Black soil degradation by rainfall erosion in Jilin, China
Bibliographic record
Abstract
Abstract Black soils, originally characterized by a deep, dark A‐horizon, are widespread in the Northeast Plain of China and have been one of the most fertile agricultural resources in the country. However, more than a half‐century of intensified management degraded its productivity, mainly with the loss of the dark‐coloured A‐horizon by rainfall erosion. Using the Revised Universal Soil Loss Equation (RUSLE), the rainfall erosion losses of black soils in YuShu and DeHui counties of Jilin Province were estimated. The rate of loss of thickness of the A‐horizon of black soils and the time over which the A‐horizons of some black soils in the region might be lost were evaluated. The results showed that about 4–45 t ha−1 topsoil could have been lost each year under corn (Zea mays L.) production. Soybean (Glycine max L. Merr) production would double the losses. Soil losses were directly related to soil type, tillage practices and crop grain yields. The thickness of the A‐horizon of black soils in the region decreased at rates of 0ċ5–4ċ5 mm yr−1, depending on soil type and management practices. Corn production may have resulted in an annual loss of 8ċ3 million tonnes of topsoil from black soils alone in Jilin Province; soybean production could have greatly increased this loss. Traditional intensified farming can accelerate the degradation of black soils; conservation tillage has great potential to prevent rainfall erosion losses for the same soils. Accordingly, to preserve and restore the productivity of black soils, conservation tillage is appropriate and should be adopted in Jilin. Copyright © 2003 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".