The potential of growing soybean in Saskatchewan and its irrigation water needs under climate change scenarios—a modelling study
Bibliographic record
Abstract
The soybean industry in Canada is seeking opportunities to expand cultivation due to economic and environmental benefits of growing soybean. Climate projections indicate that soybean expansion into Saskatchewan would be possible with the increases in the available crop heat units under a future warmer climate; however, crop water availability could limit yields. Using a crop growth model, we simulated soybean yields within the Canadian Regional Agricultural Model regions in Saskatchewan for the near-term (2030s), mid-term (2050s), and distant future (2070s) periods under different climate scenarios. Soybean yields were simulated without water stress (potential yield), with water stress (rainfed yield), and under full and partial irrigation scenarios. Irrigation water needs were estimated under the irrigation scenarios and irrigation water availability was discussed. Our results suggest that reasonable and likely more profitable yields (∼2000–2500 kg ha−1) can be achieved under rainfed conditions in the Black soil zone neighbouring Manitoba but soybean production would be less favourable in the Dark Brown soil zone and least favourable in the Brown soil zone. Northeastern regions in the Black soil zone were found to be suitable for growing soybean cultivars in the maturity group (MG) 0 in the distant future and MG 00 in the mid-term under the medium–high greenhouse gas emission scenarios. Soybean would still not be suitable in the northwestern region. Our results indicate that regions in central Saskatchewan requiring 120–170 mm of irrigation are more likely to benefit from the proposed Lake Diefenbaker Irrigation Projects in the future.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".