Genetic improvement of triticale for irrigated systems in south-eastern Australia: a study of genotype and genotype × environment interactions
Bibliographic record
Abstract
Research into winter cereal breeding in Australia has focused primarily on studying the effects of rainfed environments. These studies typically show large genotype × environment (GE) interactions, and the complexity of these interactions acts as an impediment to the efficient selection of improved varieties. Wheat has been studied extensively; however, there are no published studies on the GE interactions of triticale in Australia under irrigated production systems. We conducted trials on 101 triticale genotypes at two locations over 4 years under intensive irrigated management practices and measured the yield potential, GE interactions, heritability and estimated genetic gain of yield, lodging resistance and several other traits important for breeding triticale. We found that high yield potential exceeding 10 t ha–1 exists in the Australian germplasm tested and that, in these irrigated trials, genotype accounted for a high proportion of the variability in all measured traits. All genetic parameters such as heritability and estimated genetic gain were high compared with rainfed studies. Breeding of triticale with improved yield and lodging resistance for irrigated environments is achievable and can be pursued with confidence in breeding programs.
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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 | 0.000 | 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 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".