Factors Affecting Profitability of Smallholder Vegetable Farmers in the Shiselweni Region, Kingdom of Eswatini (Swaziland)
Bibliographic record
Abstract
Agriculture is the main anchor of the Eswatini economy and profitability in this sector still remains vital for sustainable development of the economy. This study investigated the factors affecting profitability of smallholder vegetable farmers in the Shiselweni region. Primary data was obtained using a structured questionnaire and personal interviews from 60 vegetable farmers. Data was analyzed using descriptive statistics, enterprise budget, profitability ratios and multiple linear regression models. The SPSS software was used. The results showed that the mean age of the vegetable farmers was found to be 50.5 years, the mean household size was 8 people, mean farming experience was 3 years, mean farm size was 3 hectares and the majority of the farmers had high school education. The net income of smallholder vegetable farmers was E5810.30. The results of the multiple linear regression analysis revealed that land size, gender, household size, had a direct relationship with profitability of vegetable production while age, education, experience, income and labour had a negative relationship. Farmers requested that the subsidized farm inputs should arrive on time, new engines be bought for them and dams be constructed to generate irrigation water in winter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".