Bangladesh Agricultural University, Mymensingh for the Degree Of

MATHEMATICAL MODELING OF INTEGRATED COASTAL ZONE MANAGEMENT OF BANGLADESH

Ph. D DISSERTATION

MD. ANOWER HOSSAIN

DEPARTMENT OF FARM POWER AND MACHINERY BANGLADESH AGRICULTURAL UNIVERSITY MYMENSINGH 2202

September 2011 MATHEMATICAL MODELING OF INTEGRATED COASTAL ZONE MANAGEMENT OF BANGLADESH

Dissertation submitted in accordance with the requirement of the Bangladesh Agricultural University, Mymensingh for the degree of

DOCTOR OF PHILOSOPHY

MD. ANOWER HOSSAIN

Roll No. 01/2006-07 Reg. No. 15423/1987-88

Approved as to style and content by

(Professor Dr. B. K. Bala) Supervisor

(Professor Dr. Md. Shahid Ullah Talukder) (Professor Dr. Md. Salequzzaman) Co-supervisor Member, Supervisory Committee

(Professor Dr. Md. Monjurul Alam) Chairman, Examination Committee Department of Farm Power and Machinery Bangladesh Agricultural University Mymensingh

September 2011

DEDICATED TO

MY BELOVED PARENTS, WIFE AND CHILDREN

iii

DECLARATION

I hereby declare that the dissertation titled “Mathematical Modeling of Integrated Coastal Zone Management of Bangladesh” submitted to the Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh for the degree of Doctor of Philosophy is the document of original research work, which was designed, conducted and written by me independently. I further announce that the substance or any part of the dissertation has not been submitted in any form elsewhere for the award of any degree.

Date: September 2011 Md. Anower Hossain

iv ACKNOWLEDGEMENTS

All praises are due to the Almighty Allah Subhanuta’la, the Omnipotent, Omnipresent and Omniscient, who enabled the author to complete this dissertation.

Expression of indebtedness and profound regards are not enough to acknowledge the unflinching cooperation, warmth and indomitable guidance throughout the research work and in the preparation of the manuscript in time I have received from my respected teacher and research supervisor Dr. B. K. Bala, Professor, Department of Farm Power and Machinery, Bangladesh Agricultural University (BAU), Mymensingh.

I must thank my Co-supervisor Professor Dr. Md. Shahid Ullah Talukder Department of Irrigation and Water Management, BAU, Mymensingh and member of the Supervisory Committee Professor Dr. Md. Salequzzaman, Department of Environmental Science, Khulna University, Khulna for their guidance, kind help and constructive criticism during the preparation of this manuscript.

I would like to extend my gratitude to Dr. Ayub Hossain, senior scientific officer, Bangladesh Agricultural Research institute for his constant suggestions, inspiration and affectionate feeling.

The author gratefully acknowledged to Bangladesh Agricultural Research Institute (BARI) authority for their financial support given under the award of a scholarship and for granting the deputation for the entire period of the study.

The author also acknowledged to the FAO, USAID and European Comission for assistance partial research expenses during PhD program.

I express my cordial gratitude to all respected teachers, officials and staffs of the Department of Farm Power and Machinery, BAU, Mymensingh for their kind cooperation and continuous encouragement. Special thanks are also to my friends, colleagues, officials and staffs of Bangladesh Agricultural Research Institute for their encouragements.

I would like to thank my beloved wife and children (Muaz bin Anwar Mushira zannat, and Masruk bin Anwar) for their forbearance, understanding and sacrifice.

Thanks and appreciation are also extended to Saiful Vai, Kibria, Thouhid,, Masud Alom, Nurul Amin, Sujit, Sydur, Dilip, Ershad, Rakib, Sahin, Nazir, Ayub khan, Milon, Sohel, Zakaria, Sumon, Faruk, saddam, Pantha, Zia, friends and all well wishers for their constant inspiration and encouragement.

The Author

v Biographical Sketch

The author, Md Anower Hossain was born on August 01, 1970 at Sadar upazila under Patuakhali district of Bangladesh. His father is Md. Abul Kalam and mother Mrs. Saleha Begum. He had his school education in his own village from where he passed Secondary School Certificate (SSC) from Moukaran High School in 1985 being placed in first division. Afterwards, he passed Higher Secondary Certificate (HSC) from Patuakhali Government College in 1987 obtaining first division. He obtained Bachelor of Science degree in Agricultural Engineering from Bangladesh Agricultural University (BAU), Mymensingh in 1991 (examination held in 2005) and stood first class. Following which he completed Master of Science (MS) in Irrigation and Water Management (IWM) in June 1997 obtaining first class under the National Science & Technology fellowship program. Recently, He also completed Post Graduate Diploma in ICT.

The author started his professional career in the Department of Agricultural Extension (DAE) as a Thana Agricultural Engineer in March 1997. In November 1997, he left the DAE and joined the Bangladesh Agricultural Research Institute (BARI) as a Scientific Officer. He was promoted to Senior Scientific Officer in 2008 and has been working in the same position till September, 2011. The author had been served with Irrigation and Water Management Division of BARI and done research in various aspects of irrigation water management, on-farm water management, reuse of wastewater for sustainable agriculture, etc. He is a life member of Krishibid Institution Bangladesh (KIB), Institute of Engineers’ Bangladesh (IEB), Bangladesh Society of Agricultural Engineers (BSAE) and BARISA. He published more than 30 research articles in different journals at home and abroad. He participated in many workshops, seminars and training programmes.

The author have completed a good number of foreign aided (FAO, USAID, European Union) research project with reputation. At present, the author is serving as a Senior Scientific Officer at Agricultural Statistics and Information & Communication Technology (ASICT) section, Training and Communication Wing, Bangladesh Agricultural Research Institute (BARI).

The author is married to Mustari Alom and blessed with two sons, Muaz bin Anwar & Masruk bin Anwar and one daughter, Musira Zannat.

vi MATHEMATICAL MODELING OF INTEGRATED COASTAL ZONE MANAGEMENT OF BANGLADESH M. A. Hossain ABSTRACT Abstract: This study presents the present status of food security and ecological footprint, an indicator of environmental sustainability and a computer model of integrated management of the coastal zones of Bangladesh. The study also assesses the climate change impacts on rice production in the coastal zone of Bangladesh. To estimate the present status of the food security and ecological footprint of the coastal zone of Bangladesh, primary and secondary data were collected and the present status of food security and environmental degradation (in terms of ecological footprint) were calculated. To estimate household food security primary data were also collected from all the households of a representative selected village.A quantitative method for computation of food security in grain equivalent based on economic returns (price) is developed and a method of measuring sustainable development in terms of ecological footprint developed by Wackernagel and Rees (1996) is used to estimate the environmental sustainability. Overall status of food security at upazila levels is good for all the upazilas (8.53% to 164.19%) except Shoronkhola (-23.65%), Shyamnager (-6.08%) and Morrelgonj (-30.29%), and the best is the Kalapara upazila (164.19%). But status of food security at household levels is poor. The environmental status in the coastal zones is poor for all the upazilas (-0.5076 to -0.027) except Kalapara (+0.306) and Galachipa (+0.322) and the worst is the Mongla upazila (-0.5076). Environmental status has degraded mainly due to shrimp culture. Developed computer model predicts that expanding shrimp aquaculture industry ensures high food security at upazila levels with increasing environmental degradation.The model also predicts that if shrimp aquaculture industry continues to boom from the present status to super intensive shrimp aquaculture, a collapse of the shrimp aquaculture industry will ultimately occur turning shrimp aquaculture land neither suitable for shrimp culture nor crop production. The control of population and growth of the shrimp production intensity should be considered for stabilization of the system in the long run. The sustainable development of the coastal zone of Bangladesh in the long run without control of both the growth of shrimp production intensity and population will remain mere dream. Crop growth model InfoCrop was used to predict the impact of climate change on crop production in the coastal zone of Bangladesh. Sensitivity analysis shows that the crop model is sensitive to

temperature change and CO2 levels.The simulation was carried out to predict the yields of rice under different climatic trends of temperature and carbon dioxide concentration. The effect of temperature on the yield of rice that is negative while of CO2 is positive but temperature plays dominant role. Prediction was also made to predict the climate change impacts of rice yields based on historical and IPCC climate change scenarios. Historical climate change scenario has little impacts on rice yield but there is relatively higher reduction in the yields of rice for IPCC climate change scenario. The model validated with the actual field data and found good agreement between simulated and actual data. ICZM computer model can be used to assist the policy planners to assess different policy issues and to design a policy for sustainable development of the coastal zones of Bangladesh. This study suggests that control measures are needed for affected upazilas and any further expansion of the shrimp aquaculture to enhance the food security must take into account the environmental aspects of the locality under consideration. Climate change impact study suggests taking some strategy to adapt the climate change impact situation.

vii Contents

Title Page i Approval Sheet ii Declaration iv Acknowledgements v Biographical Sketch vi Abstract vii Contents viii List of Tables x List of Figures xi List of Abbreviations and Acronyms xiv Units of Variables xv CHAPTER 1 INTRODUCTION 1 CHAPTER 2 MATERIALS AND METHODS 21 2.1 Food security and ecological footprint of the coastal zone of 21 Bangladesh 2.1.1 Site description 21 2.1.2 Site selection 30 2.1.3 Questionnaire development 32 2.1.4 Data collection and analysis 32 2.1.5 Computation of food security 33 2.1.6 Computation of ecological footprint and biocapacity 35 2.2 Modeling of integrated coastal zone management 37 2.2.1 Food security sector 40

2.2.2 Ecological footprint sector 48

2.2.3 Biocapacity sector 51

2.2.4 Validation of integrated coastal zone management model 53

2.2.5 Policy options 54 2.3 Modeling the climate change impacts on rice production in 56 the coastal zone of Bangladesh 2.3.1 Site Description 56

viii Contents(Contd.)

CHAPTER 2 MATERIALS AND METHODS 2.3.2 Selection of crop 57 2.3.3 InfoCrop model 57 2.3.4 Description of InfoCrop model 57 2.3.5 Data Collection 58 2.3.6 Modeling of climate change impacts on crop growth 60 2.3.7 Computation of climate change impact on rice production 65 CHAPTER 3 RESULTS AND DISCUSSION 67 3.1 Food security and ecological footprint at upazila level 67 3.2 Modeling of integrated coastal zone management 83

3.2.1 Validation of integrated coastal zone management model 91 3.2.2 Policy implication 94 Modeling the climate change impacts on rice production of 96 3.3 the coastal zone of Bangladesh 3.3.1 Model validation 96 3.3.2 Sensivity analysis 96

3.3.3 Impacts of temperature and carbon dioxide on rice 97 production 3.3.4 Climate change 98

3.3.5 Rice yields under Historical and IPCC trends in the coastal 99 zone 3.3.6 Adaptation to climate change impacts on rice production 100

CHAPTER 4 SUMMARY AND CONCLUSION 102

REFERENCES 108

APPENDICES 120

PUBLICATIONS AND CONFERENCE PAPERS PRODUCED FROM THIS RESEARCH WORK 192

ix List of Tables

Table Title Page No. No. 2.1 Exposed and interior upazilas in the coastal zone7ddd7777aaaaa 23

2.2 Mapping units of the coastal region of Bangladeh showing selected 28 upazilas 2.3 Category of soil salinity and saline areas of Bangladesh 28

2.4 Sea level rise (SLR) in Bangladesh and its possible impacts 29 2.5 Selected upazilas from exposed coastal zone 32 2.6 Daily balance food requirement 34 2.7 Inputs data used in InfoCrop 66 2.8 Crop management data used in the model 66 3.1 Major cropping pattern and cropping intensity of different upazilas 68 3.2 Major crop and fish area of of different upazilas 69 3.3 Ecological footprint, bio-capacity and ecological status of 52 countries 77 in the world 3.4 The present status of food security and ecological status of nine 80 upazilas of the coastal zones of Bangladesh at a glance. 3.5 Comparison between simulated and actual values of crop area of 93 Dacop upazila during 2007-2011 3.6 Comparison between simulated and actual values of pond area bagda 93 of Dacop upazila during 2007-2011 3.7 Treatments and their impacts on rice 98

3.8 Year wise assumptions of CO2 concentrations and temperature data 99

x List of Figures

Fig. Title Page No No. 1.1 Photographs of shrimp cultivation in the coastal zone of Bangladesh 4 1.2 A diagrammatic model of direct and indirect effects of shrimp farming 5 1.3 Changes in area under forest and Shrimp production in the Chakoria 7 Sundarbans over the period 1967-1988 2.1 Population density in the coastal zone of Bangladesh 24 2.2 Map of the coastal zone of Bangladesh 31 2.3 Structure of food security computation 34 2.4 Structure of ecological footprint computation 36 2.5 Structure of biological capacity computation 37 2.6 Interrelationships of integrated coastal zone management systems 39 2.7 Simplified flow diagram of integrated coastal zone management system 40 2.8 Food security sector of ICZM model 41 2.9 Changes of shrimp intensity multiplier bagda with shrimp production 44 intensity 2.10 Changes of shrimp production intensity over the year 45 2.11 Changes of cropping intensity multiplier with cropping intensity 46 2.12 Changes in cropping intensity over the year 47 2.13 Ecological footprint sector of ICZM model 49 2.14 Relationship between eco factor and shrimp production intensity 50 2.15 Biocapacity sector of ICZM model 52 2.16 Growth patterns for different policy options 55 2.17 Average monthly temperature and relative humidity of Kalapara upazila 59 in 2010 2.18 Average monthly sunshine hours of Kalapara upazila in 2010 59 2.19 Monthly rainfall of Kalapara upazila in 2010 59 2.20 Simple representation of crop growth model 61 3.1 Population in 2007 of different upazila 70 3.2 Rice production of different upazila 70 3.3 Shrimp production of different upazila 71 3.4 Food sortage/surplus of different upazila 72 3.5 Self sufficiency ratio of rice of different upazila 72 3.6 Food security status of different upazila 73

xi Contd. Fig. Title Page No No. 3.7 Contributions of crop and fish to food security 73 3.8 Percent ecological distribution of six upazilas of Khulna region 74 3.9 Percent ecological distribution of three upazilas of Barisal region 75 3.10 Ecological footprint of different upazila 76 3.11 Biological capacity of different upazila 76 3.12 Ecological status of different upazila 77 3.13 Ecological status from crop and fish of different upazila 80 3.14 Household food security status in the village Baraikhali 82 3.15 Percentage distribution of food insecurity 82 3.16 Simulated population, food availability and food security of Dacop 83 upazila. 3.17 Simulated pond area bagda, crop area and shrimp production bagda of 84 Dacop upazila. 3.18 Simulated ecological footprint, biocapacity and ecological status of 85 Dacop upazila. 3.19 (a) Simulated food security status of Dacop upazila for different 86 options (b) Simulated ecological footprint of Dacop upazila for different 86 options (c) Simulated ecological status of Dacop upazila for different options 87 3.20 (a) Simulated population, food security and food available of Dacop 88 for120 years (b) Simulated pond area bagda, shrimp production and crop area of 88 Dacop for120 yrs (c) Simulated ecological footprint , biocapacity and ecological status 89 of Dacop for120 years 3.21 (a) Simulated population, food security and food availability of Dacop 90 under control of both normal growth and population for a period of 120 years (b) Simulated pond area bagda, shrimp production and crop area of 90 Dacop under control of both normal growth and population for a period of 120 years. (c) Simulated ecological footprint, biocapacity and ecological status of 91 Dacop under control of both normal growth and population for a period of 120 years. 3.22 Simulation of crop area and pond area bagda of Dacop upazila 92 3.23 Comparison between simulated and actual field data for crop area 92 3.24 Comparison between simulated and actual field data for pond area 92 bagda

xii Fig. Title Page No No. 3.25 Impact of temperature increase on rice yield 96

3.26 Impact of CO2 increase on rice yield 97

3.27 Impacts of temperature and CO2 on rice production 98 3.28 Changes in the yields of rice for historical and IPCC climatic scenarios 100

xiii LIST OF ABBREVIATIONS AND ACRONYMS

BBS Bangladesh Bureau of Statistics BC Biological Capacity ( Bio-Capacity) BRRI Bangladesh Rice Research Institute CEC Cation Exchange Capacity DoF Department of Fisheries EEF Emergetic Ecological Footprint EF Ecological Footprint ES Ecological Status FAO Food and Agricultural Organization FS Food Security GDP Gross Domestic Product gha Global hectare ha Hectare ha/cap Hectare/capita ICZM Integrated coastal zone management IFPRI International Food Policy Research Institute INFS Institute of Nutrition and Food Science km Kilometer MOFL Ministry of Fisheries and Livestock NACA Network of Aquaculture Centres in Asia Pacific NPV Net Present Value NSF Non-Sufficient Food PDO-ICZMP Program Development Office for Integrated Coastal Zone Management Plan PRA Participatory Rural Appraisal RDRS Rangpur Dinajpur Rural Service SF Sufficient Food SRF Sunderban Reserve Forest SSR Self Sufficiency Ratio USDA United State Department of Agriculture WHO World Health Organization

xiv UNITS OF VARIABLES

Area ha Biocapacity gha Consumption ton Crop growth rate gm/m2/day Cropping intensity % Crop yield normal t/ha Ecological footprint gha Ecological status gha Equivalence factor gha/ha Food (Equivalent rice) ton Food avaiable (Equivalent rice) ton Food per capita ton/cap Food requirement (Equivalent rice) ton Food security % Food security status % Global yield t/ha Leaf area growth rate gm/m2/day Production ton Radiation Lux Shrimp production bagda ton Shrimp production galda ton Shrimp yield normal bagda t/ha Shrimp yield normal galda t/ha Thermal time hr Vegetative rate of development cm/m2/day Yield t/ha

xv CHAPTER 1

INTRODUCTION

Costal Zone is most frequently defined as land affected by its proximity to the sea and that part of the sea affected by its proximity to the land or, in other words, the areas where the processes which depend on the sea-land interactions are the most intensive. Coastal zone always include floodplains, mangroves, marshes, and fringing coral reefs. In general, there are tide flats, as well as beaches and dunes, and multiple aerial foci for ICZM (Integrated Coastal Zone Management).

The coastal zone of Bangladesh is rich in natural resources offering many tangible and intangible benefits to the nation. Excessive fishing and over exploitation of coastal resources, water quality deterioration, mangrove destruction for aquaculture and conversion of agricultural land into aquaculture pond are the major problems which need to be managed on a priority basis (Banglapedia, 2003).

The total area of Bangladesh is 147,570 km2, of which coastal zone is 47,203 km2 and it is roughly 32% of the whole country. According to 2001 population census, total population of Bangladesh is 123.15 million, of which 35.1 million people live in coastal area and it is approximately 28% of the total population of Bangladesh.

The population in the coastal zone is expected to increase from 35.1 million in 2001 to 43.9 million in 2015 and to 60.8 million by 2050 (PDO-ICZMP, 2005a). The present per capita agricultural land of 0.056 ha will decrease to 0.025 ha by 2050. On top of this, about 54% of the people of coastal Bangladesh are functionally landless and more than 30% are absolutely landless. Among the landholders, 80% are small farmers, 18% are medium farmers, and only 2% are large farmers (PDO-ICZMP, 2004b).

Out of 2.85 million ha of coastal cultivable land in Bangladesh, about 1.0 million ha of arable land are affected by varying degrees of salinity and most of these lands remain fallow in dry season (Karim et al., 1990). Out of 1.0 million ha of saline area, 0.38 million ha area is in Khulna, 0.22 million ha is in Patuakhali, 0.11 million ha is in Chittagong and

1 the rest is in Barisal and Noakhali region. For crop production in coastal zone crop selection/development of saline resistant variety and management practices are essential for maximum benefit.

Observations in the recent past indicated that due to increasing degree of salinity of some areas and expansion of salt affected area as a cause of further intrusion of saline water, normal crop production becomes more restricted. In general, soil salinity is believed to be mainly responsible for low land use as well as cropping intensity in the area (Rahman & Ahsan, 2001).

In coastal zone, T. Aman rice is mainly cultivated depending on rainfall and sometimes supplemental irrigation is applied during September-October from the low salinity river water sources and the land remains fallow due to salinity development and scarcity of irrigation water during the rest of the periods of the year. The present cropping pattern in the coastal zone is mostly T. Aman- Fallow- Fallow. Occasionally in few areas, T. Aman- Rabi crops - Fallow is followed. Cropping intensity in the coastal areas is low compared to other parts of the country. This is due to unfavorable soil and land characteristics like salinity, flood, water logging, late drainage condition, scarcity of irrigation water, acidity, low fertility status, cyclonic storm surges etc.

The main obstacle to intensification of crop production in the coastal areas is seasonally high content of salts in the root zone of the soil. The salts enter inland through rivers and channels, especially during the later part of the dry (winter) season, when the downstream flow of fresh water becomes very low. During this period, the salinity of the river water increases. The salts enter the soil by flooding with saline river water or by seepage from the rivers, and the salts become concentrated in the surface layers through evaporation. The saline river water may also cause an increase in salinity of the ground water and make it unsuitable for irrigation. The increase in water salinity of these areas has created suitable habitat for shrimp cultivation.

Scarcity of good quality of irrigation water does not permit growing dry season rice in coastal zone of Bangladesh. If suitable irrigation water can be made available, there is a scope of increasing the cropping intensity through the cultivation of dry season rice. A

2 research study (Rashid et al., 2004) was conducted in Satkhira district of Bangladesh during 2003-2004 to explore opportunities for groundwater utilization in the coastal areas for crop production and it was reported that dry season crop production is possible by utilizing the suitable groundwater.

The utilization of groundwater through tube wells is very expensive in the coastal region because the sweet water layer exists nearly at 250-300 m below the ground surface. Suitable surface water for irrigation is very scarce. However, to mitigate the demand for fresh water for irrigation, especial emphasis may be given to adopt rain water harvest technology. The existing ponds in these areas can be used to store rainwater during the monsoon period and can be used for irrigation to upland crops in dry season. Also it can be used for fish culture and duck farming through some renovations. This will add additional agricultural production and provide employment opportunity for the rural poor. It may also help reduce drought hazards and maintain ecological balance.

It is estimated that about 0.25 million ha of land has a good potential for coastal aquaculture (Ahmed, 1995) and of which, about 0.18 million ha of land area is suitable for shrimp culture (Khan and Hossain, 1996). Coastal aquaculture increased from 20,000 ha in 1994 – 1995 to 135,000 ha in 1996–1997, and production from 4000 to 35,000 metric tons in the same period (MOFL, 1997). Shrimp aquaculture in the coastal zones is expanding rapidly and agricultural lands are converted into aquaculture ponds. Shrimp areas in Bangladesh have already expanded from 51812 ha in 1983 to 137996 ha and 141353 ha in 1994 and 2002, respectively causing environmental degradation in the coastal zone (DoF, 1995, 2003). Some photographs of shrimp cultivation of coastal zone are shown in Fig. 1.1.

Most coastal lands are suitable for more than one use. Hence, many diverse uses of limited land have created conflict. Many studies have highlighted these conflicts, especially between shrimp farming and other uses (Nuruzzaman, 1979; Karim and Stellwagen, 1998). In addition, one land use or another has manifold implications for socio- environmental conditions. The introduction of shrimp farming has gradually changed the land-use patterns of the surrounding farms, transforming agriculture into shrimp farming areas (Haque, 2004). Several studies reported a reduction in land for cattle grazing

Fig. 1.1 Photographs showing shrimp cultivation in the coastal zone of Bangladesh

(Maniruzzaman, 1998), death of trees and other vegetation (Alauddin and Tisdell, 1998), increased salinity of soil and water, and a reduction in the drinking-water supply because of the introduction of shrimp farming. The rapid expansion of shrimp farming during the last decade along with the adoption of extensive and improved extensive culture techniques has caused growing concern as to its adverse effect on the coastal environment and damage to the traditional agricultural systems leading to rapid change in the socioeconomic scenarios.

Brackish water shrimp farming has altered the physical, ecological (aquatic and terrestrial) and socioeconomic environment. The practice of shrimp culture needs saline water as an input to the shrimp pond. Sluice gates are normally allowed to open two or three times when the salinity in the shrimp pond decreases and saline-water exchange from the river is necessary. As a result, heavy sedimentation from upstream water settles in the riverbed and canal bed, causing waterlogging in the shrimp ponds and on agricultural land. The

4 shrimp-processing depot and industry drain their pollutants into the river, causing water pollution. Water in the shrimp ponds is also polluted because of the application of feed and fertilizer for the production of the shrimp. Thus, the by-products of the shrimp ponds and shrimp industry pollute water and soil and degrade the quality of the overall environment. Vegetation, crops, fish and livestock are seriously damaged by the process of shrimp cultivation. The interrelationship of the direct and indirect effects of shrimp farming on physical, ecological, socioeconomic and environmental conditions are shown in Fig. 1.2.

Shrimp Depletion of Increase soil Increase farming vegetation erosion sedimentation

Decrease soil Fuel scarcity Destruction Loss of soil fertility vegetation fertility

Decrease crop Decrease Scarcity of yield livestock organic manure

Destruction of local ecosystem

Fig. 1.2 A diagrammatic model of direct and indirect effects of shrimp farming

The coastal region, especially the southwestern part (Satkhira, Khulna and Bagerhat), is one of the most promising areas for shrimp cultivation for two major reasons (MOFL, 1997): first, its fresh- and saline-water resources are abundant in almost all seasons; second, the world’s largest continuous mangrove forest, the Sundarbans, provides a food source and nursery for the offshore fishery. The mangrove forests provide a critical habitat for shrimp and other fish. Most of the shrimp culture being practiced is by the extensive and improved extensive methods known as gher culture. Gher means an enclosed area characterised by an encirclement of land along the banks of tidal rivers. Dwarf earthen dikes and small wooden sluice boxes control the free entrance of saline water into the

5 enclosed areas. In the gher, the sluice gates are opened from February to April to allow the entry of saline water containing a wide variety of fish fry and shrimp postlarvae that have grown naturally to the juvenile stage in the adjacent sea and estuarine waters. This practice of natural stocking is being progressively replaced by artificial stocking of the ghers with only the young of specific desired species of shrimp.

Aquaculture at coastal agricultural lands has adverse effects on environment and crop and animal production. Brackish-water shrimp cultivation, on a commercial scale, has brought large-scale environmental degradation. Shrimp polders retain saline water for months together, and the salinity seeps onto adjacent farms and spreads soil salinity. The entry of seawater for aquaculture causes salinization of land and groundwater thereby affecting the productivity of agricultural crops (Akteruzzaman, 2004).

The pumping of groundwater for agriculture leads to intrusion of soluble salts into aquifers and salinity gradually builds up in the soil. Remote sensing studies in Thailand indicate that 3444 ha area of shrimp ponds caused salinization of 1168 ha of agricultural lands

mostly rice fields (NACA, 1994).

Growing international demand for shrimp and stagnating catches of wild shrimp in the early 1980s created an opportunity for the development of export-oriented shrimp aquaculture industries (Csavas, 1995). Robertson and Phillips (1995) reported that depending on the shrimp pond management between 2 and 22 hectares of forest area are required to filter the nitrogen and phosphorus loads from effluent produced by a 1hectare shrimp pond. Arquitt et al., (2005) developed a system dynamics model to examine boom and burst in the shrimp aquaculture industry in Thailand and suggested that a policy that taxes the industry and rebates proceeds to licensed producers may help shift the system towards sustainability.

Forest in coastal zone plays an important role in maintaining the global system in balance and these forests are also the largest carbon sink above the soil. Deforestation for fuel wood for cooking and other purposes has adverse effect on both people and the environment, including degradation of surrounding ecosystems, reduced crop yields, loss of biodiversity, reduced timber supply, flooding, siltation, soil degradation and climate

6 irregularities (De Souza et al., 2003). Furthermore, Forest coverage in the coastal zone is below the world average.

The loss of mangrove areas to aquaculture is a common feature, with Chakoria Sunderbans being the classic example. From 1967 to 1988, areas of Chakoria Sundarban mangroves decreased from 7500 ha to only 973 ha are shown in Fig. 1.3 (Chowdhury et al., 1994).

80 Forest Cover Shrimp production area 70 ) 3 60

Area in Hectares (x 10 Area in Hectares 10

0 1967 1976 1981 1984 1988

Fig 1.3 Changes in area under forest and Shrimp production in the Chakoria Sundarbans over the period 1967-1988 (after Choudhury et al., 1994)

Sundarban is located in the coastal zone of Bangladesh and it is the largest productive mangrove forest in the world. The Sundarban Reserve Forest (SRF) comprises 45 percent of the productive forest of the country, contributing about one-half of forest-related revenue and is an important source of wood and non-wood resources (Hussain and Karim, 1994).

The people of the coastal zone are relatively income-poor compared to the rest of the country. Average per capita GDP (at current market price) in the coastal zone was Tk 18,198 in 1999-2000, compared to Tk 18,291 outside the coastal zone (BBS, 2002). Extent of poverty in terms of calorie intake is relatively high in the coastal zone, where 52 percent people are poor and 25 percent are extreme poor. Corresponding figures for Bangladesh are 49 and 23 percent respectively. (PDO-ICZMP, 2003)

The other special features of the coastal zone is its multiple vulnerabilities out of periodic cyclone and storm surges, salinity intrusion, erosion, pollution, and overall lack of physical infrastructure. Coastal natural-resource uses reflect primarily subsistence

7 agriculture with an emphasis on food production, e. g. paddy rice along with some cash crops and coastal fisheries, which provide a major food and income source. Also important, in some areas, is aquaculture with an emphasis on shrimp production for the export market, and some salt production for domestic needs.

Food Security

Food security is a situation in which people do not live in hunger or fear of starvation. Food security exists when all people at all times have access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life (FAO, 2002). Food security for a household means access by all members at all times to enough food for an active and healthy life. Food security includes at a minimum (1) the ready availability of nutritionally adequate and safe foods, and (2) an assured ability to acquire acceptable foods in socially acceptable ways (USDA,1999). FAO (1996a) defined the objective of food security as assuring to all human beings the physical and economic access to the basic food they need. This implies three different aspects: availability, stability and access.

Food security is a worldwide problem that has called the attention to Governments and the scientific community. It particularly affects developing countries. The scientific community has had increasing concerns for strategic understanding and implementation of food security policies in developing countries, especially since the food crisis in the 70s and the recent rise in the price of food commodities. The process of decision-making is becoming increasingly complex due to the interaction of multiple dimensions related to food security (Giraldo et al., 2008).

Food security is a social sustainabilty indicator and most commonly used indicators in the assessment of food security conditions are food production, income, total expenditure, food expenditure, share of expenditure of food, calorie consumption and nutritional status etc. (Riely et al., 1999). Accounting tools for quantifying food secuirty are essential for assessment of food security status and also for policy planning for sustainable development.

8 Per capita food availability in Bangladesh has declined from 458 g/day in 1990/1991 to 438 g/day in 1998/1999 while per capita fish intake has decreased from 11.7 kg/year in 1972 to 7.5 kg/year in 1990 (Begum, 2002). Also vegetables, the major dietary source of vitamin A, meet only 30 percent of recommended minimum needs.

Food security and hunger focusing on concentration and trend of poverty, pattern of household food consumption and causes of food insecurity and hunger have also been reported and the key findings are demographic and socio-economic conditions of the ultra poor, extent and trend of poverty in Bangladesh, food consumption pattern and level of food insecurity and hunger of the ultra poor (RDRS, 2005).

Mishra and Hossain (2005) reported an overview of national food security situation and identified key issues, challenges and areas of development in policy and planning; also addressed the access and utilization of food and the issues of food and nutritional security.

During the last half century, a number of individuals and institutions have used models with the aim of projecting and predicting global food security, focusing on the future demand for food, supply and variables related to the food system at different levels (MacCalla and Revoredo, 2001). The methodology used to develop the projections and predictions on food relies on correlated models. Such methodology is controlled purely by data and do not give insights into the causal relationships in the system. Several models have been developed to address the food security (Diakosavvas and Green, 1998, Coxhead, 2000, Mohanty and Peterson, 2005, Rosegrant et al., 2005, Holden et al., 2005,

Shapouri and Rosen, 2006, Ianchovichina et al., 2001, FAO, 1996b, Falcon et al., 2004).

System dynamics is a problem-oriented multidisciplinary approach that allows to identify, to understand, and to utilize the relationship between behavior and structure in complex dynamic systems. The underlying concept of the System Dynamics implies that the understanding of complex system’s behavior -such as the national food insecurity- can only be achieved through the coverage of the entire system rather than isolated individual parts. Several models have been developed using the System Dynamics around the food security (Bach and Saeed, 1992, Bala, 1999a, Gohara, 2001, Meadows, 1976, Meadows, 1977, Quinn, 2002, Saeed, et al., 1983, Georgiadis et al., 2004 and Saeed, 2000). Bala

9 (1999b) reported an integrative vision of energy, food and environment applied to Bangladesh.

Self Sufficiency Ratio

Bangladesh achieved impressive gain in food grain production in the last two decades and reached to near self-sufficiency at national level by producing about 26.76 million metric tons of cereals, especially rice and wheat in 2001 (Hossain et al., 2002 and Ministry of Finance, 2003). The Self Sufficiency ratio (SSR) calculated as per FAO’s method (FAO, 2001) was at 90.1 percent in 2001 and 91.4 percent in 2002. Estimates on food grain gap and SSR reveal that Bangladesh has a food grain gap of one to two million metric tons (Mishra and Hossain, 2005).

Based on the official and private food grain production and import figures the food grain SSR for Bangladesh is gradually declining from 94.1 in 2000-2001 to 87.7 in 2004-2005 and lowest self-sufficiency rate in Bangladesh was in 2005, which could be attributed to the crop damage during the severe flood in 2004(Mishra and Hossain, 2005).

PER PINSTRUP-ANDERSEN, Director General of IFPRI in his forward message claimed that for many years Bangladesh depended heavily on food aid, but recently it has emerged as a country approaching self-sufficiency in rice, the main staple food of its population (IFPRI, 1998).

Ecological Footprint

Ecological footprint represents the human demands, taking into account the production and supply of resources (energy, food and materials) and assimilation of the wastes (in all forms) generated by the analyzed system. Ecological footprint of a given population is the total area of productive land and water required to produce all the resources (energy, food and materials) consumed and to absorb the waste generated by that population of a region or nation using prevailing technology and resource management practices.

Ecological footprint is an ecological stability indicator. The theory and method of measuring sustainable development with the ecological footprint was developed during the

10 past decade (Wackernagel and Rees, 1996 and Chambers, et al., 2000). The Ecological Footprint is a measurement of sustainability illustrating the reality of living in a world with finite resources and it is a synthetic indicator used to estimate a population’s impact on the environment due to its consumption; it quantifies total terrestrial and aquatic area necessary to supply all resources utilized in sustainable way and to absorb all emissions produced always in a sustainable way. Apart from analyzing the present situation, ecological footprint provides framework of sustainability planning in the public and private scale.

Accounting tools for quantifying humanity’s use of nature are essential for assessment of human impact and also for policy planning towards a sustainable future. Many questions pertinent to build a sustainable society can be addressed by using ecological footprint as indicator. This tool has evolved from largely being a tool for pedagogical use to become a strategic tool for policy analysis.

Wackernagel et al., (1999) developed a simple assessment framework for national and global natural accounting and applied this technique to 52 countries and also to the world as a whole. Out of these 52 countries, only 16 countries are ecologically surplus, 35 are ecologically deficit including Bangladesh (0.2 gha/cap) and the rest one is ecologically balance. The humanity as a whole has a footprint larger than the ecological carrying capacity of the world. They also pointed out some strategies that can be implemented to reduce footprint.

Computational procedure of Ecological Footprint and Biological Capacity is described by Monfreda et al., (2004) systematically with laps and gaps to eliminate potential errors. For the meaningful comparison of the Ecological Footprint they converted all biologically productive areas into the standardized common unit global hectares (gha).

Zhao et al, (2005) reported a modified method of ecological footprint calculation by combining emergy analysis and compared their calculations with that of an original calculation of ecological footprint for a regional case. Gansu province in western China was selected for this study and this province runs ecologically deficit in both original and modified calculation.

11 Medved (2006) reported ecological footprint of Slovenia and it was found that current ecological footprint of Slovenia (3.85 gha/capita) exceeds the available biological productive areas (2.55 gha/capita) and significantly exceeds the biological productive areas of the planet (1.90 gha/capita).

Chen and Chen (2006) investigated the resource consumption of the Chinese society from 1981 to 2001 using ecological footprint and emergetic ecological footprint and suggested using emergetic ecological footprint (EEF) to serve as a modified indicator of ecological footprint (EF) to illustrate the resources, environment, and population activity, and thereby reflecting the ecological overshoot of the general ecological system.

Bagliani et al., (2008) reported ecological footprint and bio-capacity as indicators to monitor the environmental conditions of the area of Siena (Italian’s province). Among the notable results, the Siena territory is characterized by nearly breakeven total ecological balance, a result contrasting with the national average and most of the other Italian provinces.

Niccolucci et al., (2008) compared the ecological footprint of two typical Tuscan wines and the conventional production system was found to have a footprint value almost double than the organic production, mainly due to the agricultural and packing phases. These examples suggest that viable means of reducing the ecological footprint could include organic procedures, a decrease in the consumption of fuels and chemicals, and increase in the use of recycled materials in the packing phase.

