Farmers’ Knowledge on the Sweetpotato Cultivars Grown in the Teso Sub-Region, Uganda
Bibliographic record
Abstract
The farmers’ knowledge of the cultivars to use in increasing sweetpotato productivity is critically important. A study was carried out in the Teso Sub-region to investigate the role of education in sweet potato production. Using an ex post facto design, 24 out of 51 sub-counties were purposively selected applying district-county strata and used to determine the perceptions of sweetpotato farmers and of their agricultural advisers. Through interviews, observations and questionnaires, the survey covered 288 farmers randomly selected and a whole population of 33 agricultural advisers, while 329 community leaders purposively selected and farmers randomly selected were engaged in focus group discussions. A total of 650 persons participated. Data were collected on farmers’ knowledge of sweetpotato cultivars grown capturing selected attributes. Analysis of data was done qualitatively using open and axial coding and quantitatively using means, frequencies, percentages, ANOVA and multiple regressions at a confidence level of 0.05 (?). Using selected attributes, farmers identified 139 cultivars grown. The best performing cultivar was Araka yielding a range of 19,001-29,000 kg/ha but was susceptible to the sweetpotato weevil and drought. Araka also stores poorly as dried chips or sliced. The least performing cultivars were Elany ikokolak, Epaku & Ocaka amani with yields below 5,000 kg/ha but less susceptible. Commercialising the crop with value addition, farmers need up-scaling of their scientific knowledge of cultivars and production as basis for better multiplification & selection of vines for higher yields needed in processing the crop for its several uses.
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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 | 0.001 | 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".