## 1 Introduction  
A key natural resource that ensures food security, ecological security and sustainable development is cultivable land. Recently, the importance of soil has been increasingly put into focus as the general public also become more aware of it as a non-renewable resource that can be lost quickly if improperly used or managed with very little chance of regeneration. Despite the critical importance of soil productivity, not only as indicator, but also in sustaining life on Earth, knowledge of the spatial and temporal variability of soil from regional to global scales is limited or fragmented. For the creation of effective agricultural and food policies at the regional levels, accurate soil productivity predictions are essential. The limited information on soil productivity hinders national (Farmers' Soil Conservation Programme, National Rural Development Programme) and international (EU Soil Mission) programs to monitoring its changes and build future scenarios on it. The Sustainable Development Goals (SDGs) of the United Nations' Agenda 2030 framework include targets that recommend direct consideration of land and soil resources [1; 2; 3], which were adopted by all United Nations member states in 2015. Soil resources are linked to the SDGs through several soil functions [2], of which the biomass productivity function is at the core of SDGs 2.3 and 2.4., which explicitly target the sustainable increases in agricultural productivity. Furthermore, biomass productivity is proposed as an indicator of land degradation [4], which is linked to SGD 15.3 [5]. Biomass productivity is conditioned by inherent soil properties, climatic and management factors, thus variable in both space and time [6]. Spatial variability of soil productivity is traditionally assessed within the broad framework of land evaluation [7]. However, land evaluation should also include socio-economic components [8], which are not necessary for soil productivity evaluation. Nevertheless, soil is an integral part of the land with a distinct spatial location and therefore biophysical characteristics of the studied sites, such as climate and relief conditions, need to be taken into account when assessing its productivity [9]. The aim of classical quantitative land evaluation is to establish productivity indices based on actual yields in order to reflect production potentials for taxation and planning purposes [10; 11; 12; 13; 14; 15; 16; 17; 18]. A similar quantitative approach can be applied to reveal soil biomass productivity, its drivers and changes for monitoring purposes. Dynamic and simulation models [7; 16; 19; 20; 21; 22] can provide an alternative to classical productivity evaluation, but their validation still requires measured biomass or yield data. Advantages of the classical data-driven assessment, i.e., where yield is the dependent variable and biophysical factors are independent inputs, are high reliability, explicit spatial validity and easy interpretation. Process-based modeling and statistical modeling are also two frequently employed techniques for forecasting crop yield responses to climate variability. Process-based crop models are effective for predicting crop yields because they simulate physiological processes of crop growth and development in response to environmental factors and management techniques, especially at the field scale [23]. Traditional regression techniques have some drawbacks that can be addressed by statistical modeling techniques based on machine-learning algorithms. Machine-learning techniques have been used increasingly in recent years as niche-based classification modeling tools [24; 25; 26]. For our analysis we selected the Random Forest (RF) technique [27; 28], which uses the Classification and Regression Trees method as the basis for growing multiple classification trees. The study considers high-input agriculture, which is predominant in the country and uses time series information (measured crop yield statistics and satellite-derived biomass productivity indicators).