Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands)
Soil organic carbon (organic C) and pH are key soil properties and valuable indicators of soil quality, whereas phosphate retention capacity (P retention) is a diagnostic property to define andic soil properties and andic soils, with all of them typically interrelated in volcanic ash (i.e., andic) s...
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2025-05-01
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| author | Víctor Manuel Romeo Jiménez Jesús Santiago Notario del Pino José Manuel Fernández-Guisuraga Miguel Ángel Mejías Vera |
| author_facet | Víctor Manuel Romeo Jiménez Jesús Santiago Notario del Pino José Manuel Fernández-Guisuraga Miguel Ángel Mejías Vera |
| author_sort | Víctor Manuel Romeo Jiménez |
| collection | DOAJ |
| description | Soil organic carbon (organic C) and pH are key soil properties and valuable indicators of soil quality, whereas phosphate retention capacity (P retention) is a diagnostic property to define andic soil properties and andic soils, with all of them typically interrelated in volcanic ash (i.e., andic) soils. In this paper, we examined the potential of a random forest (RF) regression model to predict field-measured soil pH, organic C and P retention capacity from several biophysical (type and fraction of the plant cover), bioclimatic (maximum temperature of the warmest month, precipitation and temperature seasonality, and precipitation of the driest quarter), and topographic (ruggedness and curvature of the slope) predictors in a protected forest area in Tenerife, Canary Islands. Piecewise structural equation modeling (pSEM) was subsequently used to unravel the complex, direct and indirect relationships between the biophysical, bioclimatic and topographic variables, and the selected soil properties. The RF regression model accounted for the properties of interest with varying degrees of accuracy, from organic C (R2 = 0.67; RMSE = 29.86), to P retention capacity (R2 = 0.44; RMSE = 18.84) and soil pH (R2 = 0.31; RMSE = 0.43). The pSEM model revealed that P retention capacity is strongly linked to organic C in volcanic ash soils, and thus indirectly to the environmental variables shaping organic C variability, namely fractional vegetation cover and precipitation seasonality. |
| format | Article |
| id | doaj-art-e48a29b6d3444326aef8ed38dcece243 |
| institution | DOAJ |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-e48a29b6d3444326aef8ed38dcece2432025-08-20T02:45:18ZengElsevierEcological Informatics1574-95412025-05-018610305410.1016/j.ecoinf.2025.103054Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands)Víctor Manuel Romeo Jiménez0Jesús Santiago Notario del Pino1José Manuel Fernández-Guisuraga2Miguel Ángel Mejías Vera3Terrestrial Biodiversity and Island Conservation, University of La Laguna (ULL), SpainDepartment of Animal Biology, Soil Science and Geology, University of La Laguna (ULL), PO Box 456, Spain; Corresponding author.Department of Biodiversity and Environmental Management, Faculty of Biological an Environmental Sciences, University of León, 24701 León, Spain; Institute of Environmental Research, Natural Resources and Biodiversity (IMARENABIO), University of León, 24071 León, Spain; Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, PortugalDepartment of Geographic and History, Area of Regional Geographic Analysis, Faculty of Humanities, University of La Laguna (ULL), SpainSoil organic carbon (organic C) and pH are key soil properties and valuable indicators of soil quality, whereas phosphate retention capacity (P retention) is a diagnostic property to define andic soil properties and andic soils, with all of them typically interrelated in volcanic ash (i.e., andic) soils. In this paper, we examined the potential of a random forest (RF) regression model to predict field-measured soil pH, organic C and P retention capacity from several biophysical (type and fraction of the plant cover), bioclimatic (maximum temperature of the warmest month, precipitation and temperature seasonality, and precipitation of the driest quarter), and topographic (ruggedness and curvature of the slope) predictors in a protected forest area in Tenerife, Canary Islands. Piecewise structural equation modeling (pSEM) was subsequently used to unravel the complex, direct and indirect relationships between the biophysical, bioclimatic and topographic variables, and the selected soil properties. The RF regression model accounted for the properties of interest with varying degrees of accuracy, from organic C (R2 = 0.67; RMSE = 29.86), to P retention capacity (R2 = 0.44; RMSE = 18.84) and soil pH (R2 = 0.31; RMSE = 0.43). The pSEM model revealed that P retention capacity is strongly linked to organic C in volcanic ash soils, and thus indirectly to the environmental variables shaping organic C variability, namely fractional vegetation cover and precipitation seasonality.http://www.sciencedirect.com/science/article/pii/S1574954125000639Digital soil modelingSoil organic carbonPhosphate retention capacityMachine learningPiecewise structural equation modeling |
| spellingShingle | Víctor Manuel Romeo Jiménez Jesús Santiago Notario del Pino José Manuel Fernández-Guisuraga Miguel Ángel Mejías Vera Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) Ecological Informatics Digital soil modeling Soil organic carbon Phosphate retention capacity Machine learning Piecewise structural equation modeling |
| title | Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) |
| title_full | Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) |
| title_fullStr | Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) |
| title_full_unstemmed | Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) |
| title_short | Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands) |
| title_sort | prediction of some soil properties in volcanic soils using random forest modeling a case study at chinyero special nature reserve tenerife canary islands |
| topic | Digital soil modeling Soil organic carbon Phosphate retention capacity Machine learning Piecewise structural equation modeling |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000639 |
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