Essential oil blend and monensin for feedlot steers consuming backgrounding and finishing diets: growth performance, methane emissions using the GreenFeed system, and feeding behavior
Bibliographic record
Abstract
Abstract A study was conducted to assess growth performance, methane (CH4) emissions, and feeding behavior of feedlot steers consuming backgrounding and finishing diets with an essential oil blend (EO), monensin (Mon), and their combination (EO + Mon). The study was structured as a 2 × 2 factorial, with two feed additive treatments (Control, EO) and two monensin treatments (no Monensin, Monensin). One hundred Angus × steers were evenly distributed across each treatment into four pens, and each dietary phase consisted of four, 28-d periods. Using the GreenFeed system to estimate CH4 yield (g CH4/kg dry matter intake [DMI]), the mean CV (coefficient of variation) within the same steer across measurement periods was 14.0% and 15.2% for the backgrounding and finishing phases, respectively. The mean CV of CH4 production for steer within period and time-of-day block was up to 83.4% and 102.9% for the backgrounding and finishing phases, respectively. There was no difference in overall average daily gain (P ≥ 0.22) or gain:feed ratio (P ≥ 0.26) with EO or Monensin during the backgrounding or finishing phases. There were variable period effects on CH4 emissions, but overall control had lower (EO × Mon; P ≤ 0.01) CH4 production, yield, and CH4 energy as a percentage of gross energy intake (GEI) (Ym, Mcal CH4 % GEI), during the backgrounding phase. During the finishing phase, CH4 yield (EO × Mon; P = 0.02) and Ym (EO × Mon; P ≤ 0.01) were greatest in the EO + Mon treatment. Monensin, irrespective of EO, decreased DMI (P < 0.01) and eating rate (P = 0.01), and increased intermeal interval (P < 0.01) for the backgrounding phase. In the finishing phase, Monensin decreased (P < 0.01) DMI, variation of DMI (EO × Mon), meal size, and eating rate. Number of GreenFeed visits was negatively (P < 0.01) correlated with all feeding behavior metrics in the finishing phase. In conclusion, supplementation of EO or Monensin and their combination into backgrounding and finishing diets of feedlot steers had no effect on overall feedlot performance or CH4 emissions. Additionally, the high variability in CV of CH4 production and yield suggests a need to improve the accuracy and precision of the GreenFeed system for estimating enteric CH4 emissions from cattle.
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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) | 0.001 | 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".