Agricultural management produces soil compaction depending on intensity of use, specific management and soil properties. We used an adapted, home-built dynamic penetrometer to evaluate 20 long-term sites of arable land, tree orchards, vineyards and grassland in Europe and China, each with different tillage and cover crop strategies. To ensure comparable results across all sites, we pre-tested different penetrometer settings in the laboratory to cover all local conditions and provided Standard Operating Procedures. The laboratory tests showed that different settings in terms of falling hammer height and cone angle produced replicable results, and that narrow plough pans (3 cm) could be detected, even though their density was underestimated by 50%. The pre-tests also demonstrated the dependence of the measurements on soil water content and texture, even under preset conditions close to field capacity. Consequently, the effects of different management practices were only compared directly for each site individually. The field study showed that tillage had a greater effect on penetration resistance than different cover crop systems and intensity. The majority of the more intensively tilled fields showed penetration resistance that was up to 3 MPa lower, at least partially, up to the ploughing depth compared to less intensively tilled fields. However, in only 27% of the fields, the no-till or reduced-till management led to an SPR greater than 2.5 MPa, indicating harmful compaction, compared to conventional management. The effects of cover crops on soil compaction were unclear with differences observed between different mixtures at only one site. Nevertheless, unlike bare soil, cover crops increased penetration resistance in the tillage horizon and reduced infiltration capacity in 75% of the fields investigated. Trends depending on management practices varied due to local soil properties. We therefore recommend farmers to include penetrometer measurements in their routine to recognize soil compaction in time and apply tailored mitigation strategies.

The demand for spatially and temporally exhaustive information on soil organic carbon (SOC) is growing exponentially. This is because many national, continental, and global initiatives strongly rely on such information, including soil health monitoring, sustainable land use, climate change mitigation, land degradation neutrality, and maintenance of ecosystem services. The objective of this study is to introduce HU-SoilCarbonGrids (https://husoilcarbongrids.hu/), which aims to meet diverse demands and provide reliable and comprehensive information on spatiotemporal SOC changes across Hungary. It relies on geographically and temporally referenced SOC stock observations (n = 9385, collected every 3 years at 1048 monitoring sites) derived from the Hungarian Soil Information and Monitoring System, a large set of static and dynamic environmental covariates serving as proxies for the soil-forming factors, and advanced digital soil mapping techniques to predict annually the space-time variability of SOC stock at a resolution of 100 m between 1992 and 2023 across the country, with the associated prediction uncertainty. In the case of dynamic covariates, their long-term effects on SOC stock were also investigated and considered using various weighting functions. Based on the compiled map series, which provides a unique and reliable representation of SOC changes across Hungary, it was found that SOC stock has fluctuated at the country scale. Additionally, a net decreasing trend in SOC was obtained over the 32-year long period at the country level. It was also demonstrated that SOC stocks show diverging trends across permanent land use types, with forests increasing, wetlands gradually declining, and croplands and grasslands fluctuating. Although the current version of HU-SoilCarbonGrids meets diverse needs for dynamic SOC information, we are committed to improving it and releasing updated versions to support national and international initiatives, such as SOC conservation, soil health monitoring, sustainable land use and climate change mitigation.

Nutrient management plan (NMP) tools aim to enhance crop production while minimising environmental harm from over-fertilisation by aligning applications with crop demands and with soil and atmospheric conditions. The characteristics of 14 widely used NMP tools from nine countries (Austria, Bulgaria, China, Czech Republic, Hungary, Italy, New Zealand, Spain, and United Kingdom) were compared. All tools employed a mass balance approach at the field and seasonal scales. To evaluate the tools, matrices of the presence/absence of 24 desirable characteristics, 22 nutrient cycle processes and sources, and 38 required input data were compiled. To compare the NMPs, cumulative scores were calculated for each category evaluated. Additionally, two theoretical case studies compared fertiliser recommendations for winter wheat in arable and livestock farming systems. Cluster analysis classified the 14 tools into six clusters, reflecting distinct levels of complexity, usability, adaptability, and interoperability. The number of input data required was strongly and positively correlated with the number of nitrogen (N) processes and sources considered, confirming that input demand reflects tool sophistication. More comprehensive tools tended to recommend lower N application rates in the livestock system, suggesting that simpler tools overestimated N requirements by omitting key processes. However, practical usability characteristics did not determine different recommendations. While N recommendations were broadly aligned with national guidelines, P and K recommendations showed considerably higher variability reflecting the lack of harmonised guidelines for these macronutrients.

