Nitrogen Fertilization Impacts on Stand and Forage Mass of Cool‐Season Grass‐Legume Pastures
Bibliographic record
Abstract
Combining the benefits of legume N2fixation and N fertilization may increase the productivity and profitability of pasture systems. Our objectives were to study the effects of N fertilization on productivity and persistence of legumes in mixtures with cool‐season grasses under rotational stocking with short grazing periods. Twelve N fertilization regimes ranging from 0 to 336 kg of N per ha were applied annually to smooth bromegrass and reed canarygrass in monoculture and mixture with alfalfa, birdsfoot trefoil, and kura clover. Alfalfa was the dominant legume in mixtures with cool season grasses in 1999. As kura clover developed, it became the dominant legume species and by the trials end stands averaged over 70% in mixtures with both smooth bromegrass and reed canarygrass and across N treatments. Nitrogen fertilization did not affect alfalfa stands, but reduced kura clover stands by 17%. Smooth bromegrass‐legume mixtures with no N fertilization produced more forage (10.5 Mg DM/ha) than any smooth bromegrass monoculture with N treatment (336 kg of N per ha produced 8.0 Mg DM/ha). Cost of forage mass in smooth bromegrass‐legume mixtures was less than 50% of smooth brome monocultures. While N fertilization did not increase forage production in treatments with legumes, legumes were able to maintain vigorous stands with up to 336 kg of N per ha.
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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 | 0.000 |
| 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".