Comparative Analysis of the Profitability of Major Value-added Activities Along the Pineapple Value Chain in Ghana
Bibliographic record
Abstract
This study aimed to analyze the profitability of sampled pineapple farmers, processors, and marketers in Ghana, which will help to assess how these actors optimize available resources to generate profits and achieve production efficiency. A cross-sectional descriptive survey design was used with interview schedules as the data collection instruments. The sample size was 320, 66, and 169, pineapple farmers, processors, and marketers respectively. The study found that pineapple production and processing were profitable, but marketing was not. The results showed a significant difference in the profit share of the group actors, highlighting that the profit share of each actor along the pineapple value chain is different. The results also showed that income, capital, and planting materials were the main determinants of farmers' profits. On the other hand, capital, pineapples, and packaging materials were the predictors of processors' profits. While transport, revenue, and loading and unloading costs predicted the marketer's profit. Based on these findings, the study recommended that NGOs and other partner agencies promote the pineapple industry in various ways to reduce poverty by providing credit facilities to actors to increase their productivity, profitability, and sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".