An Analysis of Yield Gap and Some Factors of Cocoa (Theobroma cacao) Yields in Ghana
Bibliographic record
Abstract
Although cocoa productivity has recently been increasing in Ghana, it is still low compared with that of other countries such as Cote d’Ivoire and Malaysia. This situation has been attributed to the low adoption of cocoa production technologies. The study was aimed at analysing the yield gap as well as some cocoa yield factors. Cross-sectional socio-economic survey was conducted in six (6) cocoa growing districts: Nkawie, Goaso, Enchi, Oda, Twifo Praso/Assin Fosu and Hohoe. A structured questionnaire was employed in the collection of data from 300 respondents who were randomly chosen with multi-stage cluster sampling technique. The yield gaps and their proportion to yield potentials were estimated using data from the survey and on-station trials. The findings indicated an experimental yield gap of 1 553.4 kg ha-1, accounting for 82.1% of the experimental yield potential whereas farmer-based yield gap was 1 537.2 kg ha-1, also accounting for 82.0% of the farmer (survey) yield potential. The Ordinary Least Square (OLS) regression analysis indicated that frequency of spraying fungicides against black pod disease, spraying insecticides against capsids, weeding of cocoa farms, cocoa variety planted by farmer, area of cocoa farm and total cocoa production variables had a significant impact on cocoa yield. It is recommended that the Government should encourage cocoa farmers, through pragmatic measures, to adopt improved technologies for enhancing productivity instead of focusing on excessive land expansion which eventually leads to low productivity.
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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 | 0.001 | 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) | 0.001 | 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".