determinants of land use change in south-west region of bangladesh

Determinants of Land Use Change in South-west Region of

Bangladesh

Jahangir Alam

Economics Discipline

Social Science School

Khulna University

Khulna, Bangladesh

October, 2014

ii


Bangladesh

………………………………………..

Jahangir Alam

Student No.: 101502

Session: 2012-13

Supervisor

………………….………..

Md. Firoz Ahmed

Assistant Professor


Khulna University

Khulna, Bangladesh

This thesis paper submitted to Economics Discipline, Social Science

School, Khulna University, Khulna, Bangladesh in partial fulfillment of

the degree of Bachelor of Social Science (BSS Hons.) in Economics

October, 2014

iii


Bangladesh

………………………………………………………….

Mohammed Ziaul Haider, Ph.D

Head


Social Science School

Khulna University

Khulna, Bangladesh

October, 2014

iv

Statement of Originality


Bangladesh

The findings of this thesis paper are entirely of the candidate’s own research

and any part of it is neither been accepted for any degree nor it is being

concurrently submitted for any other degree.

………………………………………….

Jahangir Alam

Student No.: 101502

Session: 2012-13

October, 2014

Determinants of Land Use Change in South-west Region of Bangladesh

v

Acknowledgement

This thesis paper is prepared as a requirement of completing graduation in

Economics from Khulna University since October, 2014. However, the author being

grateful like to thanks Almighty because His great provision, protection and support

throughout his whole life and especially during this research work.

The author can’t but feel owe to supervisor, Mr. Md. Firoz Ahmed, for his

constructive suggestion, criticism and encouragement throughout the research work to

prepare such a representative research work by such a short span of time and despite

all obstacles. Appreciation then goes to Economics Discipline as well as all the

faculties and staff for their effort, suggestion and cooperation towards my progress of

life since I have become a member of Economics Discipline of Khulna University and

especially in this research work.

The author is also grateful to the respondents, the secretary as well as other

staffs of Dhalbaria Union Parishad and the local representatives for the friendly

behaviors and help towards my successful completion of the thesis paper. Moreover,

the writer likes to empress his gratitude towards his friends, well-wishers and others

who are not being mentioned here for their cooperation during the research work and

whole life. It is author’s privilege to express gratefulness and deep sense of appreciation

to all those individuals and institutions whose direct as well as indirect invaluable

contributions and support have helped me in writing up this thesis.

Last but not the least, the author like to remember the devotion and

contribution of his family members for their encouragement, support and help

throughout the whole life. He is also grateful to all the teachers and others who have

teach and support him in gaining knowledge and experience till now.

Finally the author like to ask reader and evaluator to take the mistakes as

unnoticed by the author during the completion of this paper in such a short time.

Moreover, the author being a new comer in research likes to acknowledge the errors

in this paper because of his low experience and expertise in research.

Jahangir Alam

BSS 101502


Khulna University, Khulna

Economics Discipline, Khulna University, Khulna, Bangladesh

vi

Abstract

Like all other parts of the world, land use patterns in Bangladesh especially of

south-west part have been observed to change rapidly since late of 20th century.

Lands of south-west region were generally used for rice farming since the middle of

20th century but polderization project of Bangladesh during 1970s caused major

changes in land use pattern either through transformation or modification of land

cover and cropping. Literature shows that single cropped rice areas of past decades

have already been cultivated twice or thrice per year while some such lands have

already been converted for shrimp farming. This paper examines the determinants of

land use patterns and their corresponding changes (i.e. rice and shrimp farming) over

time at pirozpur village of Kaligonj upazila under Satkhira district of Khulna division

in Bangladesh. The study is being done on the basis of cross-sectional data collected

from the decision maker or head of each sample household. Here data have been

collected through questionnaire as well as focus group discussion from a sample size

of 80 households; each forty from shrimp and rice farming. Here logistic regression

considering rice farming land as the reference dummy as well as cost-benefit analysis

is being done to know the extents of land use determinants. However, the study area

being close to river Hariavanga, shrimp farming has become predominant in the

study area and young people are more interested in shrimp farming than in any other

land use alternatives. Analysis shows that cost free irrigation for shrimp farming as

well as higher profit, lower cost and available inputs are the major factors of increased

shrimp farming in the study area. The study also finds that if rice can be cultivated

thrice per year then shrimp is less attractive while there lacks training facilities for the

rice farmers which may cause dissatisfaction to land owners causing conversion of

rice land into shrimp. Available land holders primarily decide their land use pattern

based on short run cost benefit calculation rather than long run impact of land use in

their livelihood as well as ecology. The study finds age, natural calamities, family

type and availability of credit to be negatively related with shrimp farming while land

engagement process, accessibility, economically active family number, proximity to

service sector, neighborhood land use patterns, land ownership and land rent to be

positively related. Whatever be the determinants of land use and their corresponding

extents, mass awareness should be emphasized for optimal land use.

Key Words: Land Use Change, Determinants, South-west Region, Bangladesh


vii

Table of Contents

Title of Content Page No.

Acknowledgement v

Abstract vi

Table of Contents vii-x

List of Maps xi

List of Tables xii-xii

List of Figures and Graphs xiii

Acronyms xiv

Abbreviations xv

Chapter One: Introduction 1-9

1.1 Background of the Study 1

1.2 Objective of the Study 3

1.3 Rationale of the Study 3

1.4 Scope of the Study 5

1.5 Operational Definitions 5

1.6 Limitation of the Study 7

1.7 Structure of the Study 8

Chapter Two: Theoretical Background 10-17

2.1 Land Use Models 10

2.2 History and Trends of Land Use Models 11

2.3 Land Use Modeling Approaches and Models 12

2.3.1 Agent-Based Perspective 12

2.3.2 Systems Perspective 12

2.3.3 Narrative Perspective 12

2.3.4 The Fitting Data Model 13

2.3.5 Simulation Processes 13

2.3.6 Structural Models 13

2.3.7 Statistical or Reduced Form Models 13

2.3.8 Geographic Models 13

2.3.9 Economic Models 13

2.3.10 Stochastic Markov Model 13

2.3.11 Ecological Models 14

2.3.12 Dyna-CLUE model 14

2.3.13 Spatial Economical Model 14

2.3.14 Cellular Automata Model 14

2.3.15 Species-distribution Model 14

2.4 Economics, Econometrics and Land Use Research 15

Chapter Three: Literature Review 18-29

3.1 Land 18

3.2 Land Use 19

3.3 Land Use Change 19


viii


3.4 Land Cover 19

3.5 Land Cover Change 20

3.6 Land Use and Cover Change 20

3.7 Land Use Planning 20

3.8 Land Use Conflict 20

3.9 Methods Used to Identify Patterns and Changes of Land Use

and Cover

21

3.10 Variable Used in Modeling Land Use and Cover Changes 22

3.11 Type and Scope of Land Use and Cover Change 23

3.12 Observed Land Use Pattern 23

3.13 Global Land Use and Cover Trends 24

3.14 Land Use Trends in Bangladesh 24

3.15 Causes of Land Use and Cover Change 24

3.16 Impact of Land Use and Cover Change 26

3.17 Initiatives for Land Use and Cover Changes 26

3.18 Findings and Results of Land Research 27

3.19 Problems and Limitation of Land Use and Cover Researches 28

3.20 Research Gap 29

Chapter Four: Methods and Materials 30-38

4.1 Conceptualization of the Research Problem 30

4.2 Study Area 30

4.3 Research Design 31

4.4 Target Group 31

4.5 Sample Design 31

4.5.1 Sampling Techniques 32

4.5.2 Sample Size 32

4.5.3 Data Collection Method 32

4.6 Type of Data Used 32

4.7 Variables and Indicators 33

4.8 Model Specification 34

4.8.1 Logistic Regression for Land Use Change 34

4.8.2 Empirical Analysis of Land Use Determinants 35

4.9 Data Collection 37

4.9.1 Primary Data Collection 37

4.9.2 Secondary Information 37

4.10 Data Processing and Analysis 38

4.11 Writing the Thesis Paper 38

Chapter Five: Land Use Patterns and Changing Trends 39-47

5.1 Global Land Use Patterns 39

5.2 Land Use Trends of Bangladesh 40

5.3 Trends of Land Availability in Khulna Division 43

5.4 Land Use Trend in South-west Part of Bangladesh 45


ix


5.5 Land Use Policies in Bangladesh 46

Chapter Six: Overview of Study Area and Respondent 48-63

6.1 Overview of Study Area 48

6.2 Information of the Respondents 52

6.2.1 Age and Gender of the Sample Population 52

6.2.2 Educational Status 52

6.2.3 Family Size and Composition of the Respondents 53

6.2.4 Occupational Distribution 55

6.2.5 Engagement Process in Present Land Use Pattern 56

6.2.6 Land Ownership Pattern of Households 56

6.2.7 Scenario of Assets and Non-assets of the Sample

Households

57

6.2.8 Household Yearly Income 57

6.2.9 Household Yearly Expenditure 58

6.2.10 Households’ Farming Experience 59

6.2.11 Training Facilities of Sample Population 59

6.2.12 Credit Facility 60

6.2.13 Plan to Change Land Use Pattern in Near Future 60

6.2.14 Pressure and Regulation on Current Land Use

Pattern

62

Chapter Seven: Results and Discussion 63-88

7.1 Lands Cultivated over Time 63

7.2 Variation in Land Use Pattern 64

7.3 Change in Land Use Pattern 64

7.4 Location of Land 65

7.5 Land Elevation 66

7.6 Fertility of Land 67

7.7 Salinity and Sand in Land 68

7.8 Neighborhood Land Use Pattern 68

7.9 Water Management Facilities 69

7.10 Distance of Water Management Sources 70

7.11 Way Used for Water Management System 70

7.12 Cost of Water Management System 71

7.13 Proximity to Nearest Infrastructure 71

7.14 Land Rent 72

7.15 Accessibility to Land 73

7.16 Transport Mode and Available Facilities to Specific Land 74

7.17 Cost of Transportation per Trip 75

7.18 Availability of Input 75

7.19 Demand for Final Product 76

7.20 Market Location 76

7.21 Price Distribution of Final Output 77


x


7.22 Changes in Land Use Patterns of the Households 77

7.23 Conversion and Maintenance Cost 78

7.24 Cost-benefit of Land Use 79

7.25 Estimation of the Determinants of Land Use Change 81

Chapter Eight: Findings and Conclusion 89-93

8.1 Information through Focus Group Discussion 89

8.2 Findings of the Research 90

8.3 Comparison of Findings 91

8.4 Conclusion 92

8.5 Further Scope 94

List of References 95-113

List of Web References 114

Appendix I xvi-xix

Appendix II xx-xxvii


xi

List of Maps


Map 6.1 Map of Bangladesh 48

Map 6.2 Map of Kaligonj Upazila 51


xii

List of Tables

Title of Content Page No. Table 4.1 Description of Independent Variable 33 Table 4.2 Explanation of Variables in Empirical Analysis 36 Table 5.1 Land Use Trends in Bangladesh during 1977-2008 41 Table 5.2 Scenario of per Capita Arable and Irrigated Land 42 Table 5.3 Total Land Area of Bangladesh during 1976-2010 42 Table 5.4 Rice and Shrimp Farming Area during 1976-2010 43 Table 5.5 Land Use Statistics of Khulna Division in 2008 43 Table 5.6 Land Use Pattern in Khulna Division during 1976-2010 45 Table 6.1 Khulna Division at a Glance 49 Table 6.2 General Information of Kaligonj Upazila 50 Table 6.3 Age and Gender Distribution 52 Table 6.4 Educational Status of the Decision maker 53 Table 6.5 Literacy Status of Sample Population 53 Table 6.6 Family Type of Sample Population 54 Table 6.7 Distribution of Economically Active Family Member 54 Table 6.8 Occupational Distribution of Sample Household 55 Table 6.9 Engagement Process in Current Land Use Pattern 56 Table 6.10 Information on Land and Non-land Assets 57 Table 6.11 Distribution of Income from Land and Non-land Assets 58 Table 6.12 Yearly Expenditure of Sample Household 58 Table 7.1 Amount of Land Cultivated over Time 63 Table 7.2 Variation in Land Use Pattern 64 Table 7.3 Distribution of Water Source 69 Table 7.4 Distances of Water Source and Disposal Location 70 Table 7.5 Way used for Water management 70 Table 7.6 Cost of Irrigation and Water Disposal 71 Table 7.7 Proximity to Nearest Infrastructures 72 Table 7.8 Land Rent Scenario per Year 73 Table 7.9 Cost of Input and Output Transportation 75 Table 7.10 Price Distribution of Final Output 77 Table 7.11 Summary Statistics 81 Table 7.12 Estimation of Determinants of Land Use Change 84 Table 7.13 Marginal Analysis of Determinants of Land Use Change 86 Table Annex_II.1 Description of Sample Data used in Logistic Regression xx Table Annex_II.2 Summary of Sample Data used in Logistic Regression xxi Table Annex II.3 Summary Statistics of Categorical Variable xxi Table Annex II.4 Classification Table xxi Table Annex_II.5 Classification Table xxi Table Annex_II.6 Omnibus Tests of Model Coefficients xxi Table Annex_II.7 Hosmer and Lemeshow Test xxii Table Annex_II.8 Contingency Table for Hosmer and Lemeshow Test xxii Table Annex_II.9 Model Summary of Land Use Determinants xxi Table Annex_II.10 Wald Test of Sample Data xxii Table Annex_II.11 Test of Data Classification xxii Table Annex_II.12 Goodness-of-fit Test xxii Table Annex_II.13 Results of Binary Logit Model xxiii Table Annex_II.14 Results of Logistic Regression xxiii Table Annex_II.15 Marginal Analysis of Sample Data xxiv Table Annex_II.16 Variables in the Equation xxv Table Annex_II.17 Observed and Probable Land Use Pattern of Each Sample xxvi


xiii

List of Figures and Graphs


Figure 2.01 Economic Dynamics of Land Use System 15

Figure 5.1 Land Use Statistics of Khulna Division in 2008 44

Figure 5.2 Percentage Land Uses during 1989-2010 46

Figure 6.1 Land Ownership Pattern of the Sample Population 56

Figure 6.2 Farming Experience 59

Figure 6.3 Training Facilities on Specific Land Use 59

Figure 6.4 Credit Facilities on Specific Land Use 60

Figure 6.5 Expectation of Change in Current Land Use 60

Figure 6.6 Expected Land Use Pattern in Future 61

Figure 6.7 Determinants of Expected Changes in Land Use 61

Figure 6.8 Pressure and Regulation Scenario on Land Use 62

Figure 7.1 Land Use Statistics of Sample Households during (2010-2014) 64

Figure 7.2 Changes in Total Land Size during 2010-2014 65

Figure 7.3 Location of Sample Land 66

Figure 7.4 Land Elevation Scenario of Sample Land 66

Figure 7.5 Fertility Scenario of Sample Land 67

Figure 7.6 Distributions of Salinity and Sand in Land 68

Figure 7.7 Neighborhood Land Use Patterns 68

Figure 7.8 Accessibility to Sample Land 73

Figure 7.9 Mode of Transport Used 74

Figure 7.10 Transport Facilities for Specific Land Use Pattern 74

Figure 7.11 Availability of Input for Specific Land Use 75

Figure 7.12 Demand Prototypes for Final Output 76

Figure 7.13 Distribution of Market for Final Product 76

Figure 7.14 Changes in Land use Patterns (early 2008- mid 2014) 78

Figure 7.15 Initial Conversion Cost for Specific Land Use Pattern 78

Figure 7.16 Yearly Land Maintenance Expenditure 79

Figure 7.17 Cost-benefit Analysis of Rice and Shrimp Farming 80

Figure 7.18 Change in Profit based on Cropping Frequency 80

Figure Annex_II.1 Area under ROC Curve xxiv

Figure Annex_II.2 Sensitivity and Specificity versus Probability Cutoff xxv


xiv

Acronyms

coef. Coefficient

Freq. Frequency

ha Hectare

km Kilometer

govt. Government

ha Hectares

mha Million Hectare

mm millimeter

sq Square

sq km Square Kilometer

st. dev. Standard deviation

st. err. Standard error

Tk. Taka


xv

Abbreviations

BBS Bangladesh Bureau of Statistics

BCE before Christian era

BDT Bangladesh Taka

BSCIC Bangladesh Small and Cottage Industries Corporation

CV Coefficient of Variation

EEZ Exclusive Economic Zone

EPZ Export Processing Zone

EU European Union

FAO Food and Agricultural Organization

FGD Focus Group Discussion

FY Fiscal Year

GIS Global Information System

GOs Government Organizations

LUCC Land-Use and Cover Change

MB Marginal Benefit

MC Marginal Cost

MES Meghna Estuary Study

MoWR Ministry of Water Resource

NASA National Aeronautics and Space Administration

NFPCSP National Food Policy Capacity Strengthening Program

NGOs Non-Government Organizations

PC Planning Commission

PDO-ICZMP Program Development Office- Integrated Coastal Zone

Management Plan

SPSS Statistical Packages for Social Sciences

US United States

WB World Bank


1

Chapter One

Introduction

Though land is an important component of nature to maintain ecological as

well as bio-physical balance (Agarwal et al., 2001; Mohammad, 2009), there remains

very little landscape on earth in their natural state (Zubair, 2006). Researchers have

already reported that our universe has been changing rapidly through urbanization and

industrialization with a corresponding decline of green lands and alteration of

structure and functioning of ecosystem (Vitousek et al., 1997; Schneider and Pontius,

2001). Thus, changes of land use patterns i.e. forest into farmland, farmland into

periphery; with shifting and melting of shorelines and glaciers have attracted global

concern (NASA, 2006). Like many other parts of the world, land use patterns have

been changing in Bangladesh (Qusem, 2011) with appalling impacts on livelihood

pattern of her people chiefly who are dependent on land (Mondal, 2008). Moreover,

south-west region of Bangladesh has already gone through dynamic but unsustainable

changes in land uses (Rahman and Begum, 2011) because most of the areas are being

observed to transfer its agro-land to non-agro uses i.e. rice to shrimp farming (Zubair,

2006; Quasem, 2011). Therefore, this paper is an attempt to address and discuss some

of the existing land use patterns of south-west region and their determinants.

1.1 Background of the Study

Since 10,000 BCE, world population was less than 10 million with negligible

land uses (NASA, 2006) but with the industrial revolution as well as rapid population

growth especially in developing states (Lambin et al., 2003), researchers now claim

that human footprint has affected 83% global terrestrial surface while degraded more

than 60% ecosystem in last century (Nkonya et al., 2012). Moreover, settlements and

sprawl development have become much influential both in underdeveloped,

developed and developing countries (Oluseyi, 2006) with rapid and continuous

changes in land use patterns (Minar et al., 2013). Therefore land has now been

considered to have locally pervasive and globally significant influence on ecology and

sustainability (Agarwal et al., 2001) mainly because of its limited size (Zubair, 2006).

Humans have been altering land uses through clearance of patches of land (Shi, 2008)

since the beginning of civilization and it is now claimed that during last three

centuries, nearly 1.2 million sq km of forest and woodland as well as 5.6 million sq


2

km of grassland and pasture have been transformed (Ramankutty and Foley, 1999)

while cropland has increased by 12 million square km (Vitousek et al., 1997).

It is also demanded that most populated areas are located along coastal zones

and major waterways in Indian Sub-continent, East Asia and western Europe (Lambin

et al., 2003) and have witnessed major land use changes in last decades (Nkonya et

al., 2012) through aggregated global influences in trade and commerce (Lambin et al.,

2001). Researchers have also demanded that economy expands in size and growth

with the expansion of population, invention and innovation (Houghton, 1994) which

in turn causes a transfer of agro land to non-agro uses (Quasem, 2011). For instance,

though by 1970 there were two megacities (e.g. populations more than 10 millions),

today there are 23 megacities and is estimated to be 37 in 2025 of which most are in

developing countries (Rui, 2013). Researches also show that land has both positive

and negative influence on human life and environment based on the pattern of uses

over time (Li, 1996; Vitousek et al., 1997; Zenga et al., 2008). In this point, Coleman

(1987) and Lambin et al. (2001) has pointed out that large proportion of world’s

problems observed recently have originated from the misuse, disuse, overuse, under

use or abuse of land resources either directly or indirectly.

Coming from world scenario to Bangladesh, we observe that Bangladesh as an

agricultural country with the involvement of more than 47.5% (MES, 2010; as cited in

BBS, 2013) who contributes about 19.41% to total GDP in FY2011-12 (BBS, 2013)

while that in FY2004-05 was 22% (BBS, 2005). Over the last 30-40 years, availability

of agricultural land in Bangladesh has been declining at the rate of 1% per year

(NFPCSP, 2011) while data available from BBS (2005) and BBS (2009) showed that

between 1985 and 2007, net cropped area decreased by 11% (i.e. from 8770 to 7800

thousand ha). Moreover being a land of rivers, Bangladesh loses about 80 thousand ha

of agro lands yearly (MoWR, 2005; as cited in Iftekhar, 2006) while nearly one

percent land is being converted to non-agro uses particularly for settlements and

infrastructure (Quasem, 2011) per year. In this regard, Islam et al. (2004; as cited in

Mia and Islam, 2005) showed that about 220 ha arable land is being reduced daily due

to uses such as road construction, industrialization and housing while at least, 86,000

ha of land has already been lost to river erosion during 1973-2000 (MES, 2001).

About 70% of total lands in Barisal and Khulna divisions are now affected by

different degree of salinity (Mia and Islam, 2005) which are causing reduced agro

production (PDO-ICZMP, 2004). PDO-ICZMP (2004) also showed that per capita


3

agro land since 2003 was 0.056 ha (BBS, 2009) and will be decreased to only 0.025

ha by 2050 because of substitution by shrimp farming which started during eighties of

last century (Mia and Islam, 2005). Therefore, marginal and small farmers are

becoming more vulnerable (Quasem, 2011). Recent reports show that majority of her

population being poor and exposed to various natural and artificial hazards (Iftekhar,

2006), frequent land use changes are occurring especially in south-west region (FAO,

1999; Mia and Islam, 2005; Minar et al., 2013). However, bio-physical, socio-

economic and environmental objectives of sustainable development are not only

affected by land use changes but also affect LUCC effectively (Müller, 2003).

1.2 Objective of the Study

Based on information through literature survey, the author has formulated a

broad issue of land use problems under the objective of identifying the major land use

patterns and their corresponding determinants in South-west region of Bangladesh

over time. Moreover, author has reviewed the objective more critically as follows.

i. To trace out the major land use patterns and their corresponding changes

ii. To explore observed determinants of land use change from rice farming to

shrimp farming and their respective extents

To achieve the above objectives, the author has collected information from

various secondary sources to represent the land use patterns and their evolution both

in regional and global context along with necessary local information collected

through questionnaire survey with the aim of empirical and comparable analysis.

1.3 Rationale of the Study

Though the earth started her journey with few people (NASA, 2006), she now

possesses millions of inhabitants and has been experiencing modern lifestyle and

unplanned urbanization since industrial revolution (Chase et al., 1999; Schneider and

Pontius, 2001). Moreover, land being one of the scarce natural resources as well as

factors of production (Mohammad, 2009), has been observed to have diversified uses

both in reversible and irreversible ways (Islam, 2000). Researches also show that

economy experiences rapid expansion in size and growth to keep pace with the rapid

increase in and expansion of population, invention and innovation (Houghton, 1994).

Moreover, observations from last century particularly last decades have showed that


4

changes in land use patterns are global in nature (Houghton, 1994; Dale et al., 2000)

especially because of high dependency of human being on land (NFPCSP, 2011) for

livelihoods, modern shelter in superb places, desired food for eating (NASA, 2006).

Therefore, lands are becoming scarce natural resource (Mohammad, 2009) day by day

causing acute conflicts (Ruben et al., 2008) especially due to lack of coordinated

action amongst the various parties concerned with land management especially in

developing nations (Mohammad, 2009). Change in land use patterns or the physical

changes in land cover caused by human action is now a concern (Müller, 2003) due to

its disastrous changes (Schneider and Pontius, 2001) at unparalleled rates, magnitudes

and spatial scales (Turner, 1994a; Vitousek et al., 1997).

With high pressure on its natural resource base (NFPCSP, 2011), Bangladesh

is in threat of declining agro lands (Zubair, 2006) with devastating consequences on

country’s ability to sustainably achieve and maintain self-sufficiency in food and

agro-based livelihoods (NFPCSP, 2011). Besides, shifting rate of agricultural land to

non-agricultural uses is alarming with respect to crop production and food security in

Bangladesh (PC, 2009; Rahman and Hasan, 2003). In this connection, SRDI (2010)

estimated approximately 0.13% land was transferred from agro to non-agro sector per

year during 1963 and 1983 (Rahman and Hasan, 2003) while PC (2009) demanded

that at least one quarter of country’s agricultural land has already been lost since

independence. Researches also show that shifting rate of productive lands towards

unproductive purposes may be faster in 21st century because of rapid economic

growth and infrastructural development (Hasan et al., 2013). Though shrimp farming

was initially introduced in coastal as well as in the South-western part (Rahman et al.,

2013), production of shrimp has now been growing at an annual rate of 20-30% since

1990 (Primavera, 1997). Moreover shrimps cultivated in coastal Bangladesh now

accounts more than 2.5% of global production of shrimp with its position as the 7th

exporter to the markets of Japan, EU and USA (Rahman et al., 2013). Despite all

concerning reports on land use issues, very little attention has yet been paid to

formulate a dynamic as well as suitable national land use policy to conserve and make

best possible use of this highly scarce land (Mohammad, 2009).


5

1.4 Scope of the Study

Land use pattern and its corresponding changes are in a close dependency with

natural, socio-cultural and economic factors (Dale et al., 2000) and also influence the

behaviors and decision making over time and space (Houghton, 1994; Dale et al.,

2000; Ruben et al., 2008). Therefore, better understanding of the determinants of land

use patterns as well as corresponding determinants is necessary (Agarwal et al., 2001;

Lesschen et al., 2005) to assess present situation and possible future impact on

sustainable development of environment, economy and society at large (Verburg et

al., 2004; NASA, 2006). From this perspective, this study is primarily an attempt to

consider what are the major determinants of lands used for rice and shrimp farming

with an emphasis on the mode of interaction among the different driving forces of this

two land uses. And for this purpose, collection of primary data, its analysis and

presentation of analyzed data is being shown in a simple but effective way both using

descriptive statistics and econometric models.

