The Impact of Credit on Technical Efficiency Among Vegetable Farmers in Swaziland
Bibliographic record
Abstract
Access to credit is regarded as an important intervention for improving the incomes of the rural population, mainly by mobilizing resources to more productive uses. Production of vegetables by smallholder farmers in Swaziland is inconsistent and lower than the national demand, hence the gap is filled by imports from South Africa. The purpose of the study was to assess the influence of credit on technical efficiency of smallholder vegetable farmers in Swaziland. Data were collected in 2013 from farmers through a structured questionnaire, which was administered using personal interviews. A two-stage sampling procedure was used by stratifying the vegetable farmers in the Hhohho region according to the four Rural Development Areas (RDAs). This was followed by a simple random sampling technique used to select the number of vegetable farmers from each stratum. A sample size of 120 farmers was selected from a population of 289. The Stochastic Frontier production function was used to analyze the data using the STATA program (version 12). The results revealed that credit had a negative effect on technical efficiency of cabbage and green pepper farmers, while it had a positive effect on the technical efficiency of tomato, and beetroot farmers. The technical efficiency of tomatoes and cabbage farmers was affected by age, education level, farming experience and access to credit (p <0.01), while beetroot and green pepper was affected by farmer’s age, and off-farm income. (p<0.05). The study recommended that vegetable farmers should increase the amount of seeds, fertilizer and chemicals used in order to improve yields. The Government of Swaziland should subsidize farming inputs and financial institutions should make credit more available to agribusinesses in order to improve the efficient use of input resources.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".