Search

A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach—where crop yield is the dependent variable—was followed using measured yield and net primary productivity data derived from satellite images, together with digital soil and climate maps. In addition to characterizing of soil biomass productivity based on measured data, the weight of soil properties on productivity was also quantified to provide measured soil health and soil quality indicators as an information base for designing sustainable land management practices. To produce these results, we used only the Random Forest method for our calculations. The study considers high-input agriculture, which is predominant in the country. Biomass productivity indices for the main crops (wheat, maize and sunflowers) and general productivity indices were calculated for the whole agricultural area of Hungary. Results can be implemented in cadastral systems, in applied in agricultural and rural development programs. The assessment can be repeated for monitoring purposes to support general monitoring objectives as well as for reporting in relation to the United Nations Sustainable Development Goals. However, on the basis of the results, we also propose a method for periodically updating the assessment, which can also be used for monitoring biomass productivity in the context of climate change, land degradation and the development of cultivation technology. [ABSTRACT FROM AUTHOR]

High-resolution ecosystem maps increase the efficiency of policy implementation. However, due to challenges related to both data and methods, such maps of appropriate scale and quality are still rarely available for nationwide analyses. We present solutions to some typical challenges of national-scale ecosystem mapping through the new Ecosystem Map of Hungary. It is a comprehensive, spatially and thematically detailed map with a hierarchical typology. The mapping methodology combined several novel elements from the integration of various large-scale databases in a (theoretical) data cube to the use of image-based predictive mapping (with a Random Forest classifier, using Sentinel-1 and -2 and environmental data). A participatory method involving local experts was used for validation, addressing the lack of suitable reference data as well as improving map-maker and map-user interaction. Besides the original objective of supporting conservation-related decision-making, further uses emerged from a variety of fields including spatial planning, education and recreation. [ABSTRACT FROM AUTHOR]

There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. Physical soil properties, especially particle size distribution play important role in this context. A few of the requirements can be satisfied by the sand-, silt-, and clay content maps compiled according to global standards such as GlobalSoilMap (GSM) or Soil Grids. Soil texture classes (e. g. according to USDA classification) can be derived from these three fraction data, in this way texture map can be compiled based on the proper separate maps. Soil texture class as well as fraction information represent direct input of crop-, meteorological- and hydrological models. The model inputs frequently require maps representing topsoil features, which refer most commonly to 0–30 cm depth. This is covered by three consecutive depth intervals according to standard specifications: 0–5 cm, 5–15 cm, 15–30 cm. Becoming GSM and SoilGrids the most detailed freely available spatial soil data sources, the common model users (e. g. meteorologists, agronomists, or hydrologists) would produce input map from (the thickness-weighted mean of) these three layers. However, if the basic soil data and proper knowledge is obtainable, a soil texture map targeting directly the 0–30 cm layer could be independently compiled. In our work we compared Hungary's soil texture maps compiled using the same reference and auxiliary data and inference methods but for differing layer distribution. We produced the 0–30 cm clay, silt and sand map as well as the maps for the three standard layers (0–5 cm, 5–15 cm, 15–30 cm). Maps of sand-, silt-, and clay content were computed through composite regression kriging applying Additive Log-Ratio (alr) transformation. In addition to the Hungarian Soil Information and Monitoring System as reference soil data, digital elevation model and its derived components, soil physical property maps, remotely sensed images, land use-, geological-, as well as meteorological data were applied as auxiliary variables. We compared the directly compiled and the synthetized clay-, sand content, and texture class maps by different tools. In addition to pairwise comparison of basic statistical features (histograms, scatter plots), we examined the spatial distribution of the differences. We quantified the taxonomical distances of the textural classes, in order to investigate the differences of the map-pairs. We concluded that the directly computed and the synthetized maps show various differences. In the case of clay-, and sand content maps, the map-pairs have to be considered statistically different. On the other hand, the differences of the texture class maps are not significant. However, in all cases, the differences rather concern the extreme ranges and categories. Using of synthetized maps can intensify extremities by error propagation in models and scenarios. Based on our results, we suggest the usage of the directly composed maps. • Maps of physical soil properties were compiled for differing layer depth distribution. • Synthetized maps were aggregated of maps created for standard depth layers. • Directly compiled and synthetized maps were compared by various tools. • Differences and similarities of the two types of prediction were identified. • Conclusions were drawn on possible risks of soil map synthetization. [ABSTRACT FROM AUTHOR]

