Seasonal ammonia emissions from an intensive beef cattle feedlot in Victoria Australia
Bibliographic record
Abstract
Ammonia (NH3) emitted from concentrated animal feeding operations can cause environmental and health problems, and indirectly contribute to greenhouse gas emissions. Cattle feedlots are known to be large sources of NH3, but few studies have documented seasonal emissions from Australian feedlots. We conducted two field campaigns to measure NH3 emissions from an intensive beef cattle feedlot in southeast Australia, and these results were combined with previous measurements at the same feedlot to document seasonal variations in emissions and to derive annual feedlot emission factors (EFs). Emission rates were calculated with an inverse dispersion modelling (IDM) technique, based on NH3 concentrations measured at the feedlot with open-path lasers (OPLs). The average area emission rates in spring, summer, autumn and winter were 90.5, 167.4, 96.2 and 86.8 μg NH3 m−2 s−1 from the cattle pens, and 22.5, 18.1, 7.7 and 20.7 μg NH3 m−2 s−1 from the manure stockpile area, respectively. The total per-animal EFs ranged from 126.0 (autumn) to 190.2 g NH3 animal−1 d−1 (summer), representing a loss of 47.5–64.6% of the fed N. Seasonal variations in emissions were related to air temperature. Slight changes in crude protein content of the cattle diet may also have impacted seasonal variability. Taking seasonal variations into consideration, the average feedlot EF was 160.4 g NH3 animal−1 d−1, with 90% of the emissions coming from the cattle pens. Extrapolating the EF to all feedlot cattle in the country, the direct NH3 emissions from Australian feedlots amount to 65.2 Gg NH3 annually, or 3.7% of the national total. Our study benchmarks seasonal and annual EFs and N losses for Australian commercial feedlots, and provides a baseline for extrapolating the impacts of mitigation efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".