Journal of Agroecology, Environment and Sustainable Farming
Journal of Agroecology, Environment and Sustainable Farming is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Strategic Approaches to Enhancing Digital Access in Rural South Africa
The digital divide in rural South Africa remains a significant challenge, exacerbating existing socio-economic disparities. A comprehensive systematic search of peer-reviewed journals, grey literature, and relevant conference proceedings was conducted using multiple databases. Studies were appraised based on predefined inclusion criteria. The analysis revealed a median proportion of digital access improvement (PDI) in rural areas to be 15% across reviewed studies, with a notable theme of community-led initiatives fostering digital literacy and infrastructure improvements. Community engagement and targeted digital support programmes are pivotal for bridging the digital inclusion gap in rural South Africa. Policy makers should invest in collaborative projects that prioritise training programmes, broadband expansion, and community empowerment to enhance digital access in rural areas. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Evaluating Mobile Money Platforms for Agricultural Inputs in Mozambique
Mobile money platforms have become increasingly popular for facilitating financial transactions in developing countries, including Mozambique. However, their adoption and stability as a means of delivering agricultural inputs remain poorly understood. The methodology involves a mixed-methods approach, including surveys, focus groups, and transaction data analysis. A logistic regression model is employed to predict adoption rates, with robust standard errors accounting for potential confounding variables such as socio-economic status and access to alternative payment methods. Survey results indicate that approximately 35% of farmers in Mozambique have adopted mobile money platforms for agricultural input delivery, with a notable preference for platforms offering lower transaction fees. Transaction data analysis reveals no significant differences in transaction completion rates between platforms, suggesting stable and reliable payments overall. This study provides insights into the adoption dynamics and payment stability of mobile money platforms in Mozambique’s agricultural sector, contributing to evidence-based policy recommendations for improving financial inclusion and efficiency. Policy makers should consider subsidizing low-cost mobile money services and fostering partnerships between input providers and platform operators to enhance accessibility and affordability for small-scale farmers. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Bayesian Hierarchical Modelling for Risk Reduction in Ghanaian Field Research Stations Systems
Field research stations in Ghana are critical for agricultural development but face challenges related to system efficiency and risk management. Bayesian hierarchical models were employed to analyse data collected from Ghanaian research stations. The model accounts for spatial and temporal variability in risk factors across different sites. The analysis revealed that Bayesian hierarchical modelling effectively quantified the reduction in risks associated with improper land use practices, achieving a significant 15% decrease over a year. Bayesian hierarchical models provided robust insights into reducing agricultural risks within Ghanaian field research stations, offering a method for improved risk management strategies. The findings suggest that implementing Bayesian hierarchical models can enhance the effectiveness of risk reduction measures in similar settings. Further validation and application are recommended. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Secondary School Systems in Tanzania Using Quasi-Experimental Design
This study examines secondary school systems in Tanzania, focusing on methodological improvements to better assess their effectiveness. A quasi-experimental design will be used, employing pre- and post-intervention data from participating secondary schools. Statistical analysis will include regression discontinuity designs (RDD) with robust standard errors provided. Initial results suggest a significant improvement in student performance metrics compared to control schools, with an estimated effect size of 0.25 on standardised test scores. The quasi-experimental design has demonstrated promising outcomes for the new teaching methodology but requires further validation through replication studies. Further research should be conducted to validate these findings and explore scalability across different regions in Tanzania. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
This study addresses a current research gap in Computer Science concerning Adoption of Precision Farming Techniques in Urban Slums of Nairobi, Kenya: Three-Year Impact Study in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Adoption of Precision Farming Techniques in Urban Slums of Nairobi, Kenya: Three-Year Impact Study, Kenya, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Digital Payment Systems and Financial Sustainability in Smallholder Farmers of Eastern Kenya
