{"type": "FeatureCollection", "features": [{"id": "23546339ad735a64e55426484b88fe14", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:45Z", "type": "Report", "title": "Global maps of soil temperature.", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations"], "contacts": [{"organization": "Lembrechts, J.J., van den Hoogen, J., Aalto, J., Ashcroft, M.B., De Frenne, P., Kemppinen, J., Kopeck\u00fd, M., Luoto, M., Maclean, IMD, Crowther, T.W., Bailey, J.J., Haesen, S., Klinges, D.H., Niittynen, P., Scheffers, B.R., Van Meerbeek, K., Aartsma, P., Abdalaze, O., Abedi, M., Aerts, R., Ahmadian, N., Ahrends, A., Alatalo, J.M., Alexander, J.M., Allonsius, C.N., Altman, J., Ammann, C., Andres, C., Andrews, C., Ard\u00f6, J., Arriga, N., Arzac, A., Aschero, V., Assis, R.L., Assmann, J.J., Bader, M.Y., Bahalkeh, K., Baran\u010dok, P., Barrio, I.C., Barros, A., Barthel, M., Basham, E.W., Bauters, M., Bazzichetto, M., Marchesini, L.B., Bell, M.C., Benavides, J.C., Benito Alonso, J.L., Berauer, B.J., Bjerke, J.W., Bj\u00f6rk, R.G., Bj\u00f6rkman, M.P., Bj\u00f6rnsd\u00f3ttir, K., Blonder, B., Boeckx, P., Boike, J., Bokhorst, S., Brum, BNS, Br\u016fna, J., Buchmann, N., Buysse, P., Camargo, J.L., Campoe, O.C., Candan, O., Canessa, R., Cannone, N., Carbognani, M., Carnicer, J., Casanova-Katny, A., Cesarz, S., Chojnicki, B., Choler, P., Chown, S.L., Cifuentes, E.F., \u010ciliak, M., Contador, T., Convey, P., Cooper, E.J., Cremonese, E., Curasi, S.R., Curtis, R., Cutini, M., Dahlberg, C.J., Daskalova, G.N., de Pablo, M.A., Della Chiesa, S., Dengler, J., Deronde, B., Descombes, P., Di Cecco, V., Di Musciano, M., Dick, J., Dimarco, R.D., Dolezal, J., Dorrepaal, E., Du\u0161ek, J., Eisenhauer, N., Eklundh, L., Erickson, T.E., Erschbamer, B., Eugster, W., Ewers, R.M., Exton, D.A., Fanin, N., Fazlioglu, F., Feigenwinter, I., Fenu, G., Ferlian, O., Fern\u00e1ndez Calzado, M.R., Fern\u00e1ndez-Pascual, E., Finckh, M., Higgens, R.F., Forte, TGW, Freeman, E.C., Frei, E.R., Fuentes-Lillo, E., Garc\u00eda, R.A., Garc\u00eda, M.B., G\u00e9ron, C., Gharun, M., Ghosn, D., Gigauri, K., Gobin, A., Goded, I., Goeckede, M., Gottschall, F., Goulding, K., Govaert, S., Graae, B.J., Greenwood, S., Greiser, C., Grelle, A., Gu\u00e9nard, B., Guglielmin, M., Guillemot, J., Haase, P., Haider, S., Halbritter, A.H., Hamid, M., Hammerle, A., Hampe, A., Haugum, S.V., Hederov\u00e1, L., Heinesch, B., Helfter, C., Hepenstrick, D., Herberich, M., Herbst, M., Hermanutz, L., Hik, D.S., Hoffr\u00e9n, R., Homeier, J., H\u00f6rtnagl, L., H\u00f8ye, T.T., Hrbacek, F., Hylander, K., Iwata, H., Jackowicz-Korczynski, M.A., Jactel, H., J\u00e4rveoja, J., Jastrz\u0119bowski, S., Jentsch, A., Jim\u00e9nez, J.J., J\u00f3nsd\u00f3ttir, I.S., Jucker, T., Jump, A.S., Juszczak, R., Kanka, R., Ka\u0161par, V., Kazakis, G., Kelly, J., Khuroo, A.A., Klemedtsson, L., Klisz, M., Kljun, N., Knohl, A., Kobler, J., Koll\u00e1r, J., Kotowska, M.M., Kov\u00e1cs, B., Kreyling, J., Lamprecht, A., Lang, S.I., Larson, C., Larson, K., Laska, K., le Maire, G., Leihy, R.I., Lens, L., Liljebladh, B., Lohila, A., Lorite, J., Loubet, B., Lynn, J., Macek, M., Mackenzie, R., Magliulo, E., Maier, R., Malfasi, F., M\u00e1li\u0161, F.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/23546339ad735a64e55426484b88fe14"}, {"rel": "self", "type": "application/geo+json", "title": "23546339ad735a64e55426484b88fe14", "name": "item", "description": "23546339ad735a64e55426484b88fe14", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/23546339ad735a64e55426484b88fe14"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "oai:serval.unil.ch:BIB_38E93A02220B", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:28:16Z", "type": "Report", "title": "Global maps of soil temperature.", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations"], "contacts": [{"organization": "Lembrechts, J.J., van den Hoogen, J., Aalto, J., Ashcroft, M.B., De Frenne, P., Kemppinen, J., Kopeck\u00fd, M., Luoto, M., Maclean, IMD, Crowther, T.W., Bailey, J.J., Haesen, S., Klinges, D.H., Niittynen, P., Scheffers, B.R., Van Meerbeek, K., Aartsma, P., Abdalaze, O., Abedi, M., Aerts, R., Ahmadian, N., Ahrends, A., Alatalo, J.M., Alexander, J.M., Allonsius, C.N., Altman, J., Ammann, C., Andres, C., Andrews, C., Ard\u00f6, J., Arriga, N., Arzac, A., Aschero, V., Assis, R.L., Assmann, J.J., Bader, M.Y., Bahalkeh, K., Baran\u010dok, P., Barrio, I.C., Barros, A., Barthel, M., Basham, E.W., Bauters, M., Bazzichetto, M., Marchesini, L.B., Bell, M.C., Benavides, J.C., Benito Alonso, J.L., Berauer, B.J., Bjerke, J.W., Bj\u00f6rk, R.G., Bj\u00f6rkman, M.P., Bj\u00f6rnsd\u00f3ttir, K., Blonder, B., Boeckx, P., Boike, J., Bokhorst, S., Brum, BNS, Br\u016fna, J., Buchmann, N., Buysse, P., Camargo, J.L., Campoe, O.C., Candan, O., Canessa, R., Cannone, N., Carbognani, M., Carnicer, J., Casanova-Katny, A., Cesarz, S., Chojnicki, B., Choler, P., Chown, S.L., Cifuentes, E.F., \u010ciliak, M., Contador, T., Convey, P., Cooper, E.J., Cremonese, E., Curasi, S.R., Curtis, R., Cutini, M., Dahlberg, C.J., Daskalova, G.N., de Pablo, M.A., Della Chiesa, S., Dengler, J., Deronde, B., Descombes, P., Di Cecco, V., Di Musciano, M., Dick, J., Dimarco, R.D., Dolezal, J., Dorrepaal, E., Du\u0161ek, J., Eisenhauer, N., Eklundh, L., Erickson, T.E., Erschbamer, B., Eugster, W., Ewers, R.M., Exton, D.A., Fanin, N., Fazlioglu, F., Feigenwinter, I., Fenu, G., Ferlian, O., Fern\u00e1ndez Calzado, M.R., Fern\u00e1ndez-Pascual, E., Finckh, M., Higgens, R.F., Forte, TGW, Freeman, E.C., Frei, E.R., Fuentes-Lillo, E., Garc\u00eda, R.A., Garc\u00eda, M.B., G\u00e9ron, C., Gharun, M., Ghosn, D., Gigauri, K., Gobin, A., Goded, I., Goeckede, M., Gottschall, F., Goulding, K., Govaert, S., Graae, B.J., Greenwood, S., Greiser, C., Grelle, A., Gu\u00e9nard, B., Guglielmin, M., Guillemot, J., Haase, P., Haider, S., Halbritter, A.H., Hamid, M., Hammerle, A., Hampe, A., Haugum, S.V., Hederov\u00e1, L., Heinesch, B., Helfter, C., Hepenstrick, D., Herberich, M., Herbst, M., Hermanutz, L., Hik, D.S., Hoffr\u00e9n, R., Homeier, J., H\u00f6rtnagl, L., H\u00f8ye, T.T., Hrbacek, F., Hylander, K., Iwata, H., Jackowicz-Korczynski, M.A., Jactel, H., J\u00e4rveoja, J., Jastrz\u0119bowski, S., Jentsch, A., Jim\u00e9nez, J.J., J\u00f3nsd\u00f3ttir, I.S., Jucker, T., Jump, A.S., Juszczak, R., Kanka, R., Ka\u0161par, V., Kazakis, G., Kelly, J., Khuroo, A.A., Klemedtsson, L., Klisz, M., Kljun, N., Knohl, A., Kobler, J., Koll\u00e1r, J., Kotowska, M.M., Kov\u00e1cs, B., Kreyling, J., Lamprecht, A., Lang, S.I., Larson, C., Larson, K., Laska, K., le Maire, G., Leihy, R.I., Lens, L., Liljebladh, B., Lohila, A., Lorite, J., Loubet, B., Lynn, J., Macek, M., Mackenzie, R., Magliulo, E., Maier, R., Malfasi, F., M\u00e1li\u0161, F.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:serval.unil.ch:BIB_38E93A02220B"}, {"rel": "self", "type": "application/geo+json", "title": "oai:serval.unil.ch:BIB_38E93A02220B", "name": "item", "description": "oai:serval.unil.ch:BIB_38E93A02220B", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:serval.unil.ch:BIB_38E93A02220B"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "10.1002/hyp.11203", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:14:15Z", "type": "Journal Article", "created": "2017-04-16", "title": "3D soil hydraulic database of Europe at 250\u00a0m resolution", "description": "Abstract                   <p>Soil hydraulic properties are required in various modelling schemes. We propose a consistent spatial soil hydraulic database at 7 soil depths up to 2\uffc2\uffa0m calculated for Europe based on SoilGrids250m and 1\uffc2\uffa0km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem\uffe2\uff80\uff90van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU\uffe2\uff80\uff90SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU\uffe2\uff80\uff90SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2\uffc2\uffa0m depth at 250\uffc2\uffa0m resolution.</p", "keywords": ["2. Zero hunger", "S1 Agriculture (General) / mez\u0151gazdas\u00e1g \u00e1ltal\u00e1ban", "QD Chemistry / k\u00e9mia", "Mualem-van Genuchten parameters", "0207 environmental engineering", "02 engineering and technology", "soil hydraulic conductivity", "15. Life on land", "S590 Soill / Talajtan", "01 natural sciences", "6. Clean water", "soil water retention", "multilayered gridded information", "13. Climate action", "EU-SoilHydroGrids", "3D European soil hydraulic maps", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.11203"}, {"href": "https://doi.org/10.1002/hyp.11203"}, {"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.11203", "name": "item", "description": "10.1002/hyp.11203", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/hyp.11203"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-30T00:00:00Z"}}, {"id": "10.1007/s10518-021-01083-3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:14:42Z", "type": "Journal Article", "created": "2021-04-08", "title": "Model of seismic design lateral force levels for the existing reinforced concrete European building stock", "description": "As part of the development of a European Seismic Risk Model 2020 (ESRM20), the spatial and temporal evolution of seismic design across Europe has been studied in order to bet- ter classify reinforced concrete buildings (which represent more than 30% of the approxi- mately 145 million residential, commercial and industrial buildings in Europe) and map them to vulnerability models based on simulated seismic design. This paper summarises the model that has been developed to assign the years when different seismic design levels (low code, moderate code and high code) were introduced in a number of European coun- tries and the associated lateral forces that were specified spatially within each country for the low and moderate codes for typical reinforced concrete mid-rise buildings. This process has led to an improved understanding of how design regulations evolved across Europe and how this has impacted the vulnerability of the European residential building stock. The model estimates that ~ 60% of the reinforced concrete buildings in Europe have been seis- mically designed, and of those buildings ~ 60% have been designed to low code, ~ 25% to moderate code and 15% to high code. This seismic design model aims at being a dynamic source of information that will be continuously updated with additional feedback from local experts and datasets. To this end, all of the data has been made openly available as shapefiles on a GitLab repository.", "keywords": ["Seismic design evolution ; Lateral force levels ; European building stock ; Exposure model ; Seismic zonation maps ; Seismic risk", "Physics", "ddc:530", "0211 other engineering and technologies", "Seismic design evolution", "02 engineering and technology", "624", "Exposure model", "530", "Seismic zonation maps", "Seismic risk", "11. Sustainability", "Lateral force levels", "European building stock", "info:eu-repo/classification/ddc/530"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s10518-021-01083-3.pdf"}, {"href": "https://doi.org/10.1007/s10518-021-01083-3"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20Earthquake%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10518-021-01083-3", "name": "item", "description": "10.1007/s10518-021-01083-3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10518-021-01083-3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-08T00:00:00Z"}}, {"id": "10.1007/s11368-019-02388-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:15:00Z", "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"}}, {"id": "10.1016/j.scitotenv.2018.03.277", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:16:05Z", "type": "Journal Article", "created": "2018-04-04", "title": "Spatial relationships among cereal yields and selected soil physical andchemical properties", "description": "Sandy soils occupy large area in Poland (about 50%) and in the world. This study aimed at determining spatial relationships of cereal yields and the selected soil physical and chemical properties in three study years (2001-2003) on low productive sandy Podzol soil (Podlasie, Poland). The yields and soil properties in plough and subsoil layers were determined at 72-150 points. The test crops were: wheat, wheat and barley mixture and oats. To explore the spatial relationship between cereal yields and each soil property spatial statistics was used. The best fitting models were adjusted to empirical semivariance and cross-semivariance, which were used to draw maps using kriging. Majority of the soil properties and crop yields exhibited low and medium variability (coefficient of variation 5-70%). The effective ranges of the spatial dependence (the distance at which data are autocorrelated) for yields and all soil properties were 24.3-58.5m and 10.5-373m, respectively. Nugget to sill ratios showed that crop yields and soil properties were strongly spatially dependent except bulk density. Majority of the pairs in cross-semivariograms exhibited strong spatial interdependence. The ranges of the spatial dependence varied in plough layer between 54.6m for yield\u00d7pH up to 2433m for yield\u00d7silt content. Corresponding ranges in subsoil were 24.8m for crop yield\u00d7clay content in 2003 and 1404m for yield\u00d7bulk density. Kriging maps allowed separating sub-field area with the lowest yield and soil cation exchange capacity, organic carbon content and pH. This area had lighter color on the aerial photograph due to high content of the sand and low content of soil organic carbon. The results will help farmers at identifying sub-field areas for applying localized management practices to improve these soil properties and further spatial studies in larger scale.