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Preface

Human beings evolved under conditions of high mortality due to

famines, accidents, illnesses, infections and war and therefore the

relatively high fertility rates were essential for species survival. In

spite of the relatively high fertility rates it took all the time from

evolution of mankind to the middle of the 19th century for the global

population to reach one billion. The twentieth century witnessed an

unprecedented rapid improvement in health care technologies and

access to health care all over the world; as a result there was a

steep fall in the mortality and steep increase in longevity. The

population realized these changes and took steps to reduce their

fertility but the decline in fertility was not so steep. As a result the

global population has undergone a fourfold increase in a hundred

years and has reached about 7 billion.

The State of World Population 2011 looks at the trendsthe

dynamicsthat are defining our world of 7 billion and shows what

people in vastly different countries and circumstances are doing in

their own communities to make the most of our world of 7 billion.

Some of the trends are remarkable: Today, there are 893 million

people over the age of 60 worldwide. By the middle of this century

that number will rise to 2.4 billion. About one in two people lives in a

city, and in only about 35 years, two out of three will. People under

the age of 25 already make up 43 per cent of the worlds population,

reaching as much as 60 percent in some countries. India has the

second largest population in the world, with 1.21 billion people

comprising 623.7 million males and 586.5 million females, according

to the provisional 2011 Census report. In the last ten years, 181

million people were added and, since 1947, the population of India

has more than tripled. Interestingly, the addition of 181 million

people to the population during 2001-11 is slightly lower than the

total population of Brazil, the fifth most populous country in the

world. Significantly, the growth is slower compared to the previous

decade. India accounts for 17.5 percent of the world population.

The present book is an initiative to bring out the innovative

ideas of authors and scholars on population issues, I am highly

indebted and gratitude to all authors and scholars who have given

consent for publishing their articles in the edited book form. It gives

me great pleasure to express my thanks to all, who have extended

their helping hands in the completion of this book. I am very grateful

to Dr. M. M. Sheikh, Associate Professor, Dept. of Geography, Govt.

Lohia PG Collage, Churu for his valuable guidance and supervision.

I deeply express my gratitude to my father Mr. Shiv Kumar Ojha,

mother Mrs. Uma Ojha, Brother Mr. Anil Kumar Ojha, and sister in

law Nisha Ojha, Sister Manju Sharma, brother in law Mr. Manoj

Sharma and my aunty Hemlata Ojha for their co-operation in this

work. I also extend special thanks to our all friends who motivated

me to bring out this book. It is hoped this book will be useful for

researchers, environmental activists, subject experts and policy

makers.

Place: Churu, Rajasthan (India) (Anju Ojha)

Dated: 21th March, 2014

Contents

Preface iContents iii

List of Authors iv

1. Population Growth Dynamics in IndiaAnju Ojha

1

2. Detecting Fallow Agricultural Land and Correlationwith Demographic Indicators in the Branicevo andPomoravlje Districts, SerbiaDarko Jaramaz and Veljko Perovic

34

3. Population Dynamics in Rajasthan StateM. M. Sheikh

60

4. Population Explosion Menace: An OverviewMalti P. Sharma

81

5. Declining Sex Ratio in IndiaVibhuti Patel

90

6. Education and Women Population in IndiaL. R. Patel and Pankaj Rawal

138

7. In-Vitro Fertilization in India: Negotiating Genderand ClassSneha Annavarapu

151

8. Woman Literacy in Rajasthan State of IndiaRatan Lal

172

9. Population Challenges and Development GoalsPreeti Sharma and Devendra Kumar Sharma

180

10. Population Growth Trends in IndiaPardeep Sharma

201

11. Population Trends and Policy in selectedCountriesAkshita Chotia, Pratibha Sharma and Preeti Sharma

217

List of Authors

1. Akshita Chotia Assistant ProfessorDept of Geography, R.K.J.K Barasia PG College, Surajgarh, Jhunjhunu (Raj)

2. Anju OjhaAssistant ProfessorDepartment of GeographyGovt. Lohia PG College, Churu, Rajasthan, INDIA

3. Darko Jaramaz and Veljko PerovicInstitute of Soil Science, Teodora Drajzera 7, Belgrade, Serbia

4. Devendra Kumar SharmaAssistant Professor, Department of GeographyRKJK Barasia PG College, Surajgarh (Jhunjhunu )Raj.

5. Education and Women Population in IndiaL. R. Patel Department .of GeographyJRN University, Udaipur (Raj.) India

6. M. M. SheikhAssociate ProfessorDepartment of GeographyGovt. Lohia PG College, Churu, Rajasthan, India

7. Malti P. Sharma Associate Professor (English), S.K. Govt. (P.G.)College Sikar (Rajasthan), India

8. Pankaj Rawal Department .of GeographyJRN University, Udaipur (Raj.) India

9. Pardeep SharmaAssistant ProfessorDept of GeographyR.K.J.K Barasia PG College, Surajgarh, Jhunjhunu(Raj)

10. Pratibha Sharma Assistant ProfessorDept of Geography, R.K.J.K Barasia PG College, Surajgarh, Jhunjhunu (Raj)

11. Preeti Sharma Assistant Professor, Department of GeographyRKJK Barasia PG College, Surajgarh (Jhunjhunu )Raj.

12. Ratan Lal Govt. School, Badi Kallan (Jodhpur), India

13. Sneha AnnavarapuResearch ScholarDepartment of Humanities and Social Sciences, IITMadras.

14. Vibhuti Patel Professor & Head, Department of Economics, SNDT Womens University, Churchgate, Mumbai

1

Chapter-1

Population Growth Dynamics in India

Anju OjhaAssistant Professor

Department of GeographyGovt. Lohia PG College, Churu, Rajasthan, INDIA

Long years ago we made a tryst with destiny, and now the time

comes when we shall redeem our pledge, not wholly or in full

measure, but very substantially. At the stroke of the midnight hour,

when the world sleeps, India will awake to life and freedom. A

moment comes, which comes but rarely in history, when we step out

from the old to the new, when an age ends, and when the soul of a

nation, long suppressed, finds utterance. It is fitting that at this

solemn moment we take the pledge of dedication to the service of

India and her people and to the still larger cause of humanity. That

future is not one of ease or resting but of incessant striving so that

we may fulfill the pledges we have so often taken and the one we

shall take today. The service of India means the service of the

millions who suffer. It means the ending of poverty and ignorance

and disease and inequality of opportunity. The ambition of the

greatest man of our generation has been to wipe every tear from

every eye. That may be beyond us, but as long as there are tears

and suffering, so long our work will not be over. (Jawahar Lal Nehru,

1947)

With these evocative words, an independent India began her

tryst with destiny. It is fitting that we celebrate the awakening of the

2

Indian economy and an era of faster annual growth by remembering

this pledge of service to the 1.2 billion-plus population in diverse

corners of India. This book is dedicated to exploring the contours of

the day to day lives of Indians in 2004 and 2005, nearly 60 years

after this pledge was made. This search must acknowledge the

achievements of the last century as well as anticipate the challenges

of the twenty-first century. It must document the lived experiences of

Indian families in cities and villages from Kashmir to Kanyakumari as

they go about negotiating their daily lives in a globalizing India.

The high rate of population is a major problem of the Country

and the State as well. Population control remains the most

challenging task before our nation and our state today. Although

India was the first developing country to adopt the Family Planning

Program in 1951, the efforts towards population stabilization in the

last five decades did not fetch the desired results. After the 1994

International Conference on Population and Development (ICPD) at

Cairo, the country as a whole approaching the issue of population

stabilization from a Reproductive Child Health (RCH) perspective.

Due to socio-economic and demographic heterogeneity it is,

however, not possible to implement all the components of the

program of action adopted at ICPD, in all the states all at once.

There is an urgent need, therefore, to take the regional variations

into account while developing programs and action plans that are

state specific. The population of an area is the outcome of its

physical, socio-economic environment. Population study, gives an

idea not only about the region but also gives an idea about the

resource because, population is also one on the major resource of

3

any region. Population study is concerned not only with the

population variables but also with the relationship between

population variables and social, economic, political, biological,

geographical variables etc. It includes both qualitative and

quantitative aspects of human population.

