PARTICIPATORY IDENTIFICATION OF FARMER ACCEPTABLE IMPROVED RICE VARIETIES FOR RAIN-FED LOWLAND ECOLOGIES IN UGANDA
Why this work is in the frame
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Bibliographic record
Abstract
Rice ( Oryza sativa L.) is increasingly an important food and income generating crop in eastern Africa. Unfortunately, its production is characterised by low yields largely caused by minimal utilisation of improved varieties and poor production techniques. In response to the rising rice demand, rain-fed lowland rice production in the country is associated with field expansion rather than intensification. Consequently, farmers are encroaching on vulnerable ecologies, especially the wetlands. The objective of this study was to identify farmer preferred and rain-fed lowland adapted improved rice varieties. Six varieties (IR 64, Basmat 370, Supa, Wita 9, K85, Buyu) were evaluated in four trials in the Kyoga plains agro-ecological zone in eastern Uganda. Varieties K85 and Wita 9 yielded 6133 and 5553 kg ha-1, respectively; significantly higher (P<0.05) than Buyu, the local check. Basmat, IR64 and Supa yielded 4191, 3554 kg and 3608 kg ha-1, respectively; though not significantly different (P>0.05) from the local check. Variety K85 was preferred by 59% of the farmers; and this was followed by Wita 9. Basimat 370 and Supa were selected by 50.4% as the worst performing varieties. Gender based preference for K85 was 54.5 and 36.4% for male and female, respectively. The criteria for variety preference were level of grain yield, short maturity time, plant height and resistance to lodging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it