Nitrogen turnover and N<sub>2</sub>O production in incubated soils after receiving field applications of liquid manure and nitrification inhibitors
Bibliographic record
Abstract
Applying abundant manure to soils can accelerate nitrogen (N) transformations and nitrous oxide (N2O) emissions. We conducted a laboratory incubation to examine the turnover of labile N in manured soils. Soils were collected from agricultural fields that had recently received spring-injected liquid dairy manure with or without admixing nitrification inhibitors. Bands and interbands of the manure plots were incubated separately. Time courses of ammonium (NH4+) and nitrate (NO3−) were used to derive and contrast zero-, first-, and second-order kinetics models. We found that nitrification rates were consistently better represented by first-order kinetics (k1). Furthermore, across all evaluated soils, the dependency of nitrification rate (k1 of NH4+) on initial NH4+ concentration was modelled by Michaelis–Menten kinetics reasonably well, with an affinity (Km) of 63 mg N·kg−1 soil (R2 = 0.82). Compared with the manure interbands, the initially NH4-enriched bands had a much faster nitrification rate, with half-life for NH4+ of only 4 d and rapid k1 (0.186 versus 0.011 d−1). Soil N substrate and k1 exerted control on N2O production. Nitrous oxide production increased linearly with both measured NH4+ intensity (R2 = 0.47) and modelled k1–NH4+ (R2 = 0.48). Conversely, N2O production increased non-linearly with NO3− intensity (R2 = 0.68), where high NO3− caused a saturation plateau with a threshold of 96 mg N·kg−1·d−1 — beyond which no additional N2O was produced. During peak N transformations, measured N2O-N flux was 1.4% ± 0.3% of the inorganic N undergoing nitrification. Heavily manured soils exhibited augmented N turnover that increased N2O fluxes, but inhibitors reduced these emissions by half.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".