12 Integrated Coastal Zone Management

Integrated Coastal Zone Management (ICZM) is an internationally accepted approach for achieving sustainable development. Coastal area is different from the rest of the country and an ICZM program is needed. The natural resources of the coastal areas are as different from their terrestrial counterparts as to require different and special forms of management. Coastal areas are important ecologically, as they provide a number of environmental goods and services. Thus frequently contain critical terrestrial and aquatic habitats, such as the mangrove forests, wetlands and tidal flats.

Fabbri (1998) reported a method and tool for improved decision aid in integrated coastal zone management (ICZM) and discussed the advantage of implementing, in a spatial decision support system, the most efficient strategies for data capture, integration, analysis and modeling, for the assessment of impacts deriving from possible development scenarios. The importance of integrating socio-economic and biophysical parameters in the context of ICZM and the need to define environmental indicators on which decision- making processes are based are also discussed.

Belt et al., (1998) applied computer modeling as a consensus building tool as part of the development of the Patagonia Coastal Zone Management Plan (PCZMP) and the model provides some interesting preliminary conclusions. The model indicates that the total net present value (NPV) of the fisheries sector over a period of 40 years may be increased by 13% compared with current income, with a decrease in hake fishing levels by ≈50% and the natural capital on which the fishery sector depends would be used in a more sustainable way, both ecologically and economically. The model also simulates possible impacts of oil spills and dumping of tanker ballast water on the penguin population which can have a significant negative impact on tourist industry incomes. The model implies that the importance of the tourist sector in Patagonia could in the future greatly exceed the value of the fishing industry (by 29%).

The key problem of developing capacities for integrated approaches to coastal zone management, especially in the context of newly industrialized and developing countries was examined (Pedersen et al., 2005). Through the discussion from an integrated coastal

13 zone management project in Malaysia it was learnt that some practical approaches have to be needed to develop capacities for acquiring and performing integrated approaches to the management of the coastal zone.

Siry (2006) analyzed decentralized coastal zone management in two neighbouring countries, Malaysia and Indonesia, and discussed in the details significant differences in the pattern of coastal zone management in these two countries. The lessons learnt from this study provide insight into how far decentralized coastal zone management has taken place in Malaysia and Indonesia. Finally it was reported that co-management and community- based approaches can be appropriate in dealing with coastal zone management.

Chua et al., (2006) studied the dynamics of integrated coastal management (ICM) in China and discussed the role of the interactions between the dynamic forces and essential elements of ICM to address the environmental and management issues at the local level. Also the tangible, intangible and socioeconomic issues were addressed in their study and concluded that dynamism in integrated coastal management mobilizes significant benefits of intangible assets.

Sonak et al., (2008) documented several issues involved in the recovery of tsunami- affected areas in India and the application of the ICZM concept to the reconstruction efforts and assessed the damage caused by the tsunami and its impact on the coastal states of India. The status of ecology such as: mangroves, coastal fisheries, agricultural lands and wet lands, ground water etc. after affected by tsunami were assessed.

Cao and Wong (2007) examined Social-economic and environmental issues recently emerged in China's coastal zone and identified the pollution from agriculture, livestock, domestic and industrial sources, ecosystem degradation, coastal reclamation, aquatic water depletion and coastal erosion as the main issues in the coastal zone of China. It was reported that comprehensive coastal management in China is a big challenge, facing with many difficulties and finally made recommendations for tackling these issues for China's coastal zone management.

14 Nguyen and Kok (2007) discussed the inherent complexity of the integrated systems model, the philosophical debate about the model validity and validation; the uncertainty in model inputs, parameters and future context and the scarcity of field data that complicate model validation and selected three tests, namely, Parameter-Verification, Behaviour- Anomaly and Policy-Sensitivity to test the model for coastal-zone management. To facilitate these three tests they used Morris sensitivity analysis and Monte Carlo uncertainty analysis.

Integrated coastal zone management (ICZM) consists of the population, crop production, aquaculture and forestry with two unique features of food security and environmental degradation (ecological footprint). There is a need to assess the present status of food security and environmental degradation (ecological footprint) of the coastal zone of Bangladesh to find out the leaverge points and also to explore management scenarios of integrated coastal zone management system for policy planning.

Dynamic behaviour of physical system can be studied by experimentation. Sometimes it may be expensive and time consuming. Full scale experimentation of integrated coastal zone management system is neither possible nor feasible. Most inexpensive and less time consuming method is to use mathematical model or computer model.

Integrated coastal zone management system is a highly complex system containing biological, agricultural, aquacultural, environmental, technological, and socio-economic components. The problem can not be solved in isolation, an integrated and systems approach is needed. For clear understanding of this complex system before its implementation, it must be modeled and simulated. System Dynamics, a methodology for constructing computer model for dynamic and complex systems, is the most appropriate technique to model such a complex system.

There is a need to develop a dynamic model to explore management scenarios of policy planning and management of integrated coastal zone management system (Iftekhar, 2006 and Klinger, 2004). This type of integrated study in the field of coastal zone management is relatively new in Bangladesh. Therefore, a dynamics of integrated costal zone management need to be studied in the Khulna-Barisal region for a sustainable

15 management of food production, ecology and environment aiming to alleviate the poverty of coastal population and ensure food security.

Climate Change Impacts on Rice

Climate change is a change in the statistical distribution of weather over periods of time that range from decades to millions of years. It can be a change in the average weather or a change in the distribution of weather events around an average (for example, greater or fewer extreme weather events). Climate change may be limited to a specific region, or may occur across the whole earth.

Industrialization, population growth and depletion of natural resources are threatening the ecosystems all over the world. Climate change is one of the results of this development. Climate changes include both rapid changes in climatic variables such as temperature, radiation and precipitation, as well as changes in the atmospheric concentration of greenhouse gases, soil water and nutrient cycling. Climate changes affect food security, supply of fishes and forest ecosystems. Predicted climate change impacts are essential to design plans and programs to adapt for future conditions.

Agriculture plays a dominant role in supporting rural livelihoods and economic growth of Bangladesh. Rice is the staple food crop in Bangladesh. Despite impressive success in increasing the food production in Bangladesh to meet the demands of the rapidly increasing population, the ability to sustain the increasing population is a major concern. Agricultural systems are vulnerable to variability in climate and it can be viewed as a function of the sensitivity of agriculture to changes in climate, the adaptive capacity of the system and the degree of exposure to climate hazards (IPCC, 2001). The productivity of food crops from year to year is sensitive to variability in climate and it affects the food security. Furthermore, Bangladesh is the most vulnerable to the impacts of climate variability and change. In the last two decades, there has been rapid development of crop models that can simulate the response of crop production to a variety of environment and management factors. With such models, it is feasible to assess the variations in yields for different crops or management options under given climatic change.

16 The climate change problem is related to changes in the concentration of the greenhouse gases (water vapor, CO2, CH4, N2O, and CFCs), which trap infrared radiation from the earth's surface and thus cause the greenhouse effect. This effect is a natural phenomenon, which helps maintain a stable temperature and climate on Earth. Human activities, such as fossil fuel combustion, deforestation, and some industrial processes have led to an increase in greenhouse gases concentration. Consequently, more infrared radiation has been captured in the atmosphere, which causes changes in the air temperature, precipitation patterns, sea-level rise, and melting of glaciers.

The knowledge and technology required for adaptation includes understanding the patterns of variability of current and projected climate, seasonal forecasts, hazard impact mitigation methods, land use planning, risk management, and resource management. Adaptation practices require extensive high quality data and information on climate, and on agricultural, environmental and social systems affected by climate, with a view to carrying out realistic vulnerability assessments and looking towards the near future.

Several studies have been reported on climate change impacts on rice (Karim et al., 1996, Aggarwal, et al., 1997, Saseendran et al., 2000, De Silva et al., 2007 and Yao et al., 2007, Basak et al., 2011; Islam and Saila, 2011 and Bala et al., 2011). Tubiello et al., (2000) investigated the effects of climate change and elevated CO2 on cropping systems at two Italian locations and the results suggested that the combined effects of elevated atmospheric CO2 and climate change at both sites would depress crop yields if current management practices were not modified.

Bala and Masuduzzaman (1998) developed system dynamics version of crop growth model based on the Wageningen Agricultural University crop growth model to predict the potential yield and yield under water stress of wheat. Bala et al., (2000) also adapted this model to project crop production (rice and wheat) in Bangladesh.

Farm level analyses have shown that large reductions in adverse impacts from climate change are possible when adaptation is fully implemented (Mendelsohn and Dinar 1999). Major classes of adaptation are seasonal changes and sowing dates, different variety or species, water supply and irrigation system, other inputs (fertilizer, tillage methods, grain

17 drying, other field operations), and new crop varieties and the types of responses needed are reduction of food security risk, identifying present vulnerabilities, adjusting agricultural research priorities, protecting genetic resources and intellectual property rights, strengthening agricultural extension and communication systems, adjustment in commodity and trade policy and increased training and education.

Challinor et al., (2007) reported three aspects of the vulnerability of food crops systems in Africa: the assessment of the sensitivity of crops to variability in climate, the adaptive capacity of farmers and the role of institutions in adapting to climate. Most studies show a negative impact of climate change on crop productivity in Africa. Farmers have proved highly adaptable in the past to short- and long-term variations in climate and in their environment. Key to the ability of farmers to adapt to climate variability and change is the access to relevant knowledge and information.

Tao et al., (2008) reported around food security presenting a covariant relationship between changes in cereal productivity due to climate change and the cereal harvest area required to satisfy China’s food demand and also estimated the effects of changing harvest area on the productivity required to satisfy the food demand; and of the productivity and land use changes on the population at risk of under nutrition.

Smith and Olesen (2010) reported that there exists a large potential for synergies between mitigation and adaptation in agriculture and suggested for development of new production systems that integrate bioenergy and food and feed production systems

Srivani et al., (2007) evaluated the climate change impacts on the productivity of major cereal crop, rice for 2020, 2050 and 2080 for IPCC climate change scenarios using INFOCROP and this study reveals prominent negative impacts of climate change on rice crop.

Rosenzweig et al., (2010) reported preliminary outlook for effects of climate change on Bangladeshi rice and this study shows that aus crop is not strongly affected and aman crop simulations project highly consistent production increase.

18 Basak et al., (2011) predicted the climate change impacts on two varieties of boro rice using the DSSAT modeling system and the weather data required for the model were generated using the regional climate model PRECIS. The model predicted significant reduction in yield of both varieties of boro rice due to climate change and the variations in rainfall over the growing period were found to affect rice yield and water requirement. Islam and Saila (2011) reported that the overall impact of climate change crop production in Bangladesh would probably be small in 2030. Shahid (2011) estimated the change of irrigation water demand in dry season Boro rice field in northwest Bangladesh in the context of global climate change and reported that there will be no appreciable change in the total irrigation water requirement due to climate change, but there will be an increase in the daily use of water for irrigation. As groundwater is the main source of irrigation in northwest Bangladesh, higher daily pumping rate in dry season may aggravate the situation of groundwater security in the region.

More recently Bala et al., (2011) predicted the climate change impacts on the yields of rice, wheat and maize in Bangladesh. Historical climate change scenario has little or no negative impacts on rice and wheat yields in Mymensingh and Dinajpur but IPCC climate change scenario has higher negative impacts than historical climate change scenario. There is also almost no change in the yields of rice and maize for the historical climate change scenario in the Hill Tracts of Chittagong, but there is a comparatively higher decrease in the yields of rice and maize for IPCC climate change scenario.

For proper understanding and implementations of the plans and programs of the adaptation strategies of the climate change impacts, the climate change impact systems must be modeled and simulated. Simulation models can assist in examining the effect of different scenarios of future development and climate change impacts on crop production and several crop models are available.

Therefore, the challenges of climate change impacts are faced by mitigation and adaption. The agricultural sectors require systematic integration of environmental and economic development measures for a sustainable agricultural growth. So, predicted climate change impacts are essential to design plans and programs to adapt for future conditions.

19 Objectives of the study

Rapid conversion of agricultural lands into aquaculture ponds and the growth of penaeid shrimp culture are considered to increase the food security with increased environmental degradation of the coastal zones in Bangladesh. Also boom and burst of shrimp culture work against agriculture and aquaculture in the long run (Arquitt. et al, 2005). Farmers in the coastal zones are also in panic for the long term consequences of shrimp culture. The purpose of this research is to examine the present status of food security and environmental degradation; address the short term and long term policy options for sustainable food security to assist the policy planners to design the policies for enhancing food security improving agriculture and aquacultural technology and at the same time to assess the impact of climate change on crop production in the coastal zone of Bangladesh for sustainable food security and suggest adaptations to face the challenges of the climate change impacts.

Specific objectives are: a) To estimate the present status of the contribution of expanding population, decreasing agriculture, expanding aquaculture for shrimp farming and forests to food security and ecological factor. b) To develop a computer model to simulate integrated coastal zone management systems for sustainable development. c) To determine the management strategies for sustainable development of the coastal zone system. d) To assess the impact of climate change on crop production in the coastal zone of Bangladesh and suggest adaptations.

20 CHAPTER 2

MATERIALS AND METHODS

2.1 Food Security and Ecological Footprint of the Coastal Zone of Bangladesh

2.1.1 Site Description With an area of about 144,000 sq. km, Bangladesh is situated between latitudes 20o 34' and 26o 38' north and longitude 88o 01' and 92o 41' east with an average altitude of 12 m above mean sea level (MSL). The country is bordered by India on the east, west and north and by the Bay of Bengal on the south. There is also a small strip of frontier with Myanmer (Burma) on the southeastern edge. The land is a deltaic plain with a network of numerous rivers and canals. Bangladesh is the seventh most populous country in the world. Officially known as the People's Republic of Bangladesh, it is the second largest Muslim country in the world after Indonesia. The country is divided into seven administrative divisions and the divisions are further subdivided into districts or zila. The districts are further divided into sub-districts or upazila.

The terrain of Bangladesh is mostly flat alluvial plain, but it is hilly in southeast. The lowest point in the country is Indian Ocean (0 m) while the highest point is Keokradong (1,230 m). Throughout the year, Bangladesh experiences different types of climate. From October to March the weather is tropical and mild winter; from March to June it is summer season where the climate is hot and humid. From June to October, Bangladesh experiences warm rainy monsoon. Natural hazards which occur in Bangladesh include droughts, cyclones and floods in most part of the country during the summer monsoon season. Cox’s Bazar in Bangladesh is the longest unbroken sea beach in the world.

Bangladesh is a developing economy; since 1996 the economy has developed at a pace of 5-6% per year. More than half of the country's GDP is generated from the service sector.

21 However, more than 65% of Bangladesh's population is engaged in agriculture. Rice is the most important agricultural product. Production and export of jute was once the major source of income for the country. But it started to decline after products made from polypropylene began to replace goods made of jute. Presently, production and export of garment makes a significant contribution to the economy of the country.

Along the coast in easterly direction, the physical features change, as well as the social makeup. The western part is a moribund delta (in this part the largest mangrove forest, Sundarban, is located); the middle part (the Meghna estuary area) is an active delta; and the eastern part (Chittagong coast) is a stable landmass. These parts have also some cultural differentiations rooted in the political history. The western part (Khulna) belonged to the territory of Rarh, the Barisal area (formerly called Bakerganj) was known as Chandradwip, the Comilla-Noakhali area was part of Samatat and the eastern part (Chittagong coast) belonged to Horikel. These “regional” entities continued for centuries until the Mughals integrated them in the seventeenth century (PDO-ICZMP, 2003b).

The three basic natural system processes and events that govern opportunities and vulnerabilities of the coastal zone of Bangladesh are: tidal fluctuations; salinities (soil, surface water or groundwater); and cyclone and storm surge risk. Considering these three criteria, an assessment has been done on the delineation of the coastal zone. For each of these criteria, threshold values have been specified and corresponding impacted areas were determined. Based on these criteria, area of the coastal zone was delineated.

Nineteen southern districts facing the Bay of Bengal or having proximity to the Bay and the exclusive economic zone (EEZ) in the Bay are grouped into a zone in terms of three geo-physical characteristics that distinguish the coastal zone from rest of the country: these are interplay of tidal regime, salinity in soil and water and cyclone and storm surge; with economic and social implications on the population. These districts are Bagerhat, Barguna, Barisal, Bhola, Chandpur, Chittagong, Cox’s Bazar, Feni, Gopalganj, Jessore, Jhalkati, Khulna, Lakshmipur, Narail, Noakhali, Patuakhali, Pirojpur, Satkhira and Shariatpur (PDO-ICZMP, 2003a).

The coastal zone of Bangladesh covers 147 upazilas (sub-district) within 19 districts. Further, a distinction has been made between upazilas facing the coast or the estuary and the upazilas located behind them. A total of 48 upazilas with cyclone risks, salinity and

22 tidal movement above threshold level facing the sea or the lower estuary in 12 districts that are exposed to the sea and or lower estuaries, are defined as the exposed coast and the remaining 99 upazilas of the coastal districts are termed interior coast. Exposed and Interior Upazilas in the Coastal Zone are shown in Table 2.1.

Table 2.1 Exposed and Interior Upazilas in the Coastal ZoneQq7ddd7777aaaaa

District Upazilas t Exposed Interior Bagerhat Mongla, Saran Khola, Morrelganj Bagerhat Sadar, Chitalmari, Fakirhat, Kachua, Mollahat, Rampal Barguna Amtali, Barguna Sadar, Patharghata, Betagi Bamna Barisal Agailjhara, Babuganj, Bakerganj, Gaurnadi,Hizla, Mehendiganj, Muladi, Wazirpur, Banari Para, Barisal Sadar Bhola Bhola Sadar, Burhanuddin, Char Fasson, Daulatkhan, Lalmohan, Manpura, Tazumuddin Chandpur Chandpur Sadar, Faridganj, Haimchar, Hajiganj, Kachua, Matlab, Shahrasti Chittagong Anowara, Banshkhali, Chittagong port, Boalkhali, Chandanaish, Lohagara, Double Mooring, Mirsharai, Pahartali, Rangunia,Chandgaon, Fatikchhari, Panchlaish, Sandwip, Sitakunda, Hathazari, Patiya,Raozan, Satkania, Patenga, Halisahar, Kotwali, Boijid Bakalia, Karanaphuli, Kulshi Bostami, Cox's Bazar Chakaria, Cox’s Bazar Sadar, Kutubdia, Ukhia, Maheshkhali, Ramu, Teknaf Feni Sonagazi Chhagalnaiya, Feni Sadar, Parshuram, Daganbhuiyan Gopalganj Gopalganj Sadar, Kashiani, Kotali Para, Muksudpur, Tungipara Jessore Bagher Para, Chaugachha, Jhikargachha, Manirampur, Abhaynagar, Keshabpur, Jessore Sadar, Sharsha Jhalokati Jhalokati Sadar, Kanthalia, Nalchity, Rajapur Khulna Dacope, Koyra Batiaghata, Daulatpur, Dumuria, Dighalia, Khalishpur, Khan Jahan Ali, Khulna Sadar, Paikgachha, Phultala, Rupsha, Sonadanga, Terokhada Lakshmipur Ramgati Lakshmipur Sadar, Raipur, Ramganj Narail Lohagara, Narail Sadar, Kalia, Narigati Noakhali Companiganj, Hatiya, Noakhali Sadar Chatkhil, Senbagh, Begumganj

23 Patuakhali Dashmina, Rangabali, Galachipa, Kala Bauphal, Mirzaganj, Patuakhali Sadar Para Pirojpur Mathbaria Bhandaria, Kawkhali, Nazirpur, Pirojpur Sadar, Swraupkati Satkhira Assasuni, Shyamnagar Debhata, Kalaroa, Kaliganj, Satkhira Sadar, Tala Shariatpur Bhederganj, Damudya, Goshairhat, Naria, Palong, Zanjira

More than a quarter of the population of the country lives in a coastal environment with multiple vulnerabilities and opportunities. Their despair and dream, their plight and struggle, their vulnerability and resilience, are uniquely situated in an intricate ecological and social setting that makes their livelihoods distinctive from other parts of the country to a considerable extent. Although Bangalees are the major ethnic community, there are 10 other ethnic communities who also live in the coastal zone. Among them are Chakma, Khiyang, Marma, Munda, Murang, Rakhaine, Tanchangya, Tripura, Mahato and Khatrio. Their total population was estimated at 0.2 million in 1991 (Kamal et al., 2001).

Population density in exposed coast is 482 persons per square kilometer whereas it is 1,012 for the interior coast. Average population density of the zone is 743 per sq. km., and the same value for Bangladesh average is 839 (Fig. 2.1). Population density in interior coast is much higher than that of exterior coast and the country’s average. There are about 6.8 million households in the zone, of which 52 percent are absolute poor (Islam, 2004).

1200

1000

800

600

400 Population per sq. km sq. per Population

200

0 Bagladesh Coastal zone Exposed coast Interior coast

Fig. 2.1 Population density in the coastal zone of Bangladesh (Sarwar and Wallman, 2005)

24 Livelihood activities in the coastal zone may be clustered into some broad categories: These are: a. natural resource based activities, such as: agriculture, salt making, fishing, aquaculture, shrimp fry collection, fuel collection, extraction of forest products, etc; and b. human resource based activities, such as: livestock and poultry keeping, boat building (carpentry), net making, kantha making, fish processing, trading, etc.

These two categories broadly correspond to farm and non-farm activities. Both categories of activities may be based on self-employment and wage employment. It is also true that one engaged in self-employment is also available for wage employment and vice versa.

However, livelihood activities at the household level are diversified. Farmer along with farming are also involved in fishing, animal husbandry and trading (selling crops or dairy products to buy other essentials). Increasingly the people are engaging themselves with a secondary occupation along with the primary one. The driving force behind opting for multiple occupations is to maximize household income and to minimize risk.

Certain activities are common everywhere and some are typical of the coastal zone. Coastal zone-specific activities are like: salt production; fishing; fish processing (drying); net making; fry collection; shrimp farming; crab/shell collection; extraction of forest products (wood, honey, golpata and wax collection from Sundarban); and boat building (boat carpentry) (PDO-ICZMP, 2004a).

Soil fertility is an important factor for crop production. In general the coastal regions of Bangladesh are quite low in soil fertility. Thus in addition to salinity, plant nutrients in soils affect plant growth. Soil reaction values (pH) range from 6.0-8.4 with the exception of Chittagong and Patuakhali, where the pH values range from 5.0-7.8. Most of the soils are moderate to strongly alkaline, the pH values of the surface soils being lower than those of the subsurface soils. In places with higher pH values, micronutrients’ deficiencies are expected.

The soils are in general poor in organic matter content with the excepton of Paikgachha upazila of Khulna district, where the topsoils contain high organic matter (7%). The

25 organic matter content of the top soils ranges from less than 1% to 1.5%. The low organic content in soils indicates poor physical condition of the coastal soils. The CEC (Cation Exchange Capacity) of the soils range from 9.4-40.6 m.e.%. The higher CEC values of Khulna and Bagerhat soils are due to finer texture and higher organic matter contents. Soils having CEC below 15.0 m.e.% is considered as of poor status (Singaraval et al., 1996). The soils contain variable levels of exchangeable bases, but a general feature is the higher Ca and K saturation of the exchange complex compared to Na and Mg in most of the soils. The Na and Mg saturation of the exchange complex is harmful because they destroy the soil physical properties and offset plant nutrition. Magnesium has synergistic effect of plant uptake of Na as well as antagonistic effect on the uptake of Ca and K.

The total N contents of the soils are generally low, mostly around 0.1%. The low N content may be attributed to low organic matter contents of most of the soils. Available P status of the soils ranges from 15-25 ppm. Some deficient P soils are also found in Chttagong, Barguna, Satkhira and Patuakhali districts. Widespread Zn and Cu deficiencies have been observed in the coastal regions (Karim et al., 1990).

In the coastal saline soils rice, jute, sugarcane, pulses, oilseeds, spices, vegetables and fruits are grown, but their contributions to cropping intensity vary greatly with regions. In salt affected highlands of Barisal, Khulna and Patuakhali regions, local transplanted Aman rice (July-November) is the dominant crop, whereas in the same land type of Chittagong region HYV Aman rice is the major crop. In medium highlands of Barisal, Khulna, Noakhali, Patuakhali and Chittagong regions the dominant crop is local transplanted Aman rice. The dominant crop in the medium low lands of the former three regions is broadcast aman rice, whereas in Chittagong region broadcast aus rice is the dominant crop.

During wet season, local aman rice is grown extensively in the coastal saline areas with normal yields between 2.5 and 3.0 tons per hectare. Transplanted aman-fallow is the most dominant cropping pattern in the Khulna, Barisal and Patuakhali regions. In Noakhali and Chittagong, aus-local transplanted aman pattern covers 25-28% area. Next to this is the transplanted aman-fallow pattern, represents about 18-20% area. Winter crops, such as wheat, potato and vegetables are grown, which cover a small area (11.5%). This is

26 practiced in the district of Noakhali with transplanted aman-winter crop cropping pattern (Haque, 2006).

Shrimp farmers in the coastal zone of Bangladesh mainly cultivate Indian Tiger Shrimp (Peneaus monodon). Water salinity required for maximum growth of this species is 5-25 ppt (Chanratchakool, 2003). It can not be cultivated in fresh water. Salinity intrusion in the freshwater zone of the coastal area has opened the door to shrimp farmers to cultivate tiger shrimp in the area. Vast number of land area is converted to saline water field day by day. Shrimp farm areas in the coastal districts increasing day by day that indicate salinity intrusion.

Shrimp farm areas in the year 2004 were 87 fold more than that of the year 1975. It is another indicator of salinity intrusion in the coastal zone. In last thirty years time period, salinity intrusion has degraded land quality and farmers can’t grow any agricultural crops in their fields. Thus farmers become zero productive land owners, in one sense landless with their existing saline land. Salinity intrusion causes loss in agriculture, loss in biodiversity, loss in fresh water and its resources. Salinity area in the coastal zone increased from 0.83 mha in 1973 to 1.06 mha in 2009 (SRDI, 2010).

Coastal soils widely in nature salinity depth and fluctuation of groundwater and the seasonal variation in salinity of surface water (MPO, 1986). The sub-soil and sub-strata remain saline throughout the year and shallow groundwater also remains very harmful stage in coastal areas (SRDI, 1991). Soil Resources Development Institute (SRDI) has developed 5 mapping units of the entire zone (SRDI, 2000). These are presented in Table 2.2.

Depending on the intensity of saline, it has been catagorized by S1 ( 2.0 – 4.0 dS/m), S2

(4.1 – 8.0 dS/m), S3 (8.1 – 12.0 dS/m), S4 (12.1 – 16.0 dS/m) and S5 (>16.0 dS/m). The total land area under each category of 5 mapping units is given in Table 2.3. According to the meteorologists and environmentalists more non saline areas will be under salinity with the rise of sea level. Thus, apprehension is that more agricultural lands will be lost due to salinity engulfing a huge non-saline cropped area, which makes it imperative that appropriate techniques are to be developed for the cultivation of crops in saline soils.

27 Table 2.2 Mapping units of the coastal region of Bangladeh

Mapping Characteristics of Location units mapping units 1 No saline to A large parts of Satkhira, Khulna, Bagerhat, Narail, Jessore, slightly saline Gopalgonj, Madaripur, Pirojpur, Jhalokathi, Barisal, Bhola, Patuakhali, Borguna, Laxmipur, Noakhali, Feni and Chittagong 2 Slightly saline to Many parts of Satkhira, Khulna, Bagerhat, Narail, Jessore, low saline Madaripur, Pirojpur, Bhola, Patakhali, Borguna, Laxmipur, Noakhali, Feni, Chittagong and Cox,s Bazar. 3 Low saline to Many parts of Satkhira, Khulna, Bagerhat, Narail, Jessore, medium saline Madaripur, Pirojpur, Bhola, Potakhali, Borguna, Laxmipur, Noakhali, Feni, Chittagong and Cox,s Bazar. 4 Medium to high Many parts of Satkhira, Khulna, Bagerhat, Bhola, Patakhali, saline Borguna, Noakhali, Feni, Chittagong and Cox,s Bazar. 5 High saline to Many parts of Satkhira, Khulna, Bagerhat, Bhola, Patakhali, very high saline Borguna, Noakhali, Feni, Chittagong and Cox’s Bazar. Source: SRDI (2000)

Table 2.3 Category of soil salinity and saline areas of Bangladesh

Mapping Saline area Category of salinity (dS/m) Unit (ha) S1 S2 S3 S4 S5 2 – 4 4.1 – 8 8.1 – 12 12.1 – 16 .16 1 1,15,370 82260 31590 1520 - - 2 3,09,190 170380 110390 29420 - - 3 2,40,220 35490 113890 61240 25870 2650 4 1,98,890 1630 36060 73400 55130 32750 5 1,57,080 - 15270 25900 64100 51740 Total 10,20,750 289760 307200 191550 145100 87140 Source: SRDI (2000)

Soil salinity is a major problem in the coastal region during the dry period. Soil salinity starts increasing from last week of December and reaches to its peak level in the month of March and April and minimum salinity occurs in the months of July and August after the onset of the monsoon rains (Mondal, 1997). Salinity level varies from 3 to 18 dS/m.

Bangladesh is highly vulnerable to sea level rise, as it is a densely populated coastal country of smooth relief comprising broad and narrow ridges and depressions (Brammer et al., 1993). World Bank (2000) showed 10 cm, 25 cm and 1 m rise in sea level by 2020,

28 2050 and 2100; affecting 2%, 4% and 17.5% of total land mass respectively (Table 2.4). Frihy (2003) reported 1.0 cm per year sea level rise in Bangladesh.

Table 2.4 Sea level rise (SLR) in Bangladesh and its possible impacts

Year 2020 2050 2100 Sea level rise 10cm 25cm 1m(high end estimate) 2% fo land (2,500 4% fo land (6,300 km2) 17% fo land (2,500 km- Land below km2) 2). Patuakhali, khalna SLR and barisal regions will be most affected 1991cyclone happens Storm surge goes from again with a 10% 7.4 to 9.1 m with 1m increase in intensity, STR. wind speed increase Storm surge - from 225 to 248 km/h; storm surge goes from 7.1 to 8.6 m with 0.3m STR. 20% increase in Increase flooding in Both inundation area inundation. Meghna and Ganges and flood intensity will Flooding floodplain. Monsoonal increase tremendously. fioods increase yield loss. Inundate 0.2 Mmt. 0.3 m inundate 0.5 Mmt. Devasting flood may Agriculture of production; <1% of production; <2% of cause crop failure for of current total. current total. any year. Inundates 15% of Inundates 40% of the The sundarbans the sundarbans sundarbans would be lost. Lost of the sundarbansand other Ecosystem costal wetlands would reduce breeding ground for many estuarine fish, which wouid reduce their population. Salinity increase increase increase Source: World Bank (2000)

The coast of Bangladesh is prone to severe natural disasters, such as cyclones, storm surges, and floods. There are other natural and man-made hazards, such as erosion, high arsenic contents of ground water, water logging, water and soil salinity, tectonic process and deteriorating coastal ecosystems and various forms of pollution. These hazards occur as shocks (sudden changes such as natural disasters, conflict or collapsing market prices),

29 seasonality (low demand for farm labor between plantation and harvesting periods) and trends (gradual environmental degradation, oppressive political systems or deteriorating terms of trade). The intensities and magnitudes of these hazards vary over space and time.

In addition, coastal zone faces predictable extreme impact of climate change; and is the ultimate recipient of pollution load. Like elsewhere in the world, the coastal zone of Bangladesh has the highest concentration of natural hazards.

People in the coastal zone of Bangladesh have been fighting from the beginning of human habitation. Cyclone and tidal surge are their main enemy that broken the backbone of coastal people. Cyclone and tidal surge snap away properties as well as lives.

Records of the last 2000 years show that at least 70 major cyclones hit the coastal belt of Bangladesh. During last 35 years, nearly 900,000 people died due to catastrophic cyclones.The Noakhali- Chittagong coast received 40 percent of the cyclones, which is the most vulnerable area for the landfall of cyclones. The Chittagong- Cox”s Bazar coast received around 27 percent, while Khulna/Sundarban and Barisal-Noakhali coast are relatively less vulnerable (Rahman,2001).

Four major natural calamities occured in the coastal zone since last fifties. 1970 – Cyclone and tidal surge

29 April 1991 – Cyclone and tidal surge

15 November 1997 – SIDR

25 May 2009 – Aila

2.1.2 Site Selection

To address the food security and ecological footprint, an indicator of environmental sustainability of the coastal zones of Bangladesh, five districts from the exposed coast and nine upazilas from the five districts having exposure to sea and or lower estuaries were randomly selected. Selected upazilas are shown in Fig 2.2 indicated Exposed and interior coastal zones of Bangladesh in the map The purpose of this random selection was also to include penaeid shrimp culture as well as rice cultivation in the upazilas under study. The selected upazilas are given in Table 2.5 and are representative of the exposed coast of Bangladesh.

Exposed coast

Interior coast

Fig. 2.2. Map of the coastal zone of Bangladesh showing selected upazilas

31 Table 2.5. Selected upazilas from exposed to the coastal zone of Bangladesh.

District Upazila Patuakhali Kalapara, Galachipa Borguna Patharghata Satkhira Shyamnagar Khulna Dacop, Koyra Bagerhat Mongla,, Morrelgonj, Sharonkhola

2.1.3 Questionnaire Development

To estimate the present status of the food security and ecological footprint of the integrated coastal zone management systems two sets of questionnaire for primary and secondary data collection were developed and the questionnaire were structured into three parts: (i) general information, (ii) information for computation of food security and (iii) information for computation of ecological footprint. These are shown in Appendix A and B respectively. Two sets of questionnaire were pre-tested and necessary improvement was made. .

2.1.4 Data Collection and Analysis

Data on population, crop production, aquaculture, livestock and forestry were collected to estimate the present status of the food security and environmental degradation of the coastal zones of Bangladesh from upazila office of Government Department of Statistics, Agriculture, Fishery and Livestock. Purposeful random sampling was conducted for primary data collection and four different categories of farm size were considered and these are landless (<0.02 ha), small (0.02- 1.0 ha), medium (1.0-3.0 ha) and large (> 3.0 ha). Pre-tested questionnaire was used for primary data collection from individual farmers with emphasis on food security and ecological footprint. Primary data also served as a cross check for the secondary data as well a measure to fill up the missing gaps in the secondary data.

Collected data and information were compiled, edited, summarized and analyzed, and the present status of food security and environmental degradation (in terms of ecological

32 footprint) were calculated. Database was prepared in Excel format separately for computation of food security and ecological footprint from primary and secondary information for the nine upazilas of the coastal zone of Bangladesh. Excel format permits easy change or refinement of any data and the subsequent computation of food security and ecological footprint for changed or refined data in the designed Excel computation mode automatically. A database prepared for the nine upazilas of the coastal zone of Bangladesh are shown in Appendix C 1.1 to Appendix C 9.2.

A typical village named Baraikhali was also selected from Dacop upazila of Khulna distict to find out the individual household food security status. Data were collected from every household of the village using pre-designed questionnaire. This village is in the exposed coast and includes shrimp culture and rice cultivation. Total number of households of the village was 182.

2.1.5 Computation of Food Security

United State Department of Agriculture (USDA) evaluated food security based on the gap between projected domestic food consumption and a consumption requirement (USDA, 2007). All food aid commodities were converted into grain equivalent based on calorie content. Based on USDA concept the food security is defined as

Food Security = (Food available from different sources and also equivalent food from Different sources-Food requirement)/Food requirement (2.1)

Yusuf and Islam (2005) reported that the daily food requirement data of BBS (Bangladesh Bureau of Statistics and INFS (Institute of Nutrition and Food Science) are not adequate and consumption of such a diet would produce physiological deficiencies of both energy and protein leading to protein-energy malnutrition as well as micronutrient malnutrition and proposed a dietary composition for balanced nutrition in Bangladesh as shown in Table 2.6. The total food intake proposed is 2345 kcal/capita and it is midway between the values suggested by WHO (2310 kcal/capita) and FAO (2400 kcal/capita). The proposed 2345 kcal/capita is equivalent to 1.357 kg of rice based on price. All food aid commodities were converted into grain equivalent based on economic returns (price) to compute the food security.

33 Table 2.6 Daily balanced food requirement SL. Food Item Amount Price Total price Equi rice kcal No. (gm) (Tk. /kg) (Tk.) (kg) 1 Rice 312 26.60 8.30 0.312 1086 2 Wheat 60 28.00 1.68 0.063 209 3 Pulse 66 55.00 3.63 0.136 228 4 Animal products 126 110.00 13.86 0.521 176 5 Fruits 57 30.00 1.71 0.064 41 6 Vegetables 180 12.00 2.16 0.081 113 7 Potato 80 12.00 0.96 0.036 71 8 Oil 36 80.00 2.88 0.108 324 9 Sugar and Gur 22 30.00 0.66 0.025 88 10 Spices 14 20.00 0.28 0.011 09 Total 953 36.12 1.357 2345

Source: Yusuf and Islam (2005) Based on this concept the food security is computed as Food Security = [(Food available from crops + Food available from aquaculture and equivalent food from income of aquaculture + Food available from livestock and equivalent food from income of livestock + Food available from forestry and equivalent food from income of forestry)– Total food requirement] / Total food requirement (2.2) Positive food security means surplus food and negative food security means shortage in food supply to lead healthy life. The structure of food security computation is shown in Fig. 2.3.

