Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Abstract

Abstract Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between –3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey’s ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and cross-validations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R2 in MLR. The UK fitted with exponential variogram model has the highest R2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

Keywords

• Geostatistics
• Mountain ridge proximity
• Multiple linear regression
• Ordinary kriging
• Simple kriging
• Universal kriging