218 Methane and Carbon Dioxide Emissions from Crossbred Beef Cattle Fall-Grazed on Native Aspen Parkland Pastures
Bibliographic record
Abstract
Abstract Over 20 M ha of grazing land is utilized for beef production in western Canada, significantly contributing to the Canadian economy. Cattle are recognized for contributing to methane (CH4) and carbon dioxide (CO2), and individual animal contribution varies with several factors, one of them being feed efficiency as measured using residual feed intake adjusted for off-test backfat thickness (RFIfat). Given that commercial cattle spend a large portion of their production lifecycle grazing on diverse pastures in western Canada, understanding whether and how RFIfat measured in drylot and CH4 emissions reflect animal performance on pasture remains essential. This study quantified CH4 and CO2 production from beef cattle while grazing diverse diets on open-range aspen parkland pastures during fall. Cattle had been previously measured for RFIfat in drylot. Production of CH4 and CO2 (g/day) from crossbred beef cows (n = 22, with a range of -2.2 to +1.3 kg DM/day in RFIfat) and replacement heifers (n = 15; with a range of -2.6 to + 3.0 kg DM/day in RFIfat) were monitored using the GreenFeed emissions monitoring system over 40 days while grazing on native rangeland (70 ha) in the fall of 2022. Fall grazing was divided into two 20 ± 1 day' grazing periods; 20 days in September (SEP) vs. 20 days in October (OCT). Total spot measurements of CH4 and CO2 emissions in SEP and OCT were 1,096 vs. 1,054 for cows and 644 vs. 571 for heifers, respectively. The average number of daily visits per animal to the GreenFeed unit for cows and heifers were 2.8 ± 0.1 vs. 2.5 ± 0.1 in SEP, and 2.5 ± 0.1 vs. 2.0 ± 0.1 in OCT, respectively. Cows had greater average daily CH4 (SEP: 268.4 ± 6.1 vs. 196.1 ± 4.3; OCT: 243.9 ± 6.1 vs. 185.0 ± 4.3 g/day) and CO2 emission (SEP: 9,105.0 ± 143.1 vs. 6,485.3 ± 108.6; OCT: 8,544.3 ± 143.2 vs. 6,308.7 ± 108.9 g/day) than heifers (all P < 0.01). A negative relationship was evident between the CH4 emission and RFIfat in heifers (SEP: R2 = 0.043 vs. OCT: R2 = 0.014; P < 0.01), as well as CO2 emission and RFIfat (SEP: R2 = 0.083 vs. OCT: R2 = 0.020; P < 0.01), although the amount of variation explained by RFIfat is very small and is in disagreement with previous data. We speculate that the decline in CH4 and CO2 emissions from cattle in Oct might be due to various factors, including but not limited to changes in overall intake, daylight hours, air temperature, plant community, feeding and ruminating patterns, and rumen microbial profiles. Further analysis of forage quality, step counts, and GPS location, as well as rumen bacterial community, may shed more light on methane production in the fall.
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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".