Multispecies grasslands produce more yield from lower nitrogen inputs across a climatic gradient
Bibliographic record
Abstract
High-yielding forage grasslands frequently contain low species diversity and receive high inputs of nitrogen fertilizer. To investigate multispecies mixtures as an alternative strategy, the 26-site international LegacyNet experiment systematically varied the diversity of sown grasslands using up to six high-yielding forage species (grasses, legumes, and herbs) managed under moderate nitrogen inputs. Multispecies mixtures outyielded two widely used grassland practices: a grass monoculture with higher nitrogen fertilizer and a two-species grass-legume community. High yields in multispecies mixtures were driven by strong positive grass-legume and legume-herb interactions. In warmer sites, the yield advantage of legume-containing multispecies mixtures over grass monocultures with higher nitrogen fertilizer inputs increased. Improved design of grassland mixtures can inform more environmentally sustainable forage production and may enhance adaptation of productive grasslands to a warming climate.
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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 | 0.001 |
| 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".