## 3 Results
### Characteristics of the Spatial-Variation Structure of SOM  
The spatial-variability structure of the SOM can be fitted using the semivariance function. The ratio of C\({}_{0}\) to (C\({}_{0}\) + C) is nugget coefficients, which indicate the proportion of the random components in the spatial-variation structure. The higher the nugget coefficient, the weaker the structural components and the stronger the random components [25]. By fitting the semivariance function of SOM under different sampling points, it was found that when the number of samples was large, the system could also greatly satisfy the spatial autocorrelation. Taking a subset of 800 sample points as an example (Table 3), the determining coefficient (R\({}^{2}\)) of the semivariance function was 0.22. The nugget value was 9.09 and the sill value was 13.02. The nugget coefficient was reached at 69.81%, indicating that the system also had a moderate degree of autocorrelation. However, when the number of sample points decreased, the overall fitting accuracy of the semivariance function of each subset was low; the R\({}^{2}\) was mostly below 0.22. The value of nugget coefficient was overall higher, mostly close to 80%, and the value of nugget coefficient did not change much with  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Minimum** \\ **(g/kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **(g/kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **(g/kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Standard** \\ **(p/kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Skewness** \\ **(g/kg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Kurtosis** \\ **(\%/kg)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Median** \\ **Variation(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Coefficient of** \\ **Variation(\%)** \\ \end{tabular} } \\ \hline
900 & 1.61 & 3.31 & 2.33 & 0.35 & 0.10 & 2.53 & 2.33 & 14.85 \\
800 & 1.61 & 3.31 & 2.33 & 0.35 & 0.15 & 2.55 & 2.32 & 15.09 \\
700 & 1.61 & 3.31 & 2.33 & 0.35 & 0.16 & 2.54 & 2.33 & 15.23 \\
600 & 1.61 & 3.31 & 2.33 & 0.35 & 0.07 & 2.48 & 2.34 & 15.19 \\
500 & 1.61 & 3.24 & 2.34 & 0.35 & 0.01 & 2.49 & 2.34 & 14.81 \\
400 & 1.61 & 3.20 & 2.35 & 0.34 & \(-0.10\) & 2.42 & 2.36 & 14.68 \\
300 & 1.61 & 3.20 & 2.36 & 0.34 & \(-0.13\) & 2.53 & 2.38 & 14.51 \\
200 & 1.61 & 3.20 & 2.36 & 0.34 & \(-0.03\) & 2.56 & 2.38 & 14.52 \\
150 & 1.61 & 3.14 & 2.34 & 0.35 & 0.10 & 2.43 & 2.36 & 15.12 \\
100 & 1.65 & 3.20 & 2.38 & 0.35 & 0.10 & 2.43 & 2.36 & 14.89 \\
75 & 1.65 & 3.20 & 2.39 & 0.36 & 0.11 & 2.45 & 2.38 & 15.17 \\
50 & 1.76 & 3.20 & 2.39 & 0.33 & 0.23 & 2.47 & 2.37 & 13.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of SOM content after logarithmic transformation.  
Figure 2: Histogram of normal distribution of SOM at 900 sample points after logarithmic transformation.  
the decrease in sample numbers. Therefore, we are not going to discuss the impact of the number of points on the spatial variation structure in SOM.