Estimate of soil erosion on cultivated soils using <sup>137</sup>Cs measurements and calibration models: A case study from Nakhla watershed, Morocco
Bibliographic record
Abstract
Despite the seriousness of the erosion problem in the Nakhla watershed, there are still only limited data on the rates of soil loss on cultivated land on steep slopes. Thus, there is a need for more information for optimizing strategies for soil conservation. Recent concern over problems of soil degradation and the off-site impacts of accelerated erosion has highlighted the need for improved methods of estimating rates and patterns of soil erosion by water. In this paper, soil erosion and deposition rates have been studied using the 137Cs technique in upstream part in the Nakhla watershed on cultivated soils (Larbaa Beni Hassan area), which is known to suffer soil erosion. Multiple transect sampling was established parallel to the slope, and 137Cs inventories were determined for the sampling points. After establishing the local reference inventory (3073 Bq m-2), soil erosion and deposition rates were estimated using the 137Cs inventories on all the transects with three calibration models: proportional model (PM), mass balance model 1 (MBM1) and mass balance model 2 (MBM2). The magnitude of the soil erosion rates depends on many factors, including the location of sampling, the local topography, and the soil properties. Estimated soil erosion rates for the study area varied from 0.4 to 28.9 t ha-1 yr-1 using PM; from 0.5 to 66.9 t ha-1 yr-1 using MBM1; and from 0.4 to 56.4 t ha-1 yr-1 using MBM2. The deposition ranged, respectively, from 0.3 to 8.0 t ha-1 yr-1 for PM, from 0.4 to 14.5 t ha-1 yr-1 for MBM1, and from 0.3 to 11.5 t ha-1 yr-1 for MBM2. The sediment delivery ratio is about 98%. For one selected plot, L11, the erosion rates show a clear relationship with the range of slopes in its three different horizontal parts. Estimates of soil redistribution rates were interpolated by means of kriging, using Surfer 7.0 software. Two representative transects were selected to identify the contribution of tillage on 137Cs redistribution by using mass balance model 3. It is observed that the pattern of 137Cs redistribution is dominated by water erosion and that the contribution of tillage redistribution remain is smaller. Key words: 137Cs method, soil erosion, erosion rate, soil redistribution, conversion models
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".