Microbial Community Dynamics of Soybean (Glycine max) Is Affected by Cropping Sequence
Bibliographic record
Abstract
The microbial composition of the rhizosphere soil could be an important determinant of crop yield, pathogen resistance, and other beneficial attributes in plants. However, little is known about the impact of cropping sequences on microbial community dynamics, especially in economically important species like soybean. Using 2-year crop sequences of corn-soybean, canola-soybean, and soybean-soybean, we investigated how crops from the previous growing season influenced the structure of the microbiome in both the bulk soil and soybean rhizosphere. A combination of marker-based Illumina sequencing and bioinformatics analyses was used to show that bacterial species richness and evenness in the soybean rhizosphere soil were similar following canola and soybean compared to a previous corn sequence. However, fungal species richness and evenness remained unaffected by crop sequence. In addition, bacterial and fungal species diversity in both the bulk and soybean rhizosphere soil were not influenced by crop sequence. Lastly, the corn-soybean sequence significantly differed in the relative abundance of certain bacterial and fungal classes in both the soybean rhizosphere and bulk soil. While canola-soybean and a continuous soybean sequence did not, suggesting that a preceding corn sequence may reduce the occurrence of overall bacterial and fungal community members. For the present study, crop sequence impacts bacterial diversity and richness in both the bulk soil and soybean rhizosphere soil whereas fungal diversity and richness are resilient to crop sequence practices. Together, these findings could help drive decision making for annual crop and soil management 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 | 0.000 |
| 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 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".