India is one of the very few countries in the world, which has a

proud history of holding Census after every ten years. The Indian

Census has a very long history behind it. The earliest literature Rig

Veda reveals that some kind of population count was maintained

during 800-600 BC. Kautilyas Arthasastra, written around 321-296

BC, laid stress on census taking as a measure of State policy for

purpose of taxation. During the regime of Mughal king Akbar the

Great, the administrative report Ain-e-Akbari included

comprehensive data pertaining to population, industry, wealth and

many other characteristics.

The History of Census began with 1800 AD when England had

begun its census but the population of dependencies was not known

at that time. In its continuation , based on this methodology census

was conducted in town of Allahabad in 1824 and in the city of

Banaras in the year 1827-28 by James Princes. The first complete

census of an Indian city was conducted in 1830 by Henry Walter in

Dacca. In this census the statistics of population with sex and broad

age group and also the houses with their amenities were collected.

The first census in India, commonly referred to as 1872 census, was

conducted over five years between 1867 and 1872, and thus was

not synchronous. The exercise was started by the British who

wanted to know the size, composition and characteristics of

4

population in their colonies but it was not conducted over the entire

territory controlled by the British. The subsequent censuses were

synchronous and gradually were canvassed throughout the country.

Despite political and other problems, Censuses in India have

continued to be conducted every 10 years. After Independence,

Parliament passed the Census Act of 1948 and created a post of

Census Commissioner. Earlier, the whole operation used to be

temporarily set up for 2-3 years and wound up after the census was

conducted and results published. The Act empowered census

department to ask certain questions and made answering them

obligatory for citizens. Information collected is treated as confidential

and can be used only for statistical purposes.

Population in the World currently growing at a rate of 1.1.0

percent per year. India has more people than Europe, more than

Africa, more than the entire Western Hemisphere. Indias population

will exceed that of China before 2030 to become the worlds most

populous country, a distinction it will almost certainly never lose.

Just one group, Indian boys below age 5, numbers 62 million-more

than the total population of France. Indias annual increase of nearly

19 million contributes far more to annual world population growth

than any other country (Population Bulletin, Sep. 2006). The

average population changes currently estimated at round 75 million

per year. Indias population in 1901 was about 238.4 million which is

increased by more than four times in 110 years to real population of

1210 million in 2011. India is often described as a collection of many

countries held together by a common destiny and a successful

democracy. Its diverse ethnic, linguistic, geographic, religious, and

5

demographic features reflect its rich history and shape its present

and future. No fewer than 16 languages are featured on Indian

rupee notes. It is also only the second country to achieve a

population of 1 billion. While it is an emerging economic power, life

remains largely rooted in its villages. India, accounting for nearly 18

percent of the world population has been experiencing slow but

steady demographic transition since the second half of the last

century. In recent years, however, the fertility transition in India has

accelerated resulting in rapid changes in the age structure of the

population. This change creates unique opportunities along with

significant challenges both for the economy and society. The

Census 2011 was the largest such exercise in the world. Our census

history goes back to 1872 when although a census was conducted,

it is not regarded as a regular census as it was not conducted at the

same time. Since 1881 India has conducted decennial censuses

without any interruption. We have numerous tables on the

demographic, social and economic life of the people in this country

of great demographic diversity

The census in India collects and publishes information on

various characteristics of the population, such as, age and sex

distribution, social and cultural factors such as religion, literacy,

languages known, migration and economic activities of the people.

Besides, during housing census conducted a year before the

population count, information is also collected on type of housing,

amenities and assets possessed by households. Analysis of the

data collected from several Censuses provide a unique opportunity

to understand the dynamics of and trends in various facets of the

6

diverse population of the country. A population Census is the

process of collecting, compiling, analyzing and disseminating

demographic, social, cultural and economic data relating to all

persons in the country, at a particular time in ten years interval.

Conducting population census in a country like India, with great

diversity of physical features, is undisputedly the biggest

administrative exercise of peace time. The wealth of information

collected through census on houses, amenities available to the

households, socio economic and cultural characteristics of the

population makes Indian Census the richest and the only source for

planners, research scholars, administrators and other data users.

The planning and execution of Indian Census is challenging and

fascinating.

7

BackgroundIndia is the largest democratic country in the world. It accounted for

more than 17 percent of the worlds population in 2010 according to

the estimates prepared by the United Nations (United Nations,

2008). This 17 per cent of the world population lives on less than 2.5

percent of the total land area of the planet Earth. Between 2000 and

2010, worlds population is been estimated to have increased at the

rate of 1.22 percent per year, adding an average of 79 million

persons each year. Very close to 22 percent of this increase is

estimated to have accounted for by the increase in population in

India and this contribution has been the largest, even larger than the

contribution of China, the most populous country in the world today

8

(United Nations, 2008). Projections prepared by the United Nations

suggest that by the year 2050, population of India will increase to

1614 million which will account for almost 19 percent of the

estimated world population of 9150 million at that time. This means

that of the projected 2854 million increases in world population in the

50 years between 2000 and 2050, more than 571 million or almost

19 percent increase in the world population will be confined to India

alone. These projections also indicate that by the year 2050, India

will become the most populous country in the world.

During the nineties, the government of India has taken a

number of key policy initiatives that have relevance to future

population growth in the country. The first of these initiatives was the

National Population Policy 2000 which aimed at achieving zero

population growth in the country by the year 2045 through reducing

fertility to the replacement level by the year 2010 (Government of

India, 2000). At the same time the process of economic reforms that

started in 1990 continued with varying pace throughout this period.

A revival of economic reforms and better economic policies during

the first decade of the present century has accelerated the economic

growth rate. Today, India is the second fastest growing major

economy of the world. These facts explain the special interest with

which the results of the 2011 population census in India have been

published. They supply basic information about population size, rate

of population growth, population sex ratio and levels of literacy for

the country as a whole as well as for its constituent states and Union

Territories.

9

Trends of Growth Rate The population of India as of 1 March 2011 was 1,210,193,422

persons. This implies an increase of 17.653 percent in the ten-year

period since the 2001 population census. The proportionate

increase in the population of the country during the decade 1991-

2001 was 21.353 per cent which means that the population increase

in the country has continued to slow down and the rate of retardation

in population growth appears to have increased. In terms of the

average annual growth rate, the population of the country increased

at a rate of 1.626 percent per year, well below the average annual

increase of 1.935 percent per year during 1991-2001. A notable

feature of the population figures is that they are very close to the

population projected by the Government of India for the period 2001-

2011 on the basis of the 2001 population census. Government of

India had projected that the population of the country will increase to

1,192,506 thousand by the year 2011 (Government of India, 2006).

Similarly, United Nations had estimated that Indias population would

increase to more than 1214 million by the year 2010 (United

Nations, 2008). The population figures of 2011 population census

suggest that the enumerated population in the country exceeded the

projected population by almost 18 million. During the period 1991-

2001, the enumerated population of the country exceeded the

project population by around 16 million whereas, the enumerated

population exceeded the projected population by less than 9 million

during the period 1981-91(Chaurasia and Gulati, 2008). In fact, the

average annual population growth rate during the period 2001-2011

based on the figures of the 2011 population census works out to be

almost 1.63 percent per year which is substantially higher than the

10

project average annual growth rate of 1.48 percent per year. This

suggests that demographic transition - reduction in fertility and

mortality - in the country has been slower than the projected one.

Population projections prepared by the Government of India are

based on the assumption that the replacement fertility will be

achieved by the year 2021 not in 2010 as aimed in the National

Population Policy 2000. However, the average annual population

growth rate during the period 2001-2011 derived from the figures of

the 2011 population census suggests that the decrease in fertility in

the country has been slower than the project one which means that

the country will not able to achieve replacement fertility even by the

year 2021. This means that there is only a distant possibility of

achieving stable population by the year 2045 as stipulated in

National Population Policy 2000.

11

Table 1: India: Population and Population Growth, 1901-2011.

