Organic Carbon Convergence in Diverse Soils toward Steady State: A 21‐Year Field Bioassay
Bibliographic record
Abstract
Core Ideas We report long‐term SOM change in different soils transplanted to one plot. Mainly, initial SOM concentrations highly influence change over time. Sand and land management history influence SOM change. High C soils lose C and low C soils gain C; thus, soils converge to new equilibrium. Light‐fraction OM converges at a faster rate than C and N. The response of soil organic matter (SOM) to an abrupt change in environment and management was studied in a long‐term field bioassay. Thirty‐six soils, with a wide range of SOM reflecting large differences in management, climate, and ecological histories, were selected and transported to a common site at Lethbridge, Alberta, Canada. The experiment included control and N‐fertilized treatment plots. The plots were seeded annually to spring wheat and managed under no‐till rain‐fed conditions, with annual removal of aboveground residues. The soils were sampled every 7 yr and analyzed for organic C (OC), total N (TN), and light fraction C (LF‐C) and N (LF‐N). Most of the soils lost OC and TN over 21 yr, and the amount of loss was highly related to the original contents (9–74 g C kg −1 , 1.4–6.4 g N kg −1 ). Similarly, LF‐C and LF‐N, indices of labile OM, declined in most soils, but the rate of loss was faster than for OC. In a few soils, SOM slowly increased from initially low levels. The soils seem to be progressing toward a steady state, although complete convergence of OC and TN may not occur for a century or longer based on an exponential model. Labile OM may converge within a few decades, so that these soils, conceivably, will still have widely different OC and TN but similar labile SOM, with possible implications for soil health. Our findings demonstrate the value of long‐term field bioassays for improved understanding of SOM dynamics in response to management and climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".