Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

Kingsley John, Isong Abraham Isong, Ndiye Michael Kebonye, Esther Okon Ayito, Prince Chapman Agyeman and Sunday Marcus Afu
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Kingsley John: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences, Kamýcká 129, 16500 Prague, Czech Republic
Isong Abraham Isong: Department of Soil Science, Faculty of Agriculture, University of Calabar, Calabar P.M.B. 1115, Nigeria
Ndiye Michael Kebonye: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences, Kamýcká 129, 16500 Prague, Czech Republic
Esther Okon Ayito: Department of Soil Science, Faculty of Agriculture, University of Calabar, Calabar P.M.B. 1115, Nigeria
Prince Chapman Agyeman: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences, Kamýcká 129, 16500 Prague, Czech Republic
Sunday Marcus Afu: Department of Soil Science, Faculty of Agriculture, University of Calabar, Calabar P.M.B. 1115, Nigeria

Land, 2020, vol. 9, issue 12, 1-20

Abstract: Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R 2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R 2 = 0.68) followed by the cubist model (R 2 = 0.51), SVM (R 2 = 0.36), ANN (R 2 = 0.36) and MLR (R 2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Keywords: geostatistic; machine learning; geospatial modeling; predictive mapping; soil fertility indices; environmental covariates (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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