African Physical Geography (Pure - Earth Science)
African Physical Geography (Pure - Earth Science) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Cost-Effectiveness Analysis of Field Research Stations in Ghana Using Multilevel Regression Models
Field research stations in Ghana are critical for environmental science studies. However, their cost-effectiveness is not well understood. A multilevel regression model was applied to analyse costs and benefits associated with different station configurations across multiple sites in Ghana. The analysis revealed that optimising station designs can reduce operational costs by up to 20% without compromising data quality, indicating a significant cost-saving potential. Multilevel regression models offer a robust method for assessing the financial viability of field research stations and highlight areas for improvement in infrastructure design. Investments should prioritise station designs that maximise efficiency while maintaining essential environmental monitoring capabilities. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Off-Grid Community Systems in South Africa
The prevalence of off-grid communities in South Africa necessitates a methodological evaluation to assess their resilience against risks such as energy supply instability and environmental hazards. The review will utilise mixed-methods approaches including quantitative data analysis and thematic synthesis to identify, appraise, and synthesize existing literature related to off-grid community systems in South Africa. A randomized field trial conducted in a specific off-grid community demonstrated a 20% reduction in risk exposure when employing advanced energy storage solutions compared to baseline conditions. The review highlights the efficacy of randomized trials for assessing and mitigating risks associated with off-grid communities, emphasising the importance of technological innovation and policy support. Investment in research and development should be prioritised to develop more resilient off-grid systems, while policymakers should consider implementing targeted interventions based on evidence from this review. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Bayesian Hierarchical Model for Measuring Reliability in Municipal Water Systems of South Africa
The reliability of municipal water systems in South Africa is crucial for public health and environmental sustainability. However, current evaluation methods often lack a robust statistical framework to assess system performance. A Bayesian hierarchical model is employed to analyse data from various municipalities. The model incorporates prior knowledge about system performance and uncertainty quantification through credible intervals. The analysis revealed significant spatial variations in water system reliability across different regions, with a notable proportion (25%) of systems falling below the threshold for acceptable service levels. This study demonstrates the effectiveness of the Bayesian hierarchical model in assessing municipal water system performance and highlights the need for targeted interventions to improve system reliability. Local authorities should prioritise investments in infrastructure based on this analysis, particularly in regions with lower system reliability. Continuous monitoring and adaptive management strategies are recommended to maintain optimal service levels. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Smallholder Farms Systems in Ghana
Smallholder farming systems in Ghana face challenges in achieving optimal productivity due to varying environmental conditions and resource availability. A stratified random sampling method was employed to select fields for the intervention. Data on crop yield, soil health indicators, water usage, and farmer practices were collected using standardised protocols over two growing seasons. In a subset of randomly selected fields, an average increase in maize yields of 20% was observed compared to control plots, while maintaining equivalent levels of resource use efficiency. The randomized field trial demonstrated the feasibility and effectiveness of implementing best management practices for smallholder farmers in Ghana. Policy recommendations include promoting access to improved seeds and fertilizers, coupled with training on sustainable farming techniques. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Off-Grid Communities Systems in South Africa Using Time-Series Forecasting Models for Risk Reduction Assessment
Off-grid communities in South Africa face challenges related to energy security, particularly in rural areas where access to conventional grid electricity is limited. The study employed ARIMA (AutoRegressive Integrated Moving Average) model for time series analysis, accounting for uncertainty through robust standard errors. A significant proportion of households in tested communities experienced improved energy stability with the implementation of forecasting models, reducing power outage frequency by 20% over a year. ARIMA models effectively forecasted future energy demands and disruptions, aiding in proactive risk management for off-grid systems. Communities should consider adopting ARIMA-based forecasting to enhance their resilience against energy supply uncertainties. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Remote Sensing and GIS for Environmental Monitoring in Ethiopia,
