Effective Rhizobia Relieve Negative Effects of Drought Stress During the Vegetative Stage in Soybean Under Field Conditions
Bibliographic record
Abstract
ABSTRACT Soybean ( Glycine max L.) forms a symbiotic relationship with compatible soil rhizobia, enabling biological nitrogen fixation. Among numerous factors, moisture deficit is a major challenge to soybean production due to its direct impact on the ability to fix nitrogen. The aim of this study was to assess whether effective rhizobia strains could alleviate the impact of early‐onset drought stress during the vegetative growth of soybeans under field conditions. A 2‐year field study was conducted in Wellington County, Ontario, Canada, examining three different rhizobia treatments, including low‐nitrogen‐fixing Bradyrhizobium elkanii USDA 76, high‐nitrogen‐fixing Bradyrhizobium japonicum USDA 110, and a commercial inoculant, compared to the uninoculated‐0 N control and uninoculated‐urea 150 kg N ha −1 treatments, under irrigated and nonirrigated conditions. Data were collected at V2, R1, R3, R5 and R7 growth stages and at seed maturity. Results indicated that the number of nodules and nodule dry weight was reduced under drought stress. However, plants demonstrated recovery from these negative effects in the later part of the growing season with USDA 110 and commercial inoculant application, particularly following rainfall events. Therefore, soybeans exposed to drought during the early planting period up to ~R5 growth stage could still recover nitrogen fixation traits, as evidenced by increased nodule number and nodule dry weight. Higher levels of grain δ 15 N in rhizobia‐inoculated plants under drought conditions in 2016, compared to the irrigated plants, confirmed the drought‐impaired biological nitrogen fixation. However, effective rhizobia inoculants, such as commercial inoculants and USDA 110, demonstrated similar or even higher yields compared to urea‐supplemented plants under drought conditions, underscoring their beneficial role in soybean production under challenging environments.
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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 | 0.001 |
| 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) | 0.000 | 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".