Genotype by environment interaction effects on fibre components in potato (Solanum tuberosum L.)
Bibliographic record
Abstract
Abstract Increasing awareness of heart health and disease prevention has led consumers to more proactive grocery food choices. Fibre and its associated health benefits remains an important area of research given the current interest in food, nutrition, and health. To position the potato as a good source of fibre, breeding efforts have focused on developing cultivars and germplasm with high fibre content. The current study examined eight elite potato clones and four commercial cultivars (checks) across six environments (three locations over two years) for their total dietary fibre (TDF), neutral detergent fibre (NDF), and soluble fibre (SF) content. Genotype by environment interaction (GEI) and stability analysis were conducted with SAS and GGE Biplot software. Significant genotypic (G), environmental (E) and GEI effects were found. The six environments differed in temperature and moisture levels, which were linked to levels of NDF and TDF. Some genotypes had high levels of stability for fibre content. GGE biplot analysis found no significant mega-environments for fibre components. Two elite clones (CV96044-3 and F05081) were identified as high fibre sources (13.3 and 14.4 %, respectively) compared to the other elite clones and commercial cultivars (e.g., Russet Burbank: 11.7 %). These lines may also be suitable as parents with high fibre and stability to breed into backgrounds with other desirable qualities.
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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 | 0.000 |
| 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 | 0.000 |
| 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".