Key soil properties and their relationships with crop yields as affected by soil–landscape rehabilitation
Bibliographic record
Abstract
Abstract Tillage and water erosion induce spatially dependent changes in soil properties that influence productivity. Soil–landscape rehabilitation (returning translocated topsoil to landscape positions of soil loss by erosion) is one method to improve the productivity of severely eroded land. The objective of this study was to investigate relationships among key soil chemical, biological, and physical factors and crop growth and grain yield in eroded and rehabilitated landform positions. Soil–landscape rehabilitation was performed by moving 15–20 cm of topsoil from the lower slope to the upper slope positions of replicate plots; adjacent plots were left in their eroded condition. Crop response was monitored for 6 years. Rehabilitation resulted in large changes in the upper slope, especially in the most eroded landscape positions, where rehabilitated plots had lower inorganic carbon (IC) and higher organic carbon, available macronutrients, water infiltration rates, fungal and bacterial populations, and other measures of soil quality compared with control plots. Differences in surface soil IC that result from extensive erosion exposing calcareous subsoils, explained 70% of the yield variability in the upper slope. In the lower slope, the soil removal treatment was the main predictor of crop yield. Areas of soil removal had relatively high early‐season water content and low fungal and bacterial populations. Improving soil properties in areas of high soil loss by erosion increased both grain yield and yield stability across climatic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".