{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jhydrol.2021.126551", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:16:28Z", "type": "Journal Article", "created": "2021-06-08", "title": "Smartphone-based tension disc infiltrometer for soil hydraulic characterisation", "description": "31 Pags.- 18 Figs.- 1 Tabl. The definitive version is available at: https://www.sciencedirect.com/science/journal/00221694 The tension disc infiltrometer has become a widespread method to measure soil hydraulic properties under unsaturated conditions. Although several automated devices have been developed, most designs include an elongated water reservoir and a pressure transducer with electronic components to register the water level, increasing its cost and limiting its application. The objective of this work is to present a new tension disc infiltrometer with a compact design of 10 cm diameter and height, where the water level is monitored by a smartphone camera. The infiltration curve is determined from the automated analysis of the images recorded by the smartphone without additional electronic components. The device was first validated in the laboratory by comparing visual measurements (V), the camera imagery (Ca) and a pressure transducer (PT). Next, it was tested on field infiltration experiments. Robust fits (R2 = 0.99) were found between the water level measured with Ca in the laboratory and those obtained with V and PT procedures. The Ca method is accurate, robust and independent of the relative camera position. Good fits were also observed between Ca water level and those obtained with PT in the field experiments. Similar hydraulic conductivity and sorptivity values were obtained with both sensors using the numerical solution of the Haverkamp (NSH) equation. The compact infiltrometer, in conjunction with the smartphone camera, is an accurate, accessible, portable and easy-to-use field-based device for soil hydraulic characterisation. This research was supported by the MINECO project ASBIO (PGC2018-094332-B-100) and the European Union\u2019s Horizon 2020 research and innovation program under grant agreement No [H2020-MSCA-RISE-777803]. Peer reviewed", "keywords": ["Automated single-ring infiltrometer", "Compact design", "0207 environmental engineering", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "Soil hydraulic properties", "Infiltration rate measurements", "NSH", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.jhydrol.2021.126551"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hydrology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jhydrol.2021.126551", "name": "item", "description": "10.1016/j.jhydrol.2021.126551", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jhydrol.2021.126551"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2023.169662", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:16:37Z", "type": "Journal Article", "created": "2023-12-28", "title": "Plant-mediated CH4 exchange in wetlands: A review of mechanisms and measurement methods with implications for modelling", "description": "Plant-mediated CH4 transport (PMT) is the dominant pathway through which soil-produced CH4 can escape into the atmosphere and thus plays an important role in controlling ecosystem CH4 emission. PMT is affected by abiotic and biotic factors simultaneously, and the effects of biotic factors, such as the dominant plant species and their traits, can override the effects of abiotic factors. Increasing evidence shows that plant-mediated CH4 fluxes include not only PMT, but also within-plant CH4 production and oxidation due to the detection of methanogens and methanotrophs attached to the shoots. Despite the inter-species and seasonal differences, and the probable contribution of within-plant microbes to total plant-mediated CH4 exchange (PME), current process-based ecosystem models only estimate PMT based on the bulk biomass or leaf area index of aerenchymatous plants. We highlight five knowledge gaps to which more research efforts should be devoted. First, large between-species variation, even within the same family, complicates general estimation of PMT, and calls for further work on the key dominant species in different types of wetlands. Second, the interface (rhizosphere-root, root-shoot, or leaf-atmosphere) and plant traits controlling PMT remain poorly documented, but would be required for generalizations from species to relevant functional groups. Third, the main environmental controls of PMT across species remain uncertain. Fourth, the role of within-plant CH4 production and oxidation is poorly quantified. Fifth, the simplistic description of PMT in current process models results in uncertainty and potentially high errors in predictions of the ecosystem CH4 flux. Our review suggest that flux measurements should be conducted over multiple growing seasons and be paired with trait assessment and microbial analysis, and that trait-based models should be developed. Only then we are capable to accurately estimate plant-mediated CH4 emissions, and eventually ecosystem total CH4 emissions at both regional and global scales.", "keywords": ["Drivers", "330", "Plants", "Carbon Dioxide", "metaani", "Modelling", "Processes", "Soil", "Wetland plants", "Wetlands", "Mechanisms", "suot", "suokasvillisuus", "Plant CH4 transport", "Biomass", "Methane", "Ecosystem"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2023.169662"}, {"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.2023.169662", "name": "item", "description": "10.1016/j.scitotenv.2023.169662", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2023.169662"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "10.1071/sr13043", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:17:32Z", "type": "Journal Article", "created": "2013-12-20", "title": "Impact Of Carbon Farming Practices On Soil Carbon In Northern New South Wales", "description": "

