{"type": "FeatureCollection", "features": [{"id": "10.1016/j.gca.2024.07.026", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:16:13Z", "type": "Journal Article", "created": "2024-07-27", "title": "Stability and transformation of jarosite and Al-substituted jarosite in an acid sulfate paddy soil under laboratory and field conditions", "description": "Open AccessGeochimica et Cosmochimica Acta, 382", "keywords": ["Redox", "2. Zero hunger", "Soil incubation", "Mossbauer spectroscopy", "Iron minerals; Mossbauer spectroscopy; Redox; Rice paddy; Soil incubation", "Rice paddy", "15. Life on land", "Iron minerals", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.gca.2024.07.026"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geochimica%20et%20Cosmochimica%20Acta", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.gca.2024.07.026", "name": "item", "description": "10.1016/j.gca.2024.07.026", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.gca.2024.07.026"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-01T00:00:00Z"}}, {"id": "10.1029/2018wr024408", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:31Z", "type": "Journal Article", "created": "2019-10-23", "title": "Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters", "description": "Abstract

Over the past decades, advances in data collection and machine learning have paved the way for the development of autonomous simulation frameworks. Among these, many are capable not only of assimilating real\uffe2\uff80\uff90time data to correct their predictive shortcomings but also of improving their future performance through self\uffe2\uff80\uff90optimization. In hydrogeology, such techniques harbor great potential for informing sustainable management practices. Simulating the intricacies of groundwater flow requires an adequate representation of unknown, often highly heterogeneous geology. Unfortunately, it is difficult to reconcile the structural complexity demanded by realistic geology with the simplifying assumptions introduced in many calibration methods. The particle filter framework would provide the necessary versatility to retain such complex information but suffers from the curse of dimensionality, a fundamental limitation discouraging its use in systems with many unknowns. Due to the prevalence of such systems in hydrogeology, the particle filter has received little attention in groundwater modeling so far. In this study, we explore the combined use of dimension\uffe2\uff80\uff90reducing techniques and artificial parameter dynamics to enable a particle filter framework for a groundwater model. Exploiting freedom in the design of the dimension\uffe2\uff80\uff90reduction approach, we ensure consistency with a predefined geological pattern. The performance of the resulting optimizer is demonstrated in a synthetic test case for three such geological configurations and compared to two Ensemble Kalman Filter setups. Favorable results even for deliberately misspecified settings make us hopeful that nested particle filters may constitute a useful tool for geologically consistent real\uffe2\uff80\uff90time parameter optimization.In recent years, vibrational spectroscopic techniques based on Fourier transform infrared (FTIR) or Raman microspectroscopy have been suggested to fulfill the unmet need for microplastic particle detection and identification. Inter-system comparison of spectra from reference polymers enables assessing the reproducibility between instruments and advantages of emerging quantum cascade laser-based optical photothermal infrared (O-PTIR) spectroscopy. In our work, IR and Raman spectra of nine plastics, namely polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polycarbonate, polystyrene, silicone, polylactide acid and polymethylmethacrylate were simultaneously acquired using an O-PTIR microscope in non-contact, reflection mode. Comprehensive band assignments were presented. We determined the agreement of O-PTIR with standalone attenuated total reflection FTIR and Raman spectrometers based on the hit quality index (HQI) and introduced a two-dimensional identification (2D-HQI) approach using both Raman- and IR-HQIs. Finally, microplastic particles were prepared as test samples from known materials by wet grinding, O-PTIR data were collected and subjected to the 2D-HQI identification approach. We concluded that this framework offers improved material identification of microplastic particles in environmental, nutritious and biological matrices.Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9\uffc2\uffa0years of free air CO2 enrichment (+200\uffc2\uffa0ppm; 2001\uffe2\uff80\uff932009) and 6\uffc2\uffa0years of soil warming (+4\uffc2\uffa0\uffc2\uffb0C; 2007\uffe2\uff80\uff932012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40\uffc2\uffa0years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above\uffe2\uff80\uff90ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7\uffc2\uffa0kg\uffc2\uffa0m\uffe2\uff88\uff922) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above\uffe2\uff80\uff90ground mass was not altered by soil warming or elevated CO2. However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (\uffe2\uff88\uff9240% for all roots <2\uffc2\uffa0mm in diameter at 0\uffe2\uff80\uff9320\uffc2\uffa0cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "Models", " Statistical", "Temperature", "Larix", "Carbon Dioxide", "15. Life on land", "Pinus", "Global Warming", "01 natural sciences", "Soil", "Species Specificity", "13. Climate action", "Biomass", "Tundra", "Switzerland"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12819", "name": "item", "description": "10.1111/gcb.12819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-30T00:00:00Z"}}, {"id": "10.1111/gcb.70130", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:40Z", "type": "Journal Article", "created": "2025-03-18", "title": "What Are the Limits to the Growth of Boreal Fires?", "description": "ABSTRACT

Boreal forest regions, including East Siberia, have experienced elevated fire activity in recent years, leading to record\uffe2\uff80\uff90breaking greenhouse gas emissions and severe air pollution. However, our understanding of the factors that eventually halt fire spread and thus limit fire growth remains incomplete, hindering our ability to model their dynamics and predict their impacts. We investigated the locations and timing of 2.2 million fire stops\uffe2\uff80\uff94defined as 300\uffe2\uff80\uff89m unburned pixels along fire perimeters\uffe2\uff80\uff94across the vast East Siberian taiga. Fire stops were retrieved from remote sensing data covering over 27,000 individual fires that collectively burned 80 Mha between 2012 and 2022. Several geospatial datasets, including hourly fire weather data and landscape variables, were used to identify the factors contributing to individual fire stops. Our analysis attributed 87% of all fire stops to a statistically significant (p\uffe2\uff80\uff89<\uffe2\uff80\uff890.01) change in one or more of these drivers, with fire\uffe2\uff80\uff90weather drivers limiting fire growth over time and landscape drivers constraining it across space. We found clear regional and temporal variations in the importance of these drivers. For instance, landscape drivers\uffe2\uff80\uff94such as less flammable land cover and the presence of roads\uffe2\uff80\uff94were key constraints on fire growth in southeastern Siberia, where the landscape is more populated and fragmented. In contrast, fire weather was the primary constraint on fire growth in the remote northern taiga. Additionally, in central Yakutia, a major fire hotspot in recent years, fuel limitations from previous fires increasingly restricted fire spread. The methodology we present is adaptable to other biomes and can be applied globally, providing a framework for future attribution studies on global fire growth limitations. In northeast Siberia, we found that with increasing droughts and heatwaves, remote northern fires could potentially grow even larger in the future, with major implications for the global carbon cycle and climate.Forests provide important ecological, economic, and social services, and recent interest has emerged in the potential for using residue from timber harvest as a source of renewable woody bioenergy. The long\uffe2\uff80\uff90term consequences of such intensive harvest are unclear, particularly as forests face novel climatic conditions over the next century. We used a simulation model to project the long\uffe2\uff80\uff90term effects of management and climate change on above\uffe2\uff80\uff90 and belowground forest carbon storage in a watershed in northwestern Oregon. The multi\uffe2\uff80\uff90ownership watershed has a diverse range of current management practices, including little\uffe2\uff80\uff90to\uffe2\uff80\uff90no harvesting on federal lands, short\uffe2\uff80\uff90rotation clear\uffe2\uff80\uff90cutting on industrial land, and a mix of practices on private nonindustrial land. We simulated multiple management scenarios, varying the rate and intensity of harvest, combined with projections of climate change. Our simulations project a wide range of total ecosystem carbon storage with varying harvest rate, ranging from a 45% increase to a 16% decrease in carbon compared to current levels. Increasing the intensity of harvest for bioenergy caused a 2\uffe2\uff80\uff933% decrease in ecosystem carbon relative to conventional harvest practices. Soil carbon was relatively insensitive to harvest rotation and intensity, and accumulated slowly regardless of harvest regime. Climate change reduced carbon accumulation in soil and detrital pools due to increasing heterotrophic respiration, and had small but variable effects on aboveground live carbon and total ecosystem carbon. Overall, we conclude that current levels of ecosystem carbon storage are maintained in part due to substantial portions of the landscape (federal and some private lands) remaining unharvested or lightly managed.\uffc2\uffa0Increasing the intensity of harvest for bioenergy on currently harvested land, however,\uffc2\uffa0led to a relatively small reduction in the ability of forests to store carbon. Climate change is unlikely to substantially alter carbon storage in these forests, absent shifts in disturbance regimes.

", "keywords": ["0106 biological sciences", "Carbon dioxide mitigation", "Forest ecology -- Oregon -- Oregon Coast Range", "Forest biomass", "13. Climate action", "Carbon cycle (Biogeochemistry)", "Biomass energy", "Forest Biology", "15. Life on land", "01 natural sciences", "7. Clean energy", "Climatic change", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1111/gcbb.12255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GCB%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcbb.12255", "name": "item", "description": "10.1111/gcbb.12255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcbb.12255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-25T00:00:00Z"}}, {"id": "10.1111/j.1365-2486.2008.01809.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:48Z", "type": "Journal Article", "created": "2008-11-04", "title": "Soil Organic Carbon Contents In Long-Term Experimental Grassland Plots In The Uk (Palace Leas And Park Grass) Have Not Changed Consistently In Recent Decades", "description": "Abstract

