Modelling infrastructure and markets for zonation of cage

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Modelling infrastructure and markets for zonation of cage aquaculture in

Lake Volta, Ghana.

Thesis submitted for the degree of MSc in Sustainable Aquaculture

By

Mohammed Madhafar Al Wahaibi

Supervised by

Professor Lindsay Ross

Institute of Aquaculture

University of Stirling

Stirling, FK9 4LA

Scotland

August 2014

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Abstract

The demand for fish products in Ghana has increased in last few years. However the fish

production from fisheries cannot satisfy this local demand. The solution could be the

establishment of cage aquaculture in Lake Volta, which is the largest artificial lake in the

world with area of 8500 km2. For that, cage farms should be located in an area close to

market in order to reduce transportation cost of fish produced and larvae and feed required.

Geographical Information Systems are used to locate the optimum sites for three different

cages size: small cages (5*5*4m), medium cages (15*15*5m) and large cages

(30*30*6m), based on three sub-models bathymetry, hydrography (physical parameters)

and market access (social parameter). The market access sub-model was developed from

transportation cost for both road and water starting from 170 districts, and the populations

in each district. The overall model was developed with using four different weightings for

marketing to illustrate the effect of market access for site selection. The number of sites for

small cages in suitable and highly suitable areas which achieved the highest market access

weight (0.6) was 310 sites. However, for medium and large cage size this was only

achieved for 272 and 228 locations respectively. These models for different cage sizes and

market access weight provide qualitative and quantitative guidelines for decision makers

for sites selection and aquaculture development.

Keywords

GIS, Cage aquaculture, Lake Volta, Ghana, Site selection, Market access

1. Introduction

Ghana is located north of the equator in West Africa. It has high potential for freshwater

aquaculture in Volta Lake which is one of the largest man-made lakes in the world. It

extends from the Akosombo Dam in southeastern Ghana to the town of Yapei, 520

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kilometers to the north. The lake generates electricity, provides inland transportation and is

a potential source for irrigation and an important source of inland fish production.

According to the FAO FishStatJ database (2015), Aquaculture production increased from

938 metric tons in 2003 to over 32513 metric tons in 2013. In 2012, aquaculture

production from cages was over 24249 metric tons compared to less than 1772 metric tons

from ponds and tanks. The main fish species cultivated are Nile tilapia (Oreochromis

niloticus) and African catfish (Clarias gariepinus) with Tilapia species representing over

90 percent of farmed fish production.

In 2003 domestic fish production only met 51.7% of Ghana's requirements from its

domestic sources and it reached only 68.1% in 2004 from both domestic production and

imports. (Awity, 2005). In 2007, Ghana imported 212000 tons of fish with value of US$

262 million. (Kwadjosse , 2009). Fisheries around the world are declining because of

overfishing, pollution and habitat destruction, whereas, human demand for fish as a source

of protein is increasing. Aquaculture is the way to cover this gap between demand and

supply of fish and in Ghana, Lake Volta provides a substantial resource for fish production

which can cover the shortfall in fish demand.

Lake Volta is a man-made reservoir created when the World Bank and International

Financial Groups built Akosombo Dam in 1961. It supplies local communities with a wide

range of freshwater fish. In order to increase fish production in Ghana, the Ghanaian

government developed a National Aquaculture Development Plan with the target to

increase farmed fish production by 1000% within 5 years (GNADP, 2012).

In lakes, cage aquaculture is an efficient method to increase fish production. Unlike ponds

and tanks, cage-culture requires lower investment costs in both capital and production. In

sites with optimum waves and currents, the fish waste and uneaten feed will wash away

from the cage, requiring less effort for net cleaning and good water exchange enables better

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fish growth. According to Kassam (2014), production from cage farms in Ghana grew from

4000 tons in 2009 to over 24000 tons in 2012 with a total production of 27451 tons.