Broadly, data both on land use patterns and its trends of world as well as

Bangladesh are being collected through secondary survey while about study area

through questionnaire survey and face to face interviews. Moreover, households who

have at least certain amount of personal lands for use (i.e. settlements, cultivation or

any other purposes but must be personally owned) are the sample population and the

decision maker of that specific household is treated as the target respondent. Data is

also being collected from local representatives (i.e. chairman, union members, agro

officer in charge) and from the oldest as well as large land holders for more accuracy

of data. Though there is variation in the socio-economic status of the target

population, only respondents living in the study area at least for five years or more are

being selected as the target population. Moreover, the simplest as well as flexible

procedures are being taken to complete the research work in time.

1.5 Operational Definitions

To avoid unnecessary confusion about the various used terms in this paper,

here is the description of commonly used terminologies with their used meaning

rather than traditional one as follows.

Household: Household is to be distinguished from family which comprises members

having blood relationship while members of a family may live in different places but

members of a household must live in the same place and share the same kitchen.


6

Illiterate: Respondent or decision maker who doesn’t have receive any education and

can’t even write his name are treated here as illiterate.

Informal Learning: When respondents are able to read and write or at least can

signature but didn’t participate in any formal institution (i.e. school, college) rather

have learnt through participating in any informal learning centre (i.e. from friend,

night courses offered by NGOs).

Land and Non-land Assets: Land assets include only the land resources possessed

by each household while non-land assets are any resources (i.e. tress, furniture,

business) except lands.

Land Owner and Farmer: Landowner and farmer are both used throughout this

paper to refer to the person making land use decisions primarily. Broadly, to be land

owner one must have his own land while farmers may or may not his own land.

Land Use: Land use refers to the manner in which people employ their land and its

resources including cultivation or use of earth surface.

Land Use Pattern: land use pattern implies to all possible as well as existing

manners in which humans are employing available land and its resources for the

benefits both in the present as well as in future context.

Land Use and Cover Change: Land use and cover changes mainly refer to the

replacement of natural lands (i.e. forests and grassland for agricultural use or agro

lands for shrimp farming or settlements) over time either due to pressure or for

expected benefits from any such conversion.

Mauza: Mauza is the lowest administrative unit having a separate jurisdiction list

number in revenue records with its well-demarcated cadastral map.

Mixed Use: When lands are used in different ways over time and doesn’t follow any

sequence, it is termed as mixed use lands. Mixed use here includes using the same

lands either in more than one use at a single time (i.e. rice and shrimp farming) or

using any lands in non-repetitive ways over some consecutive years.

Motorized, Non-motorized and Human Transport: Motorized transport takes

account of motor cycle, private cars and auto-rickshaw while non-motorized one

includes by-cycle, rickshaw (van). Human transport on the other hand includes human

labor curt run by human force for transportation.

Neighborhood Characteristics: Neighborhood characteristics consist of different

observed land use patterns in adjacent lands of the land under consideration.


7

Nuclear and Joint Family: Family which consists of only one spouse but may have

members of any number while joint family refers to having more than one spouses

under the control of single decision maker.

Other Occupation: In occupation, the terms others are being used to describe no

certain sources of income that is transitory income by the households.

Primary, Intermediate and College Education: Here primary education ranges

from preliminary stage (Class one or equivalent one) to till class eight (VIII),

intermediate from class nine (IX) to twelve (XII) and college education refers to

higher stages after intermediate education such as graduation, PhD.

Regular and Irregular Expenditure: Regular cost of household includes day to day

transaction for maintaining each household while irregular expenditure refers to

transitory expenditure (i.e. medical cost) by each household per year.

Remittance: Money (i.e. Bangladesh Taka) sent by other family member(s) who are

working either abroad or far from his houses for at least six months.

Rice Farming: Using a certain piece of land only for cultivating rice whole year or

any certain part of the year. All the rice farming lands under consideration are

cultivated using traditional methods with little modern instruments like machinery,

fertilizer while seeds are local.

Service: Service in this paper includes sale of labor hour at a single time and includes

labor income, maid servant and teaching.

Shrimp Farming: When any land is used only for producing shrimp almost all the

year round is treated here as the shrimp farming land. Shrimp farms are of different

size but use factors of input from same sources and also sell their final output to same

market at a more or less same price of both input and output.

Beside the above stated definitions as well as terminologies, some other terms

are also used as described critically during the analysis or at the point where they need

to define for easy understanding and to reduce ambiguity.

1.6 Limitation of the Study

In this study different types of data are being collected from similar types of

work around the world and Bangladesh simultaneously together with the primary data

from selected study area. Moreover, time series data are being given priority in order

to understand the trends of changes in land. But in this regard, the author failed to

manage enough time series data of land use pattern and corresponding changes due to


8

lack of availability of secondary data especially of the study area. Besides, agriculture

has a strategic function because it is the main food supplier for the people in

Bangladesh (Hasan et al., 2013) and thus different estimation methods of agricultural

statistics provide various data and information, so their reliability is questionable.

Moreover, the author couldn’t use sufficient econometric as well as statistical

tools because of lack of expertise as it is the first time to do such a research for the

author. The author has faced major problems in econometric analysis due to small

sample size mostly in case of incorporating necessary variable and due to presence of

several proxy or dummy variables in the study. Furthermore, similar answer by the

respondents in several cases made the analysis contradictory despite the truth of such

occurrence in the sample area. It is also to be noted that while calculating various

continuous data there were some mismatch which are assumed to be the result of

considering some factors but excluding some interrelated one.

The author for successful completion of the research work has used recall data

where there may some lacking of consistency as well as accuracy of data on land use

of the study area. And even in some cases there is variation in financial information

despite other information being the same. Moreover, this paper hasn’t taken time

value of money into consideration while dealing with time series cost and profit data.

1.7 Structure of the Study

The research work has been conducted in a systematic pattern which can be

described in a well mannered way for quick overview of the paper. Primarily, this

paper starts with writing of acknowledgement, abstract, table of contents for an easy

understanding of the whole paper at a glance and then includes the main body of the

research work, references and annex such as questionnaire, results of land use

determinants.

The first chapter of the paper includes the background, objective, rationale

with a clear definition of the scope of the study and faced limitations as well as

problems. The paper then, Chapter Two, shows the theoretical background (i.e.

theories and propositions on land use analysis) for explaining the research problem

and associated issues in a systematic manner. The third chapter, named literature

review has become informative with the arrangement of available literature and lastly

existing research gap. The paper in next, Chapter Four, shows the materials and

methods followed to complete the research work from research problem formulation


9

till submission with especial emphasize on variables, model formulation, target group,

research methods, tools of analysis and presentation process.

Description about the study areas and corresponding respondents are being

enumerated in Chapter Six while Chapter Five includes some qualitative as well as

quantitative overviews about land use and cover changes from global, national as well

as local context. Chapter Seven constitutes the heart of the paper because here has

been done the analysis of the collected data according to the objective. Presentation of

major findings and comparison with literature along with concluding remarks and

further scope of research are being enumerated in Chapter Eight.

Land use change is central to environmental management through its influence

on biodiversity, water and radiation budgets, trace gas emissions, carbon cycling, and

livelihoods (Lambin et al., 2000a; Turner, 1994). Wu and Li (2013) argued that world

agriculture is going to face tremendous pressure for intensification over the next 50

years especially because of increase in demand for food dramatically. Therefore, land

use modeling has attracted considerable attention (Gobim et al., 2002; Lambin, 1997;

Serneels et al., 2001; Veldkamp and Fresco, 1996; Verburg et al., 2002; Wu and Yeh,

1997) to sanctify knowledge to recognize the determinants of land use (Yadav et al.,

2012) over time and space. For example, the complexity of land use patterns and their

changes over the last decades calls for multidisciplinary analyses (Veldkamp and

Lambin, 2001) for a sustainable environment in future.


10

Chapter Two

Theoretical Background

Land use and cover change (LUCC) issues have already attracted the interest

of various researchers (Lambin et al., 2000; Verburg et al., 2004; Li, 2011; Wang,

2012; Silva and Wu, 2012) ranging from those modeling spatial and temporal patterns

of land conversion (Verburg et al., 2008; Priess and Schaldach, 2008) to those trying

to realize causes and penalties linked with these aspects (Irwin and Geoghegan, 2001;

Burgi et al., 2004). Besides, land use analysis is complex for its dynamism as well as

determinants (Lambin et al., 2003; Long et al., 2007) and asks for diverse approaches

rather than single one for consistency and precision (Verburg and Veldkamp, 2001;

Long, 2003; Cai, 2001; as cited in Long et al., 2007). Since, modeling land use issues

represents part of the complexity of land use systems (Veldkamp and Lambin, 2001),

reviews of different models on the basis of preferred variables (i.e. bio-physical and

socio-economic) have been provided by numerous disciplines over time (Verburg et

al., 2004; Priess and Schaldach, 2008; Trisurat and Duengkae, 2011).

Therefore, considering the importance of land use analysis in planning and

decision making, this paper has given a nutshell but effective depiction of prime land

researches undertaken so long to analyze land issues and to predict future problems.

2.1 Land Use Models

Models on land issues and problems range from simple system representations

including a few driving forces to simulation systems based on a deep understanding of

situation-specific interactions among a large number of factors at different spatial and

temporal scales (Verburg et al., 2008; Verburg et al., 2004; Priess and Schaldach,

2008). Moreover, the term “model” in land use research refers to the sign of a system

through mathematical, logical, physical and iconic methods (Rui, 2013) which can be

categorized in multiple ways on the basis of the subject matter of the models,

modeling techniques or methods used or actual uses of the models (Agarwal et al.,

2001; Irwin and Geoghegan, 2001; Yang et al., 2008; Veldkamp and Lambin, 2001;

Ducheyne, 2003; Torrens, 2006; Timmermans, 2003).

However, modeling methods have been developed to address when, where and

why LUCC occurs (Baker, 1989; Riebsame et al., 1994a; Lambin, 1997; Theobald

and Hobbs, 1998) to explore and predict the trends (Brown et al., 2000; Trisurat and


11

Duengkae, 2011) especially involving empirical data on historical pattern of changes

in land use patterns and then extending those for prediction (Brown et al., 2000). As a

result, huge number of models on LUCC has been described over time because of

different disciplinary perspectives and methodological approaches based on variations

in data availabilities and modeling goals (Brown et al., 2000; Long et al., 2007).

2.2 History and Trends of Land Use Models

Land use and cover change models allow testing the stability of linked social

and ecological systems (Oluseyi, 2006) through scenario building and provide

valuable information under a range of conditions despite failure of incorporating all

aspects of reality (Veldkamp and Lambin, 2001). Thus over time, LUCC modeling

has become more integrated, accurate and specialized (Nkonya et al., 2012) to ensure

the modeling of ecological interrelationships of different land uses and sustainable

development. Baker (1989) published the first reviews in the context of landscape

ecology with explicit representation of human decision making but did not discuss

models. However, with the passage of time researchers like Von Thünen (1826),

Lösch (1940), Ducheyne (2003), Timmermans (2003) and Rui (2013) have used

numerous forms theories, models and approaches to explore this issue.

Before mid nineties of last century, spatial economic theory was the base of

most land use models (Wang, 2012) while the oldest was Von Thünen’s land rent

theory of 1826 (Perraton and Baxter, 1974; Wang, 2012) showing that land close to

the city centre is used intensively (Perraton and Baxter, 1974). However, over the last

century, numbers of different clear-cut models on land issues have been made (Wang,

2012) especially following the first reviews in this context by Baker (1989). During

the last century influential models such as Weber’s classical triangle of industrial

location (1909) and Lösch’s theory of economic regions (1940) have also been

formulated (Wang, 2012) while following the advances in computational facilities,

computer-based urban models (i.e. Lowry model in 1964) arose with the domination

of micro-economic theories focusing individual landowners making land use decision

with the objective to maximize expected returns from the land (Wang, 2012).

Because of limitation of the then existing methods, spatial dimension was

introduced into land use models (Wang, 2012) based on data about landowners’

economic decision and neighborhood conditions from the end of 1980s (Irwin, 2010;

Wang, 2012). However, the most representative model of this group is CLUE model


12

which simulates geographical pattern of land uses based on locations (Veldkamp and

Fresco, 1996; Verburg et al., 1999; Verburg and Veldkamp, 2001; Verburg and

Overmars, 2009; Verbug et al., 2012). Moreover, regression analysis based on various

biophysical and socio-economic factors came into use in last century widely (Lambin

et al., 2003; Alabi, 2011; Quasem, 2011; Wang, 2012).

2.3 Land Use Modeling Approaches and Models

Studies of land use and its changes over time can be arrayed in a number of

dimensions such as theoretical versus empirical; structural versus reduced form;

disaggregate versus aggregate; extensive-margin versus intensive-margin studies;

drivers versus consequences-orientated studies, policy versus methods-orientated

studies (Wu and Li, 2013). However, addressing and sorting all available data, the

following shows a little but necessary details of how researchers have tried to deal

with various land issues over time to keep pace with evolution and social objectives.

2.3.1 Agent-Based Perspective

Land use being typically based on suitability (Wang, 2012), agent-based

models include various simulation models characterized by interacting autonomous

agents who have ability to make decisions in changing situation (Parker et al., 2003;

Wang, 2012; Oluseyi, 2006). Moreover, agent-based perception is based on general

nature and rules of decision by individuals that range from rational decision making

of neoclassical economics to socio-behavioral sciences (Lambin et al., 2003; Crooks,

2006). A familiar agent-based model is FEARLUS (Polhill et al., 2008; Wang, 2012).

2.3.2 Systems Perspective

Systems perspective explains changes through organization and institutions of

society (i.e. governments, communities) that operate closely at diverse spatial and

temporal scales; and is influenced by technical innovations, policy and institutional

changes, rural-urban dynamics and macroeconomic changes (Lambin et al., 2003).

2.3.3 Narrative Perspective

Narrative perspective seeks depth of understanding LUCC patterns through

historical details and on the same time, interpretation for a specific locality from the

historical analyses of land in particular stochastic or non-random but unpredictable

events that significantly affect it seriously (Lambin et al., 2003).


13

2.3.4 The Fitting Data Model

The fitting data model uses, theories of social sciences widely to represent

decision making as well as biophysical processes to varying degrees and therefore,

helps us understand where, how and why land are changing fast (Brown et al., 2000).

2.3.5 Simulation Processes

Simulation models are generative demonstrations of all essential practices of

agent’s decision making based on socio-economic and biophysical settings with the

intention of simulating the changes in expected outcome options (Brown et al., 2000).

2.3.6 Structural Models

Structural models are based on well established theoretical background and are

being used for hypothesis formulation and to identify variables to be incorporated in a

reduced form model based on the implicit assumption (Veldkamp and Lambin, 2001).

2.3.7 Statistical or Reduced Form Models

Statistical models are easier to put into practice because of its ability to deal

with original changes in driving forces (i.e. neighborhood land uses, experience) over

time in accordance with changes in system properties (Veldkamp and Lambin, 2001).

2.3.8 Geographic Models

Geographic models aims at optimal allocation of lands to ensure the best

possible as well as optimal uses with minimal effect on ecosystems and ecology based

on suitability of uses and spatial location of population (Nkonya et al., 2012).

2.3.9 Economic Models

Economic models stress on demand and supply of land based commodities

and effectively reflect the effect of international trade and globalization on land issues

through evaluation policies and socio-economic issues (Nkonya et al., 2012).

2.3.10 Stochastic Markov Model

Stochastic Markov Model combines both the stochastic processes as well

Markov chain analysis techniques (Basharin et al., 2004) based on probabilities with

discrete state space and continuous parameter space (Balzter, 2000). In this random

process, the state of a system(s) at time (t+1) depends only on state of the system at

time (t) not on previous states (Ahmed, 2011a).


14

2.3.11 Ecological Models

Ecological models link land allocation to species abundance and extinction,

ecological footprints and other environmental concerns assuming that prices and other

economic variables are exogenous factors (Nkonya et al., 2012).

2.3.12 Dyna-CLUE model

The Dyna-CLUE model is a spatial-explicit land use transition model that

quantifies the location preferences of different land use patterns based on logistic re-

gression models and determines relations between incidence of a land use pattern and

physical as well as socio-economic settings (Trisurat and Duengkae, 2011). It is

chosen because it explicitly addresses different future land demands driven by

expansion of agriculture, plantation and biodiversity protection (Verburg et al., 2004).

2.3.13 Spatial Economical Model

Patterns and processes of LUCC are essentially spatial processes and gives

valuable insights into associated processes and their underlying causes. Spatial

economical model emphasizes on maximization of net income in determining the land

use patterns of specific area over time (Li, 2002; Xie et al., 2014) and also account for

socioeconomic, agro-ecological, geophysical and policy variables (Müller, 2003).

Likewise, such models are useful to forecast changes (Serneels and Lambin, 2001).

2.3.14 Cellular Automata Model

Cellular Automata, originally invented by Von Neumann in the mid-1940s,

provides a proper scaffold for investigating the self-reproducing features of biological

systems (Alabi, 2011; Wang, 2012; Nkonya et al., 2012). They are more powerful for

complex systems due to their ability to simulate dynamic spatial processes from a

bottom-up perspective (Batty, 2007; Iltanen, 2012) and also for similarity to spatial

allocation models in terms of using transition rules (Wang, 2012). Moreover, data

from other models such as population growth model can easily be used (Wang, 2012;

Li and Yeh, 2000; Santé et al., 2010; Li, 2011) also.

2.3.15 Species-distribution Model

Species-distribution models refer to relationship between given pattern(s) of

interest and set of explanatory factors where the factors and associated results can be

quantified properly in dynamic ways (Guisan and Zimmermann, 2000).


15

2.4 Economics, Econometrics and Land Use Research

Economics being the field of dealing with scarce resources; has already made

enough involvement in land use and corresponding change analysis (Lambin, 1997;

Serneels et al., 2001; Veldkamp and Fresco, 1997; Verburg, et al., 2002). Researches

show that outputs are being used to reflect the value of the land use system as well as

profit scenario (Dai et al., 2005; Veldkamp and Lambin, 2001) and keeping pace with

this, equilibrium principle of microeconomics shows that under the condition of full

competition as well as economic and technological stability, marginal benefit (MB)

will decrease with the development of the land use system, whereas marginal cost

(MC) will increase with demand for land (Houghton, 1994; Dai, 2002). Therefore,

area under curve MB is the total benefit of that specific land use system and that

under the curve MC is the total cost with expanded land use while E (i.e. as described

in figure 2.01) is the point where maximum profits can be made from a land use (Dai

et al., 2005). Moreover, rational behavior as well as random utility theory implies that

transformations in use of lands are inevitable to maximize profits and to conserve

limited resources (Veldkamp and Lambin, 2001; Serneels and Lambin, 2001) in

particular when there is a divergence in suitability and target on land use (Dai, 2002;

Mia and Islam, 2005). In a purely market oriented economy, a criterion for the

transformation of land use type (LTC) can be expressed as (Dai, 2002) a point where

land type i will be transformed to type j only and only if land use pattern j generates

higher profit than that of i (Dai et al., 2005).

Figure 2.01 Economic Dynamics of Land Use System

Source: Dai et al., 2005


16

Moreover with the passage of time, various econometric analyses are also

being observed to be used along with economic theories (Lambin et al., 2003; Alabi,

2011). Most common as well as used economic tool used in land use analyses

includes regression analysis which refers to method engaged in discovering empirical

relationships between binary dependent and several independent categorical and

continuous variables (McCullagh and Nelder, 1989). However with the passage of

time, there are two basic approaches to assess spatial dependency within the

regression framework- firstly, building a complex model known as autoregressive

structure and secondly, designing a spatial sampling plot to enlarge distance interval

between sampled points (Anselin, 1988). Here is to be noted that discrete choice

model is one of the best-known ways of modeling land use patterns as well as changes

based on the concept of utility (Koppelman and Wen, 1998) while logistic regression

analysis is one of the most utilized approach during past decades (McCullagh and

Nelder, 1989; Arsanjani et al., 2013) especially to predict land uses (Verhagen, 2007).

When the dependent variable consists of more than two nominal outcomes, it is

referred to as Multinomial logistic regression or Logit but in case of two possible

outcomes logistic regression is called binary logit and when outcome may be ordered

or ranked, ordered logit is being used (Heij et al., 2004; Ntantoula, 2013).

However based on random utility and profit maximization theory, distributions

of the discrete states of land cover and use patterns in case of binary analysis can be

linked with independent variables by the following equation (Long, 1997; Lambin et

al., 2003; Alabi, 2011; Anselin, 2002).

�� = �1��

∗ > �

0��∗ ≤ �

� 2.1

The parameter � in above equation represents a threshold and for observations

of��∗ ≤ �, the observed binary variable �� takes the value zero (0) and when��

∗ < �,

the dependent variable �� is equal to 1 i.e. land use pattern will be changed into type

j. But as dependent variable �� is unobserved as well as discrete, ordinary least

squares estimation (OLS) is not appropriate and therefore, researchers need to use

maximum likelihood (ML) method (Long, 1997). ML estimation requires knowledge

about the distribution of the error terms and if the error terms are assumed to be


17

normally distributed, then probit model is used for a binary �� otherwise logit model

is applicable (Lubowski et al., 2008; Rui, 2013; Hu and Lo, 2007).

As nations and areas are going towards urbanization rapidly, land use patterns

and equivalent changes have gained increased importance by researches throughout

the world (Mia and Islam, 2005) especially for sustainable development as well as to

ensure optimal use of land and associated resources in more effective and efficient

ways (Lambin et al., 2003). Thus developing realistic and dynamic models to explore

vital drivers of changes in land use over time has no alternative (Veldkamp and

Lambin, 2001). Keeping connection with this Lambin et al. (2003) has also

emphasized on the integration of combined perspective for the best, valid and

empirical study. Therefore for more accuracy and consistency, land use analyses

should include best possible methods collectively (Zenga et al., 2008) with the

inclusion of necessary socio-economic and other associated variables (Lambin et al.,

2003; Hu and Lo, 2007; Lubowski et al., 2008; Rui, 2013).


18

Chapter Three

Literature Review

About half of the ice-free surface has been substantially modified over last

10,000 years (Lambin et al., 2003) while during last three centuries, nearly 1.2 million

sq km of forest lands as well as 5.6 million sq km of grassland and pastures have been

converted to other uses (Ramankutty and Foley, 1999). Land use changes, thus, have

become locally pervasive and globally significant (Agarwal et al., 2001) as well as

dynamic phenomenon (NASA, 2006; Mohammad, 2009) not only for its presence at

almost everywhere but also for contribution to global ecology (Houghton, 1994).

People of Bangladesh are observed to shrink per capita land by 50 percent

from 1970 and 1990 (Mohammad, 2009) and now have a per capita cultivable land of

only 12.5 decimals or less (Quasem, 2011). As a result, with the passes of time land is

becoming scarcer (Mohammad, 2009) especially with the growth and expansion of

economy (Houghton, 1994; Quasem, 2011; Yadav et al., 2012) and increasing

demand for non-farm commodities (Quasem, 2011). Moreover, land use changes have

important implications for future changes in the earth climate and ecology (Agarwal

et al., 2001) and therefore, understanding land use patterns has great role to facilitate

ecological sustainability through improving land management, enhanced capability of

assessing and predicting future trends (Veldkamp and Lambin, 2001; Wang, 2012).

3.1 Land

Land, the mother of resources (Mia and Islam, 2005; Iftekhar, 2006), is being

considered as a prerequisite for all development purposes especially for sustainable

development (Iftekhar, 2006). Land, therefore, refers to the basic natural resource that

provides habitat and nourishment for living organisms (Mia and Islam, 2005) or the

means for livelihood with potential revenue if properly utilized (Iftekhar, 2006).

Though, Stewart (1968) and Wolman (1987) defined land as the wide range of

natural resources from the atmosphere above the land surface down to some meters

below the surface, FAO (1992) defined not only as soil but also as landforms, climate

and hydrology, plant and animal population, and the physical results of human

activity like terraces and drainage works. Moreover, despite the similarity in physical

characteristics across the universe (Zubair, 2006), its supports can vary over time and

space according to the management conditions and uses (Mohammad, 2009).


19

3.2 Land Use

Land uses denote the purpose to which human puts land especially to fulfill all

their needs (Turner and Meyer, 1991; Turner and Meyer, 1994; Skole, 1994).

Moreover, land uses are considered as human activities linked with land, use of its

resources (FAO/IIASA, 1993; Veldkamp and Fresco, 1997) which have potential

ecological impact because of either permanent or cyclic interference (Vink, 1975).

Precisely, land use describes alteration of each land cover (Prakasam, 2010) or how

each parcel of land is being managed for alternative uses (FAO, 1992).

Land use, thus, is applied to the biophysical attributes of surface (Lambin et

al., 2001) through various human induced activities (Prakasam, 2010) for different

purposes i.e. habitation, forestry, agriculture (Ahmed, 2011; Yadav et al., 2012).

3.3 Land Use Change

Land use change is being considered as the single most important appearance

of human interaction on atmosphere (Mohammad, 2009) and includes alteration of

land covers (Lesschen et al., 2005) either in the form of agricultural intensification or

changes in farming system over time (Farrow and Winograd, 2001) due to influence

of population and economic expansion (Mohammad, 2009). Briassoulis (2000) has

defined land use change as the quantitative increases or decreases in the area of a

given type of land use while Wu and Li (2013) defined as any changes in

arrangements, activities and inputs that people undertake in certain land cover type.

Precisely, land use change refers to changes in land use morphology over time

with respect to particular socio-economic factors (Grainger, 1995; Zubair, 2006)

which may include both temporal and spatial dimensions (Long et al., 2007).

3.4 Land Cover

Land cover is the most vital gears of ecology (Prakasam, 2010) attributable to

functioning of ecosystem (Yadav et al., 2012). Meyer (1995) defined land cover as the

kind and state of vegetation (e.g. forest or grass cover) but Zubair (2006) has widened

the definition by including factors such as human structures, soil type, biodiversity

and ground water. Land cover, thus, refers to assemblage of biotic and abiotic

components on earth surface (Prakasam, 2010; Uddin and Gurung, 2010) or the set of

spatial units each associated with attributes (Lambin et al., 2003).


20

Precisely, land cover can be described as the layer of soils and biomass that

covers land surface (Fresco, 1994) with biota, soil, topography, surface, groundwater

and human structures (Lambin et al., 2003) which together denotes the quantity and

type of surface vegetation, water and earth materials (Turner and Meyer, 1994).

3.5 Land Cover Change

Land cover change refers to either changes in biophysical attributes (Lambin

et al., 2001; Dale et al., 2000) or complete replacement of one cover type by another

alternative (Lesschen et al., 2005). Precisely, it is the ultimate changes of the nature of

soils, vegetation and water surfaces (Houghton, 1994; Wood et al., 2004) causing

environmental modifications (Klooster and Masera, 2000; Mas et al., 2004).

