Red Clover Improves the Energy to Protein Balance of Lucerne-Grass Herbage
Bibliographic record
Abstract
Low ratio of readily fermentable carbohydrate to soluble protein concentrations in lucerne (Medicago sativa L.) leads to inefficient use of herbage N by ruminants. To improve the energy to protein balance in lucerne-grass herbage, four proportions of lucerne:red clover (Trifolium pratense L.) were compared in mixtures with and without grasses: timothy (Phleum pratense L.) and tall fescue (Schedonorus arundinaceus Schreb. Dumort.) in Quebec (QC, Canada). In the first post-seeding year, red clover proportion averaged (across grasses and four harvests) 0, 37, 59, and 74% in herbage mixtures. Increasing the proportion of red clover caused a slight but significant decrease in herbage total nitrogen (TN) concentration (32 to 31 g kg-1 DM) but substantial decreases in non-protein N (PA), rapidly (PB1) and moderately (PB2) degraded protein fractions, and a significant increase in the slowly degraded protein fractions (PB3+PC) (157 to 308 g kg-1 TN). With the inclusion of 74% of red clover, the ratio of soluble sugar to crude protein (CP) in herbage increased from 0.25 to 0.36 because of the increase in the soluble sugar concentration (48 to 66 g kg-1 DM). The inclusion of red clover in mixture with lucerne improved the energy to CP balance compared to lucerne alone, and caused a linear increase in the herbage in vitro neutral detergent fiber digestibility from 568 to 639 g kg-1 aNDF with similar herbage dry matter yield (10.3 Mg ha-1).
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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".