High-yield no-till canola production on the Canadian prairies
Bibliographic record
Abstract
Harker, K. N., O'Donovan, J. T., Turkington, T. K., Blackshaw, R. E., Lupwayi, N. Z., Smith, E. G., Klein-Gebbinck, H., Dosdall, L. M., Hall, L. M., Willenborg, C. J., Kutcher, H. R., Malhi, S. S., Vera, C. L., Gan, Y., Lafond, G. P., May, W. E., Grant, C. A. and McLaren, D. L. 2012. High-yield no-till canola production on the Canadian prairies. Can. J. Plant Sci. 92: 221–233. Relatively high prices and increasing demand for canola (Brassica napus L.) have prompted growers to produce more canola on more cropland. Here we determine if canola seed yield and oil concentration can be increased over current levels with high levels of crop inputs. From 2008 to 2010, direct-seeded experiments involving two seeding rates (75 vs. 150 seeds m−2), two nitrogen rates (100 vs. 150% of soil test recommendation), and the presence or absence of polymer-coated nitrogen or fungicides, were conducted at eight western Canada locations in canola-wheat-canola or continuous canola rotations. Herbicides, insecticides and fertilizers other than nitrogen were applied as required for optimal canola production. Increasing recommended nitrogen rates by 50% increased canola yields by up to 0.25 Mg ha−1. High (150 seeds m−2) versus lower (75 seeds m−2) seeding rates increased canola yields by 0.07 to 0.16 Mg ha−1. Fungicide treatment or polymer-coated nitrogen blended with uncoated urea increased canola yields by 0.10 Mg ha−1 in 2010, but not in 2008. The highest canola input combination treatment following wheat (3.50 Mg ha−1) yielded substantially more than the same high input treatment following canola (3.22 Mg ha−1). Average site yields were influenced by site conditions such as soil organic matter, days to maturity, and temperature, but these site and environmental predictors did not alter treatment rankings. Using higher than the soil test recommended rate of nitrogen or planting 150 versus 75 seeds m−2 increased canola yields consistently across western Canada. Canola oil concentration varied among canola cultivars, but was consistently low when N rates were high (150% of recommended). Higher than normal seeding rates led to high canola seed oil concentration in some cases, but the effect was inconsistent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".