Marginal effects of silvicultural treatments on soil nutrients following harvest in a Chinese fir plantation
Bibliographic record
Abstract
Despite the widespread recognition that silvicultural treatments (prescribed harvest residue burning, site preparation and replanting) applied following clearcutting may cause soil erosion and nutrient loss in Chinese fir (Cunninghamia Lanceolata (Lamb.) Hook) plantations, it is unclear which specific treatment leads to nutrient loss and whether an appropriate implementation of the treatments during the dry season could avert nutrient loss altogether. To address these two questions, nutrient changes in Alliti-Udic Ferrosols soils within a Chinese fir plantation located in Huitong County, Hunan Province, were investigated through the analysis of soil samples sequentially collected at depths of 0–15 cm and 15–30 cm before and after harvest with the residue material kept in place, a prescribed residue burning operation, site preparation and tree replanting initiative took place. Individual treatments significantly affected the soil pH value, organic matter and C and available N at depths of 0–15 cm, but did not significantly influence soil bulk density, total N and P contents and available P contents. The soil pH value decreased with successive application of the treatments. Soil organic C increased by way of the remaining residue after clearcutting, but declined after prescribed residue burning and ultimately returned to pre-harvest values after site preparation as a result of soil displacement and burning ash. Available N contents decreased significantly after clearcutting and residue burning, but the reduction was more or less offset after site preparation took place. Results after all silvicultural treatments were applied showed that no significant reduction in soil organic matter, C and N and P occurred to date in the Chinese fir plantation studied, suggesting that nutrient loss could be averted if the treatments were implemented during the dry season.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".