Tree diversity and soil fertility interactions drive carbon storage in degraded cocoa landscapes of Côte d’Ivoire
Bibliographic record
Abstract
Introduction: Understanding the ecological functioning of degraded cocoa landscapes is critical for restoring productivity and ecosystem services in West Africa. This study investigated how climate, geography, and land-use systems interact to shape tree diversity, soil fertility, and aboveground carbon (AGC) stocks across six cocoa-producing sites in Côte d'Ivoire. Methods: Field inventories and soil sampling to 50 cm depth were conducted across gradients of climate (1,150-1,650 mm rainfall) and land-use systems (fallow, cocoa agroforestry, and monocropping). Mixed effects ANOVA, regression, and redundancy analyses were applied to test three hypotheses linking environmental gradients, tree diversity, and soil-carbon relationships. Results: Land-use system had the strongest effect on tree diversity (P< 0.0001), with fallows supporting significantly higher richness (7.2 ± 0.51) and Shannon diversity (1.7 ± 0.05) than agroforestry or monocrop systems. Climate and site nested within climate also significantly influenced species abundance and evenness. Soil fertility indicators were primarily controlled by climate and geographic site, with significant effects on pH, exchangeable bases, and structural stability. Interactions between climate and land-use significantly affected soil pH, organic carbon, calcium, and cation exchange capacity. Stepwise regression identified aluminum, potassium, boron, and magnesium as key soil predictors of AGC (R² = 0.14; P< 0.0001), while species richness was the only diversity metric significantly associated with AGC (R² = 0.12; P< 0.0001). Discussion: These results highlight the dominant role of species richness over evenness and abundance in sustaining soil fertility and carbon storage, emphasizing biodiversity conservation as a cornerstone of cocoa landscape restoration.
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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 | 0.001 |
| 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".