3.6 Land Use and Cover Change

Land use and cover are separate terms often used interchangeably (Dimyati et

al., 1994; as cited in Yadav et al., 2012) though are semantically equivalent (Brown et

al., 2000) for their historic nature (Dale et al., 2000). However, together they refers to

the likely changes in land cover with or without unaltered existing land uses (Turner

and Meyer, 1994; Tiwari and Saxena, 2011) either directly or indirectly (Prakasam,

2010) from the interdependence between socio-economic, institutional, bio-physical,

cultural and environmental forces (Lesschen et al., 2005).

3.7 Land Use Planning

Land is influenced by personal, economic, cultural, political and historical

factors (Brown et al., 2000) and is used first and foremost for agriculture, industrial

communication and settlement purposes (Mohammad, 2009). Therefore, coherent set

of decisions about the use of land and ways needed to achieve the desired use and to

ensure optimal productive capacity are the core of land use planning (FAO, 1992; Mia

and Islam, 2005). Moreover, such planning shows fraction of total available lands for

further uses either in productive or non-productive uses (Houghton, 1994).

3.8 Land Use Conflict

Nations advancing towards development, urbanization and industrialization

face major land use conflicts in the form of converting valuable agro land to non-agro

uses (Mohammad, 2009; Mia and Islam, 2005) despite the uniqueness in cover and

attributes of each parcel of land (Zubair, 2006). About 1 to 2 million ha of croplands


21

is being taken out of production every year in developing countries to meet demand

for non-productive purposes (Houghton, 1994; Lambin et al., 2003). Moreover, most

of the lands in Bangladesh are fit for more than one use (Mia and Islam, 2005) which

leads to the diversified uses of limited land (Islam, 2000) causing acute conflict

mostly between shrimp farming and other uses (Mia and Islam, 2005). Land use

conflicts are acute under rapid population pressure and in mixed economies (Verheye,

1997) due to clumsy action among concerned parties (Mohammad, 2009).

3.9 Methods Used to Identify Patterns and Changes of Land Use and Cover

Land use research is devoted to analyze relationship among land use pattern,

socio-economic as well as biophysical variables (Lesschen et al., 2005) that act jointly

as driving forces and can be understood through monitoring and analyzing the trends

regularly (NASA, 2006). As a result, researchers have used various methods based on

existing data, techniques and facilities (Lambin et al., 2003) to explore the various

land use patterns and corresponding changes over time and place.

Scientists and environmentalists have identified fast changing magnitude of

land use patterns and corresponding changes across earth by observing and analyzing

satellite images (Loveland et al., 1999) though have poor application especially in

developing nations (IPCC, 2000). Despite all drawbacks, Mas et al. (2004) used map

comparison based on GIS while NASA (2006) as well as Kamaruzaman and Manaf

(1995) has used landsat satellites to explore changes through monitoring and

analyzing data. Tefera and Sterk (2008) and Yadav et al. (2012) used satellite images

and maps using GIS to analyze land use dynamics while Trisurat and Duengkae

(2011) used Dyna-CLUE model with logistic regression and Xie et al. (2014) used

spatially explicitly regression to describe economic drivers of agro land use change.

Brown et al. (2000) has used ‘Transition Probabilities’ while Veldkamp and

Lambin (2001) have used a spatially explicit, integrated and multi-scale manner for

the projection of alternatives into the future to test key processes and for describing

the trends in quantitative terms. Lambin et al. (2001) used simple but elegance theme

called ‘IPAT formulation’ showing interdependencies among population, affluence

and technology. Ruben et al. (2008) used optimization models (Cost-benefit analysis

based on opportunity cost of using or converting specific parcel of land at a specific

time) of the agriculture and forestry sectors. Lubowski (2002) used econometric

analysis through formulating Nested Logit model to include all major land use


22

categories in both urban and non-urban land uses and examines a comprehensive set

of transitions among the different land use categories. Lambin et al. (2003) have used

regression to address land use as well as their changes while Lesschen et al. (2005),

Alabi (2011) and Quasem (2011) have used empirical techniques to verify hypotheses

through the application of statistical and econometric tools like goodness of fit,

regression analysis, correlation analysis and descriptive statistics to predict actual

landscape change. Zhang et al. (2001) used regression analysis with cross-sectional

heteroscedasticy and simultaneous correlation analysis.

Mia and Islam (2005) in November 2004 used ‘Ground Truthing’ (an

important aspect to check information incorporated in zoning exercise) to check land

use patterns and their changes over time in southern part of Bangladesh while Uddin

and Gurung (2010) used satellite remote sensing in Bangladesh with the use of change

detection map (spatial location of changes) and change matrix (dimension of

changes). Ahmed (2011a) have widely used Remote Sensing and GIS techniques to

assess natural resources and environmental changes using time series of remotely

sensed data and linking it with socio-economic and bio-physical data in Khulna city to

detect, monitoring and mapping land cover change over time and hot spots. Rahman

and Begum (2011) used remote Sensing and GIS Application to address the land use

changes in Sundarbans areas in Khulna and Satkhira region.

3.10 Variable Used in Modeling Land Use and Cover Changes

Models of land use analysis are powerful tools to be aware of and analyze

important linkage between socio-economic processes (Lesschen et al., 2005) linked

with land and resource management and agricultural activities (Turner and Meyer,

1991; Brown et al., 2000). However, modeling land use change initially focuses on

biophysical attributes (Veldkamp and Lambin, 2001) with various socio-economic

drivers (Wilbanks and Kates, 1999). Therefore, researchers on the basis of accessible

data, techniques and problems have used different variables as described below.

Ehrlich and Holdren (1974) and Lambin et al. (2001) used population,

affluence, technology as variables despite an interdependencies and high risk of their

separation while Quasem (2011) has shown total land (decimals), homestead land

(decimals); proportion of non-crop land to total land owned (%), primary occupation

and years of schooling (number); per capita annual income (Tk.); household assets

other than housing (Tk.); disaster losses (Tk.). Agarwal et al. (2001) and Lambin et al.


23

(2003) used population density, labor availability, quantity and sensitivity of

resources, production costs, market prices, transportation costs and technology,

subsidies, taxes, property rights, infrastructure, exposure to external perturbations

while Alabi (2011) and Trisurat and Duengkae (2011) used elevation, soil type,

income, proximity to near roads, water sources, infrastructure, drainage system,

population density, road condition as major variable to quantify land use change.

3.11 Type and Scope of Land Use and Cover Change

Growing demand for urbanization as well as suburbanization is asking for

frequent alteration in using the planet surfaces in diverse ways (NASA, 2006) and as a

result, land use changes can be considered from two perspectives such as intended and

unintended (Houghton, 1994) or progressive and gradual (Lambin et al., 2003) or

reversible and irreversible (Islam, 2000). However, Lambin et al. (2001) have pointed

out that about 26 researchers of various disciplines have worked on several issues of

land use changes including tropical deforestation, rangeland modifications,

agricultural intensification and urbanization supported by quantitative assessments

with a deeper and more robust understanding of land use pattern and change

especially to adopt appropriate policy intervention.

Moreover, land use includes agricultural land, built up land, recreational area,

wildlife management area (Zhang et al., 2001; and Prakasam, 2010) and its changes

may involve shifting to a different use (i.e. from rice to built-up land) and/or

expansion or intensification of an existing one (Morita et al., 1997).

3.12 Observed Land Use Pattern

Land use and cover changes have historical sets since civilization (Dale et al.,

2000) due to growing trends of urbanization and innovation (NASA, 2006). The most

observed and important human use of land includes agriculture, settlements, forests,

water bodies, fisheries, salt production, industrial with infra-structural developments

and tourism (Turner II et al., 1994; Mia and Islam, 2005; Mohammad, 2009; Islam,

2000; Iftekhar, 2006), mixed uses restricted and vacant land (Iftekhar, 2006).

However, lands in south-west Bangladesh are being observed to be used for rice

farming, shrimp cultivation and fish farming, forestry, salt production, ports,

industries, human settlements and wetlands with some fellow lands (Alam et al.,

2002; Islam et al., 2006; Mia and Islam, 2005; Flynn et al., 2009).


24

3.13 Global Land Use and Cover Trends

Major and historical changes in land use across the world occur since humans

have controlled fire and domesticated plants and animals (Lambin et al., 2003) and

especially with the growth of population and urbanization (Dale et al., 2000).

Moreover, about half of the ice-free surface has been substantially modified by human

activities over last 10,000 years (Lambin et al., 2003) while approximately one-third

of the land surface were being converted to alternative uses (Houghton, 1994).

Estimation shows that 10-15 percent of the transformed land surface is dominated by

agricultural crop and urban-industrial areas while 6-8 percent is pasture (Vitousek et

al., 1997). According to Ramankutty and Foley (1999), during the last three centuries,

global cropland has increased by 12 million sq km.

3.14 Land Use Trends in Bangladesh

Bangladesh has a population of 153 million with an expected increasing rate

of 1.37 percent (MoF, 2013) causing direct conversion of productive lands into non-

productive uses (Mia and Islam, 2005). In last century, only 23 percent of total land

area was cultivated by tenants or owner cum tenants and 45 percent by paid laborers

(Hasan and Mulamottil, 1994). Mohammad (2009) showed that land has decreased by

about 50% during 1970-1990 while arable land per economically active person is only

0.8 ha compared to more than 12 ha in developed countries (Graff, 1993; as cited in

Mohammad, 2009). Moreover, land demand for non-agricultural purposes and urban

uses has increased sharply in last decades though still agriculture is the major activity

(Choudhury, 1987; as cited in Mohammad, 2009). Consequently, despite much fertile

land Bangladesh is marginally deficient in food grains (BBS, 2006).

Trends of land use patterns in south-west part of Bangladesh are notable over

last decades due to her major land uses (i.e. agriculture, shrimp and fish farming,

forestry, urban development and settlement) and especially due to rising demand and

huge populations in corresponding areas (Ahmed, 2011; Rahman and Begum, 2011).

3.15 Causes of Land Use and Cover Change

Land use changes can be described by the complex interaction of behavioral

and structural factors (Verburg et al., 2004) which are driven by a combination of the

so called land use drivers classified as socio-economic, political and biophysical

factors (DeKonind et al., 1999; Stomph et al., 1994; Veldkamp and Fresco, 1997)


25

along with some recent one like climatic and demographic factors, level of poverty

and economic as well as institutional structure of the resource use (Mohammad,

2009). Therefore, driving forces are generally subdivided into two groups- proximate

causes (Activities or actions that directly affect land use) and underlying causes

(Fundamental forces that underpin the proximate causes including demographic,

economic, technological, institutional and cultural factors) (Lesschen et al., 2005).

Researchers over time have pointed out numerous causes such as rapid growth

and development of civilization (NASA, 2006), population and demands of food

resources (Yadav et al., 2012), population and poverty driven deforestation, increased

presence of shifting cultivators, triggering mechanisms for rapid development,

globalization, low per capita land (Lambin et al., 2001), dam construction (Tefera and

Sterk, 2008), economic growth and development, climate change, development of

roads and electricity, improvements in irrigation, technologies, penetration of

commercial forces (Uddin and Gurung, 2010), consumer tastes, international trade,

weather, local rules (Lubowski et al., 2008), desire for profit, utility maximization,

cost minimization, (Veldkamp and Lambin, 2001), soil suitability, population density,

rainfall and accessibility, market conditions (Lesschen et al., 2005), increasing

income, urbanization, infrastructural development, national and international policies,

land tenure and property rights, bio-energy, land degradation (Nkonya et al., 2012),

soils erosion, reduced rainfall, floods and siltation (Houghton, 1994), land ownership,

non-agricultural occupation (Quasem, 2011), fertility (Mohammad, 2009). However,

according to the words of Iftekhar (2006) land use change occurs because of the

combined effect of social, political and economic conditions of a region or a country.

During past few decades Bangladesh has experienced rapid land use changes

more or less for the above stated causes (Ahmed, 2011; Iftekhar, 2006; Mohammad,

2009) while south-west regions are being observed to have frequent changes due to

the effects of increased salinity intrusion as well as natural disasters (Ahmed, 2011),

intensive agriculture practices and changing land quality (Uddin and Gurung, 2010;

Minar et al., 2013). However, Rahman and Begum (2011) showed two causes of land

use changes in Khulna and Satkhira region such as natural (i.e. global warming,

climate change, sea level rise (SLR), coastal flood, salinity intrusions, water logging)

as well as anthropogenic forces (e.g. population growth, unplanned cultivations,

salinity intrusions, water logging, misuse of Sundarbans, political unrest, illiteracy of

local people about effect of land cover changes, poverty, higher expectation).


26

3.16 Impact of Land Use and Cover Change

Land use changes have come into view as one of the key drivers of ecological

changes (Kueppers et al., 2004; Foley et al., 2005; Serneels and Lambin, 2002)

because of its potential effect of causing various sudden but catastrophic

environmental and socio-economic problems (Wang, 2012; Mia and Islam, 2005).

Human use of land has altered structure and functioning of ecosystem (Vitousek et al.,

1997) and keeping pace with this IPCC (2000) stated that expansion of agriculture

have came into present form through conversion of forests and grassland during past

140 years. Kitamura and Kobayashi (1993) and Houghton et al. (1999) have pointed

out that wrong land use has led to serious problems such as degradation and

deforestation of tropical forests, climate change with the problems of greenhouse

effect, loss of biodiversity and negative changes in regional hydrology and

biogeochemical cycles (Chase et al., 1999; Mas et al., 2004).

However, researchers have pointed out some of the frequent impacts of land

use and cover changes such as rapid conversion of potentially productive land to

unproductive purposes (Houghton, 1994; Lambin et al., 2003), change in biotic

diversity (Sala et al., 2000), important tradeoffs for sustainability, food security,

vulnerability of people and ecosystems (Lesschen et al., 2005), deforestation,

diminishing soil fertility, permanent degradation of land productivity (Islam and Weil,

2000), inundation of grazing lands, soil erosion, reduction of traditional farming,

sedimentation (Tefera and Sterk, 2008), climate change, deforestation, natural hazards

(NASA, 2006; Lubowski et al., 2008), climate variability, land degradation,

vulnerability of places and people (Veldkamp and Lambin, 2001).

Here is to be remembered that all impacts are not negative because changes in

land use patterns are also associated with increases in food and fiber production with

more efficiency and well-being (Lambin et al., 2003; Vitousek et al., 1997) despite its

externalities (Turner II et al., 1995; Lambin et al., 1999; Aylward, 2000).

3.17 Initiatives for Land Use and Cover Changes

Growing importance of land use and its policies has been approved by several

international meetings (i.e. The World Forestry Congress, The Jakarta Declaration

1978 and Paris Declaration) through holding seminars and symposiums over time

with the incorporation of socio-political and economic factors (Fresco et al., 1996;

Veldkamp and Lambin, 2001). Recognizing the significance of land use issues,


27

globally projects were prepared in 1994 for the first time (Verburg, 2006; Veldkamp,

2009; Wang, 2012) especially aiming at sustainable economic expansion and

environmental protection (Wu and Li, 2013). Moreover, considering pervasive

externalities of land use changes, a novel discipline named land use science has

already emerged (Lubowski, 2002; Wang, 2012).

In recent years, significant progresses have been observed in land use planning

in Bangladesh mainly in mapping shrimp and rice farming lands (Shahid et al., 1992),

detection of changes in Sunderbans mangrove forest (Islam et al., 1997), shrimp-

farming zone (Hossain et al., 2001), mapping suitable areas for saltpan development

(Hossain et al., 2003a), mangrove afforestation (Hossain et al., 2003b), tilapia farming

areas (Hossain et al., 2007), assessing suitable carp-farming areas (Hossain et al.,

2009; Salam et al., 2005) and giant prawn farming area (Hossain and Das, 2010).

3.18 Findings and Results of Land Research

Land use and cover changes are extensive, accelerating and significant process

driven by human actions (Xie et al., 2014) and also have influential effects on human

activities (Agarwal et al., 2001). Moreover in most societies, use of land is more or

less out of the owners’ hands and under the control of government or local authorities

though their involvements vary much across time, region and culture (Kim, 2010;

Ahmed, 2011a). Besides, when there is competition for residential land it is observed

that financially deprived people are relegated to poor and bad terrains (Alabi, 2011)

and agricultural intensification occurs at the intensive margin when more input is used

for a given land or when a less input-intensive land is converted to a more input-

intensive use i.e. conversions of grassland to crop production (Wu and Li, 2013).

Researchers over time have used various different methods on the basis of

existing data, techniques and facilities (Lambin et al., 2003; Li and Zhao, 2011; Xie et

al., 2014) and show that low income, low elevation and inefficient geography have

negative effect on residential development while is induced through favorable

ecological characteristic e.g. favorable road network, nearness to modern amenities

and facilities (Skole and Davids, 2002; Gyawali et al., 2004; and Alabi, 2011).

Lubowski (2002); Lubowski et al. (2008) and Alabi (2009) found that residential and

industrial areas are now sited on areas which were once prime agricultural lands, wet

lands and areas of physical constraints due to scarcity of land and found a

significantly positive relationship with proximity to infrastructure while significantly


28

negative relationship with elevation, road condition and population density and didn’t

indicate any notable relationship between drainage, education, land price, soil type or

flood potential. Rui (2013) showed higher value of commercial, industrial and public

service areas than that of pasture and forest area. Built-up areas and urban greenbelts

display positive relations with different centralities while agro and forest areas show

negative relationships (Riebsame et al., 1994; Zubair, 2006; Lubowski, 2002).

3.19 Problems and Limitation of Land Use and Cover Researches

Unavailability of better data for improved models and projections of land use

and cover changes especially to make a generalized conclusion (Lambin et al., 2001;

Ochoa-Gaona and Gonza´lez-Espinosa, 2000; as cited in Mas et al., 2004) together

with ignorance and misunderstanding about the cost and benefit of cropping or any

other uses (CGCR, 1999; Oluseyi, 2006) is the major problems in dealing with land

issues. Moreover, Lambin et al. (2001) and Long et al. (2007) have addressed the

problem of application of micro scale data sets in global context because they are

specific to time and place and have some common and popular myths regarding land

use changes. Lesschen et al. (2005) and Lubowski et al. (2008) have pointed out that

the misuse of different techniques described without a specific focus on land use

change issues causes much probability of uncertainty in modeling land issues. Proxy

variables, though easier to measure spatially complex variables (i.e. land management

technologies, infrastructures and policies) generate acute problems in application of

such results in policy makings (Wilbanks and Kates, 1999; Müller, 2003).

Land use pattern and corresponding changes have vital implications for future

changes in earth climate as well as ecology (Agarwal et al., 2001; NASA, 2006)

mainly in developing countries where per capita arable land is lower in contrast to that

of developed countries (Graff, 1993; as cited in Mohammad, 2009). Moreover,

changes in land use patterns occur not only for negligence and improper execution of

land use policies but also for some misconceptions (Lambin et al., 2001). Researches

also shows that despite accuracy and success of remote sensing data and GIS

(Lesschen et al., 2005), these are rarely being used especially in developing nations

(Ahmed, 2011a) and if used, the result of such studies on land use changes are placed

in complex ways which shows variation from researchers to researchers because of

geographic, demographic and climatic variations (Uddin and Gurung, 2010).


29

As a developing country Bangladesh lacks a well organized database both in

national and regional levels as a result of improper coordination among different

organizations (Oluseyi, 2006; Mohammad, 2009) and thus despite being a powerful

tool, use of satellite image is limited here (Ahmed, 2011a).

3.20 Research Gap

Relationships between population increase, economic developments and land

use changes have generated sufficient research interest recently (Agarwal et al., 2001;

Oluseyi, 2006) but little has been done in predicting long term penalties in developing

nations (Quasem, 2011). Though there are some researches in developed countries to

check relationship of land use patterns as well as their changes with sustainability,

smooth economic expansion; there has hardly any study in the area of conversion of

farm land to non-farm uses in developing nations (Quasem, 2011; Ahmed, 2011a).

However, from the literature collected and discussed above shows that there

occurs very little research on land use issues in south-west areas especially in Khulna

and Satkhira areas where both natural as well as human induced forces are responsible

for land use changes over time. Moreover, there is only some govt. information

collected over time on land use and its changes at household level but there are

enough gestation periods between data collection and publishing. Again despite being

crucial, land use change is not taken into consideration significantly on national land

policy and other policies where lands are used intensively. As a result, there are

enough spaces for research on land use issues especially to know the extent of land

use patterns and their corresponding changes in south-west region of Bangladesh.

Any activity (i.e. known as driver or determinant) associated with land use

may be on side the causes and on the other side the result of changes in land use

patterns and processes (Agarwal et al., 2001). Therefore, whatever is the planning or

policies, success depends much more on the proper implementation of the policies

which needs the establishment of integrated management through coordination,

demarcation, better preparedness against adversity and introduction of modern land

management systems (Ahmed, 2011). It is also to be noted that neither policies nor

government regulation can ensure sustainable land use until the mass people become

aware of the social cost and benefit of various alternative land use patterns and

corresponding changes.


30

Chapter Four

Methods and Materials

As this paper has already been described the rationale of the problem,

objective as well as research question of the study (Chapter one), this chapter by this

time describes all other necessary steps followed since research problem formulation

to successful completion of the research work as follows.

4.1 Conceptualization of the Research Problem

After selecting the broad research area for investigation, search for and then

reviewing of collected literature form offline (i.e. library, newspaper) and online

sources (i.e. websites) are being made continuously for conceptualization of proposed

problems as clearly as possible. Here the author has collected information with higher

emphasizes on modeling and econometric issues (i.e. for clear and easy modeling of

current study) as well as empirical analysis (i.e. for comparable findings) which have

by now been discussed in chapter two and three. Moreover, the author has also

concerned with resource persons for clear conceptualization on proposed problem.

Details but necessary information on different concepts, theories as well as

their modeling approaches and findings over time, place and culture have been

collected from previous studies such as books, journals, seminar papers, dissertations,

organizational papers and various websites (i.e. outlined in reference part in details).

4.2 Study Area

Keeping pace with the title of the research work as well as after the process of

conceptualization (i.e. developing theoretical as well as conceptual framework), the

researcher has selected the study area to answer the research questions and compare

with the existing findings in an empirical process. The author has used multi stage

sampling process to select final study area within the south-west region and primarily,

Khulna division, one of the seven divisions and the most influential coastal zones

(Ahmed, 2011) of Bangladesh, has been chosen as the broad study area. After that,

Satkhira districts out of 10 districts of Khulna division and then Kaligonj Upazila of

Satkhira district have been selected conveniently as the study area. Finally, Pirozpur

village (i.e. details in Chapter Six) of Dhalbaria union under Kaligonj upazila is being

selected as the sample study area to collect data for empirical analysis.


31

4.3 Research Design

To keep pace with the objectives, author has proposed both exploratory and

explanatory approaches in the study to address and then discuss the land use patterns as

well as their corresponding determinants both in qualitative and quantitative approach.

However, following Lambin et al. (2003); Parker et al. (2003); Oluseyi (2006);

Torrens (2006); Polhill et al. (2008); Carrión‐Flores et al. (2009); Wang (2012) and

Rui (2013), author has attempted to model land use conversion reasonably from a rich

available literature emphasizing on the economic agent who is assumed to make an

inter‐temporal, profit maximizing choice regarding the conversion of a parcel of land

to some available but towards the most persuasive alternative use.

Moreover, author has used joint approach of various models to show link

between changes in land use patterns (i.e. conversion of rice farming lands towards

shrimp) and socio-economic, bio-physical, policy variables by following Verburg et

al. (2004) and Trisurat and Duengkae (2011) on Dyna-CLUE model; Serneels and

Lambin (2001); Müller (2003); Li (2002) and Xie et al. (2014) on Spatial Economical

Model and Li and Yeh (2000); Batty (2007); Santé et al. (2010); Alabi (2011); Li

(2011); Iltanen (2012); Wang (2012) and Nkonya et al. (2012) on Cellular Automata.

Therefore, agent based approach is being used based on single survey from the

land owners or decision makers while some of the necessary but previous data (recall

data) are being collected for the proper completion of the research.

4.4 Target Group

Agent based approach is based on rational agents who emphasize on profit

maximization in choosing conversion of a parcel of land (Parker et al., 2003; Wang,

2012; Oluseyi, 2006). Hence for convenience of the study, households of the selected

study area have been primarily treated as the target group while head or decision

making individual of the each household is being taken as individual agent. It is to be

noted here that households (i.e. respondents) who are living at least for five years in

the study area are only being considered as the target sample population.

4.5 Sample Design

The author in this paper has used multistage sampling in selecting both study

area and sample population. However, the author has used the following procedures

for sampling technique, sample size and sampling methods (Next page).


32

4.5.1 Sampling Techniques

Systematic and stratified random sampling are the two agreed upon sampling

methods in logistic regression (Arsanjani et al., 2013) because of its ability to reduce

spatial dependency and complete pictogram of population (Huang et al., 2009).

Hence, following Xie et al. (2005), the author has used systematic random sampling

technique and during the survey the author had selected an initial point randomly (e.g.

household) in the study area and then has visited each tenth (10th) household

systematically for data collection. It is to be noted that when the respondent selected

was found to be landless especially if no land even for household, then the author has

taken next household as the sample for convenience. Here, head of each sample

household (i.e. those living in the study area for at least five years) is treated as the

sampling unit to conduct the research work.

4.5.2 Sample Size

As the total population (e.g. households) is not available in hand, the author

has used systematic random sampling technique to collect data from a total of 80

households e.g. each 40 households engaged in rice and shrimp farming respectively

in the study area. Here each group (i.e. both rice and shrimp farming households) is

engaged in respective occupation at least for five years while sample shrimp farmers

have changed from rice farming to shrimp farming at least five years ago.

4.5.3 Data Collection Method

After the selection of sample size and sampling technique, a semi-structured

questionnaire (Appendix I) is being used during the interview session for data

collection from target groups. Moreover, face to face interview (i.e. FGD) technique

has been used for data collection from the local authorities and old persons of the

study area. It is to be pointed here that author has used open ended as well as

unstructured questions to have the FGD.

4.6 Type of Data Used

To achieve the objective, this paper has been prepared based on cross-

sectional data primarily collected through a single survey from each respondent of

selected area. However, here some of the necessary but previous data have also been

collected from the households, local authorities and organizations for the completion

of the research. Though primary data constitutes the heart of the study, some sorts of


33

secondary data (e.g. time series data) are also being collected from necessary sources

for more accuracy and validity of data and complete presentation of the research.

