An Investigation of Challenges Facing Home Gardening Farmers in South Africa: A Case Study of Three Villages in Nkokonbe Municipality Eastern Cape Province
Bibliographic record
Abstract
This paper therefore addresses the challenges facing the home garden farmers in Eastern Cape Province of South Africa. Sixty households farmer were selected through systematic sampling from Nkokonbe municipality which were purposively selected. The small scale farmers were interviewed with the help of an interview schedule containing open and closed ended questions. Data were analysed using descriptive statistics with the help of Statistical Package for Social Sciences (SPSS). The results revealed that small scale farmers in Eastern Cape Province lack awareness of improved agricultural practices and technical knowhow because the extension staffs to farmer ratio is high. Results further reveals that they also lacked finance, experienced high interest rates on credit facilities and uncertainty of the right seed to use due to flooding of the market by many seed companies. In view of the research findings, several policy proposals are suggested. These include ensuring the availability of market information to all farmers, capacitating smallholder farmers with production and financial management skills and extension support service.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".