Optimal water supply of plants is key to high yields. However, irrigation in drier regions must be accompanied by soil conservation. Nationwide planning of irrigation needs spatially exhaustive, functional soil maps, which may support proper recommendations for the different areas. For supporting the Hungarian national irrigation strategy, a series of countrywide functional soil maps was created, which reveal the pedological constraints, conditions and circumstances of irrigation by the spatial modelling of the relevant functional features of the soil mantle. Irrigation can improve productivity, while its negative effects may lead to soil degradation. This paper focuses on threats, the spatial identification of potentially affected areas. The thematic maps spatially model the irrigability and vulnerability of soils. Estimation of salt accumulation hazard, and soil structure degradation risks were targeted. The salinization hazard assessment was carried out by two ways. We applied the steady state concept of critical water-table depth and a more dynamic, process-based method. To estimate soil structural degradation hazard, class-based relationships were developed based on soil profile data of MARTHA 1.0 (Hungarian Detailed Soil Hydraulic Database). Soil type, organic matter content, carbonate content, soil reaction and texture class (USDA) were taken into consideration to develop pedotransfer functions for modelling the correlations between primary soil properties and threats indicators. The new maps can help decision makers to improve land use management, and sustainable agronomy. [ABSTRACT FROM AUTHOR]

Spatial 3-D information on soil hydraulic properties for areas larger than plot scale is usually derived using indirect methods such as pedotransfer functions (PTFs) due to the lack of measured information on them. PTFs describe the relationship between the desired soil hydraulic parameter and easily available soil properties based on a soil hydraulic reference dataset. Soil hydraulic properties of a catchment or region can be calculated by applying PTFs on available soil maps. Our aim was to analyse the performance of (i) indirect (using PTFs) and (ii) direct (geostatistical) mapping methods to derive 3-D soil hydraulic properties. The study was performed on the Balaton catchment area in Hungary, where density of measured soil hydraulic data fulfils the requirements of geostatistical methods. Maps of saturated water content (0 cm matric potential), field capacity (-330 cm matric potential) and wilting point (-15 000 cm matric potential) for 0–30, 30–60 and 60–90 cm soil depth were prepared. PTFs were derived using the random forest method on the whole Hungarian soil hydraulic dataset, which includes soil chemical, physical, taxonomical and hydraulic properties of some 12 000 samples complemented with information on topography, climate, parent material, vegetation and land use. As a direct and thus geostatistical method, random forest combined with kriging (RFK) was applied to 359 soil profiles located in the Balaton catchment area. There were no significant differences between the direct and indirect methods in six out of nine maps having root-mean-square-error values between 0.052 and 0.074 cm 3 cm -3 , which is in accordance with the internationally accepted performance of hydraulic PTFs. The PTF-based mapping method performed significantly better than the RFK for the saturated water content at 30–60 and 60–90 cm soil depth; in the case of wilting point the RFK outperformed the PTFs at 60–90 cm depth. Differences between the PTF-based and RFK mapped values are less than 0.025 cm 3 cm -3 for 65 %–86 % of the catchment. In RFK, the uncertainty of input environmental covariate layers is less influential on the mapped values, which is preferable. In the PTF-based method the uncertainty of mapping soil hydraulic properties is less computationally intensive. Detailed comparisons of maps derived from the PTF-based method and the RFK are presented in this paper. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Questions Multiple potential natural vegetation ( MPNV) is a framework for the probabilistic and multilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional potential natural vegetation ( PNV) estimates? Location Hungary. Methods MPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models ( GBM). Vegetation data from the Hungarian Actual Habitat Database ( MÉTA) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (re-scaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. Results The main result of MPNV modelling is a series of raw and re-scaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within-type analyses, while re-scaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a location's PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. Conclusion The MPNV approach allows viewing the potential vegetation composition of locations in far more detail than the PNV approach. Re-scaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritization, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and application. [ABSTRACT FROM AUTHOR]