4.7 Variables and Indicators

Being an agent based approach to identify the existing land use pattern and

their changing trends; author has used profit maximization theory and logistic

regression in this study. Moreover, to reduce complexity and to ease interpretation,

the author has chosen rice and shrimp farming land as two of the major land use

pattern for subsequent econometric analysis as the dependent variables. Moreover,

rice and shrimp farming land are being denoted by zero (0) and one (1) respectively

where zero (0) means no change in land use (i.e. land is yet being used as rice

farming) while one (1) means land use pattern has already shifted from rice farming to

shrimp farming. On the other hand to trace out the extents of the determinants of land

use patterns, influential socio-economic, cultural and bio-physical factors and

decision variables (Table 4.1) are being treated as control variables.

Table 4.1 Description of Independent Variable

Description of Variable Unit

Age Age of the decision maker of sample household Year

Year of Schooling Total year passed by decision make in study purposes

with no study gap

Year

Land Engagement How has the decision maker got involved in current

land use pattern

Dummy

Family Type Nature of family based on family size and composition Dummy

Economic

Member

Total number of family member who are economically

active through legal job holdings

Number

Land Ownership Ownership of the concerned land of the household Dummy

Land Rent Total rent paid by household per year for sample land BDT

Neighborhood

Land Use

Land use patterns practiced by the nearby land owners Dummy

Proximity to

Service Centre

Distance of concerned service point from the sample

household/land

kilometer

Accessibility Accessibility of the land from and/or with basic

infrastructure and services

Dummy

Availability of

Credit

Availability of credit facility for each of the concerned

land use pattern

Dummy

Natural Pressure Occurrence of natural disasters and/or pressure on

sample land use

Dummy

Source: Author’s Compilation, 2014


34

4.8 Model Specification

This sub-section of methodology describes the best fitted econometric model

of land use pattern as well as the corresponding process how parameters are to be

estimated using the empirical data in following ways.

4.8.1 Logistic Regression for Land Use Change

Before land use modeling it is to be noted that discrete choice models are

based on random utility theory which assumes that decision makers use their land in

the form of optimal (i.e. land use pattern that gives highest return) alternative(s) and

the decision-makers have perfect discriminating capability. Moreover, the author has

used logistic regression because of binary or categorical nature of dependent variable

and lack of normality in the distribution of error term while independent variables are

mixture of continuous and categorical variables.

We have already discussed (Chapter Two) that logistic regression technique

yields coefficient for each independent variable based on a sample of data and also

identify the role and intensity of explanatory variables ��in the prediction of the

probability of one state of the dependent variable (i.e. defined as a categorical

variable�). Broadly, suppose � is a vector of explanatory variables and p is the

response probability to be modeled with, in the case of a dichotomous dependent

variable,� = ��(� = 1|�), with � = 0 meaning rice farming land and � = 1

meaning the presence of shrimp i.e. more critically land is converted from rice to

shrimp farming. Therefore, the general linear logistic model may be as follows.

��(�) = log[�

(��)]= � + �� + �� + ⋯ + ��(1);

Here� is the intercept and �� are slope parameters. The probability values can

thus be quantitatively expressed in terms of explanatory variables by

� =exp(� + �� + �� + ⋯ + ��)

1 + exp(� + �� + �� + ⋯ + ��)(2)

However, odds ratios are used to facilitate model interpretation as it is a

measure of association which approximates how much more likely (or unlikely) it is

for the outcome to be present for a set of values of independent variables (Serneels

and Lambin, 2001). The probability, the odds and the logit are three different ways of


35

expressing the same thing (Menard, 1995) which are computed as exponential of the

parameter estimates (Serneels and Lambin, 2001) and be expressed as follows.

��(�) = exp(� + �� + �� + ⋯ + ��)(3)

In this study, logistic regression technique is being performed using the

logistic function in the STATA software while maximum likelihood estimates (MLE)

are being used here for model estimation. Positive values of the parameter estimate

indicate that larger values of the explanatory variable will increase the likelihood of

the occurrence of the event while negative values indicate that larger values of the

explanatory variable will decrease the likelihood of the occurrence of the event. The

χ2 statistic indicates the relative weight of each explanatory variable in the model and

allows us to assess the role of each variable in the prediction of an event. In the case

of logistic models, the goodness-of-fit measure is defined as the ratio of maximized

log likelihood while pseudo-R2 or ρ2 is defined as follows.

�� = 1 −��(�)

��(�)(4);

Although ρ2 ranges in the value from 0 to 1, its value tends to be considerably

lower than the value of the coefficient of determination R2 of conventional regression

analysis. It should not be judged by the standards of what is normally considered a

“good fit” in conventional regression analysis (Serneels and Lambin, 2001).

4.8.2 Empirical Analysis of Land Use Determinants

Keeping pace with above description, author has tried to formalize an

econometric model with predetermined determinants to generate their impact on land

use pattern (i.e. rice and shrimp) and their changes over time as follows.

�� = �� + �� + ��ℎ�� + �� + �� + ��

+ �� + �� + �� + �� + ��

+ ��+ �� + �� + ��

+ ��+ ��(5)

Here, � �� denotes the dependent variable; �� is a constant term while

��, ��, … , �� are the coefficients to be estimated and��is the error term. The details

are being enumerated in the next table.


36

Table 4.2 Explanation of Variables in Empirical Analysis

Indicator Variable Name Parameter Likely Sign

�� Major Land Use Pattern (1=Shrimp

Farming, 0=Rice Farming)

N/A N/A

�� Age in year �� -

��ℎ�� Year of schooling �� +

�� 1 Engagement on current land use

(1=Inheritance, 0=Otherwise)

�� -

�� 2 Engagement on current land use

(1=Personal Interest, 0=Otherwise)

�� +

�� Family type (1=Nuclear, 0=Joint) �� +

�� Number of economically active family

member in sample household

�� +

�� 1 Land ownership pattern (1=Sole

proprietorship, 0=Otherwise)

�� +

�� 2 Land ownership pattern (1=Borrowing,

0=Otherwise)

�� -

�� Land rent per year in BDT �� +

�� Neighborhood land use pattern (1=Similar,

0=Otherwise)

�� +

�� Proximity to respective service centre in

kilometer

�� +

�� 1 Accessibility (1=High, 0=Otherwise) �� +

�� 2 Accessibility (1= Very High, 0=Otherwise) �� +

�� Availability of credit (1=Yes, 0=No) �� +

�� Occurrence of natural pressure (1=Yes,

0=No)

�� -

Source: Author’s Compilation, 2014

Here is to be noted that in case of major land use patter rice farming land is the

reference category while tradition and belief, nuclear family, joint land ownership,

dissimilar neighborhood land use, moderately accessible, no credit availability, no

natural pressure are treated as reference category in case of engagement on current

land use pattern, family type, land ownership pattern, neighborhood land use pattern,

accessibility, availability of credit and natural pressure on current land respectively.


37

4.9 Data Collection

This study has adopted data from both secondary as well as primary sources.

Here data form secondary sources (i.e. land use change in the world as well as

Bangladesh, its scenario over the past years, policies on land use, pattern of

urbanization, incentives for land use change and major macro impacts of land use)

have been collected especially for conceptualization as well as to strengthen the

discussion of the thesis. On the other hand, primary data through direct contract with

the respondents have been collected to analyze and compare the findings of the

research with the existing body of knowledge. However, three types of data were

being used in this study which is national level data, local level data and household

level data as described below on the basis of sources.

4.9.1 Primary Data Collection

A household survey was conducted to get data about land use patterns, needs

and demand for land at micro level. In general, three methods have been used in

collecting data from the sample population of study areas. Firstly, focus group

discussions (FGD) were being conducted during the field study period for overall

conceptualization on proposed field from the survey. Secondly, questionnaire survey

was being conducted through a pre-tested but semi-structured questionnaire in the

study area to assess the land use patterns and the role of different determinants. And

thirdly, data has also been collected through monitoring of the farms and households

about overall present land use information. Moreover, data have also been collected in

from the authority i.e. chairman, member (local representative); govt. officials such as

agricultural and fishery officers; organizations both govt. and NGOs.

4.9.2 Secondary Information

Secondary information and data were collected from Space Research and

Remote Sensing Organization (SPARRSO), Forest Department (FD), Department of

Agriculture Extension (DAE), Department of Fisheries (DoF), Department of

Livestock Services (DLS), Bangladesh Water Development Board (BWDB),

Bangladesh Agricultural Research Council (BARC), Soil Resources Development

Institute (SRDI), International Union for Conservation of Nature (IUCN), Bangladesh

Meteorological Department (BMD). Among the NGOs, information was collected

from Bangladesh Resource Center on Indigenous Knowledge (BARCIK), Coastal


38

Environment Conservation Center (CECC), Shushilon, Uttaran and various other

wings of GoB. Moreover, various published and unpublished documents are also

being reviewed for necessary data on the proposed field in recent years.

4.10 Data Processing and Analysis

After collection, data have been categorized and arranged according to their

nature and type using Microsoft Excel, SPSS and STATA software for further

analysis. Then, STATA as well as SPSS program and some manual procedures have

been used to analyze the data already in hand to achieve the objective of research.

However, data have been analyzed using statistical tools like correlation,

regression and dispersion analysis to present the results both in descriptive as well as

in quantitative ways. Moreover, analyzed results are being interpreted using some of

the common but well established economic theories associated with the proposed

variables in terms of relationship.

4.11 Writing the Research Paper

After the sorting of raw data and completion of necessary analysis, results are

being illustrated with the help of graph, tables, figures, charts and mostly through

descriptive statistics. Research paper and associated analysis have been revised

several times before the final submission to concerned authority.

A combined method of land use analysis is being used to complete the

proposed research work while relevant data for describing land use patterns as well as

corresponding changes are being collected directly through field survey using a

combined method of questionnaire and interview including both structured and open-

ended questions. The methodology adopted for the present study also makes extensive

use of secondary material to build up and support the objectives as well as findings of

the study.


39

Chapter Five

Land Use Patterns and Changing Trends

Land use patterns and their changes over space and time being our main

concern, this chapter describes global as well as national and local land use patterns

and their changing trends based on secondary data. Here is to be noted that we have

already summarized the major determinants of land use patterns and equivalent

changes based on secondary survey (Chapter Three).

5.1 Global Land Use Patterns

Two important drifts are evident over last century- firstly, total lands devoted

to human uses (e.g. settlement, agriculture) has increased radically; and secondly,

increased production of goods and services has intensified both use and control of

lands (Dale et al., 2000). Since early periods of civilization, about 30% lands were

being used for cropping and rest 70% as permanent pastures which together comprise

approximately 32% of earth (Houghton, 1994). But, historical changes in global land

use patterns have increased total agro land whereas approximately one-third of the

global land surface is devoted to croplands or pastures (FAO, 2001). Since humans

have controlled fire and domesticated plants and animals, they have cleared forests to

wring higher value (Lambin et al., 2003). Recent estimation also shows that

undisturbed areas characterize 46% of earth’s total surface (Mittermeier et al., 2003)

while recent forests covers only 30% which was 50% before 8000 years (Ball, 2001).

Agriculture has expanded into forests, savannas, and steppes in all parts of the

world to meet the demand for food and fiber keeping pace with development of

civilizations, economies and increasing populations (FAO, 2001). Global cropland has

enlarged from 300–400 mha since 1700 to 1500–1800 mha in 1990 (Ramankutty and

Foley, 1999) while area under pasture increased from around 500 mha since 1700 to

about 3100 mha in 1990 (Goldewijk and Ramankutty, 2003). These increases led to

decreases of forests from 6200 mha since 1700 to 4300 mha in 1990 (Ramankutty and

Foley, 1999). Steppes, savannas and grasslands also experienced a rapid decline from

around 3200 mha in 1700 to 1800 mha in 1990 (Lambin et al., 2003).

Moreover estimation also shows that 1-2 mha of cropland are being taken out

of agro production per year in developing countries to meet land demand for housing,

industry, infrastructure, and recreation (Lambin et al., 2003). Europe, Indo-Gangetic


40

Plain and China experienced the most rapid cropland expansion during the eighteenth

century while newly developed regions of North America and former Soviet Union in

early nineteenth century (Goldewijk and Ramankutty, 2003). A very gradual cropland

expansion occurred in Africa, south and South-east Asia, Latin America and Australia

until 1850s, but since then these regions have observed dramatic increases mainly at

second half of 20th century (FAO, 2001; Ramankutty et al., 2002).

On the basis of above description it may be concluded that land uses are

changing since civilization especially to cope with basic needs as well as for more

expected returns. Moreover, growing urbanization as well as globalization is causing

more rapid changes in land use patterns than the era of industrial revolution (Lambin

et al., 2003). Moreover, unplanned development in developing nations have

intensified the situation more (Hails, 2002) while migration in search of better

livelihood have caused much unplanned global development.

5.2 Land Use Trends of Bangladesh

Bangladesh, one of the poorest states with low resource base (ADB, 2000),

falls under those regions having frequent changes in land uses in last decades (FAO,

2001; Lambin et al., 2003). Moreover, national income being very low (FAO, 2001),

its residents are observed to alter land uses frequently (Quasem, 2011). Estimation

shows that only 10% people hold more than 40% of total lands while 60% of total

population is landless (ADB 2000; Kiron, 2011), as a result, most lands are cultivated

by leaseholders (Quasem, 2011; BBS, 2013). However, though initially most of the

lands in Bangladesh were being used for agricultural purposes (forestry, cropping),

changes have occurred in land uses as well as production techniques (Mohammad,

2009). During the last decades of 20th century, majority areas of the south-western

parts of Bangladesh have been observed to cultivate traditional shrimp culture which

took the first but influential changes in land use patterns (Ahmed, 2011). However,

salt intrusion and tidal surges were being then observed as the main obstacles in agro

farming in south-west as well as coastal areas (Mia and Islam, 2005) which in turn

causes heavy losses to cultivators and changes the behaviors in making the land use

changes in those areas. Moreover, crop failures due to saltwater intrusion or lack of

timely flooding in most areas (Ahmed 2011; Nishat, 1988) have caused major

changes in land uses after population and migration (FAO, 2001; Ahmed, 2011).


41

Moreover, green revolution of 1960s influenced the then land owners to have

a more intensive use of land for agriculture especially rice cultivation and as a result

govt. emphasized the need to protect coastal areas through construction and repairs of

embankments (Ahmed, 2011). Thus beside dominance of traditional agro sector,

modern varieties and technologies were introduced along with salt production,

mangrove forestry and traditional shrimp farming chiefly in south-west part (Rahman

and Begum, 2011). In this aspect Ahmed (2011) pointed out that during the 1970s and

80s, continued polderization of coastal areas became part of the natural coastal setting

and govt. established internal water management authority to enhance further agro

production. Thus, there occurred major changes in land use largely due to introducing

modern varieties and conversion of agro land to non-agro uses with the project of

coastal afforestation to protect the coast from cyclones and erosion (FAO, 2001).

Studies also show that attempts to boost rice production through large-scale

polderization in 1970s resulted in artificial embankment which in later due to poor

management were observed to hamper drainage system causing the low-lying marshy

land water logged with salinity intrusion (Ahmed, 2011; Rahman and Begum, 2011).

The acute salinity and drainage problem caused historical tradition of shrimp farming

causing a gradual transfer of crop lands and mangrove forests into shrimp farming and

fallow lands (Quasem, 2011). Moreover, agro lands declined by about 0.26% yearly

during 1976-2011 while increased during 2000-11 by 0.14% yearly (Rahman, 2010;

Ahmed, 2011). However, following table shows the land use trends since 1977-2008.

Table 5.1 Land Use Trends in Bangladesh during 1977-2008

Area in sq km Lands in 1977 Lands in 2008 Change (1977-2008) Remarks

Water Bodies 9818.11 17618.60 7800.49 Increased

Bare Land 6163.69 6831.99 668.30 Increased

Agriculture 103664.12 102119.63 -1544.49 Decreased

Closed Forest 8357.45 2961.50 -5395.95 Decreased

Open Forest 4790.39 6163.77 1373.38 Increased

Shrub land 2177.63 3760.25 1582.62 Increased

Mangrove Forest 4122.23 4117.53 -4.70 Decreased

Grass Land 5595.14 1115.49 -4479.65 Decreased

Source: Uddin and Gurung, 2010; Rahman, 2010


42

Total amount of water bodies, bare land, shrub land, open forest have

increased over time while agro lands, close forest, mangroves and grass lands are

decreasing in Bangladesh (Table 5.1). Moreover, Mia and Islam (2005) have pointed

out that there exist seasonal variations in land uses because though water bodies

during wet or rainy season are being cultivated, during dry season they remain fallow.

Thus, performance of agro sectors is continuously declining (Mohammad, 2009).

This paper by this time describes the per capita lands available over time in

Bangladesh through following table.

Table 5.2 Scenario of per Capita Arable and Irrigated Land

Area in ha Arable Land Irrigation Land

Per Capita Change (%) Per Capita Change (%)

1961 0.168 0.0 0.008 0.0

1970 0.136 -19.0 0.016 100.0

1980 0.104 -38.1 0.018 125.0

1990 0.079 -53.0 0.021 162.5

2000 0.059 -64.9 0.019 137.5

2010 0.045 -73.2 0.016 100.0

Source: Islam, 2000; IRC, 1996

Per capita cultivable lands are decreasing rapidly over time while irrigated

lands increased from 1961-1990 but decreased from 1990 and towards (Figure 5.2).

At this stage author has depicted changing trends of lands (Table 5.3).

Table 5.3 Total Land Area of Bangladesh during 1976-2010

Area in ‘000’ ha 1976 2000 2010

Area % of total Area % of total Area % of total

Agro Land 13303 91.83 12422 87.69 12176 83.53

Non-agro Land 1183 8.17 1788 12.31 2400 16.47

Total Land 14487 100.00 14530 100.00 14577 100.00

Source: Hasan et al., 2013


43

Bangladesh has gained a total area of 905 sq km (i.e. 90,512ha) during 1976-

2010 due to accretion in southern coastal zone (Table 5.3) while lands used for non-

agro lands have increased with the decrease of agro lands. However, here is the

presentation of total sizes of rice and shrimp farming lands during 1976 and 2010.

Table 5.4 Rice and Shrimp Farming Area during 1976-2010

Area in ha

Area (ha) in 1976 Area (ha) in 2000 Area (ha) in 2010

Cropland 9761450 9439541 8751937

Aquaculture 582 143506 175663


Land use data during 1976-2010 presents that agricultural lands have

decreased gradually over time while shrimp lands are observed to have positive

change at much higher rate.

5.3 Trends of Land Availability in Khulna Division

Khulna division, known as the industrial area as well as the Kuwait city of

Bangladesh (Kiron, 2011), plays an important role in agro production especially

through aquaculture along with rice, vegetables and forest commodities (Rahman and

Begum, 2011). However, in this stage, this paper is now concentrating on south-west

part of Bangladesh to show total land use scenario as follows.

Table 5.5 Land Use Statistics of Khulna Division in 2008

All

Holdings

Non-

farm

Holdings

Number

of Farm

Holdings

Number of Holdings Agro

Labor

Households

Owner Tenant

Owner

Tenant

Bagerhat 339217 106600 232617 235792 72173 31252 144577

Khulna 502835 295092 207743 319009 86292 97534 144350

Satkhira 436178 184142 252036 302240 103903 30035 227847

Cuadanga 254916 81218 173698 146363 91437 17116 102661

Jessore 591030 216407 374623 375890 158654 56484 240843

Jhenaidah 385860 129266 256594 243045 122147 20668 152857

Kustia 432249 187033 245216 265720 125990 40539 152738

Magura 189589 49390 140199 111405 69876 8308 63254

Meherpur 152544 39872 112672 85685 59340 7519 69138

Narail 151052 41520 109532 92121 51211 7720 47722

Source: BBS, 2010


44

Jessore has the highest total holdings as well as farm holdings (Table 5.5)

while Narail has the lowest in each case; on the other hand Khulna has the highest

non-farm holdings and Meherpur has the lowest. Jessore has the highest agro labor

household followed by Satkhira while owners as well as tenant owner holdings are

also highest in Jessore area while tenant holdings are higher in Khulna. The above

data of our concerned study area (i.e. Satkhira) shows that it has about 436178 total

holdings including 184142 non-farm and 252036 farm holding; 302240 owner,

103903 tenant owner, 30035 tenant holdings and 227847 ago labor holdings.

Moreover, land use statistics of Khulna division shows that urban holdings are far

lower than that of rural areas as shown below (Figure 5.1).

Figure 5.1 Land Use Statistics of Khulna Division in 2008

Source: BBS, 2010

Size of farm holdings are double than non-farm holdings while majority of the

holdings fall under owner holding followed by tenant and owner tenant holdings

(Figure 5.1). Data of agro labor holdings in urban area is very negligible in

comparison to that of rural areas which is also applicable for total holdings of both

cases. However, Khulna division has a diversified use of its land for various purposes

(Mia and Islam, 2005) as described with the help of next table.

3129699

1070444

2059255

1993139

912804

223756

1325119

305771

260096

45675

184133

28219

93419

20868

0 1000000 2000000 3000000

All Holdings

Non-farm Holdings

Number of Farm Holdings

Number of Owner Holdings

Number of Tenant Owner Holdings

Number of Tenant Holdings

Agro Labor Holdings

Urban Rural


45

Table 5.6 Land Use Pattern in Khulna Division during 1976-2010

Land Cultivated (ha) Yearly Change (ha)

1976 2000 2010 1976-

2000

2000-

2010

1976-

2010

Cropland 1330485 1322039 1234229 -352 -8781 -2831

Mangrove 409646 415047 400021 255 -1503 -283

River 209591 196629 204138 -540 751 -160

Rural Settlements 139404 151819 145276 517 -654 173

Urban & Industrial 1727 2779 5264 44 249 104


Above data shows that major areas are covered by cropland with declining

trend over time while yearly average loss of cropland was estimated as 0.03% during

1976-2000, 0.66% during 2000-2010 and 0.21% during 1976-2010. Tabulated data

also reveals that natural mangrove forest of Sunderbans covered 409646 ha in 1976

which was slightly increased to 415047 ha in 2000 due to natural regeneration but

ever-increasing human interferences and natural disasters decreased the forest to

400021 ha in 2010. Yearly average river area decreased by 0.26% during 1976-2000

but it increased by 0.38% during 2000-2010. On the other side, availability of rural

settlement increased during 1976-2000 at the rate of yearly by 0.37% but decreased

again annually by 0.43% during 2000-2010. Urban and industrial zone increased more

than three fold in Khulna division during 1976-2010 because yearly land gained in

urban and industrial area was 2.54% during 1976-2000 and 8.94% during 2000-2010.

5.4 Land Use Trend in South-west Part of Bangladesh

Land use patterns are typically conditioned by numerous socio-economic,

physiographic, climatic and biophysical factors (Ahmed, 2011). As a consequence

during last decades, significant changes took place in agro sector in Bangladesh which

include new production structure, use of high yielding varieties supported by better

fertilizers, pesticides, mechanized cultivation, irrigation (BBS, 2008). However in

south-west part of Bangladesh, the major land uses comprise agriculture, shrimp and

fish farming, forestry, urban development and other settlement because of increasing

demand and huge populations in the corresponding areas (Ahmed, 2011; Mia and

Islam, 2005; Quasem, 2011). Literature express the land use in this area as diverse,


46

competitive and often conflicting (Alam et al., 2002; Islam et al., 2006) and is

intensively used for agro and shrimp farming with changes (Mia and Islam, 2005).

Figure 5.2 Percentage Land Uses during 1989-2010

Source: Ahmed, 2011

Above figure shows that built-up are changing positively at higher speed while

vegetable lands are changing but at a slower pace than the former one. Moreover, both

agro lands and water bodies are changing negatively while changes in agro lands are

taking place rapidly than that of water bodies.

5.5 Land Use Policies in Bangladesh

In recent years, coastal planning and land use management have received staid

attention by the Government of Bangladesh as well as by various local and global

non-government organizations (Quasem, 2011). Literature shows that over the last

years govt. has taken various land use and equivalent policies i.e. The National Water

Policy-1999, The National Agricultural Policy-1999 and 2001, National Land Use

Policy-2001, Draft Shrimp Strategy-2004 and Coastal Zone Policy-2005; for

protecting the country especially south-west parts to ensure sustainable resource

management (Mia and Islam, 2005; MoA, 2011; MoWR, 2005; Iftekhar, 2006).

Moreover, recently Bangladesh govt. and its co-partners have emphasized in

creating awareness among mass people on social cost and benefits of each alternative

land use patterns (MoA, 2011; MoWR, 2005) beside formulation and implementation

of various dynamic policy and strategy directives over the last years (Kiron, 2011).

15.5

4.8

15.7

38.5

19.5

4.5

19.5

36

30.5

3.5

20

29

0

5

10

15

20

25

30

35

40

45

Built up Area Water Bodies Vegetation Agriculture

Per

cen

tag

eof

Tot

al

Lan

d

1989 1999 2009


47

Therefore, land use remains a key issue and would generate man-made disaster in

Bangladesh within the near future if not handled with necessary cautions as soon as

possible (Mia and Islam, 2005; Iftekhar, 2006).

Agriculture being the major source of foods; asks for intensive care since the

expansion of industrial revolution especially in developing nations (Kiron, 2011; Dai,

2002). Moreover, south-west regions of Bangladesh which cover an area of about

thirty percent of net cultivable land; play an extraordinary importance on ensuring

food security, sustainable growth of Bangladesh as well as whole world in coming

future (FAO, 1999; Mia and Islam, 2005; Quasem, 2011; Rahman et al., 2013).

Hence, government of Bangladesh must lay down strict policy guidelines for various

alternative cultivation systems especially shrimp cultivation as soon as possible to

tackle the problem of acute salinity, loss of biodiversity, loss of cultivable lands and

natural disasters (Ahmed, 2011; Mia and Islam, 2005).

Lastly but most importantly along with policy for sustainability of agriculture,

Bangladesh govt. should emphasizes on the projects and policies that will ensure help

and facilitates to landless, small and marginal farmers especially hard core poor and

vulnerable groups through agricultural input support and micro capital grant in

farming practices and non-farm income generating activities (Rahman et al., 2013).

Moreover, Bangladesh in this regards needs to be developed technically to ensure a

continuous monitoring system to understand land use changes and identifies the areas

with various obstacles that are to be solved as soon as possible i.e. salinity, conflict,

natural as well as human induced hazards. In this regard Bangladesh should enact

programs to aware people along with necessary policies to control land use patterns in

a sustainable manner.


