## 4 Discussion
### Spring Barley (SB)  
The model simulates the crop growth, biomass, and yield from the day of emergence to the day of complete senescence (Figure 3a-3f). The performances for simulating the above ground biomass and GAI are within the range of those of other studies. [50] implemented the SAFY model to estimate barley yield in a semi-arid region in Central Tunisia and obtain a good correlation between the measured and simulated yield with R\({}^{2}\) = 0.77. The model fit was attributed to the integration of the in-season SPOT/HRV remote sensing data and the local calibration of the most sensitive model parameters. In the study [51] barley biomass was estimated with an R\({}^{2}\) ranging from 0.39 to 0.68 and RMSE ranging from 0.42 kg\(\cdot\)m\({}^{-2}\)-0.83 kg\(\cdot\)m\({}^{-2}\) (420 g\(\cdot\)m\({}^{-2}\)-830 g\(\cdot\)m\({}^{-2}\)) using the crop surface models--generated by (a) mosaicking of collected UAV images (b) point cloud generation (c) digital surface model (DSM) export, the main limiting factors were attributed to the four lodging cultivars 10,11,12,14. Ref. [52] employed the Sirius wheat model to estimate SB biomass and yield; their model simulated biomass with an RMSE = 1.4 t\(\cdot\)ha\({}^{-1}\) (140 g\(\cdot\)m\({}^{-2}\)) and yield with an RMSE = 1.3 t\(\cdot\)ha\({}^{-1}\)(130 g\(\cdot\)m\({}^{-2}\)). The model was also able to simulate the differences in barley growth due to its mechanistic treatment of canopy development. Ref. [53] assessed the performance of nine different process-based crop models during 44 growing seasons of barley in northern and central Europe. Large uncertainties were found in estimating barley yield across sites and years, while the mean predictions from the nine models agreed well with the observations, the uncertainties were attributed to limited calibration of the models. In terms of simulated total above-ground biomass, models followed a slightly different order compared to the simulation of grain yields. Ref. [54] estimated biomass with median R\({}^{2}\) = 0.62; RMSE = 1.63 t\(\cdot\)ha\({}^{-1}\) (163 g\(\cdot\)m\({}^{-2}\)); nRMSE = 14.9% and LAI with median R\({}^{2}\) = 0.92; RMSE = 0.3 m\({}^{2}\cdot\)m\({}^{-2}\); nRMSE = 7.1% for barley crop at three sites in Germany using the remote sensing and random forest modeling approach, LAI could be predicted with high accuracy because of the strong correlation of reflectance in the visible spectrum and leaf pigments per area. Our modeling approach was found to be statistically significant (\(p\) < 0.01) for simulating biomass and GAI (Figure 4a,b); there are a few parameters that are variable and need to be manually fixed before its application, the date of sowing is one of them. Date of sowing SB in 2011 was 25 March and 3 April in 2012 and 2013. As illustrated in Figure 3a-f the simulated date of emergence is DOY 100, which represents 10 April. The simulated date of emergence for 2012 and 2013 is DOY 125 or 25 April, calibrating this parameter is important as the model starts simulating fluxes from the day of emergence. Where the actual date of sowing can be specified within the model, the model is clearly capable of simulating GAI and biomass (e.g., Figure 3) for SB. Biomass was simulated with high accuracy for all three years of available data; however the accuracy was somewhat compromised in estimating GAI. GAI is estimated using the leaf partitioning coefficients (P\({}_{\text{la}}\) and P\({}_{\text{lb}}\)) on biomass. This indicates that the sensitive parameters need to be calibrated locally or inferred from remote sensing data. Error may also have been introduced in obtaining the measurements of GAI on the ground, specifically for the year 2012, where the unusual measurement is clearly visible (Figure 3c). In comparison to studies [51; 52; 53; 54; 55] the model adapted in this workwas found to perform reasonably in simulating biomass during all the crop growth stages (Figure 3a-f).