WUE of plants is also affected by environmental conditions, which play important roles in rates of both carbon assimilation and water use. Environmental drivers can have varying degrees of influence on carbon assimilation and water use variables [10; 11; 12]. Indeed, the variables of water use and carbon uptake may be affected differently by climate, soil, vegetation, and hydrological factors. As such, variable climate and hydrological regimes have a large impact on crop resource use, and consequently, the prediction of field-scale changes in WUE is complicated by the numerous environmental interactions [9]. The varying influences of environmental controls are relevant to calculations of WUE as they can result in different patterns of WUE depending on the calculation method used. For example, the study in [13] identified nine different equations for WUE, including seven ecosystem scale equations, which resulted in inconsistencies in WUE estimates across the different methods. Moreover, the carbon and water variable used in different WUE calculations provide different insight into plant-carbon-water dynamics, which each have different advantages or disadvantages. For example, WUE calculations using T and GPP consider physiological responses and biochemical functions of vascular plants [1; 3; 14]. Calculations that use ET, however, consider physical responses (evaporation) from the environment as well as biological, offering a more complete picture of water cycles for ecosystems (including agricultural) that are important for water management [15]. Methods that use above ground biomass provide insight into yield per water use, which is important in agriculture for maximizing production. Further, in drier environments, precipitation is sometimes used due to its importance to agricultural production in water-limited environments [16]. The method of determining water and carbon variables can also influence WUE calculations. Although comparisons of field-based harvest and ecosystem flux approaches have been reported in the literature (c.f. [17]), the magnitudes of these differences vary regionally [18]. Ref. [19] investigated 29 models that used different approaches to calculate ET for two maize fields and found significant inter-seasonal and intra-seasonal variation in model performances due to soil exposure, seasonal aridity, and model parameterization. This demonstrates that some ET models can produce vastly different results from others under the same conditions due to their method of calculation and the variables considered. Furthermore, differences in the handling of eddy covariance (EC) data filtering and processing (c.f. [20; 21; 22; 23]), foot printing (c.f. [24; 25; 26]), and gap-filling techniques (c.f. [27; 28]) can also result in ET and GPP calculation discrepancies. Thus, although determinations of the WUE are relevant to understanding the functionality of plants in a field setting, WUE estimates can vary substantially depending on how the WUE and its input variables are determined. This complicates the ability to compare WUE estimates across different studies. Although attempts have been made to compare different calculations of WUE, these have typically focused on large regional scales [18] or global scales [1] and there is a paucity of observational studies that investigate field-scale discrepancies in calculation of WUE. Moreover, the few studies that have been conducted have focused on bioenergy crops, often switchgrass [17; 29], and have not specifically investigated inconsistencies in patterns between calculations. Forage crops represent 70% of agricultural land globally [30] and are therefore important to include in WUE comparison studies. Thus, there is a need to quantify the WUE of forage crops, and a need to understand if and how this varies with the method of WUE calculation.