## 6 Conclusions  
A modified DMP model was developed to include water stress based on actual evapotranspiration calculated with AgroMetShell and to adapt the existing factors (CO\({}_{2}\) fertilisation effect, temperature stress and autotrophic respiration). The best results were obtained by including different combinations of stress factors for each selected region and cumulating the modified DMP between the flowering and ripening period. A linear regression between the modified DMP and crop yield statistics showed an increased model performance as compared to the CGLS-DMP. On average, for all sites and crops studied, RMSE (t/ha) decreased from 0.84 for CGLS-DMP to 0.61 for modified DMP, RRMSE (%) reduced from 13.1-8.9, MBE (t/ha) decreased from 0.05-0.03 and \(E_{1}\) increased from 0.08-0.28. Although results did not differ much from what can be obtained using simpler BPs such as fAPAR and NDVI, obtaining similar results remains encouraging as the DMP approach may lend itself better to extrapolation in more extreme conditions. The results showed the potential of using the modified DMP for estimating crop yield at the regional scale. A combination of different stress factors produces better local yield estimates, but no single solution to the improvement of a global product could be demonstrated. The inter-annual variability of official yield statistics is low in Belgium and France, and high in Morocco. The latter enabled a better exploration of stress factors. With more accurate crop masks becoming available than the currently used GlobCover 2009, crop yield estimations might even further improve the results. Including appropriate stress factors and their impact during sensitive stages improves dry matter productivity estimates of cereal crops. For improving real RUE estimations, different remote sensing methods will be available such as chlorophyll-related vegetation indices, passive measurement of solar-induced chlorophyll fluorescence (SIF), and the photochemical reflectance index (PRI).  
**Supplementary Materials:** The following are available online at www.mdpi.com/2072-4292/8/3/170, **Figure S1:** Difference maps of \(\epsilon_{\text{AR}}\) calculated with modified DMP (this study) & CGLS-DMP (**a**); MODIS NPP/GPP ratio from 2000-2013 & \(\epsilon_{\text{AR}}\) calculated with CGLS-DMP (**b**) and MODIS NPP/GPP ratio from 2000-2013 & \(\epsilon_{\text{AR}}\) calculated with modified DMP (this study) (**c**); **Figure S2:** Scatterplots of \(\epsilon_{\text{AR}}\) calculated with modified DMP (this study) & CGLS-DMP (**a**); MODIS NPP/GPP ratio from 2000-2013 & \(\epsilon_{\text{AR}}\) calculated with CGLS-DMP (**b**) and MODIS NPP/GPP ratio from 2000-2013 & \(\epsilon_{\text{AR}}\) calculated with modified DMP (this study) (**c**). The dotted lines are the 45\({}^{\circ}\) reference lines and the red lines are trend lines.  
**Acknowledgments:** This study was funded and supported by Belspo contract nr. SD/RI/03A. We acknowledge E-AGRI Project and MARS - JRC for the data provided. The authors thank the three anonymous reviewers for their contribution in improving the quality of the manuscript.  
**Author Contributions:** [PERSON] did the experiments and wrote the original manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] supervised the process of data analysis and were responsible for manuscript revisions.  
**Conflicts of Interest:** The authors declare no conflict of interest.