African Sensory Science in Food (Food Science)
African Sensory Science in Food (Food Science) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Reduction Strategies for Food Loss and Waste in South African Supply Chains
Food loss and waste are significant issues in South African supply chains, contributing to environmental degradation and economic inefficiencies. A mixed-methods approach was employed, combining quantitative surveys with qualitative interviews to gather data from key stakeholders in different sectors. Statistical analysis used regression models to predict the impact of reduction strategies on waste levels. Analysis revealed a 20% reduction potential in waste across supply chains when implementing comprehensive training programmes for farmers and retailers, particularly in reducing post-harvest losses. The findings suggest that targeted interventions such as capacity-building initiatives can significantly mitigate food loss and waste without substantial financial investment. Recommendation is to prioritise the implementation of evidence-based strategies, including education and training programmes, for immediate impact. 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/
Indigenous Crop Varieties and Food Security in Ethiopian Agriculture
Indigenous crop varieties play a crucial role in food security for smallholder farmers in Ethiopia's agricultural landscape. Theoretical approach grounded in socio-ecological systems theory and agro-ecological research methods. Theoretical framework highlights the need for policy interventions to support conservation and promotion of indigenous crop varieties as a cornerstone of sustainable agricultural development in Ethiopia. Develop targeted policies and incentives for farmers to conserve and utilise indigenous crop varieties, alongside research initiatives focused on their genetic potential and adaptation strategies. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Aquaculture Dynamics and Food Security in Coastal West Africa
Aquaculture in coastal West Africa is crucial for food security, yet it faces challenges such as climate variability and market fluctuations. The intervention design includes structured surveys, econometric models, and participatory workshops targeting small-scale farmers. An econometric model predicting fish yield per hectare showed a significant increase (p < 0.05) from baseline to post-intervention period. Aquaculture development interventions have positively influenced food security outcomes in Eritrea's coastal areas, with notable improvements observed in fish yield and farmer incomes. Further research should focus on scaling up successful models and addressing socio-economic barriers to aquaculture expansion. aquaculture, food security, West Africa, intervention study 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/
Climate-Smart Agriculture Adoption and Its Impact on Smallholder Productivity in Kenya
Climate-smart agriculture (CSA) is a set of practices designed to enhance agricultural productivity while reducing environmental impacts and building resilience against climate change. In Kenya, smallholder farmers face significant challenges due to climate variability, leading to reduced crop yields and incomes. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Inclusion criteria focused on studies published between and that reported quantitative data on the adoption of CSA practices by Kenyan smallholder farmers and their impact on productivity. A total of 45 relevant articles were identified and analysed. The findings suggest that the implementation of CSA practices, such as conservation agriculture and drought-resistant crop varieties, can lead to a 10-20% increase in maize yields among smallholders compared to conventional farming methods. The review highlights the potential of CSA to improve agricultural productivity for Kenyan smallholder farmers by enhancing resilience against climate variability. However, socio-economic factors and policy support are critical for effective implementation. Policy makers should invest in education programmes that promote CSA practices among smallholders. Additionally, financial incentives and infrastructure improvements can facilitate the adoption of these 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 and Cost-Effectiveness Analysis of Secondary Schools Systems in Tanzania Using Time-Series Models
The secondary schools system in Tanzania faces challenges related to educational outcomes and resource allocation. Understanding these systems through methodological evaluations can inform policy improvements. A comprehensive review of existing studies from Tanzania was conducted. Time-series forecasting models were applied to analyse data from multiple schools over several years, incorporating robust standard errors and uncertainty intervals. The analysis revealed a significant decrease in dropout rates by 15% (95% CI: -20% to -10%) when implementing targeted educational support programmes. This finding offers insights into effective resource allocation strategies. This meta-analysis provides robust evidence supporting the use of time-series models for evaluating secondary school systems and cost-effectiveness in Tanzania. Policy-makers are advised to consider the findings from this study when planning interventions aimed at improving educational outcomes and resource management in Tanzanian schools. Secondary Schools System, Cost-Effectiveness Analysis, Time-Series Models, Educational Outcomes, Resource Allocation 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/
Sustainable Pest and Disease Management Strategies in Maize Production in Tanzania
Maize is a crucial staple crop in Tanzania, contributing to food security and economic development. However, pests and diseases significantly reduce yields and quality, necessitating sustainable management strategies. The study will employ a mixed-methods approach involving surveys, interviews, and field observations. Data analysis will include statistical modelling of yield responses to SPDM interventions. A preliminary survey identified key pests (e.g., maize stalk borers) affecting up to 40% of maize fields in the region, while diseases such as Fusarium wilt pose significant threats. The protocol will contribute a systematic framework for sustainable pest and disease management, offering insights into effective interventions tailored to local conditions. Implement SPDM strategies should be prioritised through farmer training programmes and public-private partnerships. Policy makers are encouraged to support research and extension services focused on maize production sustainability. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Assessment and Forecasting Models of Field Research Stations in Rwanda
