Seasonality, labor and integration of aquaculture into southern African smallhold farming systems

Fish production on Malawian smallholdings is generally limited by the quantity and quality of inputs to the pond (Brummett and Noble 1995). The timing of labor availability and other farm activities limit the amount farmers put into their ponds resulting in lower growth rates and yields. There is potential for improving production and yields through modifications of production schedules to accommodate other farming activities. Limited material and labor inputs among farming system enterprises can be better allocated by considering seasonal availability of inputs and adapting the pond and fish farming technology to the farming system. This case from Malawi demonstrates that aquaculture technology that neglects the annual cycle of events and constraints on the farm will not be easily integrated into the farming system. Focusing on technology that maximizes fish production rather than facilitation of adoption and integration has been a feature of the majority of African smallholder agriculture/aquaculture projects. Farming Systems Research (FSR) must identify niches and opportunities for system improvement for it to be worth supporting as a development intervention.

Is epizootic ulcerative syndrome (EUS) specific fungus of fishes a primary pathogen?: an opinion

Earlier findings on epizootic ulcerative syndrome (EUS) and the present observation of the authors on transmission of EUS to snakehead (Channa sp.) without skin damage provide evidence to suggest that the invasive fungus associated with EUS is a primary pathogen.

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.