Indigenous agricultural practices employed by the Vhavenda community in the Musina local municipality to promote sustainable environmental management.

Indigenous knowledge offers insights into preserving natural resources and further contributes towards improving rural livelihoods. Incorporating this knowledge into agriculture can therefore increase acceptance and innovation of sustainable technologies. In support of this argument, rural small-scale farmers have globally proven the significance of indigenous knowledge in the agricultural sector by incorporating their local strategies and practices for both commercial and subsistence agriculture. This article demonstrates the various indigenous farming practices employed by the Vhavenda Community and how these practices promote sustainable environmental management. It reflects the essential aspects crucial to indicating the value of indigenous agriculture. Using a mixed-methods approach, we administered face-to-face structured questionnaires to elderly villagers aged 50 years and above from the study area. Our results revealed various agricultural practices such as crop rotations, polyculture, intercropping, livestock farming, ploughing rituals, water harvesting, and seasonal agriculture. The Vhavenda community incorporates local knowledge, which is unique to these agricultural practices, and uses various strategies which contribute to a sustainable environment within their area. This has, in turn, allowed them to improve their livelihoods, reduce poverty, and provide life sustenance. The study's findings demonstrate the significance of indigenous knowledge in the agricultural sector and rural peoples' efforts towards supportable environmental management. The article therefore suggests that local indigenous knowledge should be understood as a collaborative concept within the agricultural industry. Furthermore, it proposes that stakeholders who influence decision-making and progress in rural communities should consider this knowledge and work together with local inhabitants. Additionally, it advocates for acknowledging the understanding of local indigenous peoples in pursuing sustainable environmental management.

Characterising landcover changes and urban sprawl using geospatial techniques and landscape metrics in Bulawayo, Zimbabwe (1984-2022).

Urbanisation is a global trend that significantly impacts sustainable urban development and the quality of urban life. Assessing urban sprawl is critical for sustainable urban planning and aligns with the key objectives of the United Nations sustainable development goals. This study employed geospatial technology and landscape metrics to comprehensively assess, map, and quantify the extent of urban sprawl in Bulawayo from 1984 to 2022. The study leveraged the Support Vector Machine (SVM) supervised machine learning algorithm coupled with landscape metrics to achieve this objective. The combined approach allowed for the classification, detection of land cover changes, analysis of urban dynamics, and quantification of the degree of urban sprawl. The results revealed a 228% increase in built-up areas between 1984 and 2022, while non-built-up areas (agricultural land, vegetation, bare land) decreased by 29.28%. The landscape metrics and change analysis indicated an encroachment of urban-like conditions into urban areas. Land use change assessment revealed that Bulawayo exhibits four district types of urban sprawl: leapfrog, strip/ribbon, low density, and infill. Urban expansion is attributed to urbanisation and evolving land use policy. Urban sprawl has numerous urban planning implications on transport management, habitat loss and deforestation, reduction and contamination of freshwater sources, and many others. This study is strategic to planners, researchers, and decision-makers/policy makers as it provides relevant, up-to-date, and accurate information for sustainable urban planning.

Indigenous local observations and experiences can give useful indicators of climate change in data-deficient regions.

This study demonstrates that Indigenous local observations and experiences can enrich knowledge of climate change, particularly in data-deficient regions that are not adequately covered by weather stations. Paradoxically, these places host groups of Indigenous Peoples who have rich knowledge about their local climates from their many years of constant interactions with the environment. The study used group-based semi-structured interviews to collaborate with keystone elderly participants who had profound knowledge and lived experiences about observed changes in their local environment (n = 13). These participants were identified through theoretical sampling from four remote Indigenous villages of Mbire District in Zimbabwe. The purpose of the study was to identify indicators of climate change impacts from communities believed not to have been much influenced by the scientific construct of climate change. Results revealed that the locals have a keen interest to closely observe changes occurring in their environment, including finer accounts of experiences with climatic events, owing to their predominantly climate-sensitive livelihoods. These results corroborate existing evidence about a warmer and drier climate and the reported increase in the frequency and severity of drought as well as floods in the area, and add finer details to the changes in ecological, hydrological and human systems, which are not sufficiently reported in existing climate impact studies. We also flagged new observations in biological systems as pointers for further intensive investigation. Given the complexity associated with understanding impacts of climate change and the urgent need to refine knowledge about the same, we argued for perforation of the boundaries of climate science to accommodate enriching perceptions of Indigenous communities who have been religiously observing changes happening in their local environment, albeit being relegated.