A recent report of widespread declines in soil organic C (SOC) in the UK over the 10\uffe2\uff80\uff9325 years until the early 2000s has focussed attention on the importance of resampling previously characterized sites to assess long\uffe2\uff80\uff90term trends in SOC contents and the importance of soils as a potentially volatile and globally significant reservoir of terrestrial C. We have used two sets of long\uffe2\uff80\uff90term experimental plots which have been under constant and known management for over a century and for which historical data exist that allow comparison over recent decades to determine what, if any, changes in SOC content have occurred. The plots used are the Palace Leas (PL) Meadow Hay Plots in north\uffe2\uff80\uff90east England (UK) established in 1897, and from the Park Grass (PG) Continuous Hay experiment established in 1856 at Rothamsted in south\uffe2\uff80\uff90east England. Collectively, these plots represent the only grassland sites in the UK under long\uffe2\uff80\uff90term management where changes in SOC over several decades can be assessed, and are probably unique in the world. The plots have received different manure and fertilizer treatment and have been under known management for at least 100 years. In 1982, total SOC contents were determined for the 0\uffe2\uff80\uff9327\uffe2\uff80\uff83cm layer of six of the PL plots using measurements of SOC concentrations, bulk density and soil depth. In 2006, the same six PL plots were resampled and SOC contents determined again. Four of the plots showed no net change in SOC content, but two plots showed net loss of SOC of 15% and 17% (amounting to decreases of 18 and 15\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921) since 1982. However, these differences in total SOC content were in a similar range to the variations in bulk density (6\uffe2\uff80\uff9331%) with changing soil water content. In 1959, the soil masses and SOC concentrations to 23\uffe2\uff80\uff83cm depth were measured on six PG plots with fertilizer and manure treatments corresponding closely with those measured on PL. In 2002, the SOC concentrations on the same plots were measured again. On three of the PG plots, SOC concentrations had declined by 2\uffe2\uff80\uff9310%, but in the other three it had increased by 4\uffe2\uff80\uff938% between 1959 and 2002. If it is assumed that the soil bulk density had not changed over this period, the losses of SOC from the top soils ranged range from 10 to 3\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921, while the gains ranged from 4 to 7\uffe2\uff80\uff83t\uffe2\uff80\uff83C\uffe2\uff80\uff83ha\uffe2\uff88\uff921. When the differences with time in SOC contents for the six PL and the six PG plots were examined using paired t\uffe2\uff80\uff90tests, that is, regarding the plots as two sets of six replicate permanent grasslands, there were no significant differences between 1982 and 2006 for the PL plots or between 1959 and 2002 for the PG plots. Thus, these independent observations on similar plots at PL and PG indicate there has been no consistent decrease in SOC stocks in surface soils under old, permanent grassland in England in recent decades, even though meteorological records for both sites indicate significant warming of the soil and air between 1980 and 2000. Because the potential influences of changes in management or land use have been definitively excluded, and measured rather than derived bulk densities have been used to convert from SOC concentrations to SOC amounts, our observations question whether for permanent grassland in England, losses in SOC in recent decades reported elsewhere can be attributed to widespread environmental change.

", "keywords": ["2. Zero hunger", "Ecology", "0401 agriculture", " forestry", " and fisheries", "biodiversity conservation", "04 agricultural and veterinary sciences", "15. Life on land", "Environmental Sciences"]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2486.2008.01809.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2008.01809.x", "name": "item", "description": "10.1111/j.1365-2486.2008.01809.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2008.01809.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-06-05T00:00:00Z"}}, {"id": "10.1111/sum.12198", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:19:04Z", "type": "Journal Article", "created": "2015-07-31", "title": "Long-Term Effects Of Tillage, Nutrient Application And Crop Rotation On Soil Organic Matter Quality Assessed By Nmr Spectroscopy", "description": "Abstract

Crop and land management practices affect both the quality and quantity of soil organic matter (SOM) and hence are driving forces for soil organic carbon (SOC) sequestration. The objective of this study was to assess the long\uffe2\uff80\uff90term effects of tillage, fertilizer application and crop rotation onSOCin an agricultural area of southern Norway, where a soil fertility and crop rotation experiment was initiated in 1953 and a second experiment on tillage practices was initiated in 1983. The first experiment comprised 6\uffe2\uff80\uff90yr crop rotations with cereals only and 2\uffe2\uff80\uff90yr cereal and 4\uffe2\uff80\uff90yr grass rotations with recommended (base) and more than the recommended (above base) fertilizer application rates; the second experiment dealt with autumn\uffe2\uff80\uff90ploughed (conventional\uffe2\uff80\uff90till) plots and direct\uffe2\uff80\uff90drilled plots (no\uffe2\uff80\uff90till). Soil samples at 0\uffe2\uff80\uff9310 and 10\uffe2\uff80\uff9330\uffc2\uffa0cm depths were collected in autumn 2009 and analysed for their C and N contents. The quality ofSOMin the top layer was determined by13C solid\uffe2\uff80\uff90stateNMRspectroscopy. TheSOCstock did not differ significantly because of rotation or fertilizer application types, even after 56\uffc2\uffa0yr. However, the no\uffe2\uff80\uff90till system showed a significantly higherSOCstock than the conventional\uffe2\uff80\uff90till system at the 0\uffe2\uff80\uff9310\uffc2\uffa0cm depth after the 26\uffc2\uffa0yr of experiment, but it was not significantly different at the 10\uffe2\uff80\uff9330\uffc2\uffa0cm depth. In terms of quality,SOMwas found to differ by tillage type, rate of fertilizer application and crop rotation. The no\uffe2\uff80\uff90till system showed an abundance of O\uffe2\uff80\uff90alkyl C, while conventional\uffe2\uff80\uff90till system indicated an apparently indirect enrichment in alkyl C, suggesting a more advanced stage ofSOMdecomposition. The long\uffe2\uff80\uff90term quantitative and qualitative effects onSOMsuggest that adopting a no\uffe2\uff80\uff90tillage system and including grass in crop rotation and farmyard manure in fertilizer application may contribute to preserve soil fertility and mitigate climate change.

", "keywords": ["Fertilizer application", "2. Zero hunger", "Crop rotation", " fertilizer application", " soil organic carbon (SOC)", " soil organic matter (SOM)", " tillage", " NMR spectroscopy.", "NMR spectroscopy", "Crop rotation", "Soil organic matter (SOM)", "13. Climate action", "Soil organic carbon (SOC)", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Tillage"]}, "links": [{"href": "https://doi.org/10.1111/sum.12198"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Use%20and%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/sum.12198", "name": "item", "description": "10.1111/sum.12198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/sum.12198"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-07-31T00:00:00Z"}}, {"id": "10261/179481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:28Z", "type": "Journal Article", "created": "2018-07-19", "title": "Molecular Fingerprinting of14C Dated Soil Organic Matter Fractions from Archaeological Settings in NW Spain", "description": "Abstract

This paper evaluates the complexities of radiocarbon (14C) dates from soil organic matter (SOM) in archaeological scenarios. The aqueous NaOH-insoluble residual SOM from Neolithic to medieval sites in NW Spain produced consistently older calibrated14C ages than NaOH-extractable SOM. Using pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and thermally assisted hydrolysis and methylation (THM-GC-MS), we analyzed the molecular composition of these SOM fractions, aiming to understand the differences in14C ages and to gain insight on SOM dynamics in relation to age fractionation. The molecular composition of the NaOH-extractable SOM, which accounts for roughly two-thirds of total SOM, has a larger proportion of microbial detritus than the NaOH-insoluble SOM. This might suggest that the discrepancies between the two fractions is due to microbial rejuvenation in the extractable fraction, leading to14C results that are younger than the activity that is to be dated. However, archaeological evidence presented here unambiguously shows that the14C age of the extractable SOM provides the more accurate age for the targeted activity, and that the insoluble fraction contains inherited old carbon. After statistical data evaluation using Partial Least Squares-Regression (PLS-R), it is concluded that this inherited SOM is a mixture of Black Carbon from wild and/or domestic fires and recalcitrant aliphatic SOM.

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.3390/agronomy11040652"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11040652", "name": "item", "description": "10.3390/agronomy11040652", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11040652"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.3390/ma14092302", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:56Z", "type": "Journal Article", "created": "2021-04-29", "title": "2D Dynamic Directional Amplification (DDA) in Phononic Metamaterials", "description": "

Phononic structures with unit cells exhibiting Bragg scattering and local resonance present unique wave propagation properties at wavelengths well below the regime corresponding to bandgap generation based on spatial periodicity. However, both mechanisms show certain constraints in designing systems with wide bandgaps in the low-frequency range. To face the main practical challenges encountered in such cases, including heavy oscillating masses, a simple dynamic directional amplification (DDA) mechanism is proposed as the base of the phononic lattice. This amplifier is designed to present the same mass and use the same damping element as a reference two-dimensional (2D) phononic metamaterial. Thus, no increase in the structure mass or the viscous damping is needed. The proposed DDA can be realized by imposing kinematic constraints to the structure\u2019s degrees of freedom (DoF), improving inertia and damping on the desired direction of motion. Analysis of the 2D lattice via Bloch\u2019s theory is performed, and the corresponding dispersion relations are derived. The numerical results of an indicative case study show significant improvements and advantages over a conventional phononic structure, such as broader bandgaps and increased damping ratio. Finally, a conceptual design indicates the usage of the concept in potential applications, such as mechanical filters, sound and vibration isolators, and acoustic waveguides.