Source: Census of India, 2011

As the result of the slowdown in the population growth, the net

addition to the population decreased in India for the first time during

the period 2001-2011. During the period 1991-2001, the net addition

to the population of the country was around 182.32 million (Table 1)

12

whereas, the net addition to the population of the country during the

period 2001-2011 was 181.6 million. This decrease in the net

addition to the population is perhaps the most remarkable feature of

population transition in India during the period 2001-2011. This is an

indication that the population growth in the country has now started

shrinking. Had the average annual population growth rate during the

period 2001-2011 would have been the same as the average annual

population growth rate during the period 1991-2001, the population

of the country would have increase to 1246.315 million and the net

addition to the population of the country would have been almost

218 million - 56 million more than the actual addition to the

population during the period 2001-2011 as revealed through figures

of the 2011 population census. This trend in the net addition to the

population of the country again confirms that population transition in

the country is picking the momentum and the net addition to the

population of the country has now peaked. However, actual slow

down in the growth of the population during the period 2001- 2011

has been slower than the projected one.

13

14

Figure 1: India Population, 1901-2011

Regional differentials in GrowthRegional diversity or inequality in the growth of population in India is

well known. Moreover, this diversity in population growth has

persisted over time. Any discussion about Indias population growth,

therefore, is incomplete without a discussion on regional differences

in the growth of population. The results of 2011 population census

15

provide information on population size and growth for all the states

and union territories of India.

It reveals considerable geographic variation in the population

growth rate across the states and union territories of the country.

Some states of the country grew relatively slowly, well below the

growth of the country as a whole. Since the size of the population of

different states and Union Territories of the country varies widely,

the population growth rate of different states and Union Territories

has different impact on the population growth rate of the country as

a whole. Because of the varying population size, it is customary to

group the states and Union Territories of the country into three

broad categories; major states (states with a population of at least

20 million at the 2001 census), small states (states with a population

of less than 20 million at the 2001 census), and Union Territories.

According to the 2001 population census, there were 17 states in

the country with a population of 20 million and more while the

population of 12 states was less than 20 million. In addition, there

are 6 Union Territories all of which had a population of less than 20

million. The provisional results of 2011 population census suggest

that the 17 major states of the country account for almost 95 per

cent of the population of the country while the 12 small states

accounted for only about 5 percent of the countrys population.

Union Territories, on the other hand, account for just around 0.3

percent of the population of the country. Trends and patterns of

Indias population growth, therefore, are primarily determined by

population growth trends and patterns in the 17 major states. The

contribution of small states and Union Territories to the growth of the

16

population of the country has always been almost negligible,

although trends and patterns of population growth in Union

Territories are themselves an important area of interest and

analysis. Among the major states of India, the population growth

during the period 2001-2011 has been the most rapid in Bihar

followed by Chhattisgarh and Jharkhand. These states are the only

three major states of India where the average annual population

growth rate was more than 2 percent year during the period under

reference. Interestingly, these three states constitute a geographical

continuity.

The average annual population growth rate has also been

more than 2 percent per year in Jammu and Kashmir, Meghalaya,

Manipur, Arunachal Pradesh and Mizoram during the period under

reference. These states are the smaller states of the country.

Population growth rate has also been quite high in Rajasthan,

Madhya Pradesh, Uttar Pradesh and Haryana. In these states,

population increased at an average annual rate of more than 1.8

percent year during the period under reference which is well above

the population growth rate of the country as a whole. In all, there are

18 states and Union Territories where the average annual

population growth rate has been estimated to be higher than the

national average during the period under reference. These states

and Union Territories account for more than 638 million or almost 53

percent of the population of the country. On the other hand,

Nagaland is the only state in the country which has recorded a

negative population growth during the period under reference.

During the period 1991-2001, the population of Nagaland increased

17

by a whopping 64.5 million but, during 2001-2011, the population of

the state decreased. This appears to be a very conspicuous finding

of the provisional results of 2011 population census. Moreover, there

are only two states - Kerala and Goa - and two Union Territories -

Andaman and Nikobar and Lakshadweep - where the average

annual growth rate during 2001-2011 is estimated to be less than 1

percent per year. The three states where the average annual

population growth rate appears to have increased during the period

2001-2011 compared to the period 1991-2001 are Tamil Nadu,

Chhattisgarh and Manipur. Among these three states, Tamil Nadu

recorded a very low growth rate during the period 1991-2001

whereas the growth rate in Chhattisgarh and Manipur was more

than 2 percent per year. It appears that rapid population growth

situation has continued in these states during the period 2001-2011

also.

The situation is however not so encouraging when the

population growth estimated on the basis of provisional figures of

2011 population census is compared with the projected population

growth based on the projected population for the year 2011. This

comparison suggests that in 20 states and Union Territories of the

country, the actual population growth has been faster than the

projected population growth rate with the difference being the largest

in Tamil Nadu followed by Bihar among the major states of the

country. In these states and Union Territories, actual population

transition during the period 2001-2011 has been slower than the

projected one. At the same time, in 9 out the 12 small states, the

actual population growth rate based on the provisional figures of

18

2011 population census has been faster than the project one.

However, in all Union Territories of the country, the actual population

growth during 2001-2011 has been slower than the project one. This

comparison suggests that the pace of population transition in the

country during the period 2001-2011 has been slower than what was

projected or expected. Obviously, the population transition scenario

in the country and in most of the states, as revealed through the

provisional figures of the 2011 population census, does not appear

to be very encouraging. It is obvious from table 3 that the country

has missed the projected target of average annual population

growth rate for the period 2001-2011, set on the basis of the results

of the 2001 population census. This means that the country will take

more time to achieve the goal of population stabilization as

stipulated in the National Population Policy 2000.

There has been considerable variation in regional changes in

the growth rate over time with acceleration in population growth in

some states and Union Territories during 2001-2011 as compared to

1991-2001 and slowdown in other states and Union Territories. Most

of the states fall very close to the 45 degree line. The deviation from

the line is marked in Andaman and Nikobar, Sikkim, Chandigarh,

Delhi and Nagaland and in Tamil Nadu, Chhattisgarh, Manipur and

Puducherry. In the first group of states and Union Territories,

average annual population growth rate has slowed down during the

period 2001-2011 as compared to the average annual growth rate

during 1991-2001 with the change in the average annual population

growth rate being the most typical in Nagaland. In the second group

of states and Union Territories, it has accelerated. In other states,

19

the average annual population growth rate registered during 2001-

2011 is what that could have been predicted on the basis of the

average annual population growth rate recorded during the period

1991-2001. This suggest that, although, the population growth rate

in the states and Union Territories of the country have shown a

decline on the basis of the provisional results of 2011 population

census, this decline appears to be, at best, a normal pattern in most

of the states and Union Territories. There are only a few marked

deviations.

Rate of Population GrowthAmong the major states, Bihar with 25.1 percent growth rate during

2001-2011is the fastest growing state. Decadal Growth rates have

exceeded 20 percent in all the core north India states Bihar, Uttar

Pradesh, Rajasthan, Madhya Pradesh including Jharkhand and

Chattisgarh. Keralas growth rate during 2001-2011 of 4.9 percent is

indicative of the state reaching stationary population in the next 10-

20 years. Growth rate around 11-13 percent is reported by Punjab,

Andhra Pradesh, and West Bengal and around 15-16 percent by

Karnataka, Maharashtra and Tamil Nadu. Southern states are the

harbinger of population stabilization.

Geographic DistributionOne implication of population growth pattern observed on the basis

of the results of 2011 population census is a change in the

distribution of the population across the states and Union Territories

of the country. An understanding of population distribution over

administrative areas can be achieved through a consideration of the

components of population distribution. Population distribution,

20

essentially, has two components - extensiveness and intensiveness.

Extensiveness is nothing but the size of the population of an

administrative unit relative to the size of other administrative units.

Intensiveness, on the other hand, implies the denseness of the

population within the administrative unit. In any analysis of the

change in population distribution, it is important to take both into

consideration.

