Influence of No-Till System with or without Cover Crops on Stomata Sensitivity of Glyphosate-Tolerant Soybeans to Vapor Pressure Deficit
Bibliographic record
Abstract
Soybeans are vulnerable to drought and temperature increases potentially induced by climate change. Hydraulic dysfunction and stomatal closure to avoid excessive transpiration are the main problems caused by drought. The vulnerability of soybeans to drought will depend on the intensity and duration of water stress. The purpose of this study was to determine if the use of cover crops (CCs) can influence the gas exchange potential of glyphosate-tolerant soybeans when the vapor pressure deficit (Vpd) increases. This two-year study was conducted in an open experimental field comprising direct seeding plots with or without CCs. Stomatal conductance (Gs) was measured five times on the same identified leaves following glyphosate-based herbicide application. These leaves were then collected in order to observe the stomata and foliar traits with a scanning electron microscope. The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CCs promotes phenotypic change in soybean leaves (more elaborate venation and a higher abaxial stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in plots with CCs compared to those without CCs with similar Vpd values. This study shows that the benefits of using CCs can be observed via the morphological development strategies of the crop plants and their higher tolerance to drought.
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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".