Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Fish are a highly perishable commodity, and unhygienic fresh fish supply chains have been documented over the past two decades in sub‐Saharan Africa. Fishers spend long hours on boats with no provision of sanitary facilities, and even after landing, they are often in environments without sanitary facilities. The purpose of the present study was to explore the impacts of water, sanitation and hygiene practices in an artisanal fishery on food safety by analysing water samples in close contact with fresh fish at various checkpoints from capture to sale at the local market along the shores of Lake Malawi (Malawi). The four checkpoints included (a) fishing boats at the fishing ground before fishing commenced (n = 85); (b) in the same boats at the landing site before offloading fresh fish (n = 85); (c) with fresh fish transporters at the landing site (n = 71); and (d) among vendors at the market (n = 63). Escherichia coli was found in a high percentage of samples at each of the four checkpoints during the dry, wet and cold seasons. The highest risk for contamination (represented by E. coli concentrations) was the transition from transport to vendor, regardless of the season during which the samples were taken. The product value chain demands food safety. The results of the present study have potential applications in informing future interventions to develop behavioural change strategies regarding handwashing and toileting practices, norms unique for highly mobile fishing communities through the integration of hardware and software solutions and using better‐quality water to store fish on the boat, in transport and at the market.

Stochastic models have proven to be practically fundamental in fields such as science, economics, and business, among others. In Malawi, stochastic models have been used in fisheries to forecast fish catches. Nevertheless, forecasting water levels in major lakes and rivers in Malawi has been given little attention despite the availability of ample historical data. Although previous multichannel seismic surveys revealed the presence of low stands (sediment bypass zone) in Lake Malawi indicating that since the beginning of its formation, important water level fluctuations have been occurring, these previous surveys failed to predict and highlight much more clearly the status of these levels in the future. Therefore, the main objective of the study was to fill these research gaps. The study used

The study was conducted to assess catch composition and economic analysis of monofilament and multifilament under-meshed gillnets (Ngongongo) from March to April 2018 around Likoma Island, Lake Malawi. Catch efficiency for targeted fish species in monofilament gillnets showed that catch per unit effort (CPUE) was three times higher than that of multifilament gillnets for Copadichromis spp. (402.2 Kg, 43.3%), Opsaridium microcephalum (315.47 Kg, 34.6%), Rhamphochromis spp. (26.2 Kg, 2.8%), Bagrus meridionalis (21.6 Kg, 2.3%), Oreochromis karongae (40.7 Kg, 4.4%), Bathyclarias spp. (23.2 Kg, 2.5%), Dimidiochromis kiwinge (14.4Kg, 1.6%) among others. Catches for Copadichromis spp. comprised of 43% for both gillnet material type combined indicating the importance of this species in gillnet fishery in the district. Monofilament gillnet caught the highest number of fish (7569) while multifilament gillnet caught the least (5427). Again monofilament gillnet has the highest weight of fish (692.87kg) while multifilament has the least (238.22kg). T-test analysis showed that the weight of fish caught by monofilament and multifilament gillnets were significantly different from one another (p=0.001). The profitability performance non-motorized monofilament and multifilament gillnets canoe fisheries in Lake Malawi (Likoma District) recorded profit margins at the end of the first year of operation with the minimum Return on Investment (ROI) of 58.9% and 34.4% respectively. On the other hand, the motorised monofilament and multifilament Gillnets canoe fisheries recorded loss 51.1% and 74.4% ROI. The study results point out to recommend for management interventions be put in place to manage the Lake Malawi fishery by imposing restrictions on effort, gear type and mesh sizes and access to illegal fishing material.

Lake Malombe fish stocks have been depleted by chronic overfishing. Various management approaches (co-management, command control, and ecosystem-based management to fisheries) have been used to manage the fishery. However, the lack of an accurate predictive model has hampered their success. Therefore, we developed and tested a time series model for Lake Malombe fishery. The seasonal fish biomass and CPUE trends were first observed and both were non-stationary. The second-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), and Akaike information criterion (AIC) were estimated, which led to the identification and construction of autoregressive integrated moving average (ARIMA) models, suitable in explaining the time series and forecasting. The results showed that ARIMA (1,2,1) provided a better prediction than its counterparts. The model satisfactorily predicted that by 2032, both fish biomass and CPUE will decrease to 3204.6 tons and 59.672 respectively, signifying the potential threat to Lake Malombe fishery. The model justified the necessity of taking precautionary measures to avoid the total collapse of the fishery.