Soybean nodulation shapes the rhizosphere microbiome to increase rapeseed yield
Bibliographic record
Abstract
• Decoding Soybean Root Secretions: By analyzing secretions within the soybean root secretome across three different soybean nodulation genotypes with the same background, we discovered that supernodulating soybeans secrete higher levels of fatty acids and carbohydrates. • Bacterial Communities Clarification: Investigating the rhizosphere bacterial communities of soybeans with varying nodulation patterns in central China, we identified Sphingomonadaceae as the predominant rhizosphere bacteria associated with soybean nodulation capacity. • Metabolites and Bacterial Attraction: The soybean root secretes a metabolite called cis -4-hydroxy-D-proline, which attracts the accumulation of Sphingomonadaceae species in the soybean rhizosphere. Furthermore, oleic acid and cis -4-hydroxy-D-proline provide essential carbon nutrients for the growth of Sphingomonadaceae species. • Promoting Oilseed Growth: Exogenous application of Sphingomonadaceae bacteria, either alone or in combination with rhizobia, can effectively promote subsequent rapeseed growth. • These findings elucidate the role of soybean nodulation in rhizosphere bacterial dynamics, highlighting its importance in sustainable agricultural practices. Crop rotation, a crucial agricultural practice that enhances soil health and crop productivity, is widely used in agriculture worldwide. Soybeans play a crucial role in crop rotation owing to their nitrogen-fixing ability, which is facilitated by symbiotic bacteria in their root systems. The soybean-rapeseed rotation is an effective agricultural practice in the Yangtze River Basin of China. However, the mechanism underlying the effectiveness of this system remains unknown. The aim of this study was to decipher the mechanisms by which previous soybean cultivation enhances the growth of subsequent rapeseed. Soybeans with three distinct nodulation genotypes were rotated with rapeseed, and the impact of previous soybean cultivation on subsequent rapeseed growth was evaluated by examining the soybean root secretome and soil rhizosphere microbiome. Soybean-rapeseed rotation significantly enhanced subsequent rapeseed growth and yield, especially when supernodulating soybean plants were used, which released the most nitrogen into the soil rhizosphere. The differences in soybean nodulation capability led to variations in root exudation, which in turn influenced the bacterial communities in the rhizosphere. Notably, the supernodulating soybean plants promoted Sphingomonadaceae family of bacteria growth by secreting oleic acid and cis -4-hydroxy-D-proline, and further attracted them through cis -4-hydroxy-D-proline. Furthermore, the exogenous application of Sphingomonadaceae bacteria, either alone or in combination with rhizobia, significantly enhanced the growth of rapeseed. Our data definitively demonstrated the crucial role of previous soybean cultivation in enhancing the yield of rapeseed, with the assistance of Sphingomonadaceae bacteria and rhizobia. This study elucidates the role of soybean nodulation in rhizosphere bacterial dynamics, highlighting its importance in sustainable agricultural practices.
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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) | 0.001 | 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".