Winter damage to perennial forage crops in eastern Canada: Causes, mitigation, and prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Harsh winter climate results in frequent losses of stands and yield reduction in many forage-growing areas of Canada and other parts of the world. Climatic conditions and crop management both affect the winter survival of perennial forage crops. In this review, we present the main causes of winter damage in eastern Canada and we discuss crop management practices that help mitigate the risks of losses. Predictive tools available to assess the risks of winter damage both spatially and temporally are also presented. Our understanding of the causes of winter damage and of the plant adaptation mechanisms to winter stresses, particularly the role of N and C organic reserves, has improved. Forage species commonly grown in eastern Canada differ in their tolerance to subfreezing temperatures and to anoxia caused by the presence of ice on fields. Some improvement in winter hardiness of forage legume species has been achieved through breeding in eastern Canada but new technologies based on laboratory freezing tests and the identification of molecular markers may facilitate the future development of winter-hardy cultivars. Crop management practices required for good winter survival are now better defined, particularly those involving cutting management and the interval between harvests. Simulation models and climatic indices derived from our current knowledge of the causes of winter damage provide general indications on the risk of winter damage but their degree of precision and accuracy is still not satisfactory. Further improvements in winter survival require a more thorough understanding of the different causes of winter damage and, primarily, of their complex interactions with genetic, climatic, and management factors. Key words: Alfalfa, organic reserves, culitvars, species, management, climate
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it