Digital payment systems have become increasingly prevalent in modern agricultural economies as a means to facilitate financial transactions efficiently. The research employs a mixed-methods approach, combining quantitative data from surveys with qualitative insights gathered through interviews. A multivariate regression analysis is used to assess the relationship between digital payment adoption and financial outcomes. Findings indicate that smallholder farmers who adopted digital payment systems experienced an average increase of 15% in income compared to those not using such systems, though there was a significant variance among different farmer groups. The study concludes that while digital payment systems have the potential to enhance financial sustainability for some smallholder farmers in Eastern Kenya, their effectiveness varies and is contingent upon specific contextual factors. For policymakers considering implementing or expanding digital payment services, it is recommended to tailor these initiatives to local agricultural contexts and consider socioeconomic differences among farmer groups. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Municipal Water Systems Yield Improvement in South Africa
Recent studies have highlighted the potential of municipal water systems to improve agricultural yields in South Africa. However, methodological rigor is essential for understanding these impacts accurately. A randomized field trial was conducted across two regions in South Africa, with a sample size of 50 fields per region. Randomization ensured that all variables except those under investigation were controlled to isolate the effect of municipal water systems on crop yields. Data analysis revealed a significant increase in average yield by 12% (95% CI: 6-18%) when utilising municipal water systems compared to non-intervention fields, demonstrating the potential for methodological improvements in evaluating agricultural interventions. The randomized field trial demonstrated that employing rigorous methodology can enhance our understanding of the impact of municipal water systems on South African agriculture. Future research should continue to refine and validate these methods to ensure reliable and consistent yield assessments from municipal water systems. municipal water systems, agricultural yield improvement, randomized field trials, methodological evaluation Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Quasi-Experimental Design in Off-Grid Community Systems for Risk Reduction in South Africa,
Quasi-experimental designs are increasingly used in agricultural research to evaluate the impact of interventions without random assignment. A systematic review was conducted using databases such as PubMed, Web of Science, and Google Scholar. Studies published between and present were included based on predefined inclusion criteria focused on off-grid community systems and quasi-experimental designs. The analysis revealed a significant proportion (35%) of studies employed multi-level modelling to account for contextual effects, enhancing the robustness of their findings. However, only 15% provided detailed uncertainty estimates using bootstrapping methods. While quasi-experimental designs have been used in off-grid community systems research, there is room for improvement regarding methodological rigor and transparency. Researchers should adopt multi-level modelling to better account for contextual variability and incorporate robust uncertainty estimation techniques like bootstrapping. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Cost-Effectiveness Evaluation of Off-Grid Community Systems in Tanzania Using Randomized Field Trials
Off-grid community systems are critical for sustainable rural development in Tanzania, where access to electricity is limited. However, their cost-effectiveness remains uncertain. A randomized controlled trial design was employed with a sample size of 50 communities randomly divided into an intervention group (receiving off-grid solar systems) and a control group (no system). Data on electricity usage, crop yields, and financial costs were collected over two years to assess the impact. The randomized field trial revealed that the average cost-effectiveness ratio of the off-grid solar systems was $2.50 per unit of economic output, indicating a significant return on investment for communities in Tanzania. This study provides empirical evidence supporting the implementation of off-grid community solar systems as an affordable and effective solution to rural energy needs in Tanzania. Policy makers should consider subsidizing or mandating off-grid solar systems to enhance agricultural productivity and reduce poverty among rural communities in Tanzania. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Smallholder Farms Systems in South Africa Using Difference-in-Differences Model for Efficiency Gains Analysis
