Gaseous Nitrogen Emissions and Forage Nitrogen Uptake on Soils Fertilized with Raw and Treated Swine Manure
Bibliographic record
Abstract
Treatments to reduce solids content in liquid manure have been developed, but little information is available on gaseous N emissions and plant N uptake after application of treated liquid swine manure (LSM). We measured crop yield, N uptake, and NH3 and N2O losses after the application of mineral fertilizer (NH4 NO3), raw LSM, and LSM that was decanted, filtered, anaerobically digested, or chemically flocculated. The experiment was conducted from 2001 to 2003 on a loam and a sandy loam cropped to timothy (Phleum pratense L.) with annual applications equivalent to 80 kg N ha(-1) in spring and 60 kg N ha(-1) after the first harvest. Raw LSM resulted in NH3 emissions three to six times larger (P < 0.05) than mineral fertilizer. The LSM treatments reduced NH3 emissions by an average of 25% compared with raw LSM (P < 0.05). The N2O emissions tended to be higher with raw LSM than with mineral fertilizer. The LSM treatments had little effect on N2O emissions, except for anaerobic digestion, which reduced emissions by >50% compared with raw LSM (P < 0.05). Forage yield with raw LSM was >90% of that with mineral fertilizer. The LSM treatments tended to increase forage yield and N uptake relative to raw LSM. We conclude that treated or untreated LSM offers an alternative to mineral fertilizers for forage grass production but care must be taken to minimize NH3 volatilization. Removing solids from LSM by mechanical, chemical, and biological means reduced NH3 losses from LSM applied to perennial grass.
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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".