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Th is study considers the meta-frontier technique to compare the effi ciency level of organic and conventional cocoa production systems in Ghana using a cross sectional data of 390 farms. Th e results reveal that the organic systems exhibit an increasing return to scale whilst, the conventional system exhibit decreasing returns to scale. All the inputs variables positively infl uence the production except the age of trees. Th e combined eff ects of operational and farm specifi c factors are identifi ed to infl uence the technical effi ciency although the individual eff ects of some variables are not signifi cant. Th e mean technical effi ciency relative to the meta-frontier is estimated to be 0.59 for the organic and 0.71 for the conventional farms. Th e study concludes that the conventional system of cocoa production is more technically effi cient than the organic system. However, the increase in the scale of production in the organic system to take advantage of the economies of scale may enhance the effi ciency of production.

The study aims to examine the technical efficiency and its determinants of fish farms in Ghana. The stochastic frontier function is employed using a cross‐sectional data of 150 farmers. The results show that elasticities of mean output for all inputs are positive, whereas the computed return to scale reveals that, on average, fish farms exhibit increasing return to scale. The combined effect of operational and farm‐specific factors influence technical efficiency although individual effects of some variables may not be significant. Mean technical efficiency is estimated to be 84%, indicating that the possibility of enhancing production given the present state of technology and input level can be achieved in the short run by increasing technical efficiency by 16% through adoption of practices of the best fish farm.

The levels of heavy metals in surface water and their potential origin (natural and anthropogenic) were respectively determined and analysed for the Obuasi mining area in Ghana. Using Hawth's tool an extension in ArcGIS 9.2 software, a total of 48 water sample points in Obuasi and its environs were randomly selected for study. The magnitude of As, Cu, Mn, Fe, Pb, Hg, Zn and Cd in surface water from the sampling sites were measured by flame Atomic Absorption Spectrophotometry (AAS). Water quality parameters including conductivity, pH, total dissolved solids and turbidity were also evaluated. Principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to identify possible sources of these heavy metals. Pearson correlation coefficients among total metal concentrations and selected water properties showed a number of strong associations. The results indicate that apart from tap water, surface water in Obuasi has elevated heavy metal concentrations, especially Hg, Pb, As, Cu and Cd, which are above the Ghana Environmental Protection Agency (GEPA) and World Health Organisation (WHO) permissible levels; clearly demonstrating anthropogenic impact. The mean heavy metal concentrations in surface water divided by the corresponding background values of surface water in Obuasi decrease in the order of Cd > Cu > As > Pb > Hg > Zn > Mn > Fe. The results also showed that Cu, Mn, Cd and Fe are largely responsible for the variations in the data, explaining 72% of total variance; while Pb, As and Hg explain only 18.7% of total variance. Three main sources of these heavy metals were identified. As originates from nature (oxidation of sulphide minerals particularly arsenopyrite-FeAsS). Pb derives from water carrying drainage from towns and mine machinery maintenance yards. Cd, Zn, Fe and Mn mainly emanate from industry sources. Hg mainly originates from artisanal small-scale mining. It cannot be said that the difference in concentration of heavy metals might be attributed to difference in proximity to mining-related activities because this is inconsistent with the cluster analysis. Based on cluster analysis SN32, SN42 and SN43 all belong to group one and are spatially similar. But the maximum Cu concentration was found in SN32 while the minimum Cu concentration was found in SN42 and SN43.

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