Soil Nitrous Oxide Emissions Following Band‐Incorporation of Fertilizer Nitrogen and Swine Manure
Bibliographic record
Abstract
Treatment of liquid swine manure (LSM) offers opportunities to improve manure nutrient management. However, N2O fluxes and cumulative emissions resulting from application of treated LSM are not well documented. Nitrous oxide emissions were monitored following band-incorporation of 100 kg N ha(-1) of either mineral fertilizer, raw LSM, or four pretreated LSMs (anaerobic digestion; anaerobic digestion + flocculation: filtration; decantation) at the four-leaf stage of corn (Zea mays L.). In a clay soil, a larger proportion of applied N was lost as N2O with the mineral fertilizer (average of 6.6%) than with LSMs (3.1-5.0%), whereas in a loam soil, the proportion of applied N lost as N2O was lower with the mineral fertilizer (average of 0.4%) than with LSMs (1.2-2.4%). Emissions were related to soil NO3 intensity in the clay soil, whereas they were related to water-extractable organic C in the loam soil. This suggests that N2O production was N limited in the clay soil and C limited in the loam soil, and would explain the interaction found between N sources and soil type. The large N2O emission coefficients measured in many treatments, and the contradicting responses among N sources depending on soil type, indicate that (i) the Intergovernmental Panel on Climate Change (IPCC) default value (1%) may seriously underestimate N2O emissions from fine-textured soils where fertilizer N and manure are band-incorporated, and (ii) site-specific factors, such as drainage conditions and soil properties (e.g., texture, organic matter content), have a differential influence on emissions depending on N source.
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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) | 0.000 | 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".