## 4 Discussion  
This study utilized seven fields from four geographic locations. The agreement between modeled and measured values is not as strong as for maize and soybean GPP at sites in southeastern Nebraska alone in the original study (13) (overall slope of 0.94 \(\pm\) 0.02, R\({}^{2}\) = 0.91, RMSE = 2.6 g C m\({}^{-2}\) d\({}^{-1}\), and MNB = 1.7%). Thus, the MNB of 30.5% across all sites in the current study is of concern (Figure 3a). It was expected that errors would be larger when using modeled input data as opposed to measured input data specific to the site. This was the case for [PERSON] et al. (1999), who attained an RMSE of 2.97 g C m\({}^{-2}\) d\({}^{-1}\) when comparing their modeled GPP data to in situ derived GPP of mixed land cover in the Heihe River Basin, China. Three of the seven fields, which include 36 of 65 total field years, are located in southeastern Nebraska and are the same as those used in the original study. With over half of the field years from this specific location, a calibration bias may have occurred. This bias may have led to smaller errors for the Nebraska sites and larger errors for the non-Nebraska sites than would be the case if the field years were more evenly represented for model calibration (Table 2). In addition, there are inherent errors and uncertainties in the gridded Daymet data (29), attributed to weaker agreement between measured and modeled daily GPP values. Additionally, the gLAI algorithms used in this study were developed using the Mead, NE sites (20) and may not be ideal estimators of gLAI for the other sites due to characteristics not captured in the site-specific algorithms  
Figure 6: Measured AmeriFlux vs. LUEc modeled daily GPP for all soybean validation years by site: (**a**) US-Bo1; (**b**) US-Br1; (**c**) US-Br3; (**d**) US-Ne2; (**e**) US-Ne3; and (**f**) US-Ro1. used in this study. Unfortunately, ground data were not available at the other AmeriFlux locations to generate more generic gLAI algorithms. The underestimation of early and late season GPP is attributed to the sensitivity of gLAI algorithms to soil background at low LAI and the inaccurate representation of canopy architecture with the given k constants when foliage density is low. In both cases, any noise in the approach is amplified due to low values of GPP during these periods. The correlation between measured and modeled GPP values tended to be higher with maize than with soybean datasets (Figure 3b,c). The association among individual sites varied as well (Figures 5 and 6). Factors contributing to a trend of a stronger association with the maize data include (1) the increased sensitivity to diffuse light of soybean plants compared to maize (\(\beta\) of \(0.294\pm 0.002\) for soybean, \(0.181\pm 0.014\) for maize) combined with the concerns regarding PARin data (discussed below); (2) estimates of gLAI may be a factor as gLAI algorithms for maize and soybean were developed for the Mead, NE training sites (US-Ne1, US-Ne2, and US-Ne3) and may not adequately estimate gLAI for the other locations which may have different plant densities (there is also uncertainty associated with separating green and nongreen leaf material during senescence [30]); (3) there were fewer soybean field years (17) than maize field years (30) with which to train the scalars, which may have allowed for more error in the soybean scalars due to the smaller sample size (Table 2); (4) the soybean growing season is much shorter than for maize, so there are fewer data points to calibrate/validate the model; (5) the temperature scalar was selected based on optimal temperatures for maize. There are physiological differences between C3 and C4 plants that may account for minor shifts in the optimal temperature range. However, using crop-specific temperature ranges will complicate the model and require in-season crop type maps. This would reduce the applicability of generating regional estimates.