Yield component variation in winter wheat grown under drought stress
Bibliographic record
Abstract
Crops produced in the semiarid environment of western Canada are subjected to variable and unpredictable periods of drought stress. The objective of this study was to determine the inter-relationships among yield components and grain yield of winter wheat (Triticum aestivum L) so that guidelines could be established for the production of cultivars with high yield potential and stability. Five hard red winter wheat genotypes were grown in 15 field trials conducted throughout Saskatchewan from 1989–1991. Although this study included genotypes with widely different yield potential and yield component arrangements, only small differences in grain yield occurred within trials under dryland conditions. High kernel number, through greater tillering, was shown to be an adaptation to low-stress conditions. The ability of winter wheat to produce large numbers of tillers was evident in the spring in all trials; however, this early season potential was not maintained due to extensive tiller die-back. Tiller die-back often meant that high yield potential genotypes became sink limiting with reduced ability to respond to subsequent improvements in growing season weather conditions. As tiller number increased under more favourable crop water conditions genetic limits in kernels spike −1 became more identified with yield potential. It is likely then, that tillering capacity per se is less important in winter wheat than the development of vigorous tillers with numerous large kernels spike −1 . For example, the highest yielding genotype under dryland conditions was a breeding line, S86-808, which was able to maintain a greater sink capacity as a result of a higher number of larger kernels spike −1 . It appears that without yield component compensation, a cultivar can be unresponsive to improved crop water conditions (stable) or it can have a high mean yield, but it cannot possess both characteristics. Key words: Triticum aestivum L., wheat, drought stress, kernel weight, kernel number, spike density, grain yield
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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 | 0.000 | 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) | 0.001 | 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 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".