Overview
With the rapid increase in population and continuing expectations of growth in the standard of living, pressures on natural resources have become intense.
The World Food Summit in Rome, in November 1996 organized by the Food and Agriculture Organization of the United Nations (FAO) endorsed the previously agreed Summit Declaration and Plan of Action for a renewed global commitment to solve the problem of chronic food insecurity causing over 840 million people, mainly in developing countries, to remain undernourished (FAO, 1996a; Fertilizers and Agriculture, 1997).
Africa is an area of the world in which chronic hunger continuous to be widespread. Two hundred and four million people were affected in sub-Saharan Africa (SSA) by malnutrition in 1990. According to the Human Development Report (1996), 22.5 million African children are malnourished. It is estimated that 40 percent of the population of SSA goes hungry, and that the figure will increase by the year 2000 (FAO, 1996b).
Fish are an important source of both food and income to many people in developing countries. In Africa, as much as 5 percent of the population, some 35 million people, depend wholly or partly on the fisheries sector for their livelihood (FAO, 1996c). While capture fisheries based on species that are presently exploited seem to have reached their natural limits (FAO, 1996c), there is considerable potential to expand aquaculture in Africa in order to improve food security (Kapetsky, 1994, 1995; Engle, 1997).
Even though there is potential for fish farming in Africa, very few African countries have a quantified long term or even mid-term national plan (Coche, Haight and Vincke, 1994), it has therefore been difficult to develop production targets for their aquaculture sectors which could be used to set realistic actions and financial commitments. The only two regional assessments that may serve as a guidelines for strategic planning are those by Coche, Haight and Vincke (1994) and Kapetsky (1994).
The present study is based on the development of analytical strategies that could be used to stimulate improved planning for aquaculture development in Africa. This was done by developing Geographical Information System (GIS)-based models aimed to gain a better understanding among siting criteria required for fish farming. This study is built mainly on two GIS studies: the analysis of factors important for aquaculture development and operation (Kapetsky, 1994); and the development of farming system models and fish yield models in Latin America (Kapetsky and Nath, 1997).
Objectives
This study was aimed at stimulating and/or supporting the development of two important planning schemes:
The development of national
level studies which could improve planning in those countries with relatively large
potential for fish farming development,
The development of
comprehensive plans for technical and financial assistance by FAO and other national and
international organizations, as well as national governments and financing institutions
for fish farming development.
The first GIS study that evaluates inland fish farming potential in Africa was that by Kapetsky (1994), who found that 40 out of 49 countries, in the continent have areas with some potential for warm-water fish farming at small-scale and commercial levels. It was estimated that about 31% of the land area in Africa is potentially suitable for warm-water fish farming at a small-scale level, and that about 13% of the land area is suitable for commercial farming. These results clearly indicate that the availability of land area for warm-water fish farming is apparently not a constraint for aquaculture development.
Kapetsky (1995) estimated the potential contribution of African warm-water fish farming to food security by the Year 2000. He found that an increase in pond surface area per farm would provide a significant increase in fish production. Thus, warm-water fish farming could play an increasingly important role in filling the gap between fish supply and demand. Most certainly, it was concluded that, from those few countries where fish farming is already well established, significant contributions could be made to food security by warm-water fish farming by the Year 2000.
In addition to the above, economic studies for fish farming development in Africa have also proven that fish farming in African can be a good source of income. Findings by (Molnar, Rubagumya and Adjavon (1991) and Engle, Brewster and Hitayezu (1993) showed that fish production in Rwanda represented the main cash crop for over 50% of the group members and private pond holders. Engle, Brewster and Hitayezu (1993) indicate that fish farming provides cash to a family in addition to supplementing the diet of Rwandan farmers. Finally, Engle (1997) used a mathematical programming model to demonstrate how fish can be an important cash crop, even for limited-resource Rwandan farmers.
The above studies thus demonstrate that fish farming can be a viable enterprise for African producers and that gains in economic and food security goals can be achieved at reasonable costs.
The motivation for the current study was to provide a more thorough assessment of the potential for inland aquaculture in Africa compared to that provided by Kapetsky (1994) both by building upon the methodological framework developed by Kapetsky and Nath (1997) for Latin America, and by the use of more comprehensive spatial datasets currently available for Africa. Differences between the current study and the previous fish farming GIS effort for Africa (Kapetsky, 1994) are summarized in Table 1.1.
Table 1.1 Enhancements to the first African fish-farming study
(Kapetsky, 1994).
