Diet and landscape controls on greenhouse gas emissions from cattle excreta in a semi-arid environment
Bibliographic record
Abstract
Sod-seeding low productivity, or depleted, pastures with legumes, and non-bloat legumes in particular, is considered a viable method of restoring productivity to the pasture. However, the impact of the change in plant composition of the pastures on GHG emissions from the urine and dung deposited by cattle grazing the pastures is as yet unknown. Excreta were collected from beef cattle grazing a low productivity, depleted meadow bromegrass-alfalfa mixed pasture (D-MA) and D-MA pastures rejuvenated by sod-seeding with a non-bloat legume, cicer milkvetch (R-CM) or sainfoin (R-SF). The excreta were subsequently applied back to the respective pastures at locations in upper and lower slope positions. In general, plant composition of the pastures had a small but significant impact on the C and N content of the cattle excreta; however, this yielded no significant differences among treatments in either cumulative CO 2 emissions or cumulative CH 4 uptake for either the urine or dung. Yet, whereas CH 4 uptake was unaffected by the application of either urine or dung, urine applications yielded CO 2 emissions that were greater than those from the control or dung-amended treatments. Nitrous oxide emissions were significantly impacted by the chemical composition of the urine, and here we report distinct N 2 O emission factors for urine and dung—with an average EF N2O of 0.034 ± 0.024 % for dung and 0.12 ± 0.10 % for the urine from cattle that grazed the depleted and rejuvenated pastures. Our data also suggest that, for urine at least, diet can significantly impact the EF N2O , with urine from cattle grazing the R-CM pasture yielding an EF N2O of 0.24 ± 0.10 % and urine from confined beef cattle fed a high crude protein, total mixed ration (TMR) diet yielding an EF N2O of 0.39 ± 0.13 %. These findings suggest that a disaggregation of emission factors based on excreta type and animal diet, while also considering temporal (seasonal) and spatial (landscape-scale) variability, can lead to improved accuracy of GHG emissions inventories. • Landscape position is an important regulator of excreta patch GHG emissions. • The composition of urine and dung deposited by beef cattle is related to the grazing diet. • Urine patch N 2 O emissions are diet-related and depend on more than just N concentration of the urine. • Nitrous oxide emission factors (EF N2O ) for beef cattle urine and dung were 0.19 % and 0.034 %, respectively. • The EF N2O for beef cattle urine and dung should be disaggregated for pastures in the semi-arid Canadian Prairies.
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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 | 0.000 | 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 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".