Determinants of Land Use Change in South-west Region of
Bangladesh
Jahangir Alam
Economics Discipline
Social Science School
Khulna University
Khulna, Bangladesh
October, 2014
Determinants of Land Use Change in South-west Region of
Bangladesh
………………………………………..
Jahangir Alam
Student No.: 101502
Session: 2012-13
Supervisor
………………….………..
Md. Firoz Ahmed
Assistant Professor
Economics Discipline
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
ii
Determinants of Land Use Change in South-west Region of
Bangladesh
………………………………………………………….
Mohammed Ziaul Haider, Ph.D
Head
Economics Discipline
Social Science School
Khulna University
Khulna, Bangladesh
October, 2014
iii
Statement of Originality
Determinants of Land Use Change in South-west Region of
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
iv
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
Economics Discipline
Khulna University, Khulna
v
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
vi
Table of Contents
Title of Content
Acknowledgement
Abstract
Table of Contents
List of Maps
List of Tables
List of Figures and Graphs
Acronyms
Abbreviations
Chapter One: Introduction
1.1 Background of the Study
1.2 Objective of the Study
1.3 Rationale of the Study
1.4 Scope of the Study
1.5 Operational Definitions
1.6 Limitation of the Study
1.7 Structure of the Study
Chapter Two: Theoretical Background
2.1 Land Use Models
2.2 History and Trends of Land Use Models
2.3 Land Use Modeling Approaches and Models
2.3.1 Agent-Based Perspective
2.3.2 Systems Perspective
2.3.3 Narrative Perspective
2.3.4 The Fitting Data Model
2.3.5 Simulation Processes
2.3.6 Structural Models
2.3.7 Statistical or Reduced Form Models
2.3.8 Geographic Models
2.3.9 Economic Models
2.3.10 Stochastic Markov Model
2.3.11 Ecological Models
2.3.12 Dyna-CLUE model
2.3.13 Spatial Economical Model
2.3.14 Cellular Automata Model
2.3.15 Species-distribution Model
2.4 Economics, Econometrics and Land Use Research
Chapter Three: Literature Review
3.1 Land
3.2 Land Use
3.3 Land Use Change
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Title of Content
3.4 Land Cover
3.5 Land Cover Change
3.6 Land Use and Cover Change
3.7 Land Use Planning
3.8 Land Use Conflict
3.9 Methods Used to Identify Patterns and Changes of Land Use
and Cover
3.10 Variable Used in Modeling Land Use and Cover Changes
3.11 Type and Scope of Land Use and Cover Change
3.12 Observed Land Use Pattern
3.13 Global Land Use and Cover Trends
3.14 Land Use Trends in Bangladesh
3.15 Causes of Land Use and Cover Change
3.16 Impact of Land Use and Cover Change
3.17 Initiatives for Land Use and Cover Changes
3.18 Findings and Results of Land Research
3.19 Problems and Limitation of Land Use and Cover Researches
3.20 Research Gap
Chapter Four: Methods and Materials
4.1 Conceptualization of the Research Problem
4.2 Study Area
4.3 Research Design
4.4 Target Group
4.5 Sample Design
4.5.1 Sampling Techniques
4.5.2 Sample Size
4.5.3 Data Collection Method
4.6 Type of Data Used
4.7 Variables and Indicators
4.8 Model Specification
4.8.1 Logistic Regression for Land Use Change
4.8.2 Empirical Analysis of Land Use Determinants
4.9 Data Collection
4.9.1 Primary Data Collection
4.9.2 Secondary Information
4.10 Data Processing and Analysis
4.11 Writing the Thesis Paper
Chapter Five: Land Use Patterns and Changing Trends
5.1 Global Land Use Patterns
5.2 Land Use Trends of Bangladesh
5.3 Trends of Land Availability in Khulna Division
5.4 Land Use Trend in South-west Part of Bangladesh
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5.5 Land Use Policies in Bangladesh
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Chapter Six: Overview of Study Area and Respondent
48-63
6.1 Overview of Study Area
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6.2 Information of the Respondents
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6.2.1 Age and Gender of the Sample Population
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6.2.2 Educational Status
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6.2.3 Family Size and Composition of the Respondents
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6.2.4 Occupational Distribution
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6.2.5 Engagement Process in Present Land Use Pattern
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6.2.6 Land Ownership Pattern of Households
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6.2.7 Scenario of Assets and Non-assets of the Sample
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Households
6.2.8 Household Yearly Income
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6.2.9 Household Yearly Expenditure
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6.2.10 Households’ Farming Experience
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6.2.11 Training Facilities of Sample Population
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6.2.12 Credit Facility
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6.2.13 Plan to Change Land Use Pattern in Near Future
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6.2.14 Pressure and Regulation on Current Land Use
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Pattern
Chapter Seven: Results and Discussion
63-88
7.1 Lands Cultivated over Time
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7.2 Variation in Land Use Pattern
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7.3 Change in Land Use Pattern
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7.4 Location of Land
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7.5 Land Elevation
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7.6 Fertility of Land
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7.7 Salinity and Sand in Land
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7.8 Neighborhood Land Use Pattern
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7.9 Water Management Facilities
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7.10 Distance of Water Management Sources
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7.11 Way Used for Water Management System
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7.12 Cost of Water Management System
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7.13 Proximity to Nearest Infrastructure
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7.14 Land Rent
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7.15 Accessibility to Land
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7.16 Transport Mode and Available Facilities to Specific Land
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7.17 Cost of Transportation per Trip
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7.18 Availability of Input
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7.19 Demand for Final Product
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7.20 Market Location
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7.21 Price Distribution of Final Output
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Title of Content
7.22 Changes in Land Use Patterns of the Households
7.23 Conversion and Maintenance Cost
7.24 Cost-benefit of Land Use
7.25 Estimation of the Determinants of Land Use Change
Chapter Eight: Findings and Conclusion
8.1 Information through Focus Group Discussion
8.2 Findings of the Research
8.3 Comparison of Findings
8.4 Conclusion
8.5 Further Scope
List of References
List of Web References
Appendix I
Appendix II
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List of Maps
Title of Content
Map 6.1 Map of Bangladesh
Map 6.2 Map of Kaligonj Upazila
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List of Tables
Title of Content
Table 4.1 Description of Independent Variable
Table 4.2 Explanation of Variables in Empirical Analysis
Table 5.1 Land Use Trends in Bangladesh during 1977-2008
Table 5.2 Scenario of per Capita Arable and Irrigated Land
Table 5.3 Total Land Area of Bangladesh during 1976-2010
Table 5.4 Rice and Shrimp Farming Area during 1976-2010
Table 5.5 Land Use Statistics of Khulna Division in 2008
Table 5.6 Land Use Pattern in Khulna Division during 1976-2010
Table 6.1 Khulna Division at a Glance
Table 6.2 General Information of Kaligonj Upazila
Table 6.3 Age and Gender Distribution
Table 6.4 Educational Status of the Decision maker
Table 6.5 Literacy Status of Sample Population
Table 6.6 Family Type of Sample Population
Table 6.7 Distribution of Economically Active Family Member
Table 6.8 Occupational Distribution of Sample Household
Table 6.9 Engagement Process in Current Land Use Pattern
