Spatial Variability Modeling of Soil Erodibility Index in Relation to Some Soil Properties at Field Scale
Bibliographic record
Abstract
Soil erosion is a major land degradation issue affecting various facets of human lives. To curtail soil erosion occurrence requires understanding of soil properties and how they influence soil erosion. To this end, the soil erodibility index which gives an indication of the susceptibility of soils to erosion was examined. In particular, we aimed to determine soil erodibility index at field scale and establish relationships that exist between selected soil properties and soil erodibility index. It was hypothesized that for soil erodibility index to vary spatially, then the existing soil properties should have varying spatial structure. Hundred disturbed and 100 undisturbed soil samples were collected from a 7.3 ha gridded area. The samples were analyzed for particle size distribution, bulk density, particle density, organic matter content and porosity. All soil analyses were conducted following standard procedures. Data were analyzed statistically and geostatistically on the basis of semivariograms. Sandy clay loam was the dominant soil texture in the studied field. Results indicate significant negative relationship between sand content, bulk density, particle density and organic matter with soil erodibility index. Silt correlated significantly with a positive relation with soil erodibility. Estimated erodibility for the sampled field ranged from 0.019 t.ha.hr/ha.MJ.mmto 0.055 t.ha.hr/ha.MJ.mm. The order of dominance of erodibility ranges were 0.038-0.042 t.ha.hr/ha.MJ.mm> 0. 036-0.08 t.ha.hr/ha.MJ.mm> 0.032-0.036 t.ha.hr/ha.MJ.mm> 0.019-0.032 t.ha.hr/ha.MJ.mm> 0.042-0.055 t.ha.hr/ha.MJ.mm. Regression analysis revealed silt to be the most significant variable that influences soil erodibility. The best regression of soil properties on soil erodibility index gave an R2 of 0.90. A comparison of the regression equation with other studies indicated good performance of the equation developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".