Can Spring Wheat‐Growing Megaenvironments in the Northern Great Plains Be Dissected for Representative Locations or Niche‐Adapted Genotypes?
Bibliographic record
Abstract
ABSTRACT Characterizing variety testing sites and identification of sites with negligible genotype × environment crossover interaction is important for plant breeders wishing to identify superior germplasm and (or) cultivars for a wide range of environments. Long‐term multilocation grain yield data from the regional hard red spring wheat (Triticum aestivum L.) variety trials from 1981 to 2002 (472 location years assessing 64 wheat genotypes) in Alberta, Canada, were employed for this study. The shifted multiplicative model (SHMM) and the site regression model (SREG) were used to group testing sites into subsets with reduced crossover interaction. Both models identified yearly subsets of testing sites with negligible crossover interaction. However, the yearly site groupings did not generally follow a repeatable pattern over years. Clustering did not correspond with provincial agroclimatic classification, nor did it correspond with site‐specific yield potential. Genotype × environment patterns were therefore inconsistent over the years, mainly because of complex, highly variable, and unpredictable year × location effects. We identified sites appearing to be more discriminative and predictive of average genotype performance. This suggests that regional variety trials may be conducted at a fewer more representative locations predictive of average varietal performance. We conclude that the spring wheat growing areas in Alberta (and in the northern Great Plains in general) belong to a single megaenvironment with unpredictable crossover interaction patterns. Because of the highly variable and unpredictable genotype × environment interaction patterns in Alberta, genotypic selection targeting wide adaptation is recommended. Although genotype × environment patterns were not repeatable, the yearly high yielding and stable varieties were repeatedly selected over years. These varieties were the most popular varieties grown by farmers during the testing time period.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".