## 4 Discussion  
The most influential spatial variables in the NEP maps were typically those that were dynamic at the weekly time step (for example, NDVI, PAR, Week, and DOY). Precipitation (PCP), a major driver in many process-based models, had only moderate utilization in NEP modeling of 33% and 21% for grass and crop, respectively. The reduced usage or precipitation in the regression tree models may be related to the difficulty of extrapolating precipitation away from weather stations as regression tree models will only employ spatial inputs when and where they contribute to explaining the spatial or temporal variability of weekly NEP. Temperature inputs are much more reliably extrapolated away from weather stations but temperature inputs were more utilized in the crop model than the grass model. Incorporation of PAR and its high utilization in the grass model may have reduced the impact of the phenological variables (SOSN, SOST, AMP, MAXN, and EOST) relative to the crop model. East to west moisture and productivity gradients were observed in annual and multiple year summaries (Figures 5 and 7), agreeing with the general flux trends in other studies [5; 11; 70]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dependent Variable (\(\gamma\))** & **Independent Variable (\(\chi\))** & \(R^{2}\) & **Equation** \\ \hline Annual Precipitation & Grass NEP & 0.69 & Y = 1.935X + 372.6 \\ Annual Precipitation & Crop NEP & 0.90 & Y = 1.617X + 629.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Spatially averaged per grassland biomass strata (\(n=8\), Figure 12) 30 year annual precipitation (mm) regressed on grass and non-irrigated crop 2000–2008 mean NEP (g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)). Figure 12: Grass NEP regressed on non-irrigated crop NEP using regional class rangeland biomass median 2000–2008 mean NEP. These mapping approaches provide logical and useful means to reliably extend flux tower data across space and time. Currently, available flux towers grossly under sample the Great Plains ecosystems in both time and space relative to the mapped population (area \(\times\) years) of this study. The 76 site years of flux tower data used to develop the NEP mapping models represented only 0.00003% of the space for time population that was mapped (28,817,873 pixels at 250 m resolution \(\times\) 9 years). Particularly, limited flux tower representation occurs in cropland areas in the extreme northern, southern, and western portions of the U.S. Great Plains (Figure 1), where high RMSE values were observed (Figure 8). Crop flux towers are primarily located in either corn or soybean fields, with little or no data available for other crops, such as cotton, spring wheat, and sorghum. Grassland areas along the southwestern edge of the Great Plains are also deficient in flux tower representation [70]. A focused expansion to the current flux tower network would improve the spatial mapping accuracies and help to ensure that extreme weather and environmental conditions are captured. Capturing extreme weather and environmental conditions with additional flux towers would result in more robust modeling results to better inform the carbon cycle science communities. Referencing Figures 5 and 7, areas with persistent high carbon sinks (\(>\)300 g C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\)) mapped in this study aligned with: (1) the grassland-dominated Flint Hill ecoregion (9.4.4) primarily in eastern Kansas where shallow rocky soils have allowed native tallgrass prairie systems to persist; (2) the northwestern Central Great Plains ecoregion (9.4.2) which is a mix of grassland and cropland; and (3) the eastern portions of the Northwestern Claciated Plains ecoregion (9.3.1), a grassland-dominated region with some cropland. Another notable high carbon sink region is the edges for the Prairie Coteau in northeastern South Dakota, where steep slopes have precluded crop expansion in these grasslands which are considered by many as tallgrass systems [71].