Soil health and synergy of ecological determinants of green cocoa productivity in different soil ecotypes in Ghana
Bibliographic record
Abstract
Introduction Soil health is critical for the efficient management of soil fertility and crop yield in “green” cocoa (GC) ( Theobroma cacao L .) agroforestry systems. However, knowledge about agroecosystem factors that affect healthy soil productivity in “green” cocoa agroforestry systems is patchy in West Africa. Based on organic cocoa (OC) and conventional cocoa (CC) agroforestry systems in Ghana, this study examined the soil health and synergy of ecological factors that determine the yield of GC. Methods Using multi-stage random sampling, 11 CC and 11 OC farms were sampled from three soil types (ferralsols, lixisols, and leptosols) within selected agroecological zones. Socioeconomic and farm data, including bulked soil samples, were collected at 0–30 cm depth for analysis of soil chemical and physical properties. Results The results showed intricate relationships between the ecological factors and the yield of GC (1.07 t ha −1 ), which comprised dry beans of OC (1.24 t ha −1 ) and CC (0.89 t ha −1 ). The green cocoa yield increased for fields owned by female farmers and for native farmers who inherited or outrightly owned farmlands. The cocoa yield was also positively related to physicochemical factors such as soil organic carbon (0.21%), pH (5.8), and carbon–nitrogen ratio (40.8%). The carbon–nitrogen ratio and pH together exerted the highest positive influence (0.62%) on the yield. Biological factors such as plant density (>7 cocoa trees per 23.4 m 2 ) and black pod rots reduced the cocoa yield. Discussion This study provides comprehensive empirical determinants of green cocoa productivity and offers a more reliable estimate of cocoa plant density. The findings suggest that Ghana's cocoa can be much greener if stakeholders promote healthy farm soil productivity and empower women who engage in soil organic carbon-conserving agroforestry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".