Grasslands and Croplands Have Different Microbial Biomass Carbon Levels per Unit of Soil Organic Carbon
Bibliographic record
Abstract
Primarily using cropped systems, previous studies have reported a positive linear relationship between microbial biomass carbon (MBC) and soil organic carbon (SOC). We conducted a meta-analysis to explore this relationship separately for grasslands and croplands using available literature. Studies were limited to those using fumigation–extraction for MBC for field samples. Trials were noted separately where records were distinct in space or time. Grasslands were naturally occurring, restored, or seeded. Cropping systems were typical of the temperate zone. MBC had a positive linear response to increasing SOC that was significant in both grasslands (p < 0.001; r2 = 0.76) and croplands (p < 0.001; r2 = 0.48). However, MBC increased 2.5-fold more steeply per unit of increasing SOC for grassland soils, as compared to the corresponding response in cropland soils. Expressing MBC as a proportion of SOC across the regression overall, slopes corresponded to 2.7% for grasslands and 1.1% for croplands. The slope of the linear relationship for grasslands was significantly (p = 0.0013) steeper than for croplands. The difference between the two systems is possibly caused by a greater proportion of SOC in grasslands being active rather than passive, relative to that in croplands, with that active fraction promoting the formation of MBC.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".