Forage Quality and Yield in Grass-Legume Mixtures in Northern Europe and Canada
Bibliographic record
Abstract
Research has shown that increased biodiversity (e.g. number of species) may improve ecosystem services (e.g. give more yield). A mixture experiment was carried out in five sites in Northern Europe and one in Canada to investigate if mixtures of grasses and legumes gave more yield than monocultures. The resistance of the mixtures to unsown species invasion was also investigated and the quality of the forage in the mixtures was compared to the forage quality of the monocultures. The experimental layout followed a simplex design, where four species, timothy (Phleum pratense L.), smooth meadow grass (Poa pratensis L.), red clover (Trifolium pratense L.) and white clover (Trifolium repens L.) were sown in field plots in various combinations. The species were grown in monocultures and 11 different mixtures with systematically varying proportions of the four species. The plots were harvested 2-3 times a year, depending on site, and yield, species composition and forage quality were measured. The results showed positive diversity effects leading to greater yield than expected from species sown in monocultures. The diversity effects were strong enough to achieve transgressive overyielding, i.e. the mixtures were more productive than the most productive monoculture and the mixtures were more resistant to unsown species invasion than the monocultures. Mixing legumes with grasses improved the forage quality compared to the grass monocultures, especially increasing the crude protein content of the mixtures. These benefits persisted over the three harvest years of the experiment and were consistent between sites.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".