<i>Paenibacillus polymyxa</i> biofertilizer application in a tea plantation reduces soil N<sub>2</sub>O by changing denitrifier communities
Bibliographic record
Abstract
Increasing the use of nitrogen fertilizers in tea orchards has led to intense nitrous oxide (N2O) emissions. Foliar application of Paenibacillus polymyxa biofertilizer has been proven to be beneficial for organic tea production. In this study, tea yield and quality were significantly improved after application of P. polymyxa biofertilizer compared with the control but were not significantly different from chemical fertilizer treatments. However, the average N2O fluxes in tea fields treated with chemical fertilizers and biofertilizers (225 kg N·ha−1·year−1 for both) were 50.6–973.7 and 0.6–29.1 times higher than those in the control treatment, respectively. Pot experiments conducted to explore the mechanism of N2O reduction induced by P. polymyxa biofertilizer showed that applying P. polymyxa in addition to urea could reduce N2O fluxes by 36.5%–73.1%. Quantitative PCR analysis suggested that a significant increase in the quantity of nirK and nosZ genes was linked to the reduction of N2O, and high-throughput sequencing of nosZ revealed active and potentially efficient denitrifiers in different treatments. Our findings suggest that P. polymyxa biofertilizer is in line with the requirements of modern agriculture, which aims to increase product yield and quality while reducing negative environmental impacts.
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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 | 0.001 |
| 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".