## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Ecosystem** & **Year** & **Annual NEE** & **Latitude** & **Longitude** & **Researcher** \\ \hline Kiwifruit & 2018 & \(-444.25\) & \(30^{\circ}19^{\prime}00^{\prime\prime}\) N & \(103^{\circ}25^{\prime}00^{\prime\prime}\) E & Our study \\ \hline Apple & 2016 & \(-698.00\) & \(35^{\circ}08^{\prime}52^{\prime\prime}\) N & \(108^{\circ}18^{\prime}00^{\prime\prime}\) E & [15] \\ \hline Pear & 2012 & \(-600.00\) & \(37^{\circ}47^{\prime}44^{\prime\prime}\) N & \(114^{\circ}55^{\prime}57^{\prime\prime}\) E & [45] \\ \hline Vineyard & 2008 & \(-820.00\) & & & \\  & 2009 & \(-824.00\) & \(37^{\circ}51^{\prime}00^{\prime\prime}\) N & \(102^{\circ}51^{\prime}00^{\prime\prime}\) E & [46] \\  & 2010 & \(-961.00\) & & & \\ \hline Citrus & 2009 & \(-385.50\) & \(39^{\circ}27^{\prime}15^{\prime\prime}\) N & \(0^{\circ}33^{\prime}32^{\prime\prime}\) W & [47] \\ \hline Peach & 2011 & \(-1052.00\) & \(40^{\circ}10^{\prime}25^{\prime\prime}\) N & \(116^{\circ}00^{\prime\prime}53^{\prime\prime}\) E & [48] \\ \hline Kiwifruit & 2003 & \(-320.00\) & \(44^{\circ}20^{\prime}39^{\prime\prime}\) N & \(11^{\circ}59^{\prime}02^{\prime\prime}\) E & [44] \\  & 2012 & \(-493.30\) & & & \\ \hline Winter wheat & 2006 & \(-437.35\) & \(35^{\circ}14^{\prime}00^{\prime\prime}\) N & \(107^{\circ}41^{\prime}00^{\prime\prime}\) E & [49] \\ Agroforest & 2006 & \(-545.42\) & \(35^{\circ}14^{\prime}00^{\prime\prime}\) N & \(107^{\circ}41^{\prime}00^{\prime\prime}\) E & [49] \\ \hline Grassland & 2005 & 54.80 & \(30^{\circ}51^{\prime}00^{\prime\prime}\) N & \(91^{\circ}05^{\prime}00^{\prime\prime}\) E & \\  & 2005 & \(-51.70\) & \(37^{\circ}39^{\prime}55^{\prime\prime}\) N & \(101^{\circ}19^{\prime}52^{\prime\prime}\) E & [50] \\  & 2005 & \(139.90\) & \(43^{\circ}32^{\prime}00^{\prime\prime}\) N & \(116^{\circ}40^{\prime}00^{\prime\prime}\) E & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of annual net ecosystem exchange (NEE, g C m\({}^{-2}\) yr\({}^{-1}\)) under different ecosystems. Our study found that although the total \(\mathrm{R_{e}}\) was comparable between the two growing seasons, GPP and NEE in 2019 were relatively lower than those of the 2018 growing season. On the one hand, this may be attributed to the lower SWC and higher VPD in the summer of 2019 (Figure 2), which limited photosynthesis due to reduced water availability. GPP would be reduced significantly due to a lack of precipitation and seasonal high temperatures [44]. On the other hand, the significant decrease in the LAI caused by spring pruning at the end of April 2019 likely resulted in overall lower carbon flux values. It can be speculated that farmland ecosystems' carbon flux is strongly disturbed by both environmental and human management interventions [51; 52]. The correlation analysis of diurnal and daily carbon fluxes indicated that PAR was the primary driver for GPP and NEE, but \(\mathrm{R_{e}}\) was predominantly influenced by \(\mathrm{T_{a}}\)[53; 54]. Previous studies have shown that water availability controls carbon fluxes in arid regions, whereas in humid regions, radiation and temperature are the primary factors, and excessive moisture conditions may restrict the efficiency of photosynthesis [55; 56; 57]. Our study area is located in the low-altitude Sichuan Basin, where frequent cloudy and rainy conditions limit available radiation, making radiation crucial for GPP. Owing to its direct involvement in photosynthesis, PAR can directly enhance GPP and NEE [58]. \(\mathrm{R_{e}}\) is primarily driven by a series of enzymatic reactions, leading to an increase with rising temperature [55; 59]. Soil temperature regulated surface temperature through heat exchange and radiation dynamics, influencing enzymatic activities and thereby altering carbon exchange rates. The results revealed that the influence of \(\mathrm{T_{s}}\) at the depth of 5 cm on diurnal \(\mathrm{R_{e}}\) was not as strong as on daily \(\mathrm{R_{e}}\) (Figure 4).