Soil bacterial community response to cover crop introduction in a wheat-based dryland cropping system
Bibliographic record
Abstract
The incorporation of cover crops into cropping systems is important for enhancing soil health in agricultural systems. Soil microbes contribute to soil health by supplying key nutrients and providing protection against plant pests, diseases, and abiotic stress. While research has demonstrated the connection between cover crops and the soil microbiology, less is known regarding the impact of cover crops on the soil microbial community in semi-arid regions of the Northern Great Plains. Our objectives were to evaluate changes in the soil bacterial community composition and community networks in wheat grown after multi-species cover crops. Cover crops were compared to continuous cropping and crop/fallow systems and the effects of cover crop termination methods were also evaluated. Cover crops consisted of a cool season multispecies mix, mid-season multispecies mix, and a warm season multispecies mix, which were grown in rotation with winter wheat. A continuous cropping (wheat/barley) and wheat/fallow system were also included along with cover crop termination by grazing, herbicide application, and haying. Cover crop treatments and termination methods had no significant impact on microbial community alpha diversity. Cover crop termination methods also had no significant impact on microbial community beta diversity. Families belonging to the phyla Actinobacteria, Bacterioidota, and Proteobacteria were more abundant in the cool season cover crop treatment compared to the warm season cover crop treatment. Co-occurrence network analysis indicated that incorporation of cool season cover crops or mid-season mixes in a wheat-based cropping system led to greater complexity and connectivity within these microbial networks compared to the other treatments which suggests these communities may be more resilient to environmental disturbances.
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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".