", "keywords": ["phononic", "metamaterials", "damping", "13. Climate action", "0103 physical sciences", "02 engineering and technology", "0210 nano-technology", "7. Clean energy", "01 natural sciences", "dynamic directional amplifier", "Article"]}, "links": [{"href": "http://www.mdpi.com/1996-1944/14/9/2302/pdf"}, {"href": "https://www.mdpi.com/1996-1944/14/9/2302/pdf"}, {"href": "https://doi.org/10.3390/ma14092302"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Materials", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ma14092302", "name": "item", "description": "10.3390/ma14092302", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ma14092302"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-29T00:00:00Z"}}, {"id": "10.5061/dryad.1v87f", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:22Z", "type": "Dataset", "title": "Data from: Post-fire changes in forest carbon storage over a 300-year chronosequence of Pinus contorta-dominated forests", "description": "unspecifiedA warming climate may increase the frequency and severity of stand-replacing wildfires, reducing carbon (C) storage in forest ecosystems. Understanding the variability of post-fire C cycling on heterogeneous landscapes is critical for predicting changes in C storage with more frequent disturbance. We measured C pools and fluxes for 77 lodgepole pine (Pinus contorta Dougl. ex Loud var. latifolia Engelm.) stands in and around Yellowstone National Park (YNP) along a 300-year chronosequence to examine how quickly forest C pools recover after a stand-replacing fire, their variability through time across a complex landscape, and the role of stand structure in this variability. Carbon accumulation after fire was rapid relative to the historical mean fire interval of 150-300 years, recovering nearly 80% of pre-fire C in 50 years and 90% within 100 years. Net ecosystem carbon balance (NECB) declined monotonically from 160 g C m-2 yr-1 at age 12 to 5 g C m 2 yr-1 at age 250, but was never negative after disturbance. Decomposition and accumulation of dead wood contributed little to NECB relative to live biomass in this system. Aboveground net primary productivity was correlated with leaf area for all stands, and the decline in aboveground net primary productivity with forest age was related to a decline in both leaf area and growth efficiency. Forest structure was an important driver of ecosystem C, with ecosystem C, live biomass C, and organic soil C varying with basal area or tree density in addition to forest age. Rather than identifying a single chronosequence, we found high variability in many components of ecosystem C stocks through time; a > 50% random subsample of the sampled stands was necessary to reliably estimate the non-linear equation coefficients for ecosystem C. At the spatial scale of YNP, this variability suggests that landscape C develops via many pathways over decades and centuries, with prior stand structure, regeneration, and within-stand disturbance all important. With fire rotation projected to be < 30 years by mid century in response to a changing climate, forests in YNP will store substantially less C (at least 4.8 kg C/m2 or 30% less).", "keywords": ["Pinus contorta var. latifolia", "13. Climate action", "Yellowstone", "lodgepole pine", "net ecosystem carbon balance", "15. Life on land", "Carbon"]}, "links": [{"href": "https://doi.org/10.5061/dryad.1v87f"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.1v87f", "name": "item", "description": "10.5061/dryad.1v87f", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.1v87f"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-12-03T00:00:00Z"}}, {"id": "10.5061/dryad.cz8w9gj78", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:27Z", "type": "Dataset", "title": "Soil microbial relative resource limitation exhibited contrasting seasonal patterns along an elevational gradient in Yulong snow mountain", "description": "unspecified", "keywords": ["2. Zero hunger", "mountain ecosystems", "13. Climate action", "microbial metabolic mechanisms", "microbial relative C limitation", "microbial relative P limitation", "C use efficiency", "FOS: Earth and related environmental sciences", "15. Life on land", "elevations"], "contacts": [{"organization": "Zhang, Dandan, Wu, Baoyun, Li, Jinsheng, Cheng, Xiaoli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.cz8w9gj78"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.cz8w9gj78", "name": "item", "description": "10.5061/dryad.cz8w9gj78", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.cz8w9gj78"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.5194/bg-15-1933-2018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:37Z", "type": "Journal Article", "created": "2017-11-21", "title": "Straw incorporation increases crop yield and soil organic carbon sequestration but varies under different natural conditions and farming practices in China: a system analysis", "description": "

Abstract. Loss of soil organic carbon (SOC) from agricultural soils is a key indicator of soil degradation associated with reductions in net primary productivity in crop production systems worldwide. Simple technical and locally appropriate solutions are required for farmers to increase SOC and to improve cropland management. In the last 30 years, straw incorporation has gradually been implemented across China in the context of agricultural intensification and rural livelihood improvement. A meta-analysis of data published before the end of 2016 was undertaken to investigate the effects of straw incorporation on crop production and SOC sequestration. The results of 68 experimental studies throughout China in different edaphic, climate regions and under different farming regimes were analyzed. Compared with straw removal, straw incorporation significantly sequestered SOC (0\uffe2\uff80\uff9320\uffe2\uff80\uff89cm depth) at the rate of 0.35 (range 0.31\uffe2\uff80\uff930.40)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921, increased crop grain yield by 13.4\uffe2\uff80\uff89% (range 9.3\uffe2\uff80\uff89%\uffe2\uff80\uff9318.4\uffe2\uff80\uff89%) and had a conversion efficiency of the applied straw-C as 16\uffe2\uff80\uff89%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff892\uffe2\uff80\uff89% across the whole of China. The combined straw incorporation at the rate of 3\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 with mineral fertilizer of 200\uffe2\uff80\uff93400\uffe2\uff80\uff89kg N\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 was demonstrated to be the best combination for farmers to use with crop yield increased by 32.7\uffe2\uff80\uff89% (range 17.9\uffe2\uff80\uff89%\uffe2\uff80\uff9356.4\uffe2\uff80\uff89%) and SOC sequestrated by the rate of 0.85 (range 0.54\uffe2\uff80\uff931.15)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921. Straw incorporation achieved higher SOC sequestration rate and crop yield increment when applied to clay soils, under high cropping intensities, and in areas like Northeast China where the soil is being degraded. SOC responses were the greatest in the initial starting phase of straw incorporation and then declined and finally were negligible after 28\uffe2\uff80\uff9362 years, however, crop yield responses were initially low and then increased reaching their highest level at 11\uffe2\uff80\uff9315 years after straw incorporation. Overall, our study confirmed that straw incorporation did create a positive feedback loop of SOC enhancement together with increased crop production, and this is of great practical significance to straw management as agricultural intensifies in China and other regions in the world with different climate conditions.

", "keywords": ["2. Zero hunger", "QE1-996.5", "info:eu-repo/classification/ddc/550", "Ecology", "Life", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "QH540-549.5"]}, "links": [{"href": "https://doi.org/10.5194/bg-15-1933-2018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-15-1933-2018", "name": "item", "description": "10.5194/bg-15-1933-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-15-1933-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-21T00:00:00Z"}}, {"id": "10.5281/zenodo.14761001", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:22:38Z", "type": "Software", "title": "PyretoClustR (executable version)", "description": "PyretoClustR (executable version) Distilling the Pareto Optimal Front into Actionable Insights ##Highlights: Open-access tool to cluster and visualize complex multi-dimensional Pareto solutions Bridging the gap between decision and objective space through intuitive visualizations Implements k-means and k-medoids clustering without expert knowledge Uses frequency maps to display hotspot locations for spatial optimization problems Increasing stakeholder accessibility to Pareto solutions Program languages: Python, RSoftware availability (source code version): https://github.com/SydneyEWhite/Pareto_ClusteringSoftware availability (executable version, with python and libraries implemented): Zenodo (10.5281/zenodo.14761001) ## Overview This framework performs k-means and k-medoids clustering on a set of Pareto optimal solutions derived from a multi-objective optimization algorithm. (Optional) A correlation matrix of the input variables is returned (the goal is to help users reduce the input variable count). Before clustering occurs, the data is cast onto principal component axes and extreme solutions are handled (if desired). The code iterates through different possible inputs for the number of clusters, the number of principal components, and the variables that define 'extreme solutions'. After these iterations, the best solution, as defined by silhouette score, is visualized in several ways: Representative solutions are plotted on up to 4 dimensions Distributions of values within the clusters are plotted in a violin plot (optional) Maps with the frequency of a trait can be plotted, if locational data (*.shp file) is provided ## Background: executable version Unlike the version of PyretoClustR available on GitHub (https://github.com/SydneyEWhite/PyretoClustR), this version on Zenodo represents a standalone version of PyretoClustR not requiring a Python installation. The python_files folder contains three executables and one folder called _internal. The executables can be run by clicking on them. The _internal folder is crucial for the standalone operation of the executables and should not be modified or deleted. ## Example Data The data provided in the input folder (pareto_and_scen_solutions.csv and the shapefiles) are part of the BiodivERsA project TALE (\u2018Towards multifunctional agricultural landscapes in Europe\u2019). This project optimized four objectives, Agricultural gross margin (AY), Breeding habitat (BH), Low flow (LF), Nitrate load (NL) and four different land use scenarios (SQ, BAU, EXT, INT) were distinguished in the decision space.\u00a0The config.ini has been adapted to run with this dataset. To run it with your own data please alter the respective input values as outlined in config.ini. ## Directory Structure project_root/||\u2500\u2500 README.md||\u2500\u2500 input/|\u00a0 |\u2500\u2500 config.ini|\u00a0 |\u2500\u2500 [your_input_data].csv| \u2514\u2500\u2500 [shape_files]||\u2500\u2500 python_files/|\u00a0 |\u2500\u2500 _internal|\u00a0 |\u2500\u2500 correlation_matrix.exe|\u00a0 |\u2500\u2500 kmeans.exe| \u2514\u2500\u2500 kmedoid.exe||\u2500\u2500 r_files/| \u2514\u2500\u2500 plot_frequency_maps.R|\u2514\u2500\u2500 output/ (populated once code is run)\u00a0\u00a0 |\u2500\u2500 correlation_matrix.csv (if run)\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions.csv (created with kmeans.exe when Extreme Solutions are not handled)\u00a0\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions_outliers.csv (created with kmeans.exe when Extreme Solutions are handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions.csv (created with kmedoid.exe when Extreme Solutions are not handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions_outliers.csv (created with kmedoid.exe when Extreme Solutions are handled)\u00a0 \u2514\u2500\u2500 freq_map_cluster_X.png (if run)", "keywords": ["Multi-objective optimization", "Pareto pruning", "Land management", "Pareto optimal data", "visualization", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14761001"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14761001", "name": "item", "description": "10.5281/zenodo.14761001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14761001"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-29T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:22:46Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers. The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions. For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes. The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables. The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location. The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions. Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15680931", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:22:59Z", "type": "Journal Article", "created": "2025-06-15", "title": "Investigating the extent of PFAS contamination in the Upper Danube Basin across environmental compartments", "description": "Abstract Background

Per- and polyfluoroalkyl substances (PFAS) are emerging organic pollutants widely detected in environmental systems, posing risks to human health and the ecosystem. Despite increasing efforts to monitor PFAS in river systems, knowledge gaps remain regarding sources and emissions via different pathways. This study investigates PFAS contamination across multiple environmental compartments in the Upper Danube Basin, including surface water, groundwater, wastewater, landfill leachate, surface runoff, and atmospheric deposition. The primary objectives are to assess the extent of PFAS contamination, identify key emission sources and transport pathways, and evaluate associated risks in terms of the potential exceedance of current and proposed environmental regulatory thresholds in the European Union.

