Contribution of Arbuscular Mycorrhizal Fungi, Phosphate–Solubilizing Bacteria, and Silicon to P Uptake by Plant
Bibliographic record
Abstract
Phosphorus (P) availability is usually low in soils around the globe. Most soils have a deficiency of available P; if they are not fertilized, they will not be able to satisfy the P requirement of plants. P fertilization is generally recommended to manage soil P deficiency; however, the low efficacy of P fertilizers in acidic and in calcareous soils restricts P availability. Moreover, the overuse of P fertilizers is a cause of significant environmental concerns. However, the use of arbuscular mycorrhizal fungi (AMF), phosphate-solubilizing bacteria (PSB), and the addition of silicon (Si) are effective and economical ways to improve the availability and efficacy of P. In this review the contributions of Si, PSB, and AMF in improving the P availability is discussed. Based on what is known about them, the combined strategy of using Si along with AMF and PSB may be highly useful in improving the P availability and as a result, its uptake by plants compared to using either of them alone. A better understanding how the two microorganism groups and Si interact is crucial to preserving soil fertility and improving the economic and environmental sustainability of crop production in P deficient soils. This review summarizes and discusses the current knowledge concerning the interactions among AMF, PSB, and Si in enhancing P availability and its uptake by plants in sustainable agriculture.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".