An Analysis of the Vegetables Supply Chain in Swaziland
Bibliographic record
Abstract
The total consumption of fresh vegetables in Swaziland is estimated around 40,000 tonnes per year and this translate into 40 kg per capita consumption per year. Individuals who are not economically challenged consume above the annual per capita of 40 kg in contrast to a poor individuals living in rural areas, who consume less than the per capita vegetables. The study sought to describe the performance of vegetable vegetables supply chain in Swaziland. A descriptive research design was used in the study and data were collected using personal interviews from 100 randomly selected vegetable farmers. Data were analysed using market margins and marketing channel analysis to identify existing marketing channels used by vegetable farmers. The revealed marketing channels that producers used to obtain attractive prices and a higher share of the consumer price. The largest producer’s share was obtained through direct sale to consumers. Channels that included restaurants had high total gross margins and low producer’s share of the consumer price. The concern for issues on post-harvest and marketing should form an integral part of policy development and research programmes and also the public and private sectors should facilitate contractual arrangements for vegetables farmers. Commercialising vegetable production should not be overemphasised because it encourages farmers to be market oriented as opposed to production oriented. Farmers need to form cooperatives in order to assist in bargaining of prices within the vegetable supply chain.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".