Yield and nutritive value of grazed complex legume–grass mixtures under increasing nitrogen application rates
Bibliographic record
Abstract
Complex pasture mixtures are advantageous, but little information exists on the best forage species and nitrogen (N) management in eastern Canada. We compared under mob stocking four complex mixtures of one of two legume species [alfalfa (Medicago sativa L.) and birdsfoot trefoil (Lotus corniculatus L.)] plus one of two grass mixes [No. 1 — timothy (Phleum pratense L.), meadow fescue (Schedonorus pratensis (Huds.) P. Beauv.), reed canarygrass (Phalaris arundinacea L.), and Kentucky bluegrass (Poa pratensis L.); No. 2 — tall fescue (Schedonorus arundinaceus (Schreb.) Dumort., nom. cons.), meadow bromegrass (Bromus biebersteinii Roem. and Schult.), reed canarygrass, and Kentucky bluegrass] under three N application rates at three sites over the first three post-seeding years. Legume species had little effect on most forage attributes mainly because of their low contribution to forage dry matter (DM) yield (<30%) in second and third years. The grass mix with tall fescue and meadow bromegrass (No. 2) yielded similar or slightly better than the one with timothy and meadow fescue (No. 1) but tended to have a greater neutral detergent fibre concentration, and lower N and total digestible nutrient concentrations. Nitrogen fertilization increased forage DM yield only in second and third years when the legume contribution to forage DM yield was <30%, and it increased N concentration and decreased nonstructural carbohydrate concentration. These results highlight the challenge of maintaining legume species in rotationally grazed pastures of eastern Canada and confirm recommendations of applying no or little N fertilizer on grazed legume–grass mixtures when the legume contribution to forage DM yield is >30%.
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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 | 0.000 | 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) | 0.001 | 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".