Carbon, Nitrogen Balances and Greenhouse Gas Emission during Cattle Feedlot Manure Composting
Bibliographic record
Abstract
Carbon and N losses reduce the agronomic value of compost and contribute to greenhouse gas (GHG) emissions. This study investigated GHG emissions during composting of straw-bedded manure (SBM) and wood chip-bedded manure (WBM). For SBM, dry matter (DM) loss was 301 kg Mg(-1), total carbon (TC) loss was 174 kg Mg(-1), and total nitrogen (TN) loss was 8.3 kg Mg(-1). These correspond to 30.1% of initial DM, 52.8% of initial TC, and 41.6% of initial TN. For WBM, DM loss was 268 kg Mg(-1), TC loss was 154 kg Mg(-1), and TN loss was 1.40 kg Mg(-1), corresponding to 26.5, 34.5, and 11.8% of initial amounts. Most C was lost as CO2 with CH4 accounting for <6%. However, the net contribution to greenhouse gas emissions was greater for CH4 since it is 21 times more effective at trapping heat than CO2. Nitrous oxide (N2O) emissions were 0.077 kg N Mg(-1) for SBM and 0.084 kg N Mg(-1) for WBM, accounting for 1 to 6% of total N loss. Total GHG emissions as CO2-C equivalent were not significantly different between SBM (368.4 +/- 18.5 kg Mg(-1)) and WBM (349.2 +/- 24.3 kg Mg(-1)). However, emission of 368.4 kg C Mg(-1) (CO2-C equivalent) was greater than the initial TC content (330.5 kg Mg(-1)) of SBM, raising the question of the net benefits of composting on C sequestration. Further study is needed to evaluate the impact of composting on overall GHG emissions and C sequestration and to fully investigate livestock manure management options.
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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 | 0.001 | 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) | 0.000 | 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".