Farmer uptake of cassava-whitefly management technologies and implications for future breeding and promotional efforts
Bibliographic record
Abstract
Cassava stands as Uganda's second most vital staple food after bananas, playing a crucial economic role for smallholder farmers. However, whiteflies significantly reduce cassava yields, jeopardizing farmers' incomes and food security . Aside from direct damage, the cassava whitefly transmits cassava brown streak disease (CBSD) and cassava mosaic disease (CMD), leading to potential yield losses ranging from 70 % to 100 %. The control of whiteflies in cassava cultivation is complicated by the prevalence of varieties susceptible to these pests and the farmers' limited knowledge of effective insecticide use. A study employing both quantitative and qualitative survey methods was conducted to assess smallholder farmers' awareness and adoption of the whitefly-tolerant cassava variety, Mkumba, and the systemic insecticide imidacloprid . Findings reveal that 35.2 % of farmers grew Mkumba, while 31.9 % utilized chemical control. Furthermore, 34.7 % identified whiteflies on cassava, with 45.4 % associating sooty moulds on leaves with whitefly feeding. Awareness of these control technologies was evident among farmers. However, factors such as the farmer's age and sex influenced the adoption of Mkumba, with barriers including the limited availability and high costs of insecticides and certain undesirable traits of Mkumba hindering broader uptake. Addressing these challenges may enhance the adoption and demand for these technologies in cassava farming.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".