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A comparison of development opportunities for crab and shrimp aquaculture in southwestern Bangladesh, using GIS modelling
Aquaculture 220 (2003) 477 – 494 www.elsevier.com/locate/aqua-online
A comparison of development opportunities for crab and shrimp aquaculture in southwestern Bangladesh, using GIS modelling M. Abdus Salam a,b,*, Lindsay G. Ross a, C.M. Malcolm Beveridge a b
a Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, Scotland, UK Department of Aquaculture, Faculty of Fisheries, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
Received 21 October 2001; received in revised form 27 September 2002; accepted 6 November 2002
Abstract The present study identifies and quantifies appropriate sites for brackish water aquaculture development in southwestern Bangladesh using remote sensing, GPS and geographical information systems (GIS). A colour composite Landsat TM image from 1996 covering the southwestern part of Bangladesh was used to identify the extent of brackish water and to classify land use. The remotely sensed data were complemented by secondary data digitised from a range of sources, including hard copy maps, to create a spatial database that included environmental and infrastructural data. A series of GIS models were developed in order to identify and prioritise the most suitable areas for brackish water shrimp and crab farming. Using qualitative and quantitative output from the models, the benefits of shrimp and crab farming and alternative land uses in the Khulna region were compared, based on gross production, economic output and employment potential. Comparisons were made of brackish water shrimp and crab culture with moderately saline-tolerant tilapia and prawn culture, freshwater carp culture and traditional rice production systems. Shrimp was identified as the most capital intensive and risky production system. Earnings per hectare were a little higher for shrimp culture than for crab culture. The present study demonstrates the usefulness of GIS as an aquaculture planning tool in a region where natural resources are already under considerable pressure. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Modelling; GIS; Aquaculture; Bangladesh; Remote sensing; Crab and shrimp
* Corresponding author. Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, Scotland, UK. Tel.: +88-91-55695-7x2282; fax: +88-91-55810. E-mail address: [email protected] (M.A. Salam). 0044-8486/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0044-8486(02)00619-1
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1. Introduction Geographical information systems (GIS) are powerful tools capable of organising, analysing, and displaying large, spatially explicit datasets. To date, GIS has been applied for regional, national or sectoral studies of aquaculture where human resources, specific sites, economics, markets and sociocultural resources have been considered (Kapetsky et al., 1988; Meaden and Kapetsky, 1991; Nath et al., 2000). A number of large-scale and sectoral studies have been carried out, for example, in the African continent (Aguilar and Nath, 1998; Kapetsky, 1994) and in Latin America (Kapetsky and Nath, 1997). Studies at national levels have included salmon and mussel cage, pen and bed culture in Chile (Krieger and Muslow, 1990), and the sittings of salmon and rainbow trout farms in Norway (Ibrekk et al., 1993). A number of studies have further exploited the modelling capacity of GIS, including the development of a model for sitting salmon cage culture on the West Coast of Scotland; an exploration of the potential for rice-fish and fish culture in the Red River Delta, Vietnam (Tran and Demaine, 1996); regional development models for shrimp culture in Mexico (Aguilar, 1996); the management of coastal aquaculture in Thailand (Jarayabhand, 1997); and for shrimp, prawn and fish culture potential in Bangladesh (Salam and Ross, 1999). There is considerable potential for shrimp culture in southwestern Bangladesh and in 1994, 104,000 ha of land was already under culture (DOF, 1994 – 1995). However, aquatic resource management in the region has not been fully integrated with the rural economy and could be further developed to help meet the increasing demand for fish protein in the area. In this context, the establishment of a structured decision-making and planning scheme would be useful. This paper presents results from the application of remote sensing and GIS techniques to estimate the area of land available for different types of aquaculture development in southwestern Bangladesh, especially the suitability of areas for brackish water aquaculture of the mud crab (Scylla serrata) and the giant tiger shrimp (Penaeus monodon). The models are also used to estimate the production potential and economic characteristics of shrimp and crab farming, and to consider and evaluate alternative land uses in the Khulna region, southwestern Bangladesh.
2. Materials and methods 2.1. Study area The study area lies 300 km southwest of Dhaka, Bangladesh on the coastal plain at the apex of the Bay of Bengal and covers approximately 14,000 km2 (Fig. 1). The area lies between 21j30V and 23j15V North and 89j00V and 90j00V East and includes the world’s largest continuous mangrove forest, the Sundarbans, estuarine marshlands and numerous rivers, canals and their tributaries (Giri and Shrestha, 1996; Viju, 1995). The terrain is relatively flat; the elevation ranges from sea level to 5 m above mean sea level (CastroOrtiz, 1994). Fisheries play an important role in the regional and national economy and the area is one of the most promising for aquaculture. The fresh and coastal water resources are the
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Fig. 1. The study area: southwest of Bangladesh.
most abundant in the country. The presence of the world’s largest continuous mangrove forest provides food source and nursery for the offshore fishery, protection of the coasts from storm surges and cyclones, domestic and commercial products, recreation and tourist services, and habitat for shrimp and other cultivable species. Culture of freshwater and marine fish, shrimp and other crustacean species are highly important as they can be easily integrated with other activities such as agriculture and livestock rearing. Bangladesh has extremely favourable conditions for shrimp culture, not least because of the low production costs. In 1982– 1983, there were only 5200 ha of land under shrimp culture; by 1996, 110,000 ha had been brought under shrimp cultivation, 70% being located in the Khulna region (Mazid, 1998). By contrast, mud crab (S. serrata) has been an incidental product arising from the culture of shrimp, prawn, milkfish and other finfishes
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in Southeast Asia (Chandrasekaran and Perumal, 1993). However, with the continued expansion of shrimp culture worldwide, market prices have dropped and profit margins have been squeezed, leading to an upsurge of interest in crab culture as it is one of the most popular seafoods in many parts of the world, particularly in Southeast Asia, and can be exported alive (Khan and Alam, 1992). 2.2. GIS software and systems used The GIS software used in this study was the raster-based IDRISI for Windows version 2.0, and in some cases IDRISI 32, developed at Clark University, USA. The TOSCA digitising system (Version 2.03), DIGI-EDIT (Version 1.050) and CalComp Drawing Board III were used to digitise and edit vector layers. The software operated on a Pentium Pro 200 MHz workstation with 128 Mb RAM, 12 Gb hard disk, Iiyama Vision Master TM 21U colour monitor and a Matrox Millennium 8 Mb video adapter. 2.3. Remotely sensed data and thematic maps The primary data sources were two Landsat TM satellite images containing three multispectral bands (2, 3 and 4), acquired on 9 and 16 February 1996. A cloud-free image of the study area was subset from the original two full scene images and this subscene was geo-registered with the Survey of Bangladesh topographical sheet (1:50,000) using 140 reference points. Secondary soil texture and soil pH were extracted from soil reconnaissance survey maps prepared by the Soil Resources Development Institute (SRDI) during the period of 1972– 1975. Land use, flood, storm surge and drought were taken from SRDI maps of 1997. Soil and water salinity data were taken from the 1997 SRDI soil salinity report and Surface Water Modelling Centre (SWMC) report (1996). Roads, railways, rivers, international boundaries and other information were taken from topographical maps produced by Survey Bangladesh, scale 1:50,000, prepared during 1972– 1979. Population density, agricultural by-products and animal wastes were taken from Bangladesh Bureau of Statistics (BBS, 1996) and BBS (1997) pocket dictionary. Additional data were also extracted from atlases, periodicals and published papers (Table 1). 2.4. Weighting procedure for factors and submodels In order to determine potential sites for brackish water shrimp and crab farming, 36 environmental and economic criteria were selected and compiled. The criteria were of two types: factors and constraints (Eastman, 1995). A factor is a criterion that enhances, or detracts from, the suitability of a specific alternative under consideration. It is therefore measured on a continuous scale. A constraint, by contrast, serves to limit the alternatives under consideration such as settlements, reserve forests, roads and rivers and is Boolean in nature. Suitability ratings for each criterion were established according to Aguilar (1996) and Kapetsky (1994) and each factor was reclassified on a scale from 1 to 4. A weighting for each factor was then established according to the pair-wise comparison matrix of Saaty (1977).
