PSXI-1 Evaluating the yield and nutritive value of perennial forage species for stockpile grazing of beef cows under western Canadian conditions.
Bibliographic record
Abstract
This project assessed the yield and nutritive value of 8 perennial pure grass or pure legume crops and 15 grass-legume binary mixtures for their potential for stockpile grazing of beef cows in Manitoba, Canada. The perennial forage cultivars that were used in the evaluation were: Killarney orchardgrass (K; Dactylis glomerata); Courtenay tall fescue (C; Schedonorus arundinaceum); Success hybrid brome (S; Bromus riparius × B. inermis), cv.’s Fleet (F) and Armada (AR) meadow bromes (Bromus riparius); cv.’s Algonquin (AL) and Yellowhead (Y) alfalfa (Medicago sativa); and Oxley II cicer milkvetch (O; Astragalus cicer). The effect of early (ES; June) or late (LS; July) stockpiling of forages was also assessed. Plots (4 replicates/treatment) were seeded at Carman, Manitoba in 2014 in a RCBD and forage measurements conducted in 2015. Mixed treatments were seeded at a 50:50 grass:legume rate. Forages were harvested on October 15 for DM yield (DMY) and subsampled for nutritive value. Courtenay, C+AL, C+Y, and C+O had the highest DMY (P<0.05) for ES ranging from 8,376 to 10,274 kg ha-1 with C+O highest overall. For LS, C+O had a higher DMY (6,508 kg ha-1; P<0.05) than any other forage. All ES treatments had higher DMY than the corresponding LS treatments. For ES, few differences in CP between forage treatments were observed excellt all grass only treatments, K+Y, S+Y and F+Y had significantly lower CP values than pure legumes and other mixed treatments. For LS, the legume only, S and S+O, S+AL and S+Y had significantly higher CP than all other treatments (P<0.05), ranging from 147 to 179 g/kg. For NDF, F had the highest mean value across both stockpile treatments, ranging from 597 to 674 g/kg. In conclusion ES Courtenay-legume treatments held the best potential for stockpile grazing beef cows in terms of yield and nutritive value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".