Interactive effects of composts and liquid pig manure with added nitrate on soil carbon dioxide and nitrous oxide emissions from soil under aerobic and anaerobic conditions
Bibliographic record
Abstract
The composting process results in immobilization of inorganic N. When high-Ndemanding crops are grown in compost- amended soils, additional N fertilizer is often applied. The combination of elevated nitrate levels from N fertilizer and high C inputs from the compost may result in enhanced greenhouse gas emissions. Hence, the objective of this laboratory incubation study was to characterize CO2 and N2O emissions from a Brookston clay loam soil that has received organic amendments in the presence or absence of added nitrate. The organic amendments included urban yard waste compost (YWC), liquid pig manure + wheat straw compost (PMC), and liquid pig manure (LPM). The nitrate treatments included added nitrate (100 mg KNO3-N kg soil-1) or no added nitrate. Total CO2 emissions during aerobic incubation followed the pattern: YWC > LPM > PMC > control (no organic amendments) for both nitrate treatments. Nitrate addition increased CO2 emissions from the YWC- and LPM-amended soils by 9 and 43%, respectively, but had no significant effect (P < 0.05) on CO2 emissions from the control or the PMC-amended soil. All organic amendments increased N2O emissions compared to the control. When nitrate was added to aerobically incubated LPM-amended soils, N2O emissions were increased over seven times; however, N2O emissions were decreased by 93% for PMC-amended soils and by 50% for YWC-amended soils. These decreases in N2O production occurred when nitrate was added to the YWC and PMC treatments under aerobic conditions but not under anaerobic conditions. Composted liquid pig manure was found to be more environmentally friendly than raw liquid pig manure as it stabilized the manure C and reduced CO2 and N2O emissions compared to the liquid pig manure. Key words: Compost, liquid pig manure, carbon dioxide, nitrous oxide, denitrification
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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) | 0.001 | 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 | 0.000 |
| 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".