Micrometeorological Methods for Measuring Methane Emission Reduction at Beef Cattle Feedlots: Evaluation of 3‐Nitrooxypropanol Feed Additive
Bibliographic record
Abstract
It is highly desirable to test agricultural emission mitigation strategies in a whole-farm environment to ensure that all aspects of management and production operations are included. However, the large spatial scale of commercial operations makes the dual measurements of control and treatment(s) difficult. We evaluated the application of two micrometeorological methods, a novel concentration ratio method and an inverse dispersion method, where both were used to measure methane (CH) emission reductions in cattle fed the compound 3-nitrooxypropanol compared with cattle fed just the basal diet. In total, there were 1344 cattle used that were located in six pens (∼222 animals per pen). Three adjacent pens to the east and three to the west were designated as the treatment and control blocks, respectively. Underlying the emission reduction method was the assumption of site symmetry between the treatment and control pen blocks in the feedlot. There was, on average, a large CH emission reduction of ∼70% (±18%) due to the additive as found by both micrometeorological methods. Both methods also show a change in the diel distribution (peak emissions after initial morning feeding) and seasonal pattern (a decrease in emission reduction of 7.5 and 26.1% over 90 d). The simplicity of the developed concentration ratio method is expected to have applications for evaluating other mitigation strategies at large commercial scales (e.g., the application of manure additives to pens to reduce odors and ammonia emissions).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".