Societal context-dependent multi-modal transportation network augmentation in Johannesburg, South Africa.

In most developing countries, formal and informal transportation schemes coexist without effective and smart integration. In this paper, the authors show how to leverage opportunities offered by formal and informal transportation schemes to build an integrated multi-modal network. Precisely, the authors consider integration of rickshaws to a bus-train network, by taking into account accessibility and societal constraints. By modelling the respective networks with weighted graphs, a graph augmentation problem is solved with respect to a composite cost taking into account constraints on the use of rickshaws. The solution, is based on finding a minimum cost spanning tree of a merged graph. The method is applied in the South African context, in the city of Johannesburg where rickshaws are not yet a significant part of the transportation system. The implications of the study reveal that using non-motorised transportation services is a viable option of improving mobility in the city. The composite cost introduced herein could be used for new routing algorithm including societal, environmental, architectural contexts and commuter experiences through rating.

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery.

Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41-0.46; Root Mean Square Error (RMSE): 0.60-0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three-five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three-five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery.

Past, current, and future perspectives on eco-tourism: a bibliometric review between 2001 and 2018.

With increasing attentions on climate change, solid wastes, over-tourism, and improved environmental awareness worldwide, eco-tourism has been widely promoted worldwide. This requires that governmental agencies at different levels should prepare appropriate policies to facilitate eco-tourism and local communities should take necessary actions to preserve their natural resources, protect their environment, and support sustainable tourism. Academically, literatures on eco-tourism have increased faster with an annual growth rate of 10-30% during the recent years. Under such a circumstance, it is important to conduct a comprehensive review so that research progress on eco-tourism can be summarized and future research directions can be identified. Based on 1771 publications published during 2001-2018, a systematic method combining bibliometric analysis and network analysis is applied in this study to uncover the dynamic trends, academic collaboration, and research hotspots related with eco-tourism. Results show that the total number of relevant publications has gradually increased. Key journals include Journal of Hospitality and Tourism Management, Annals of Tourism Research, Conservation Biology, and Biological Conservation. Authors from USA have the most publications and international co-authorships, while the most influential institution is the Chinese Academy of Science. Moreover, research keywords have been identified, including eco-tourism, management, biodiversity, national park, sustainability, and sustainable tourism. Research findings of this study provide valuable insights to further improve eco-tourism research so that this emerging research field can be proactively fostered.

Data on strategically located land and spatially integrated urban human settlements in South Africa.

In developing countries like South Africa processed geographic information systems (GIS) data on land suitability, is often not available for land use management. Data in this article is based on a published article "The strategically located land index support system for humans settlements land reform in South Africa" (Musakwa et al., 2017) [1]. This article utilities data from Musakwa et al. (2017) [1] and it goes on a step further by presenting the top 25th percentile of areas in the country that are strategically located and suited to develop spatially integrated human settlements. Furthermore the least 25th percentile of the country that are not strategically located and spatially integrated to establish human settlements are also presented. The article also presents the processed spatial datasets that where used to develop the strategically located land index as supplementary material. The data presented is meant to stir debate on spatially integrated human settlements in South Africa.

Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa.

Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatial extent and not up-to-date. Consequently, the dataset presented in this paper illustrates the spatial and temporal coverage of cycling patterns and trends in Johannesburg for the year 2014 derived from the geolocation based mobile application Strava. To the best knowledge of the authors, there is little or no comprehensive dataset that describes cycling patterns in Johannesburg. Perhaps this dataset is a tool that will support evidence based transportation planning and smart mobility.