BEEKEEPING AND LOCAL SELF-RELIANCE IN RURAL SOUTHERN AFRICA*

ABSTRACT. Beekeeping has the potential to supplement incomes in rural southern Africa. In light of regional economic constraints, self-reliance strategies that draw on local knowledge and skills take on a renewed importance. We consider the advantages and disadvantages of beekeeping and examine appropriate forms of development support. A short case study from Zimbabwe illustrates these issues.     

Modeling and optimizing a gas—water reservoir: Enhanced recovery with waterflooding

Accepted practice dictates that waterflooding of gas reservoirs should commence, if ever, only when the reservoir pressure has declined to the minimum production pressure. Analytical proof of this hypothesis has yet to appear in the literature however. This paper considers a model for a gas-water reservoir with a variable production rate and enhanced recovery with waterflooding and, using an initial dynamic programming approach, confirms the above hypothesis.     

Rb-Sr age of lunar igneous rocks 62295 and 14310

We report an age of crystallization for spinel-troctolite (VHA basalt) 62295 of 4.00 ± 0.06 × 10⁹ yr (I = 0.69956 ± 6) and an age of crystallization for KREEP-rich basalt 14310 of [3.94 ± 0.03 × 10⁹yr (I = 0.70041 ± 5). The ages probably date the cooling of shock melts.     

A test of Sporormiella representation as a predictor of megaherbivore presence and abundance

Spores of the dung fungus Sporormiella have been suggested to indicate the presence, perhaps also the abundance of past megaherbivore populations. Nonetheless, basic studies demonstrating a correlation between Sporormiella concentration in lacustrine sediments and modern herbivore abundance are lacking. This study of Sporormiella representation in grazed and ungrazed landscapes provides supporting evidence for the application of Sporormiella as an indicator of megaherbivore presence and abundance in ancient landscapes. However, Sporormiella representation is spatially sensitive to the distance from the dung source. In lakes where Sporomiella are abundant in shoreline sediments, they decline sharply with increasing distance from the lake edge. Although this study provides supporting evidence for the application of Sporormiella as a proxy for herbivore presence and abundance, independent proxies should be applied in conjunction with Sporormiella to control for changes in lake size.     

Satellite observations of phytoplankton variability during an upwelling event

A sequence of satellite images of near-surface phytoplankton pigment concentrations and sea surface temperature together with concurrent surface measurements are used to study an upwelling event during the Coastal Ocean Dynamics Experiment off northern California. These data sets show a high degree of temporal and spatial variability during this episode. Recurrent patterns in this variability give insight into the dynamics of coastal upwelling and its effects on biological distributions. Simple models of coastal upwelling cannot explain the observed phenomena in the CODE region. Satellite estimates of phytoplankton growth rates were about 0.8 day⁻¹ near persistent upwelling centers.     

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.