Optimizing nitrogen fertilization for hybrid canola (Brassica napus L.) production across Canada
Bibliographic record
Abstract
Context or problem Hybrids are currently the dominant varieties in canola production, but their yield response to nitrogen (N) application across Canada has not been adequately updated. As a result, there is a lack of effective N management guidelines for modern hybrid canola to reach their full yield potential and cope with growing abiotic stresses caused by climate change . Objectives or research questions This study was designed to investigate the responses of yield and N use efficiency (NUE) of modern canola hybrids to N fertilization for determining site-specific economic optimum N rates (EONR). Additionally, the key driving factors of canola yields and N recommendations were identified. Methods A 32 site-year field study across Canada was conducted to test 8 combinations of N rates and application methods on 2 site-specific hybrids in each trial. Results Nitrogen fertilization greatly increased canola yield by an average of 41%, with significant responses in 19 out of 32 trials. Split-N strategy led to similar yield, NUE, and yield response index compared to preplant-only N application. However, these traits varied among hybrids due to different growing environments and hybrid-specific tolerance to abiotic stresses. The number of heat-stress days and heat-induced thermal accumulation surrounding the 4 weeks before and post-flowering stage directly determined the canola responses to N fertilization and EONR. Conclusions Our results suggest a site-specific EONR of 146–166 kg N ha −1 in the Black soil zone, 85–100 kg N ha −1 in the low-yielding Brown soil zone, and 140 kg N ha −1 in Ontario, preferably with a split-N strategy for hybrid canola production. The split-N fertilization is generally recommended, as it provides an opportunity to adjust the amount of topdressing N based on historical and early season weather conditions to achieve the dual goals of increasing canola productivity and reducing greenhouse gas (GHG) emissions from fertilizer use. The energy and time costs must be considered when making practical decisions. Environment-specific selection of canola hybrids also played an important role in the response to N, with ‘6074RR’ in favorable weather and ‘L233P’ in drought-prone conditions appearing to be good choices for specific ecoregions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".