Lessons Learned and Challenges of Biopesticide Usage for Locust Management—The Case of China
Bibliographic record
Abstract
Using qualitative methods, this study assessed the stakeholders and management processes involved in locust outbreaks in China, including factors influencing the use of biopesticides. Study findings show that China has an integrated national locust response protocol, which involves various institutions from all administrative levels of the government. The process is inherently highly complex but efficient, with multisectoral agencies working closely together to prevent and/or manage locust outbreaks. In addition, the process has been successful in combating recent outbreaks, due to dedicated government funding, decisive administrative and technical actions, and the empowerment of local government administration. This is the case with the county level acting as a ‘first-responder’ that is capacitated financially and technically to respond to a locust invasion in their jurisdiction. Additionally, study findings show that despite the availability of biopesticides in local markets, their use is dampened by inadequate information about market availability, negative perceptions by decision makers about their efficacy, and concerns about their costs, as well as limited knowledge of their application techniques. Actions are therefore needed by relevant authorities to enhance stakeholder awareness of biopesticide market availability, efficacy, and field application processes. Future areas of research should focus on modelling the expected impact and cost effectiveness of chemicals vs. biopesticides, thus increasing the evidence base for promoting biopesticide use.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".