The state of Uttar Pradesh with 199.6 million people is Indias

most populous state accounting for 16.5 percent of countrys

population. Bihar (103.8) and Maharashtra (112.4) are other two

states with more than 100 million people. Other large states are

West Bengal with 91, Andhra Pradesh with 85, Madhya Pradesh

with 73, and Tamil Nadu with 72 million people. Nearly 42.4 percent

of Indians now live in formerly undivided Bihar, Uttar Pradesh,

Madhya Pradesh and Rajasthan; a proportion that has increased

from 40 percent in 1991. Conversely, the proportion of Indians living

in the four southern states of Kerala, Tamil Nadu, Karnataka and

Andhra Pradesh has decreased from 22.5 percent in 1991 to 20.8

percent in 2011, causing concerns about their representation in

parliamentary democracy.

Sex Ratio of PopulationThe good news is that female to male sex ratio of population has

began to improve from 927 in 1991 to 933 in 2001 to 940 in 2011.

Yet, compared to what is observed elsewhere in most countries in

the world, Indias sex ratio is anomalous. The British Census

commissioners also noted it and were quite puzzled. Quite

systematically, they examined a number of factors to understand

21

why there were fewer women in India compared to men in the total

population. The possible reasons dwelt upon by them and by other

noted population scientists were: under enumeration of women,

more masculine sex ratio at birth compared to observed in other

populations, higher mortality experienced by women compared to

men due to epidemics (such as plague, malaria and influenza) or

deficiency diseases, or due to neglect, premature cohabitation and

unskillful midwifery. Except for the persistent survival disadvantage

that women experienced from early infancy well into the

reproductive period, evidence did not support any of the other

factors. The female to male sex ratio of population historically noted

in the contiguous area of Punjab, Haryana, Chandigarh and Delhi,

has improved between 2001 and 2011, but it is still below 900

women per 1000 men. On the other hand, sex ratio close to unity is

recorded in the southern states of Kerala, Tamil Nadu and Andhra

Pradesh. This phenomenon observed since the beginning of the

20th Century has persisted even now.

Child Sex RatioSince 1981 Indian Censuses have made available data on

population in the age group 0-6 by sex, as a byproduct of

information on literacy rates which are calculated for 7+ population,

enabling calculation of sex ratio of children in the age group 0-6.

(Typically, age data are generated in five year age groups and thus

most populations would provide data on children in the age group 0-

4 and not 0-6.) The Census Commissioners office has calculated

sex ratio of children aged 0-6 from the previous Censuses of 1961

and 1971 also showing the trend over 50 years. The child sex ratio

22

has steadily declined from 976 in 1961 to 927 in 2001 and further to

914 in 2011. This phenomenon has drawn worldwide attention and

is largely attributed to the increasing practice of sex detection and

selectively aborting female foetuses. Between 2001 and 2011, child

sex ratio fell in practically the whole country, giving credence to a

belief that the practice of female selective abortion is spreading to

parts of the country, where it was not noted earlier. Child sex ratio

improved in 2011 from the level in 2001 in Himachal Pradesh,

Haryana, Punjab and marginally in Gujarat; the states where it was

below 850. In 2011 in these states, there are still less than 900 girls

for 1000 boys.

23

Table 2: Sex Ratio, 1991-2011

Literacy Trends in IndiaThe pace of progress in literacy rates as revealed by decennial

census is very slow in India. In the span of fifty years i.e. from1951

24

(18.33) to 2001(64.83), there has been only marginal increase of

46.5 percent in literacy rate. Between 1951 to 2001, female literacy

shows a mere 44.7 percent increase which is only five times for the

whole point. According to census 2011, out of 74.04 percent of

literacy rate, the corresponding figures for male and female are

82.14 and 65.46 percent respectively which means four out of every

five males and two out of every three females of the age seven and

above are literate in the country. Though the target set by Planning

Commission to reduce the gender gap by 10 percent in 2011-12 has

not been achieved yet the reduction by 5 percent (4.99 percent) has

been achieved which is a positive stride towards decreasing

illiteracy. A significant milestone of Census 2011 is that the total

number of illiterates has come down from 30.4 crores in 2001 to

27.2 crores showing a decline of 3.1 crore. Out of total 21.7 crores

literates, female (11.0 crores) outnumber males (10.7 crores).

Another striking feature is that, out of total decrease of 3.1 crore of

illiterates, the females (1.7 crores) top male (1.4 crore) in the list.

This trend of rising female literacy will have far reaching

consequences which may lead to development of the society. When

we portray the literacy picture of India we find that the ordering of

the states are almost same as it was in 2001 as Kerela still

continues to top the list with 93.91 percent literacy rate whereas

Bihar remains at the bottom of the ladder with 63.82 percent.

Although Bihar has performed well in 2011census compared to

literacy rate in 2001 (47.00 percent) still it lies in the lowest rank.

States like Punjab (76.68 percent), Haryana ( 76.64 percent),

Madhya Pradesh ( 70.63 percent), Andhra Pradesh (75.60 percent),

Karnataka (67.66 percent) and Tamil Nadu ( 80.33 percent) and UTs

25

like Andaman & Nicobar Islands (86.27 percent), Chandigarh (86.43

percent) were downgraded from their previous rank whereas Tripura

(87.75 percent), Sikkim (82.20 percent), Manipur (79.85 percent),

Nagaland (80.11 percent) and UTs like Dadra & Nagar Haveli (77.65

percent), NCT of Delhi (86.34 percent), Puducherry (86.55 percent)

and Lakshadweep (92.28 percent) have shown higher rankings than

before.

Table 3: Literacy Rate in India, 2011

Source: Census of India, 2011

26

27

Demographic Dividend Demographic dividend refers to a change in the age distribution of

population from child ages to adult ages. It leads to larger proportion

of population in the working age group compared to younger and old

age groups. Apparently, given the diversity in the fertility transition in

India, the demographic dividend is likely to continue as it shifts from

one state to another based on the pace of demographic changes in

the respective states. It is generally argued that the demographic

change in India is opening up new economic opportunities (James

2008). There is generally high optimism both based on the

experience of many other countries and from India that demographic

changes will take the country to newer economic heights (Bloom

and Williamson, 1998; Aiyer and Modi 2011; James 2008).

Along with high optimism, there are also larger concerns on

the ability of the nation to take full advantage of the demographic

dividend. It is often argued that demographic dividend might turn into

a nightmare given the composition of the Indian population in terms

of educational level and skill levels (Altbach and Jayaram, 2010;

Chandrasekhar, Ghosh and Roychwdhury, 2006). It is argued that

large segments of adult population in the country are illiterate and do

not have the capacity to contribute substantially to the modern

economy. Perhaps, demographic dividend needs to be understood

more critically and in a proper perspective. Many of the good

empirical studies estimating the impact of age structure changes on

the economic progress have indicated very high impact of age

structure change and positive demographic dividend in the country

(Aiyer and Modi 2011; Bloom et al, 2006; James 2008). In other

28

words, these studies bring out clearly that those states moving faster

in demographic and age structure change are also experiencing

rapid economic growth. The best examples come from southern and

western states in India where the demographic changes are also

leading to sustained economic changes both in the aggregate

economy and in the lives of people.

The 2011 census results show that there has been significant

inflow of migration to many southern states in India. Tamil Nadu,

Karnataka and Andhra Pradesh are attracting huge inflow of

migrants from other states. In these states, the enumerated

population has been far higher than the projected population.

Perhaps, it points towards a replacement migration taking place into

these states. The replacement migration refers to migration

occurring as a result of age structure changes. With the

demographic and age structure changes, there will be scarcity of

labour particularly in the unskilled sector. This labour has to be

replaced from other places with abundance of labour due to lack of

any significant demographic changes. In the context of Western

countries, the replacement migration mainly came from poor

developing countries. On the contrary, India is able to take care of

the replacement migration from within due to large diversity in the

nature of demographic transition. The replacement migration into

Kerala is well known and many studies have pointed out large inflow

of such migrants from other parts of the country (Zachariah and

Rajan 2004).

