Influence of Genotype and Environment on Wheat Grain Fructan Content
Bibliographic record
Abstract
Fructans are naturally occurring plant polymers composed of fructose molecules. Approximately 15% of flowering plant species contain fructans, including wheat (Triticum aestivum L.). Fructans serve as carbon stores in plants and exhibit potentially beneficial effects on human health. The main objectives of this study were to examine the effects of genotype and environment on winter wheat grain fructan content and to assess the feasibility of using genomic selection for grain fructan content. Total grain fructan content was determined for 288 winter wheat genotypes grown across 2 yr at three locations each year. Observed variation in wheat grain fructan content was significantly influenced by genotype, environment, and genotype × environment interactions. The high genetic correlation, small impact of genotype × environment interactions on genomic predictability, and lack of significant hits in a genome‐wide association study suggest that genomic selection is a suitable tool in breeding for wheat grain fructan content. The results of this study will be useful for implementing recurrent genomic selection in winter wheat and guiding future decisions regarding breeding methodologies for total fructan content in wheat. This study provides a deeper understanding of the effects of genotype, environment, and genotype × environment interaction on fructan content, which will have implications for breeders seeking to develop nutritionally improved, climate‐resilient wheat cultivars.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".