Genetic variation for effects of drought stress on yield formation traits among commercial soybean [Glycine max (L.) Merr.] cultivars adapted to Ontario, Canada
Bibliographic record
Abstract
Drought stress significantly limits soybean [Glycine max (L.) Merr.] yields in Ontario, Canada. Many studies of genetic variation for drought tolerance compare commercial lines with exotic, unadapted germplasm. We hypothesized that even current commercial cultivars adapted to Ontario would differ significantly for traits related to drought tolerance. In a greenhouse experiment, we grew fifteen soybean cultivars in field soil amended with sand in 1-m rooting columns, which allowed for simulation of field-like soil water profiles and rooting depths. Two watering treatments were imposed from the first flower until maturity by daily restoration of soil water to either 100% (control), or 50% (drought stress) of the maximum soil water holding capacity. Throughout the experiment, we measured volumetric soil water content at different depths in the soil profile, but found no evidence at any developmental stage that the cultivars differed for their ability to extract soil water from different depths. Drought stress reduced seed yield by 51% on average. Similar to the effects of drought in the field, pod number was the yield component most affected, with effects on seeds per pod and single-seed weight being comparatively minor. There were significant cultivar × treatment interactions for seed yield, pod number, shoot dry matter, and water use. We identified two drought-sensitive (Saska and OAC Drayton) and three drought-tolerant (OAC Lakeview, OAC Champion, and PRO 2715R) cultivars based on their ratios of seed yield under drought stress to seed yield under control conditions (seed yield ratio, SYR). Regression and principal component analyses revealed that drought-tolerant (high-SYR) cultivars were consistently those that maintained relatively high values for water use, biomass accumulation and pod number under drought stress; high water use efficiency under drought stress was also associated with a high SYR. One of the cultivars, OAC Lakeview, displayed a distinct mode of drought tolerance, maintaining a very high fraction of its control pod number under drought stress. This study helps define the physiological basis of soybean cultivar differences in drought tolerance, and provides direction for soybean breeders to select traits that could improve yield under drought stress.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".