# A Modeling Application for GHG Fluxes Estimates in Betel Nuts Plantations in Taiwan
## 3 Results  
Figure 2 shows the January GPP spatial variability in the Chiayi County area. During the three months when detection proceeded, the mean GPP per square meter considerably changed. As we can see in Table 1, March GPP values were higher than the ones in January and February, when daytime was shorter and solar radiation less intense. If the model had considered the entire surface, the GPP could have reached 1466.42 tons in January, 1517.23 tons in February and 2718.61 tons in March. Because a light use efficiency model was used, as expected, the most important driving factor was the solar radiation, which increased, as did the GPP (Table 2). Even though the GPP and radiation mean values changed so closely, when a linear model was applied to these variables, it did not show a strong correlation between them (R\({}^{2}\) = 0.06). The lack of correlation could be explained by the high variability introduced by the remote sensing data, such as EVI. In Table 1, the coefficients of variation (CV) for GPP, EVI, and radiation were reported; focusing on Figure 3a, it can be noticed that the GPP trend and variability strictly followed those of EVI, with R\({}^{2}\) = 0.67. However, when we considered the radiation absorbed by vegetation, calculated like in [26], using EVI instead of NDVI, it was possible to see a relation with GPP. In fact, like GPP, the absorbed radiation depended on vegetation intensity and its light processing efficiency [27]; GPP increased exponentially when absorbed radiation increased (Figure 3b), while when we used a logarithmic scale for GPP, a satisfactory linear relationship was confirmed by an R\({}^{2}\) of 0.47 (Figure 3c). The model validation was highly problematic; in fact, data regarding gas exchanges directly measured in the field were completely absent. To overcome this lack of data, we took advantage of the close coupling of photosynthesis with H\({}_{2}\)O fluxes and used evapotranspiration to evaluate the performance of the VPM model (Figure 3d). In this case, too, the high variability, due to the remote sensing inputs, prevented us from noticing a strong correlation, showing a very slight positive correlation with R\({}^{2}\) = 0.17. The GPP represents the whole amount of C absorbed by a plant without considering the CO\({}_{2}\) released through the plant and soil respiration. To better outline the carbon cycle and the permanent amount of carbon stored in vegetation, NEP must also be considered. The net storage is sensitively reduced to less than a half of GPP (Table 1). When we considered also the plant respiration, we observed NEP average values of 135.93 \(\pm\) 28.81 gC m\({}^{-2}\) month\({}^{-1}\) in January, 141.10 \(\pm\) 29.45 gC m\({}^{-2}\) month\({}^{-1}\) in February, and 255.31 \(\pm\) 50.68 gC m\({}^{-2}\) month\({}^{-1}\) in March. For the whole area, we recorded 670.05 tC month\({}^{-1}\) in January, 695.87 tC month\({}^{-1}\) in February, and 1259.14 tC month \begin{table}
\begin{tabular}{c c c c c} \hline  & **GPP** & **NEP** & **EVI** & **Solar Radiation** \\  & **(gC m\({}^{-2}\) month\({}^{-1}\))** & **(gC m\({}^{-2}\) month\({}^{-1}\))** & **(MJ m\({}^{-2}\))** \\ \hline January & 297.34 \(\pm\) 62.55 & 135.86 \(\pm\) 28.81 & 0.47 \(\pm\) 0.09 & 298.94 \(\pm\) 14.62 \\ February & 307.7 \(\pm\) 63.66 & 141.10 \(\pm\) 29.45 & 0.48 \(\pm\) 0.08 & 326.62 \(\pm\) 5.52 \\ March & 551.24 \(\pm\) 108.83 & 255.31 \(\pm\) 50.68 & 0.49 \(\pm\) 0.07 & 498.60 \(\pm\) 17.98 \\ Variation coefficient & 20\% & 20\% & 16\% & 3\% \\ \hline \end{tabular}
\end{table} 
Table 2: GPP, NEP, EVI, and Solar radiation means and variation coefficients during the detection period in Taiwan. Figure 2: The betel nuts GPP spatial variability in the area of Chiayi County, Taiwan. These output data were distinguished by high variability, probably also due to the lack of a dense network of weather stations.