Wind erosion susceptibility of Hungarian soils was mapped on the national level integrating three factors of the complex phenomenon of deflation (physical soil features, wind characteristics, and land use and land cover). Results of wind tunnel experiments on erodibility of representative soil samples were used for the parametrization of a countrywide map of soil texture compiled for the upper 5 cm layer of soil, which resulted in a map representing threshold wind velocity exceedance. Average wind velocity was spatially estimated with 0.50 resolution using the Meteorological Interpolation based on Surface Homogenised Data Basis (MISH) method elaborated for the spatial interpolation of surface meteorological elements. The probability of threshold wind velocity exceedance was determined based on values predicted by the soil texture map at the grid locations. Ratio values were further interpolated to a finer 1 ha resolution using sand and silt content of the uppermost (0-5 cm) layer of soil as spatial covariables. Land cover was also taken into account, excluding areas that are not relevant to wind erosion (forests, water bodies, settlements, etc.), to spatially assess the risk of wind erosion. According to the resulting map of wind erosion susceptibility, about 10% of the total area of Hungary can be identified as susceptible to wind erosion. The map gives more detailed insight into the spatial distribution of wind-affected areas in Hungary compared to previous studies. [ABSTRACT FROM AUTHOR]

Due to former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific 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 significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil 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 matter content. Classification 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 identified and expressed in decision rules made the compilation of spatially refined 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 first 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. [ABSTRACT FROM AUTHOR]

Hungary has long traditions in soil survey and mapping. Large amount of soil information is available in various dimensions and generally presented in maps, serving different purposes as to spatial and/or thematic aspects. Increasing the proportion of soil-related data has been digitally processed and organized into various spatial soil information systems (SSISs). The most current countrywide ones have been elaborated by and available at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences (RISSAC HAS). The existing maps, data and systems served the society for many years; however, the available data are no longer fully satisfactory for the recent needs of policy making. There were numerous initiatives for the digital processing, completion, improvement and integration of the existing soil datasets. In our paper, we briefly present the national SSISs developed and maintained by our institute and some examples are given regarding how their functionality was extended by digital soil mapping for the solution of specific soil-related demands. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents habitat maps from two years in which precipitation was extreme - 2000 was one of the driest years while 2010 was one of the wettest years in the Carpathian Basin. The study area is in northern Hungary, beside the River Ipoly, in the municipality of Drégelypalánk, with a smaller proportion in Hont and Ipolyvece. Its extent is 621.5 hectares. During the field survey habitat polygons were recorded using a hand-held GPS device, with aerial photographs helping to identify the exact location. Processing of the data, establishment of a database of the mapped area, and editing of maps were performed using ESRI ArcView GIS 3.2 and ESRI ArcGIS 10.0. The maps, at a scale of 1:15,000, show vegetation and habitats of the study area. They display changes occurring within habitats, in habitat types determined by their nature, composition and changes that occurred along their polygon borders. Large vegetation changes caused by the changes in precipitation over the time period can be tracked with the maps which display habitat changes. The area of wetlands increased significantly, whilst areas of meadow and marsh decreased and in their place new complex aquatic habitat forms appeared. The number of habitat complexes also increased because of the presence of water linked to habitat types and fragments. In the humid period, a sustained floodplain groundwater-level increase was observed that resulted in a mosaic appearance in habitats, but led to species degradation. [ABSTRACT FROM AUTHOR]

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier. [ABSTRACT FROM AUTHOR]

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care. [ABSTRACT FROM AUTHOR]

Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors. [ABSTRACT FROM AUTHOR]

