The experimental cage culture was conducted at Shirati bay, Lake Victoria from February to August 2013, to investigate the impacts of the small scale cage culture on the environment. Three locations along the cages, at the intermediate and one in the offshore (control) were sampled for water quality parameters, phytoplankton and macro invertebrates. A notable increase in nutrient concentration was observed after the set of cages among the stations. However DO, pH, and water transparency showed no major changes and was within the recommended ranges. Cyanophytes an indicator of inorganic pollution dominated before and after the set of cages, an increase in phytoplankton numerical abundance was observed after stocking of fish in cages. In addition there was an increase in the invertebrate community especially bivalves and gastropods. In conclusion we found no consistent environmental change caused by cage culture, and therefore it can be allowed in Lake Victoria, Tanzania part, with close monitoring of its impacts.

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Biomass of the schooling fish Rastrineobola argentea (dagaa) is presently estimated in Lake Victoria by acoustic survey following the simple “rule” that dagaa is the source of most echo energy returned from the top third of the water column. Dagaa have, however, been caught in the bottom two-thirds, and other species occur towards the surface: a more robust discrimination technique is required. We explored the utility of a school-based random forest (RF) classifier applied to 120 kHz data from a lake-wide survey. Dagaa schools were first identified manually using expert opinion informed by fishing. These schools contained a lake-wide biomass of 0.68 million tonnes (MT). Only 43.4% of identified dagaa schools occurred in the top third of the water column, and 37.3% of all schools in the bottom two-thirds were classified as dagaa. School metrics (e.g. length, echo energy) for 49 081 manually classified dagaa and non-dagaa schools were used to build an RF school classifier. The best RF model had a classification test accuracy of 85.4%, driven largely by school length, and yielded a biomass of 0.71 MT, only c. 4% different from the manual estimate. The RF classifier offers an efficient method to generate a consistent dagaa biomass time series.

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