Participatory identification of farmer acceptable improved rice\nvarieties for rain-fed lowland ecologies in Uganda
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".