## 3 Results
### Effect of Sampling-Point Numbers on the Expression of Spatial Distribution of SOM Content  
In order to further understand the effect of sampling-point numbers on the spatial-distribution characteristics of SOM content in the study area, an ordinary Kriging interpolation method was conducted for the 12 sample-point sets to obtain the spatial-distribution maps of SOM at different sampling-point densities (Figure 4). Since the sampling site did not cover the Yellow River Delta National Nature Reserve in the east of the study area, we did not analyze the eastern region when studying the spatial variability of the SOM.  
According to Figure 4, when the numbers of samples were between 400 and 900, the SOM in the study area showed a similar spatial distribution pattern: soils with a medium content of SOM (9 g/kg-12 g/kg) occupied most of the study area; soils with relatively low content of SOM (<9 g/kg) were distributed in the southern part of the study area with the form of spots; while soils with relatively high SOM content (12 g/kg-14 g/kg) were mainly distributed in the central and northern parts with the form of discontinuous patches. Because of the smooth effect of the Kriging interpolation method, soils with SOM content greater than 14 g/kg formed narrow band regions with high value in the northeast when the number of sample points were 500 and 600. When the number of sample points dropped to 300-150, the ability to depict details was slightly weakened. Furthermore, when the blocky low-value area in the southern part of the study area was no longer obvious, the overall trend of SOM spatial variation could still be kept largely unchanged. When the sampling point number was 100, the distribution proportion of soils with relatively high SOM content (12 g/kg to 14 g/kg) was increased significantly within the study area. A large area of soils with relatively high SOM content appeared in the southern part where the SOM content was actually low, and the distribution continuity of the patches also increased. When the number of sample points dropped to 75, the SOM spatial-distribution information with the highest or lowest value could not displayed in the map. The expression information of SOM spatial distribution was relatively simple, and many details were difficult to be described. When only 50 sample points were left, there was a marked local overestimation in the southern area. Combined with the case of 100 and 75 sampling-point sets, it can be shown that when the number of sample points was less than 150, the Kriging interpolation method overestimated the variability of SOM spatial prediction, especially in the truly low-value region. These characteristics of the spatial expressions were consistent with the performance of spatial-prediction errors.  
Overall, with the decrease in sample numbers, the spatial distribution of SOM showed the gradually weakened ability of detail characterization. Moreover, with the further reduction of the number of sample points, the predicted spatial distribution of SOM by Kriging interpolation might also have appeared to be a false characterization that was inconsistent with the actual situation. Combined with the independent verification index of SOM spatial prediction, when predicting the spatial distribution of SOM in Kenli County, it essentially required at least 150 sample points to ensure the accuracy of spatial prediction and the correctness of SOM spatial-distribution trend.