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "soil variability", "crop yields", "04 agricultural and veterinary sciences", "15. Life on land", "low productive area", "Soil", "cross-semivariograms", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "Edible Grain", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2018.03.277"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2018.03.277", "name": "item", "description": "10.1016/j.scitotenv.2018.03.277", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2018.03.277"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-01T00:00:00Z"}}, {"id": "10.1016/j.still.2017.07.015", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:16:27Z", "type": "Journal Article", "created": "2017-08-23", "title": "Spatial variability of soil properties and cereal yield in a cultivated field on sandy soil", "description": "Abstract   Sandy soils are used in agriculture in different regions of the world. In Poland soils derived from sands occupy about 50% of agricultural area. Productivity of the soils depend on the soil properties that vary in the scale of field. This study aimed at determining and mapping the within-field variation of soil physical and chemical properties and grain yield of oats, rye, oats and triticale in 2001, 2002, 2003, 2015, respectively. The experiment was set up in a field (200\u00a0\u00d7\u00a050\u00a0m) on sandy soil in Trzebieszow (region Podlasie, Poland). The soil measurements included sand, silt, clay, and organic carbon (SOC) contents, cation exchange capacity (CEC), pH in the topsoil (0\u201310\u00a0cm) and subsoil (30\u201340\u00a0cm) layers in 2001, and water content and bulk density in the topsoil layer in spring and summer 2002\u20132003. The yields of oats were assessed in 2001 and 2003 and those of rye and triticale in 2002 and 2015, respectively. The soil properties and cereal yields were determined at 33\u201355 points in a grid evenly covering the whole field area. The results were analyzed using classic statistics and geostatistics by constructing semivariograms and 2D mapping by Inverse Distance Weighting (IDW). The cereal grain yields were significantly positively correlated with the topsoil water content (SWC) (r\u00a0=\u00a00.295\u20130.711), clay content (r\u00a0=\u00a00.081\u20130.174), and SOC in the subsoil (r\u00a0=\u00a00.208\u20130.271) and CEC in both layers (r\u00a0=\u00a00.123\u20130.298) and negatively correlated with bulk density (BD) (r\u00a0=\u00a0\u20130.065 to \u22120.279). The spatial dependence determined by the \u201cnugget-to-sill\u201d ratio was moderate or weak for the silt and clay content, CEC, and pH (29\u201379%) and strong for SOC, BD, SWC, and crop yield (0.2\u201313.2%). The effective range of the spatial dependence for all studied quantities varied from 9.9 to 120\u00a0m. The cereal yields were positively and significantly correlated between all study years (r\u00a0=\u00a00.141\u20130.734), which indicates inter-annual similarity in their spatial distribution. The 2D maps based on the IDW allowed assessing how gradual or sharp the changes in the studied quantities from one place to another are. Similar spatial patterns of the SWC, SOC and CEC, and crop yields were observed. This is of importance in precise and sustainable field management aimed at increasing and aligning spatial crop productivity of the studied low-productivity sandy soils that will have to be used in crop production due to the current shortage of land resources and food supplies on a global scale.", "keywords": ["2. Zero hunger", "2D maps", "soil properties", "sandy soil", "spatial variation", "0401 agriculture", " forestry", " and fisheries", "cereal yield", "04 agricultural and veterinary sciences", "semivariograms", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2017.07.015"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2017.07.015", "name": "item", "description": "10.1016/j.still.2017.07.015", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2017.07.015"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "10.1038/s41598-019-56266-5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:16:53Z", "type": "Journal Article", "created": "2019-12-27", "title": "Determining the effect of exogenous organic materials on spatial distribution of maize yield", "description": "Abstract<p>Knowledge on spatial distribution of crop yield in relation to fixed soil fertilisation with exogenous organic materials is essential for improving precise crop and soil management practices within a field. This study assessed the effect of various application rates and types of exogenous (recycled) organic materials (EOMs) containing different organic matter and nitrogen contents vs. mineral nitrogen on the yield of maize by means of linear regressions (trends), spatial kriging-interpolated maps, and Bland-Altman statistics. The experiments were conducted in 2013 and 2014 on two soils, i.e. loam silt in Braszowice (Poland) and clay silt loam in Pust\uffc3\uffa9 Jakartice (Czech Republic) under a cross-border cooperation project. The organic materials included 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 fries factory (Dg). The following 3 application rates of each EOM were adjusted according to the reference 100%\uffe2\uff80\uff89=\uffe2\uff80\uff89200\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921: 50 (50% N from EOM and 50% mineral N), 75 (75% N from EOM and 25% mineral N), and 100 (100% N from EOM). 100% mineral N was applied on control plots. All treatments were carried out in 4 replicates. The linear regressions between the EOM application rates and the maize yield were in general ascending in the Braszowice soil and descending in the more productive Pust\uffc3\uffa9 Jakartice soil. The spatial kriging-interpolated maps allowed separating zones of lower and higher yields with EOMs compared to the control. They were attributed in part to the different EOM application rates and soil water contents. The Bland-Altaman statistics showed that addition of 50% of N from EOMs in 2013 caused a decrease and an increase in the maize grain yield in Braszowice and Pust\uffc3\uffa9 Jakartice, respectively, whereas the inverse was true with the 75 and 100% EOM additions. In 2014, the yield of maize for silage increased with the increasing EOM application rate in Braszowice and decreased in Pust\uffc3\uffa9 Jakartice, but it was smaller on all EOM-amended plots than in the control. As shown by the limits of agreement lines, the maize yields were more even in Pust\uffc3\uffa9 Jakartice than Braszowice. These results provide helpful information for selection of the most yield-producing EOM rates depending on the site soil conditions and prevalent weather conditions.</p", "keywords": ["2. Zero hunger", "Composting", "04 agricultural and veterinary sciences", "crop yield", "15. Life on land", "Zea mays", "7. Clean energy", "01 natural sciences", "Article", "Crop Production", "6. Clean water", "12. Responsible consumption", "recycled organic matter", "Soil", "Bland-Altman statistics", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "Fertilizers", "spatially variable application", "Czech Republic", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Usowicz, Bogus\u0142aw, Lipiec, Jerzy,", "roles": ["creator"]}]}, "links": [{"href": "https://www.nature.com/articles/s41598-019-56266-5.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56266-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-56266-5", "name": "item", "description": "10.1038/s41598-019-56266-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56266-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-27T00:00:00Z"}}, {"id": "10.1038/s41598-021-86862-3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:16:54Z", "type": "Journal Article", "created": "2021-04-15", "title": "Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale", "description": "Abstract                   <p>                     Saturated hydraulic conductivity (K) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of K have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of K at the larger regional scale in south-eastern Poland and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC) and temporally variable water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0\uffe2\uff80\uff9320\uffc2\uffa0cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied region (140\uffc2\uffa0km                     2                     ) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 and 0.86 and their ranges (in %) are 45\uffe2\uff80\uff9395 and 0.002\uffe2\uff80\uff933.75, respectively. The number of individual samples varied from 216 to 228 (for K, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, K was most variable (coefficient of variation 77.3%) and significantly (                     p                     \uffe2\uff80\uff89&lt;\uffe2\uff80\uff890.05) positively correlated with total porosity (r\uffe2\uff80\uff89=\uffe2\uff80\uff890.300) and negatively correlated with soil bulk density (r\uffe2\uff80\uff89=\uffe2\uff80\uff89\uffe2\uff80\uff93\uffe2\uff80\uff890.283). The normal or close to the normal distribution was obtained by natural logarithmic and root square transformations. The mean K was 2.597\uffc2\uffa0m\uffc2\uffa0day                     \uffe2\uff88\uff921                     and ranged from 0.01 up to 11.54\uffc2\uffa0m\uffc2\uffa0day                     \uffe2\uff88\uff921                     . The spatial autocorrelation (range) of K in the single (direct) semivariograms was 0.081\uffc2\uffb0 (8.1\uffc2\uffa0km), while it favourably increased up to 0.149\uffc2\uffb0\uffe2\uff80\uff930.81\uffc2\uffb0 (14.9\uffe2\uff80\uff9381\uffc2\uffa0km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high K above 3.0\uffc2\uffa0m\uffc2\uffa0day                     \uffe2\uff88\uff921                     in the northern sandier (sand content\uffe2\uff80\uff89&gt;\uffe2\uff80\uff8974%) and less silty (silt content\uffe2\uff80\uff89&lt;\uffe2\uff80\uff8922%) part and, with lower K in the southern part of the study region. Generally, the spatial distribution of the K values in the study region depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between K and the intrinsic and relatively stable soil properties were much larger (from\uffe2\uff80\uff89~\uffe2\uff80\uff8915 to 81\uffc2\uffa0km) than between K and the temporally variable soil properties (0.3\uffe2\uff80\uff930.9\uffc2\uffa0km). This knowledge is supportive for making decisions related to land management aimed at alteration of hydraulic conductivity to improve soil water resources and crop productivity and reduce chemical leaching.                   </p", "keywords": ["2. Zero hunger", "Science", "saturated hydraulic conductivity", "Q", "R", "04 agricultural and veterinary sciences", "15. Life on land", "commune-scale variability", "Article", "6. Clean water", "kriging maps", "intrinsic and dynamic soil properties", "Medicine", "0401 agriculture", " forestry", " and fisheries", "geostatistics"], "contacts": [{"organization": "Usowicz, Boguslaw, Lipiec, Jerzy,", "roles": ["creator"]}]}, "links": [{"href": "https://www.nature.com/articles/s41598-021-86862-3.pdf"}, {"href": "https://doi.org/10.1038/s41598-021-86862-3"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-021-86862-3", "name": "item", "description": "10.1038/s41598-021-86862-3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-021-86862-3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-15T00:00:00Z"}}, {"id": "10.1080/17445647.2022.2088305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:17:18Z", "type": "Journal Article", "created": "2022-08-11", "title": "Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy)", "description": "In the Northern Apennines, significant modifications to the characteristic historical features of landscapes have occurred since the 1950s as agriculture declined in importance and villages were progressively depopulated. Today, European policies are promoting the repopulation of these regions to help preserve the cultural identity of territories and reduce demographic pressure inurban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of strategies to underpin the preservation of landscape heritage and foster local development based on the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect the more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components.", "keywords": ["local indicators for categorical data", "point pattern analysis", "G3180-9980", "Landscape archaeology", "Maps", "11. Sustainability", "landscape management", "15. Life on land", "01 natural sciences", "historic landscape characterisation", "spatial statistics", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/17445647.2022.2088305"}, {"href": "https://eprints.ncl.ac.uk/fulltext.aspx?url=284595/39618FDF-222E-4078-8426-E55819A569AD.pdf&pub_id=284595"}, {"href": "https://doi.org/10.1080/17445647.2022.2088305"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Maps", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/17445647.2022.2088305", "name": "item", "description": "10.1080/17445647.2022.2088305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/17445647.2022.2088305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1111/ejss.70132", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:17:37Z", "type": "Journal Article", "created": "2025-06-14", "title": "An Open Framework for Downscaling Soil Carbon and Clay Maps Using Sensor Data: Five Case Studies Across Diverse European Landscapes", "description": "ABSTRACT                   <p>                     Sustainable soil management is recognised as a pivotal solution for addressing current and future global challenges, but existing global and national soil property maps often lack the fine\uffe2\uff80\uff90scale resolution required for local or intra\uffe2\uff80\uff90field assessments. Here, we aimed to develop an open access framework to downscale soil property maps using remote and proximal sensor data and test it for predicting soil organic carbon (SOC) and clay across different regions of Europe. To facilitate the dissemination of this framework, we developed the R package \uffe2\uff80\uff9c                     soilscaler                     \uffe2\uff80\uff9d, which contains integrated functions for producing downscaled soil maps. This approach uses coarse resolution maps as a baseline, incorporating sensor data and soil observations to train a model explaining local variation of soil properties. We tested the framework in Denmark, Northern Ireland, Lithuania, The Netherlands, and Turkey. For comparison, we also created high\uffe2\uff80\uff90resolution maps using a conventional digital soil mapping (DSM) approach for each field independently. We found that the downscaling performance depends on the quality of the coarse\uffe2\uff80\uff90resolution soil maps, the spatial variability of soil properties within a given field, and the range of inter\uffe2\uff80\uff90field variations in each country. Although the downscaling process showed lower performance than the conventional DSM approach, the results indicate that the downscaled maps better represent local variability than existing national and global soil maps. Additionally, we found that remote sensing sensors generally better represent the spatial distribution of SOC, while proximal soil sensors better capture clay contents. Future studies should focus on gathering more sensor data and correlating it with soil properties to improve predictions based solely on sensor data.                   </p", "keywords": ["soil organic carbon", "satellite", "downscaling", "fusion data", "soil management", "high-resolution maps"]}, "links": [{"href": "https://bsssjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/ejss.70132"}, {"href": "https://doi.org/10.1111/ejss.70132"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/ejss.70132", "name": "item", "description": "10.1111/ejss.70132", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/ejss.70132"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-01T00:00:00Z"}}, {"id": "10.15482/usda.adc/1518485", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:18:24Z", "type": "Dataset", "title": "Data from: Genome sequence of the chestnut blight fungus Cryphonectria parasitica EP155: A fundamental resource for an archetypical invasive plant pathogen", "description": "The ascomycete fungus Cryphonectria parasitica is the causal agent of chestnut blight disease. This deadly fungal pathogen was introduced into North America from Asia before the turn of the 20th century, quickly spreading throughout the natural range of the American chestnut tree. In the course of a single generation, chestnut blight destroyed billions of American chestnut trees in forests across North America, driving it almost to extinction. The genome assembly for C. parasitica EP155 (v. 2.0, available at https://mycocosm.jgi.doe.gov/Crypa2/Crypa2.info.html) contains 26 main genome scaffolds totaling 43.9 Mb, and was sequenced at the U.S. Department of Energy Joint Genome Institute. The information and documents contained within this Ag Data Commons dataset provide supplementary data about the EP155 genome assembly, including scaffold summaries, genetic maps, mitochondrial DNA, P450s, secondary metabolite clusters, vegetative incompatibility genes, and transposable elements. These data are freely available for research purposes.", "keywords": ["15. Life on land", "chestnut blight", "Cryphonectria parasitica", "vegetative incompatibility", "transposons", "P450", "secondary metabolite", "mitochondria", "fungi", "Forest &amp; Plant Health", "american chestnut", "genome assembly", "transposable elements", "genetic maps", "NP303", "Cryphonectria parasitica", "fungi", "invasive species", "plant pathogens", "Asia", "Castanea dentata", "trees", "forests", "extinction", "genome assembly", "United States", "silver", "data collection", "mitochondrial DNA", "secondary metabolites", "genes", "transposons", "phylogeny", "cytochrome P-450", "enzymes", "nuclear genome", "mitochondria"]}, "links": [{"href": "https://doi.org/10.15482/usda.adc/1518485"}, {"rel": "self", "type": "application/geo+json", "title": "10.15482/usda.adc/1518485", "name": "item", "description": "10.15482/usda.adc/1518485", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15482/usda.adc/1518485"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/foods9030322", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:15Z", "type": "Journal Article", "created": "2020-03-12", "title": "Comparison of Faba Bean Protein Ingredients Produced Using Dry Fractionation and Isoelectric Precipitation: Techno-Functional, Nutritional and Environmental Performance", "description": "<p>Dry fractionated faba bean protein-rich flour (FPR) produced by milling/air classification, and faba bean protein isolate (FPI) produced by acid extraction/isoelectric precipitation were compared in terms of composition, techno-functional properties, nutritional properties and environmental impacts. FPR had a lower protein content (64.1%, dry matter (DM)) compared to FPI (90.1%, DM), due to the inherent limitations of air classification. Of the two ingredients, FPR demonstrated superior functionality, including higher protein solubility (85%), compared to FPI (32%) at pH 7. Foaming capacity was higher for FPR, although foam stability was similar for both ingredients. FPR had greater gelling ability compared to FPI. The higher carbohydrate content of FPR may have contributed to this difference. An amino acid (AA) analysis revealed that both ingredients were low in sulfur-containing AAs, with FPR having a slightly higher level than FPI. The potential nutritional benefits of the aqueous process compared to the dry process used in this study were apparent in the higher in vitro protein digestibility (IVPD) and lower trypsin inhibitor activity (TIA) in FPI compared to FPR. Additionally, vicine/convicine were detected in FPR, but not in FPI. Furthermore, much lower levels of fermentable oligo-, di- and monosaccharides, and polyols (FODMAPs) were found in FPI compared to FPR. The life cycle assessment (LCA) revealed a lower environmental impact for FPR, partly due to the extra water and energy required for aqueous processing. However, in a comparison with cow\uffe2\uff80\uff99s milk protein, both FPR and FPI were shown to have considerably lower environmental impacts.</p>", "keywords": ["Isoelectric precipitation", "carbon footprint", "fodmaps", "TP1-1185", "faba bean", "Article", "Life cycle assessment", "0404 agricultural biotechnology", "life cycle assessment", "Faba bean", "isoelectric precipitation", "FODMAPs", "Nutrition", "Functional properties", "2. Zero hunger", "functional properties", "Protein", "Chemical technology", "dry fractionation", "04 agricultural and veterinary sciences", "Carbon footprint", "nutrition", "13. Climate action", "Dry fractionation", "antinutrients; carbon footprint; dry fractionation; Faba bean; FODMAPs; functional properties; isoelectric precipitation; life cycle assessment; nutrition; protein", "protein", "antinutrients", "0405 other agricultural sciences", "Antinutrients"]}, "links": [{"href": "http://www.mdpi.com/2304-8158/9/3/322/pdf"}, {"href": "https://iris.uniroma1.it/bitstream/11573/1661889/1/VogelsangO%e2%80%99Dwyer_Comparison-of-Faba_2022.pdf"}, {"href": "https://www.mdpi.com/2304-8158/9/3/322/pdf"}, {"href": "https://doi.org/10.3390/foods9030322"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Foods", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/foods9030322", "name": "item", "description": "10.3390/foods9030322", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/foods9030322"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-11T00:00:00Z"}}, {"id": "7d17628cc22c5a84c405669037b92bc8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:24:04Z", "type": "Report", "title": "Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images", "description": "Open AccessA major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.", "keywords": ["Appariement de graphes", "Traitement d\u2019images", "Extracellular matrix", "Statistical parametric maps", "Cartes statistiques des parametres", "Image processing", "Matrice extracellulaire", "Machine learning", "Fibronectine", "Apprentissage machine", "Fibronectin", "Graph-matching", "Cancer", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"], "contacts": [{"organization": "Grapa, Anca-Ioana", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "self", "type": "application/geo+json", "title": "7d17628cc22c5a84c405669037b92bc8", "name": "item", "description": "7d17628cc22c5a84c405669037b92bc8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/foods9020230", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:15Z", "type": "Journal Article", "created": "2020-02-25", "title": "Techno-Functional, Nutritional and Environmental Performance of Protein Isolates from Blue Lupin and White Lupin", "description": "<p>Similarly prepared protein isolates from blue lupin (Lupinus angustifolius) and white lupin (L. albus) were assessed in relation to their composition, functional properties, nutritional attributes and environmental impacts. Blue lupin protein isolate (BLPI) and white lupin protein isolate (WLPI) were found to be quite similar in composition, although differences in the electrophoretic protein profiles were apparent. Both lupin protein isolates (LPIs) had good protein solubility (76.9% for BLPI and 69.8% for WLPI at pH 7) and foaming properties. However, a remarkable difference in heat gelation performance was observed between BLPI and WLPI. WLPI had a minimum gelling concentration of 7% protein, whereas BLPI required 23% protein in order to form a gel. WLPI also resulted in stronger gels over a range of concentrations compared to BLPI. Nutritional properties of both LPIs were similar, with no significant differences in in vitro protein digestibility (IVPD), and both had very low trypsin inhibitor activity (TIA) and fermentable oligo-, di- and monosaccharides, and polyols (FODMAP) content. The amino acid profiles of both LPIs were also similar, with sulfur-containing amino acids (SAAs) being the limiting amino acid in each case. Environmental impacts revealed by the life cycle assessment (LCA) were almost identical for BLPI and WLPI, and in most categories the LPIs demonstrated considerably better performance per kg protein when compared to cow\uffe2\uff80\uff99s whole milk powder.