Stochastic simulation of cultivated soil organic matter spatial variability

Soil organic matter is one of important features of soil fertility. Studying the spatial variability of the soil organic matter can provide consults to improve the saline soil quality. Based on plentiful information that obtained by field-survey, soil sampling and lab analysis, the study was conducted on an area of typical saline soil improvement districts in Yucheng City. The unobserved values of soil organic matter were estimated by sequential Gaussian Simulation to achieve variance (SGSV), sequential Gaussian simulation of the average achieved (SGSA) and the ordinary Kriging interpolation (OK) were applied on cultivated soil organic matter. Their statistic characteristics, semi-variances and spatial distribution trend were compared. The results indicated that the estimated values by OK and SGSA changed the original data configuration with obviously smoothing-average effect, and SGSA was better than OK method in eliminating smoothing-average effects. While SGSV had the same data configuration as the measured values，and the simulated value was uncertain for the unobserved points. So SGSV was efficient in analyzing the uncertain and risk variants of soil organic matter, and could bring some negative effect on uncertain and risk variants.