Cropping sequence affects the structure and diversity of pathogenic and non-pathogenic soil microbial communities
Bibliographic record
Abstract
Abstract Aims Current understanding of how cropping sequence affects pathogen-suppressive microbiomes in soil is limited. We investigated the effects of several cropping sequences from the 2020–2021 growing seasons, including cereals, pulses, and an oilseed, on microbial communities in rhizosphere and bulk soils in two western Canadian field locations. Methods The fungi and bacteria were characterized by Internal Transcribed Spacer (ITS) and 16S rRNA gene sequencing, respectively. The QIIME 2™ bioinformatic pipeline was used to measure the diversity and abundance of microbial species. Additionally, the concentration of the soil mineral chemicals, including macro and micro nutrients, was determined by colorimetric analysis. Results Ascomycota (62.5%) was the most common fungal phylum, followed by Glomeromycota (11.1%), Mucoromycota (8.9%), and Basidiomycota (6.8%). Pseudomonadota (35.0%), Actinomycetota (21.1%), and Bacillota (10.1%) were the three most common bacterial phyla. Fungal OTU richness and phylogenetic diversity were highest in the cereal-pulse cropping sequencing, and bacterial OTU richness was highest in the pulse-oilseed sequences. Fusarium was the fungal genus most commonly associated with cereal-cereal monoculture and least common in the oilseed-pulse cropping sequences. The fungi ( Mortierella , Funneliformis , and Diversispora ) and bacteria ( Rhizobium , Bradyrhizobium , Flavobacterium , and Candidatus ) were higher in the cropping sequences involving pulses. The most prevalent bacteria were Streptomyces in cereal-related sequences and Solirubrobacter and Pseudomonas in oilseed-related sequences. Among soil mineral chemicals, nitrate-nitrogen, copper, calcium, potassium, and chlorine were associated with a number of beneficial fungal and bacterial genera but not with pathogenic fungal genera. Conclusions The results highlight the consequences of crop species selection in cropping sequences and the management of agrochemicals in the agricultural production system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".