Farmers’ Perceptions of Finger Millet Production Constraints, Varietal Preferences and Their Implications to Finger Millet Breeding in Uganda
Bibliographic record
Abstract
Finger millet is an important food security and cash crop in Uganda but its production is constrained by a number of factors. However, information on farmers’ perceptions of constraints and varietal preferences is limited. A study was conducted to; identify varieties and varietal preferences in finger millet, and assess farmers’ constraints to finger millet production and coping mechanisms. The study involved a participatory rural appraisal, and a survey. Farmers identified the major constraint as high labour requirements especially for weeding since over 95% of the farmers used broadcasting as a method of planting. Other constraints that occurred across all the districts were blast disease and low yielding cultivars. Farmers also reported to have developed some coping mechanisms to counter the constraints. In terms of preference for new cultivars, farmers preferred high grain yield, brown seed colour, compact head shape, tolerance to blast disease, high tillering ability, moderate plant height (1 ± 0.2 m), early maturity, tolerance to shattering and ease of threshing without compromising other preferred attributes. The study further revealed that a considerable proportion of the farmers had limited or no knowledge on finger millet blast disease, its causes and mechanisms of coping. Farmers also reported that blast disease symptoms in all locations were on the increase over the years and pointed out the most susceptible and tolerant cultivars. These findings therefore, present an urgent need for information sharing with farmers and other agricultural development partners, and continuous development of blast resistant cultivars with farmer preferred attributes.
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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 | 0.000 | 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".