Projecting spring wheat yield changes on the Canadian Prairies: effects of resolutions of a regional climate model and statistical processing
Bibliographic record
Abstract
ABSTRACT In addition to the uncertainty associated with crop models, climate scenarios are still a major source of uncertainty in projecting crop yield changes under climate change. Regional climate models (RCMs) are used as tools for dynamic downscaling of climate scenarios from global climate models (GCMs) to regional scales for climate change impact studies. It is known that running an RCM is more expensive and time consuming at a higher resolution than at a lower resolution. Therefore, it is interesting to investigate how resolutions of an RCM and statistical processing of RCM data may result in differences in projected crop yield changes. We used the decision support system for agrotechnology transfer (DSSAT)–CERES‐Wheat model to simulate yield changes of spring wheat at 13 locations across the Canadian Prairies, with climate scenarios from a Canadian RCM (CanRCM4) driven by a Canadian earth system model (CanESM2) with the forcing scenarios RCP4.5 and RCP8.5 at 25 and 50 km resolutions. Bias correction and a stochastic weather generator referred to as AAFC‐WG were used as statistical processing tools to develop future climate scenarios as input to the crop model. The results showed that when changes were averaged across the locations, whether 25‐ or 50‐km resolution CanRCM4 data were used, the projected yield changes were fairly consistent with those based on its driving GCM–CanESM2, especially if AAFC‐WG was used to develop future climate scenarios. The spatial patterns of the projected yield changes were also similar for the two resolutions of CanRCM4, although the magnitude of the projected changes had a relatively large range among the scenarios at some locations. These results indicated that using future climate scenarios based on climate change simulations by GCMs might be sufficient for projecting regional crop yield changes on the Canadian Prairies.
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 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".