## 3 Results  
### GPP Response to Limiting Factors Per Land Cover Class: Statistical Analysis  
The highest correlations were found between GPP and N deposition and between GPP and temperature. The lowest correlations were found between GPP and precipitation. In particular, very low correlation or no correlation between both variables were found in 2007 and 2010 for grasslands (\(\approx\)0.06 and \(\approx\)-0.04, respectively, \(p<0.01\)) and croplands/natural vegetation mosaic (\(\approx\)0.07 \(p<0.01\) and \(-0.04\)\(p<0.05\)) (Figure 2 and Appendix A, Tables A1-A3). In grasslands, the two variables that mostly explained the GPP variance were N deposition and precipitation (Table 3). In croplands, N deposition was the independent variable that influenced the most GPP response. Precipitation followed N deposition in relevance for the years 2000 and 2010. In 2007, temperature was, however, the variable that followed N deposition in importance and the remaining variables were excluded from the model (Table 3). In croplands/natural vegetation mosaics, N deposition largely explained the GPP response followed by precipitation in the years 2000 and 2007 and sunshine in 2010 (Table 3). In grasslands, [PERSON]'s correlations [65] showed that N deposition and temperature were the variables with the highest correlation with GPP (Figure 2 and Appendix A, Table A1). The model with the lowest RMSE (including all explanatory variables) reached collinearity values of 0.23 for T and 4.5 for VIF. The addition of variables to the ones already in the model (e.g., model 1: N deposition, model 2: N deposition and precipitation) increased the coefficient of determination (R2) up to 0.77 in 2000, 0.75 in 2007, and 0.8 in 2010 (Table 4). In croplands, N deposition and temperature were the most correlated independent variables with GPP in all the years (Figure 2 and Appendix A, Table A2). However, correlation between these variables resulted either in exclusion of the one less correlated with GPP or a later addition into the model. The final model achieved collinearity statistics between 0.49 and 0.88 for T and between 1.14 and 2.06 for VIF. The explanatory variables reached adjusted R2 values ca. 0.4 (Table 5). In croplands/natural vegetation mosaics, the explanatory variables that reached high correlation with GPP were N deposition followed by positive correlations with temperature and negative correlations with sunshine (Figure 2 and Appendix A, Table A3). The model with the lowest RMSE achieved values between 0.61 and 0.99 for T and 1 and 1.6 for VIF. Adjusted R2 values reached ca. 0.18 in 2000 and 2010, and 0.16 in 2007 (Table 6). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**2000**} & \multicolumn{4}{c}{**2007**} & \multicolumn{4}{c}{**2010**} \\ \hline \multirow{2}{*}{**Order**} & \multicolumn{2}{c}{**Adjusted**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Order**} & \multicolumn{2}{c}{**Adjusted**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Order**} & \multicolumn{2}{c}{**Adjusted**} \\  & \multicolumn{2}{c}{**R2**} & & & & & \multicolumn{1}{c}{**R2**} & & \multicolumn{1}{c}{**R2**} & \\ \hline
**Ln N dep** & 0.677 *** & 0.225 & Ln N dep & 0.637 *** & 0.246 & Ln N dep & 0.660 *** & 0.220 \\
**Precip** & 0.759 *** & 0.194 & Precip & 0.731 *** & 0.212 & Precip & 0.796 *** & 0.171 \\
**Sunsh** & 0.764 *** & 0.192 & Temp & 0.746 *** & 0.206 & Temp & 0.798 *** & 0.170 \\
**Temp** & 0.766 *** & 0.191 & Sunsh & 0.747 *** & 0.206 & Sunsh & 0.800 *** & 0.169 \\ \hline \end{tabular}
\end{table} 
Table 4: Order of variables entered into the model and adjusted R2 for grasslands, according to the addition of a new variable to the model: Ln N dep: Natural logarithm of N deposition, Temp: Temperature, Precip: Precipitation, and Sunsh: Sunshine. *** Statistically significant \(p<0.001\).