Forage Accumulation, Nutritive Value, and Botanical Composition of Grass–Cicer Milkvetch Mixtures under Two Harvest Managements
Bibliographic record
Abstract
ABSTRACT Limited information is available on cicer milkvetch (CMV, Astragalus cicer L.) performance in mixtures with grasses. A field trial was sown at Melfort, SK, Canada, in May 2013 to evaluate forage accumulation, nutritive value, and botanical composition of different mixtures of grasses and CMV under two‐cut and three‐cut harvest management from 2014 to 2017. Thirteen forage mixtures consisted of (i) monocultures of Bromus riparius Rehm. (MB), Bromus riparius Rehm. × B. inermis Leyss. (HB), Agropyron cristatum (L.) (CWG), Agropyron intermedium Beauv. (IWG), and CMV; (ii) binary mixtures of each of the grass + CMV; (iii) a four‐grass mixture (MB, HB, CWG, and IWG) (Mix 4); (iv) Mix 4 + CMV (Mix 5); (v) a six‐grass mixture (Mix 4) + Dactylis glomerata L. and Phleum pratense L. (Mix 6); and (vi) Mix 6 + CMV (Mix 7). The two‐cut system resulted in greater total forage accumulation (10.3 ± 2.8 Mg ha −1 ) compared with the three‐cut system (8.6 ± 2.3 Mg ha −1 ) with the exception of 2017. On average, grass mixtures containing CMV and CMV monoculture (11.1 ± 1.7 Mg ha −1 ) had greater forage accumulation than grass monocultures and grass only mixtures (7.5 ± 2.4 Mg ha −1 ). Cicer milkvetch dominated in all mixtures (44.0–68.8% of forage mass) in 2016 compared with its initial proportion of 2.1 to 31.2%. Concentration of crude protein was significantly increased with the inclusion of CMV compared with the grass‐only treatments. Acid detergent fiber and neutral detergent fiber (NDF) concentrations were similar, but mixtures without CMV tended to have a greater NDF concentration. In conclusion, the inclusion of CMV in forage mixtures increased forage accumulation and protein concentration. Binary CMV–grass mixtures produced similar or greater forage accumulation than more complex CMV–grass mixtures.
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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 | 0.001 |
| 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".