Exploring bacterial communities and their function for soil health under different cropping systems
Bibliographic record
Abstract
Rhizosphere and soil bacteria are important drivers in nearly all biochemical cycles in terrestrial ecosystems and participate in maintaining health and productivity of soil in agriculturally managed systems. However, the effect of agricultural management systems on bacterial communities is still poorly understood. In this study, cultural methods and advanced molecular methods (terminal restriction fragment length polymorphism (TRFLP) and 454- pyrosequencing) were used to identify shifts in soil and rhizosphere bacterial diversity, community composition, and functions under different cropping systems in Manitoba, Canada. This included monoculture vs. rotation, zero tillage vs. conventional tillage, and organic farming vs. conventional farming. Results showed that: (1) different cropping systems did not significantly influence the diversity of bacterial communities. However, a significant variation in relative abundances of bacterial communities at both the phylum and genus level was observed among different cropping systems. Compared to conventional farming systems, organic farming system had a higher percentage of the phylum Proteobacteria (many Plant Growth Promoting Rhizosbacteria) and a lower percentage of the phylum Actinobacteria. When canola monoculture was compared to wheat-oat-canola-pea rotation, a significantly higher percentage of Proteobacteria and a lower percentage of Actinobacteria were found in the rotational system. Wheat monoculture shared similar bacterial communities with wheat-oat-canola-pea rotation. Zero tillage did not change bacterial community profiles except for an increase in Firmicutes (many PGPR), compared to conventional tillage. At the genus level, significant differences were found for the dominant genera Pseudomonas, Rhizobium, Stenotrophomonas, Brevundimonas, Burkholderia, Marmoricola, Microlunatus, and Solirubrobacter. The bacterial distribution was strongly associated with soil pH. (2) The cropping systems also influenced the antibiotic-producing Pseudomonas populations determined through PCR-based screening for the detection of genes involved in the biosynthesis of antibiotics. It was found that pyrrolnitrin- and phenazine- producing Pseudomonas spp. were more prevalent in the soil under zero tillage and organic farming systems, while 2,4-DAPG and pyoluteorin-producing strains were not found in this study. This comprehensive study provided fundamental information on how different cropping systems affect soil and rhizosphere bacterial communities, which can be used to guide Manitoba farmers to choose proper farming systems to maintain soil health and increase PGPR populations in soil.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".