Income from Equivalent Crop crop rice (ton)

Income from Equivalent Fish fish rice (ton) ÷ = (ton) sources

Income from Equivalent rice (ton) Animal animal rice (ton)

Food security ratio

Production from different Income from Equivalent Forest Total income in equivalent rice in equivalent Total income forest rice (ton) Food requirement in equivalent

Fig. 2.3 Structure of food security computation

34 This indicator of food security gives a quantitative measure of food security that can be achieved from the available foods and incomes derived from different sources. The major drawback of this indicator is that it does not give the nutritional status that actually prevails.

Self Sufficiency Ratio (SSR) is calculated as per FAO’s method (FAO, 2001) to express magnitude of production in relation to domestic utilization as well as food deficiency in the country. SSR is defined as:

SSR = Production / (production + imports – exports) (2.3)

2.1.6 Computation of Ecological Footprint and Biological Capacity

The ecological footprint calculation is based on the average consumptions data that are converted into uses of productive lands. The bioproductive land is divided into 6 categories according to the classification of the World Conservation Union: (1) cropland; (2) grazing land; (3) forest; (4) fishing ground; (5) build-up land; (6) energy land. Total ecological footprint is the sum of the ecological footprints of all categories of land areas which provide for mutually exclusive demands on the bio-sphere. Each of these categories represents an area in hectares, which is then multiplied by its equivalence factor to obtain the footprint in global hectares. One global hectare is equal to 1 ha with productivity equal to the avarage of all the productive ha of the world. Thus, one ha of highly productive land is equal to more global hectares than 1 ha of less productive land (Monfreda et al. 2004). The ecological footprint can be expressed as:

Ecological footprint (gha) = Area (ha) × Equivalence Factor (gha/ha) (2.4) Where, Area (ha) = All categories of land areas, in ha Equivalence Factor = the world average productivity of a given bioproductive area / the world average potential productivity of all bioproductive areas. Equivalence factor represents the world average productivity of a given bioproductive area relative to the world average potential productivity of all productive areas and it is the quantity of global hectares contained within an average hectare of cropland, build-up land, forest, pasture or fishery.

35 Specifically, an equivalence factor is the quantity of global hectares contained within an average hectare of cropland, build-up land, forest, pasture or fishery. The structure of the computation of ecological footprint is shown in Fig. 2.4.

equivalence crop yield global crop occupied crop factor crops [t/yr] yield [t/ha/yr] area [gha] / × [gha/ha] =

animal equivalence occupied global pasture products factor pasture pasture area yield [t/ha/yr] [t/yr] / × [gha/ha] = [gha]

equivalence occupied fish products global fisheries ]

factor fisheries fisheries area ha

[t/yr] / yield [t/ha/yr] × = g [gha/ha] [gha]

forest equivalence REGION REGION global timber occupied forest products 3 factor forest 3 / yield [m /ha/yr] × = area [gha] hectares or [m /yr] [gha/ha]

equivalence occupied build-up area yield factor factor crops build-up area [ha] crop × × [gha/ha] = [gha]

equivalence occupied

NET CONSUMPTION (= production + import - export) OF OF - export) + import production (= NET CONSUMPTION energy fuel wood yield factor forest energy area [global REGION OF FOOTPRIENT ECOLOGICAL TOTAL [GJ/yr] [GJ/ha/yr] / × [gha/ha] = [gha] Fig. 2.4 Structure of ecological footprint computation

An important part of the ecological footprint analysis of a region or zone is represented by the calculation of its Biological Capacity (Biocapacity) that takes into account the surfaces of ecologically productive land located within the area under study. Biological capacity represents the ecologically productive area that is locally available and it indicates the local ecosystems potential capacity to provide natural resources and services. Biological capacity is the total annual biological production capacity of a given biologically productive area (Monfreda et al. 2004). Biological capacity can be expressed as:

Biocapacity (gha) = Area (ha) × Equivalence Factor (gha/ha) × Yield factor (2.5) Where, Yield factor = Local yield/ global yield

Total biocapacity is the sum of all bioproductive areas expressed in global hectares by multiplying its area by the appropriate equivalence factor and the yield factor specific to that country/locality. The structure of the computation of biocapacity is shown in Fig.2.5.

equivalence existing crop yield factor equivalence factor crops area [ha] × crop × = crop area [gha] [gha/ha]

existing equivalence equivalence yield factor pasture area factor pasture pasture area × pasture × = [ha] [gha/ha] [gha]

existing equivalence equivalence yield factor fisheries area factor fisheries fisheries area × fisheries × =

REGION [hectares or ha] REGION [ha] [gha/ha] [gha]

equivalence equivalence existing forest yield factor factor forest forest area area [ha] × forest × = [gha/ha] [gha]

gha] or hectares [global equivalence equivalence existing build- yield factor factor crops build -up area up area [ha] × crop × = [gha/ha] [gha]

existing energ OF REGION CAPACITY BIOLOGICAL TOTAL

TOTAL EXISTING AREA OF AREA TOTAL EXISTING equivalence equivalence biomass yield factor factor forest energy area accumulation × forest × = [gha/ha] [gha] area [ha]

Fig.2.5. Structure of biological capacity computation

Biological capacity can be compared with the ecological footprint, which prodides an estimation of the ecological resources required by the local population. The ecological status is expressed as the difference between biocapacity and eclogical footprint. A negative ecological status (BC < EF) indicates that the rate of consumption of natural resources is greater than the rate of production (regeneration) by local ecosystems (Rees, 1996). Thus, an ecological deficit (BC < EF) or surplus (BC > EF) provides an estimation of a local territory’s level of environmental sustainability or unsustainability. This also indicates how close to sustainable development the specific area is.

2.2 Modeling of Integrated Coastal Zone Management.

Planning of Integrated Coastal Zone Management (ICZM) has to address several interdependent issues such as food security and environmental degradation (ecological footprint) of the coastal zone of Bangladesh to find out the leaverge points and also to explore management scenarios of integrated coastal zone management system. The ICZM is a complex, dynamic and multi-faceted system depending not only on available

37 technology but also upon economic and social factors. Experimentation with an actually existing coastal zone management system containing economic, social, technological, environmental and political elements is totally unrealistic. Simulating an ICZM by a computer model one can conduct a series of experiments. Computer models clearly are of great value to understand the dynamics of such complex systems (Bala, 1999a). Owing to the intrinsically complex nature of ICZM problems, it is advantageous to implement ICZM policy options only after careful modeling analyses. Forrester’s system dynamics methodology provides a foundation for constructing computer models to do what the human mind cannot do that is rationally analyze the structure, the interactions and mode of behavior of complex socio-economic, technological, and environmental systems Forrester (1968). Hence, the system dynamics approach is the most appropriate technique to handle this type of complex problem.

System dynamics model can be written in a general purpose language such as FORTRAN and C++. But many software programs and packages are now available that offer dynamic modeling capabilities and sophisticated graphics interfaces. These are icon operated modeling softwares such as STELLA, POWERSIM and VENSIM. In this study software STELLA 8 is used for modeling of integrated coastal zone management system.

System dynamics methodology is based on the feedback concept of control theory and the feedback loops simulate dynamic behaviour (Bala, 1999a). Two basic building blocks in system dynamics studies are stock or level and flow or rate. Stock variables (symbolized by rectangles) are state variables and stocks represent accumulation in the system. Flow variables (symbolized by valves) are the rate of change in the stock variables and flows represent the activities and decision function in the system. Converters (represented by circles) are intermediate variables used for miscellaneous calculations. Finally, the connectors (represented by simple arrows) represent cause and effect links within the model structure (Bala, 1999a).

The integrated coastal zone management system consists of population, crop production, aquaculture, forestry and ecological sector. These sub-models are integrated for sustainable development. The system as a whole can be described in terms of interconnected blocks. Block diagram representation of the integrated coastal zone management system is shown in Fig. 2.6. The major influences to a sector from other

38 sectors and its influences on the other sectors are shown in the diagram. Crop area is converted into aquaculture pond area and the shrimp production is highly dependent shrimp production intensity and pond area. Major contributions to the food security of coastal zone come from the shrimp production and crop production and the environmental degradation i.e. ecological footprint comes from mainly shrimp production intensity and pond area and cropping intensity and crop area. The simplified flow diagram of integrated coastal zone management system is shown in Fig. 2.7. In Fig. 2.7 pond area is a stock variable and pond growth rate is inflow to the stock – pond area and outflow for the stock - crop area. The line starting from the population to population growth with arrow towards the population growth indicates that population level depends on population growth. Fundamental equations that correspond to major state variables shown in Fig. 2.6 are as follows:

pond area (t) = pond area (t-Δt) + pond growth rate × Δt (2.6)

crop area (t) = crop area (t-Δt) - pond growth rate × Δt (2.7) population (t) = population (t-Δt) + population growth rate × Δt (2.8)

Population Crop production

Forestry Aquaculture

Food Security Environment

Fig. 2.6 Interrelationships of integrated coastal zone management systems

39 Simplif ied f low diagram

population

shrimp production intensity

energy consumption population growth

crop area pond area

cropping intensity per capita f ood requiremenr pond growth rate

shrimp production

crop production

f ood av ailability f ood requirement

food security

ecological f ootprint biocapacity

ecological status

Fig. 2.7 Simplified flow diagram of integrated coastal zone management system.

This model is essentially a detailed mathematical description of the system and it is a system of finite-difference integral equations. The system of equations of the model is given in Appendix-D. The system of equations of the model is solved using Runge-Kutta 4th order method. The model is organized into three major sectors; (i) food security (ii) ecological footprint and (iii) Biocapacity. All of the three sectors are interlinked with each other. The STELLA flow diagram and description of each sector is given below.

2.2.1. Food Security Sector Food Security is calculated based on the difference between food available from different sources of a region and food requirement for the population of that region. All food aid commodities were converted into grain equivalent based on economic returns (price) to compute the food security. Fig. 2.8 presents the food security sector of ICZM model. Crop area is converted into aquaculture pond area and the aquaculture pond area is divided into two categories. One is rice fish integrated farming area (galda) and another is solely shrimp pond area (bagda). Land transfer rate for bagda is a fraction of crop (rice) area.

40 Food security sector

y ield other f ish other f ish production f ood per capita eclogical f ootprint ~ population f or shrimp culture shrimp production intensity ~ shrimp ecological shrimp y ield bagda Area of canal riv er & pond f oot print multiplier ~ population growth shrimp intensity multiplier bagda f ood f rom other f ish Yield of shrimp rcp shrimp production bagda Shrimp production rcp equiv alent f actor other f ish pond area bagda

shrimp y ield normal bagda f ood f rom bagda population growth f actor land transf er rate f or bagda ecological f ootprint f or crop crop area f ood requirement transf er f raction f or bagda equiv alent f actor shrimp f ood f rom shrimp rcp ~ transf er f raction crop ecological f or crop plus f ish no of day s f oot print multiplier rice f ish integrated Graph 2 food security Graph 3 f arming area

Table 1 ~ non rice y ield cropping intensity Graph 4 non rice arealand transf er rate f or crop f ish f ood av ailable ~ cropping intensity multiplier non rice area growth rate non rice production

equiv alence f actor non rice crop y ield Table 2 f ood f rom non rice non rice growth f raction f ood f rom crop area crop y iled normal crop y ield f or crop f ish food from forest normal integrated f arming food from forest f ish y ield galda shrimp production galda

f ood f rom crop plus f ish f ood f rom animal normal f ood f rom galda animal area forest area f ood f rom animal

animal growth rate forest growth forest growth factor animal growth f raction

Fig. 2.8 Food security sector of ICZM model

41 Similarly, land transfer rate for rice fish is also a fraction of crop (rice) area. Pond area bagda at any time calculated from the area of pond bagda at previous time and the land transfer rate for bagda multiplied by solution interval. Similarly, rice fish integrated farming area at any time calculated from the area of crop fish integrated farming at previous time and land transfer rate for rice fish multiplied by solution interval. In STELLA it is expressed as: crop_area(t) = crop_area(t - dt) + (- land_transfer_rate_for_bagda – land_transfer_rate_for_rice_fish) * dt (2.9) pond_area_bagda(t) = pond_area_bagda(t - dt) + (land_transfer_rate_for_bagda)*dt (2.10) land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda (2.11) rice_fish_integrated_farming_area(t) = rice_fish_integrated_farming_area(t - dt) +(land_transfer_rate_for_rice_fish) * dt (2.12) land_transfer_rate_for_rice_fish = crop_area*transfer_fraction_for_crop_plus_fish (2.13)

Food Security is computed as

Food Security = (Food available from different sources and also equivalent food from Different sources-Food requirement)/Food requirement (2.14)

In STELLA the equation is expressed as: food_security = ((food_available-food_requirement)/food_requirement)*100 (2.15)

Food requirement is directly related to number of population and amount of food consumed per person.

In STELLA the equation is expressed as: food_requirement = population*food_per_capita*no_of_days (2.16)

Population at any time calculated from the population at previous time and population growth multiplied by solution interval. Population growth is calculated by multiplying the existing population by the population growth factor. Population and population growth at time, t can be expressed by the following equations.

42 population(t) = population(t - dt) + (population_growth) * dt (2.17) population_growth = population*population_growth_factor (2.18)

Food available is the sum of all available sources of food i. e. food from crops, food from aquaculture, food from livestock and food from forestry. For computing food security, all food aid commodities were converted into grain equivalent based on economic returns (price).

In STELLA it is expressed as: food available = food_from_galda+food_from_bagda+ food_from_shrimp_rcp +food_from_other_fish+ food_from_crop_area +food_from_crop_plus_fish+food_from_non_rice +food_from_forest+food_from_animal (2.19)

Penaied (Galda) shrimp is cultivated mainly in rice field with integrated rice farming. Food available from galda is the product of shrimp production galda and equivalent factor from shrimp. The term equivalent factor is used as a conversion factor to convert the amount of production of shrimp into the amount of rice equivalent. In STELLA the eqation is expressed as: food_from_galda = shrimp_production_galda*equivalent_factor_shrimp (2.20)

Shrimp production (galda) is determined by multiplying the area of galda and yield of galda and it is affected on shrimp ecological foot print multiplier. Shrimp production or shrimp yield is decreased over the year which is defined as shrimp ecological foot print multiplier.

In STELLA the equation is expressed as: shrimp_production_galda = shrimp_yield_galda*shrimp_ecological_footprint_ multiplier*crop_fish_integrated_farming_area (2.21)

Bagda shrimp is cultivated mainly in shrimp pond. Food available from bagda is the product of shrimp production bagda and equivalent factor for shrimp. Where, shrimp production (bagda) is the product of area of bagda and yield of bagda.

43 In STELLA the equations are expressed as: food _from_bagda = shrimp_production_bagda*equivalent_factor_shrimp (2.22) shrimp_production_bagda = pond_area_bagda*shrimp_yield_bagda (2.23) Shrimp yield (bagda) is highly depending on shrimp production intensity and shrimp ecological foot print multiplier. In STELLA the equation is expressed as: shrimp_yield_bagda = shrimp_yield_normal_bagda*shrimp_intensity_multiplier_ bagda*shrimp_ecological_footprint_multiplier (2.24)

Shrimp intensity multiplier bagda is dependent on shrimp production intensity. Shrimp intensity multiplier bagda gradually increases with the increase of shrimp production intensity. It is shown in Fig. 2. 9.

Shrimp intensity multiplier bagda multiplier intensity Shrimp 1

0 1 10.9 20.8 30.7 40.6 50.5 60.4 70.3 80.2 90.1 100 Shrimp production intensity

Fig. 2.9 Changes of shrimp intensity multiplier bagda with shrimp production intensity

Approximately 10% of the farms in the world are currently using intensive or super- intensive production strategies. There is a tendency for Asian farms to be smaller in size but more intensive in the methods of production. This is particularly true in Taiwan and Thailand, where the industry is extremely well-developed while Asia has a greater percentage of farms that are intensive,

Shrimp production intensity increases over the year which is shown in Fig.2.10.

20 Shrimp production intensity production Shrimp 10

0 0123456789101112 Year

Fig. 2.10 Changes of shrimp production intensity over the year

Food available from shrimp rcp (river, canal and pond) is the product of equivalent factor shrimp and shrimp production rcp. In STELLA the equation is expressed as: food_from_shrimp_rcp = equivalent_factor_shrimp*Shrimp_production_rcp (2.25)

Food available from other fish is calculated by multiplying the other fish production by equivalent factor other fish. Here, equivalent factor other fish refers to conversion of the production of different fishes into rice equivalent. Other fish production means the all other fish production except shrimp obtained from different fish growing sources (e.g. canal, pond, river etc.).

In STELLA the equation is expressed as: food_from_other_fish = equivalent_factor_other_fish*other_fish_production (2.26)

Food available from crop is obtained by multiplying the crop area by crop yield.

In STELLA the eqation is expressed as: food_from_crop_area = crop_area*crop_yield (2.27)

45 Crop yield is the product of three factors: crop yield normal, crop ecological footprint multiplier and cropping intensity multiplier. Crop production or crop yield is decreased over the year which is defined as crop ecological foot print multiplier.

In STELLA the equation is expressed as: crop_yield = crop_yield_normal*crop_ecological_footprint_multiplier*cropping_intensity_multiplier (2.28)

Cropping intensity multiplier is dependent on cropping intensity. Cropping intensity multiplier gradually increases with the increase of cropping intensity. It is shown in Fig. 2.11. Crop yield at a given period depends on cropping intensity. Cropping intensity may be increased in very slight and slightly alkaline areas by adopting proper soil and water management practices with introduction of salt tolerant varieties of different crops.

1.5

1.45

1.4

1.35

1.3

1.25

1.2

1.15

1.1 Cropping intensity multipiier intensity Cropping

1.05

1 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

Cropping intensity

Fig. 2.11 Changes of cropping intensity multiplier with cropping intensity

Rainwater harvesting could be another alternative as a source of irrigation water. Cropping intensity increases over the year which is shown in Fig.2.12. Food available from crop plus fish is obtained by multiplying the crop fish integrating farming area by respective crop yield.

In STELLA the equation is expressed as: food_from_crop_plus_fish = crop_fish_integrated_farming_area*crop_yield_for_crop_fish_integrated_farming (2.29)

2.3

2.2

2.1

1.9

1.8 Cropping intensity Cropping 1.7

1.6

1.5 0123456789101112 Year

Fig. 2.12 Changes in cropping intensity over the year

Food available from non rice is calculated by multiplying the non rice production by equivalent factor non rice. Here, conversion of the production of non rice crops into rice equivalent is termed as equivalent factor non rice. Non rice production means the all other crop production except rice and it is the product of non rice area and non rice yield. Non rice area is dependent on the non rice growth fraction over the year. In STELLA the equations are expressed as: food_from_non_rice = equivalent_factor_non_rice*non_rice_production (2.30) non_rice_production = non_rice_area*non_rice_yield (2.31) non_rice_area(t) = non_rice_area(t - dt) + (non_rice_area_growth_rate) * dt (2.32) non_rice_area_growth_rate = non_rice_area*non_rice_growth_fraction (2.33)

Food available from forest is obtained by multiplying the forest production by equivalent factor forest. Where, forest production is the product of forest area and forest yield. Here, conversion of the production of forest into rice equivalent is termed as equivalent factor forest. Existing forest area depends on previous area and forest growth in a particular time. Forest growth is calculated by multiplying the forest area by forest growth factor.

In STELLA the equations are expressed as: food_from_forest = forest_production*equivalent_factor_forest (2.34)

47 forest_production = forest_area*yield_from_forest (2.35) forest_area(t) = forest_area(t - dt) + (forest_growth) * dt (2.36) forest_growth = forest_area*forest_growth_factor (2.37)

Food from animal is the product of animal production and equivalent factor animal. Here, conversion of the production of animal into rice equivalent is termed as equivalent factor animal. Animal production means the production of milk, meat and egg which comes from different types of animal.

In STELLA the equation is expressed as: food_from_animal = animal_production*equivalent_factor_animal (2.38)

2.2.2. Ecological Footprint Sector

The ecological footprint of a given population is “the total area of productive land and water required on a continuous basis to produce the resources consumed, and to assimilate the wastes produced, by that population, wherever on Earth the land (and water) is located. Fig. 2.13 presents the ecological footprint sector of ICZM model. Total ecological footprint is the sum of the ecological footprints of all categories of land areas which provide for mutually exclusive demands on the bio-sphere.

In STELLA the equation is expressed as: ecological_foot_print_per_capita = eclogical_footprint_for_shrimp_culture+ecological_footprint_for_animal+ecological_foot print_for_build_up_area+ecological_footprint_for_crop+ecological_footprint_for_energy +ecological_footprint_for_fish+ecological_footprint_for_forest+ecological_footprint_for_ non_rice (2.39)

Depending on the shrimp pond management between 2 and 22 hectares of forest area are required to filter the nitrogen and phosphorus loads from effluent produced by a 1hectare shrimp pond (Robertson and Phillips, 1995). To absorb the waste produced from shrimp culture the term eco factor is used for calculating the ecological footprint for shrimp culture. Total pond area is the sum of two categories of shrimp culture: i) solely shrimp pond area, ii) crop fish integrated farming area.

48 Ecological footprint sector

population pond area bagda ~ food consumption per capita shrimp production intensity food consumption

global yield for crop fish consumption per capita ecological footprint for crop equivalence factor for crop total pond area

fish consumption energy consumption per capita ~ eco factor for semi crop fish integrated intensive culture farming area global yield for fish ecological footprint for fish consumption equivalence factor for fish

global average of energy consumption energy consumption ecological foot print per capita

ecological footprint for energy eclogical footprint per capita animal consumption for shrimp culture

equivalence factor for energy

global average of animal consumption animal consumption equivalence factor for animal global average of forest consumption ecological footprint for animal

non rice consumption per capita forest consumption equivalence factor for forest

global average of non ecological footprint for forest rice consumption equivalence factor for non rice

forest consumption per capita non rice consumption

build up growth factor ecological footprint ecological footprint buildup area for non rice for build up area yield factor crop

buildup area growth rate

Fig. 2.13 Ecological footprint sector of ICZM model

49 In STELLA the equations are expressed as: eclogical_footprint_for_shrimp_culture = total_pond_area*eco_factor_for_semi_intensive_culture/population (2.40) total_pond_area = crop_fish_integrated_farming_area+pond_area_bagda (2.41)

To simulate the model the value of eco factor has been used depending on shrimp production intensity which is shown in Fig.2.14. The value of eco factor increases with the increase of shrimp production intensity.

200

180

160

140

120

100

Eco factor 80

0 1 34 67 100 9.25 17.5 25.8 42.3 50.5 58.8 75.3 83.5 91.8 Shrimp production intensity

Fig.2.14 Relationship between eco factor and shrimp production intensity

STELLA equations for computation of ecological footprint for animal, build up area, crop, energy, forest and non rice are expressed as: ecological_footprint_for_animal = (animal_consumption/global_average_of_animal_consumption)*equivalence_factor_for_a nimal/population (2.42) animal_consumption = population*per_capita_animal_consumption (2.43)

Build-up area is the land occupies infrastructure for housing, transportation, industrial production and capturing hydroelectricity. ecological_footprint_for_build_up_area = buildup_area*yield_factor_crop*equivalence_factor_for_non_rice/population (2.44)

50 buildup_area(t) = buildup_area(t - dt) + (buildup_area_growth_rate) * dt (2.45) buildup_area_growth_rate = buildup_area*build_up_growth_factor (2.46) ecological_footprint_for_crop = ((food_consumption/global_yield_for_crop)*equivalence_factor_for_crop)/population (2.47) food_consumption = population*food_consumption_per_capita (2.48) ecological_footprint_for_energy = ((energy_consumption/global_average_of_energy_consumption)*equivalence_factor_for_ energy)/population (2.49) energy_consumption = population*energy_consumption_per_capita (2.50) ecological_footprint_for_fish= ((fish_consumption/global_yield_for_fish)*equivalence_factor_for_fish)/population(2.51) fish_consumption = population*fish_consumption_per_capita (2.52) ecological_footprint_for_forest = (forest_consumption*equivalence_factor_for_forest)/global_average_of_forest_consumpti on/population (2.53) forest_consumption = population*forest_consumption_per_capita (2.54) ecological_footprint_for_non_rice = (non_rice_consumption*equivalence_factor_for_non_rice)/global_average_of_non_rice_c onsumption/population (2.55) non_rice_consumption = population*non_rice_consumption_per_capita (2.56)

2.2.3 Biocapacity Sector

Biological capacity is the total annual biological production capacity of a given biologically productive area. Fig. 2.15 presents the biocapacity sector of ICZM model. Total biocapacity is the sum of all bioproductive areas expressed in global hectares by multiplying its area by the appropriate equivalence factor and the yield factor specific to that country/locality.

In STELLA the equations are expressed as: total_biocapacity = biocapacity_for_animal+biocapacity_for_buildup_area+biocapacity_for_crop+biocapacity _for_fish+biocapacity_for_forest+biocapacity_for_non_rice (2.57)

Biocapacity sector

equiv alence f actor f or crop biocapacity f or crop crop area y ield f actor f or crop non rice area Boro Aus area

biocapacity f or non rice

equiv alence f actor f or f orest equiv alence f actor f or f ish pond area bagda biocapacity f or f ish crop f ish integrated f arming area yield factor for fish forest area Area of canal riv er & pond total biocapacity y ield f actor f or f orest biocapacity f or f orest

equiv alence f actor f or animal animal area y ield f actor f or animal biocapacity f or animal

buildup area

biocapacity f or buildup area

population ecological status ecological f oot print per capita biocapacity per capita

Fig. 2.15 Biocapacity sector of ICZM model

Total biologically productive land is not available to human use. According to the World Commission on Environment and Development, at least 12% of the ecological capacity, representing all ecosystems types, should be preserved for biodiversity protection (WCED, 1987). Considering 12% biodiversity protection, total available biocapacity is calculated. biocapacity_per_capita = (total_biocapacity-0.12*total_biocapacity)/population (2.58)

STELLA equations for computation of biocapacity for animal, build up area, crop, forest and non rice are expressed as:

52 biocapacity_for_animal = animal_area*equivalence_factor_for_animal*yield_factor_for_animal (2.59) biocapacity_for_buildup_area = buildup_area*equivalence_factor_for_crop*yield_factor_for_crop (2.60) biocapacity_for_crop = (crop_area+crop_fish_integrated_farming_area+Boro_Aus_area)*yield_factor_for_crop*e quivalence_factor_for_crop (2.61) biocapacity_for_fish = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*e quivalence_factor_for_fish*yield_factor_for_fish (2.62) biocapacity_for_forest = forest_area*equivalence_factor_for_forest*yield_factor_for_forest (2.63) biocapacity_for_non_rice = non_rice_area*equivalence_factor_for_crop*yield_factor_for_crop (2.64)

At the final stage, comparing the biological capacity and the ecological footprint of the system, ecological status (BC < EF) or ecological deficit (BC < EF) is determined. ecological_status = biocapacity_per_capita-ecological_foot_print_per_capita (2.65) A negative value of this equation indicates that the specific area is ecologically deficit.

2.2.4 Validation of Integrated Coastal Zone management (ICZM) Model

Validity, a method is defined as a coherent procedure or set of rules, directed toward the attainment of a goal. Essentially, there are two types of methods: algorithms and heuristics. An algorithm is a fixed procedure with a high level of robustness by which a well-defined objective is achieved. From the perspective of system dynamics, most available validation methods have the characteristics of heuristics. A system dynamicist does not have a fixed toolbox of validation methods at hand, whose application would automatically guarantee models with high validity. Instead, the available methods are heuristic devices which, if properly selected and correctly applied, enhance model validity. This explains the difficulty in selecting the most adequate tests—especially for novice modelers (Groesser and Schwaninger, 2012).

53 Validation means the process of establishing confidence in the soundness and usefulness of the model. In validation mode the model should behave plausibly and generate problem symptoms of modes of behavior observed in the real world (Bala, 1999). To build up confidence in the predictions of the model there are various ways of validating a system dynamics model such as comparing the model predictions with historic data, checking whether the model generates plausible behavior and the checking the parameter values (Bala, 1999; Heuvelink, 1999).

In this study the model predictions were compared with the observed data in terms of standard error of the estimate, index of agreement and mean relative deviation modulus and the expressions of these three indicators are given below: i) The index of agreement (d) (Willmott, 1982) (eq. 2.66).

(2.66)

Where Si and Mi are the simulated and actual values, respectively, and M is the mean of the n number of observations. The value of d ranges from –∞ to 1.0; and the model’s fit improves as d approaches unity. ii) Standard error of estimate (SEE) (eq. 2.67).

n 2 ∑(M i − Si ) SEE = i=1 (2.67) n

Standard error of estimate determines the accuracy of the model. More smaller is standard error, more accurate is the model. iii) Mean relative deviation modulus (Ertekin and Yaldiz, 2004)

100 n M − S E(%) = ∑ i i (2.68) n i=1 M i

The values of E less than 5.0 indicate an excellent fit, while values greater than 10 are indicative of a poor fit (Lomauro et al., 1985; Gencturk et al., 1986).

54 2.2.5 Policy Options

The model was simulated to assess different policy options and to explore management scenarios of integrated coastal zone management system. Basic scenario is the projection of the system behaviour based on the present trends of the growth of the system i.e. it is based on existing trend of the growth of the shrimp production intensity termed as normal growth. The system behaviour for super intensive shrimp production intensity is termed as super intensive and the system behaviour under stabilized shrimp production intensity is termed as control growth. Fig. 2.16 shows the growth patterns for different policy options.

120

100

) Normal growth

80 Super-intensive Control growth

40 Shrimp production intensity (% Shrimp

0 0123456789101112 Year Fig. 2.16 Growth patterns of the different policy options.

55 2.3 Modeling the Climate Change Impacts on Rice Production in the Coastal Zone of Bangladesh

2.3.1 Site Description

Kalapara upazila in the coastal zone of Bangladesh was selected to address the climate change impacts on rice production. Kalapara (also known as Khepupara) is an Upazila of Patuakhali District in the Division of Barisal, Bangladesh. Kalapara is located at 21.9861°N 90.2422°E . It has 31324 units of households and total area of 483.08 km². Kalapara thana was established in 1906 and was turned into an upazila in 1983. The population density is 838 per sq km. Kalapara has an average literacy rate of 34.9% (7+ years) whereas the national average is 32.4% literate.

Duration of winter is shorter than that of other parts of the country. Average minimum temperature in the coolest month January is 13.7 °C and maximum is 25.6 °C. The hottest month is May and its average minimum temperature is 25.4 °C and maximum is 33.2 °C. Depending on climatic variation Bangladesh has divided into 30 agro ecological zones. This upazila is under AEZ-13. The rainfall starts from June and continues up to middle of October and the dry season occurs during mid October to May. Average annual rainfall is 2707 mm. Rainfall distribution in recent years is not uniform as compared to few decades ago. Frequency of natural calamities like SIDR, Aila etc. is also increasing. Crops, livestock, fruit trees, houses are heavily damaged by these natural calamities.

The major soil types of this upazila is clay to clay loam. Soil salinity is a major problem in this area during the dry period. Soil salinity starts increasing from last week of December and reaches to its peak level in the month of April and prevails up to monsoon rain starts. Salinity level varies from 3 to 18 dS/m. Rainfall pattern is not well distributed but salinity is increasing in trend. Soil organic matter is around 1% and nitrogen level is low to very low, phosphorus very low, potassium optimum to very high, zinc low, sulphur optimum to high.

Number of cropping pattern is limited by soil and water salinity during dry period. In the kharif-II season there is no land fallow. In the rabi and kharif-I season lands remain

56 fallow. In the year 2009-2010 the major cropping pattern of Kalapara upazilla and their area coverage are given below: Fallow-Fallow-T. aman rice – 38% Fallow-Aus rice-T. aman rice – 13% Cowpea-Aus rice-T. aman rice – 13% Cowpea- Fallow -T. aman rice – 11% Mungbean-Aus rice-T. aman rice – 3%

The major portions of the study area is covered with modern rice variety (60 %) and rest of the area is covered with local variety because of the fact that water height is high during planting time. The seedling height of the modern variety is short and not suitable for transplanting in the medium and low lying area.

2.3.2 Selection of Crop

Aman rice is the main crop of the coastal zone of Bangladesh. So, Aman rice crop is selected for simulation of climate change impacts.

2.3.3 InfoCrop Model

InfoCrop is a decision support system based on crop models that has been developed by a network of scientists of Indian Agricultural Research Institute (IARI) to provide a platform to scientists and extension workers to build their applications around it and to meet the goals of stakeholders need for information. These models are designed to simulate the effects of weather, soils, agronomic management, nitrogen, water and major pests on crop growth and yield, water and nitrogen management, and greenhouse gases emission. We used the InfoCrop model for assessment of the climate change impacts on crop growth, yield and plant behavior of rice crop of the coastal zone of Bangladesh.

2.3.4 Description of InfoCrop Model

InfoCrop considers following processes of crop growth and development, soil water, nitrogen, and carbon, and crop-pest interactions. Each process is described by a set of equations, in which the parameters vary depending upon the crop/cultivar. • Crop growth : Photosynthesis, respiration, partitioning, leaf area growth, storage organ numbers, and source: sink balance, phenology, transpiration, N-uptake,

57 allocation and redistribution, and effects of water, nitrogen, temperature, flooding and frost stresses. • Crop-pest interactions: Damage mechanisms of insects and diseases. • Soil water balance: Root water uptake, drainage, evaporation, runoff. • Soil nitrogen balance: Mineralization uptake, nitrification, volatilization, inter layer movement, denitrification, leaching. • Soil organic carbon dynamics: Mineralization and immobilization.

• Emissions of green house gasses: Carbon dioxide (CO2), methane (CH4), nitrous

oxide (N2O).

The Masters contain all the data available for selection in the Project screen while carrying out a simulation. The values in the database in the Masters should be appropriate, in the desired units, and validated. The results of the simulation could be biased and even erroneous depending upon the database in Masters. The Masters are divided into the following sub-modules: Crop Master Variety Master Soil Texture Master Soil District Master Weather Master Pest Master Organic Matter Master

2.3.5 Data Collection

To assess the climate change impact on crop production in the coastal zone of Bangladesh weather data such as daily average maximum and minimum temperature, daily precipitation, solar radiation, humidity etc were collected from weather station in Kalapara upazila of Patuakhali district and crop related data were collected from the office of Bangladesh Agricultural Research Institute located in this location to generate the base line scenario and historical trend and IPCC assumptions for temperature increase were used to develop policy scenarios. Average monthly maximum, minimum temperature, relative humidity, sunshine hour and monthly rainfall of Kalapara upazila in 2010 are shown in Fig. 2.17, Fig. 2.18 and Fig. 2.19, respectively.

40 90 35 80 30 70 60 25

c 50 0 20 Max Temp 40

15 R. H. (%) 30 Temp Min temp 10 R. H 20 5 10 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Fig. 2.17 Average monthly temperature and relative humidity of Kalapara upazila in 2010

9 8 7 6 5 4 3

Sunshine hours 2 1 0 Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec Month

Fig.2.18 Average monthly sunshine hours of Kalapara upazila in 2010

700

600

500

400

300

200 Rainfall (mm) Rainfall

100

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Fig.2.19 Monthly rainfall of Kalapara upazila in 2010

59 From Fig. 2.17 it is shown that temperature is almost same from February to November. During this period the maximum temperature ranged from 30 °C to 34 °C while it was around 25 °C in January and December. The minimum temperature was also in similar pattern ranged from 21 °C to 27 °C during February to November. However, minimum temperature was very low (12-15 °C) in January and December. Fig. 2.18 depicts that the maximum sunshine hour (~7) occurred from February to May and lowest sunshine hour (~3) occurred in the month of July and August. From Fig. 2.19 it is shown that there was no rainfall in January and December. The rainfall gradually increased from the month of February with a peak in July (602 mm). However, the rainfall gradually decreased from the month of August up to November.

2.3.6 Modeling of Climate Change Impacts on Crop Growth

Computation of climate change impacts on crop yields is based on the crop growth model InfoCrop developed by Aggarwal et al. (2006 a&b). Computation of canopy photosynthesis from the incoming photosynthetically active radiation forms the central part of the crop growth simulation models. The crop development and growth processes and their relationships for the crop growth model InfoCrop are shown in Fig. 2.20. Under favorable growth conditions, light, temperature, and the crop characteristics for phenological, morphological, and physiological processes are the main factors determining the growth rate of the crop on a specific day. The model follows a daily calculation scheme for the rates of dry matter production of the plant organs, the rate of leaf area development, and the rate of phenological development (growth stages). By integrating these rates over time, dry-matter production of the crop is simulated throughout the crop growing season and the yield of the crop is computed. Development and growth processes are dry matter production, dry matter partitioning, leaf area growth and phenology and these are described below:

60 Dry matter production

Several models including SUCROS, MACROS, WTGROWS and ORYZA calculate dry matter production as a function of gross canopy photosynthesis, depending on the detailed calculations of the distribution of light within the canopies, the radiation absorbed by the canopy, and photosynthesis light response curve of leaves (Bouman et al., 2000). Growth and maintenance respirations are calculated as a function of tissue N-content, temperature and crop-specific coefficients. This methodology although yields very accurate results, poses practical difficulties because of its requirement for detail and careful measurements. More or less similar results can generally be obtained under normal radiation situations by calculating the net dry matter production as a function of the radiation use efficiency. This approach was utilized in the present model.

