Wheat distillers grains in feedlot cattle diets: Feeding behavior, growth performance, carcass characteristics, and blood metabolites1,2
Bibliographic record
Abstract
A study was conducted to evaluate feed intake, ADG, carcass quality, eating behavior, and blood metabolites in feedlot beef steers fed diets that varied in proportion of wheat dried distillers grains with solubles (DDGS) replacing barley grain or barley silage. Two hundred crossbred steers (BW = 489 ± 30 kg) were blocked by BW and randomly allotted to 20 pens (5 pens per treatment). Steers were fed 1 of 4 diets: control without DDGS (CON), 25% (25DDGS), 30% (30DDGS), or 35% (35DDGS) wheat DDGS (DM basis). The CON diet consisted of 15% barley silage and 85% barley-based concentrate; the 3 wheat DDGS diets were formulated by substituting 20% barley grain and 5, 10, or 15% silage, respectively, with 25, 30, or 35% wheat DDGS so that the 35DDGS diet contained no silage. The diets were formulated such that wheat DDGS was substituted for both barley grain and barley silage to evaluate whether wheat DDGS can be fed as a source of both energy and fiber in feedlot finishing diets. Dry matter intake of steers fed 25DDGS was greater (P < 0.01), but final BW, ADG, and G:F were not different compared with steers fed CON diet. Carcass characteristics and liver abscess score were not different between CON and 25DDGS. Steers fed 25DDGS had longer eating time (min/d; P < 0.01), greater meal frequency (P < 0.04), but a slower eating rate (P < 0.04). Replacing barley silage with increasing amounts of wheat DDGS (from 25DDGS to 35DDGS) linearly reduced (P < 0.01) DMI. Final BW, ADG, and G:F were not affected by increasing amounts of wheat DDGS. Carcass traits were not different, whereas liver abscess scores linearly (P < 0.01) increased as more barley silage was replaced by wheat DDGS. Eating time (min/d) and duration of each meal linearly (P < 0.02) decreased, whereas eating rate (min/g of DM) linearly (P < 0.01) increased with increasing replacement of barley silage. Blood urea N was doubled (P < 0.01) compared with CON by inclusion of wheat DDGS. Results indicate that wheat DDGS can be used effectively in feedlot diets, decreasing the need for barley grain or silage without negatively affecting growth performance and carcass characteristics. A reduction in the amount of roughage required to maintain growth performance is a potential advantage in feedlot operations because forage is costly and often of limited availability. Thus, DDGS can be a possible alternative as long as they are available and cost effective; however, increased incidence of liver abscess and increased N content of manure need to be considered when greater amounts of wheat DDGS are included in finishing diets.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".