Forage Yield and Species Composition in Years following Kura Clover Sod‐Seeding into Grass Swards
Bibliographic record
Abstract
Sod‐seeding legumes into grass pastures improves forage productivity and quality, but legumes currently used lack persistence. Field experiments were established in Québec and Minnesota to compare postseeding year performance of two cultivars (‘Cossack’ and ‘Endura’) of Kura clover ( Trifolium ambiguum M. Bieb.) against that of red clover ( Trifolium pratense L.) and white clover ( Trifolium repens L.) sod‐seeded using different intensities of herbicide sod suppression [paraquat (0.9 kg a.i. ha −1 ) and glyphosate (0.8 or 3.3 kg a.i. ha −1 )] with or without seeding year N fertilization (110 kg N ha −1 ). Red clover had the greatest yield and contribution to total forage yield in the first postseeding year [avg. 2.7 Mg dry matter (DM) ha −1 , 50% clover], white clover (WC) was intermediate (avg. 1.5 Mg DM ha −1 , 32% clover), and Kura clover (KC) ranked last (avg. 1.2 DM Mg ha −1 , 27% clover). Yields of KC were, however, similar to WC in three of five sites. Clover yields and content in the first postseeding year were positively associated with intensity of sod suppression. Kura clover content increased over time; at the first harvest of the second postseeding year, it had greater clover yield and content (avg. 750 kg DM ha −1 , 45% clover) than red clover (avg. 160 kg DM ha −1 , 25% clover) and WC (avg. 60 kg DM ha −1 , 11% clover). Seeding year N fertilization, which enhanced seeding year yields, had inconsistent effects on postseeding year yield and botanical composition but rarely had negative effects on clover. Kura clover can be established in permanent pastures via sod‐seeding.
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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".