Effects of Silage-Based Diets and Cattle Efficiency Type on Performance, Profitability, and Predicted CH4 Emission of Backgrounding Steers
Bibliographic record
Abstract
Feed costs are the largest expense in commercial beef production. Increasing cattle (Bos taurus) feed efficiency should reduce feed costs and increase beef profitability. This study used data from two years of a backgrounding trial conducted in Lacombe, Alberta, Canada. The evaluation looked at economic and predicted CH4 emission impacts of diet quality and cattle efficiency type in backgrounding systems. The hypothesis was that diet quality from use of barley (Hordeum vulgare c.v. Canmore) or triticale (xTriticosecale c.v. Bunker) silage-based diets and cattle efficiency type defined by residual feed intake would interact to affect profitability and CH4 emissions. Effects of diet and cattle efficiency type on profitability and CO2e emissions were assessed using statistical and stochastic risk simulation. The profitability of beef backgrounding was affected by cattle efficiency type and diet quality with higher quality barley silage also lowering CO2e emissions. The difference in certainty equivalent (CAD~30 steer−1) of efficient steers on barley silage and inefficient steers on barley silage or efficient or inefficient steers on triticale silage supports a beef backgrounding producer focus on diet quality and cattle efficiency type. This study did not address potential agronomic differences, including yield, which could provide nuance to forage choice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".