Productivity and persistence of Kura clover and white clover mixtures with grasses
Bibliographic record
Abstract
Abstract There is a lack of persistent forage legumes for permanent pastures in eastern Canada. Kura clover (Trifolium ambiguum M. Bieb.; KC) is a long‐lived species, with demonstrated potential in various regions, however, there is a lack of information on its performance in mixtures with grasses and no comparisons with white clover (Trifolium repens L.; WC), the main legume species recommended for pastures in eastern Canada. Objectives were to evaluate the potential of KC when grown in mixtures with grasses and compare its performance with WC based‐mixtures and N‐fertilized grass monocultures. Twenty treatments that included KC, WC, and six perennial grasses species (Kentucky bluegrass [Poa pratensis L.; KB], meadow bromegrass [Bromus biebersteinii Roem. & Schult., MB], orchardgrass [Dactylis glomerata L., OR], smooth bromegrass [Bromus inermis L.; SB], tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.; TF], and timothy [Phleum pratense L.; TI]) seeded alone and in binary mixtures were seeded in 2 yr at two sites and monitored for up to four production years. During the first production year, WC‐based mixtures yields were overall greater than those of KC‐based mixtures, while in the second production year and onwards, KC treatments had similar or greater yields than WC, illustrating the greater persistence of KC. Adding KC to grasses also increased total forage yields by 13% on average compared to N‐fertilized solo‐seeded grasses. The performance of specific KC–grass mixtures was variable across sites‐years with no specific mixture being consistently more productive. Kura clover is adapted to contrasting environments of Québec and appears to be a suitable alternative to WC.
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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 | 0.001 | 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".