Grazing Effects on Herbage Mass and Composition in Grass–Birdsfoot Trefoil Mixtures
Bibliographic record
Abstract
Grass–legume mixtures have the ability to supply more consistent forage yields across a wide range of environments throughout the grazing season than do grass monocultures. The suitability of diverse grass species in binary mixtures with birdsfoot trefoil ( Lotus corniculatus L.) in rotational stocking systems has not been extensively studied. The objective of this study was to evaluate binary mixtures of five cool‐season grasses with the birdsfoot trefoil cultivar Norcen for herbage mass, botanical composition, and cattle ( Bos taurus ) grazing preference under a rotational stocking. Experiments were established at Lake City and Chatham, MI, in 1994. Binary mixtures were grazed for 2 yr with beef or dairy cows three times yearly at predetermined periods from spring to fall. Total herbage dry mass production ranged from 3 to 10 Mg ha −1 yr −1 over two years and locations. The grass fraction in binary mixtures was 327 to 946 g kg −1 in swards over two years and locations. Perennial ryegrass ( Lolium perenne L.) failed to persist at Lake City, probably due to less consistent snow cover. Birdsfoot trefoil fraction was highest in binary mixtures with smooth bromegrass ( Bromus inermis Leyss) and timothy ( Phleum pratense L.). Binary mixtures with orchardgrass ( Dactylis glomerata L.) and tall fescue ( Festuca arundinacea Schreb.) produced the highest herbage biomass but were less preferred by grazing animals while binary mixtures with timothy and smooth bromegrass were associated with the highest apparent herbage utilization at both locations (84–100%).
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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".