Effects of Prescribed Fire on Soil Biogeochemistry in a Mixed Grass Prairie
Bibliographic record
Abstract
Limited information exists regarding the effects of prescribed fire on soil biogeochemistry in the mixed grasslands of North America. This study investigated the effects of prescribed grassland fire on soil biogeochemistry over two growing seasons in the mixed grass prairies of Southern Saskatchewan. Spring burning was conducted in continuously grazed native and tame pastures. Soils were sampled (0-10 cm) 2, 3, 4 and 15, 16, 17 months after fire in burned and adjacent control plots. Investigation of soil biogeochemical changes include the analysis of soil total carbon (C), nitrogen (N), microbial biomass C (MBC), microbial biomass N (MBN), C stock, pH, electrical conductivity (EC) and characterization of the microbial community through phospholipid fatty acid biomarker extraction. Results indicate that the mixed grasslands in southern Saskatchewan are largely resistant and resilient to the effects of disturbance by fire. Slight changes in the microbial community structure were observed in both pastures; burning increased the homogeneity in microbial community composition. Attributed to a post fire nutrient flush, the tame forage pasture had an increase in soil fungi 2-4 months following fire and an increase in the ratio of Gram-negative bacteria to Gram-positive bacteria throughout the duration of the study. Temporal effects on soil biogeochemistry were stronger than fire effects. Pastures responded differently over time, likely due to differences in vegetation composition and abundance as well as land use history. This research shows that the use of prescribed fire is compatible with soil conservation principles, as negative effects on soil biogeochemistry were not observed. Reducing barriers to the use of prescribed fire in grassland management is important for the preservation and productivity of remnant grassland parcels, and the ecosystem services grasslands provide.
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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) | 0.000 | 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".