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Although a variety of weighting techniques exist, the pair-wise comparison developed by Saaty known as the Analytical Hierarchy Process (AHP) was used to develop a set of relative weights for a group of factors in a multicriteria evaluation. Providing a series of pair-wise comparisons of the relative importance of factors to the suitability of image pixels for the activity being evaluated develops the weights. These pair-wise comparisons are then analysed to produce a set of weights that sum to 1 (Table 2). The factors and their resulting weights can then be used as input for the multicriteria evaluation (MCE) module by weighted linear combination (WLC). The relative rating of a pair of factors is systematically scored on a 17-point continuous scale from the least important (1/9, 1/8, 1/7,. . ., 1/2) to the most important (1, 2,. . ., 9). 2.5. Submodels In a decision-making process, the selected and scored criteria can be developed into a series of submodels that can logically group certain factors within a general model. For example, some factors are grouped to form natural submodels, such as a soil classification based on texture, pH and soil salinity. Similarly, population density, fish processing plants and markets could be grouped to form a simple market potential submodel. One reason for developing submodels in a decision-making process is that multicriteria evaluation (MCE) is more manageable if less than 10 factors in a particular objective are being considered simultaneously (Ross, 1998). When the number of factors is greater than 10, the logic of the modelling for the MCE module becomes difficult. This process was first used by Aguilar (1992) and Aguilar and Ross (1995). Aguilar and Ross (1993) divided the modelling into primary, secondary and tertiary stages within the general model. They noted that the number of stages may vary with the application but the overall approach would remain the same (Fig. 2). Based on the score and weightings, nine submodels were developed (Table 2), from which system-oriented models were generated for brackish water shrimp and crab culture by using different combinations and weightings of the submodels. The weighting of each submodel was again derived from Saaty’s matrix and the weights were finally used in MCE to show the potential sites for shrimp and crab aquaculture. In the final MCE module, a constraint image (settlements, rivers, roads, forests, etc.) was incorporated to exclude from the consideration. 2.6. Verification Model verification was carried out by making comparisons between predicted suitable sites and existing farm locations by cross-tabulation matrix. The layer of existing farm locations was prepared from reference data amassed during two field campaigns during January 1998 and February 1999 and existing farm locations from maps. A stratified random series of sites were identified and these were visited and assessed. Navigation and data collection were based on a Garmin GPS III in conjunction with a laptop using CARTALINX (Clark Labs) and a series of backdrop LANDSAT images. The interest of this verification was to find out whether the existing shrimp farm matched with the suitable sites or not.
482
Criteria
Type of data collected
Sources
Location
Scale
Rainfall Temperature Mangroves
BBS, 1996 Graphosman, 1998 Overseas Development Administration (ODA), 1985 Soil Research and Development Institute (SRDI) 1972 – 1975 SRDI, 1998(a – d) Survey Bangladesh 1973 – 1981
Bangladesh Bangladesh Khulna region
Tablea 1:10,000,000 1:10,000
Khulna region
1:25,000
Khulna region Bangladesh
1:10,000,000 1:50,000
Natural postlarvae
Report Annual mean temperature Sundarbans Forest Inventory Project, Bangladesh Reconnaissance soil survey maps and soil map Soil map Topographical survey map of Bangladesh Availability of natural postlarvae
Khulna region
Hatcheries
DFID report
Bangladesh
Tablea 1:10,000,000 Tablea
Shrimp farm locations Industries Mangroves and settlements Urban developments Roads
Landsat TM false colour image Types of industries Landsat TM false colour image
Bangladesh Fisheries Research Institute Report, 1998 Department for International Development, 1996 Nysel Press Associates, 1996 Graphosman, 1998 Nysel Press Associates, 1996
Bangladesh Bangladesh Khulna region
30 30 m 1:10,000,000 30 30 m
Graphosman, 1998 Graphosman, 1998
Khulna region Bangladesh
1:10,000,000 1:10,000,000
ODA, 1985
Khulna region
1:10,000
BBS, 1996
Bangladesh
Tablea
Soil texture
Topography
Conservation areas Population density
Population centre Transport systems of Bangladesh, Landsat image Sundarbans Forest Inventory Project, Bangladesh Population census report
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Table 1 Sources of the data for environmental and socioeconomic criteria for aquaculture development in the Khulna region, Bangladesh
Agglomeration
Location of shrimp farms
Markets
Topographical survey maps and population size Administrative maps Directory of NGOs
Government support NGO support
Field data Report
Soil salinity Surface water Ground water
Report Landsat TM false colour image Hydrological Network, Bangladesh
Tidal chart
Bangladesh Tide Tables
Fish processing plant Flood Cyclone Drought Animal waste Inputs Land use
Report Flood-prone areas Cyclone-affected areas Drought-affected areas Report Report Land use map and Landsat image
a
Administrative maps
SRDI, 1998(a – d) Association of Development Agencies in Bangladesh, 1999 SRDI, 1998(a – d) District Fisheries Office, GPS reading Surface Water Modeling Centre, Dhaka, 1996 SRDI, 1997 Nysel Press Associates, 1996 Bangladesh Water Development Board, 1996 Bangladesh Inland Water Transport Authority, 1996 Janata Bank, 1999 SRDI, 1998(a – d) SRDI, 1998(a – d) SRDI, 1998(a – d) BBS, 1997 BBS, 1997 SRDI, 1998(a – d)
Khulna region
1:10,000,000
Bangladesh
1:50,000
Bangladesh Bangladesh
1:10,000,000 Tablea
Bangladesh
1:10,000,000
Khulna region Khulna region
GPS reading Tablea
Coastal Belt Khulna region Bangladesh
Tablea 30 30 m 1:175,000
Bangladesh
Tablea
Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh
Tablea 1:10,000,000 1:10,000,000 1:10,000,000 Tablea Tablea 1:10,000,000
Table data were plotted on to the 1:50,000 scale map and INTERPOLATION module was used to create continuous surface.
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Support form research station Salinity Salinity
Space Research and Remote Sensing Organization, 1996 Survey Bangladesh, 1972 – 79
483
484
Water chemistry Available water Land use Soil chemistry Infrastructure Support Inputs Risk factors Mangrove as indicator
Water chemistry
Available water
1 1 1/2 1/2 1/3 1/6 1/6 1/7 1/4
1 1/2 1/2 1/2 1/6 1/3 1/7 1/3
Coincidence ratio (CR) = 0.07.
Land use
1 1/2 1/3 1/4 1/6 1/7 1/5
Soil chemistry
1 1/3 1/5 1/3 1/4 1/4
Infrastructure
1 1 1/6 1/3 1/6
Support
1 1 1/2 1/2
Inputs
1 1/2 1/2
Risk factors
1 1/3
Mangrove as indicator
1
Weights 0.2286 0.2028 0.1818 0.1373 0.0978 0.0466 0.0420 0.0328 0.0303
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Table 2 The pair-wise comparison matrix for assessing the relative importance of the nine factors for aquaculture development in the Khulna region, Bangladesh (numbers show the ratings of the row factor relative to the column factor)
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Fig. 2. Integration of criteria into models for development of shrimp and crab culture in the Khulna region, Bangladesh. 485
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Table 3 Input parameters and calculated indicators of economic benefits from the potential sites Economic benefits
Tilapia
Carp
Culture potential area identified (ha) Minimum wage (MW) in US$ MW/person/year (US$ 12) Estimated productivity (tons ha/crop) Earnings ha/year (US$) Farmer’s earnings MW ha/year Available land after 15% excluded for dikes, guard rooms and office construction and 5% for rice culture Total production Total farming job potential at MW level
172,876 137,073 178,442 198,673 191,141 322,883 30 30 30 30 30 30 360 360 360 360 360 360 2 41 0.7 0.6 1.5 2.4 759 982 2051 1518 1385 140 2 2.73 6 4 3.85 0.39 146,945 116,512 151,676 168,872 162,470 306,739
293,889 466,048 293,890 318,078
Prawn
618,295 910,054
Shrimp
101,323 675,488
Crab
243,705 625,509
Rice
736,173 119,628
Carp culture was calculated one crop/year and others were one crop/6 months. 50 Taka = US$1 in 2000. DOF (1993, 1996), Chong (1993), Saha et al. (1997), Overton and Macintosh (1997), BFRI (1994a,b, 1997), Chanda and Khandaker (1994) and Jainal (1997).
