Climate change is expected to affect the livelihood of rural farmers in South Africa particularly the smallholder farmers, due to their overwhelming dependence on rain-fed agriculture. This study examines smallholder farmers' perception of climate change, the adaptation strategies adopted and factors that influences their adaptive decisions. The unit of data collection was household interview and focus group discussion. Climate data for the Olifants catchment (1986–2015) were also collected to validate farmers' perception of climate change with actual climate trend. Data collected were analysed using descriptive statistics, Mann–Kendall trend, Sen's slope estimator and multinomial logit regression model. Results revealed that smallholder farmers are aware of climate change (98%), their perception of these changes aligns with actual meteorological data, as the Mann–Kendall test confirms a decreasing inter-annual rainfall trend (−0.172) and an increasing temperature trend (0.004). These changes in temperature and precipitation have prompted the adoption of various adaptation responses, among which the use of improved seeds, application of chemical fertilizer and changing planting dates were the most commonly practised. The main barriers to the adoption of adaptation strategies were lack of access to credit facility, market, irrigation, information about climate change and lack of extension service. The implication of this study is to provide information to policy-makers on the current adaptation responses adopted by farmers and ways in which their adaptive capacity can be improved in order to ensure food security.
The outbreak of Covid-19 pandemic has not only caused fear and uncertainty in the education systems across the globe, but it brought about a fundamental paradigm shift in the mode of teaching and learning. Higher education drastically transitioned to remote/ online delivery even for the students who had enrolled for face-to-face mode of teaching and learning. The paper is premised in the context of a developing country that such a drastic change could have widened the digital divide between students from privileged homes and those from disadvantaged families as students did not receive adequate technological training and to even acquire the necessary electronic devices. Consequently, the study sought to establish the levels of adaptation to remote teaching and learning by university students herein referred to as pre-service teachers. Following a quantitative research design, an online questionnaire survey was administered to 157 pre-service teachers enrolled in a Life Sciences Methodology module at a South African university. Data was analysed using SPSS version 26 and descriptive statistics, exploratory analysis of the questionnaire constructs and One-Way ANOVA tests were conducted to compare pre-service teachers` perceptions, experiences and preparedness. The results showed that the disparities and inequalities that exist in different South African contexts in which pre-service teachers hail from, dictated their levels of adaptations to remote teaching and learning. Those from disadvantaged backgrounds were less adapted as they struggled more when it comes to acquisition of electronic gadgets and connectivity to facilitate remote learning compared to those from advantaged backgrounds. This study affirms the call for education institutions and governments to rethink ways of closing the gap between the poor and the rich in education in terms of resource and other support mechanisms.
Hypertension has become a major public health challenge and a crucial area of research due to its high prevalence across the world including the sub-Saharan Africa. No previous study in South Africa has investigated the impact of blood pressure risk factors on different specific conditional quantile functions of systolic and diastolic blood pressure using Bayesian quantile regression. Therefore, this study presents a comparative analysis of the classical and Bayesian inference techniques to quantile regression. Both classical and Bayesian inference techniques were demonstrated on a sample of secondary data obtained from South African National Income Dynamics Study (2017–2018). Age, BMI, gender male, cigarette consumption and exercises presented statistically significant associations with both SBP and DBP across all the upper quantiles [Formula: see text]. The white noise phenomenon was observed on the diagnostic tests of convergence used in the study. Results suggested that the Bayesian approach to quantile regression reveals more precise estimates than the frequentist approach due to narrower width of the 95% credible intervals than the width of the 95% confidence intervals. It is therefore suggested that Bayesian approach to quantile regression modelling to be used to estimate hypertension.
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