## 4 Discussion  
### Comparison of the Rational Sampling Numbers in Different Regions  
In the spatial-prediction process of soil attribute, the number of sampling points or sampling-point density were important factors related to prediction accuracy and prediction  
Figure 4: Spatial distribution of the SOM content under 12 sampling-point sets in Kenli County. Note: The number of sampling point sets represented by (**a**–I) is 50, 75, 100, 150, 200, 300, 400, 500, 600, 700, 800 and 900, respectively. cost [13]. In the area where the terrain was relatively flat and the soil salinization was more severe, such as Kenli County, it was believed that the minimum number of sample points was 150. It was equivalent to sampling more than 107 points on the area of 1000 km\({}^{2}\) for satisfying the prediction accuracy of SOM. This differs from the results found by other scholars at different regions and scales. For example, in a small karst-basin scale with an area of 75 km\({}^{2}\) in Guizhou Province, southern China, 357 sample points could better express the spatial variation of 0-20 cm soil organic carbon, equivalent to 4760 sample points collected on an area of 1000 km\({}^{2}\)[26]. In a small undulating hilly area with an area of 184 hm\({}^{2}\) in Sao Paulo state of southwestern Brazil, collecting one sample point per 3.75 hm\({}^{2}\) and one sample point per 7.2 hm\({}^{2}\) could satisfy the expression of the spatial variability of SOM and clay, which was equivalent to 26,667 samples and 13,889 samples collected on 1000 km\({}^{2}\), respectively [27]. [PERSON] et al. found that sampling 200 points could better describe spatial distribution of soil salinity within a 288 hm\({}^{2}\) scale in the Senegal River valley, correspond to sampling 69,444 soil samples per 1000 km\({}^{2}\)[28]. These studies all showed that a high number of samples were needed to express spatial variability of soil properties. It may be due to the fact that these study areas were located with undulating terrain and complex topography (e.g., hills, valleys, mountains, etc. ), which resulted in high spatial variability of soil properties. Therefore, more sampling points were needed for spatial-distribution prediction. The results in the Yangtze River Delta region showed that the reasonable sampling numbers of SOM were about 170 sample points per 1000 km\({}^{2}\)[29; 30]. Although the topography of the Yangtze River Delta was similar to the Yellow River Delta, it had developed agriculture, high farming intensity and high geographical complexity, so the minimum sampling number was slightly higher than that of the Yellow River Delta region. In a typical agricultural planting area of Huang-Huai-Hai Plain in North China (Yucheng County), [PERSON] et al. concluded that 82 samples points per 1000 km\({}^{2}\) area could satisfy the expression of the spatial variability of soil organic carbon [31]. Yucheng County belongs to a temperate monsoon climate zone and is located in the alluvial plains of the middle and lower reaches of the Yellow River. The county has similar climate and topographic characteristics as Kenli County, the study area of this paper, so the rational number of sampling points from Yucheng County was similar to that of this paper. At the same time, Yucheng County is located in the hinterland of the Huang-Huai-Hai plain, and the salinity degree was light at present, while Kenli County in this paper was affected by seawater infiltration and hydrogeology, and the soil salinity degree was relatively high. Thus, the environmental conditions of Kenli County were more complex than Yucheng, and furthermore, the minimum sampling number of SOM spatial variation was slightly more than that of Yucheng. Therefore, the minimum sampling number of 107 sample points per 1000 km\({}^{2}\) determined in this paper was only suitable for areas with similar geographical environment to the Kenli County.