Bibliographic record
Abstract
The objective of this systematic review is to explore recent advances in biological control methods for managing sugarcane insect pests and to evaluate their role in integrated pest management (IPM). By synthesizing current research, this review highlights key biological control agents and their efficacy against major sugarcane insect pests, emphasizing classical, augmentative, and conservation strategies. Classical biological control approaches focus on the introduction of exotic natural enemies, such as parasitoids and predators, which have shown significant success in managing pests like the sugarcane borer ( Diatraea saccharalis ) and root borer ( Diaprepes abbreviatus ). Augmentative strategies involve mass rearing and periodic release of natural enemies like Trichogramma spp. and Cotesia flavipes , which have proven effective in reducing pest populations. Conservation biological control emphasizes habitat management practices that enhance the survival and efficacy of native and introduced natural enemies. Furthermore, microbial control agents such as entomopathogenic fungi ( Beauveria bassiana ), bacteria ( Bacillus thuringiensis ), and viruses are gaining prominence in sugarcane pest management due to their specificity and environmental safety. This review provides insights into the potential of these biological control methods in sustainable sugarcane pest management and underscores the importance of integrating them into broader IPM frameworks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".