Benefits of mixing grasses and legumes for herbage yield and nutritive value in Northern Europe and Canada
Bibliographic record
Abstract
Increased biodiversity may improve ecosystem services, including herbage yield. A mixture experiment was carried out at five sites in Northern Europe and one in Canada to investigate whether mixtures of grasses and legumes would give higher herbage yield than monocultures. Resistance of the mixtures to weed invasion and nutritive value of the herbage were also investigated. The experimental layout followed a simplex design, where four species differing in specific functional traits, timothy (Phleum pratense L.), smooth meadow grass (Poa pratensis L.), red clover (Trifolium pratense L.) and white clover (Trifolium repens L.), were grown in monocultures and eleven different mixtures with systematically varying proportions of the four species. Positive diversity effects (DE) were observed, leading to greater herbage dry‐matter (DM) yield in mixtures than expected from species sown in monocultures. For centroid mixtures, the DE generated on average an additional 32, 25 and 21% of the DM yield than would be expected from the monocultures in the first, second and third year respectively. On average, the mixtures were 9, 15 and 7% more productive than the most productive monoculture (transgressive overyielding) in the first, second and third year respectively. These benefits persisted over the three harvest years of the experiment and were consistent among most sites. This positive effect was not accompanied by a reduction in herbage digestibility and crude protein concentration that is usually observed with increased DM yield. Mixtures also reduced the invasion of weeds to <5% of herbage yield compared to monocultures (10–60% of herbage yield).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".