Role of Biopesticides in Integrated Pest Management for Maize
Bibliographic record
Abstract
Biopesticides play a crucial role in the Integrated Pest Management (IPM) of maize, offering an environmentally sustainable alternative to chemical pesticides. This study assesses the efficacy, benefits, and challenges of using biopesticides in maize pest management. Key biopesticides, including microbial agents such as Bacillus thuringiensis and botanical extracts like neem, have demonstrated significant effectiveness in controlling major pests, particularly the fall armyworm ( Spodoptera frugiperda ). This study highlights advancements in biopesticide formulation, including encapsulation technologies and genetic modifications, which have enhanced the stability and application of these agents in varying environmental conditions. Additionally, the integration of biopesticides with precision agriculture and other IPM components has proven to optimize pest control while reducing the ecological footprint of maize farming. Despite their potential, challenges such as production costs, regulatory barriers, and pest resistance are limiting factors for wider adoption. The review concludes by discussing future directions in research and policy needed to accelerate the global use of biopesticides in maize IPM, contributing to more sustainable agricultural practices.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".