Enhancing Crops Sustainably Through The Combined Use Of Microbiological And Silicon Resources
Bibliographic record
Abstract
As global food demand escalates due to a rapidly growing population, sustainable agricultural practices are essential to enhance crop productivity while minimizing environmental impact. This review explores the synergistic effects of silicon-solubilizing bacteria (SSB) and phosphate-solubilizing bacteria (PSB) in conjunction with silicon fertilizers on plant growth and yield. Silicon, the second most abundant element in the Earth's crust, plays a crucial role in improving soil health and enhancing plant resilience against abiotic stresses such as drought and salinity. SSB and PSB contribute to nutrient mobilization, promoting the availability of silicon and phosphorus, which are vital for plant development. The combined application of these microorganisms not only improves root architecture and nutrient uptake but also fosters beneficial soil microbial communities that enhance overall soil fertility. Furthermore, the indirect benefits of these practices extend to human health by improving food security and reducing reliance on chemical fertilizers. Ultimately, this review highlights the potential of integrating microbiological resources with silicon applications to create a more sustainable agricultural framework.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".