Site selection is an important factor for sustainable production. There are many parameters

involved in site selection which include: bathymetry, hydrography (wave height and

currents), water quality (temperature, pH, dissolved oxygen and nutrients, etc),

infrastructure (transport, electricity supply systems) and socio-economic factors (potential

market, labour sources, goods and services) (Nath et al., 2000). A Geographic information

systems (GIS) is an integrated assembly of computer hardware, software, geographic data

and personnel designed to efficiently acquire, store, manipulate, retrieve, analyze, display

and report all forms of spatially referenced information geared towards a particular set of

purposes (Nath et la., 2000). The use of GIS allows combination and processing of many

variables to select the optimum aquaculture sites. This technique is relatively new and it

has been used previously for site selection such as tilapia cages in Mexico (Ross et al.,

2010), sea bream and sea bass cages in Tenerife (Pérez, 2005), Shrimp ponds in Vietnam

(Giap et al., 2005).

GIS-based site selection has most often focused upon physical and biological parameters

with few attempts to incorporate and model market influences. The aim of this study was

to construct a spatial database of relevant parameters to enable modelling of optimal

locations for cage aquaculture in Lake Volta, Ghana based on market access, coupled with

key environmental parameters. This will allow to identification of optimal sites for

development of aquaculture.

2. Materials and Methods

2.1. Study area

Lake Volta is located in West Africa within the Republic of Ghana. It is the largest artificial

lake by surface area in the world with a total length of 520 km and covering approximately

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8,500 km2

(Zwieten, 2011). The study area covers the main body of the lake from the

Akosombo Dam to the three main river inflow locations (White Volta River, Black Volta

River and Oti River) and has a surface area of 5895 km2. There are around 148 fish farm in

the lake found from Google Earth (Fig. 1). Ghana has two main seasons: wet and dry. In

the north there is a single wet season (May – November) and the dry season is between

(December and March). In the south, there are two wet seasons, one in March to July, and

a shorter wet season in September to November (McSweeney et al, 2010).

Figure 1. Fish cage farms locations at Lake Volta, Ghana.

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2.2. GIS systems

Data analysis and model development used IDRISI SELVA edition (Version of 17.02)

[Clarks Labs, MA, USA] as well as QGIS software. The software operated on an Intel(R)

Xeon(R) CPU X5460 @ 3.16 GHz workstation with 8.00 GB RAM, 3 TB hard disk, and

twin Dell 21” 2408WFP monitors.

2.3. Model Development

Baseline satellite imagery was developed from a mosaic of six recent LANDSAT 8 OLI

images which were pansharpened to 15m resolution (Xia, 2014). The study area straddles

two UTM sectors, UTM 30N and UTM 31N. All data were re-projected to the UTM 31N

georeference system, as four of the six images were UTM-31N and this is the projection

used in the field surveys.

Three sub-models were used in the development of the site selection models: bathymetry,

hydrography and market access (Fig. 2). In each sub-model the results maps were

reclassified to a scoring system where 1 is highly unsuitable and 5 is highly suitable for

cage aquaculture. Similar studies have used other scoring systems, such as a 1-4 scoring

system developed for southwestern Bangladesh (Salam et al., 2003); 1-8 scoring system

used in Tenerife, Canary Islands (Pérez et al., 2005) and a 1 to 15 scoring system used in

the State of Sinaloa, Mexico (Aguilar-Manjarrez and Ross, 1995). However, a 5 point

system was adopted in order to link with a previous study for Lake Volta in Ghana by Xia

(2014). This allowed data sensitivity but was not too complex to manage. Scores were

assigned based on values from the literature and/or expert opinion.

Three cage sizes were selected for use in the modeling process: small cages (5*5 m, depth

of 4 m and 2 m for net hang); medium cages (15*15 m, depth of 5 m and 2.5 m for net

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hang) and large cages (30*30 m, depth of 6 m and 3 m for net hang). Those sizes can all

potentially be used in Lake Volta and the range allows decision makers to evaluate the

suitability of the lake for three different scales of aquaculture. The small cages are suitable

for local market farming, while the medium and large cages are more suitable for

commercial farming for local and international markets. The data was reclassified in terms

of suitability for each of the three cage types.

Figure 2: Structure of the overall model processes for cage site selection in Lake Volta,

Ghana.

2.4. Bathymetry sub-model

Bathymetry is an important parameter for cage farming. They should not be in areas that

are too shallow or too deep. In the previous study (Xia, 2014) there were no bathymetric

datasets available for Lake Volta and so a new bathymetric map using an empirical

approach based on Landsat 8 images and partially verified using bathymetric data collected

from the lake. It is based on the principle that light passing through water becomes

attenuated so shallow water areas appear bright and deep areas look dark in an image

(Gholamalifard et al., 2013).