Results

The findings reveal a widespread presence of PFAS, with PFOA, PFOS and short-chain compounds being predominant. The Alz River and Gendorf chemical park emerge as hotspots with far-reaching effects downstream, contributing significantly to diffuse legacy contamination of PFOA and being a significant source of two industrial PFOA substitutes, ADONA and GenX. Wastewater treatment plants, old municipal landfills, and sites with a history of fire-fighting foam application are identified as key pathways or sources of legacy pollution, exhibiting higher concentrations compared to the other matrices. Notably, no significant removal is observed when comparing influent and effluent samples from conventional WWTPs. The study further demonstrates that groundwater is vulnerable to contamination from point sources and to infiltration from rivers, with bank filtration proving largely ineffective in preventing PFAS contamination.

Conclusions

The study underscores the necessity for source and pathway control measures to mitigate PFAS pollution, the implementation of advanced treatment technologies to safeguard drinking water and surface water quality, and targeted remediation for legacy soil and groundwater contamination. Additionally, strong use regulations should be explored to minimize ongoing emissions. The multi-compartment monitoring proves to be a crucial approach to understand the complexity of PFAS distribution at the catchment scale. Comparative analysis and risk assessment highlight challenging situations for water management, offering an indispensable basis for emission modeling as a next step for quantitative assessment of the relevance of different sources and pathways for surface water pollution.

", "keywords": ["Emerging contaminants", "Emerging Pollutants", "PFAS", "Source identification", "Watershed management", "Environmental sciences", "Emission", "Water Framework Directive", "Environmental law", "Water pollution", "GE1-350", "K3581-3598", "Catchment monitoring", "Environmental Monitoring"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1186/s12302-025-01141-6.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15680931"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Sciences%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15680931", "name": "item", "description": "10.5281/zenodo.15680931", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15680931"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15772619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:00Z", "type": "Dataset", "title": "Dataset to: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Near-Infrared (NIR) Austrian Soil Spectral Library (ASSL, 680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL \u2018Towards climate-smart sustainable management of agricultural soils\u2019 (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "Spectroscopy", " Near-Infrared", "data"], "contacts": [{"organization": "Fohrafellner, Julia", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15772619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15772619", "name": "item", "description": "10.5281/zenodo.15772619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15772619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-07T00:00:00Z"}}, {"id": "10.5281/zenodo.15781488", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:01Z", "type": "Report", "title": "Results of stakeholder surveys on preferred NSWRM implementation plans. Deliverable D5.3 of the EU Horizon 2020 project OPTAIN.", "description": "Deliverable report D5.3 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) The objective of this deliverable is to convey OPTAIN\u2019s optimisation approach, methodologies and results to stakeholders of each case study\u2019s Multi-Actor Reference Groups. More importantly, it will create a common understanding of the potential of the NSWRMs for improving water and nutrient retention in the CS, as well as of the associated trade-offs such as costs and potential reductions in crop production. Finally, this task will determine those NSWRM implementation plans preferred by individual actors using the tool, ParetoPick-R, developed in the previous task 5.3. This sets the stage for the subsequent in-depth, cross-sectoral discussion about a spatially targeted implementation of NSWRM. Summary\u00a0 This deliverable from the EU Horizon 2020 OPTAIN project presents the results from stakeholder interviews across eleven European case studies, focusing on the identification of preferred implementation plans for Natural/Small Water Retention Measures (NSWRMs).\u00a0It builds on the modelling and multi-objective optimisation workflows employed in OPTAIN, which explored numerous options for potential measure implementation optimised for environmental and economic objectives. Stakeholders of each case study\u2019s Multi-Actor Reference Groups (MARG) participated in structured interviews. Using the interactive ParetoPick-R app, they developed a common understanding of the potential of NSWRMs and explored trade-offs among four optimisation objectives, such as water/nutrient retention, crop production, and cost. They then selected their preferred implementation plans based on weights assigned to each objective and filter options applied to the solution space. Key Findings: Trade-offs & preferences: Stakeholders' preferences varied significantly across sectors and case studies. Agricultural actors typically prioritised crop production and cost-efficiency, while those in the water and nature conservation sectors leaned towards environmental benefits. Common measures: Frequently preferred NSWRMs included soil and/or crop management measures, followed by greening measures and engineered solutions. Feasibility issues: Technical feasibility, land ownership, and institutional hurdles (e.g., need for permits) influenced stakeholder choices. Tool feedback: The ParetoPick-R tool was generally well-received for visualising trade-offs and supporting decision-making. However, some users found it too complex and suggested improvements in usability, guidance, and map functionality. This deliverable D5.3 sets the foundation for the final MARG workshops in the case studies, which will seek to negotiate compromise solutions that are acceptable to all actors. The report underscores the importance of participatory modelling tools and multi-sector engagement in water and land management planning.", "keywords": ["multiobjective optimisation", "trade-offs", "NSWRM", "agricultural production", "H2020", "OPTAIN", "SWAT", "NWRM", "stakeholder", "water retention"], "contacts": [{"organization": "Strauch, Michael, Wittekind, Cordula,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15781488"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15781488", "name": "item", "description": "10.5281/zenodo.15781488", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15781488"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15797289", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:01Z", "type": "Dataset", "title": "Database of soil characteristics under specific pesticide management", "description": "Database of soil characteristics under specific pesticide management Contributors: Mark\u00e9ta Mayerov\u00e1 and Veronika \u0158ez\u00e1\u010dov\u00e1 Affiliation: Czech Agrifood Research Center, Drnovsk\u00e1 507/73, CZ-160 00 Prague 6, Czech Republic Database of soil characteristics will contribute to the realization of the project\u00b4s goal to identify appropriate and inappropriate pesticides from the point of the view of their impact on the non-target organisms and soil characteristics. Field I. The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The experiment took place at the site of the experiment that had been running there since 2013 and included 5 different herbicide treatments in 4 replications (Mayerov\u00e1 et al. 2018) The new trial area was split into 20 randomised plots with 2 different herbicide treatments in 8 replications and control without herbicides in 4 replications. Herbicide treatments differed in the mode of action (Table 1) Table 1. Summary of the herbicides and active ingredients used in the trial. Classification Group by Herbicide Resistance Action Committee (HRAC). herbicide dose formulation active ingredient content of a.i. HRAC group target weeds Agritox 50 SL 1.5 l/ha EC MCPA 500 g/l O dicot Glean 75 PX 15 g/ha WG chlorsulfuron 750 g/l B dicot + annual grasses \u00a0 The area of each plot was 100 m2 and the 10 by 10 m plots were separated from field boundaries and from each other by 2 m on all sides to eliminate interaction between plots. Herbicides were applied post emergency in spring (April 26, 2024) from the tillering crop stage to the beginning of stem elongation (BBCH 21\u201331) by the Agrio-Napa 12 sprayer. Winter wheat was grown in the experimental field in 2024. At the beginning of March, it was mineral fertilized with LAD (ammonium nitrate with dolomite - NH4NO3\u00a0+\u00a0CaMg (CO3)2; 27 % N) at a dose 100 kg/ha. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer in each replication before herbicide application (April 24, 2024), 14 days after herbicide application (May 9, 2024) and 7 weeks after herbicide application (June 14, 2024). \u00a0A total of 20 soil samples were collected at each sampling. The soil samples were subsequently dried and sieved through a 2 mm sieve, thus simultaneously homogenised. The following soil properties were determined: pH (H2O), electric conductivity, available P and K, concentration NH4 and NO3, soil organic carbon, and total organic nitrogen content. Available P and K were assessed according to the Mehlich III method (Mehlich, 1984) on an Agilant ICP-OES 5110 VDV instrument. NO3 and NH4 were determined using calcium chloride solution as extractant according to ISO 14255:1998 on automated chemistry analyser SKALAR. Soil organic carbon and soil organic matter content were determined by sulfochromic oxidation according to ISO 14235:1998. Field II The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The total area of the experiment is about 11 ha including the protective area around the entire experiment. The experimental area is divided into two halves, 120m wide and 300m long.\u00a0 One half was treated on June 17, 2024, with insecticide Decis forte (active ingredient deltamethrin) at a dose 62.5ml/ha, the other half was without insecticide treatment. Both areas are further divided into other halves. One half was treated on May 15, 2024, with herbicide Agritox (active ingredient MCPA) at a dose 1.5l/ha, the other was treated with hoeing only. We thus obtained 4 strips 60m wide with following treatment combinations: (A) herbicide + insecticide; (B) hoeing + insecticide; (C) hoeing; (D) herbicide. Spring wheat was grown in the experimental field in 2024. It was fertilized with mineral nitrogen at a dose of 150 kg N/ha before sowing and with 39 kg N/ha (DAM 390 - ammonium nitrate with urea) in the tillering phenophase. In the middle of each strip (i.e. treatment), 8 sampling sites were marked in a row, 20 m apart from each other. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer at each sampling site 14 days after herbicide application and 14 days after insecticide application. A total of 32 soil samples were collected at each sampling. Further sample processing was the same as for Field I. The database will be gradually supplemented in the following years. Funding: Development for this work is funded primarily by the Technology Agency of the Czech Republic, project SS07020100: \u201cThe impact of plant protection products on non-target biodiversity: soil microorganisms, invertebrates and wild plants\u201d, and the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0425. The database was approved on September 2, 2025, by the Ministry of Agriculture of the Czech Republic. References: Mayerov\u00e1 M., Mikulka J., Soukup J. (2018): Effects of selective herbicide treatment on weed community in cereal crop rotation. Plant Soil Environ., 64: 413\u2013420. https://doi.org/10.17221/289/2018-PSE \u00a0Mehlich A. (1984): Mehlich 3 Soil Test Extractant. A Modification of the Mehlich 2 Extractant. Commun. Soil Sci. Plant Anal. 15, 1409-1416. http://dx.doi.org/10.1080/00103628409367568.", "keywords": ["field trial", " herbicides", " insecticides", " soil properties"], "contacts": [{"organization": "Mayerov\u00e1, Mark\u00e9ta, \u0158ez\u00e1\u010dov\u00e1, Veronika,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15797289"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15797289", "name": "item", "description": "10.5281/zenodo.15797289", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15797289"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.16017208", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:02Z", "type": "Dataset", "title": "Cashew orchard soil properties, Dodamarg, Northern Western Ghats, India", "description": "Soil properties of cashew orchards of the Northern Western Ghats, India This project contains chemical properties of soil collected from cashew orchards of Dodamarg, Northern Western Ghats, for a study investigating the factors influencing the effects of forest cover, flower abundance, temperature and (potentially) soil composition on cashew pollinators. Taxonomic Coverage:\u00a0Not applicable Geographic Coverage: Dodamarg, Sindhudurg District, Maharashtra, India Temporal Coverage: March 2025 \u00a0 Description of field and lab methods Soil collection: Soil samples were collected from 30 cashew orchards, using soil core sampler. The diameter of the core sampler was measured before soil collection. All soil samples were collected from 10 cm depth after removing all the leaf litter from the ground. From each orchard, 10 soil columns were collected for analysis of chemical properties. Chemical Properties: We estimated thirteen soil chemical properties for all soil samples collected. The following parameters were analyzed by Zuari Farmhubs Laboratory: pH, electrical conductivity (E.C.) at 25\u00b0C, organic carbon (O.C.), available phosphorus (P\u2082O\u2085), available potassium (K\u2082O), available calcium (Ca), magnesium (Mg), sulfur (S), boron (B), zinc (Zn), iron (Fe), copper (Cu), and manganese (Mn). More details about the data can be obtained from Aditya Satish (adityasatish@ncf-india.org) and Rohit Naniwadekar (rohit@ncf-india.org) from the Nature Conservation Foundation (www.ncf-india.org). File Descriptions: Data file: Dodamarg_2025_Cashew_Soil_Properties.csv We have also included a ReadMe.txt file that explains the data file, akin to the description in the metadata. Description of the columns of the data file: Sl no: Serial number Site: Site ID Code: Site code (General location) Latitude: latitude co-ordinate of the plot (in decimal degrees, \u00b0N) Longitude: longitude co-ordinate of the plot (in decimal degrees, \u00b0E) pH: pH of the soil E.C.: Electrical Conductivity at 25\u00b0C (in dS/m) O.C.: Organic Carbon (in %) P\u2082O\u2085: Available P\u2082O\u2085 (in Kg /acre) K\u2082O: Available Potassium (in Kg /acre) Ca: Available Calcium (in mg/Kg) Mg: Available Magnesium (in mg/Kg) S: Available Sulphur (in mg/Kg) B: Available Boron (in mg/Kg) Zn: Available Zinc (in mg/Kg) Fe: Available Iron (in mg/Kg) Cu: Available Copper (in mg/Kg) Mn: Available Manganese (in mg/Kg) Funding:\u00a0 Godrej Consumer Products Limited Arvind Datar Rohini Nilekani Philanthropies", "keywords": ["Soil chemical properties", "Cashew orchards", "Ecology", "FOS: Biological sciences", "Northern Western Ghats"], "contacts": [{"organization": "Sadekar, Vishal, Satish, Aditya, Naniwadekar, Rohit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16017208"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16017208", "name": "item", "description": "10.5281/zenodo.16017208", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16017208"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16261617", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:02Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16261617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16261617", "name": "item", "description": "10.5281/zenodo.16261617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16261617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.17941270", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:08Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17941270"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17941270", "name": "item", "description": "10.5281/zenodo.17941270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17941270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.4487144", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:15Z", "type": "Dataset", "title": "Eddy Covariance data from ICOS-associated station IT-NIV \u2013 August-November 2019", "description": "RestrictedData stored here refer to Eddy Covariance (EC) data measured in 2019 between August and November at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO2 flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.", "keywords": ["13. Climate action", "alpine grassland", "15. Life on land", "Gran Paradiso National Park", "Mountain", "EO_Data", "Eddy Covariance", "Net Ecosystem Exchange", "ecosystem-atmosphere carbon exchange"], "contacts": [{"organization": "Vivaldo, Gianna, Raco, Brunella, Baneschi, Ilaria, Giamberini, Maria Silvia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4487144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4487144", "name": "item", "description": "10.5281/zenodo.4487144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4487144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:18Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": "

