Perceptions of Communal Farmers on Extension Support Services Accessibility in the Port St Johns, Eastern Cape Province
Bibliographic record
Abstract
Communal farming is mainly practised in most rural areas of South Africa, and agricultural production plays a significant part in rural livelihoods. Lack of access to adequate resources has led to high vulnerability. Farmers' understanding, awareness, and experience of extension services are important. Extension services are vital in supporting farmers in acquiring information, gaining knowledge and skills, and engaging in agricultural production to solve farming-related problems. Therefore, the paper seeks to determine farmers' perceptions of extension services accessed. The study used a cross-sectional research design to collect data using a 5-Likert scale questionnaire. A snowball sampling method was used to select 115 communal farmers from Ntsimbini village in Port St Johns Local Municipality. Descriptive statistics and principal component analysis were used to analyse the collected data. The study's findings revealed that production challenges associated with limited access to support services affect crop and livestock production. Findings on farmers' perceptions revealed poor access to production inputs and infrastructural support. Therefore, extension services accessibility affects production inputs and infrastructural support. The study recommends that access and use of extension support services be improved through communication strategies conducive to all stakeholders involved in communal farming, as this will help improve access to support services for farmers.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".