Table 6.10 Information on Land and Non-land Assets
Table 6.11 Distribution of Income from Land and Non-land Assets
Table 6.12 Yearly Expenditure of Sample Household
Table 7.1 Amount of Land Cultivated over Time
Table 7.2 Variation in Land Use Pattern
Table 7.3 Distribution of Water Source
Table 7.4 Distances of Water Source and Disposal Location
Table 7.5 Way used for Water management
Table 7.6 Cost of Irrigation and Water Disposal
Table 7.7 Proximity to Nearest Infrastructures
Table 7.8 Land Rent Scenario per Year
Table 7.9 Cost of Input and Output Transportation
Table 7.10 Price Distribution of Final Output
Table 7.11 Summary Statistics
Table 7.12 Estimation of Determinants of Land Use Change
Table 7.13 Marginal Analysis of Determinants of Land Use Change
Table Annex_II.1 Description of Sample Data used in Logistic Regression
Table Annex_II.2 Summary of Sample Data used in Logistic Regression
Table Annex II.3 Summary Statistics of Categorical Variable
Table Annex II.4 Classification Table
Table Annex_II.5 Classification Table
Table Annex_II.6 Omnibus Tests of Model Coefficients
Table Annex_II.7 Hosmer and Lemeshow Test
Table Annex_II.8 Contingency Table for Hosmer and Lemeshow Test
Table Annex_II.9 Model Summary of Land Use Determinants
Table Annex_II.10 Wald Test of Sample Data
Table Annex_II.11 Test of Data Classification
Table Annex_II.12 Goodness-of-fit Test
Table Annex_II.13 Results of Binary Logit Model
Table Annex_II.14 Results of Logistic Regression
Table Annex_II.15 Marginal Analysis of Sample Data
Table Annex_II.16 Variables in the Equation
Table Annex_II.17 Observed and Probable Land Use Pattern of Each Sample
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List of Figures and Graphs
Title of Content
Figure 2.01 Economic Dynamics of Land Use System
Figure 5.1 Land Use Statistics of Khulna Division in 2008
Figure 5.2 Percentage Land Uses during 1989-2010
Figure 6.1 Land Ownership Pattern of the Sample Population
Figure 6.2 Farming Experience
Figure 6.3 Training Facilities on Specific Land Use
Figure 6.4 Credit Facilities on Specific Land Use
Figure 6.5 Expectation of Change in Current Land Use
Figure 6.6 Expected Land Use Pattern in Future
Figure 6.7 Determinants of Expected Changes in Land Use
Figure 6.8 Pressure and Regulation Scenario on Land Use
Figure 7.1 Land Use Statistics of Sample Households during (2010-2014)
Figure 7.2 Changes in Total Land Size during 2010-2014
Figure 7.3 Location of Sample Land
Figure 7.4 Land Elevation Scenario of Sample Land
Figure 7.5 Fertility Scenario of Sample Land
Figure 7.6 Distributions of Salinity and Sand in Land
Figure 7.7 Neighborhood Land Use Patterns
Figure 7.8 Accessibility to Sample Land
Figure 7.9 Mode of Transport Used
Figure 7.10 Transport Facilities for Specific Land Use Pattern
Figure 7.11 Availability of Input for Specific Land Use
Figure 7.12 Demand Prototypes for Final Output
Figure 7.13 Distribution of Market for Final Product
Figure 7.14 Changes in Land use Patterns (early 2008- mid 2014)
Figure 7.15 Initial Conversion Cost for Specific Land Use Pattern
Figure 7.16 Yearly Land Maintenance Expenditure
Figure 7.17 Cost-benefit Analysis of Rice and Shrimp Farming
Figure 7.18 Change in Profit based on Cropping Frequency
Figure Annex_II.1 Area under ROC Curve
Figure Annex_II.2 Sensitivity and Specificity versus Probability Cutoff
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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
xiv
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
xv
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
1
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
2
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, socioeconomic 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
3
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 7 th
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).
4
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.
5
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.
6
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
7
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
8
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.
9
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
10
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
11
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).
12
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).
13
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 regression 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).
14
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
15
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 𝑖𝑓 𝑦𝑖∗ > 𝜏
𝑦𝑖 = {
0 𝑖𝑓 𝑦𝑖∗ ≤ 𝜏
2.1
The parameter 𝜏 in above equation represents a threshold and for observations
of 𝑦𝑖∗ ≤ 𝜏, the observed binary variable 𝑦𝑖 takes the value zero (0) and when 𝑦𝑖∗ < 𝜏,
the dependent variable 𝑦𝑖 is equal to 1 i.e. land use pattern will be changed into type
j. But as dependent variable 𝑦𝑖 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
16
normally distributed, then probit model is used for a binary 𝑦𝑖 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).
17
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).
18
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).
19
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
20
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
21
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.
22
(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).
23
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 nonproductive 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)
24
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).
25
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,
26
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), shrimpfarming 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 inputintensive 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
27
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).
28
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.
29
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.
30
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).
31
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
32
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
Age
Age of the decision maker of sample household
Year of Schooling Total year passed by decision make in study purposes
with no study gap
Land Engagement How has the decision maker got involved in current
land use pattern
Family Type
Nature of family based on family size and composition
Economic
Total number of family member who are economically
Member
active through legal job holdings
Land Ownership
Ownership of the concerned land of the household
Land Rent
Total rent paid by household per year for sample land
Neighborhood
Land use patterns practiced by the nearby land owners
Land Use
Proximity to
Distance of concerned service point from the sample
Service Centre
household/land
Accessibility
Accessibility of the land from and/or with basic
infrastructure and services
Availability of
Availability of credit facility for each of the concerned
Credit
land use pattern
Natural Pressure
Occurrence of natural disasters and/or pressure on
sample land use
Source: Author’s Compilation, 2014
33
Unit
Year
Year
Dummy
Dummy
Number
Dummy
BDT
Dummy
kilometer
Dummy
Dummy
Dummy
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 𝑋𝑛 in the prediction of the
probability of one state of the dependent variable (i.e. defined as a categorical
variable 𝑌). Broadly, suppose 𝑋 is a vector of explanatory variables and p is the
response probability to be modeled with, in the case of a dichotomous dependent
variable, 𝑝 = 𝑃𝑟(𝑌 = 1|𝑋), with 𝑌 = 0 meaning rice farming land and 𝑌 = 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.
𝑝
𝑙𝑜𝑔𝑖𝑡(𝑝) = log[(1−𝑝)] = 𝛼 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ + 𝛽𝑛 𝑋𝑛
(1);
Here 𝛼 is the intercept and 𝛽𝑛 are slope parameters. The probability values can
thus be quantitatively expressed in terms of explanatory variables by
𝑝=
exp(𝛼 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ + 𝛽𝑛 𝑋𝑛 )
1 + exp(𝛼 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ + 𝛽𝑛 𝑋𝑛 )
(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
34
expressing the same thing (Menard, 1995) which are computed as exponential of the
parameter estimates (Serneels and Lambin, 2001) and be expressed as follows.
𝑂𝑑𝑑𝑠(𝑝) = exp(𝛼 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ + 𝛽𝑛 𝑋𝑛 )
(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.
log(𝛽)
𝜌2 = 1 − log(𝐶)
(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.
