Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

The objective of our study was to compare the performance of the empirical best linear unbiased predictor (E-BLUP) with residual maximum likelihood (REML) with that of regression kriging (RK) for predicting soil organic matter (SOM) with the presence of different external drifts. The study was conducted on a 933 km2 area in Pinggu district of Beijing. Terrain attributes (elevation, slope and topographic wetness index) calculated from DEM were used as external drift variable. The root mean squared errors (RMSE) and the mean squared deviation ratio (MSDR) were used to assess the accuracy of prediction and the goodness of fit of the theoretical estimate of error respectively. RK resulted in both the most and least accurate predictions. REML-EBLUP provided more correct residual variogram models than RK for each trend model. Our results have shown that when the value of adjusted R2 is greater than 0.45, there is litter difference in the ability to increase the accuracy between REML-EBLUP and RK; and when the value is less than 0.45, the performance of REML-EBLUP is significantly better than RK. It also suggested that topographical data can further improve the accuracy of the spatial predictions of SOM by using RK and REML-EBLUP.