48

Chapter Six

Overview of Study Area and Respondent

The so long discussion of the research shows either the blueprint of the paper

or the previous findings of some similar researches but from here starts the main

empirical study of the thesis. This chapter describes the basic information in details

about the study area and the sample population with their various bio-physical, socio-

economic and cultural features as follows.

6.1 Overview of Study Area

Bangladesh (Map 6.1) has a total area of 147,570 sq km sited in the Indo-

Gangetic plain of South Asia

between 20°34′ and 26°38′ North

as well as 88°01′ and 92°41′ East,

bordered by India to the West,

North and North-east, Myanmar to

the south-east and Bay of Bengal

just to the South (BBS, 2013). With

a sub-tropical monsoon climate, it

experiences three seasons a year: a

hot or summer from March to June;

a warm and humid monsoon from

June to September and a cool dry

from October to February while

annual rainfall varies between

1500-5000 mm (Mohammad,

2009). Bangladesh has seven

divisions, 68 districts, 609 thanas,

485 upazilas, 4501 unions, 87319 villages (Kiron, 2011).

Khulna Division (i.e. total red colored area in Map 6.1) is in the south-west of

the country having total population of 15,563,000 as per Census-2011 (BBS, 2013)

with in an area of 22,285 sq km [i]. She contains ten districts subdivided into 59 sub-

districts and is bordered by the West Bengal of India to the west, Rajshahi Division to

the north, Dhaka and Barisal Divisions to the east and has a coastline with the Bay of

http://en.wikipedia.org/wiki/West_Bengal

http://en.wikipedia.org/wiki/Rajshahi_Division

http://en.wikipedia.org/wiki/Dhaka_division

http://en.wikipedia.org/wiki/Barisal_Division


49

Bengal to the south [ii]. It is part of the Ganges River delta or Greater Bengal Delta

including the Madhumati River, the Bhairob River and the Kopotokkho River with

several islands in the Bay of Bengal (Mohammad, 2009). However, the next table

gives an overview of Khulna division at a glance.

Table 6.1 Khulna Division at a Glance

Density (sq km) District Upazila Union Village Pourashava Literacy

700 10 64 61 9284 28 41%

Source: Kiron, 2011

Satkhira is a district Khulna division located at the South-western part of

Bangladesh and is bordered to the north by Jessore district, on the south by the Bay of

Bengal, to the east by Khulna district and to the west by Pargana district of West

Bengal [iv]. However, Satkhira subdivision is now consist of seven upazila, two

pourasavas and seventy eight unions [iii].

Kaligonj Upazila, located in between 22°19´ and 22°33´ north latitudes and in

between 88°58´ and 89°10´ east longitudes, has an area of 333.79 sq km [iv]. It is

bounded by Debhata and Assasuni Upazila on the north, Shyamnagar Upazila on

south, Assasuni Upazila at east and West-Bengal state of India on the west. The

Upazila has a total population of 256384 including 130929 male and 125455 female

(BBS, 2013). Here are the flows Jamuna, Kakshiali, Kalindi, Gutiakhali; Bilgali,

Banshtala, Hariavanga and Bagarkhali river which play an influential role in the land

use pattern of this area [v]. Present Kaliganj Thana has 12 unions, 243 mouzas and

253 villages [iv] with a population density of 768 people per sq km while the literacy

rate is 50% [v].

About 52.48% of the total population in Kaligonj Upazila possess own land

while about 47.52% people are landless (BBS, 2011). About 43.45% of urban

population and 53.03% of rural population possess own and cultivate agro lands

primarily for paddy and vegetables [v]. There is about 107 km pucca road, 21.43 km

semi-pucca road and 698.40 km mud road in Kaligonj Upazila while all the unions

are under rural electrification network and 8.82% of the dwelling households have

access to electricity (BBS, 2011). The next table gives an overview of the information

about area, demography and educational affairs of Kaligonj upazila.

http://en.wikipedia.org/wiki/Bay_of_Bengal

http://en.wikipedia.org/wiki/Bay_of_Bengal


50

Table 6.2 General Information of Kaligonj Upazila

Name of Union and

GO Code

Area (Acre) Population Literacy

Rate (%) Male Female

Kushlia (55) 5552 10923 9921 50.41

Krishnanagar (47) 6405 11912 12621 43.75

Champaphul (23) 7475 7853 7313 49.03

Tarali (94) 9138 10365 9602 45.01

Dakshin Sreepur (31) 4601 8323 8115 46.25

Dhalbaria (39) 8432 9798 9331 50.30

Nalta (79) 11431 16750 15676 42.40

Brisnupur (15) 4336 10067 9615 49.00

Mathureshpur (63) 8301 13648 13375 48.20

Mautala (71) 3164 8767 8721 52.20

Ratanpur (87) 6885 10699 10113 42.72

Bhara Simla (07) 22878 11824 11052 46.80

Source: BBS, 2011a

Dhalbaria Union, established in 1973 under local govt. act, is under Kaligonj

Upazilla having a total area of about 3412 ha with about 20000 populations in her 15

Villages [v]. The union is respectively 8 and 42 kilometers away from upazila and

district. It is an agro based economy with a large forest and trans-boundary river in the

western part [v] and consequently, large share of income comes from agro and

forestry sector. However, Dhalbaria Union (i.e. red color circle in Map 6.2) is

bounded in North by Mathurespur Union, in South by Ratanpur Union, in East by

Ratanpur Union, in West by West Bengal of India (SRDI, 2010).

The sample study named Pirozpur (i.e. shown by the colored area in Map 6.2)

is under the ward number 10 of Dhalbaria union and is situated at the south-western

part of Bangladesh just close to Hariavanga River and West-Bengal of India. The

study area is surrounded by Gandhulia in the east, Bajuagor in the north, West-

Bengal in west and Muragasa in south with about 200 ha area (SRDI, 2010; BBS,

2011). Though the sample study area is considered as core zone of agricultural uses

including forest, water bodies and cultivable land; alternative land use patterns (i.e.

shrimp farming; settlements) are taking the place of agriculture rapidly [vi].


51

Map 6.2 Map of Kaligonj Upazila

Source: [iv]


52

6.2 Information of the Respondents

This subsection gives an overview of the sample population on the basis of

which further analysis is to be done in empirical basis.

6.2.1 Age and Gender of the Sample Population

Based on the age information from sample households, the decision makers of

the study area are being classified into three categories (i.e. young aged (Age<35),

middle aged (36<Age<50) and old aged (Age>51). The frequency distribution of age

of the sample population is being enumerated below.

Table 6.3 Age and Gender Distribution

Rice Farming Shrimp Farming

Total Male Percent Female Percent Male Percent Female Percent

Young 01 1.25 01 1.25 05 6.25 02 2.5 09

Middle 11 13.75 05 6.25 14 17.5 0 0 30

Old 22 27.5 0 0 15 18.75 04 5 41

Total 34 42.5 06 7.5 34 42.5 06 7.5

Source: Author’s Compilation Based on Field Survey, 2014

Majority of the sample respondents (i.e. about 51%) are old aged (Table 6.3)

followed by middle (38%) and young aged (11%). Here minimum and maximum age

is respectively 25 and 83 years while mean age of sample population is 50.74 years.

Table also shows that rice farming decision makers are more aged than that of shrimp.

Data also shows that about 15% of total sample households are being ran by

female decision maker while 85% by male. It is to be noted that most of the female

member(s) constitute the position of decision making because male member(s) in

such family is (are) either absent due to job purpose or has already died. In many

houses, though female is the decision maker, yet she doesn’t generate any income.

6.2.2 Educational Status

Education being considered as the most influential pioneer of changes in

world civilization, educational status of the sample households are being collected

primarily on the basis both year of schooling and literacy level categorized as

i)Illiterate and ii)Literate. However, frequency distributions of educational status of

sample population are given in next page (Table 6.4).


53

Table 6.4 Educational Status of the Decision maker


Total Literate Illiterate Literate Illiterate

Male 26 08 28 06 68

Female 05 01 05 01 12

Total 31 09 33 07


Number of female literate as well as illiterate decision makers are same in

both land use patterns (Table 6.4) while male shrimp farmers are more literate as well

as less illiterate in number than that of rice farmers in the sample population. The

tabulated data also shows that about 20% of decision makers are illiterate while 80%

are literate in the sample population. This paper with next table describes the

frequency distribution of literate decision makers as shown in next page.

Table 6.5 Literacy Status of Sample Population

Informal Learning Primary Intermediate College

Total Rice Shrimp Rice Shrimp Rice Shrimp Rice Shrimp

Male 02 02 08 07 0 01 16 18 54

Female 01 0 0 02 01 0 03 03 10

Total 03 02 08 09 01 01 19 21

05 17 02 40 64


Half of the sample has college education followed by primary (21%), informal

(06%) and intermediate (2.5%) level respectively (Table 6.5). here data shows that

shrimp farmers are slightly educated than the sample rice farmers.

6.2.3 Family Size and Composition of the Respondents

The size of family in this study has been defined as the number of persons

living together under the control of one head and taking meal from the same kitchen.

The following table on next page represents the frequency distribution of family

composition of sample households of the study area.


54

Table 6.6 Family Type of Sample Population

Decision Maker of the Household in


Male Female Male Female

Nuclear 22 03 17 05

Joint 12 03 17 01

Total 34 06 34 06


Rice farming households are more nuclear in nature (Table 6.6) than that of

shrimp. Moreover, out of 80 households, 85% are run by male decision makers out of

which 49% are nuclear family while female counterpart runs 08 nuclear and 04 joint

households. However, the author has found average family size with 4.96 people

while the highest family size was found with 12 members and the minimum one is 2.

Author has also collected economically active family member of each sample

household to know whether it has any impact on land use decision making or not.

Following table shows the distribution of economically independent family member.

Table 6.7 Distribution of Economically Active Family Member

Family Type Current Land Use Pattern

Nuclear Joint Rice Farming Shrimp Farming

1 Person 27 04 24 07

2 Persons 20 06 09 17

3 Persons 15 07 08

4 Persons 04 04

5 Persons 02 02

6 Persons 01 01

8 Persons 01 01


About 34% nuclear family possesses only one (1) economically active family

member in the sample households while the rate is 25% in case of family containing 2

persons and 19% with 3 members (Table 6.7). Aggregate data shows that nuclear

families possess about 59% economically active persons though ranges only between

1 and 2 persons.


55

Family engaged currently in rice farming possess 50% economically active

member (Table 6.7) including about 30 percent household with 1 person, 11% with 2

persons and the rest consists of 3 persons. But when the family is engaged in shrimp

farming respectively 21%, 10%, 09%, 05%, 03%, 01% and 01% of the sample

households contain 2, 3, 1, 4, 5, 6 and 8 person(s) who are economically active.

6.2.4 Occupational Distribution

Occupation being directly related to land use patterns in rural areas, the author

has tried to present the occupational status of each sample household in the following

table. It is to be remembered that when the household has more than one major

occupation, the most influential occupation is taken into consideration.

Table 6.8 Occupational Distribution of Sample Household

Primary Occupation Secondary Occupation Gender

Frequency Frequency Male Female

No Occupation 0 02 02 0

Rice Farming 11 33 38 06

Shrimp Farming 12 29 35 06

Mixed Use 01 05 05 01

Business 18 02 18 01

Govt. Job 02 0 01 01

Non-govt. Job 05 0 02 03

Service 04 01 04 01

Remittance 18 03 19 03

Others 09 05 12 02


Rice farming is being recognized to be the major occupation followed by

shrimp farming, remittance and business (Table 6.8). Households controlled by

female members are mostly engaged in rice and shrimp farming with a contribution of

remittance by their counterpart. Moreover, in the study area remittance and business

plays important role as occupation with 23% contribution by each in the sample

population followed by shrimp farming with 15% and rice farming through 14%

contribution. There is no contribution of female in govt. as well as non-govt. jobs in

the study area though male personnel are observed to participate there.

6.2.5 Engagement Process

In this sub-point, a

and engaged itself to the current land use pattern

Table 6.9 Engagement Proc

Through Inheritance

Personal Interest

Tradition and Belief


Most of the households are

followed by tradition as well as belief and personal interest respectively while

shrimp farming because of personal interest

inheritance respectively.

headed households have taken shrimp farming rather than rice farming,

headed household has shifted to shrimp farming

contrary, female headed

6.2.6 Land Ownership Pattern

As already prominent

some lands for settlement if marginalized in nature while the well to do households

possess lands for cultivation, pasture and

lands. The next figure

Figure 6.1 Land Ownership Pattern of the Sample Population


0

10

20

30

Fre

qu

ency


56

Process in Present Land Use Pattern

point, author has tried to note how the sample household

and engaged itself to the current land use pattern as presented through below table

Engagement Process in Current Land Use Pattern


Frequency Percent Frequency

21 26 09

03 04 16

16 20 15


ost of the households are observed in agro farming through in

followed by tradition as well as belief and personal interest respectively while

shrimp farming because of personal interest followed by tradition as well as

respectively. Analysis of collected data also shows that

headed households have taken shrimp farming rather than rice farming,

headed household has shifted to shrimp farming by dint of personal interest

contrary, female headed domestic are engaged in the inherited land use pattern

Ownership Pattern of Households

prominent that each and every sample household possesses

lands for settlement if marginalized in nature while the well to do households

possess lands for cultivation, pasture and various purposes along with homestead

figure shows land ownership scenario of sample population

Land Ownership Pattern of the Sample Population


Sole Proprietorship

Joint Borrowing

30

37

20

812

Rice Shrimp


households has come

through below table.

ess in Current Land Use Pattern

Shrimp Farming Total

Percent

11 30

20 19

19 31

observed in agro farming through inheritance

followed by tradition as well as belief and personal interest respectively while that in

as well as belief and

Analysis of collected data also shows that though male

headed households have taken shrimp farming rather than rice farming, no female

by dint of personal interest. On the

land use pattern.

household possesses at least

lands for settlement if marginalized in nature while the well to do households

purposes along with homestead

land ownership scenario of sample population.

Land Ownership Pattern of the Sample Population



57

Most of the lands possessed by the sample households are solely owned which

is about 62% of total sample while joint ownership and borrowing land is only 14%

and 24% respectively. Data also shows that solely owned lands are mostly used for

rice farming while borrowing and joint lands are highly used for shrimp farming.

6.2.7 Scenario of Assets and Non-assets of the Sample Households

Literature survey showed that land use pattern is not only dependent on but

also determines holding of land and non-land assets possessed by each household.

Therefore, this paper now attempts to show holding of assets (in BDT) as follows.

Table 6.10 Information on Land and Non-land Assets

Land Assets Non-Land Assets

Frequency Frequency

40000-150000 06 19

150000-400000 06 25

400000-700000 04 16

700000-1500000 15 13

1500000-3000000 18 04

3000000-5000000 16 02

5000000-7000000 5 01

More than 7000000 10

Mean 29,83,900 6,65,940


The data of assets from the sample population shows that the mean value of

land assets is BDT 29,83,900 while mean of non-land assets is BDT 6,68,940. The

information also shows that value of land assets ranges more than that of non-land

assets while the highest and lowest value of land assets is 1,24,50,000 and BDT

46,000 and that for non-land assets are BDT 7,50,000 and BDT 45,000 respectively.

6.2.8 Household Yearly Income

Opportunity cost and random utility theory suggests that rational households

use their lands either for direct or indirect benefit. Therefore, the author has collected

data on the annual income (in BDT) of both land and non-land assets as in next page.


58

Table 6.11 Distribution of Income from Land and Non-land Assets

Land Assets Non-Land Assets

Frequency Frequency

Less than 30000 03 06

30000-50000 19 26

50000-100000 27 29

100000-150000 13 10

150000-250000 10 09

250000-350000 07

More than 350000 01

Mean 1,26,088 81,263


Land assets shows a minimum income of BDT 25,000 and maximum of

12,00,000 while that of non-land assists are BDT 0 and BDT 2,50,000 respectively.

Majority of the households’ income from land assets as well as non-land assets fall in

between BDT 30,000 and BDT 2,50,000 while the frequency is highest between

income from both assets ranging from BDT 50,000 and BDT 1,00,000.

6.2.9 Household Yearly Expenditure

Next table shows households’ expenditure scenario where irregular costs refer

to cost other than regular expenditure such as medical cost, sudden expenditure.

Table 6.12 Yearly Expenditure of Sample Household

Regular Expenditure Irregular Expenditure

Frequency Frequency

Less than 20000 01

20000-40000 26 26

40000-60000 25 33

60000-80000 20 18

More than 80000 08 03

Total 54,700 31,438


Regular expense shows a minimum value of BDT 20,000 and maximum

1,75,000 with a mean value of BDT 54,700 while those for irregular expenditure is

BDT 10,000 and BDT 80,000 correspondingly with respective mean value of BDT

31,438. Moreover, major sample households spend an amount ranging between BDT

20,000 and BDT 80,000 for regular as well as irregular purposes.

6.2.10 Households’ Farming Experience

Though in this world of globalization,

given more priority than

especially in rural areas

Source: Author’s Compilation Bas

It can be interpreted that sample population has more experience on rice

farming than on shrimp farming in the study area

experience ranging between 5

6.2.11 Training Facilities of Sample Population

Bangladesh govt. has been providing various training facilities for optimal as

well as profitable uses of each parcel of lands across the country especially in rural

areas through its various partner organization.

Figure


Sample data shows that

shrimp farmers and 15%

sample population doesn’t

4

00

4

8

12

16

20

Less than 5

Fre

qu

ency

0

10

20

30

40

Fre

qu

ency

Determinants of Land Use Change in South

59

Farming Experience

Though in this world of globalization, flexibility and desire for change is

given more priority than experience, in agriculture experience plays an important role

especially in rural areas.

Figure 6.2 Farming Experience


can be interpreted that sample population has more experience on rice

farming than on shrimp farming in the study area (Figure 6.2). Shrimp farmers have

experience ranging between 5-20 years while that of rice is more diversified.

Training Facilities of Sample Population



areas through its various partner organization.

Figure 6.3 Training Facilities on Specific Land Use


shows that only 16 out of 80 sample land users including 25%

and 15% rice (i.e. 15%) have got training while

doesn’t have any training.

35 4

1315

17

8

0

Less than 5 05--10 10--15 15-20 20-30

Year of Experience

Rice Shrimp

6

34

10

30

Training No Training

Rice Shrimp


flexibility and desire for change is

experience plays an important role

can be interpreted that sample population has more experience on rice

Shrimp farmers have

diversified.



Training Facilities on Specific Land Use


sample land users including 25%

while about 80% of the

11

0

30 More than 30

6.2.12 Credit Facility

Since there is some time lag between production

land use pattern along with

endemic in land use

availability of credit among

Figure


About 79% of the total

in the study area while credits are more available in case of rice farming than that of

shrimp. Moreover, about 21% sample

and 13 out of 40 shrimp land

6.2.13 Plan to Change Land Use Pattern in Near Future

As literature shows that shrimp farming are more attractable than rice farming

while shrimp farming has negative effects on environment and surrounding

author has identified how much the current land use patter is attractive to each of the

sample population. The author thus addressed about their expectation of changing

land use pattern in coming future

Figure 6.


10

20

30

40

Fre

qu

ency

0

20

40

60

Fre

qu

ency


60

is some time lag between production and getting return from each

along with investment deficiency during land use, credit

in land use decision. Hence, this paper now describes the scenario of

among sample population as follows.

Figure 6.4 Credit Facilities on Specific Land Use


About 79% of the total population has said positively that credits are available


about 21% sample population including 4 out of 40 rice farmers

and 13 out of 40 shrimp land holders has said that credits are not available.

Plan to Change Land Use Pattern in Near Future


while shrimp farming has negative effects on environment and surrounding



in coming future which is enumerated below.

6.5 Expectation of Change in Current Land Use


36

4

27

13

0

10

20

30

40

Credit No Credit

Rice Shrimp

14

26

0

40

Expectation for Change No Expectation for Change

Rice Shrimp


and getting return from each

, credit has become

his paper now describes the scenario of

Credit Facilities on Specific Land Use


population has said positively that credits are available


population including 4 out of 40 rice farmers

said that credits are not available.


while shrimp farming has negative effects on environment and surroundings, the



Expectation of Change in Current Land Use


40

No Expectation for Change

It can be said that only 18% of the total sample

for change in their current land use pattern while all of them (14 respondents) are

engaged now in rice farming.

expected land use patterns by

Figure


Above information shows that o

convert their land for shrimp and mixed farming respectively

water bodies i.e. for carp fish farming.

changes in land use pattern

Figure 6.7

Source: Author’s

Above portrayed data implies that land owners are expecting to change their

lands mostly for economic benefit followed by pressure from external sources, family

demand and for neighborhood land characteristics.

questionnaire survey they added that besides the above they also take cost of farming,

cost of land maintenance, availability of input, demand for final product, land use

regulation and returns from that use as the major determinant of la

Mixed Use

43%

5

0

2

4

6

Economic Benefit

Fre

qu

ency


61

that only 18% of the total sample (Figure 6.5)


engaged now in rice farming. Keeping pace with this, this paper now describes the

cted land use patterns by respondents expecting changes in their land

Figure 6.6 Expected Land Use Pattern in Future


Above information shows that out of 14 respondents, each 43% likes to

convert their land for shrimp and mixed farming respectively while rest 14% into

water bodies i.e. for carp fish farming. Moreover, the underlying reasons of expected

changes in land use patterns have been portrayed through Figure 6.7

7 Determinants of Expected Changes in Land Use




demand and for neighborhood land characteristics. Moreover, during the



returns from that use as the major determinant of land use decision.

Shrimp Farming

643%

Mixed Use6

43%

Water Bodies

214%

23

Economic Benefit Neighborhood Characteristics

Family Demand

Frequency


(Figure 6.5) has expectation


Keeping pace with this, this paper now describes the

respondents expecting changes in their land use pattern.

Expected Land Use Pattern in Future


ut of 14 respondents, each 43% likes to

while rest 14% into

reasons of expected

Figure 6.7 below.

in Land Use

Compilation Based on Field Survey, 2014



eover, during the



nd use decision.

4

Pressure

6.2.14 Pressure and Regulation

Literature shows that

important determinant in this age where govt. intervention is common to ensure the

optimal uses of each resource.

pressure (i.e. hazards like flood,

high competition) and land use regulation (i.e. from the local govt., land owner, large

land holders) for each of the concerned land use pattern

Figure 6.8


Rice farming lands are getting pressure

well as natural phenomenon

higher land use regulation in the study area

Keeping pace with the broad study areas, village

changes in land use patterns especially lands close to the river through shrimp farming

from rice and other lands with non

communication. However, land uses are not only dependent on household but also on

external factors especially on land characteristics and neighborhood land use patterns

along with the major occupation of the area. H

land use change, mass people needs to be careful about unplanned and hazardous use

of available lands and should use each parcel of land for optimal uses as efficiently

and effectively as possible.

0

5

10

15

20

25

30

Human Induced

Fre

qu

ency


62

and Regulation on Current Land Use Pattern

Literature shows that natural as well as human induced pressures are the most


optimal uses of each resource. therefore, the author has collected data on

(i.e. hazards like flood, drought), human activities (i.e. intentional conflict,


land holders) for each of the concerned land use pattern as given in next figure

8 Pressure and Regulation Scenario on Land Use


ice farming lands are getting pressure more from human induced activities as

well as natural phenomenon than shrimp lands while shrimp farming lands are facing

land use regulation in the study area (Figure 6.8).

Keeping pace with the broad study areas, village Pirozpur


from rice and other lands with non-productive uses like settlements, roads and

However, land uses are not only dependent on household but also on


along with the major occupation of the area. However, to tackle the adverse effect of



and effectively as possible.

18

27

161517

Human Induced Pressure

Natural Pressure Land Use Regulation

Rice Shrimp


natural as well as human induced pressures are the most


, the author has collected data on natural

drought), human activities (i.e. intentional conflict,


as given in next figure.

ion Scenario on Land Use


from human induced activities as

while shrimp farming lands are facing

Pirozpur has also observed


ductive uses like settlements, roads and

However, land uses are not only dependent on household but also on


owever, to tackle the adverse effect of



23

Land Use Regulation


63

Chapter Seven

Results and Discussion

This chapter checks which one of the two land use patterns (i.e. rice and

shrimp farming) the rational land owners or farmers will choose at a specific time

through the analysis of collected primary data on land uses from sample population.

Here is presentation of results obtained through the application of profit maximization

theory as well as cost-benefit analysis and logistic regression as follows.

7.1 Lands Cultivated over Time

Land being a non-depreciable asset varies in their uses over time based on the

level of fertility, salinity, ownership, communication facilities and mostly for water

management system. Whatever be the reason of changes in land use patterns, the

author has found following variation in farming area by sample households over time.

Table 7.1 Amount of Land Cultivated over Time

Present (2014) 2010 - 2013 Before 2010

Rice Shrimp Total Rice Shrimp Total Rice Shrimp Total

Less than 3 Bigha 07 0 19 12 02 20 13 15 24

3 - 5 Bigha 16 21 11 14 22 14 15 13 15

5 - 7 Bigha 09 07 19 07 04 19 03 07 18

7 - 10 Bigha 03 05 10 03 05 08 06 02 03

More than 10 Bigha 05 07 21 04 07 19 03 03 20

Average 6.05 5.64 5.55 8.18 7.35 4.63 8.11 7.13 6.54

CV (%) 65.5 73.1 83.8 86.2 69.4 124.0 77.5 78.0 87.3

N.B: �� = ��/�� ∗ 100


At present majority of the land size in rice and shrimp farming ranges between

3 bigha and 5 bigha (Table 7.1). However, the average cultivable land size at present

is 5.55 bigha while that was 4.63 bigha and 6.54 bigha during year (2010-2013) and

before 2010 respectively per household. Moreover, average land size cultivated as

rice farming land increased between (2010-2013) than what it was before 2010 but

shows a fall again in 2014 which is similar to that of shrimp farming also. Rice

farming lands are more acceptable than shrimp for optimal use except year (2010-13).


64

7.2 Variation in Land Use Pattern

Bangladesh being a riverine country with six seasons and high rainfall, there

are frequent changes in land use patterns in a single year as well as territory because

of availability of necessary facilities such as water supply, water disposal and

communication with lands and accessibility through machinery, seeds and fertilizers.

Table 7.2 Variation in Land Use Pattern

Frequency of using land in Frequency of not using land in

Summer Rainy Winter Summer Rainy Winter

Rice 27 (68) 33 (83) 28 (70) 13 (32) 07 (17) 12 (30)

Shrimp 40 (100) 40 (100) 40 (100)

N.B.: Parenthesis contains percentage (%) value of frequency


Shrimp lands are used all the year round while 68%, 83% and 70% of rice

lands are used in rainy season (Aush farming), winter season (Amon) and summer

season (Boro) respectively (Table 7.2) while rest of the rice farming lands remain

fellow in respective seasons of each years. It is to be noted that shrimp farming lands

remain also unused for week or more but less than a month; hence isn’t considered.

