The effect of drought stress on nodulation, plant growth, and nitrogen fixation in soybean during early plant growth
Bibliographic record
Abstract
Abstract Soybean [ Glycine max (L.) Merr.] is one of the most important grain legume crops in the world. Soybean forms a symbiotic relationship with the nitrogen‐fixing rhizobia bacteria that reside in root nodules and fix atmospheric nitrogen. Most of the soybean's nitrogen demand is derived from symbiotic nitrogen fixation (SNF). Drought is becoming a major threat to crop production in the world. Legumes are sensitive to drought stress, and drought can negatively affect SNF, grain yield, and seed protein production. The effect of drought stress on soybean growth, morphological traits, nodulation, water use efficiency, and SNF, particularly during early plant growth, remains poorly characterized. We hypothesized that early‐onset drought stress can negatively affect nodulation, plant growth, and SNF in soybean during the vegetative growth stages. To test this hypothesis, a controlled‐environment study was conducted using soybean plants grown in pots, where plants were subjected to moderate drought stress (40% field capacity) and well‐watered (80% field capacity) from seeding until the flowering stage. Drought stress significantly reduced the nodule number and nodule dry weight, root and shoot biomass, shoot total nitrogen content, shoot carbon to nitrogen ratio, and shoot total fixed nitrogen compared to the well‐watered control. Conversely, root‐to‐shoot ratio and shoot nitrogen concentration were higher under drought stress compared to well‐watered conditions. These findings enhance our understanding of adverse impacts of drought in the early growth stage of soybean while suggesting that reduced total nitrogen assimilation during the vegetative phase can lead to less nitrogen reserves available for translocating at the seed filling stages for seed protein production. This information should aid in generating drought‐tolerant soybean cultivars.
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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 | 0.001 |
| 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 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".