Rainfed spring canola yield response to changing heat and water stress in the Canadian Prairie region
Bibliographic record
Abstract
Canola is a significant crop in Canadian agriculture and the economy. However, Canada's average temperatures have risen rapidly over the past eight decades, changing temperature patterns and water availability for canola production. This study aims to explore the impacts of air temperature and soil water availability on spring canola production from 2025 to 2050. Accordingly, this study introduces DSSAT calibration and simulation of the current hybrid InVigor®L340PC, integrating the Shared Socioeconomic Pathways. Leveraging DSSAT-Pythia, gridded simulations capture spatial variability in water and temperature stress interactions, driven by a large ensemble of climate models. The analysis reveals how precipitation and temperature changes jointly influence spring canola development. Yield projections under these conditions provide critical insights into the future viability of rainfed spring canola and inform adaptation strategies for growers and policymakers. Findings demonstrate negative impacts on exclusively rainfed spring canola production in the Canadian Prairie Region under diverse climate scenarios from 2025 to 2050. The main canola growing ecozone (Aspen Parkland) is expected to have higher air temperatures and lower soil water content if greenhouse gas emissions keep rising. An average increase of 1.5°C in air temperature and 0.025 in the water stress factor indices may result in annual yield reductions of 203 ± 4.3 and 121 ± 13.6 kg ha −1 , in Lake Manitoba Plain and Aspen Parkland ecoregions, respectively. Given that future canola production is expected to continue in the same ecoregions it is recommended that adaptation and mitigation strategies are developed and adopted to improve canola production conditions in these ecoregions. ● By 2050, canola yields may reduce by up to 20 % in Canadian Prairie rainfed systems. ● Aspen Parkland and Lake Manitoba Plain may have the highest yield drops. ● Drops occur due to heat and water stress under Shared Socioeconomic Pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".