Performance of Vegetable Production and Marketing in Peri-Urban Kumasi, Ghana
Bibliographic record
Abstract
Vegetable production and marketing play an important role in providing income and employment for a significant proportion of small holder farmers and traders in Ghana. Yet, farmers are dissatisfied, claiming that they earn less marketing margins than is due them as compared to traders in the value chain. Due to lack of a holistic assessment of actors’ performance, this assertion remains unjustifiable. It is against this backdrop that this study investigates the performance of farmers, wholesalers and retailers along the investment channels of three major leafy vegetables (spring onions, lettuce and cabbage) in peri-urban Kumasi. Using a two-stage sampling technique, a total of 217 actors comprising 147 farmers, 30 wholesalers and 40 retailers, were sampled. Marketing margin analysis and returns on investments (ROIs) were used to assess the performance of actors’ investments. Results show that vegetable production is dominated by males (91 percent) and trading by females (83 percent of wholesalers and 100 percent of retailers) respectively. Wholesalers recorded the highest yearly marketing margins for spring onions and cabbage (GH¢ 3 369 and GH¢ 17 376) (1US$ = GH¢ 3.6), while farmers obtained the highest yearly margins (GH¢ 3 630) for lettuce. Farmers obtained the most ROIs of 28, 145 and 79 percents for spring onions, lettuce and cabbage respectively. Based on accrued ROIs, the study concludes that farmers are more efficient in the investments in these vegetables than traders. Information flow gap was found to be a major cause of farmers’ scepticism on margin distribution because 76 percent of farmers had no information on market prices of products. It is recommended that an efficient policy on market price information system for vegetables be implemented via convenient means such as farmer associations and weekly radio broadcasts of product prices to all actors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".