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Landsat 8 band 3 (Green, wavelength from 0.525 to 0.600 um) was used to calculate water

depth while band 6 (Short Wavelength Infrared) was used to locate the water body

borderline because of its clear reflection from the water surface and minimal atmospheric

interference. Water depth was calculated using a model described by Landmap

Geoknowledge (2005) and depends on the empirical tuning by Jerlov (1976), Lyzenga

(1981) and Jupp (1988). Field data was collected by the Water Research Institute (Accra,

Ghana) and used to partially verify the results. The final bathymetric map is shown in

Figure 3.

Figure 3: The bathymetric map of Lake Volta, Ghana in meter (from Xia et al, 2014).

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The satellite data was collected in March 2014 near the start of the rainy season so the

depth of the water in Lake Volta was the lowest of the year which allowed development of

a model which avoided any damage to cages due to depth change as well as allowing for

sufficient water exchange and discharge of wastes (Beveridge, 2004). However areas that

are too deep can also generate problems because of increase costs associated with mooring

systems (Beveridge, 2004; Pérez et al., 2005). According to Beveridge (2004) the nets

should be at least 4-5 m above the sediments. However, Lake Volta is a man-made

reservoir which covers submerged trees, buildings and other obstacles. This could result in

problems for cage aquaculture and to allow for that the depth under cages should be twice

the cage depth. A reclassified scheme for the three cage sizes modeled is shown in Table 1.

Table 1: Water depth (in meters) with classification for three different cage size (from Xia,

2014).

2.5. Hydrography sub-model

2.5.1 Wave height

Wave height is an important factor for cage siting because it can damage and make stress

Factor 0 (Not suitable) 1 (Highly unsuitable) 2 (Unsuitable) 3 (Moderate) 4 (Suitable) 5 (Highly

suitable)

Small cages

(5*5*4m)

0-8;

40-100

8-9;

35-40

9-10;

30-35

10-11;

25-30

11-12;

20-25

12-20

Medium cages

(15*15*5m)

0-11;

45-100

11-12;

40-45

12-13;

35-40

13-14;

30-35

14-15;

25-30

15-25

Large cages

(30*30*6m)

0-14;

50-100

14-15;

45-50

15-16;

40-45

16-17;

35-40

17-18;

30-35

18-30

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on cage structures. Also, high wave unsafe environment for fish farmers. Xia et al. (2014)

modelled maximum wave height in Lake Volta. Although the maximum potential

significant wave height occurs infrequently, when it does occur the cages would experience

significant impacts and damage potentially leading to a loss of stock. The wave height was

calculated using the method first developed by the US Army Corps of Engineers (1984)

which uses data on wind direction and speed, fetch and water depth and is suitable for

depths less than 90m. This method was also been used by Scott (2004) and Falconer et al.

(2013) for aquaculture site selection. The final wave height model for Lake Volta,

combining models for the 8 principal wind directions, is shown in Fig. 4.

Meters

Figure 4: The wave height layer of Lake Volta, Ghana (from Xia et al, 2014).

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Cage site selection for aquaculture should considering the different cage sizes and materials which have different

tolerance to wave action. Most studies have focused on cages in exposed areas of the marine environment where the wave

height is over 2 m (Pérez et al., 2003; Ma et al., 2009; Huang et al., 2008) though this can act as a guide to conditions in

large lake systems. The data from Fig. 5 were reclassified based on expert opinion and field experience (Table 2) Table

2: Wave height in meters with classification for three different cage size (Xia et al., 2014)

2.5.2 Current velocity

Current velocity is considered an important parameter for cage site selection to develop

aquaculture. Good current velocity will allow good water exchange for cage aquaculture

and remove waste cause by feeding and metabolites as well as renewing oxygen in water.

On the other hand, if currents are too strong they can damage cages and stress fish. For

that, it is important to measure the currents at a site before development cage project.

Unfortunately, there little current data is available for Lake Volta and so this is not

considered in this study.

2.6. Market Access sub-model

Market access is an important factor which should be evaluated when a site is selected. In

this study, the two major factors considered were population size and transportation costs.