Spatially explicit assessment (quantification and mapping) of soil functions and services is still a challenge. Soil property maps provide quantified spatial information, which can be properly utilized in the spatial inference of soil functions and services. Digital process or crop models properly simulate the soil-plant-water environment conditioned by various factors based on actual, predicted or presumed data. Specific outputs of the modelled processes provide adequate information on certain soil functions. Although these models do not necessarily work in space, they can be built in as engines into spatial inference systems. Once the input is spatialized, the output is also produced in map format.Council of the European Union has established the Nitrates Directive covering all Member States to avoid nitrate pollution, which is also valid regulation for Hungary. Good Agricultural Practice describes the elements of the protection against nitrate pollution, according to which farmers are obliged to provide data on their nutrition supply each year in areas classified as nitrate sensitive (nearly 70% of the territory of Hungary). From ecosystem service point of view, agricultural soils in these areas provide simultaneously filtering and provisioning functions which are heavily affected by anthropogenic factors realized in their management. We carried out the agri-environmental assessment of the nitrate database for the year 2016 using the 4M crop model with appropriate spatial soil information provide by DOSoReMI.hu. By the crop production simulation model, the expected yields of the 5 main crops (wheat, maize, barley, sunflower and rapeseed) and the amount of nitrogen uptaken by crops were estimated at total of 1.3 million hectares on 50,000 Agricultural Parcel Identification System (MePAR) blocks. The needed input parameters to model running were supplied by the nitrate database (manure amounts, cultivated crop, and yield) and 100 meter resolution digital soil property maps, in depth of 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm (clay, silt and sand and organic matter content aggregated for MePAR blocks). In view of the total amount of nitrates from nitrate database we estimated the nitrogen balance as well as the nitrate content that leach under the 0-90 cm soil layer. The model also allowed to run different fertilization time scenarios using the year of 2016 nitrate database.Our results verified that the nutrition of Hungarian agriculture is environmentally friendly, as the fertilizer doses are adapted to the needs of grown plants or in many cases it is under the requisite. In this context, the nutrient balances are negative. We found strong correlation between the adequacy of production site and the amount of used fertilizer, so the agri-ecological possibilities are taken into account by farmers in the planning of nutrient management.According to results of 4M model runs, we established that the extension of manuring season did not show any significant increase in agri-environmental risk, that is the soil nitrate content under the 90 cm layer do not increase. Based on the results, we suggested to change the relevant regulation, so the beginning of close season should be modified from 31 October onwards 30 November. [ABSTRACT FROM AUTHOR]

As a member state of the EU, Hungary is also obliged to asses and map its most important ecosystem services. The Hungarian national ecosystem service assessment was started in 2016, a four-year project led by the Ministry of Agriculture and co-financed by the EU. After several steps of involvement (interviews, stakeholder analysis, participatory prioritizing workshops) thirteen ecosystem services were selected for detailed study. Majority of the thirteen prioritized ES is related to the state and functions of soils. Additionally, characterization of ecosystem condition as well as the elaboration of the high resolution ecosystem base map, which constitutes the fundament of the spatial assessment process, also demand the proper spatial representation of the soil mantle.Provisioning and regulating services attributed to soils are generally represented by proxies and indicators, which are widely used in ecosystem service mapping. Although soil functions and services could be approximated by elementary soil features, either by the association of soil types with services as basic principle, soil property maps provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. The activity of DSM goes beyond mapping purely primary and secondary soil properties, the regionalization of further levels of soil related features (processes, functions and services) is also feasible. Target specific digital soil mapping can produce directly applicable products.In the course of ecosystem service assessment, it is essential to take ecosystem conditions into consideration, since only ecosystems in proper condition are able to provide the potential services. Among both the so-called service-based status indicators and indicators describing the general state of ecosystems spatial information on the state and functions of soils is of paramount importance.The category system of the current ecosystem base map, which serves as the basis for the assessment, includes natural and near-natural habitats that cannot be identified or delineated with sufficient certainty on the basis of available spatial databases and remote sensing information. Therefore, the idea has arisen to use environmental information on habitats, because the occurrence of certain feature combinations may make the presence of a particular habitat and thus the ecosystem probable. Spatial representation of abiotic habitat characteristics with relatively large spatial resolution at national level is mainly constrained to digital soil property maps on the physical and chemical properties of soils. During the investigations of the relationship between abiotic habitat features and habitat types, pedological and habitat characteristics of grasslands, wetlands and certain forest types have been examined using data mining methods based on map reference data classified according to Á-NÉR (General National Habitat Classification System). The identified relationships and the elaborated classification rules have been used to the spatial estimation of habitats.Our poster present, how formerly elaborated together with intentionally targeted digital soil maps have been supporting the national mapping and assessment of ecosystem services in Hungary. [ABSTRACT FROM AUTHOR]

