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The European Joint Programme Cofound on Agricultural Soil Management has set the goal of harmonizing national soil databases with the continental LUCAS topsoil database for the purpose of monitoring soil carbon, fertility and land degradation. Up-to-date soil surveys have poor spatial resolution and are very cost- and labour intensive, so harmonizing the existing datasets allows us to create more accurate soil maps with better resolution. The LUCAS topsoil database can be used to complement the Hungarian Soil Information and Monitoring System (SIMS) for the creation of more detailed soil-property maps. Due to the different laboratory analysis methods and sampling strategies used by the two databases, conversion was needed, so the datasets can be directly compared to each other and used together for digital soil mapping. Two sets of topsoil data were used from 2009 and 2015 from both LUCAS and SIMS respectively, so the swiftly changing soil properties (pH, CaCO3, OC, P, K) were available for a more accurate side-by-side comparison. Map products were created for the chemical soil properties of both the LUCAS and SIMS database with the ancillary data of 28 environmental covariates using random forest kriging with 10-fold cross-validation. The spatial resolution of the maps was 100 x 100 m. The raster maps were compared directly to each other using linear regression. In conclusion the results show that the LUCAS and national soil databases can and should be harmonized, merged and used together for creating more accurate soil maps with better spatial resolution at national and continental scale.

EJP SOIL, which is a European Joint Programme Cofund on Agricultural Soil Management, has posed questions concerning the application of various soil observation datasets to account, monitor and map agricultural soil carbon, fertility and degradation. One of the most exciting issue is, how the continental LUCAS Topsoil dataset and national soil observation/monitoring databases could be harmonized and/or should complement or improve each other to produce Europe wide spatial soil information to support European contribution towards international reporting on soils, and the accuracy of European agri-environmental policies. Methods are being searched by a broad international team. In our paper we present a countrywide case study for the comparison of (i) the representativity of the Hungarian Soil Information and Monitoring System (SIMS) versus LUCAS Topsoil dataset and (ii) some map products, which were modelled by the usage of the two reference data sources.The difference between national monitoring systems and LUCAS Topsoil dataset is mainly due to (i) the different measurement methods applied to determine soil properties, and (ii) the different sampling strategy, both in terms of sampling location and sampling depth. While transformation (using unit conversions, mass-preserving splines to derive soil properties for similar soil depth, pedotransfer functions for methods conversion, etc.) of the SIMS soil data into the units, methods and soil depth used in the LUCAS dataset could be carried out more or less straightforwardly, the spatial representativity, which strongly affects the performance of any digital soil map based on the given observation dataset, is a challenging feature, which could and should be checked.From a statistical point of view, a sample is said to be representative if it reflects the characteristics of the population the best. First we analysed whether the two sets of soil data represent the same population applying two approaches: by the comparison of (i) empirical cumulative distribution function and (ii) the mean values aggregated for different land use categories of the selected soil properties coming from the two system. Although based on the Kolmogorov-Smirnov test we could conclude that the Hungarian subset of the LUCAS Topsoil dataset and the data of the Hungarian Soil Information and Monitoring System come from the same population in the case of particle size distribution, pH, organic carbon and carbonate content, the land use based comparison did not give satisfying results.In the next round the countrywide spatial predictivity of the two datasets have been tested. Primary soil property map pairs have been compiled using the same ancillary datasets and digital mapping methods but the two different observation datasets. The two map products for the same property have been compared by both global measures and cell-to-cell statistics. In addition to pairwise comparison of basic statistical features (histograms, scatter plots), we have examined the spatial distribution of the differences. In our presentation our findings and experiences will be discussed. Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820), European Union’s Horizon 2020 Research and Innovation programme under grant agreement No. 862695.

Due to certain socio-economic processes and technical pressure, the number of potential data sources targeting the Earth’s surface increases rapidly as well as the data generated by them. Soil mapping heavily relied on these changes in the paradigm shift, which took place in the population and interpretation of spatial soil information in the last decade. In digital soil mapping practice, auxiliary, environmental co-variables, which are related to soil forming factors and processes, have been taken into account in spatially exhaustive form. However, the potential hidden in spatially non-exhaustive (most frequently point-like), ancillary information – originating from observations also targeting the soil mantle – is far from being exploited. In their thematic features, accuracy and reliability they are inferior to primary field and/or laboratory measurements collected directly, but they are generated in more facile, cheaper way, in greater volume, with denser temporal and spatial coverage and characteristically they are available in significantly easier form. Data sequences of various installed field sensors, data collections by proximal sensing techniques, information supply by farmers and land managers as well as citizen science are considered as possible information sources. Essentially, the (soft) data supplied by them don’t provide spatially exhaustive coverage, neither direct pedological reference, nevertheless they are hypothesized to be utilized as auxiliary information within DSM framework. In a recently started project we started to investigate, (i) in which way and with what efficiency these ancillary information originating from different secondary sources can be applied, furthermore (ii) in what manner their application influences (hopefully improves) the results, accuracy and reliability of goal-specific spatial predictions. The elaborated digital mapping procedures, which are based on (i) large amount of data with differing quality and (ii) integrated geostatistical and data mining methods can be absorbed in various earth and environmental science applications. Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and Gábor Szatmári by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390).

The concept of precision agriculture is straightforward at the scientific level but even basic goals are blurred at the level of everyday practice in the Hungarian crop production despite the fact that several elements of the new technology have already been applied. The industrial and the service sectors offer many products and services to the farmers but crop producers do not get enough support to choose between different alternatives. Agricultural higher education must deliver this support directly to the farmers and via the released young graduates. The price of agricultural land must be higher if well-organized data underpin the production potential of the fields. Accumulated database is a form of capital. It must be owned by the farmers but in a data-driven economy its sharing will generate value for both farmers and the society as a whole. We present a methodological approach in which simple models were applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models were used to test different sampling and interpolation approaches commonly applied in precision agriculture. Three strategies for composite sample collection and three interpolation methods were compared. Multiple regression models were developed to predict yields. R2 values were used to select among the applied methods.

Existing soil information systems contain mainly qualitative data on soilscapes, however, quantitative data would be necessary to more eff ectively guide digital soil mapping eff orts. Detailed analysis of small scale overview maps off ers the most appropriate way to delineate soilscapes where they are available. In our study, the genetic soil map of Hungary have been used which displays the most complete representation of the Hungarian Soil Classifi cation System. Our goal was to analyse spatial association structure based on the boundary segments between soil polygons. We transformed the polygons into lines. The features of each line segment were the names (or codes) of the soil polygons on both sides. Aft er omission soils with low representation (less than three polygons) and boundaries beside state border, forests and cities, 69 soil units were retained. We calculated a similarity matrix among soil types based on logarithm of ratios between existing segment lengths and theoretical segment lengths. The theoretical lengths were calculated with a Chi-squared calculation by using sums of lengths in rows and columns in the 69 × 69 matrix. The similarity matrix was converted into dissimilarity matrix to distinguish between complete dissimilarity (missing values) and complete similarity (main diagonal). Dissimilarity matrix was clustered and represented in a form of dendrogram both in original form and aft er dimension reduction with multidimensional scaling method. Our method has resulted a promising approach for delineating soilscapes in presence of overview soil maps. The study resulted fuzzy soilscapes with broad transition zones. The method could be refi ned by using variable sized moving window method and by combining boundary data with terrain, geology etc.