Evaluation of bacterial and fungal diversity in a long-term spring wheat – field pea rotation field under different tillage practices
Bibliographic record
Abstract
Soil management practices have the potential to modify the diversity and function of microbes in agricultural fields. The aim of this study was to investigate bacterial and fungal diversity in a 15 yr wheat–pea rotation tillage experiment. The treatments included conventional tillage with stubble removed, no-till with stubble removed, no-till with stubble retained (NTS), and conventional tillage with stubble incorporated. Illumina high-throughput sequencing platform was employed to sequence bacteria 16S rRNA (V3V4) and fungi internal transcribed spacer (ITS2) region genes in 0–10 cm and 10–30 cm of soil sampled. The dominant bacterial and fungal phyla identified at 97% similarity cutoff across both depths of treatments were Proteobacteria (26.3%), Actinobacteria (25.1%), Acidobacteria (15.0%), Gemmatimonadetes (8.8%), Ascomycota (85.8%), and Basidiomycota (8.0%). NTS had significantly (p < 0.05) higher microbial diversity indices, total organic carbon, soil microbial biomass carbon and nitrogen, NO3-N, and NH4-N at 0–10 cm depth. Tillage and stubble effects had a significant correlation with some phyla such as Proteobacteria, Actinobacteria, Gemmatimonadetes, JL-ETNP-Z39, Nitrospirae, Chloroflexi, Firmicutes, and other identified and unidentified minor microbial phyla. No-till and residue retention practices influenced fungal and bacterial species diversity through improved soil chemical properties, which have potential to affect the habitat and activity of soil microbes. Therefore, no-till and stubble retention could improve soil quality and promote sustainable agriculture in the rainfed Loess Plateau.
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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".