Comparative Analysis of the Profitability of Major Value-added Activities Along the Pineapple Value Chain in Ghana
Bibliographic record
Abstract
Abstract The purpose of this study was to analyze the profitability for sampled pineapple farmers, processors, and marketers in Ghana which will help evaluate how these actors optimize available resources to generate profits and achieve production efficiencies. It employed the descriptive survey while the interview schedules were used as the instruments for the data collection. A sample size of 320, 66, and 169 pineapple farmers, processors, and marketers was obtained respectively. Empirically, it revealed that pineapple production and processing were profitable whereas marketing was not. It rejected the null hypothesis identifying no significant difference in the profit share of the group actors and concluded that the profit share of each actor along the pineapple value chain is different. That is revenue, capital, and planting materials were the determinants of farmers’ profit; capital, pineapple fruits, and packaging materials were the predictors of the processors’ profit, while transportation, revenue, and loading and off-loading costs predict the profit of the marketer. The study recommended, among others, that NGOs and other partner agencies promote the pineapple industry in various ways in hopes of decreasing the country's poverty by providing the actors with credit facilities that will enhance their productivity, profitability, and sustainability.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".