An Analysis of Institutional Factors Influencing Vegetable Production amongst Small-Scale Farmers in Six Vegetable Projects of the Nkonkobe Local Municipality
Bibliographic record
Abstract
The specific roles of institutions in mediating production and marketing within the smallholder sector have not been fully investigated and understood especially in the parts of South Africa designated independent homelands prior to the end of Apartheid. This paper investigated institutional factors influencing vegetable production in six small-scale vegetable projects in Alice town in the Nkonkobe Municipality of Eastern Cape Province of South Africa. Amidst worsening poverty in the wider society it was the intention to know how vegetable production can contribute to enhancing food security and if it is in a position to do so. Seeking some insights on effectiveness of the agrarian reforms on smallholder farmers in South Africa, the objectives of the study were to identify and explore institutional factors that influence vegetable production. The data were drawn from all the 62 farmers in the projects investigated. Descriptive analysis and binary logistic regression were employed to analyze the data and explain the patterns of interactions among the identified institutional factors influencing vegetable production. The study results revealed that some institutional factors need to be addressed to enhance vegetable production. The binary logistic results show that formal rules and informal norms are important in vegetable production. The most significant institutional variables revealed by the analysis were attributes of the formation and organizational structure of the projects, land tenure, extension service, collective action in production and marketing. The findings suggest that institutional changes in respect to aforementioned variables and other complementary institutions such as contract farming and credit access can significantly contribute to increased, efficient and sustainable vegetable production.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".