*2.2. Databases*

2.2.1. National Plot-Level Field Soil, Fertilization and Yield Databases (AIIR Field Database)

The AIIR database [31] contains crop type, yield, fertilization and soil information for each cultivated parcel, summing up to 80,000 cultivated parcels of Hungary for 5 years (1985–1989). The data were provided by the Central Plant and Soil Conservation Service (Budapest) for the purpose of land evaluation research. The sampling for the soil tests was carried out in such a way that the parcels were divided into 12 ha sections and then, along the diagonals of the selected sections, soil samples were taken from at least 20 locations using the so-called parallel sampling method. The subsamples were taken homogenized, so that an average sample was taken from the subplots of each agricultural field. For areas with a slope greater than 12%, average samples were taken separately for each (upper, middle, lower) section of the slope, taking into account erosion and different soil nutrient supply. The database was digitized in 2000 and in 2014 was upgraded to a modern geospatial database (point data with coordinates). We have selected the points that still fall on arable land at the time of our study. The database includes the following three major types of data:


Distribution of data by soil types is presented in Table 1.

**Table 1.** Main features of the AIIR dataset, based on Hungarian [32] and World Reference Base for Soil Resources [33] classification.


2.2.2. Remote Sensing Derived Biomass Productivity Indicators

Long term (2003–2018) time series remote sensing data were used to derive mean gross primary productivity (GPP) values as proposed by Jin and Eklundh (2014) [34]. The MODIS dataset (MOD17) [35] was used at a nominal 500 m spatial resolution to produce GPP datasets for the whole country. It is important to note that crop yields and GPP represent different aspects of productivity. However, in managed cropland there is a strong correlation between the two [36]. We used the normalized productivity (value range 1–100) as the target variable, and all of our results were normalized between 1 and 100, making it easier to integrate into our model.

#### 2.2.3. Time Series Meteorological Data

The Central-European FORESEE meteorological database [37], which covers the whole area of the country with a 0.1 × 0.1 degree grid, was used to derive mean temperature and total precipitation at monthly scales (between 1951 and 2013). Mean temperature and precipitation values were linked to the spatial units (100 m pixels) of the assessment. The downscaling was performed by the bilinear resampling method.

#### 2.2.4. Topographic Data

The Shuttle Radar Topography Mission [38,39] provides a dataset of 30 m resolution grid cells as the basis for the digital elevation model (DEM). SRTM mapped Earth's topography between 56 degrees south and 60 degrees north of the equator. SRTM has a vertical

accuracy of 5.3 m (RMSE) in Hungary [40]. The SRTM-derived DEM was used to include a topographic component to the land evaluation model.