Simplif ied model of crop growth

temperature solar radiation CO2

leaf area index biomas

radiation use ef f iciency leaf area growth photosy nthesis rate leaf area loss dev elopment state

water stress dev elopment rate partitioning of dry matter

biomass & N

nutrient stress root storage organ leav es stem photoperiod

potential y ield economic y ield

Fig. 2.20 Simple representation of crop growth model

Pre-determined values of the radiation use efficiency were input in the model as a function of crop/cultivar. This was further modified by the development stage, abiotic and biotic

61 factors. The effect of temperature mimics a crop-specific decrease in photosynthesis due to adverse mean daytime temperature. But, CO2 increases the relative photosynthesis in C3 plants, whereas this effect on C4 plant is negligible. This was simulated by a crop-specific input that increases radiation use efficiency as a function of ambient CO2. Radiation interception of crops has been calculated as a function of total Leaf Area Index (LAI), incident solar radiation, radiation captured by the pests and weeds and a crop/cultivar- specific extinction coefficient. The latter is also sensitive to the age of the plant. The growth rate of the crop were calculated as a function of radiation use efficiency, radiation intercepted by the crop, total leaf area index, radiation captured by the pests and crop/cultivar specific extinction coefficient as follows: GCROP = RUE ∗ PAR ∗ (1− EXP(− KDF ∗ (LAI − PSTPAR))) (2.69) Where, GCROP = net crop growth rate RUE = radiation use efficiency PAR = photosynthetically active radiation KDF = extinction coefficient LAI = leaf area index PSTPAR = radiation captured by the pests

Dry matter partitioning

The net dry matter available each day for crop growth was partitioned into roots, leaves, stems, and storage organs as a crop-specific function of development stage. Allocation was made first to roots, which gets increased in case the crop experiences water, or nitrogen stress. The remaining dry matter was allocated to the above ground shoot from which a fraction was allocated to leaves and stems. The balance dry matter was automatically allocated to the storage organs.

A fraction of carbohydrates partitioned to the stems was treated as non-structural reserves depending on the crop and development stage. These reserves accumulate more if the growth rate of storage organs lags behind the current dry matter production. After anthesis, in addition to current assimilates, 10% of the previously accumulated reserves are mobilized every day and used for storage organ growth (Penning de Vries et al., 1989).

62 The net growth rates of leaves, stems, roots and storage organs were calculated based on the growth rate of the crop, fractions allocated, death due to senescence, and losses due to pests and during transplanting if any. The weights of green leaves, dead leaves, stem, roots, and storage organs were updated every day based on their initial weights at seedling emergence and the daily growth rates were calculated. The net weight of the storage organs was adjusted for their energy content (Penning de Vries et al., 1989). Allocation to leaves is computed as: RWLVG = GCROP ∗ FSH ∗ FLV − (DLV − SUCKLV ) (2.70) Where RWLVG = net growth rates of leave GCROP = net crop growth rate FSH = fraction allocated to shoots FLV = fraction allocated to leaves Similar procedure is adopted for stems and roots.

Leaf area growth

The leaf area growth was calculated based on initial leaf area index and its growth rate. The latter was obtained by multiplying the increment in leaf weight by the specific leaf area. During initial stages, there is a greater control over the area formation, and hence for this period net growth rate is calculated based on a thermal time-dependent relative growth rate of leaf area index (Kropff et al., 1994). The integrated photosynthetic areas of stems, sheaths and spikes have been estimated to be between 10 and 100% of green leaf lamina areas depending upon the crop. Since the number of the tillers/branches is not simulated, the non-lamina area was calculated as a crop-specific function of the maximum leaf lamina area index and a sequence rate that is accelerated by temperature. The photosynthesis characteristics of the non-lamina green areas were assumed to be the same as those of leaves. Simulation of sequences (DLAI) is based on several empirical constants relating to shading, ageing, nitrogen mobilization, temperature, water stress and death due to pests and diseases. The loss leaf area and weight due to ageing and tiller mortality were assumed to commence once stems starts expanding. Shading in dense stands accelerates senescence. Higher or lower temperatures can accelerate rate of senescence depending

63 upon the crop. The water stress also accelerates senescence depending upon its severity. After anthesis, considerable nitrogen is mobilized from leaves for the grain development in most annual crops. This can induced rapid senescence. This was simulated in this model by making senescence of leaves also dependent on the fraction of nitrogen mobilized from leaves everyday once the storage organ start filling up. Net effective leaf area for photosynthesis and transpiration are thus the sum of the leaf areas and non–lamina green area after subtracting all losses due to senescence and insect feeding. RLAI = LAII + GLAI − DLAI − LALOSS (2.71)

Where, RLAI = net leaf area growth rate LAII = initial leaf area index GLAI = leaf area growth rate DLAI = death rate of leaf area index LALOSS = net loss of leaf area index due to pests

Phenology The total development of a crop has been quantified based on development stages (DS), a dimensionless variable having a value of 0 at sowing, 0.1 at seedling emergence, 1.0 at flowering and 2.0 at maturity (Keulen and Seligman, 1987). This was calculated by integrating the temperature-driven development rates of the phases from sowing to seedling emergence, seedling emergence to anthesis, and storage organ filling phases. The rate of development of sowing to seedling emergence phase is controlled by the thermal time. Since water stress delays emergence in many crop plants, the thermal time can be increased depending upon the available water fraction in surface soil layer. Thus, two parameters have been used to quantify duration of this phase for different crop/varieties: thermal time from sowing to emergence and base temperature for this period.

Seedling emergence to anthesis phase is generally divided into three major sub-phases depending upon the environmental factors affecting these and the organs formed and these are basic juvenile phase, photosensitive phase and storage organ formation phase. Considering the fact that the thermal times for different sub-phases may not be easily available, the entire duration of this phase is governed by a single thermal time. The latter

64 is calculated based on base, optimum and maximum temperatures. The rate of development was linearly related to the daily mean temperature above base temperature up to the optimum temperature. Above this optimum temperature, the rate decreases until the maximum temperature is reached. If temperature goes below the base temperature or above the maximum temperature, the rate of development becomes zero. The rate of crop development is therefore, accelerated depending upon the crop/intensity of stress.

DRV = HUVG ∗ DAYLC ∗ MAXSTD /TTVG (2.72)

Where, DRV = rate of development during vegetative phase HUVG = thermal time of the day DAYLC = correction factor for the photoperiod-dependent thermal time MAXSTD = stress effect of water and nitrogen TTVG = thermal time required for entire phase

2.3.7 Computation of Climate Change Impact on Rice Production

Crop growth model discussed above was used to simulate the crop production for climate change conditions. Radiation use efficiency changes for the changes in temperature and

CO2 levels as a result of climate change and these changes have been incorporated in this crop model to assess the climate change impacts on crop production. Crop data were provided for simulation in InfoCrop model is shown in Table 2.7.

The major crop management input data used in the model for simulation in the present study are shown in Table 2.8. The simulation was carried out to predict the yields of rice under different climatic trends of temperature and carbon dioxide concentration for assessment of rice production of the coastal zone of Bangladesh. The simulation was also carried out to predict the climate change impacts on the yields of rice for historical and

IPCC trends of both the temperature and CO2 changes for a period of 2020-2050.

65 Table 2.7 Inputs data used in InfoCrop

Category of inputs Details of required inputs Crop/variety Name of crop Weather Latitude, longitude and altitude of weather station, solar radiation, maximum and minimum temperature, rainfall, wind speed, vapor pressure, and humidity. Location Soil texture, saturation fraction, field capacity, wilting and air dry levels, initial soil moisture, bulk density, saturated hydraulic conductivity, organic carbon, PH, depth, fertility and Electrical conductivity. Sowing Dates of planting, seed rate, depth of planting., sowing method, age of transplanting, plant/hill, hills per sq. m Irrigation Amount, time, depth of irrigation, irrigation method. Inorganic nitrogen Amount and time of application of N fertilizer. fertilization Organic matter Amount of different organic matters Pests Population/severity of pests and their timing of presence

Table 2.8 Crop management data used in the model

Planting method Transplanted Transplanting date 15 July Planting distribution Hill Seed rate 40 kg/ha Planting depth 25 mm Age of transplanting 30 days Plant per Hill 3 Hills per Sq. m 25 Fertilizer (N) application N stress not considered for both crop Application of irrigation Check basin: Water stress not considered

66 CHAPTER 3

RESULTS AND DISCUSSION

3.1 Food Security and Ecological Footprint of the Coastal Zone of Bangladesh

The major cropping patterns and cropping intensity of nine upazilas in the coastal zone of Bangladesh are shown in Table 3.1. Cropping patterns of six upazilas of Satkhira, Khulna and Bagerhat district are almost similar while the cropping patterns of the other three upazilas of Patuakhali and Barguna district are also similar. The cropping pattern T. Aman – Fallow – Fallow has the highest coverage in all the upazilas. This pattern has the highest coverage in Mongla and Shyamnagar upazila (93.6 %) followed by Morrelgonj Upazila (82.8 %) and the lowest is found in Galachipa upazila (24.4%). Some areas were cultivated for production of high yielding varieties during Boro season, where the irrigation facilities were available either from surface water or groundwater sources. The Boro area could be expanded by introducing salt tolerant Boro Variety BRRI Dhan47, where the water salinity ranges upto 8 dS/m. According to report (SRDI, 2000) about 0.6 mha of land is under 0-8 dS/m range of salinity. So, 0.6 mha of saline land can be brought under production using salt tolerant varieties. Pulse followed by T. Aman pattern dominated in Pathargata, Kalapara and Galachipa upazilas of Barguna and Patuakhali district. The highest cropping intensity of 199% was observed in Kalapara upazila followed by Galachipa upazila (195%). This is mainly due to the coverage of pulse and Aus crop. The lowest cropping intensity of 103% was observed in Mongla upazila. Mongla is the second seaport in Bangladesh. Therefore, it is one of the commercial area in Bangladesh. It is a shrimp dominated upazila. T. aman is the only crop in this area. There is little Boro or Aus rice and other non rice crop cultivation in this upazila. For this reason, cropping intensity is the lowest in this upazila.

67 Table 3.1. Major cropping patterns and cropping intensity in 2006-2007 of nine upazilas

Sl. Upazila Major cropping pattern % Cropping No. Coverage intensity (%) 1 Shyamnagar T. Aman – Fallow – Fallow 93.6 115 T. Aman – Boro - Fallow 5.4 2 Dacop T. Aman – Fallow – Fallow 56.5 159 T. Aman – Fish 42.2 3 Koyra T. Aman – Fallow – Fallow 63.8 138 T. Aman – Boro – Fallow 16.8 T. Aman – Fish 4.5 T. Aman – Potato – Vegetables 4.6 4 Shoronkhola T. Aman – Fallow – Fallow 53.9 148 T. Aman – Khesari – Fallow 28.2 T. Aman – Fallow – T. Aus 6.1 5 Morrelgonj T. Aman – Fallow – Fallow 82.8 128 T. Aman – Fallow – Aus 13.4 T. Aman – Boro – Fallow 3.1 6 Mongla T. Aman – Fallow – Fallow 93.6 103 7 Patharghata T. Aman – Fallow – Fallow 45.0 187 T. Aman – Khesari – Fallow/ T. Aus 30.0 T. Aman – Mung – Fallow/ T. Aus 7.0 T. Aman – Sweet potato/ Chilli – Fallow 7.0 8 Kalapara T. Aman – Fallow – Fallow 27.7 199 T. Aman – Khesari – Fallow/ T. Aus 18.9 T. Aman – Mung – Fallow/ T. Aus 6.8 T. Aman – Fallow – Aus 13.0 T. Aman – Cowpea – Aus 12.7 T. Aman – Cowpea – Fallow 11.0 9 Galachipa T. Aman – Fallow – Fallow 24.4 195 T. Aman – Khesari – T. Aus 13.0 T. Aman – Mung – T. Aus 12.0 T. Aman – Groundnut – Fallow 10.3 T. Aman – Khesari – Fallow 9.5 T. Aman – Chilli – Fallow 9.6

68 Major crop and aquaculture areas are shown in Table 3.2. T. Aman is the major crop for all the upazilas. Boro cultivation is limited and limited to mainly Galachipa (2610 ha), Shyamnagar (1500 ha), Koyra (1400 ha) and Morrelgonj (680 ha). This is mainly due to the limited irrigation facilities for Boro rice cultivation. The highest Gher area is in Shyamnagar (15622 ha) followed by Dacop (13395 ha) while the highest rice-fish integrated area is in Morrelgonj (11437 ha) followed by Mongla (9806 ha). There is no Gher in Shoronkhola, Morrelgonj, Mongla and Patharghata and also there is no rice-fish integrated farming in Dacop and Kalapara.

Table 3.2 Major crop and fish area in 2006-2007 of different upazilas

Sl. Upazila Total area T. Aman Boro area Rice-fish Gher area No. (ha) area (ha) (ha) integrated (ha) area (ha) 1 Shyamnagar 196824 21370 1500 357 15622 2 Dacop 133736 19500 15 0 13395 3 Koyra 181343 15220 1400 470 5203 4 Shoronkhola 74615 9200 10 48 0 5 Morrelgonj 43830 28280 680 11437 0 6 Mongla 18688 11220 0 9806 0 7 Patharghata 49210 18500 0 55 0 8 Kalapara 48347 40450 10 0 985 9 Galachipa 126891 69500 2610 40 2600

The present status of population, food security, food self sufficiency ratio, contributions of crop production and aquaculture to food security, and environmental degradation in terms of ecological footprint of nine upazilas of the coastal zones of Bangladesh are estimated and these upazilas are Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. Fig. 3.1 shows the present levels of population in these nine upazilas. Morrelgonj (384479) has the largest population followed by Shyamnagar (347178) and Galachipa (351026) while Shoronkhola (128021) has the lowest population level.

69 450

400

) 350 Population

300

250

200

150

Population (in thousand Population (in 100

0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala . Fig. 3.1 Population in 2007 of different upazilas

Fig. 3.2 shows the present production levels of rice production in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. Galachipa (167198 tons) and Kalapara (158464 tons) have the largest rice production among these nine upazilas and the levels of rice production of Galachipa and Kalapara are almost same followed by Shyamnagar (64598 tons). Rice productions of Dacop (60958 tons) and Koyra (62144 tons) are also almost same. The production level of rice in Galachipa and Kalapara is more than double of that of Shyamnagar, Dacop and Koyra. The production level of rice is double because of the more area coverage of rice production in these upazilas. Shoronkhola has the lowest level of rice production. Thus, Galachipa and Kalapara are rich in rice production but Shoronkhola (21630 tons) is poor in rice production having the lowest population level.

180

160

140 Rice Production

120

100

40 Rice Production ('ooo tons) ('ooo Production Rice 20

0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

Fig. 3.2 Rice production of different upazilas

70 Fig. 3.3 shows the present levels of shrimp production in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. Shyamnagar (4213 tons), Dacop (3467 tons) and Mongla (3461 tons) are the largest shrimp producers while the shrimp production in Shoronkhola (81 tons) and Patharghata (16 tons) is almost absent. Galachipa (2128 tons) and Koyra (2163 tons) is a moderate shrimp producer with high level of rice production, but Shoronkhola is poor both in terms of shrimp and rice production.

4500

4000 Shrimp Production

) 3500

3000

2500

2000

1500

Shrimp Production (tons Production Shrimp 1000

500

0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

Fig. 3.3 Shrimp production of different upazilas

Fig. 3.4 shows the food situation in terms of surplus or shortage in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. Galachipa, Kalapara, Dacop, Koyra, Mongla and Patharghata are the food surplus upazilas while Shyamnagar, Morrelgonj and Shoronkhola are food deficit upazilas. Galachipa has the largest surplus (223,272 tons) followed by Kalapara (179,166 tons) and Patharghata is marginally surplus (7617 tons). Morrelgonj is the largest food deficit upazila (57,695 tons) and Shyamnagar (10456 tons) and Shoronkhola (14995 ton) are food deficit by a small margin.

Fig. 3.5 shows the SSR (Self Sufficiency Ratio) of rice in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, Morrelgonj, Patharghata, Kalapara and Galachipa. Out of nine upazilas 5 upazilas are self sufficient in rice and four upazilas are deficit in rice. Kalapara has the largest SSR (3.06) and the SSR for Patharghata is marginally surplus (1.10). Morrelgonj has the largest deficit (0.70).

71 250000

200000 Food sortage/surplus

) 150000

100000

50000

Equivalent rice (tons Equivalent rice 0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala -50000

-100000

Fig.3.4. Food sortage/surplus of different upazilas

3.5

2.5 SSR of Rice

1.5

Self sufficiency ratio 1 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala 0.5

Fig. 3.5. Self sufficiency ratio of rice of different upazilas

Fig. 3.6 shows the food security status in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, Morrelgonj, Patharghata, Kalapara and Galachipa. Kalapara (+164.19%), Galachipa (+128.42%), Dacop (+77.24%), Koyra (+34.42%), Mongla (+36.87%) and Patharghata (+8.53%) have positive food security status and Shyamnagar (-6.08%), Shoronkhola (-23.65%) and Morrelgonj (-30.29) have negative food security status. This implies that Kalapara, Galachipa, Dacop, Koyra, Mongla and Patharghata are food surplus and Shyamnagar, Shoronkhola and Morrelgonj are food deficit upazilas in terms of available foods and incomes derived from different sources.

72 200 Food Security Status (%) 150

100

0 Food Security (%)Status Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

-50 . Fig.3.6. Food security status of different upazilas Fig. 3.7 shows the contributions of crop and fish to food security in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, Morrelgonj, Patharghata, Kalapara and Galachipa. Galachipa (69%) has the largest contribution to food security from crop followed by Kalapara (57%) and Patharghata (53%) and these upazilas are crop dominated while Mongla (71%) has the largest contribution to food security from fish followed by Shyamnagar (47%) and Dacop (44%) and these upazilas are aquaculture dominated. Koyra and Morrelgonj have almost equal contributions from crop and fish. This implies that aquaculture plays vital role to insure food security at upazila levels. Higher contributions to food security from shrimp culture in the coastal zones of Bangladesh may be attributed to two reasons: first, the potential of shrimp culture in the coastal zone; second, the export market of the shrimp which prompted to expansion of shrimp culture in the coastal zone.

80 70

) 60 Crop Fish 50 40 30

Contribution (% Contribution 20 10 0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

Fig. 3.7. Contributions of crop and fish to food security of different upazilas

73 Fig. 3.8 shows the contributions to ecological footprint from different resources in the Khulna region (Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, and Morrelgonj). For all these upazilas the contributions to ecological footprint from crop is 29-54%, from energy is 17-35% and from fishery is 5-40%. But the contribution from fishery is the largest in Mongla and it is 40%. Thus, in this region shrimp culture is popular and its contribution to environmental degradation is large.

Shyamnagar Dacop Ener gy Ener gy 23% 17% Crop Crop 29% Forest 38% Forest 0% 0%

Fishery Animal 25% Fishery Animal 8% Build-up 37% Build-up 9% 5% 9%

Koyra Shoronkhola Ener gy 28% Ener gy 35% Crop 46% Crop Forest Forest 54% 0% 0% Fishery Fishery Animal 5% 18% Build-up Build-up Animal 5% 3% 2% 4%

Morrelgonj Mongla

Energy Energy 23% 18% Crop Forest 35% Forest Crop 0% 0% 45%

Fishery Animal 24% Animal 5% Build-up Fishery Build-up 6% 2% 40% 2%

Fig: 3.8. Percent ecological distribution of six upazilas of Khulna region

74 Fig. 3.9 shows percents of contributions to ecological footprint from different resources in the Barisal region (Patharghata, Kalapara and Galachipa). For all these upazilas the major contribution comes from crop (49-56%) followed by energy (24-26%). But the contribution from fishery is 4 to 9%. Thus, in this region shrimp culture is still not popular and its contribution to environmental degradation is very small.

Patharghata Kalapara Ener gy Energy 26% 24% Forest 0% Crop Forest Fishery 51% 0% Crop 4% 56% Fishery Build-up 8% 11% Animal Build-up Animal 8% 2% 10%

Galachipa Energy 24%

Forest 0% Crop 49% Fishery 9%

Build-up 10% Animal 8%

Fig.3.9. Percent ecological distribution of three upazilas of Barisal region

Fig. 3.10 shows the ecological footprint in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, Patharghata, Kalapara and Galachipa. The largest ecological footprint is at Dacop (0.74 gha/cap) followed by Mongla (0.664 gha/cap) and the lowest ecological footprint is at Shoronkhola (0.389 gha/cap). This implies that Dacop and Mongla have suffered serious environmental degradation and Shoronkhola is the least suffered upazila.

75 0.8

) 0.75 Ecological footprint 0.7 0.65 0.6 0.55 0.5 0.45 0.4

Ecological footprint (gha/cap footprint Ecological 0.35 0.3 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

Fig. 3.10. Ecological footprint of different upazilas

Fig. 3.11 shows the biocapacity in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Mongla, Morrelgonj, Patharghata, Kalapara and Galachipa. Kalapara and Galachipa have the largest biocapacity (+0.802 gha/cap) and the lowest is at Mongla (+0.157 gha/cap).

0.9 0.8 Bio capacity ) 0.7 0.6 0.5 0.4 0.3 0.2 Biocapacity (gha/cap 0.1 0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala

Fig. 3.11. Biological capacity of different upazilas

Fig. 3.12 shows the ecological status the nine upazilas of Shyamnagar, Dacop, Koyra Shoronkhola, Mongla, Morrelgonj, Patharghata, Kalapara and Galachipa. The ecologial status of Kalapara and Galachipa is surplus (+0.306 gha/cap, +0.322 gha/cap) and this implies that these upazilas are not facing any environmental degradation. These two upazilas are crop dominated. The upazilas that have suffered the most are Mongla, Shyamnagar, Dacop, and Morrelgonj where shrimp culture is at commercial level for export market. The highest and the least suffered upazilas are Mongla (-0.5076 gha/cap)

76 and Patharghata (-0.027 gha/cap) respectively. Wackernagel et al., (1999) also reported that the ecological status for Bangladesh as a whole is -0.20 gha/cap. The ecologial footprints of 52 countries of the world are shown in Table 3.3. The largest ecological suprplus country among these 52 countires is New Zealand (+12.8) and the lowest ecological deficit country is Singapore (-6.8). The average ecologial status (-0.2) of Bangladeesh is marginally deficit, but the ecologial status (-0.51) of Mongla is 2.5 times of the national average of Bangladesh and needs policy and programs to arrest the growth and reduce the degradation.

0.4

0.3

0.2 Ecological Status ) 0.1

0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala -0.1

-0.2

-0.3 Ecological status (gha/cap -0.4

-0.5

-0.6

Fig.3.12. Ecological status of different upazilas

Table 3.3. Ecological footprint, bio-capacity and ecological status of 52 countries in the world Sl Country Ecological Available Ecological No. footprint (ha/cap) bio-capacity (ha/cap) status (ha/cap) 1 Argentina 3.9 4.6 0.7 2 Australia 9.0 14.0 5.0 3 Austria 4.1 3.1 -1.0 4 Bangladesh 0.5 0.3 -0.2 5 Belgium 5.0 1.2 -3.8 6 Brazil 3.1 6.7 3.6

77 7 Canada 7.7 9.6 1.9 8 Chile 2.5 3.2 0.7 9 China 1.2 0.8 -0.4 10 Colombia 2.0 4.1 2.1 11 Costa Rica 2.5 2.5 0.0 12 Czech Rep 4.5 4.0 -0.5 13 Denmark 5.9 5.2 -0.7 14 Egypt 1.2 0.2 -1.0 15 Ethiopia 0.8 0.5 -0.3 16 Finland 6.0 8.6 2.6 17 France 4.1 4.2 0.1 18 Germany 5.3 1.9 -3.4 19 Greek 4.1 1.5 -2.6 20 Hong Kong 5.3 0.0 -5.1 21 Hungary 3.1 2.1 -1.0 22 Iceland 7.4 21.7 14.3 23 India 0.8 0.5 -0.3 24 Indonesia 1.4 2.6 1.2 25 Ireland 5.9 6.5 0.6 26 Israel 3.4 0.3 -3.1 27 Italy 4.2 1.3 -2.9 28 Japan 4.3 0.9 -3.4 29 Jordan 1.9 0.1 -1.8 30 Korea 3.4 0.5 -2.9 31 Malaysia 3.3 3.7 0.4 32 Mexico 2.6 1.4 -1.2 33 Netherlands 5.3 1.7 -3.6 34 New Zealand 7.6 20.4 12.8 35 Nigeria 1.5 0.6 -0.9 36 Norway 6.2 6.3 0.1 37 Pakistan 0.8 0.5 -0.3 38 Peru 1.6 7.7 6.1

78 Sl Country Ecological Available Ecological No. footprint (ha/cap) bio-capacity (ha/cap) status (ha/cap) 39 Philippines 1.5 0.9 -0.6 40 Poland, Rep 4.1 2.0 -2.1 41 Portugal 3.8 2.9 -0.9 42 Russian 6.0 3.7 -2.3 Federation 43 Singapore 6.9 0.1 -6.8 44 South Africa 3.2 1.3 -1.9 45 Spain 3.8 2.2 -1.6 46 Sweden 5.9 7.0 1.1 47 Switzerland 5.0 1.8 -3.2 48 Thailand 2.8 1.2 -1.6 49 Turkey 2.1 1.3 -0.8 50 United 5.2 1.7 -3.5 Kingdom 51 USA 10.3 6.7 -3.6 52 Venezuela 3.8 2.7 -1.1 World 2.8 2.0 -.0.8 Source: Wackernagel et al., (1999)

Fig. 3.13 shows the contributions of crop and fish to ecological status in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. The contributions of both crop and fish to ecological status of Shyamnagar, Morrelgonj and Mongla are negative resulting ecologically deficit upazilas while the rest of the upazilas have surplus ecological status from crop production. However, fish production (shrimp) always creates deficit ecological footprint and Dacop and Mongla are mainly affected (ecological deficit) by the shrimp production. This implies that the increase in shrimp culture moves the coastal zone towards unsustainable development.

79 0.6

0.5

0.4 Crop Fish

0.3

0.2

0.1

0 Shyam Dacop Koyra Shorn Morrl Mong Pathr Kala Gala -0.1

Ecological status (gha/cap) -0.2

-0.3

-0.4

Fig. 3.13. Ecological status from crop and fish of different upazilas

The present status of food security, food self sufficiency ratio, contributions of crop production and aquaculture to food security and environmental degradation in terms of ecological footprint in the nine upazilas of the coastal zones of Bangladesh at a glance are given in Table 3.4.

Table 3.4 The present status of food security and ecological status of nine upazilas of the coastal zones of Bangladesh at a glance.

Name of Contribution Food self Food Ecological Bio- Ecologic Upazila to food sufficiency security footprint capacity al status security (%) Ratio status (gha/cap) (gha/cap) (gha/cap Crop Fish (%) ) Shyamnagar 34 47 0.86 -6.08 0.601 0.207 -0.394 Dacop 32 44 1.72 77.24 0.741 0.418 -0.322 Koyra 41 38 1.24 40.06 0.530 0.309 -0.22 Shoronkhola 37 19 0.92 -23.65 0.389 0.220 -0.169 Morrelgonj 36 40 0.70 -30.29 0.482 0.192 -0.2896 Mongla 20 71 0.85 36.87 0.664 0.157 -0.5076 Patharghata 53 18 1.10 8.53 0.495 0.467 -0.027 Kalapara 57 15 3.06 164.19 0.461 0.768 +0.306 Galachipa 69 16 2.12 128.42 0.480 0.802 +0.322

80 This research shows that the overall status of food security at upazila levels is good for all the upazilas (8.53% to 164.19%) except Shoronkhola (-23.65%), Shyamnager (-6.08%) and Morrelgonj (-30.29%), and the best is the Kalapara upazila (164.19%). The environmental status in the coastal zones is poor for all the upazilas (-0.5076 to -0.027) except Kalapara (+0.306) and Galachipa (+0.322) and the worst is the Mongla upazila (- 0.5076). The environmental status in the coastal zones has degraded mainly due to shrimp culture. This suggests that the control of shrimp production and increasing the yield factor of the crops without additional load on the environment can lead towards sustainable development.

Household food security Fig. 3.14 shows the household level food security of the village Baraikhali. Only about 37.4% of the population of the village Baraikhali has sufficient food (SF) i.e. food security for round the year and the rest of 62.6% people have not sufficient food for round the year. Non-sufficient food (NSF) people are divided into three categories: i) NSF for 0-3 months ii) NSF for 3-6 months and iii) NSF for more than 6 months. Figure also shows that out of total population about 42.3% of the households of the village Baraikhali have not sufficient food for 0-3 months followed by 10.4% of the households for 3-6 months and 9.9% for more than 6 months.The picture of food security at village level is different from that of upazila level where the overall status of food security is good. This happens mainly due to the fact that shrimp production in the village is dominated by local/non-local private enterprises who care mainly for profit maximization rather than poverty alleviation of the local poor and also who care little to protect the local environment.

81 70 62.6 60

50 42.3 40 37.4

20 Percentage of household 10.4 9.9 10

0 SF Non-SF 0-3 M NSF 3-6 M NSF >6 M NSF

Fig. 3.14. Household food security status in the village Baraikhali

Fig. 3.15 shows the percentage distribution of food insecurity in the village Baraikhali. Out of Non-sufficient food (NSF) people almost 67% of the households of the village Baraikhali have suffered from food insecurity for 0-3 months followed by 17% of the households for 3-6 months and 16% for more than 6 months.

>6 M NSF 16%

3-6 M NSF 17%

0-3 M NSF 67%

Fig. 3.15. Percentage distribution of food insecurity

82 3.2 Modeling of Integrated Coastal Zone Management

The computer model was simulated to predict the contributions of coastal zones of Bangladesh to food security and environmental degradation. The initial values of the stock variables and the values of the parameters were taken from the primary and secondary data. To build up confidence in the predictions of the model various ways of validating a system dynamics model, such as comparing the model results with historical data, checking whether the model generates plausible behavior and checking the quality of parameter values were considered. To judge the plausibility of the model, the behavior of the key variables in the base run were examined. The validated model was used for base line scenario and policy analysis, assessment of management strategies and searching for sustainable development policy for food security. The simulated results of a typical upazila, Dacop is presented here. The simulated results of other upazilas are presented in Appendix E.

Fig. 3.16 shows the simulated population, food availability and food security of Dacop upazila for simulation period of 12 years. The population is increasing rapidly from 172613 in 2007 to 207650 in 2019 but the food availability (380207 tons) become almost constant after 9 years and the food security (204%) become almost constant after 8 years of increasing trend. This indicates the sustainability in term of food security is ensured in the short run in Dacop upazila.

1: population 2: food security 3: f ood av ailable 1: 210000 2: 250 3: 350000

2 3 2 1 3

2 1: 190000 2: 150 3 1 3: 250000

1 2 3

1: 170000 1 2: 50 3: 150000 0.00 3.00 6.00 9.00 12.00 Page 1 Years 12:28 PM Thu, Sep 11, 2008 Simulation of f ood security of Dacop (Normal growth) Fig. 3.16. Simulated population, food availability and food security of Dacop upazila.

83 Fig. 3.17 shows the simulated penaeid (bagda) shrimp pond area, crop area and penaeid (bagda) shrimp production of Dacop upazila. Cropped area is converted into aquaculture pond area at the rate of 1.2%. This causes the increase of pond area with the decrease of cropped area resulting shrimp production of 3257 tons in 2007 to 12425 tons in 2019.

1: pond area bagda 2: shrimp production bagda 3: crop area 1: 16000 2: 13000 3: 19500 3 2 1 2

3 1 1: 14500 2 2: 8000 3: 17000 3 1

3 1 2 1: 13000 2: 3000 3: 14500 0.00 3.00 6.00 9.00 12.00 Page 1 Years 12:12 AM Mon, Oct 20, 2008 Simulation of crop and shrimp production of Dacop (Normal growth) Fig. 3.17. Simulated penaeid (bagda) shrimp pond area, crop area and penaeid shrimp production of Dacop upazila

Fig. 3.18 shows the short run simulated ecological footprint per capita, biocapacity per capita and ecological status of Dacop upazila. The ecological footprint per capita increases rapidly from 0.741 gha/cap to 9.90 gha/cap within a period of 12 years, but in the same period biocapacity decreases from 0.42 gha/cap to 0.32 gha/cap. As a consequence the ecological status decreases rapidly from -0.74 gha/cap to -9.9 gha/cap. This implies that the environmental degradation is also rapid in Dacop mainly due to increased shrimp culture.

84 1: ecological f oot print per capita 2: ecological status 3: biocapacity per capita 1: 11 2: 0 3: 11 2

1 2

1: 6 1 2: -5 3: 6 2

1 2

1: 1 2: -10 1 3 3 3 3 3: 0 0.00 3.00 6.00 9.00 12.00 Page 1 Years 12:34 PM Sat, Oct 18, 2008 Fig. 25(b) Simulation of Ecological Footprint of Dacop (Normal growth) Fig. 3.18. Simulated ecological footprint, biocapacity and ecological status of Dacop upazila.

Fig. 3.19 shows the simulated food security, ecological footprint and ecological status of Dacop upazila for normal growth (current trend), super intensive culture and control growth (stable growth) for a simulation period of 12 years. In Fig. 3.19 (a) the simulated results show that food security increases up to 9 years and then it drops quickly to 122% from 224% under super intensive culture whereas the food security is almost constant for a simulation period of last 5 years for both normal and control scenarios. This implies that if shrimp aquaculture industry continues to boom from the present status to super intensive shrimp aquaculture, a collapse of the shrimp aquaculture industry will ultimately occur turning shrimp aquaculture land neither suitable for shrimp culture nor crop production. Arquitt et al., (2005) reported similar results for super intensive shrimp production in Thailand.

85 250

200

150

100 FS (Normal growth) FS (Super-intensive) Food security(%) 50 FS(Control growth)

0 01234567891011Final Year

Fig. 3.19(a). Simulated food Security status of Dacop upazila for different options

Fig. 3.19 (b) shows the simulated ecological footprint increases exponentially from a value +0.74 gha/cap to +17.24 gha/cap under super intensive culture whereas the ecological footprint under normal growth increases linearly from a value +0.74 gh/cap to +9.9 gha/cap and the ecological footprint under control growth increases from a value +0.74 gha/cap to +8.0 gha/cap and becomes almost contant towards end of the simulated period. This implies that if shrimp aquaculture industry continues to boom in terms of super intensive shrimp aquaculture, the environment is seriously affected resulting ecological foot print of +17.24 gha/cap within 12 years.

20 18 ) 16 EF(Normal growth) 14 EF (Super-intensive) 12 EF(Control growth) 10 8 6 4 2 Ecological footprint (gha/cap footprint Ecological 0 0 1 2 3 4 5 6 7 8 9 10 11 Final Year Fig. 3.19(b). Simulated ecological footprint of Dacop upazila for different options

86 Fig. 3.19(c) shows that the simulated ecological status decreases exponentially under super intensive culture from a value of -0.32 gha/cap to -16.92 gha/cap whereas the ecological status under normal decreases linearly from a value of -0.32 gha/cap to -9.58 gh/cap. Ecological status under control growth decreases from a value of -0.32 gha/cap to -7.72 gha/cap and the rate of growth of control growth is lower than that of the normal growth. The absolute magnitude of the ecological status is almost same as the absolute magnitude of the ecological footprint since the magnitude of initial biocapacity and its growth are very small. This implies that if shrimp aquaculture industry continues to boom from the present status to super intensive shrimp aquaculture, the environment is seriously affected resulting ecological status of-18 gha/cap.

Year 0 01234567891011Final -2

-4

-6

-8

-10 ES (Normal growth)

-12 ES (Super-intensive) ES (Control growth) -14 Ecological(gha/cap) status -16 -18 Fig. 3.19 (c). Simulated ecological status of Dacop upazila for different options

Fig. 3.20 shows the simulated population, food security and ecological status under normal growth for a period of 120 years. Fig.3.20(a) shows that the population increases exponentially from 172613 in 2007 to 1095594 in 2119 but the food security increase initially up to 12 years and reach a value of 205 and then decreases linearly for rest of the period and ultimately collapses because of the population explosion. This implies that sustainable development of the coastal zone in the long run through the control of shrimp production intensity without the control of population is a mere dream.

1: population 2: food security 3: f ood av ailable 1: 1150000 2: 300 3: 650000 3

3 2

1 1: 650000 3 2: 150 2 3: 400000

3 2 1

2 1 1: 150000 2: 0 1 3: 150000 0.00 30.00 60.00 90.00 120.00 Page 1 Years 7:00 PM Mon, Oct 20, 2008 Simulation of f ood security of Dacop (Normal growth long term) Fig. 3.20(a). Simulated population, food security and food available of Dacop for120 years.

Fig. 3.20(b) shows that the pond area increases gradually and becomes almost stable at 23272 ha within 120 years. As a consequence the crop area decreases gradually and becomes almost stable at 1391 ha within 120 years. This implies that although pond area and crop area become almost stable in the long run, food security decreases because of the population explosion.

