Climate Variability and Outlook of Cocoa Production in Côte D’ivoire under Future Climate
Bibliographic record
Abstract
Cocoa supports about 3.5 million people. Farmers produce each year 1.5 million ton. This performance hides production constraints, the most is climate variability. The climatic variables, temperature, precipitation, and 16 climatic indices were identified to assess the potential impacts on cacao in the past year, currently and under future climate. The climate data in the southern and central cocoa production zone were analysed for periods of 2021–2050 and 2041–2070. The climate reference period is 1981–2010. The climate projections are from the CORDEX RCP 4.5 and 8.5. The results suggest an increase in daily temperature of 1.0–2.1°C in the central region and 0.9–2.0°C in the southern region by 2041–2070. Cocoa could be affected by the projected changes, especially in the central region where the maximum daily temperature at which production is reduced (33°C) would be exceeded between 92 and 142 days per year by this time horizon. The direction of changes in precipitation cannot be established due to a lack of consensus between the climate models analysed. However, the little rainy season would start slightly earlier, potentially reducing the duration of the little dry season between the rainy seasons. The climate scenarios enhanced deterioration of growing environment conditions. It is necessary to take adaptation measures to mitigate climate impacts.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".