Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics

Jacob Kaingo; Siza D. Tumbo; Nganga I. Kihupi; Boniface P. Mbilinyi

Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics

2018

Jacob Kaingo | Siza D. Tumbo | Nganga I. Kihupi | Boniface P. Mbilinyi

Soil moisture-holding capacity data are required in modelling agrohydrological functions of dry subhumid environments for sustainable crop yields. However, they are hardly sufficient and costly to measure. Mathematical models called pedotransfer functions (PTFs) that use soil physicochemical properties as inputs to estimate soil moisture-holding capacity are an attractive alternative but limited by specificity to pedoenvironments and regression methods. This study explored the support vector machines method in the development of PTFs (SVR-PTFs) for dry subhumid tropics. Comparison with the multiple linear regression method (MLR-PTFs) was done using a soil dataset containing 296 samples of measured moisture content and soil physicochemical properties. Developed SVR-PTFs have a tendency to underestimate moisture content with the root-mean-square error between 0.037 and 0.042 cm3·cm−3 and coefficients of determination (R2) between 56.2% and 67.9%. The SVR-PTFs were marginally better than MLR-PTFs and had better accuracy than published SVR-PTFs. It is held that the adoption of the linear kernel in the calibration process of SVR-PTFs influenced their performance.

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