𝑀𝐿𝑈𝑃 = 𝛽0 + 𝛽1 𝐴𝑔𝑒 + 𝛽2 𝑆𝑐ℎ𝑌𝑟 + 𝛽3 𝐷𝑢𝑚𝐿𝑎𝑛𝐸𝑛𝑔1 + 𝛽4 𝐷𝑢𝑚𝐿𝑎𝑛𝐸𝑛𝑔2 + 𝛽5 𝐹𝑇
+ 𝛽6 𝐸𝑐𝑜𝐴𝑐𝑡𝐹𝑀 + 𝛽7 𝐷𝑢𝑚𝐿𝑂1 + 𝛽8 𝐷𝑢𝑚𝐿𝑂2 + 𝛽9 𝐿𝑅 + 𝛽10 𝑁𝑒𝑖𝐿𝑈
+ 𝛽11 𝑆𝑒𝑟𝑃𝑟𝑜 + 𝛽12 𝐷𝑢𝑚𝐴𝑐𝑐1 + 𝛽13 𝐷𝑢𝑚𝐴𝑐𝑐2 + 𝛽14 𝐶𝑟𝑒𝐴𝑣𝑎
+ 𝛽15 𝑁𝑎𝑡𝑃𝑟𝑒 + 𝑢𝑖
(5)
Here, 𝑀𝐿𝑈𝑃 denotes the dependent variable; 𝛽0 is a constant term while
𝛽1 , 𝛽2 , … , 𝛽15 are the coefficients to be estimated and 𝑢𝑖 is the error term. The details
are being enumerated in the next table.
35
Table 4.2 Explanation of Variables in Empirical Analysis
Indicator
𝑀𝐿𝑈𝑃
Variable Name
Major
Land
Use
Pattern
(1=Shrimp
Parameter
Likely Sign
N/A
N/A
Farming, 0=Rice Farming)
𝐴𝑔𝑒
Age in year
𝛽1
-
𝑆𝑐ℎ𝑌𝑟
Year of schooling
𝛽2
+
𝐷𝑢𝑚𝐿𝑎𝑛𝐸𝑛𝑔1
Engagement
on
current
land
use
𝛽3
-
land
use
𝛽4
+
(1=Inheritance, 0=Otherwise)
𝐷𝑢𝑚𝐿𝑎𝑛𝐸𝑛𝑔2
Engagement
on
current
(1=Personal Interest, 0=Otherwise)
𝐹𝑇
Family type (1=Nuclear, 0=Joint)
𝛽5
+
𝐸𝑐𝑜𝐴𝑐𝑡𝐹𝑀
Number of economically active family
𝛽6
+
(1=Sole
𝛽7
+
Land ownership pattern (1=Borrowing,
𝛽8
-
member in sample household
𝐷𝑢𝑚𝐿𝑂1
Land
ownership
pattern
proprietorship, 0=Otherwise)
𝐷𝑢𝑚𝐿𝑂2
0=Otherwise)
𝐿𝑅
Land rent per year in BDT
𝛽9
+
𝑁𝑒𝑖𝐿𝑈
Neighborhood land use pattern (1=Similar,
𝛽10
+
𝛽11
+
0=Otherwise)
𝑆𝑒𝑟𝑃𝑟𝑜
Proximity to respective service centre in
kilometer
𝐷𝑢𝑚𝐴𝑐𝑐1
Accessibility (1=High, 0=Otherwise)
𝛽12
+
𝐷𝑢𝑚𝐴𝑐𝑐2
Accessibility (1= Very High, 0=Otherwise)
𝛽13
+
𝐶𝑟𝑒𝐴𝑣𝑎
Availability of credit (1=Yes, 0=No)
𝛽14
+
𝑁𝑎𝑡𝑃𝑟𝑒
Occurrence of natural pressure (1=Yes,
𝛽15
-
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.
36
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
37
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 openended 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.
38
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
39
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).
40
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
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
Agriculture
Source: Uddin and Gurung, 2010; Rahman, 2010
41
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
Irrigation Land
Arable 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
1976
Agro Land
Non-agro Land
Total Land
Area in ‘000’ ha
2010
2000
Area
% of total
Area % of total
Area
% of total
13303
91.83
12422
87.69
12176
83.53
1183
8.17
1788
12.31
2400
16.47
14487
100.00
14530
100.00
14577
100.00
Source: Hasan et al., 2013
42
Bangladesh has gained a total area of 905 sq km (i.e. 90,512ha) during 19762010 due to accretion in southern coastal zone (Table 5.3) while lands used for nonagro 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 2010
Area (ha) in 1976
Area (ha) in 2000
9761450
9439541
8751937
582
143506
175663
Cropland
Aquaculture
Source: Hasan et al., 2013
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
Bagerhat
Khulna
Satkhira
Cuadanga
Jessore
Jhenaidah
Kustia
Magura
Meherpur
Narail
339217
502835
436178
254916
591030
385860
432249
189589
152544
151052
Nonfarm
Holdings
106600
295092
184142
81218
216407
129266
187033
49390
39872
41520
Number
of Farm
Holdings
232617
207743
252036
173698
374623
256594
245216
140199
112672
109532
Source: BBS, 2010
43
Number of Holdings
Agro
Labor
Owner
Tenant Tenant
Households
Owner
235792
72173
31252
144577
319009
86292
97534
144350
302240 103903
30035
227847
146363
91437
17116
102661
375890 158654
56484
240843
243045 122147
20668
152857
265720 125990
40539
152738
111405
69876
8308
63254
85685
59340
7519
69138
92121
51211
7720
47722
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
Urban
Rural
20868
Agro Labor Holdings
1325119
93419
223756
Number of Tenant Holdings
28219
Number of Tenant Owner Holdings
912804
184133
Number of Owner Holdings
1993139
45675
Number of Farm Holdings
2059255
260096
Non-farm Holdings
1070444
305771
All Holdings
3129699
0
1000000
2000000
3000000
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.
44
Table 5.6 Land Use Pattern in Khulna Division during 1976-2010
Land Cultivated (ha)
1976
2000
2010
Yearly Change (ha)
1976-
2000-
1976-
2000
2010
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
1727
2779
5264
44
249
104
Urban & Industrial
Source: Hasan et al., 2013
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,
45
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
Percentage of Total Land
45
1989
1999
2009
38.5
40
36
35
30.5
29
30
25
20
19.5 20
19.5
15.7
15.5
15
10
4.8 4.5 3.5
5
0
Built up Area
Water Bodies
Vegetation
Agriculture
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).
46
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.
47
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, socioeconomic 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
1500-5000
2009).
varies
mm
Bangladesh
between
(Mohammad,
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 subdistricts 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
48
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
700
10
64
61
Village Pourashava Literacy
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.
49
Table 6.2 General Information of Kaligonj Upazila
Name of Union and
Area (Acre)
Population
GO Code
Male
Literacy
Female
Rate (%)
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
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
22878
11824
11052
46.80
Nalta (79)
Bhara Simla (07)
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, WestBengal 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].
50
Map 6.2 Map of Kaligonj Upazila
Source: [iv]
51
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
Male
Percent
Female
Shrimp Farming
Percent
Male
Percent
Female
Total
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).
52
Table 6.4 Educational Status of the Decision maker
Rice Farming
Shrimp Farming
Total
Literate
Illiterate
Literate
Illiterate
Male
26
08
28
06
68
Female
05
01
05
01
12
Total
31
09
33
07
Source: Author’s Compilation Based on Field Survey, 2014
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
Rice
Primary
Intermediate
College
Shrimp Rice Shrimp Rice Shrimp Rice Shrimp
Total
Male
02
02
08
07
0
01
16
18
54
Female
01
0
0
02
01
0
03
03
10
03
02
08
09
01
01
19
21
Total
05
17
02
40
64
Source: Author’s Compilation Based on Field Survey, 2014
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.
53
Table 6.6 Family Type of Sample Population
Decision Maker of the Household in
Rice Farming
Shrimp Farming
Male
Female
Male
Female
Nuclear
22
03
17
05
Joint
12
03
17
01
Total
34
06
34
06
Source: Author’s Compilation Based on Field Survey, 2014
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
54
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
55
6.2.5
Engagement Process in Present Land Use Pattern
In this sub-point, author has tried to note how the sample households has come
and engaged itself to the current land use pattern as presented through below table.
