Biochar and Bacillus subtilis co-drive dryland soil microbial community and enzyme responses
Bibliographic record
Abstract
Introduction To investigate the impact of soil amendments on the structure of the soil microbial community. Methods This study focuses on dryland soil and employs indoor static cultivation as the experimental approach. It analyzes the impact and mechanism of adding rice straw biochar (S), rapeseed straw biochar (Y), and Bacillus subtilis agent (J) separately and in combination on the soil microbial community structure. Results The experimental results indicated that, compared to the blank control (CK), the Y treatment increased the relative abundance of Proteobacteria by approximately 3.03% and significantly reduced the abundance of Acidobacteria (from 70.56% to 82.81%). The application of biochar and microbial inoculants significantly increased the relative abundance of Ascomycota (2.85% to 33.53%) and Rozellomycota (0.58% to 27.73%). Furthermore, the addition of soil amendments enhanced the richness (3.02% to 7.07%) and diversity (3.22% to 3.77%) of soil bacteria, as well as the microbial nitrogen content (3.7 to 9.3 times). Meanwhile, except for the YJ treatment, the richness of the fungal community decreased, while the diversity index increased. The experimental results showed that the application of rapeseed straw biochar or the compound microbial inoculant alone significantly increased soil urease activity, reaching 40.34 µg of NH + 4 -N g −1 of soil h −1 and 40.29 µg of NH + 4 -N g −1 of soil h −1 at the end of the incubation period, respectively. Discussion In conclusion, rapeseed straw biochar not only enhances the soil microbial community but also significantly influences soil enzyme activity. This study offers a scientific foundation for utilizing biochar and Bacillus subtilis to improve dryland soil, providing valuable insights for sustainable soil management.
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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) | 0.001 | 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".