Research on Land Use and Land Cover (LULC) dynamics, and an understanding of the drivers responsible for these changes, are very crucial for modelling future LULC changes and the formulation of sustainable and robust land-management strategies and policy decisions. This study adopted a mixed method consisting of remote sensing and Geographic Information System (GIS)-based analysis, focus-group discussions, key informant interviews, and semi-structured interviews covering 586 households to assess LULC dynamics and associated LULC change drivers across the Dedza district, a central region of Malawi. GIS-based analysis of remotely sensed data revealed that barren land and built-up areas extensively increased at the expense of agricultural and forest land between 1991 and 2015. Analysis of the household-survey results revealed that the perceptions of respondents tended to validate the observed patterns during the remotely sensed data-analysis phase of the research, with 57.3% (n = 586) of the respondents reporting a decline in agricultural land use, and 87.4% (n = 586) observing a decline in forest areas in the district. Furthermore, firewood collection, charcoal production, population growth, and poverty were identified as the key drivers of these observed LULC changes in the study area. Undoubtedly, education has emerged as a significant factor influencing respondents’ perceptions of these drivers of LULC changes. However, unsustainable LULC changes observed in this study have negative implications on rural livelihoods and natural-resource management. Owing to the critical role that LULC dynamics play to rural livelihoods and the ecosystem, this study recommends further research to establish the consequences of these changes. The present study and future research will support decision makers and planners in the design of tenable and coherent land-management strategies.

Drought is a creeping phenomenon whose effects evolve with time, yet the start and end is often only clear in the hindsight. The present study assessed drought conditions using two categories of drought indicators computed from precipitation data sets measured by weather stations across the Western Cape Province, South Africa for the period 1985 to 2016. The first category was the Standardized Precipitation Index (SPI) accumulated over 3-, 6- and 12-months (hereafter of SPI-3, SPI-6 and SPI-12 respectively). The second category consists of the four Drought Monitoring Indicators (DMI) i.e., Drought Duration (DD), Severity (DS), Intensity (DI) and Frequency (DF). Firstly, analysis of SPI-3, SPI-6 and SPI-12 illustrate that between 1985 and 2016, the Western Cape Province experienced recurrent mild drought conditions. This suggests that the drought conditions experienced during 2015/2016 hydrological year (hereafter current) in the Western Cape Province is a manifestation of past drought conditions. Secondly, analysis of trends in DMI series depict a noticeable spatial-temporal dependence wherein the southern and western regions experienced more severe droughts compared to the eastern and northern regions of the Western Cape Province. Results also show that the DMI trends exhibit up to ~8% variability over the past decade. Overall, the current drought conditions in the Western Cape Province continues to adversely affect agricultural production while the water reservoirs are at below 30% capacity implying that the socio-economic impacts of these droughts will continue to reverberate for many months to come. Though the on-going drought conditions in the Western Cape Province is a regular part of nature’s cycle, analysis of historical drought characteristics based on drought indicators is an important first step towards placing the current drought conditions into perspective, and contribute to triggering action and response thereof. All these lay the foundation for drought monitoring and contribute towards the development of drought early warning.

The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R² of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers.

Notwithstanding the dispersed nature of the water, energy and food (WEF) nexus scholarship in the African continent, its strategic importance to the African agenda has gained widespread attention in research and planning circles. In this regard, the bibliometric science mapping and content analysis of the WEF nexus scientific publication trends, the conceptual, intellectual and social structures, as well as the inherent paradigmatic shifts in the WEF nexus body of knowledge in the African continent have been undertaken, using the nexus body of literature accessed from the Web of Science and Scopus core collection databases. The review results confirmed that, whilst the WEF nexus scholarship has expanded since 2013, there is also evidence of growth in the conceptual, intellectual and social structures of the WEF nexus in the African continent. These shifts have resulted in the emergence of hot topics (subfields) including modelling and optimization, climate variability and change, environmental ecosystem services sustainability, and sustainable development and livelihoods. The review further determined that these structures have evolved along two main perspectives of WEF nexus research development, i.e., the interdisciplinary and transdisciplinary domains. In support of the interpretation of the visual analytics of the intellectual structure and changing patterns of the WEF nexus research, the shifts in positivist, interpretivist and pragmatic paradigmatic perspectives (these are underpinned by the ontology, epistemology, and methodology and methods) are considered when explaining WEF nexus research shifts: (a) From the unconnected silo paradigms that focus on water, energy and food (security concerns) to interconnected (and sometimes interdependent or nested) linkages or systems incorporating environmental, social-economic and political drivers (also viewed as subfields) in a bid to holistically support the Sustainable Development Goals (SDGs) across the African continent; and (b) in the evaluation of the WEF nexus scholarship based on novel analytical approaches. We contend that whilst the theories of science change underpin this apparent expansion, the macro-economic theory will find use in explaining how the WEF nexus research agenda is negotiated and the Integrative Environmental Governance (IEG) is the duly suited governance theory to bridge the inherent disconnect between WEF nexus output and governance processes uncovered in the literature. Overall, operational challenges and opportunities of the WEF nexus abound, transitioning the WEF nexus research to practice in Africa, motivating the need to take advantage of the scholar–practitioner research underpinnings, as contemplated in the transdisciplinary research approach, which is characterised by the dual quest for new knowledge and considerations of use. Yet, there is need for more coordinated and collaborative research to achieve impact and transition from WEF nexus thinking to WEF nexus practice.

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer—December, January, and February (DJF)—can be explained by spring—September, October, and November (SON)—rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.