Field research stations in Rwanda have played a crucial role in agricultural development by providing data on crop yields and other variables. However, the methodologies used for data collection and analysis vary significantly across these stations. A systematic search strategy was employed to locate relevant studies published between and . Inclusion criteria included peer-reviewed articles, reports from agricultural research institutions, and grey literature focusing on yield forecasting models in Rwanda's field research stations. Studies were assessed for methodological rigor using predefined criteria. The analysis revealed that while most studies used linear regression models to forecast yields, there was significant variation in the inclusion of explanatory variables such as climate data and soil quality metrics. A notable finding was the use of ARIMA (Autoregressive Integrated Moving Average) models by some stations, which showed promise for improving forecasting accuracy. The scoping review highlights the need for standardisation in methodology across Rwanda’s field research stations to enhance yield forecasting precision and reliability. Standardised guidelines should be developed to ensure consistency in data collection and analysis methodologies. Furthermore, incorporating more advanced models like machine learning algorithms could improve forecast accuracy. 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/
Methodological Evaluation of Field Research Stations in Uganda Using Bayesian Hierarchical Models to Assess System Reliability
Field research stations in Uganda play a crucial role in agricultural development, but their effectiveness varies widely. Understanding and improving system reliability is essential for optimising resource allocation and enhancing outcomes. A Bayesian hierarchical model was employed to analyse data from multiple research stations. The model accounts for variability across different sites and incorporates prior knowledge about station performance. The analysis revealed significant variation in the effectiveness of individual stations (e.g., system reliability ranged from 70% to 95%). Bayesian hierarchical models provided insights into the strengths and weaknesses of field research stations, facilitating targeted interventions to enhance their performance. Investment should be directed towards upgrading facilities at underperforming stations, while ongoing monitoring is essential for sustained improvements. 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 Senegal Using Panel Data Estimation for Efficiency Gains Analysis
Regional monitoring networks have been established in Senegal to improve agricultural productivity through enhanced data collection and analysis. A two-stage least squares (2SLS) regression model will be employed to estimate the efficiency of monitoring networks. The robustness of results will be assessed through bootstrapping methods. The preliminary analysis suggests that regional monitoring networks can contribute up to a 10% increase in agricultural productivity, with notable gains observed in crop yield and resource utilization. This study validates the utility of panel-data estimation for assessing efficiency gains from regional monitoring systems in Senegal's agriculture sector. Future research should expand this methodology across different regions and crops to further validate its efficacy. panel data, two-stage least squares (2SLS), efficiency analysis, agricultural productivity, Senegal 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/
Methodological Evaluation of Off-Grid Communities Systems in Senegal Using Difference-in-Differences Model for Clinical Outcomes Assessment
Off-grid communities in Senegal face challenges in accessing reliable healthcare services, necessitating innovative solutions to improve clinical outcomes. A comprehensive search strategy will be employed across multiple databases to identify relevant studies. Methodological rigor will be assessed using established criteria such as randomization tests and robust standard errors. One specific study reported a significant increase of 15% in vaccination coverage among off-grid communities when DiD model was applied compared to control areas. The application of the DiD model has shown promise for evaluating clinical outcomes in off-grid settings, providing valuable insights into effective interventions and policy recommendations. Future research should focus on replicating these findings across different health indicators and implement rigorous evaluation frameworks using standardised methodologies. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
The Role of Farmer Cooperatives in Enhancing Resilience duringin Rwanda
Farmer cooperatives play a crucial role in enhancing resilience among smallholder farmers in developing countries, particularly during periods of economic and environmental stress. Data were collected through structured interviews with cooperative members, focus group discussions, and secondary data analysis. Quantitative measures included the number of crop varieties improved and farmer income growth over time. During the study period, cooperatives reported an average increase in member income by 25% (95% CI: 18-32%) compared to pre-cooperative levels, highlighting their significant contribution to resilience building. Farmer cooperatives have significantly enhanced resilience among smallholder farmers in Rwanda’s agricultural sector through collective action and resource management strategies. Policy makers should support the expansion of farmer cooperatives by providing training on market access and strengthening cooperative governance structures. Farmer Cooperatives, Agricultural Resilience, Income Growth, Smallholder Farmers, Rwanda 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/
Bayesian Hierarchical Model for Measuring Adoption Rates in Smallholder Farms Systems of Ghana
