Evaluation of Wheat Cultivars to Test Indirect Selection for Organic Conditions
Bibliographic record
Abstract
There is debate regarding direct or indirect selection for organic conditions. Our objective was to evaluate the progress of indirectly selecting organic cultivars in conventional environments. Canadian spring wheat ( Triticum aestivum L.) cultivars, developed for conventional environments from 1885 to 1999 and from 1975 to 2009, respectively, were grown in two separate experiments to assess progress of yield and associated agronomic traits due to breeding. The first experiment evaluated 27 cultivars in organic and conventional conditions for 3 yr (2002, 2003, and 2004), on three sites in western Canada. In the second experiment, eight cultivars were evaluated in organic conditions in 2010 and 2011 at the University of Alberta, Canada. The first experiment showed that breeding had improved yield and most associated traits only in conventional systems and a few associated traits in organic conditions. The second experiment showed that breeding had made significant improvements in yield and test weight in organic conditions. This study suggests that with sufficient quality and disease resistance criteria in place for the breeding of wheat in conventional environments, it may be possible to concomitantly improve wheat yield destined for organic growing conditions. However, fewer associated traits showed significant improvement in organic conditions and improvement rates were lower than in conventional conditions. This suggests that optimizing trait performance in organic conditions should include organic conditions during breeding and selection.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".