Global Potential to Increase Soil Carbon Storage by Reducing Rotational Fallow in Semiarid Regions
Bibliographic record
Abstract
ABSTRACT Intensification of cropping systems improves crop productivity and soil organic carbon (SOC) storage by maintaining or enhancing existing SOC stocks. We compiled published data on SOC changes in agricultural soils globally from experiments evaluating the impact of bare‐fallow reduction to determine the change in SOC storage that results from management change. Overall, the intensification of cropping systems by eliminating fallow led to an average increase in SOC stocks that were 3.2 (±0.3) Mg C ha −1 than in cropping systems with bare‐fallow. To account for variation in fallow frequency among study sites, we estimated the SOC change on a per year of fallow reduction basis and found the difference in SOC stocks was 443 (±34) kg C ha −1 for each year of fallow reduction. Soil texture influenced the amount of SOC change, with average differences of 552 (±85), 406 (±38), and 430 (±92) kg C ha −1 yr −1 for fallow reduction in fine‐, medium‐, and coarse‐textured soils, respectively. The rate of SOC storage declined over time with SOC increases of 615 (±74), 433 (±45), and 360 (±60) kg C ha −1 associated with fallow reduction for < 10 years, 11 to 20 years, and > 21 years, respectively. Soil type and aridity index had an impact on SOC storage when comparing crop systems with and without fallow. Countries with significant amounts of bare fallow could promote intensification of cropping systems by reducing bare fallow as part of their nationally determined contributions to the Paris Agreement. Canada has reduced the annual area of fallow from 1990 to 2022, resulting in a cumulative gain of SOC storage of 66.3 Mt C. Based on global statistics of annual fallow area, a significant reduction in this practice is feasible on a global scale with cumulative changes in SOC storage of 0.54 Gt C for a period of 20 years.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".