The Impact of Afforestation on Soil Organic Carbon Sequestration on the Qinghai Plateau, China
Bibliographic record
Abstract
Afforestation, the conversion of non-forested land into forest, is widespread in China. However, the dynamics of soil organic carbon (SOC) after afforestation are not well understood, especially in plateau climate zones. For a total of 48 shrub- and/or tree-dominated afforestation sites on the Qinghai Plateau, Northwestern China, post-afforestation changes in SOC, total nitrogen (TN), the carbon-to-nitrogen ratio (C/N) and soil bulk density (BD) were investigated to a soil depth of 60 cm using the paired-plots method. SOC and TN accumulated at rates of 138.2 g C m(-2) yr(-1) and 4.6 g N m(-2) yr(-1), respectively, in shrub-dominated afforestation sites and at rates of 113.3 g C m(-2) yr(-1) and 6.7 g N m(-2) yr(-1), respectively, in tree-dominated afforestation sites. Soil BD was slightly reduced in all layers in the shrub-dominated afforestation plots, and significantly reduced in soil layers from 0-40cm in the tree-dominated afforestation plots. The C/N ratio was higher in afforested sites relative to the reference sites. SOC accumulation was closely related to TN accumulation following afforestation, and the inclusion of N-fixing species in tree-dominated afforestation sites additionally increased the soil accumulation capacity for SOC (p < 0.05). Multiple regression models including the age of an afforestation plot and total number of plant species explained 75% of the variation in relative SOC content change at depth of 0-20 cm, in tree-dominated afforestation sites. We conclude that afforestation on the Qinghai Plateau is associated with great capability of SOC and TN sequestration. This study improves our understanding of the mechanisms underlying SOC and TN accumulation in a plateau climate, and provides evidence on the C sequestration potentials associated with forestry projects in China.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".