Faculty Opinions recommendation of Phosphate-solubilising microorganisms for improved crop productivity: a critical assessment.
Bibliographic record
Abstract
Phosphate-solubilising microorganisms (PSM) are often reported to have positive effects on crop productivity through enhanced phosphorus (P) nutrition. Our aim was to evaluate the validity of this concept. Most studies that report 'positive effects' of PSM on plant growth have been conducted under controlled conditions, whereas field experiments more frequently fail to demonstrate a positive response. Many studies have indicated that the mechanisms seen in vitro do not translate into improved crop P nutrition in complex soil-plant systems. Furthermore, associated mechanisms are often not rigorously assessed. We suggest that PSM do not mobilise sufficient P to change the crops' nutritional environment under field conditions. The current concept, in which PSM solubilise P 'for the plant' should thus be revised. Although PSM have the capacity to solubilise P to meet their own needs, it is the turnover of the microbial biomass that subsequently provides P to plants over a longer time. Therefore, the existing concept of PSM function is unlikely to deliver a reliable strategy for increasing crop P nutrition. A further mechanistic understanding is needed to determine how P mobilisation by PSM as a component of the whole soil community can be manipulated to become more effective for plant P nutrition.© 2020 The Authors New Phytologist © 2020 New Phytologist Trust. PMID: 32929739
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.063 |
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".