## 4 Discussion  
Biomass productivity is a dynamic property that changes over time, partly due to changing climatic conditions (within and between years) and changing soil properties (pH, organic matter, soil nutrients content, etc. ), but also due to new crop varieties and advances in crop management having an important influence. The effect of the changes in the biophysical factors may be synergistic or in the opposite direction. Nevertheless, it is possible to estimate the weight of the factors in productivity on a reasonable time scale. Twenty to thirty years seem to be an adequate time scale for estimating soil biomass productivity and for identifying the weights of different factors in it. A moving timeframe with intervals of 3 to 6 years can be proposed for the updating of the biomass productivity indices. If the system is to be used for monitoring purposes, soil biomass productivity will need to be compared on the basis of different time periods, e.g., on moving time windows or trends and supplemented by the monitoring of soil properties, subject to degradation. The moving time window for biomass productivity monitoring can be harmonized with the periodic assessments in soil monitoring, i.e., 3-6 years. Our validation results (predicted data vs. measured data in the test set) showed that there is a significant difference in the prediction accuracy between the different crops. The sunflower model has a lower performance in calculating biomass productivity, which may be due to the fewer number of data available for model training. Furthermore, sunflowers are a cash crop grown on very diverse soils and not so much linked to bioclimatic factors and soil parameters [58; 59], while the R\({}^{2}\) value (0.41) for the biomass productivity map can be regarded as adequate for a national estimate. The MAPE value indicates that only 18.06% of the model is inaccurate, that is, above the accuracy of results published in other studies [60; 61]. The MAE indicator values indicate an average deviation of 6.78, which is not outstanding on a scale of 1 to 100. [PERSON] et al. (2022) [25] found similar R\({}^{2}\) values in the case of maize and wheat based on MODIS GPP values, with stronger correlation in the case of maize. However, other studies in the case of wheat presented lower values [24; 61; 62]. Although the performance of the sunflower productivity model is rather low, its inclusion in the assessment provides a more comprehensive overview of plant-specific productivities and their differences, including major factors and the varying weights of the factors in plant-specific productivities. Furthermore, the inclusion of an additional plant-specific model extends the potential of the applied method to provide a general soil productivity assessment considering multiple crops, which is often needed in land use planning. A new soil biomass productivity map was created by applying the biomass productivity model using national soil, climate and topography geodatabase. Crop-specific productivity maps, which shall be the ultimate source of multicriteria land use planning [63], were also produced using the same technique. The spatial distribution of biomass potential is shown on the general productivity map, which can be used to plan land use and crop production. Further to that, weights of individual soil and climate parameters of crop-specific productivity indices were also derived. For our final model results (soil biomass productivity) we applied a correction that takes into account the topography. Slope angle and orientation both matter for solar radiation to be taken into account. Our solution for incorporating terrain indices from an earlier national land evaluation model [51] was tailored for Hungarian conditions (average slope under 2.3%), instead of applying more complex methods [64; 65] used in other pedoclimatic conditions. Random Forest is considered to be an appropriate method to predict crop-specific biomass productivity, as proven by [PERSON] et al. [23] and as also highlighted in our country assessment.