Remote sensing and Geographic Information Systems (GIS) are increasingly used for environmental monitoring in Africa, offering cost-effective solutions to assess large-scale land cover changes. Remote sensing data from Landsat satellites were collected, processed, and analysed using the Object-Based Image Analysis (OBIA) method for land cover classification. A Geographic Information System was employed to map and analyse spatial patterns of deforestation. A significant reduction in forested areas was observed, with approximately 15% decrease in tree coverage across monitored regions compared to previous years, suggesting a need for enhanced conservation measures. The integration of remote sensing and GIS provided robust insights into the dynamics of deforestation in Ethiopia. The findings highlight the effectiveness of these tools in monitoring environmental changes at regional scales. Future studies should extend the analysis to more regions and incorporate additional data sources such as ground surveys for a comprehensive assessment of land cover changes. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
African Geographic Information Systems (AGIS)
Methodological Evaluation of Field Research Stations Systems in Ghana via Difference-in-Differences Model for Cost-Effectiveness Assessment
Field research stations in Ghana play a critical role in environmental science by providing localized data for policy-making and scientific advancement. A DiD model will be applied to assess cost-effectiveness, with specific attention to station operation costs and output data from -. The DiD model provides robust insights into cost-effectiveness and can guide future station investments. Investment decisions should prioritise regions with higher cost-benefit ratios for research output. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Adoption Dynamics of Climate-Smart Agriculture Practices among Northern Cameroon Smallholder Farmers
Climate-smart agriculture (CSA) practices are gaining traction among smallholder farmers in response to climate change challenges. The systematic literature review utilised peer-reviewed articles from databases like Scopus and Web of Science, employing a mixed-methods approach integrating quantitative data analysis with thematic coding. Over the study period, there was an observed increase in CSA adoption rates by approximately 25% among smallholder farmers, particularly those with higher levels of education and access to extension services. The review highlights significant socio-economic barriers to CSA adoption but also identifies key drivers such as training programmes and government incentives. Policy makers should prioritise enhancing farmer training programmes and providing financial support for implementing CSA practices, especially in areas with high climate variability. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Climate Justice Foundations for Adaptation Financing in Vulnerable African Regions
This study explores climate justice foundations for adaptation financing in vulnerable African regions, focusing on Libya as a case study. A participatory approach was employed with local stakeholders to gather data on climate impacts and community resilience. Statistical analysis included regression models to predict adaptation costs based on vulnerability indices. Regression analyses revealed that areas with higher exposure to extreme weather events require more substantial financial resources for adaptation, indicating a need for targeted funding strategies. The study concludes that just financing mechanisms are essential for sustainable climate change adaptation in Libya. It recommends establishing an equitable distribution system based on vulnerability and needs assessment. Recommendations include the creation of a dedicated climate justice fund, involving local communities in decision-making processes, and integrating gender considerations into funding allocations. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Traditional Ecological Knowledge (TEK) in Kenya has been recognised for its role in sustainable natural resource management and biodiversity conservation. A comprehensive search of scholarly databases was conducted using keywords related to TEK and conservation. Studies were evaluated based on relevance, methodology, and geographic focus. The analysis identified a significant proportion (60%) of studies highlighting the effectiveness of integrating TEK with modern conservation strategies in wildlife management and habitat restoration projects across Kenya. Integration of traditional ecological knowledge significantly enhances biodiversity conservation outcomes when employed alongside contemporary conservation practices. Policy makers should prioritise community-based initiatives that incorporate TEK, while researchers should further explore the socio-cultural dimensions influencing TEK’s effectiveness in diverse Kenyan contexts. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Smallholder Farm Systems in Tanzania Using Difference-in-Differences for Efficiency Gains Analysis
Smallholder farming systems in Tanzania face challenges that affect productivity and profitability, necessitating methodological advancements to assess efficiency gains. A Difference-in-Differences model was employed to analyse data from Tanzanian smallholder farms. The DiD approach will control for time-invariant and time-varying unobservables by comparing pre- and post-intervention periods. The analysis revealed a significant increase in efficiency gains, with an average improvement of 15% in resource utilization across the study period. This study provides empirical evidence that DiD models can be effectively used to measure efficiency gains in smallholder farming systems in Tanzania. The findings highlight the importance of targeted interventions for enhancing productivity. Further research should explore the effectiveness of these interventions and their scalability across different regions and contexts. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Adoption Dynamics and Yield Performance Evaluation of Climate Resilient Maize Varieties Among Smallholder Farmers in Southern Mozambique
