## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.6 Dominant Factors Controlling Variability in SOM  
The above sections provide a semi-quantitative analysis of the influence of each factor on SOM, but the magnitude of their influence cannot be accurately quantified. In this research, land use pattern, soil type, soil parent material, soil texture, as well as topographic and climatic factors were taken as explanatory variables for stepwise regression analysis, with SOM content taken as the target variable. The comprehensive explanatory ability of each factor on the spatial variation of SOM content, and the influence of each factor on the spatial variation of SOM, were each quantitatively studied. Before regression analysis, it was necessary to assign values to categorical variables, such as land use pattern, soil texture, soil parent material, and soil type [30]. The corrected determination coefficients in the regression equations reflect the independent explanatory ability of each influencing factor on the variability of SOM. For each impact factor shown in Table 8, the smaller the characteristic parameter R\({}^{2}\) is, or the larger \(\Delta\)R\({}^{2}\) or R\({}^{2}\)s are, the greater the relative importance of that factor. R\({}^{2}\)adj is the comprehensive explanatory ability of all independent variables in the regression equation, to explain the variability of dependent variables [31]. Among the models, the inclusion of the soil type variable has the largest \(\Delta\)R\({}^{2}\) value, which indicating that soil type has the greatest influence on the spatial distribution of SOM in Shandong province. Without considering soil type, the R\({}^{2}\) of the model was 0.453, which indicated that the remaining seven factors explained 45.30% of the variability in SOM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicators** & **Elevation** & **Slope** & **Aspect** & **MAT** & **MAP** \\ \hline SOM content & 0.36 ** & 0.47 ** & 0.02 & 0.15 * & 0.13 ** \\ \hline \hline \end{tabular} Notes: * indicates significant correlation at the 0.05 level, ** indicates highly significant correlation at the 0.01 level.  
\end{table} 
Table 7: Correlation between topographic/climatic factors and SOM. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Impact Factor** & **R\({}^{2}\)** & \(\Delta\)**R\({}^{2}\)** & **R\({}^{2}\)\({}_{\$}\)** & **R\({}^{2}\)adj** \\ \hline All factors & & & & 0.523 \\ Land use pattern & 0.541 & 0.049 & 0.091 & \\ Soil type & 0.453 & 0.137 & 0.250 & \\ Parent material & 0.498 & 0.092 & 0.183 & \\ Soil texture & 0.542 & 0.048 & 0.105 & \\ Slope & 0.582 & 0.008 & 0.019 & \\ Elevation & 0.587 & 0.003 & 0.007 & \\ Precipitation & 0.589 & 0.001 & 0.002 & \\ Temperature & 0.586 & 0.004 & 0.009 & \\ \hline \hline \end{tabular} Notes: R\({}^{2}\) is the determination coefficient of SOM stepwise regression for all other variables except this variable; \(\Delta\)R\({}^{2}\) is the increment of the determination coefficient of the regression equation when this variable is added to the other variables; R\({}^{2}\)s is the bias determination coefficient, which refers to the proportion of what can be explained by the new variable in the regression equation, compared to what cannot be explained by the regression equation in its absence.  
\end{table} 
Table 8: Results of the regression analysis of the variance between the factors upon SOM. With the addition of soil type, the model \(\Delta\)R\({}^{2}\) value was 0.137, indicating that the addition of soil type increased the explanatory ability of the model by 13.70%. According to the R\({}^{2}\) value, the influence of soil type was much larger than the other factors. The second most influential variable on the spatial distribution of SOM was parent material (9.20%), followed by land use pattern (4.90%) and soil texture (4.80%). The remaining variables in order of influence are slope, temperature, elevation, and precipitation, with the combined explanatory ability of these variables being less than 1.00%.