Assessment and mapping of climate change impacts on spring wheat yield in Southern Saskatchewan using DSSAT and high-resolution RCM projections
Bibliographic record
Abstract
Climate change presents both potential benefits and drawbacks for crop production in Canada. An assessment and mapping of future climate change impacts on spring wheat yield in Saskatchewan is crucial because the province largely accounts for Canada’s share of the global wheat market. We applied ten climate change scenarios (high-resolution (0.22°) regional simulations) to the CERES-Wheat module of the Decision Support System for Agro-technology Transfer (DSSAT) model using a historical baseline period (1975–2004) and three 30-year future periods: near (2010–2039), middle (2040–2069), and far (2070–2099). The model showed high accuracy with d-values always higher than 0.75 for the calibration (d-value = 0.84) and the validation (d-value = 0.82) periods. Furthermore, the model showed a remarkable similarity between the observed and simulated spring wheat yields. Results indicate a median yield increase of 16.1% in the near future, 27.5% in the middle future, and 10.9% in the far future. While there is a general rise in the annual wheat yield under future climate conditions and elevated CO 2 , yield in the far future exhibits a notable decline, particularly in the extreme southern and southwestern areas of Saskatchewan which is exposed to increased aridity under higher temperatures. Conversely, the northern regions are expected to experience significantly higher yields, primarily due to a shift towards wetter conditions and longer growing season. Whereas DSSAT is a commonly used model, the current research is novel by extending the analysis at high-resolution over a large area representing more than 40% of Canada’s crop land.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".