1203 Grazing management and farm greenhouse gas emission intensity of beef production systems
Bibliographic record
Abstract
The objective of the study was to evaluate the impact of grazing management on greenhouse gas (GHG) emission intensity at the farm-gate for beef production systems in western Canada using life cycle analysis. A life cycle analysis over an 8-yr period was conducted on a beef farm that managed 120 cows, 4 bulls, and their progeny. Calves were stocked on pasture and market cattle were finished on grain for 136 d. Four grazing management systems were evaluated: i) light continuous grazing (LC), ii) heavy continuous grazing (HC), iii) light continuous grazing for the cow-calf pairs and moderate deferred-rotational grazing for the stocker cattle (LCDR), and iv) heavy continuous grazing for the cow-calf pairs and moderate deferred-rotational grazing for the stocker cattle (HCDR). Primary data for pasture quality, animal performance and soil were from short- and long-term grazing studies. GHG emissions from different sources within the farm were estimated using the whole-farm model, Holos. Soil carbon change related to the different grazing managements was estimated using the Introductory Carbon Balance Model. Emissions intensity of beef varied among grazing management strategies and ranged between 14.4–15.9 kg CO2e kg−1 live weight. Emissions intensity decreased with increasing stocking rate where the LC management had 9% greater GHG emission intensity than the HC treatment (14.4 kg CO2e kg−1 live weight). There was no difference in emission intensity estimates between LC and LCDR or between HC and HCDR, indicating that the use of moderate deferred-rotational grazing for the stocker operation in LCDR and HCDR has no effect on emission intensity. However, the LCDR management had 7% greater emission intensity than HCDR (14.5 kg CO2e kg−1 live weight). Regardless of the grazing management, methane emission from enteric fermentation was the major contributor (67–68%) followed by nitrous oxide from manure management (14–16%). Similarly, in all the grazing managements, emissions from the cow-calf herd were the major contributor (68–70%) for the total farm GHG emissions. When soil carbon sequestration was included into the total farm emissions, intensity estimate was reduced by 25–30% and were similar among the grazing management scenarios. Overall, the outcome from our study emphasizes the impact of grazing management on farm emissions as well as the importance of accounting for all the emission sources and sinks within the beef production system while estimating its environmental footprint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".