&lt;p&gt;Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (&lt; 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.&lt;/p&gt;

", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.6202061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:20Z", "type": "Dataset", "title": "Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies", "description": "Open AccessBias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for: -period 1981-2099/2100, -6 RCM, -3 scenarios (RCPs 2.6, 4.5 and 8.5), -7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and -18 domains and 23 locations within these domains. Bias correction and further downscaling to 0.1\ufffd\ufffd was done using ERA5-Land reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging. Organization of the data The name of the files are domain-type.zip, where type is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: experiment/modelNumber/variable.nc for gridded and experiment/modelNumber/variable-pilotFieldNumber.txt for point data, where experiment is rcp26, rcp45 or rcp85. domain and pilotFieldNumber domain domain location (min and max. Longitude, min and max latitude) pilotFieldNumber pilot field location (longitude, latitude) case study number country Name (OPTAIN case study) 01 50.95 51.45 14.55 15.05 1 DEU Schoeps 02 46.35 47.05 6.55 7.15 2 46.816667 6.95 2 CHE Petite Glane 02_1 46.75 47.25 7.25 7.75 1 46.983333 7.466667 02_34 47.35 47.85 8.35 3 4 47.433333 8.516667 47.683333 8.616667 02_5 46.15 46.65 5.95 6.45 5 46.4 6.233333 03a 46.65 47.15 17.45 17.95 1 2 3 4 46.92649 17.68246 46.9166 17.68976 46.91283 17.69754 46.91283 17.69723 3a HUN Csorsza 03b 46.45 46.95 16.65 17.15 3b HUN Felso Valicka 04 52.35 52.85 18.45 18.95 1 52.597469 18.728617 4 POL Upper Zglowiaczka 05 46.35 46.85 15.35 15.85 5 SVN Pesnica 06 46.45 46.95 16.15 16.65 6 HUN/SVN Kebele/Kobiljski 07 49.85 50.35 4.75 5.25 7 BEL La Wimbe 08 55.15 55.75 23.55 24.05 1 2 55.522057 23.799235 55.42233194 23.82580339 8 LTU Dotnuvele 09 45.45 45.95 9.65 10.15 9 ITA Cherio 10 59.45 59.95 10.75 11.25 1 2 3 4 5 6 7 8 59.71949 10.83576 59.6833306 10.8833298 59.6833306 10.8833298 59.665 10.9475 59.665 10.9475 59.841012 10.903597 59.757631 11.072031 59.539623 10.856447 10 NOR Krogstad 11 46.45 46.95 17.55 18.05 1 2 46.658333 17.75583 46.656944 17.75833 11 HUN Tetves 12 49.35 49.85 14.75 15.25 1 49.616837 15.078266 12 CZE Cechticky 13 55.85 56.35 25.85 26.45 13 LVA Dviete 14 59.75 60.25 17.55 18.05 14 SWE Ingvastaan Lehstaan modelNumber modelNumber Driving Model (GCM) Ensemble RCM End date 1 EC-EARTH r12i1p1 CCLM4-8-17 31.12.2100 2 EC-EARTH r3i1p1 HIRHAM5 31.12.2100 3 HadGEM2-ES r1i1p1 HIRHAM5 30.12.2099 4 HadGEM2-ES r1i1p1 RACMO22E 30.12.2099 5 HadGEM2-ES r1i1p1 RCA4 30.12.2099 6 MPI-ESM-LR r2i1p1 REMO2009 31.12.2100 variable variable description Unit Tmean Mean temperature \ufffd\ufffdC Tmin Min temperature \ufffd\ufffdC Tmax Max temperature \ufffd\ufffdC prec Precipitation mm solarRad Solar radiation MJ/m2 windSpeed Wind speed at 2m m/s relHum Relative humidity % Methodolody Bias correction was done using non-parametric empirical quantile mapping with modified method from R package qmap. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010. The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages automap and gstat with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model Shuttle Radar Topography Mission was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z. Funding This project has received funding from the European Union\ufffd\ufffd\ufffds Horizon 2020 research and innovation programme under grant agreement No 862756.", "keywords": ["CORDEX", "13. Climate action", "RCM", "ERA5-Land", "OPTAIN", "EURO-CORDEX", "bias correction"], "contacts": [{"organization": "Honzak, Luka", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6202061"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6202061", "name": "item", "description": "10.5281/zenodo.6202061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6202061"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-21T00:00:00Z"}}, {"id": "10.5281/zenodo.7307449", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:30Z", "type": "Dataset", "title": "Components of the complete budget for SAFE intensive carbon plots", "description": "Description: Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.

These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.

SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf. Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification Funding: These data were collected as part of research funded by: Sime Darby Foundation (Grant, SAFE Core data) European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131) NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1) NERC standard grant: The multi-year impacts of the 2015/2016 El Ni\u00f1o on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1) HSBC Malaysia (Grant) The University of Zurich (Grant) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. Permits: These data were collected under permit from the following authorities: Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76)) XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx SAFE_CarbonBalanceComponents.xlsx This file contains dataset metadata and 1 data tables: Carbon balance components data (described in worksheet Data) Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project Number of fields: 64 Number of data rows: 11 Fields: ForestType: Old-growth or Logged (Field type: categorical) SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (Field type: location) PlotName: Plot name (used in field work) (Field type: id) ForestPlotsCode: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id) WoodyNPP_Stem: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_CoarseRoot: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_BranchTurnover: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_Total: Total woody net primary producivity (Field type: numeric) CanopyNPP_Leaf: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Twig: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Reproductive: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Miscellaneous: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Herbivory: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Total: Total canopy net primary producivty (Field type: numeric) FineRootNPP: Fine root productivity (Field type: numeric) TotalNPP_WithoutMycorrhiza: Total net primary productivity without mycorrhiza (Field type: numeric) TotalNPP_WithMycorrhiza: Total net primary productivity including mycorrhiza (Field type: numeric) GPP_WithoutMycorrhiza: Gross primary productivity without mycorrhiza (Field type: numeric) GPP_WithMycorrhiza: Gross primary productivity including mycorrhiza (Field type: numeric) R_Stem: Respiration from woody stems (Field type: numeric) R_Leaf: Leaf Respiration (Field type: numeric) R_FineRoots: Respiration from fine roots (Field type: numeric) R_CoarseRoots: Respiration from coarse roots (Field type: numeric) R_SOM: Respiration from soil organic matter (Field type: numeric) R_Mycorrhiza: Respiration from mycorrhiza (Field type: numeric) R_Litter: Respiration from litter layer (Field type: numeric) R_Deadwood: Deadwood respiration (Field type: numeric) R_auto: Total autotrophic respiration (Field type: numeric) R_het: Total heterotrophic respiration (Field type: numeric) R_eco: Total ecosystem respiration (Field type: numeric) NEP_WithoutMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) NEP_WithMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) AbovegroundBiomassCarbonStock: Plot above-ground biomass carbon stock (Field type: numeric) CoarseRootBiomassCarbonStock: Biomass carbon stock of coarse roots (Field type: numeric) SE_WoodyNPP_Stem: Standard error of woody stem productivity (Field type: numeric) SE_WoodyNPP_CoarseRoot: Standard error of coarse root productivity (Field type: numeric) SE_WoodyNPP_BranchTurnover: Standard error of branch turnover productivity (Field type: numeric) SE_WoodyNPP_Total: Standard error of total woody net primary producivity (Field type: numeric) SE_CanopyNPP_Leaf: Standard error of leaf productivity (Field type: numeric) SE_CanopyNPP_Twig: Standard error of twig productivity (Field type: numeric) SE_CanopyNPP_Reproductive: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric) SE_CanopyNPP_Miscellaneous: Standard error of unidentified canopy debris (Field type: numeric) SE_CanopyNPP_Herbivory: Standard error of leaf productivity lost to herbivory (Field type: numeric) SE_CanopyNPP_Total: Standard error of total canopy net primary producivty (Field type: numeric) SE_FineRootNPP: Standard error of fine root productivity (Field type: numeric) SE_TotalNPP_WithoutMycorrhiza: Standard error of total net primary productivity without mycorrhiza (Field type: numeric) SE_TotalNPP_WithMycorrhiza: Standard error of total net primary productivity including mycorrhiza (Field type: numeric) SE_GPP_WithoutMycorrhiza: Standard error of gross primary productivity without mycorrhiza (Field type: numeric) SE_GPP_WithMycorrhiza: Standard error of gross primary productivity including mycorrhiza (Field type: numeric) SE_R_Stem: Standard error of respiration from woody stems (Field type: numeric) SE_R_Leaf: Standard error of leaf Respiration (Field type: numeric) SE_R_FineRoots: Standard error of respiration from fine roots (Field type: numeric) SE_R_CoarseRoots: Standard error of respiration from coarse roots (Field type: numeric) SE_R_SOM: Standard error of respiration from soil organic matter (Field type: numeric) SE_R_Mycorrhiza: Standard error of respiration from mycorrhiza (Field type: numeric) SE_R_Litter: Standard error of litter layer respiration (Field type: numeric) SE_R_Deadwood: Standard error of deadwood respiration (Field type: numeric) SE_R_auto: Standard error of total autotrophic respiration (Field type: numeric) SE_R_het: Standard error of total heterotrophic respiration (Field type: numeric) SE_R_eco: Standard error of total ecosystem respiration (Field type: numeric) SE_NEP_WithoutMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_NEP_WithMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_AbovegroundBiomassCarbonStock: Standard error of plot above-ground biomass carbon stock (Field type: numeric) SE_CoarseRootBiomassCarbonStock: Standard error of biomass carbon stock of coarse roots (Field type: numeric) Date range: 2011-08-25 to 2018-07-17 Latitudinal extent: 4.1830 to 5.0700 Longitudinal extent: 114.0190 to 117.8200", "keywords": ["2. Zero hunger", "Soil carbon cycle", "Soil organic matter", "Flux", "Respiration", "15. Life on land", "Carbon balance", "Autotrophic respiration", "6. Clean water", "SAFE core data", "13. Climate action", "SAFE project", "Heterotropchic respiration", "Litter", "Carbon plot", "Carbon flux", "Productivity"], "contacts": [{"organization": "Riutta, Terhi, Ewers, Robert M, Malhi, Yadvinder, Majalap, Noreen, Khoon, Kho Lip, Mills, Maria,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307449"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307449", "name": "item", "description": "10.5281/zenodo.7307449", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307449"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}, {"id": "10.7910/DVN/GVNJAB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:20Z", "type": "Dataset", "created": "2019-06-24", "title": "Physical topsoil properties in Murugusi, Western Kenya", "description": "Open Access<b>General:</b> Lab determined topsoil bulk density, contents of sand, clay and organic carbon in Murugusi, W. Kenya, together with spatial coordinates of where the soil samples were taken (rounded to the closest center point of a 250 m \u00d7 250 m raster). All lab analyses were carried out at the ILRI/CIAT lab in Nairob, Kenya. <br> <b>Soil sampling:</b> At each sample location, one composite topsoil sample was taken; three cores of 7 cm in diameter taken within an area of one square meter. The soil was taken from 0-0.2 m depth below any organic (O) horizon. <br> <b>Determination of soil properties:</b> The bulk density of the soil was determined by taking two undisturbed soil samples (0-10 cm and 10-20 cm depth) of known volume (100 cm2) and weighting them after air drying. Soil fractions of clay (<0.002 mm) and sand (0.05-2 mm) were determined by the hydrometer method (Estefan et al., 2014), using 10% sodium hexametaphosphate as the dispersing agent. Soil pH was determined potentiometrically on a soil suspension of 1:2 (soil: water). Total carbon was measured after dry combustion using an elemental analyser (Elementar Vario max cube; ISO 10694, first edition 1995-03-01) <br> <b>Reference: </b>Estefan G., Sommer R., Ryan J. (2014) Analytical Methods for Soil-Plant and Water in Dry Areas. A Manual of Relevance to the West Asia and North Africa Region. 3rd Edition, International Center for Agricultural Research in the Dry Areas, Aleppo, 255 pp. Available online at: http://repo.mel.cgiar.org:8080/handle/20.500.11766/7512?show=full. Verified: October 9, 2018. <br> <b>Acknowledgements: </b> We are deeply thankful for the good services provided by John Mukulama (soil sampling), John Yumbya Mutua (soil sampling) and Francis Mungthu Njenga (lab analyses) The project was carried out within the CGIAR Research Program on Water, Land and Ecosystems (WLE).", "keywords": ["Soil organic matter", "Agricultural Sciences", "Soil organic carbon", "sand", "Kenya", "Carbon", "Latin America and the Caribbean", "soil", "Soil", "Soil bulk density", "Sand", "soil organic matter", "Earth and Environmental Sciences", "Soil texture", "Murugusi", "Africa", "Clay", "Texture", "Western Kenya", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Piikki, Kristin, S\u00f6derstr\u00f6m, Mats, Sommer, Rolf, Da Silva, Mayesse,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/GVNJAB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/GVNJAB", "name": "item", "description": "10.7910/DVN/GVNJAB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/GVNJAB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/HXAH87", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:20Z", "type": "Dataset", "title": "Arbuscular and ectomycorrhizal fungi diversity in the Indian subcontinent", "description": "Mycorrhizal fungi (MF) are below-ground organisms playing a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Arbuscular and ectomycorrhizal fungi are the two mycorrhizal types most relevant to worldwide ecosystems, but areas like the Indian sub-continent remain under-represented in global maps. The dataset presented here reports the available information regarding arbuscular and ectomycorrhizal fungi diversity in cultivated and natural ecosystems of the Indian subcontinent. We have selected studies published in English in ISI Web of Science during the years 2005 - 2020 that provided a taxonomic classification of MF and their associated abundance in terms of percentage of root colonization or number of spores per quantity of soil. From the screening of 74 studies, we have recorded: i. the scientific or common name of the plant or the generic habitat sampled for MF identification; ii the MF genus and species; iii. the location of the study with associated altitude and geographic coordinates; iv. main soil physico-chemical properties (soil pH, texture, organic Carbon, Total Nitrogen, available Phosphorus); climatic variables such as mean annual precipitation and temperature.<br><br>", "keywords": ["ecosystem management", "Asia", "Agricultural Sciences", "CGIAR Research Program on Water", " Land and Ecosystems", "Multifunctional Landscapes", "gesti\u00f3n de ecosistemas", "soil biology", "MYCORRHIZAE", "CGIAR Research Program", "Earth and Environmental Sciences", "SOIL BIOLOGY", "BIODIVERSITY", "mycorrhizae", "biolog\u00eda del suelo"], "contacts": [{"organization": "Beggi, Francesca, Dasgupta, Debarshi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HXAH87"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HXAH87", "name": "item", "description": "10.7910/DVN/HXAH87", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HXAH87"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/ZTMDUR", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:21Z", "type": "Dataset", "created": "2005-01-01", "title": "Pilot Analysis of Global Ecosystems (PAGE), Agroecosystems dataset", "description": "<br>The Pilot Analysis of Global Ecosystems (PAGE): Agroecosystems was one of four pilot studies undertaken as precursors to the Millennium Ecosystem Assessment. The study identifies linkages between crop production systems and environmental services such as food, soil resources, water, biodiversity, and carbon cycling, in the hopes that a better understanding of these linkages might lead to policies that can contribute both to improved food output and to improved ecosystem service provision. Th e PAGE Agroecosystems report includes a series of 24 maps that provide a detailed spatial perspective on agroecosystems a nd agroecosystem services. Pilot Analysis of Global Ecosystems (PAGE): Agroecosystems Dataset offers the 9 geospatial datasets used to build these maps. </br> <br>The datasets are:</br> <br>PAGE Global Agricultural Extent. The data describe the location and extent of global agriculture and are derived from GLLCCD 1998; USGS EDC1999a.</br> <br>PAGE Global Agricultural Extent version 2. The data are an update of the original PAGE Global Agricultural Extent, based on version 2 of the Global Land Cover Characteristics Dataset (GLCCD v2.0, USGS/EDC 2000). The methods used to create this dataset were the same as those employed to create the origina l PAGE Global Agricultural Extent.</br> <br>Mask of the Global Extent of Agriculture. This dataset displays the global extent of agricultural areas as defined by the PAGE study. The other datasets made available on this site (eg. tree cover, soil carbon, area free of soil constraints) only show values for areas within this agricultural extent.</br> <br>PAGE Global Agroecosystems. These data characterize agroecosystems, defined as 'a biological and natural resource system managed by humans for the primary purpose of producing food as well as other socially valuable nonfood products and environmental services.' </br> <br>Percentage Tree Cover within the Extent of Agriculture. This is a raster dataset that shows the proportion of land area within the PAGE agricultural extent that is occupied by 'woody vegetation' (mature vegetation whose approximate height is greater than 5 meters).</br> <br>Carbon Storage in Soils within the PAGE Agricultural Extent. The data give a global estimate of soil organic carbon storage in agricultural lands, calculated by applying Batjes' (1996 and 2000) soil organic carbon content values by soil type area share of each 5 x 5 minute of the Digital Soil Map of the World (FAO 1995). </br> <br>Agriculture Share of Watershed. This dataset depicts agricultural area as a share of total watershed area. The share of each watershed that is agricultural was calculated by applying a weighted percentage to each PAGE agricultural land cover class.</br> <br>Area Free of Soil Constraints. The data show the proportional area within the PAGE agricultural extent that is free from soil constraints. The area free of soil constraints is based on fertility capability classification (FCC) app lied to FAO's Digital Soil Map of the World (1995).</br> <br>Outline of Land and Water Area. These data are used to provide a boundary for land areas and facilitate the readability of maps.</br>", "keywords": ["Pilot Analysis of Global Ecosystems (PAGE)", "Agroecosystems"], "contacts": [{"organization": "Wood, Stanley, Sebastian, Kate, Scherr, Sara,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/ZTMDUR"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/ZTMDUR", "name": "item", "description": "10.7910/DVN/ZTMDUR", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/ZTMDUR"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2005-01-01T00:00:00Z"}}, {"id": "11369/372709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:48Z", "type": "Journal Article", "created": "2018-09-07", "title": "Soil resources and element stocks in drylands to face global issues", "description": "Abstract

Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth\uffe2\uff80\uff99s land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in the face of future challenges including increased population, food security, desertification, and climate change. Aridity limits plant production and results in poorly developed soils, with coarse texture, low C:N and C:P, scarce organic matter, and high vulnerability to erosion. Dryland soils store 646 Pg of organic C to 2\uffe2\uff80\uff89m, the equivalent of 32% of the global soil organic C pool. The magnitude of the historic loss of C from dryland soils due to human land use and cover change and their typically low C:N and C:P suggest high potential to build up soil organic matter, but coarse soil textures may limit protection and stabilization processes. Restoring, preserving, and increasing soil organic matter in drylands may help slow down rising levels of atmospheric carbon dioxide by sequestering C, and is strongly needed to enhance food security and reduce the risk of land degradation and desertification. The ecotoxicological effects of microplastics in soil ecosystems are complex, particularly in areas of intensive agriculture and livestock production, where plant protection products and veterinary drugs commonly coexist with plastic residues. In this study, we investigated the impact, under laboratory conditions, of 3 MP types (non-biodegradable low-density polyethylene (LDPE) and biodegradable polybutylene adipate terephthalate-based (PBAT-based) and a starch-based polymer) on the soil-dwelling species Folsomia candida (Willem, 1902) in soils contaminated with the anthelmintic albendazole and the fungicide pyraclostrobin. These organic pollutants (OPs) are frequently found in areas of intensive agriculture and livestock production. F. candida individuals were exposed for 28\uffc2\uffa0days to soils contaminated by the OPs at 0.0001 w/w% (1\uffc2\uffa0mg/kg), with and without MPs at 0.01 and 0.1 w/w% concentrations (100 and 1000\uffc2\uffa0mg/kg respectively), under laboratory conditions (21\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 C\uffc2\uffb0, 80%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 RH). Adults\uffe2\uff80\uff99 survival, egg production, and juveniles\uffe2\uff80\uff99 occurrence were recorded as endpoints. Our findings indicate that microplastics alone did not significantly affect the survival and reproductive outcomes of F. candida . However, in soils contaminated with albendazole and pyraclostrobin, the presence of biodegradable MPs resulted in significant effects compared to the control and the treatment with only microplastics. Specifically, PBAT-based MPs significantly impacted adult survival, juvenile occurrence, and egg counts, while starch-based MPs primarily affected egg counts. On the contrary, co-exposure to OPs and LDPE MPs did not show significant effects. These results suggest that different MPs influence the bioavailability and toxicity of co-occurring fungicides and veterinary drug in soil ecosystems in different ways, with implications for assessing the ecological risks of biodegradable and non-biodegradable plastics in contaminated soils. The potential of MPs to influence the spatial distribution and bioavailability of organic pollutants for soil mesofauna needs further investigation. Plastics used in agriculture, commonly known as agriplastics (AP), offer numerous advantages in terrestrial agriculture, forestry, fisheries and aquaculture, but the diffusion of AP-intensive practices has led to extensive pollution. This review aims to synthesise scientific and policy discussions surrounding AP, examining evidence of their benefits and detrimental environmental and agricultural impacts. Following the proposal of a preliminary general taxonomy of AP, this paper presents the findings from a survey conducted among international experts from the plastic industry, farmer organisations, NGOs and environmental research institutes. This analysis highlights knowledge gaps, demands and perspectives for the sustainable future use of AP. Stakeholder positions vary on the options of \uffe2\uff80\uff98rejection\uffe2\uff80\uff99 or \uffe2\uff80\uff98reduction\uffe2\uff80\uff99 of AP, as well as the role of alternative materials such as (bio)degradable and compostable plastics. However, there is consensus on critical issues such as redesign, labelling, traceability, environmental safety standards, deployment and retrieval standards, as well as innovative waste management approaches. All stakeholders express concern for the environment. A \uffe2\uff80\uff98best practice\uffe2\uff80\uff99-based circular model was elaborated capturing these perspectives. In the context of global food systems increasingly reliant on AP, scientists emphasise the need to simultaneously preserve nature-based and traditional knowledge-based sustainable agricultural practices to enhance food system resilience.Soil moisture is among the most important factors regulating soil biodiversity and functioning. Models forecast changes in the precipitation regime in many areas of the planet, but how these changes will influence soil functioning, and how biotic drivers modulate such effects, is far from being understood. We evaluated the responses of C and N fluxes, and soil microbial properties to different soil water regimes in soils from the main three ecotypes of the world's largest and most diverse tropical savanna. Further, we explored the direct and indirect effects of changes in the ecotype and soil water regimes on these key soil processes. Soils from the woodland savanna showed a better nutritional status than the other ecotypes, as well as higher potential N cycling rates, N2O emissions, and soil bacterial abundance but lower bacterial richness, whereas potential CO2 emissions and CH4 uptake peaked in the intermediate savanna. The ecotype also modulated the effects of changes in the soil water regime on nutrient cycling, greenhouse gas fluxes, and soil bacterial properties, with more intense responses in the intermediate savanna. Further, we highlight the existence of multiple contrasting direct and indirect (via soil microbes and abiotic properties) effects of an intensification of the precipitation regime on soil C- and N-related processes. Our results confirm that ecotype is a fundamental driver of soil properties and functioning in the Cerrado and that it can determine the responses of key soil processes to changes in the soil water regime.Catchment scale hydrological process studies in southern Chile are of special interest as little research at this scale has been carried out in this region. In particular, the young volcanic ash soils, which are typical for this area, are not well understood in their hydrological behaviour. In addition, extensive land use changes require detailed knowledge of hydrological processes in disturbed as well as undisturbed catchments in order to estimate resulting risks of erosion, eutrophication, floods and droughts. This study focuses on data collection and experimental determination of relevant processes in an undisturbed forested catchment in the Andes of southern Chile. The here gained understanding of runoff generation can serve as a reference for comparison with sites subject to human intervention, improving estimation of the effects of land use change. Owing to the lack of long\uffe2\uff80\uff90term data for this catchment it was necessary to replace long time series by a multitude of experimental methods covering as many aspects of the runoff generation process as possible. The methods used in this investigation include: measurements of streamflow, rainfall, throughfall, water chemistry, soil water dynamics, groundwater dynamics, soil physics, soil mineralogy, geo\uffe2\uff80\uff90electrical sounding, and tracer techniques. Methods and equipment used during field campaigns are described and evaluated for usefulness versus expenditure (labour and financial costs). Selected results and the hypotheses developed from these findings are presented. The results suggest the importance of fast processes for rainfall runoff response on the one hand as well as considerable dampening effects of a large subsurface storage on the other hand. Copyright \uffc2\uffa9 2008 John Wiley & Sons, Ltd.

