###### Abstract  
Gross Primary Productivity (GPP) for cropland is often estimated using a fixed value for maximum light use efficiency (LUE\({}_{\text{max}}\)) which is reduced to light use efficiency (LUE) by environmental stress scalars. This may not reflect variation in LUE within a crop season, and environmental stress scalars developed for ecosystem scale modelling may not apply linearly to croplands. We predicted LUE on several vegetation indices, crop type, and agroclimatic predictors using supervised random forest regression with training data from flux towers. Using a fixed LUE\({}_{\text{max}}\) and environmental stress scalars produced an overestimation of GPP with a root mean square error (RMSE) of 6.26 gC/m\({}^{2}\)/day, while using predicted LUE from random forest regression produced RMSEs of 0.099 and 0.404 gC/m\({}^{2}\)/day for models with and without crop type as a predictor, respectively. Prediction uncertainty was greater for the model without crop type. These results show that LUE varies between crop type, is dynamic within a crop season, and LUE models that reflect this are able to produce much more accurate estimates of GPP over cropland than using fixed LUE\({}_{\text{max}}\) with stress scalars. Therefore, we suggest a paradigm shift from setting the LUE variable in cropland productivity models based on environmental stress to focusing more on the variation of LUE within a crop season. light use efficiency; Gross Primary Productivity; random forest 20221414951002  
critical component of the error budget in remote sensing GPP models [14; 15; 16]. Various approaches to setting the LUE parameter exist, which include assuming a globally fixed value, assuming a fixed value for each functional type of vegetation (C3 grass, C4 grass, and trees), or varying LUE dynamically with vegetation type and environmental stressors [8; 14]. The latter approach, known as the Carnegie-Ames-Stanford Approach (CASA) has been widely applied to both NPP and GPP estimation for field crops [17; 18] (Equation (2)):  
\[GPP_{\text{CASA}}=fPAR\times PAR\times LUE_{\text{max}}\times TS\times VS \times SMS, \tag{2}\]  
where maximum LUE (\(LUE_{\text{max}}\)) is reduced below its potential by temperature stress (\(TS\)), vapor stress (\(VS\)), and soil moisture stress (\(SMS\)) scalars to give \(\text{LUE}_{\text{CASA}}\). This is a simple yet effective means of capturing environmental variation in LUE [8]. Alternatively, [PERSON] et al. [14] varied LUE based on the proportion of diffuse radiation which met or outperformed the stress scalar approach for accurate GPP estimation at a continental scale. However, each method relies on an accurate starting value for \(LUE_{\text{max}}\). [PERSON] et al. [8] recognised that LUE, and thereby \(\text{LUE}_{\text{max}}\), could be predicted based on relationships with satellite-derived vegetation indices [19; 20]. For example, canopy greenness, as a proxy for crop maturity and stress, such as nutrient deficiency, may be useful predictors of LUE [21; 22]. We sought to combine vegetation indices with crop type and agroclimatic information to test whether a machine learning method could accurately predict cropland LUE. A contrasting approach for setting LUE for agricultural applications is setting a fixed \(\text{LUE}_{\text{max}}\) based on information such as crop type, and sometimes soil fertility and fertiliser regimes [8]. For example, the Global Yield Mapper in Google Earth Engine (GYMEE) notes uncertainty in defining \(\text{LUE}_{\text{max}}\) for crop type and applies 2.5 gC/MJ/day for C3 crops and 3.5 gC/MJ/day for C4 crops [1]. Similarly, the Python implementation for the Surface Energy Balance Algorithm (PySEBAL) documentation for estimating crop biomass production and evapotranspiration recommends \(\text{LUE}_{\text{max}}\) values of 2.5 gC/MJ/day and 4.5 gC/MJ/day for C3 and C4 crops, respectively [12; 13; 23].