Soil organic carbon as an indicator of environmental quality at the national scale: Inventory monitoring methods and policy relevance
Bibliographic record
Abstract
Soil organic carbon (SOC) storage is an indicator of environmental quality for mineral soils because of the influence that organic matter has on key functional properties, such as fertility, soil structure and water relations. Historically, agricultural management has caused large losses of SOC relative to native ecosystems, leading to degradation. However, new technologies and conservation practices have been developed during the past few decades that can enhance SOC storage, and thus improve environmental quality. Our objective was to describe a national inventory procedure to estimate SOC storage for purposes of monitoring environmental quality. The major steps in this procedure include: (1) model selection/development, (2) model verification, (3) identification of model input data, (4) uncertainty assessment, (5) model implementation, and (6) validation of results. Applying this approach with a simple C accounting method, the upper 30 cm of US agricultural soils were estimated to have accumulated 10.8 Tg C yr -1 between 1982 and 1997, with an uncertainty of ± 40%. A simple index was developed to relate estimated SOC stocks to the potential amounts under native conditions and conventional agricultural management. An index value of 0% on the proposed scale would be equivalent to the SOC under conventional a gricultural use, while an index value of 100% would be equivalent to native levels. With an estimated 1997 stock of 22 400 Tg C, the index value for US agricultural soils was about 60%. Using this inventory procedure, environmental issues related to soil, water and air quality could be informed by SOC in combination with other key indicators, in addition to using the inventory for evaluating sustainability of agricultural lands for food and fiber production. Key words: Natural resource inventory, environmental quality indicators, soil organic carbon, land use and management, national inventory, Organization for Economic Co-operation and Develop
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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 | 0.002 | 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 | 0.001 |
| 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".