## 3 Results
### Driving Factors of Carbon Fluxes  
#### 3.3.1 Correlations Among the GPP, NEE, and \(\rm R_{e}\)  
Both diurnal and daily GPP, \(\rm R_{e}\), and NEE exhibited significant positive correlations (\(p<0.01\)) among themselves (Figure 4). Diurnal GPP showed the strongest correlation with NEE, followed by \(\rm R_{e}\), with a Pearson correlation coefficient of 0.98 and 0.80 (\(p<0.01\)), respectively. The correlation between diurnal NEE and \(\rm R_{e}\) was relatively weaker, with a Pearson correlation coefficient of 0.66 (\(p<0.01\), Figure 4a,b). Daily GPP showed the strongest correlation with NEE and \(\rm R_{e}\), with a Pearson correlation coefficient of 0.89 and 0.80 (\(p<0.01\)), and the correlation between NEE and \(\rm R_{e}\) was relatively lower, with a value of 0.44 (\(p<0.01\), Figure 4c,d), respectively. During the vigorous growth period, the correlation between GPP and \(\rm R_{e}\), \(\rm R_{e}\), and NEE was relatively weak, with the correlation coefficient being 0.62 (\(p<0.01\)) and 0.13 (\(p<0.1\)), while the value was 0.80 and 0.33 (\(p<0.01\)) at the non-vigorous growth period (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Carbon**} & \multicolumn{5}{c}{**Month**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Total**} \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\ \hline \multirow{3}{*}{2018} & GPP & 2.75 & 5.51 & 9.41 & 10.25 & 9.85 & 8.18 & 5.13 & 3.67 & 2.66 & 6.77 & 1684.66 \\  & \(\rm R_{e}\) & 3.45 & 4.58 & 5.29 & 5.65 & 6.22 & 7.31 & 4.88 & 3.21 & 2.52 & 4.99 & 1240.41 \\  & NEE & 0.70 & \(-0.93\) & \(-4.11\) & \(-4.61\) & \(-3.63\) & \(-0.87\) & \(-0.25\) & \(-0.46\) & \(-0.14\) & \(-1.79\) & \(-444.25\) \\ \hline \multirow{3}{*}{2019} & GPP & 2.82 & 4.98 & 6.95 & 6.74 & 8.47 & 9.46 & 5.65 & 3.89 & 2.24 & 5.88 & 1526.27 \\  & \(\rm R_{e}\) & 3.21 & 4.87 & 4.58 & 6.07 & 6.02 & 6.50 & 4.61 & 3.67 & 2.66 & 4.78 & 1242.09 \\ \cline{1-1}  & NEE & 0.39 & \(-0.11\) & \(-2.4\) & \(-0.67\) & \(-2.48\) & \(-2.96\) & \(-1.04\) & \(-0.22\) & 0.42 & \(-1.10\) & \(-285.77\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Monthly mean daily, annual mean, and total gross primary productivity (GPP, g C m\({}^{-2}\) d\({}^{-1}\)), ecosystem respiration (\(\rm R_{e}\), g C m\({}^{-2}\) d\({}^{-1}\)), and net ecosystem exchange (NEE, g C m\({}^{-2}\) d\({}^{-1}\)) in the kiwifruit orchard during the growing seasons in 2018 and 2019.  
Figure 4: Pearson correlation coefficient between biophysical factors and carbon fluxes (GPP, gross primary productivity; \(\rm R_{e}\), ecosystem respiration; NEE, net ecosystem exchange) at diurnal and daily scales in 2018 (left) and 2019 (right). Biophysical factors are vapor pressure deficit (VPD), concentration of air carbon dioxide (\(\rm CCO_{2}\)), concentration of air water vapor concentration (\(\rm CH_{2}O\)), air temperature (\(\rm T_{a}\)), air relative humidity (RH), photosynthetically active radiation (PAR), soil temperature (\(\rm T_{s}\)) at 5 cm depth, soil water content (SWC) at 20 cm depth, and the leaf area index (LAI). \({}^{*}\) and \({}^{**}\) represent a significance level of \(p<0.05\) and \(p<0.01\), respectively.