29

Thus it is clear that the demographic changes create

demographic opportunities and dividend and the concern that India

may not be able to experience demographic dividend is perhaps not

empirically validated. There is also ample evidence to suggest that

demographic changes enhance economic changes. Micro level

evidence also suggests that age structure changes lead to

substantial investment in children both in terms of education and

health (Bhat, 2002). Thus the demographic dividend emanates from

rapid changes in fertility which has several positive impacts both at

macro and at household level.

Urbanization and Economic GrowthOnly 30 percent of India's population lives in urban areas. This is

much lower than in China, Indonesia, South Korea, Mexico, and

Brazil. Some of this may be due to much lower per capita incomes in

India. The Committee's projections suggest that India's urban

population as presently defined will be close to 600 million by 2031,

more than double that in 2001. Already the number of metropolitan

cities with population of 1 million and above has increased from 35

in 2001 to 50 in 2011 and is expected to increase further to 87 by

2031. The expanding size of Indian cities will happen in many cases

through a process of peripheral expansion, with smaller

municipalities and large villages surrounding the core city becoming

part of the large metropolitan area, placing increasing strain on the

country's urban infrastructure. Future growth is likely to concentrate

in and around 60 to 70 large cities having a population of one million

or more. Decentralization of municipal governance and greater

reliance on institutional financing and capital markets for resource

30

mobilization are likely to increase the disparity between the larger

and smaller urban centers. A satisfying outcome will depend on the

formulation of effective public policies to accelerate all-round

development of smaller urban centers and to refashion the role of

the state as an effective facilitator to compensate for the deficiencies

of market mechanisms in the delivery of public goods. Three

decades of rapid economic growth would normally have propelled

migration from rural areas but growth in India has not had this effect

thus far. This is because industrialization has been capital intensive

and the services boom fuelled by the knowledge economy has also

been skill intensive. A few cities of India have acted as centers of

knowledge and innovation. As more cities provide economies of

agglomeration and scale for clusters of industries and other non-

agricultural economic activity, the urban sector will become the

principal engine for stimulating national economic growth.

Industrialization will absorb more people as India advances further in

its integration with the world economy. At the present juncture, India

faces the challenge of continuing on its high growth trajectory while

making growth more broad-based and labour intensive. The fortunes

of the agricultural sector are crucially linked to the manner in which

growth in the industry and services sectors unfolds. People living in

rural areas typically tap the opportunities that cities provide for

employment, entrepreneurial avenues, learning, and monetary

repatriation. As urbanization grows, demand for food items other

than food grains, i.e. vegetables, lentils, milk, eggs, etc., also grows.

This leads to investments in infrastructure, logistics, processing,

packaging, and organized retailing. These investments and other

economic inter-linkages connect and build synergy between rural

31

and urban centers. Of course, government policy should also focus

on enhancing the productive potential of the rural economy. From

the report, that India's urban future promises to be an inclusive one,

with the benefits extending to rural areas as well. Already, there is

evidence to suggest that rising standards of living in India's urban

areas in the post-reform period have had significant distributional

effects favoring the country's rural poor.

ConclusionThere are unmistakable signs that population transition in India has

progressed and the average rate of population growth in the country

has declined substantially during 2001-2011. However, the actual

growth of population between 2001 and 2011 has been faster than

the population growth projected by the Government of India on the

basis of the results of the 2001 population census and observed

trends in fertility, mortality and migration (Government of India,

2006). Obviously, efforts to moderate the growth of the population

during 2001-2011 appear to have fallen short of the projected, most

likely, path. Results of the 2011 population census also indicate that

there is little possibility of realizing the expectations laid down in the

National Population Policy 2000 and there is little probability that the

country will be able to reach stable population by the year 2045.

These results do not provide any indication that the country will be

able to achieve the cherished goal of population stabilization during

the current century until and unless a serious effort is made to

reinvigorate population stabilization efforts. It is in this context that

there is a need of revisiting the goals and objectives of the National

Population Policy and reviewing ongoing population stabilization

32

efforts after taking into consideration the results of the 2011

population census.

In the brief, demographic changes are inevitable and generally

contribute positively to the nation. The demographic changes are

also accompanied by considerable social and economic changes. It

is important that the nation is prepared to take care of such rapid

changes. In the future, the success of a nation will critically depend

upon its ability to address such sweeping demographic changes

effectively though policies and programs. India is on the course of

rapid demographic changes.

ReferencesCarl Haub and O.P. Sharma, "Examining Literacy Using India'sCensus" October 2008.

Census of India, www.censusindia.gov.in/.

Chaurasia Alok Ranjan, Gulati SC (2008) India: The State of Population 2007. National Population Commission, OxfordUniversity Press, New Delhi.

Government of India (2000) National Population Policy 2000. Ministry of Health and Family Welfare, New Delhi.

Government of India (2005) National Rural Health Mission. Ministryof Health and Family Welfare, New Delhi.

Government of India (2006) Census of India 2001. PopulationProjections for India and States 2001-2026. Report of the TechnicalGroup of Population Projections. National Commission onPopulation, Ministry of Health and Family Welfare. New Delhi.

Government of India Ministry of Home Affairs, SampleRegistration System Report 2008, Ministry of Home Affairs, NewDehli.

33

Human Development in India 2010, Oxford University Press, NewDelhi.

International Institute for Population Sciences (IIPS) and MacroInternational, National Family Health Survey (NFHS-3), 2005-2006 (Mumbai: IIPS, 2007).

Mari Bhat PN (1999) Population projections for Delhi: Dynamiclogistic model versus cohort component method. Demography India28(2).

Nehru, Jawaharlal (1946). The Discovery of India. Oxford UniversityPress. New Delhi.

PRB's Discuss Online in 2009 with Leela Visaria, researcher andpresident of the Asian Population Association.

Reports, United Nations Population Fund, New York, USA

United Nations (2008) World Population Prospects. 2008 Revision. Department of Economic and Social Affairs. Population Division, New York.

United Nations Development Programme. 2010. HumanDevelopment Report 2010. Human Development Report Office, NewYork.

United Nations. 2011. The Millennium Development Goals Report 2011. Department of Economic and Social Affairs, New York.

World Bank (2010). World Development Indicators, 2010.

Yojna, July 2011. GoI, New Delhi.

34

Chapter-2

Detecting Fallow Agricultural Land and Correlation withDemographic Indicators in the Branicevo and Pomoravlje

Districts, Serbia

Darko Jaramaz and Veljko PerovicInstitute of Soil Science, Teodora Drajzera 7, Belgrade, Serbia

IntroductionAgricultural land is a non-renewable natural resource whose

development strategy is of the highest importance to each country.

The Republic of Serbia has great potential in the sector of

agricultural production due to favorable climatic conditions, good

natural characteristics of the land and available water resources, but

this potential is not fully utilized, partly because there is a lot of area

with fallow agricultural land. The fallow agricultural land can be

defined as plowed and left unseeded agricultural land for one or

more seasons.

Branicevo district is located in the northeastern part of

Republic of Serbia. District area is approximately 3,862 km2, and

according to the Census 2011 district have 185,165 inhabitants. The

relief of district is divided into two parts: lowland on the western part

and highlands on the eastern half of district. District are consists of

the following eight municipalities: Zagubica, Kucevo, Petrovac,

Pozarevac, Blace, Veliko Gradiste, Malo Crnice and Zabari.

Pomoravlje districts is located in the central part of Republic of

Serbia. Covering a area of approximately 2,613 km2, and have a

total of 212,304 inhabitants by Census 2011 data. District includes

35

the following six municipalities: Despotovac, Paracin, Cuprija,

Rekovac, Svilajnac and Jagodina.

The research aims to within Branicevo and Pomoravlje

districts identify fallow agricultural land based on the analysis of

aerophoto images, and also to evaluate demographic characteristics

for specified area base on Census 2011 data, by performing

Multivariate analyses (Principal Components Analysis and Cluster

Analysis) and Descriptive statistics methods. With the intention that

the ultimate result gave the response on the question, which

demographic indicators can be correlated with the appearance of

fallow agricultural land.

