Forest Carbon Management: a Review of Silvicultural Practices and Management Strategies Across Boreal, Temperate and Tropical Forests
Bibliographic record
Abstract
Abstract Purpose of Review Carbon sequestration and storage in forest ecosystems is often promoted as a solution for reducing CO 2 concentrations in the atmosphere. Yet, our understanding is lacking regarding how forest management strategies affect the net removal of greenhouse gases and contribute to climate change mitigation. Here, we present a review of carbon sequestration and stock dynamics, following three strategies that are widely used in boreal, temperate and tropical forests: extensive forest management, intensive forest management and old-growth forest conservation. Recent Findings Several studies show that specific forest management strategies can improve carbon sequestration capacity and soil carbon storage. Within these studies, the old-growth forest conservation strategy results in greater carbon storage in soils than do extensive and intensive forest management. Intensive forest management enhances forest carbon sequestration capacity through afforestation using fast-growing species, mechanical soil preparation from low to moderate intensity and N fertilization. Extensive forest management is an intermediate compromise regarding carbon sequestration and soil carbon storage, between conservation and intensive forest management strategies. With respect to silvicultural treatments, partial cutting is a practice that increases forest carbon sequestration rates and maintains higher carbon storage in soils compared to clear-cuts. Each silvicultural practice that is discussed in this review showed a similar effect on forest carbon in all biomes, although the magnitude of these effects differs mainly in terms of heterotrophic respiration. Summary To achieve sustainable management and fulfill industrial demand and profitability, specific gaps must be dealt with to improve our scientific knowledge regarding forest carbon sequestration in a climate change context, mainly through the integration of the three aforementioned strategies in a functional zoning approach at the landscape scale. We present a review with promising strategies for guiding sustainable forest management in such a global context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".