Microbial communities in soils with different population densities of soybean cyst nematode
Bibliographic record
Abstract
The relationship of abiotic factors with soybean cyst nematode (SCN), the most damaging pathogen of soybean, has been studied extensively, but characterization of microbial communities in infested soils and on soybean roots under field conditions remains unknown. This study investigated the relationship of different SCN population density levels with soil physical, chemical and microbial properties in soils and on soybean roots. A commercial soybean field with different levels of SCN densities (low, medium, high) was sampled, and microbial communities were characterized using both dilution plating assays and molecular approaches. Population densities of species of Trichoderma, actinomycetes, Pseudomonas and other bacteria were significantly higher (P ≤ 0.05) in soils with low SCN (SCN-C) compared with high (SCN-A) and medium levels (SCN-B). Diversity indices of Trichoderma communities on roots based on denaturing gradient gel electrophoresis (DGGE) were significantly higher in soils with SCN-C compared with SCN-A and SCN-B. Cluster analyses and canonical correspondence analysis (CCA) generally separated microbial communities into three groups based on different SCN levels, and CCA showed that low SCN population levels were positively correlated with high levels of nitrogen (N), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), cation exchange capacity (CEC), base saturation (BS), the populations of Trichoderma and bacteria. Moreover, diverse Trichoderma, Pseudomonas, Bacillus and actinomycetes were found on roots in SCN-C soils based on dilution plating and DGGE, suggesting that the presence of these microbes on soybean roots might be associated with improved plant health and reduction of SCN.
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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".