Assessment of post-harvest losses in Nigeria fishery: the Kainji Lake model

The paper provides a description of a methodology used for quantitative assessment of post harvest losses in the Kainji Lake Fishery (Nigeria). The sample population was made up of 314 fisherfolk, 115 processors, 125 fish buyers and 111 fish sellers. For the determination of handling losses, 24,839 fishes weighing 2,389.31 kg belonging to 43 species were examined of which 10% by number and 9% by weight deteriorated at checking and 4% by number and 3% by weight at landing. Processing losses recorded 22% by number and 16% by weight deteriorated prior to and during smoking with the traditional 'Banda' kiln. During marketing, 16% of fish sold had deteriorated and 6% by weight of fish bought also deteriorated, mainly due to insect infestation during storage. Based on the 1995 yield estimate for Kainji Lake fishery, approximately 1000 tons of fish estimated at 80 million Naira were lost during handling alone. This figure would be much higher if the level of losses during processing and marketing are included. This assessment technique is recommended for use in obtaining quantifiable data on post harvest losses from other water bodies in Nigeria

Influence of soak time and fish accumulation on catches of reef fishes in a multispecies trap survey

Catch rates from fishery-independent surveys often are assumed to vary in proportion to the actual abundance of a population, but this approach assumes that the catchability coefficient (q) is constant. When fish accumulate in a gear, the rate at which the gear catches fish can decline, and, as a result, catch asymptotes and q declines with longer fishing times. We used data from long-term trap surveys (1990–2011) in the southeastern U.S. Atlantic to determine whether traps saturated for 8 reef fish species because of the amount of time traps soaked or the level of fish accumulation (the total number of individuals of all fish species caught in a trap). We used a delta-generalized-additive model to relate the catch of each species to a variety of predictor variables to determine how catch was influenced by soak time and fish accumulation after accounting for variability in catch due to the other predictor variables in the model. We found evidence of trap saturation for all 8 reef fish species examined. Traps became saturated for most species across the range of soak times examined, but trap saturation occurred for 3 fish species because of fish accumulation levels in the trap. Our results indicate that, to infer relative abundance levels from catch data, future studies should standardize catch or catch rates with nonlinear regression models that incorporate soak time, fish accumulation, and any other predictor variable that may ultimately influence catch. Determination of the exact mechanisms that cause trap saturation is a critical need for accurate stock assessment, and our results indicate that these mechanisms may vary considerably among species.