Poplar plantations in coastal China: towards the identification of the best rotation age for optimal soil carbon sequestration
Bibliographic record
Abstract
Abstract Poplar plantations are an important resource in China, which possess significant potential to offset carbon (C) emissions through the sequestration of atmospheric carbon dioxide (CO 2 ) within biomass and soil. The traditional rotation age of poplar plantations is determined by maximizing the economic return from timber production. However, the optimal rotation age that results in the highest level of carbon sequestration within the soil remains unclear. In this study, we examined the total C, nitrogen (N) and microbial biomass ( SMB ) content of soils, as well as other properties in 0–10, 10–25 and 25–40 cm soil profiles along a 0‐ to 20‐yr chronosequence in a coastal region of Eastern China. Soil C stocks were determined for 1 m soil profiles, and the stand biomass in poplar plantations of different ages was investigated. We found that C concentrations within soils increased with plantation age, primarily in the topsoil layers. The periodic annual increment of C in soils peaked between stand ages of from 6 to 10 yr (0.71 t/ha/yr) and then decreased considerably at 17.5 yr, while the mean annual increment of C in soils was the highest at 15 yr (0.573 t/ha/yr). Soil C accumulation (i.e. soil C sequestration) was positively correlated with poplar biomass, soil N and SMB , and negatively correlated with soil potassium (K), calcium (Ca), magnesium (Mg) and sodium (Na), but not with sulphur (S) or phosphorus (P). Our results suggest that a rotation age of 15 yr is optimal for the sequestration of atmospheric CO 2 in poplar plantations in the coastal region of Eastern China. The C sequestration capacity of soil was primarily controlled by poplar biomass, soil N and SMB .
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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 | 0.001 | 0.001 |
| 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 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".