Comparison of six zero-till seed drill openers for establishment and persistence of sod-seeded alfalfa (<i>Medicago sativa</i> L.) in a cool-season perennial grass pasture
Bibliographic record
Abstract
Zero-till seed drills are readily available for crop seeding in the prairie region of western Canada but have not been evaluated for sod-seeding in perennial forage. Sod-seeding a legume, such as alfalfa (Medicago sativa L.), into the existing perennial grass vegetation improves forage yield and quality for ruminant livestock grazing and production. Suppression of the existing vegetation with herbicide has been reported to increase alfalfa seedling establishment. We evaluated six commercially available zero-till seed drill openers by sod-seeding alfalfa into a crested wheatgrass [Agropyron cristatum (L.) Gaertn.] pasture with or without glyphosate suppression of the existing vegetation in Lanigan Saskatchewan. Alfalfa seedling establishment (52.2 vs. 21.9 seedlings m−1 ± 6.8), alfalfa composition (42.5% vs. 0.2% ± 8.6), and alfalfa yield (1105 kg·ha−1 vs. 12 kg·ha−1 ± 206) in 2011 were increased by the application of herbicide. Seed drill openers performed similarly for all three variables. Nine years later, in June 2020, there was greater alfalfa composition (13.3% vs. 0% ± 2.3) and forage yield (1325 vs. 957 kg·ha−1 ± 127) in the sod-seeded plots than in the unseeded controls but the alfalfa plant cover, alfalfa composition, alfalfa yield and forage yield were similar among seed drill openers. There was also no difference in these variables due to herbicide application nine years prior. Livestock producers can utilize currently available zero-till seeding equipment for sod-seeding alfalfa to rejuvenate grass pastures in the thin Black soil zone of western Canada with the expectation of persistence of alfalfa for nine years.
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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 | 0.001 |
| 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 | 0.001 | 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".