## 4 Discussion
### Difference in Forecasting Accuracy among Various Fluxes and Sites  
Adopting the same inputs for all fluxes with the continuous six-year measurement data can offer a reliable and valuable means for benchmarking the prediction of different carbon fluxes. Among the three carbon fluxes at the five sites, our proposed models generally provided the best estimates for both GPP and \(R\), but obtained inferior estimates for NEE, which is in good agreement with previous results found by [PERSON] et al. [PERSON] et al. (2017). Despite the fact that higher accuracy for both GPP and \(R\) was obtained by only using four climatic factors as explanatory variables, which made it more convenient and easy to develop the relevant models, it turned out that omitting certain driving factors as model inputs, especially for NEE, may lead to slight under-fitting in the model training and generalization periods. In addition to the variables used in this study, numerous recent studies have focused on the responses of carbon fluxes to other factors, such as drought, precipitation, vapor pressure deficit, and soil moisture content [PERSON] and [PERSON] (2011); [PERSON] and [PERSON] (2012); [PERSON] and [PERSON] (2013). Therefore, identifying the important factors controlling the processes of terrestrial carbon exchanges and adding some key factors should be able to enhance the predictive capability of the models. Furthermore, the forecasting accuracy of various carbon fluxes is constrained by the quality of carbon flux measurement data induced by the eddy covariance method. This is because, when using these data for diverse applications, there are still many problems and challenges, such as data uncertainty mainly from random and systematic errors [PERSON] and [PERSON] (2013), energy balance closure [PERSON] and [PERSON] (2013), and the partitioning of NEE flux into GPP and \(R\)[PERSON] (2014). Moreover, appropriately selecting an effective gap-filling method for flux data may be capable of enhancing the predictive accuracy of carbon fluxes [PERSON] and [PERSON] (2013); [PERSON] and [PERSON] (2013). It should be noted that the use of daily flux data obtained from the gap-filled half-hourly flux data may lead to additional error. However, based on the daily time scale, relatively less time is required to train the applied models, and their estimates can provide helpful insight into carbon budget studies. Considerable difference among various sites was found in the simulated accuracy of each flux. More specifically, our developed models generally performed the best for the three fluxes in forest ecosystems, including CA-Oas and CA-Obs sites, but provided the worst estimates for both GPP and \(R\) in a cropland ecosystem (US-Bo1) and for NEE in a grassland ecosystem (AT-Neu). These estimates highlighted the importance of discussing the differences among different terrestrial ecosystems in the estimation of carbon fluxes. It has been acknowledged that many biophysical factors affect the photosynthetic and respiration capacity in terrestrial ecosystems, such as stomatal conductance [84] and light use efficiency [85; 86]. However, these factors are either often poorly described or omitted in the estimation of carbon fluxes. In addition, the superior generalization ability in the two mature forest ecosystems may be attributed predominantly to the obvious periodic fluctuation in the carbon fluxes within the continuous multi-year measurements and their relatively stable interactions with the change and variation in environmental factors [87]. In contrast, uncontrolled grazing, vegetation removal, and fire can cause severe damage to grassland ecosystems [88; 89; 90]. During the long-time human management activities, a variety of practices, such as irrigation, fertilization, harvesting, and rotation, substantially affect the ecological processes and ecological functions of cropland ecosystems [91; 92; 93].