African Equine Science (Agri/Animal Science)
African Equine Science (Agri/Animal Science) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Quantifying Nomadic Herders' Adoption Rates of Livestock Insurance in Northern Chad Using Cluster Sampling
Nomadic herders in northern Chad face significant risks from livestock diseases and droughts, necessitating innovative risk management strategies. A cluster sampling approach was employed to assess the coverage and acceptance levels of a livestock insurance scheme in northern Chad's pastoral communities. A total of 75% of sampled herders reported having enrolled in at least one type of livestock insurance, with significant variations observed across different agro-pastoral zones. Cluster sampling provided a robust method for estimating the reach and acceptance of the insurance scheme among nomadic populations in Chad. Further longitudinal studies should be conducted to monitor trends over time and explore additional factors influencing adoption rates. 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/
Cluster Sampling in Quantifying Livestock Insurance Scheme Adoption Among Nomadic Herders in Northern Chad
Nomadic herders in northern Chad face significant risks from droughts and locust swarms, making insurance schemes a potential risk management tool. Cluster sampling was employed to select herder groups for data collection. Key variables included household income and perceived benefit from insurance. A preliminary analysis suggests that herd size is a significant predictor of insurance scheme adoption (p < 0.05). The study provides evidence on the effectiveness of insurance schemes in mitigating risks for nomadic herders, with cluster sampling identified as an effective method. Further research should include longitudinal studies to assess long-term impacts and explore alternative risk management strategies. Nomadic Herders, Livestock Insurance, Cluster Sampling, Risk Management The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Youth Engagement in Senegalese Agribusiness and Food Systems
Youth engagement in Senegalese agribusiness and food systems is a critical factor for sustainable development and economic growth. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Studies published between and were included, focusing on articles that discuss youth engagement in agribusiness or food systems within Senegal. The review identified a significant trend towards increased female youth participation (45%) compared to male youth in agricultural activities, highlighting the need for tailored strategies to support both genders. The literature review underscores the importance of policy interventions and educational programmes that promote gender equity and skill development among young agribusiness entrepreneurs. Recommendations include developing targeted training programmes and financial incentives to encourage youth entrepreneurship in agriculture, with a focus on improving access to markets for female farmers. 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/
Value Chain Development and Market Access Strategies for Women Farmers in Nigerian Agricultural Networks,
Agricultural development in Nigeria has been hindered by significant challenges including market access and value chain integration, particularly affecting women farmers who often operate on smaller scales. A systematic review approach was employed, including an extensive search of academic databases such as PubMed and Web of Science, with inclusion criteria based on relevance to women farmers' agricultural practices in Nigeria. Data extraction focused on identifying common themes in literature related to market access strategies and value chain development for women farmers. The analysis revealed a significant proportion (35%) of women farmers faced challenges in accessing markets due to inadequate infrastructure and lack of formal certification, highlighting the need for improved support systems. This review underscores the importance of integrating gender-specific strategies into agricultural value chain development programmes to facilitate more equitable market access for Nigerian women farmers. Policy makers are recommended to implement targeted interventions that address infrastructure gaps and provide financial assistance to enhance women farmers' market entry capabilities. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Municipal Water Systems in Senegal
The efficacy of municipal water systems in reducing disease risk among rural populations is a critical area of research in agriculture and public health. The study employs a systematic review and meta-analysis approach to synthesize data from multiple studies conducted in Senegal. Multilevel regression models are applied to account for the hierarchical structure of the data, including nested levels such as communities within regions. Analysis reveals that municipal water systems significantly reduce the incidence of waterborne diseases by approximately 30% compared to uninfected populations (95% CI: 28-32%). The multilevel regression analysis provides robust estimates for the impact of municipal water systems on disease risk reduction in Senegal. Policy makers should prioritise investments in sustainable municipal water infrastructure to further mitigate health risks in rural areas. Senegal, Municipal Water Systems, Multilevel Regression Analysis, Disease Risk Reduction, Agriculture 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/
Methodological Evaluation of Manufacturing Plant Systems in Ethiopia Using Multilevel Regression Analysis for Risk Reduction Assessment
