79 Maximizing the utilization of wheat straw in finishing beef cattle diets with canola or flax screenings supplementation: Growth performance, carcass characteristics and economic analysis.
Bibliographic record
Abstract
Abstract Inclusion of crop residues and byproducts in feedlot diets can be an attractive alternative during feed shortages, potentially decreasing feed costs and enhancing beef production sustainability. However, limited data is available evaluating the use of wheat straw (WS) supplemented with oilseed screenings in finishing beef cattle diets. This study’s objective was to evaluate the effect of increasing WS inclusion, supplemented with canola or flax screenings, in finishing feedlot cattle diets compared to a standard dry-rolled barley grain (DRB):barley silage diet on growth performance, carcass characteristics and cost of gain. In a completely randomized design study, 300 steers (initial body weight [BW]: 366 ± 32 kg) were stratified by weight, randomly allocated to 20 pens (15 steers/pen) and fed for 148 d. Each pen was randomly assigned to one of five diet treatments (n=4/treatment). The diets contained (DM basis): 1) 85.64% DRB, 10.00% barley silage (CTL); 2) 78.14% DRB, 12.50% canola screenings, 5.00% WS, (LSC); 3) 78.14% DRB, 12.50% flax screenings, 5.00% WS (LSF); 4) 73.14% DRB, 12.50% canola screenings, 10.00% WS (HSC); and 5) 73.14% DRB, 12.50% flax screenings, 10.00% WS (HSF). Supplement (4.36% of dietary DM) was included in all diets. Data were analyzed using the MIXED procedure of SAS with diet as fixed effect and pen the experimental unit. Diet did not affect (P=0.47) DMI (11.7 kg/d). Steer final BW, average daily gain (ADG) and hot carcass weight (HCW) decreased (P<0.05) as dietary WS inclusion increased; however, final BW and HCW did not differ between steers fed CTL and low WS inclusion diets (P>0.05). There was a tendency (P=0.05) for gain:feed to decrease with increasing WS inclusion. The proportion of AAA carcasses was greatest (P=0.02) for CTL and lowest for steers fed diets with high WS inclusion; however, carcasses from steers fed low WS inclusion diets did not differ from CTL (P=0.47). The proportion of yield grade 2 (Y2) carcasses tended to increase with increasing WS inclusion (P=0.09). Supplementing WS diets with canola compared to flax screenings did not affect any performance metrics (P>0.05) except carcass quality grade distribution. Flax screenings inclusion promoted a decrease (P=0.04) in AA-classified carcasses and tended (P=0.07) to increase AAA grades (P< 0.05). Diet cost ($/steer/d) decreased (P=0.01) with higher WS inclusion. No differences were observed across treatments (P >0.33) for backfat thickness, lean meat yield, and cost:gain. Increasing WS inclusion in finishing diets (5 to 10% of diet DM) negatively affected some carcass characteristics and live performance metrics; however, overall performance of steers fed low WS inclusion diets was comparable to steers fed the CTL diet. These findings suggest that feeding finishing steers diets with low WS inclusion (5%) supplemented with oilseed screenings may be an attractive feeding strategy during feed shortages.
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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".