Pulse Frequency in Crop Rotations Alters Soil Microbial Community Networks and the Relative Abundance of Fungal Plant Pathogens
Bibliographic record
Abstract
Including pulse crops in cereal-based cropping systems has become a widely accepted and useful agronomic practice to increase crop diversification and biologically fixed nitrogen in agroecosystems. However, there is a lack of knowledge regarding how the intensification of pulses in crop rotations influence soil microbial communities. In this study, we used an amplicon sequencing approach to examine the bulk and rhizosphere soil bacterial and fungal communities from the wheat (Triticum aestivum L.) phase (final year of 4 years rotations) of a long-term pulse intensification field trial in the semi-arid region of the Canadian Prairies. Our results revealed pulse frequency had a minimal impact on microbial α-diversity, but caused a significant shift in the composition of the fungal (rhizosphere and bulk soil) and bacterial (bulk soil) communities. This effect was the most pronounced in the Ascomycete and Bacteroidete communities. Increasing pulse frequency also promoted a higher proportion of fungal pathotrophs in the bulk soil, particularly those putatively identified as plant pathogens. The network analysis revealed that rotations with higher pulse frequency promoted increased competition within the soil microbial networks in the rhizosphere and bulk soil. However, we also detected more negative interactions among the dominant pathotrophic taxa with increased pulse frequency, suggesting higher soil-borne disease potential. These findings highlight the potential drawbacks and reduced sustainability of increasing pulse frequency in crop rotations in semiarid environments.
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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".