Deep vertical rotary tillage increases the diversity of bacterial communities and alters the bacterial network structure in soil planted to corn
Bibliographic record
Abstract
Deep vertical rotary tillage (DVRT) is a novel technique; however, its influence on soil bacterial diversity and community structure remains unclear. Herein, it was hypothesized that this tillage method significantly improves the bacterial diversity and alters the bacterial community structure and therefore it supports enhanced soil ecosystem functions in cultivated land. We investigated the soil bacterial communities and performed molecular ecological network analysis of cultivated land soils under different tillage regimes using high-throughput 16S rRNA gene Illumina sequencing. Soil samples were collected from the experimental field under 2 treatments: DVRT and conventional rotary tillage (CT) in Shizuishan City, Ningxia, China, in a 2-year field experiment. The α-diversity indices showed that DVRT resulted in higher bacterial diversity. In addition, the principal coordinate analysis results revealed a clear separation among the groups of cultivated land soils under the 2 treatment regimes. The key physicochemical factors that significantly influenced bacterial diversity and community structure were pH and total potassium concentration. The network analysis indicated that the bacterial network of DVRT soils consisted of more functionally interrelated bacterial modules than that of soils under CT, and the topological roles of characteristic bacteria and key bacteria were also different. In relation to CT, the relative abundances of organisms belonging to the functional groups of “Xenobiotics biodegradation and metabolism”, “Signal transduction”, and “Metabolism of cofactors and vitamins” were significantly increased in cultivated land soils under DVRT. It was concluded that DVRT treatment could improve bacterial diversity, alter the bacterial network structure, and enhance potential ecosystem functions in soils of cultivated land.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".