Fall armyworm management: lessons learnt from Ghana
Bibliographic record
Abstract
The invasive pest, fall armyworm (FAW) was confirmed to be in Ghana in 2016.Stakeholders, including CABI, worked to support the development of a national FAW management plan.A review of the management plan implementation was undertaken using outcome harvesting, a Sprockler inquiry and key informant interviews.Results showed evidence of stakeholder collaboration, leading to increased public awareness of FAW and related management practices, and more coordinated research into low-risk management options.Key factors for the success of the FAW response were: establishment of the multidisciplinary taskforce, with common goals and ownership; mobilization of financial, human and material resources at national and district levels; effective coordination and communication, limiting duplication of efforts by different actors; farmer sensitization to identify and manage FAW and other pests.Steps to ensure future preparedness include: implementation of the National Invasive Species Strategy and Action Plan (NISSAP); establishment of a standing taskforce and emergency fund to address new pest outbreaks; improved monitoring and surveillance especially at borders and ports of entry; strengthened research capacity especially in pest risk analyses; and development of emergency response guidelines for future outbreaks.Fall armyworm management: lessons learnt from Ghana Key highlights• An initial slow response to the FAW outbreak was mainly due to inadequate funds and lack of knowledge of the pest.• Once the impact of FAW became apparent, funding was released and the response effectiveness increased.• Stakeholders at central level (e.g.central government officials, researchers, media, NGOs)considered key response success factors to be collaboration through the multidisciplinary taskforce, knowledge sharing, and effective planning, coordination, and communication.• Stakeholders at the local level (e.g.local government officials, extension workers, local leaders, farmers) considered the pesticide distribution scheme and the information shared through the communication plan as contributing to the response success.• Future preparedness measures should be put in place to ensure quick responses to new pest outbreaks in the future.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".