Analyzing the Impact of COVID-19 on Agricultural Cooperatives in Rwanda: Coping Strategies of the Dukunde Umurimo Cooperative
Bibliographic record
Abstract
This study aims to unravel the profound impacts of the COVID-19 pandemic on Rwanda's agricultural cooperatives, concentrating on the nuanced dynamics within the Dukunde Umurimo cooperative. Its primary focus lies in assessing the extent of Covid-19's influence on the cooperative's members, exploring their adopted coping mechanisms, identifying post-pandemic challenges, and proposing strategies to alleviate these identified challenges. Employing a mixed-methods approach combining qualitative and quantitative research designs, the study encompassed 755 members of Dukunde Umurimo Cooperative, with a sample of 88 derived through the Yamane formula utilizing simple random and purposive sampling techniques. Data collection involved interviews, questionnaires, and documentation techniques. Utilizing descriptive statistics—frequency and percentage analysis—and comparative methods, the research evaluated cooperative productivity across pre-Covid, during, and post-Covid periods. Findings revealed a notable decrease in income and sales quantities during the Covid-19 period, followed by a significant post-pandemic increase. Concurrently, reductions in production, sales, and demand emerged as prominent challenges faced by Dukunde Umurimo cooperative members. Based on these findings, recommendations include strengthening resilience through diversification, and enhance the insurance risks management for the agricultural produce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".