Table 6.9 Engagement Process in Current Land Use Pattern
Rice Farming
Shrimp Farming
Total
Frequency
Percent
Frequency
Percent
Through Inheritance
21
26
09
11
30
Personal Interest
03
04
16
20
19
Tradition and Belief
16
20
15
19
31
Source: Author’s Compilation Based on Field Survey, 2014
Most of the households are observed in agro farming through inheritance
followed by tradition as well as belief and personal interest respectively while that in
shrimp farming because of personal interest followed by tradition as well as belief and
inheritance respectively. Analysis of collected data also shows that though male
headed households have taken shrimp farming rather than rice farming, no female
headed household has shifted to shrimp farming by dint of personal interest. On the
contrary, female headed domestic are engaged in the inherited land use pattern.
6.2.6
Land Ownership Pattern of Households
As already prominent that each and every sample household possesses at least
some lands for settlement if marginalized in nature while the well to do households
possess lands for cultivation, pasture and various purposes along with homestead
lands. The next figure shows land ownership scenario of sample population.
Figure 6.1 Land Ownership Pattern of the Sample Population
Rice
Shrimp
30
Frequency
30
20
20
12
8
10
7
3
0
Sole
Proprietorship
Joint
Borrowing
Source: Author’s Compilation Based on Field Survey, 2014
56
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
57
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
Source: Author’s Compilation Based on Field Survey, 2014
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
58
6.2.10 Households’ 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
Frequency
Rice
20
16
12
8
4
0
Shrimp
17
15
13
11
8
4
5
3
4
0
0
Less than 5
05--10
10--15
15-20
20-30
0
More than 30
Year of Experience
Source: Author’s Compilation Based on Field Survey, 2014
It 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.
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 6.3 Training Facilities on Specific Land Use
Rice
Shrimp
34
Frequency
40
30
30
20
10
6
10
0
Training
No Training
Source: Author’s Compilation Based on Field Survey, 2014
Sample data shows that only 16 out of 80 sample land users including 25%
shrimp farmers and 15% rice (i.e. 15%) have got training while about 80% of the
sample population doesn’t have any training.
59
6.2.12 Credit Facility
Since there is some time lag between production and getting return from each
land use pattern along with investment deficiency during land use, credit has become
endemic in land use decision. Hence, this paper now describes the scenario of
availability of credit among sample population as follows.
Figure 6.4 Credit Facilities on Specific Land Use
Rice
40
Frequency
Shrimp
36
27
30
20
13
4
10
0
Credit
No Credit
Source: Author’s Compilation Based on Field Survey, 2014
About 79% of the total population has said positively that credits are available
in the study area while credits are more available in case of rice farming than that of
shrimp. Moreover, 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.
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 surroundings, the
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 which is enumerated below.
Figure 6.5 Expectation of Change in Current Land Use
Rice
Shrimp
Frequency
60
40
40
20
26
14
0
0
Expectation for Change
No Expectation for Change
Source: Author’s Compilation Based on Field Survey, 2014
60
It can be said that only 18% of the total sample (Figure 6.5) has expectation
for change in their current land use pattern while all of them (14 respondents) are
engaged now in rice farming. Keeping pace with this, this paper now describes the
expected land use patterns by respondents expecting changes in their land use pattern.
Figure 6.6 Expected Land Use Pattern in Future
Water
Bodies, 2,
14%
Shrimp
Farming,
6, 43%
Mixed
Use, 6,
43%
Source: Author’s Compilation Based on Field Survey, 2014
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 below.
Figure 6.7 Determinants of Expected Changes in Land Use
Frequency
6
5
Frequency
4
4
3
2
2
0
Economic Benefit
Neighborhood
Characteristics
Family Demand
Pressure
Source: Author’s Compilation Based on Field Survey, 2014
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. Moreover, during the
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 land use decision.
61
6.2.14 Pressure and Regulation on Current Land Use Pattern
Literature shows that natural as well as human induced pressures are the most
important determinant in this age where govt. intervention is common to ensure the
optimal uses of each resource. therefore, the author has collected data on natural
pressure (i.e. hazards like flood, drought), human activities (i.e. intentional conflict,
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 as given in next figure.
Figure 6.8 Pressure and Regulation Scenario on Land Use
Frequency
Rice
30
25
20
15
10
5
0
Shrimp
27
23
18
17
15
Human Induced
Pressure
Natural Pressure
16
Land Use Regulation
Source: Author’s Compilation Based on Field Survey, 2014
Rice farming lands are getting pressure more from human induced activities as
well as natural phenomenon than shrimp lands while shrimp farming lands are facing
higher land use regulation in the study area (Figure 6.8).
Keeping pace with the broad study areas, village Pirozpur has also observed
changes in land use patterns especially lands close to the river through shrimp farming
from rice and other lands with non-productive uses like settlements, roads and
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. However, to tackle the adverse effect of
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.
62
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: 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛/𝑀𝑒𝑎𝑛 ∗ 100
Source: Author’s Compilation Based on Field Survey, 2014
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).
63
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
Frequency
Figure 7.1 Land Use Statistics of Sample Households during (2010-2014)
30
25
20
15
10
5
0
24
20 (60%)
(50%)
Rice
Shrimp
15
15
(38%) (38%)
5
(12%) 1
(2%)
Increased
Decreased
Remain Constant
Source: Author’s Compilation Based on Field Survey, 2014
64
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
30
Frequency
25
24
(60%)
20
Rice
14
(35%)
15
10
23
(58%)
Shrimp
13
(33%)
3
3
(7%) (7%)
5
0
Increased
Decreased
No Change
Source: Author’s Compilation Based on Field Survey, 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
65
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
information which are given with the frequency of each as follows.
Figure 7.3 Location of Sample Land
Frequency
Rice
50
40
30
20
10
0
Shrimp
40 (100%)
20
(50%)
20
(50%)
0
0
0
Close to Sweet Water Close to Saline Water No Certain Source
Source
Source
Source: Author’s Compilation Based on Field Survey, 2014
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 the
land for the use of rice farming (Figure 7.3). Here, no certain source includes farmers
who either use rain water if available or irrigated water for farming.
7.5
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, data on land
elevation 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
population on land elevation are being enumerated below.
Figure 7.4 Land Elevation Scenario of Sample Land
Frequency
Rice
25
20
15
10
5
0
13
(16%)
Shrimp
15
(19%)
21
(26%) 16
(20%)
6
(8%)
5
(6%)
4
(5%)
0
Very low
Low
Moderate
High
Source: Author’s Compilation Based on Field Survey, 2014
66
Here, land elevation is being classifies into five categories such as very low
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. However, above presentation of data
shows that there is no rice farming in very low as well as in very highly elevated lands
while no shrimp farming in very highly elevated land. Sample data also shows that
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 that
of shrimp is 19%, 20% and 5% respectively (Figure 7.4). The data shows that shrimp
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 (i.e. very low fertility
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 7.5 Fertility Scenario of Sample Land
Frequency
Rice
30
25
20
15
10
5
0
Shrimp
26
26
12
7
7
2
0
Low fertility
Moderate fertility
High fertility
0
Very high fertility
Source: Author’s Compilation Based on Field Survey, 2014
Sample data shows that low fertile as well as moderately fertile lands are
highly used for shrimp farming followed by little high fertile lands while rice farming
lands are mostly very high, high and moderate in fertility in the sample lands.
67
7.7
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 describes the
scenario of the salinity and sand situation in the sample land as described follows.