Literature ReviewThe case for linking statistical data analysis techniques to

Geographic information system (GIS) is grounded in the idea that

additional explanation, understanding, and insight can be gleaned

when data is viewed and examined from both a spatial and

statistical perspective [1]. The integration of these two perspectives

in an environment that supports flexible methods for data retrieval,

manipulation, and display, is argued to yield more than the sum of

the component parts [2]. A number of researchers have explored

different approaches to integrating statistical analysis within GIS [3,

4, 5].

1. The Geographic Information Systems (GIS)The Geographic Information Systems (GIS) are a powerful set

of tools that are intended to store, visualize, process and analyze

digital spatial data, with the most common application for information

36

visualizations. In the recent past many science disciplines adopted

GIS as a tool for solving various spatial problems. Furthermore,

should be noted that currently there is a little consensus about which

criteria define a one geographic information system. The core GIS

idea that the world can be understood as a series of layers of

different types of information, that can be added together

meaningfully through overlay analysis to arrive at conclusions [6].

2. Statistics AnalysisIn our research, will be evaluated characteristics of Branicevo and

Pomoravlje districts using the Multivariate analyses and Descriptive

statistics methods.

2.1. Multivariate Analyses As the name implies, multivariate statistics refers to an assortment

of descriptive and inferential techniques that have been developed

to handle situations in which sets of variables are involved either as

predictors or as measures of performance [7]. Multivariate analysis

deals with issues related to the observations of many (usually

correlated) variables on units of a selected random sample [8].

Multivariate analysis also may be defined as the branch of statistics

which is concerned with the relationships among sets of dependent

variables and the individuals which bear them [9]. Conventional

multivariate analysis analyzed a numerical multivariate data set. On

the other hand, in functional data analysis, each data is not a

numerical data set but a set of functions, and in this case functions

are directly analyzed [10].

37

In this research, inside the multivariate analyzes is employed

Principal Components Analysis (PCA) and Cluster Analysis

techniques.

2.1.1. Principal Components Analysis (PCA)The classical procedures of statistics are principal components

analysis, which reduce dimensionality by forming linear

combinations of the features. The PCA is a technique that can be

used to supply to a statistic analysis of the data set, being used as

pre-processing stage to the prediction [11]. The PCA is indicated for

the analysis of variables that have linear relations. Each principal

component brings statistic information different from the others.

However, the first principal components are such more relevant that

we can even disdain the others [12].

This multivariate technique has a the central aim to reduce the

dimensionality of a multivariate data set while accounting for as

much of the original variation as a possible present in the data set,

this aim is achieved by transforming to a new set of variables (the

principal components) those represent linear combinations of the

original variables. The principal components are uncorrelated and

are ordered so that the first few of them account for most of the

variation in all the original variables. The object of principal

components analysis is to find a lower-dimensional representation

that accounts for the variance of the features. The result of a

principal components analysis would be the creation of a small

number of new variables that can be used as surrogates for the

originally large number of variables and consequently provide a

38

simpler basis for graphing or summarizing of the data, and also

perhaps when undertaking further multivariate analyses of the data

[13].

If we think of the problem as one of removing or combining

(i.e., grouping) highly correlated features, then it becomes clear that

the techniques of clustering are applicable to this problem. In terms

of the data matrix, whose n rows are the d- data dimensional

samples, ordinary clustering can be thought of as a grouping of the

rows, matrix with a smaller number of cluster centers being used to

represent the data, whereas dimensionality reduction can be thought

of as a grouping of the columns, with combined features being used

to represent the data. Roughly speaking, the most interesting

features are the ones for which the difference in the class means is

large relative to the standard deviations, not the ones for which

merely the standard deviations are large.

This analysis is concerned with the extraction of the factors

that better represent the structure of interdependence between

variables of large dimensions. Therefore, all the variables are

analyzed simultaneously, each one in relation to all the others,

aiming at determining factors (principal components) that maximize

the explanation of variability existing in the data [14].

2.1.2 Hierarchical Cluster Analysis Cluster analysis is a technique to classify an original data set

into some subsets (called clusters) by using some distance or

similarity/dissimilarity criterion. Cluster analysis can be roughly

divided into hierarchical clustering and non-hierarchical clustering:

39

Hierarchical clustering makes an original data set a hierarchy

of clusters which may be represented in a tree structure

(called dendrogram) based on some linkage criterion (single

linkage, complete linkage, median method, centroid method.

Ward's method, Mcquitty's method, group average method,

etc.).

On the other hand, non-hierarchical (or partitional) clustering

assigns each data to the cluster whose center is nearest. Af-

means algorithm is a typical technique of non-hierarchical

clustering.

When clusters have subclusters, these have sub-subclusters, and

so on. In fact, this kind of hierarchical clustering permeates

classifactory activities in the sciences. The most natural

representation of hierarchical clustering is a corresponding tree,

called a dendrogram, which shows how the samples are grouped. If

it is possible to measure the similarity between clusters, then the

dendrogram is usually drawn to scale to show the similarity between

the clusters that are grouped. Because of their conceptual simplicity,

hierarchical clustering procedures are among the best-known of

unsupervised methods.

2.2 Descriptive Statistics In essence, descriptive statistical procedures enable you to turn a

large pile of numbers that cannot be comprehended at a glance into

a very small set of numbers that can be more easily understood [15].

Roughly can be said that, descriptive statistics condense data sets

to allow for easier interpretation [16]. Mann [17] defines descriptive

40

statistics like a discipline of quantitatively that describe the main

features of a collection of data. The descriptive statistics, unlike

inferential statistics, are not developed on the basis of probability

theory [18].

There are also many different ways of obtaining descriptive

statistics. There is not a right way or a wrong way; it simply comes

down to personal preference on procedure and the way the statistics

are formatted in the output [19].

Materials and Methods1. The Geographic Information Systems (GIS)Analysing aerophoto images to determine the fallow agricultural land

are started by field research when the samples were taken for each

researched municipality. Preliminary work also included the

purchase of vector and raster data covering researched area, but

the basic layer was aerophoto images of the national territory of the

Republic of Serbia in the resolution of 2.5 meters per pixel, from

2005th - 2008th years.

Softwares that are used for interpretation of aerophoto images

have implemented algorithms for raw data processing, and also

enables that the results can be stored for each pixel individually. The

purpose of the aerophoto images classification were categorization

of all image pixels in a classes. It should be noted that the

classification can be done using two main groups of computer

operations: supervised (semi-automated) classification and

unattended (automated) classification. Unsupervised classification is

determined by the research area, this method compare pixels and

categorize them into class base on color similarity. Unattended

41

classification do not use fixed classes, but algorithms that

interpreting the unknown pixels and collects them in classes.

In this research, supervised classification is applied to obtain

map of land use. Supervised classification can be performed at

aerophoto images, when the resolution is less than 5 meters per

pixel. Given that, in this research, images have the resolution of 2.5

meters per pixel, the obtained result of supervised classification will

be with an accuracy of over 70 percent.

Set of classes need to be identified before entering the

supervised classification process. Set of classes integrate values of

research area as much as possible (arable agricultural land, fallow

agricultural land, forests, pastures and meadows, water surface,

urban areas), during which are created a sample grid for each class.

Represented samples were the ones which are taken from the field,

at the beginning of the research, by GPS devices. Each created

sample must be relatively homogeneous, and as such it represents

his class. With the supervised classification process, each sample

area need to be analyzed for the determination of the statistical

characteristics of the class for the selected raster object, afterwards

these class characteristic need to be applied for the classification of

aerophoto images, that are used as input.

After the classification, the quality of the obtained results is

estimate, and repairs are made if the results were not acceptable.

Repairs includes a precise determination of the class by new

classification of pixel.

