No abstract

Malaria incidence in South Africa is highest in the three endemic provinces: KwaZulu-Natal, Mpumalanga and Limpopo. The contribution to malaria transmission by several mosquito species, variation in their resting behaviours and low levels of insecticide resistance makes it necessary to periodically monitor Anopheles species assemblages and resistance phenotypes in vector populations. The aim of this study was therefore to assess Anopheles species assemblage in northern KwaZulu-Natal and to collect insecticide susceptibility data for An. arabiensis, the primary vector of malaria in that province. Anopheles specimens were collected from Mamfene, Jozini, northern KwaZulu-Natal from November 2019 to April 2021. Progeny of wild-collected An. arabiensis females were used for standard insecticide susceptibility tests and synergist bioassays. Anopheles arabiensis contributed 85.6% (n=11 062) of the total catches. Samples for subsequent insecticide susceptibility bioassays were selected from 212 An. arabiensis families. These showed low-level resistance to DDT, permethrin, deltamethrin, and bendiocarb, as well as full susceptibility to pirimiphos-methyl. Synergist bioassays using piperonyl butoxide and triphenyl phosphate suggest oxygenase-based pyrethroid and esterase-mediated sequestration of bendiocarb. These low levels of resistance are unlikely to be operationally significant at present. It is concluded that northern KwaZulu-Natal Province remains receptive to malaria transmission despite ongoing control and elimination interventions. This is due to the perennial presence of the major vector An. arabiensis and other secondary vector species. The continued detection of low-frequency insecticide resistance phenotypes in An. arabiensis is cause for concern and requires periodic monitoring for changes in resistance frequency and intensity.

Accurate and detailed studies in crop mapping are crucial in precision agriculture, yield estimations, and crop monitoring. This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified. The utility and contribution of different bands for classification were evaluated using RF mean decrease GINI for variable importance. Validation of results was done using a confusion matrix which produced overall accuracy, errors and prediction measures. The best performance was attained by SVM with an overall accuracy of 95% and a kappa value of 94%. RF also performed fairly well with 85% of overall accuracy and kappa value of 83%. It was concluded that Sentinel-2 data performs better using the SVM classifier compared to RF classifier.