Will Phosphate Bio-Solubilization Stimulate Biological Nitrogen Fixation in Grain Legumes?
Bibliographic record
Abstract
Biological nitrogen fixation (BNF) refers to a bacterially mediated process by which atmospheric N2is reduced, either symbiotically or non-symbiotically, into ammonia (NH3) in the presence of the enzyme complex nitrogenase. In N2-fixing grain legumes, BNF is often hampered under low phosphorus (P) availability. The P status of legumes, particularly nodules, as well as P availability in the rhizosphere, play a vital role in regulating BNF. Aside from increasing P availability via fertilization, other plant traits (i.e., extensive rooting system and their spatial distribution, hyper-nodulation, root exudates, rhizosphere acidification, and heterogeneity) contribute to greater P uptake and hence more effective BNF. The positive interaction between P availability and BNF can be exploited through beneficial soil P solubilizing microorganisms (PSM). These microorganisms can increase plant-available P by modifying either rhizosphere soil processes or promoting plant traits, which lead to increased P uptake by the production of plant growth-promoting substances, both of which could indirectly influence the efficiency of BNF in legumes. In this review, we report on the importance of microbial P bio-solubilization as a pathway for improving BNF in grain legumes via PSM and P solubilizing bacteria (PSB). Because BNF in legumes is a P-requiring agro-ecological process, the ability of soil PSB to synergize with the rhizobial strains is likely a key belowground process worth investigating for advanced research aiming to improve rhizosphere biological functions necessary for sustainable legume-based cropping systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".