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7.1 Accuracy of the results

Several important aspects of the results should be kept in mind. The first is that the results are dependent on data acquired by the mission, but originally generated by other organizations. Examples are the data on cattle, sheep and pigs which come from the Department of Animal Health and Production and the data on rice which comes from the Statistics Division of the policy, Planning, Monitoring and Evaluation Department. These, themselves, are estimates which have a certain amount of built-in error. The second aspect is that, where data were lacking, the results are based on a number of assumptions. An example is the geographic distribution of the per capita consumption of fish upon which depends, in part, our estimate of local markets for farmed fish. We have tried to make our assumptions as realistic as possible, but there are bound to be errors. Finally, at a broader level, our models are intended to reflect how various factors influence fish farming development. Our weighting of the various criteria is based on experience in many developing countries, but hard data for weighting purposes are lacking. In conclusion, we caution against a too literal interpretation of the results. They are indicative of the real situation, not a precise measure of it.

7.2 Recommendations for Additional Data and Analyses

It is clear that the Land and Water Index has to be modified to better account for fish farming opportunities in irrigation schemes.

As a logical follow up to our work and in order to confirm our results, we recommend a study of the factors which have influenced the development of fish farming. The study should be conducted in districts where fish farming is now well established, in districts where it has not prospered and in districts in which it has yet to take off. The results of this study can be used to refine the the present models and to redefine the districts with the best opportunities.

Our intention was to use a fifth input, by-products of palm oil processing, to assess the ability of districts to support fish farming. However, the data by district could not be located. As this is an important industry in Ghana and potentially a significant fish farming input, we have requested the Fisheries Department to obtain the data and to send them to Rome for analysis.

The qualitative effect of adding oil palm byproducts as inputs will be to enhance development opportunities for districts mainly in Ashanati, Western and Eastern Regions where about 80% of the area is and to some extent in Central, Brong Ahafo and Volta which share the remainder.

We collected data on the production of many types of crops for which byproducts could be used for fish farming. Although these are in relatively small supply in comparison with the manures, rice and oil palm byproducts, incorporating them in the analysis could be a useful refinement. We intend to do this for the more detailed report in preparation for “Integrated Approach to Aquaculture Development in Africa, RAF/87/077”.

Despite the apparent spatial differences in the results of the four integrated models (Figs. 25–28), overall, the results are fairly closely related as shown by their statistical frequency distribution (Fig 29). Thus, to provide more “contrast”, weightings could be increased for various criteria.

Finally, it is worth noting that the approach used here need not be confined only to tilapia and catfish farming in ponds. The GIS can be extended to other kinds of farming systems and to culture-based fisheries. For example, for the latter important criteria for development and management are size of water bodies, permanency and distance to government fish farms. These can be manipulated to assess stocking requirements and to compare them with present stocking capabilities in order to estimate staff and equipment necessities.

7.3 Conclusions

By using the Ghana Geographical Information System, we have been able to deal comprehensively and quantitatively with a variety of criteria important for the development of tilapia and catfish farming in ponds. By this approach, we have been able to go further than our predecessors to predict where fish farming is most likely to be successful. Although our results are indicative, not precise measures, we believe that our predictions can be used by the Fisheries Department, the applied research institutes, banks and private individuals to better plan their own aquaculture development or investment activities. Important follow up activities are to undertake field studies to confirm our results and to gather additional information in order to refine our predictions and to expand the analyses to other kinds of aquaculture or culture-based fishery systems.

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