Worldwide cotton production and trade during COVID-19 pandemic: An empirical analysis for a three-year observation
Bibliographic record
Abstract
The COVID-19 pandemic has posed a significant impact on agriculture. Due to its importance in world trade and human life, the effects of the pandemic on the cotton economy were evaluated by using the data of important organizations such as the U.S. Department of Agriculture, the World Trade Organization, and International Cotton Advisory Committee in this study. With the Chow test, which measures of structural breaks, the effects of COVID-19 on cotton production and trade were examined. According to the Chow test results, the pandemic had no significant effect on cotton production, exports and imports in the People’s Republic of China and Türkiye, while being highly influential on cotton production and exports in the U.S. and Brazil. Distinctively, in Pakistan, it had a significant impact on cotton production and import. It was observed that although the demand, trade and prices for cotton were descended, the cotton prices started to recover with the increase in demand in the third quarter of 2020. In June 2022, the highest peak in cotton prices was observed. As a conclusion, it is shown that cotton production and trade during the pandemic were affected in all countries except People’s Republic of China and Türkiye. However, the marks of the effects of factors such as decreasing stocks, uncertainties in national economies, high inflation and increase in production costs on the cotton economy will be better understood in the coming years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".