar

9363

4.00

0.

25

2161

30.0

0 0.

22

77.7

1 0.

88

205.

00

0.29

90

3.20

0.

87

1375

094.

75

0.25

19

70.0

0 0.

14

34.0

7 Pa

nch

Mah

als

6592

3.00

0.

18

1052

72.0

0 0.

11

66.2

5 0.

75

478.

00

0.69

93

9.60

0.

91

2094

037.

40

0.38

22

74.0

0 0.

17

33.8

5 S

ourc

e: D

ata

anal

ysis

by

auth

ors.


Fig

ure

3: D

istr

ict-

wis

e So

cio-

econ

omic

Cla

sses

(20

05)

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.


Fig

ure

4: D

istr

ict-

wis

e Sp

atia

l Dis

trib

utio

n of

SEI

(19

85, 1

995

and

2005

)

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.


Fig

ure

5: S

EI a

s Po

int

Laye

r (1

985,

199

5 an

d 20

05)

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.



calculation and organisation of taluk-wise population data within the basin as part of the analysis. The output population data is then attached to the spatial database of taluk boundary and rasterised to generate the population thematic raster of 1985, 1995 and 2005 (Figure 9).

l Correlation of socio-economic and demographic drivers with land use categories of the basin for three time period.

In order to analyse the relation between the human drivers and land use dynamics, it is required to organise the temporal changes in both the land use categories and the drivers database on the same level, so as to identify the sequences of changes. For the purpose, the land use changes for all categories are incorporated in the taluk layer along with its respective driver database, for all three time periods separately, so that we can get the distribution of change in various land use categories along with their driver values in each taluk.

In Arcmap, Union function has been used to bring the land use data at the same base with the driver data, so that, the change in the land use area for a particular class with respect to the change in the driver data for a particular taluk can be well recognised (Figure 10).

Impact of the drivers on the land use categories can be assessed with the help of Correlation Matrix, which is built on the land use database for three years separately (Table 6). A correlation Matrix is an array of row and column, having the correlation values, which depicts the strength of relation among different variables of land use categories and its drivers. A correlation value (denoted by r) ranges from 1 to +1. A high value of r whether positive or negative, shows a higher dependency among two variables, which means with a slight change in the first variable, there will be a significant change in the second one and vice versa. On the other hand, when the value of r will be more near to the zero, it depicts a lower strength of relation between the two variables. The sign of r indicates the direction of relationship; + means that with the increase in independent variable, the dependent variable increases; means that with increase in independent variable, the dependent variable decreases.

Figure 6: SEI as TIN (2005)Source: Data analysis by authors.



Figure 7: SEI Raster (1985, 1995, and 2005)Source: Data analysis by authors.


Fig

ure

8: S

patia

l Rep

rese

ntat

ion

of T

aluk

-wis

e SE

I Dat

a (1

985,

199

5, a

nd 2

005)

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.


(Tab

le 5

Con

tinue

d)

Tab

le 5

: Org

anis

atio

n of

Tal

uk-w

ise

Popu

latio

n (1

985,

199

5, a

nd 2

005)

in N

arm

ada

Riv

er B

asin

TA

LUK

PO

P_81

PO

P_

1991

PO

P_

2001

A

rea

19

81

Gro

wth

ra

te

(91

2001

)

Gro

wth

ra

te

(81

91)

Gro

wth

ra

te

(01

06)

Gro

wth

ra

te

per

yr

(200

191

)

Gro

wth

ra

te

per

yr

(91

81)

Gro

wth

ra

te

per

yr

(200

120

06)

Log(

e)

Pop

20

05

Log(

e)

Pop

1995

Lo

g(e)

Po

p 19

85

Ahm

adab

ad

City

25

3092

0 32

8069

2 42

2004

8 60

0 26

.61

24.2

5 16

0.

0266

1 0.

0242

5 0.

016

1.01

2534

44

3564

4 1.

0209

33

3564

136

1.01

9059

27

2945

2

Dah

egam

18

3633

21

4321

25

0253

62

0.2

26.6

1 24

.25

16

0.02

661

0.02

425

0.01

6 1.

0125

34

2630

38 1

.020

933

2328

37.8

1.

0190

59 1

9803

7.6

Das

kroi

27

8107

30

8214

45

9183

69

8.4

26.6

1 24

.25

16

0.02

661

0.02

425

0.01

6 1.

0125

34

4826

42 1

.020

933

3348

43

1019

059

2999

22.4

Dha

ndhu

ka

2137

48

2521

83

4346

62 2

682.

9 26

.61

24.2

5 16

0.