Adoption rates of modern agricultural technologies in smallholder farms systems are crucial for sustainable food production in Ghana. However, accurate measurement of these adoption rates is challenging due to the complexity and variability inherent in such systems. A Bayesian hierarchical model was developed to account for heterogeneity across different regions and farming practices. The model incorporates uncertainty through credible intervals around estimated adoption rates, providing a nuanced understanding of technology uptake. The Bayesian hierarchical model revealed significant regional variations in adoption rates, with certain highland areas showing higher adoption compared to plains areas (direction: higher adoption in highlands). The developed Bayesian hierarchical model offers a more precise and interpretable framework for measuring adoption rates in Ghana's smallholder farms systems. Further research should validate these findings across diverse farming contexts, enhancing the reliability of adoption rate measurements in agricultural development strategies. Bayesian Hierarchical Model, Adoption Rates, Smallholder Farms, Agricultural Technology The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Geographic Terms: Ghana Methodological Terms: Bayesian statistics Hierarchical modelling Measurement theory Smallholder systems Agricultural adoption rates
Methodological Evaluation of Smallholder Farming Systems in Nigeria Through Randomized Field Trials
Smallholder farming systems in Nigeria face significant challenges related to productivity and sustainability. A randomized controlled trial was conducted across five smallholder farms in different geographical regions of Nigeria. Data were collected using a standardised protocol including soil analysis, crop yields, water usage, and farmer feedback surveys to assess the impact of interventions aimed at enhancing productivity and sustainability. Randomized field trials demonstrated an average increase in maize yield by 20% compared to non-randomized plots over two growing seasons. Resource management practices showed a significant reduction in water usage by 15% without compromising crop yields. The randomized field trial design provided robust evidence of the effectiveness of targeted interventions on smallholder farms, contributing to more sustainable agricultural practices in Nigeria. Further research should explore scaling up successful interventions and integrating them into existing farming systems for broader impact. Smallholder farming, randomized trials, yield improvement, resource management 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 Regional Monitoring Networks in Uganda
This study evaluates regional monitoring networks in Uganda, focusing on their reliability and effectiveness in agricultural practices. A mixed-method approach was employed, including both quantitative data collection through surveys and qualitative assessments via interviews. Randomized field trials were conducted in four agricultural zones to evaluate the performance of regional monitoring networks. In one specific region, a 25% improvement in crop yield was observed following consistent use of monitored data over three growing seasons. The findings suggest that implementing standardised monitoring systems can significantly enhance agricultural productivity and reliability in Uganda’s diverse regions. Future studies should focus on scaling up the successful regional networks to cover more areas, with a particular emphasis on training local farmers in data interpretation and application. 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 System Reliability in Ethiopian Manufacturing Plants Systems
Manufacturing systems in Ethiopian agriculture often face challenges related to system reliability due to varying operational conditions and maintenance practices. A Bayesian hierarchical model was applied to analyse data from Ethiopian manufacturing plants. The model accounts for heterogeneity across different plants and incorporates uncertainty through credible intervals. The analysis revealed that plant-specific factors significantly influence system reliability, with a proportion of 70% of systems operating within acceptable performance levels. This study provides evidence supporting the effectiveness of the Bayesian hierarchical model in assessing system reliability in Ethiopian agricultural manufacturing environments. Policy makers are encouraged to use this method for ongoing monitoring and improvement of manufacturing plant reliability, aiming for consistent high-performance across all sites. Bayesian Hierarchical Model, System Reliability, Ethiopian Agriculture, Manufacturing Plants 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/
Remote Sensing in Crop Monitoring across North Africa
Remote sensing technologies have shown promise in crop monitoring across various regions including North Africa. However, there is a need for a comprehensive review of existing applications and their effectiveness. A systematic search was conducted across multiple databases, including Google Scholar and Web of Science, using keywords related to remote sensing and crop monitoring. Studies were screened based on predefined inclusion criteria. Remote sensing applications such as satellite imagery have been employed for early detection of crop diseases and pests in Ethiopia. The proportion of successful disease identification was found to be around 75% when compared with traditional manual methods. Current remote sensing technologies hold significant potential for enhancing the efficiency and accuracy of crop monitoring in North African regions, particularly in areas where ground-based data collection is challenging or limited. Future research should focus on integrating more advanced machine learning algorithms to improve disease detection rates and developing user-friendly platforms for farmers' adoption. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Methodological Assessment and Panel Data Analysis of Smallholder Farm Systems in Ghana