Figure 7.6 Distributions of Salinity and Sand in Land
Rice
Frequency
25
Shrimp
20
20
17
17
13
15
8
10
5
2
2
1
0
0
0
Very low
Low
Moderate
High
Very high
Source: Author’s Compilation Based on Field Survey, 2014
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 identified various neighborhood land uses of the study area
such as rice farming, shrimp farming, fellow land, mixed farming, water bodies and
homestead along with the identifying of various existing land use patterns. The
following are the demonstrations of the neighborhood characteristics observed.
Figure 7.7 Neighborhood Land Use Patterns
Rice Farming
Frequency
25
Shrimp Farming
Fellow Land
Mixed Use
Water bodies
23
(58%)
22
(56%)
20
15
10
5
6
(15%)
6
4 (15%)
1
1 (10%)
(2%) (2%)
8
(20%)
7
(18%)
0
1
1
(2%) (2%)
0
Rice
Shrimp
Source: Author’s Compilation Based on Field Survey, 2014
68
Homestead
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
Irrigation
Disposal
Sources for Shrimp Farming
Irrigation
Disposal
Freq. Percent Freq. Percent Freq. Percent Freq. Percent
River
0
12
30%
40
Pond
11
27.5%
20
50%
Shallow Tube Well
11
27.5%
Rain Water
18
50.0%
Others
0
39
98%
0
01
2%
0
0
0
0
0
0
0
0
08
20%
100%
Source: Author’s Compilation Based on Field Survey, 2014
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.
69
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
Irrigation
Disposal
Irrigation
Disposal
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: Author’s Compilation Based on Field Survey, 2014
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
Irrigation
Disposal
Irrigation
Disposal
Canal
05
30
40
40
Machinery
17
0
0
0
Human Labor
06
0
0
0
Uncertain
12
10
0
0
Source: Author’s Compilation Based on Field Survey, 2014
70
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
Irrigation
Disposal
Irrigation
Disposal
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
1971.25
83.75
5275.00
1006.25
Mean
Source: Author’s Compilation Based on Field Survey, 2014
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.
71
Table 7.7 Proximity to Nearest Infrastructures
Input
Output
Nearest
Agro/Fishery
Market
Market
Road
Office
Rice Shrimp Rice Shrimp Rice Shrimp
No Distance
01
05
04
35
36
Rice
Shrimp
0 – 1 km
06
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
07
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
72
Table 7.8 Land Rent Scenario per Year
Rice Farming Land
Freq. Percent
Mean
Shrimp Farming Land
St. Err. Freq. Percent
Mean St. Err.
No Rent
32
40
0
0
20
25
0
0
1 – 15000
04
05 13000
1225
01
01 10000
-
15000 - 30000
03
04 18000
1000
10
13 22400
1360
More than 30000
01
01 45000
-
09
11 62333
11741
Summary
40
50
1416
40
50 19875
4694
3775
Source: Author’s Compilation Based on Field Survey, 2014
Households pay less rent for rice farming land than that of shrimp. However,
32 rice farmers and 20 shrimp farmers pay no rent for their land while the rest sample
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.
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 7.8 Accessibility to Sample Land
Rice
25
24
(62%) (60%)
30
Frequency
25
20
15
10
5
Shrimp
10
(25%)
12
(30%)
5
(13%)
4
(10%)
0
Moderate
High
Very high
Source: Author’s Compilation Based on Field Survey, 2014
About 25% of the rice lands are moderately accessible (Figure 7.8) while the
rate is only 10% in case of shrimp lands. However, both sample rice and shrimp lands
show similar scenario in terms of highly accessibility which is 62% and 60%
respectively, but when dealing with very high accessibility, shrimp farms show higher
ratio (about 30%) than that of rice farming lands (13% only).
73
7.16
Transport Mode and Available Facilities to Specific Land
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 used by the
sample households for their concerned land use as follows.
Figure 7.9 Mode of Transport Used
Rice
22
25
Shrimp
23
18
20
15
10
7
10
5
0
0
Motorized
Non-Motorized
Human Labor
Source: Author’s Compilation Based on Field Survey, 2014
Sample households used three types of transport modes i.e. motorized, nonmotorized and 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 nonmotorized vehicles than shrimp farms and even use human labor for transport.
Keeping pace with this, author has described the nature of transport facility as below.
Figure 7.10 Transport Facilities for Specific Land Use Pattern
Frequency
Rice
30
25
20
15
10
5
0
Shrimp
25
23
16
10
5
1
Moderate
High
Very high
Source: Author’s Compilation Based on Field Survey, 2014
Lands with higher transport facilities are observed to be used mostly for
shrimp farming rather than rice among the sample households (Figure 7.10).
74
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. Therefore, this paper here describes
the transport cost per trip incurred by each land use patterns as follows.
Table 7.9 Cost of Input and Output Transportation
Input Transport Cost
Rice
Shrimp
Output Transport Cost
Rice
Shrimp
03
39
37
01
No Cost
0 - 500
40
38
500 - 1000
01
1000 - 1500
More than 1500
01
Mean
134.88
281.50
230.75
Source: Author’s Compilation Based on Field Survey, 2014
63.50
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 to generate
less transport cost in case of output than the rice farming lands. Moreover, transport
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 that land use pattern and vice versa. Moreover, field
survey as well as literature demonstrates that shrimp farming in south-west
Bangladesh are flourishing because of locally available inputs.
Figure 7.11 Availability of Input for Specific Land Use
Rice
Frequency
25
20
22
19 (55%)
(48%)
Shrimp
20
(50%) 17
(43%)
15
10
1
1
(2%) (2%)
5
0
Moderate
High
Very High
Source: Author’s Compilation Based on Field Survey, 2014
75
Inputs of rice farming lands are 48% moderately, 50% highly and rest 2% are
very highly available while that of shrimp farming are respectively 55%, 43% and 2%
(Figure 7.11). Inputs of rice farming are more available locally than that of shrimp.
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 7.12 Demand Prototypes for Final Output
Rice
Shrimp
28
Frequency
30
21
20
15
12
10
4
0
0
Moderate
High
Very high
Source: Author’s Compilation Based on Field Survey, 2014
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.
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. Therefore, this paper has
demonstrated market location of each final output as follows.
Figure 7.13 Distribution of Market for Final Product
Rice
Frequency
40
27
Shrimp
30
20
13
10
0
Local
External
Source: Author’s Compilation Based on Field Survey, 2014
76
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
Source: Author’s Compilation Based on Field Survey, 2014
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.
77
Figure 7.14 Changes in Land use Patterns (early 2008- mid 2014)
Rice
Total
47
50
Frequency
Shrimp
40
33
24
30
23
16
20
17
10
0
Change
No Change
Source: Author’s Compilation Based on Field Survey, 2014
About 59% of sample population has changed their land use patterns over the
last five years while rest 41% remained the same land use patterns. Here is to be noted
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-depreciable asset, each type of changes in land use patterns
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
30
28
Rice
Shrimp
Frequency
25
20
15
10
10
5
6
2
7
5
0
1
7
0
7
0
6
0
0 1
0
No Cost 0 - 10000 10000 - 20000 - 30000 - 50000 - 70000 20000 30000 50000 70000 100000
Source: Author’s Compilation Based on Field Survey, 2014
78
More
than
100000
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.
Frequency
Figure 7.16 Yearly Land Maintenance Expenditure
18
16
14
12
10
8
6
4
2
0
17
Rice
17
Shrim
13
10
6
6
5
3
0
0
1
0
2
0
0
0
Source: Author’s Compilation Based on Field Survey, 2014
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.