Transportation cost from or to fish farm has an effect on fish price. The transportation cost

is affected by the road type and good road access can reduce the cost. Access to the sites

Factor 0 (Not suitable) 1 (Highly unsuitable) 2 (Unsuitable) 3 (Moderate) 4 (Suitable) 5 (Highly suitable)

Small cages

(5*5*4m)

1.0-0.8 0.8-0.64 0.64-0.48 0.48-0.32 0.32-0.16 0.16-0

Medium cages

(15*15*5m)

/ 1.0-0.8 0.8-0.6 0.6-0.4 0.4-0.2 0.2-0

Large cages

(30*30*6m)

/ 1.0-0.9 0.9-0.8 0.8-0.6 0.6-0.3 0.3-0

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for supply of goods and services from cities and small towns is most important (Ross et al.,

2010). The second factor in marketing is population in around Ghana which not equal in

different distract. The demand for fish is higher in distract with more population.

2.6.1 Population distribution

Population data for Ghana was obtained from Ghana Statistical Service population and

housing census for 2010 (GSS, 2012). The population was converted to an IDRISI 17.0

database for 170 districts. Each of district locations were found with help of Google Maps

in QGIS (Fig 4), and were used as starting point to calculate transportation costs.

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Figure 4: The location of 170 districts using QGIS with Google Maps.

2.6.2 Product transportation costs

Transport by road

The road maps were highlighted 23 different types of road. All roads around Lake Volta

were connected to the lake by using QGIS. The roads types were classified to three

categories based on the product cost of transportation (1= expensive, 2= medium, 3=

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cheap) (Table 3, Fig. 5), though footpaths and bridleways were ignored. According to

(Taiwo and Kumi, 2013) the average transportation cost per tonne of product for a “cheap”

road is Ghȼ 0.90 per km to the market centres, for one tonne of product on an expensive

road the cost is Ghȼ 1.43 per kilometre, and for a medium road the cost is Ghȼ 1.25 per

tonne-kilometre.

Table 3: classification of road types around Ghana

NO Road type Reclassified

1 bridleway 0

2 construction 3

3 cycleway 0

4 footway 0

5 living_street 1

6 mini_roundabout 1

7 motorway_link 3

8 path 0

9 pedestrian 0

10 primary 3

11 primary_link 3

12 residential 0

13 road 3

14 secondary 3

15 secondary_link 3

16 service 0

17 steps 1

18 tertiary 2

19 tertiary_link 2

20 Track 1

21 trunk 1

22 trunk_link 1

23 unclassified 1

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Figure 5: Ghana road map classification

Transport over water

According to Gulbrandsen (2012), motorised canoe is the traditional fishing boat in Ghana.

It is normally 14 m long and it can carry 3 tonnes of produce. The maximum speed for a

canoe is 14.8 km/hr and it consumes 8 litres of diesel per hour or 0.54 L/km. The cost of

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diesel in Ghana is 3.25 Ghȼ (Global Petrol Prices, 2015), thus the cost of transportation

over the Lake Volta is 1.755 Ghȼ per kilometre. The Lake map was reclassified to 1 is

equal to 1.755 to create lake cost map (Fig 6).

Figure 6: Transportation cost over Lake Volta

2.6.3 The combined access model

Both road and lake cost layers were combined to form a single cost layer using image

overlay. The cost of product transportation was calculated in (QGIS) for each one of the

170 districts at 60m resolution. The results were then imported into IDRISI for further

processing. Each district cost map was divided by the head of population in the district to

produce 170 layers of the cost of transportation from each district per person. The 170

district costs per person were then added to form a single layer. The cost image was then

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masked to show lake cost only (Fig. 8). The lake transportation costs were not different for

different cages sizes. The road transport costs were then reclassified based on expert

opinion and experience in the field (Table 4)

Figure 8: Product transportation cost per kilometre per person over Volta Lake, Ghana

Table 4: Cost of product transportation in Ghȼ per tonne fish per kilometre per person.

Factor 0 (Not suitable) 1 (Highly unsuitable) 2 (Unsuitable) 3 (Moderate) 4 (Suitable) 5 (Highly suitable)

Cost per

person

< .0.4 0.59 – 1 0.54 – 0.59 0.49 – 0.54 0.44 - 0.49 0.40 – 0.44

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2.7. Combining the access sub-model with previous sub-models

A final set of model outputs was generated by using the three sub-models: bathymetry,

hydrography and market access. A one stage Multi-Criteria Analysis (MCE) process was

used to construct the separate models for the three cage sizes (Fig 8).