(See page xv for an explanation of the abbreviations used in the table)
| SUBJECT | KAPETSKY (1994) | THIS STUDY |
| Resolution | 10 minute grid (18 km x 18 km). | 3 minute grid ( 5 km x 5 km). |
| Date range of data sources used. | 1931 - 1988 | 1920 - 1997 |
| Factors included in the analysis. | Water temperature (air temperature), water availability (water from rainfall runoff and water from streams and rivers), soils (texture and topography), inputs (LGP), local market demand (population density), and roads. | Water temperature (air temperature and wind velocity), water requirement (precipitation, potential evapotranspiration and seepage), soils (soil type and topography), inputs (manure and crops), farm-gate sales and urban market size (population density) and proximity (roads). |
| Constraints | Water bodies (main lakes). | Protected areas, water bodies (main lakes), and major cities. |
| Farming system analyzed | Small-scale and commercial. | Small-scale and commercial. |
| Farming System Models | Simple. Only farming system models. Factors are not assigned weights for integration of the models. |
Complex. Based on experience from Latin America study (Kapetsky and Nath, 1997) and Sinaloa study (Aguilar-Manjarrez, 1996). Fish yield models, farming system models and overall models (fish yield models combined with farming system models). Factors assigned weights for model integration. |
| Bioenergetics model | Not used | Bioenergetic model adapted from the Latin America study (Kapetsky and Nath, 1997). |
| Fish species used | Nile tilapia (Oreochromis niloticus) and Catfish (Clarias gariepinus). | Nile tilapia (Oreochromis niloticus), African catfish (Clarias gariepinus) and Common carp (Cyprinus carpio). |
| Basis of water temperature estimates | Air temperature alone used to estimate water temperature using interpolation and regression. Annual estimate. | Air temperature and annual wind velocity data used as input variables into a simulation model to estimate monthly water temperatures. |
| Basis of fish yield estimates | Water temperature thresholds for the model species. | Water temperature, food consumption rates, feeding levels, feed composition, fish size, fish biomass and photoperiod. |
| Water requirement | Based on annual rainfall runoff, and water from perennial streams and rivers. | Function of monthly precipitation, monthly potential evapotranspiration, and seepage. |
| Soils | Soil texture and topography (slope), based on the FAO-UNESCO Soil Map of Africa at 1:5 million scale (FAO-UNESCO, 1977). Slope thresholds imposed by FAO-UNESCO soil classification. | Soil texture, effective soil depth, gravel and stones %,
salinity and pH. Derived from DSMW, CD-ROM (version 3.5, FAO, 1995). Slope thresholds chosen from a 1 km DEM. |
| Inputs from agricultural by-products. | Crop yield and variety estimated using the LGP. | Inputs estimated by manipulating manure and crop data. Manure availability estimated from livestock data.Cropland areas extracted from a land cover image. |
| Local market demand | Function of population and the occurrence of fish farms. | Farm-gate sales of population size. Urban market size and proximity based on road conditions and population size respectively. |
| Road infrastructure (paved and motorable roads) | Important only for commercial fish farming. | Important only for commercial fish farming. |
Although the history of aquaculture is relatively recent in sub-Saharan Africa compared to Asia, it is not new to the majority of the countries. In fact, most known aquaculture systems have been introduced over the last 35 years (FAO Fisheries Department, 1996a; 1996b).
During the 60's, aquaculture development not only stopped but regressed sharply. Most ponds were abandoned because of limited security of land tenures, reluctance of farmers to adopt technology, labor shortages, lack of stocking material, drought and political unrest (Harrison, 1994; Coche, 1994). It is since the late 60's that aquaculture has started to develop again, on more solid basis following the increased technical assistance financed by multilateral and bilateral donors.
To date, aquaculture in Africa is still essentially a rural, secondary and part-time activity taking place in small farms with small freshwater ponds. The continent contributes only 0.2 percent of total global production (FAO Fisheries Department, 1996a). Extensive to semi-intensive cultural systems produce limited fish yields which are mostly consumed directly, bartered or sold locally as cash crop. Almost all fish farming is carried out by rural small-scale operators in small freshwater ponds as a secondary activity to agriculture (Coche, Haight and Vincke, 1994).
Aquaculture development in most African countries has primarily had social objectives such as nutrition improvement in rural areas, generation of supplementary income, diversification of activities and income, and creation of employment especially in rural communities where opportunities for economic activities are limited. Only in recent years has aquaculture also been viewed (only on a relatively small scale) as an activity likely to meet national shortfalls in fish supplies thereby reducing fish imports, as well as a direct source of foreign exchange mainly through the production of high value marine finfish, crustaceans and molluscs.
Aquaculture production in Africa has more than doubled during the period of 1985 to 1995, the year of the most recent data (Figure 1.1). However, the contribution of aquaculture production in absolute terms is still very small in comparison to that from inland fisheries. In 1995 inland aquaculture amounted to 35,000 tonnes, while mariculture provided less than half this amount (16,000 tonnes). Inland fisheries at 1,990,000 tonnes, exceeded both types of aquaculture by a wide margin.
The major species cultured include finfish (tilapias, catfish, and carp), molluscs and shrimp. Freshwater fish make up over 80 percent of the total aquaculture harvests.Inland aquaculture is dominated by Nigeria (15,489 tonnes) and Egyt (5,645 tonnes) which together accounted for 56% of the total production in 1995 (Figure 1.2). There are only 5 additional countries (Zambia, Madagascar, Togo, Kenya and Sudan) that each produce 1,000 tonnes or more.
There is a lack of regular aquaculture development planning exercises in most African countries. However, there is a growing recognition of the need for proper planning of the fisheries sector in general and the aquaculture sub-sector in particular (Text Box 1.1).
In 1983, the FAO Inland Water Resources and Aquaculture Service of the Fishery Resources and Environment Division embarked upon a medium-term programme to collect updated information useful in the preparation of national aquaculture development plans for 12 African countries with the highest potential for aquaculture (Coche, 1994). These 12 national reports are a good source of environmental and socio-economic information. However, they are essentially catalogues of information rather than a set of integrated information that can be used to develop clear policies and strategies for aquaculture development.
The national reports cited above have been assembled into a single document thus making a useful summary of the data collected in the original reports (Coche, 1994). Additionally, the original reports have been also used to develop an indicative plan for aquaculture development and research (Coche, Haight and Vincke, 1994). However, all of these studies could have or may still (i.e. since some data might still be useful if not outdated) benefit from the use of computers, particularly with regard to electronic databases and GIS. The former provide support for storing large and diverse kinds of information, and the latter a powerful mechanism of manipulating and integrating spatial information into a format that can be very useful for decision-making (Meaden and Kapetsky, 1991).
