Peaks of in situ N<sub>2</sub>O emissions are influenced by N<sub>2</sub>O‐producing and reducing microbial communities across arable soils
Bibliographic record
Abstract
Abstract Agriculture is the main source of terrestrial N 2 O emissions, a potent greenhouse gas and the main cause of ozone depletion. The reduction of N 2 O into N 2 by microorganisms carrying the nitrous oxide reductase gene ( nosZ ) is the only known biological process eliminating this greenhouse gas. Recent studies showed that a previously unknown clade of N 2 O‐reducers ( nos Z II ) was related to the potential capacity of the soil to act as a N 2 O sink. However, little is known about how this group responds to different agricultural practices. Here, we investigated how N 2 O‐producers and N 2 O‐reducers were affected by agricultural practices across a range of cropping systems in order to evaluate the consequences for N 2 O emissions. The abundance of both ammonia‐oxidizers and denitrifiers was quantified by real‐time qPCR , and the diversity of nosZ clades was determined by 454 pyrosequencing. Denitrification and nitrification potential activities as well as in situ N 2 O emissions were also assessed. Overall, greatest differences in microbial activity, diversity, and abundance were observed between sites rather than between agricultural practices at each site. To better understand the contribution of abiotic and biotic factors to the in situ N 2 O emissions, we subdivided more than 59,000 field measurements into fractions from low to high rates. We found that the low N 2 O emission rates were mainly explained by variation in soil properties (up to 59%), while the high rates were explained by variation in abundance and diversity of microbial communities (up to 68%). Notably, the diversity of the nos Z II clade but not of the nos Z I clade was important to explain the variation of in situ N 2 O emissions. Altogether, these results lay the foundation for a better understanding of the response of N 2 O‐reducing bacteria to agricultural practices and how it may ultimately affect N 2 O emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".