The Fall Armyworm and Larger Grain Borer Pest Invasions in Africa: Drivers, Impacts and Implications for Food Systems
Bibliographic record
Abstract
Invasive alien species (IAS) are a major biosecurity threat affecting globalisation and the international trade of agricultural products and natural ecosystems. In recent decades, for example, field crop and postharvest grain insect pests have independently accounted for a significant decline in food quantity and quality. Nevertheless, how their interaction and cumulative effects along the ever-evolving field production to postharvest continuum contribute towards food insecurity remain scant in the literature. To address this within the context of Africa, we focus on the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), and the larger grain borer, Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae), two of the most important field and postharvest IAS, respectively, that have invaded Africa. Both insect pests have shown high invasion success, managing to establish themselves in >50% of the African continent within a decade post-introduction. The successive and summative nature of field and postharvest damage by invasive insect pests on the same crop along its value chain results in exacerbated food losses. This systematic review assesses the drivers, impacts and management of the fall armyworm and larger grain borer and their effects on food systems in Africa. Interrogating these issues is important in early warning systems, holistic management of IAS, maintenance of integral food systems in Africa and the development of effective management strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".