Nitrogen Fertilization and Rhizobial Inoculation Effects on Kura Clover Growth
Bibliographic record
Abstract
Kura clover ( Trifolium ambiguum M.B.) is a persistent, rhizomatous forage legume; however, its use is currently limited by slow establishment. We determined the effects of rhizobial inoculation and N fertilization on kura clover growth and N 2 fixation in the seeding year. Kura clover was seeded with or without a commercial rhizobial inoculant and with and without N fertilization in three environments. Fertilization treatments consisted of 100 kg N ha −1 either applied at seeding or split in 10 kg N ha −1 applications every other week after seeding. Nitrogen fertilization increased seeding‐year herbage accumulation in all locations, but the response to fertilizer N was greater on a loamy sand with low organic matter and available N than on a silt loam with high soil organic matter. Rhizobial inoculation failed to consistently improve seeding‐year herbage accumulation compared with no inoculation; a positive response was observed in only one of three environments. Dry matter accumulation responses of root and rhizome to N fertilization and rhizobial inoculation were similar to that of herbage. Dinitrogen fixation in the seeding year varied between 9 and 25 kg ha −1 fixed N, depending on the environment. Seeding‐year inoculation increased postseeding year herbage yield. Also, when a positive response to N fertilization occurred in the seeding year, the response was maintained in the postseeding year. A commercial rhizobial inoculant was ineffective in establishing adequate nodulation in the seeding year in a N‐limited soil, indicating the need to identify more effective rhizobia for kura clover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".