7.3 Change in Land Use Pattern

Keeping pace with literature, this sub-section describes the gradual changes in

cultivated land size of each land use under consideration based on land use data of

sample households. Here is to be noted that this sub-section only denotes any change

in cultivable land size not on land use pattern (Figure 7.1) in next page.

Figure 7.1 Land Use Statistics of Sample Households during (2010-2014)


20(50%) 15

(38%)

5(12%)

24(60%)

15(38%)

1(2%)

0

5

10

15

20

25

30

Increased Decreased Remain Constant

Fre

qu

ency

Rice Shrimp


65

In case of rice farming, half of the respondents witnessed an increase in their

land uses during 2010-14 while the average increase amount is 2.88 bigha and 13%

respondents have found no change in their land size but 37% respondents have

observed an average land decrease by 2.5 bigha during the same period. On the

contrary, shrimp lands have increased in case of 60% respondents on an average by

6.33 bigha while only one respondent has showed that his land has decreased and rest

37% have no change in their land size. Abruptly, shrimp lands have increased more in

size than that of rice while rice lands are constant more over time than that of shrimp.

Keeping pace with above data, author now likes to present how current land

use practices have changed the total land size of the sample households (Figure 7.2).

Figure 7.2 Changes in Total Land Size during 2010-2014


Though only 14% of total sample land users (i.e. each 7% of rice as well as

shrimp farmers) have observed a decrease in their total lands while 60% rice farming

households and 35% shrimp farmers have found their total land size to be increased.

In the mean time, about 45% of total respondents including 33% of rice and 58% of

shrimp farming households have found no change in their land use during 2010-14.

Comparing figure 7.1 and 7.2, it can be concluded that though size of shrimp

farms have increased during 2010-14, total land size have increased much for the rice

farming than the shrimp farming households.

7.4 Location of Land

Location plays an influential role in land use decision making because of the

influence of weather, salinity, rainfall and other bio-physical land characteristics.

Therefore, the author has divided geographic location into three categories (Close to

24(60%)

3(7%)

13(33%)

14(35%)

3(7%)

23(58%)

0

5

10

15

20

25

30

Increased Decreased No Change

Fre

qu

ency

Rice Shrimp

saline water sources such as river and canal

deep tube well and no certain water source which implies to ra

information which are given with the frequency of each as follows.


Lands close to saline water sources are used mainly for shrimp farming while

no certain source of water for irrigation as well as sweet water sources influences

land for the use of rice farming

who either use rain water if available or irrigated water for

7.5 Land Elevation

Water bearing capacity

the land use decisions in south

elevation are important in our analysis

opposite to each other

farming asks for medium or highly elevated land

population on land elevation are being enumerated below

Figure


(50%)

01020304050

Close to Sweet Water

Fre

qu

ency

00

5

10

15

20

25

Very low

Fre

qu

ency


66

saline water sources such as river and canal; close to sweet water sources like pond,

deep tube well and no certain water source which implies to rain water) to collect


Figure 7.3 Location of Sample Land


ands close to saline water sources are used mainly for shrimp farming while

source of water for irrigation as well as sweet water sources influences

land for the use of rice farming (Figure 7.3). Here, no certain source include

either use rain water if available or irrigated water for farming.

Land Elevation

Water bearing capacity or duration of water logging plays an important role in

the land use decisions in south-west region of Bangladesh. Moreover,

are important in our analysis because our desired land use patterns are just

opposite to each other (i.e. shrimp farming land needs low elevated land while rice

farming asks for medium or highly elevated land). However, the data from the sample

on on land elevation are being enumerated below.

Figure 7.4 Land Elevation Scenario of Sample Land


20(50%)

0

20(50%)

0

40 (100%)

0

Close to Sweet Water Source

Close to Saline Water Source

No Certain Source

Rice Shrimp

13(16%)

21(26%)

(8%)5

(6%)

15(19%)

16(20%)

Very low Low Moderate

Rice Shrimp


close to sweet water sources like pond,

in water) to collect



ands close to saline water sources are used mainly for shrimp farming while

source of water for irrigation as well as sweet water sources influences the

Here, no certain source includes farmers

.

duration of water logging plays an important role in

west region of Bangladesh. Moreover, data on land

because our desired land use patterns are just

i.e. shrimp farming land needs low elevated land while rice

. However, the data from the sample

of Sample Land


0

No Certain Source

6(8%)

4(5%)

High

Here, land elevation is being classifies into five categories such as

land which holds water the whole year, low land holding water for at least six month,

moderate land with water only in rainy season, high land with water logging for week

or less and very high land with no water logging.

shows that there is no rice farming in very low as well as in very highly elevated

while no shrimp farming in very highly elevated land. Sample data

low and very low lands are used mainly as shrimp farming area while moderate lands

are observed to use both for agro and shrimp based on the neighborhood land use

pattern, water management system and infrastructure facilities. However, about 16%,

26% and 8% of total lands used for rice farming is low, moderate and high while

of shrimp is 19%, 20% and

farming lands are lower than that of rice farming in terms of elevation.

7.6 Fertility of Land

Since fertility is the prerequisite of productivity as well as return from specific

land use, the author has divided total land into five categories (

with no rice farming, low fertility with very little rice farming, moderate fertility

which is suitable for both shrimp and agriculture, high fertility where rice farming is

done for at least two times in year and very high fertility with whole year rice

farming) to trace out the fertility of sample land.

Figure 7.


Sample data shows tha

highly used for shrimp farming followed by little high fertile lands while rice farming

lands are mostly very high, high and moderate in fertility

00

5

10

15

20

25

30

Low fertility

Fre

qu

ency


67

Here, land elevation is being classifies into five categories such as

water the whole year, low land holding water for at least six month,


or less and very high land with no water logging. However, above presentation of data

is no rice farming in very low as well as in very highly elevated

no shrimp farming in very highly elevated land. Sample data


e both for agro and shrimp based on the neighborhood land use


26% and 8% of total lands used for rice farming is low, moderate and high while

of shrimp is 19%, 20% and 5% respectively (Figure 7.4). The data shows that shrimp


Land


author has divided total land into five categories (i.e.



or at least two times in year and very high fertility with whole year rice

t the fertility of sample land.

Figure 7.5 Fertility Scenario of Sample Land


Sample data shows that low fertile as well as moderately fertile lands are


lands are mostly very high, high and moderate in fertility in the sample lands

7

26

12

26

2

Low fertility Moderate fertility High fertility Very high fertility

Rice Shrimp

Use Change in South-west Region of Bangladesh

Here, land elevation is being classifies into five categories such as very low

water the whole year, low land holding water for at least six month,


bove presentation of data

is no rice farming in very low as well as in very highly elevated lands

no shrimp farming in very highly elevated land. Sample data also shows that


e both for agro and shrimp based on the neighborhood land use


26% and 8% of total lands used for rice farming is low, moderate and high while that

The data shows that shrimp



i.e. very low fertility



or at least two times in year and very high fertility with whole year rice


t low fertile as well as moderately fertile lands are


in the sample lands.

7

0

Very high fertility

7.7 Salinity and Sand in Land

Literature shows that south

problem than any other problems, therefore t

scenario of the salinity and sand situation in the sample land as described follows.

Figure


Most rice farming sample lands contain less salinity and sand than that of

shrimp (Figure 7.6) or in other words, shrimp farming lands are more saline and sandy

than the rice farming lands in the study area.

7.8 Neighborhood Land Use Pattern

This paper has

such as rice farming, shrimp farming, fellow land, mixed

homestead along with the identifying of various existing land use patterns.

following are the demonstrations of the neighborhood

Figure


0

5

10

15

20

25

Fre

qu

ency

22(56%)

6(15%)

0

5

10

15

20

25

Fre

qu

ency



68

Salinity and Sand in Land

Literature shows that south-west regions are acutely affected with salinity

problem than any other problems, therefore this sub-point of this paper


Figure 7.6 Distributions of Salinity and Sand in Land



or in other words, shrimp farming lands are more saline and sandy

than the rice farming lands in the study area.

Neighborhood Land Use Pattern

This paper has identified various neighborhood land uses of the study area

such as rice farming, shrimp farming, fellow land, mixed farming

along with the identifying of various existing land use patterns.

following are the demonstrations of the neighborhood characteristics

Figure 7.7 Neighborhood Land Use Patterns


1720

2 1 02

17

8

13

Very low Low Moderate High Very high

Rice Shrimp

8(20%)

(15%)

23(58%)

1(2%) 0

1(2%)

7(18%)4

(10%)(2%)

6(15%)

Rice Shrimp

Shrimp Farming Fellow Land Mixed Use Water bodies


west regions are acutely affected with salinity

point of this paper describes the


Distributions of Salinity and Sand in Land



or in other words, shrimp farming lands are more saline and sandy

s of the study area

farming, water bodies and

along with the identifying of various existing land use patterns. The

characteristics observed.


0

Very high

1(2%)

1(2%)

Water bodies Homestead


69

Figure 7.7 shows that in case of rice farming about 55% neighborhood lands

are being used for the same purpose while the other influential neighborhood land

uses are homestead and shrimp farming representing almost 15% each, 10% water

bodies and lastly 2% of each fellow and mixed farming lands. On the other hand,

shrimp farming lands followed by rice, mixed use, water bodies and homestead

constitute the major neighborhood land use patterns when considering shrimp farming

lands with a share of 58%, 20%, 17%, 2% and 2% respectively.

7.9 Water Management Facilities

Water management system, not only source of water for irrigation but also

disposal source of water, plays an important role in the land use decision making.

Keeping pace with this ideology, the following table shows data of sources used for

irrigation and disposal where the option ‘others’ include uncertain sources.

Table 7.3 Distribution of Water Source

Sources for Rice Farming Sources for Shrimp Farming

Irrigation Disposal Irrigation Disposal

Freq. Percent Freq. Percent Freq. Percent Freq. Percent

River 0 12 30% 40 100% 39 98%

Pond 11 27.5% 20 50% 0 01 2%

Shallow Tube Well 11 27.5% 0 0 0

Rain Water 18 50.0% 0 0 0

Others 0 08 20% 0 0


All the shrimp farms get their irrigated water from river while none of rice

lands use river water for irrigation (Table 7.3). Moreover, about 97% shrimp lands (39

out of 40 farms) use river for water disposal while the rate for rice farming is about

30% only (12 out of 40 rice farming lands). About 18 out of 40 Most of the rice lands

are observed to be dependent on rain water for irrigation followed by pond and

shallow tube well with a share of 27.5% each. Though half of the sample rice farms

are observed to use dispose water in nearby ponds, about 20% rice farming lands are

facing uncertainty in water disposal while one shrimp farming land is observed to

dispose water in ponds as the pond is also used for fish farming.


70

7.10 Distance of Water Management Sources

Not only available irrigation and disposal sources but also their distance plays

important role in land use decision making. Therefore here is an attempt to represent

the data on distance of water sources both of disposal and irrigation as follows.

Table 7.4 Distances of Water Source and Disposal Location

Distance for Rice Farming Distance for Shrimp Farming


No Distance 11 06 01 02

0 km - 1 km 24 22 34 37 1 km - 2 km 04 05 04 01 2 km - 3 km 01 03 0 0 More than 3 km 0 04 01 0

Mean 0.37 1.03 0.57 0.39


Source of irrigation and disposal of rice farming lands has a mean distance of

0.37 km and 1.03 km respective while that in case of shrimp farming is 0.57 km and

0.39 km respectively (Table 7.4). Here major sources of irrigation and disposal lies

between 0 and 2 kilometers both for rice and shrimp farming lands. Rice farms are

much closer to irrigation sources than that of shrimp while disposal sources of shrimp

farming are closer than that of rice farming.

7.11 Way Used for Water Management System

Keeping pace with the above presentation it is now time to represent the

scenario how the cornered land owners or farmers get or dispose water from their land

to the concerned sources. Though most of the shrimp lands get their water from and

dispose also to the rivers basically through natural canal, some of the land owners and

farmers need to prepare artificial one for both disposal and irrigation from the rivers.

Table 7.5 Way used for Water management

Sources for Rice Farming Sources for Shrimp Farming


Canal 05 30 40 40

Machinery 17 0 0 0

Human Labor 06 0 0 0

Uncertain 12 10 0 0



71

It can be said that all shrimp farms use canal both for irrigation and disposal of

water while 30 rice farming lands are observed to dispose water through canal (Table

7.5). The data also shows that rice farming lands uses diversified ways of irrigation as

well as disposal while 12 and 10 rice farming lands have no certain irrigation and

disposal source respectively.

7.12 Cost of Water Management System

Rational land owners and farmers are very much conscious about the cost

associated with each alternative land use pattern and therefore, cost of irrigation may

have a considerable role in land use decision making. However, the following table

shows the water management cost scenario of each of the sample land holdings.

Table 7.6 Cost of Irrigation and Water Disposal

Cost for Rice Farming Cost for Shrimp Farming


No Cost 11 25 04 12

0 – 1000 BDT 10 15 0 12

1000 – 3000 BDT 08 0 14 15

3000 – 5000 BDT 06 0 11 01

More than 5000 BDT 05 0 11 0

Mean 1971.25 83.75 5275.00 1006.25


Rice farming lands incur lower cost both in case of irrigation and water

disposal than sample shrimp farming lands (Table 7.6). It is also to be noted that

disposal charges are much higher in shrimp lands than the rice farming lands.

7.13 Proximity to Nearest Infrastructure

As already discussed land use decision not only depends on household

demand and intention but also on external factors such as proximity to nearest and

necessary infrastructure both in terms of cost and distance. Therefore, the following

table shows proximity state of sample lands to nearest and necessary infrastructure.

Here the data of proximity to agro/fishery office also shows how far the

land/households are from the nearest town or centre area.


72

Table 7.7 Proximity to Nearest Infrastructures

Input

Market

Output

Market

Nearest

Road

Agro/Fishery

Office

Rice Shrimp Rice Shrimp Rice Shrimp Rice Shrimp

No Distance 01 05 04

0 – 1 km 06 07 35 36

1 – 2 km 14 04 14 08

2 – 3 km 09 01 12 03 02

3 – 5 km 08 08 06 16 02 04

5 – 7 km 03 09 01 08 08 05

7 – 10 km 02 01 05 03

10 – 13 km 05 02 15 10

13 – 15 km 04 01 04 13

15 – 20 km 02 01 04

More than 20 km 05 01


Only one respondent engaged in shrimp farming has said to have no distance

between his land and output market (Table 7.7) while 5 and 4 respondents of rice and

shrimp farming respectively (i.e. about 11% of total sample population) have said to

have no distance between their land and road. Moreover, distance between rice lands

and input market shows a lower range than that of shrimp land and its input market

while the ratio of distance is also true in case of rice farming land and its output

market as well as shrimp lands and its output market. But in case of distance between

land and nearest road it is found to range with in 1 km for both rice and shrimp

farming land. In conclusion it can be said that sample rice farming lands are closer to

input as well as output market and service centre than that of shrimp farming lands.

7.14 Land Rent

Land generates income over time either through production or in the form of

rent for certain period. Therefore for the clarity about respondents on using joint and

borrowing land, land rents paid by sample households per year are as follows. Here

rent are given in BDT per year both for borrowing and joint lands because joint farms

either pay rent in cash taka or through output to the land owners.

Table 7.

Freq.

No Rent

1 – 15000

15000 - 30000

More than 30000

Summary


Households pay less rent for rice farming land than that of

32 rice farmers and 20 shrimp farmer

farmers pay some rents per year

rent for rice and shrimp farming lands are BDT 3

7.15 Accessibility to Land

How each land should be used depends much on how easily accessible the

concerned land is in terms of necessary machinery, inputs and labor forces. However,

the next figure depicts the nature of accessibility of each parcel of sample land.

Figure


About 25% of the rice lands are moderately accessible

rate is only 10% in case of shrimp lands. However, both sample rice and shrimp lands

show similar scenario in terms of

respectively, but when dealing with very high accessibility, shrimp farms show higher

ratio (about 30%) than that of r

10(25%)

0

5

10

15

20

25

30

Moderate

Fre

qu

ency


73

Table 7.8 Land Rent Scenario per Year

Rice Farming Land Shrimp Farming Land

Freq. Percent Mean St. Err. Freq. Percent

32 40 0 0 20 25

04 05 13000 1225 01 01

03 04 18000 1000 10 13

01 01 45000 - 09 11

40 50 3775 1416 40 50


ouseholds pay less rent for rice farming land than that of

and 20 shrimp farmers pay no rent for their land while the rest

farmers pay some rents per year. The row named summary shows that average land

rent for rice and shrimp farming lands are BDT 3,775 and BDT 19,875 respectively.

Accessibility to Land



figure depicts the nature of accessibility of each parcel of sample land.

Figure 7.8 Accessibility to Sample Land


bout 25% of the rice lands are moderately accessible (Figure 7.8)


similar scenario in terms of highly accessibility which is 62


ratio (about 30%) than that of rice farming lands (13% only).

25(62%)

5(13%)

4(10%)

24(60%)

Moderate High Very high

Rice Shrimp


Shrimp Farming Land

Percent Mean St. Err.

25 0 0

01 10000 -

13 22400 1360

11 62333 11741

50 19875 4694

ouseholds pay less rent for rice farming land than that of shrimp. However,

no rent for their land while the rest sample

The row named summary shows that average land

875 respectively.



figure depicts the nature of accessibility of each parcel of sample land.

(Figure 7.8) while the


highly accessibility which is 62% and 60%


(13%)

12(30%)

Very high

7.16 Transport Mode and Available Fa

Transports are becoming part and parcel in our daily life as well as to decide

the land use pattern because accessibility as well as profitability depends much on

transport. However, the author has described the mode of transport

sample households for their concerned land use as follows.


Sample households used three types of transport modes i.e. motorized, non

motorized and human l

farming than that of rice (Figure 7.9)

motorized vehicles than shrimp farms and even

Keeping pace with this, author

Figure 7.10


Lands with higher transport facilities are

shrimp farming rather than rice

0

5

10

15

20

25

16

0

5

10

15

20

25

30

Moderate

Fre

qu

ency


74

Transport Mode and Available Facilities to Specific Land



transport. However, the author has described the mode of transport

sample households for their concerned land use as follows.

Figure 7.9 Mode of Transport Used


Sample households used three types of transport modes i.e. motorized, non

human labor while motorized transport are used more in shrimp

farming than that of rice (Figure 7.9). Data also shows that rice farms uses more non

motorized vehicles than shrimp farms and even use human labor for transport

Keeping pace with this, author has described the nature of transport facility

Transport Facilities for Specific Land Use Pattern


ands with higher transport facilities are observed to be

rather than rice among the sample households (Figure 7.10)

7

23

10

2218

0

Motorized Non-Motorized Human Labor

Rice Shrimp

23

1

10

25


Rice Shrimp




transport. However, the author has described the mode of transport used by the


Sample households used three types of transport modes i.e. motorized, non-

while motorized transport are used more in shrimp

rice farms uses more non-

use human labor for transport.

the nature of transport facility as below.

Transport Facilities for Specific Land Use Pattern


observed to be used mostly for

(Figure 7.10).

Human Labor

5

Very high

7.17 Cost of Transportation per Trip

Transport cost constitutes a vital part in the total cost of production in any

productive sector especially in land use decision

the transport cost per trip incurred by each land use patterns as follows.

Table

No Cost

0 - 500

500 - 1000

1000 - 1500

More than 1500

Mean


It is seen that 3

their output are sold from their lands

input transaction cost than that of shrimp while shrimp lands are observed

less transport cost in case of output than

cost of rice is more because output is more in volume than that of shrimp.

7.18 Availability of Input

The higher the availability of input for land uses, the more would be the

tendency by the farmer towards th

survey as well as literature demonstrates that shrimp farming in south

Bangladesh are flourishing because of locally available inputs.

Figure 7.11


(48%)

0

5

10

15

20

25

Fre

qu

ency


75

Cost of Transportation per Trip


especially in land use decision. Therefore, this paper here describes


Table 7.9 Cost of Input and Output Transportation

Input Transport Cost Output Transport Cost

Rice Shrimp Rice

40 38 39

01 01

01

134.88 281.50 230.75


It is seen that 3 shrimp land holders need no output transaction cost because

their output are sold from their lands (Table 7.9). Rice farming lands generate lower

input transaction cost than that of shrimp while shrimp lands are observed

less transport cost in case of output than the rice farming lands. Moreover,

more because output is more in volume than that of shrimp.

Availability of Input

igher the availability of input for land uses, the more would be the

tendency by the farmer towards that land use pattern and vice versa.

survey as well as literature demonstrates that shrimp farming in south

Bangladesh are flourishing because of locally available inputs.

Figure 7.11 Availability of Input for Specific Land Use


19(48%)

20(50%)

1(2%)

22(55%) 17

(43%)

1(2%)

Moderate High Very High

Rice Shrimp



Therefore, this paper here describes


Cost of Input and Output Transportation

Output Transport Cost

Rice Shrimp

03

37

230.75 63.50

need no output transaction cost because

Rice farming lands generate lower

input transaction cost than that of shrimp while shrimp lands are observed to generate

Moreover, transport

more because output is more in volume than that of shrimp.

igher the availability of input for land uses, the more would be the

at land use pattern and vice versa. Moreover, field

survey as well as literature demonstrates that shrimp farming in south-west

Availability of Input for Specific Land Use


(2%)

Very High

Inputs of rice farming lands are 48

very highly available while

(Figure 7.11). Inputs of rice farming

7.19 Demand for Final Product

So long we have discussed about the production side of the two land uses, here

is the expected demand scenario of final output as follows assuming that lands owners

converted lands towards an alternative that has higher demand.

Figure 7.


Final outputs from both of the land use patterns don’t

demand throughout the whole year

shows that demand for shrimp is higher than that of rice in the study area

7.20 Market Location

Market location is crucial in determining land use because demand as well as

price varies on the basis of market location and output level.

demonstrated market location of each final output as follows.

Figure


0

10

20

30

Fre

qu

ency

0

20

40

Fre

qu

ency


76

Inputs of rice farming lands are 48% moderately, 50% highly and rest 2

very highly available while that of shrimp farming are respectively 55%, 43

nputs of rice farming are more available locally than that of shrimp.

Demand for Final Product



ands towards an alternative that has higher demand.

Figure 7.12 Demand Prototypes for Final Output


Final outputs from both of the land use patterns don’t have low or very low

demand throughout the whole year (Figure 7.12). Data collected from respondents

shows that demand for shrimp is higher than that of rice in the study area

Market Location


price varies on the basis of market location and output level. Therefore, this paper has

demonstrated market location of each final output as follows.

Figure 7.13 Distribution of Market for Final Product


15

21

40

28

12


Rice Shrimp

27

13

30

10

Local External

Rice Shrimp


derately, 50% highly and rest 2% are

are respectively 55%, 43% and 2%

are more available locally than that of shrimp.



rototypes for Final Output


have low or very low

Data collected from respondents

shows that demand for shrimp is higher than that of rice in the study area.


Therefore, this paper has

Market for Final Product



77

Analysis of sample data shows that majority of the sample households (i.e.

about 71% of total sample lands) sell their final output to local market which is

situated at Ratanpur and Kadamtala while the rest to external market of Kaligonj and

Moutala because of large output. Above data also shows that shrimps are mostly sold

in local market because of physical nature, complexity in storing and low durability

while large shrimp farmers are engaged in shrimp trading also which are causes the

selling of output at external market located at Shyamnagar, Parulia and Satkhira.

7.21 Price Distribution of Final Output

Random utility theory suggests that price works as the basic determinants of

any land use decision. Therefore, the next table shows the price from each of the land

use patterns taken by the sample households. Here actual price of rice is measured per

basta (50kg) while that of shrimp per kg as expressed by sample population based on

last year’s price and therefore, they can’t be compared directly.

Table 7.10 Price Distribution of Final Output

Rice Shrimp

Actual Price (Freq.) Actual Price (Freq.)

850 - 1000 06 450 - 550 03

1000 - 1100 18 550 - 650 26

1100 - 1200 14 650 - 750 11

1200 - 1300 02

Mean 1104.25 631.5


Average price of rice per basta is observed to be BDT 1,104.25 while

corresponding price of shrimp per kg is BDT 631.5.

7.22 Changes in Land Use Patterns of the Households

Though the study area is known mostly as an agricultural area with high land

consumption for rice, vegetables and jute farming, recently aquaculture (i.e. especially

shrimp and carp fish farming) has been taking the place of prior land uses in a notable

amount especially close to saline water source. Here is to be noted that change in land

use pattern denotes that sample household have changes any of available lands into

another one in last five years not necessarily the concerned land use.

Figure 7.14 Changes in Land use Patterns


About 59% of

last five years while rest 41% remained the same land use patterns.

that the rice farming households that supports the option that they have changed their

land use pattern denotes that they have changed some of their lands uses from one

form to other not necessarily into shrimp farming only.

7.23 Conversion and Maintenance Cost

Despite being non

generates more or less some cost during each conversion period and even user needs

to have some regular or irregular maintenance cost during the use of each parcel of

land further. However, in this study the initial conversion as well as maintenance cost

of each of the selected land uses is being presented with the help of following figure.

Figure 7.15 Initial Conversion Cost for Specific Land Use Pattern


0

10

20

30

40

50

Fre

qu

ency

28

2

0

5

10

15

20

25

30

No Cost 0

Fre

qu

ency


78

Changes in Land use Patterns (early 2008-


of sample population has changed their land use patterns over the

last five years while rest 41% remained the same land use patterns.


use pattern denotes that they have changed some of their lands uses from one

form to other not necessarily into shrimp farming only.

Conversion and Maintenance Cost

Despite being non-depreciable asset, each type of changes in land use patterns

ates more or less some cost during each conversion period and even user needs



h of the selected land uses is being presented with the help of following figure.

Initial Conversion Cost for Specific Land Use Pattern


2416

2317

47

33

0

10

20

30

40

50

Change No Change

Rice Shrimp Total

6 5

1 0 0 00

107 7 7 6

0 - 10000 10000 -20000

20000 -30000

30000 -50000

50000 -70000

70000 100000

Rice Shrimp


mid 2014)


sample population has changed their land use patterns over the

Here is to be noted


use pattern denotes that they have changed some of their lands uses from one

depreciable asset, each type of changes in land use patterns

ates more or less some cost during each conversion period and even user needs



h of the selected land uses is being presented with the help of following figure.