• It was assumed that soils covered by forest can be considered as saturated in SOC. • Cubist-based PTF was developed for predicting and mapping saturated SOC content. • Environmental conditions are also important factors in predicting SOC saturation. • Significant SOC deficit was revealed on large part of Hungary with high variability. • Highest SOC deficits occur in areas with medium to high SOC content. Stakeholders and policymakers have been becoming more and more interested not just in the potential organic carbon (SOC) saturation level of soils but also in spatially explicit information on the degree of SOC deficit, which can support future policy and sustainable management strategies, and carbon sequestration-associated spatial planning. Thus the objective of our study was to develop a cubist-based pedotransfer function (PTF) for predicting and mapping the saturated SOC content of the topsoils (0–30 cm) in Hungary and then compare the resulting map with the actual SOC map to determine and assess the degree of SOC deficit. It was assumed that topsoils covered by permanent forests can be practically considered as saturated in SOC. Using the monitoring points of the Hungarian Soil Information and Monitoring System located in forests as reference soil profiles, we developed a cubist-based PTF. The transparent model structure provided by cubist allowed to show that not just the physicochemical properties of soils (e.g., texture, and pH) but also environmental conditions, such as topography (e.g., slope, altitude, and topographical position) and climate (e.g., long-term mean annual temperature, and evaporation), characterizing landscape are important factors in predicting the level of SOC saturation. Our results also pointed out that there is SOC deficit on large part of the country (∼80%) showing high spatial variability. It was also revealed that the most considerable potential for additional SOC sequestration can be found related to soils with medium to high actual SOC content. [ABSTRACT FROM AUTHOR]

• Topsoil organic carbon (SOC) stocks were mapped for the years 1992 and 2010. • Quantile regression forest was used for spatial prediction and uncertainty quantification. • Total SOC stock was found to be 424.41 Tg in 1992 and 451.59 Tg in 2010. • SOC stock increased under forest and pasture, and did not changed under arable. • SOC stock map of 1992 provides a baseline reference for Hungary. We compiled maps for the topsoil (0–30 cm) organic carbon (SOC) stock and its prediction uncertainty in Hungary at 100 m resolution for the years 1992 and 2010 using a machine learning algorithm, namely, quantile regression forest. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications for both years. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases (0.15 and 0.30 for 1992 and 2010), root mean square errors (21.99 and 21.39 for 1992 and 2010), accuracy plots and the G statistics (0.96 for both years) as well. Based on the compiled SOC stock maps, we assessed the spatio-temporal change of SOC stocks on the territory of Hungary. A scheme was elaborated based on the quantified prediction uncertainties for identifying and delimiting significant and tendentious changes of SOC stock during the respective period. The total SOC stock in the topsoil was found to be 424.41 Tg (1 teragram = 1012 grams) in 1992 and 451.59 Tg in 2010. Thus SOC stock increased by 27.18 Tg over the respective period. On those areas where the land use types did not change, we observed that the SOC stock increased under forests (by 16.29 Tg) and pastures (by 2.48 Tg), decreased under wetlands (by 0.49 Tg) and did not change under agricultural areas. On those areas where the land use has been changed during the 18-year period, we found that afforestation has increased the SOC stock, whereas cultivation of pastures has decreased it. Due to soil sealing 34,000 ha of soil have been lost resulting in approximately 1.7 Tg carbon loss. We compared our own total SOC stock estimate and map referring to 1992 with other estimates and maps provided by global and continental initiatives. The comparisons have pointed out that the SOC stock map of 1992 outperformed these maps. We recommend applying the SOC stock map of 1992 as a baseline for Hungary. [ABSTRACT FROM AUTHOR]

The exotic honey locust (Gleditsia triacanthos) is often planted as ornamental tree in urban parks. In Hungary, it occasionally escapes cultivation, in other countries it has already become invasive, and thus, further spread cannot be ruled out. The production of copious long-lived seeds may contribute to its invasiveness. We investigated the soil seed bank of honey locust growing in urban parks of Budapest (Hungary). Soil samples of 1200 cm 3 were taken under the canopy of 48 solitary female trees, seeds were extracted by washing, and their viability was assessed by germination test following scarification. For each tree, trunk circumference and level of park maintenance were recorded. Seed bank density varied between 0 and 500 seeds/m 2 and in 17 out of 48 cases, it exceeded 100 seeds/m 2 . Larger trees tended to form better-developed soil seed bank than smaller ones, yet the level of park maintenance has a much stronger effect: trees in neglected parks produced five-times higher density on average than those in perfectly managed parks (126.4 and 24.5 seeds/m 2 , respectively). For a better understanding of the species' invasion risk, detailed seed ecological studies are needed and to prevent its further spreading, a more careful treatment of its litter is recommended., (© 2020. The Author(s).)