Surface soil sampling underestimates soil carbon and nitrogen storage of long-term cover cropping
Bibliographic record
Abstract
Cover cropping is a promising management practice for soil health and climate change mitigation by improving soil organic carbon (SOC) and total nitrogen (TN) stocks. However, limited studies focused on deeper soil layers (>30 cm depth) where soil C is more stable than that in surface soil (≤30 cm depth). Here, deep soil sampling was conducted in a 15-year cover cropping experiment, in a horticulture-grain system on sandy loam soil. The SOC and TN stocks were expressed on an equivalent soil mass basis using a cubic spline model. Overall, long-term cover cropping had significantly greater SOC and TN stocks by 22 % (95 %CI: 5–43 %) and 26 % (95 %CI: 6–49 %), respectively in the 0–120 cm depth, compared to no cover cropping. Additionally, the mean SOC and TN sequestration rate (0–30 cm depth) was 0.53 Mg C ha −1 yr −1 and 0.06 Mg N ha −1 yr −1 , respectively. However, if only 0–15 cm depth was evaluated, long-term cover cropping did not significantly affect SOC and TN stocks. These results indicated that shallow sampling (<15 cm depth) may not provide comprehensive information on the effect of long-term cover cropping on soil C and N storage. To better understand the mechanism of bulk soil C and N storage, we investigated their distribution between particulate and mineral-associated organic matter pools (POM and MAOM). We found POM pool was the main store of bulk SOC and TN stocks in surface soils while it was the MAOM pool in deeper soil layers, without soil texture change with soil depth. These findings indicated that soil C and N sources for bulk SOC and TN accrual differed in surface and deeper soils. Our study demonstrated that long-term cover cropping can facilitate SOC accumulation in the soil below 15 cm deep, which calls into question carbon capture protocols that focus on shallow soil depths.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".