Impact of Varietal Differences in Water Use Efficiency on Soybean Yield under Water Stress
Bibliographic record
Abstract
Naturally-occurring water stress causes yield losses in Ontario soybean. The role of water use efficiency (WUE) in drought tolerance of soybean is investigated. There is genetic variation for WUE within commercially grown varieties. We hypothesized that high WUE conveys resistance to yield loss under rain-fed conditions in Ontario. Varieties whose WUE varied over a range of 16% under greenhouse conditions, were grown under two soil water conditions, rain-fed and water-replete, in 2011-2013 in a replicated field experiment. Yield loss due to water deficit occurred yearly, even with above-average rainfall, with pod number being the most impacted yield component. Contrary to the hypothesis, high-WUE varieties did not have a measurable yield advantage under rain-fed conditions. This may be due to the physiological basis of the variation in WUE, since in the greenhouse screening a high WUE was consistently associated with high biomass production but also increased water use, not water conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 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 teacher head, 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".