Cutting schedule and species composition to improve energy‐to‐protein ratios in alfalfa‐based mixtures
Bibliographic record
Abstract
Abstract Energy‐to‐protein ratios have been suggested as a potential forage attribute related to nitrogen (N) use efficiency in dairy cows but how these ratios vary with the cutting management of alfalfa‐based mixtures is poorly known. Our objective was to determine the effect of cutting schedules of alfalfa‐based mixtures and their species composition on two forage energy‐to‐protein ratios from a large number of samples from four field experiments with previously published results on forage dry matter yield and nutritive value. The two energy‐to‐protein ratios were (1) nonfiber carbohydrates (NFC) to crude protein (CP), NFC/CP, and (2) NFC to the sum of nonprotein N (NPN) and rapidly degradable protein fraction (PB1), NFC/(NPN + PB1). Cutting alfalfa‐based mixtures at the early bloom stage rather than at the early bud stage of alfalfa and taking a fall harvest increased mostly the NFC/CP ratio. Including at least one grass species with alfalfa had variable effects on the two ratios. Although several grass species were tested, we cannot conclude on the best grass species to use. Observed increases in the energy‐to‐protein ratios, however, were relatively small (<8%) and not always consistent in different experiments. Because the relative changes in the energy (NFC) and protein (CP or NPN + PB1) components of the ratios often varied in the same direction when induced by cutting schedules or the presence of grasses with alfalfa, opportunities to improve energy‐to‐protein ratios with those practices remain limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".