Production Determinants of the Pearl Millet Cropping System in Uganda and Implications to Productivity
Bibliographic record
Abstract
Pearl millet is an important crop for people living in semi-arid areas in Uganda but not much is known about its production environment. A survey was conducted in eastern and northern regions of Uganda to characterise the pearl millet cropping system and to identify the most important production determinants. Using questionnaires, data was collected from 160 households through face-to-face interviews with the respondents. Results showed that pearl millet was mainly grown for food and source of income. The production environment was low input as farmers planted unimproved genotypes, used no artificial chemicals or manure, and had minimal access to financial credit and agricultural trainings or extension services. Planting was done in the second rains with no optimal use of important resources like family labour and seed due to seed broadcasting. Farmers desired genotypes with traits such as; stay green, being tall, high tillering, high yield, early maturity and being ergot resistant. The most important constraints were ergot and rust diseases susceptibility, low yield, low tillering, late maturity, sterile panicles, rodents, moulds/rotting and insect pests; while lack of market, low prices and price fluctuation were the important market constraints. Results further showed that farmers lacked knowledge about the common diseases like rust and ergot. The area planted, spouse age and years of pearl millet cultivation were the important factors enhancing production while age of household head, amount of seed planted and distance to the market negatively affected grain yield.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".