1: pond area bagda 2: shrimp production bagda 3: crop area 1: 24000 2: 30000 3: 20000 1

1 2 3 2

1: 18500 1 2 2: 15000 3: 10000 2 3

1: 13000 1 3 2: 0 3: 0 0.00 30.00 60.00 90.00 120.00 Page 1 Years 7:16 PM Mon, Oct 20, 2008 Simulation of Dacop (Control growth long term) Fig. 3.20(b). Simulated penaeid (bagda) shrimp pond area, penaeid shrimp production and crop area of Dacop for120 yrs

88 Fig. 3.20(c) shows that the ecological footprint increase rapidly from 0.74 to +8.07 gha/cap within 13 years and then decreases linearly to +2.97 gha/cap at the end of the simulation period of 120 years. As a consequence ecological status decreases rapidly from -0.32 to -7.80 gha/cap within 16 years and then again increase linearly to -2.92 gha/cap at the end of the simulation period of 120 years since the contribution of biocapacity is very small. This implies that sustainable environmental development of the coastal zone in the long run through the control of shrimp production intensity without the control of population remains a mere dream.

1: ecological f oot print per capita 2: ecological status 3: biocapacity per capita 1: 9 2: 0 3: 9 1

1: 5 2 2: -4 3: 5 1

2 1 2

2 1: 1 2: -8 3 3 3 3: 0 3 0.00 30.00 60.00 90.00 120.00 Page 1 Years 4:59 PM Sat, Oct 18, 2008 Simulation of Ecological Footprint of Dacop f or 120 y ears Fig. 3.20(c). Simulated ecological footprint, biocapacity and ecological status of Dacop upazila for120 years

Fig. 3.21 shows the simulated population, food security and ecological status under control of both normal growth and population for a period of 120 years. Fig. 3.21(a) The population initially increases to 321816 in 90 years and then becomes stabilized but the food security increase initially to a value of 203 in 10 years, then decreases slowly for a the period of 60 years to a value of 150 and then becomes stabilized. This implies that sustainable development of the coastal zone in terms of population and food security in the long run can be realized through the control of shrimp production intensity and population control.

89 1: population 2: food security 3: f ood av ailable 1: 350000 2: 250 3: 450000 1 3 3 2 1 3

2 1: 250000 3 1 2: 150 2 3: 300000 2

1 1: 150000 2: 50 3: 150000 0.00 30.00 60.00 90.00 120.00 Page 1 Years 9:55 AM Sat, Oct 18, 2008 Simulation of f ood security of Dacop (Control growth) Fig. 3.21(a). Simulated population, food security and food availability of Dacop under control of both normal growth and population for a period of 120 years

Fig. 3.21(b) shows that the pond area increases gradually and becomes stable at almost 23372 ha within 120. As a consequence the crop area decreases gradually and becomes stable at almost 1391 ha within 120 years. This implies that sustainable development of the coastal zone in terms of crop and shrimp production in the long run can be realized through the control of shrimp production intensity and population control.

1: pond area bagda 2: shrimp production bagda 3: crop area 1: 24000 2: 20000 3: 1 2 1 2 3 2

1: 18500 2 1 2: 10000 3: 3

1: 13000 1 3 2: 3: 0 0.00 30.00 60.00 90.00 120.00 Page 1 Years 12:28 AM Mon, Oct 20, 2008 Simulation of crop and shrimp area of Dacop (Control growth) Fig. 3.21(b). Simulated penaeid (bagda)shrimp pond area, penaeid shrimp production and crop area of Dacop under control of both normal growth and population for a period of 120 years

90 Fig. 3.21(c) shows that the ecological footprint increases rapidly from almost 0.74 to +8.20 gha/cap within 12 years and then becomes stabilized at +8.60 gha/cap. The ecological status decreases rapidly from almost -0.32 to -7.90 gha/cap within 12 years and then remains almost constant at -8.35 gha/cap. This implies that sustainable development of the coastal zone in terms of environment in the long run can be realized through the control of shrimp production intensity and population control.

1: ecological f oot print per capita 2: ecological status 3: biocapacity per capita 1: 10 2: 0 3: 10 1 1 1

1: 5 2: -5 3: 5

2 1

1: 1 2 2 2 2: -9 3 3 3 3 3: 0 0.00 30.00 60.00 90.00 120.00 Page 1 Years 4:46 PM Sun, Oct 19, 2008 Simulation of Ecological Footprint of Dacop (Control growth) Fig. 3.21(c). Simulated ecological footprint, biocapacity and ecological status of Dacop under control of both normal growth and population for a period of 120 years

3.2.1 Validation of Integrated Coastal Zone Management (ICZM) Model

Integrated coastal zone management (ICZM) model was validated by comparing the simulated crop area and pond area of bagda with those of the actual field data of Dacop upazla during the period 2007-2011. Simulated crop area and pond area of bagda of Dacop upazila under existing growth condition during the study period are given in Fig. 3.22 and the comparisons of the simulated results of crop area and pond area of bagda with those of the field level data are given in Fig. 3.23 and Fig. 3.24, respectively. Fig. 3.22 shows that pond area of bagda is increasing with the decrease of cropped area. The changes in simulated crop area and pond area of bagda follow similar trends to those of the observed field level data (Fig. 3.23 and Fig. 3.24).

91 1: pond area bagda 2: crop area 1: 16000 2: 19500 2 2 2 2

1: 14500 2: 17000

1 1 1 1

1: 13000 2: 14500 0.00 1.00 2.00 3.00 4.00 Page 1 Years 12:25 PM Thu, Jan 19, 2012 Simulation of crop area and pond area bagda (Normal growth) Fig. 3.22 Simulation of crop area and pond area bagda of Dacop upazila

20000 Simulated 19500 Actual

19000

18500

Crop AreaCrop (ha) 18000

17500

17000 2007 2008 2009 2010 2011

Year

Fig. 3.23 Comparison between simulated and actual field data for crop area

15000

14600 Simulated Actual

14200

13800 Pond bagda (ha) area 13400

13000 2007 2008 2009 2010 2011

Year

Fig. 3.24 Comparison between simulated and actual field data for pond area bagda

92 The simulated and observed crop area and pond area of bagda of Dacop upazila and the values of different statistical measurements are given in Table 3.7 and Table 3.8, respectively. From Table 3.7 and Table 3.8 we can see that the standard error of estimate is 1.518%, index of agreement is 99.99% and mean relative deviation modulus is 1.307 for crop area while the standard error of estimate is 1.421%, index of agreement is 99.99% and mean relative deviation modulus is 1.219 for pond area for bagda.. These results indicate that the model predicts well the changes in crop areas and pond areas of bagda .

Table 3.5 Comparison between simulated and actual values of crop area of Dacop upazila during 2007-2011

Crop area (ha) Year d SEE E (%) Simulated Actual Deviation (%) (%) 2007 19500 19500 0 2008 19075 18750 -1.73 2009 18660 18970 1.63 0.999927 1.518 1.307 2010 18254 18080 -0.1 2011 17857 17460 -2.27

Table 3.6 Comparison between simulated and actual values of pond area bagda of Dacop upazila during 2007-2011

Pond area bagda (ha) Year d SEE E (%) Simulated Actual Deviation (%) (%) 2007 13395 13395 0 2008 13626 13900 1.97 2009 13852 13700 -1.1 0.999937 1.421 1.219 2010 14074 14250 1.23 2011 14291 14550 -1.78

93 3.2.2 Policy Implications

Shoronkhola, Shyamnager and Morrelgonj are poor in food security as well as in environmental degradation. To improve the situation any further expansion of shrimp aquaculture should be considered with cautions and rice – shrimp aquaculture should be explored on a priority basis. Mongla upazila is the worst environmentally affected upazila and this implies that the shrimp aquaculture in Mongla should be restricted. Since all the upazilas except Kalapara and Galachipa are environmentally affected, any further expansion of shrimp aquaculture to enhance food security should be considered with cautions. Since the status of food security at household levels is poor, action programmes are needed to improve the food security at household and also to ensure the payment of reasonable rent of the land used in shrimp aquaculture to the poor farmers.

A system dynamics model of integrated management of coastal zone for food security has been developed. This model can be used to predict food security and environmental degradation in terms of ecological footprint of the coastal zone of Bangladesh. This model can provide better insights and understanding of integrated coastal zone system. This model can be used to assist the policy planners to assess different policy issues and to design a policy for sustainable development of the coastal zones of Bangladesh.

Super intensive shrimp aquaculture must not be allowed to avoid the collapse of the shrimp aquaculture industry ultimately turning shrimp aquaculture land neither suitable for shrimp culture nor crop production. The control of population and growth of the shrimp production intensity should be considered for the long run sustainability of the coastal zone of Bangladesh.

Higher contributions to food security from shrimp culture in the coastal zones of Bangladesh may be attributed to two reasons: first, the potential of shrimp culture in the coastal zone; second, the export market of the shrimp which prompted to expansion of shrimp culture in the coastal zone.

However, the ecosystem of the coastal zone already has degradeded due to the expansion of shrimp culture and there is a scope for sustainable development of the coastal zones of Bangladesh. The policy regime of shrimp farming may also have induced some additional distortion to environmental degradation. As already mentioned farmers are converting

94 agricultural land into shrimp farming pond and intruding saline water for penaeid shrimp culture.

Practically there is no regulation on conversion of agricultural land into shrimp culture intruding saline water. Local elites and private enterprises are attracted to shrimp farming for export. The findings in this study give rise to a number of observations regarding shrimp farming and environmental protection. In practice, the shrimp is not intensive. In principle, it requires not only simply restricting the conversion of agriculture land into intensive shrimp pond but also restricting the growth of the shrimp farming intensity. The findings, therefore suggest that policy planning of coastal zone development should take into account the following:

* Effort to achieve higher food security must be combined with effort to achieve lower ecological footprint (environmental degradation). This can be done via policies designed to establish through application of restriction on conversion of agriculture land into conversion of shrimp ponds and restricted growth of intensity and development of salinity resistant rice variety. Right policies and programs to boost up shrimp culture in rice field using improved technology and extension services and sustainable penaeid shrimp culture with restriction on uncontrolled growth of the shrimp culture pond and intensity of shrimp culture in the high salinity areas with tax to reduce ecological footprint need promotion to increase the food security with reduced environmental degradation.

* For the success of the sustainable development of coastal zone of Bangladesh requires awareness based on participatory approach with shrimp culture technology, crop production in saline water and environment related problems. Effective extension service should be further strengthened for awareness development regarding environmental degradation and sustainable development of coastal zone i. e. restricted growth of shrimp farming and farming intensity.

* Sustainable development of the coastal zone in the long run without any population control would remain a mere dream amd hence policies targeted for sustainable development must include population control measures.

95 3.3 Modeling the Climate Change Impacts on Rice Production in the Coastal Zone of Bangladesh 3.3.1 Model Validation

To build up confidence in the predictions of the model, various ways of validating a model such as comparing the model predictions with observed data, checking whether the model generates plausible behaviour and checking the quality of the parameter values were considered. The validated model was used for base line scenario and policy analysis. The sensitivity of the important parameters was also estimated.

3.3.2 Sensivity Analysis

3.3.2.1 Impacts of Temperature on Rice Production

The model was simulated to assess the impacts of temperature increase of 0.5oC, 1oC, 1.5oC and 2oC for rice in the coastal zone and the simulated results are shown in Fig 3.25. The simulated results show that for an increase of 2oC temperature rice yield reduces from 4555.2 to 3281.7 kg/ha in the coastal zone. For every 0.5oC increase of temperature from 0oC to 2oC rice yield is reduced by 5.14%, 15.59%, 24.42% and 27.95%, respectively. Fig. 3.22 also shows that for increase of temperature 0.5oC, 1oC, 1.5oC and 2oC, rice yield is reduced from 4555.2 kg/ha to 4320.8, 3845, 3442.5 and 3281.7 kg/ha, respectively. This implies that temperature has a negative impact on rice yield.

5000

4000

3000

2000 Yield, kg/ha

1000

0 00.511.52

Temperature increase, oC

Fig: 3.25 Impact of temperature increase on rice yield

96 3.3.2.2 Impacts of Carbon Dioxide on Rice Production

The model was simulated to assess the impacts of CO2 levels of 370, 380 ppm, 390 ppm, 400 ppm and 410 ppm on the yields of rice in the coastal zone and the simulated results are shown in Fig. 3.26.

5000

4900

4800

4700

4600 Yield, kg/ha 4500

4400

4300 370 380 390 400 410

CO2 level, ppm

Fig: 3.26 Impact of CO2 increase on rice yield

Predictions have been made using a fixed concentration of atmospheric CO2 of 370 ppm and then increased at a level of 380 ppm, 390 ppm, 400 ppm and 410 ppm. Simulated results show that the increase in CO2 concentration from 370 ppm to 430 ppm increases rice yield from 4555.2 kg/ha to 4905.7 kg/ha in the coastal zone. Simulated results also show that increase in CO2 concentration has positive impacts on the yields of rice.

3.3.3 Impacts of Temperature and Carbon Dioxide on Rice Production

The simulation was carried out to predict the yields of rice under different climatic trends of temperature and carbon dioxide concentration. Treatments of temperature and CO2 and their impacts on the yields of rice are shown in Table 3.7.

Fig. 3.27 shows the impacts of different treatments of temperature and CO2 on the yields of rice. All of the tratment show the negative effect on rice in the coastal zone of

Bangladesh. It is observed from the previous section that due to increases of CO2 level yield is increased but from the combined effect of temperature and carbon dioxide, the yield of rice is decreased. From predictions of the rice yields under different treatments, the sensitivity analysis shows that the effect of temperature increase on the crop yields is

97 Table 3.7: Treatments and their impacts on rice

Treatment Temperature change, °C CO2 level, Rice yield, Yield No. ppm kg/ha decrease (%) Maximum Minimum

T1 0.0 0.0 370 4555.2 -

T2 0.5 0.5 380 4407.8 3.2

T3 1.0 1.0 390 4008.3 12.0

T4 1.5 1.5 400 3678.3 19.2

T5 2.0 2.0 410 3602.7 20.9 more pronounced than that of CO2 level increase i.e. temperature is more sensitive to crop yield in comparison to the CO2 level.

5000

4000

3000

2000 Yield, kg/ha Yield,

1000

0 T1 T2 T3 T4 T5 Treatments

Fig: 3.27 Impacts of temperature and CO2 on rice production

3.3.4 Climate Change

Simulation study was carried out for the coastal zone for 2020, 2030, 2040 and 2050 for both historical trend and IPCC assumptions. The data on temperature and CO2 concentration changes for both historical trend and IPCC assumptions are shown in Table 3.8. These assumptions are based simply on the trend line analysis. From the analysis of historical data over the last 30 years (1976-2005), it is found that the monthly average maximum and minimum temperatures increase about 0.020C per year and if this trend line is extended up to 2020, 2030, 2040 and 2050, temperature will increase by 0.30C, 0.50C, 0.70C and 0.90C respectively.

98 Temperature is projected to rise in a range from 1.8°C (with a range from 1.1°C to 2.9°C for SRES B1) to 4.0°C (with a range from 2.4°C to 6.4°C for A1) by 2100 under IPCC trend. On the basis of the IPCC report temperature will increase at a rate of 0.0420C per year and it may be 0.670C, 1.090C, 1.510C and 1.930C in respectively 2020, 2030, 2040 and 2050. Carbon dioxide concentration increases at a rate of 1.9 ppm per year and if this rate of increase in CO2 concentration is constant for the future, CO2 concentration would be 409 ppm, 428 ppm, 447 ppm and 466 ppm for the years of 2020, 2030, 2040 and 2050 respectively (simply trend line analysis).

Table 3.8: Year wise assumptions of CO2 concentrations and temperature data

Year Historical Data Analysis IPCC CO2 Maximum Minimum Average 379,ppm in 2005 Temperature Temperature Temperature 2008(Base) 0.02,oC/yr 0.02,oC/yr 0.042 1.9,ppm/yr 2020 0.3 0.3 0.67 409 2030 0.5 0.5 1.09 428 2040 0.7 0.7 1.51 447 2050 0.9 0.9 1.93 466

3.3.5 Rice Yields under Historical and IPCC Trends in the Coastal Zone

As the global warming is continuing, the weather parameters are also changing in conjuncture, which gradually creating a new unacquainted ambient environment for the field crops. What will be the yield, duration and spatial dispersion of the field crops in the changing scenarios are the burning issue to the global intellectual community?

The model was simulated to predict the yields of rice and wheat for both historical and IPCC trends of climate change and the simulated climate change impacts on the yields of rice for historical and IPCC trends of both the temperature and CO2 changes for a period of 2020-2050 are shown in Fig. 3.28. From the simulation studies, it is found that Rice yield decreases from 4704.4 kg/ha in 2020 to 4657 kg/ha in 2050 and from 4588.8 kg/ha in 2020 to 3993.2 in 2050 kg/ha for historical and IPCC climatic change scenarios, respectively in Patuakhali i.e. in the coastal zone of Bangladesh. For this period the rice yield decreases by 1% for historical trend and decreases by 13% for IPCC trend. From the simulation studies, it is clear that there is a very small change in the yields of rice for the

99 historical climate change scenario, but there is a relatively higher reduction in the yields of rice for IPCC climate change scenario.

6000 Historical 5000 IPCC

4000

3000

Yield, kg/ha Yield, 2000

1000

0 2020 2030 2040 2050 Time, year

Fig. 3.28 Changes in the yields of rice for historical and IPCC climatic scenarios

The decrease in yield due to climate change is more pronounced for IPCC trend of climate change. The decrease in yield is mainly due to temperature increase, which might be due to acceleration in maturity. The rice yield was predicted to vary with locations and it ranges 2% to 13%. Basak et al., (2011) reported that there will be a significant decrease in the yields of boro rice in Bangladesh due to climate change. But Bala et al (2011) and Islam and Saila (2011) reported that the overall impact of climate change on crop production in Bangladesh would probably be small in 2030. This study agrees well with the predictions of Islam and Saila (2011).

3.3.6 Adaptation to Climate Change Impacts on Rice Production

Farmers should adapt to the climate change conditions. Some of the strategies would be easy to perform and the others would be costly. There are two main types of adaptations and these are autonomous and planned adaptation. Autonomous adaptation for example is the reaction of a farmer to changing precipitation patterns to change the crops or use different harvest and planting/sowing dates. Planned adaptation measures are conscious policy options or response strategies. Planned adaptation for example are the deliberate crops selection and distribution strategies across different agri-climatic zones, substitution of new crops for old ones and resource substitution induced by scarcity. Large reductions

100 in adverse impacts from climate change are possible if adaptation is fully implemented (Mendelsohn and Dinar 1999). Major classes of adaptation are: ■ seasonal changes and sowing dates; ■ different variety or species; ■ water supply and irrigation system; ■ other inputs (fertilizer, tillage methods, grain drying, other field operations); ■ new crop varieties;

The simplest measure to reduce the negative impacts of climate change on crop production is to switch the planting dates, which can be done without extra investments.

It is also very likely that the evaporative demand of water will increase as a result of increasing temperature affecting the crop productivity, if water availability is not enough to counterbalance the evaporative demand. As a result, irrigation need could rise because of the higher evaporative demand. However, if CO2 effects are considered, irrigation requirements could decrease.

In a warming climate the use of fertilizers should be carefully surveyed. Another important issue is that warmer temperatures could induce changes in the dynamics of pests, diseases and weeds. Increased monitoring and adoption of integrated pest management practices will be necessary. However, more complex and time consuming measures are genetic improvement and development of new varieties tolerant to temperature increase, water stress and resistant to pathogens.

Finally, for sustainable development management of the agricultural systems for climate change conditions participatory approach of multi agent systems involving all the stakeholders for knowledge sharing and collective decision making should be considered.

101 CHAPTER 4

SUMMARY AND CONCLUSION

SUMMARY

Costal Zone is most frequently defined as land affected by its proximity to the sea and that part of the sea affected by its proximity to the land or, in other words, the areas where the processes which depend on the sea-land interactions are the most intensive. The coastal zone of Bangladesh is 47,203 km2 and it is roughly 32% of the whole country. According to 2001 population census, total population of Bangladesh is 123.15 millions. Of which 35.1 millions live in coastal area and it is approximately 28% of the total population of Bangladesh.

Integrated coastal zone management (ICZM) consists of the population, crop production, aquaculture and forestry with two unique features of food security and environmental degradation (ecological footprint). There is lack of integration of environmental consideration in the integrated coastal zone management of Bangladesh. The problem can not be solved in isolation, an integrated and systems approach is needed. For clear understanding of this complex system before its implementation, it must be modeled and simulated.

The purposes of this study are: (i) to estimate the present status of the contribution of expanding population, decreasing agriculture, expanding aquaculture for shrimp farming and forests to food security and ecological factor, (ii) to develop a computer model to simulate integrated coastal zone management systems for sustainable development (iii) to determine the management strategies for sustainable development of the coastal zone

102 system and (iv) d) to assess the impact of climate change on crop production in the coastal zone of Bangladesh.

Food security and ecological footprint

To address the food security and ecological footprint, an indicator of environmental sustainability of the coastal zones of Bangladesh, nine upazilas of the coastal zones in the five districts of Bangladesh were selected and data on population, crop production, aquaculture, livestock and forestry were collected to estimate the present status of the food security and environmental degradation of the coastal zones of Bangladesh from upazila office of Government Department of Statistics, Agriculture, Fishery and Livestock. A typical village named Baraikhali was selected from Dacop upazila of Khulna district to find out the individual household food security status. Total number of household of the village was 182. The collected data and information were compiled, edited, summarized and analyzed and the present status of food security and environmental degradation (in terms of ecological footprint) were determined.

A quantitative method for computation of food security in grain equivalent based on economic returns (price) is developed. The food security and ecological footprint of the coastal zone of Bangladesh are estimated and a database has been prepared.

This research shows that the overall status of food security at upazila levels is good for all the upazilas (8.53% to 164.19%) except Shoronkhola (-23.65%), Shyamnager (-6.08%) and Morrelgonj (-30.29%), and the best is the Kalapara upazila (164.19%). But status of food security at household levels is poor. Shoronkhola, Shyamnager and Morrelgonj are poor in food security as well as in environmental degradation. To improve the situation any further expansion of shrimp aquaculture should be considered with cautions and rice – shrimp aquaculture should be explored on a priority basis. Since the status of food security at household levels is poor, action programmes are needed to improve the food security at household and also to ensure the payment of reasonable rent of the land used in shrimp aquaculture to the poor farmers.

103 The environmental status in the coastal zones is poor for all the upazilas (-0.5076 to - 0.027) except Kalapara (+0.306) and Galachipa (+0.322) and the worst is the Mongla upazila (-0.5076). The environmental status in the coastal zones has degraded mainly due to shrimp culture. Mongla upazila is the worst environmentally affected upazila and this implies that the shrimp aquaculture in Mongla should be restricted. Since all the upazilas except Kalapara and Galachipa are environmentally affected, any further expansion of shrimp aquaculture to enhance food security should be considered with cautions.

The control of growth of the shrimp production intensity stabilizes the system at least in the short run. The control of population and growth of the shrimp production intensity should be considered for stabilization of the system in the long run. The sustainable development of the coastal zone of Bangladesh in the long run without control of both the growth of shrimp production intensity and population will remain mere dream.

This model can be used to predict food security and environmental degradation in terms of ecological footprint of the coastal zone of Bangladesh. This model can provide better insights and understanding of integrated coastal zone system.

The boost up of coastal agriculture and restriction on rapid growth of shrimp culture and its intensity to reduce ecological footprints are two pathways for sustainable development of food security in the coastal zones of Bangladesh. This study examines the short term and long term policy options for sustainable food security.

Climate change impact on rice

Climate has been changing due to natural forcing. Climate factors such as temperature, rainfall, atmospheric carbon dioxide, solar radiation, etc. are closely linked with agricultural production. Rice production would be major concern in recent years due to changing climatic conditions.

To assess the impact of climate change on rice production in the coastal zone of Bangladesh weather data such as daily average maximum and minimum temperature, daily precipitation, solar radiation, humidity etc were collected from weather station in Kalapara upazila of Patuakhali district and crop related data were collected from the office

104 of Bangladesh agricultural Research Institute located in the location. InfoCrop model was used for assessment of the climate change impacts on crop growth, yield and plant behavior of rice crop of the coastal zone of Bangladesh.

The simulation was carried out to predict the yields of rice under different climatic trends of temperature and carbon dioxide concentration. The effect of temperature on the yield of rice that is negative while of CO2 is positive but temperature plays dominant role. Prediction was also made to predict the climate change impacts of rice yields based on historical and IPCC climate change scenarios. Rice yield decreases from 4704.4 kg/ha in 2020 to 4657 kg/ha in 2050 and from 4588.8 kg/ha in 2020 to 3993.2 in 2050 kg/ha for historical and IPCC climatic change scenarios, respectively in Patuakhali i.e. in the coastal zone of Bangladesh. For this period the rice yield decreases by 1% for historical trend and decreases by 13% for IPCC trend. From the simulation studies, it is clear that there is a very small change in the yields of rice for the historical climate change scenario, but there is a relatively higher reduction in the yields of rice for IPCC climate change scenario.

Some adaptation options to climate change impacts are also discussed. In a warming climate the use of fertilizers should be carefully surveyed. Another important issue is that warmer temperatures could induce changes in the dynamics of pests, diseases and weeds. Increased monitoring and adoption of integrated pest management practices will be necessary. However, more complex and time consuming measures are genetic improvement and development of new varieties tolerant to temperature increase, water stress and resistant to pathogens.

105 CONCLUSION

Overall status of food security at upazila levels is good for all the upazilas (8.53% to 164.19%) except Shoronkhola (-23.65%), Shyamnager (-6.08%) and Morrelgonj (- 30.29%), and the best is the Kalapara upazila (164.19%). But status of food security at household levels is poor.

The environmental status in the coastal zones is poor for all the upazilas (-0.5076 to - 0.027) except Kalapara (+0.306) and Galachipa (+0.322) and the worst is the Mongla upazila (-0.5076). The environmental status in the coastal zones has degraded mainly due to shrimp culture.

A system dynamics model of integrated management of coastal zone for food security and ecological footprint has been developed. This model predicts that expanding shrimp aquaculture industry ensures high food security (77% in 2007 and 202% in 2016) at upazila levels with increasing environmental degradation (0.74 gha/cap in 2007 and 8.37 gha/cap). The model validated with the actual field data and found good agreement between simulated and actual data.

The model also predicts that if shrimp aquaculture industry continues to boom from the present status to super intensive shrimp aquaculture, a collapse of the shrimp aquaculture industry will ultimately occur turning shrimp aquaculture land neither suitable for shrimp culture nor crop production. Such as, food availability from crop area decreases from 45239 ton to 5638 ton during the period of 2007 to 2107.

The control of growth of the shrimp production intensity stabilizes the system at least in the short run. Food security status increases from 77% in 2007 to 209% in 2017 and then decreases to 55% in 2107. The control of population and growth of the shrimp production intensity should be considered for stabilization of the system in the long run. This model can be used to assist the policy planners to assess different policy issues and to design a policy for sustainable development of the coastal zones of Bangladesh.

InfoCrop model based on radiation energy use efficiency in photosynthesis was used to simulate crop growth and to predict climate change impacts on rice production in the

106 coastal zone of Bangladesh. The simulation was carried out to predict the yields of rice under different climatic trends of temperature and carbon dioxide concentration. The effect of temperature on the yield of rice is negative while of CO2 is positive but temperature plays dominant role. Prediction was also made to predict the climate change impacts of rice yields based on historical and IPCC climate change scenarios. Rice yield decreases from 4704.4 kg/ha in 2020 to 4657 kg/ha in 2050 and from 4588.8 kg/ha in 2020 to 3993.2 in 2050 kg/ha for historical and IPCC climatic change scenarios, respectively in the coastal zone of Bangladesh. For this period the rice yield decreases by 1% for historical trend and decreases by 13% for IPCC trend. From the simulation studies, it is clear that there is a very small change in the yields of rice for the historical climate change scenario, but there is a relatively higher reduction in the yields of rice for IPCC climate change scenario.

107 REFERENCES

Aggarwal, P. K., Kropff, M. J., Cassman, K. G. and Berge, H. F. M. 1997. Simulating genotypic strategics for increasing rice yield potential in irrigated, tropical environments. Field Crops Research, 51: 5-17.

Aggarwal, P. K., Kalra. N., Chander, S., and Pathak, H. 2006a. InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description. Agricultural Systems, 89(1): 1-25.

Aggarwal, P. K., Banerjee, B., Daryaei, M. G., Bhatia, A., Bala, A., Rani, S., Chander, S., Pathak, H. and Kalra, N. 2006b. InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro- ecosystems in tropical environments. II. Performance of the model. Agricultural Systems, 89(1): 47-67.

Ahmed, A.T.A. 1995. Impacts of shrimp culture on the coastal environment of Bangladesh. Proceedings of the National Workshop on Coastal Aquaculture and Environmental Management. Institute of Marine Sciences, 25-28 April 1995. Nuruddin Mahmood, University of Chittagong/UNESCO, Bangladesh. pp. 77–84.

Akteruzzaman, M. 2004. A cost benefit analysis of breakish water management for shrimp farming in southwest coastal belt of Bangladesh. 9th Biannual Research and Training Workshop. 01-09 November. Banglore, India.

Alauddin, M. and Tisdell, C. (1998) The Environment and Economic Development in South Asia: An Overview Concentrating on Bangladesh. Macmillan, London.

Arquitt, S., Honggang, X. and Johnstone, R. 2005. A system dynamics analysis of boom and burst in the shrimp aquaculture industry. Systems Dynamics Review. 21(4), 305-324.

Bach, N.L., & Saeed, K. 1992. Food self-sufficiency in Vietnam: a search for aviable solution. System Dynamics Review, 8(2), 129-148.

Bagliani, M., Galli, A., Niccolucci, V., Marchettini, N. 2008. Ecological footprint analysis applied to a sub-national area: The case of the Province of Siena Italy). Environmental Management. 86(2).354-364.

108 Bala, B. K. and Masuduzzaman, M. 1998. Irrigation Scheduling using System Dynamics Approach. Proceedings of the International Conference on System Dynamics ICSD-98 Held on December 15-18, 1998 at Kharagpur, India. pp. 133-141.

Bala, B. K. 1999a. Principles of System Dynamics. Agrotech Publishing Academy, Udaipur, India.

Bala, B. K. 1999b. Computer Modelling of Energy, Food and Environment: The case of Bangladesh. Paper presented at the 17th International Conference of the System Dynamics Society and 5th Australian & New Zealand Systems Conference,Wellington, New Zealand.

Bala, B.K; Matin, M.A.; Rahman, M.M; Biswas, B.K; and Ahmed, Farid Uddin. 2000. Computer Modelling of Integrated Farming Systems and Environment: The Case of Bangladesh. Proceedings of the Ninth National Conference on System Dynamics, December 26-29, 2000, Hyderabad, India.

Bala, B. K., Munnaf, M. A., Ruma, F. Y. and Alam, M. S., 2011. Modeling of climate change impacts on agriculture, forests and fishery. Proceedings of the 4th International Conference on Environmentally Sustainable Development - ESDev 2011 on 24-26 July 2011, Abbottabad, Pakistan, 2011.

Banglapedia, 2003. National Encyclopedia of Bangladesh. Vol. 03. p. 56. Asiatic Society of Bangladesh.

Basak, J. K., Ali, M. A., Islam, M. N. and Rashid, M. A. 2011. Assessment of the effect of climate change on boro rice production in Bangladesh using DSSAT model. Journal of Civil Engineering, 38(2): 95-108.

BBS, 2002. Statistical Pocketbook Bangladesh.2001. Bangladesh Bureau of Statistics. Semtember. 2002.

Begum, S. 2002. Proceedings of the APO seminar on role of rural women in food security in Asia and the Pacific. Held in Thailand from 21-25 August.2000. Asian Productivity Organization, Tokyo, Japan.

Belt, M. V. D., Deutsch, L. and Jansson, A. 1998. A consensus-based simulation model for management in the Patagonia coastal zone. Ecol. Model. 110. 79-103.

109 Bouman, B. A. M., Kropff, M. J., Tuong, t. P., Wopereis, M. C. S., ten Berge, h. F. M. And van Larr, G. G. 2000. modelling lowland rice, International Rice Research Iinstitute, Philippines, 235pp.

Brammer, H., Asaduzzaman M. & Sultana, P., 1993. Effects of Climate and Sea-level Changes on the Natural Resources of Bangladesh. Briefing Ducument No. 3, Bangladesh Unnayan Parishad (BUP), Dhaka.

Cao, W. and Wong, M. H. 2007. Current status of coastal zone issues and management in China: A review. Environment International. 33. 985-992.

Challinor, A., Wheeler, T., Garforth, C., Craufurd, P. and Kassam, A. 2007. Assessing the vulnearability of food crops systems in Africa to climate change. Climate Change. 83: 381-399.

Chambers, N., Simmons, C. and Wackernagel, M. 2000. Sharing Nature’s Interest- Ecological Footprint as an indicator of Sustainability. Earthscan Publication Ltd, London, UK, ISBN 1-85383-739-3.

Chanratchakool, P., 2003. Problems in Penaeus monodon culture in low salinity areas, Aquaculture Asia,VIII (1), pp.54-56.

Chen, B. and Chen, G. Q. 2006. Ecological footprint accounting based on emergy- A case study of the Chinese society. Ecol. Model. 198, 101-114.

Chowdhury, A.M., Quadir, D.A., Islam, M.J. (1994) Study of Chokoria Sundarbans using remote sensing techniques. ISME (International Society of Mangrove Ecosystems), Japan. Technical Report 4, 1–33.

Chua, T. E., Bonga, D. and Atrigenio, N. B. 2006. Dynamics of integrated coastal management: PEMSEA’s Experience. Coastal Management. 34:303-322.

Coxhead, Ian. 2000. Consequences of a food security strategy for economicwelfare, income distribution and land degradation the Philippine case. World Development Vol. 28, No. 1, pp. 111-128.

Csavas, I. 1995. Development of shrimp farming with special reference to Southeast Asia. Presented at INDAQUA 1995. Exposition of Indian Aquaculture, Madras.

110 De Silva, C.S; Weathearhead, E.K; Knox, J.W; and Rodriguez-Diaz, J.A. 2007. Predicting the impact of climate change- A case study of paddy irrigation water requirements in Srilanka. Agricultural Water Management, 93: 19-29.

De Souza, R., Williams, J. S. and Meyerson Frederick, A. B. 2003. “Critical Links. Population, Health and Environment”. In population Bulletin, 58 (3).

Diakosavvas and Green. 1998. Assessing the impact on food security of alternative compensatory financing schemes. A simulation approach with an application to India. World Development Vol. 26, No. 7, pp. 1251-1265.

DoF (Department of Fisheries). 1995. Shrimp resources statistics. Central Shrimp Cell, Department of Fisheries, Dhaka.

DoF (Department of Fisheries). 2003. Fishery Statistical Yearbook of Bangladesh: 2002- 2003. Department of Fisheries, Dhaka, 41 pp.

Ertekin, C. and Yaldiz O. (2004). Drying of eggplant and selection of a suitable thin layer drying model. Journal of Food Engineering. 63: 349-359.

Fabbri, K. P. 1998. A methodology for supporting decision making in integrated coastal zone management. Ocean and Coastal management. 39. 51-62.

Falcon, Walter P., Naylor, R. L., Smith, W. L. S. W. L., Burke, M. and McCullough. 2004. Using climate models to improve Indonesian Food Security. Bulletin of Indonesian Economic Studies, Vol. 40, No. 3, 355–77

FAO (Food and Agricultural Organization). 1996a. “Implications of Economic Policy for Food Security: A Training Manual”, Rome.

FAO (Food and Agricultural Organization). 1996b. Technical Background Document, prepared for the World Food Summit, Rome,

FAO. 2001. Food Balance Sheet- A Handbook. Food and Agricultural Organization of the United Nations, Rome.

FAO. 2002. The State of Food Insecurity in the World 2001. Rome. Forrester, J. W. 1968. Principles of Systems. Wright-Allen Press, MIT, Massachusetts.

Frihy, O.E., 2003. The Nile Delta-Alexandria Coast: Vulnerability to Sea-Level Rise; Consequences and Adaptation, Mitigation and Adaptation Strategies for Global Change 8,pp.115–138.

111 Gencturk, M. B., A. S. Bakshi, Y. C. Hong and T. P. Labuza. (1986). Moisture transfer properties of wild rice. . Food Process. Engineering. 8(4):243-261.

Georgiadis, P., Vlachos, D. and Iakavou, E. 2004. A system dynamics modeling framework for the strategic supply chain management of food chains. Journal of Food Engineering, 70 351–364.

Giraldo, D. P., Betancur, M. J. and Arango, S. 2008. Food Security in Developing Countries: A systemic perspective. Paper presented at the International Conference of the System Dynamics Society held on July 20-24, 2008 at Athens, Greece.

Gohara, R. 2001. A System Dynamics Model for Estimation of Future World FoodProduction Capacity. Unpublished Thesis (M.S.) University of New Hampshire.

Groesser, S. N. and Schwaninger, M. 2012. Contribution to model validation: hierarchy, process, and cessation. System Dynamics Review. (wileyonlinelibrary.com)

Haque, A.K.E. (2004) Sanitary and Phyto-sanitary Barriers to Trade and Its Impact on the Environment: The Case of Shrimp Farming in Bangladesh. IUCN Bangladesh Country Office, Dhaka, Bangladesh, 63 pp.

Haque, S. A. 2006. Salinity problems and crop production in coastal regions of Bangladesh. Pak. J. Bot., 38(5): 1359-1365.

