When Coconut Trees Die: Spatio-Temporal Land-Use Dynamics on Grand-Lahou Island, Côte d’Ivoire
Bibliographic record
Abstract
In Côte d’Ivoire, coconut cultivation represents both a major cash crop and an important source of subsistence for rural communities. However, coconut plantations are increasingly threatened by Lethal Yellowing Disease, which has destroyed large areas of coconut trees and profoundly altered land-use patterns on Grand-Lahou Island. This study aims to analyze the spatiotemporal dynamics of land-use on the island from 1990 to 2025. The methodological approach combined digital processing of multi-temporal Landsat satellite images acquired in 1990, 2000, 2016, and 2025. Image classification and change detection techniques were used to quantify land-use transitions over time. The results reveal an increase in coconut plantation area between 1990 and 2000, followed by a sharp decline between 2000 and 2025, mainly due to the impact of Lethal Yellowing Disease. A significant conversion of coconut plantations into food crop areas was observed, with 43.91% of former coconut lands transformed between 2016 and 2025. These findings illustrate the severe consequences of Lethal Yellowing Disease on the local economy and landscape structure. To mitigate further losses, the establishment of an early detection system using drone imagery is recommended to identify infected coconut trees and help contain the spread of the disease.
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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 | 0.000 |
| 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".