Bacterial endophytes in sustainable agriculture: perspectives and advancements as biostimulants and fungal biocontrol agents in crops
Bibliographic record
Abstract
Agricultural intensification, to meet the nutritional needs of the growing world population, has been made possible through the extensive use of agrochemicals, such as synthetic fertilizers and pesticides. However, these practices pose significant health and environmental risks, including groundwater contamination, soil degradation and microbial resistance. Also, predictions indicate that relying solely on synthetic chemicals to boost production may not be enough to meet the future global need for food. Sustainable agricultural intensification involves the use of novel tools to enhance production while addressing environmental concerns using eco-friendly strategies, such as microbial inoculants. These can improve soil fertility, nutrient cycling and crop yield, while enhancing stress tolerance and overall crop fitness. This review outlines the key aspects of the global presence of plant diseases, plant defense responses and disease management strategies, and examines bacterial endophytes as crop biostimulants and biocontrol agents for sustainable control of mycotoxigenic fungi. It also proposes strategies to increase microbial product adoption by addressing technical limitations, such as field stability, delivery precision and shelf-life.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".