Soil Carbon and Nitrogen Dynamics Following Application of Pig Slurry for the 19th Consecutive Year II. Nitrous Oxide Fluxes and Mineral Nitrogen
Bibliographic record
Abstract
Agricultural soils often receive annual applications of manure for long periods. Our objective was to quantify the effects of 19 consecutive years of pig ( Sus scrofa ) slurry (PS) application to a loamy soil (loamy, mixed, frigid Aeric Haplaquept) on N 2 O emissions. Soil surface N 2 O fluxes ( F N2O ) were measured 36 times in 1 yr. Nitrous oxide concentration profiles, soil NH + 4 ‐ and NO − 3 ‐N contents, denitrifying enzyme activity (DEA), and denitrification rate (DR) in soil were also determined to explain the variation in F N2O Long‐term (19 yr) treatments on continuous silage maize ( Zea mays L.) were 60 (PS60) and 120 Mg ha −1 yr −1 (PS120) of pig slurry and a control receiving mineral fertilizer at a dose of 150 kg ha −1 each of N, P 2 O 5 , and K 2 O. Denitrifying enzyme activity, soil N 2 O concentrations, and F N2O (<25 ng m −2 s −1 ) were low in the control plots receiving mineral fertilizer. Annual applications of PS to the soil for 18 yr had positive residual effects on the DEA compared with the long‐term fertilized control plots. Following PS application, there was a strong and rapid increase of F N2O (up to 350 ng m −2 s −1 ) on manured plots. The PS‐induced F N2O increased with increasing quantity of PS, probably as the result of a greater availability of NO − 3 ‐N for denitrification. The effects of PS on F N2O were mostly limited to the 30 d following application, with low fluxes (<10 ng m −2 s −1 ) during the rest of the measurement period. Total N 2 O–N emissions represented 0.62, 1.23, and 1.65% of total N applied in control, PS60, and PS120 plots, respectively. These emission factors for the PS plots agreed with values previously suggested for N‐fertilized soils (1.25%).
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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 | 0.000 |
| 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".