Adaptation to climate variability: farmers' practices and perspectives in cocoa farming in Côte d'Ivoire
Bibliographic record
Abstract
Côte d'Ivoire supplies over 40% of cocoa production worldwide. Climatic variability threatens to significantly reduce the area suitable for cocoa cultivation. Cocoa farmers have already leveraged their intimate knowledge of the local climate to adapt their production systems to climate change. However, their practices have not been well documented or evaluated. In response, this study aims to assess climate-smart cocoa practices for scalability recommendations. The study was conducted across three zones of predicted climate impacts on cocoa production: low impact, high impact, and a transformational impact. Climate-smart practices were inventoried, analyzed, and synthesized in different production contexts and by household categories in terms of well-being and asset endowment. The list of practices was then validated in a national stakeholder workshop in terms of agricultural productivity, food security, income generation, climate resilience and ecosystem services, economic viability and sustainability, and adoption probability. The resulting recommended practices are presented according to climate hazard. These recommendations represent local experiential knowledge consensus and offer valuable options for sustainable cocoa management in a changing climate. Our results show that Ivorian cocoa farmers broadly believe climate change will continue to worsen and have already widely adopted several of the recommended practices, particularly agroforestry. However, nearly none of the farmers across all three impact zones anticipate that cocoa production will become unviable. Climate information services offer significant potential for addressing this information gap and further supporting farmers' decision making in the face of climate change. Keywords: Climate Change, climate-smart practices, diversification, resilience, West Africa
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".