**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 R2 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. Cheng et al. (2022) [25] found similar R<sup>2</sup> 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 Jeon et al. [23] and as also highlighted in our country assessment. Results of

RF-based models can be applied to plan agricultural land use in order to increase the yield and make it sustainable, without environmental side effects. One of the most important and interesting results in our perspective is the quantification of the relative importance of explanatory variables, which best reflects the different edaphic and climatic needs of the observed crop species. For wheat, soil characteristics are the most important factors, while temperature and precipitation are less important [66]. In case of maize, soil parameters are still important but temperature and precipitation have more importance than in the case of wheat, highlighting that, even in a relatively small country like Hungary, climate tolerance of plants is a differentiating factor. This observation becomes more evident when studying sunflowers, where the importance of mean temperature and precipitation outweighs those of soil type and soil textures as earlier presented by Kern et al. (2018) in case studies from Hungary. Nevertheless climatic variables, such as precipitation in October and November and temperature in January and February are also important for winter wheat [66]. Our results also show the importance of summer rainfall totals (May, June, July) for maize, while for sunflowers the most important parameters are spring and autumn temperatures. We have to emphasize that it is often difficult to compare our results with those of other researchers, because the bioclimatic variables of the study area differ. For example, the work of Vannoppen and Gobin (2018) from northern Belgium, investigating the importance of climatic variables in winter wheat yield estimation, found similar parameters to be important, but in a different order. While in Hungary, the mean temperature in January and the amount of precipitation in November are the most important, in Belgium, winter precipitation is the most important [67]. The model fit can be further improved by adding information on management factors such as nutrient levels and fertilizer inputs [52,68].

Soil plays an important role in increasing crop production. The soil science community is trying to define the appropriate indicators. The presented analysis on the importance of variables in calculating productivity also provides a good basis for SDG indicators, as the related target of SDG is to improve land and soil quality progressively. Addressing soil health and soil quality are the main criteria for achieving sustainable agriculture. Climate change largely affects the minimum and maximum temperatures and the amount of precipitation per month [69–72]. Our results suggest that these variables are also important for winter wheat, maize and sunflowers, and that changes in these variables could change soil productivity in the future.

We established a baseline prediction model for biomass productivity applicable for Hungarian croplands using Earth observation data and yield statistics, identified the importance of soil and climatic determinants of biomass productivity, and proposed a methodology for integrated monitoring of biomass productivity.