2.7. Economic evaluation of different land uses In order to compare the economic value of potential culture developments, revenues in terms of income were calculated for shrimp and crab farming and alternative agricultural land uses. Six land use options were considered in terms of economic output and job potential: shrimp, crab, carp, tilapia, prawn culture and rice production. Parameters for 1 ha of lease land were taken for all culture operations and the culture period was determined as 6 months due to the seasonality of each crop. To calculate total outcomes from the GIS-predicted suitable land areas, it was necessary to estimate the area required for roads, dikes, inlet and outlet channels, guardrooms and offices. Fifteen percent of the land was allowed for that purpose, the rest being considered as the pond area available for culture. Similarly, 5% of the land was excluded from the rice fields for the same reasons. Average revenues from shrimp, crab, carp, tilapia and prawn farming were compared with those of rice culture systems. Input parameters are summarised in Table 3.
3. Results 3.1. Areal distribution of submodels The reclassified surface areas for nine submodels are summarised in Table 4. Based on actual intensity of rice and other crop farming systems, about 43% of the land was classified as very suitable for brackish water shrimp and crab culture, while a further 5%, 49%, and 3% were classified as moderately, marginally and unsuitable, respectively (Table 4). Fig. 3 shows the range of water availability for brackish water shrimp and crab farming in the region.
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Table 4 Scoring of submodels in terms of suitability for shrimp and crab culture in southwestern Bangladesh Name of submodel
Very suitable (%)
Moderately suitable (%)
Marginally suitable (%)
Unsuitable (%)
Land use Water sources Soil chemistry Risk Water chemistry Inputs Support Infrastructure Natural indicators
43.0 29.1 10.0 4.1 26.6 19.6 1.02 3.3 6.3
4.7 23.7 70.4 55.6 58.0 19.8 22.0 47.4 6.1
49.2 25.2 19.5 40.3 15.5 53.5 68.9 44.6 11.5
3.2 22.0 0.2 0 0 7.0 8.2 4.7 76.1
Values are percentages of the total study area.
Optimum suitability of soils occurs in the middle of the study area where other physical factors are more or less favourable. There are large areas of moderately suitable soils found in Satkhira, Bagerhat, Khulna, Jessore and Narail districts. However, most of the areas in Gopalganj district and a few patches in Khulna and Bagerhat districts fall into the marginally suitable class due to the presence of peat soils (Fig. 4). In addition, much of the
Fig. 3. Available water for brackish water aquaculture in Southwest Bangladesh.
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Fig. 4. Suitable lands for brackish water shrimp culture in Southwest Bangladesh.
unsuitable area in the risk submodel is located in the lower part of the region that is lowlying and extremely prone to flooding. Areas with very suitable or moderately suitable water chemistry are widely distributed in the lower part of the region because of uniform salinity, temperature and dissolved oxygen. However, in the south and northeastern part of the region, by-product inputs are potentially insufficient for shrimp farming due to low crop yields and less cattle and poultry rearing. Similarly, areas with support potential for shrimp and crab farming at an optimum level are patchily distributed except for a few areas in the Khulna and Bagerhat districts, where most of the NGOs and government offices are located, far away from the farming sites. The lower part of the area has unsuitable infrastructural potential for brackish water aquaculture, although some small patches of land in the northwestern and central region are very suitable to moderately suitable for both culture species. Most of the area is marginal to unsuitable due to the absence of major roads and distance from processing plants. Image interpretation showed that crab culture had much more suitable land in terms of infrastructure (40%) than shrimp (3%). This is due to the abundance of crab postlarvae inside and surrounding the Sundarbans mangrove forest and their higher survival rate during transportation. Also, adult crabs are not as perishable as shrimp and can travel long distances for marketing. Overall, Figs. 4 and 5 clearly highlight the suitability of the southern parts of the region for both brackish water shrimp and crab farming, principally due to the high quality of water, appropriate salinity and consistent flow of tidal water. These areas also have good
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Fig. 5. Suitability of lands for brackish water crab culture in Southwest Bangladesh.
access to road transport, high agglomeration potential and are not too far from market outlets. Since the weights of the primary criteria and ranking of submodels were different for shrimp and crab, it was obvious that the spatial outcome would be different. A comparison of the results for the two species revealed that the salinity, water availability and infrastructure strongly influenced the outcome. Moreover, there are no areas that are totally unsuitable for either of the species, whereas, some northern areas are only marginally suitable for both species because of the lack of saline water. A greater area is suitable for farming crab than shrimp because the former is more tolerant of environmental stress and little processing is needed prior to export. It is important to note that some of the areas that have already been developed for shrimp farming only scored as moderately suitable. These included most of the Bagerhat and Satkhira region and some parts of Khulna district. In these areas, soil quality is lower because of a high proportion of fine clay, although the major problem is the distance from markets and processing plants. However, some areas were predicted to be suitable for shrimp and crab culture in the lower part of Jessore district. These rated well on certain criteria, but are not in culture at present due to the low salinity of the available water. Fifty-five percent of the very suitable and 45% of the moderately suitable model output matched with the existing shrimp farm locations. Among the 287 known farm locations, 158 were exactly matched with the very suitable model output and 129 falls within moderately suitable output. A few farms fell just within the mangrove area, although no shrimp farms were seen inside the Sundarbans mangrove forest in the Landsat TM image,
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Table 5 Cross-tabulation for error matrix analysis of shrimp culture potential site (columns) against existing farm location (rows) Existing farm location
Total number Pixels that fall Outside farm Existing shrimp of pixel/cell into different category (%) location farm location Aquaculture potential outside (0) 18,062,244 site after modeling marginally suitable (2) 0 moderately suitable (3) 0 very suitable (4) 0
0 2 2381 2913
18,062,244 2 2381 2913
100 0.04 44.96 55.00
Cramer’s V = 1.00 and overall Kappa = 0.78.
it was considered to be an artefact of a minor geo-registration error that was 12.26 m. The result of the cross-tabulation of shrimp culture potential site (columns) against existing farm location (rows) is shown in Table 5 that summarises the number of cells that fall within each possible combination of location. In addition to the cross-tabulation matrix, the CROSSTAB module also generates the Kappa Index of Agreement (KIA). This index is particularly important as it is used to determine the degree of agreement between the two images (Eastman, 1995). Kappa ranges in value from 1 to 1. A value of 1 indicates that the two images are in perfect agreement (no change occurred), whereas if the two images are completely different from one another, then the Kappa value is 1. If all the changes that occurred could be accounted for by chance, then Kappa is 0. The overall KIA of the two images is 0.78, indicating that there is a considerable agreement between suitable model output and existing farm location. Cross-tabulation also generates a correlation coefficient of Cramer’s V whose value ranges from 0.0, indicating no correlation, to 1.0, indicating perfect correlation between the two images.
