When is short-season soybean most susceptible to water stress?
Bibliographic record
Abstract
Fourteen soybean (Glycine max L. Merr.) cultivars were grown at Ottawa from 1993 to 2004 in a replicated design. Phenology, yield and seed quality data were collected. Climate data were merged into the data set. Seven key phenological growth stages were identified and the total precipitation (ppt) between stages was calculated per cultivar for all possible durations. Mean cumulative ppt among the growth stage durations was correlated with mean seed yield, 1000-seed weight (tsw) seed protein and oil content. Variation in ppt prior to flowering did not influence yield. Yield and tsw were found to be most susceptible to water stress from flowering to the end of seed development. The most sensitive stage occurred during a period from the beginning to the end of pod development (R4 to mid R5). Seed protein was correlated with ppt from the beginning of flowering to the beginning of seed development. Seed oil content was reduced by late season precipitation. The identification of the most sensitive stage of development in soybean to water stress will be useful for producers forecasting yield response to precipitation and for plant breeders targeting selection for water stress tolerance. Key words: Water stress, soybean, Glycine max L. Merr.
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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 | 0.000 | 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) | 0.001 | 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".