Influence of composts and liquid pig manure on CO<sub>2</sub> and N<sub>2</sub>O emissions from a clay loam soil
Bibliographic record
Abstract
Application of raw animal manure to agricultural land has resulted in environmental and health problems. Alternatives, such as composting, are now being developed to alleviate this situation. However, very little information is available regarding the fate of composts when added to fine-textured and poorly drained soils, especially with respect to the initial decomposition of composts and their effects on soil-derived CO2 and N2O emissions. We evaluated food waste compost (FW), yard waste compost (YW), liquid pig manure (LPM), LPM + yard waste compost (PMY) and LPM + wheat straw compost (PMS). Red clover (Trifolium pratense L.) (RC) and an unamended control soil were also tested. The solid amendments were divided into ground and unground treatments. Carbon dioxide emissions were increased relative to the control soil for all treatments except PMY. Total CO2 emissions over the 144-h incubation from unground material followed the pattern (P < 0.05): YW (849 mg CO2-C kg-1 soil) > RC (554 mg CO2-C kg-1 soil) > LPM (444 mg CO2-C kg-1 soil) > FW (203 mg CO2-C kg-1 soil) ≈ PMS (194 mg CO2-C kg-1 soil) ≈ PMY (157 mg CO2-C kg-1 soil). The RC, YW and PMS significantly increased N2O emissions compared to all other treatments and the control. Total N2O emissions from unground material over the 144-h incubation followed the pattern (P < 0.05): RC ≈ YW ≈ PMS (1230–1490 μg N2O-N kg-1 soil) > LPM ≈ FW ≈ PMY ≈ Control (44-404 μg N2O-N kg-1 soil). Grinding was found to increase CO2 emissions from YW, PMY, PMS, and N2O emissions from RC. The CO2 emissions were due primarily to amendment composition and not particle size, as the relative differences in CO2 emission among ground and unground treatments remained nearly constant. Carbon dioxide emissions from LPM were reduced substantially by composting the manure with yard waste (PMY). Key words: Compost, pig manure, carbon dioxide, nitrous dioxide, available N, incubation
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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".