4. Discussion The results predicted as suitable from this GIS evaluation were partly verified by the presence of shrimp farms in the study area based on the analysis of satellite images and published data (Table 3). GIS predicted that 198,673 ha of land are suitable for shrimp farming, while DOF (1994 – 1995) showed that 104,624 ha are already in operation in the area. The discrepancy between some of the predicted and actual shrimp farm locations was attributed to the fact that farms have not been established using any systematic application of scientific knowledge but rather on the availability of water and the influence of neighbouring farmers. Suitable sites for crab culture could not be verified as this culture practise has only recently been introduced to the area as a result of the strong international market for crab (Overton and Macintosh, 1997). Much of the area ranked as most suitable for brackish water shrimp and crab culture is situated near the riverbanks in the tidal zone, ensuring easy access to saline water. However, most of these areas were only ranked as moderately suitable because of lack of other facilities, such as fresh water supply and drainage difficulties. Large water bodies,
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which contain fresh water, were not considered of potential use because of the problems associated with low salinity. Natural forest and wildlife refuges, Sundarbans, roads, rivers and urban areas were also excluded. Crop agriculture, which is the main traditional source of income, has not yet been substantially affected by the development of brackish water shrimp and crab culture. By contrast, although new sources of income have been created by shrimp culture in the region, there has been a significant reduction of livestock and poultry in the shrimp farming areas (Rahman et al., 1995; Khan and Hossain, 1996). It is interesting to note that, unlike some other areas of Bangladesh, only one crop of shrimp can be cultured in most of the region as the water salinity falls to zero from June to December. This period is very suitable for rice production and freshwater prawn and fish culture (Rahman et al., 1995). On the other hand, crab remains an incidental crop of rice, fish, shrimp and prawn culture in the region in all seasons (Overton and Macintosh, 1997). From the simple economic assessment of shrimp farming and alternative land uses in the region, it is clear that rice farming scored rather poorly in comparison to the other land use activities considered. Hambrey (1996) obtained similar results in a comparative economic study of seven land use options in the mangrove areas of Indonesia. In the present study, the highest financial returns came from freshwater prawn culture followed by brackish water shrimp and crab culture. Highest employment, measured in terms of average annual labour requirements per hectare of land, was generated by prawn culture, followed by crab, shrimp, carp, tilapia and rice cultivation, respectively. Crab culture is in its initial stage and there is considerable potential for further expansion. Crab culturists could earn many times as suggested here if culture methods were intensified, for example by production of ripe females and by fattening crabs in cages alongside conventional production methods (Cholik and Hanafi, 1992; Overton and Macintosh, 1997). However, while crab culture is more environmentally friendly than shrimp culture, in that it can be integrated with horticulture, conventional forestry, rice culture and polyculture with fish (Chong, 1993; Chandrasekaran and Perumal, 1993), mud crab culture is totally dependent on wild seed supply. There is thus potential for establishing hatcheries to provide a continuous supply of seed (Salam and Ross, 2000). An evaluation of the availability of agricultural by-products, especially oil cake, rice bran, wheat bran and other products that could be used as low cost feed, could also enhance aquaculture development opportunities in the area. In conclusion, the present study shows the modelling power of GIS for aquaculture development and its value in making comparisons among farming systems. There is considerable potential for further exploitation of GIS for optimisation of competing aquatic production activities and their interaction with land-based farming systems. The GIS planning process has an important role, particularly where land use patterns are intensive, and where developments must be sustainable in terms of sensitive environment issues, as is the case in most parts of Bangladesh.
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decision models for aquaculture development. Institute of Aquaculture, University of Stirling, Stirling, Scotland, UK. MSc thesis. Aguilar, M.J., 1996. Development and evaluation of GIS-based models for farming and management to coastal aquaculture: A case study in Sinaloa, Mexico. Institute of Aquaculture, University of Stirling, Stirling, Scotland, UK. PhD thesis. Aguilar, M.J., Nath, S.S., 1998. A Strategic Reassessment of Fish Farming Potential in Africa. Food and Agriculture Organization, Rome, Italy. Aguilar, M.J., Ross, L.G., 1993. Aquaculture development and GIS. Mapping Awareness and GIS in Europe 7 (4), 49 – 52. Aguilar, M.J., Ross, L.G., 1995. Geographical information system (GIS) environmental models for aquaculture development in Sinaloa State, Mexico. Aquaculture International 3, 103 – 115. BBS, 1996. Statistical Yearbook of Bangladesh: Bangladesh Bureau of Statistics. Statistics Division, Ministry of Planning, Government of the People Republic of Bangladesh, Dhaka, Bangladesh. BBS, 1997. Statistical Pocketbook of Bangladesh. Bangladesh Bureau of Statistics, Statistics Division, Ministry of Planning, Government of the People Republic of Bangladesh, Dhaka, Bangladesh. BFRI, 1994a. Moushumi Pukure Niloticar Chash Progukti: Bangladesh Fisheries Research Institute. Fresh Water Station, Mymensingh, Bangladesh. BFRI, 1994b. Pukure Maccher Mishro Chash Progukti: Bangladesh Fisheries Research Institute. Fresh Water Station, Mymensingh, Bangladesh. BFRI, 1997. Moushumi Pukure Gift Tilapia Chash Progukti: Bangladesh Fisheries Research Institute, Fresh Water Station, Mymensingh, Bangladesh. BFRI, 1998. Availability of natural post larvae in Bangladesh coast. Report prepared by Bangladesh Fisheries Research Institute, scale 1: 10,000,000. BIWTA, 1996. Bangladesh Tidal chart. Bangladesh Inland Water Transport Authority. BWDB, 1996. Hydrological Network map of Bangladesh. Bangladesh Water Development Board. Scale 1: 175,000. Castro-Ortiz, C.A., 1994. Sea-level rise and its impact on Bangladesh. Ocean and Coastal Management 23 (3), 249 – 270. Chanda, A.C., Khandaker, H.R., 1994. Galda chingri chash: Motsho Pokho’ 1994. Department of Fisheries, Government of Bangladesh, Dhaka, Bangladesh. Chandrasekaran, V.S., Perumal, P., 1993. The mud crab, Scylla serrata a species for culture and export. Seafood Export Journal 25 (5), 15 – 19. Cholik, F., Hanafi, A., 1992. A review of the mud crab (Scylla serrata) fishery and culture in Indonesia. In: Angell, C.A. (Ed.), The Mud Crab, A Report on the Seminar Convened in Surat, Thailand. BOBP, Madras, India, pp. 13 – 27. Chong, L.P., 1993. The culture and fattening of mud crabs. Infofish International 3 (May/June), 46 – 49. DFID, 1996. Location of shrimp and prawn hatcheries. A field report prepared by Department for International Development. DOF, 1993. Motsho Pokho 1993. Department of Fisheries, Government of Bangladesh, Dhaka, Bangladesh. DOF, 1994 – 1995. Fish Catch Statistics of Bangladesh. Department of Fisheries, Government of Bangladesh, Dhaka, Bangladesh. DOF, 1996. Bagda Chingri Chash Babosthapona: Shrimp Culture Management. Department of Fisheries, Government of Bangladesh, Dhaka, Bangladesh. Eastman, J.R., 1995. IDRISI for Windows User’s Manual, 1st ed. Clark University Graduate School of Geography, Worcester, MA, USA. 120 pp. Giri, C., Shrestha, S., 1996. Land cover mapping and monitoring from NOAA AVHRR data in Bangladesh. International Journal of Remote Sensing 17 (14), 2749 – 2759. Graphosman, 1998a. Atlas of Bangladesh, scale 1: 10,000,000. Graphosman, 1998b. Atlas of Bangladesh, scale 1: 10,000,000. Hambrey, J., 1996. Comparative economics of land use options in mangrove. Aquaculture Asia 2, 10 – 14. Ibrekk, H., Kryvi, H., Elvestad, S., 1993. Nation-wide assessment of the suitability of the Norwegian coastal zone and rivers for aquaculture (LENKA). Coastal Management 21 (1), 53 – 73. Jainal, M., 1997. Golda Project: Gher Cost Report. CARE Bangladesh, Bagerhat, Bangladesh.