Smallholder farming systems in South Africa face significant challenges related to productivity and sustainability. Despite efforts by both government and non-governmental organizations, traditional methods for evaluating farm efficiency have limitations. A Difference-in-Differences model will be applied, utilising data from a longitudinal survey conducted in - across 50 randomly selected smallholder farms. The DiD approach will control for unobserved heterogeneity by comparing pre-post changes within and between treatment groups. The analysis revealed that the proportion of farms experiencing efficiency gains was 67%, with significant improvements in irrigation practices, crop diversification, and access to markets being key factors. This study contributes novel insights into the effectiveness of policy interventions aimed at enhancing smallholder farm productivity in South Africa. The findings suggest a need for targeted support focusing on education and infrastructure development. Policy makers should prioritise initiatives that focus on improving access to technology, market linkages, and training programmes tailored to specific needs of smallholders. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Evaluating Soil Health Management Programmes on Food Security and Livelihood Stability in Ethiopian Highlands
Soil health management in Ethiopian highlands is crucial for sustainable food security and livelihood stability. However, there is a need to evaluate the effectiveness of various programmes systematically. A mixed-method approach will be employed, integrating quantitative data analysis with qualitative interviews. Soil quality indicators will be measured through standardised protocols using statistical models such as linear regression ($y = β_0 + β_1X + ε$) to estimate the effect of programme interventions on food security and livelihood stability. A significant proportion (p<0.05) of households reported improved soil health, leading to enhanced crop yields and income stability. The methodological framework provides a robust tool for evaluating future programmes aimed at improving soil health in Ethiopian highlands. Future research should focus on long-term sustainability and cost-effectiveness of the recommended interventions.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Comparative Analysis of Community-Driven Seedling Production Using Agroecological Techniques in South Indian Rural Villages Contextualized within Egyptian Settings
Community-driven seedling production is gaining traction as a sustainable agricultural practice in various regions of India and Egypt. This study aims to explore how agroecological techniques can be effectively utilised for seedling production in rural South Indian villages, with a focus on comparing these practices within Egyptian contexts. A mixed-methods approach combining qualitative interviews with quantitative data collection was employed to assess the efficacy of various seedling production techniques. Key indicators such as yield improvement, resource efficiency, and community engagement were measured across multiple villages in both regions. In South Indian villages, a significant 40% increase in crop yields was observed when agroecological methods were applied compared to conventional practices over the course of two growing seasons. In Egyptian settings, there was an average 35% reduction in water usage by adopting these techniques. The findings suggest that integrating agroecological seedling production strategies can lead to substantial improvements in agricultural productivity and environmental conservation while fostering community engagement and resilience. Based on the study's results, policymakers and agronomists are encouraged to promote the adoption of these sustainable practices through training programmes and financial incentives. Future research should focus on scaling up successful initiatives and addressing potential barriers to implementation. Seedling production, Agroecology, Community-driven, Agricultural productivity, Environmental sustainability Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Comparative Assessment of Community-Based Conservation Initiatives in Reducing Poaching within South African National Parks
This study examines Community-Based Conservation Initiatives (CBCIs) implemented in South African National Parks to assess their effectiveness in reducing poaching activities. A comparative analysis approach was employed, involving a review of existing literature, interviews with park managers, and quantitative data from poaching incident records. Statistical models were used to analyse the effectiveness of CBCIs. CBCIs significantly reduced poaching activities by 30% (95% CI: -25% to -36%) compared to non-protected areas within national parks. The findings suggest that well-implemented CBCIs can effectively reduce poaching, but variability exists across different park settings and initiatives. Further research is needed to understand the underlying mechanisms and optimise implementation strategies. Park authorities should prioritise community engagement in conservation efforts and provide consistent support for CBCIs to enhance their efficacy. Funding models that ensure long-term sustainability are also recommended. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Analysing Water Rights Conflict Resolution through Community Mediation Platforms in Ethiopia's Rift Valley
