Potato yield projections under climate change in Canada
Bibliographic record
Abstract
Abstract Potato ( Solanum tuberosum L.) is an important staple crop in Canada. Past studies have forecasted future yield decreases under climate change, which could have major consequences for the economy of some regions. However, limitations in those studies suggest that further investigations are needed. In this study, we simulated the effect of 15 climate change scenarios (classified from low to moderate and high) on potato potential (no N and water stresses) and rainfed (no N stress) yields at 59 locations across Canada representing current and future potential production regions using three crop models (Decision Support System for Agrotechnology Transfer [DSSAT], DeNitrification and DeComposition [DNDC], and Simulateur mulTI‐disciplinaire pour les Cultures Standard [STICS]). Simulation trends were generally consistent across all three crop models and suggested (1) an increase in potential and rainfed yields in the future (up to 4.4 t ha −1 dry matter in 2051–2080 compared with 1991–2020) in the northern regions where production is currently limited, if not impossible, due to a too short growing season; (2) a slight‐to‐moderate increase in potential and rainfed yields in the near future (2021–2050) for the remaining regions with greater increases for drier regions (0.7–3.1 t ha −1 ) than in wetter regions (0.5–1.4 t ha −1 ); and (3) stable or lower yields (up to −2.7 t ha −1 ) in the distant future (2051–2080), for most regions except the northern ones, due to excessively high temperatures, especially in the moderate and high‐climate change scenarios. This study gave the first extensive projections of future potato yield in Canada, including northern locations where production may become possible.
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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 | 0.001 |
| 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".