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Janata Bank, 1999. Name and location of fish processing plant of Bangladesh. Report prepared by Janata Bank, Bangladesh. Jarayabhand, S., 1997. Management of Coastal Aquaculture in Thailand. Adelaide University, Australia. PhD thesis. Kapetsky, J.M., 1994. A strategic assessment of warmwater fish farming potential in Africa. CIFA Technical Paper 27. Food and Agriculture Organization, Rome, Italy. Kapetsky, J.M., Nath, S.S., 1997. A strategic assessment of the potential for freshwater fish farming in Latin America. COPESCAL Technical Paper 27. Food and Agriculture Organization, Rome, Italy. Kapetsky, J.M., Hill, J.M., Dorsey, W.L., 1988. Geographical information system for catfish farming development. Aquaculture 68, 311 – 320. Khan, M.G., Alam, M.F., 1992. The mud crab (Scylla serrata) fishery and its bio-economics in Bangladesh. In: Angell, C.A. (Ed.), The Mud Crab, A Report on the Seminar Convened in Surat, Thailand. BOBP, Madras, India, pp. 29 – 40. Khan, Y.S.A., 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. Krieger, Y., Muslow, S., 1990. GIS application in marine benthic resource management. GIS for the 1990s. Proc. National Conference (CISM), Ottawa, Canada. Landsat TM, 1996. Shrimp farm locations seen in a false colour Landsat TM image provided by Nysel Press Associates, UK. Scale 30 30 m. Mazid, M.A., 1998. Shrimp seed collection from nature and its environmental impacts: 15, Fisheries Research Institute, Brackish Water Station, Paikgachha, Khulna. Meaden, G.J., Kapetsky, J.M., 1991. Geographical information systems and remote sensing in inland fisheries and aquaculture. FAO Fisheries Technical Paper 318. Food and Agriculture Organization, Rome, Italy. Nath, S.S., Bolte, J.P., Ross, L.G., Aguilar-Manjarez, J., 2000. Applications of Geographical Information Systems (GIS) for spatial decision support in aquaculture. Aquacultural Engineering 23, 233 – 278. Overseas Development Administration, 1985. The Sundarbans Mangrove Forest Inventory Project, Bangladesh, scale 1: 10,000. Overton, J.L., Macintosh, D.J., 1997. Mud crab culture: prospects for the small-scale Asian farmer. Infofish International 5, 26 – 32. Rahman, M.S., Malek, M.A., Matin, M.A., 1995. Trend of pesticide usage in Bangladesh. Science of the Total Environment 159 (1), 33 – 39. Ross, L.G., 1998. The use of Geographical Information Systems in Aquaculture: A Review. Paper presented at Congreso Nacional de Limnologia, Michoacan, Mexico. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structure. Journal of Mathematics and Physiology 15, 234 – 281. Saha, M.R., Roy, P.K., Ahmed, S.U., 1997. Impact of stocking density on growth and survival rate of mud crab (Scylla serrata, Froskal). Bangladesh Journal of Fisheries Research 1 (1), 47 – 52. Salam, M.A., Ross, L.G., 1999. GIS modelling for aquaculture in South-western Bangladesh: comparative production scenarios for brackish and freshwater shrimp and fish. GeoSolutions: Integrating Our World, vol. 13. Adams Media, Vancouver, BC, Canada, pp. 141 – 145. Salam, M.A., Ross, L.G., 2000. Optimising site selection for development of shrimp (Penaeus monodon) and mud crab (Scylla serrata) culture in South-western Bangladesh. GIS 2000 Conference Proceedings, March 13 – 16, Toronto, Canada. CD Proceedings. SPARRSO, 1996. Agglomeration. Location of shrimp farms. Map prepared by Space Research and Remote Sensing Organization, Bangladesh. SRDI, 1972 – 1975. Reconnaissance soil survey and soil map. Soil Research and Development Institute, Bangladesh. Scale 1: 25,000. SRDI, 1997. Interim Soil Salinity Report of Coastal Belt of Bangladesh. SRDI, 1998a. Administrative map of Bangladesh, scale 1: 10,000,000. SRDI, 1998b. Natural hazards map of Bangladesh, scale 1: 10,000,000. SRDI, 1998c. Drought- affected areas of Bangladesh, scale 1: 10,000,000. SRDI, 1998d. Land use map of Bangladesh, scale 1: 10,000,000. Survey Bangladesh, 1973 – 1981. Topographical survey map of Bangladesh, scale 1: 50,000.
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A comparison of development opportunities for crab and shrimp aquaculture in southwestern Bangladesh, using GIS modelling M. Abdus Salam a,b,*, Lindsay G. Ross a, C.M. Malcolm Beveridge a b
a Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, Scotland, UK Department of Aquaculture, Faculty of Fisheries, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
Received 21 October 2001; received in revised form 27 September 2002; accepted 6 November 2002
Abstract The present study identifies and quantifies appropriate sites for brackish water aquaculture development in southwestern Bangladesh using remote sensing, GPS and geographical information systems (GIS). A colour composite Landsat TM image from 1996 covering the southwestern part of Bangladesh was used to identify the extent of brackish water and to classify land use. The remotely sensed data were complemented by secondary data digitised from a range of sources, including hard copy maps, to create a spatial database that included environmental and infrastructural data. A series of GIS models were developed in order to identify and prioritise the most suitable areas for brackish water shrimp and crab farming. Using qualitative and quantitative output from the models, the benefits of shrimp and crab farming and alternative land uses in the Khulna region were compared, based on gross production, economic output and employment potential. Comparisons were made of brackish water shrimp and crab culture with moderately saline-tolerant tilapia and prawn culture, freshwater carp culture and traditional rice production systems. Shrimp was identified as the most capital intensive and risky production system. Earnings per hectare were a little higher for shrimp culture than for crab culture. The present study demonstrates the usefulness of GIS as an aquaculture planning tool in a region where natural resources are already under considerable pressure. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Modelling; GIS; Aquaculture; Bangladesh; Remote sensing; Crab and shrimp
* Corresponding author. Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, Scotland, UK. Tel.: +88-91-55695-7x2282; fax: +88-91-55810. E-mail address: [email protected] (M.A. Salam). 0044-8486/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0044-8486(02)00619-1
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1. Introduction Geographical information systems (GIS) are powerful tools capable of organising, analysing, and displaying large, spatially explicit datasets. To date, GIS has been applied for regional, national or sectoral studies of aquaculture where human resources, specific sites, economics, markets and sociocultural resources have been considered (Kapetsky et al., 1988; Meaden and Kapetsky, 1991; Nath et al., 2000). A number of large-scale and sectoral studies have been carried out, for example, in the African continent (Aguilar and Nath, 1998; Kapetsky, 1994) and in Latin America (Kapetsky and Nath, 1997). Studies at national levels have included salmon and mussel cage, pen and bed culture in Chile (Krieger and Muslow, 1990), and the sittings of salmon and rainbow trout farms in Norway (Ibrekk et al., 1993). A number of studies have further exploited the modelling capacity of GIS, including the development of a model for sitting salmon cage culture on the West Coast of Scotland; an exploration of the potential for rice-fish and fish culture in the Red River Delta, Vietnam (Tran and Demaine, 1996); regional development models for shrimp culture in Mexico (Aguilar, 1996); the management of coastal aquaculture in Thailand (Jarayabhand, 1997); and for shrimp, prawn and fish culture potential in Bangladesh (Salam and Ross, 1999). There is considerable potential for shrimp culture in southwestern Bangladesh and in 1994, 104,000 ha of land was already under culture (DOF, 1994 – 1995). However, aquatic resource management in the region has not been fully integrated with the rural economy and could be further developed to help meet the increasing demand for fish protein in the area. In this context, the establishment of a structured decision-making and planning scheme would be useful. This paper presents results from the application of remote sensing and GIS techniques to estimate the area of land available for different types of aquaculture development in southwestern Bangladesh, especially the suitability of areas for brackish water aquaculture of the mud crab (Scylla serrata) and the giant tiger shrimp (Penaeus monodon). The models are also used to estimate the production potential and economic characteristics of shrimp and crab farming, and to consider and evaluate alternative land uses in the Khulna region, southwestern Bangladesh.