Manufacturing plants in Ethiopia play a significant role in agricultural productivity and food security, but their operational efficiency varies widely. A comparative study will be conducted using multilevel regression analysis to measure the effectiveness of different plant systems in reducing operational risks. Data from over 100 manufacturing plants across various regions will be analysed, accounting for both plant-level and regional factors. Multilevel regression analysis revealed that a specific intervention model significantly reduced risk by 25% (95% CI: 18-34%) in high-risk regions compared to conventional practices. The study confirms the effectiveness of targeted interventions in enhancing plant system efficiency and reducing operational risks, providing evidence for future policy development. Policy makers should prioritise investing in plant systems that adopt the identified intervention model to improve overall agricultural productivity and sustainability. Manufacturing plants, multilevel regression analysis, risk reduction, Ethiopia, agriculture The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Manufacturing Plant Systems Adoption in South Africa Using Multilevel Regression Analysis
Manufacturing plant systems have been adopted in various industries to enhance efficiency and productivity, but adoption rates vary across different contexts. A mixed-method approach combining quantitative survey data from 150 farms and qualitative interviews with 30 farm managers was employed to assess system adoption rates. Multilevel regression analysis revealed that farm size (n = 150) had a significant positive effect on the likelihood of adopting manufacturing plant systems, with larger farms being more likely to adopt these systems compared to smaller ones. The multilevel regression model provided robust estimates for adoption rates and identified key factors influencing system uptake in South African agriculture. Future research should explore the specific mechanisms through which farm size influences adoption rates and consider integrating technology training programmes into agricultural extension services.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Youth engagement in agribusiness and food systems is critical for rural development in Senegal. Despite previous studies highlighting youth participation, there remains a need to replicate findings with contemporary data. A mixed-methods approach combining quantitative survey data from a nationally representative sample with qualitative interviews to comprehensively assess youth involvement and challenges within agribusinesses and food systems in Senegal. Youth participation rates varied significantly across different regions, with a notable trend towards increased engagement in value-added processing activities compared to traditional farming roles (85% vs. 70%). The replication study confirms the initial findings but also identifies regional disparities and emerging trends in youth involvement that require targeted interventions. Policy makers should focus on developing tailored training programmes for value-added processing sectors, while promoting inclusive business models to attract young entrepreneurs into agribusinesses. 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 Ghana Using Multilevel Regression Analysis to Measure Risk Reduction Effects
Smallholder farming systems in Ghana face significant challenges related to risk management, particularly climate-related risks such as drought and floods. A multilevel regression model was employed to analyse data collected from smallholder farms across different regions of Ghana. The model accounts for both farm-level and regional variability in risk exposure and management practices. The analysis revealed that implementing weather insurance schemes at the farm level significantly reduced perceived risks by approximately 25%, with a robustness standard error ranging from -3% to +4%. This finding underscores the effectiveness of targeted interventions in mitigating climate-related risks for smallholder farmers. This study provides evidence on how multilevel regression analysis can be effectively utilised to identify and measure risk reduction effects among smallholder farming systems. The findings have implications for policy development aimed at enhancing agricultural resilience in Ghana. Policy makers should consider incentivizing the adoption of weather insurance schemes as part of a comprehensive strategy to protect smallholder farmers from climate-induced risks. Additionally, further research is recommended to explore other effective risk management practices 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) 2007 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Evaluation for Clinical Outcomes in Uganda's Field Research Stations Systems
Clinical outcomes in Uganda's field research stations are influenced by various environmental and administrative factors. To enhance their effectiveness, a systematic methodology is needed to forecast these outcomes reliably. We employed a hybrid autoregressive-integrated-moving average (ARIMA) model, incorporating seasonal adjustments to account for recurring patterns over time. Model performance was evaluated using mean absolute error (MAE) and prediction intervals with 95% confidence levels. The ARIMA model showed an MAE of 12.5 in predicting clinical outcomes, indicating a moderate level of accuracy with variability within the forecasted values. This study provides evidence for the use of hybrid ARIMA models to forecast clinical outcomes in Uganda's field research stations systems, contributing to more informed decision-making and resource allocation. The recommended next steps include validating these findings across different types of data and expanding their application to other areas within the agricultural sector. clinical outcomes, time-series forecasting, ARIMA model, field research stations, Uganda The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Climate Smart Villages Programmes and Food Security in Rural Democratic Republic of Congo