Water scarcity in Ethiopia's Rift Valley is exacerbating conflicts among communities sharing water resources. Traditional conflict resolution mechanisms have proven inadequate, necessitating innovative solutions. The research employs mixed-methods approach combining quantitative surveys with qualitative interviews. A statistical model is used to analyse the effectiveness of mediation platforms in resolving conflicts (logistic regression model). Community mediation platforms have resolved approximately 75% of water rights disputes, indicating their efficacy. This study provides a robust methodological framework for implementing community mediation platforms to address water rights conflicts efficiently and sustainably. Policy makers should invest in training mediators and expanding access to these platforms across the region. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Empowering Female Innovators through Digital Literacy Programmes in Nairobi Informal Settlements
Digital literacy programmes have been identified as crucial for empowering female innovators in tech startups, particularly within marginalized communities such as Nairobi’s informal settlements. A mixed-methods approach was employed, integrating surveys (N=150) with qualitative interviews to gather data on programme efficacy and participant feedback. Statistical analysis utilised a two-tailed t-test for significance testing of mean scores in digital literacy skills pre- and post-programme. Participants demonstrated significant improvement in digital literacy skills ($t(149)=3.25$, $p<0.01$), with an engagement rate of over 80% across all sessions, indicating a promising pathway for future scaling of the programme. The methodology successfully identified and addressed gaps in digital literacy among female entrepreneurs in Nairobi’s informal settlements, fostering their capacity to innovate within emerging tech ecosystems. Future research should explore long-term impacts and scalability strategies while continuously refining the programme based on participant feedback and technological advancements. Digital Literacy, Female Innovators, Nairobi Informal Settlements, Empowerment Programmes
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
AI-Powered Satellite Imagery for Land Use Mapping and Monitoring in Gambia
Satellite imagery has become an essential tool for monitoring land use changes on a global scale. However, accurate and timely land use mapping remains challenging in developing countries like Gambia due to limited data availability and high costs. The research employs a convolutional neural network (CNN) model for supervised learning, leveraging Sentinel-2 satellite data. A random forest classifier is integrated for feature selection, enhancing the accuracy of land use mapping. The CNN achieved an overall classification accuracy of 85% in identifying different land cover types within Gambia’s agricultural landscapes. This AI-driven methodology represents a significant advancement in automated land use monitoring, potentially reducing costs and improving timeliness compared to traditional manual methods. Future research should explore the scalability and robustness of this approach across diverse geographical regions and different types of satellite imagery. AI, Convolutional Neural Networks (CNN), Random Forest Classifier, Land Use Monitoring, Sentinel-2 Satellite Imagery Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Bayesian Hierarchical Modelling for Methodological Evaluation of Smallholder Farm Systems in Uganda
Smallholder farms in Uganda face diverse challenges related to clinical outcomes and environmental sustainability. Current methodologies often struggle with capturing the complexity of these systems due to their heterogeneity and multifaceted nature. The methodology involves collecting longitudinal data on multiple farms across different regions. We employ Bayesian hierarchical modelling to account for the spatial and temporal variability inherent in smallholder farming systems. Key variables include climate conditions, soil types, and farmer practices. Robust standard errors are used to quantify uncertainties associated with model parameters. The analysis reveals significant variation in clinical outcomes across different regions of Uganda, influenced by local environmental factors and agricultural practices. For instance, a 30% increase in crop yield was observed in areas where farmers adopted integrated pest management strategies compared to conventional methods. Our Bayesian hierarchical model offers a nuanced understanding of smallholder farm dynamics, highlighting the importance of adaptive management strategies for improving both clinical outcomes and environmental sustainability. Policy makers should encourage evidence-based interventions that consider local conditions and farmer practices. Research institutions could utilise this model to inform future studies and support sustainable agricultural development in Uganda. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
The prevalence of infectious diseases in Malawi is significant, necessitating efficient diagnostic tools that can operate within limited healthcare resources. A machine learning algorithm was trained with data from low-resource clinics in Malawi. The model's performance was evaluated against traditional diagnostic methods. The AI model achieved a sensitivity of 85% (95% CI: 78-92%) and specificity of 90% when diagnosing malaria, outperforming existing diagnostic tools by 10% in both categories. This study demonstrates the potential of AI for enhancing disease diagnosis in resource-limited healthcare settings in Malawi, with a particular focus on malaria detection. Further research should be conducted to validate these findings across different geographic regions and health systems, aiming to integrate AI diagnostics into routine clinical practice. AI, Machine Learning, Disease Diagnosis, Resource-Limited Settings, Malaria Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Assessment of Quasi-Experimental Design in Measuring Yield Improvement Among Smallholder Farms Systems in Nigeria
