Evaluating the Addition of Water to a Barley-Based Finishing Diet on Feed Sorting Behaviour, Digestibility, Steer Performance, and Carcass Characteristics
Bibliographic record
Abstract
The objectives of the studies within this thesis were to evaluate the effects of adding water to a dry-rolled barley grain-based finishing diet on dry matter intake (DMI) and feed sorting behaviour. In Chapter 3, 8 ruminally cannulated beef steers (341.5 ± 25.1 kg starting body weight (BW)) were used in a study designed as a replicated 4 × 4 Latin square, with 21-d periods consisting of 16 d for diet adaptation and 5 d for data and sample collection. Chapter 4 utilized 120 beef steers (331.0 ± 31.0 kg starting BW) that were stratified by BW and randomly assigned to 1 of 20 pens (6 steers/pen, 5 pens/treatment) in a finishing growth performance study lasting 150 to 181 d. Dietary treatments for both studies included water at 0% (CON), 10% (10W), 20% (20W), and 30% (30W) relative to the barley grain weight. Both studies used barley-based finishing diets consisting of (dry matter (DM) basis) barley grain (88%), barley silage (7.7% in Chapter 3, 9.6% in Chapter 4), mineral and vitamin premix (4.1% in Chapter 3, 2.4% in Chapter 4), and titanium dioxide (0.2% in Chapter 3 only). The major difference between experiments was that Chapter 3 utilized aggressively processed barley grain with a processing index (PI) of 62.2 ±2.1% and 3.2 ±1.0% percent fines, whereas the barley grain in Chapter 4 had a PI of 84.2 ± 3.4% and 2.1 ± 1.0% percent fines. In Chapter 3, increasing water inclusion linearly increased DMI and water intake (P < 0.01 and P = 0.04, respectively). As water inclusion increased, the sorting index for the pan approached 100% (P < 0.01) indicating that steers consumed more fine particles. The increase in DMI and fine particle consumption led to linear decreases for mean (P < 0.01) and maximum ruminal pH (P = 0.02), and linear increases for the duration that ruminal pH was <5.5 (P = 0.02) and the ruminal lipopolysaccharide concentration (P < 0.01). In Chapter 4, DMI, average daily gain, and the gain:feed ratio were not affected by water inclusion (P ≥ 0.46). Sorting index values for particles retained on the 19-, 4-, and 1.18-sieves, and the pan were quadratically affected by the addition of water (P ≤ 0.02) such that the magnitude of the sorting decreased (values moved towards 100%) at a decreasing rate as water inclusion increased. Carcass characteristics (hot carcass weight, cold carcass weight, dressing percentage, and ribeye area) did not differ among treatments (P ≥ 0.15). However, increasing water linearly reduced variability within a pen for marbling scores (P = 0.05). Collectively, these results are interpreted to suggest that adding water to a barley-based finishing diet may be an effective strategy to reduce feed sorting behaviour without altering digestibility, thereby reducing the variance for carcass marbling scores.
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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 | 0.001 |
| 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) | 0.001 | 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".