204 Evaluation of the Chemical Composition and Animal Performance of Intermediate Wheatgrass (<i>Thinopyrum intermedium</i>) Regrowth for Potential Fall Grazing of Beef Cattle
Bibliographic record
Abstract
Abstract This study assessed forage yield and chemical composition, feed intake, and performance of beef heifers grazing intermediate wheatgrass (IWG) regrowth post-grain harvest compared with a stockpiled grass/legume control in the late fall. Pastures were established in 2020 at Glenlea, MB, Canada and treatments were: 1) Courtenay tall fescue/Algonquin alfalfa/Oxley II cicer milkvetch (50:25:25; FORCON); 2) IWG, pure stand, no fertilizer post establishment (NFERT); 3) IWG, pure stand+50 kg N· ha-1 post-grain harvest (FERT); 4) IWG with Alsike clover (50:50; AL). The FORCON and IWG treatments were harvested in June (hay) and August (grain) 2021, respectively. Thirty-two beef heifers were assigned to eight paddocks, four heifers per paddock in a randomized complete block design with two blocks. Each block had four paddocks, each paddock receiving one of the four treatments. Heifers began grazing on Oct 6 for a 14-d adaptation period and a 22-d experimental period. Forage samples were obtained once every 2 weeks and analyzed for CP and TDN. Individual feed intake was estimated using the titanium oxide method with fecal samples collected on d 21 and 22 and pooled. Bodyweight and blood samples (blood urea nitrogen, BUN) were obtained on d 1 and d 22. Data were analyzed using a mixed model in R studio, with the fixed effect of forage treatment and the random effects of block and paddock. Forage yield and CP were greater for FORCON (3,515 kg· ha-1 and 14.9% DM, respectively) than the IWG treatments (mean 2778 kg· ha-1 and 12.83% DM, respectively), which did not differ (P < 0.0001 and P = 0.150, respectively). The TDN was less for FORCON (60.6% DM) than IWG treatments (mean 61.5% DM), which did not differ (P = 0.394). Dry matter, CP and TDN intake were lower for FORCON (7.91 kg· DM d-1, 1.15 kg· DM d-1, 4.98 kg· DM d-1, respectively) than IWG treatments (mean 8.54 kg· DM d-1, 1.16 kg· DM d-1, 5.44 kg DM d-1), which did not differ (P = 0.237, P = 0.218, P = 0.137, respectively). The ADG was less for FORCON (0.53 kg· d-1) than IWG treatments (mean 0.77 kg· d-1), with the greatest gains observed in IWG AL (0.90 kg d-1; P = 0.046). Blood urea nitrogen was greater for FORCON (3.95 mmol· L-1) than IWG treatments (mean 3.78 mmol· L-1), which did not differ (P = 0.203) on d 1. However, on d 22 the opposite response was observed with decreased BUN in FORCON (2.97 mmol ·L-1) than in IWG treatments (mean 3.51 mmol· L-1), which did not differ (P = 0.350). In conclusion, IWG offered comparable performance as a feed alternative to the conventional perennial grass/legume mix commonly used for extended grazing. However, the effects of fertilizer or legume on IWG may differ under different soil conditions due to high fertility at the experimental site.
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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".