Effects of environmental factors on the influence of tillage conversion on saturated soil hydraulic conductivity obtained with different methodologies: a global meta-analysis
Bibliographic record
Abstract
Abstract. The saturated hydraulic conductivity (Ksat) is a key soil hydraulic property governing agricultural production. However, the influence of the conversion from the conventional tillage (CT) to conservation tillage (CS; including no tillage, NT, and reduced tillage, RT) on the Ksat of soils is not well understood and still debated. In this study, we applied a global meta-analysis method to synthesize 227 paired observations for soil Ksat from 69 published studies and investigated factors influencing the effects of conversion to CS on Ksat. Results showed that soil layer, conservation tillage type, soil texture type, and cropping system management did not have significant effects on the influence of conversion to CS on Ksat. When the Ksat was measured by the rainfall simulator, the conversion to CS significantly (p<0.05) increased the surface and subsurface soil Ksat by 41.7 % and 36.9 %, respectively. In addition, the subsurface Ksat also tended to increase under CS practices when the Ksat was measured by a tension disc infiltrometer. However, when the Ksat was measured by a hood infiltrometer, ring infiltrometer, constant/falling head, and Guelph permeameter, the conversion to CS had no significant effects on the Ksat. It is observed that, when the conversion period was less than 15 years, the Ksat under CS showed a greater increase for a longer conversion period. Climatic and topographic factors, including the mean annual temperature (MAT) and the mean annual precipitation (MAP), were statistically related to the responses of Ksat to tillage conversion at the global scale. Quadratic polynomials can describe the relationships between them. These findings suggested that quantifying the effects of tillage conversion on soil Ksat needed to consider experimental conditions, especially the measurement technique and conversion period.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".