Factors Affecting the Choice of Marketing Channel by Vegetable Farmers in Swaziland
Bibliographic record
Abstract
Vegetables as a group of horticultural crops are important for their contribution as an income support to a large proportion of the rural households. However, enhancing vegetable farmers to reach markets and actively engage in the markets is a key challenge influencing vegetable production in Swaziland. The perishable nature of vegetables necessitates effective marketing channels. The aim of this paper was to investigate factors affecting farmers’ choice of marketing channels using survey data gathered during the 2011 production season. Data were collected from 100 randomly selected vegetable farmers. Descriptive and multinomial logistic regression analyses were used. The results indicated that age of the farmer, quantity of baby corn produced and level of education were significant predictors of the choice to sell vegetables to NAMBoard market channel instead of selling to other-wholesale market channel. The age of the farmer, distance from production area to market, membership in farmer organization and marketing agreement were significant determinants of the choice to use non-wholesale market channel over other-wholesale market channel. It is therefore important to promote collective action as an institutional vehicle for linking farmers to agribusiness supply chains. Farmers should establish networks since they aid in sharing knowledge, farmers can improve produce grades as required by market.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".