42

2. Statistics AnalysisIn our research, SPSS software is used in order to perform

multivariate analyses (Principal Components Analysis and Cluster

Analysis) and Descriptive statistics methods. The SPSS is software

designed to enable: data mining, customer relationship

management, business intelligence and data analysis. Demographic

data are obtained from Republic of Serbia Census 2011 [20]. For

this research, the following 18 demographic factors are employed:

1. Population aged 0-4, 2. Population aged 5-14, 3. Population aged 15-24, 4. Population aged 25-44, 5. Population aged 45-64, 6. Population aged over 65, 7. Employed, 8. Unemployed, 9. Employed - Women, 10. Employed - Man, 11. Unemployed - Women, 12. Unemployed - Man, 13. Unemployed - Without qualification, 14. Unemployed - With qualification, 15. Unemployed - Without work experience, 16. Unemployed - With work experience, 17. Employed in the legal entities (companies, enterprises,

institutions, co-operatives and other organizations), and18. Employed in private entrepreneurs (people self-employed and

their employees).

Before beginning the statistical analysis each of these factors

will obtain a numeric value, which will be later transformed to

percentages to be suitable for analysis inside SPPS.

43

2.1. Multivariate AnalysesAs in the previous part of the paper indicated, inside the

multivariate analyzes are employed Principal Components Analysis

(PCA) and Cluster Analysis techniques.

2.1.1. The Principal Components AnalysisThe Principal components analysis (PCA) is Factor Analysis,

and its represents a method of data reduction. When data set

contains numerous variables that are correlated, researchers use

principal components analysis to reduce measures to a small

number of principal components. The PCA analyzes the total

variance, assuming that each original measure is retrieved without

measurement error. By obtaining the component scores, we are

able to perceive the dimensionality of the data. PCA method can be

used the correlation matrix and covariance matrix. The correlation

matrix standardized the variables and the total variance will

equivalent the amount of variables used in the analysis (because

each standardized variable has a variance equal to 1). On the other

hand, in the covariance matrix, the variables will stay in their original

metric. In our research is applied Correlation matrix technique.

Performing the PCA inside SPSS software, the obtained

results have the following forms:

The Communalities table; represents the proportion of every

variable's variance that can be explained by the principal

components analysis, and contains the Initial values and

Extraction values.

The total variance explained table; display the Initial Eigen

values (represents the variances of the factors) and the

44

Extraction Sums of Squared Loadings (precisely replicate the

values given on the same row on the left side of the table

inside SPSS).

The scree plot graphs; display the eigenvalue in opposition to

the component number. Performing the PCA, researchers are

interested only for saving principal components, which

eigenvalues are greater than 1.

The Component Matrix table; contains component loadings,

which represents the correlations between the components

and the variables, in values range from -1 to +1.

The factor score variables are obtained on the end of Principal

components analysis, this variables will be used like an input for the

Hierarchical cluster analysis. The factor score is equal to the

summation of the standardized value of each variable multiplied by

its factor loading [21].

2.1.2 Hierarchical Cluster AnalysisThe classification aims to capture the important socio-

economic dimensions of, and differences between, areas. The goal

of classification is to arrange N units into M clusters such that the

inter-M variation in attributes is maximise, and the intra-M variation

in attributes is minimized [22].

The Hierarchical cluster analysis is the major statistical

method, that use measured characteristics, for discovering relatively

standardized clusters, that are based on cases. The analysis starts

with each case as a separate cluster, and then combines the

clusters sequentially by further reducing the number of clusters. This

45

method calculates the distances and dissimilarities among research

items when forming the clusters.

Performing the Hierarchical cluster analysis, inside SPSS

software, obtained results have a following forms:

The agglomeration schedule table; display cases information

about all stages of a hierarchical clustering process.

The Cluster membership table; specified cases belongs to the

clusters.

The dendrogram (hierarchical tree diagram); is a graphical tool

for displaying clustering results, where the clusters that are

joined together are connected with lines.

2.2. The Descriptive StatisticsBased on previously obtained Clusters results inside the

Hierarchical cluster analysis, the Descriptive statistics analysis are

performed to describe each cluster based on his model significant

variables mean values. The descriptive statistics give us

informations about the variables distribution, by presenting the

maximum, minimum, mean and standard deviation values for each

cluster variable.

Inside Descriptive statistics table, obtained results produce

following columns:

The N value; represents a number of clusters.

The Maximum value; represents the largest value in the

distribution.

The Minimum value; represents the smallest value in the

distribution.

46

The Mean value; represents the average value of the

distribution.

The standard deviation value; represents a measure of

variability.

Evaluation of the clusters are performed based on the

obtained results, mentioned evaluation represents description of

demographic characteristics for Branicevo and Pomoravlje districts.

ResultsAnalysing aerophoto images are obtained results (represented

at Map 1) that displays Branicevo and Pomoravlje districts area,

divided into following six classes:

Forests,

Water surface,

Urban areas,

Pastures and meadows,

Fallow agricultural land, and

Arable agricultural land.

47

Map 1. Branicevo and Pomoravlje districts area, divided into sixclasses

The same results are represented on the Map 2, where are

(using the charts) displayed the ratio inside each municipality of:

Arable agricultural land, Fallow agricultural land and other four

classes.

48

Map 2. Ratio inside each municipality of Arable and Fallowagricultural land

The all six classes area (Arable agricultural land, Fallow

agricultural land, Pastures and meadows, Forests, Urban areas and

Water surface) of Branicevo and Pomoravlje districts municipalities,

are represented inside Table 1 (expressed in hectares).

49

Table 1. The all six classes area of Branicevo and Pomoravljedistricts municipalities (ha)

Municip-alities

Arableagric-ultural land (ha)

Fallowagric-ultural land (ha)

Pasturesand mea. (ha)

Forests(ha)

Urbanareas(ha)

Watersurface(ha)

Total area (ha)

Pozarevac 33968.61 3517.62 3074.41 3670.79 2296.42 1831.31 48359.16

VelikoGradiste

20090.04 3020.60 2806.44 6247.66 347.52 1803.93 34316.19

Golubac 6338.29 2338.19 5848.00 20004.85 194.66 2037.58 36761.57

MaloCrnice

18251.81 2033.31 3181.45 3220.82 220.65 4.59 26912.63

Zabari 16310.35 2418.66 2532.84 4577.71 423.82 92.57 26355.95

Petrovac 33459.65 4990.83 9809.54 15713.41 1408.70 55.95 65438.08

Kucevo 10394.03 2419.21 22789.12 36111.08 371.38 8.99 72093.81

Zagubica 11434.49 3892.78 17882.37 42133.89 611.57 11.93 75967.03

Jagodina 26323.87 3008.86 6098.52 10469.51 1339.08 183.00 47422.84

Cuprija 17711.91 1731.89 3027.05 5212.72 939.08 210.95 28833.60

Paracin 20969.15 4281.90 9483.47 16297.85 3040.75 167.48 54240.60

Svilajnac 19082.95 2445.86 4648.38 5550.15 515.79 260.04 32503.17

Despotovac 16325.62 3177.82 13718.54 28573.89 451.44 16.47 62263.78

Rekovac 16747.65 3357.59 6741.26 9450.16 265.29 12.47 36574.42

Total: 267408.42 42635.12 111641.39 207234.49 12426.15 6697.26 648042.83

The relationship between all six classes (Arable agricultural

land, Fallow agricultural land, Pastures and meadows, Forests,

Urban areas and Water surface) inside each total municipality area,

are represented inside Table 2 (expressed in percentage).

50

Table 2. Relationship between all six classes inside eachmunicipality (%)

Municipalities Arableagricultural land (%)

Fallowagricultural land (%)

Pasturesandmea. (%)

Forests(%)

Urbanareas(%)

Watersurface(%)

Total (%)

Pozarevac 70.24 7.27 6.36 7.59 4.75 3.79 100.00VelikoGradiste

58.54 8.80 8.18 18.21 1.01 5.26 100.00

Golubac 17.24 6.36 15.91 54.42 0.53 5.54 100.00Malo Crnice 67.82 7.56 11.82 11.97 0.82 0.02 100.00Zabari 61.88 9.18 9.61 17.37 1.61 0.35 100.00Petrovac 51.13 7.63 14.99 24.01 2.15 0.09 100.00Kucevo 14.42 3.36 31.61 50.09 0.52 0.01 100.00Zagubica 15.05 5.12 23.54 55.46 0.81 0.02 100.00Jagodina 55.51 6.34 12.86 22.08 2.82 0.39 100.00Cuprija 61.43 6.01 10.50 18.08 3.26 0.73 100.00Paracin 38.66 7.89 17.48 30.05 5.61 0.31 100.00Svilajnac 58.71 7.52 14.30 17.08 1.59 0.80 100.00Despotovac 26.22 5.10 22.03 45.89 0.73 0.03 100.00Rekovac 45.79 9.18 18.43 25.84 0.73 0.03 100.00

The relationship between Arable agricultural land and Fallow

agricultural land, base on total Agricultural land inside each

municipality, are represented inside Table 3 (expressed in

percentage).

