Carbon (δ13C) dynamics in agroecosystems under traditional and minimum tillage systems: a review
Bibliographic record
Abstract
Following cultivation, substantial loss of soil organic matter occurs in surface soil layers. No-till is an agronomic practice to reverse or slow the loss of soil organic matter. We reviewed 95 research papers that used 13C natural abundance of soils to quantify the impact of tillage on the C dynamics of cropping systems. New C (from current cropping systems) accumulated in the surface soil under no-till, whereas the most extreme cultivation (mouldboard ploughing) mixed new C throughout the soil. There was a decline in soil C with years of cultivation. Compared with land that had been tilled, no-till generally had little impact on the accumulation on soil organic C. Tillage and residue retention caused stratification in C stocks that depended on tillage depth, with the highest C concentrations and stocks found in the surface under no-till. Shifts in the δ13C signature indicated significant exchange of ‘new’ C for the original (old) C. Tillage methods had no impact on the size and δ13C signature of the microbial biomass pool. Change in δ13C indicates that microbial biomass rapidly incorporates new carbon. The largest change in the δ13C values (Δ13C) was observed in the coarse sand fraction, whereas the smallest change occurred in the clay fraction. Comparison of conventional vs no-till showed inconsistent results on the effect of tillage on C in the different particle size fractions. Natural 13C abundance data show that no-till cropping systems do not result in increases in soil organic C in the top 0.30 m of soil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".