2. Materials and methods 2.1. Study area The study area lies 300 km southwest of Dhaka, Bangladesh on the coastal plain at the apex of the Bay of Bengal and covers approximately 14,000 km2 (Fig. 1). The area lies between 21j30V and 23j15V North and 89j00V and 90j00V East and includes the world’s largest continuous mangrove forest, the Sundarbans, estuarine marshlands and numerous rivers, canals and their tributaries (Giri and Shrestha, 1996; Viju, 1995). The terrain is relatively flat; the elevation ranges from sea level to 5 m above mean sea level (CastroOrtiz, 1994). Fisheries play an important role in the regional and national economy and the area is one of the most promising for aquaculture. The fresh and coastal water resources are the
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Fig. 1. The study area: southwest of Bangladesh.
most abundant in the country. The presence of the world’s largest continuous mangrove forest provides food source and nursery for the offshore fishery, protection of the coasts from storm surges and cyclones, domestic and commercial products, recreation and tourist services, and habitat for shrimp and other cultivable species. Culture of freshwater and marine fish, shrimp and other crustacean species are highly important as they can be easily integrated with other activities such as agriculture and livestock rearing. Bangladesh has extremely favourable conditions for shrimp culture, not least because of the low production costs. In 1982– 1983, there were only 5200 ha of land under shrimp culture; by 1996, 110,000 ha had been brought under shrimp cultivation, 70% being located in the Khulna region (Mazid, 1998). By contrast, mud crab (S. serrata) has been an incidental product arising from the culture of shrimp, prawn, milkfish and other finfishes
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in Southeast Asia (Chandrasekaran and Perumal, 1993). However, with the continued expansion of shrimp culture worldwide, market prices have dropped and profit margins have been squeezed, leading to an upsurge of interest in crab culture as it is one of the most popular seafoods in many parts of the world, particularly in Southeast Asia, and can be exported alive (Khan and Alam, 1992). 2.2. GIS software and systems used The GIS software used in this study was the raster-based IDRISI for Windows version 2.0, and in some cases IDRISI 32, developed at Clark University, USA. The TOSCA digitising system (Version 2.03), DIGI-EDIT (Version 1.050) and CalComp Drawing Board III were used to digitise and edit vector layers. The software operated on a Pentium Pro 200 MHz workstation with 128 Mb RAM, 12 Gb hard disk, Iiyama Vision Master TM 21U colour monitor and a Matrox Millennium 8 Mb video adapter. 2.3. Remotely sensed data and thematic maps The primary data sources were two Landsat TM satellite images containing three multispectral bands (2, 3 and 4), acquired on 9 and 16 February 1996. A cloud-free image of the study area was subset from the original two full scene images and this subscene was geo-registered with the Survey of Bangladesh topographical sheet (1:50,000) using 140 reference points. Secondary soil texture and soil pH were extracted from soil reconnaissance survey maps prepared by the Soil Resources Development Institute (SRDI) during the period of 1972– 1975. Land use, flood, storm surge and drought were taken from SRDI maps of 1997. Soil and water salinity data were taken from the 1997 SRDI soil salinity report and Surface Water Modelling Centre (SWMC) report (1996). Roads, railways, rivers, international boundaries and other information were taken from topographical maps produced by Survey Bangladesh, scale 1:50,000, prepared during 1972– 1979. Population density, agricultural by-products and animal wastes were taken from Bangladesh Bureau of Statistics (BBS, 1996) and BBS (1997) pocket dictionary. Additional data were also extracted from atlases, periodicals and published papers (Table 1). 2.4. Weighting procedure for factors and submodels In order to determine potential sites for brackish water shrimp and crab farming, 36 environmental and economic criteria were selected and compiled. The criteria were of two types: factors and constraints (Eastman, 1995). A factor is a criterion that enhances, or detracts from, the suitability of a specific alternative under consideration. It is therefore measured on a continuous scale. A constraint, by contrast, serves to limit the alternatives under consideration such as settlements, reserve forests, roads and rivers and is Boolean in nature. Suitability ratings for each criterion were established according to Aguilar (1996) and Kapetsky (1994) and each factor was reclassified on a scale from 1 to 4. A weighting for each factor was then established according to the pair-wise comparison matrix of Saaty (1977).
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Although a variety of weighting techniques exist, the pair-wise comparison developed by Saaty known as the Analytical Hierarchy Process (AHP) was used to develop a set of relative weights for a group of factors in a multicriteria evaluation. Providing a series of pair-wise comparisons of the relative importance of factors to the suitability of image pixels for the activity being evaluated develops the weights. These pair-wise comparisons are then analysed to produce a set of weights that sum to 1 (Table 2). The factors and their resulting weights can then be used as input for the multicriteria evaluation (MCE) module by weighted linear combination (WLC). The relative rating of a pair of factors is systematically scored on a 17-point continuous scale from the least important (1/9, 1/8, 1/7,. . ., 1/2) to the most important (1, 2,. . ., 9). 2.5. Submodels In a decision-making process, the selected and scored criteria can be developed into a series of submodels that can logically group certain factors within a general model. For example, some factors are grouped to form natural submodels, such as a soil classification based on texture, pH and soil salinity. Similarly, population density, fish processing plants and markets could be grouped to form a simple market potential submodel. One reason for developing submodels in a decision-making process is that multicriteria evaluation (MCE) is more manageable if less than 10 factors in a particular objective are being considered simultaneously (Ross, 1998). When the number of factors is greater than 10, the logic of the modelling for the MCE module becomes difficult. This process was first used by Aguilar (1992) and Aguilar and Ross (1995). Aguilar and Ross (1993) divided the modelling into primary, secondary and tertiary stages within the general model. They noted that the number of stages may vary with the application but the overall approach would remain the same (Fig. 2). Based on the score and weightings, nine submodels were developed (Table 2), from which system-oriented models were generated for brackish water shrimp and crab culture by using different combinations and weightings of the submodels. The weighting of each submodel was again derived from Saaty’s matrix and the weights were finally used in MCE to show the potential sites for shrimp and crab aquaculture. In the final MCE module, a constraint image (settlements, rivers, roads, forests, etc.) was incorporated to exclude from the consideration. 2.6. Verification Model verification was carried out by making comparisons between predicted suitable sites and existing farm locations by cross-tabulation matrix. The layer of existing farm locations was prepared from reference data amassed during two field campaigns during January 1998 and February 1999 and existing farm locations from maps. A stratified random series of sites were identified and these were visited and assessed. Navigation and data collection were based on a Garmin GPS III in conjunction with a laptop using CARTALINX (Clark Labs) and a series of backdrop LANDSAT images. The interest of this verification was to find out whether the existing shrimp farm matched with the suitable sites or not.
