Farmers’ Knowledge and Management of Rice Diseases in Uganda
Bibliographic record
Abstract
Rice in the recent past has picked up as a food security and income generating crop among farming households in Uganda. Even though, the crop is playing a significant role in reducing food insecurity and poverty within the country, its productivity has remained constant and low at about 1.5 metric tonnes per hectare. Using cross-sectional data collected from rice farmers in Northern (Lira, Dokolo, Otuke and Alebtong Districts); Eastern (Iganga, Bugiri and Kamuli Districts) and Central (Kayunga District) regions of the country, this paper establishes farmers’ knowledge and management of rice diseases from a total of 224 farmers, as a baseline for research initiatives focusing on improving rice productivity within the country. Farmers ranked diseases as the second most important constraint in rice production, after insect pests. Moreover, farmers ranked rice yellow mottle virus (RYMV) as the most important disease in rice production, followed by leaf blast (LB), brown spot (BS), sheath rot (ShR), panicle blast (PB) and bacterial leaf blight (BLB). However, 60% of farmers identified RYMV while less than 30% could clearly diagnose the symptoms of each of the other diseases. Surprisingly, only about 18% of farmers employed management options against RYMV, whereas less than 6% practiced some form of management for each of the other major diseases. The predominant disease control measure was uprooting diseased plants followed by insecticide application. About 10% of farmers reported sedges (Cyperus spp.), goat weed (Ageratum conyzoides), black jack (Bidens pilosa), and cut grass (Leersia spp.) as alternative host plants for rice diseases. An integrated disease management approach, whose development and implementation recognizes farmers as the key players in the rice production chain, is required to achieve improved and sustainable rice productivity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".