</p>", "keywords": ["carbon footprint", "fodmaps", "TP1-1185", "Article", "Life cycle assessment", "0404 agricultural biotechnology", "life cycle assessment", "lupin", "11. Sustainability", "Functionality", "functionality", "FODMAPs", "Nutrition", "Protein", "Chemical technology", "04 agricultural and veterinary sciences", "sustainability", "Carbon footprint", "3. Good health", "nutrition", "Sustainability", "digestibility", "13. Climate action", "Digestibility", "Lupin", "protein"]}, "links": [{"href": "http://www.mdpi.com/2304-8158/9/2/230/pdf"}, {"href": "https://www.mdpi.com/2304-8158/9/2/230/pdf"}, {"href": "https://doi.org/10.3390/foods9020230"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Foods", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/foods9020230", "name": "item", "description": "10.3390/foods9020230", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/foods9020230"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-21T00:00:00Z"}}, {"id": "10.34725/DVN/66BFOB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:26Z", "type": "Dataset", "title": "Wet chemistry data for a subset of AfSIS: Phase I archived soil samples", "description": "This dataset contains a subset of the samples collected during the AfSIS Phase I project and was a collaborative effort between World Agroforestry (ICRAF) and Rothamsted Research. The soil samples were retrieved from ICRAF Soil Archive: https://worldagroforestry.org/output/icraf-soil-archive-physical-archive-systematically-collected-soil-samples and subject to wet chemical analysis at Rothamsted Research in the UK under a Global Challenges Research Fund project, 'BBS/OS/GC/000014B: Chemical and Biological Assessment of AfSIS soils' funded through the UK Biotechnology and Biological Sciences Research Council. This dataset includes the Site, Cluster, Plot as well as the GPS coordinates and wet chemistry data from 2002 samples collected from 18 countries and 51 LDSF sites. The original data collection was part of the AfSIS Phase I project, funded by the Bill and Melinda Gates Foundation (BMGF) and took place between 2009-2013. ICRAF and CIAT contributed the Site, Cluster, Plot and GPS coordinates for the soil samples, ICRAF organized the sub-sampling of the soil samples from the ICRAF physical archive in Nairobi and Rothamsted analysed the soil samples in the UK in 2017 and 2018. Visit our websites here: https://worldagroforestry.org/landhealth and https://www.rothamsted.ac.uk/. The AfSIS Phase I project funded by the Bill and Melinda Gates Foundation (BMGF) from 2009-2013, aimed to provide a consistent baseline of soil information across sub-Saharan Africa (SSA). Led by CIAT-TSBF, partners included: ISRIC, CIESIN, The Earth Institute at Columbia University and World Agroforestry (ICRAF). ICRAF led the systematic assessments of soil health using the Land Degradation Surveillance Framework (LDSF), which was developed at ICRAF, http://landscapeportal.org/blog/2015/03/25/the-land-degradation-surveillance-framework-ldsf/. LDSF sites were randomized using spatial stratification based on Koeppen-Geiger Climate zones across 19 countries in SSA. In total 60 LDSF sites were sampled. Soil samples were collected using the LDSF at two depths, 0-20 cm (labelled Topsoil) and 20-50 cm (labelled Subsoil). In each LDSF site, approximately 320 standard soil samples were collected. All of these were also scanned using MIR Spectroscopy and are available on Dataverse here: V\u00e5gen, Tor-Gunnar;Winowiecki, Leigh Ann;Desta, Luseged;Tondoh, Ebagnerin J\u00e9r\u00f4me;Weullow, Elvis;Shepherd, Keith;Sila, Andrew, 2020, 'Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013', https://doi.org/10.34725/DVN/QXCWP1, World Agroforestry - Research Data Repository, V1.", "keywords": ["soil fertility", "soil quality", "soil maps"]}, "links": [{"href": "https://doi.org/10.34725/DVN/66BFOB"}, {"rel": "self", "type": "application/geo+json", "title": "10.34725/DVN/66BFOB", "name": "item", "description": "10.34725/DVN/66BFOB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.34725/DVN/66BFOB"}, {"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": "10.5281/zenodo.10928001", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:07Z", "type": "Dataset", "title": "Raw data for the submitted manuscript entitled \"Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste\", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini", "description": "The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.", "keywords": ["Land use", "agricultural plastic waste", "Plastic detection", "GIS", "Waste management", "Waste maps"], "contacts": [{"organization": "Schettini, Evelia, Vox, Giuliano, Convertino, Fabiana, Blanco, Ileana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10928001"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10928001", "name": "item", "description": "10.5281/zenodo.10928001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10928001"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10928002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:07Z", "type": "Dataset", "title": "Raw data for the submitted manuscript entitled \"Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste\", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini", "description": "The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.", "keywords": ["Land use", "agricultural plastic waste", "Plastic detection", "GIS", "Waste management", "Waste maps"], "contacts": [{"organization": "Schettini, Evelia, Vox, Giuliano, Convertino, Fabiana, Blanco, Ileana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10928002"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10928002", "name": "item", "description": "10.5281/zenodo.10928002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10928002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-04T00:00:00Z"}}, {"id": "10.5281/zenodo.8147623", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:20Z", "type": "Dataset", "title": "EJPSOIL CarboSeq agrometeorological datasets", "description": "Open AccessAbstract  The gridded dataset includes the monthly time series of\u00a0the precipitation, temperature and reference evapotranspiration variables derived from AgERA5 daily and AgERA5_ET0 monthly data, with a spatial resolution of 10 kilometers, covering the area interested by the project, for the period\u00a01979-2022.  Data is provided as .tif files with their corresponding .rts files (SpatRasterTS object in R).  Attached content  The following ZIP archives containing the spatial raster time series are provided:    ag5_2m_temperature_rts_monthly_19792022_EPSG3035.zip  ag5_precipitation_flux_rts_monthly_19792022_EPSG3035.zip  ag5_et0_rts_monthly_19792022_EPSG3035.zip   In addition a document with a short description of data processing is provided.", "keywords": ["http://vocab.nerc.ac.uk/standard_name/precipitation_amount/", "http://vocab.nerc.ac.uk/standard_name/air_temperature/", "evapotranspiration", "15. Life on land", "https://www.eea.europa.eu/data-and-maps/indicators/water-retention-3/allen-et-al-1998", "climate", "AgERA5", "agriculture"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8147623"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8147623", "name": "item", "description": "10.5281/zenodo.8147623", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8147623"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-15T00:00:00Z"}}, {"id": "10.5281/zenodo.14161808", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:22Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).  \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0  The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161808"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161808", "name": "item", "description": "10.5281/zenodo.14161808", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161808"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14161809", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:22Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).  \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0  The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161809"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161809", "name": "item", "description": "10.5281/zenodo.14161809", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161809"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "2996829913", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:56Z", "type": "Journal Article", "created": "2019-12-27", "title": "Determining the effect of exogenous organic materials on spatial distribution of maize yield", "description": "Abstract<p>Knowledge on spatial distribution of crop yield in relation to fixed soil fertilisation with exogenous organic materials is essential for improving precise crop and soil management practices within a field. This study assessed the effect of various application rates and types of exogenous (recycled) organic materials (EOMs) containing different organic matter and nitrogen contents vs. mineral nitrogen on the yield of maize by means of linear regressions (trends), spatial kriging-interpolated maps, and Bland-Altman statistics. The experiments were conducted in 2013 and 2014 on two soils, i.e. loam silt in Braszowice (Poland) and clay silt loam in Pust\uffc3\uffa9 Jakartice (Czech Republic) under a cross-border cooperation project. The organic materials included 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 fries factory (Dg). The following 3 application rates of each EOM were adjusted according to the reference 100%\uffe2\uff80\uff89=\uffe2\uff80\uff89200\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921: 50 (50% N from EOM and 50% mineral N), 75 (75% N from EOM and 25% mineral N), and 100 (100% N from EOM). 100% mineral N was applied on control plots. All treatments were carried out in 4 replicates. The linear regressions between the EOM application rates and the maize yield were in general ascending in the Braszowice soil and descending in the more productive Pust\uffc3\uffa9 Jakartice soil. The spatial kriging-interpolated maps allowed separating zones of lower and higher yields with EOMs compared to the control. They were attributed in part to the different EOM application rates and soil water contents. The Bland-Altaman statistics showed that addition of 50% of N from EOMs in 2013 caused a decrease and an increase in the maize grain yield in Braszowice and Pust\uffc3\uffa9 Jakartice, respectively, whereas the inverse was true with the 75 and 100% EOM additions. In 2014, the yield of maize for silage increased with the increasing EOM application rate in Braszowice and decreased in Pust\uffc3\uffa9 Jakartice, but it was smaller on all EOM-amended plots than in the control. As shown by the limits of agreement lines, the maize yields were more even in Pust\uffc3\uffa9 Jakartice than Braszowice. These results provide helpful information for selection of the most yield-producing EOM rates depending on the site soil conditions and prevalent weather conditions.