Using Machine Learning Algorithms for Spatial Prediction of Soil Organic Carbon Based on Environmental Variables and Soil Physicochemical Parameters in the Mediterranean Region
Bibliographic record
Abstract
Soil plays a key role in storing organic carbon, which is a critical indicator of soil fertility and overall quality. Understanding the spatial distribution of soil organic carbon stock (SOCS) and its influencing factors is essential for promoting sustainable land management. This study applied four machine learning models such as Random Forest (RF), k-nearest neighbors (kNN), Support Vector Machine (SVM), and Cubist to enhance SOCS prediction in the Srou catchment, part of the Upper Oum Er-Rbia watershed in Morocco. A dataset of 120 samples was collected, with 80% used for model training and 20% for validation. Boruta’s feature selection and multicollinearity tests identified nine key factors influencing SOCS. Spatial maps generated from the models were validated using statistical indicators. The RF model showed the highest predictive accuracy (R² = 0.76, RMSE = 0.52 Mg C/ha), followed by SVM and Cubist, while kNN had the lowest performance (R² = 0.31, RMSE = 0.94 Mg C/ha). Key predictors for SOCS included bulk density, pH, electrical conductivity, and calcium carbonate. The proposed machine learning approach demonstrates significant potential for mapping SOCS in similar semi-arid environments.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Machine learning spatial prediction of soil organic carbon in a Moroccan catchment; the object is soil carbon mapping.
The work predicts soil organic carbon using machine learning, not research practice.
ML prediction of soil organic carbon; environmental domain application, not metaresearch.
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 | 0.003 |
| 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".