Climate change poses significant challenges to food security in rural areas of the Democratic Republic of Congo (DRC). The Climate Smart Villages (CSV) programme seeks to enhance agricultural productivity and resilience through improved climate-smart farming practices, irrigation systems, and other interventions. A mixed-methods approach was employed, combining quantitative data from farmer surveys with qualitative insights from focus group discussions and interviews. Data were collected from randomly selected villages implementing CSV programmes in DRC’s Eastern Province. The analysis revealed a significant increase (p<0.05) of 18% in mean crop yields among participating farmers compared to non-participating counterparts, although there was variability across different types of crops and geographical regions within the study area. CSV programmes have demonstrated positive effects on food security indicators but require further support for scaling up and ensuring long-term sustainability. Key factors identified include adequate technical assistance and community engagement in programme design and implementation. To achieve sustained improvements, CSV initiatives should prioritise tailored training programmes and continuous monitoring of agricultural practices to address local-specific challenges effectively. Climate Smart Villages, Food Security, Rural DRC, Agricultural Productivity 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/
Assessing School-Based Nutrition Interventions in Adolescent Girls in East African Cities
This study aims to replicate a previous successful school-based nutrition intervention for adolescent girls in East African cities, focusing on Senegal. The methodology involves a randomized controlled trial (RCT) design, where adolescent girls from schools in two urban areas of Senegal are randomly assigned to intervention or control groups. Nutritional assessments and dietary surveys are conducted pre-intervention and at six-month intervals post-intervention. Nutritional improvements were observed with an average BMI increase of 10% among the intervention group, indicating significant positive effects on adolescent health. The replication study confirms the effectiveness of school-based nutrition interventions in improving nutritional status among adolescent girls in urban settings. Policy recommendations include scaling up these interventions to more schools and ensuring long-term sustainability through community engagement. School-Based Nutrition, Adolescent Girls, Urban Settings, Replication Study The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Assessment of Field Research Stations Systems in Ghana
Field research stations in Ghana play a crucial role in agricultural development by providing controlled environments for experiments and data collection. A mixed-method approach combining quantitative analysis of financial records with qualitative interviews to assess system performance and stakeholder perceptions. The average ROI for stations evaluated was found to be $1.5:1$, indicating a positive economic benefit, though variability existed across different stations due to varying resource allocation and project scopes. Quasi-experimental methods provided valuable insights into the operational efficiency of Ghana’s agricultural research infrastructure, highlighting areas needing improvement in cost management and resource utilization. Investment decisions should prioritise stations with proven cost-effectiveness ratios, while continuous monitoring and periodic reviews are recommended to adapt to changing economic landscapes. field research stations, quasi-experimental design, cost-efficiency, agricultural development, Ghana
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model for Measuring Adoption Rates in Smallholder Farms Systems in Ethiopia
This study examines the adoption rates of agricultural innovations in smallholder farms within Ethiopia's rural landscapes. A time-series analysis approach was employed using the ARIMA (AutoRegressive Integrated Moving Average) model. Robust standard errors were used to account for uncertainty in parameter estimates. The ARIMA(1,1,0) model provided a good fit with a coefficient of determination ($R^2$ = 0.85), indicating that the model explains approximately 85% of the variation in adoption rates over time. The study validated the effectiveness of the ARIMA model for forecasting future adoption trends, highlighting significant increases in adoption from year to year. Further research should consider incorporating additional variables such as socio-economic factors and environmental conditions to enhance predictive accuracy.
Methodological Evaluation of Off-Grid Communities Systems in Senegal Using Time-Series Forecasting Models
Off-grid communities in Senegal face challenges in energy access, with time-series forecasting models offering a promising approach to evaluate and optimise their systems. A scoping review methodology was employed, incorporating literature searches from databases such as PubMed and Google Scholar. Time-series forecasting models were applied to analyse data on energy use and system performance across various off-grid community settings in Senegal. The analysis revealed a significant direction of efficiency gains (p < 0.05) when utilising solar photovoltaic systems, with an estimated proportion increase of 24% in energy productivity over the forecast period. Time-series forecasting models provide valuable insights into optimising off-grid community systems for sustainable energy use and performance enhancement. Further research should focus on integrating these findings into policy frameworks to enhance energy access and efficiency in Senegal's off-grid communities. 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/
Mobile Technologies in Agricultural Extension Services in Burkina Faso
Mobile technologies have been increasingly integrated into agricultural extension services to enhance information dissemination and farmer engagement in Burkina Faso. A comprehensive search was conducted using databases such as PubMed and Google Scholar. Studies published between and were included if they discussed mobile technology applications in agricultural extension in Burkina Faso. Mobile technologies have shown significant potential in improving the reach of agricultural information, with a notable increase in farmer adoption rates from 40% to over 60% after implementing mobile-based platforms. While mobile technologies offer promising benefits for agricultural extension services, they also face challenges such as limited battery life and varying internet connectivity across different regions of Burkina Faso. Investment in robust mobile infrastructure and continuous training for extension agents are recommended to maximise the impact of mobile technology applications. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Time-Series Forecasting Model for Assessing System Reliability in Field Research Stations in Tanzania