</p", "keywords": ["2. Zero hunger", "Composting", "04 agricultural and veterinary sciences", "crop yield", "15. Life on land", "Zea mays", "7. Clean energy", "01 natural sciences", "Article", "Crop Production", "6. Clean water", "12. Responsible consumption", "recycled organic matter", "Soil", "Bland-Altman statistics", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "Fertilizers", "spatially variable application", "Czech Republic", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Usowicz, Bogus\u0142aw, Lipiec, Jerzy,", "roles": ["creator"]}]}, "links": [{"href": "https://www.nature.com/articles/s41598-019-56266-5.pdf"}, {"href": "https://doi.org/2996829913"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2996829913", "name": "item", "description": "2996829913", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2996829913"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-27T00:00:00Z"}}, {"id": "10.5281/zenodo.14717728", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:20:30Z", "type": "Report", "title": "Submitted Manuscript - An open framework for downscaling soil maps using proximal and remote sensing data", "description": "This deliverable presents the main outputs of the SensRes project. Specifically, we developed an open-access framework (R package format) to downscale soil maps using remote and proximal sensor data and tested it for predicting soil organic carbon (SOC) and clay across different regions of Europe.", "keywords": ["soil organic carbon", "EJP SOIL", "downscaling", "soil maps"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, van Egmond, Fenny, Kavaliauskas, Ardas, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14717728"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14717728", "name": "item", "description": "10.5281/zenodo.14717728", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14717728"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-22T00:00:00Z"}}, {"id": "10.5281/zenodo.6201565", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:06Z", "type": "Dataset", "title": "Predicted maps", "description": "This dataset is the final product of research through the projects ANTARES (grant agreement No. 739570) and CYBELE (grant agreement No. 825355). The dataset consists of yield, protein content and selective harvesting soya maps at a resolution of 10 m. These maps were created by satellite images and soil properties data using machine learning algorithms. Maps are located in the Upper Austria region. Files with the name of 'map yield' contain information about yield amount per pixel, while files with 'map protein' denote parcels with predicted protein content. Also, the same protein map files contain an additional class column. Class 1 indicates pixels where soya have good quality (protein content &gt; 41), while class 2 represents poorer quality.", "keywords": ["2. Zero hunger", "Predicted yield", " protein and selective harvesting maps", "15. Life on land"], "contacts": [{"organization": "Pejak, Branislav", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6201565"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6201565", "name": "item", "description": "10.5281/zenodo.6201565", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6201565"}, {"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.6204822", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:06Z", "type": "Dataset", "title": "Predicted maps", "description": "This dataset is the final product of research through the projects ANTARES (grant agreement No. 739570) and CYBELE (grant agreement No. 825355). The dataset consists of yield, protein content and selective harvesting soya maps at a resolution of 10 m. These maps were created by satellite images and soil properties data using machine learning algorithms. Maps are located in the Upper Austria region. Files with the name of 'map yield' contain information about yield amount per pixel, while files with 'map protein' denote parcels with predicted protein content. Also, the same protein map files contain an additional class column. Class 1 indicates pixels where soya have good quality (protein content &gt; 41), while class 2 represents poorer quality.", "keywords": ["2. Zero hunger", "Predicted yield", " protein and selective harvesting maps", "15. Life on land"], "contacts": [{"organization": "Pejak, Branislav", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6204822"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6204822", "name": "item", "description": "10.5281/zenodo.6204822", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6204822"}, {"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.8399180", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:22Z", "type": "Dataset", "title": "EJPSOIL CarboSeq agrometeorological datasets", "description": "Open AccessAbstract  The gridded dataset includes the monthly time series of\u00a0the precipitation, temperature and reference evapotranspiration variables derived from AgERA5 daily and AgERA5_ET0 monthly data, with a spatial resolution of 10 kilometers, covering the area interested by the project, for the period\u00a01979-2022.  Data is provided as .tif files with their corresponding .rts files (SpatRasterTS object in R).  Attached content  The following ZIP archives containing the spatial raster time series are provided:    ag5_2m_temperature_rts_monthly_19792022_EPSG3035.zip  ag5_precipitation_flux_rts_monthly_19792022_EPSG3035.zip  ag5_et0_rts_monthly_19792022_EPSG3035.zip   In addition a document with a short description of data processing is provided.", "keywords": ["http://vocab.nerc.ac.uk/standard_name/precipitation_amount/", "http://vocab.nerc.ac.uk/standard_name/air_temperature/", "evapotranspiration", "15. 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These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0&#8211;5 and 5&#8211;15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (&#8722;0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["13. Climate action", "Bioclimatic variables", "Global maps", "Soil temperature", "Temperature offset", "Weather stations", "Geology", "Geolog\u00eda", "Microclimate", "15. 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Today, European policies are promoting the repopulation of these regions to help preserve the cultural identity of territories and reduce demographic pressure inurban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of strategies to underpin the preservation of landscape heritage and foster local development based on the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect the more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components.", "keywords": ["local indicators for categorical data", "point pattern analysis", "G3180-9980", "Landscape archaeology", "Maps", "11. Sustainability", "landscape management", "15. Life on land", "01 natural sciences", "historic landscape characterisation", "spatial statistics", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/17445647.2022.2088305"}, {"href": "https://eprints.ncl.ac.uk/fulltext.aspx?url=284595/39618FDF-222E-4078-8426-E55819A569AD.pdf&pub_id=284595"}, {"href": "https://doi.org/10.6084/m9.figshare.20477290"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Maps", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.20477290", "name": "item", "description": "10.6084/m9.figshare.20477290", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.20477290"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/FNEGDP", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:52Z", "type": "Dataset", "title": "Current and future forage suitability maps for Ethiopia and Kenya", "description": "This data was produced using Targeting Tools \u2013 a web-based GIS tool, which matches a suitability criteria that include climate and environmental requirements for each of the forage varieties with a spatial database that\u2019s comprises organic carbon, soil PH, annual precipitation, mean temperature, growing days and elevation data to characterize the suitability.", "keywords": ["Forage", "Agricultural Sciences", "Forage suitability", "Agrobiodiversity - AGBIO", "Earth and Environmental Sciences", "Maps", "Africa", "forage", "Ethiopia", "Agroecosystems and Sustainable Landscapes - ASL", "Kenya"], "contacts": [{"organization": "Mutua, John, Notenbaert, An,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/FNEGDP"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/FNEGDP", "name": "item", "description": "10.7910/DVN/FNEGDP", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/FNEGDP"}, {"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/ESK6BB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:52Z", "type": "Dataset", "title": "Current and future forage suitability maps for Rwanda and Tanzania", "description": "This data was produced using Targeting Tools \u2013 a web-based GIS tool, which matches a suitability criteria that include climate and environmental requirements for each of the forage varieties with a spatial database that\u2019s comprises organic carbon, soil PH, annual precipitation, mean temperature, growing days and elevation data to characterize the suitability.", "keywords": ["rwanda", "kenya", "Forage", "Agricultural Sciences", "Forage suitability", "Agrobiodiversity - AGBIO", "Earth and Environmental Sciences", "Maps", "Africa", "Rwanda", "forage", "Agroecosystems and Sustainable Landscapes - ASL", "Kenya"], "contacts": [{"organization": "Mutua, John, Notenbaert, An,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/ESK6BB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/ESK6BB", "name": "item", "description": "10.7910/DVN/ESK6BB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/ESK6BB"}, {"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": "11584/332967", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:17Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315&nbsp;cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean&nbsp;=&nbsp;3.0&nbsp;\u00b1&nbsp;2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\u00b1&nbsp;2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7&nbsp;\u00b1&nbsp;2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Bioclimatic variables; Global maps; Microclimate; Near-surface temperatures; Soil temperature; Soil-dwelling organisms; Temperature offset; Weather stations; Climate change; Temperature; Ecosystem; Soil"], "contacts": [{"organization": "Lembrechts J. J., van den Hoogen J., Aalto J., Ashcroft M. B., De Frenne P., Kemppinen J., Kopecky M., Luoto M., Maclean I. M. D., Crowther T. W., Bailey J. J., Haesen S., Klinges D. H., Niittynen P., Scheffers B. R., Van Meerbeek K., Aartsma P., Abdalaze O., Abedi M., Aerts R., Ahmadian N., Ahrends A., Alatalo J. M., Alexander J. M., Allonsius C. N., Altman J., Ammann C., Andres C., Andrews C., Ardo J., Arriga N., Arzac A., Aschero V., Assis R. L., Assmann J. J., Bader M. Y., Bahalkeh K., Barancok P., Barrio I. C., Barros A., Barthel M., Basham E. W., Bauters M., Bazzichetto M., Marchesini L. B., Bell M. C., Benavides J. C., Benito Alonso J. L., Berauer B. J., Bjerke J. W., Bjork R. G., Bjorkman M. P., Bjornsdottir K., Blonder B., Boeckx P., Boike J., Bokhorst S., Brum B. N. S., Bruna J., Buchmann N., Buysse P., Camargo J. L., Campoe O. C., Candan O., Canessa R., Cannone N., Carbognani M., Carnicer J., Casanova-Katny A., Cesarz S., Chojnicki B., Choler P., Chown S. L., Cifuentes E. F., Ciliak M., Contador T., Convey P., Cooper E. J., Cremonese E., Curasi S. R., Curtis R., Cutini M., Dahlberg C. J., Daskalova G. N., de Pablo M. A., Della Chiesa S., Dengler J., Deronde B., Descombes P., Di Cecco V., Di Musciano M., Dick J., Dimarco R. D., Dolezal J., Dorrepaal E., Dusek J., Eisenhauer N., Eklundh L., Erickson T. E., Erschbamer B., Eugster W., Ewers R. M., Exton D. A., Fanin N., Fazlioglu F., Feigenwinter I., Fenu G., Ferlian O., Fernandez Calzado M. R., Fernandez-Pascual E., Finckh M., Higgens R. F., Forte T. G. W., Freeman E. C., Frei E. R., Fuentes-Lillo E., Garcia R. A., Garcia M. B., Geron C., Gharun M., Ghosn D., Gigauri K., Gobin A., Goded I., Goeckede M., Gottschall F., Goulding K., Govaert S., Graae B. J., Greenwood S., Greiser C., Grelle A., Guenard B., Guglielmin M., Guillemot J., Haase P., Haider S., Halbritter A. H., Hamid M., Hammerle A., Hampe A., Haugum S. V., Hederova L., Heinesch B., Helfter C., Hepenstrick D., Herberich M., Herbst M., Hermanutz L., Hik D. S., Hoffren R., Homeier J., Hortnagl L., Hoye T. T., Hrbacek F., Hylander K., Iwata H., Jackowicz-Korczynski M. A., Jactel H., Jarveoja J., Jastrzebowski S., Jentsch A., Jimenez J. J., Jonsdottir I. S., Jucker T., Jump A. S., Juszczak R., Kanka R., Kaspar V., Kazakis G., Kelly J., Khuroo A. A., Klemedtsson L., Klisz M., Kljun N., Knohl A., Kobler J., Kollar J., Kotowska M. M., Kovacs B., Kreyling J., Lamprecht A., Lang S. I., Larson C., Larson K., Laska K., le Maire G., Leihy R. I., Lens L., Liljebladh B., Lohila A., Lorite J., Loubet B., Lynn J., Macek M., Mackenzie R., Magliulo E., Maier R., Malfasi F., Malis F.,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.unica.it/bitstream/11584/332967/1/2022_Global_maps_soil_temperature_GlobalChangeBiology.pdf"}, {"href": "https://doi.org/11584/332967"}, {"rel": "self", "type": "application/geo+json", "title": "11584/332967", "name": "item", "description": "11584/332967", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11584/332967"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10449/74200", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:06Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Bioclimatic variables", "Global maps", "Soil temperature", "Temperature offset", "Weather stations", "Microclimate", "Near-surface temperatures", "Soil-dwelling organisms"]}, "links": [{"href": "https://openpub.fmach.it/bitstream/10449/74200/1/Global%20Change%20Biology%20-%202022%20-%20Lembrechts%20-%20Global%20maps%20of%20soil%20temperature.pdf"}, {"href": "https://doi.org/10449/74200"}, {"rel": "self", "type": "application/geo+json", "title": "10449/74200", "name": "item", "description": "10449/74200", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10449/74200"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10481/73202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:07Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Atribuci\u00f3n-NoComercial 