Interactions Between the Fall Armyworm and Sugarcane: Challenges and Management Strategies
Bibliographic record
Abstract
The Fall Armyworm (FAW), Spodoptera frugiperda , represents a significant threat to global sugarcane production due to its rapid life cycle, high reproductive capacity, and extensive migratory behavior, which collectively result in substantial yield reductions and compromised sugar quality, thereby causing considerable economic losses. This review explores the intricate interactions between FAW and sugarcane ( Saccharum officinarum L.), emphasizing the pest's biological and ecological characteristics, the extent of damage inflicted, and the economic repercussions. Current management strategies, including chemical, biological, and cultural methods, as well as integrated pest management (IPM) approaches, are critically assessed. While chemical controls are prevalent, issues of resistance development and environmental concerns persist, highlighting the need for more sustainable alternatives such as biological controls and cultural practices, though these are often hindered by socio-economic constraints. Nonetheless, challenges remain, particularly regarding resistance development, environmental and health risks, and socio-economic barriers faced by smallholder farmers. Future directions focus on genetic advancements, such as developing genetically engineered sugarcane resistant to FAW, and leveraging technological innovations like drones and sensors for enhanced monitoring and control. The review underscores the importance of policy frameworks and international cooperation in implementing effective management strategies to mitigate the global impact of FAW on sugarcane production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".