Smallholder farmers’ knowledge, attitudes and practices towards biological control of papaya mealybug in Kenya
Bibliographic record
Abstract
Abstract Background Farmer perceptions are highly important in influencing on-farm pest management decision-making. Biological control is extremely sustainable in the smallholder production context, but in Sub Saharan Africa (SSA) few attempts using this pest control method for arthropod pests have been successful, with one of the key reasons cited as poor involvement of farming communities and extension in the dissemination of information. Although farmers’ knowledge and attitudes are hugely important for the successful implementation of biological control, they are often disregarded. Papaya mealybug ( Paracoccus marginatus ) (PMB) has rapidly spread and established in suitable areas across Kenya becoming a serious pest. The objective of this study is to determine smallholder farmers’ knowledge, attitudes and practices towards biological control; farmers’ willingness to reduce their chemical pesticide use; and levels of support for a classical biological control initiative for PMB management. Methods Household surveys were conducted covering 383 farming households (148 women) in four papaya producing counties in Kenya alongside key informant interviews with eight extension agents and thirty agro-dealers, and eight focus group discussions. Results Although some farmers demonstrated awareness of the concept of biological control they lacked knowledge, experience and technical support from extension or agro-dealers. Reasons for not using biological control included inadequate awareness and concerns over efficacy and safety. Farmers expressed high levels of interest and willingness to support biological control, and were willing to reduce their chemical pesticide use to help conserve, and support the establishment of natural enemies. County, perception of biological as safe, training in IPM and gender were all highly significant factors determining farmers willingness to support biological control. Conclusions Previously, poor attention has been paid to farmer perceptions and participation in biological control, which has resulted in limited success in developing countries. With high levels of interest and willingness to support biological control, the next step is to engage with farming communities impacted by PMB. By building awareness and capacity, and developing a management plan with farmers that will support the release and establishment of the biological control agent, Acerophagus papayae , long-term, sustainable control of PMB in Kenya is possible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".