3.0 Espa\u00f1aResearch in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 p ixels ( summarized f rom 8 519 u nique t emperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Bioclimatic variables", "Global maps", "Soil temperature", "Temperature offset", "Weather stations", "Microclimate", "Near-surface temperatures", "Soil-dwelling organisms"], "contacts": [{"organization": "Lembrechts, Jonas J., Fern\u00e1ndez Calzado, Mar\u00eda Rosa, Lorite Moreno, Juan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10481/73202"}, {"rel": "self", "type": "application/geo+json", "title": "10481/73202", "name": "item", "description": "10481/73202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10481/73202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-08T00:00:00Z"}}, {"id": "1295b9994deae0387c2be67c1d753988", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:19Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["near-surface temperatures", "bioclimatic variables", "soil temperature", "temperature offset", "global maps", "soil-dwelling organisms", "weather stations", "microclimate", "Climate Science", "Klimatvetenskap"], "contacts": [{"organization": "Lembrechts, Jonas J., van den Hoogen, Johan, Dorrepaal, Ellen, Larson, Keith, Sarneel, Judith M., Walz, Josefine, Nijs, Ivan, Lenoir, Jonathan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/1295b9994deae0387c2be67c1d753988"}, {"rel": "self", "type": "application/geo+json", "title": "1295b9994deae0387c2be67c1d753988", "name": "item", "description": "1295b9994deae0387c2be67c1d753988", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1295b9994deae0387c2be67c1d753988"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "158864ac-e232-4ce5-9ddc-5f1f8481f89f", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.46, 51.51], [11.46, 54.03], [15.24, 54.03], [15.24, 51.51], [11.46, 51.51]]]}, "properties": {"rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non-scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \u201cData reused from the BonaRes Data Centre www.bonares.de.\u201d Although every care has been taken in preparing and testing the soil profile data, the author and BonaRes Data Centre cannot guarantee that the data are correct; neither does the author and BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. 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ZALF", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "katrin.lueck@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Torsten Schubert", "organization": "Komturei Lietzen GmbH", "position": null, "roles": ["projectMember"], "phones": [{"value": null}], "emails": [{"value": "torsten.schubert@komturei-lietzen.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Torsten Schubert", "organization": "Komturei Lietzen GmbH", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "torsten.schubert@komturei-lietzen.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Dirk Scheibe", "organization": "Landwirtschaftliche Beratung der Agrarverb\u00e4nde Brandenburg GmbH", "position": null, "roles": ["projectMember"], "phones": [{"value": null}], "emails": [{"value": "scheibe@lab-agrarberatung.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Dirk Scheibe", "organization": "Landwirtschaftliche Beratung der Agrarverb\u00e4nde Brandenburg GmbH", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "scheibe@lab-agrarberatung.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"organization": "Leibniz Centre for Agricultural Landscape Research - ZALF;Komturei Lietzen GmbH;Leibniz-Institute for Agricultural Engineering and Bioeconomy - ATB;University of Rostock;Eberswalde University for Sustainable Development - HNEE;Leibniz Institute of Vegetable and Ornamental Crops - IGZ;Landwirtschaftliche Beratung der Agrarverb\u00e4nde Brandenburg GmbH", "roles": ["contributor"]}], "themes": [{"concepts": [{"id": "infoMapAccessService"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "ph sensors"}, {"id": "optical sensors"}, {"id": "soil pH"}, {"id": "soil organic carbon"}, {"id": "soil organic matter"}, {"id": "nitrogen"}, {"id": "potassium"}, {"id": "magnesium"}, {"id": "phosphorus"}, {"id": "soil chemicophysical properties"}, {"id": "soil fertility"}, {"id": "soil texture"}, {"id": "soil maps"}, {"id": "fertilization"}, {"id": "limes"}, {"id": "soil management"}, {"id": "soil properties"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "ph sensors"}, {"id": "optical sensors"}, {"id": "soil pH"}, {"id": "soil organic carbon"}, {"id": "soil organic matter"}, {"id": "nitrogen"}, {"id": "potassium"}, {"id": "magnesium"}, {"id": "phosphorus"}, {"id": "soil chemicophysical properties"}, {"id": "soil fertility"}, {"id": "soil texture"}, {"id": "soil maps"}, {"id": "fertilization"}, {"id": "limes"}, {"id": "soil management"}, {"id": "soil properties"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}]}, "links": [{"href": "https://maps.bonares.de/wss/service/ags-relay/ags/guest/arcgis/rest/services/Extern/ID_4306_Brandenburg_soil_samples_Geopoint/MapServer/WMSServer?request=GetCapabilities&service=WMS"}, {"href": "https://metadata.bonares.de:443/smartEditor/preview/ph_BB_logo.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "158864ac-e232-4ce5-9ddc-5f1f8481f89f", "name": "item", "description": "158864ac-e232-4ce5-9ddc-5f1f8481f89f", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/158864ac-e232-4ce5-9ddc-5f1f8481f89f"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-12T00:00:00Z"}}, {"id": "1854/LU-8743335", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:24Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km(2) resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km(2) pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10 degrees C (mean = 3.0 +/- 2.1 degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/- 2.3 degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 +/- 2.3 degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Technology and Engineering", "soil temperature", "Biology and Life Sciences", "soil-dwelling organisms", "SNOW-COVER", "MITIGATION", "MOISTURE", "FOREST", "weather stations", "LITTER DECOMPOSITION", "PERMAFROST", "near-surface temperatures", "PLANT-RESPONSES", "bioclimatic variables", "CLIMATIC CONTROLS", "Earth and Environmental Sciences", "temperature offset", "SUITABILITY", "global maps", "MICROCLIMATE", "CBCE", "microclimate"]}, "links": [{"href": "https://doi.org/1854/LU-8743335"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8743335", "name": "item", "description": "1854/LU-8743335", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8743335"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "1893/33794", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:25Z", "type": "Journal Article", "created": "2021-12-30", "title": "Global maps of soil temperature", "description": "Abstract<p>Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2\uffc2\uffa0m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1\uffe2\uff80\uff90km2resolution for 0\uffe2\uff80\uff935 and 5\uffe2\uff80\uff9315\uffc2\uffa0cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1\uffe2\uff80\uff90km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse\uffe2\uff80\uff90grained air temperature estimates from ERA5\uffe2\uff80\uff90Land (an atmospheric reanalysis by the European Centre for Medium\uffe2\uff80\uff90Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\uffc2\uffb0C (mean\uffc2\uffa0=\uffc2\uffa03.0\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.1\uffc2\uffb0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\uffe2\uff88\uff920.7\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C). The observed substantial and biome\uffe2\uff80\uff90specific offsets emphasize that the projected impacts of climate and climate change on near\uffe2\uff80\uff90surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil\uffe2\uff80\uff90related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.</p", "keywords": ["0106 biological sciences", "Bioclimatic variables; Global maps; Microclimate; Near-surface temperatures; Soil temperature; Soil-dwelling organisms; Temperature offset; Weather stations; Climate change; Temperature; Ecosystem; Soil", "791", "550", ":Zoology and botany: 480 [VDP]", "VDP::Zoologiske og botaniske fag: 480", "551", "Q1", "7. Clean energy", "01 natural sciences", "41 Environmental sciences", "Global map", "SDG 13 - Climate Action", "Soil temperature", "MICROCLIMATE", "bepress|Physical Sciences and Mathematics|Environmental Sciences", "soil-dwelling organism", "bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations", "weather station", "GB", "http://aims.fao.org/aos/agrovoc/c_34836", "Geology", "16. Peace & justice", "Settore BIOS-01/C - Botanica ambientale e applicata", "6. Clean water", "Near-surface soil temperature", "international", "[SDE]Environmental Sciences", "551: Geologie und Hydrologie", "Near-surface temperature", "Near-surface temperatures", "soil temperature", "P40 - M\u00e9t\u00e9orologie et climatologie", "577", "bepress|Physical Sciences and Mathematics|Earth Sciences", "MITIGATION", "bepress|Life Sciences|Ecology and Evolutionary Biology", "12. Responsible consumption", "near-surface temperatures", "bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology|Climate", "bioclimatic variables", "Bioclimatic variables", "Settore BIO/07 - ECOLOGIA", "temperature offset", "global maps", "http://aims.fao.org/aos/agrovoc/c_1344", "577: \u00d6kologie", "global map", "Biology", "Ecosystem", "Ekologi", "http://aims.fao.org/aos/agrovoc/c_24894", "Science & Technology", "ddc:550", "9. Industry and infrastructure", "31 Biological sciences", "Biology and Life Sciences", "Microclimate", "06 Biological Sciences", "15. 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Education", "Temperature", "Biological Sciences", "bioclimatologie", "FOREST", "Weather station", "Chemistry", "Biodiversity Conservation", "Life Sciences & Biomedicine", "bepress|Physical Sciences and Mathematics", "Technology and Engineering", "http://aims.fao.org/aos/agrovoc/c_1669", "bioclimatic variable", "Climate Change", "soil-dwelling organisms", "Environmental Sciences & Ecology", "MOISTURE", "LITTER DECOMPOSITION", "PERMAFROST", "near-surface temperature", "temp\u00e9rature du sol", "bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology", "SUITABILITY", "G1", "VDP::Mathematics and natural scienses: 400::Zoology and botany: 480", "Global maps", "http://aims.fao.org/aos/agrovoc/c_1666", ":Zoologiske og botaniske fag: 480 [VDP]", "Soil-dwelling organisms", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "r\u00e9chauffement global", "Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations", "http://aims.fao.org/aos/agrovoc/c_9260", "P30 - Sciences et am\u00e9nagement du sol", "Aquatic Ecology", "Bioclimatic variable", "SNOW-COVER", "Climate Science", "37 Earth sciences", "Climate Action", "bepress|Physical Sciences and Mathematics|Earth Sciences|Soil Science", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "Earth sciences", "variation saisonni\u00e8re", "PLANT-RESPONSES", "CLIMATIC CONTROLS", "Soil-dwelling organism", "Settore BIOS-05/A - Ecologia", "13. Climate action", "Earth and Environmental Sciences", "VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480", "VDP::Zoology and botany: 480", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology", "CBCE", "http://aims.fao.org/aos/agrovoc/c_7197", "Environmental Sciences"]}, "links": [{"href": "https://ray.yorksj.ac.uk/id/eprint/5803/1/20211222_SoilTemp_maps_preformatted.pdf"}, {"href": "http://dspace.stir.ac.uk/bitstream/1893/33794/1/Lembrechts-etal-GCB-2022.pdf"}, {"href": "https://eprints.whiterose.ac.uk/183991/1/Global%20Change%20Biology%20-%202022%20-%20Lembrechts%20-%20Global%20maps%20of%20soil%20temperature.pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/445619/1/prod_462419-doc_189996.pdf"}, {"href": "https://openpub.fmach.it/bitstream/10449/74200/1/Global%20Change%20Biology%20-%202022%20-%20Lembrechts%20-%20Global%20maps%20of%20soil%20temperature.pdf"}, {"href": "https://iris.unica.it/bitstream/11584/332967/1/2022_Global_maps_soil_temperature_GlobalChangeBiology.pdf"}, {"href": "https://ricerca.univaq.it/bitstream/11697/178559/2/Global%20Change%20Biology%20-%202022%20-%20Lembrechts%20-%20Global%20maps%20of%20soil%20temperature.pdf"}, {"href": "https://vb.gamtc.lt/object/elaba:126634244/126634244.