Initial Conversion Cost for Specific Land Use Pattern


0

6

1

70000 -100000

More than

100000


79

About 70% (i.e. 28 out of 40 rice lands) of the rice farming didn’t have any

conversion cost because the lands were plain and cultivable from the beginning while

the rest land owners have some conversion cost to get engaged in rice farming. On the

contrary, only two shrimp farming lands didn’t generate any cost because they have

either inherited it as successor or have been cultivated the shrimp farming land as a

lease holder which was being prepared before his ownership as a leaseholder.

Moreover, the rest 47% shrimp farming lands are observed to generate conversion

cost ranging between BDT 11000 and BDT 150000. The data also shows that initial

conversion cost of shrimp farming lands are higher than that of rice farming lands.

Now this paper describes the annual maintenance cost (BDT) by sample

households in using land in the best possible way to maximize utility from that land.

Figure 7.16 Yearly Land Maintenance Expenditure


Above figure shows that 15% rice farming lands show that they don’t have

any maintenance cost while annual conversion costs are lower in case of rice farming

lands than that of shrimp farming by the sample population in the study area.

7.24 Cost-benefit of Land Use

Profit maximization theory suggests that each and every rational user chooses

a land use that generates the highest optimal value at specific time period. As a result,

the author now presents the cost and benefits of using the land per year as follows.

Here production cost includes cost of input, machinery and labor cost while total cost

includes production cost as well as yearly maintenance cost of that specific land.

6

17

10

6

10 0 00 0 0

35

17

13

2

02468

1012141618

Fre

qu

ency

Rice Shrim


80

Figure 7.17 Cost-benefit Analysis of Rice and Shrimp Farming

(Rice Farming thrice per year)

In BDT


Shrimp farming lands, though, generate higher average production cost, total

average cost and earnings; rice farming by the sample population generates higher

average profit in the study area. As a result, here asks for analysis how changes in

cropping time affect the profit of each land use. Here is to be noted that shrimp

farming in the study area are done almost full year or more than or equal to11 months

per year while rice farming are done once, twice or thrice based on various factors.

Therefore, changes in profit distribution are being shown in next page (Figure 7.17)

assuming the profit from shrimp constant while changing in rice farming.

Figure 7.18 Change in Profit based on Cropping Frequency


42811.25 50115.13

139793.8

89678.63104625

142235.5

229525

87289

0

50000

100000

150000

200000

250000

Production Cost Total Cost Total Earnings Profit per Year

Yea

rly

Cos

t a

t B

DT

Rice Shrimp

0

20000

40000

60000

80000

100000

120000

Profit (Thrice)

Profit (Summer)

Profit (Rainy)

Profit (Winter)

Profit (Sum and Winter)

Profit (Sum and

Rainy)

Profit (Rainy and

Winter)

Pro

fit

in B

DT

Rice Shrimp


81

Shrimp farming lands give higher profit except when rice are cultivated thrice

per year or collectively in summer and winter season of year (Figure 7.18) in the

study area. Therefore, based on sample data we can conclude that if rice may be

cultivated trice or consecutively in summer and winter season then farming rice than

any other alternatives should be considered as the optimal land use pattern. The

analysis of production cost of and corresponding return from rice farming shows that

cost is higher in case of summer season than other seasons which distinguish rice

more profitable among the sample population.

7.25 Estimation of the Determinants of Land Use Change

This sub-section basically describes the nature of data used for empirical

determination of the extents of determinants of land use change (Table 4.1; Table 4.2)

and the corresponding results after running logistic regression. As already described

that this study is based on a sample population of 80 households (each 40 farmers

engaged on rice and shrimp farming at least for last five years) of Pirozpur village.

Table 7.11 Summary Statistics

Rice Farmers Shrimp Farmers

Mean St. Err. CV (%) Mean St. Err. CV (%)

Age 52.53 2.08 25.07 48.95 2.02 26.15

Year of Schooling 5.43 0.97 112.34 5.68 0.94 104.23

Land Engagement through

inheritance 0.53 0.08 96.38 0.23 0.07 188.00

Land Engagement by

personal interest 0.08 0.04 356.00 0.40 0.08 124.00

Family Type 1.38 0.08 35.64 1.45 0.08 34.76

Economically active family

member 1.58 0.12 49.59 2.63 0.23 56.38

Land ownership by sole

proprietorship 0.75 0.07 58.53 0.50 0.08 101.20

Land ownership by

borrowing 0.08 0.42 365.00 0.20 0.06 202.50

Land rent 3775.00 1415.79 237.20 19875.00 4694.40 149.38

Neighborhood land use

pattern 0.55 0.08 91.64 0.58 0.80 0.87

Proximity to service centre 10.03 0.51 32.36 11.71 0.65 35.18

High accessibility 0.63 0.08 78.45 0.60 0.08 82.67

Very high accessibility 0.13 0.05 268.00 0.30 0.07 154.67

Availability of credit 0.10 0.05 304.00 0.33 0.08 145.85

Natural pressure 0.68 0.08 70.22 0.43 0.08 117.88

Source: Author’s Estimation, 2014


82

Age of rice farmer gives a higher average than that of shrimp farmers while

age of shrimp farmers has more variation than that of rice farmers (Table 7.11).

Likewise, rice farmers, on an average, have lower schooling year with higher

variability than that of shrimp farmers in the sample population. Average number of

economically active family members is higher in case of shrimp farming households

than that of rice arming households which also shows that there is greater variability

in case of shrimp farming households also. Average land rent shows higher value in

case of shrimp farming while variability is higher in rice farming lands. Average

shrimp farms are closer to the service centre with higher variability in collected data

than that of rice farming. It is here to be noted that in case of dummy variables, vale

of CV is high because in case of one unit change in each dummy (i.e. from 0 to 1)

there occurs a change of 100 units as they are dummy.

Based on collected data from sample population, this paper has done logistic

regression analysis using STATA and SPSS program for the generation of necessary

results to empirically prove the fitness of data as well as to know the extents of land

use change determinants. Therefore, before going to describe the extents of land use

determinants in land use decision making we need to clarify how the model fits the

data under consideration in this paper and analysis. Classification table (Table Annex

II.5) shows that classification accuracy rate has changed from the initial 50% (Table

Annex II.4) to 97.5% with the addition of more variables in the model or in other

words, the model has showed more accuracy to predict the dependent variable with

the selected independent variables. Though this model appears to be good but need to

evaluate the fitness and significance of the model yet and for this reason we are going

to use Omnibus Tests of Model Coefficient or more specifically through Chi-square

test which is derived from the likelihood of observing the actual data under the

assumption that the model that has been fitted is accurate. In this regard this paper

assumes following hypothesis in relation to the overall fit of the model.

H0: Adopted model is a good fitting model.

H1: Model is not a good fitting model (i.e. predictors have significant effect).

In our case of our model, chi-square has 15 degrees of freedom with a value of

93.514 and a probability of p<0.000 (Table Annex_II.6) which indicates that the

model has a good fit. So we accept the null hypothesis i.e. the model is a good fitting

model. Yet for more accuracy this paper has also used various other tests as in next


83

portion. As there is no close equivalent statistic in logistic regression to the coefficient

of determination R2, we need some approximation. Based on likelihood, Cox & Snell

R Square indicates that 68.90% of the variation in the dependent variable is explained

by the logistic model under consideration (Table Annex_II.9). Moreover, as per rule

of thumb, Nagelkerke R Square, a more reliable measure of the relationship, shows a

higher value (i.e. 0.919) than that of Cox & Snell R Square and indicates a very strong

relationship of 91.90% between the predictors and the predictions.

Alternative to model chi-square is the Hosmer and Lemeshow Test which

divided the subjects under condition in 9 ordered groups and then compares the

number actually in each group to the number predicted by the logistic model we have

chosen based on their estimated probability (Table Annex_II.8). A probability (p)

value is being computed from the chi-square distribution with 7 degrees of freedom to

test the fit of the logistic model. As the H-L goodness-of-fit test statistics is greater

than o.5 (Table Annex_II.7), we fail to reject the null hypothesis that there is no

difference between observed and model-predicted values implying that the model’s

estimates fit the data at an acceptable level. More specifically, this desirable outcome

of non-significance indicates that the model prediction does not significantly differ

from the observed. Our H-L goodness-of-fit test statistic has a significance of 0.721

meaning that it is not statistically significant and assumed model is quite a good fit.

Rather than using a goodness-of-fit statistic, researchers often emphasizes on

the fact that what proportion of cases we have managed to classify correctly through

our adopted model. Though in a perfect model, all cases remain on the diagonal and

overall percent correct is 100%, in our study 97.5% of the data is being correctly

classified in each individual case (Table Annex II.5) as well as in case of overall data

set. At this stage this paper has used Wald statistics and associated probabilities

provided with an index of the significance of each predictor in the equation (Table

Annex_II.16). As per rule of Wald statistic, we this paper may drop independent

variables such as Dum_Lan_Eng1, Nei_LU, Cre_Ava from the model under

consideration because their effect isn’t statistically significant at 5% level.

This paper has used expected value of coefficient (Table Annex_II.16) which

shows the extent to which raising the corresponding measure (i.e. independent

variable) by one unit influences the odds ratio. As per rule of thumb, when the value

exceeds 1, the odds of an outcome occurring also increases while decreases when the

figure is less than 1. In our analysis, in case of increase in variable age,


84

Dum_Lan_Eng2, FT, Nei_LU, Acc2, there would be a decrease in the occurrence of

outcome while the increase in rest variable will lead to an increase in outcome

occurring in favor of shrimp farming. Moreover, the fit of the model is adequate since

the Pearson chi-square value is 34.58 on 64 degrees of freedom (Table Annex_II.12)

while the probability is 0.999. The goodness-of-fit of the model can also be evaluated

with the area under the ROC curve and the analysis shows that the area is closer to 1

implying that the curve passes through the left corner and the model in perfect (Figure

Annex_II.1). Pseudo R-square with a value of 0.8432 implies that 84.32% pseudo

variance of dependent variable is perfectly explained by the independent variables and

the model is fit enough to use for analysis. Moreover, case wise list of each

observation shows that only two observations- each one from shrimp (Obs.-77) and

rice farming (Obs.-13) households- is being shown as misclassified (Table

Annex_II.17). Observed as well as predicted major land use pattern of each sample

shows that there is no significant difference except two misclassifications in sample.

However, the above description shows that adopted model in this paper fits the

data and therefore, we have tried to get the extents of land use determinants.

Table 7.12 Estimation of Determinants of Land Use Change

Coefficient St. Err. p>|z|

Age -0.588** 0.250 0.019

Year of Schooling 1.702** 0.821 0.038

Land Engagement through inheritance 7.296* 3.726 0.050

Land Engagement by personal interest 41.034** 18.629 0.028

Family Type -46.843** 20.970 0.026

Economically active family member 32.007** 14.292 0.025

Land ownership by sole proprietorship 58.267** 27.528 0.034

Land ownership by borrowing 24.926** 12.236 0.042

Land rent 0.004** 0.002 0.030

Neighborhood land use pattern 9.600* 4.998 0.055

Proximity to service centre 3.220** 1.492 0.031

High accessibility 25.270** 11.078 0.023

Very high accessibility 24.540** 10.583 0.020

Availability of credit -8.551* 4.902 0.081

Natural pressure -19.193** 8.855 0.030

Constant -97.468** 46.361 0.036

LR Chi-square Value (15) 93.5100

Pseudo R Square 0.8432

Probability > Chi Square 0.0000

N.B.:** and * shows 5% and 10% significant level respectively



85

Socio-economic as well as bio-physical variables included in the model such

as age, year of schooling, land engagement by personal interest, family type,

economically active family member, land ownership pattern, land rent, proximity to

service centre (i.e. agriculture or fishery office), accessibility, natural pressure are

significant variables at 5% significant/probability level (Table 7.12) while land

engagement through inheritance, neighborhood land use pattern, availability of credit

are significant at 10% level based on a two-tailed test at 95% confidence level (see

Annex_II.13 for more details). Moreover, age, family type, availability of credit and

natural pressure has shown negative association with major land use patterns while

the rest variables have shown positive one (Table 7.12).

Based on odds ratios (Table Annex_II.14) it can be interpreted that variable

such as age, family type, credit availability and natural pressure shows less likely to

influence the major land use patterns towards shrimp farming while schooling year,

land engagement process, economically active family member, land ownership, land

rent, neighborhood land use, service centre proximity, accessibility are to more likely

influence the owners to use his land for shrimp farming. However more precisely, age

shows negative significant result which indicates that log likelihood of major land use

pattern will be shrimp at lower age and vice versa (Table Annex_II.14) or in other

word, one year increase in age causes the odds of major land use pattern decreased by

a factor of 0.555, on an average (i.e. coefficient is -0.314 and odd ratio is 0.555) while

the estimate is significant at 5% level if other things remaining the same. Likewise,

odds ratio from the logit result shows positive relationship between major land use

pattern and year of schooling indicating that the higher the year of schooling the more

likely the probability to have shrimp farming as the major land use or one year

increase in year of schooling leads to increase the odds of major land use towards

shrimp farming by a factor of 5.487 (i.e. coef. is 0.906) which is significant at 5%

level if ceteris paribus. Abruptly, positive change in age, family type, availability of

credit and natural pressure causes the land owners to convert their lands less likely

towards shrimp farming from rice while positive change in year of schooling, land

engagement by personal interest, economically active family member land ownership

pattern (i.e. sole proprietorship and borrowing), land rent, proximity to service centre

(i.e. agro or fishery office), accessibility (i.e. high and low), existence of natural

pressure causes farmers more likely to change their land uses towards shrimp farming


86

from existing land use i.e. rice farming. For more accuracy in interpretation this paper

has used marginal analysis of the land use determinates (Table 7.13).

Table 7.13 Marginal Analysis of Determinants of Land Use Change

Variable dy/dx Std. Err. P>|z|

Age -.5882493 .24999 0.019

Year of Schooling 1.702376 .82101 0.038

Land Engagement through inheritance* 7.296162 3.72622 0.050

Land Engagement by personal interest* 41.03385 18.629 0.028

Family Type* -46.84293 20.971 0.026

Economically active family member 32.00656 14.293 0.025

Land ownership by sole proprietorship* 58.26666 27.529 0.034

Land ownership by borrowing* 24.92581 12.236 0.042

Land rent .0036388 .00167 0.030

Neighborhood land use pattern* 9.599267 4.99781 0.055

Proximity to service centre 3.220036 1.49183 0.031

High accessibility* 25.26952 11.078 0.023

Very high accessibility* 24.53952 10.583 0.020

Availability of credit* -8.551443 4.90146 0.081

Natural pressure* -19.19279 8.85445 0.030

N.B.: (*) dy/dx is for discrete change of dummy variable from 0 to 1


Marginal analysis (see Table Annex_II.15 for more information) shows that

when age increases by 1 year, probability of changing from rice farming towards

shrimp farming decreases by 0.59 percent on an average if other things remaining the

same while one year increase in year of schooling produces 1.70% probability of

shrimp farming on an average if cetaris paribus while the estimates are statistically

significant at 5 percent level. Likewise, other things remaining the same when

engagement on land use is occurred through inheritance rather than tradition and

belief, probability of converting rice farming into shrimp farm increases by 7.30% on

an average which is statistically true at 10% significant level. Again, when someone

gets engaged in land use pattern through personal interest, probability of shifting from

rice to shrimp farming land increases by 41.03% on an average which is statistically

significant at 5 percent level if cetaris paribus. Moreover, probability towards shrimp

farming from rice farming decreases, on an average, by 46.84 percent when family

type is nuclear rather than joint which is statistically significant at 5 percent level if

other things remaining the same. If number of economically active family member

increases by 1 person, probability of changing current major land use pattern from


87

rice to shrimp farming increases by 32.01 percent, on an average, which is statistically

significant at 5% level if cetaris paribus while probability of shifting a parcel of land

from rice farming towards shrimp increases on an average by 58.27 percent and 24.93

percent respectively while land is correspondingly owned solely (i.e. sole

proprietorship) and through borrowing (i.e. lease holder) which is statistically

significant at 5 percent level when other things remaining the same.

If rent of any land increases by BDT 1000, probability of shifting the land use

pattern from rice to shrimp also increases by 3.6 percent on an average if cetaris

paribus and the estimate is statistically significant at 5 percent level. Likewise, when

neighborhood land characteristics are similar rather than dissimilar one (i.e. other land

use patterns), probability of shifting each parcel of land towards shrimp from rice

farming increases by 9.60 percent on an average if other things remaining the same

while the result is statistically significant at 10 percent level. Moreover, other things

remaining the same, if proximity to service center increases by 1 kilometer,

probability of shifting land use pattern towards shrimp increases by 3.02 percent on an

average which is statistically significant at 5 percent level. When any land is highly

and very highly accessible rather than moderate accessibility, shifting the land use

towards shrimp farming from rice increases, on an average, by 25.27 percent and

24.54 percent respectively which are true at 5% statistically significant level if other

things remaining the same. Likewise when credit facilities are available, probability

of changing land use from rice towards shrimp farming reduces on an average by 8.55

percent which is statistically significant at 10 percent level if cetaris paribus. But other

things remaining the same, if there are frequent natural pressures, probability of

changing rice farming lands into shrimp farming lands decreases by 19.19 percent, on

an average, which is statistically significant at 5 percent level.

However, figure showing sensitivity and specificity versus probability cutoff

(Figure Annex_II.2) shows that most of sample lands are classified properly while

some are yet sensitive showing that changes in any of the variables may lead to

change the results in major land use pattern. Abruptly, sensitivity portion shows that

these land owners are yet confused in land use decision and any change in

independent variables may lead to opposite results in land use pattern which is also

supported by predicted probability list (Table Annex_II.16).


88

Pirozpur is an agro based rural area where education level as well as labor

migration generates a larger share of total income of that area. As result of multi-

profession at a single time has caused the land owners to be engaged in a land use

alternative that generates higher yields. As a result, at the last of 20th century, major

land use changes occurred in the study area especially shifting of agro land close to

river area towards shrimp farming. As a result with the passage of time, shrimp

farming lands have gained a larger share of total cultivable land with more income

generation to the households. Though shrimp farming is more appealing than any

other land use alternatives, the analysis of collected data shows something

contradictory with literature. Rice farming is more profitable as well as less costly

than shrimp farming if cultivated optimally (i.e. thrice per year). Moreover, shrimp

farming has been generating more and more conflict both in the form of natural

vulnerability as well as human induced conflicts in the study area. Though land use

changes are occurring in the study are that is found to be conflicting with the current

findings. Moreover, the empirical findings have contradicted with some of our

proposition (Table 4.2). The analysis and collected data shows that there are enough

land users who are far away from the optimal use of each parcel of land over time in

the sample population. Individual probability analysis shows that some of the land

users are yet confused of their optimal land use patterns which ask for intervention of

authority as much as possible for sustainable land use in study area as well as other

parts of Bangladesh.


89

Chapter Eight

Findings and Conclusion

Agriculture is yet the most imperative livelihood option in Bangladesh (BBS,

2010) especially in rural south-west region (Alam et al., 2002) and has a key role to

play in tackling challenges of growing population, poverty alleviation, maintaining

food security and adapting to climate change (BBS, 2013; IPCC, 2000). Keeping this

in mind, this research work has been done in such a study area which has been

observing frequent shift of rice farming lands towards shrimp as well as non-

productive uses. Before going to the major findings it is to be remembered that this

study is done on two groups- one who were engaged in rice farming before five years

ago but now are being engaged in shrimp farming and the other who have been using

their land for rice farming at least for five years and more. However, this study has

found some exclusive information regarding land use decision during the analysis of

finding out the determinants of land use change in south-west region of Bangladesh as

summarized in later sections.

8.1 Information through Focus Group Discussion

During the study period several pilot surveys were done to get the overall land

use change scenario of the study area through focus group discussion, interview

process of local representatives and talking with old aged or informative persons.

However, the author through focus group discussion (FGD) came to know that before

2000 there were very insignificant uses of lands for shrimp farming except some lands

just close to the embankments of Hariavanga River. But during the mid of first

decade of 21st century, several natural calamities caused the total area flooded for

several times especially during rainy season while the longest floods remained active

for more than a month and from then shrimp farming came in force in Pirozpur area

widely. But author has also noticed that yet majority of the land owners engaged in

rice farming are using their lands thrice per year while are getting loans from govt. as

well as non-govt. organization. A large number of rice processing firms are observed

in the study area while agricultural officer and associated staffs are much conscious

about rice and vegetable farming to discourage the shrimp and irreversible uses. Local

authorities have already become more conscious about management and construction

of embankments with the formulation and implementation of land regulation to stop


90

the misuse or disuse of each parcel of land. Rice farmers expressed positive view

about rice farming in the sense that if rainfall is enough and timely available or

irrigation facilities are enough, then rice farming is more profitable than that of

shrimp. Rice farmers have showed various observed adverse effect of shrimp farming

such as salinity intrusion in nearby lands, loss of biodiversity and lower agro

production in nearby areas of shrimp lands.

However, it is a matter of surprise that lands engaged once in shrimp farming

have become more saline and less fertile than before. Moreover, farmers engaged in

shrimp farming are also changing their land use because of natural hazards like attack

of virus, high salinity compare to the endurance limit and especially for high tax

imposition by local authorities. Majority of the lands of the households are observed

to be used either for rice farming or shrimp farming followed by mixed farming,

water bodies, road and communication, business, fellow and mostly homestead land

for settlements, farming vegetables, recreation and irregular activities.

8.2 Findings of the Research

Agricultural occupations are predominant in the study area but because of

highly available saline water near the lands as well as higher demand of shrimp in

local as well as international market have influenced the sample population to switch

from rice farming to shrimp in last century and next years. Moreover, west side of the

study area being located near the Hariavanga River, shrimp farming has got more

priority in the study area due to highly available irrigation water and locally available

factors of input (i.e. prawn). One interesting information in this regard is that family

engaged in business farming are more interested in shrimp farming while families

which are influenced more by remittance shows a positive outlook towards rice

farming than shrimp. But despite increase in salinity and favorable environment, yet

many land owners are yet engaged in rice farming especially in lands far enough from

the river and canal along with some nearby one. It is to be noted that shrimp lands in

the study area are also cultivated for rice along with shrimp in rainy season which

aren’t included in our study. Moreover, farmers are observed not only to change land

uses from rice to shrimp but also from shrimp to rice and even from other uses to both

practices in the study area. Population growth has caused the much of the agro land

conversion for settlement purposes followed by roads and communication, business

infrastructure, fellow lands.


91

Majority of sample households are being maintained by male decision maker

while households with young aged decision maker are engaged more in shrimp

farming in the study area. Most of the rice lands are solely owned and cultivated by

sample population while shrimp farming lands are mostly joint and borrowing in

nature. Moreover, average land size on current land use is lower than what it was

before 2010 but higher than that of during 2010-2013 among the sample population.

Though literature shows that both training and credit facilities are available in

Bangladesh but analysis shows that credit are available but training are rarely

available for the sample population. Findings show that shrimp farmers have got more

training facility than that of rice in the study area. More to the point, lands with low

salinity is used indiscriminately for either rice or shrimp based on other influential

factors while no farming is done in very highly saline and sandy lands. The

respondents have said that agro offices are now becoming more conscious about their

services and as a result most of the sample farmers are getting benefited from their

services offered. On the contrary fishery office though in the initial stage encouraged

the land owners for shrimp farming, now are encouraging only the existing land

farmers in keeping pace with the present difficulties of shrimp farming and to have

the optimum use of the existing lands.

It is also found that income has a positive relation with the number of

economically active member of each household while is negatively related with the

number of members engaged in non-income generating activities like study.

Moreover, households having business, govt. or non-govt. job and remittance as

source of income has more income than the rest households while expenditure are

more or less similar among all the sample households. Data of field survey also shows

that most of the rice farmers and/or land owners are now using their lands for rice

cultivation thrice per year with the irrigation system either personal or rented.

8.3 Comparison of Findings

The average size of sample population shows an average of 4.96 persons

which is little higher than the national average of 4.85 (BBS, 2011). Moreover, the

occupational distribution of sample household shows rice cultivation as the major

occupation followed by shrimp and farming, business, services while remittance has

highest share in income generation of in study village followed by business, service,

shrimp farming and rice farming which are likely to be similar to that of national


92

statistics (BBS, 2013). During the land use decision, sample households are observed

to take factors like economic benefit (i.e. expected returns), neighborhood land use,

family demand, natural as well as human pressure and land use cost chronologically

which is also supported by literature.

The findings though shows similarity with most of the propositions (Table

4.2), there is also contradiction with variables such as land engagement through

inheritance, family type, availability of credit, land ownership by sole proprietorship (Table

7.11). However, the findings of this research paper (i.e. extents of land use change

determinants) shows similarity with the findings of Skole and Davids (2002), Gyawali

et al. (2004), Alabi (2011), Lubowski (2002), Lubowski et al. (2008) and Alabi (2009)

in terms of accessibility, proximity to infrastructure and neighborhood land use

pattern but contradics with the findings of Lubowski et al. (2008), Alabi (2009) and

Rui (2013) in terms of population density, education. Moreover, findings of Riebsame

et al. (1994), Zubair (2006) and Lubowski (2002) shows similar results with different

significant level.

8.4 Conclusion

Despite steady progress towards industrialization, agriculture remains the most

important sector in Bangladesh with a share of about 19% in total Gross Domestic

Product (GDP) of the country (BBS, 2013). Bangladesh is an agricultural country and

over 60% of its population is directly or indirectly involved in agricultural activities

contributing about 19.41% to the GDP of the country (BBS, 2013). The polderization

project in the last of 20th century along with frequent natural calamities is the pioneer

of shrimp farming in the study area (SRDI, 2010). Since the level of salinity is

increasing continuously, traditional farmers are not able to produce sufficient

agricultural crops and thereby are found to shift from rice farming to shrimp farming

over time especially lands close to saline water sources like river, canal. The existing

rice varieties may not be adapted to grow under increased soil salinity conditions and

consequently, food production does not seem to have a better future in light of climate

change [v]. It is now reported that lands with intensive agricultural practices 10 years

ago are major shrimp cultivation lands now [v]. Therefore, agricultural lands have

decreased and at present standing at the position of vanishing in many areas because

of flood, river erosion and mostly due to intentional conflict among competitors [iv].


93

Like all other parts of Bangladesh, Pirozpur has already gone though major land use

changes over the last decades which have already influenced the ecology negatively.

However, analysis of the study shows that if lands can be cultivated trice or at

least during winter and summer then rice farming generates higher income than that

of shrimp farming over the year. Moreover, the young aged people are positive

towards shrimp farming in the study area which asks for immediate steps by

authorities to tackle the problems originated from inefficient land use over time.

