490 Estimating enteric methane emission from beef heifers with different residual feed intake using greenfeed and respiration chambers
Bibliographic record
Abstract
Greenhouse gas emission from the livestock sector is mainly contributed from enteric methane (CH4) production. Improving feed efficiency to reduce CH4 emission while maintaining productivity as well as accurate and robust measurement is of great environmental and economic importance. The objectives of this study were to: compare CH4 emissions measured using respiration chambers (RC) and the GreenFeed (GF) emission monitoring system and evaluate the relationship between residual feed intake (RFI) and enteric CH4 production. Sixteen crossbred replacement heifers (8 low-RFI, 8 high-RFI, 377 kg initial BW) were used to measure enteric CH4 emission. Heifers were group-housed in a pen and fed barley silage ad libitum and their individual feed intakes were recorded by automated feeding bunks. Heifers also received pellets dispensed from the GF emission monitoring system, used to attract and keep the animals in the unit for emission measurement. Enteric CH4 emission of individual animals was measured over two 25-d periods using RC (2 days/period) and GF systems (all days when not in chambers). Data were analyzed using the mixed procedure of SAS and differences are discussed at P ≤ 0.05. Estimates of CH4 (g/d) were greater for GF than RC (P < 0.001), but for CH4 yield the systems only differed for the high-RFI cattle (P = 0.01). Average CH4 emission was 202 and 222 g/d (P = 0.02) from the GF system, and 156 and 164 g/d (P = 0.40) in RC for the low- and high-RFI heifers, respectively. As expected, high-RFI heifers consumed 6.9% more feed (P = 0.03) compared to their more efficient counterparts (7.1 vs 6.6 kg DM/d). However, when adjusted for feed intake, CH4 yield (g/kg DMI) was similar for high- and low-RFI heifers (GF: 27.7 and 28.5, P = 0.25; RC: 26.5 and 26.5, P = 0.99). Intake declined for both groups when they were moved to the RC, and as such DMI was similar (P = 0.29) between groups when they were in the chambers. Our study found that the two measurement techniques differ in estimating CH4 emission, partially due to differences in conditions (lower feed intakes of cattle while in chambers, fewer days measured in chambers) during measurement. Furthermore, high- and low-efficiency cattle produce similar CH4 yield but different daily CH4 emission. We conclude that, when intake of animals is known, the GF offers a robust and accurate means of estimating CH4 emissions from animals under field conditions.
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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".