Towards Robust Subsidence-Based Soil Carbon Emission Factors for Peat Soils in South-East Asia, With Special Reference to Oil Palm Plantations
Bibliographic record
Abstract
Oil palm and Acacia pulpwood plantations are being established at a rapid rate on drained peatland in south-east Asia. Accurate measurements of associated carbon losses are still scarce, however, due mainly to difficulties of excluding autotrophic carbon fluxes from chamber-based flux measurements and uncertainties about the extent of waterborne losses. Here, we demonstrate a simple approach to determining total net carbon loss from subsidence records that is applicable to steady state conditions under continuous land use. We studied oil palm and Acacia plantations that had been drained for 5–19 years. Very similar subsidence rates and dry bulk density profiles were obtained, irrespective of crop type or age of the plantation, indicating that the peat profiles were in a steady state. These are conditions that allow for the deduction of net carbon loss by multiplying the rate of subsidence by the carbon density of the peat below the water table. With an average subsidence rate of 4.2 cm y - 1 and a carbon density of 0.043 g cm -3 , we arrive at a net carbon loss of ~18 t ha -1 y -1 (~66 t CO 2 -eq ha -1 y -1 ) for typical oil palm and Acacia plantations more than five years after drainage, without large differences between the plantation types. The proposed method enables calculation of regional or project-specific carbon loss rates to feed into mitigation schemes of the UN Framework Convention on Climate Change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".