Heuvelink, E., 1999. Evaluation of a Dynamic Simulation Model for Tomato Crop Growth and Development. Annals of Botany, 83: 413-422.

Holden, S., Shiferaw, B., and Pender, J. 2005. Policy Analysis for Sustainable Land Management and Food Security in Ethiopia. A Bioeconomic Model with Market Imperfections. IFPRI, Washington D.C.

Hossain, S. M. A., Ahmad, S., Halim, A., Dewan, S., Talukder, M. S., Islam, M. S. and Bhuiya, M. S. U. 2002. Fact searching and intervention 1999-2001. Studies on integrated farming. FSES Publication No. 78. Farming Systems and Environmental Studies, Bangladesh Agricultural University, Mymensingh.

Hussain, Z. and Karim, A. 1994. Introduction. In: Hussain, Z. and Acharya, G. (eds.). Mangrove of the Sundarban.Volume 2: pp. 1-10. IUCN- The World Conservation Union, Bangkok.

112 Ianchovichina, E., Darwin, R. and Shoemaker, R. 2001. Resource use and technological progress in agriculture: a dynamic general equilibrium analysis. Ecological Economics 38, 275–291.

IFPRI, 1998. Sustainable Food Security for All by 2020. Proceedings of an International Conference. September 4-6. Bonn, Germany.

Iftekhar, M. S. 2006. Conservation and management of the Bangladesh coastal ecosystem: overview of an integrated approach. Natural Resources Forum. 30. 230-237.

IPCC,. Climate Change. The science of Climate Change. 2001. Cambridge University Press, Cambridge, UK.

Islam, M. F. and Saila, P. 2011. Food security in the face of climatic change, population growth and resource constraints: Implications for Bangladesh.. Environmental Management, 34(4): 467 – 498.

Islam, M.R. (ed.), 2004. Where Land Meets the Sea: A Profile of the Coastal Zone of Bangladesh, The University Press Limited, Dhaka

Kamal Mesbah, Avijit Reaz Quazi and Sanzida Akhter, 2001. Resource Use by Indigenous Communities in the Coastal Zone. PDO-ICZMP, July.

Karim, M. and Stellwagen, J. (1998) Shrimp aquaculture. Final Report, Volume 6. Fourth Fisheries Project, Department of Fisheries, Dhaka, 101 pp.

Karim, Z., Hossain, S. G. and Ahmed, M. 1990. Salinity problem and crop intensification in the coastal region of Bangladesh, BARC, Dhaka.

Karim, Z., Hussain, S. G. and Ahmed, M. 1996. Assessing impacts of climatic variations of foodgrain production in Bangladesh. Water, Air and Soil Pollution. 92. 53-62.

Keulen, H. and Seligman, N. G. 1987. Simulation of water use, N nutrition and growth of a spring wheat crop. Pudoc, Wageningen, 310 pp.

Khan, Y. S. A. and Hossain, M.S. 1996. Impact of shrimp culture on the coastal environment of Bangladesh. International Journal of Ecology and Environmental Sciences 22(2), 145–158.

Klinger, T., 2004. International ICZM: In search of successful outcomes. Ocean and Coastal Management, 47: 195–196.

113 Kropff, M.J., Van Laar, H.H. and Matthews, R.B. (Eds.). 1994. ORYZA1: An ecophysiological model for irrigated rice production. SARP Research Proceedings, IRRI, Wageningen, 110 pp

Lomauro, C. J., A. S. Bakshi and T. P. Labuza. (1985). Evaluation of food moisture sorption isotherm equations. Part I: Fruit, vegetable and meat products. Lebensm. Wiss. Technol. 18(2): 111-117.

MacCalla, A. and Revoredo, C. 2001. Prospects for Global Food Security. A Critical Appraisal of Past Projections and Predictions. International Food Policy Research Institute. Washington, D.C. U.S.A.

Maniruzzaman, M. (1998) Intrusion of Commercial Shrimp Farming in Three Rice- growing Villages of Southern Bangladesh: Its Effects on Poverty, Environment and Selected Aspects of Culture. PhD thesis. University of the Philippines, Quezon City, Philippines.

Meadows, D. H. 1976. Food and Population: Policies for the United States. In D.Baldwin (Ed.), America as an Interdependent World. Hanover, NH: Univ. Press of New England.

Meadows, D. H. 1977. The World Food Problem: Growth Models and Nongrowth Solution. In D. L. Meadows (Ed), Alternatives to Growth-I: A Search for Sustainable Futures. Cambridge MA: Ballinger Publishing Co.

Medved, S. 2006. Present and future ecological footprint of Slovenia- The influence of energy demand scenarios. Ecol. Model. 192, 25-36.

Mendelsohn, R. and Dinar, A. 1999. Climate change, agriculture and developing countries: Does adaptation matter? The World Bank Research Observer. 14(2): 277-293.

Ministry of Finance. 2003. Bangladesh: A National Strategy for Economic Growth, Poverty reduction and Social Development. Economic Relation Division, Ministry of Finance, Government of the People’s Republic of Bangladesh, Dhaka.

Mishra, U. and Hossain, S. A. K. 2005. Current food security and challenges: Achieving 2015 MDG hunger milepost. Food Security in Bangladesh. Paper presented in the national workshop. pp 01-06. October 19-20. Dhaka. Bangladesh.

114 MOFL (Ministry of Fisheries and Livestock) 1997. Fisheries resources development and management. Paper presented at the National Workshop, 29 October to 1 November 1997, MOFL, Dhaka, Bangladesh.

Mohanty, S. and Peterson, E. 2005. Food security and government interventions. A study of Indian grain markets. J. Int. Trade & Economic Development Vol. 14, No. 3. 337-352. September.

Mondal, M.K. 1997. Management of soil and Water Resources for Higher productivity of the Coastal Saline rice land of Bangladesh. Ph.D. Thesis, UPLB. Philippines.

Monfreda, C., Wackernagel, M. and Deumling, D. 2004. Establishing national natural capital accounts based on detailed ecological footprint and biological capacity assessment. Land Use Policy 21. 231-246.

MPO. 1986. National Water Plan. Master Plan Organization. Bangladesh Water Development Board, Dhaka, Bangladesh.

NACA (Network of Aquaculture Centres in Asia Pacific). 1994. Environmental management for coastal aquaculture. An assessment of shrimp culture in Southern Thailand.

Nguyan, T. G.and Kok, J. L. 2007. Systematic testing of an integrated systems model for coastal zone management using sensitivity and uncertainty analyses. Environmental Model. and Software. 22. 1572-1587.

Niccolicci, V., Gall, A., Kitzes, J, Pulselli, R. M., Borsa, S. and Marchettini, N. 2008. Ecological footprint analysis applied to the production of two Italian wines. Agriculture, Ecosystems and Environment. 128:162-166.

Nuruzzaman, K.M. (1979) Physical planning legislation in Bangladesh: a study of proper legislation needs. Master’s degree thesis, University of Sheffield, UK.

PDO-ICZMP. 2003. Coastal Livelihoods – an introductory analysis. Working Paper WP011. January 2003.

PDO-ICZMP. 2003a. Delineation of the Coastal Zone. Working Paper WP005. December 2003.

PDO-ICZMP, 2003b. Coastal Livelihoods – Situation and Context. Working Paper WP015. June 2003.

115 PDO-ICZMP (Program Development Office for Integrated Coastal Zone Management Plan). 2004a. Proceedings of Technical Discussion on Coastal Land Zoning. PDO- ICZMP, Water Resources Planning Organisation, Dhaka, 80 pp.

PDO-ICZMP (Program Development Office for Integrated Coastal Zone Management Plan).2004b. Where Land Meets the Sea: A Profile of the Coastal Zone of Bangladesh. The University Press Limited, Dhaka, 317 pp.

PDO-ICZMP (Program Development Office for Integrated Coastal Zone Management Plan). 2005a. Living in the Coast. PDO-ICZMP, Water Resources Planning Organisation, Dhaka, 36 pp.

Pedersen, J. D., Beck. S., Johansen, H. B. and Jensen, H. B. 2005. Capacity development in integrated coastal zone management: Some lessons learned from Malaysia. Coastal Management. 33. 353-372.

Penning de vries, F.W.T., Jansen, D.M., ten Berge, H.F.M. and Bakema, A.H. 1989. Simulation of ecophysiological processes in several annual crops. Simulation Monograph. PUDOC, Wageningen. The Netherlands, 271 pp.

Quinn, P. M., 2002. Nation State Food Security: A Simulation of Food Production, Population Consumption, and Sustainable Development. Paper presented at the Proceedings of the 20th International Conference of the System Dynamics Society, Palermo, Italy.

Rahman, M. S. 2001. Tropical Cyclone in Bangladesh. Quarterly Nirapad Newsletter. 4 th issue, Dhaka, September.

Rahman, M.M. and Ahsan, M.2001. Salinity constraints and agricultural productivity in coastal saline area of Bangladesh. In: Soil Resources in Bangladesh- Assessment and Utilization, pp. 1–14. Proceedings of the Annual Workshop on Soil Resources, 14 to 15 February, Dhaka, Soil Resources Development Institute.

Rashid, M. A., Ghani, M. A., Shah, A. L. and Rahman, M. S. 2004. Opportunities for groundwater utilization in the coastal areas for crop production.paper presented in the “proceedings of the workshop on integrated nutrient management and water resoureces utilization for crop production in the costal saline zone of Bangladesh” held in 26 june, 2004.BRRI Gazipur-1701

116 RDRS. 2005. A report of survey on food security and hunger in Bangladesh. ISBN 984- 32-2562-7.

Rees, W. E. 1996. Revisiting carrying capacity: area-based indicators of sustainability. Population and Environment 17, 195-215.

Riely, F., Mock, N., Cogill, B., Bailey, L. and Kenefick, E. 1999. Food security indicators and framework for use in the monitoring and evaluation of food aid programs. Food and Nutrition Technical Assistance. Washington D. C.

Robertson, A. I. and Phillips, M. J. 1995. Mangroves as filters of shrimp pond effluent: predictions and biogeochemical research needs. Hydrobiologia. 295, 311-321.

Rosegrant, M. W., meijer, S. and Cline, S. A. 2005 The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT-WATER). International Food Policy Research Institute. Washington, D.C.

Rosenzweig, C; Ruane, A.C; Major, D.C; Horton, R; Goldberg, R; Pervez, M.S; Yu, W; Alam, M; Hossain, S.G; Khan, A.S; Hassan, A. 2010. 5106220- 1234469721549/20.2_Modeling_the_impact_of_CC_on_ Agriculture. pdf

Saeed, K., Satter, M. A., & Singh, G. 1983. Rice Crop Production Policies and Food Supply in Bangladesh. Paper presented at the International System Dynamics Conference, Chestnut Hill, MA.

Saeed, K. 2000. Defining Developmental Problems for System Dynamics Modeling: An Experiential Learning Approach. Social Science and Policy Studies Department. Worcester Polytechnic Institute. USA.

Sarwar, M.G. M. and Wallman, P. 2005. Impact of sea level rise on the coastal zone of Bangladesh. Masters thesis. Lund University, Sweden, 21 November.

Saseendrain, S. A; Singh, K. K; Rathore, L. S; Singh, S. V; and Sinha, S. K. ; 2000. Effects of climate change on rice production in the tropical humid climate of Kerala, India. Climate Change, 44: 495-514.

Shahid, S. 2011. Impacts of climate change on irrigation water demand of dry season Boro rice in northwest Bangladesh. Climatic Change, 105: 433-453.

Shapouri, S. and Rosen, S. 2006. Food Security Assessment. United States Department of Agriculture USDA.

117 Singaraval, R., S.K. Balasundaram, K. Johnson. 1996. Physicochemical characteristics and nutrient status of coastal saline soils of Tamil Nadu. J. Ind. Soc. Coastal Agric. Res., 14 (1&2): 59-61.

Siry, H. Y. 2006. Decentralized coastal zone management in Malaysia and Indonesia: A comparative perspective. Coastal Management. 34:267-285.

Smith, P. and Olesen, J.E. 2010. Synergies between the mitigation of, and adaptation to, climate change in agriculture. Journal of Agricultural Science, 148(5): 543-552.

Sonak, S., Pangan, P. and Giriyan, A. 2008. Green reconstruction of the tsunami-affected areas in India using the integrated coastal zone management concept. Environmental Management. October 89(1):14-23.

SRDI. 1991. Soil and Water Salinity Monitoring Report. Soil Resources Development Institute, Ministry of Agriculture, People’s Republic of Bangladesh.

SRDI. 2000. Soil Salinity in Bangladesh. Soil Resources Development Institute, Ministry of Agriculture, People’s Republic of Bangladesh.

SRDI. 2010. Soil and Water Salinity Monitoring Report. Soil Resources Development Institute, Ministry of Agriculture, People’s Republic of Bangladesh

Srivani, O., Geetalakshmi, V., Jagannathan, R., Bhuvaneswari, K. and Guruswamy, L. 2007. Impacts of future climate change on growth and productivity of rice crop in Tamil Nadu. Asian Journal of Agricultural Research. 1(3): 119-124.

Tao, F., Yokozawa, M., Liu, J. and Zhang, Z. 2008. Climate change, land use change and China’s food security in the twenty-first century: an integrated perspective. Climate Change. 93. 433-445.

Tubiello, F. N., Donatelli, M., Roacnzweig, C. and Stockle, C. O. 2000. Effects of climate

change and elevated CO2 on cropping systems: model predictions at two Italian locations. European Journal of Agronomy. 13: 179-189.

USDA. 1999. Food Security Assessment. USDA Economic Research Service. Situation and Outlook series GFA-11 Washington DC

USDA. 2007. Food Security Assessment. USDA Economic Research Service. Appendix- Food Security Model: Definition and Methodology.GFA-19. Washington DC

118 Wackernagel. M. and Rees, W.E. 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society, Gabrioala, BC, Canada.ISBN 1-55092-251-3.

Wackernagel, M., Onisto, L;, Bello, P., Belllo, P., Linares, A.C., Falfn, I.S.L., Garca, J.M. Guerrere, A.I.S. and Guerrero, M.G.S. 1999. National Natural capital accounting with the ecological footprint concept. Ecol. Econ. 29, 375-390.

WCED, 1987. The World Commission on Environment and Development, 1987. Our Common Future.Oxfortd University Prees, Oxford. Willmott, C.J. 1982. Some comments on the evaluation of model performance, Bulletin of American Meteorological Society 3: 309–1313.

World Bank, 2000. Bangladesh: Climate Change & Sustainable Development. Report No.21104BD,Dhaka.

Yao, F; Xu, Y; Lin, E; Yokozawa, M; and Zhang, J. 2007. Assessing the impact of climate change on rice yields in the main rice areas of China. Climatic Change, 80: 395- 409.

Yusuf, H. K. M. and Islam, A. 2005.Setting standard cereal intake for balanced nutrition in Bangladesh. Paper presented in the national workshop on Food Security in Bangladesh. pp. 51-60. October, 19-20. IDB Bhaban, Agargaon, Dhaka. Bangladesh.

Zhao, S., Li, Z. and Li, W. 2005. A modified method of ecological footprint calculation and its application. Ecol. Model. 185, 65-75.

119 Appendix-A Questionnaire for secondary data collection from different sources Integrated Management of Coastal Zone for Food Security

Information to be collected from secondary sources

Name of Upazila Name of District

1. Population information Total Male Female No. of M/F Birth Death Family population Children ratio rate rate size

2. Household information No. of Non-farm Number of farm holding household household Total Small Medium Large

3. Area related information a. Total area of upazila (ac/ha) b. River area (ac/ha) c. Total household area (ac/ha) d. Total cultivated land area (ac/ha) e. Irrigated land area(ac/ha) f. Fallow land area (ac/ha) g. Fallow land in dry season area (ac/ha) h. Crop land area (ac/ha) i. Forest area (ac/ha) j. Aqua cultural land area (ac/ha) k. Roads and highways area (ac/ha) l. Market area (ac/ha) m. Cropping intensity (%)

120 4. Year wise area Year Crop area Forest Area Aquaculture Area Others Area Total Area (ac/ha) (ac/ha) (ac/ha) (ac/ha) (ac/ha)

5. Cropping pattern: Sl. Cropping pattern Area Percentage No. (%)

6. Crop information A. Crop Crop Area Yield Straw No. of Cost/ha Price of Price of (ac/ha) (t/ac/ha ) yield irrigation (Tk.) grain(Tk.) straw (t/ac/ha ) (Tk.)

121 B. Fertilizer and pesticides Crop Area Fertilizer Pesticides (ha) Urea TSP MP Amount Price Amount Price Amount Price Amount Price Amount Price (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.)

7. Livestock and poultry Cattle Buffalo Goats/sheep Poultry/ duck Male Female Male Female Male Female Male Female No. Meat Milk

8. Aquaculture Category No. Area Production Price (Tk./ Cost (Tk/ Gross income (ha) (ton) ton) ha) (Tk.) Marine Shrimp Others River Shrimp Others Canal Shrimp Others Fish+ Shrimp Rice Others Gher Shrimp Others Pond Shrimp Others Total

122 9. Forestry Sl. No. Area Density Growth Age Ave dia Unit Total Prod (ac/ha) (No./ha) rate ht (m) vol vol (m3/yr) (m) (m3) (m3)

Agro- forestry agro- forestry products

10. Information needed to compute Food Security and Ecological Footprint Category Existing Yield Production Inside Outside Consumpt Footprint Area supply supply ion component (ac/ha) (t/ac/ha) (ton) (ton) (ton) (ton) (ha/capita) A. Crop Aus Aman Rice Boro

B. Animal Product

y Meat r Egg Poult

y Meat

Dair Milk C. Fishery Marine Riverine Shrimp

uacul Others q A

123 D. Build-up Area: - Transportation: i) Length of road (km): ii) Average width (km): iii) Total Area (ac/ha): Mode No. Average Area (ac/ha) Total Area (ac/ha) Housing Industry Market Others Roads Total

E. Energy: (a) Cultivation No. Area Operating Average Field Fuel Total of PT cultivated by no. of capacity consumption fuel PT hrs/day passes (ac/ha/hr) (lit/hr) (ac/ha) (lit)

(b) Irrigation No. of Irrigated No. of Ave Fuel consumption Total STW land (ac/ha) irrig/ time/irrigation/ha (lit/hr) fuel season (hr) (lit)

(c) Threshing and Milling Item No. Total Average Fuel Total fuel operating operating consumption (lit) days hrs/day (lit/hr) Power thresher Mill

124 (d) Transportation Mode No. of Avg. Average Fuel Total vehicles distance hrs/day consumption fuel (lit) (km/day) (lit/hr) Launch Bus Track Tempo/motor vehicle Engine boat PT/Tractor Others

(f) Electricity Heads No. Average Total consumption Total consumption consumption (kwh/month) (kwh/year) (kwh/month) Domestic Commercial Industrial Irrigation Total

(g) Cooking fuel energy (Mds/month) Heads No. Fire Leave Straw Tree Jute Cowdung Others wood branches sticks cake Households Brick Kiln Bazar

Total

Name of the Interviewer

125 Appendix-B Questionnaire for primary data collection from farmers

Integrated Management of Coastal Zone for Food Security

Questionnaire Field level data for computing Food Security and Ecological Footprint

Sl. No. Date

1. Name of the household

Age Education Profession

2. Address of the household Father’s name Village Electrified P.O. Union Upazila District (Y/N)

3. Family details of the household No. of No. No. of No. of Earning Yearly Yearly family of. female children member(s) income expenditure members male (Tk.) (Tk.)

4. Land distribution Land area Homestead Cropped Aquaculture Forest Others (ha/ac/bigha) (ha/ac/bigha) area (ha/ac/bigha) (ha/ac/bigha) (ha/ac/bigha) (ha/ac/bigha)

126 5. Crop information A. Crop Crop Area grain Straw No. of Cost Price of Price of (Bigha) yield yield irrigation (Tk/bigha) grain straw (md/big) (md/big) (Tk/md) (Tk/md)

B. Fertilizer and pesticides Crop Area Fertilizer Pesticides (big) Urea TSP MP Amount Price Amount Price Amount Price Amount Price Amount Price (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.) (Kg) (Tk.)

6. Livestock and poultry Cattle Buffalo Goats/sheep Poultry/ duck Male Female Male Female Male Female Male Female No. Meat Milk

127 7. Aquaculture Name of fish/ Area Production (ton) Price Cost Gross Net integrated (ac/ha) Fish others (Tk/kg) (Tk./kg) income income crops (Tk.) (Tk.)

8. Forestry Sl. No. Area Density Growth Age Ave dia Unit Total Prod (ac/ha) (No./ha) rate ht (m) vol vol (m3/yr (m) (m3) (m3) )

Forestry Forestry products

9. Energy (a) Power tiller No. Cultivated Operating Average no. Field Fuel Total of PT area hrs of passes capacity consumption fuel (lit) (bigha) (hr/bigha) (lit/hr)

(b) Irrigation Irrigated No. of Ave Fuel consumption Total land (ha) irrigation/ time/irrigation/bigha (lit/hr) fuel (lit) season (hr)

(c) Threshing and Milling Item Total operating Average Fuel consumption Total day operating hrs/day (lit/hr) fuel (lit) Power thresher Mill

128 (d) Transportation Mode Avg. distance/day Average Fuel Total fuel (km) hrs/day consumption (lit) (lit/hr) Tempo/motor vehicle Engine boat PT/Tractor Others

(e) Electricity Heads Total consumption Total consumption Total cost (kwh/month) (kwh/year) (Tk/year) Domestic Commercial Industrial Irrigation Total

(f) Cooking fuel energy (Mds/month) Heads Fire Leave Straw Tree Jute Cowdung wood branches sticks cake Households Brick Kiln Bazar Total

10. Daily/Monthly food consumption (kg) Item No. of Rice Wheat Potato Fish Meat Milk Egg Pulse Veg Spi meal/day Amount Tk/kg Total

Name of the Interviewer

129 Appendix C Database in Excel for computation of food security and ecological footprint in the nine upazilas of Shyamnagar, Dacop, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa. Appendix C-1.1 Database in Excel for computation of food security of Shyamnagar upazila FOOD SECURITY CALCULATION Name of Upazila: Shayammnagar District : Satkhira A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton) Aman (U) 20,570 2.8 57596 18750 1.08E+09 40598.68 Aus 180 2.67 480.6 17000 8170200 307.1504 Boro 1500 3.6 5400 17500 94500000 3552.632 Potato 410 8 3280 13000 42640000 1603.008 Khesari 250 1 250 30000 7500000 281.9549 Veg (S) 187 2.304 430.848 10000 4308480 161.9729 Veg (W) 650 34.5 22425 8000 179400000 6744.361 Straw 43065 750 32298750 1214.239 Total 24547 134047.4 133750 1.47E+09 55253.47 B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton) Marine Shrimp Others River Shrimp 4591 15 450000 6750000 253.759398 Others 356 140000 49840000 1873.68421 Canal Shrimp 1338 0 450000 0 0 Others 248 120000 29760000 1118.79699 Fish+ Rice Shrimp 357 140 450000 63000000 2368.42105 Others 60 80000 4800000 180.451128 Gher Shrimp 15622 4050 450000 1822500000 68515.0376 Others 322 80000 25760000 968.421053 Pond Shrimp 150 4 450000 1800000 67.6691729 Others 173 70000 12110000 455.263158 Total 5368 2740000 2016320000 75801.5038

130 C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.)

Poultry Meat 9.28 1898 80000 1.5E+08 5708.27068 Egg 5380000 3.4 1.8E+07 687.669173 Dairy Meat 54.56 2500 150000 3.8E+08 14097.7444 Milk 1900 22000 4.2E+07 1571.42857 Total 63.84 5.9E+08 22065.1128 D. Forestry i) Fruit Tree Gross Equi rice Area Production income (ton) Name No. (ha) (ton) Price (Tk./ton) (Tk.)

Mango 105 840 30000 25200000 947.368 Guava 43 1240 25000 31000000 1165.41 Coconut 36 124 12000 1488000 55.9398 Jackfruit 10 140 6000 840000 31.5789 Bar 17 140 35000 4900000 184.211 Palm 30 250 6000 1500000 56.391 Berry 10 45 25000 1125000 42.2932 Banana 14 280 18000 5040000 189.474 Total 71093000 2672.67 ii) Non-fruit Tree: Category Quantity (ton) Price Gross income Equi rice (Tk./ton) (Tk.) (ton)

Wood 42578 1625 69189250 2601.0996 Treebranch 66124 1250 82655000 3107.3308 Total 151844250 5708.4305

Food from all sources Food Requirement Food Security Food Security Equivalent rice (ton) Equivalent rice ratio status (%) (ton) 161501.2 171957 0.9392 -6.08

131 Appendix C-1.2 Database in Excel for computation of ecological footprint of Shyamnagar upazila ECOLOGICAL FOOTPRINT CALCULATION Name of Upazila: Shayammnagar District : Satkhira Category Produ Inside Outsi Consu Global Equiva Populat Footprint ction supply de mption yield factor ion component (ton) (ton) suppl (ton) (t/ha) (gha/ha) (gha/cap) y (ton) Crop Rice 64597 10393 0 74990 3.75 2.8 347178 0.161279 Wheat 0 3750 0 3750 2.62 2.8 347178 0.0115435 Potato 3280 14426 0 17706 16.47 2.8 347178 0.0086703 Pulses 250 1503 0 1753 0.837 2.8 347178 0.0168913 Vegetable 22856 17764 0 40620 18 2.8 347178 0.0182001 Oils 0 1000 0 1000 2.24 2.8 347178 0.0036005 Spices 0 2118 0 2118 14.17 2.8 347178 0.0012055 Tea 0 139 0 139 0.56 2.8 347178 0.0020019 Sugar 0 2711.4 0 2711 6.82 2.8 347178 0.0032064 Sub-total 0.2265983 Animal Meat 4398 2200 2925 3673 0.457 1.1 347178 0.0254651 Egg 316 909 0 1225 0.304 1.1 347178 0.0127674 Milk 1900 700 0 2600 0.52 1.1 347178 0.015842 Sub-total 0.0540745 Fishery Shrimp 4213 0 4043 170 3.25 0.2 347178 3.013E-05 Others 1159 0 116 1043 0.05 0.2 347178 0.0120169 Waste 0.1380761 Sub-total 0.1501232 Forest Fruit 3059 1000 569 3490 18 1.1 347178 0.0006143 Sub-total 0.0006143 Total 0.4314103

132 Build-up Yield factor Equivalence factor Population Footprint Area: (crop) (gha/ha) component (gha/capita)

4771 0.85 2.8 347178 0.032706508

Energy Name Amount Convers Amount Global Equivale Population Footprint consum ion consume averag nce component ed factor d e factor (gha/capita) (GJ/year) (GJ/ha (gha/ha) /yr)

Fire wood 42578 15.4 655701 59 1.1 0.0352123 (ton) 347178 Twigs (ton) 66124 15.4 1E+06 59 1.1 347178 0.054685 Straw (ton) 13193 12.23 161350 32 1.1 0.0159757 347178 Cowdung 13232 8.75 115780 49 1.1 0.0074865 (ton) 347178 Diesel 539944 0.038 205179 71 1.1 0.0091562 (litre) 5 347178 Petrol (litre) 359999 0.034 122400 71 1.1 0.0054621 5 347178 Kerosine 192355 0.037 71171 71 1.1 0.0031761 (litre) 0 347178 Electricity 325000 0.0036 11700 1000 1.1 3.707E-05 (kwh) 0 347178 Coal (ton) 3500 27 94500 55 1.1 347178 0.0054439

Total 0.13663487

Category Existing Yield Equivalence Population Bio- Ecological Ecological Area factor factor capacity Footprint Status (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Animal 63.84 150.84 1.1 347178 0.0305105 0.0540745 -0.394093 Build-up 4771 0.85 2.8 0.0327065 0.0327065 347178 Fishery 22058 0.147 0.2 347178 0.0018679 0.1501232 Forest 583 0.8 1.1 347178 0.0014777 0.0006143 Energy 0.1366349 Total 0.2348391 0.6007517 Available BC (-12% for Biodiversity) 0.2066584

133 Appendix C-2.1 Database in Excel for computation of food security of Dacop upazila

FOOD SECURITY CALCULATION

Name of Upazila: Dacop District : Khulna

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Aman 10500 2.8 29400 18750 551250000 20723.68 (L) Aman 9,000 3.5 31500 18750 590625000 22203.95 (U) Boro 15 3.9 58.5 17500 1023750 38.48684 Veg (S) 270 22.38 6042.6 10000 60426000 2271.654 Veg (W) 284 28.54 8105.36 8000 64842880 2437.702

Straw 40842 750 30631500 1151.56 Total 20069 115948.5 73750 1.299E+09 48827.03

B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton)

Marine Shrimp Others River Shrimp 1947 40.89 450000 18400500 691.748 Others 27.26 130000 3543800 133.226 Canal Shrimp 352 62.69 450000 28210500 1060.55 Others 94.04 110000 10344400 388.887 Fish+ Rice Shrimp 0 0 0 0 0 Others 0 0 0 0 Gher Shrimp 13395 3363 450000 1513350000 56892.9 Others 1880 80000 150400000 5654.14 Pond Shrimp 254 0 0 0 0 Others 508.28 75000 38121000 1433.12 Total 15948 5976.16 1745000 1762370200 66254.5

134

C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.)

Poultry Meat 10.11 183 80000 1.5E+07 550.376 Egg 5000000 3.5 1.8E+07 657.895 Dairy Meat 73.16 3416 160000 5.5E+08 20547.4 Milk 14400 23000 3.3E+08 12451.1 Total 83.27 9.1E+08 34206.8

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Mango 35 280 30000 8400000 315.789 Guava 12 345 25000 8625000 324.248 Coconut 12 41 12000 492000 18.4962 Jackfruit 3 42 6000 252000 9.47368 Bar 5 41 35000 1435000 53.9474 Palm 8 67 6000 402000 15.1128 Berry 9 40 25000 1000000 37.594 Banana 7 140 18000 2520000 94.7368 Total 996 23126000 869.398 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 14891 1625 24197875 909.69455 Treebranch 9943 1250 12428750 467.24624 Total 36626625 1376.9408

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton)

151534.7 85495 1.772438856 77.24388556

135 Appendix C-2.2 Database in Excel for computation of ecological footprint of Dacop upazila. ECOLOGICAL FOOTPRINT CALCULATION Name of Upazila: Dacop District : Khulna

Categor Production Inside Outsid Consu Globa Equiva Populati Footprint y (ton) supply e mptio l yield factor on component (ton) supply n (t/ha) (gha/ha (gha/cap) (ton) (ton) )

Crop Rice 60958 0 25538 35420 3.75 2.8 172613 0.15321519 Wheat 0 1771 0 1771 2.62 2.8 172613 0.01096483 Potato 0 8362 0 8362 16.47 2.8 0.00823571 172613 Pulses 0 828 0 828 0.837 2.8 172613 0.01604684 Vegeta 14148 5038 0 19186 18 2.8 172613 0.01729006 Oils 0 472 0 472 2.24 2.8 172613 0.00341805 Spices 0 990 0 990 14.17 2.8 172613 0.00113331 Tea 0 62 0 62 0.56 2.8 172613 0.00179592 Sugar 0 1267 0 1267 6.82 2.8 172613 0.00301354 Sub- 0.21511345 total Animal Meat 3599 0 908 2691 0.457 1.1 172613 0.03752465 Egg 294 112 0 406 0.304 1.1 172613 0.00851082 Milk 14400 0 13109 1291 0.52 1.1 172613 0.0158213 Sub- 0.06185677 total Fishery Shrimp 3467 0 3432 35 3.25 0.2 172613 1.2478E-05 Others 2509 600 1604 1505 0.05 0.2 172613 0.0348757 Waste 0.23280402 Sub- 0.2676922 total Forest Fruit 996 765 200 1561 18 1.1 172613 0.00055265 Sub- 0.00055265 total Total 0.54521507

Build-up Area: Area (ha) Yield factor Equivalence factor Population Footprint component (crop) (gha/ha) (gha/capita) 4199 0.99 2.8 172613 0.067431932

136 Energy Name Amount Convers Amount Global Equivale Populati Footprint consumed ion consumed average nce on component factor (GJ/year) (GJ/ha/yr) factor (gha/cap) (gha/ha)

Fire wood 14891 15.4 229321.4 59 1.1 0.0247692 (ton) 172613 Twigs 9943 15.4 153122.2 59 1.1 0.0165389 (ton) 172613 Straw 16398 12.23 200547.5 32 1.1 0.039938 (ton) 172613 Cowdung 26445 8.75 231393.8 49 1.1 0.0300936 (ton) 172613 Diesel 2461095 0.038 93521.61 71 1.1 0.0083941 (litre) 172613 Petrol 366500 0.034 12461 71 1.1 0.0011184 (litre) 172613 Kerosine 2199490 0.037 81381.13 71 1.1 0.0073044 (litre) 172613 Electricity 291995 0.0036 1051.182 1000 1.1 6.699E-06 (kwh) 172613 Coal (ton) 0 27 0 55 1.1 172613 0

Total 0.1281633

Category Existing Yield Equivalence PopulationBio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Crop 20069 0.99 2.8 172613 0.322289 0.2151134 Animal 83.27 150.84 1.1 172613 0.0800432 0.0618568 -0.322318 Build-up 4199 0.99 2.8 0.0674319 0.0674319 172613 Fishery 15948 0.227 0.2 172613 0.0041946 0.2676922 Forest 314 0.8 1.1 172613 0.0016008 0.0005526 Energy 0.1281633 Total 0.4755595 0.7408103 Available BC (-12% for Biodiversity) 0.4184923

137 Appendix C-3.1 Database in Excel for computation of food security of Koyra upazila.

FOOD SECURITY CALCULATION

Name of Upazila: Koyra District : Khulna

A. Crop Crop Area Yield Production Price Gross Equi rice (ha) (t/ha ) (ton) (Tk./ton) income (Tk.) (ton)

Aman (L) 900 2.4 2160 18750 40500000 1522.556 Aman (U) 14,320 3.7 52984 18750 993450000 37347.74 Boro 1400 5 7000 17000 119000000 4473.684 Watermel 190 50 9500 15000 142500000 5357.143 Potato 80 16 1280 12500 16000000 601.5038 Veg (S) 310 19.03 5899.3 8000 47194400 1774.226 Veg (W) 296 15.55 4602.8 9500 43726600 1643.857 0 0 0 0 0 0 Straw 41636 750 31227000 1173.947 Total 17496 125062.1 100250 1.434E+09 53894.66

B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton)

Marine Shrimp Others River Shrimp 732 28 450000 12600000 473.684 Others 112 150000 16800000 631.579 Canal Shrimp 1485 594 450000 267300000 10048.9 Others 2376 140000 332640000 12505.3 Fish+ Rice Shrimp 470 209 450000 94050000 3535.71 Others 870 80000 69600000 2616.54 Gher Shrimp 5203 1290 450000 580500000 21823.3 Others 94 80000 7520000 282.707 Pond Shrimp 214.4 42 450000 18900000 710.526 Others 798 75000 59850000 2250 Total 8104.4 6413 2775000 1459760000 54878.2

138 C. Animal Category Area Production Price Gross income Equi rice (ton) (ha) (ton/No.) (Tk./ton/No.) (Tk.)

Poultry Meat 2.1 113 75000 8475000 318.609 Egg 9300000 3.5 3.3E+07 1223.68 Dairy Meat 74.64 4760 150000 7.1E+08 26842.1 Milk 88 21000 1848000 69.4737 Total 7.6E+08 28453.9

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Mango 8 64 30000 1920000 72.1805 Guava 12 346 25000 8650000 325.188 Coconut 10 34 12000 408000 15.3383 Jackfruit 12 168 6000 1008000 37.8947 Bar 6 49 35000 1715000 64.4737 Palm 3 26 6000 156000 5.86466 Berry 7 32 25000 800000 30.0752 Banana 12 240 18000 4320000 162.406 Total 70 959 18977000 713.421 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Treebranch 20094 1250 25117500 944.26692 Total 65581625 2465.4746

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton)

140405.6 104450.35 1.344233169 34.423

139 Appendix C-3.2 Database in Excel for computation of ecological footprint of Koyra upazila. ECOLOGICAL FOOTPRINT CALCULATION Name of Upazila: Koyra District : Khulna

Category Produc Inside Outside Consum Global Equiva Populat Footprint tion supply supply ption yield factor ion component (ton) (ton) (ton) (ton) (t/ha) (gha/ha) (gha/cap)

Crop Rice 62144 0 12039 50105 3.75 2.8 210883 0.1774 Wheat 0 2004 0 2004 2.62 2.8 210883 0.0102 Potato 1280 8937 0 10217 16.47 2.8 0.0082 210883 Pulses 0 1118 0 1118 0.837 2.8 210883 0.0177 Vegetables 10502 16639 0 27141 18 2.8 210883 0.0200 Oils 0 577 0 577 2.24 2.8 210883 0.0034 Spices 0 1351 0 1351 14.17 2.8 210883 0.0013 Tea 0 85 0 85 0.56 2.8 210883 0.0020 Sugar 0 1581 0 1581 6.82 2.8 210883 0.0031 Sub-total 0.2433 Animal Meat 4873 0 3819 1054 0.457 1.1 210883 0.0120 Egg 547 0 180 367 0.304 1.1 210883 0.0063 Milk 88 790 0 878 0.52 1.1 210883 0.0088 Sub-total 0.0271 Fishery Shrimp 2163 0 2120 43 3.25 0.2 210883 0.0000 Others 4250 400 3800 850 0.05 0.2 210883 0.0161 Waste 0.0807 Sub-total 0.0968 Forest Fruit 959 728 0 1687 18 1.1 210883 0.0005 Sub-total 0.0005

