Simulated Canola Yield Responses to Climate Change and Adaptation in Canada
Bibliographic record
Abstract
Core Ideas Responses of canola to climate change in Canada were simulated using a crop model. An overall negative impact of climate change on canola yield was simulated. Yield reductions are due to the increased heat stress and/or water stress. The effects of earlier seeding could be very limited as an adaptation measure. Developing canola cultivars tolerant to heat and water stresses is an urgent need. A projected future warmer climate implies significant impacts on canola (Brassica napus L.) production in Canada. We aimed to use a modeling approach to simulate climate change impacts on canola yield in Canada and to evaluate potential adaptation measures. The CSM‐CROPGRO‐Canola model was used to simulate the responses of canola to the projected climate change at Brandon on the Prairies, and West Nipissing and Normandin in eastern Canada. Future climate scenarios for the near (2041–2070) and distant (2071–2100) future under two representative concentration pathways (RCP4.5 and RCP8.5) were developed based on climate change simulations by a regional climate model CanRCM4. Seeding dates were estimated from air temperature, precipitation, and soil moisture to account for the potential of earlier seeding as an adaptation measure. Compared to the baseline climate, simulated seed yield reduction was 42, 21, and 24% in the near future and of 37, 27, and 23% in the distant future, under RCP4.5, respectively for Brandon, West Nipissing, and Normandin. A larger reduction was simulated under RCP8.5, especially in the distant future at Brandon and West Nipissing. The simulated seed yield reduction was associated with increases in heat and water stresses under rainfed conditions with current N fertilizer application rates. Coping with heat and water stresses is a big challenge for canola production in Canada under the projected climate change, especially on the Canadian Prairies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".