Response of Deep Soil Carbon Pools to Forest Management in a Highly Productive Andisol
Bibliographic record
Abstract
Core Ideas Deep soils are rarely included in studies of management effects on soil carbon. The majority of soil carbon at this site was stored in subsurface (>30 cm) horizons. Forest management did not significantly affect total carbon pools to a depth of 3 m. Control of competing vegetation increased carbon storage deep in the soil profile. Soil contains more C than the atmosphere and plant biomass combined. Consequently, it is the most important long-term sink for C within terrestrial ecosystems. An understanding of the potential to induce C sequestration in soils through management is crucial in light of increasing anthropogenic CO2 emissions. Nevertheless, soil has historically been under-represented in C cycling research, especially regarding subsurface (>30 cm) layers and processes. Research on the effects of forest management practices on deep soil C has been lacking. To test the effects of biomass removal and vegetation control treatments on deep soil C, soils were sampled to a depth of 3 m at the Fall River Long-term Soil Productivity Site in western Washington State. Treatments were installed 15 yr previously in a complete randomized block design. No difference was found in total soil C among treatments, but there was significantly less (a = 0.10) C stored at the deepest interval measured (250–300 cm) in the plots with vegetation control (8.6 Mg C ha-1) than in those without (16.3 Mg C ha-1). These results suggest the stability of soil C pools at Fall River and indicate that more intensive management practices may not deplete C pools at this site, but imply that these deep soil pools may be more sensitive to change than shallow pools. Here, 58.2% of the soil C pool is located below 30 cm, which demonstrates that shallow sampling significantly underestimates soil C pools and highlights the importance of understanding processes that control deep soil C.
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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 | 0.000 | 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) | 0.001 | 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".