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 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".