Legume addition to alfalfa‐based mixtures improves the forage energy to protein ratio
Bibliographic record
Abstract
Abstract Alfalfa ( Medicago sativa L., AL)‐based forage mixtures are a major constituent of ruminant rations, and optimizing their energy‐to‐protein ratio has been identified as a way to improve N use efficiency. This study aimed to determine whether the energy‐to‐protein ratio could be improved by adding red clover ( Trifolium pratense L., RC) or birdsfoot trefoil ( Lotus corniculatus L., BT) at different seeding proportions, and/or one grass species [timothy, Phleum pratense L., or tall fescue, Schedonorus arundinaceus (Schreb.) Dumort] to AL. Annual forage yield, species proportion in botanical composition, and nutritive value of forage were measured at three sites in Canada for 2 post‐seeding years. The addition of RC or BT did not affect the annual forage yield but it increased the concentration of forage nonfiber carbohydrates (NFCs), particularly of soluble sugars, and decreased concentrations of crude protein (CP), nonprotein nitrogen (NPN), and rapidly degradable protein (PB1) of AL‐based mixtures. The addition of one percentage unit of RC or BT to forage botanical composition improved the NFC/CP ratio by 0.005, and the NFC/(NPN + PB1) ratio by 0.024. The addition of either grass species to AL mixtures also increased the two ratios, but it was related to a CP decrease with no increase in NFC concentrations. Adding RC or BT to AL‐based mixtures is therefore a valuable strategy to increase the forage energy to protein ratio.
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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".