Efficiency of Vegetable Marketing in Peri-Urban Areas of Ogun State, Nigeria
Bibliographic record
Abstract
Against the backdrop evidenced in the substantial wastage, deterioration in quality, and frequent mismatch between demand and supply of vegetables spatially and overtime; this study examined the efficiency of vegetable marketing in Ifo and Ado-Odo L.G.As of Ogun State, Nigeria. Primary data were employed for the study. Data were collected from 120 respondents with the aid of structured questionnaire using multistage sampling procedure. Analytical tools used included, Descriptive statistics, budgetary and marketing efficiency analyses. The study revealed that women (78.3%) were the major players in the enterprise and most had basic education with majority having business experience of more than five years. However, they relied on their personal savings to run their enterprise. Indigenous vegetable marketing was found to be profitable and efficient as indicated by the positive net margin of N29,180.05. As an indication of the profit maximization motive of the marketers, various marketing efficiency scores were computed for the selected indigenous vegetables. The scores are 10.85%, 3.88%, 5.27%, 2.54%, 5.32%, and 2.46% for ugu, tomato, okra, amaranthus, celocia and chocorus, respectively. It is recommended that extension trainings on preservation of indigenous vegetables should be conducted and accessible funds should be made available to these marketers, to forestall the problem of spoilage and lack of funds, as these constituted major drawbacks on marketing efficiency in the study areas.
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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) | 0.001 | 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".