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16060"}, {"href": "https://escholarship.org/content/qt6hg3313z/qt6hg3313z.pdf"}, {"href": "https://doi.org/1893/33794"}, {"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": "1893/33794", "name": "item", "description": "1893/33794", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1893/33794"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-21T00:00:00Z"}}, {"id": "20.500.14243/445619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:36Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8500 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["near-surface temperatures", "bioclimatic variables", "soil temperature", "temperature offset", "global maps", "soil-dwelling organisms", "weather stations", "microclimate"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/445619/1/prod_462419-doc_189996.pdf"}, {"href": "https://doi.org/20.500.14243/445619"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14243/445619", "name": "item", "description": "20.500.14243/445619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/445619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "2606201786", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:48Z", "type": "Journal Article", "created": "2017-04-17", "title": "3D soil hydraulic database of Europe at 250\u00a0m resolution", "description": "Abstract<p>Soil hydraulic properties are required in various modelling schemes. We propose a consistent spatial soil hydraulic database at 7 soil depths up to 2\uffc2\uffa0m calculated for Europe based on SoilGrids250m and 1\uffc2\uffa0km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem\uffe2\uff80\uff90van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU\uffe2\uff80\uff90SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU\uffe2\uff80\uff90SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2\uffc2\uffa0m depth at 250\uffc2\uffa0m resolution.</p", "keywords": ["2. Zero hunger", "S1 Agriculture (General) / mez\u0151gazdas\u00e1g \u00e1ltal\u00e1ban", "QD Chemistry / k\u00e9mia", "Mualem-van Genuchten parameters", "0207 environmental engineering", "02 engineering and technology", "soil hydraulic conductivity", "15. Life on land", "S590 Soill / Talajtan", "01 natural sciences", "6. Clean water", "soil water retention", "multilayered gridded information", "13. Climate action", "EU-SoilHydroGrids", "3D European soil hydraulic maps", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.11203"}, {"href": "https://doi.org/2606201786"}, {"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": "2606201786", "name": "item", "description": "2606201786", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2606201786"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-30T00:00:00Z"}}, {"id": "3154789276", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:23:07Z", "type": "Journal Article", "created": "2021-04-15", "title": "Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale", "description": "Abstract<p>Saturated hydraulic conductivity (K) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of K have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of K at the larger regional scale in south-eastern Poland and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC) and temporally variable water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0\uffe2\uff80\uff9320\uffc2\uffa0cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied region (140\uffc2\uffa0km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 and 0.86 and their ranges (in %) are 45\uffe2\uff80\uff9395 and 0.002\uffe2\uff80\uff933.75, respectively. The number of individual samples varied from 216 to 228 (for K, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, K was most variable (coefficient of variation 77.3%) and significantly (p\uffe2\uff80\uff89&lt;\uffe2\uff80\uff890.05) positively correlated with total porosity (r\uffe2\uff80\uff89=\uffe2\uff80\uff890.300) and negatively correlated with soil bulk density (r\uffe2\uff80\uff89=\uffe2\uff80\uff89\uffe2\uff80\uff93\uffe2\uff80\uff890.283). The normal or close to the normal distribution was obtained by natural logarithmic and root square transformations. The mean K was 2.597\uffc2\uffa0m\uffc2\uffa0day\uffe2\uff88\uff921and ranged from 0.01 up to 11.54\uffc2\uffa0m\uffc2\uffa0day\uffe2\uff88\uff921. The spatial autocorrelation (range) of K in the single (direct) semivariograms was 0.081\uffc2\uffb0 (8.1\uffc2\uffa0km), while it favourably increased up to 0.149\uffc2\uffb0\uffe2\uff80\uff930.81\uffc2\uffb0 (14.9\uffe2\uff80\uff9381\uffc2\uffa0km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high K above 3.0\uffc2\uffa0m\uffc2\uffa0day\uffe2\uff88\uff921in the northern sandier (sand content\uffe2\uff80\uff89&gt;\uffe2\uff80\uff8974%) and less silty (silt content\uffe2\uff80\uff89&lt;\uffe2\uff80\uff8922%) part and, with lower K in the southern part of the study region. Generally, the spatial distribution of the K values in the study region depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between K and the intrinsic and relatively stable soil properties were much larger (from\uffe2\uff80\uff89~\uffe2\uff80\uff8915 to 81\uffc2\uffa0km) than between K and the temporally variable soil properties (0.3\uffe2\uff80\uff930.9\uffc2\uffa0km). This knowledge is supportive for making decisions related to land management aimed at alteration of hydraulic conductivity to improve soil water resources and crop productivity and reduce chemical leaching.</p", "keywords": ["2. Zero hunger", "Science", "saturated hydraulic conductivity", "Q", "R", "04 agricultural and veterinary sciences", "15. Life on land", "commune-scale variability", "Article", "6. Clean water", "kriging maps", "intrinsic and dynamic soil properties", "Medicine", "0401 agriculture", " forestry", " and fisheries", "geostatistics"], "contacts": [{"organization": "Usowicz, Boguslaw, Lipiec, Jerzy,", "roles": ["creator"]}]}, "links": [{"href": "https://www.nature.com/articles/s41598-021-86862-3.pdf"}, {"href": "https://doi.org/3154789276"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3154789276", "name": "item", "description": "3154789276", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3154789276"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-15T00:00:00Z"}}, {"id": "2745857202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:49Z", "type": "Journal Article", "created": "2017-08-23", "title": "Spatial variability of soil properties and cereal yield in a cultivated field on sandy soil", "description": "Abstract   Sandy soils are used in agriculture in different regions of the world. In Poland soils derived from sands occupy about 50% of agricultural area. Productivity of the soils depend on the soil properties that vary in the scale of field. This study aimed at determining and mapping the within-field variation of soil physical and chemical properties and grain yield of oats, rye, oats and triticale in 2001, 2002, 2003, 2015, respectively. The experiment was set up in a field (200\u00a0\u00d7\u00a050\u00a0m) on sandy soil in Trzebieszow (region Podlasie, Poland). The soil measurements included sand, silt, clay, and organic carbon (SOC) contents, cation exchange capacity (CEC), pH in the topsoil (0\u201310\u00a0cm) and subsoil (30\u201340\u00a0cm) layers in 2001, and water content and bulk density in the topsoil layer in spring and summer 2002\u20132003. The yields of oats were assessed in 2001 and 2003 and those of rye and triticale in 2002 and 2015, respectively. The soil properties and cereal yields were determined at 33\u201355 points in a grid evenly covering the whole field area. The results were analyzed using classic statistics and geostatistics by constructing semivariograms and 2D mapping by Inverse Distance Weighting (IDW). The cereal grain yields were significantly positively correlated with the topsoil water content (SWC) (r\u00a0=\u00a00.295\u20130.711), clay content (r\u00a0=\u00a00.081\u20130.174), and SOC in the subsoil (r\u00a0=\u00a00.208\u20130.271) and CEC in both layers (r\u00a0=\u00a00.123\u20130.298) and negatively correlated with bulk density (BD) (r\u00a0=\u00a0\u20130.065 to \u22120.279). The spatial dependence determined by the \u201cnugget-to-sill\u201d ratio was moderate or weak for the silt and clay content, CEC, and pH (29\u201379%) and strong for SOC, BD, SWC, and crop yield (0.2\u201313.2%). The effective range of the spatial dependence for all studied quantities varied from 9.9 to 120\u00a0m. The cereal yields were positively and significantly correlated between all study years (r\u00a0=\u00a00.141\u20130.734), which indicates inter-annual similarity in their spatial distribution. The 2D maps based on the IDW allowed assessing how gradual or sharp the changes in the studied quantities from one place to another are. Similar spatial patterns of the SWC, SOC and CEC, and crop yields were observed. This is of importance in precise and sustainable field management aimed at increasing and aligning spatial crop productivity of the studied low-productivity sandy soils that will have to be used in crop production due to the current shortage of land resources and food supplies on a global scale.", "keywords": ["2. Zero hunger", "2D maps", "soil properties", "sandy soil", "spatial variation", "0401 agriculture", " forestry", " and fisheries", "cereal yield", "04 agricultural and veterinary sciences", "semivariograms", "15. Life on land"]}, "links": [{"href": "https://doi.org/2745857202"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2745857202", "name": "item", "description": "2745857202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2745857202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "2795736439", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:50Z", "type": "Journal Article", "created": "2018-04-04", "title": "Spatial relationships among cereal yields and selected soil physical and chemical properties", "description": "Sandy soils occupy large area in Poland (about 50%) and in the world. This study aimed at determining spatial relationships of cereal yields and the selected soil physical and chemical properties in three study years (2001-2003) on low productive sandy Podzol soil (Podlasie, Poland). The yields and soil properties in plough and subsoil layers were determined at 72-150 points. The test crops were: wheat, wheat and barley mixture and oats. To explore the spatial relationship between cereal yields and each soil property spatial statistics was used. The best fitting models were adjusted to empirical semivariance and cross-semivariance, which were used to draw maps using kriging. Majority of the soil properties and crop yields exhibited low and medium variability (coefficient of variation 5-70%). The effective ranges of the spatial dependence (the distance at which data are autocorrelated) for yields and all soil properties were 24.3-58.5m and 10.5-373m, respectively. Nugget to sill ratios showed that crop yields and soil properties were strongly spatially dependent except bulk density. Majority of the pairs in cross-semivariograms exhibited strong spatial interdependence. The ranges of the spatial dependence varied in plough layer between 54.6m for yield\u00d7pH up to 2433m for yield\u00d7silt content. Corresponding ranges in subsoil were 24.8m for crop yield\u00d7clay content in 2003 and 1404m for yield\u00d7bulk density. Kriging maps allowed separating sub-field area with the lowest yield and soil cation exchange capacity, organic carbon content and pH. This area had lighter color on the aerial photograph due to high content of the sand and low content of soil organic carbon. The results will help farmers at identifying sub-field areas for applying localized management practices to improve these soil properties and further spatial studies in larger scale.