Lignite Improved the Quality of Composted Manure and Mitigated Emissions of Ammonia and Greenhouse Gases during Forced Aeration Composting
Bibliographic record
Abstract
Lignite amendment of livestock manure is considered a viable ammonia (NH3) emission mitigation technique. However, its impact on the subsequent composting of the manure has not been well studied. This work compared changes in biochemical parameters (e.g., organic matter loss and nitrogen (N) transformation) and also the emissions of NH3 and greenhouse gases (GHGs) between lignite-amended and unamended cattle manure during forced aeration composting. Amending manure with lignite did not alter the time to compost stability despite delaying the onset of the thermophilic temperatures. Lignite treatments retained N in the manure by suppressing NH3 loss by 35–54%, resulting in lignite-amended manure composts having 10–19% more total N than the unamended compost. Relative to manure only, lignites reduced GHG emissions over the composting period: nitrous oxide (N2O) (58–72%), carbon dioxide (CO2) (12–23%) and methane (CH4) (52–59%). Low levels of CH4 and N2O emissions were observed and this was attributed to the continuous forced aeration system used in the composting. Lignite addition also improved the germination index of the final compost: 90–113% compared to 71% for manure only. These findings suggest that lignite amendment of manure has the potential to improve the quality of the final compost whilst mitigating the environmental release of NH3 and GHGs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".