Effect of Encapsulated Nitrate and Microencapsulated Blend of Essential Oils on Growth Performance and Methane Emissions from Beef Steers Fed Backgrounding Diets
Bibliographic record
Abstract
A long-term study (112 days) was conducted to examine the effect of feeding encapsulated nitrate (NO3−), microencapsulated blend of essential oils (EO), and their combination on growth performance, feeding behavior, and enteric methane (CH4) emissions of beef cattle. A total of 88 crossbred steers were purchased and assigned to one of four treatments: (i) control, backgrounding high-forage diet supplemented with urea (1.17% in dietary DM); (ii) encapsulated NO3− (EN), control diet supplemented with 2.5% encapsulated NO3− as a replacement for urea (1.785% NO3− in the dietary DM); (iii) microencapsulated blend of EO (MBEO), control diet supplemented with 150 mg/kg DM of microencapsulated blend of EO and pepper extract; and (iv) EN + MBEO, control diet supplemented with EN and MBEO. There was no interaction (p ≥ 0.080) between EN and MBEO on average dry matter intake (DMI), average daily gain (ADG), gain to feed ratio (G:F), feeding behavior, and CH4 emission (using GreenFeed system), implying independent effects of feeding EN and MBEO. Feeding MBEO increased CH4 production (165.0 versus 183.2 g/day; p = 0.005) and yield (18.9 versus 21.4 g/kg DMI; p = 0.0002) but had no effect (p ≥ 0.479) on average DMI, ADG, G:F, and feeding behavior. However, feeding EN had no effect on ADG and G:F (p ≥ 0.119) but reduced DMI (8.9 versus 8.4 kg/day; p = 0.003) and CH4 yield (21.5 versus 18.7 g/kg DMI; p < 0.001). Feeding EN slowed (p = 0.001) the feeding rate (g of DM/min) and increased (p = 0.002) meal frequency (events/day). Our results demonstrate that supplementing diets with a blend of EO did not lower CH4 emissions and there were no advantages of feeding MBEO with EN. Inclusion of EN as a replacement for urea reduced CH4 emissions but had no positive impact on animal performance.
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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) | 0.000 | 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".