Feeding strategies for improving productivity of growing steers fed grass silage
Bibliographic record
Abstract
Two experiments were conducted to determine the relationship between corn particle size and soybean meal treatment on growth, diet digestibility, and nitrogen balance of growing steers. In the first experiment, 40 medium-frame beef steers (250 ± 11 kg) were fed individually for 140 d a diet based on grass silage offered for ad libitum consumption and supplemented with either 3.5 kg of DM d-1 of cracked corn (CC) or ground corn (GC) and with 450 g of DM d-1 of solvent extracted (SS) or lignosulfonate-treated soybean meal (Soypass™ SP). Dry matter intake was not affected by treatments and averaged 8.6 ± 0.3 kg d-1 (P > 0.10). Average daily gain was higher for animals receiving the ground corn than those fed cracked corn. Feed to gain ratio was not affected by treatments. There was an interaction between treatments for plasma urea-N concentration, with source of soybean meal having no effect with CC. When compared with SS, SP supplement reduced plasma urea-N when fed with GC. No effect of soybean meal and its interaction with corn processing was observed on growth performance. In the second experiment four additional steers were used in a 4 × 4 Latin square design to evaluate diet digestibility and nitrogen balance. There was an interaction between treatments for starch apparent digestibility and N retained, the values being respectively, 90, 86, 93, and 92% (SEM = 1.2; P < 0.01) and 36, 42, 44 and 41 g d-1 (SEM = 2.8; P < 0.03) for CCSS, CCSP, GCSS and GCSP, respectively. Altogether, the results indicate a slight advantage to reduce particle size of corn in growing steers fed grass silage. However, soybean meal treatment resulted in limited effects on growth and digestion. Key words: Rumen carbohydrate, undegradable protein, performance, steers
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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) | 0.000 | 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".