Traditionally in Hungary the soil cover under agricultural and forestry management is typically characterized independently and just approximately identically. Soil data collection is carried out and the databases of soil features are managed irrespectively. As a consequence, nationwide soil maps cannot be considered homogeneously predictive for soils of croplands and forests, plains and hilly/mountainous regions. In order to compile a national soil type map with harmonized legend as well as with spatially relatively homogeneous predictive power and accuracy, the authors unified their resources. Soil profile data originating from the two sources (agriculture and forestry) were cleaned up and harmonized according to a common soil type classification. Various methods were tested for the compilation of the target map: segmentation of a synthesized image consisting of the predictor variables, multi stage classification by Classification and Regression Trees, Random Forests and Artificial Neural Networks. Evaluation of the results showed that the object based, multi-level mapping approach performs significantly better than the simple classification techniques. A combination of best performing classifiers, when each classifier's vote on the same object is weighted according to its confidence in the voted class, led to the final product: a unified, national, soil type map with spatially consistent predictive capabilities.
Soil hydraulic properties are among the most important parameters that determine soil quality and its capability to serve the ecosystem. Land use can significantly influence soil properties, including its hydraulic conditions; however, additional factors, such as changes in climate (temperature and precipitation), can further influence the land use effects on soil hydraulic properties. In order to develop possible adaptation measures and mitigate any negative effects of land use and climatic changes, it is important to study the impact of land use and changes in land use on soil hydraulic properties. In this paper, we summarize recent studies examining the effect of land use/land cover and the associated changes in soil hydraulic properties, mainly focusing on agricultural scenarios of cultivated croplands and different tillage systems.
Due to former soil surveys and mapping activities signifi cant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfi lled with these available datasets either by their direct usage or aft er certain specifi c and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project we would like to signifi cantly extend the potential, how soil information requirements could be satisfi ed in Hungary. We started to compile digital soil maps, which fulfi l optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. Regression kriging has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matt er content. Classifi cation and regression trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identifi ed and expressed in decision rules made the compilation of spatially refi ned category-type soil maps (like genetic soil type and soil productivity maps) possible with the aid of high resolution environmental auxiliary variables. In our paper, we give a short introduction to soil mapping and information management concentrating on the driving forces for the renewal of soil spatial data infrastructure provided by the framework of Digital Soil Mapping. The fi rst results of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project are presented in the form of brand new national and regional soil maps.
There are global aspirations to harmonize soil particle-size distribution data measured by the laser diffraction method and by traditional sedimentation techniques, e.g. sieve-pipette methods. The need has arisen therefore to build up a database, containing particle-size distribution values measured by the sieving and pipette method according to the Hungarian standard (sieve-pipette methods-MSZ) and the laser diffraction method according to a widespread and widely used procedure. In our current publication, 155 soil samples measured with sieve-pipette methods-MSZ and laser diffraction method (Malvern Mastersizer 2000, HydroG dispersion unit) were compared. Through the application of the usual size limits at the laser diffraction method, the clay fraction was under-and the silt fraction was overestimated compared to the sieve-pipette methods-MSZ results, and subsequently the soil texture classes were determined according to the results of both methods also differed significantly from each other. Based on our previous experience, the extension of the upper size limit of the clay fraction from 2 to 7 µm increases the comparability of sievepipette methods-MSZ and laser diffraction method, in this way the texture classes derived from the particle-size distributions were also more in accordance with each other. The difference between the results of the two kinds of particle-size distribution measurement methods could be further reduced with the pedotransfer functions presented. K e y w o r d s: laser diffraction, particle-size distribution, pedotransfer function, soil texture triangle
After several years of digital processing of legacy soil data collected by the Kreybig soil survey, the nationwide development of the digital Kreybig soil information system (DKSIS) made possible the compilation of soil property and function maps for the territory of Hungary at a scale of approximately 1:25,000-1:50,000. The Kreybig legacy data are spatially most detailed nationwide dataset related to soils which covers the whole area of the country. It simultaneously contains two types of geometric datasets: approximately 100,000 soil mapping units (SMUs) and 250,000 sampling plots. SMUs are characterized by several complex soil physical and chemical categories and detailed soil properties which are provided for soil profiles whose description in the digital environment is supported by a specific relational database. Primary digital soil maps can be compiled based on the polygons-type entities, while suitable spatial inference of profile-related variables makes the composition of secondary, regionalized digital soil maps possible, too. In our paper, we present example for both types.
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