Preliminary studies on bamboo floating cage and net enclosure culture in Kainji Lake basin

The main objective of this paper is to introduce bamboo floating cage and net enclosure fish culture technology aimed at producing fish from almost all available inland bodies of water in Nigeria. The experimental approach embarked upon at Kainji Lake Research Institute is discussed. Results obtained from these experiments would help in identifying the inherent problems of this culture system and in determining the urgently needed information that will serve as management and production guidelines for adapting the technology to local conditions of varying ecological characteristics in Nigeria. Ultimately, the project is aimed at increasing the productivity of fishermen/fish farmers and hastening the development of rural communities

Small scale commercial production of tilapia fingerlings in floating bamboo net-hapas

The inadequate supply of tilapia seed is considered as one of the major present constraints to the development of the culture industry in Nigeria. The floating bamboo net-hapa hatchery/nursery system was observed to be very efficient in the mass production of tilapia (Oreochromis niloticus) fry and fingerlings at Kainji Lake Research Institute. This system was therefore, recommended for small-scale (artisanal) commercial operators consisting of fishermen families in order to increase their productivity and hasten development of rural communities. The economic analysis of this system showed that loan obtained for the recommended scale of operation can be amortized within 2 years of the project. It was emphasized that the operational and managerial skills of the fish farm operators account largely to the production cost and profitability of the enterprise

Commercial aquaculture in Nigeria: a vertically integrated approach in the commercial freshwater farming of tilapia in floating bamboo net-hapas and cages systems

Tilapia once termed "poor man's" fish, still remains as the highly-priced food fish in many developing countries. The good attributes of this fish prompt its use in intensive aquaculture vertically integrated systems (VIS) which embrace broodstock development, hatchery/nursery and growout phase. Based on the series of studies carried out at Kainji Lake Research Institute, in New Bussa, Nigeria using Oreochromis (Tilapia niloticus) in floating bamboo hapas/cages, the recommended intensive modular systems were estimated to be capable of producing 4 million Tilapia fingerlings and 729 tons fish (Market-size) annually. Cost-benefit analysis showed the venture to have high prospects. It is recommended that priority be given to Tilapia cage culture at the national level in order to contribute immensely towards increased fish production

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.