Genotype–environment interaction of no-till winter wheat in Western Canada
Bibliographic record
Abstract
Differences among cultivars in their response to changes in crop water availability are reflected in genotype–environment (GE) interactions for grain yield. With the recent expansion of the winter wheat production area in western Canada, it is important that plant breeders and agronomists have an understanding of the significance of GE interactions as they relate to regional adaptation of genotypes. Consequently, the objective of this study was to determine the phenotypic stability of recent high-yielding winter wheat genotypes grown under drought and low stress conditions on the Canadian prairies and to assess the effect that crop water status has on GE interactions. Eighteen field trials were conducted throughout Saskatchewan over a 3-yr period. Five hard red winter wheat genotypes were selected for evaluation in these trials on the basis of unique characteristics identified in earlier studies. Natural variation in weather among locations and years and irrigation produced a wide range in the timing and intensity of drought stress. The high yield potential of recent winter wheat selections was confirmed. A nonsignificant genotype-location effect meant that geographic subregions requiring specific adaptive traits could not be identified. In contrast, significant effects of years and genotype-year and location-year interactions indicated that annual differences in weather had a greater influence on relative genotype performance than weather differences among locations. Significant within-site genotypic variation for grain yield was observed only at high rainfall and irrigated sites, and the GE interaction was larger than the genotypic variance component when there were wide differences in environmental conditions. The GE interaction effect was not significant when only dryland sites were considered. A poor association between yield rank at the highly productive and drought-stressed sites was attributed to genotypic differences in yield potential and the effect of drought on the expression of yield potential. Joint regression, pairwise correlated response, stability, and convergence analyses were conducted in an effort to better interpret the practical importance of the GE interactions. A tendency for the genotype regression lines to converge below the range of grain yields expected in the region indicated that genotypes with the highest mean yield were widely adapted and that winter wheat breeders should select for high yield potential in low stress environments. However, the expression of grain yield potential was reduced enough to suggest that winter wheat yields in western Canada are likely to benefit from this “high” yield potential only under moderate and low stress conditions. Therefore, because there is a wide diversity of crop water conditions in this region, trial locations should also include targeted high stress environments to identify genotypes with high performance over a wide range of environments. Key words: Triticum aestivum L., drought stress, stability, regression analyses, 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) | 0.000 | 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 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".