Net greenhouse gas balance of fibre wood plantation on peat in Indonesia
Bibliographic record
Abstract
Abstract Tropical peatlands cycle and store large amounts of carbon in their soil and biomass1–5. Climate and land-use change alters greenhouse gas (GHG) fluxes of tropical peatlands, but the magnitude of these changes remains highly uncertain6–19. Here we measure net ecosystem exchanges of carbon dioxide, methane and soil nitrous oxide fluxes between October 2016 and May 2022 from Acacia crassicarpa plantation, degraded forest and intact forest within the same peat landscape, representing land-cover-change trajectories in Sumatra, Indonesia. This allows us to present a full plantation rotation GHG flux balance in a fibre wood plantation on peatland. We find that the Acacia plantation has lower GHG emissions than the degraded site with a similar average groundwater level (GWL), despite more intensive land use. The GHG emissions from the Acacia plantation over a full plantation rotation (35.2 ± 4.7 tCO2-eq ha−1 year−1, average ± standard deviation) were around two times higher than those from the intact forest (20.3 ± 3.7 tCO2-eq ha−1 year−1), but only half of the current Intergovernmental Panel on Climate Change (IPCC) Tier 1 emission factor (EF)20 for this land use. Our results can help to reduce the uncertainty in GHG emissions estimates, provide an estimate of the impact of land-use change on tropical peat and develop science-based peatland management practices as nature-based climate solutions.
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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".