Figure 8. Structure of sub-models final output processes for cages site selection.

By consultation with a group of experts, four different weightings for market access were

assigned for each cage size, while retaining equal weights for the bathymetry and

hydrography sub-models. (Table 5), thus creating twelve outcomes. To preserve sensitivity,

each sub-model was rescaled to a 25 point scale before running the MCE by applying a

scalar operation on the source layers. In addition, a depth constraint layer was created for

each cage size (Fig. 9) representing areas in which it is not possible for aquaculture at each

cage size. The MCE results were reclassified from the 25 point scale to score out of 5 to

calculate the total area in each class (Table 6). The distance between the sites were 3 km

which is based on expert opinion and experience in the field. The number of possible cage

sites were calculated by development of a sample map overlaid with each MCE map

Number of sites were calculated in image histogram.

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Table 5: The MCE weightings for final model

Small cage Medium cage Large cage

Bathymetry 0.5 0.4 0.3 0.2 0.5 0.4 0.3 0.2 0.5 0.4 0.3 0.2

Hydrography 0.5 0.4 0.3 0.2 0.5 0.4 0.3 0.2 0.5 0.4 0.3 0.2

rket access 0 0.2 0.4 0.6 0 0.2 0.4 0.6 0 0.2 0.4 0.6

Figure 9: The constraint area in three cage size (small, medium and large)

Table 6: Classification scores used for the final models from a 25 point scale.

Reclassified

Suitability

0 (Not

suitable)

1 (Highly

unsuitable)

2

(Unsuitable)

3

(Moderate)

4

(Suitable)

5 (Highly

suitable)

Suitability

from initial

outcomes

0 - 4 5 – 8 9 – 13 14 – 17 18 – 23 24 - 25

In order to propose a maximum number of cage site on Lake Volta, a distance of 3 km

between sites was used which is based upon expert opinion, experience in the field and

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guidelines form other countries concerned with biosecurity (e.g. Scottish Environment

Protection Agency, SEPA). A grid of sites at 3km spacing was generated using a sample

generation procedure. This was overlaid on the outcomes of the MCE processes to give a

visual indication of site distribution, from which, the maximum possible number of sites in

the different suitability zones could be calculated using an image histogram.

3. Results

3.1. Bathymetry sub-model output:

The results of the bathymetry developed from Landsat 8 OLI imagery by Xia (2014) are

shown in Fig. 3 and Table 7. From this, it was calculated that there are 1810 km2 of

suitable and highly suitable areas available for large cages, 3653 km2 for medium cages

and 4896 km2 for small cages. Fig. 10, shows the depth suitability for the three cage sizes,

The highly suitable area for small cages (4104 km2) and medium cages (2992 km

2) are

found in the center and the southwest edge, although medium cages also have more highly

suitable areas in the south part of the lake. The highly suitable area for large cages (1447

km2) sites can found in south part of Lake Volta.

Table 7: The suitability result of bathymetry sub-model.

0

(Not suitable)

1

(Highly

unsuitable)

2

(Unsuitable)

3

(Moderate)

4

(Suitable)

5

(Highly suitable)

Small cages

(5*5*4m)

1.83%

(108 km2)

4.62%

(272 km2)

4.56%

(269 km2)

5.97%

(352 km2)

13.43%

(792 km2)

69.60%

(4104 km2)

Medium cages

(15*15*5m)

16.03%

(945 km2)

5.97%

(352 km2)

7.38%

(435 km2)

9.84%

(580 km2)

11.21%

(661 km2)

49.56%

(2992 km2)

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Large cages

(30*30*6m)

39%

(2302 km2)

10%

(616 km2)

11%

(665 km2)

9%

(502 km2)

6%

(363 km2)

25%

(1447 km2)

Figure 10: Suitability of water depths for three different sizes cages in Lake Volta, Ghana.

3.2. Hydrography sub-model output:

The results of the Hydrography sub-models for the three cages sizes are shown in Table 8

and Fig 11, representing the suitability of the cages with respect to wave height. 84% of

Lake Volta is considered highly suitable for large cages with 16% being suitable area. This

because large cages are more tolerance to waves. For medium cages the highly suitable and

suitable areas are 39.88% and 54.30%, respectively, with some moderately suitable areas

located in the south of the lake where they would be exposed to higher waves. The highly

suitable and suitable areas for small cages are located around the edge of the lake where

the cages would be less exposed to high waves.