Total 0.3678

Build-up Area: Area (ha) Yield factor Equivalence factor Population Footprint (crop) (gha/ha) component (gha/capita)

1043 1.16 2.8 210883 0.016064187

140

Energy Name Amount Convers Amount Global Equivale Population Footprint consume ion consumed average nce (gha/cap) d factor (GJ/year) (GJ/ha/yr) factor (gha/ha)

Fire wood 24901 15.4 383475.4 59 1.1 0.0339 (ton) 210883 Twigs 20094 15.4 309447.6 59 1.1 0.0274 (ton) 210883 Straw (ton) 12020 12.23 147004.6 32 1.1 0.0240 210883 Cowdung 24040 8.75 210350 49 1.1 0.0224 (ton) 210883 Diesel 9826250 0.038 373397.5 71 1.1 0.0274 (litre) 210883 Petrol 766500 0.034 26061 71 1.1 0.0019 (litre) 210883 Kerosine 2831473 0.037 104764.5 71 1.1 0.0077 (litre) 210883 Electricity 779635 0.0036 2806.686 1000 1.1 0.0000 (kwh) 210883 Coal (ton) 500 27 13500 55 1.1 210883 0.0013

Total 0.1460

Category Existing Yield Equivalence PopulationBio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Crop 17496 1.16 2.8 210883 0.2694717 0.2433322 Animal 76.74 150.84 1.1 210883 0.0603795 0.0271347 -0.220079 Build-up 1043 1.16 2.8 0.0160642 0.0160642 210883 Fishery 8104 0.48 0.2 210883 0.0036892 0.0968388 Forest 567 0.8 1.1 210883 0.0023661 0.0004889 Energy 0.1459545 Total 0.3519706 0.5298132 Available BC (-12% for Biodiversity) 0.3097342

141 Appendix C-4.1 Database in Excel for computation of food security of Shoronkhola upazila FOOD SECURITY CALCULATION

Name of Upazila: Shoronkhola District : Bagerhat

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Aus 2000 1.5 3000 17000 51000000 1917.293 Aman 9,200 2 18400 18000 331200000 12451.13 (U) Boro 10 3 30 17000 510000 19.17293 Potato 200 1 200 14000 2800000 105.2632 Veg (S) 112 12.13 1358.56 11000 14944160 561.8105 Veg (W) 521 15.25 7945.25 9000 71507250 2688.242 Straw 14358 750 10768500 404.8308 Total 12043 45291.81 106750 482729910 18147.74

B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton)

Marine Shrimp 0 Others River Shrimp 1515 16 450000 7200000 270.677 Others 780 200000 156000000 5864.66 Canal Shrimp 39.68 0 0 0 0 Others 0.9 150000 135000 5.07519 Fish+ Rice Shrimp 48 64.5 525000 33862500 1273.03 Others 7 120000 840000 31.5789 Gher Shrimp 0 0 0 0 0 Others 0 0 0 0 Pond Shrimp 192.1 0 0 0 0 Others 474 100000 47400000 1781.95 Total 1342.4 1545000 245437500 9226.97

142

C. Animal Category Area Production Price Gross income Equi rice (ha) (ton/No.) (Tk/ton/No.) (Tk.) (ton)

Poultry Meat 2.67 143 80000 1.1E+07 430.075 Egg 93465 3.5 327128 12.298 Dairy Meat 29.14 1933 150000 2.9E+08 10900.4 Milk 19.75 23000 454250 17.0771 Total 3E+08 11359.8

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Bar 32 190 38000 7220000 271.429 Mango 110 760 32000 24320000 914.286 Banana 270 5720 18000 102960000 3870.68 Coconut 600 2100 12000 25200000 947.368 Sofeda 28 700 11000 7700000 289.474 Guava 45 700 25000 17500000 657.895 Palm 50 1050 6000 6300000 236.842 Papaya 70 1260 8000 10080000 378.947 Total 12480 201280000 7566.92 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 15754 1625 25600250 962.41541 Treebranch 24466 1250 30582500 1149.718 Total 56182750 2112.1335

Food from all Food Requirement Food Security ratio Food Security Status sources Equivalent Equivalent rice (%) rice (ton) (ton)

48413.59 63408.8 0.763515342 -23.65

143 Appendix C-4.2 Database in Excel for computation of ecological footprint of Shoronkhola upazila ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Shoronkhola District : Bagerhat

Category Product Inside Outsid Consu Global Equiva Populati Footprint ion supply e mptio yield factor on (gha/cap) (ton) (ton) supply n (t/ha) (gha/ha) (ton) (ton)

Crop Rice 21630 7174 0 28804 3.75 2.8 128021 0.1680 Wheat 0 1410 0 1410 2.62 2.8 128021 0.0118 Potato 2000 3223 0 5223 16.47 2.8 0.0069 128021 Pulses 0 484 0 484 0.837 2.8 128021 0.0126 Vegetables 9303 2679 0 11982 18 2.8 128021 0.0146 Oils 0 331 0 331 2.24 2.8 128021 0.0032 Spices 0 711 0 711 14.17 2.8 128021 0.0011 Tea 0 49 0 49 0.56 2.8 128021 0.0019 Sugar 0 799 0 799 6.82 2.8 128021 0.0026

Sub-total 0.22271 Animal Meat 2076 0 1820 256 0.457 1.1 128021 0.00481 Egg 5.5 220.5 0 226 0.304 1.1 128021 0.00639 Milk 19.75 172.25 0 192 0.52 1.1 128021 0.00317 Sub-total 0.01437 Fishery Shrimp 80.5 0 78.89 1.61 3.25 0.2 128021 7.7E-07 Others 1261.9 0 756.9 505 0.05 0.2 128021 0.01578 Waste 0.00112 Sub-total 0.0169 Forest Fruit 12480 1300 11020 3120 18 1.1 128021 0.00149 Sub-total 0.00149 Total 0.25548

Build-up area:

560 0.67 2.8 128021 0.008206154

144 Energy Name Amount Convers Amount Global Equiva Population Footprint consumed ion consumed average factor (gha/cap) factor (GJ/year) (GJ/ha/yr) (gha/ha)

Fire wood 15754 15.4 242611.6 59 1.1 0.0353 (ton) 128021 Twigs 24466 15.4 376776.4 59 1.1 0.0549 (ton) 128021 Straw 4881 12.23 59694.63 32 1.1 0.0160 (ton) 128021 Cowdung 1295 8.75 11331.25 49 1.1 0.0020 (ton) 128021 Diesel 1997794 0.038 75916.17 71 1.1 0.0092 (litre) 128021 Petrol 1331998 0.034 45287.93 71 1.1 0.0055 (litre) 128021 Kerosine 577108 0.037 21353 71 1.1 0.0026 (litre) 128021 Electricity 595892 0.0036 2145.211 1000 1.1 0.0000 (kwh) 128021 Coal (ton) 0 27 0 55 1.1 128021 0.0000 Total 0.1255

Category Existing Yield Equivalence PopulationBio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Crop 12043 0.65 2.8 128021 0.1712083 0.2227139 Animal 31.81 150.84 1.1 128021 0.0412279 0.0143735 -0.168749 Build-up 560 0.67 2.8 0.0082062 0.0082062 128021 Fishery 1795 0.45 0.2 128021 0.0012619 0.0169043 Forest 4158 0.8 1.1 128021 0.0285816 0.0014893 Energy 0.1254894 Total 0.2504859 0.3891766 Available BC (-12% for Biodiversity) 0.2204276

145 Appendix C-5.1 Database in Excel for computation of food security of Morrelgonj upazila FOOD SECURITY CALCULATION

Name of Upazila: Morrelgonj District : Bagerhat

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Aman 24150 1.6 38640 18500 714840000 26873.68 (L) Aman 4,130 2.6 10738 18500 198653000 7468.158 (U) Aus 1550 2.4 3720 17000 63240000 2377.444 Boro 91 1.6 145.6 17000 2475200 93.05263 Boro 779 2.72 2118.88 17000 36020960 1354.171 Potato 275 12 3300 14000 46200000 1736.842 Veg (S) 760 11.1 8436 11000 92796000 3488.571 Veg (W) 750 12.88 9660 9000 86940000 3268.421 Straw 37092 750 27819000 1045.827 Total 32485 113850.5 122750 1.269E+09 47706.17

B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton)

Marine Shrimp Others River Shrimp 2332 4 450000 1800000 67.6692 Others 188 200000 37600000 1413.53 Canal Shrimp 1537 1.28 450000 576000 21.6541 Others 127 120000 15240000 572.932 Fish+ Rice Shrimp 11437 2620 450000 1179000000 44323.3 Others 291 70000 20370000 765.789 Gher Shrimp 0 0 0 0 0 Others 0 0 0 0 Pond Shrimp 1217 0 0 0 0 Others 2608 60000 156480000 5882.71 Total 5839.28 1800000 1411066000 53047.6

146

C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.)

Poultry Meat 6.19 332 80000 2.7E+07 998.496 Egg 549578 3.5 1923523 72.3129 Dairy Meat 54.99 3589 160000 5.7E+08 21588 Milk 228.5 22500 5141250 193.28 Total 6.1E+08 22852.1

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Mango 100 850 30000 25500000 958.647 Guava 0 0 0 0 0 Coconut 0 0 0 0 0 Jackfruit 0 0 0 0 0 Bar 0 0 0 0 0 Palm 0 0 0 0 0 Papaya 50 500 8000 4000000 150.376 Banana 1100 8800 15000 132000000 4962.41 Total 161500000 6071.43 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 33091 1625 53772875 2021.5367 Treebranch 22095 1250 27618750 1038.2989 Total 81391625 3059.8355

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton) 132737.1 190432.45 0.69702978 -30.29

147 Appendix C-5.2 Database in Excel for computation of ecological footprint of Morrelgonj upazila ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Morrelgonj District : Bagerhat

Category Produ Inside Outside Consump Global Equiva Population Footprint ction supply supply tion yield factor (ton) (ton) (ton) (ton) (t/ha) (gha/ha) (gha/cap)

Crop Rice 55362 23533 0 78895 3.75 2.8 384479 0.1532 Wheat 0 3654 0 3654 2.62 2.8 384479 0.0102 Potato 3300 17288 0 20588 16.47 2.8 0.0091 384479 Pulses 0 1903 0 1903 0.837 2.8 384479 0.0166 Vegetab 18096 24638 0 42734 18 2.8 384479 0.0173 Oils 0 1162 0 1162 2.24 2.8 384479 0.0038 Spices 0 2462 0 2462 14.17 2.8 384479 0.0013 Tea 0 169 0 169 0.56 2.8 384479 0.0022 Sugar 0 2699 0 2699 6.82 2.8 384479 0.0029 Sub-total 0.2164 Animal Meat 3921 0 653 3268 0.457 1.1 384479 0.0205 Egg 32.32 419.68 0 452 0.304 1.1 384479 0.0043 Milk 228.5 130 0 358.5 0.52 1.1 384479 0.0020 Sub-total 0.0267 Fishery Shrimp 2625. 0 2573.28 52 3.25 0.2 0.0000 28 384479 Others 3214 0 553 2571 0.05 0.2 384479 0.0267 Waste 0.0892 Sub-total 0.1160 Forest Fruit 8800 900 6232 3468 18 1.1 384479 0.0006 Sub-total 0.0006 Total 0.3597

Build-up Area: Area (ha) Yield factor Equivalence factor Population Footprint component (crop) (gha/ha) (gha/capita)

1986 0.69 2.8 384479 0.009979614

148

Energy Name Amount Conversion Amount Global Equiva Populat Footprint consumed factor consumed average factor ion component (GJ/year) (GJ/ha/yr) (gha/ha) (gha/cap)

Fire wood 33091 15.4 509601.4 59 1.1 0.0247 (ton) 384479 Twigs 22095 15.4 340263 59 1.1 0.0165 (ton) 384479 Straw 36440 12.23 445661.2 32 1.1 0.0398 (ton) 384479 Cowdung 33056 8.75 289240 49 1.1 0.0169 (ton) 384479 Diesel 6152737 0.038 233804 71 1.1 0.0094 (litre) 384479 Petrol 916250 0.034 31152.5 71 1.1 0.0013 (litre) 384479 Kerosine 2495128 0.037 92319.74 71 1.1 0.0037 (litre) 384479 Electricity 2730613 0.0036 9830.207 1000 1.1 0.0000 (kwh) 384479 Coal (ton) 0 27 0 55 1.1 384479 0.0000

Total 0.1124

Category Existing Yield Equivalence PopulationBio- Ecological Ecologic Area factor factor capacity Footprint al Status (crop) (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Crop 32485 0.69 2.8 384479 0.1632365 0.2164463 Animal 61.18 150.84 1.1 0.0264026 0.0266854 - 384479 0.289639 Build-up 1986 0.69 2.8 0.0099796 0.0099796 384479 Fishery 16523 0.214 0.2 384479 0.0018393 0.1159965 Forest 7500 0.8 1.1 384479 0.0171661 0.0005512 Energy 0.1123696 Total 0.2186241 0.4820286 Available BC (-12% for Biodiversity) 0.1923892

149 Appendix C-6.1 Database in Excel for computation of food security of Mongla upazila

FOOD SECURITY CALCULATION

Name of Upazila: Mongla District : Bagerhat

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Aman 11220 2.45 27489 18750 515418750 19376.64 (L) Potato 45 1.1 49.5 18750 928125 34.89192 Veg 332 1.2 398.4 17000 6772800 254.6165 Chilli 30 1.1 33 17500 577500 21.71053 Garlic 6 2.1 12.6 13000 163800 6.157895 Khesari 2 0.9 1.8 30000 54000 2.030075

Straw 18118 750 13588500 510.8459 Total 537503475 20206.9

B. Fish Category Area Production Price (Tk./ Gross income Equi rice (ha) (ton) ton) (Tk.) (ton)

Marine Shrimp 0 Others River Shrimp 2746 30 450000 13500000 507.5188 Others 1500 140000 210000000 7894.7368 Canal Shrimp 101 0 450000 0 0 Others 3.5 120000 420000 15.789474 Fish+ Rice Shrimp 9806 3187 450000 1434150000 53915.414 Others 1372 80000 109760000 4126.3158 Gher Shrimp 0 0 450000 0 0 Others 0 80000 0 0 Pond Shrimp 253 144.45 450000 65002500 2443.703 Others 672.78 70000 47094600 1770.4737 Total 6909.73 2740000 1879927100 70673.951

150

C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.) Poultry Meat 4.98 268 80000 2.1E+07 806.015038 Egg 5225000 3.4 1.8E+07 667.857143 Dairy Meat 13.79 940 150000 1.4E+08 5300.75188 Milk 472 22000 1E+07 390.37594 Total 1.9E+08 7165

D. Forestry i) Fruit Tree Name No. Area Production Price Gross Equi rice (ton) (ha) (ton) (Tk./ton) income (Tk.)

Coconut 8 25 11000 275000 10.3383459 Mango 7 56 28000 1568000 58.9473684 Guava 10 280 24000 6720000 252.631579 Papaya 5 50 8000 400000 15.037594 Bar 4 32 32000 1024000 38.4962406 Sofeda 3 55 11000 605000 22.7443609 Banana 3 50 16000 800000 30.075188 Total 548 11392000 428.270677 ii) Non-fruit Tree: Category Quantity Price (Tk./ton) Gross income (Tk.) Equi rice (ton) (ton)

Wood 11580 1625 18817500 707.42481 Tree branch 8530 1250 10662500 400.84586 Total 29480000 1108.2707

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton)

99582.39 72755.6 1.368724745 36.87247447

151 Appendix C-6.2 Database in Excel for computation of ecological footprint of Mongla upazila ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Mongla District : Bagerhat

Category Production Inside Outside Consu Global Equiva Populati Footprint (ton) supply supply mption yield factor on (gha/cap) (ton) (ton) (ton) (t/ha) (gha/ha)

Crop Rice 27970 5080 0 33050 3.75 2.8 146892 0.1680 Wheat 0 1652 0 1652 2.62 2.8 146892 0.0120 Potato 13 8066 0 8079 16.47 2.8 0.0094 146892 Pulses 2 746 0 748 0.837 2.8 146892 0.0170 Vegeta 398 17229 0 17627 18 2.8 146892 0.0187 Oils 0 381 0 381 2.24 2.8 146892 0.0032 Spices 0 998 0 998 14.17 2.8 146892 0.0013 Tea 0 62 0 62 0.56 2.8 146892 0.0021 Sugar 0 1013 0 1013 6.82 2.8 146892 0.0028 Sub-total 0.2346 Animal Meat 1208 0 398 810 0.457 1.1 146892 0.0133 Egg 307 38 0 345 0.304 1.1 146892 0.0085 Milk 472 155 0 627 0.52 1.1 146892 0.0090 Sub-total 0.0308 Fishery Shrimp 3361 0 3328 33 3.25 0.2 146892 0.0000 Others 3548 0 1065 2483 0.05 0.2 146892 0.0676 Waste 0.2003 Sub-total 0.2679 Forest Fruit 548 480 0 1028 18 1.1 146892 0.0004 Sub-total 0.0004 Total 0.5337

Build-up Area: Area (ha) Yield factor Equivalence factor Population Footprint component (crop) (gha/ha) (gha/capita) 850 0.65 2.8 146892 0.010531547

152 Energy Name Amount Conver Amount Global Equiva Population Footprint consumed sion consumed average factor (gha/cap) factor (GJ/year) (GJ/ha/yr) (gha/ha)

Fire wood 11580 15.4 178332 59 1.1 0.0226 (ton) 146892 Twigs (ton) 8530 15.4 131362 59 1.1 146892 0.0167 Straw (ton) 13417 12.23 164089.9 32 1.1 0.0384 146892 Cowdung 25440 8.75 222600 49 1.1 0.0340 (ton) 146892 Diesel 1476657 0.038 56112.97 71 1.1 0.0059 (litre) 146892 Petrol (litre) 91625 0.034 3115.25 71 1.1 146892 0.0003 Kerosine 649357 0.037 24026.21 71 1.1 0.0025 (litre) 146892 Electricity 1340984 0.0036 4827.542 1000 1.1 0.0000 (kwh) 146892 Coal (ton) 0 27 0 55 1.1 146892 0.0000

Total 0.1205

Category Existing Yield Equivalence PopulationBio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/capita) (gha/cap) (gha/capita)

Crop 11259 0.65 2.8 146892 0.1394996 0.2345935 Animal 18.77 150.84 1.1 146892 0.0212019 0.0308007 -0.50766 Build-up 850 0.65 2.8 0.0105315 0.0105315 146892 Fishery 12906 0.324 0.2 146892 0.0056934 0.2678977 Forest 273 0.8 1.1 146892 0.0016355 0.0004277 Energy 0.1205434 Total 0.178562 0.6647945 Available BC (-12% for Biodiversity) 0.1571345

153 Appendix C-7.1 Database in Excel for computation of food security of Patharghata upazila FOOD SECURITY CALCULATION

Name of Upazila: Patharghata District : Barguna

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Rice 20615 2.3 47414.5 18750 889021875 33421.88 Potato 700 20 14000 8000 112000000 4210.526 S. potato 625 14 8750 5000 43750000 1644.737 G. Nut 275 1 275 30000 8250000 310.1504 Chilli 485 1.3 630.5 40000 25220000 948.1203 Pulses 7275 0.67 4874.25 35000 170598750 6413.487 Veg (S) 425 13.13 5580.25 12000 66963000 2517.406 Veg (W) 180 14.25 2565 11000 28215000 1060.714 Straw 31767 750 23825250 895.6861 Total 30580 115856.5 160500 1.368E+09 51422.7

B. Fish Category Area Production Price Gross income Equi rice (ha) (ton) (Tk./ ton) (Tk.) (ton)

Marine Shrimp Others River Shrimp 296 0 0 0 0 Others 370 150000 55500000 2086.47 Canal Shrimp 228 0 0 0 0 Others 3430 100000 343000000 12894.7 Fish+ Rice Shrimp 55 16 500000 8000000 300.752 Others 60.3 100000 6030000 226.692 Gher Shrimp 0 0 0 0 0 Others 0 0 0 0 Pond Shrimp 461.86 0 0 0 0 Others 616.325 100000 61632500 2317.01 Total 1040.86 4492.625 950000 474162500 17825.7

154

C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.)

Poultry Meat 6.92 372 80000 3E+07 1118.8 Egg 200000 3.4 680000 25.5639 Dairy Meat 48.7 3227 160000 5.2E+08 19410.5 Milk 120 21000 2520000 94.7368 Total 55.62 5.5E+08 20649.6

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Mango 22 704 30000 21120000 793.985 Jackfruit 25 1300 5000 6500000 244.361 Banana 60 1800 15000 27000000 1015.04 Papaya 10 260 8000 2080000 78.1955 Guava 20 320 20000 6400000 240.602 Coconut 160 4950 10000 49500000 1860.9 Others 25 400 5000 2000000 75.188 Total 9734 114600000 4308.27 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 21234 1375 29196750 1097.6222 Tree branch 33062 1250 41327500 1553.6654 Total 70524250 2651.2876

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton) 96857.54 89241.17 1.0853 8.53

155 Appendix C-7.2 Database in Excel for computation of ecological footprint of Patharghata upazila ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Patharghata District : Barguna

Category Produ Inside Outside Consum Global Equiva Population Footprint ction supply supply ption yield factor component (ton) (ton) (ton) (ton) (t/ha) (gha/ha) (gha/cap)

Crop Rice 47414 1900 6420 42894 3.75 2.8 180176 0.1778 Wheat 0 1950 0 1950 2.62 2.8 180176 0.0116 Potato 1325 7882 0 9207 16.47 2.8 0.0087 180176 Pulses 4874 500 4174 1200 0.837 2.8 180176 0.0223 Vegeta 8145 12160 0 20305 18 2.8 180176 0.0175 Oils 0 490 0 490 2.24 2.8 180176 0.0034 Spices 630 490 100 1020 14.17 2.8 180176 0.0011 Tea 0 65 0 65 0.56 2.8 180176 0.0018 Sugar 200 1450 0 1650 6.82 2.8 180176 0.0038 Sub-total 0.2479 Anima l Meat 3599 0 1763 1836 0.457 1.1 180176 0.0245 Egg 12 355 0 367 0.304 1.1 180176 0.0074 Milk 120 580 0 700 0.52 1.1 180176 0.0082 Sub-total 0.0401 Fisher y Shrimp 16 0 14 2 3.25 0.2 180176 0.0000 Others 4476 0 3581 895 0.05 0.2 180176 0.0199 Waste 0.0009 Sub-total 0.0208 Forest Fruit 9734 450 8551 1633 18 1.1 180176 0.0006 Sub-total 0.0006

Total 0.3094

Build-up Area: Area (ha) Yield factor (crop) Equivalence factor Population Footprint (gha/ha) component (gha/capita) 4187 0.87 2.8 180176 0.056608716

156

Energy Name Amount Conve Amount Global Equiv Population Footprint consumed rsion consume average factor component factor d (GJ/ha/yr) (gha/ha) (gha/cap) (GJ/year)

Fire wood 21234 15.4 327003.6 59 1.1 0.0338 (ton) 180176 Twigs (ton) 33062 15.4 509154.8 59 1.1 180176 0.0527 Straw (ton) 7546 12.23 92287.58 32 1.1 0.0176 180176 Cowdung 6232 8.75 54530 49 1.1 0.0068 (ton) 180176 Diesel 1413168 0.038 53700.38 71 1.1 0.0046 (litre) 180176 Petrol 127227 0.034 4325.718 71 1.1 0.0004 (litre) 180176 Kerosine 1245826 0.037 46095.56 71 1.1 0.0040 (litre) 180176 Electricity 541872 0.0036 1950.739 1000 1.1 0.0000 (kwh) 180176 Coal (ton) 3000 27 81000 55 1.1 180176 0.0090

Total 0.1289

Category Existing Yield Equiva Population Bio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/cap) (gha/cap) (gha/cap)

Crop 30580 0.87 2.8 180176 0.413445 0.2479026 Animal 55.62 150.84 1.1 180176 0.051220 0.0401163 -0.027091 Build-up 4187 0.87 2.8 0.056608 0.0566087 180176 Fishery 1041 1.65 0.2 180176 0.001906 0.0207859 Forest 1712 0.8 1.1 180176 0.008361 0.0005539 Energy 0.1288808 Total 0.531542 0.4948481 Available BC (-12% for Biodiversity) 0.467757

157 Appendix C-8.1 Database in Excel for computation of food security of Kalapara upazila

FOOD SECURITY CALCULATION

Name of Upazila: Kalapara District : Patuakhali

A. Crop Crop Area Yield Production Price Gross income Equi rice (ha) (t/ha ) (ton) (Tk./ton) (Tk.) (ton)

Rice 48460 3.27 158464.2 18750 2.971E+09 111699.4 Potato 35 12 420 8000 3360000 126.3158 S. Potato 504 10 5040 5000 25200000 947.3684 Chilli 410 1.5 615 35000 21525000 809.2105 Pulse 7675 0.92 7061 35000 247135000 9290.789 W melon 1091 40 43640 20000 872800000 32812.03 G. Nut 530 1.5 795 24000 19080000 717.2932 Maize 58 5 290 15000 4350000 163.5338 Til 70 0.9 63 22000 1386000 52.10526 Veg (S) 160 8.78 1404.8 11000 15452800 580.9323 Veg (W) 850 13.1 11135 10000 111350000 4186.09 Straw 106170 750 79627500 2993.515 Total 59843 4.372E+09 164378.6

B. Fish Category Area Production Price Gross income Equi rice (ton) (ha) (ton) (Tk./ ton) (Tk.)

Others River Shrimp 241 22 200000 4400000 165.413534 Others 1300 140000 182000000 6842.10526 Canal Shrimp 1091 5 200000 1000000 37.593985 Others 4680 120000 561600000 21112.782 Fish+ Rice Shrimp 0 0 0 0 0 Others 0 0 0 0 Gher Shrimp 985 375 500000 187500000 7048.87218 Others 0 0 0 0 Pond Shrimp 1196 147 500000 73500000 2763.15789 Others 1175 110000 129250000 4859.02256 Total 3513 7704 1770000 1139250000 42828.9474

158 C. Animal Category Area Production Price Gross Equi rice (ha) (ton/No.) (Tk./ton/No.) income (ton) (Tk.)

Poultry Meat 53.44 534 70000 3.7E+07 1405.26 Egg 5024708 3.4 1.7E+07 642.256 Dairy Meat 198.26 13648 120000 1.6E+09 61569.9 Milk 12790 18000 2.3E+08 8654.89 Total 251.7 1.9E+09 72272.3

D. Forestry i) Fruit Tree Name No. Area Production Price Gross income Equi rice (ha) (ton) (Tk./ton) (Tk.) (ton)

Mango 35 280 25000 7000000 263.158 Jackfruit 22 308 6000 1848000 69.4737 Banana 112 2040 16000 32640000 1227.07 Papaya 13 208 7000 1456000 54.7368 Coconut 160 550 11000 6050000 227.444 Bar 40 328 25000 8200000 308.271 Guava 230 3890 22000 85580000 3217.29 Litchi 22 53 22000 1166000 43.8346 Palm 37 222 6000 1332000 50.0752 Total 7879 145272000 5461.35 ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 26824 1375 36883000 1386.5789 Treebranch 41658 1250 52072500 1957.6128 Total 88955500 3344.1917

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton)

288285.4 109118.55 2.6419 164.19

159 Appendix C-8.2 Database in Excel for computation of ecological footprint of Kalapara upazila ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Kalapara District : Patuakhali

Category Produ Inside Outside Consum Global Equiva Popula Footprint ction supply supply ption yield factor tion component (ton) (ton) (ton) (ton) (t/ha) (gha/ha) (gha/cap)

Crop Rice 158464 2500 109221 51743 3.75 2.8 220308 0.1754 Wheat 0 2250 0 2250 2.62 2.8 220308 0.0109 Potato 5390 6119 0 11509 16.47 2.8 0.0089 220308 Pulses 7061 600 6392 1269 0.837 2.8 220308 0.0193 Veget 7300 17884 0 25184 18 2.8 220308 0.0178 Oils 0 620 0 620 2.24 2.8 220308 0.0035 Spices 615 656 0 1271 14.17 2.8 220308 0.0011 Tea 0 82 0 82 0.56 2.8 220308 0.0019 Sugar 400 1362 0 1762 6.82 2.8 220308 0.0033 Sub-total 0.2420 Animal Meat 14182 0 11868 2314 0.457 1.1 220308 0.0253 Egg 296 196 0 492 0.304 1.1 220308 0.0081 Milk 12790 0 10970 1820 0.52 1.1 220308 0.0175 Sub-total 0.0508 Fishery Shrimp 594 0 587 7 3.25 0.2 220308 0.0000 Others 7155 0 5724 1431 0.05 0.2 220308 0.0260 Waste 0.0134 Sub-total 0.0394 Forest Fruit 7879 1300 6981 2198 18 1.1 220308 0.0006 Sub-total 0.0006

Total 0.3328

Build-up Area: Area Yield factor Equivalence factor Population Footprint component (ha) (crop) (gha/ha) (gha/capita)

848 0.87 2.8 220308 0.009376546

160 Energy Name Amount Conversion Amount Global Equiv Popul Footprint consumed factor consumed average factor ation (gha/cap) (GJ/year) (GJ/ha/yr) (gha/ha)

Fire wood 26824 15.4 413089.6 59 1.1 0.0350 (ton) 220308 Twigs 41658 15.4 641533.2 59 1.1 0.0543 (ton) 220308 Straw (ton) 7916 12.23 96812.68 32 1.1 0.0151 220308 Cowdung 8601 8.75 75258.75 49 1.1 0.0077 (ton) 220308 Diesel 1943106 0.038 73838.03 71 1.1 0.0052 (litre) 220308 Petrol 159033 0.034 5407.122 71 1.1 0.0004 (litre) 220308 Kerosine 574086 0.037 21241.18 71 1.1 0.0015 (litre) 220308 Electricity 2450769 0.0036 8822.768 1000 1.1 0.0000 (kwh) 220308 Coal (ton) 0 27 0 55 1.1 220308 0.0000

Total 0.1191

Category Existing Yield Equivalence PopulationBio- Ecological Ecological Area factor factor capacity Footprint Status (crop) (gha/ha) (gha/capita) (gha/capita) (gha/cap)

Crop 59843 0.87 2.8 220308 0.6616988 0.2420165 Animal 251.7 150.84 1.1 220308 0.1895667 0.0508382 0.3066846 Build-up 848 0.87 2.8 0.0093765 0.0093765 220308 Fishery 3513 1.33 0.2 220308 0.0042416 0.0393828 Forest 1976 0.8 1.1 220308 0.0078929 0.0006097 Energy 0.1191351 Total 0.8727767 0.4613588 Available BC (-12% for Biodiversity) 0.7680435

161 Appendix C-9.1 Database in Excel for computation of food security of Galachipa upazila.

FOOD SECURITY CALCULATION

Name of Upazila: Galachipa District : Patuakhali

A. Crop Crop Area (ha) Yield (t/ha ) Production Price Gross income Equi rice (ton) (Tk./ton) (Tk.) (ton)

Rice 88935 1.88 167197.8 18750 3.135E+09 117855.6 Pulse 23,500 1.2 28200 40000 1.128E+09 42406.02 Chilli 6700 1.1 7370 50000 368500000 13853.38 Potato 1200 22 26400 9000 237600000 8932.331 S.Potato 5500 16 88000 6000 528000000 19849.62 W. melon 1200 48 57600 20000 1.152E+09 43308.27 Veg (S) 425 17.83 7577.75 12000 90933000 3418.534 Veg (W) 2645 18 47610 11000 523710000 19688.35 Straw 112022 750 84016500 3158.515 Total 130105 541977.6 167500 7.248E+09 272470.6

B. Fish Category Area (ha) Production Price (Tk./ Gross income Equi rice (ton) ton) (Tk.) (ton)

Marine Shrimp Others River Shrimp 810 1510 200000 302000000 11353.4 Others 5300 140000 742000000 27894.7 Canal Shrimp 404 20 200000 4000000 150.376 Others 213 90000 19170000 720.677 Fish+ Rice Shrimp 40 10 550000 5500000 206.767 Others 50 100000 5000000 187.97 Gher Shrimp 2600 586 500000 293000000 11015 Others 300 100000 30000000 1127.82 Pond Shrimp 2095 1 550000 550000 20.6767 Others 2936 105000 308280000 11589.5 Total 594910926 2535000 1709500000 64266.9

162 C. Animal Category Area (ha) Production Price (Tk./ton/No.) Gross Equi rice (ton/No.) income (ton) (Tk.) Poultry Meat 16.27 874 75000 6.6E+07 2464.29 Egg 29200000 3.4 9.9E+07 3732.33 Dairy Meat 116.33 8028 150000 1.2E+09 45270.7 Milk 1095 21000 2.3E+07 864.474 Total 132.6 1.4E+09 52331.8

D. Forestry i) Fruit Tree Name No. Area Production Price Gross Equi rice (ha) (ton) (Tk./ton) income (ton) (Tk.)

Mango 40 320 25000 8000000 300.752 Jackfruit 20 280 6000 1680000 63.1579 Banana 60 1200 16000 19200000 721.805 Papaya 15 240 7000 1680000 63.1579 Coconut 170 580 11000 6380000 239.85 Bar 35 280 25000 7000000 263.158 Guava 80 1240 22000 27280000 1025.56 Litchi 12 40 30000 1200000 45.1128 Palm 27 135 6000 810000 30.4511 0 Total 4315 73230000 2753.01

ii) Non-fruit Tree: Wood Category Quantity (ton) Price (Tk./ton) Gross income (Tk.) Equi rice (ton)

Wood 42590 1375 58561250 2201.5508 Treebranch 66200 1250 82750000 3110.9023 Total 141311250 5312.453

Food from all Food Requirement Food Security ratio Food Security status sources Equivalent Equivalent rice (%) rice (ton) (ton)

397134.8 173863.18 2.2842 128.42

163 Appendix C-9.2 Database in Excel for computation of ecological footprint of Galachipa upazila.

ECOLOGICAL FOOTPRINT CALCULATION

Name of Upazila: Galachipa District : Patuakhali

Category Produ Inside Outside Consum Global Equiva Popula Footprint ction supply supply ption yield factor tion (gha/cap) (ton) (ton) (ton) (ton) (t/ha) (gha/ha)

Crop Rice 167198 4200 92408 78990 3.75 2.8 351026 0.1680 Wheat 0 3780 0 3780 2.62 2.8 351026 0.0115 Potato 114400 0 96790 17610 16.47 2.8 0.0085 351026 Pulses 28200 800 27040 1960 0.837 2.8 351026 0.0187 Veget 55188 2500 16968 40720 18 2.8 351026 0.0180 Oils 0 998 0 998 2.24 2.8 351026 0.0036 Spices 7370 855 6210 2015 14.17 2.8 351026 0.0011 Tea 0 135 0 135 0.56 2.8 351026 0.0019 Sugar 700 2200 0 2900 6.82 2.8 351026 0.0034

Sub-total 0.2348 Animal Meat 8902 0 5632 3270 0.457 1.1 351026 0.0224 Egg 1718 0 693 1025 0.304 1.1 351026 0.0106 Milk 1095 200 0 1295 0.52 1.1 351026 0.0078 Sub-total 0.0408 Fishery Shrimp 2127 0 2107 20 3.25 0.2 351026 0.0000 Others 8799 0 7039 1760 0.05 0.2 351026 0.0201 Waste 0.0226 Sub-total 0.0426 Forest Fruit 4315 550 1585 3280 18 1.1 351026 0.0006 Sub-total 0.0006

Total 0.3188

Build-up Area: Area (ha) Yield factor Equivalence factor Population Footprint component (crop) (gha/ha) (gha/capita) 7334 0.72 2.8 351026 0.042120367

164 Energy Name Amount Conver Amount Global Equiva Population Footprint consumed sion consumed average factor component factor (GJ/year) (GJ/ha/yr) (gha/ha) (gha/cap)

Fire wood 42590 15.4 655886 59 1.1 0.0348 (ton) 351026 Twigs 66200 15.4 1019480 59 1.1 0.0541 (ton) 351026 Straw 13290 12.23 162536.7 32 1.1 0.0159 (ton) 351026 Cowdung 12900 8.75 112875 49 1.1 0.0072 (ton) 351026 Diesel 2826336 0.038 107400.8 71 1.1 0.0047 (litre) 351026 Petrol 254454 0.034 8651.436 71 1.1 0.0004 (litre) 351026 Kerosine 1203945 0.037 44545.97 71 1.1 0.0020 (litre) 351026 Electricity 2709359 0.0036 9753.692 1000 1.1 0.0000 (kwh) 351026 Coal (ton) 0 27 0 55 1.1 351026 0.0000

Total 0.1192

Category Existing Yield Equivalence Populati Bio- Ecological Ecological Area factor factor on capacity Footprint Status (crop) (gha/ha) (gha/cap) (gha/cap) (gha/cap)