There is a growing demand for comprehensive environmental information driven by increasingly complex environmental challenges. Addressing these issues requires multidisciplinary approaches supported by harmonized datasets derived from diverse data sources. We developed HU-EnviroGrids, a spatially exhaustive, gridded environmental dataset covering the entire territory of Hungary. The dataset was created using a multidisciplinary data integration approach that harmonized diverse data sources across multiple environmental domains, such as topography, hydrology, climate, biosphere & land surface and pedosphere. The derived quantitative and qualitative environmental variables were transformed into a common reference system, providing a consistent spatial coverage at a 100 × 100 m spatial resolution across Hungary. This dataset provides foundational inputs to a wide range of applications for applied research in ecology, hydrology, climatology, agriculture, forestry, and Earth system sciences. By making these data openly available, HU-EnviroGrids seeks to strengthen the scientific and practical basis for addressing complex environmental issues, and to foster more informed, data-driven decision-making at multiple scales.

Enhancing social capital, improving market connections for healthy goods and services, and increasing business and technical management capacities can help farmers adopt more sustainable and profitable business models. The aim of the study was to map the business models used by farmers in three agricultural systems (tree crops, cereal-based rotations, and pastures) across European countries. The study used a simplified Business Model Canvas to define the elements of the business model. Then, a two-step cluster analysis was applied to identify the characteristics that define each business model. As a result, four business models were described. A comparative analysis was then made between them. Based on these analyses, conclusions were drawn about the socioeconomic opportunities for their further development.

Soil health is a significant problem in agriculture which demands a tailor-made approach. The study aims to develop a methodological approach for farm typology construction in terms of soil health. Thus, the focus is on EU farms, which produce in the three key cropping systems - grassland, cereal-based rotation, and tree crops. It was applied principal component analysis based on which it was constructed four factors, related to soil health. This approach bridges soil health problems with socioeconomic, environmental, and technology assessments. Soil health farm typology determinations is an essential step in any realistic evaluation of constraints and opportunities that farmers face and helps develop appropriate technological solutions, policy interventions, and comprehensive environmental assessment. It can be used to describe the possibilities and implications at larger regional scales of new strategies for promoting soil restoring and best fertilization technologies in agriculture and its inclusion in agricultural and environmental policies. The farm typology in term of soil health was constructed applying two sequential multivariate techniques: principal component analysis (PCA), and cluster analysis (CA).Key words: soil health, farm typology, crops, tree crops, cereal-based rotation, grasslandAcknowledgement: The research is made under the project “Transforming Unsustainable management of soils in key agricultural systems in EU and China. Developing an integrated platform of alternatives to reverse soil degradation” – TUdi. This project receives funding from the European Union’s Horizon 2020 Research and Innovation action under grant agreement No 101000224.

Climate change is an emerging threat to global ecosystems, thus assessing human influences on soil carbon and nitrogen cycles is essential for mitigation policies. We evaluated the effects of different fertilizer applications on CO2, N2O, and CH4 emissions of an Endocalcic Chernozem under maize cropping in a long-term experiment for two consecutive years. We examined soil temperature (Ts), soil water content (SWC), soil chemical parameters, yield and greenhouse gas intensity indexes (GHGI) in the control (C), the manure (M), the fertilized (NPK), and the combined (NPK+M) parcels. We found higher mean CO2 emissions (0.056 +/- 0.040 mg CO2 m(-2)s(-1)) in the M treatment compared to the NPK (0.048 +/- 0.057 mg CO2 m(-2)s(-1)). CO2 emission showed inconsistent results in both years, highlighting the importance of the duration of the investigations. N2O emissions were higher under NPK or NPK+M treatments (0.014 +/- 0.025 and 0.017 +/- 0.026 mu g N2O m(2) s(-1)) than under M or control (0.003 +/- 0.002 and 0.003 +/- 0.002 mu g N2O m(2) s(-1)). These results can be attributed to the higher nitrogen contents and lower pH values in the NPK parcels. There were no significant CH4 emissions under any treatments. Mean Ts and SWC were similar in each treatment indicating their influence on the emissions was rather temporal, than between treatments. Mean GHGI was the lowest in NPK+M, since the yields compensated the elevated emissions. This research highlights the benefits of combined fertilization for chernozem soils in terms of yield and GHGI, which can be useful for selecting proper fertilizer technologies in areas with similar soil characteristics.

