The Role of Genetic Engineering in Enhancing Sugarcane Resistance to Insect Pests
Bibliographic record
Abstract
Sugarcane ( Saccharum officinarum ) is a vital crop for sugar production globally, yet it faces significant yield losses due to insect pest attacks. Traditional breeding methods have struggled to enhance pest resistance due to the complex genetic makeup of sugarcane and the absence of inherent resistance genes. Genetic engineering has emerged as a promising alternative, enabling the introduction of genes that confer resistance to pests. This study explores various genetic engineering strategies employed to enhance sugarcane resistance to insect pests. Key approaches include the overexpression of cry proteins, vegetative insecticidal proteins (VIP), lectins, and proteinase inhibitors (PI), as well as the application of advanced biotechnological tools such as host-induced gene silencing (HIGS) and CRISPR/Cas9. This study also discusses the integration of multiple resistance genes, such as Cry1Ab and EPSPS, and their impact on pest resistance and agronomic traits. The findings highlight the potential of genetic engineering to develop transgenic sugarcane lines with robust resistance to insect pests, thereby contributing to sustainable sugarcane production.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".