Review: Annual crop adaptation to abiotic stress on the Canadian prairies: Six case studies
Bibliographic record
Abstract
Bueckert, R. A. and Clarke, J. M. 2013. Review: Annual crop adaptation to abiotic stress on the Canadian prairies: Six case studies. Can. J. Plant Sci. 93: 375-385. More than half of Canada's grain crop production comes from the Canadian prairies, a region that experiences short growing seasons characterized by temperature and moisture stress. Historically, the region was dominated by temperate cereal production, but in recent decades crops have included canola (Brassica species) and pulses (chickpea, Cicer arietinum L.; dry bean, Phaseolus vulgaris L.; pea, Pisum sativum L.; lentil, Lens culinaris L.). Here we describe climatic conditions and the resulting abiotic stresses that are common in prairie crop production. We also showcase how specific cultivars have been successfully adapted to fit a short growing season of 95 to 120 d, and examine current strategies to improve crop performance on the Canadian prairies. Durum wheat (Triticum turgidum L. var. durum) production has been increased by incorporating stress escape through early flowering, and stress avoidance through increased seasonal water extraction, water use efficiency and reduced loss from leaves. Dry bean, a warm-season crop, has been improved by selecting for rapid emergence in cool soils. The indeterminate crops chickpea, lentil, and canola (Brassica juncea L.) have been improved through breeding for early flowering, double podding (chickpea), high harvest index, and a longer reproductive duration (lentil and canola). Enhanced drought tolerance in chickpea is in progress using early flowering for drought escape, and rooting traits that improve water extraction and canopy transpiration to avoid water and heat stress. Crops grown on the Canadian prairies have superior quality profiles and two crops, durum and lentil, have become dominant in global exports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
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 teacher head, 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".