Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province, China
Bibliographic record
Abstract
The Revised Universal Soil Loss Equation (RUSLE) is widely used to estimate regional soil erosion. However, quantitative impacts of soil and water conservation (SWC) measures on conservation practice factor (P) of the RUSLE remain largely unclear, especially for the mountainous and hilly areas. In this study, we improved the RUSLE by considering quantitative impacts of different SWC measures on the P factor value. The improved RUSLE was validated against the long-term (2000–2015) soil erosion monitoring data obtained from 96 runoff plots (15–35°) in mountainous and hilly areas of Hubei Province, China; the result presented a high accuracy with the determination coefficient of 0.89. Based on the erosion monitoring data of 2018 and 2019, the Root Mean Square Error of the result by the improved RUSLE was 28.0% smaller than that by the original RUSLE with decrement of 19.6%–24.0% in the average P factor values, indicating that the soil erosion modelling accuracy was significantly enhanced by the improved RUSLE. Relatively low P factor values appeared for farmlands with tillage measures (P < 0.53), grasslands with engineering measures (P < 0.23), woodlands with biological measures (P < 0.28), and other land use types with biological measures (P < 0.51). The soil erosion modulus showed a downward trend with the corresponding values of 1681.21, 1673.14, 1594.70, 1482.40 and 1437.50 t km−2 a−1 in 2000, 2005, 2010, 2015 and 2019, respectively. The applicability of the improved RUSLE was verified by the measurements in typical mountainous and hilly areas of Hubei Province, China, and arrangements of SWC measures of this area were proposed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".