## 4 Discussion
The high variation in grass flux tower NEP is probably related to the inclusion of international grassland flux towers and because the maximum, minimum, and mean NEPs were estimated from a NEP frequency graph (Figure 11A in [PERSON] et al. (2016)). Regional grassland annual means averaged across moisture and latitudinal gradients in the Great Plains tended to minimize inter-annual regional variations relative to flux tower estimates. Only alfalfa regional means and inter-annual NEP data ranges were higher than the flux tower site-year observations (Figure 13). The regional distributions of long-term alfalfa cropping are more prevalent along the drier western edge of the Great Plains and appear to be irrigation dependent due to proximities to major river systems (Arkansas, Platte, Yellowstone, and Milk Rivers, Figure S2). Flux tower alfalfa NEP estimates (5 site years) included towers east of Mandan, ND and towers in moist ecosystems (Michigan, Pennsylvania, and Italy) (Michigan, 2016). Recall also that alfalfa-mapped NEP was the average of the grass and crop NEP predictions (Section 2.3), which could add uncertainty. The lowest alfalfa regional annual means were in 2000 and 2002, both drier than normal years (Figure 5). Flux tower data on irrigated alfalfa in arid and semiarid ecosystems would be useful in quantifying alfalfa flux uncertainty and improving mapped NEP accuracy in these alfalfa-dominant areas. Figure 13: Agreement of NEP between flux tower synthesis analysis ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016) and regional dominant land cover mapped areas. Utilizing spatial, temporal, synoptic, remotely sensed, and ancillary digital map products to interpolate carbon fluxes through space and time is a strong approach. Our regression tree approach of using mapped versions of all input spatial variables in model development ensures that input variables are used only if prediction utility persists despite any mapping errors in the input drivers. Improved mapping of NEP fluxes would be realized with improved resolution and spatial accuracy of weather, climate, and soils information. Another limiting factor is the low density of carbon flux tower observations relative to the spatial and temporal area mapped. In particular, extreme events (droughts, wet years, early and late freezes, etc.) need to be captured to make the regression tree mapping model robust to expected and witnessed increased weather variability. Representation of major crops by flux towers is weak, particularly in non-irrigated dry environments. Regression tree mapping models should be assessed for possible over fitting or over fitting tendencies to help ensure better final map products. Our experience has been that regression trees can be quite site specific as they are tuned to optimize prediction for a specific study area. Therefore, we do not recommend applying the Great Plains NEP mapping models in this manuscript (Tables S2 and S3) to other areas. However, we have successfully applied regression trees to subsequent (newer) years with reasonable success. Future plans include mapping of the partitioned carbon fluxes associated with GPP and RE, which allow more functional prediction of carbon fluxes through time and space ([PERSON], 2014; [PERSON], 2014) than the direct mapping of the somewhat functionally confounded NEP. Further GPP and RE spatial and temporal maps would improve understanding of the causes of carbon sinks and sources. The authors intend to apply the GPP and Re mapping approach to grass and croplands across the conterminous U.S. by adding additional flux tower site years.