The Impact of Near Natural Forest Management on the Carbon Stock and Sequestration Potential of Pinus massoniana (Lamb.) and Cunninghamia lanceolata (Lamb.) Hook. Plantations
Bibliographic record
Abstract
Quantifying the impact of forest management on carbon (C) stock is important for evaluating and enhancing the ability of plantations to mitigate climate change. Near natural forest management (NNFM) through species enrichment planting in single species plantations, structural adjustment, and understory protection is widely used in plantation management. However, its long-term effect on forest ecosystem C stock remains unclear. We therefore selected two typical coniferous plantations in southwest China, Pinus massoniana (Lamb.) and Cunninghamia lanceolate (Lamb.) Hook., to explore the effects of long-term NNFM on ecosystem C storage. The C content and stock of different components in the pure plantations of P. massoniana (PCK) and C. lanceolata (CCK), and their corresponding near natural managed forests (PCN and CCN, respectively), were investigated during eight years of NNFM beginning in 2008. In 2016, there was no change in the vegetation C content, while soil C content in the 0–20 cm and 20–40 cm layers significantly increased, compared to the pure forests. In the P. massoniana and C. lanceolata plantations, NNFM increased the ecosystem C stock by 31.8% and 24.3%, respectively. Overall, the total C stock of soil and arborous layer accounted for 98.2%–99.4% of the whole ecosystem C stock. The increase in the biomass of the retained and underplanted trees led to a greater increase in the arborous C stock in the near natural forests than in the controls. The NNFM exhibited an increasingly positive correlation with the ecosystem C stock over time. Long-term NNFM enhances ecosystem C sequestration by increasing tree growth rate at individual and stand scales, as well as by likely changing the litter decomposition rate resulting from shifts in species composition and stand density. These results indicated that NNFM plays a positive role in achieving multi-objective silviculture and climate change mitigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".