Biocontrol for papaya mealybug: lessons learnt from Kenya
Bibliographic record
Abstract
ImpactImports and Exports (KSTCIE).Following mass rearing and subsequent small-scale field releases at six research sites around Mombasa, Kwale, and Kilifi coastal counties, the parasitoid was effectively established at papaya farms, confirming A. papayae's potential as a valuable form of PMB biocontrol in Kenya and elsewhere in Africa.Prior to any field releases of A. papayae in Kenya, a study was also conducted to determine smallholder farmers' knowledge, attitudes, and practices (KAP) towards biocontrol.Obtaining their perceptions was key, as farmers are often overlooked despite being recognised as hugely important players in decision-making on pest management practices.Subsequently, following initial field releases of A. papayae, a follow-up study took place in 2022 to determine any changes in farmers' KAP towards biocontrol, as well as any changes in papaya yield and subsequent farmer income. Key highlights• The highest parasitism rate of PMB (73%) was recorded after more than 1,000 parasitoids were released on farm, and parasitism was still observed four months after release.• In all the research sites, PMB population counts reduced significantly after releases, to a level where it was difficult to find the pest.• Farmers' perception of biocontrol was very positive across KAP survey years, with a 12% increase in awareness of biocontrol observed from 2021 to 2022.• Farmers carried out activities that enhanced pest natural enemies, e.g.intercropping and crop diversification, and demonstrated some initial uptake of natural enemy field reservoirs to support A. papayae population establishment and spread.• On average, treatment farms achieved approximately 196 kg higher harvest than control farms, and the control farms lost a higher amount of income (USD 94) than the treatment farms across the survey years. How to cite this paper
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".