Willingness to Adopt Certifications and Sustainable Production Methods among Small-Scale Cocoa Farmers in the Ashanti Region of Ghana
Bibliographic record
Abstract
The main objective of this research project was to identify current cocoa production practices and determine the principal factors that affect the adoption of sustainable farming practices and socio-environmental certifications among small-scale cocoa farmers in Ghana. The study was conducted in two cocoa districts (Atwima Mponua and Ahafoano North) in the Ashanti Region of Ghana. A combination of stratified, systematic and random sampling techniques was employed to select 439 cocoa producing households for the study. A standardized structured questionnaire was used to gather field data through personal interviews. Results showed that membership in farmers’ organizations, awareness of certification and size of cocoa farm were the main determinants of willingness to adopt sustainable cocoa production methods and certifications. Whereas membership in farmer-based organizations and awareness about different aspects related to certification had a significant positive effect on adoption of cocoa certification, farm size tended to have a significant negative effect on adoption of certification. Formation of cocoa farmers’ associations/organizations in various communities, creation of awareness about certification and continuous education of cocoa farmers are recommended to stimulate adoption of cocoa certification to achieve sustainability in the Ghanaian cocoa industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".