0266

1 0.

0242

5 0.

016

1.01

2534

45

6868

.2 1

.020

933

2739

711.

0190

59 2

3051

4.9

Dho

lka

2610

92

3073

43

2148

36 1

692.

1 26

.61

24.2

5 16

0.

0266

1 0.

0242

5 0.

016

1.01

2534

22

5811

.6 1

.020

933

3338

96.7

1.

0190

59 2

8157

2.7

Sana

nd

1373

46

1615

25

1933

35

790.

7 26

.61

24.2

5 16

0.

0266

1 0.

0242

5 0.

016

1.01

2534

20

3212

.2 1

.020

933

1754

80.4

1.

0190

59 1

4811

9.8

Vir

amga

m

2709

48

2775

34

1724

00 1

714.

1 26

.61

24.2

5 16

0.

0266

1 0.

0242

5 0.

016

1.01

2534

18

1207

.7 1

.020

933

3015

12.3

1.

0190

59 2

9220

1.9

Am

reli

1762

33

1982

32

2175

01

838.

5 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

22

8612

.8 1

.005

034

2022

54

1.01

1723

184

643.

3

Babr

a 91

911

1078

09

1229

83

793.

1 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

12

9266

1.0

0503

4 10

9996

.4

1.01

1723

962

97.2

2

Dha

ri M

ahal

12

6581

14

4232

13

6253

109

22

6.45

14

.97

16

0.00

645

0.01

497

0.01

6 1.

0125

34

1432

14 1

.005

034

1471

58.4

1.

0117

23 1

3262

1.8

Jafa

raba

d M

ahal

59

153

7364

1 90

732

355.

6 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

95

367.

36 1

.005

034

7513

5.12

1.

0117

23 6

1975

.93

Kha

mbh

a M

ahal

46

556

5304

5 84

529

407.

5 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

88

847.

46 1

.005

034

5412

1.25

1.

0117

23 4

8777

.77

Kod

inar

13

8970

16

5795

19

8181

53

6.8

6.45

14

.97

16

0.00

645

0.01

497

0.01

6 1.

0125

34

2083

05.8

1.0

0503

4 16

9158

.9

1.01

1723

14

5602


(Tab

le 5

Con

tinue

d)

TA

LUK

PO

P_81

PO

P_

1991

PO

P_

2001

A

rea

19

81

Gro

wth

ra

te

(91

2001

)

Gro

wth

ra

te

(81

91)

Gro

wth

ra

te

(01

06)

Gro

wth

ra

te

per

yr

(200

191

)

Gro

wth

ra

te

per

yr

(91

81)

Gro

wth

ra

te

per

yr

(200

120

06)

Log(

e)

Pop

20

05

Log(

e)

Pop

1995

Lo

g(e)

Po

p 19

85

Kun

kava

wa

Dia

15

3092

14

8538

15

7770

83

9.2

6.45

14

97

16

0.00

645

0.01

497

0.01

6 1.

0125

34

1658

30.2

1.0

0503

4 15

1551

.7

1.01

1723

160

397.

9

Lath

i D

amna

gar

1004

87

1193

04

1321

39

632.

8 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

13

8889

.8 1

.005

034

1217

24.6

1.

0117

23 1

0528

2.5

Lilia

Mah

al

5384

6 62

646

6072

2 39

5 6.

45

14.9

7 16

0.

0064

5 0.

0149

7 0.

016

1.01

2534

63

824.

19 1

.005

034

6391

7.04

1.

0117

23 5

6415

.66

Raj

ula

1322

68

1601

77

1456

28

850

6.45

14

.97

16

0.00

645

0.01

497

0.01

6 1.

0125

34

1530

67.9

1.0

0503

4 16

3426

.9

1.01

1723

138

580.

2

Dan

ta

1018

45

1314

76

1733

66

860.

4 26

.31

30.8

7 16

0.

0263

1 0.

0308

7 0.

016

1.01

2534

18

2223

1.0

2069

4 14

2701

.8

1.02

4325

112

121.

8

Dha

nera

14

2288

19

1633

18

1174

118

8.5

26.3

1 30

.87

16

0.02

631

0.03

087

0.01

6 1.

0125

34

1904

29.9

1.0

2069

4 20

7995

.2

1.02

4325

156

645.

7

Dis

a 26

0532

35

2040

45

8303

148

1.5

26.3

1 30

.87

16

0.02

631

0.03

087

0.01

6 1.

0125

34

4817

17 1

.020

694

3820

98.3

1.

0243

25 2

8682

1.3

Diy

odar

14

5075

19

0077

14

6393

101

1.5

26.3

1 30

.87

16

0.02

631

0.03

087

0.01

6 1.