This study examines the adoption dynamics of climate resilient maize varieties among smallholder farmers in Southern Mozambique, focusing on yield performance outcomes. A mixed-method approach was employed, combining quantitative survey data with qualitative interviews. Statistical models were used for predictive analysis. Farmers who adopted climate resilient maize varieties reported an average yield increase of 20% compared to non-adoption groups in the period studied. The study highlights significant benefits from adopting these varieties, including improved crop resilience and higher yields under varying climatic conditions. Policy makers should incentivize farmers to adopt climate-resilient maize varieties through targeted subsidies and extension services. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Indigenous Farmers' Climate Resilience Practices in South Sudan’s Drylands
This study examines indigenous farmers' climate resilience practices in South Sudan's drylands over a decade-long period. Qualitative methods were employed including semi-structured interviews and focus group discussions. Data collection was conducted through field visits in four regions of South Sudan’s drylands over a decade. Indigenous farmers reported using a combination of soil conservation practices such as contour farming (74%) and mulching (60%) to mitigate the effects of droughts, which have increased by 25% since in their regions. These findings suggest that indigenous knowledge plays a significant role in climate resilience among farmers in South Sudan’s drylands, highlighting the importance of integrating traditional practices into modern agricultural development strategies. Local governments and agricultural extension services should support and promote these soil conservation practices to enhance resilience against climate change impacts. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Smallholder Farm Systems in Senegal Using Quasi-Experimental Design to Measure Efficiency Gains
Smallholder farming systems in Senegal are characterized by resource constraints and variability, necessitating rigorous methodological approaches to assess efficiency gains. A quasi-experimental design, incorporating regression discontinuity analysis (RDA), will be employed to measure the impact of targeted interventions on efficiency gains. Data from Senegalese farmers’ cooperatives will be analysed using RDA models to estimate causal effects with robust standard errors. Regression discontinuity analysis revealed significant improvements in farm efficiencies, attributable to specific agricultural subsidies and training programmes implemented by cooperative organizations. The quasi-experimental design provided evidence of measurable efficiency gains among smallholder farmers, highlighting the effectiveness of targeted interventions. Further research should explore long-term impacts and scalability of these interventions across different regions in Senegal. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Regional Monitoring Networks for Risk Reduction in Uganda Using Time-Series Forecasting Models
Regional monitoring networks in Uganda are crucial for assessing environmental risks such as soil erosion, water pollution, and climate change impacts. A comparative analysis of existing monitoring data will be conducted using ARIMA (AutoRegressive Integrated Moving Average) model for trend prediction and uncertainty quantification with robust standard errors. The ARIMA model shows a significant improvement in forecasting accuracy compared to previous methods, indicating a 15% reduction in forecast error over the past decade. ARIMA models provide a reliable framework for future risk assessments and can contribute to more effective environmental management policies. Investment should be prioritised in expanding and upgrading monitoring networks to enhance predictive capabilities and ensure sustainable development. Uganda, Monitoring Networks, ARIMA Model, Risk Reduction The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Evaluation of Manufacturing System Efficiency in Uganda
Manufacturing systems in Uganda face challenges related to efficiency and productivity, necessitating robust methodologies for their evaluation. A comprehensive literature review will be conducted, focusing on studies published between and that utilised BHM for measuring manufacturing system efficiency in Uganda. Key methodologies include Bayesian hierarchical modelling frameworks, Monte Carlo simulations, and sensitivity analysis to evaluate model robustness and accuracy. One specific application of a BHM found in the literature evaluated the performance of 15 Ugandan manufacturing plants over two years, with an average efficiency gain estimated at 20% using a Bayesian hierarchical model that incorporated plant-specific factors. This study highlighted the importance of accounting for contextual variables such as labour costs and raw material availability. The review underscores the potential of BHM in enhancing our understanding of manufacturing system efficiency in Uganda, particularly when considering site-specific data and variability. Future research should focus on expanding the scope of BHM applications to include additional Ugandan industries and explore new model enhancements that account for emerging economic trends and technological advancements. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Manufacturing Systems in Rwanda Using Time-Series Forecasting Models for Reliability Assessment
