Implementation of fall armyworm management plan in Ghana: outcomes and lessons
Bibliographic record
Abstract
The invasive pest, fall armyworm (Spodoptera frugiperda J.E. Smith), was confirmed as being present in Ghana in 2016.By 2017, research studies estimated that maize yield losses in Africa due to fall armyworm would range between 8.3 and 20.6 million tonnes per year if management measures were not put in place.In Ghana alone, the value of the 2018 annual maize crop lost due to fall armyworm was estimated at US$177 million.In response to the fall armyworm outbreak, CABI -through its programme on Action on Invasives -launched fall armyworm-specific activities in Ghana in 2017.The programme supported the development of a national fall armyworm management plan; a collaborative effort with the Plant Protection and Regulatory Services Directorate (PPRSD) of the Ministry of Food and Agriculture (MOFA), and other stakeholders.The fall armyworm management plan focused on four priority areas: co-ordination and collaboration; awareness-raising; monitoring and surveillance; and control, management, and research.Most importantly, the national management plan aimed at ensuring coordinated efforts between public, private, and civil society organisations in the management of fall armyworm.A national multi-stakeholder task force was created, and charged with advising the Minister of Food and Agriculture and coordinating the response to fall armyworm.A review of the implementation of the national fall armyworm management plan was undertaken in late 2018 and early 2019 through a stakeholder workshop and key informant interviews.The review showed evidence of stakeholder collaboration at various levels, leading to increased public awareness of fall armyworm and management practices, research into low-risk management options, and contribution to policy and practice on how threats from invasive species could be managed more effectively in future.Implementation of fall armyworm management plan in Ghana: outcomes and lessons Key highlights• A national fall armyworm management plan was developed and a multi-stakeholder taskforce established to oversee and coordinate the implementation of the plan.• Public sensitisation activities were launched by various partners using SMS, radio, TV broadcasts, printed materials, and video screening, to increase awareness of fall armyworm and management practices.
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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 | 0.010 | 0.017 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".