How does management legacy, nitrogen addition, and nitrification inhibition affect soil organic matter priming and nitrous oxide production?
Bibliographic record
Abstract
Abstract Long‐term management of croplands influences the fluxes and sources of nitrous oxide (N2O). We examined this premise in a greenhouse study by using soils collected from a 38‐yr‐old field experiment. The sampled treatments were continuous barley (Hordeum vulgare L.; CB), continuous fescue (Festuca rubra L., F. arundinacea Schreb; CF), and two phases of an 8‐yr rotation: faba bean (Vicia faba L.; FB) and alfalfa (Medicago sativa L.)–bromegrass (Bromus inermis Leyss) hay. Barley was grown as a test crop in the greenhouse in each soil. The ranking of N2O emissions was hay > FB > CB > CF (P < .001). We quantified the 15N‐site preference to assess the N2O‐producing processes. Denitrification was the predominant source, contributing 77.4% of the N2O production. We also evaluated nitrogen (N) additions: urea alone or urea with a nitrification inhibitor (nitrapyrin or DMPSA). Compared with urea alone, nitrapyrin and DMPSA reduced N2O emissions by 16 and 25%, respectively. We used urea labeled with 15N to trace N to N2O emissions, aboveground plant N uptake, and N retention by soils. Total 15N‐recovery (N2O + plant + soil) was highest under FB (86%) and lowest under CB (29%). We further separated the N2O derived from urea versus N2O from soil organic matter (SOM). The inhibitor DMPSA reduced the N2O derived specifically from added urea‐N by more than half (P < .001). With the addition of urea, N2O production from mineralization of SOM‐N accelerated over the control (without urea), termed the priming effect. This priming of SOM‐N contributed with 13% of the total N2O production when averaged across the four management legacies. The CB soil had the highest proportion of priming‐derived N2O (24%). Management legacies clearly differed in soil carbon and N, which governed N2O production from denitrification and SOM priming.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".