Detailed mapping of soil texture of a paddy growing soil using multivariate geostatistical approaches

Abstract

This study was conducted to explore performances of multivariate geostatistical techniques, co-kriging and regression kriging in contrast to univariate ordinary kriging, to generate detailed maps of soil texture by using proximally sensed apparent electrical conductivity (ECa) as secondary information. The survey of ECa (n = 21110) was conducted in a paddy tract (2.5 ha) located in Kurunegala, Sri Lanka using DUALEM-1S proximal soil sensor. Twenty-five soil samples were collected on the basis of conditioned Latin hypercube sampling approach. Soil texture was determined using pipette method. Additive log transformed sand and clay values were used to produce soil texture maps using multivariate co-kriging, regression kriging and univariate ordinary kriging. Data ranges of clay (3.3 – 19.5%) and sand (62.5 – 90.1%) showed a considerable variability within the study area. Correlation analysis revealed a strong relationship of clay% with horizontal (r = 0.86) and perpendicular (r = 0.89) coplanar ECa and their geometric mean (r = 0.89). Sand % showed strong negative relationships with horizontal (r = -0.89) and perpendicular (r = -0.89) coplanar ECa and their geometric mean (r = 0.90). Second order polynomial regression models were best fitted for the prediction of clay (R2 = 0.83), and sand (R2 = 0.83) and these relationships were used for regression kriging. Prediction accuracies of geostatistical approaches were investigated by leave-one-out cross validation procedure and estimation of mean error, mean absolute error and root mean square error. Detailed maps resolution = 1 x 1 m) of clay and sand generated using co-kriging, regression kriging and ordinary kriging showed similar spatial patterns. However, multivariate geostatistical techniques produced more accurate detailed soil texture maps. Further, the comparison of mean error, mean absolute error and root mean square error values of three different interpolation techniques indicated that regression kriging produced the most accurate soil texture maps. This study emphasizes the high potential and robustness of regression kriging combined with proximally sensed apparent electrical conductivity to create high accurate detailed texture maps in efficient and cost effective manners.