Regional and national 3D soil hydraulic maps enhance understanding of soil hydraulic properties, essential for environmental assessments. However, data aggregation is often necessary in large-scale models to facilitate the modelling of complex soil characteristics. This study presents a soil hydrologic groups map for Hungary, derived through k-means clustering and expert-based rules. Clustering was applied to the 100 m resolution 3D HU-SoilHydroGrids database, considering eight hydraulic parameters across six depths. The accuracy of these maps is limited for rare soil types with extreme characteristics due to their small spatial extent and sparse representation in national datasets. To account for these underrepresented soil types, we refined each statistics-based cluster using expert-based rules incorporating soil profile depth, genetic type, electrical conductivity, and exchangeable sodium content. The final classification includes 68 soil hydrologic groups, defined by distinct hydraulic properties, such as van Genuchten parameters to describe water retention, and saturated hydraulic conductivity. This national map supports country-wide hydrological modelling, environmental management, and agricultural planning in Hungary by enabling consistent treatment of similar soils.

Herein, we report a 6-year-long investigation on the CO2 emission (soil respiration) of a chernozem soil under conventional moldboard plowing (MP) and two conservation tillage techniques, namely shallow cultivation (SC) and no-tillage (NT). This study aims to compare soil respiration data among SC, and MP or NT treatments and investigate the underlying processes influencing the magnitude of soil-derived emissions. CO2 fluxes were measured using static and dynamic chamber methods in seven replicates weekly during and biweekly to monthly outside growing seasons. We investigated postharvest yield and root biomass, post-tillage mulch thickness, soil water content (SWC) and temperature (Ts) via a monitoring system and portable instruments, soil chemical parameters via wet chemical analyses, and community-level physiological profiles of the soil microbial community using the MicroRespTM technique. The 6-year average soil respiration under SC (0.093 mgCO2 m-2 s-1) was the same as the mean emission in NT. Both of these conservation treatments showed significantly elevated CO2 emissions compared with the mean soil respiration under conventional MP (0.081 mgCO2 m-2 s-1). We found that vegetation biomass via root respiration and denser straw residue cover could be major factors of higher CO2 emission under SC. Additionally, the higher soil respiration in SC compared with MP could result from the high soil organic carbon (SOC) content. Similarly, elevated soil respiration in NT can occur because of the highest mean SOC and SWC as well as the densest straw residue layer among the three treatments. Micro-RespTM measurements revealed differences in the substrate use efficiency of the microbial community under the three treatments, therefore suggesting that the treatment effect on CO2 emission is caused by differences in microbial communities. Following crop production and soil respiration together, the CO2 emission to yield ratio was the lowest under SC, similar to MP, and highest under NT treatment. The CO2 emissions of the treatments exhibited variability over the years. Therefore, longer experimental time is essential to find more established

The present study aimed to investigate specific vegetation indices (VI) at three research sites - one grassland and two vineyards - and evaluate the potential of grassland remote sensing (RS) data to refine Normalized Difference Vegetation Index (NDVI) values in grass-covered inter-row vineyards. The vineyards differed in soil texture, with silt loam at BCS and clay at GB, located on 8-15% slopes. Field monitoring included NDVI, Photochemical Reflectance Index (PRI), and Photosynthetically Active Radiation (PAR) data at different slope positions. We also downloaded spectral data from Sentinel-2 (S2; n = 124) to see how well the NDVI field and S2 data correlate. Afterward, different machine learning techniques were used to refine the accuracy of the measurements, such as linear regression (LR), random forest (RF), and XGBoost. Significant differences in VI were observed between the research sites, mainly correlating with soil chemistry. While NDVI is an indicator of overall canopy vigor, PRI was more responsive to short-term physiological changes, showing higher sensitivity under stress conditions. Ground truth and RS NDVI were well correlated (r = 0.68), with RF providing the best accuracy when trained with day of year and the grassland data (r = 0.787). Each model moderately predicted grapevine VI using basic inputs (r > 0.61), however, all three models performed well when grassland NDVI included in the training.

