Agricultural Information Needs of Rural Women Farmers in Nkonkobe Municipality: The Extension Challenge
Bibliographic record
Abstract
Access to agricultural information is vital for improving food security at the village level. This study accessed the agricultural information needs of women farmers in Nkonkobe Municipality of the Amathole District, Eastern Cape Province, South Africa. Data was obtained from 118 households. The women farmers were identified from four villages using the snowball sampling technique. Findings revealed that backyard gardening (87.2%; n = 103) was common in addition to the rearing of indigenous chicken (65.2%; n = 77) to complement food security. Most (80.5%; n = 95) were confronted with weed problems after applying cow dung as manure. There was a high report (70.3%; n = 83) of insect attack on leaves of cabbage, spinach and carrot, while seed dormancy was low (24.58; n = 29). Problems of fowls’ theft (66.95%; n = 49) and fowl predators (40.68%; n = 48) were common. More than average (54.2%; n = 64) depends on friends, neighbors and farmers’ colleagues for agricultural information but the majority (99.1%; n = 117) preferred extension workers coupled with farm demonstration for agricultural information. The study identified the importance of farmer-to-farmer model of technology transfer among farmers. It is recommended that farmer-to-farmer model could further be investigated to complement efforts of the extension services towards providing agricultural information to the smallholder 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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".