482
Criteria
Type of data collected
Sources
Location
Scale
Rainfall Temperature Mangroves
BBS, 1996 Graphosman, 1998 Overseas Development Administration (ODA), 1985 Soil Research and Development Institute (SRDI) 1972 – 1975 SRDI, 1998(a – d) Survey Bangladesh 1973 – 1981
Bangladesh Bangladesh Khulna region
Tablea 1:10,000,000 1:10,000
Khulna region
1:25,000
Khulna region Bangladesh
1:10,000,000 1:50,000
Natural postlarvae
Report Annual mean temperature Sundarbans Forest Inventory Project, Bangladesh Reconnaissance soil survey maps and soil map Soil map Topographical survey map of Bangladesh Availability of natural postlarvae
Khulna region
Hatcheries
DFID report
Bangladesh
Tablea 1:10,000,000 Tablea
Shrimp farm locations Industries Mangroves and settlements Urban developments Roads
Landsat TM false colour image Types of industries Landsat TM false colour image
Bangladesh Fisheries Research Institute Report, 1998 Department for International Development, 1996 Nysel Press Associates, 1996 Graphosman, 1998 Nysel Press Associates, 1996
Bangladesh Bangladesh Khulna region
30 30 m 1:10,000,000 30 30 m
Graphosman, 1998 Graphosman, 1998
Khulna region Bangladesh
1:10,000,000 1:10,000,000
ODA, 1985
Khulna region
1:10,000
BBS, 1996
Bangladesh
Tablea
Soil texture
Topography
Conservation areas Population density
Population centre Transport systems of Bangladesh, Landsat image Sundarbans Forest Inventory Project, Bangladesh Population census report
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Table 1 Sources of the data for environmental and socioeconomic criteria for aquaculture development in the Khulna region, Bangladesh
Agglomeration
Location of shrimp farms
Markets
Topographical survey maps and population size Administrative maps Directory of NGOs
Government support NGO support
Field data Report
Soil salinity Surface water Ground water
Report Landsat TM false colour image Hydrological Network, Bangladesh
Tidal chart
Bangladesh Tide Tables
Fish processing plant Flood Cyclone Drought Animal waste Inputs Land use
Report Flood-prone areas Cyclone-affected areas Drought-affected areas Report Report Land use map and Landsat image
a
Administrative maps
SRDI, 1998(a – d) Association of Development Agencies in Bangladesh, 1999 SRDI, 1998(a – d) District Fisheries Office, GPS reading Surface Water Modeling Centre, Dhaka, 1996 SRDI, 1997 Nysel Press Associates, 1996 Bangladesh Water Development Board, 1996 Bangladesh Inland Water Transport Authority, 1996 Janata Bank, 1999 SRDI, 1998(a – d) SRDI, 1998(a – d) SRDI, 1998(a – d) BBS, 1997 BBS, 1997 SRDI, 1998(a – d)
Khulna region
1:10,000,000
Bangladesh
1:50,000
Bangladesh Bangladesh
1:10,000,000 Tablea
Bangladesh
1:10,000,000
Khulna region Khulna region
GPS reading Tablea
Coastal Belt Khulna region Bangladesh
Tablea 30 30 m 1:175,000
Bangladesh
Tablea
Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh
Tablea 1:10,000,000 1:10,000,000 1:10,000,000 Tablea Tablea 1:10,000,000
Table data were plotted on to the 1:50,000 scale map and INTERPOLATION module was used to create continuous surface.
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Support form research station Salinity Salinity
Space Research and Remote Sensing Organization, 1996 Survey Bangladesh, 1972 – 79
483
484
Water chemistry Available water Land use Soil chemistry Infrastructure Support Inputs Risk factors Mangrove as indicator
Water chemistry
Available water
1 1 1/2 1/2 1/3 1/6 1/6 1/7 1/4
1 1/2 1/2 1/2 1/6 1/3 1/7 1/3
Coincidence ratio (CR) = 0.07.
Land use
1 1/2 1/3 1/4 1/6 1/7 1/5
Soil chemistry
1 1/3 1/5 1/3 1/4 1/4
Infrastructure
1 1 1/6 1/3 1/6
Support
1 1 1/2 1/2
Inputs
1 1/2 1/2
Risk factors
1 1/3
Mangrove as indicator
1
Weights 0.2286 0.2028 0.1818 0.1373 0.0978 0.0466 0.0420 0.0328 0.0303
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Table 2 The pair-wise comparison matrix for assessing the relative importance of the nine factors for aquaculture development in the Khulna region, Bangladesh (numbers show the ratings of the row factor relative to the column factor)
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Fig. 2. Integration of criteria into models for development of shrimp and crab culture in the Khulna region, Bangladesh. 485
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Table 3 Input parameters and calculated indicators of economic benefits from the potential sites Economic benefits
Tilapia
Carp
Culture potential area identified (ha) Minimum wage (MW) in US$ MW/person/year (US$ 12) Estimated productivity (tons ha/crop) Earnings ha/year (US$) Farmer’s earnings MW ha/year Available land after 15% excluded for dikes, guard rooms and office construction and 5% for rice culture Total production Total farming job potential at MW level
172,876 137,073 178,442 198,673 191,141 322,883 30 30 30 30 30 30 360 360 360 360 360 360 2 41 0.7 0.6 1.5 2.4 759 982 2051 1518 1385 140 2 2.73 6 4 3.85 0.39 146,945 116,512 151,676 168,872 162,470 306,739
293,889 466,048 293,890 318,078
Prawn
618,295 910,054
Shrimp
101,323 675,488
Crab
243,705 625,509
Rice
736,173 119,628
Carp culture was calculated one crop/year and others were one crop/6 months. 50 Taka = US$1 in 2000. DOF (1993, 1996), Chong (1993), Saha et al. (1997), Overton and Macintosh (1997), BFRI (1994a,b, 1997), Chanda and Khandaker (1994) and Jainal (1997).
2.7. Economic evaluation of different land uses In order to compare the economic value of potential culture developments, revenues in terms of income were calculated for shrimp and crab farming and alternative agricultural land uses. Six land use options were considered in terms of economic output and job potential: shrimp, crab, carp, tilapia, prawn culture and rice production. Parameters for 1 ha of lease land were taken for all culture operations and the culture period was determined as 6 months due to the seasonality of each crop. To calculate total outcomes from the GIS-predicted suitable land areas, it was necessary to estimate the area required for roads, dikes, inlet and outlet channels, guardrooms and offices. Fifteen percent of the land was allowed for that purpose, the rest being considered as the pond area available for culture. Similarly, 5% of the land was excluded from the rice fields for the same reasons. Average revenues from shrimp, crab, carp, tilapia and prawn farming were compared with those of rice culture systems. Input parameters are summarised in Table 3.
3. Results 3.1. Areal distribution of submodels The reclassified surface areas for nine submodels are summarised in Table 4. Based on actual intensity of rice and other crop farming systems, about 43% of the land was classified as very suitable for brackish water shrimp and crab culture, while a further 5%, 49%, and 3% were classified as moderately, marginally and unsuitable, respectively (Table 4). Fig. 3 shows the range of water availability for brackish water shrimp and crab farming in the region.
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Table 4 Scoring of submodels in terms of suitability for shrimp and crab culture in southwestern Bangladesh Name of submodel
Very suitable (%)
Moderately suitable (%)
Marginally suitable (%)
Unsuitable (%)
Land use Water sources Soil chemistry Risk Water chemistry Inputs Support Infrastructure Natural indicators
43.0 29.1 10.0 4.1 26.6 19.6 1.02 3.3 6.3
4.7 23.7 70.4 55.6 58.0 19.8 22.0 47.4 6.1
49.2 25.2 19.5 40.3 15.5 53.5 68.9 44.6 11.5
3.2 22.0 0.2 0 0 7.0 8.2 4.7 76.1
Values are percentages of the total study area.
Optimum suitability of soils occurs in the middle of the study area where other physical factors are more or less favourable. There are large areas of moderately suitable soils found in Satkhira, Bagerhat, Khulna, Jessore and Narail districts. However, most of the areas in Gopalganj district and a few patches in Khulna and Bagerhat districts fall into the marginally suitable class due to the presence of peat soils (Fig. 4). In addition, much of the
Fig. 3. Available water for brackish water aquaculture in Southwest Bangladesh.