0125

34

1538

72 1

.020

694

2063

06.4

1.

0243

25

1597

14

Kan

krej

23

875

1463

72

1853

52

795.

5 26

.31

30.8

7 16

0.

0263

1 0.

0308

7 0.

016

1.01

2534

19

4821

.4 1

.020

694

1588

69.7

1.

0243

25 2

6284

.13

Pala

npur

30

7683

39

7437

38

0707

147

3.1

26.3

1 30

.87

16

0.02

631

0.03

087

0.01

6 1.

0125

34

4001

56.7

1.0

2069

4 43

1371

.4

1.02

4325

338

730.

1

Rad

hanp

ur

8117

4 94

669

1201

77

595.

7 26

.31

30.8

7 16

0.

0263

1 0.

0308

7 0.

016

1.01

2534

12

6316

.7 1

.020

694

1027

52.1

1.02

4325

893

64.9

5

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.



Figure 9: Population Raster (1985, 1995, and 2005)Source: Data analysis by authors.


Fig

ure

10: U

nion

of L

ULC

with

Dri

ver

Dat

abas

e (2

005)

Sou

rce:

Dat

a an

alys

is b

y au

thor

s.



Result and Discussion

For Narmada River basin, the significant socio-economic factors are the economic factors and the drinking water facilities, followed by the health facilities and the road connectivity, as shown in Table 3. The correlation values have validated the authentication of the socio-economic model, chosen for the computation of socio-economic index for the basin.

Table 6: Correlation Analysis between Land Use Classes and Corresponding Driver Database (1985, 1995, and 2005)

2005 Forest

(1) Sp. Veg

(2) Water Body(3)

Built Up (4) Cropland(S)

Waste Land(6)

Fallow Land(7)

rain05 0.215 0.036 0.127 0.002 0.103 0.114 0.028

temp05 0.367 0.038 0.078 0.055 0.117 0.052 0.023

ele 0.520 0.120 0.136 0.105 0.080 0.129 0.159

slope% 0.505 0.290 0.091 0.137 0.227 0.071 0.012

sd 0.192 0.183 0.149 0.092 0.173 0.067 0.059

pop05 0.065 0.073 0.010 0.837 0.194 0.042 0.097

sei05 0.277 0.223 0.047 0.094 0.010 0.009 0.022

drainge05 0.527 0.233 0.590 0.056 0.556 0.352 0.423

1995

rain95 0.327 0.009 0.202 0.063 0.144 0.210 0.151

temp95 0.388 0.017 0.119 0.092 0.243 0.113 0.138

ele 0.515 0.039 0.196 0.136 0.242 0.161 0.130

slope% 0.506 0.213 0.112 0.149 0.037 0.106 0.248

sd 0.197 0.061 0.194 0.107 0.014 0.051 0.194

pop95 0.073 0.022 0.020 0.883 0.196 0.047 0.066

sei95 0.185 0.127 0.026 0.132 0.119 0.004 0.195

drng95 0.437 0.141 0.079 0.019 0.454 0.081 0.175

1985

rain85 0.296 0.035 0.159 0.034 0.216 0.273 0.252

temp85 0.334 0.003 0.074 0.089 0.310 0.109 0.173

ele 0.497 0.093 0.197 0.131 0.295 0.178 0.229

slope% 0.522 0.142 0.116 0.141 0.072 0.093 0.250

sd 0.217 0.050 0.172 0.111 0.123 0.084 0.055

pop85 0.078 0.080 0.033 0.824 0.251 0.078 0.087

sei85 0.034 0.038 0.046 0.073 0.036 0.099 0.041

drng85 0.320 0.028 0.028 0.019 0.402 0.020 0.343




According to the output socio-economic index map of Narmada basin, the highest index value within the basin is found in the regions of Ahmadabad and Surat of Gujarat and Nasik of Maharashtra and the lowest socio economy is found in the Dangs district of Gujarat, because of the existing tribal community (Figure 11).

Similar to the socio-economic factors, the demographic factor has also followed the same trend of change over time, as evaluated from the population data. The highest value for total population per district of the Narmada basin is found in the Ahmadabad and Nasik district of Gujarat and Maharashtra and the Dangs being the lowest, as shown in Figure 12.

Since land resources have been used for human benefit since ages, population and socio-economic growth of a human community have strong impact on the land use dynamics. With increased population, there would be the demand for more land for agriculture or looking for fuel wood or timber. Larger numbers of

Figure 11: Graphical Representation of District-wise Socio-economic Index of Narmada River Basin, 2005.