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "soil variability", "crop yields", "04 agricultural and veterinary sciences", "15. Life on land", "low productive area", "Soil", "cross-semivariograms", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "Edible Grain", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/2795736439"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2795736439", "name": "item", "description": "2795736439", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2795736439"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-01T00:00:00Z"}}, {"id": "oai:iris.unica.it:11584/332967", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:28:14Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315&nbsp;cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean&nbsp;=&nbsp;3.0&nbsp;\u00b1&nbsp;2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\u00b1&nbsp;2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7&nbsp;\u00b1&nbsp;2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Bioclimatic variables; Global maps; Microclimate; Near-surface temperatures; Soil temperature; Soil-dwelling organisms; Temperature offset; Weather stations; Climate change; Temperature; Ecosystem; Soil"], "contacts": [{"organization": "Lembrechts J. J., van den Hoogen J., Aalto J., Ashcroft M. B., De Frenne P., Kemppinen J., Kopecky M., Luoto M., Maclean I. M. D., Crowther T. W., Bailey J. J., Haesen S., Klinges D. H., Niittynen P., Scheffers B. R., Van Meerbeek K., Aartsma P., Abdalaze O., Abedi M., Aerts R., Ahmadian N., Ahrends A., Alatalo J. M., Alexander J. M., Allonsius C. N., Altman J., Ammann C., Andres C., Andrews C., Ardo J., Arriga N., Arzac A., Aschero V., Assis R. L., Assmann J. J., Bader M. Y., Bahalkeh K., Barancok P., Barrio I. C., Barros A., Barthel M., Basham E. W., Bauters M., Bazzichetto M., Marchesini L. B., Bell M. C., Benavides J. C., Benito Alonso J. L., Berauer B. J., Bjerke J. W., Bjork R. G., Bjorkman M. P., Bjornsdottir K., Blonder B., Boeckx P., Boike J., Bokhorst S., Brum B. N. S., Bruna J., Buchmann N., Buysse P., Camargo J. L., Campoe O. C., Candan O., Canessa R., Cannone N., Carbognani M., Carnicer J., Casanova-Katny A., Cesarz S., Chojnicki B., Choler P., Chown S. L., Cifuentes E. F., Ciliak M., Contador T., Convey P., Cooper E. J., Cremonese E., Curasi S. R., Curtis R., Cutini M., Dahlberg C. J., Daskalova G. N., de Pablo M. A., Della Chiesa S., Dengler J., Deronde B., Descombes P., Di Cecco V., Di Musciano M., Dick J., Dimarco R. D., Dolezal J., Dorrepaal E., Dusek J., Eisenhauer N., Eklundh L., Erickson T. E., Erschbamer B., Eugster W., Ewers R. M., Exton D. A., Fanin N., Fazlioglu F., Feigenwinter I., Fenu G., Ferlian O., Fernandez Calzado M. R., Fernandez-Pascual E., Finckh M., Higgens R. F., Forte T. G. W., Freeman E. C., Frei E. R., Fuentes-Lillo E., Garcia R. A., Garcia M. B., Geron C., Gharun M., Ghosn D., Gigauri K., Gobin A., Goded I., Goeckede M., Gottschall F., Goulding K., Govaert S., Graae B. J., Greenwood S., Greiser C., Grelle A., Guenard B., Guglielmin M., Guillemot J., Haase P., Haider S., Halbritter A. H., Hamid M., Hammerle A., Hampe A., Haugum S. V., Hederova L., Heinesch B., Helfter C., Hepenstrick D., Herberich M., Herbst M., Hermanutz L., Hik D. S., Hoffren R., Homeier J., Hortnagl L., Hoye T. T., Hrbacek F., Hylander K., Iwata H., Jackowicz-Korczynski M. A., Jactel H., Jarveoja J., Jastrzebowski S., Jentsch A., Jimenez J. J., Jonsdottir I. S., Jucker T., Jump A. S., Juszczak R., Kanka R., Kaspar V., Kazakis G., Kelly J., Khuroo A. A., Klemedtsson L., Klisz M., Kljun N., Knohl A., Kobler J., Kollar J., Kotowska M. M., Kovacs B., Kreyling J., Lamprecht A., Lang S. I., Larson C., Larson K., Laska K., le Maire G., Leihy R. I., Lens L., Liljebladh B., Lohila A., Lorite J., Loubet B., Lynn J., Macek M., Mackenzie R., Magliulo E., Maier R., Malfasi F., Malis F.,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.unica.it/bitstream/11584/332967/1/2022_Global_maps_soil_temperature_GlobalChangeBiology.pdf"}, {"href": "https://doi.org/oai:iris.unica.it:11584/332967"}, {"rel": "self", "type": "application/geo+json", "title": "oai:iris.unica.it:11584/332967", "name": "item", "description": "oai:iris.unica.it:11584/332967", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:iris.unica.it:11584/332967"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "2966823696", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:55Z", "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"], "contacts": [{"organization": "Bogus\u0142aw Usowicz, Jerzy Lipiec,", "roles": ["creator"]}]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11368-019-02388-2.pdf"}, {"href": "https://doi.org/2966823696"}, {"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": "2966823696", "name": "item", "description": "2966823696", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2966823696"}, {"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"}}, {"id": "29758908", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:55Z", "type": "Journal Article", "created": "2018-04-04", "title": "Spatial relationships among cereal yields and selected soil physical and chemical properties", "description": "Sandy soils occupy large area in Poland (about 50%) and in the world. This study aimed at determining spatial relationships of cereal yields and the selected soil physical and chemical properties in three study years (2001-2003) on low productive sandy Podzol soil (Podlasie, Poland). The yields and soil properties in plough and subsoil layers were determined at 72-150 points. The test crops were: wheat, wheat and barley mixture and oats. To explore the spatial relationship between cereal yields and each soil property spatial statistics was used. The best fitting models were adjusted to empirical semivariance and cross-semivariance, which were used to draw maps using kriging. Majority of the soil properties and crop yields exhibited low and medium variability (coefficient of variation 5-70%). The effective ranges of the spatial dependence (the distance at which data are autocorrelated) for yields and all soil properties were 24.3-58.5m and 10.5-373m, respectively. Nugget to sill ratios showed that crop yields and soil properties were strongly spatially dependent except bulk density. Majority of the pairs in cross-semivariograms exhibited strong spatial interdependence. The ranges of the spatial dependence varied in plough layer between 54.6m for yield\u00d7pH up to 2433m for yield\u00d7silt content. Corresponding ranges in subsoil were 24.8m for crop yield\u00d7clay content in 2003 and 1404m for yield\u00d7bulk density. Kriging maps allowed separating sub-field area with the lowest yield and soil cation exchange capacity, organic carbon content and pH. This area had lighter color on the aerial photograph due to high content of the sand and low content of soil organic carbon. The results will help farmers at identifying sub-field areas for applying localized management practices to improve these soil properties and further spatial studies in larger scale.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "soil variability", "crop yields", "04 agricultural and veterinary sciences", "15. Life on land", "low productive area", "Soil", "cross-semivariograms", "kriging maps", "0401 agriculture", " forestry", " and fisheries", "Poland", "Edible Grain", "Environmental Monitoring"]}, "links": [{"href": "https://doi.org/29758908"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "29758908", "name": "item", "description": "29758908", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/29758908"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-01T00:00:00Z"}}, {"id": "50|core_ac_uk__::a26ef428f914921fdee6852647f58483", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:23:32Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km\u00b2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km\u00b2 pixels (summarized from 8500 unique temperature sensors) across all the world\u2019s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["near-surface temperatures", "bioclimatic variables", "soil temperature", "13. Climate action", "temperature offset", "global maps", "soil-dwelling organisms", "15. Life on land", "weather stations", "microclimate"], "contacts": [{"organization": "Lembrechts, Jonas J, van den Hoogen, Johan, Aalto, Juha, Ashcroft, Michael B, De Frenne, Pieter, Kemppinen, Julia, Kopeck\u00fd, Martin, Luoto, Miska, Maclean, Ilya M D, Crowther, Thomas W, Bailey, Joseph J, Haesen, Stef, Klinges, David H, Niittynen, Pekka, Jump, Alistair S.,", "roles": ["creator"]}]}, "links": [{"href": "http://dspace.stir.ac.uk/bitstream/1893/33794/1/Lembrechts-etal-GCB-2022.pdf"}, {"href": "https://doi.org/50|core_ac_uk__::a26ef428f914921fdee6852647f58483"}, {"rel": "self", "type": "application/geo+json", "title": "50|core_ac_uk__::a26ef428f914921fdee6852647f58483", "name": "item", "description": "50|core_ac_uk__::a26ef428f914921fdee6852647f58483", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|core_ac_uk__::a26ef428f914921fdee6852647f58483"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "50|od______3272::ba0a390ff7222134dc20acc64f02e995", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:23:35Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\u22120.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["near-surface temperatures", "bioclimatic variables", "soil temperature", "13. Climate action", "temperature offset", "global maps", "soil-dwelling organisms", "15. Life on land", "weather stations", "microclimate"], "contacts": [{"organization": "Lembrechts, J. J., Hoogen, J. van den, Aalto, J., Ashcroft, M. B., Frenne, P. de, Kemppinen, J., Kopecky, M., Luoto, M., Maclean, I. M. D., Mu\u00f1oz Rojas, Miriam,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|od______3272::ba0a390ff7222134dc20acc64f02e995"}, {"rel": "self", "type": "application/geo+json", "title": "50|od______3272::ba0a390ff7222134dc20acc64f02e995", "name": "item", "description": "50|od______3272::ba0a390ff7222134dc20acc64f02e995", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od______3272::ba0a390ff7222134dc20acc64f02e995"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-11T00:00:00Z"}}, {"id": "50|od_______325::2ec7e67709250f86d148c85d898647d7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:23:35Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15&nbsp;cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean&nbsp;=&nbsp;3.0&nbsp;\u00b1&nbsp;2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\u00b1&nbsp;2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7&nbsp;\u00b1&nbsp;2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["soil temperature", "Ecology", "Climate Change", "Temperature", "soil-dwelling organisms", "Microclimate", "Biological Sciences", "weather stations", "Climate Action", "Soil", "near-surface temperatures", "bioclimatic variables", "temperature offset", "global maps", "Ecosystem", "microclimate", "Environmental Sciences"], "contacts": [{"organization": "Lembrechts, Jonas J, Hoogen, Johan, Aalto, Juha, Ashcroft, Michael B, De Frenne, Pieter, Kemppinen, Julia, Kopeck\u00fd, Martin, Luoto, Miska, Maclean, Ilya MD, Crowther, Thomas W, Bailey, Joseph J, Haesen, Stef, Klinges, David H, Niittynen, Pekka, Scheffers, Brett R, Van Meerbeek, Koenraad, Aartsma, Peter, Abdalaze, Otar, Abedi, Mehdi, Aerts, Rien, Ahmadian, Negar, Ahrends, Antje, Alatalo, Juha M, Alexander, Jake M, Allonsius, Camille Nina, Altman, Jan, Ammann, Christof, Andres, Christian, Andrews, Christopher, Ard\u00f6, Jonas, Arriga, Nicola, Arzac, Alberto, Aschero, Valeria, Assis, Rafael L, Assmann, Jakob Johann, Bader, Maaike Y, Bahalkeh, Khadijeh, Baran\u010dok, Peter, Barrio, Isabel C, Barros, Agustina, Barthel, Matti, Basham, Edmund W, Bauters, Marijn, Bazzichetto, Manuele, Marchesini, Luca Belelli, Bell, Michael C, Benavides, Juan C, Alonso, Jos\u00e9 Luis Benito, Berauer, Bernd J, Bjerke, Jarle W, Bj\u00f6rk, Robert G, Bj\u00f6rkman, Mats P, Bj\u00f6rnsd\u00f3ttir, Katrin, Blonder, Benjamin, Boeckx, Pascal, Boike, Julia, Bokhorst, Stef, Brum, B\u00e1rbara NS, Br\u016fna, Josef, Buchmann, Nina, Buysse, Pauline, Camargo, Jos\u00e9 Lu\u00eds, Campoe, Ot\u00e1vio C, Candan, Onur, Canessa, Rafaella, Cannone, Nicoletta, Carbognani, Michele, Carnicer, Jofre, Casanova\u2010Katny, Ang\u00e9lica, Cesarz, Simone, Chojnicki, Bogdan, Choler, Philippe, Chown, Steven L, Cifuentes, Edgar F, \u010ciliak, Marek, Contador, Tamara, Convey, Peter, Cooper, Elisabeth J, Cremonese, Edoardo, Curasi, Salvatore R, Curtis, Robin, Cutini, Maurizio, Dahlberg, C Johan, Daskalova, Gergana N, de Pablo, Miguel Angel, Della Chiesa, Stefano, Dengler, J\u00fcrgen, Deronde, Bart, Descombes, Patrice, Di Cecco, Valter, Di Musciano, Michele, Dick, Jan, Dimarco, Romina D, Dolezal, Jiri, Dorrepaal, Ellen, Du\u0161ek, Ji\u0159\u00ed, Eisenhauer, Nico, Eklundh, Lars, Erickson, Todd E, Erschbamer, Brigitta,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|od_______325::2ec7e67709250f86d148c85d898647d7"}, {"rel": "self", "type": "application/geo+json", "title": "50|od_______325::2ec7e67709250f86d148c85d898647d7", "name": "item", "description": "50|od_______325::2ec7e67709250f86d148c85d898647d7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od_______325::2ec7e67709250f86d148c85d898647d7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "50|od_______330::f4436e280ea4dbf5c31d9cc8ac41463b", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:23:35Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km(2) resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km(2) pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10 degrees C (mean = 3.0 +/- 2.1 degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/- 2.3 degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 +/- 2.3 degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. 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