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Table 8: The suitability details of hydrography sub-model.

0

(Not suitable)

1 (Highly

unsuitable)

2

(Unsuitable)

3

(Moderate)

4

(Suitable)

5 (Highly

suitable)

Small cages

(5*5*4m)

0.37%

(22 km2)

3.27%

(193 km2)

15.59%

(919 km2)

36.17%

(2132 km2)

34.76%

(2050 km2)

9.84%

(580 km2)

Medium cages

(15*15*5m)

0%

(0 km2)

0%

(0 km2)

0.03%

(1.6 km2)

5.8%

(342 km2)

54.30%

(3201 km2)

39.88%

(2351 km2)

Large cages

(30*30*6m)

0%

(0 km2)

0%

(0 km2)

0%

(0 km2)

0%

(0 km2)

16%

(926 km2)

84%

(4970 km2)

Figure 11: Suitability of wave heights for three different sizes cages in Lake Volta, Ghana.

3.3. Market access sub-model output:

The market access sub-model was the same for the three cages size (small, mediun and

large). The results of the sub-models (Table 9 and Fig. 12) Indicate that there are 2221.5

km2 of highly suitable and suitable areas for cages covering 37.7% of Lake Volta. These

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areas are mainly located at the south part of lake with some suitable area in the north, as

the result of the good road system at this part of the lake and higher population in adjacent

districts. Moreover, there are 3022km2 (51.3%) classified as moderately suitable in the

centre and north part of the lake.

Table 9: The suitability market access sub-model.

1

(Highly

unsuitable)

2

(Unsuitable)

3

(Moderate)

4

(Suitable)

5

(Highly suitable)

Percentage (%)

Area (km2)

0.6%

(36.3km2)

10.3 %

(606 km2)

51.34%

(3021.98 km2)

29.85%

(1756.95 km2)

7.89%

(464.57 km2)

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Figure 12: Suitability of access and market for in Lake Volta, Ghana.

3.4. Final outputs

3.4.1 Small cages

The final result of the overall site selection model for small cages is shown in Fig. 13 (A,

B, C and D) which show the suitability categories for small cage sites with changing

market access weight. In Table 10 and Fig 13, suitable areas decrease when market weight

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increases, whereas moderately suitable areas increase with market weight. The highly

suitable area is small and almost constant at the south part of the lake, with small areas also

found in the north. Based on the idealised and maximised site spacing shown in Fig 14 and

Table 11, shows the maximum number of sites in each category with the change of market

weight. The total number of sites in both suitable and highly suitable categories decreases

from 526 sites at 0 market weight to 310 sites at 0.6 market weight.

Table 10: The suitability details of final outputs for small cage size.

Table 11: Number of sites suitable for small cage systems in Lake Volta.

Suitability 0 0.2 0.4 0.6

Area

(km2) (%)

Area

(km2) (%) Area (km2) (%) Area (km2) (%)

Constraint area 0 108.00 1.83 108.00 1.83 108.00 1.83 108.00 1.83

Highly unsuitable 1 62.00 1.05 0.00 0.00 0.00 0.00 2.00 0.03

unsuitable 2 479.00 8.13 380.00 6.45 120.00 2.04 155.00 2.63

moderate 3 466.00 7.91 1193.00 20.24 1701.00 28.86 2886.00 48.96

suitable 4 4421.00 75.00 4122.00 69.93 3680.00 62.43 2464.00 41.80

Highly suitable 5 358.00 6.07 91.00 1.54 279.00 4.73 279.00 4.73

Suitability score market access weight

0 0.2 0.4 0.6

Highly unsuitable 5-8 7 0 0 0

unsuitable 9-13 57 41 12 18

moderate 14-17 54 146 203 316

suitable 18-23 481 450 400 281

Highly suitable 24-25 45 7 29 29

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A: the market access weight is 0 B: the market access weight is 0.2

C: the market access weight is 0.4 D: the market access weight is 0.6

Figure 13: Final MCE result for small cage size.

