Identifying Key Canopy Architecture Traits of Spring Wheat for Plant Selection to Drought and Heat Stress Avoidance
Bibliographic record
Abstract
In Canada, wheat (Triticum aestivum L.) is one of the key staple crops, contributing an estimated $42.7 billion to the Canadian economy yearly. However, climate change is posing a major threat to the growth, development, and productivity of wheat, as it is a heat and drought sensitive crop, especially in Western Canada. Canopy architecture influences yield, and erect canopies have been reported to produce a higher grain yield than planophile plants. Our research aims to identify how different canopy architectures and their significant traits improve yield stability under high temperature and drought stress conditions. Our research was conducted in a new unique system of 12 field-established, environmentally controlled high tunnels (30 ft x 50 ft each) in a randomized complete block experimental design. Four contrasting wheat genotypes were grown in each tunnel. Plants were exposed to four treatments, including control (ambient air temperature with 90% field capacity), drought (ambient air temperature with 30% field capacity), heat (ambient air temperature +12 °C with 90% field capacity), and combined drought and heat stress (ambient air temperature +12 °C with 30% field capacity) at the heading stage. Canopy architecture was graded according to the visual UPOV scoring scale, and grain yield was obtained at the end of the growing period. The identified promising physiological traits linked to erectophile and planophile canopy architectures under stress will be presented. This represents an important initial step toward the selection and development of high-yield crops that are tolerant to drought and heat, benefiting not only crop producers but also wheat breeding programs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.018 | 0.005 |
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".