## 3 Results and Discussion
### Importance of Input Variables and Processing Methods on WUE Estimates
#### 3.2.3 Importance of Processing Method for Ecosystem Water Use Efficiency Method  
Not only is the physical variable selection important when comparing different WUE techniques, the processing method for determining these variables is also important to consider. EddyPro/REddyProc and FVS processing (Fluxpart) approaches use with the same calculation variables for EWUES and EWUEF, and the same high frequency data files, but there is disagreement between these methods where half-hourly EWUES values were substantially lower (Figure 3g,h). The discrepancies between these techniques are caused by lower magnitudes of ET (Figure 3a,b; Spearman correlation for alfalfa: R = 0.87, \(p\)-value \(<\) 0.001, n = 2104; for maize: R = 0.88, \(p\)-value \(<\) 0.001, n = 1304), and higher, more variable, GPP (Figure 3c,d; Spearman correlation for alfalfa: R = 0.73, \(p\)-value \(<\) 0.001, n = 2056; maize: R = 0.77, \(p\)-value \(<\) 0.001, n = 1314) under FVS processing. Previous studies have shown that Fluxpart results coincide with agricultural flux trends that are expected when accounting for vegetation dynamics and harvesting [61], which was also observed in this study despite the magnitude of these fluxes differing from EddyPro/REddyPro methods. Fluxpart is considerably dependent on leaf WUE estimates, which is frequently not available and therefore estimated by the program [53; 78; 79; 80; 52] tested a few LWUE parameterization scenarios using FVS partitioning and found that component fluxes can be biased by up to 30% based on the accuracy of estimated internal leaf-to-ambient CO\({}_{2}\), with the poorest performing model being the one that used a constant for internal leaf-to-ambient CO\({}_{2}\) concentration. In this study, internal leaf CO\({}_{2}\) was assigned a default constant, based on photosynthetic pathway [22], which may have been partly responsible for the inconsistencies between EWUES and EWUEF.  
Figure 2: Half-hourly EWUEF (mg C g\({}^{-1}\) H\({}_{2}\)O m\({}^{-2}\) s\({}^{-1}\)) versus half-hourly EWUEC (\(x\)-axis) for alfalfa (**a**) and maize (**c**), and transpiration: evapotranspiration ratios for alfalfa (**b**) and maize (**d**) over the 2018 growing season. Alfafa was separated by cuts 1 (light green, n = 678), 2 (dark green, n = 548), 3 (beige, n = 541), and 4 (brown, n = 594), while maize was divided into the four growth stages: early growth (Stage 1; yellow, n = 296), mid-season accelerating growth (Stage 2; light orange, n = 257), pre-reproductive phase (Stage 3; dark orange, n = 518), and reproductive phase (Stage 4; brown, n = 442).  
The magnitude of disagreement between these two programs also differed between alfalfa and maize. EWUES ranged from 21-26% of corresponding median half-hourly EWUEF for each cut of alfalfa, but was 30-34% of corresponding median half-hourly EWUEF estimates during GS2-4 in maize. This could be a limitation in FVS partitioning since it has been reported elsewhere that lower measurement height-canopy height ratio improves results produced, while immature shorter stature plants tend to reduce accuracy [53]. This is further supported by this study since Fluxpart was unable to produce consistent estimates in the early growing season of maize (n = 258), particularly before sprouting was observed (n = 19).  
Impact of Physiological Stage and Anthropogenic Influences on WUE Methods at Both Seasonal and Shorter Timescales