79
Figure 7.17 Cost-benefit Analysis of Rice and Shrimp Farming
(Rice Farming thrice per year)
In BDT
Rice
Shrimp
229525
Yearly Cost at BDT
250000
200000
142235.5
150000
139793.8
89678.63
104625
87289
100000
50000
42811.25
50115.13
0
Production Cost
Total Cost
Total Earnings
Profit per Year
Source: Author’s Compilation Based on Field Survey, 2014
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
Rice
120000
Shrimp
Profit in BDT
100000
80000
60000
40000
20000
0
Profit
(Thrice)
Profit
(Summer)
Profit
(Rainy)
Profit
(Winter)
Profit
Profit
Profit
(Sum and (Sum and (Rainy and
Winter)
Rainy)
Winter)
Source: Author’s Compilation Based on Field Survey, 2014
80
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
Age
Year of Schooling
Land Engagement through
inheritance
Land Engagement by
personal interest
Family Type
Economically active family
member
Land ownership by sole
proprietorship
Land ownership by
borrowing
Land rent
Neighborhood land use
pattern
Proximity to service centre
High accessibility
Very high accessibility
Availability of credit
Natural pressure
Rice Farmers
Mean St. Err. CV (%)
Shrimp Farmers
Mean St. Err. CV (%)
52.53
5.43
2.08
0.97
25.07
112.34
48.95
5.68
2.02
0.94
26.15
104.23
0.53
0.08
96.38
0.23
0.07
188.00
0.08
0.04
356.00
0.40
0.08
124.00
1.38
0.08
35.64
1.45
0.08
34.76
1.58
0.12
49.59
2.63
0.23
56.38
0.75
0.07
58.53
0.50
0.08
101.20
0.08
0.42
365.00
0.20
0.06
202.50
237.20 19875.00 4694.40
149.38
3775.00 1415.79
0.55
0.08
91.64
0.58
0.80
0.87
10.03
0.63
0.13
0.10
0.68
0.51
0.08
0.05
0.05
0.08
32.36
78.45
268.00
304.00
70.22
11.71
0.60
0.30
0.33
0.43
0.65
0.08
0.07
0.08
0.08
35.18
82.67
154.67
145.85
117.88
Source: Author’s Estimation, 2014
81
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
82
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,
83
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
Age
-0.588**
Year of Schooling
1.702**
Land Engagement through inheritance
7.296*
Land Engagement by personal interest
41.034**
Family Type
-46.843**
Economically active family member
32.007**
Land ownership by sole proprietorship
58.267**
Land ownership by borrowing
24.926**
Land rent
0.004**
Neighborhood land use pattern
9.600*
Proximity to service centre
3.220**
High accessibility
25.270**
Very high accessibility
24.540**
Availability of credit
-8.551*
Natural pressure
-19.193**
Constant
-97.468**
LR Chi-square Value (15)
Pseudo R Square
Probability > Chi Square
N.B.:** and * shows 5% and 10% significant level respectively
Source: Author’s Estimation, 2014
84
St. Err.
0.250
0.821
3.726
18.629
20.970
14.292
27.528
12.236
0.002
4.998
1.492
11.078
10.583
4.902
8.855
46.361
p>|z|
0.019
0.038
0.050
0.028
0.026
0.025
0.034
0.042
0.030
0.055
0.031
0.023
0.020
0.081
0.030
0.036
93.5100
0.8432
0.0000
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
85
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.
Age
-.5882493
.24999
Year of Schooling
1.702376
.82101
Land Engagement through inheritance*
7.296162
3.72622
Land Engagement by personal interest*
41.03385
18.629
Family Type*
-46.84293
20.971
Economically active family member
32.00656
14.293
Land ownership by sole proprietorship*
58.26666
27.529
Land ownership by borrowing*
24.92581
12.236
Land rent
.0036388
.00167
Neighborhood land use pattern*
9.599267
4.99781
Proximity to service centre
3.220036
1.49183
High accessibility*
25.26952
11.078
Very high accessibility*
24.53952
10.583
Availability of credit*
-8.551443
4.90146
Natural pressure*
-19.19279
8.85445
N.B.: (*) dy/dx is for discrete change of dummy variable from 0 to 1
P>|z|
0.019
0.038
0.050
0.028
0.026
0.025
0.034
0.042
0.030
0.055
0.031
0.023
0.020
0.081
0.030
Source: Author’s Estimation, 2014
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
86
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).
87
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 multiprofession 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.
88
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 nonproductive 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
89
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.
90
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
91
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].
92
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.
93
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.
94
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List of Web References
[i] http://en.wikipedia.org/wiki/Khulna_Division; website of Wikipedia (Accessed on
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114
Appendix I
A Questionnaire
On
Determinants of Land Use Change in South-west Region of Bangladesh
(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
A
Gender
Age
Education
(Code 3)
(Year)
(Code 4)
B
C
D
Year
of Mobile
Schooling
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
xvi
A.4
General Information about the Household:
Family Type (Code 7)
A
Family Member
B
Total Assets (BDT)
C
Occupation (Code 8)
D
Male
Land
Primary
Secondary
1
2
1
Female
2
Non-Land
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
A.5.1
A.5.2
A.6
Income
A
Land Assets
Non-land Assets
A.7
What are the major land use patterns over time from the following (Bigha)?
Present (2014)
A
Before 2010
C
Shrimp
Seasonal Variation in Land Use Pattern (If uncertain, take data of last year):
(Code 9)
A
A.8
2010 - 2013
B
Rice
Ownership
A.7.1
A.7.2
Cost
B
Regular
Irregular
Land Use Pattern
A.6.1
A.6.2
Expenditure Type
Seasonal Use (Code 1)
Summer Rainy Winter
B
C
D
Reason of Variation
E
Rice
Shrimp
Code 9: 1= Sole Proprietorship, 2=Joint, 3=Borrowing, 4=Others
Cost and Benefit of Specific Land Use Pattern in Last Year (2013):
Size
(Bigha)
A
Land
Total Cost (BDT)
Rent Summer Rainy Winter
B
C
D
E
Total Earning (BDT)
Summer
Rainy
Winter
F
G
H
A.8.1 Rice
A.8.2 Shrimp
A.9
Proximity to Necessary Infrastructure and Service (in Km):
Input
Market
A
A.9.1
A.9.2
Output
Market
B
Rice
Shrimp
xvii
Nearest
Roads
C
Nearest
Town
D
Agro/Fishery
Office
F
A.10
Characteristics of Land Cultivated by the Respondent (at least of Year 2013):
Rice
A
A.10.1
A.10.2
A.10.3
A.10.4
A.10.5
Shrimp
B
Geographic Location (Code 10)
Land Elevation (Code 11)
Land Fertility (Code 12)
Salinity and Sand (Code 2)
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 Location
of Market Price Expected
Market (Code 14) (Per Mound/Kg) Price
(Code: 2)
A
B
C
D
A.11.1 Rice
A.11.2 Shrimp
Code 14: 1=Local, 2=External, 3=Uncertain, 4=Others
A.12
A.12.1
A.12.2
A.12.3
A.13
A.13.1
A.13.2
Have you changed your land use pattern since 2010 (Code 1)? ………………….
