Harvest Date Effect on Forage Yield, Botanical Composition, and Nutritive Value of Novel Legume-Grass Mixtures
Bibliographic record
Abstract
The potential for novel forage mixtures to address reduced herbage for late season grazing was investigated. Forage legumes, sainfoin (Onobrychis viciifolia Scop.) (SF) cvs. AC Mountainview, Shoshone, and Nova (MountainSF, ShoshoneSF, and NovaSF), cicer milkvetch (Astragalus cicer L.) cv. AC Veldt (CMV), Canadian milkvetch (Astragalus canadensis L.) cv. Great Plains (CaMV), and alfalfa (Medicago sativa L.) cv. AC Yellowhead (ALF) were evaluated in binary mixtures with meadow bromegrass (Bromus riparius Rehm.) cv. Admiral (MBG), hybrid bromegrass (B. riparius × B. inermis Leyss.) cv. AC Success (HBG) and Russian wildrye [Psathyrostachys junceus (Fisch.) Nevski.] cv. Tom (RWR) for yield, botanical composition, and nutritive value on July and September harvest dates at Saskatoon and Swift Current, Saskatchewan, Canada from 2016 to 2018. Hybrid bromegrass-legume mixture produced 16–38% greater forage compared to RWR-legume (7.5 vs. 5.6 Mg ha−1 in July and 6.1 vs. 5.1 Mg ha−1 in September at Saskatoon, and 3.2 vs. 2.0 Mg ha−1 in July at Swift Current). MountainSF and ALF had the greatest legume contribution to total yield at July harvest at Swift Current (67.7 ± 3.2%) and Saskatoon (62.1 ± 2.1%), respectively, while CaMV had lowest composition at Swift Current (20.2 ± 2.5%) and Saskatoon (12.6 ± 3.5%). The CMV and ALF-grass mixtures at Saskatoon and legume-RWR mixtures at both sites in July had greatest CP content. The July harvest had greatest yield, legume content and nutritive value compared to the September harvest at both sites. Study results suggest if yield is the objective, then either ALF or CMV with HBG may be considered. If nutritive value is the goal, any legume with RWR is an option. Finally, ALF or CMV in mixture with either HBG or RWR could be summer or fall stockpile forage in the Northern Great Plains of western Canada.
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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 | 0.000 |
| 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".