Bibliographic record
Abstract
Competition with weeds decreases crop yields globally. Some traits are known to confer a competitive advantage to spring bread wheat (Triticum aestivum L.), but complex relationships between the competitive traits makes breeding for competitive ability difficult. Prairie organic producers use spring wheat cultivars which have been bred for conventional management systems or heritage cultivars released before the widespread use of synthetic fertilizers and pesticides. Breeding spring wheat specifically for organic production has been suggested. The International Triticeae Mapping Initiative (ITMI) population was used to study the genetics of traits associated with competitive ability. Grain yield without weed competition and under experimentally sown cultivated oat competition exhibited similar heritability. Similar heritability estimates between competition treatments suggest that selection in a weed free environment can lead to improvements in a weedy environment, but some high yielding lines under competition would be eliminated during selection. Quantitative trait loci (QTL) analysis of the population found QTL associated with vigour, days to heading, anthesis, and maturity, and cultivated oat grain yield suppression on chromosome 5A. The genetic correlations support the idea that early maturity provides a competitive advantage in northern grain growing regions. To investigate the feasibility of organic wheat breeding we used a random population of 79 F6-derived recombinant inbred sister lines from a cross between the Canadian hard red spring wheat cultivar AC Barrie and the CIMMYT derived cultivar Attila. The population, including the parents, was grown on conventionally and organically managed land in 12 environments over three years. Six environments had detailed agronomic data and heritability estimates differed between systems for five of the 14 traits recorded. Direct selection in each management system (10% selection intensity) resulted in 50% or fewer lines selected in common for four of the traits. Over all 12 environments direct selection within management system resulted in three lines retained specific to each system. The results of the management studies suggest that selection differences occur across multi-location tests, and selection for grain yield in organic systems should be conducted within organic systems. However, data garnered from conventional yield trials does have some relevance towards breeding for organic environments.
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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 | 0.001 | 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) | 0.002 | 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".