# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 4 Discussion  
In this study, the 2016 corn and soybean GPP\({}_{\rm calc}\) in four counties were upscaled using ground measurements of GPP, NDVI, and meteorological data as well as MODIS fPAR products. In contrast to previous studies, seasonal GPP, NEP, and RE from several EC stations concentrated on a relatively small land area were retrieved. It was therefore possible to estimate the spatial variation of GPP under similar climate conditions and for a relatively small land area as compared to previous studies [12; 13; 14; 15]. The calculated 95% CI indicate a substantial variation in GPP, NEP, and RE among sites, despite that the studied fields are well represented in the four counties [5] and the relatively high density of EC stations used. Individual spatial variation in GPP is probably connected to climate, soil and crop management (Table 1, Table 2), such as precipitation, cloud cover, crop growth, crop varieties, fertilizer application, tillage, and amount of crop residues among others. Analyzing for these impacting factors was beyond the scope of this study and would require more information of each field but may be important to further increase the accuracy of GPP\({}_{\rm calc}\). In a previous study, substantial variation in corn and soybean F\({}_{\rm c}\) and precipitation using 12 EC stations in a 5000-ha Midwestern US watershed was found, and this was accounted to difference in ground cover, plant growth and biomass accumulation  
Figure 6: Daily GPP\({}_{\rm calc}\) (g C m\({}^{-2}\) d\({}^{-1}\)) on DOY 193 in 2016 for four Iowa counties. Triangles and circles show the location of the eddy covariance (EC) stations in soybean and corn fields, respectively. among fields [47]. Differences in soil tillage has also impacted GPP [4], and soil respiration varies with nitrogen fertilizer application, irrigation, and tillage [48]. This indicates that a higher density of EC stations than typically used in field studies is needed to cover the spatial variation of GPPcalc. The higher NEP values in corn are connected to higher photosynthetic rates of corn as a C4 crop, compared to soybean as a C3 crop. In addition, high amounts of corn residues from the previous season remain in the soybean fields, which further decreases NEP due to higher residue mineralization [27]. The GPPcalc was more accurate than GPPMODIS, as evidenced by lower RMSE in comparison to EC-derived GPP. Field measurements of RUEmax, improved land use maps, and a higher resolution of IPAR, Ta, and VPD measurements increased the accuracy of GPPcalc compared to GPPMODIS. One important tool to increase the accuracy of GPPcalc compared to GPPMODIS was the ACPF land use map [18], which enabled distinction between corn and soybean fields within each county. The ACPF land use maps are based on an ensemble of aerial photographs, satellite imagery, and other land use map products [18]. Earlier studies on corn and soybean production in the Midwestern US were constraint to the Cropland Data Layer (CDL) and an area-weighted, crop-specific value was used to estimate GPP contribution by crop [12; 13]. To the best of our knowledge, this is the first time that ACPF land use maps have been used to upscale GPPcalc. The field boundaries provided by ACPF land use maps were the basis to create GPPcalc maps as separated by crop. Approximately, \(\frac{3}{4}\) of the total land area in the studied counties were used for corn and soybean production, which demonstrates the economic importance and its impact on the carbon cycle in the region. There were more fields used for corn than for soybean production in the studied counties in 2016, and corn GPP (as a C4 crop) was higher than soybean GPP (as a C3 crop) (Figure 2D). Consequently, the tempo-spatial variation in corn and soybean fields determines the amount of county-wide, seasonal cropland GPPcalc. Accurate and annually updated land use maps are therefore imperative to upscale GPPcalc and is important to improve GPPMODIS.