Establishment of Kura Clover No‐Tilled into Grass Pastures with Herbicide Sod Suppression and Nitrogen Fertilization
Bibliographic record
Abstract
Sod‐seeding legumes into grass‐dominated pastures improve forage productivity and quality. Identical field experiments were established in May 2001–2002 at two sites in Québec and three in Minnesota. Our objective was to compare the establishment in perennial cool‐season grass sods of two sod‐seeded cultivars (‘Cossack’ and ‘Endura’) of Kura clover ( Trifolium ambiguum M.B.) against that of red clover ( Trifolium pratense L.) and white clover ( Trifolium repens L.) using different herbicide sod suppression intensities {paraquat, 1,1'‐dimethyl‐4,4'‐bipyridinium (0.9 kg a.i. ha −1 ) and glyphosate [ N ‐(phosphonomethyl) glycine] (0.8 or 3.3 kg a.i. ha −1 )}, without or with N fertilization (110 kg N ha −1 ). Establishment year plant density and dry matter (DM) production of both Kura clover cultivars were similar (avg. 90 plants m −2 , 390 kg DM ha −1 ), but were generally inferior to white clover (avg. 110 plants m −2 , 740 kg DM ha −1 ) and red clover (avg. 170 plants m −2 , 1450 kg DM ha −1 ). Paraquat did not sufficiently suppress the sod, resulting in lower legume populations and yields than glyphosate. Sod suppression using glyphosate, however, led to heavy seeding‐year weed infestation at two of three sites in Minnesota (avg. 2.2 Mg weed DM ha −1 ). Sod‐seeded Kura clover successfully established with glyphosate; however, its contribution to forage production in the sod‐seeding year remained minimal (<0.5 Mg ha −1 at four of five sites). Effects of N fertilization varied with species and herbicides; effects on Kura clover were inconsistent but rarely detrimental, while increasing total forage yields by an average of 40%. It is thus possible to establish Kura clover via sod‐seeding; however, its productivity in the seeding year remains minimal.
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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 | 0.001 |
| 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".