Agricultural Supply Chain Analysis During Supply Chain Disruptions: Case of Teff Commodity Supply Chain in Ethiopia in the era of COVID-19
Bibliographic record
Abstract
Current state agricultural supply chain analysis for essential commodities like Teff in Ethiopia is necessary to avoid supply chain disruption caused by events like COVID 19. The objective of this study is to assess the effect of COVID-19 on the agriculture and food sector. It has taken into account both qualitative and quantitative mixed approaches. The study has been conducted to analyze the resilient teff value chain across teff supply chain members from production to consumption by comparing two production areas in Ethiopia. Cross-sectional descriptive surveys at different stages of the supply chain are identified. Data collection has been made based on the purposive sampling technique. It has then, analyzed the data and reach on conclusion. The findings revealed that wealth was not accumulated by farmers to create a sustainable supply of Teff to the consumers which is not enough to respond to the demand gap created in the event of supply chain disruption. Teff value chain analysis also indicated that consumers have low price expectations of Teff regardless of its high price at the retail shop. Since March 2020 Due to COVID 19 prevalence in Ethiopia, lockdown that disrupts goods and people move from rural to urban has resulted in a sharp Teff price increase from an average of 4200 per Quintal to 5000 Birr per Quintal in just one month at the retail shop. This has significantly benefited downstream supply chain members like wholesalers and retailers by hoarding Teff supply to consumers until regulatory bodies took action on price hikes by retailers. Finally, recommendations have been forwarded among the others include: to enlarge subsidies for Ethiopian farmers to boost agricultural production, hedge farmers against price fluctuation and avail warehouses for stocking agricultural commodities to buffer against future uncertainties are the major ones that the government has to apply.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".