Nitrous oxide emissions and soil mineral nitrogen status following application of hog slurry and inorganic fertilisers to acidic soils under forage grass
Bibliographic record
Abstract
Field application of livestock slurry often results in higher nitrous oxide (N2O) emissions than inorganic fertiliser, because slurry contains large amounts of available N and C, and when applied it increases soil water content, thus enhancing denitrification. This study evaluated the impact of hog (Sus scrofa) slurry and inorganic fertilisers on N2O emissions and soil inorganic N. Three short-term (3 wk) field experiments were conducted during summer 2005 on two contrasting acidic soils seeded to forage grass. Treatments included hog slurry (Slurry) at 126 kg N ha-1, potassium nitrate (Nitrate) at 120 kg N ha-1, ammonium sulphate (Ammonium) at 120 kg N ha-1, Carbon (Dextrose) at 500 kg ha-1 and an unamended control (Control). Potassium nitrate increased (P< 0.05) cumulative N2O losses compared with the other treatments. Emissions of N2O from Slurry and Ammonium were similar, but higher than from Dextrose and Control, which were similar. Soil NH4+-N contents for Slurry and Ammonium treatments were generally similar but higher than for the other treatments, particularly during the first and second sampling dates. Soil NO3−-N contents, meanwhile, were higher with the Nitrate treatment compared with the other treatments, especially at the first sampling date. These results imply that N2O production in these acid soils was limited by NO3-availability. Therefore, N2O emissions from these soils can be minimised by using ammonium-based fertilisers including hog slurry rather than nitrate-based fertilisers. Key words: Acidic soils, hog slurry, mineral fertiliser, soil nitrogen, N2O emissions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".