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Fig. 4. Suitable lands for brackish water shrimp culture in Southwest Bangladesh.
unsuitable area in the risk submodel is located in the lower part of the region that is lowlying and extremely prone to flooding. Areas with very suitable or moderately suitable water chemistry are widely distributed in the lower part of the region because of uniform salinity, temperature and dissolved oxygen. However, in the south and northeastern part of the region, by-product inputs are potentially insufficient for shrimp farming due to low crop yields and less cattle and poultry rearing. Similarly, areas with support potential for shrimp and crab farming at an optimum level are patchily distributed except for a few areas in the Khulna and Bagerhat districts, where most of the NGOs and government offices are located, far away from the farming sites. The lower part of the area has unsuitable infrastructural potential for brackish water aquaculture, although some small patches of land in the northwestern and central region are very suitable to moderately suitable for both culture species. Most of the area is marginal to unsuitable due to the absence of major roads and distance from processing plants. Image interpretation showed that crab culture had much more suitable land in terms of infrastructure (40%) than shrimp (3%). This is due to the abundance of crab postlarvae inside and surrounding the Sundarbans mangrove forest and their higher survival rate during transportation. Also, adult crabs are not as perishable as shrimp and can travel long distances for marketing. Overall, Figs. 4 and 5 clearly highlight the suitability of the southern parts of the region for both brackish water shrimp and crab farming, principally due to the high quality of water, appropriate salinity and consistent flow of tidal water. These areas also have good
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Fig. 5. Suitability of lands for brackish water crab culture in Southwest Bangladesh.
access to road transport, high agglomeration potential and are not too far from market outlets. Since the weights of the primary criteria and ranking of submodels were different for shrimp and crab, it was obvious that the spatial outcome would be different. A comparison of the results for the two species revealed that the salinity, water availability and infrastructure strongly influenced the outcome. Moreover, there are no areas that are totally unsuitable for either of the species, whereas, some northern areas are only marginally suitable for both species because of the lack of saline water. A greater area is suitable for farming crab than shrimp because the former is more tolerant of environmental stress and little processing is needed prior to export. It is important to note that some of the areas that have already been developed for shrimp farming only scored as moderately suitable. These included most of the Bagerhat and Satkhira region and some parts of Khulna district. In these areas, soil quality is lower because of a high proportion of fine clay, although the major problem is the distance from markets and processing plants. However, some areas were predicted to be suitable for shrimp and crab culture in the lower part of Jessore district. These rated well on certain criteria, but are not in culture at present due to the low salinity of the available water. Fifty-five percent of the very suitable and 45% of the moderately suitable model output matched with the existing shrimp farm locations. Among the 287 known farm locations, 158 were exactly matched with the very suitable model output and 129 falls within moderately suitable output. A few farms fell just within the mangrove area, although no shrimp farms were seen inside the Sundarbans mangrove forest in the Landsat TM image,
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Table 5 Cross-tabulation for error matrix analysis of shrimp culture potential site (columns) against existing farm location (rows) Existing farm location
Total number Pixels that fall Outside farm Existing shrimp of pixel/cell into different category (%) location farm location Aquaculture potential outside (0) 18,062,244 site after modeling marginally suitable (2) 0 moderately suitable (3) 0 very suitable (4) 0
0 2 2381 2913
18,062,244 2 2381 2913
100 0.04 44.96 55.00
Cramer’s V = 1.00 and overall Kappa = 0.78.
it was considered to be an artefact of a minor geo-registration error that was 12.26 m. The result of the cross-tabulation of shrimp culture potential site (columns) against existing farm location (rows) is shown in Table 5 that summarises the number of cells that fall within each possible combination of location. In addition to the cross-tabulation matrix, the CROSSTAB module also generates the Kappa Index of Agreement (KIA). This index is particularly important as it is used to determine the degree of agreement between the two images (Eastman, 1995). Kappa ranges in value from 1 to 1. A value of 1 indicates that the two images are in perfect agreement (no change occurred), whereas if the two images are completely different from one another, then the Kappa value is 1. If all the changes that occurred could be accounted for by chance, then Kappa is 0. The overall KIA of the two images is 0.78, indicating that there is a considerable agreement between suitable model output and existing farm location. Cross-tabulation also generates a correlation coefficient of Cramer’s V whose value ranges from 0.0, indicating no correlation, to 1.0, indicating perfect correlation between the two images.
4. Discussion The results predicted as suitable from this GIS evaluation were partly verified by the presence of shrimp farms in the study area based on the analysis of satellite images and published data (Table 3). GIS predicted that 198,673 ha of land are suitable for shrimp farming, while DOF (1994 – 1995) showed that 104,624 ha are already in operation in the area. The discrepancy between some of the predicted and actual shrimp farm locations was attributed to the fact that farms have not been established using any systematic application of scientific knowledge but rather on the availability of water and the influence of neighbouring farmers. Suitable sites for crab culture could not be verified as this culture practise has only recently been introduced to the area as a result of the strong international market for crab (Overton and Macintosh, 1997). Much of the area ranked as most suitable for brackish water shrimp and crab culture is situated near the riverbanks in the tidal zone, ensuring easy access to saline water. However, most of these areas were only ranked as moderately suitable because of lack of other facilities, such as fresh water supply and drainage difficulties. Large water bodies,
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which contain fresh water, were not considered of potential use because of the problems associated with low salinity. Natural forest and wildlife refuges, Sundarbans, roads, rivers and urban areas were also excluded. Crop agriculture, which is the main traditional source of income, has not yet been substantially affected by the development of brackish water shrimp and crab culture. By contrast, although new sources of income have been created by shrimp culture in the region, there has been a significant reduction of livestock and poultry in the shrimp farming areas (Rahman et al., 1995; Khan and Hossain, 1996). It is interesting to note that, unlike some other areas of Bangladesh, only one crop of shrimp can be cultured in most of the region as the water salinity falls to zero from June to December. This period is very suitable for rice production and freshwater prawn and fish culture (Rahman et al., 1995). On the other hand, crab remains an incidental crop of rice, fish, shrimp and prawn culture in the region in all seasons (Overton and Macintosh, 1997). From the simple economic assessment of shrimp farming and alternative land uses in the region, it is clear that rice farming scored rather poorly in comparison to the other land use activities considered. Hambrey (1996) obtained similar results in a comparative economic study of seven land use options in the mangrove areas of Indonesia. In the present study, the highest financial returns came from freshwater prawn culture followed by brackish water shrimp and crab culture. Highest employment, measured in terms of average annual labour requirements per hectare of land, was generated by prawn culture, followed by crab, shrimp, carp, tilapia and rice cultivation, respectively. Crab culture is in its initial stage and there is considerable potential for further expansion. Crab culturists could earn many times as suggested here if culture methods were intensified, for example by production of ripe females and by fattening crabs in cages alongside conventional production methods (Cholik and Hanafi, 1992; Overton and Macintosh, 1997). However, while crab culture is more environmentally friendly than shrimp culture, in that it can be integrated with horticulture, conventional forestry, rice culture and polyculture with fish (Chong, 1993; Chandrasekaran and Perumal, 1993), mud crab culture is totally dependent on wild seed supply. There is thus potential for establishing hatcheries to provide a continuous supply of seed (Salam and Ross, 2000). An evaluation of the availability of agricultural by-products, especially oil cake, rice bran, wheat bran and other products that could be used as low cost feed, could also enhance aquaculture development opportunities in the area. In conclusion, the present study shows the modelling power of GIS for aquaculture development and its value in making comparisons among farming systems. There is considerable potential for further exploitation of GIS for optimisation of competing aquatic production activities and their interaction with land-based farming systems. The GIS planning process has an important role, particularly where land use patterns are intensive, and where developments must be sustainable in terms of sensitive environment issues, as is the case in most parts of Bangladesh.
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