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Figure 14: Potential sites location map for small cages on suitable and highly suitable

areas with market weight 0.6

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3.4.2 Medium cages

The final result of the site selection model for medium cages is shown in Fig. 17 (A, B, C

and D) which show the suitability categories for medium cages sites with change of market

access weight. In Table 12 and Fig 15, highly suitable areas increase slightly from 2.03%

to 4.34% when the market weight increases from 0.2 to 0.6. However, the suitable area

decreases by more than 20% with higher market weight. Moderately suitable area for

medium cages changes little with different market weighting. The increase of market

weight has only a small effect to the number of sites available for medium cages (Fig. 16

and Table 13). The total number of the highly suitable and suitable sites declines by only

25 sites (from 297 to 272) when market weighting increases from 0.2 to 0.6.

Table 12: The suitability details of final output of medium cage size.

market access

weight 0 0.2 0.4 0.6

Area

(km2) (%)

Area

(km2) (%) Area (km2) (%) Area (km2) (%)

Constraint area 0 945.00 16.03 945.00 16.03 945.00 16.03 945.00 16.03


unsuitable 2 271.24 4.60 230.54 3.91 90.78 1.54 324.12 5.50

moderate 3 647.05 10.98 1975.48 33.51 2183.52 37.04 2107.88 35.76

suitable 4 3552.23 60.26 2624.04 44.51 2420.76 41.07 2263.04 38.39

Highly suitable 5 458.41 7.78 119.37 2.03 255.98 4.34 255.98 4.34

Table 13: Number of site suitable for medium cage size in Lake Volta

Suitability Score Market access weight

0 0.2 0.4 0.6


unsuitable 9-13 30 27 11 37

moderate 14-17 76 217 240 232

suitable 18-23 378 287 262 244


29



Figure 15: Final MCE result for medium cage size.

30

Figure 16: Sites location for medium cages on suitable and highly suitable area with

market weight 0.6

31

3.4.3 Large cages

Large size cages are more tolerant to high waves compared to small cage. However, large

cages required more water depth. Because of that, the constraint area was 39% of the total

Lake Volta area which is shown by the white colour in Fig 17 (A, B, C & D). The suitable

and highly suitable areas decreased from 48% when market weight is zero to 35% at a

market weight of 0.6 (Table 14). This resulted in a decrease in the number of cage sites

from 318 to 228 (Table 15). The suitable and highly suitable areas for large cages are

located in the south of Lake Volta (Fig. 18).

Table 14: The suitability details of final output of medium cage size.

market access

weight 0 0.2 0.4 0.6

Area

(km2) (%)

Area

(km2) (%) Area (km2) (%) Area (km2) (%)

Constraint area 0 2302.00 39.05 2302.00 39.05 2302.00 39.05 2302.00 39.05


unsuitable 2 43.35 0.74 42.16 0.72 150.77 2.56 313.63 5.32

moderate 3 662.98 11.25 1207.25 20.48 1159.08 19.66 1190.73 20.20

suitable 4 1863.16 31.61 1668.61 28.31 1979.65 33.58 1783.13 30.25

Highly suitable 5 1024.45 17.38 675.91 11.47 304.44 5.16 304.44 5.16

Table 15: Number of site suitable for large cage size in Lake Volta.

Suitability score Market access weight

0 0.2 0.4 0.6


unsuitable 9-13 4 4 18 32

moderate 14-17 76 129 119 138

suitable 18-23 208 194 226 193


32



Figure 17: Final MCE result for large cage size.

33

Figure 18: Sites location for large cages on suitable and highly suitable area with market

weight 0.6

Fig 19, shows that the number of sites for small cage size was affected when market weight

was increased. The number of sites in the suitable area decreased from 450 to 281,

however, in highly suitable the sites number increased from 7 to 29. As well as, moderate

sites increased from 146 to 316 sites. It is also, shown the similar affect for medium cage

size. On the other hand, for large cage size the number of sites in highly suitable areas

decrease to 35 at 0.4 & 0.6 market weight compare with 71 sites at 0.2 market weight.

Whereas in suitable area, the number of sites has small change.

34

Figure 19: The suitability area for the three cage sizes with four different market

weightings.