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Table 3. The relationship between Arable agricultural land andFallow agricultural land inside each municipality (%)

Municipalities Arableagricultural land(%)

Fallowagricultural land(%)

Agricultural land(%)

Pozarevac 90.62 9.38 100.00Veliko Gradiste 86.93 13.07 100.00Golubac 73.05 26.95 100.00Malo Crnice 89.98 10.02 100.00Zabari 87.09 12.91 100.00Petrovac 87.02 12.98 100.00Kucevo 81.12 18.88 100.00Zagubica 74.60 25.40 100.00Jagodina 89.74 10.26 100.00Cuprija 91.09 8.91 100.00Paracin 83.04 16.96 100.00Svilajnac 88.64 11.36 100.00Despotovac 83.71 16.29 100.00Rekovac 83.30 16.70 100.00

The Scree plot graphs (Picture 1) displays the PCA Total

Variance Explained where each successive component accounts

less and less variance, for all 18 demographic variables that are

employed in this research. In our research, five principal

components are obtained with eigenvalues 1 or greater than 1 (first

6.252, second 3.743, third 2.964, fourth 2.305 and fifth 1.148). Also

the PCA inside SPSS provide cumulative value for each component,

for example the fifth component displays a cumulative value of

91.178, which means that the first five components mutually account

for 91.178% of the total variance.

52

Picture 1 Scree plot graphs (PCA)

On the Hierarchical cluster analysis visually presented by

Dendrogram (picture 2) that visually displayed cluster results. The

clusters are linked by the increasing levels of similarity, and the

dendrogram graphically displays these linkage points.

53

Picture 2 - Dendrogram (Hierarchical cluster analysis)

In this research are employed three to five numbers of

solutions, and following solutions with five clusters was selected as

the most suitable (Map 3):

Cluster 1: Pozarevac, Jagodina, Cuprija and Paracin,

Cluster 2: Veliko Gradiste, Malo Crnice and Petrovac,

Cluster 3: Golubac,

Cluster 4: Zabari, Svilajnac, Despotovac and Rekovac , and

Cluster 5: Kucevo and Zagubica.

54

Map 3. Clusters

Based on the results obtained inside Descriptive Statistics

table (due to its size, whole table will not be displayed in the paper),

following cluster labels are created:

55

Cluster 1 - Mainly employed population, under 45 years.

Cluster 2 - Employed population, with a large percentage work

as private entrepreneurs.

Cluster 3 - Unemployed population, with large percentage of

unemployed without qualification,

Cluster 4 - Population employed in the legal entities, and

unemployed without work experience.

Cluster 5 - Employed population at age 45-64, and population

over 65 years.

ConclusionInside this study, different methods are used for obtaining the

results for relationship between fallow agricultural land and

demographic indicators inside the Branicevo and Pomoravlje

districts, Serbia. Applying the first the Geographic Information

Systems (GIS) technique for the detecting and mapping fallow

agricultural land. Second applying the multivariate analysis

(Principal Components Analysis and Cluster Analysis) and

Descriptive statistics methods on the Census 2011 attribute data

inside SPSS software. For previously stated, 18 variables are used

for getting the final result in the form of the cluster classifications

with five clusters types that describe the studied areas. Each cluster

type carries its own characteristics, which are described in detail

using the Descriptive statistical analysis. Third applying the

Geographic Information Systems (GIS) technique for the mapping

multivariate analysis and descriptive statistics results by the joining

with the vector data (borders of the municipalities that are inside

Branicevo and Pomoravlje districts) by ArcGIS software.

56

The Cluster 3 (which includes municipality Golubac) are

labeled by descriptive statistics as Unemployed population, with

large percentage of unemployed without qualification, and the

Cluster 5 (which includes municipalities Kucevo and Zagubica) are

labeled by descriptive statistics as Employed population at age 45-

64, and population over 65 years. These three previously

mentioned municipalities have the highest percentage of fallow

agricultural land in regards to entirely agricultural land inside

municipality (Table 3); Golubac 26.95%, Zagubica 25.40% and

Kucevo 18.88%.

The Cluster 1 (which includes municipalities Pozarevac,

Jagodina, Cuprija and Paracin) are labeled by descriptive statistics

as Mainly employed population, under 45 years, and he basically

contains (with the exception of Paracin municipality) municipalities

with lowest percentage of fallow agricultural land in regards to

entirely agricultural land inside municipality (Table 3); Cuprija 8.91%,

Pozarevac 9.38%, Jagodina 10.26% and Paracin 16.96%.

Base on the obtained results we can conclude that percentage

or fallow agricultural land is directly related to the population age

group; the highest percentage of fallow agricultural land is

characteristic for the municipalities that have high percentage of old

population and otherwise. Also, although this is typical for Republic

of Serbia and other countries that are in period of transition

economy, the highest percentage of fallow agricultural land in

combination with the highest percentage of unemployed population

can be related with former Agricultural companies that are currently

in bankruptcy, liquidation process or in restructuring.

57

Research has shown that combining Geographic Information

Systems with the statistical methods can provide good foundation

for understanding the situation in agriculture base on demographic

characteristics of the population.

AcknowledgementThe paper is the result of research carried out within the

scientific projects TR37006 "Impact of soil quality and irrigation

water quality on agricultural production and environmental

protection"; financed by the Ministry of Education, Science and

Technological Development of the Republic of Serbia for the period

2011-2014.

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60

Chapter-3

Population Dynamics in Rajasthan State

M. M. SheikhAssociate Professor

Department of GeographyGovt. Lohia PG College, Churu, Rajasthan, India

Rajasthan is situated in the northern part of India. It is the largest

State in India by area constituting 10.4 percent of the total

geographical area of India and it accounts for 5.67 percent of

population of India. Topographically, deserts in the State constitute a

large chunk of the land mass, where the settlements are scattered

and the density of population is quite low. It is administratively

divided into 7 divisions, 33 districts, 244 Tehsils, 249 Panchayat

Samities, 9,177 Gram Panchayats, inhabited villages and 184 urban

local bodies as of Census 2011. The State has a population of 6.86

crore according to the provisional totals of Census 2011. Compared

to Indian averages, Rajasthan has slightly better proportion of total

cropped area and net shown area. The net irrigated area for

Rajasthan is at par with all India averages. While the countrys forest

area constitutes about 23.6 per cent of total land; in Rajasthan the

corresponding figure is only 9.5 per cent. This indicates Rajasthan

has comparative low forest coverage.

Geographic Profile of RajasthanRajasthan state, initially constituted in 1949 after the merger of 19

princely states and later further consolidated in 1956 with the

incorporation of Ajmer (earlier a central territory), has for long best

61

been known for its colourful history: forts and palaces built in the

yesterera and the valour and sacrifice of its princes andprincesses, which apparently has also been its main tourist rallying

point. It has not been a major contender for heralding

industrialisation or economic growth in the countrybeing

landlocked and having more than 60 per cent of its area coveredby desertdespite that among the major trading communities in the

country, many (e.g. Marwaris) hail from Rajasthan. This state, like

any other in the country, is not a monolith: there are regional

diversities, nuances and issues that need to be put forth at the

outset. Seen from an agroclimatic and social point of view, thereare four loose geographic groupings:

(1) The west (Jaisalmer, Barmer, Bikaner, Jalore, Jodhpur, Nagaur

Pali), which lies in the heart of the Thar Desert, is arid, sparsely

popu