Figure 12: Graphical Representation of District-wise Total Population of Narmada River Basin, 2005.


0102030405060708090

SEI 2005 (%)

Rajna

ndgao

n

Bana

s Kan

tha

Sabar

Kanth

a

Ahma

daba

dRa

jkot

Porba

ndar

Amrel

i

Khed

a

Vado

daraSu

ratVa

lsad

Ratla

mJha

bua

Indor

e

East-

Nima

r

Raise

nHa

rda

Jabalp

ur

Mand

laSe

oni

Nand

urbar

Jalgao

nAk

ola

Nashi

kSir

ohi

Dung

arpur

Chitta

urgarh

TOTAL POPULATION 2005

01000000200000030000004000000500000060000007000000

Rajna

ndan

gaon

Bana

s Kan

tha

Saba

r Kan

tha

Ahmad

abad

Rajko

t

Porban

dar

Amreli

Khed

a

Vado

daraSu

rat

Valsa

d

Ratla

mJha

bua

Indor

e

East-

Nim

ar

Raise

n

Harda

Jabalp

ur

Man

dlaSe

oni

Nan

durbar

Jalgaon

Akola

Nashik

Siroh

i

Dung

arpu

r

Chittau

rgarh



people would also ensure that more labourers would be available, forcing wages down and making activities that need labour, such as agriculture, more profitable. An effect in the same direction may occur if the demand for agricultural products expands because of the growing number of people who need to be fed (Contreras-Hermosella, 2000; Panayotou, 1995). In the Table 6, the relation between various drivers (both human and physical) with the land use categories prevailing over Narmada basin, at a scale of 250,000, has been shown. Looking to its history of economy, over a period of 20 years, Narmada basin has grown as a prominent agricultural economy. This is because of development of extensive irrigation facilities induced by the Narmada River dam. Thus, the major changes in land use classes that have taken place from 1985 to 2005, is mainly found in crop land, built up area and forested areas. As evident from the Table 6, the highlighted values refer to those drivers which are having a significant level of impact on the corresponding land use category. The level of significance depends on the level of analysis of the concerned data which in case of Narmada were 331 (number of taluks) and the spatial scale of data analysis were 250,000 scale. Depending on the level of analysis, which is referred to as unit of freedom, the threshold of correlation significance is determined. According to this threshold, the values of correlation coefficient (r) are highlighted in the table, which represent the significant drivers for a class in a particular year. The land use change measurement depends on the spatial scale of analysis, thus higher the spatial scale, larger the areas of land use change can be detected and measured. Since the scale of analysis considered for Narmada basin is at 2,50,000 for its whole areal coverage, thus the correlation values (Table 6), the drivers are having with the land use change though significant but are not very high (for example, the relation SEI is having with the land use classes in 1995 and 2005).

It is also evident from the table, that there is a difference in the drivers of change for a particular class from year to year. For example, the socio-economic driver is an important agent of change for some of the classes in 1995 and 2005, but in 1985, it has no impact on the land use change for any class. Similarly, the population growth is affecting the classes like settlement and cropland the most rather than any other land use class. The most affected land use categories like the forest, built up and agriculture have been largely impacted by the human drivers of population, socio economy and drainage parameters (including both natural and canal drainage), especially after the construction of Narmada dam on the river. The Narmada River dam, has developed the irrigational facilities, leading to an increase in crop area, and has ensured the availability of drinking water facilities, to the remote areas. It has become the source of employment, for the power plant set ups, thus improving the socio economy of the basin. Henceforth, in a period of 20 years, the socio economy condition of the basin has improved due to the various social and economic developmental aspects, along with a steady rise in population, which has acted as a feedback in changing the land use scenarios, for the betterment of human life.

Following the data analysis, it can be said that humans and their ever increasing needs, has a significant relation with the land cover change over time. This is where, it brings the term land use, which means, human utilisation of land. It



is quiet tough to assess the land use change over time and predict for the future, without considering the human drivers as the agent of land use change. Land use change hence, is associated with population growth, global market enhancement, technological innovation and rural developmental policy. Opportunities and constraints for new land uses are thus created by markets and policies. Land cover changes are mostly driven by the peoples response to these opportunities and constraints (Lambin et al., 2003). Sometimes, the population growth also induces certain technological progress and institutional changes that contribute to reduced pressures on forests. It may result in agricultural intensification by increasing the soil fertility using various agricultural techniques instead of agricultural areal expansion. In response to the global market, there is also a gradual shift to cash crops on the existing fields and an expansion of agriculture into more fragile, marginal areas. At more local levels, population density is determined by road connectivity, soil fertility, employment opportunities, infrastructural availability and access to markets. Several studies show that population growth in any forested and sparsely populated areas occurs in response to road construction, available high-quality soils, and growing demand for agricultural products (Angelsen and Kaimowitz, 1999). This implies that these factors inevitably lead to an increase in population and constantly modifying the land use and land cover scenarios of the region in response to their needs.