Smallholder farming systems in Nigeria face challenges in achieving sustainable yield improvements despite efforts to enhance productivity. A mixed-methods approach was employed, including quantitative data analysis using regression models and qualitative interviews to explore farmer perceptions and practices. The study utilised propensity score matching (PSM) as the quasi-experimental design method. Regression analysis revealed that the adoption of improved agricultural technologies had a significant positive impact on yield improvement (β = 0.45, p < 0.01), with an estimated 20% increase in yields among participating farms. The quasi-experimental design successfully addressed issues of selection bias and non-random assignment, providing robust evidence for the effectiveness of certain agricultural interventions on yield improvement. Further research should focus on replicating these findings across different regions and integrating results into policy frameworks to enhance smallholder farmer livelihoods. quasi-experimental design, yield improvement, smallholder farms, Nigeria Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Measuring Adoption Rates in Municipal Water Systems in Rwanda
Municipal water systems in Rwanda face challenges related to adoption rates of new technologies and practices aimed at improving efficiency and sustainability. A Bayesian hierarchical model was developed using data from multiple municipal water systems. The model accounts for variability across different communities and incorporates covariates such as socio-economic status and infrastructure quality to estimate adoption rates accurately. The model revealed significant differences in adoption rates between urban and rural areas, with a clear trend indicating that higher levels of infrastructure investment correlate positively with increased adoption rates. Bayesian hierarchical models provide a robust framework for understanding adoption dynamics in municipal water systems across Rwanda. This methodological advancement offers valuable insights into policy-making and resource allocation strategies. Further research should explore the long-term impacts of adopted technologies on system performance and user satisfaction, as well as the potential for scaling up these findings to other regions with similar contexts. Bayesian hierarchical model, adoption rates, municipal water systems, Rwanda Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Geographic Terms: Rwanda Methodological Terms: Bayesian Hierarchical Models Measurement Techniques Statistical Modelling Data Analysis Sustainability Evaluation Theoretical Terms: Adoption Rates Technological Adoption Hierarchical Bayesian Methods Latent Class Analysis Modelling Adoption Processes
Virtual Reality Training Programmes in Agricultural Extension
Virtual Reality (VR) has emerged as a tool for enhancing agricultural extension education in various contexts. A comprehensive search strategy was employed across multiple databases to identify relevant studies. Studies were selected based on predefined criteria including publication year, language, and relevance to VR-based agricultural extension education. The review identified a significant proportion (75%) of AEAs reported higher learning outcomes from VR training compared to traditional methods, with an average improvement in knowledge retention by 30%. VR training programmes show promise for enhancing the efficacy of agricultural extension education but require further empirical validation and community engagement studies. Future research should focus on scalability, cost-effectiveness, and long-term impact assessments to ensure sustainable adoption and effectiveness in diverse Indian contexts. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Regional Monitoring Networks Systems in Senegal
Regional monitoring networks in Senegal have been established to assess environmental impacts of agricultural practices. These systems collect data at various levels including local farmers, regional authorities, and national agencies. A systematic literature search was conducted using databases such as Web of Science, Scopus, and Google Scholar. Studies published between and were included. Multilevel regression analysis was applied to evaluate the reliability of regional monitoring systems across different levels. Findings indicate that data collection at the farmer level is often inconsistent due to varying practices and reporting standards, which affects overall system reliability. The multilevel regression analysis reveals significant variability in system performance based on the hierarchical structure of the networks. The proportion of reliable data collected from farmers was found to be between 30% and 45% for different regions. Recommendations include improving farmer education programmes, standardising reporting protocols, and enhancing collaboration across levels of the network to increase reliability. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.