Climate Change and Winter Survival of Perennial Forage Crops in Eastern Canada
Bibliographic record
Abstract
Severe winter climatic conditions cause recurrent damage to perennial forage crops in eastern Canada. Predicted increases of 2 to 6°C in minimum temperature during winter months due to global warming will likely affect survival of forage crops. Potential impacts of climate change on overwintering of perennial forage crops in eastern Canada were assessed using climatic indices reflecting risks of winter injuries related to cold intensity and duration, lack of snow cover, inadequate cold hardiness, soil heaving, and ice encasement. Climatic indices were calculated for 22 agricultural regions in eastern Canada for the current climate (1961–1990) and future climate scenarios (2010–2039 and 2040–2069). Climate scenario data were extracted from the first‐generation Canadian Global Coupled General Circulation Model. Compared with current conditions, the hardening period in 2040 to 2069 would be shorter by 4.0 d, with a lower accumulation of hardiness‐inducing cool temperatures. The period during which a temperature ≤−15°C can occur (cold period) would be reduced by 23.8 d, and the number of days with snow cover of at least 0.1 m would be reduced by 39.4 d. Consequently, the number of days with a protective snow cover during the cold period would be reduced by 15.6 d. Under predicted future climate, risks of winter injury to perennial forage crops in eastern Canada will likely increase because of less cold hardening during fall and reduced protective snow cover during the cold period, which will increase exposure of plants to killing frosts, soil heaving, and ice encasement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".