Impact of Sampling Depth on Differences in Soil Carbon Stocks in Long‐Term Agroecosystem Experiments
Bibliographic record
Abstract
The depth of sampling has recently been highlighted as critical to making accurate measurements of changes in SOC stocks. This paper aimed to determine the effects of land management changes (LMC) on soil organic carbon (SOC) by re‐sampling long‐term agoecosystem experiments (LTAEs) across Canada using identical sampling and laboratory protocols. The impact of sampling depth on the monitoring of LMC‐induced differences in SOC stock in LTAEs in Canada, and the implications on statistical power and sampling design, were assessed. In most cases, four cores would be suitable for detecting a significant difference in SOC stock of 5 Mg ha −1 at 95% confidence for LMCs in western Canada. The impact of eliminating fallow on SOC stocks was typically restricted to the surface 15 cm. The impact of perennial forages on the average cumulative SOC was sufficiently large to be detectable at all sampling depths (to 60 cm). In three of the six LTAEs sampled in western Canada comparing conventional tillage to no‐till, there was a significantly greater SOC storage in the 0‐ to 30‐depth than the 0‐ to 15‐cm depth, suggesting that sampling below 15 cm could be necessary. The same comparisons in eastern Canada suggested that sampling often must exceed the 30‐cm depth to account for any changes in SOC due to moldboard plow tillage. Nonetheless, there was little evidence to suggest that increasing sampling intensity or sampling deeper would improve the ability to detect a difference in SOC stocks for this LMC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".