Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States
Bibliographic record
Abstract
Abstract Because soil microbes drive many of the processes underpinning ecosystem services provided by soils, understanding how cropping systems affect soil microbial communities is important for productive and sustainable management. We characterized and compared soil microbial communities under restored prairie and three potential cellulosic biomass crops (corn, switchgrass, and mixed prairie grasses) in two spatial experimental designs – side‐by‐side plots where plant communities were in their second year since establishment (i.e., intensive sites) and regionally distributed fields where plant communities had been in place for at least 10 years (i.e., extensive sites). We assessed microbial community structure and composition using lipid analysis, pyrosequencing of rRNA genes (targeting fungi, bacteria, archaea, and lower eukaryotes), and targeted metagenomics of nifH genes. For the more recently established intensive sites, soil type was more important than plant community in determining microbial community structure, while plant community was the more important driver of soil microbial communities for the older extensive sites where microbial communities under corn were clearly differentiated from those under switchgrass and restored prairie. Bacterial and fungal biomasses, especially biomass of arbuscular mycorrhizal fungi, were higher under perennial grasses and restored prairie, suggesting a more active carbon pool and greater microbial processing potential, which should be beneficial for plant acquisition and ecosystem retention of carbon, water, and nutrients.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".