Estimation of carbon dioxide production and energy expenditure of grazing cattle by the sulphur hexafluoride (SF<sub>6</sub>) tracer gas technique
Bibliographic record
Abstract
The purpose of the study was to explore the suitability of the sulphur hexafluoride (SF6) tracer gas technique to estimate CO2 production and energy expenditure (EE) for grazing animals on pastures typical of western Canada. During each year of a 3-yr study, CO2 emissions were collected from 60 yearling steers on grass pastures receiving one of three fertility treatments: no manure, liquid hog manure applied as a split application (74 kg available N ha-1) in the spring and fall, and a single application of liquid hog manure applied at a rate of 155 kg available N ha-1 each spring. The mean CO2 production estimate across all treatments was 5424 ± 2218 L d-1 (mean ± SD) with a range of 1099 to 11548 L d-1. When compared across three grazing periods in June, July and August, steers produced more (P < 0.05) CO2 in June than in either July or August. Metabolic body weight (BW0.75), average daily gain (ADG), standing forage biomass, and forage neutral detergent fibre (NDF) and crude protein (CP) contents explained 33% of the variation in CO2 production. Energy expenditure (EE) was estimated from CO2 production by adopting an energetic equivalent of 22 kJ L-1 CO2 and EE was calculated for animals that were not losing body weight at time of measurement. The average EE estimate was 1.2 ± 0.5 MJ kg-1 BW0.75 d-1 with a range of 0.2 to 2.8 MJ kg-1 BW0.75 d-1. Energy expenditure declined as ADG and forage biomass declined, but EE was not influenced (P > 0.05) by fertility treatment. The EE estimate was higher (P < 0.05) early in the grazing season (June) and declined as the grazing season progressed. The SF6 tracer gas technique was able to estimate EE of individual animals without interfering with herd dynamics. The technique was also able to show differences in EE in response to pasture conditions. The results of this study suggest that the SF6 tracer gas technique shows potential as a simple and non-invasive method of estimating CO2 production and EE for grazing animals. Further validation under different grazing conditions and with animals undertaking different degrees of activity is required. Comparisons with other field techniques of estimating EE are also important. Key words: Energy expenditure, carbon dioxide, sulphur hexafluoride, grazing animals
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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) | 0.000 | 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".