Effects of feeding corn silage from short-season hybrids and extending the backgrounding period on production performance and carcass traits of beef cattle
Bibliographic record
Abstract
Corn silage (CS) acreage in western Canada continues to expand with CS being used increasingly in feedlot cattle diets where barley silage (BS) previously was the main forage fed. Our study evaluated the effects of increasing the amounts of CS in backgrounding (BKGN) diets on performance of cattle by 1) replacing BS with early-maturing CS, 2) increasing the proportion of CS in the diet, and 3) extending the BKGN duration. A total of 160 steers (mean BW ± SD; 272 ± 22.4 kg) were used in a completely randomized design. Steers were assigned to 16 pens and fed BKGN diets (4 pens per diet) that contained 60% BS (DM basis; CON), 60% CS (60CS), 75% CS (75CS), or 90% CS (90CS) until reaching a mean pen BW of either 380 (SBKGN) or 430 ± 15 kg (LBKGN) with 2 pens per treatment. All steers were finished (FIN diet; 9% CS, 86% barley grain, and 5% supplement) to an equal-BW end point (700 ± 15 kg LW). During BKGN and FIN phases, DMI, ADG, and G:F were measured for all pens. Carcass data also were collected. No BKGN diet × duration interactions were statistically significant (P ≥ 0.412) for most production measures. As dietary CS content was increased during BKGN, DMI and ADG decreased (quadratic; P ≤ 0.003). There also was a tendency (P = 0.078) for a decrease in G:F at the highest level of CS. As expected, LBKGN steers took longer (105 vs. 71 d; P = 0.001) than SBKGN steers to reach the BKGN target end weight. In addition, as a result of their heavier weight at the beginning of FIN, LBKGN steers also had a higher DMI (11.6 vs. 11.0 kg/d; P = 0.045) than SBKGN steers. However, no carryover effects (P ≥ 0.354) of BKGN diet on DMI, ADG, and G:F were significant during the FIN phase. Similarly, the BKGN diet and duration had no effect (P ≥ 0.219) on carcass traits including HCW, dressing percentage, and quality grade. However, we detected an interaction between BKGN diet and duration on backfat thickness (P = 0.009); SBKGN steers that were fed 75CS during BKGN accumulated more backfat during FIN than LBKGN steers that were fed 75CS during BKGN, and because they were lighter at the beginning of FIN they took longer to reach 700 kg BW, their end point. In conclusion, dietary inclusion of CS (up to 90% of diet DM) in place of BS and extending the BKGN period had marginal effects on FIN performance or carcass traits in our study. Therefore, beef producers can make extensive use of early-maturing CS in growing cattle diets without compromising animal performance or carcass quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".