Pea-Wheat Rotation Affects Soil Microbiota Diversity, Community Structure, and Soilborne Pathogens
Bibliographic record
Abstract
Intensive cultivation based on monocultures has a significant impact on ecosystem function, and sustainable agriculture must rely on alternative methods, including crop rotation. On the Canadian prairies, the use of pulse crops is a common practice, but few studies have investigated the impact on soil microorganisms. Here, we studied the effect of pea, wheat, pea–wheat rotation, and fallow in bulk soil bacterial and fungal communities. We characterized soil microbiota by high-throughput sequencing of 16S and 18S rRNA genes for bacteria and eukaryotes. Different crop rotations and fallow significantly modified soil community composition, as well as bacterial and fungal diversity. Pea alone caused a strong reduction of bacterial and fungal richness and diversity compared to wheat, pea–wheat rotation, and fallow. Notably, pea–wheat rotation increased the abundance of Fusarium graminearum compared to other management practices. The bacterial community was less responsive to crop rotation identity compared to the fungal microbiota, and we found minor differences at the phylum level, with an increase in Actinobacteria in fallow and Firmicutes in wheat. In summary, our study demonstrated that rotations alter bulk soil microbial community diversity and composition in Canadian prairies. The frequent use of pea in rotation with wheat should be carefully evaluated, balancing their ecological effects on nitrogen mineralization, water conservation, and impact on beneficial, as well as pathotrophic, fungi.
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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".