Analyzing the impact of the COVID-19 pandemic on vegetable market supply in Northwestern Ethiopia
Bibliographic record
Abstract
Purpose Due to global trade and transportation, the COVID-19 pandemic has rapidly reached all corners of the world; it has most impacted the poor communities and rural areas with limited or no access to recovery. This paper aims to understand the pandemic's impact on the market supply of vegetables (agrifood) in Ethiopia and other countries in sub-Saharan Africa and to implement scientifically based recommendations that can improve the challenges caused by the pandemic and improve the livelihoods of vegetable producers. Design/methodology/approach This study assessed the major incidents that follow the occurrence of the COVID-19 pandemic in Ethiopia with the factors that significantly influence onion market supply in the Mecha district of Northwestern Ethiopia using linear multiple regression. Findings The study revealed that producers, processors, local collectors, cooperatives, wholesalers, retailers and consumers are the main actors. The most important difference that happened on the onion market channel after the outbreak of coronavirus was brokers were removed from the market channel after the pandemic and this saved the cost producers pay as a commission. The pandemic disrupted input distribution, extension and cooperative services and created labor scarcity. Access to market, postharvest value addition, price and marketing contract were significant factors that influence the market supply of onion. The study identified the need for policy interventions by the government to overcome the postpandemic challenges and ensure the sustainable development of onion production and marketing in the Mecha district. Research limitations/implications The limitations of the study are primarily related to the methodology as data are collected at a single moment in time. However, the study observes that those changes after the pandemic are better understood if we collect data at different time. Therefore, the future study needs to provide longitudinal data to examine stability of response and to observe performance of the market that occur over time. Originality/value This original research is the first to study the impacts of the COVID-19 pandemic in Northwestern Ethiopia. The data used in the analysis are primary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".