Performance of current canola (Brassica napus) hybrids under future rainfed production management
Bibliographic record
Abstract
Canola is vulnerable to the current changing weather conditions, mainly due to moisture and temperature-related stresses. Adaptation strategies such as shifting planting dates allow producers to improve canola's response to environmental conditions. This study aims to explore the optimal setting to increase canola productivity within the Canadian Prairies under future scenarios from the Shared Socioeconomic Pathways. Hence, we define the optimal planting period to avoid water and temperature stresses as well as the optimal nitrogen (N) concentration in fertilization to maximize canola productivity. We used DSSAT-Pythia to simulate four canola hybrids, 24 planting dates, five nitrogen concentrations, and four future climate scenarios, with a spatial resolution of 0.25° × 0.25° in the Canadian Prairies. The model's performance showed satisfactory predictions of canola phenology and grain yield for all hybrids. On spatial and temporal averages, the second hybrid showed highest yield values, with most values between 2500 and 3000 kg ha −1 . In addition, spatial analysis shows that the first hybrid can complete the crop cycle in all growing zones when planted early (April), and the second and third hybrids completed the cycle when planted later (June and July). Nitrogen uptake was affected by weather conditions. The higher the temperature, especially during the bolting stage, the less nitrogen uptake from the plant. Fertilization with high N concentration (200 kg ha −1 ) is expected to be more effective before May 19 under very hot scenarios and before June 08 under mild temperatures. Overall, canola yield increased with an increase in N concentration.
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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".