A review of sod-seeding for pasture improvement in Saskatchewan, Canada
Bibliographic record
Abstract
The Canadian province of Saskatchewan is located on the Great Plains of North America and is an important beef (Bos taurus) production region.Pasture rejuvenation through sod-seeding has been little adopted by beef producers in the province despite forty years of research.We reviewed the research results to determine the successes and gaps in knowledge to guide current and future research and extension activities and crop insurance programs.Alfalfa (Medicago sativa L.) sod-seeding results have been more consistent than other legumes and is currently the standard legume, though both Cicer milkvetch (Astragalus cicer L.) and alfalfa established well in sub-humid (wetter) locations.As non-bloat legume species and other such varieties are released by plant breeding programs, more research on alternative legumes, such as sainfoin, (Onobrychis viciifolia Scop.), should be conducted.P fertilizer with sod-seeding species improved seedling establishment.Vegetation control of existing pasture species was found to improve sod-seeding success in semi-arid locations, while it was not required in sub-humid locations.The cost of broad-spectrum herbicide to reduce existing pasture species competition has decreased dramatically since much of the research was done.Therefore, the economics of herbicide suppression for successful sod-seeding should be re-examined at current pricing and across a range of soil zones represented in the province.Modern zero-till seed drills can be used for sod-seeding in semi-arid soil zones, while broadcast seeding can be used in subhumid soil zones.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".