Supply-Side Interventions in Cocoa Production in Ghana: A Regional Decomposition of Technical Efficiency and Technological Gaps
Bibliographic record
Abstract
Although Ghana Cocoa Board (COCOBOD) promotes technical change in cocoa farming with innovative technologies and input support, crop productivity is better advanced by improving on the efficiency of input use by farmers. This study thereby investigates the technical efficiency of cocoa farmers in Ghana. The study uses cross sectional field data covering Western North, Ashanti, Eastern, Volta and Brong-Ahafo regions of Ghana on a sample of 899 cocoa farmers and adopts Meta frontier stochastic frontier analysis to derive production efficiencies for each region. The findings are that supply-side interventions such as hand pollination, hybrid seedlings, farm pruning and extension services can improve on technical efficiency of cocoa farmers, more especially in Ashanti, Eastern and Western region. Notably, the CODAPEC input support programme which encapsulates insecticides and fungicides spraying has failed to improve on production efficiency as compared to the Hi-Tech (fertilizer application) programme. Eastern region cocoa farmers stand out as the most efficient producers, producing about 87% of their potential output given technology, whereas Western North produces 76% of its output potential, the lowest of the five regions. The three other regions, namely, Brong-Ahafo, Ashanti and Volta can produce on average 83%, 80% and 78% of their output potential in cocoa respectively. Averagely, cocoa growing regions are underutilizing 21.5% of available technology in the industry while losing 36.5% of output potential due to technical inefficiencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".