Influence of forest management on soil carbon dioxide efflux in temperate forests of Ontario, Canada
Bibliographic record
Abstract
Forests play an important role in the global carbon (C) cycle, storing a large amount of C in biomass (trunks, branches, leaves, and roots) and soils, and exchanging C with the atmosphere through photosynthesis and respiration. Global forest policy initiatives, such as those under the Kyoto protocol, currently aim to extend forest cover and wisely manage forest ecosystems to sequester more C from the atmosphere for mitigating climate change. CO 2 flux from soils is second in magnitude only to photosynthesis in most forest ecosystems, amounting to 60–90% of total ecosystem respiration. Forest management practices, such as harvesting, thinning, vegetation control, and fertilization influence soil CO2 efflux (FCO2) as a result of changes in microclimatic conditions, soil properties, root dynamics, and microbial community structure. This thesis investigates the influence of forest management practises, including the uneven-aged selection harvesting silviculture system, thinning, fertilization, liming, and litter removal, on soil FCO2 in temperate forest ecosystems in Ontario, Canada. Field measurements show that soil FCO 2 changes greatly with time since harvest and thinning, showing an initial peak related to soil disturbance and root decomposition, followed by a reduction driven by declines in fine root production. Phosphorus (P) plus potassium (K) fertilization and liming in the forest soil also significantly affect soil FCO2. P+K fertilization had a significant negative effect on soil FCO2, fine root production, and microbial respiration (MR) in sugar maple stands. Liming resulted in increased FCO2 when coupled with thinning, whereas litter removal suppressed soil FCO2 in red pine plantations. In both study systems soil FCO2 was strongly positively affected by temperature, soil water content, depth to bedrock, but soil pH and soil organic matter (SOM) had no detectable effects on soil FCO2. Results suggest that forest management practices involving partial harvests, either in the form of uneven-aged selection harvesting or thinning, and strategic additions of phosphorus, potassium, calcium and magnesium mineral nutrients, may reduce C losses from forest soil, and thereby enhance C sequestration in managed forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".