There is a recurring question in environmental science, whether the spatial heterogeneity of soils is also accompanied by notable variation of soil hydrological behaviour. Our aim was to investigate this so-called “structural heterogeneity versus functional homogeneity” issue by using variably saturated zone simulations and the Hungarian soil database MARTHA v3.1.4. The purpose of the applied functional classification method is to simulate the water balance components of different soils under the same meteorological forcing and then cluster them based on their hydrological response.We used 2552 soil samples with adequate data availability (fitted van Genuchten parameters of the soil moisture retention curve, saturated hydraulic conductivity) to set up and run 200 cm deep homogeneous soil profile models in Hydrus-1D, which differed only in their soil hydraulic parametrization. The simulations covered a 1 year period with daily time steps. Two types of upper boundary were applied: (i) 1 mm/day constant precipitation for 30 days then no precipitation, (ii) measured precipitation time series from Hungary over the whole period. The bottom boundary condition was free drainage.Hydrological indicators were derived from the simulation results (surface runoff, average root zone saturation, storage change, bottom boundary flux, flowthrough volume at 40 cm depth, break through curve characteristics for the constant precipitation). These indicators were used to classify the soil profiles using k-means clustering.1984 simulations were successful, from which 9 clusters were formed. These represent distinct hydrological behaviour for the same forcing time series, indicating the applicability of the proposed classification method. Key words: soil hydrology, functional evaluation, Hydrus-1D, MARTHA database The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.

The adaption of our land use and agricultural practices requires more detailed and more reliable spatial soil physical data. The LUCAS topsoil database is an up to date collection of soil physical data, however it is spatially scarce. The soil physical data of the Hungarian Soil Information and Monitoring System (SIMS) is spatially denser and has data from multiple layers from 1992. Harmonizing and combining the two datasets can lead to the creation of better resolution and more accurate maps. Before combining the databases, we must make sure, that the sample points represent the area in the same way. The comparison of the data began with the cleansing of the datasets, followed by the conversion of the many sampling depths of the SIMS data to 0-20 cm using mass preserving splines and the conversion of the particle size limit from FAO/WRB to USDA standard. To make sure, that the sum of the sand, silt and clay fractions was 100% additive log ratio (ALR) transformation was applied on both LUCAS and SIMS. Mapping was carried out using random forest kriging with 10-fold cross-validation on a 100 m * 100 m grid using 28 environmental covariates. The ALR maps were converted back, resulting in the sand, silt and clay maps. Using the three maps, soil texture classes were calculated for both datasets using the USDA soil texture triangle. The soil texture classes were compared to each other pixel-by-pixel using the taxonomical distances of the texture classes. The particle fraction maps were compared to each other also pixel-by-pixel using linear regression. The results let us conclude that the LUCAS and SIMS databases produce very similar maps of both sand, silt a clay. The soil texture class comparison also resulted in a very close match with the majority of the country producing very close or perfect matches. The two soil monitoring systems produce very similar results when mapping sand, silt, clay and soil texture for the whole country and can safely be combined together for future use and mapping.