35

4. Discussion

Using GIS for aquaculture site selection has been shown to have positive benefits for

aquaculture development and it has been successfully used to develop complex models for

site selection for many aquaculture species (Radiarta et al., 2008; Longdill et al., 2008; Salam

et al., 2003; Pérez et al., 2005; Salam et al., 2005). Building upon earlier environmentally-

based site selection studies for Lake Volta, (AlShihi, 2013; Xia, 2014), the GIS-model

developed in this study contributes significantly to the optimal zonation of cage-culture sites

for Lake Volta, Ghana. This work has, for the first time, shown the spatial distribution of the

important effect of market access to site selection which was based on the transportation cost

and population distribution.

The market access sub-models developed in this study were combined with previous sub-

models to locate suitable site for cage aquaculture in Lake Volta. Overall, there were 12 final

models for the three cages size (four for each one). For small cages, local market access is

considered highly important for farmers because they produce relatively small quantities of

fish usually from low-input systems. In that case, when the market weighting is 0.6 the

suitable and highly suitable area for small cage was 46.5% of the lake located in south and

center of the lake. Small cage systems not only produce fish for farmer families, but also for

sale in adjacent villages and towns. By contrast, change in market weight has less effect on

the suitable and highly suitable areas for medium and large cages. The area decreased from

39.7% to 35.4% with higher market weight for large cages and from 46.5% to 42.7% for

medium cages. This is principally because depth is more important in this cages size. The

suitable and highly suitable areas for medium and large cage were mainly located at the south

of Lake Volta. Those two cage size systems require higher investment in terms of support

equipment, mooring system and operational cost. However, large cage system could provide

36

more jobs for local people for fish processing and a greater contribution to fish exporting.

According to Worldfish Center Ghana produced 24249 tons of fish from cage-culture in Lake

Volta (Kassam, 2014). Small cage farms produce between 1 – 50 tons per farm and medium

farms is producing between 50 – 1000 tons per farm, whereas, large cage farms are

approximately 4000 tons per farm. Based on the number of sites proposed for each cage size

for only the most highly suitable area from the final GIS models the total fish production

could attain 155450 tons of fish per year (Table 16). Such production would increase cultured

fish production in Ghana by a factor of six which would easily exceed the aim of 1000%

increase proposed in the National Aquaculture Development Plan (2012). Furthermore, this

estimate does not include the use of suitable areas which could increase production

massively. However, these figures do not take into account other suitability factors such as

environmental carrying capacity, environmental quality and environmental impacts, based on

ecological and assimilative factors which may have a significant effect on the maximum

prospective production limits in different regions.

Table 16: Theoretical maximised cage-culture production utilising only areas categorised as

suitable in Lake Volta, Ghana.

Cage farm size Number of sites within

the suitable area

Production per farm

(tons) Total production (tons)

Small cage farm 29 50 1450

Medium cage farm 28 500 14000

Large cage farm 35 4000 140000

total 155450

The results from this study can be used to produce an enhanced cage aquaculture

development plan for Lake Volta, based on an objective spatial assessment of suitable zones.

The model also identifies potential sites located using a GIS model based on physical

environmental data, economical and social criteria for cage aquaculture. The site suitability

37

model efficiently and effectively assesses a large area for the deployment of cages; avoiding

the need for expensive and time-consuming field evaluation (Falconer et al., 2013).

Importantly, the results from the GIS-model can narrow down and minimize the field work

required for water quality and detailed physical environmental assessment in a pre-selected

suitable area.

The aim of this study was to develop GIS-based models of market access and to use these

with environmental sub-models generated previously to identify zones for optimal

aquaculture cage site selection in Lake Volta, Ghana. In this study, the suitable and highly

suitable areas in three cage size were located in Lake Volta which provides objective support

for decision-making on aquaculture development in Ghana, contributing significantly to

attainment of the goals of the national aquaculture plan. This work has, for the first time,

shown the spatial distribution of the important effect of market access to zonation and site

selection which was based on the transportation cost and population distribution. It can be

combined with models of environmental carrying capacity and environmental quality to

assess the prospective yield and improved sustainability for fish culture in Lake Volta.

5. Acknowledgements

This work was a part of four-year project which is funded by a Leverhulme/Royal Society

Africa Grant. I wishes to thanks supervisor Professor Lindsay G Ross for his assistance,

advice and valuable comments during this work and I would like also to thank Neil

Handisyde as this work could not be accomplished without has assistance. Finally, thanks to

everybody in the Institute of Aquaculture for their friendship and high ethics.

38

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Documents

Modelling infrastructure and markets for zonation of cage