A Comparative Study of Rotation Patterns on Soil Organic Carbon in China’s Arid and Semi-Arid Regions
Bibliographic record
Abstract
The practice of crop rotation can significantly impact carbon sequestration potential. In exploring whether crop rotation has the potential to improve soil carbon sequestration in China’s Loess Plateau, soil organic carbon (SOC), soil water content (SWC), soil bulk density (SBD), and soil pH were compared across the 0–1.0 m soil profile, under four crop rotation patterns: lentil–wheat–maize, wheat–potato–lentil, wheat–maize–potato, and wheat–flax–pea. The lentil–wheat–maize and wheat–maize–potato rotations have been practiced over the past 20 years, while the wheat–potato–lentil and wheat–flax–pea rotations were established in 1978 (~40 year rotations). The results showed that under the 20-year lentil–wheat–maize rotation, SOC was not significantly different to that of the wheat–maize–potato rotation, at 6.81 g kg−1 and 6.91 g kg−1, respectively. However, under the lentil–wheat–maize rotation, SWC (9.81%) and SBD (1.19 Mg m−3) were significantly higher, but soil pH (8.42) was significantly lower than the same metrics under wheat–maize–potato rotation (8.43% and 1.16 Mg m−3, and 8.50, respectively). For the 40-year rotations, SWC (9.19%) and soil pH (8.41) under the wheat–potato–lentil were not significantly different to that of the wheat–flax–pea (8.87%, and 8.40, respectively). SOC (6.06 g kg−1) was significantly lower, but SBD (1.18 Mg m−3) was significantly higher under the wheat–potato–lentil than the wheat–flax–pea (7.29 g kg−1, and 1.15 Mg m−3, respectively) rotations. Soil carbon sequestration for the lentil–wheat–maize and wheat–potato–lentil rotations was co-influenced by SWC, SBD, and soil pH, while for wheat–maize–potato and wheat–flax–pea rotations, it was co-influenced by SWC and soil pH. The economic value of the four studied crops is, in order: potato > maize > wheat > flax. The results of the present study suggest that the lentil–wheat–maize and maize–flax–pea rotations are the most suitable patterns to optimize simultaneous economic and ecological development of the study area.
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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 | 0.001 | 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".