Soil organic carbon stocks and flows in New Zealand: System development, measurement and modelling
Bibliographic record
Abstract
An IPCC-based Carbon Monitoring System (CMS) was developed to monitor soil organic C stocks and flows to assist New Zealand to achieve its CO 2 emissions reduction target under the Kyoto Protocol. Geo-referenced soil C data from 1158 sites (0.3 m depth) were used to assign steady-state soil C stocks to various combinations of soil class, climate, and land use. Overall, CMS soil C stock estimates are consistent with detailed, stratified soil C measurements at specific sites and over larger regions. Soil C changes accompanying land-use changes were quantified using a national set of land-use effects (LUEs). These were derived using a General Linear Model to include the effects of numeric predictors (e.g., slope angle). Major uncertainties a rise from estimates of changes in the areas involved, the assumption that soil C is at steady state for all land-cover types, and lack of soil C data for some LUEs. Total national soil organic C stocks estimated using the LUEs for 0–0.1, 0.1–0.3, and 0.3–1 m depths were 1300 ± 20, 1590 ± 30, and 1750 ± 70 Tg, respectively. Most soil C is stored in grazing lands (1480 ± 60 Tg to 0.3 m depth), which appear to be at or near steady state; their conversion to exotic forests and shrubland contributed most to the predicted national soil C loss of 0.6 ± 0.2 Tg C yr -1 during 1990–2000. Predicted and measured soil C changes for the grazing-forestry conversion agreed closely. Other uncertainties in our current soil CMS include: spatially integrated annual changes in soil C for the major land-use changes, lack of soil C change estimates below 0.3 m, C losses from erosion, the contribution of agricultural management of organic soils, and a possible interaction between land use and our soil-climate classification. Our approach could be adapted for use by other countries with land-use-change issues that differ from those in the IPCC default methodology. Key words: Soil organic carbon, land-use change, stocks, flows, measurement, modelling, IPCC
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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 | 0.001 | 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) | 0.000 | 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".