Forest Proximity Positively Affects Natural Enemy Mediated Control of Fall Armyworm in Southern Africa
Bibliographic record
Abstract
The fall armyworm (FAW, Spodoptera frugiperda ) is a major crop pest in southern Africa. It threatens the livelihoods and food security of smallholder farmers in the region by negatively impacting maize yield. Although scientific evidence suggests that natural enemy-mediated predation can potentially reduce FAW infestation, the effectiveness of natural enemies such as birds, bats, parasitoids, and generalist predators on FAW is poorly understood. This study reviews existing literature to assess how birds, bats, parasitoids, and generalist predators’ control FAW infestation, as well as the role of forest or tree cover in natural enemy mediated pest control of FAW in maize in southern Africa. We then present a case study to examine the role of forest proximity in reducing FAW infestation in maize in Zimbabwe. We conclude that birds, bats, parasitoids, and generalist predators are likely drivers of the reduced success of FAW near forests in southern Africa. While predators influence FAW survival and development, their role is largely undermined by parasitoids, which are more efficient in affecting FAW populations. Birds, bats, parasitoids, and generalist predators play an important role in controlling FAW on farms in heterogenous landscapes with diverse vegetation and near-forest proximity. The findings of our case study from Zimbabwe suggest that the distance to forest had a much higher impact on FAW incidence than maize variety, planting date, or the rate of nitrogen applied. Lack of enough case studies from maize in southern Africa makes it challenging to assess the mechanism and the effectiveness of bird predation on FAW. For this reason, further research is necessary to examine how predation by birds, bats and arthropods and parasitism impacts maize yield. We discuss research barriers, recommend appropriate methods for experimental studies, and suggest possible management options to control FAW in southern Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".