The influence of climate change on crop yields in the Canadian Prairies
Bibliographic record
Abstract
Agriculture in Canada and specifically the Prairies is a key provider of global calories but is vulnerable given it is mostly rainfed and is prone to droughts. In this dissertation we examine how growing conditions and productivity in the Canadian Prairies have changed and could shift in the future. Historical trends in yields and climate are examined to understand factors that have shaped production on the Prairies. We also delve into future predictions of climate change and their impact on yields as well as exploring shifts in key agroclimatic indices under a range of possible futures. First we compare and contrast yields in key field crops for both Canada and U.S. to understand spatial and temporal trends, including crops developed predominantly through private industry and through publicly funded programs. We find lower yield growths in publicly funded crops such as wheat, particularly in the U.S. This underscores the need for more targeted investments in underperforming areas and crops as well as strategies to continue increasing yields in the high-performing areas. Second, we estimated the historical impacts of climate change on yields in the Prairies. Examining trends in agroclimatic indices and developing statistical models for assessing climate impact, we find that historical temperature trends have had a negative effect on yields for most of the Palliser triangle in Alberta and Saskatchewan. Overall from 1980-2021 climate change has resulted in an accumulated net loss of -1.9MT of barley (mostly due to negative yield impacts in Alberta) while canola and spring wheat gained 1.27MT and 0.5MT respectively, compared to a counterfactual of no climate change. We then used these statistical models to estimate the impact of climate change scenarios on future Canadian Prairie crop yields. We found that by 2050 under an intermediate scenario (SSP2-4.5) the average yields of barley, canola and spring wheat could be reduced by 25%, 19% and 21% respectively. Our findings highlight that agriculture productivity in the Canadian Prairies has experienced significant change in the last 4 decades and there is a need to focus on regionally specific resilience and adaption planning to address climate change impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".