Canola Response to ESN and Urea in a Four‐Year No‐Till Cropping System
Bibliographic record
Abstract
Nitrogen fertilizer is a major input cost in canola ( Brassica napus L.) production and farmers are interested in improving N use efficiencies. A multi‐site study in western Canada was conducted to determine the merits of polymer‐coated urea (Environmentally Smart Nitrogen, ESN) compared with urea on weed management and yield of hybrid and open‐pollinated (OP) canola. Treatments included a hybrid and OP canola cultivar, ESN and urea, 100 and 150% of soil test N fertilizer rates, and 50 and 100% of registered in‐crop herbicide rates. Canola was grown in rotation with barley ( Hordeum vulgare L.) in a no‐till system and both crops of the rotation were present each year. Fertilizer and herbicide rate treatments were applied to the same plots in four consecutive years to determine annual and cumulative effects. Hybrid compared with OP canola reduced weed tissue N concentration in 40% of the cases and reduced weed biomass in 80% of the cases. Additionally, weed tissue N concentration was lower with ESN than with urea in 70% of the cases, indicating that crop–weed competition for soil N might be reduced if ESN were used. Canola yield was greater for the hybrid cultivar in 15 of 20 site‐years. Both cultivars expressed a positive yield response to the 150% N fertilizer rate in 10 of 20 site‐years. This yield response occurred in three additional site‐years with hybrid canola, indicating that hybrid cultivars have a higher N demand under favorable growing conditions. Canola yield was similar with ESN and urea in 14 of 20 site‐years. The ESN compared with urea increased canola yield in 4 site‐years and in one additional site‐year for hybrid canola, indicating that ESN may be advantageous in some situations. Canola seed oil concentration was similar with ESN and urea in 19 of 20 site‐years. Information gained in this study will be used to develop improved fertilization strategies for canola production on the semiarid Canadian prairies.
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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 | 0.001 | 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) | 0.000 | 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".