Soil fertility increases rapidly during the 6–10 yr following conversion of cropland to grassland in China’s Loess Plateau region
Bibliographic record
Abstract
Change in land use causes changes in soil properties and soil fertility, with long-term effects on ecosystem and crop productivity. This study determined soil fertility along sequential conversion of cropland to grassland in China’s Loess Plateau. Soil samples were collected in 2015 at two sites in the semiarid region, following the conversion of cropland to grassland. Soil particle-size distribution, bulk density, pH, organic carbon (OC), total nitrogen (TN), total phosphorus (TP), available potassium, and available phosphorus were measured in this study. In addition, we analysed the changes of soil OC, TN, and TP, and evaluated soil fertility after the conversion from cropland to grassland. The establishment of grassland significantly increased soil OC, N, and P content, especially in the 0–10 cm soil layer. The highest change in soil OC, N, and P content occurred 6–10 yr after land conversion. The measured soil variables did not change significantly after 10 yr of land conversion. The overall increase in soil fertility after the land conversion was 13% at one site and 26% at the other site. The results suggested that establishing grassland could enhance soil fertility in the semiarid Loess Plateau region of China, and this enhancement is optimal 6–10 yr after the establishment of grassland.
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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 | 0.000 |
| 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) | 0.000 | 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".