Effects of Long-term Fertilization History and Current N and S Fertilizer Applications on Nitrous Oxide Production from S-deficient Soils in a Laboratory Incubation
Bibliographic record
Abstract
Nitrous oxide (N2O) production in four soils with unique fertilization management histories — collected from long-term fertility treatments receiving no fertilizer, NPKS, PKS, and NPK in a 5 yr cereal–forage rotation — in response to three sources of added N [100 kg N ha−1 urea, NH4Cl, Ca(NO3)2] with and without co-addition of elemental S (20 kg S ha−1) plus a 0-N and 0-S control was investigated in a 7 wk laboratory incubation in a loam-textured soil at 40% water-filled pore space. In all soils, cumulative N2O emissions and apparent, cumulative, net nitrification were significantly higher following addition of urea compared with other N fertilizers with and without co-addition of elemental S. Lower N2O emissions were observed in soils without a history of long-term N fertilization following addition of urea compared with soils that had historically received urea. Because the preincubation soil total N levels were similar in soils with a history of urea application (NPKS) and without urea (PKS), the results of this investigation suggest that the higher N2O production in the NPKS soil may be the result of a priming effect and (or) changes in microbial community composition induced by long-term urea applications rather than differences in the long-term soil N balance.
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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".