Forage quality, animal performance and behaviour of bred beef heifers grazing stockpiled perennial and annual forages in the late fall/early winter in Manitoba
Bibliographic record
Abstract
Stockpile grazing is practiced to extend the grazing season of beef cows in western Canada. Little research has been conducted to identify plant species for successful stockpile grazing of bred beef heifers during the late fall/early winter. Objectives of this study were to compare the three stockpiled perennial and one annual forage stand based on: 1) forage quality, yield and plant height, 2) performance (weight gain, serum urea nitrogen, intake and methane output) of bred beef heifers, 3) animal activity (distance traveled and time spent at shelter/water). Four stockpiled forage treatments were grazed for two, 28-d periods near Brandon, Manitoba, Canada. Treatments included: 1) Courtney tall fescue (T)/Fleet meadow bromegrass (M)/Yellowhead alfalfa (A; TAM), 2) Killarney orchardgrass (OG)/Algonquin alfalfa (A; OGA), 3) Courtney tall fescue/Algonquin alfalfa/Oxley II cicer milkvetch (C; TAC) and 4) Fusion corn (COR) in a randomized complete block design (RCBD) with 4 heifers/replicate/treatment. Forage biomass yield in performance pastures of COR (6,342 kg DM ha-1) was 68%, 79% and 57% higher (P=0.011) than OGA, TAC, and TAM, respectively, which did not differ from each other. Crude protein was highest in TAC (10.29% and 10.90%) and COR was the lowest (6.87% and 6.76%). However, COR had the highest in TDN (72.14% and 71.90%) when compared to perennial forage treatments. All heifers gained weight in Period 1 (1.36 kg d-1) suggesting that all treatments could be successfully stockpile grazed in the late fall. When temperature fell to <-18°C Period 2 (-2 kg d-1), all heifers lost weight and those grazing perennial pastures spent more time (30-54% of the day) near windbreak shelters. Heifers grazing COR spent 7% of the day near shelter, suggesting COR provided adequate shelter. TAC was identified as the superior perennial forage treatment for stockpile grazing, based on higher CP and lower NDF compared to OGA and TAM, as well as improved ADG compared to OGA and numerically higher ADG compared to TAM, Although COR had increased yield and TDN with lower CP compared to TAC, ADG was comparable between the two treatments. Nonetheless, significant weight loss in Period 2 indicated that all treatments required supplementation.
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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 | 0.001 | 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".