", "keywords": ["13. Climate action", "0208 environmental biotechnology", "0207 environmental engineering", "550 - Earth sciences", "02 engineering and technology", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1002/hyp.6971"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrological%20Processes", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/hyp.6971", "name": "item", "description": "10.1002/hyp.6971", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/hyp.6971"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-02-27T00:00:00Z"}}, {"id": "10.5281/zenodo.7948400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:34Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948400", "name": "item", "description": "10.5281/zenodo.7948400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}, {"id": "10.1007/s10021-004-0218-4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:14:26Z", "type": "Journal Article", "created": "2004-08-21", "title": "Climate Change Affects Carbon Allocation To The Soil In Shrublands", "description": "Climate change may affect ecosystem functioning through increased temperatures or changes in precipitation patterns. Temperature and water availability are important drivers for ecosystem processes such as photosynthesis, carbon translocation, and organic matter decomposition. These climate changes may affect the supply of carbon and energy to the soil microbial population and subsequently alter decomposition and mineralization, important ecosystem processes in carbon and nutrient cycling. In this study, carried out within the cross-European research project CLIMOOR, the effect of climate change, resulting from imposed manipulations, on carbon dynamics in shrubland ecosystems was examined. We performed a 14C-labeling experiment to probe changes in net carbon uptake and allocation to the roots and soil compartments as affected by a higher temperature during the year and a drought period in the growing season. Differences in climate, soil, and plant characteristics resulted in a gradient in the severity of the drought effects on net carbon uptake by plants with the impact being most severe in Spain, followed by Denmark, with the UK showing few negative effects at significance levels of p \u2264 0.10. Drought clearly reduced carbon flow from the roots to the soil compartments. The fraction of the 14C fixed by the plants and allocated into the soluble carbon fraction in the soil and to soil microbial biomass in Denmark and the UK decreased by more than 60%. The effects of warming were not significant, but, as with the drought treatment, a negative effect on carbon allocation to soil microbial biomass was found. The changes in carbon allocation to soil microbial biomass at the northern sites in this study indicate that soil microbial biomass is a sensitive, early indicator of drought- or temperature-initiated changes in these shrubland ecosystems. The reduced supply of substrate to the soil and the response of the soil microbial biomass may help to explain the observed acclimation of CO2 exchange in other ecosystems.", "keywords": ["2. Zero hunger", "0106 biological sciences", "decomposition", "photosynthesis", "temperature", "translocation", "plant", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "dioxide", "calluna-vulgaris", "13. Climate action", "lolium-perenne", "0401 agriculture", " forestry", " and fisheries", "rhizosphere", "respiration"]}, "links": [{"href": "https://doi.org/10.1007/s10021-004-0218-4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10021-004-0218-4", "name": "item", "description": "10.1007/s10021-004-0218-4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10021-004-0218-4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-08-06T00:00:00Z"}}, {"id": "10.1007/s00253-012-4173-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:14:14Z", "type": "Journal Article", "created": "2012-06-20", "title": "Pectin-Rich Biomass As Feedstock For Fuel Ethanol Production", "description": "The USA has proposed that 30\u00a0% of liquid transportation fuel be produced from renewable resources by 2030 (Perlack and Stokes 2011). It will be impossible to reach this goal using corn kernel-based ethanol alone. Pectin-rich biomass, an under-utilized waste product of the sugar and juice industry, can augment US ethanol supplies by capitalizing on this already established feedstock. Currently, pectin-rich biomass is sold (at low value) as animal feed. This review focuses on the three most studied types of pectin-rich biomass: sugar beet pulp, citrus waste and apple pomace. Fermentations of these materials have been conducted with a variety of ethanologens, including yeasts and bacteria. Escherichia coli can ferment a wide range of sugars including galacturonic acid, the primary component of pectin. However, the mixed acid metabolism of E. coli can produce unwanted side products. Saccharomyces cerevisiae cannot naturally ferment galacturonic acid nor pentose sugars but has a homoethanol pathway. Erwinia chrysanthemi is capable of degrading many of the cell wall components of pectin-rich materials, including pectin. Klebsiella oxytoca can metabolize a diverse array of sugars including cellobiose, one degradation product of cellulose. However, both E. chrysanthemi and K. oxytoca produce side products during fermentation, similar to E. coli. Using pectin-rich residues from industrial processes is beneficial because the material is already collected and partially pretreated to facilitate enzymatic deconstruction of the plant cell walls. Using biomass already produced for other purposes is an attractive practice because fewer greenhouse gases (GHG) will be anticipated from land-use changes.", "keywords": ["0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Bacteria", "Ethanol", "Fungi", "Industrial Waste", "Mini-Review", "15. Life on land", "Applied Microbiology and Biotechnology", "7. Clean energy", "12. Responsible consumption", "03 medical and health sciences", "13. Climate action", "Fermentation", "Food Industry", "Pectins", "Biomass", "Biotechnology"], "contacts": [{"organization": "Joy Doran-Peterson, Meredith C. Edwards,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1007/s00253-012-4173-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Microbiology%20and%20Biotechnology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00253-012-4173-2", "name": "item", "description": "10.1007/s00253-012-4173-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00253-012-4173-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-06-14T00:00:00Z"}}, {"id": "10.1007/s10021-020-00497-5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:14:28Z", "type": "Journal Article", "created": "2020-03-30", "title": "Biocrusts Modulate Responses of Nitrous Oxide and Methane Soil Fluxes to Simulated Climate Change in a Mediterranean Dryland", "description": "Little is known about the role of biocrusts in regulating the responses of N2O and CH4 fluxes to climate change in drylands. Here, we aim to help filling this knowledge gap by using an 8-year field experiment in central Spain where temperature and rainfall are being manipulated (~\u20091.9\u00b0C warming, 33% rainfall reduction and their combination) in areas with and without well-developed biocrust communities. Areas with initial high cover of well-developed biocrusts showed lower N2O emissions, enhanced CH4 uptake and higher abundances of functional genes linked to N2O and CH4 fluxes compared with areas with poorly developed biocrusts. Moreover, biocrusts modulated the responses of gases emissions and related functional genes to warming and rainfall reductions. Specifically, we found under rainfall exclusion and its combination with warming a sharp reduction in N2O fluxes (~\u200996% and ~\u2009197%, respectively) only under well-developed biocrust cover. Warming and its combination with rainfall exclusion reduced CH4 consumption in areas with initial low cover of well-developed biocrust, whereas rainfall exclusion enhanced CH4 uptake only in areas with high initial cover of well-developed biocrusts. Similarly, the combination of warming and rainfall exclusion increased the abundance of the nosZ gene compared to the rainfall exclusion treatment and increased the abundance of the pmoA gene compared to the control, but only in areas with low biocrust cover. Taken together, our results indicate that well-developed biocrust communities could counteract the impact of warming and altered rainfall patterns on soil N2O and CH4 fluxes, highlighting their importance and the need to preserve them to minimize climate change impacts on drylands. A. L. is supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness (BES-2014-067831). M.D-B. acknowledges support from the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Programme H2020-MSCA-IF-2016 under REA Grant Agreement No. 702057 (CLIMIFUN) and the BES Grant Agreement No. LRA17 1193 (MUSGONET). J.D acknowledges support from the Funda\u00e7\u00e3o para Ci\u00eancia e Tecnologia (IF/00950/2014) and the FEDER, within the PT2020 Partnership Agreement and COMPETE 2020 (UID/BIA/04004/2013). This research was supported by the European Research Council (ERC Grant Agreements 242658 [BIOCOM] and 647038 [BIODESERT]), by the Spanish Ministry of Economy and Competitiveness (BIOMOD project, ref. CGL2013-44661-R and AGL2015-64582-C3-3-R project) and by the Comunidad de Madrid and European Structural and Investment Funds (AGRISOST-CM S2013/ABI-2717). F.T.M. acknowledges support from Generalitat Valenciana (BIOMORES project, ref. CIDEGENT/2018/041). B.K.S research on the topic of biodiversity and ecosystem functions is funded by Australian Research Council (DP170104634).", "keywords": ["0301 basic medicine", "2. Zero hunger", "0303 health sciences", "arid regions", "Nitrous oxide", "nitrous oxide", "Mediterranean Region", "methane", "Ecolog\u00eda", "15. Life on land", "climatic changes", "Dryland", "03 medical and health sciences", "Methanotrophs", "13. Climate action", "XXXXXX - Unknown", "Biocrust", "crust vegetation", "Denitrifiers", "denitrifying bacteria", "Methane"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s10021-020-00497-5.pdf"}, {"href": "https://doi.org/10.1007/s10021-020-00497-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10021-020-00497-5", "name": "item", "description": "10.1007/s10021-020-00497-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10021-020-00497-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-30T00:00:00Z"}}, {"id": "10.1007/s11368-019-02388-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:15:00Z", "type": "Journal Article", "created": "2019-08-07", "title": "The effect of exogenous organic matter on the thermal properties of tilled soils in Poland and the Czech Republic", "description": "Organic matter improves soil fertility and water and thermal properties, but its content often decreases. This decrease may be mitigated by the addition of exogenous organic matter (EOM). The aim of this study was to assess the effect of EOMs, including compost from manure, slurry, and straw (Ag); industrial organic compost from sewage sludge (Ra); animal meal from animal by-products (Mb); and digestate from a biogas fry factory (Dg) on soil thermal conductivity, heat capacity, thermal diffusivity, water content, and bulk density in the top (0\u201315-cm) layer of two soils in Poland and the Czech Republic. Irrespective of EOM type, the total yearly nitrogen application rate being 200\u00a0kg N\u00a0ha\u22121 (100%) was from a given EOM at the rates 0, 50, 75, and 100% and the remaining parts from the mineral fertilizer. The study was conducted in 2013\u20132014 in Poland (Braszowice) and the Czech Republic (Puste Jakartice) on loam silt and clay silt loam, respectively, as part of a cross-border cooperation project. The soil properties were examined using classical descriptive statistics, semivariograms, and kriging-interpolated maps. Analysis of linear regressions (trends) showed that the EOM application rate influenced (positively or negatively) the soil properties in most measurement occasions. The variability of all soil properties was low and medium (coefficient of variation 7.3\u201334%). Geostatistical analysis indicated that the spatial dependence (C0/(C0\u2009+\u2009Cs)) of the soil properties on the EOM-amended plots was very strong or moderate. The maps revealed that the heterogeneity and degree of patch fragmentation were greater for thermal conductivity and heat capacity than for thermal diffusivity, water content, and bulk density. In general, all the soil properties were spatially more variable in the Braszowice than Puste Jakartice soil and in spring than autumn in both sites. The spatial analysis and maps enhance the comprehensive understanding of changes in soil thermal properties in response to EOM application. Suitability of the results from the field experiments in models predicting some thermal properties based on soil bulk density and water content in relation to EOM addition was indicated. Expressing the amount of EOMs added using the organic carbon content basis (% kg OC/kg of soil) instead of the nitrogen content basis allowed identifying areas on the kriging-interpolated maps where the distribution of soil thermal properties resembled that of soil organic carbon content, water content, and bulk density. Thus, the effect of EOMs on soil thermal properties is considered along with changes in soil water content and bulk density. The results will be helpful in forecasting effects of exogenous organic matter on the soil thermal properties affecting surface-energy partitioning, temperature distribution in soil, and plant growth.", "keywords": ["2. Zero hunger", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "recycled organic matter", "soil thermal properties", "13. Climate action", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "semivariograms", "Czech Republic", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11368-019-02388-2.pdf"}, {"href": "https://doi.org/10.1007/s11368-019-02388-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Soils%20and%20Sediments", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11368-019-02388-2", "name": "item", "description": "10.1007/s11368-019-02388-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11368-019-02388-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-07T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=EC&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=EC&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=EC&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=EC&offset=50", "hreflang": "en-US"}], "numberMatched": 8216, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-05-26T09:17:36.241694Z"}