The ability of soil to store a large amount of organic carbon (SOC) is one of its most important characteristics, making it an active and indispensable participant in the global carbon cycle. SOC influences various soil related functions and services, such as agricultural productivity, water retention and management, buffering capacity against toxic elements and compounds, which are essential to provide healthy food and clean drinking water. Furthermore, SOC is widely recognized as playing a crucial role in mitigating and addressing various environmental crises and challenges, such as climate change, land degradation, declining biodiversity, water and food security. Consequently, not only soil scientists but also researchers from other disciplines, practitioners, stakeholders, and even policymakers have shown growing interest in information on the spatial and temporal variability of SOC at various scales.In the past few years, significant efforts have been made in Hungary to predict the spatial, and more recently, the spatiotemporal variability of SOC using various digital soil mapping techniques. Recently, a space-time model of SOC was developed using a combination of machine learning and space-time geostatistics to predict SOC change at point support and various aggregation levels (i.e., 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary (Szatmári et al., 2024). This work is based on soil data derived from the Hungarian Soil Information and Monitoring System between 1992 and 2016, as well as spatially and temporally exhaustive environmental covariates. Notably, geostatistics plays a central role by accounting for the spatiotemporal correlation of errors, which is essential for reliably quantifying the uncertainty associated with the aggregated SOC change predictions. The performance of the developed model was assessed using five times repeated 10-fold cross-validation, yielding acceptable results. A series of SOC maps were compiled for the period between 1992 and 2016 for each support, along with the quantified uncertainty, representing a significant advancement in Hungary. Furthermore, the presented methodology can overcome the limitations of recent approaches in spatiotemporal SOC modelling, allowing the prediction of SOC and SOC change, with quantified uncertainty, for any year, time period and spatial scale. This capability addresses current and anticipated demands for dynamic SOC information at both national and international levels.The aim of this presentation is to outline the methodology developed, to highlight some methodological challenges, to present the resulting maps, and finally, but importantly, to discuss these findings in a broader context.Acknowledgements: This research was funded by the National Research, Development and Innovation Office (NKFIH; grant number: FK-146391) and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.References:Szatmári, G., Pásztor, L., Takács, K., Mészáros, J., Benő, A., Laborczi, A., 2024: Space-time modelling of soil organic carbon stock change at multiple scales: Case study from Hungary. Geoderma 451, 117067.

A high spatial resolution soil database is under development in Hungary consisting of legacy soil observation data originating from different soil surveys. The soil data collected for the presented pilot area situated in the Southern Great Hungarian Plain will be the part of the Profile-level Database of Hungarian Large-Scale Soil Mapping (Hungarian acronym: NATASA). Presently, the NATASA soil database contains data from about 15,000 soil profiles in the sample area. The data from the soil profile records consist of two major parts: field descriptions and results of laboratory investigations. Soil profile locations are being processed using specifically elaborated GIS tools. Digitized profile records are being revised according to the national soil system and expert-based criteria, and the content of the database is being developed according to a uniform nomenclature. Essentially, the NATASA database will form the basis for the production of target soil hydrophysical property maps using environmental auxiliary variables and proper inference methods in standardized DSM approaches providing predictions for specific soil depths.The results obtained will not only become tangible in the form of different target maps, but will also provide very valuable information on the extent of the vulnerability of the Hungarian Southern Great Plain production areas to inland water and drought caused by weather extremes under the influence of climate change. This could help in the development of a regional drought and water deficit management system, in the establishment of a basis for irrigation investments or in the further development of the methodology of the current inland water vulnerability map. A more detailed knowledge of the hydrophysical properties of soils with spatial data could help to develop natural water retention measures.Acknowledgement: The work was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project and the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (FFT NP FTA).

The introduction of the Directive on Soil Monitoring and Resilience proposed by the European Parliament and Council is supposed to be preceded by specific preparatory works at Member State level, such as the definition of so-called soil districts together with the development of a soil monitoring system based on the elaborated zonalization. Three subsequent terms of Presidency of the Council of the European Union (Belgian, Hungarian, and Polish) aimed to finalize the concept elaboration and to legislate the Directive, so far without success. As a consequence, final delineation of soil districts could not been elaborated so far. Nevertheless, certain tests were carried out to establish a proper zonalization.The first drafts of the text of the Directive introduced a set of criteria that seems relatively simple in the legislative formulation, however, their implementation by Member States poses several number of methodological challenges. In the present paper soil health is approached from soil degradation point of view and soil districts from the regionalization of soil degradation respectively, which latter has already been addressed from time to time in the last decades.In the frame of Land Degradation Mapping Sub-project of PHARE MERA ’92 -, identification, delineation and description of Hungary’s major land degradation regions at 1:500,000 scale were accomplished by building and analyzing a digital land degradation geographic database in the late ‘90s. The applied GIS analysis techniques were mainly based on traditional cartographic methods and had not exploited the opportunities, which were later emerged in DSM.The former initiative of the Commission of the European Communities by the Thematic Strategy for Soil Protection proposed a comprehensive approach to soil protection with ample freedom on how to implement its requirements on the identification of threats and specific risk areas left to Member States. In 2007, the techniques available at that time provided by DSM together with the renewed interest in spatial delineation of areas endangered by various soil threats were combined for the recompilation of land degradation regions of Hungary. Different levels of specific threats were determined in the form of categories. For the overall characterization of degradation regions, indices were introduced serving as spatial land degradation indicators.In the last decade the Hungarian soil spatial infrastructure (HSSI) has been renewed, GSM conform digital soil maps on primary together with certain secondary, derived soil properties were elaborated in the frame of DOSoReMI@hu. The work has been continued with the modelling of certain soil functions and (degradation) processes. For the support of Soil District designation all, nationally relevant soil degradation processes have been digitally (re)mapped using specific DSM approaches based on HSSI and relevant spatial environmental ancillary data. The newly (re)complied soil degradation maps have then been submitted to spatial classification procedures to regionalize the processes. The results of the various classification scenarios have been used to produce alternatives for soil districts.