Duration of Current Land Use Pattern
Conversion Cost (Initial) Per Bigha
Conversion and Maintenance Cost Yearly Per Bigha
Land Use Patterns
A
B
Rice
Shrimp
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
Rice
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
xviii
A.14
Transportation facilities and cost:
Accessibility
Facilities
(Code 2)
(Code 2)
A
A.14.1
A.14.2
Type (Code 17)
B
C
Cost from Market
Input
Output
D
E
Rice
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
A
Shrimp
B
Description
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)
A
B
Type/Nature
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)
……………………………
xix
Appendix II
Analysis and Results
Table Annex_II.1 Description of Sample Data used in Logistic Regression
Observation:
Variables:
Size
Variable Name
MLUP
Age
SchYr
Dum_Lan_Eng_1
Dum_Lan_Eng_2
FT
Eco_Act_FM
Dum_LO1
Dum_LO2
LR
Nei_LU
Ser_pro
Acc1
Acc2
Cre_Ava
Nat_Pre
80
16
5120
Storage
Type
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Float
Value
Label
MLUP
Lan1
Lan2
FT
LO1
LO2
NLU
Acc1
Acc2
YN
YN
Variable Label
Major land use pattern
Age of decision maker
Year of Schooling
Engagement process in existing land use
Engagement process in existing land use
Family Type
Economically Active Family Member
Land Ownership
Land Ownership
Land Rent
Neighborhood Land Use Pattern
Proximity to Service Point from the Land
Accessibility to Land
Accessibility to Land
Availability of Credit for Land Use
Presence of Natural Pressure
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.2 Summary of Sample Data used in Logistic Regression
Variable Name
MLUP
Age
SchYr
Dum_Lan_Eng_1
Dum_Lan_Eng_2
FT
Eco_Act_FM
Dum_LO1
Dum_LO2
LR
Nei_LU
Ser_pro
Acc1
Acc2
Cre_Ava
Nat_Pre
Obs
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
Mean
0.50
50.74
5.55
0.38
0.24
1.41
2.10
0.63
0.14
11825.00
0.56
10.87
0.61
0.21
0.21
0.55
Std. Dev.
0.50
13.03
5.97
0.49
0.43
0.50
1.29
0.49
0.35
23245.95
0.50
3.78
0.49
0.41
0.41
0.50
Min
0
25
0
0
0
0
1
0
0
0
0
3
0
0
0
0
Max
1
83
18
1
1
1
8
1
1
125000
1
21
1
1
1
1
N.B.: Obs.- Observation, Std. Dev.- Standard Deviation, Min- Minimum, Max - Maximum
Source: Author’s Compilation Based on Field Survey, 2014
xx
Table Annex II.3 Summary Statistics of Categorical Variable
Variable Name
Dum_Lan_Eng2
Dum_Lan_Eng1
FT
Dum_LO1
Dum_LO2
Nei_LU
Acc2
Acc1
Cre_Ava
Nat_Pre
Coding Name
Frequency
Parameter coding
Otherwise
Personal
Inheritance
Otherwise
Joint
Nuclear
Other
Sole
Borrowing
Other
Otherwise
Similar
Otherwise
Very High
High
Otherwise
No
Yes
No
Yes
61
19
30
50
33
47
30
50
11
69
35
45
63
17
49
31
63
17
36
44
0
1
1
0
0
1
0
1
1
0
0
1
0
1
1
0
0
1
0
1
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex II.4 Classification Table
Predicted
MLUP
Rice Farming
Shrimp Farming
0
40
0
40
Observed
Percentage Correct
Rice Farming
Shrimp Farming
Overall Percentage
N.B.: Constant is included in the model, the cut value is .500
MLUP
.0
100.0
50.0
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.5 Classification Table
Observed
Rice Farming
Shrimp Farming
Overall Percentage
N.B.: The cut value is .500
MLUP
Predicted
MLUP
Rice Farming
Shrimp Farming
39
1
1
39
Percentage Correct
97.5
97.5
97.5
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.6 Omnibus Tests of Model Coefficients
Chi-square
df
Step
93.514
15
Block
93.514
15
Model
93.514
15
N.B.: df- degrees of freedom, sig.- significant level
Sig.
.000
.000
.000
Source: Author’s Compilation Based on Field Survey, 2014
xxi
Table Annex_II.7 Hosmer and Lemeshow Test
Step
Chi-square
1
df
4.496
Sig.
7
.721
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.8 Contingency Table for Hosmer and Lemeshow Test
Step
MLUP = Rice Farming MLUP = Shrimp Farming
Observed
Expected
Observed
Expected
8
8.000
0
.000
8
8.000
0
.000
8
8.000
0
.000
7
7.739
1
.261
8
6.475
0
1.525
1
1.765
7
6.235
0
.020
8
7.980
0
.000
1
1.000
0
.000
23
23.000
1
2
3
4
5
6
7
8
9
Total
8
8
8
8
8
8
8
1
23
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.9 Model Summary of Land Use Determinants
-2 Log likelihood
17.390
Cox & Snell R Square
.689
Nagelkerke R Square
.919
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.10 Wald Test of Sample Data
Wald Chi Square
7.16
N.B.: df- degrees of freedom, pr- Probability
df
15
Pr>F
0.95
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.11 Test of Data Classification
Classified
+
Total
Correctly Classified (%)
True
D
39
01
40
͂D
01
39
40
Source: Author’s Compilation Based on Field Survey, 2014
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
Source: Author’s Compilation Based on Field Survey, 2014
xxii
Total
40
40
80
97.50%
Table Annex_II.13 Results of Binary Logit Model
Logistic regression
Log likelihood
MLUP
Age
SchYr
Dum_Lan_Eng_1
Dum_Lan_Eng_2
FT
Eco_Act_FM
Dum_LO1
Dum_LO2
LR
Nei_LU
Ser_pro
Acc1
Acc2
Cre_Ava
Nat_Pre
Constant
=
Number of observation
LR chi square (15)
Probability > chi square
Pseudo R square
-8.6949453
Coefficient
-0.588
1.702
7.296
41.034
-46.843
32.007
58.267
24.926
0.004
9.599
3.220
25.270
24.540
-8.554
-19.193
-97.468
Std. Err.
0.250
0.821
3.726
18.629
20.971
14.293
27.529
12.236
0.002
4.998
1.492
11.078
10.583
4.901
8.854
46.361
z
-2.35
2.07
1.96
2.20
-2.23
2.24
2.12
2.04
2.18
1.92
2.16
2.28
2.32
-1.74
-2.17
-2.10
P>|z|
0.019
0.038
0.050
0.028
0.026
0.025
0.034
0.042
0.030
0.055
0.031
0.023
0.020
0.081
0.030
0.036
=
=
=
=
80
93.51
0.0000
0.8432
[95% Conf. Interval]
-1.078
-0.098
0.093
3.312
-0.007
14.599
4.522
77.545
-87.945
-5.741
3.993
60.020
4.312
112.222
0.943
48.908
0.000
0.007
-1.963
19.395
0.296
6.144
3.557
46.982
3.798
45.281
-18.158
1.055
-36.547
-1.838
-188.333
-6.603
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.14 Results of Logistic Regression
MLUP
Age
SchYr
Dum_Lan_Eng_1
Dum_Lan_Eng_2
FT
Eco_Act_FM
Dum_LO1
Dum_LO2
LR
Nei_LU
Ser_pro
Acc1
Acc2
Cre_Ava
Nat_Pre
Constant
Odds Ratio
0.5552986
5.486967
1474.629
6.62e+17
4.53e-21
7.95e+13
2.02e+25
6.69e+10
1.003645
14753.96
25.02903
9.43e+10
4.54e+10
0.0001933
4.62e-09
4.68e-43
Std. Err.
