Potential use of Bacillus spp. as an effective biostimulant against abiotic stresses in crops—A review
Bibliographic record
Abstract
Environmental (abiotic) stresses significantly threaten the worldwide crop production and food security. Rapid, drastic changes in the global climate have exacerbated such stresses for crops. Plant-associated bacteria have been shown to enhance stress resistance and cope with the negative impacts of various abiotic stresses through the induction of various mechanisms. In soil, the rhizosphere and endosphere of plants, the Bacillus genus is a predominant bacterial genus. Members of this genus, which are tremendously diverse both metabolically and genetically, survive for a long time under unfavorable environmental conditions due to their ability to form long-lived, stress-tolerant spores. Bacillus spp. secrete several metabolites that trigger plant growth and enhance plants’ tolerance to biotic and abiotic stresses. Some of the Bacillus species are available commercially as phytostimulants, biopesticides, and biofertilizers. Due to this functional versatility, the Bacillus genus is one of the most widely used in the agro-biotech industry. However, the potential of the Bacillus genus has not yet been sufficiently realized, and transferring technology related to the genus from the lab environment to real world applications in the field needs to be emphasized. A better understanding of mechanisms of action of beneficial Bacillus spp. is needed for the development of products to support green biotechnology in agriculture and industries. This report comprehensively reviews the applications of Bacillus spp. in abiotic (e.g., salinity, drought, inorganic and organic pollutant toxicity, nutritional imbalance, low–high temperatures, and waterlogging) -stressed agriculture and discusses their potentials for the development of new products of biotechnological implications, highlighting gaps that remain to be explored to improve and expand on Bacillus-based biostimulants.
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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".