The sustainable management of crops in areas at risk of soil health degradation is crucial, particularly given their vulnerability in the current context of climate change. Decision Support Tools (DSTs) designed specifically for farmers are essential for assessing risks, analyzing the impact of agricultural practices, and defining strategies to mitigate negative impacts on soil health. In response to this need, the TUdi DSTs were developed (in app and web format), integrating functionalities tailored to address different types of soil degradation processes by different approaches related to soil biology, erosion, compaction, structure, organic carbon and fertilization. These DSTs are designed to restore and enhance soil health, and to optimize the use of fertilizers at the user level.However, in scenarios of high soil health degradation, the tool’s results often highlight negative outcomes, potentially leading to rejection in its adoption. It is therefore crucial to assess user acceptance of such tools in advance. To tackle this challenge, in-person workshops were conducted, engaging both farmers and stakeholders from the agricultural sector. These workshops enabled the evaluation of the TUdi DST's acceptance, and the identification of improvements aimed at optimizing its usability and fostering its broader adoption. These efforts aim to ensure that the TUdi DST becomes an effective tool for supporting farmers in sustainable soil management, while contributing to the mitigation of climate change impacts on vulnerable agricultural systems.Acknowledgments: this work was supported by the research project TUdi (Horizon 2020, GA 101000224).

The aim of the present study was to investigate stream turbidity and water chemical parameters under varying environmental conditions. We analyzed a three-year-long (2021-2023) daily and bi-weekly dataset collected at six points (P1-P6) along a small stream. We measured stream water turbidity (FNU), total dissolved inorganic nitrogen (TDIN) content, water pH, and specific conductivity (SPC). Meteorological data were collected at the catchment outlet. Daily data showed a moderate positive correlation between FNU and precipitation (r=0.42, p <0.001), while weak negative connections were observed between SPC and FNU values (r=-0.14, p =0.011, n=349). The FNU values at the groundwater spring-fed sampling point (P3) were significantly different from the other sampling points on most parameters ( p <0.05). The results of the cluster analysis revealed three main clusters based on daily turbidity data. These groups of daily precipitation totals were i) below 4.8 mm, ii) averaging 6.3 mm, and iii) averaging 23.7 mm. The clusters were most significantly separated along precipitation and FNU values. Turbidity values were strongly correlated with precipitation events for two days, after which stream water quality returned to baseline. Stream water quality was not significantly influenced by soil management or antecedent moisture content but rather by water origin (i.e., precipitation, groundwater).

Laser diffraction measurement (LDM) is an accurate, fast, and reproducible method for measuring the particle size distribution (PSD), which is the basis for texture determination. However, LDM might yield different results from those provided by previously used sedimentation-based methods and even by other LDM measurements, depending on inter alia the applied instruments, their geometry, sample preparation, and/or pretreatment methods. It is also not yet generally known how these deviations affect the texture classification of soils. In our work, results of nine different texture classifications of eight soil samples were compared using confusion matrices based on PSD data obtained by: a) the sieve pipette method (SPM) after removal of binding agents between elementary particles; b) LDM measurement (without preparation) in distilled water and tap water, with pretreatments as follows: 1) without a dispersing effect, 2) using Calgon, 3) using ultrasound, or 4) applying their combination. Our results showed that the texture classification might shift by more than two classes due to either the type of measurement or the chemical or mechanical disaggregation method used, the quality of aqueous media, the variation of soil properties, or the interactions occurring in soil-liquid phase-dispersant systems. The standardisation of the LDM requires the elimination of these sources of error.

Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can be integrated with wet analysis as an alternative. Therefore, development of national-level soil spectral libraries containing information about all soil types represented in a country is continuously increasing to serve as a basis for calibrated predictive models capable of assessing physical and chemical parameters of soils at multiple spatial scales. In this article, we present a database containing laboratory and visible-near infrared spectral data of legacy soil samples from the Hungarian Soil Degradation Observation System (HSDOS). The published data set includes the following parameters measured in 5,490 soil samples: pHKCl, soil organic matter (SOM), calcium carbonate (CaCO3), total salt content (TSC), total nitrogen (TN), soluble phosphorus (P2O5-AL), soluble potassium (K2O-AL), plasticity index according to Hungarian standard (PLI), soil profile depth and reflectance data between 350 and 2,500 nm wavelength. The presented database can be a complement for further soil related research on continental, national or regional scales to support sustainable soil management.

A talaj víztartó képességét fraktáldimenziót alkalmazó módszerrel még nem becsülték a hazai talajfizikai gyakorlatban. Többváltozós lineáris regressziós és korszerű regressziós-fa módszerű pedotranszfer függvényekkel azonban már voltak víztartó képesség becslések. Vizsgálatainkban mindhárom becslési eljárást összehasonlító módon elemeztük. TYLER és WHEATCRAFT (1990; 1992) útmutatása szerint meghatároztuk a hazai talajféleségeket reprezentáló HunSSD talajfizikai adatbázis talajain a szemcseméreteloszlás és a víztartó képesség fraktáldimenzió ( D m és D M ) értékeit. Összefüggést állapítottunk meg a D M és a talaj agyagtartalma között. GHANBARIAN-ALAVIJEH és munkatársai (2010) módszere alapján eljárást dolgoztunk ki a víztartó képesség adatok leírására általánosan használt van Genuchten (vG)-függvény (VAN GENUCHTEN, 1980) paramétereinek becslésére. A kidolgozott fraktáldimenziós (FD-vG) módszerrel a HunSSD talajokra becsült és a mért víztartó képesség értékekre illesztett regressziós egyenes meredeksége 1,01, tengelymetszete pedig 0,58 tf%, R 2 = 0,77. A fraktáldimenzió alapú becslés általános alkalmazhatóságát a HunpF adatbázison teszteltük. A HunpF talajok becsült és mért víztartó képesség értékeire illesztett egyenes meredeksége 1,01, tengelymetszete –2,78 tf %, R 2 = 0,81. RAJKAI és KABOS (1999) nyolcváltozós regressziós függvényekkel (LR8) és SZABÓ et. al. (2024) (HU-PTF v2) becslő modelljével is becsültük a HunSSD talajok víztartó képesség értékeit. A becsült és a mért értékekre illesztett egyenesek meredekségei sorrendben 0,99 és 0,94, tengelymetszetei 0,98 és 2,43, R 2 -értékei pedig sorrendben 0,80 és 0,79 voltak. A becslések pontosságának jellemzésére hibaelemzést végeztünk. Ellenőriztük, hogy a nedvességtartalom-becslés hibája független-e a vízpotenciál értékétől. Ez a feltétel csupán a HU-PTF v2 módszerre nem teljesült. A hibák eloszlása egyik becslési módszer esetében sem volt normális, és ugyanígy a hibavarianciák is vízpotenciálonként különböztek, azaz nem voltak állandóak. Emiatt a becslő modellek megítélésére általánosan használt ME (átlag hiba) és RMSE (négyzetes hiba) mutatók csak tájékoztató jellegűek. Megállapítottuk, hogy hazai talajok víztartó képességének becslésére a fraktáldimenziós módszer egy megfelelő alternatíva.