Syringic acid from rice roots inhibits soil nitrification and N2O emission under red and paddy soils but not a calcareous soil
Bibliographic record
Abstract
Syringic acid (SA) is a novel biological nitrification inhibitor (BNIs) discovered in rice root exudates with significant inhibition of Nitrosomonas strains. However, the inhibitory effect of SA on nitrification and nitrous oxide (N2O) emissions in different soils and the environmental factors controlling the degree of inhibition have not been studied. Using 14-day microcosm incubation, we investigated the effects of different concentrations of SA on nitrification activity, abundance of ammonia-oxidizing microorganisms, and N2O emissions in three typical agricultural soils. The nitrification inhibitory efficacy of SA was strongest in acidic red soil, followed by weakly acidic paddy soil, with no significant effect in an alkaline calcareous soil. Potential nitrification activity (PNA) were also greatly reduced by SA additions in paddy and red soil. Pearson correlation analysis showed that the inhibitory efficacy of SA might be negatively correlated with soil pH and positively correlated with clay percentage. SA treatments significantly reduced N2O emissions by 69.1-79.3% from paddy soil and by 40.8%-46.4% from red soil, respectively, but no effect was recorded in the calcareous soil. SA addition possessed dual inhibition of both ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) abundance in paddy and red soil. Structural equation modelling revealed that soil ammonium (NH4+) and dissolved organic carbon content (DOC) were the key variables explaining AOA and AOB abundance and subsequent N2O emissions. Our results support the potential for the use of the BNI SA in mitigating N2O emissions and enhancing N utilization in red and paddy soils.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".