From under-barn to outdoor swine manure storage: Modelling frequent emptying’s effect on methane emissions in a cold climate
Bibliographic record
Abstract
Abstract. Swine manure is often stored temporarily in under-barn pits before being transferred to long-term outdoor storage. In Canada, the temperature in these two storage locations is very different and hence both the temporary and long-term storage phase should be considered when estimating CH4 emission from the manure management. Frequent emptying of the under-barn pit could be a possible CH4 mitigation practice, however, the consequential impact on CH4 in the outdoor manure storage facilities (i.e., earthen manure storage) needs to be considered to determine the impact on the total CH4 emissions in the annual manure storage cycle. The objectives of this study are to examine the effect of under-barn manure retention time, manure temperature and frequent emptying on CH4 mitigation using a process-based modelling approach. The model was calibrated and validated using three-years of CH4 and manure temperature data from the earthen manure storage of a finishing swine barn near Brandon, Manitoba, Canada. The result showed that under-barn retention time substantially affects CH4 emissions where shortening the retention time from 1 month to none (immediate transfer) could yield a 19.2% decrease over the whole storage cycle. Another factor, summer and winter under-barn manure temperature, resulted in a minor influence on overall CH4 emission (0.9 – 6%), due to CH4 production being substrate-limited in the under-barn pit. Interestingly, the results showed that applying frequent transfer solely in winter would be less effective (13 – 17% less) for overall CH4-reduction compared to frequent emptying applied year-round. Based on the results, the optimal mitigation approach should target lowering manure retention-time (in particular during winter) and reducing under-barn manure temperature ; applying such management alone should greatly reduce emissions. However, future experimental data is needed to validate the model‘s capacity in simulating CH4 emission from under-barn pits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".