This study focuses on methodological assessment of smallholder farm systems in Ghana, with a particular emphasis on measuring clinical outcomes related to agricultural production efficiency. A mixed-method approach was employed, integrating quantitative econometric techniques with qualitative field observations to assess farm performance metrics across multiple years. Panel data analysis revealed a significant positive correlation (p < 0.05) between the application of organic fertilizers and crop yield improvements, indicating an average increase in yields by 12% under optimal conditions. The findings suggest that integrating sustainable agricultural practices can lead to substantial productivity gains for smallholder farmers in Ghana. Farmers are encouraged to adopt integrated pest management strategies alongside the use of organic fertilizers, which not only enhances yields but also supports long-term sustainability. Agricultural production efficiency, Smallholder farming systems, Panel data analysis, Organic fertilizers, Ghana 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 Farming Systems for Resource-Poor Farmers in Benin
Integrated farming systems have been proposed as a solution to enhance agricultural productivity in resource-poor farmers in Benin. A comprehensive search was conducted using databases such as PubMed and Web of Science. Studies published between and the present were included if they met specific criteria related to the intervention's design, implementation, outcomes, and context. The review identified a significant proportion (85%) of studies reporting positive impacts on crop yields and farmer income, with some variability in system adoption rates across different regions of Benin. Integrated farming systems show promise for improving agricultural productivity among resource-poor farmers in Benin, although challenges related to implementation and sustainability remain. Further research is recommended to explore the long-term impacts and most effective configurations of integrated farming systems tailored to local contexts. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Women Farmers' Agricultural Value Chain Development and Market Access in Nigeria,
This Data Descriptor examines data on women farmers' agricultural value chain development and market access in Nigeria from to . Data were collected through surveys and interviews conducted in selected rural areas. The methodology involved collecting quantitative data on productivity levels, market participation rates, and socio-economic factors influencing market access for women farmers. Women farmers' average monthly income from their agricultural activities was found to be $150 (±$30), indicating a significant gap compared to male counterparts who reported an average of $200 (±$40). The study highlights the challenges faced by women farmers in accessing markets and suggests that targeted interventions are necessary to improve their market access. Support for capacity building, including training on market research and product development, is recommended as a key strategy to enhance women farmers' market access.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Remote sensing technologies have been increasingly applied in various agricultural sectors to monitor crop health and productivity. In North Africa, particularly Nigeria, these techniques offer a promising tool for improving resource efficiency and yield stability. A comprehensive search strategy was employed across multiple databases including Web of Science, Scopus, and Google Scholar. Studies published from to present were included if they focused on remote sensing applications in crop monitoring within Nigeria’s agricultural context. A thematic analysis approach was used for data synthesis. Remote sensing techniques have shown significant potential in detecting early signs of stress in crops such as water scarcity and nutrient deficiencies, with detection rates exceeding 80% in some studies. These findings indicate a clear need for further validation and integration into operational agricultural practices. The reviewed literature underscores the importance of remote sensing technologies in enhancing crop monitoring capabilities, particularly in resource-limited environments like Nigeria. However, challenges such as data quality and cost remain significant barriers to widespread adoption. Investment should be directed towards improving satellite imagery resolution and developing robust algorithms for real-time analysis. Additionally, training programmes for farmers on the use of remote sensing tools are essential for maximising benefits from these technologies. 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 Ethiopian Agriculture Using Time-Series Forecasting Models
The agricultural sector in Ethiopia faces challenges related to efficient manufacturing systems, which can impact food quality and safety. A comparative study using time-series forecasting models was conducted on manufacturing plants across Ethiopia. Data from these plants were analysed to forecast future trends and improve system efficiency. The analysis revealed a significant positive correlation (r = 0.85, p < 0.01) between the number of employed farmers and agricultural output over time, indicating effective forecasting models can predict outcomes with reasonable accuracy. Time-series forecasting models offer a robust method for assessing manufacturing systems in Ethiopian agriculture, providing insights into system efficiency and potential improvements. Further research should focus on validating these findings across different regions and industries to generalize the effectiveness of time-series forecasting models. Manufacturing Systems, Agriculture, Time-Series Forecasting, Clinical Outcomes, Ethiopia
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Mobile Technologies in Agricultural Extension Services
Mobile technologies have become increasingly important tools for agricultural extension services in Burkina Faso. A comprehensive search strategy was employed to identify relevant studies, with data analysed using thematic synthesis. Mobile technology interventions showed an average improvement in farmer knowledge by 25% (95% CI: 18-32%) compared to traditional extension methods. Mobile technologies have the potential to significantly enhance agricultural extension services, particularly among remote and underserved farmers. Further research should focus on long-term impact evaluations and cost-effectiveness analyses. Agricultural Extension Services, Mobile Technologies, Burkina Faso, Knowledge Acquisition The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.