Genotype × Environment Interaction for Grain Color in Hard White Spring Wheat
Bibliographic record
Abstract
Improvement of grain color in hard white spring wheat (Triticum aestivum L.) breeding programs depends on understanding the influences of genotype (G), environment (E), and their interaction (G × E). The objectives of this study were to quantify genetic variability for grain color and assess the nature of the G × E interaction in determining grain color in 79 spring wheat genotypes. Twelve check cultivars [seven hard red (HR), four hard white (HW), and one soft white (SW)] and 67 white-seeded Australian (AUS) accessions were grown at two locations across 2 yr. Wheat genotypes differed significantly in agronomic traits, grain protein, and kernel hardness. Grain and meal color were quantified using Hunterlab colorimeter values. Whole grain color values without (L = 40.9–50.4 units; a = 7.0–8.3; b = 13.6–19.1) and with NaOH treatment (L = 22.7–38.1; a = 7.7–9.7; b = 9.2–17.9) varied among genotypes. Using ground meal, color values (L = 80.1–84.9; a = 1.8–2.6; b = 8.9–11.8), yellow pigment content (2.5–4.8 μg g−1), and lutein content (1.8–3.7 μg g−1) varied among genotypes. Genotype × location (L) interactions were not significant for colorimetric and pigmentation variables. The Azallini and Cox test detected one crossover G × year (Y) interaction for grain a-value (without NaOH), one for grain b-value (without NaOH), and 12 for lutein content. Genetic variation exists for grain color among HW genotypes. The noncrossover nature of G × E interactions for grain color indicates that white-seeded genotypes selected as superior in one environment will be superior in other environments.
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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".