Crop 130105 0.72 2.8 351026 0.7472144 0.234783 Animal 132.6 150.84 1.1 351026 0.0626778 0.040792 0.3219556 Build-up 7334 0.72 2.8 0.0421204 0.042120 351026 Fishery 5949 1.11 0.2 351026 0.0037623 0.042619 Forest 22210 0.8 1.1 351026 0.0556791 0.000571 Energy 0.119238 Total 0.9114539 0.480124 Available BC (-12% for Biodiversity) 0.8020795

165 Appendix- D. The equations of Integrated Coastal Zone Management (ICZM) model

Equations for normal growth of Dacop upazila Biocapacity sector biocapacity_for_animal = animal_area*equivalence_factor_for_animal*yield_factor_for_animal biocapacity_for_buildup_area = buildup_area*equivalence_factor_for_crop*yield_factor_for_crop biocapacity_for_crop = (crop_area+crop_fish_integrated_farming_area+Boro_Aus_area)*yield_factor_for_crop*e quivalence_factor_for_crop biocapacity_for_fish = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*e quivalence_factor_for_fish*yield_factor_for_fish biocapacity_for_forest = forest_area*equivalence_factor_for_forest*yield_factor_for_forest biocapacity_for_non_rice = non_rice_area*equivalence_factor_for_crop*yield_factor_for_crop biocapacity_per_capita = (total_biocapacity-.12*total_biocapacity)/population Boro_Aus_area = 15 ecological_status = biocapacity_per_capita-ecological_foot_print_per_capita total_biocapacity = biocapacity_for_animal+biocapacity_for_buildup_area+biocapacity_for_crop+biocapacity _for_fish+biocapacity_for_forest+biocapacity_for_non_rice yield_factor_for_animal = 151 yield_factor_for_crop = .99 yield_factor_for_fish = .227 yield_factor_for_forest = .8

Ecological footprint sector buildup_area(t) = buildup_area(t - dt) + (buildup_area_growth_rate) * dt INIT buildup_area = 4199

166 INFLOWS: buildup_area_growth_rate = buildup_area*build_up_growth_factor animal_consumption = population*per_capita_animal_consumption build_up_growth_factor = .0012 eclogical_footprint_for_shrimp_culture = total_pond_area*eco_factor_for_semi_intensive_culture/population ecological_footprint_for_animal = (animal_consumption/global_average_of_animal_consumption)*equivalence_factor_for_a nimal/population ecological_footprint_for_build_up_area = buildup_area*yield_factor_crop*equivalence_factor_for_non_rice/population ecological_footprint_for_crop = ((food_consumption/global_yield_for_crop)*equivalence_factor_for_crop)/population ecological_footprint_for_energy = ((energy_consumption/global_average_of_energy_consumption)*equivalence_factor_for_ energy)/population ecological_footprint_for_fish_consumption = ((fish_consumption/global_yield_for_fish)*equivalence_factor_for_fish)/population ecological_footprint_for_forest = (forest_consumption*equivalence_factor_for_forest)/global_average_of_forest_consumpti on/population ecological_footprint_for_non_rice = (non_rice_consumption*equivalence_factor_for_non_rice)/global_average_of_non_rice_c onsumption/population ecological_foot_print_per_capita = eclogical_footprint_for_shrimp_culture+ecological_footprint_for_animal+ecological_foot print_for_build_up_area+ecological_footprint_for_crop+ecological_footprint_for_energy +ecological_footprint_for_fish_consumption+ecological_footprint_for_forest+ecological_ footprint_for_non_rice energy_consumption = population*energy_consumption_per_capita energy_consumption_per_capita = 5.81 equivalence_factor_for_animal = 1.1 equivalence_factor_for_crop = 2.8

167 equivalence_factor_for_energy = 1.10 equivalence_factor_for_fish = 0.20 equivalence_factor_for_forest = 1.1 equivalence_factor_for_non_rice = 2.8 fish_consumption = population*fish_consumption_per_capita fish_consumption_per_capita = .0089 food_consumption = population*food_consumption_per_capita food_consumption_per_capita = 0.216 forest_consumption = population*forest_consumption_per_capita forest_consumption_per_capita = .009 global_average_of_animal_consumption = .452 global_average_of_energy_consumption = 49.92 global_average_of_forest_consumption = 18 global_average_of_non_rice_consumption = 8.63 global_yield_for_crop = 3.75 global_yield_for_fish = .05 non_rice_consumption = population*non_rice_consumption_per_capita non_rice_consumption_per_capita = .180 per_capita_animal_consumption = .025 total_pond_area = crop_fish_integrated_farming_area+pond_area_bagda yield_factor_crop = .99 eco_factor_for_semi_intensive_culture = GRAPH(shrimp_production_intensity) (1.00, 3.00), (9.25, 18.8), (17.5, 34.5), (25.8, 50.3), (34.0, 66.0), (42.3, 78.8), (50.5, 93.6), (58.8, 106), (67.0, 124), (75.3, 139), (83.5, 156), (91.8, 172), (100, 197)

Food security sector animal_area(t) = animal_area(t - dt) + (animal_growth_rate) * dt INIT animal_area = 83.27

INFLOWS: animal_growth_rate = animal_area*animal_growth_fraction crop_area(t) = crop_area(t - dt) + (- land_transfer_rate_for_bagda - land_transfer_rate_for_crop_fish) * dt INIT crop_area = 19500

168 OUTFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish crop_fish_integrated_farming_area(t) = crop_fish_integrated_farming_area(t - dt) + (land_transfer_rate_for_crop_fish) * dt INIT crop_fish_integrated_farming_area = 0

INFLOWS: land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish forest_area(t) = forest_area(t - dt) + (forest_growth) * dt INIT forest_area = 314

INFLOWS: forest_growth = forest_area*forest_growth_factor non_rice_area(t) = non_rice_area(t - dt) + (non_rice_area_growth_rate) * dt INIT non_rice_area = 554

INFLOWS: non_rice_area_growth_rate = non_rice_area*non_rice_growth_fraction pond_area_bagda(t) = pond_area_bagda(t - dt) + (land_transfer_rate_for_bagda) * dt INIT pond_area_bagda = 13395

INFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda population(t) = population(t - dt) + (population_growth) * dt INIT population = 172613

INFLOWS: population_growth = population*population_growth_factor animal_growth_fraction = 0.0012 Area_of_canal_river_&_pond = 2553 crop_yield = crop_yiled_normal*crop_ecological_foot_print_multiplier*cropping_intensity_multiplier crop_yield_for_crop_fish_integrated_farming = 2.20 crop_yiled_normal = 1.95 equivalence_factor_non_rice = 0.332

169 equivalence_factor_shrimp = 16.91 equivalent_factor_other_fish = 3.03 fish_from_crop_plus_fish = shrimp_production_galda*equivalence_factor_shrimp fish_yield_galda = 0.39 food_available = fish_from_crop_plus_fish+food_equivalent_from_bagda+food_from_animal+food_from_ crop_area+food_from_crop_plus_fish+food_from_forest+food_eqivalent_other_fish+food _from_non_rice+food_from_shrimp_rcp food_eqivalent_other_fish = equivalent_factor_other_fish*other_fish_production food_equivalent_from_bagda = shrimp_production_bagda*equivalence_factor_shrimp food_from_animal = animal_area*food_from_animal_normal food_from_animal_normal = 410.8 food_from_crop_area = crop_area*crop_yield food_from_crop_plus_fish = crop_fish_integrated_farming_area*crop_yield_for_crop_fish_integrated_farming food_from_forest = forest_area*food_from_forest_normal food_from_forest_normal = 7.15 food_from_non_rice = equivalence_factor_non_rice*non_rice_production food_from_shrimp_rcp = equivalence_factor_shrimp*Shrimp_production_rcp food_per_capita = 0.001357 food_requirement = population*food_per_capita*no_of_days food_security = ((food_available-food_requirement)/food_requirement)*100 forest_growth_factor = .0015 non_rice_growth_fraction = 0.0012 non_rice_production = non_rice_area*non_rice_yield non_rice_yield = 25.5 no_of_days = 365 other_fish_production = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*yi eld_other_fish population_growth_factor = .0154 shrimp_production_bagda = pond_area_bagda*shrimp_yield_bagda

170 shrimp_production_galda = fish_yield_galda*shrimp_ecological_foot_print_multiplier*crop_fish_integrated_farming _area Shrimp_production_rcp = Area_of_canal_river_&_pond*Yield_of_shrimp_rcp shrimp_yield_bagda = shrimp_yield_normal_bagda*shrimp_intensity_multiplier_bagda*shrimp_ecological_foot _print_multiplier shrimp_yield_normal_bagda = 0.251 transfer_fraction_for_bagda = .0120 transfer_fraction_for_crop_plus_fish = .010 Yield_of_shrimp_rcp = 0.04 yield_other_fish = 0.157 cropping_intensity = GRAPH(TIME) (0.00, 1.59), (1.00, 1.73), (2.00, 1.84), (3.00, 1.86), (4.00, 1.92), (5.00, 1.96), (6.00, 2.02), (7.00, 2.09), (8.00, 2.12), (9.00, 2.10), (10.0, 2.13), (11.0, 2.15), (12.0, 2.15) cropping_intensity_multiplier = GRAPH(cropping_intensity) (1.00, 1.01), (1.20, 1.12), (1.40, 1.19), (1.60, 1.24), (1.80, 1.28), (2.00, 1.33), (2.20, 1.35), (2.40, 1.38), (2.60, 1.41), (2.80, 1.43), (3.00, 1.45) crop_ecological_foot_print_multiplier = GRAPH(ecological_footprint_for_crop) (0.00, 1.00), (0.3, 0.965), (0.6, 0.94), (0.9, 0.925), (1.20, 0.9), (1.50, 0.87), (1.80, 0.845), (2.10, 0.815), (2.40, 0.8), (2.70, 0.765), (3.00, 0.73) shrimp_ecological_foot_print_multiplier = GRAPH(ecological_foot_print_per_capita) (0.00, 1.00), (2.00, 0.91), (4.00, 0.814), (6.00, 0.71), (8.00, 0.605), (10.0, 0.512), (12.0, 0.429), (14.0, 0.356), (16.0, 0.269), (18.0, 0.176), (20.0, 0.098) shrimp_intensity_multiplier_bagda = GRAPH(shrimp_production_intensity) (1.00, 1.00), (10.9, 2.04), (20.8, 2.84), (30.7, 3.61), (40.6, 4.51), (50.5, 5.18), (60.4, 6.09), (70.3, 6.80), (80.2, 7.34), (90.1, 7.84), (100, 8.15) shrimp_production_intensity = GRAPH(TIME) (0.00, 1.00), (1.00, 5.95), (2.00, 10.9), (3.00, 15.9), (4.00, 21.3), (5.00, 26.2), (6.00, 31.2), (7.00, 35.6), (8.00, 40.1), (9.00, 44.6), (10.0, 49.5), (11.0, 54.5), (12.0, 59.9)

Not in a sector

171 Equations for super-intensive growth of Dacop upazila

Biocapacity sector biocapacity_for_animal = animal_area*equivalence_factor_for_animal*yield_factor_for_animal biocapacity_for_buildup_area = buildup_area*equivalence_factor_for_crop*yield_factor_for_crop biocapacity_for_crop = (crop_area+crop_fish_integrated_farming_area+Boro_Aus_area)*yield_factor_for_crop*e quivalence_factor_for_crop biocapacity_for_fish = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*e quivalence_factor_for_fish*yield_factor_for_fish biocapacity_for_forest = forest_area*equivalence_factor_for_forest*yield_factor_for_forest biocapacity_for_non_rice = non_rice_area*equivalence_factor_for_crop*yield_factor_for_crop biocapacity_per_capita = (total_biocapacity-.12*total_biocapacity)/population Boro_Aus_area = 15 ecological_status = biocapacity_per_capita-ecological_foot_print_per_capita total_biocapacity = biocapacity_for_animal+biocapacity_for_buildup_area+biocapacity_for_crop+biocapacity _for_fish+biocapacity_for_forest+biocapacity_for_non_rice yield_factor_for_animal = 151 yield_factor_for_crop = .99 yield_factor_for_fish = .227 yield_factor_for_forest = .8

Ecological footprint sector buildup_area(t) = buildup_area(t - dt) + (buildup_area_growth_rate) * dt INIT buildup_area = 4199

172 INFLOWS: buildup_area_growth_rate = buildup_area*build_up_growth_factor animal_consumption = population*per_capita_animal_consumption build_up_growth_factor = .0012 eclogical_footprint_for_shrimp_culture = total_pond_area*eco_factor_for_semi_intensive_culture/population ecological_footprint_for_animal = (animal_consumption/global_average_of_animal_consumption)*equivalence_factor_for_a nimal/population ecological_footprint_for_build_up_area = buildup_area*yield_factor_for_crop*equivalence_factor_for_non_rice/population ecological_footprint_for_crop = ((food_consumption/global_yield_for_crop)*equivalence_factor_for_crop)/population ecological_footprint_for_energy = ((energy_consumption/global_average_of_energy_consumption)*equivalence_factor_for_ energy)/population ecological_footprint_for_fish_consumption = ((fish_consumption/global_yield_for_fish)*equivalence_factor_for_fish)/population ecological_footprint_for_forest = (forest_consumption*equivalence_factor_for_forest)/global_average_of_forest_consumpti on/population ecological_footprint_for_non_rice = (non_rice_consumption*equivalence_factor_for_non_rice)/global_average_of_non_rice_c onsumption/population ecological_foot_print_per_capita = eclogical_footprint_for_shrimp_culture+ecological_footprint_for_animal+ecological_foot print_for_build_up_area+ecological_footprint_for_crop+ecological_footprint_for_energy +ecological_footprint_for_fish_consumption+ecological_footprint_for_forest+ecological_ footprint_for_non_rice energy_consumption = population*energy_consumption_per_capita energy_consumption_per_capita = 5.81 equivalence_factor_for_animal = 1.1 equivalence_factor_for_crop = 2.8

173 equivalence_factor_for_energy = 1.10 equivalence_factor_for_fish = 0.20 equivalence_factor_for_forest = 1.1 equivalence_factor_for_non_rice = 2.8 fish_consumption = population*fish_consumption_per_capita fish_consumption_per_capita = .0089 food_consumption = population*food_consumption_per_capita food_consumption_per_capita = .205 forest_consumption = population*forest_consumption_per_capita forest_consumption_per_capita = .009 global_average_of_animal_consumption = .452 global_average_of_energy_consumption = 49.92 global_average_of_forest_consumption = 18 global_average_of_non_rice_consumption = 8.63 global_yield_for_crop = 3.75 global_yield_for_fish = .05 non_rice_consumption = population*non_rice_consumption_per_capita non_rice_consumption_per_capita = .180 per_capita_animal_consumption = .025 total_pond_area = crop_fish_integrated_farming_area+pond_area_bagda eco_factor_for_semi_intensive_culture = GRAPH(shrimp_production_intensity) (1.00, 3.00), (9.25, 18.8), (17.5, 34.5), (25.8, 50.3), (34.0, 66.0), (42.3, 78.8), (50.5, 93.6), (58.8, 106), (67.0, 124), (75.3, 139), (83.5, 156), (91.8, 172), (100, 197)

Food security sector animal_area(t) = animal_area(t - dt) + (animal_growth_rate) * dt INIT animal_area = 83.27

INFLOWS: animal_growth_rate = animal_area*animal_growth_fraction crop_area(t) = crop_area(t - dt) + (- land_transfer_rate_for_bagda - land_transfer_rate_for_crop_fish) * dt INIT crop_area = 19500

174 OUTFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish crop_fish_integrated_farming_area(t) = crop_fish_integrated_farming_area(t - dt) + (land_transfer_rate_for_crop_fish) * dt INIT crop_fish_integrated_farming_area = 0

INFLOWS: land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish forest_area(t) = forest_area(t - dt) + (forest_growth) * dt INIT forest_area = 314

INFLOWS: forest_growth = forest_area*forest_growth_factor non_rice_area(t) = non_rice_area(t - dt) + (non_rice_area_growth_rate) * dt INIT non_rice_area = 554

INFLOWS: non_rice_area_growth_rate = non_rice_area*non_rice_growth_fraction pond_area_bagda(t) = pond_area_bagda(t - dt) + (land_transfer_rate_for_bagda) * dt INIT pond_area_bagda = 13395

INFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda population(t) = population(t - dt) + (population_growth) * dt INIT population = 172613

175 equivalence_factor_non_rice = 0.332 equivalence_factor_shrimp = 16.91 equivalent_factor_other_fish = 3.03 fish_from_crop_plus_fish = shrimp_production_galda*equivalence_factor_shrimp fish_yield_galda = 0.39 food_available = fish_from_crop_plus_fish+food_equivalent_from_bagda+food_from_animal+food_from_ crop_area+food_from_crop_plus_fish+food_from_forest+food_eqivalent_other_fish+food _from_non_rice+food_from_shrimp_rcp food_eqivalent_other_fish = equivalent_factor_other_fish*other_fish_production food_equivalent_from_bagda = shrimp_production_bagda*equivalence_factor_shrimp food_from_animal = animal_area*food_from_animal_normal food_from_animal_normal = 410.8 food_from_crop_area = crop_area*crop_yield food_from_crop_plus_fish = crop_fish_integrated_farming_area*crop_yield_for_crop_fish_integrated_farming food_from_forest = forest_area*food_from_forest_normal food_from_forest_normal = 7.15 food_from_non_rice = equivalence_factor_non_rice*non_rice_production food_from_shrimp_rcp = equivalence_factor_shrimp*Shrimp_production_rcp food_per_capita = 0.001357 food_requirement = population*food_per_capita*no_of_days food_security = ((food_available-food_requirement)/food_requirement)*100 forest_growth_factor = .0015 non_rice_growth_fraction = 0.0012 non_rice_production = non_rice_area*non_rice_yield non_rice_yield = 25.5 no_of_days = 365 other_fish_production = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*yi eld_other_fish population_growth_factor = .0154 shrimp_production_bagda = pond_area_bagda*shrimp_yield_bagda

176 shrimp_production_galda = fish_yield_galda*shrimp_ecological_foot_print_multiplier*crop_fish_integrated_farming _area Shrimp_production_rcp = Area_of_canal_river_&_pond*Yield_of_shrimp_rcp shrimp_yield_bagda = shrimp_yield_normal_bagda*shrimp_intensity_multiplier_bagda*shrimp_ecological_foot _print_multiplier shrimp_yield_normal_bagda = .251 transfer_fraction_for_bagda = .0120 transfer_fraction_for_crop_plus_fish = .010 Yield_of_shrimp_rcp = 0.04 yield_other_fish = 0.157 cropping_intensity = GRAPH(TIME) (0.00, 1.59), (1.00, 1.73), (2.00, 1.84), (3.00, 1.92), (4.00, 1.97), (5.00, 2.02), (6.00, 2.07), (7.00, 2.11), (8.00, 2.16), (9.00, 2.19), (10.0, 2.22), (11.0, 2.24), (12.0, 2.26) cropping_intensity_multiplier = GRAPH(cropping_intensity) (1.00, 1.00), (1.20, 1.08), (1.40, 1.14), (1.60, 1.22), (1.80, 1.28), (2.00, 1.32), (2.20, 1.36), (2.40, 1.38), (2.60, 1.41), (2.80, 1.43), (3.00, 1.45) crop_ecological_foot_print_multiplier = GRAPH(ecological_footprint_for_crop) (0.00, 1.00), (0.3, 0.965), (0.6, 0.94), (0.9, 0.925), (1.20, 0.9), (1.50, 0.87), (1.80, 0.845), (2.10, 0.815), (2.40, 0.8), (2.70, 0.765), (3.00, 0.73) shrimp_ecological_foot_print_multiplier = GRAPH(eclogical_footprint_for_shrimp_culture) (0.00, 1.00), (2.00, 0.91), (4.00, 0.814), (6.00, 0.71), (8.00, 0.605), (10.0, 0.512), (12.0, 0.429), (14.0, 0.356), (16.0, 0.269), (18.0, 0.176), (20.0, 0.098) shrimp_intensity_multiplier_bagda = GRAPH(shrimp_production_intensity) (1.00, 1.00), (10.9, 2.04), (20.8, 2.84), (30.7, 3.61), (40.6, 4.51), (50.5, 5.18), (60.4, 6.09), (70.3, 6.80), (80.2, 7.34), (90.1, 7.84), (100, 8.15) shrimp_production_intensity = GRAPH(TIME) (0.00, 1.00), (1.00, 5.95), (2.00, 10.9), (3.00, 15.9), (4.00, 21.3), (5.00, 26.2), (6.00, 33.7), (7.00, 41.1), (8.00, 51.0), (9.00, 61.9), (10.0, 71.8), (11.0, 83.7), (12.0, 99.0)

Not in a sector

177 Equations for control growth of Dacop upazila

Ecological footprint sector buildup_area(t) = buildup_area(t - dt) + (buildup_area_growth_rate) * dt INIT buildup_area = 4199

178 INFLOWS: buildup_area_growth_rate = buildup_area*build_up_growth_factor animal_consumption = population*per_capita_animal_consumption build_up_growth_factor = .0012 eclogical_footprint_for_shrimp_culture = total_pond_area*eco_factor_for_semi_intensive_culture/population ecological_footprint_for_animal = (animal_consumption/global_average_of_animal_consumption)*equivalence_factor_for_a nimal/population ecological_footprint_for_build_up_area = buildup_area*yield_factor_crop*equivalence_factor_for_non_rice/population ecological_footprint_for_crop = ((food_consumption/global_yield_for_crop)*equivalence_factor_for_crop)/population ecological_footprint_for_energy = ((energy_consumption/global_average_of_energy_consumption)*equivalence_factor_for_ energy)/population ecological_footprint_for_fish_consumption = ((fish_consumption/global_yield_for_fish)*equivalence_factor_for_fish)/population ecological_footprint_for_forest = (forest_consumption*equivalence_factor_for_forest)/global_average_of_forest_consumpti on/population ecological_footprint_for_non_rice = (non_rice_consumption*equivalence_factor_for_non_rice)/global_average_of_non_rice_c onsumption/population ecological_foot_print_per_capita = eclogical_footprint_for_shrimp_culture+ecological_footprint_for_animal+ecological_foot print_for_build_up_area+ecological_footprint_for_crop+ecological_footprint_for_energy +ecological_footprint_for_fish_consumption+ecological_footprint_for_forest+ecological_ footprint_for_non_rice energy_consumption = population*energy_consumption_per_capita energy_consumption_per_capita = 5.81 equivalence_factor_for_animal = 1.1 equivalence_factor_for_crop = 2.8

179 equivalence_factor_for_energy = 1.10 equivalence_factor_for_fish = 0.20 equivalence_factor_for_forest = 1.1 equivalence_factor_for_non_rice = 2.8 fish_consumption = population*fish_consumption_per_capita fish_consumption_per_capita = .0089 food_consumption = population*food_consumption_per_capita food_consumption_per_capita = 0.216 forest_consumption = population*forest_consumption_per_capita forest_consumption_per_capita = .009 global_average_of_animal_consumption = .452 global_average_of_energy_consumption = 49.92 global_average_of_forest_consumption = 18 global_average_of_non_rice_consumption = 8.63 global_yield_for_crop = 3.75 global_yield_for_fish = .05 non_rice_consumption = population*non_rice_consumption_per_capita non_rice_consumption_per_capita = .180 per_capita_animal_consumption = .025 total_pond_area = crop_fish_integrated_farming_area+pond_area_bagda yield_factor_crop = .99 eco_factor_for_semi_intensive_culture = GRAPH(shrimp_production_intensity) (1.00, 3.00), (9.25, 18.8), (17.5, 34.5), (25.8, 50.3), (34.0, 66.0), (42.3, 78.8), (50.5, 93.6), (58.8, 106), (67.0, 124), (75.3, 139), (83.5, 156), (91.8, 172), (100, 197)

Food security sector animal_area(t) = animal_area(t - dt) + (animal_growth_rate) * dt INIT animal_area = 83.27

INFLOWS: animal_growth_rate = animal_area*animal_growth_fraction crop_area(t) = crop_area(t - dt) + (- land_transfer_rate_for_bagda - land_transfer_rate_for_crop_fish) * dt INIT crop_area = 19500

180 OUTFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish crop_fish_integrated_farming_area(t) = crop_fish_integrated_farming_area(t - dt) + (land_transfer_rate_for_crop_fish) * dt INIT crop_fish_integrated_farming_area = 0

INFLOWS: land_transfer_rate_for_crop_fish = crop_area*transfer_fraction_for_crop_plus_fish forest_area(t) = forest_area(t - dt) + (forest_growth) * dt INIT forest_area = 314

INFLOWS: forest_growth = forest_area*forest_growth_factor non_rice_area(t) = non_rice_area(t - dt) + (non_rice_area_growth_rate) * dt INIT non_rice_area = 554

INFLOWS: non_rice_area_growth_rate = non_rice_area*non_rice_growth_fraction pond_area_bagda(t) = pond_area_bagda(t - dt) + (land_transfer_rate_for_bagda) * dt INIT pond_area_bagda = 13395

INFLOWS: land_transfer_rate_for_bagda = crop_area*transfer_fraction_for_bagda population(t) = population(t - dt) + (population_growth) * dt INIT population = 172613

181 equivalence_factor_non_rice = 0.332 equivalence_factor_shrimp = 16.91 equivalent_factor_other_fish = 3.03 fish_from_crop_plus_fish = shrimp_production_galda*equivalence_factor_shrimp fish_yield_galda = 0.39 food_available = fish_from_crop_plus_fish+food_equivalent_from_bagda+food_from_animal+food_from_ crop_area+food_from_crop_plus_fish+food_from_forest+food_eqivalent_other_fish+food _from_non_rice+food_from_shrimp_rcp food_eqivalent_other_fish = equivalent_factor_other_fish*other_fish_production food_equivalent_from_bagda = shrimp_production_bagda*equivalence_factor_shrimp food_from_animal = animal_area*food_from_animal_normal food_from_animal_normal = 410.8 food_from_crop_area = crop_area*crop_yield food_from_crop_plus_fish = crop_fish_integrated_farming_area*crop_yield_for_crop_fish_integrated_farming food_from_forest = forest_area*food_from_forest_normal food_from_forest_normal = 7.15 food_from_non_rice = equivalence_factor_non_rice*non_rice_production food_from_shrimp_rcp = equivalence_factor_shrimp*Shrimp_production_rcp food_per_capita = 0.001357 food_requirement = population*food_per_capita*no_of_days food_security = ((food_available-food_requirement)/food_requirement)*100 forest_growth_factor = .0015 non_rice_growth_fraction = 0.0012 non_rice_production = non_rice_area*non_rice_yield non_rice_yield = 25.5 no_of_days = 365 other_fish_production = (crop_fish_integrated_farming_area+pond_area_bagda+Area_of_canal_river_&_pond)*yi eld_other_fish population_growth_factor = .0154 shrimp_production_bagda = pond_area_bagda*shrimp_yield_bagda

182 shrimp_production_galda = fish_yield_galda*shrimp_ecological_foot_print_multiplier*crop_fish_integrated_farming _area Shrimp_production_rcp = Area_of_canal_river_&_pond*Yield_of_shrimp_rcp shrimp_yield_bagda = shrimp_yield_normal_bagda*shrimp_intensity_multiplier_bagda*shrimp_ecological_foot _print_multiplier shrimp_yield_normal_bagda = 0.251 transfer_fraction_for_bagda = .0120 transfer_fraction_for_crop_plus_fish = .010 Yield_of_shrimp_rcp = 0.04 yield_other_fish = 0.157 cropping_intensity = GRAPH(TIME) (0.00, 1.59), (1.00, 1.73), (2.00, 1.84), (3.00, 1.86), (4.00, 1.92), (5.00, 1.96), (6.00, 2.02), (7.00, 2.09), (8.00, 2.12), (9.00, 2.17), (10.0, 2.17), (11.0, 2.15), (12.0, 2.15) cropping_intensity_multiplier = GRAPH(cropping_intensity) (1.00, 1.01), (1.20, 1.12), (1.40, 1.19), (1.60, 1.24), (1.80, 1.28), (2.00, 1.33), (2.20, 1.35), (2.40, 1.38), (2.60, 1.41), (2.80, 1.43), (3.00, 1.45) crop_ecological_foot_print_multiplier = GRAPH(ecological_footprint_for_crop) (0.00, 1.00), (0.3, 0.965), (0.6, 0.94), (0.9, 0.925), (1.20, 0.9), (1.50, 0.87), (1.80, 0.845), (2.10, 0.815), (2.40, 0.8), (2.70, 0.765), (3.00, 0.73) shrimp_ecological_foot_print_multiplier = GRAPH(ecological_foot_print_per_capita) (0.00, 1.00), (2.00, 0.91), (4.00, 0.814), (6.00, 0.71), (8.00, 0.605), (10.0, 0.512), (12.0, 0.429), (14.0, 0.356), (16.0, 0.269), (18.0, 0.176), (20.0, 0.098) shrimp_intensity_multiplier_bagda = GRAPH(shrimp_production_intensity) (1.00, 1.00), (10.9, 2.04), (20.8, 2.84), (30.7, 3.61), (40.6, 4.51), (50.5, 5.18), (60.4, 6.09), (70.3, 6.80), (80.2, 7.34), (90.1, 7.84), (100, 8.15) shrimp_production_intensity = GRAPH(TIME) (0.00, 1.00), (1.00, 5.95), (2.00, 10.9), (3.00, 14.4), (4.00, 19.8), (5.00, 23.8), (6.00, 27.7), (7.00, 32.2), (8.00, 35.2), (9.00, 38.6), (10.0, 42.1), (11.0, 45.1), (12.0, 47.0) Not in a sector

183 Appendix-E Simulated results in the eight upazilas of in the nine upazilas of Shyamnagar, Koyra, Shoronkhola, Morrelgonj, Mongla, Patharghata, Kalapara and Galachipa.

160

140 FS (Normal growth)

120 FS (Super-intensive) FS(Control growth) 100

40 Food (%) Security 20

0 01234567891011Final -20 Year

Fig. A.E.1.1 Food Security status of Shyamnagar upazila for different options

12 ) 10 EF(Normal growth) EF (Super-intensive) 8 EF(Control growth)

2 Ecological Footprint (gha/cap Footprint Ecological

0 01234567891011Final Year

Fig. A.E.1.2 Ecological footprint of Shyamnagar upazila for different options

Year 0 01234567891011Final ) -2

-4

-6 ES (Normal growth) ES (Super-intensive) -8 ES (Control growth)

Ecological Status (gha/cap Status Ecological -10

-12

Fig. A.E.1.3 Ecological status of Shyamnagar upazila for different options

184 140

120

100

60 FS (Normal growth) FS (Super-intensive) Food(%) Security 40 FS(Control growth) 20

0 01234567891011Final Year

Fig. A.E.2.1 Food Security status of Koyra upazila for different options

8 ) 7 EF(Normal growth) 6 EF (Super-intensive) 5 EF(Control growth)

1 Ecological Footprint (gha/cap Footprint Ecological 0 0 1 2 3 4 5 6 7 8 9 10 11 Final Year

Fig. A.E.2.2 Ecological footprint of Koyra upazila for different options

Year 0 0 1 2 3 4 5 6 7 8 9 10 11 Final -1 )

-2

-3

-4 ES (Normal growth) -5 ES (Super-intensive)

-6 ES (Control growth) Ecological (gha/cap Status -7

-8

Fig. A.E.2.3 Ecological status of Koyra upazila for different options

185 50

40 FS (Normal growth) 30 FS (Super-intensive)

20 FS(Control growth)

0 01234567891011Final Food (%) Security -10

-20

-30 Year

Fig. A.E.3.1 Food Security status of Shoronkhola upazila for different options

3.5 ) 3

2.5 EF(Normal growth) EF (Super-intensive) 2 EF(Control growth) 1.5

0.5 Ecological Footprint (gha/cap Footprint Ecological

0 0 1 2 3 4 5 6 7 8 9 10 11 Final Year

Fig. A.E.3.2 Ecological footprint of Shoronkhola upazila for different options

Year 0 01234567891011Final ) -0.5

-1

-1.5 ES (Normal growth) -2 ES (Super-intensive) -2.5 ES (Control growth)

Ecological (gha/cap Status -3

-3.5

Fig. A.E.3.3 Ecological status of Shoronkhola upazila for different options

186 Year 0 0 1 2 3 4 5 6 7 8 9 10 11 Final -5

-10 FS (Normal growth) -15 FS (Super-intensive)

-20 FS(Control growth) Food (%) Security -25

-30

-35

Fig. A.E.4.1 Food Security status of Morrelgonj upazila for different options

) 7 EF(Normal growth) 6 EF (Super-intensive) 5 EF(Control growth) 4

1 Ecological Footprint (gha/cap Footprint Ecological 0 01234567891011Final Year

Fig. A.E.4.2 Ecological footprint of Morrelgonj upazila for different options

Year 0

) 01234567891011Final -1

-2

-3 ES (Normal growth) -4 ES (Super-intensive) -5 ES (Control growth) Ecological (gha/cap Status -6

-7

Fig. A.E.4.3 Ecological status of Morrelgonj upazila for different options

187 40

30 FS (Normal growth) FS (Super-intensive) 20 FS(Control growth)

0 Food (%) Security 0 1 2 3 4 5 6 7 8 9 10 11 Final -10

-20 Year

Fig. A.E.5.1 Food Security status of Mongla upazila for different options

14 ) 12 EF(Normal growth) 10 EF (Super-intensive)

8 EF(Control growth)

2 Ecological Footprint (gha/cap Footprint Ecological 0 0 1 2 3 4 5 6 7 8 9 10 11 Final Year

Fig. A.E.5.2 Ecological footprint of Mongla upazila for different options

Year 0 01234567891011Final ) -2

-4

-6 ES (Normal growth) -8 ES (Super-intensive) ES (Control growth) Ecological(gha/cap Status -10

-12

Fig. A.E.5.3 Ecological status of Mongla upazila for different options

188 160

140 FS (Normal growth) 120 FS (Super-intensive) 100 FS(Control growth) 80

Food (%) Security 40

0 01234567891011Final Year

Fig. A.E.6.1 Food Security status of Pathargata upazila for different options

4.5 ) 4 3.5 EF(Normal growth) 3 EF (Super-intensive) 2.5 EF(Control growth) 2

1.5

Ecological Footprint (gha/cap Footprint Ecological 0.5

0 01234567891011Final Year

Fig. A.E.6.2 Ecological footprint of Pathargata upazila for different options

Year 0

) 01234567891011Final -0.5

-1

-1.5

-2 ES (Normal growth) -2.5 ES (Super-intensive) -3 ES (Control growth)

Ecological (gha/cap Status -3.5

-4

-4.5

Fig. A.E.6.3 Ecological status of Pathargata upazila for different options

189 400

350

300

250

200 FS (Normal growth) 150 FS (Super-intensive) Food Security(%) 100 FS(Control growth) 50

0 01234567891011Final Year

Fig. A.E.7.1 Food Security status of Kalapara upazila for different options

) 9 8 7 EF(Normal growth) 6 5 EF (Super-intensive) 4 EF(Control growth) 3 2 1 Ecological Footprint (gha/cap Footprint Ecological 0 01234567891011Final Year

Fig. A.E.7.2 Ecological footprint of Kalapara upazila for different options

Year 1

) 0 01234567891011Final -1

-2

-3 ES (Normal growth) -4

-5 ES (Super-intensive)

-6 ES (Control growth) Ecological(gha/cap Status -7 -8

Fig. A.E.7.3 Ecological status of Kalapara upazila for different options

190 250

200

150 FS (Normal growth) 100 FS (Super-intensive) FS(Control growth) Food (%) Security 50

0 0 1 2 3 4 5 6 7 8 9 10 11 Final Year

Fig. A.E.8.1 Food Security status of Galachipa upazila for different options

) 9 8 EF(Normal growth) 7 EF (Super-intensive) 6 EF(Control growth) 5 4 3 2

Ecological Footprint (gha/cap Footprint Ecological 1 0 01234567891011Final Year

Fig. A.E.8.2 Ecological footprint of Galachipa upazila for different options

Year 1

) 0 -1 0 1 2 3 4 5 6 7 8 9 10 11 Final -2 -3 -4 ES (Normal growth) -5 -6 ES (Super-intensive) -7 ES (Control growth)

Ecological (gha/cap Status -8 -9 -10

Fig. A.E.8.3 Ecological status of Galachipa upazila for different options

191 PUBLICATIONS AND CONFERENCE PAPERS PRODUCED FROM THIS RESEARCH WORK

PUBLICATIONS 1. Bala, B. K. and Hossain, M. A. 2010. Food security and ecological foot print of the coastal zone of Bangladesh. Environment, Development and Sustainability.12.531-545.

2. Bala, B. K. and Hossain, M. A. 2010. Modeling of food security and ecological foot print of the coastal zone of Bangladesh. Environment, Development and Sustainability.12, 511-529.

CONFERENCE PAPERS 1. Bala, B. K. and Hossain, M. A. 2009. Food security and ecological foot print of coastal zone of Bangladesh for sustainable development. Paper presented in the third International Conference Environmentally Sustainable Development ESDev-2009. August 16-18, Abbottabad, Pakistan.

2. Hossain, M. A and Bala, B. K. 2011. Modeling the climate change impacts on rice production in the coastal zone of Bangladesh. Paper is to be presented in the Conference Crop Production under Unfavorable Ecosystems in Bangladesh.October 8, BARI, Gazipur, Bangladesh..

192