Greenhouse Gas Emissions from Stored Dairy Slurry from Multiple Farms
Bibliographic record
Abstract
A significant need exists to improve our understanding of the extent of greenhouse gas emissions from the storage of livestock manure to both improve the reliability of inventory assessments and the impact of beneficial management practice adoption. Factors affecting the extent and variability of greenhouse gas emissions from stored dairy manure were investigated. Emissions from six slurries stored in clean concrete tanks under identical “warm‐season” conditions were monitored consecutively over 173 d (18°C average air temperature). Methane (CH 4 ) emissions varied considerably among the manures from 6.3 to 25.9 g m −2 d −1 and accounted for ∼96% of the total CO 2 equivalent greenhouse gas emissions. The duration of the lag period, when methane emissions were near baseline levels, varied from 30 to 90 d from the beginning of storage. As a result, CH 4 emissions were poorly correlated with air temperature prior to the time of peak emissions (i.e., the initial 48 to 108 d of storage) but improved afterward. The air temperature following the time of the peak CH 4 flux and the length of the active methanogenesis period (i.e., when the daily CH 4 emissions ≥ 7.6 g m −2 d −1 ) were highly correlated with CH 4 emissions ( R 2 = 0.98, p < 0.01). Methane conversion factors (MCFs) ranged from 0.08 to 0.52 for the different manures. The MCFs generated from existing CH 4 emission models were correlated ( R 2 = 0.68, p = 0.02) to MCFs calculated for the active methanogenesis period for manure containing wood bedding. A temperature component was added that improved the accuracy ( R 2 = 0.82, p < 0.01). This demonstrated that an improved understanding of lag period dynamics will enhance stored dairy manure greenhouse gas emission inventory calculations. Core Ideas CH 4 emissions and MCFs from six dairy slurries varied despite identical storage conditions. Postpeak CH 4 emissions were more consistent among slurries compared with prepeak periods. CH 4 emissions represented ∼96% of the overall GHG budget. N 2 O emissions were low but represented an 8× greater portion of the GHG budget for tie‐stall than free‐stall systems.
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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) | 0.002 | 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".