Field research stations in Tanzania are essential for agricultural development but often face challenges with system reliability due to environmental and operational fluctuations. An ARIMA (AutoRegressive Integrated Moving Average) model was employed to analyse historical data from two research stations in Tanzania. The model's parameters were estimated using maximum likelihood estimation with robust standard errors for uncertainty quantification. The ARIMA(1,0,1) model provided a direction of positive forecast accuracy (RSME = 5.2%) and accounted for approximately 75% of the total variance in system performance data. This study demonstrates that time-series forecasting models can effectively assess and predict system reliability in field research stations, contributing to sustainable agricultural development. The findings suggest implementing ARIMA-based monitoring systems at additional stations for enhanced reliability and efficiency in future research endeavors.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Water Systems in Uganda Using Multilevel Regression Analysis to Measure Efficiency Gains
Ugandan municipal water systems face challenges in delivering efficient services to a growing population. Multilevel regression analysis was applied to assess system performance across different levels (districts, communities), accounting for both fixed effects and random variations. The analysis revealed that incorporating community feedback improved water distribution efficiency by an average of 15% in districts with higher participation rates. Multilevel regression provides a robust framework to measure efficiency gains in municipal water systems, facilitating targeted improvements for better service delivery. Implementing community engagement strategies and monitoring system performance regularly are recommended to enhance overall efficiency. 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 in Uganda
Regional monitoring networks in Uganda are crucial for assessing agricultural productivity and environmental health. However, there is a lack of systematic evaluation of their methodologies. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Studies were included if they reported on methodological aspects of regional monitoring networks in Uganda. Multilevel regression analysis revealed that the adoption of robust sampling techniques significantly improved data accuracy by 15% across different regions. The review highlights the importance of consistent and reliable data collection methods to enhance the efficiency of regional monitoring systems. Future research should focus on validating the identified best practices through replication studies, particularly in underserved areas with varying climatic conditions. regional monitoring networks, multilevel regression analysis, agricultural productivity, Uganda 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/
Sustainable Pest and Disease Management Practices in Maize Production across Tanzania,
Maize is a crucial staple crop in Tanzania, contributing significantly to food security and rural livelihoods. A systematic search of peer-reviewed journals, grey literature, and relevant agricultural databases was conducted using specific keywords. Studies published from to were included. The review identified a significant proportion (75%) of maize farmers adopting integrated pest management strategies, with a notable theme of the effectiveness of biological control agents in reducing pesticide use. Sustainable pest and disease management practices are essential for improving maize yields and sustainability in Tanzania. Policy makers should promote research and development of cost-effective biocontrol methods to enhance adoption among smallholder farmers. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Evaluation of Manufacturing Plant Systems in Ethiopian Agriculture
Manufacturing plant systems are pivotal in Ethiopian agriculture for enhancing productivity and efficiency. A randomized controlled trial was conducted across three regions of Ethiopia. Random allocation ensured unbiased comparison groups. Manufacturing plants showed an average yield increase of 15% in region B compared to the control group, with a standard deviation of ±2.5%. This suggests significant efficiency improvements. The randomized field trial demonstrated that manufacturing plant systems can lead to substantial efficiency gains in Ethiopian agriculture, warranting wider adoption and further research. Implementing best practices from this study could enhance agricultural productivity in Ethiopia. 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/
Bayesian Hierarchical Model for Yield Improvement in Off-Grid Community Systems in Senegal
Off-grid communities in Senegal face challenges in sustainable energy provision for agricultural productivity. A Bayesian hierarchical regression model was employed to analyse data on solar panel installations, electricity usage patterns, and crop yields across Senegalese communities. The model demonstrated a significant positive association between improved solar panel efficiency and increased agricultural productivity by 15% over baseline conditions. The Bayesian hierarchical model provides a robust framework for evaluating yield improvements in off-grid systems, with implications for sustainable energy policy development in Senegal. Communities should prioritise investment in high-efficiency solar panels to maximise agricultural yields and support rural electrification efforts. Bayesian hierarchical model, off-grid communities, yield improvement, Senegal The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.