An evaluation of carbon indicators of soil health in long-term agricultural experiments
Bibliographic record
Abstract
Soil organic carbon (SOC) is closely tied to soil health. However, additional biological indicators may also provide insight about C dynamics and microbial activity. We used SOC and the other C indicators (potential C mineralization, permanganate oxidizable C, water extractable organic C, and β-glucosidase enzyme activity) from the North American Project to Evaluate Soil Health Measurements to examine the continental-scale drivers of these indicators, the relationships among indicators, and the effects of soil health practices on indicator values. All indicators had greater values at cooler temperatures, and most were greater with increased precipitation and clay content. The indicators were strongly correlated with each other at the site-level, with the strongest relationship between SOC and permanganate oxidizable C. The indicator values responded positively to decreased tillage, inclusion of cover crops, application of organic nutrients, and retention of crop residue, but not the number of harvested crops in a rotation. The effect of decreased tillage on the C indicators was generally greater at sites with higher precipitation. The magnitude and direction of the response to soil health practices was consistent across indicators within a site but measuring at least two indicators would provide additional confidence of the effects of management, especially for tillage. All C indicators responded to management, an essential criterion for evaluating soil health. Balancing the cost, sensitivity, interpretability, and availability at commercial labs, a 24-hr potential C mineralization assay could deliver the most benefit to measure in conjunction with SOC.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".