Soil scientists can aid in an essential part of ecological conservation and rehabilitation by quantifying soil properties, such as soil organic carbon (SOC), and is stock (SOCs) SOC is crucial for providing ecosystem services, and, through effective C-sequestration, the effects of climate change can be mitigated. In remote mountainous areas with complex terrain, such as the northern Maloti-Drakensberg in South Africa and Lesotho, direct quantification of stocks or even obtaining sufficient data to construct predictive Digital Soil Mapping (DSM) models is a tedious and expensive task. Extrapolation of DSM model and algorithms from a relatively accessible area to remote areas could overcome these challenges. The aim of this study was to determine if calibrated DSM models for one headwater catchment (Tugela) can be extrapolated without re-training to other catchments in the Maloti-Drakensberg region with acceptable accuracy. The selected models were extrapolated to four different headwater catchments, which included three near the Motete River (M1, M2, and M3) in Lesotho and one in the Vemvane catchment adjacent to the Tugela. Predictions were compared to measured stocks from the soil sampling sites (n = 98) in the various catchments. Results showed that based on the mean results from Universal Kriging (R2 = 0.66, NRMSE = 0.200, and ρc = 0.72), least absolute shrinkage and selection operator or LASSO (R2 = 0.67, NRMSE = 0.191, and ρc = 0.73) and Regression Kriging with cubist models (R2 = 0.61, NRMSE = 0.184, and ρc = 0.65) had the most satisfactory outcome, whereas the soil-land inference models (SoLIM) struggled to predict stocks accurately. Models in the Vemvane performed the worst of all, showing that that close proximity does not necessarily equal good similarity. The study concluded that a model calibrated in one catchment can be extrapolated. However, the catchment selected for calibration should be a good representation of the greater area, otherwise a model might over- or under-predict SOCs. Successfully extrapolating models to remote areas will allow scientists to make predictions to aid in rehabilitation and conservation efforts of vulnerable areas.
Curationis 33 (2): 60-68The Acquired Immune Deficiency Syndrome epidemic, caused by the Human Immu nodeficiency Virus, is a global crisis which threatens development gains, economies, and societies. Within sub-Saharan Africa, where the epidemic began the earliest and the HIV prevalence is the highest, African countries have death rates not seen be fore. In South Africa the epidemic has a devastating impact which creates profound suffering on individuals and their families, and the impact on the socio-economic level is o f great concern. The eradication of HIV/AIDS represents one of humanity's greatest challenges, which requires co-operation and comprehensive collaboration between many different role players. In this endeavour clinical information plays a major role.To combat the effect o f the disease, the Free State Department of Health started with the provisioning o f antiretroviral therapy in the public health sector. The objective of this paper was to address the challenges they faced in order to develop and imple ment an information system to manage the rollout of antiretroviral treatment effec tively. They started with a paper-based system to collect vital information. It was followed by a palm computer project that was initiated to electronically capture the data collected by the paper-based system. This system was then replaced by a comprehensive Hospital and Clinic Information System which was acquired and customised for the antiretroviral data collection process. Research partners devel oped a standalone antiretroviral data warehouse for collecting information associ ated with the monitoring and evaluation o f the Free State antiretroviral and HIV/ AIDS treatment programme. The data warehouse successfully produced several management information reports to the antiretroviral management team. A need was identified to design a comprehensive antiretroviral data warehouse that will integrate " data from several operational sources which are all associated with HIV/AIDS.
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