Cutting management of alfalfa‐based mixtures in contrasting agroclimatic regions
Bibliographic record
Abstract
Abstract Cutting schedules affect forage yield, nutritive value, and persistence but few studies have recently assessed the effect of intensive cutting schedules on alfalfa ( Medicago sativa L.)‐based mixtures. We determined the effects of (a) cutting at early bud vs. early bloom of alfalfa, (b) a fall cut, (c) alfalfa–grass mixture vs. pure alfalfa, (d) one vs. two grasses, and (e) tall fescue ( Schedonorus arundinacea [Schreb.] Dumort.) vs. timothy ( Phleum pratense L.) in an experiment over four post‐seeding years at four sites with four cutting schedules on four alfalfa‐based mixtures. Cutting alfalfa at early bud rather than early bloom reduced annual forage dry matter (DM) yield by 2.03 Mg ha −1 and alfalfa contribution to DM yield by 17 percentage units, increased forage total digestible nutrient (TDN) concentration by 44 g kg −1 DM but did not increase estimated annual milk production per hectare. A fall cut did not improve annual forage DM yield and estimated annual milk production per hectare but reduced alfalfa contribution to DM yield. Pure alfalfa resulted in 1.09 Mg DM ha −1 less annual forage DM yield than alfalfa grown with one or two grasses. Two forage grasses with alfalfa compared with just one grass had no effect on forage DM yield and estimated annual milk production per hectare. The response of forage DM yield and estimated annual milk production per hectare to timothy or tall fescue with alfalfa varied with site but forage TDN concentration and alfalfa contribution to DM yield were generally greater with timothy than tall fescue.
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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".