## 6 Discussion and Conclusions
The comparison with data from recent literature on GPP/NPP in similar vegetation types also proofed the reliability of the modelling results. Estimates of the NEE are more difficult to evaluate than the GPP or NPP because of the greater uncertainty about the modelled NEE. Quantifying the NEE requires estimates of carbon budget components that each has associated errors. NEE can range from 10-50% of NPP depending on the soil carbon pools available for decomposition [73]. The results of the present study revealed the value of NEE for short grassland accounts to about 10% of NPP, while steppe grassland was approximately in balance with a slightly negative value. For the modelled values, one of the greatest uncertainties is the amount of soil respiration. In this study, soil respiration was calculated using empirical equation for estimation soil respiration at the global scale [55]. Obviously, more accurate results would be achieved in the presented model if we had had _in situ_ measurements of soil respiration from the study area, for example, using the chamber approach. Such measurements are planned for the prospected research work in the study area. Certainly, they will greatly improve the model's accuracy. Remotely sensed data provide necessary inputs, such as land cover categories and leaf area index, but other required spatial data such as root-leaves ratio, litter-leaves ratio, base rate of maintenance or growth respiration etc. are poorly known and considered as biome-dependent constants, which limit the accuracy of model predictions. In the model presented here, most of these constants were calculated from the _in situ_ measurements of carbon residing in the vegetation and soil at a number of test sites. This improves the general accuracy of the model. However, the values of these constants were averaged over the two land cover categories without taking into account spatial variance of them between localities. Obviously, values of all input variables might demonstrate significant spatial variability within each land cover category. This variability was not incorporated into the model presented in the study. Additional studies are required in order to investigate possibilities of mapping the input variables at the per-pixel scale. The use of such information would significantly improve modelling results at localities. Finally, considering the applicability of the model, we should also take the different time scales of the application into account. In this study, _in situ_ measurements of biomass were carried out at the end of June. This period is considered to represent the peak of the growing season in the study area. Normally, the middle part and the end of June in this region are characterized by the peak of precipitation [30,74]. However, there is a lot of variability in distribution of precipitation throughout the growing season, and this intra-annual variability is overlapped with inter-annual variability of precipitation. An additional source for uncertainty in localization of the growing season peak is a different reaction time of the vegetation to precipitation. Previous studies proved that vegetation types in Central Asia and the study region are characterized by different time-lags in their response to precipitation. Thus, the short grassland in the region of the study shows the peak of plant growth about 10-20 days after the precipitation peak, while the time-lag for the steppe grassland accounts to about 20-40 days [74]. For this reason, it is difficult to forecast exactly the peak of the growing season for a certain year before making in situ measurements of the peak biomass. This may limit to some extent the applicability of the LUE model based the one-time measurements of biomass. To reduce the uncertainty in the model's applicability, data on biomass from different periods within the growing season are needed.