Moreover, as the study area is known as agro based rural economy, govt. especially

local representatives should take steps to control the unplanned land use in the area

especially to avoid the use of lands in unproductive uses. River water is the major

sources of irrigation in shrimp farms which are causing nearby lands either to shift

their land use or to keep the land fellow, therefore authority should control the land

use patterns through controlling the water supply system in regulation on using river

water or taxing high for using river water. Govt. has already formulated dynamic

policies and programs to control the land use patterns optimally and efficiently, there

is no space for recommendation but what is now important is to ensure the proper as

well as optimal implementation of formulated policies through proper monitoring by

the local authorities over time. Govt. as well as other concerned authority should

emphasizes on creating more and more awareness among mass population to stop the

unplanned use of lands especially through seminar and symposiums over time in

affected areas. Educational institution should emphasize on the negative impacts of

unplanned and wrong land use pattern with necessity of using lands optimally.

Lastly as the area is agro based yet, authority should emphasize in controlling

the conversion of suitable lands for rice farming so that such lands mayn’t shift

towards shrimp or any other non-productive uses. Here, the most important factor to

be considered here is to create awareness rather than policy formulation and its

implementation to ensure sustainable land use pattern in the study area as well as

other parts of the world. In this regard, coordination of concerned parties such as

ministries, land owners, business parties and other users should come forward with

positive outlook towards the optimal use of land use rather than using for profit

maximization in the short run. So govt. as well as all other parties should emphasize

on land uses to ensure its sustainable development rather than short term benefits. So

the concluding speech is that each and every individual should be aware of the

optimal alternative uses of each parcel of land for better future.


94

8.5 Further Scope

Though land use changes are occurring as a consequence of national economic

growth and development to meet the demand of urbanization and industrialization, it

is important to evaluate land use changes in the regional and the local context in order

to assist in anticipating the impacts associated with change and contribute to an

understanding of productive environmental sustainability (Oluseyi, 2006). Although

understanding of land use and cover changes has improved since early studies on

deforestation by Myers (1980) and Mather (1990), it does appear that theoretical

elaboration is in underdeveloped stage yet (Irwin and Geoghegan, 2001) especially in

developing nations like Bangladesh (Walker and Solecki, 2004). Moreover, land use

and cover change analysis needs to use geo-informatics technologies (Anderson et al.,

2002; Brannstrom et al., 2008; Trisurat et al., 2009) for accuracy and consistency.

Here is to be noted that land use researches should be based on panel or at least time

series data to capture the trends of land use patterns, their changes and the major

determinants. Keeping pace with the problems associated with land use patterns

globally especially in developing nations, researches can be taken on the reason for

which valuable agricultural lands are shifting towards the non-agro purposes

especially for residential purposes?

From the so long discussion of the paper, it may now be concluded that land is

one of the major constraints to cope with the growing demand of increased population

as well as evolutionary civilization. Therefore, researchers and planners should

consider land issues deeply for a planned and sustainable economy. Keeping this in

mind, researches may be carried out researches under the broad heads like the trends

of changing patterns of land use, explore the extent of determinants responsible for

changes in land use pattern, relationship between urbanization and industrialization

with the land use patterns, land use and transportation, land use and planned

urbanization, land use and food security, land use and sustainable development and

mostly impact of land use patterns and their changes on eco-system as well as climate.


95

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List of Web References

[i] http://en.wikipedia.org/wiki/Khulna_Division; website of Wikipedia (Accessed on

24 August 2014 at 07:34 PM)

[ii] http://www.mapsofworld.com/bangladesh/divisions/khulna.html; website of maps

of world (Accessed on 24 August 2014 at 07:36 PM)

[iii] http://www.lged.gov.bd/DistrictLGED.aspx?DistrictID=39; website of Local

Government Engineering Department (LGRD), Satkhira (Accessed on 24 August

2014 at 08:07 PM)

[iv] http://maps-of-bangladesh.blogspot.com/2010/10/political-map-of-satkhira-

district.html; website of country window IT centre (Accessed on 24 August 2014

at 08:28 PM)

[v] http://www.lged.gov.bd/DistrictArea2.aspx?Area=UnionParishad&DistrictID=39;

website of local government Bangladesh- Satkhira district (Accessed on 24

August 2014 at 08:13 PM)

[vi] http://www.banglapedia.org/HT/K_0046.htm; website of banglapedia (Accessed

on 24 August 2014 at 08:16 PM)


xvi

Appendix I

A Questionnaire

On


(All the collected data are to be used only for academic purpose)

The author, Jahangir Alam, is a student (BSS Honors) of Economics Discipline under Social

Science School at Khulna University and conducting a research work under the supervision of

Md. Firoz Ahmed, a faculty member of Economics Discipline on Determinants of Land Use

Change in South-west Region of Bangladesh. Hence, for the successful completion of the

research work on proposed title, the following questionnaire has been prepared to collect

some relevant information from you and your area. We are very interested to let you know

that your responses would never be used for any further purpose without your concern. So, if

you are interested and fell free, please join shortly without tension or risk of confidentiality.

General Instruction

Sample household must have enough land for subsistence for last five years.

Moreover, respondent must be the head and/or primary decision maker.

If the answer of any question is others, please specify the option in details.

If the respondent has more than one plot, take data of the largest one.

Code 1: 1=Yes, 0=No

Code 2: 1=Very Low, 2=Low, 3=Moderate, 4=High and 5=Very High

All data are to be taken in BDT not in any other measurement unit i.e. Kg, Mound.

Sample No.: ____________ Date: ____/____/_______

A. Information of the Respondent (Household)

A.1 General Information about the Respondent:

Name Gender (Code 3)

Age (Year)

Education (Code 4)

Year of

Schooling

Mobile

A B C D E F

A.1

Code 3: 0=Female and 1=Male

Code 4: 0=Illiterate, 1=Informal Learning, 2=Primary, 3=Intermediate, 4=College

A.2 Major Land use Pattern (Code 5) ……………………..

Code 5: 1=Rice Farming, 2=Shrimp Farming

A.3 How have you engaged yourself to this land use pattern? (Code 6) ……………..

Code 6: 1=through Inheritance, 2=Personal Interest, 3=Tradition and Belief


xvii

A.4 General Information about the Household:

Family Type (Code 7) Family Member Total Assets (BDT) Occupation (Code 8)

A B C D

Male Female Land Non-Land Primary Secondary

1 2 1 2 1 2

A.4

Code 7: 1=Nuclear, 2=Joint, 3=Others

Code 8: 1=Rice Farming, 2=Shrimp Farming, 3=Mixed Use, 4=Business, 5=Govt.

Job, 6=Non-govt. Job, 7=Service, 8=Remittance, 9=Others

A.5 Information of Household Yearly Income and Expenditure (BDT):

Type of Assets Income Expenditure Type Cost

A B

A.5.1 Land Assets Regular

A.5.2 Non-land Assets Irregular

A.6 What are the major land use patterns over time from the following (Bigha)?

Land Use Pattern Present (2014) 2010 - 2013 Before 2010

A B C

A.6.1 Rice

A.6.2 Shrimp

A.7 Seasonal Variation in Land Use Pattern (If uncertain, take data of last year):

Ownership

(Code 9)

Seasonal Use (Code 1) Reason of Variation

Summer Rainy Winter

A B C D E

A.7.1 Rice

A.7.2 Shrimp

Code 9: 1= Sole Proprietorship, 2=Joint, 3=Borrowing, 4=Others

A.8 Cost and Benefit of Specific Land Use Pattern in Last Year (2013):

Size (Bigha)

Land

Rent

Total Cost (BDT) Total Earning (BDT)

Summer Rainy Winter Summer Rainy Winter

A B C D E F G H

A.8.1 Rice

A.8.2 Shrimp

A.9 Proximity to Necessary Infrastructure and Service (in Km):

Input

Market

Output

Market

Nearest

Roads

Nearest

Town

Agro/Fishery

Office

A B C D F

A.9.1 Rice

A.9.2 Shrimp


xviii

A.10 Characteristics of Land Cultivated by the Respondent (at least of Year 2013):

Rice Shrimp

A B

A.10.1 Geographic Location (Code 10)

A.10.2 Land Elevation (Code 11)

A.10.3 Land Fertility (Code 12)

A.10.4 Salinity and Sand (Code 2)

A.10.5 Neighborhood Land Use (Code 13)

Code 10: 1=Close to saline water sources (River, Canal), 2=Close to sweet water

sources (Pond, Deep Tube well), 3=No certain water source (Rain)

Code 11: 1=Very Low (Whole year water logging), 2=Low (At least six month water

logging), 3=Moderate (Water logging only in rainy season), 4=High (Water logging

for week or less) and 5=Very High (No water logging)

Code 12: 1=Very Low (No rice farming), 2= Low (Very little rice farming),

3=Moderate (Both shrimp and agriculture), 4=High (Rice farming at least two times

in year) and 5=Very High (Whole year rice farming)

Code 13: 1=Rice Farming, 2=Shrimp Farming, 3=Mixed Farming, 4=Water Bodies,

5=Homestead, 6=Fallow Land

A.11 Market demand for the final output and corresponding price:

Product

Type

Market Demand (Code: 2)

Location of

Market (Code 14) Market Price (Per Mound/Kg)

Expected

Price

A B C D

A.11.1 Rice

A.11.2 Shrimp

Code 14: 1=Local, 2=External, 3=Uncertain, 4=Others

A.12 Have you changed your land use pattern since 2010 (Code 1)? ………………….

Land Use Patterns

A B

A.12.1 Duration of Current Land Use Pattern Rice Shrimp

A.12.2 Conversion Cost (Initial) Per Bigha

A.12.3 Conversion and Maintenance Cost Yearly Per Bigha

A.13 Source of water for irrigation and water disposal:

Source (Code 15) Way (Code 16) Distance (Km) Cost (BDT) Irrigation Disposal Irrigation Disposal Irrigation Disposal Irrigation Disposal

A B C D E F G H

A.13.1 Rice A.13.2 Shrimp

Code 15: 1=River, 2=Pond, 3=Shallow Tube well, 4=Rain water 5=others

Code 16: 1=Canal, 2=Machinery, 3=Human Labor, 4=Uncertain, 5=others


xix

A.14 Transportation facilities and cost:

Accessibility (Code 2)

Facilities (Code 2)

Type (Code 17) Cost from Market

Input Output

A B C D E

A.14.1 Rice

A.14.2 Shrimp

Code 17: 1=Motorized, 2=Non-motorized, 3= Human Labor and 4=others

A.15 Availability of input, training and credit facilities for specific land use:

Rice Shrimp Description

A B A.15.1 Availability of Input (Code 18) A.15.2 Training Facility (Code 1) A.15.3 Credit Facility (Code 2)

Code 18: 1=Very low (Locally not available), 2= Low (Rarely available in local

context), 3=Moderate (Variation in availability in local area), 4=High (Mostly

available in local area) and 5=Very High (Always available locally)

A.16 Do you have plans to change land use patterns in coming future (Code 1)? ……..

A.16.1 If yes, what would be the expected change in land use pattern (Code 13)? ………

A.16.2 What would be the reasons behind your land conversion (Code 19)? ……………

Code 19: 1=Economic Benefit, 2=Neighborhood Characteristics, 3=Family Demand,

4=Land Fertility, 5=Land Elevation, 6=Pressure, 7=Others

A.17 Miscellaneous Questions on Land Use Pattern and Corresponding Regulation:

Rice (Code 1) Shrimp (Code 1) Type/Nature

A B C

A.17.1 Human Induced Pressure (Code 20)

A.17.2 Natural Pressure (Code 21)

A.17.3 Land Use Regulation (Code 22)

Code 20: Pressure from 1=Land owner, 2=Neighborhood land users, 3=Local

authorities, 4=Large/rich land owners, 5=Intentional land use conflict, 6=Others

Code 21: 1=Floods, 2=Lack of timely rainfall, 3=Salinity, 4=Others

Code 22: Regulation from 1= Land owner, 2=Local authority, 3=Others

With Thanks

The Enumerator (Sign with Date & Time)

……………………………


xx

Appendix II

Analysis and Results

Table Annex_II.1 Description of Sample Data used in Logistic Regression

Observation: 80 Variables: 16 Size 5120 Variable Name Storage

Type Value Label

Variable Label

MLUP Float MLUP Major land use pattern Age Float Age of decision maker SchYr Float Year of Schooling Dum_Lan_Eng_1 Float Lan1 Engagement process in existing land use Dum_Lan_Eng_2 Float Lan2 Engagement process in existing land use FT Float FT Family Type Eco_Act_FM Float Economically Active Family Member Dum_LO1 Float LO1 Land Ownership Dum_LO2 Float LO2 Land Ownership LR Float Land Rent Nei_LU Float NLU Neighborhood Land Use Pattern Ser_pro Float Proximity to Service Point from the Land Acc1 Float Acc1 Accessibility to Land Acc2 Float Acc2 Accessibility to Land Cre_Ava Float YN Availability of Credit for Land Use Nat_Pre Float YN Presence of Natural Pressure


Table Annex_II.2 Summary of Sample Data used in Logistic Regression

Variable Name Obs Mean Std. Dev. Min Max MLUP 80 0.50 0.50 0 1

Age 80 50.74 13.03 25 83 SchYr 80 5.55 5.97 0 18

Dum_Lan_Eng_1 80 0.38 0.49 0 1 Dum_Lan_Eng_2 80 0.24 0.43 0 1

FT 80 1.41 0.50 0 1 Eco_Act_FM 80 2.10 1.29 1 8

Dum_LO1 80 0.63 0.49 0 1 Dum_LO2 80 0.14 0.35 0 1

LR 80 11825.00 23245.95 0 125000 Nei_LU 80 0.56 0.50 0 1 Ser_pro 80 10.87 3.78 3 21

Acc1 80 0.61 0.49 0 1 Acc2 80 0.21 0.41 0 1

Cre_Ava 80 0.21 0.41 0 1 Nat_Pre 80 0.55 0.50 0 1

N.B.: Obs.- Observation, Std. Dev.- Standard Deviation, Min- Minimum, Max - Maximum



xxi

Table Annex II.3 Summary Statistics of Categorical Variable

Variable Name Coding Name Frequency Parameter coding

Dum_Lan_Eng2 Otherwise 61 0 Personal 19 1

Dum_Lan_Eng1 Inheritance 30 1 Otherwise 50 0

FT Joint 33 0 Nuclear 47 1

Dum_LO1 Other 30 0 Sole 50 1

Dum_LO2 Borrowing 11 1 Other 69 0

Nei_LU Otherwise 35 0 Similar 45 1

Acc2 Otherwise 63 0 Very High 17 1

Acc1 High 49 1 Otherwise 31 0

Cre_Ava No 63 0 Yes 17 1

Nat_Pre No 36 0 Yes 44 1


Table Annex II.4 Classification Table

Observed Predicted

MLUP Percentage Correct


MLUP Rice Farming 0 40 .0 Shrimp Farming 0 40 100.0

Overall Percentage 50.0 N.B.: Constant is included in the model, the cut value is .500


Table Annex_II.5 Classification Table

Observed Predicted

MLUP Percentage Correct


MLUP Rice Farming 39 1 97.5 Shrimp Farming 1 39 97.5

Overall Percentage 97.5 N.B.: The cut value is .500


Table Annex_II.6 Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 93.514 15 .000

Block 93.514 15 .000

Model 93.514 15 .000

N.B.: df- degrees of freedom, sig.- significant level



xxii

Table Annex_II.7 Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 4.496 7 .721


Table Annex_II.8 Contingency Table for Hosmer and Lemeshow Test

MLUP = Rice Farming MLUP = Shrimp Farming Total

Observed Expected Observed Expected

Step

1 8 8.000 0 .000 8

2 8 8.000 0 .000 8

3 8 8.000 0 .000 8

4 7 7.739 1 .261 8

5 8 6.475 0 1.525 8

6 1 1.765 7 6.235 8

7 0 .020 8 7.980 8

8 0 .000 1 1.000 1

9 0 .000 23 23.000 23


Table Annex_II.9 Model Summary of Land Use Determinants

-2 Log likelihood Cox & Snell R Square Nagelkerke R Square 17.390 .689 .919


Table Annex_II.10 Wald Test of Sample Data

Wald Chi Square df Pr>F 7.16 15 0.95

N.B.: df- degrees of freedom, pr- Probability


Table Annex_II.11 Test of Data Classification

True Classified D ͂͂ D Total

+ 39 01 40 - 01 39 40

Total 40 40 80 Correctly Classified (%) 97.50%


Table Annex_II.12 Goodness-of-fit Test

Number of Observations = 80

Number of Covariate Patterns = 80

Pearson Chi Square (64) = 34.58

Probability > Chi Square = 0.9990



xxiii

Table Annex_II.13 Results of Binary Logit Model

Logistic regression Number of observation = 80

LR chi square (15) = 93.51

Probability > chi square = 0.0000

Log likelihood = -8.6949453 Pseudo R square = 0.8432

MLUP Coefficient Std. Err. z P>|z| [95% Conf. Interval]

Age -0.588 0.250 -2.35 0.019 -1.078 -0.098

SchYr 1.702 0.821 2.07 0.038 0.093 3.312

Dum_Lan_Eng_1 7.296 3.726 1.96 0.050 -0.007 14.599

Dum_Lan_Eng_2 41.034 18.629 2.20 0.028 4.522 77.545

FT -46.843 20.971 -2.23 0.026 -87.945 -5.741

Eco_Act_FM 32.007 14.293 2.24 0.025 3.993 60.020

Dum_LO1 58.267 27.529 2.12 0.034 4.312 112.222

Dum_LO2 24.926 12.236 2.04 0.042 0.943 48.908

LR 0.004 0.002 2.18 0.030 0.000 0.007

Nei_LU 9.599 4.998 1.92 0.055 -1.963 19.395

Ser_pro 3.220 1.492 2.16 0.031 0.296 6.144

Acc1 25.270 11.078 2.28 0.023 3.557 46.982

Acc2 24.540 10.583 2.32 0.020 3.798 45.281

Cre_Ava -8.554 4.901 -1.74 0.081 -18.158 1.055

Nat_Pre -19.193 8.854 -2.17 0.030 -36.547 -1.838

Constant -97.468 46.361 -2.10 0.036 -188.333 -6.603


Table Annex_II.14 Results of Logistic Regression

MLUP Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

Age 0.5552986 0.1388183 -2.35 0.019 .3402012 .9063946

SchYr 5.486967 4.504864 2.07 0.038 1.097706 27.42703

Dum_Lan_Eng_1 1474.629 5494.791 1.96 0.050 .9929336 2190007

Dum_Lan_Eng_2 6.62e+17 1.23e+19 2.20 0.028 92.06531 4.76e+33

FT 4.53e-21 9.51e-20 -2.23 0.026 6.40e-39 .0032112

Eco_Act_FM 7.95e+13 1.14e+15 2.24 0.025 54.22771 1.16e+26

Dum_LO1 2.02e+25 5.55e+26 2.12 0.034 74.57368 5.46e+48

Dum_LO2 6.69e+10 8.18e+11 2.04 0.042 2.568943 1.74e+21

LR 1.003645 0.0016781 2.18 0.030 1.000362 1.00694

Nei_LU 14753.96 73737.47 1.92 0.055 .8218002 2.65e+08

Ser_pro 25.02903 37.33899 2.16 0.031 1.344616 465.8968

Acc1 9.43e+10 1.04e+12 2.28 0.023 35.07538 2.53e+20

Acc2 4.54e+10 4.81e+11 2.32 0.020 44.61406 4.63e+19

Cre_Ava 0.0001933 0.0009473 -1.74 0.081 1.30e-08 2.872676

Nat_Pre 4.62e-09 4.09e-08 -2.17 0.030 1.34e-16 .1590741

Constant 4.68e-43 2.17e-41 -2.10 0.036 1.61e-82 .001356



xxiv

Table Annex_II.15 Marginal Analysis of Sample Data

Marginal effects after logistic

y = Linear prediction (log odds) (predict, xb)

= 27.24829

Variable dy/dx Std. Err. z P>|z| [95% Conf. Interval] x Age -.5882493 .24999 -2.35 0.019 -1.07822 -.098281 50.7375

SchYr 1.702376 .82101 2.07 0.038 .093222 3.31153 5.55

Dum_Lan_Eng_1* 7.296162 3.72622 1.96 0.050 -.007092 14.5994 .375

Dum_Lan_Eng_2* 41.03385 18.629 2.20 0.028 4.5225 77.5452 .2375

FT* -46.84293 20.971 -2.23 0.026 -87.9447 -5.74112 1.4125

Eco_Act_FM 32.00656 14.293 2.24 0.025 3.99319 60.0199 2.1

Dum_LO1* 58.26666 27.529 2.12 0.034 4.31179 112.222 .625

Dum_LO2* 24.92581 12.236 2.04 0.042 .943494 48.9081 .1375

LR .0036388 .00167 2.18 0.030 .000362 .006916 11825

Nei_LU* 9.599267 4.99781 1.92 0.055 -.196258 19.3948 .5625

Ser_pro 3.220036 1.49183 2.16 0.031 .296108 6.14396 10.8675

Acc1* 25.26952 11.078 2.28 0.023 3.5575 46.9815 .6125

Acc2* 24.53952 10.583 2.32 0.020 3.79805 45.281 .2125

Cre_Ava* -8.551443 4.90146 -1.74 0.081 -18.1581 1.05524 .2125

Nat_Pre* -19.19279 8.85445 -2.17 0.030 -36.5472 -1.83839 .55

N.B.: (*) dy/dx is for discrete change of dummy variable from 0 to 1


Figure Annex_II.1 Area under ROC Curve


0.0

00

.25

0.5

00

.75

1.0

0S

en

sitiv

ity

0.00 0.25 0.50 0.75 1.001 - Specificity

Sensitivity Reference

Area under ROC curve = 0.9919


xxv

Figure Annex_II.2 Sensitivity and Specificity versus Probability Cutoff


Table Annex_II.16 Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Age -.588 .250 5.537 1 .019 .555

SchYr 1.702 .821 4.299 1 .038 5.487

Dum_Lan_Eng1 7.296 3.726 3.834 1 .050 1474.630

Dum_Lan_Eng2 -41.034 18.629 4.852 1 .028 .000

FT -46.843 20.971 4.990 1 .026 .000

Eco_Act_FM 32.007 14.293 5.015 1 .025 79482945900397.050

Dum_LO1 -58.267 27.528 4.480 1 .034 .000

Dum_LO2 24.926 12.236 4.150 1 .042 66856103260.270

LR .004 .002 4.737 1 .030 1.004

Nei_LU -9.599 4.998 3.689 1 .055 .000

Ser_Pro 3.220 1.492 4.659 1 .031 25.029

Acc1 25.270 11.078 5.203 1 .023 94279064817.382

Acc2 -24.540 10.583 5.377 1 .020 .000

Cre_Ava 8.551 4.901 3.044 1 .081 5174.216

Nat_Pre 19.193 8.854 4.698 1 .030 216432796.464

Constant -38.616 21.019 3.375 1 .066 .000

N.B.: B-Coefficient, S.E.- Standard Error, df- Degrees of freedom, Sig.- Significant level, Exp(B)-

Expected coefficient


0.0

00

.25

0.5

00

.75

1.0

0S

en

sitiv

ity/

Sp

eci

ficity

0.00 0.25 0.50 0.75 1.00Probability cutoff

Sensitivity Specificity


xxvi

Table Annex_II.17 Observed and Probable Land Use Pattern of Each Sample

Case Selected Statusa Observed Predicted Predicted Group Temporary Variable

MLUP Resid ZResid

1 S R .000 R .000 -.015

2 S R .243 R -.243 -.567

3 S R .000 R .000 .000

4 S R .000 R .000 .000

5 S R .000 R .000 -.002

6 S R .000 R .000 .000

7 S R .000 R .000 .000

8 S R .000 R .000 .000

9 S R .092 R -.092 -.318

10 S R .000 R .000 .000

11 S R .000 R .000 .000

12 S R .207 R -.207 -.511

13 S R** .782 S -.782 -1.893

14 S R .000 R .000 .000

15 S R .000 R .000 .000

16 S R .000 R .000 .000

17 S R .293 R -.293 -.644

18 S R .014 R -.014 -.117

19 S R .018 R -.018 -.135

20 S R .000 R .000 -.002

21 S R .000 R .000 .000

22 S R .000 R .000 .000

23 S R .000 R .000 .000

24 S R .000 R .000 .000

25 S R .000 R .000 .000

26 S R .000 R .000 -.001

27 S R .000 R .000 .000

28 S R .000 R .000 -.004

29 S R .083 R -.083 -.301

30 S R .000 R .000 .000

31 S R .112 R -.112 -.356

32 S R .000 R .000 .000

33 S R .000 R .000 .000

34 S R .079 R -.079 -.293

35 S R .000 R .000 .000

36 S R .030 R -.030 -.177

37 S R .138 R -.138 -.400

38 S R .000 R .000 .000

39 S R .089 R -.089 -.312

40 S R .350 R -.350 -.734

41 S S 1.000 S .000 .

42 S S 1.000 S .000 .

43 S S 1.000 S .000 .

44 S S 1.000 S .000 .

45 S S .989 S .011 .108

46 S S .521 S .479 .958

47 S S 1.000 S .000 .

48 S S 1.000 S .000 .

49 S S .813 S .187 .479

50 S S 1.000 S .000 .

51 S S 1.000 S .000 .

52 S S .804 S .196 .493

53 S S 1.000 S .000 .

54 S S .992 S .008 .088


xxvii

55 S S 1.000 S .000 .

56 S S 1.000 S .000 .000

57 S S 1.000 S .000 .

58 S S 1.000 S .000 .

59 S S 1.000 S .000 .

60 S S 1.000 S .000 .

61 S S 1.000 S .000 .

62 S S 1.000 S .000 .

63 S S 1.000 S .000 .

64 S S 1.000 S .000 .

65 S S 1.000 S .000 .000

66 S S .859 S .141 .405

67 S S 1.000 S .000 .

68 S S 1.000 S .000 .002

69 S S 1.000 S .000 .000

70 S S 1.000 S .000 .000

71 S S 1.000 S .000 .

72 S S .889 S .111 .353

73 S S 1.000 S .000 .000

74 S S .650 S .350 .734

75 S S 1.000 S .000 .

76 S S .999 S .001 .032

77 S S** .036 R .964 5.138

78 S S 1.000 S .000 .

79 S S 1.000 S .000 .

80 S S .916 S .084 .303

N.B.: S = Selected, U = Unselected cases, and ** = Misclassified cases.


determinants of land use change in south-west region of bangladesh

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