## 3 Results
### The Relative Contribution Rate of Influencing Factors  
To investigate the correlation between NPP and its influence factors in various land cover types in the Songnen Plain, this study constructed a multisource spatial dataset based on annual data of NPP (gC\(\cdot\)m\({}^{-2}\).a\({}^{-1}\)), PRE (mm), TEM (\({}^{\circ}\)C), SUN (h), EVA (mm), SM (mm), DEM (m), and slope (\({}^{\circ}\)) in the study area from 2001 to 2020; spatially balanced sampling was conducted for land cover types at an interval of 5 km (Figure 14, taking 2001 as an example). Figure 14: Spatially balanced sampling points in each land cover type. Figure 13: (**a**) Spatial Distribution of slope in the Songnen Plain. (**b**) The mean NPP for each land cover type within each slope level. These influencing factors exhibit strong autocorrelation in the modeling process. In this case, linear regression may lead to overfitting, and coefficients of the independent variables in the regression equation cannot accurately represent the actual significance of the influencing factors. Therefore, it is necessary to diagnose the covariance of each influencing factor before constructing the regression model. The variance inflation factor (VIF) is often used as a criterion for covariance diagnosis, with a VIF greater than 10 indicating severe multicollinearity. Covariance diagnosis (Figure 15) showed that the VIFs of PRE and SM were greater than 10, and the multicollinearity was severe. To reduce the disturbance of multiple covariates of independent variables, this study conducted elastic net regression analysis for reliable regression results at the cost of excluding some information. The elastic net regression analysis (Figure 15, Table 2) shows that the multicollinearity among the influencing factors was significantly attenuated (VIF \(<\) 10), and the model coefficient of determination (R\({}^{2}\)) exceeded 0.6 for all land cover types. DEM and PRE had a positive effect on NPP for all land cover types, while TEM had a negative effect on NPP across all land cover types. SUN had a significant negative effect on NPP of DRY and PAD and a weak positive effect on NPP in other land cover types; SM and slope only had a negative effect on NPP in SWA and exerted a positive effect on NPP in other land cover types. EVA only had a negative effect on NPP in FOR. To analyze the degree of influence of these factors on NPP, we calculated their relative contribution to NPP in the cases of various land cover types based on the results of elastic net regression analysis. As can be seen in Figure 16, the cumulative contribution of PRE to NPP ranked first in all land cover types, reaching 61.2% and 50.6% in GRA and FOR, respectively; and therefore, PRE is the most significant influencing factor of NPP. The cumulative contributions of PRE, SM, and DEM were close to 100% in GRA and more than 70% in FOR, SWA, and DRY, making them dominant influencing factors in most of the land types in the Songnen Plain. The contribution of DEM in FOR exceeded that of SM. In the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Land Cover** & **Factors** & **GRA** & **DRY** & **FOR** & **PAD** & **SWA** \\ \hline R\({}^{2}\) & 0.62 & 0.64 & 0.66 & 0.65 & 0.65 \\ PRE & 0.612 & 0.320 & 0.506 & 0.333 & 0.400 \\ TEM & \(-\)0.002 & \(-\)0.013 & \(-\)0.059 & \(-\)0.131 & \(-\)0.118 \\ SUN & 0.003 & \(-\)0.116 & 0.004 & \(-\)0.250 & 0.064 \\ EVA & 0.007 & 0.102 & \(-\)0.056 & 0.058 & 0.003 \\ SM & 0.289 & 0.222 & 0.081 & 0.156 & \(-\)0.284 \\ DEM & 0.093 & 0.193 & 0.279 & 0.046 & 0.126 \\ Slope & 0.007 & 0.034 & 0.018 & 0.028 & \(-\)0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Elastic net regression analysis results. Figure 15: Variance inflation factors (VIFs) of influencing factors in each land cover: (**a**) the collinearity diagnosis result; (**b**) VIFs after elastic net regression. case of DEM, its contribution in FOR was the highest (27.9%) among the five land types, possibly because of the most comprehensive distribution of FOR in all classes of DEM.