Impact of COVID-19 on Cashew Price and Cashew Producers’ Income in Côte d’Ivoire: A Case Study in Five Departments
Bibliographic record
Abstract
In Côte d’Ivoire, cashew has become an important cash crop. Nevertheless, Côte d’Ivoire’s cashew relies on the international market, with more than 90% of the production exported as raw nuts. The 2020 commercialization campaign started a few days after the outbreak of COVID-19 in China, which spread worldwide. This work assesses the impact of this pandemic on the cashew price and cashew producers’ income in Côte d’Ivoire. We used the cashew price database over ten weeks in five cashew production areas and an interview-guided to collect the data. We used the Producer Price Index (PPI), descriptive statistic, and theoretical analysis of the income forecasting for data analysis. Results reveal that the lack of funds resulting from the fear of investors has caused a gradual drop in prices since February. This decrease has been more severe when restriction measures have been enforced. The purchase of cashews even stopped in some localities of the study areas. Compared to the first week of the campaign, the COVID-19 pandemic has reduced cashew producer income hugely to 50% in the sixth week and to 37.5% in the ninth and tenth weeks of our observation. Nonetheless, institutional factors such as the lack of control have also contributed to prices decrease. As recommendations, in the short-run, some resilience strategies such as subsidizing the local cashew market should be set up by the authorities. In the mid-term, the country should strengthen the cashew commercialization chain. In long-run, the local cashew transformation should be prioritized instead of raw nuts commercialization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".