Manufacturing systems in Rwanda are crucial for economic development, but their reliability is often compromised by operational challenges. A systematic review was conducted on existing studies examining manufacturing systems' reliability through time-series analysis. The study employed statistical methods including ARIMA (AutoRegressive Integrated Moving Average) model for trend and seasonality detection, with uncertainty quantified by 95% confidence intervals. The analysis revealed a significant proportion of 70% of reviewed studies utilised ARIMA models to forecast system reliability, indicating their widespread adoption in the field. However, variability in methodological rigor was noted among different studies. ARIMA model has proven effective for measuring manufacturing system reliability in Rwanda, but there is room for improving consistency and depth in methodology across studies. Future research should focus on harmonizing methodologies to ensure comparability of results and enhance the robustness of conclusions drawn from time-series forecasting models. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Integrated Watershed Framework for Sustainable Agriculture and Water Supply in South Africa
Integrated watershed management (IWM) is a holistic approach to managing water resources in complex landscapes, aimed at balancing environmental, social, and economic needs. Not applicable The IWM framework integrates hydrological modelling with socioeconomic data to predict water availability and demand under varying conditions. Implementing this framework requires collaboration between government agencies, farmers, and local communities for effective watershed management. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Climate Justice Perspectives on Adaptation Financing in Vulnerable African Regions
Climate change exacerbates vulnerabilities in African regions, necessitating equitable adaptation financing. A comprehensive search of peer-reviewed journals and grey literature was conducted using keywords related to climate change, adaptation funding, and climate justice. Studies from to present were included. Findings indicate that climate justice frameworks emphasise equitable distribution of resources among vulnerable communities in Guinea. Climate justice principles offer a robust framework for addressing the financing gaps in adaptation efforts within Guinea’s vulnerable regions. Policy recommendations include integrating climate justice into national adaptation strategies and enhancing public participation in decision-making processes. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Revisiting Time-Series Forecasting Models for Adoption Rates in Nigerian Manufacturing Plants,
This study revisits time-series forecasting models to assess adoption rates of advanced manufacturing technologies in Nigerian plants from . A time-series analysis was conducted using the ARIMA model (Autoregressive Integrated Moving Average), which accounts for past values and their influence on future periods. Uncertainty in forecasts is estimated through robust standard errors. The analysis revealed that investment levels have a significant positive correlation with technology adoption rates, accounting for approximately 60% of the variation observed. This replication confirms the reliability of ARIMA models for forecasting adoption trends in Nigerian manufacturing settings. Further research should explore additional socio-economic factors impacting technology diffusion. Future studies could incorporate more granular data and longitudinal analysis to enhance predictive accuracy, particularly focusing on small-scale plants that may not have full historical records. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Waste Management and the Circular Economy in South African Municipalities
Waste management in South African municipalities is a critical aspect of environmental sustainability, with increasing emphasis on adopting circular economy principles to reduce waste and promote resource efficiency. A comprehensive search strategy was employed using multiple databases including Web of Science, Scopus, and Google Scholar. Studies published between and were included, with rigorous screening and data extraction procedures applied to ensure the quality and relevance of the literature. The analysis revealed a significant proportion (46%) of municipalities have implemented circular economy initiatives, primarily focusing on waste reduction and recycling programmes. However, there is substantial heterogeneity in implementation strategies across different regions. Despite notable progress, gaps remain in policy coherence and stakeholder engagement, which are essential for effective circular economy adoption at the municipal level. Municipalities should prioritise cross-sector collaboration and invest in education programmes to foster public awareness of waste reduction practices. Additionally, a more standardised approach to circular economy implementation is recommended. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Community Acceptance and Technological Adoption of Renewable Energy in Off-Grid South African Cities
This study examines the community acceptance of renewable energy technologies in off-grid South African cities, focusing on their impact on household power supply. A mixed-methods approach combining quantitative surveys with qualitative interviews was employed to gather data from 300 households across five off-grid cities in South Africa. Community acceptance varied significantly, with a majority (65%) expressing willingness to adopt solar energy systems. A regression analysis revealed that this acceptance positively correlated with improved household power supply reliability by an average of 21%. Findings suggest that targeted community engagement strategies are essential for successful renewable energy adoption in off-grid South African cities, particularly those focusing on education and empowerment programmes. Policy recommendations include enhancing educational outreach initiatives and providing financial incentives to encourage greater participation in renewable energy projects. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.