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<p>Reliable soil organic carbon (SOC) estimation is crucial for ensuring agricultural production and environmental amenity. Under data-scarcity conditions, geospatial modeling is a suitable tool for mapping and managing SOC. This study evaluated the spatial variability of SOC and compared the predictive performance of five modelling approaches: Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost) using environmental covariates, including temperature, soil texture, drainage, soil depth, erosion, altitude, soil type, and land use, in the Fogera Plain, northwestern Ethiopia. Descriptive statistics showed that grassland contained the highest mean SOC (1.43%), followed by shrubland (1.40%) and cultivated land (1.35%), indicating significant land-use effects on carbon storage. Correlation analysis identified soil erosion (r = − 0.756, <i>p</i> &lt; 0.01) as the dominant negative driver of SOC, whereas soil texture exhibited a strong positive relationship (r = 0.501, <i>p</i> &lt; 0.01). Variable importance analysis consistently ranked erosion and soil texture as the most influential predictors across all machine-learning models. Among the evaluated algorithms, SVM produced the highest predictive accuracy (R<sup>2</sup> = 0.936), outperforming LR (R<sup>2</sup> = 0.925), RF (R<sup>2</sup> = 0.851), XGBoost (R<sup>2</sup> = 0.842), and ANN (R<sup>2</sup> = 0.774). Semivariogram analysis indicated weak spatial dependence of SOC (nugget/sill = 79.07–91.02%), with the exponential model providing the best geostatistical fit (R<sup>2</sup> = 0.88). Spatial prediction revealed higher SOC concentrations in the western and southwestern parts of the landscape and lower values in the northern and southeastern areas. These findings demonstrate that integrating machine-learning techniques, particularly SVM, with environmental covariates provides an effective framework for accurate SOC prediction and uncertainty assessment. The resulting high-resolution SOC maps provide valuable information for site-specific soil management, carbon accounting, erosion mitigation, and sustainable land-use planning in tropical agricultural landscapes. Future research should integrate high-resolution remote sensing data, long-term field monitoring, additional environmental covariates, and ensemble deep-learning approaches to improve the transferability, temporal prediction, and uncertainty quantification of SOC models under changing climate and land-use conditions.</p>

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Spatial distribution and modeling of soil organic carbon using machine learning and a geostatistical approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Gizachew Ayalew Tiruneh,
  • Ciro Abbud Righi,
  • Tiringo Yilak Alemayehu,
  • Genetu Fekadu,
  • Simeneh Demissie,
  • Marcelo H. Procópio Pelegrino,
  • Carlos Rodrigues Pereira

摘要

Reliable soil organic carbon (SOC) estimation is crucial for ensuring agricultural production and environmental amenity. Under data-scarcity conditions, geospatial modeling is a suitable tool for mapping and managing SOC. This study evaluated the spatial variability of SOC and compared the predictive performance of five modelling approaches: Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost) using environmental covariates, including temperature, soil texture, drainage, soil depth, erosion, altitude, soil type, and land use, in the Fogera Plain, northwestern Ethiopia. Descriptive statistics showed that grassland contained the highest mean SOC (1.43%), followed by shrubland (1.40%) and cultivated land (1.35%), indicating significant land-use effects on carbon storage. Correlation analysis identified soil erosion (r = − 0.756, p < 0.01) as the dominant negative driver of SOC, whereas soil texture exhibited a strong positive relationship (r = 0.501, p < 0.01). Variable importance analysis consistently ranked erosion and soil texture as the most influential predictors across all machine-learning models. Among the evaluated algorithms, SVM produced the highest predictive accuracy (R2 = 0.936), outperforming LR (R2 = 0.925), RF (R2 = 0.851), XGBoost (R2 = 0.842), and ANN (R2 = 0.774). Semivariogram analysis indicated weak spatial dependence of SOC (nugget/sill = 79.07–91.02%), with the exponential model providing the best geostatistical fit (R2 = 0.88). Spatial prediction revealed higher SOC concentrations in the western and southwestern parts of the landscape and lower values in the northern and southeastern areas. These findings demonstrate that integrating machine-learning techniques, particularly SVM, with environmental covariates provides an effective framework for accurate SOC prediction and uncertainty assessment. The resulting high-resolution SOC maps provide valuable information for site-specific soil management, carbon accounting, erosion mitigation, and sustainable land-use planning in tropical agricultural landscapes. Future research should integrate high-resolution remote sensing data, long-term field monitoring, additional environmental covariates, and ensemble deep-learning approaches to improve the transferability, temporal prediction, and uncertainty quantification of SOC models under changing climate and land-use conditions.