Biochar and Wood Ash Impacts on Soil Microbial Community Structure and Biogeochemical Functioning in Managed Ontario Forests
Bibliographic record
Abstract
Biochars and wood ash could be used as valuable soil amendments in managed Canadian forests to counteract effects of full-tree harvesting and N deposition, such as soil acidification and nutrient depletion. However, large-scale application cannot be recommended without a thorough assessment of the impacts of these amendments on key aspects of the ecosystem. Characterizing microbial responses is crucial for predicting the overall forest response to biochar and wood ash additions, because soil microbial communities affect ecosystem health and functioning and aboveground plant communities. However, for biochars in particular, the long-term effects on the soil microbial community in temperate forested ecosystems are still unknown. Similarly, while there have been ongoing ash-addition experiments in northern European and South American forests, the variability in microbial responses makes it challenging to extrapolate to other forest types and few field-scale ash addition studies have been conducted in Canadian forests. This thesis examines the effects of adding biochars and wood ash to Great Lakes' St. Lawrence and Boreal forest soils, particularly focusing on changes to the structure of the soil microbial community and its biogeochemical functional ability. In a north-temperate, selection-harvested forest, biochar application had only minor effects on bacterial and fungal community composition, fungi:bacteria ratios, microbial biomass, and microbial C mineralization two years after addition. Ash addition to a clear-cut boreal forest and the same selection-harvested forest had similarly minor effects on microbial community composition and did not alter overall microbial biomass or microbial C mineralization. In a microcosm experiment, biochar and ash addition effects on P bioavailability depended on soil texture and tree species. A comparison of four-year-old biochar particles and adjacent soil showed that even when there were phylogenetic differences between biochar and bulk soil microbial communities, both groups had very similar functional abilities. Across all experiments, site-specific factors played a strong role in chemical and biological responses to biochar and ash addition. However, most observed effects were minor and transient, indicating that biochar and ash can likely be used as soil amendments in these systems without negatively disrupting the soil microbial community.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".