Impacts of Stone Quarrying on Local Vegetation in Mount Korok Area, Juba, Central Equatoria State, South Sudan

Abstract

This study was carried out in three quarry sites at Mount Korok, also known as Jebel Kujur area, which is located within Juba Town Council in Central Equatoria State, South Sudan. The main aim was to assess the impact of quarrying activities on the local ground cover vegetation, mainly grasses and low-lying non-woody herbaceous plants. The methods used included, besides direct observations, iron frame quadrats of 1 × 1 m2 in size, for random sampling of attributes and community characteristics of the plants in three different sites. The first site was an old, abandoned stone-quarrying site; the second was where quarrying work was actively going on at the time of the study; and the third was an area never before exposed to stone quarrying (hence, acting as a control). Data were analyzed using descriptive statistics such as frequency distribution, density measures, diversity indices, and correlations. The research was carried out during the wet season when most plants were green and at different stages of flowering and/or fruiting from July through September, 2020. The results revealed that over 44 species of ground cover plants were identified, some of which were more abundant and had the widest distribution and frequency in the three study sites, including Cynodon lemfuensis, Cyperus rotundus, Bracharia ramose, Merremia pinata, Cyanodon dactylon, and Digitaria fernatad, whereas others were limited to one site or the other. Results also indicated that though stone-quarrying activities have impacts on ground cover plants, however, they are not the only factor affecting ground cover plants. More than 80% of the impacts on ground cover plants are caused by factors other than stone quarrying but were not identified during this study. It was therefore recommended that future studies in the area on the same theme should isolate the effects of stone quarrying on ground cover plants from these other operating factors through discriminant functional analysis.