Evaluating the determinants of deforestation and approaches to reforestation by cocoa farmers in Côte d'Ivoire
Bibliographic record
Abstract
Societal Impact Statement Cocoa‐driven deforestation threatens tropical forests, particularly in Côte d'Ivoire, the world's largest cocoa producer. This study examines the motivations behind cocoa cultivation in classified forests, the selection of retained tree species on farms, and the economic drivers of wooded land conservation among farmers. Findings reveal that land tenure security, soil fertility, and socioeconomic factors shape farmers' decisions, with species preferences influenced by ethnicity. Understanding these drivers informs policies for sustainable cocoa agroforestry, integrating reforestation efforts with farmer livelihoods. Targeted interventions, such as land tenure reforms and incentives for tree retention, can promote forest restoration while ensuring the sustainability of cocoa farming. Translated Societal Impact Statement La cacaoculture contribue à la déforestation en zone tropicale, particulièrement en Côte d'Ivoire, principal producteur mondial de cacao. Cette étude analyse les motivations des agriculteurs dans les forêts classées, les critères de sélection des arbres conservés et les facteurs économiques influençant la préservation des terres boisées. Les résultats montrent que la sécurité foncière, la fertilité des sols et les facteurs socioéconomiques façonnent ces choix, avec des préférences d'espèces variant selon l'ethnicité. Des réformes foncières et des incitations à la conservation peuvent favoriser la restauration forestière tout en garantissant une cacaoculture durable et des moyens de subsistance aux agriculteurs. Summary Cocoa cultivation is widely recognized as a major driver of deforestation in Côte d'Ivoire. To effectively restore forest cover using a participatory approach, it is essential to understand the dynamics of cocoa‐driven deforestation. This study investigates the factors motivating cocoa cultivation in classified forests, criteria influencing the retention of specific tree species when clearing land for cocoa establishment, and the economic motivations for preserving wooded plots among cocoa farmers in Côte d'Ivoire. Findings indicate that motivations for cultivating cocoa in classified forests vary by ethnicity and region: native farmers are more active in the Southwest, while foreign migrants concentrate in the East. Socioeconomic factors such as education, cooperative membership, and household structure, as well as land availability, soil fertility, and limited oversight in classified forests, also play significant roles. Among the 753 farmers cultivating cocoa in classified forests, key motivations include land tenure assurances from presumed landowners (39.4%) and soil fertility (22.8%). Additionally, farmers prioritize income potential, shade provision, and food production when selecting trees to retain for cocoa plantations, with preferences varying slightly across ethnic groups (native farmers, in‐country migrants, and foreign migrants). The most desired species for integration include Garcinia kola , Ricinodendron heudelotii , and Terminalia superba . Of the farmers preserving wooded lands, 64.1% maintain fallow areas primarily for family use and bequest value. These findings underscore the complex socio‐economic and environmental drivers behind cocoa farming in classified forests, highlighting the need for policies that balance conservation efforts with farmer livelihoods.
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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 | 0.000 | 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 | 0.000 | 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".