In social science studies, the variables are always correlated and interdependent. Thus, in order to derive a composite index of socio-economic parameters using any statistical technique, special care is to be taken on the nature of analysis, availability of the data and objective and scale of analysis. A nations development in terms of social and economic growth can be categorised in broad sectors of health, economy and communication. The growth in each sector further depends on several correlated variables. For a large scale analysis of regional socio-economic growth of each sector, it is essential to identify the key variables, accountable for most of the variations in the socio-economic patterns of the region (Adhikari, 2006). Since the scale of analysis in Narmada basin case study is as small as 250,000, thus contributions of the variables are considered with respect to their weightage they are having on the socio-economic condition of the basin. The Australian Bureau of Statistics in their Socio-Economic Index for Areas (SEIFA) has identified the Principal Component Analysis as an important technique of dimension reduction. It has enabled them to summarise the whole set of inter-dependent variables in each sector into a manageable form without much loss of original information. Their main objective was to find a few set of key variables which can account for most of the variation in the growth of a particular socio-economic sector. This technique of dimension reduction is significantly important in the large scale evaluation of socio-economic growth at any local or regional level. Depending upon the purpose of summarising variables and how the variables group together, one or more of the principal components are used to create the final index (Adhikari, 2006). On the other hand, the New Zealand SEI depends more on the concept of Return to Human Capital model of social stratification (Davis et al., 1997). It follows a fundamental relationship between cultural resources (education) and access



to material reward (income) and this relation is mediated through occupational structure. The NZSEI, identified three variables of age, income and education to derive a value for occupational socio-economic status. Each value of the status is an optimally weighed combination of income and education corrected for age based on the assumption of the model.

The main purpose of this research work was to derive a relationship between the land use change of any region and its socio-economic growth represented by socio-economic index values. The socio-economic growth of any region depends largely on the overall development of human life, expanding human capabilities and enlarging their choices to live full and creative lives (Fukuda-Parr, 2003). The people are the beneficiaries of such a development and the courses of the change that it brings on the land cover to make it more resourceful for their use. Thus, in order to assess the socio-economic growth of the river basin in true sense and to analyse how it affects its land use land cover scenario with time, UNDPs Human Development Index is considered as a mean to derive the socio-economic index for the Narmada basin. With the use of four basic sectors of development, that is, economy, health, education and infrastructure, the index is computed, the value of which relates well to the corresponding land use scenario of the region. This type of socio-economic Index suits best for the small scale study of overall social and economic development of a river basin affecting the land use dynamics of the region. The major sensitivity of such indices lies in the arbitrariness and unavailability of the data records as well as the uncertainty of the fact which variable is more crucial for the growth of a particular sector of socio economy.

Conclusion

In this article we presented a study on land use dynamics in relation to the impact of selected socio-economic conditions and demographic conditions prevailing in the Narmada River basin.

In general, development can be viewed as a multi-dimensional phenomenon, which defines the existing land use scenarios over the region. Here in this study, a composite index of development is constructed in the name of socio-economic index using four broadly accepted components: (i) economic production and economic condition or in other words level of economic development; (ii) basic needs to survive; (iii) health and health-related services and (iv) communication.

The values of the index support the general socio-economic conditions prevailing over the concerned states covering the basin. The factors, which are found out to be more important for the overall development process in Narmada, relate to basic needs like education, employment opportunities and facilities like safe drinking water, Health care infrastructure, etc. Temporal development in the socio-economic status along with population growth has a direct impact on the change of land use classes like agriculture, forestry and human settlement, as it is evident from the correlation analysis. Thus, it can be concluded that to assess the dynamicity of land use classes over time and to predict them for future, its



essential to consider the human drivers along with the physical ones and organise them according to the scale of analysis, in order to select the significant agent of change for a particular land use class.

AcknowledgementsThe present study as been carried out as part of ISRO Geosphere Biosphere Programme (IGBP) on landuse/land cover dynamics in Indian river basins and impact of human drivers thereof.

The authors are thankful to Director, IIRS for all necessary support and to Dr Kanchan Chopra for reviewing the article and providing valuable suggestions.

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Journal of Land and Rural Studies-2015-Palchoudhuri-1-28 (1)