Assessment of the lateral and vertical variability of soil organic carbon
Bibliographic record
Abstract
Accurate predictions of changes in soil organic matter are difficult, at least in part, because of the lack of precision in measurements of soil organic carbon (SOC). This lack of precision is mostly due to the spatial variability in SOC that occurs with depth through the profile and laterally across the soil surface. The objective of this study was to assess the lateral and vertical variability of SOC in several pedologically distinct agricultural soils across Canada. Our goal was to determine the effect of different sampling methods on the precision of SOC measurements, namely: the effect of sampling either by fixed depth or by genetic soil horizon, the influence of compositing samples from different depth increments, and the number of cores required for a minimum detectable difference. Soils were sampled in increments down to 60 cm using a 4 × 3 m grid at six sites: two each from Ontario (Gleysol and Melanic Brunisol), Quebec (Humic Gleysol and Humo Ferric Podzol) and Saskatchewan (Dark Brown Chernozem). At four of the six sites, sampling by genetic soil horizon appeared to increase the precision of SOC measurements, but only when the surface 30 cm of the soil profile was considered. At the other two sites (soil types: Gleysol and Melanic Brunisol) sampling by fixed depth increments was more effective for increasing the precision of SOC measurements than sampling by genetic horizon. The effect of compositing samples from different depth increments had little influence on the precision of SOC measurements for all six soil types. These results suggest that sampling more than two depth increments per soil core has limited advantages for increasing statistical power to detect change in SOC. The high background SOC levels in the Gleysol soil would require a large number of soil cores in order to detect a small change in SOC such as that which would occur in a typical monitoring project. The Chernozem soils had lower spatial variability in SOC than the soil types in eastern Canada. Determining a statistically significant change in SOC of 5 Mg ha -1 would be difficult with the sampling design used in this study. Key words: Soil organic carbon, statistical power, sampling design, coefficient of variation, spatial variability, Canada
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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 | 0.001 |
| 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".