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 (CH4) emissions varied considerably among the manures from 6.3 to 25.9 g m−2 d−1 and accounted for ∼96% of the total CO2 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, CH4 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 CH4 flux and the length of the active methanogenesis period (i.e., when the daily CH4 emissions ≥ 7.6 g m−2 d−1) were highly correlated with CH4 emissions (R2 = 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 CH4 emission models were correlated (R2 = 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 (R2 = 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 CH4 emissions and MCFs from six dairy slurries varied despite identical storage conditions. Postpeak CH4 emissions were more consistent among slurries compared with prepeak periods. CH4 emissions represented ∼96% of the overall GHG budget. N2O 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".