0.1388183
4.504864
5494.791
1.23e+19
9.51e-20
1.14e+15
5.55e+26
8.18e+11
0.0016781
73737.47
37.33899
1.04e+12
4.81e+11
0.0009473
4.09e-08
2.17e-41
z
-2.35
2.07
1.96
2.20
-2.23
2.24
2.12
2.04
2.18
1.92
2.16
2.28
2.32
-1.74
-2.17
-2.10
P>|z|
0.019
0.038
0.050
0.028
0.026
0.025
0.034
0.042
0.030
0.055
0.031
0.023
0.020
0.081
0.030
0.036
Source: Author’s Compilation Based on Field Survey, 2014
xxiii
[95% Conf. Interval]
.3402012
.9063946
1.097706
27.42703
.9929336
2190007
92.06531
4.76e+33
6.40e-39
.0032112
54.22771
1.16e+26
74.57368
5.46e+48
2.568943
1.74e+21
1.000362
1.00694
.8218002
2.65e+08
1.344616
465.8968
35.07538
2.53e+20
44.61406
4.63e+19
1.30e-08
2.872676
1.34e-16
.1590741
1.61e-82
.001356
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|
-.5882493
.24999
-2.35
0.019
Age
1.702376
.82101
2.07
0.038
SchYr
7.296162
3.72622
1.96
0.050
Dum_Lan_Eng_1*
41.03385
18.629
2.20
0.028
Dum_Lan_Eng_2*
-46.84293
20.971
-2.23
0.026
FT*
32.00656
14.293
2.24
0.025
Eco_Act_FM
58.26666
27.529
2.12
0.034
Dum_LO1*
24.92581
12.236
2.04
0.042
Dum_LO2*
.0036388
.00167
2.18
0.030
LR
9.599267
4.99781
1.92
0.055
Nei_LU*
3.220036
1.49183
2.16
0.031
Ser_pro
25.26952
11.078
2.28
0.023
Acc1*
24.53952
10.583
2.32
0.020
Acc2*
-8.551443
4.90146
-1.74
0.081
Cre_Ava*
-19.19279
8.85445
-2.17
0.030
Nat_Pre*
N.B.: (*) dy/dx is for discrete change of dummy variable from 0 to 1
[95% Conf. Interval]
-1.07822 -.098281
.093222
3.31153
-.007092
14.5994
4.5225
77.5452
-87.9447 -5.74112
3.99319
60.0199
4.31179
112.222
.943494
48.9081
.000362
.006916
-.196258
19.3948
.296108
6.14396
3.5575
46.9815
3.79805
45.281
-18.1581
1.05524
-36.5472 -1.83839
x
50.7375
5.55
.375
.2375
1.4125
2.1
.625
.1375
11825
.5625
10.8675
.6125
.2125
.2125
.55
Source: Author’s Compilation Based on Field Survey, 2014
0.50
0.25
0.00
Sensitivity
0.75
1.00
Figure Annex_II.1 Area under ROC Curve
0.00
0.25
0.50
1 - Specificity
Sensitivity
0.75
Reference
Area under ROC curve = 0.9919
Source: Author’s Compilation Based on Field Survey, 2014
xxiv
1.00
0.00
0.25
0.50
0.75
1.00
Figure Annex_II.2 Sensitivity and Specificity versus Probability Cutoff
0.00
0.25
0.50
Probability cutoff
Sensitivity
0.75
1.00
Specificity
Source: Author’s Compilation Based on Field Survey, 2014
Table Annex_II.16 Variables in the Equation
Age
SchYr
Dum_Lan_Eng1
Dum_Lan_Eng2
FT
Eco_Act_FM
Dum_LO1
Dum_LO2
LR
Nei_LU
Ser_Pro
Acc1
Acc2
Cre_Ava
Nat_Pre
Constant
N.B.: B-Coefficient,
B
S.E.
Wald
df
Sig.
Exp(B)
-.588
.250
5.537
1
.019
.555
1.702
.821
4.299
1
.038
5.487
7.296
3.726
3.834
1
.050
1474.630
-41.034
18.629
4.852
1
.028
.000
-46.843
20.971
4.990
1
.026
.000
32.007
14.293
5.015
1
.025
79482945900397.050
-58.267
27.528
4.480
1
.034
.000
24.926
12.236
4.150
1
.042
66856103260.270
.004
.002
4.737
1
.030
1.004
-9.599
4.998
3.689
1
.055
.000
3.220
1.492
4.659
1
.031
25.029
25.270
11.078
5.203
1
.023
94279064817.382
-24.540
10.583
5.377
1
.020
.000
8.551
4.901
3.044
1
.081
5174.216
19.193
8.854
4.698
1
.030
216432796.464
-38.616
21.019
3.375
1
.066
.000
S.E.- Standard Error, df- Degrees of freedom, Sig.- Significant level, Exp(B)-
Expected coefficient
Source: Author’s Compilation Based on Field Survey, 2014
xxv
Table Annex_II.17 Observed and Probable Land Use Pattern of Each Sample
Selected Statusa
Case
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
Observed
MLUP
R
R
R
R
R
R
R
R
R
R
R
R
R**
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
S
S
S
S
S
S
S
S
S
S
S
S
S
S
Predicted
.000
.243
.000
.000
.000
.000
.000
.000
.092
.000
.000
.207
.782
.000
.000
.000
.293
.014
.018
.000
.000
.000
.000
.000
.000
.000
.000
.000
.083
.000
.112
.000
.000
.079
.000
.030
.138
.000
.089
.350
1.000
1.000
1.000
1.000
.989
.521
1.000
1.000
.813
1.000
1.000
.804
1.000
.992
xxvi
Predicted Group
R
R
R
R
R
R
R
R
R
R
R
R
S
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
S
S
S
S
S
S
S
S
S
S
S
S
S
S
Temporary Variable
Resid
ZResid
.000
-.015
-.243
-.567
.000
.000
.000
.000
.000
-.002
.000
.000
.000
.000
.000
.000
-.092
-.318
.000
.000
.000
.000
-.207
-.511
-.782
-1.893
.000
.000
.000
.000
.000
.000
-.293
-.644
-.014
-.117
-.018
-.135
.000
-.002
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
-.001
.000
.000
.000
-.004
-.083
-.301
.000
.000
-.112
-.356
.000
.000
.000
.000
-.079
-.293
.000
.000
-.030
-.177
-.138
-.400
.000
.000
-.089
-.312
-.350
-.734
.000
.
.000
.
.000
.
.000
.
.011
.108
.479
.958
.000
.
.000
.
.187
.479
.000
.
.000
.
.196
.493
.000
.
.008
.088
55
S
S
1.000 S
56
S
S
1.000 S
57
S
S
1.000 S
58
S
S
1.000 S
59
S
S
1.000 S
60
S
S
1.000 S
61
S
S
1.000 S
62
S
S
1.000 S
63
S
S
1.000 S
64
S
S
1.000 S
65
S
S
1.000 S
66
S
S
.859 S
67
S
S
1.000 S
68
S
S
1.000 S
69
S
S
1.000 S
70
S
S
1.000 S
71
S
S
1.000 S
72
S
S
.889 S
73
S
S
1.000 S
74
S
S
.650 S
75
S
S
1.000 S
76
S
S
.999 S
77
S
S**
.036 R
78
S
S
1.000 S
79
S
S
1.000 S
80
S
S
.916 S
N.B.: S = Selected, U = Unselected cases, and ** = Misclassified cases.
Source: Author’s Compilation Based on Field Survey, 2014
xxvii
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.141
.000
.000
.000
.000
.000
.111
.000
.350
.000
.001
.964
.000
.000
.084
.
.000
.
.
.
.
.
.
.
.
.000
.405
.
.002
.000
.000
.
.353
.000
.734
.
.032
5.138
.
.
.303