This study sought to quantify the influence of \uffe2\uff80\uff98carbon farming\uffe2\uff80\uff99 practices on soil carbon stocks, in comparison with conventional grazing and cropping, in northern New South Wales. The study had two components: assessment of impacts of organic amendments on soil carbon and biological indicators in croplands on Vertosols of the Liverpool Plains; and assessment of the impact of grazing management on soil carbon in Chromosols of the Northern Tablelands. The organic amendment sites identified for the survey had been treated with manures, composts, or microbial treatments, while the conventional management sites had received only chemical fertilisers. The rotational grazing sites had been managed so that grazing was restricted to short periods of several days, followed by long rest periods (generally several months) governed by pasture growth. These were compared with sites that were grazed continuously. No differences in total soil carbon stock, or soil carbon fractions, were observed between sites treated with organic amendments and those treated with chemical fertiliser. There was some evidence of increased soil carbon stock under rotational compared with continuous grazing, but the difference was not statistically significant. Similarly, double-stranded DNA (dsDNA) stocks were not significantly different in either of the management contrasts, but tended to show higher values in organic treatments and rotational grazing. The enzymatic activities of \uffce\uffb2-glucosidase and leucine-aminopeptidase were significantly higher in rotational than continuous grazing but statistically similar for the cropping site treatments. Relative abundance and community structure, measured on a subset of the cropping sites, showed a higher bacteria\uffe2\uff80\uff89:\uffe2\uff80\uff89fungi ratio and provided evidence that microbial process rates were significantly higher in chemically fertilised sites than organic amendment sites, suggesting enhanced mineralisation of organic matter under conventional management. The higher enzyme activity and indication of greater efficiency of microbial populations on carbon farming sites suggests a greater potential to build soil carbon under these practices. Further research is required to investigate whether the indicative trends observed reflect real effects of management.

", "keywords": ["2. Zero hunger", "Land Capability and Soil Degradation", "550", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "Carbon Sequestration Science", "04 agricultural and veterinary sciences", "15. Life on land", "Land capability and soil productivity"]}, "links": [{"href": "https://doi.org/10.1071/sr13043"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1071/sr13043", "name": "item", "description": "10.1071/sr13043", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1071/sr13043"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-01-01T00:00:00Z"}}, {"id": "10.1098/rstb.2011.0313", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:17:50Z", "type": "Journal Article", "created": "2012-03-26", "title": "The Role Of N2o Derived From Crop-Based Biofuels, And From Agriculture In General, In Earth'S Climate", "description": "

In earlier work, we compared the amount of newly fixed nitrogen (N, as synthetic fertilizer and biologically fixed N) entering agricultural systems globally to the total emission of nitrous oxide (N 2 O). We obtained an N 2 O emission factor (EF) of 3\uffe2\uff80\uff935%, and applied it to biofuel production. For \uffe2\uff80\uff98first-generation\uffe2\uff80\uff99 biofuels, e.g. biodiesel from rapeseed and bioethanol from corn (maize), that require N fertilizer, N 2 O from biofuel production could cause (depending on N uptake efficiency) as much or more global warming as that avoided by replacement of fossil fuel by the biofuel. Our subsequent calculations in a follow-up paper, using published life cycle analysis (LCA) models, led to broadly similar conclusions. The N 2 O EF applies to agricultural crops in general, not just to biofuel crops, and has made possible a top-down estimate of global emissions from agriculture. Independent modelling by another group using bottom-up IPCC inventory methodology has shown good agreement at the global scale with our top-down estimate. Work by Davidson showed that the rate of accumulation of N 2 O in the atmosphere in the late nineteenth and twentieth centuries was greater than that predicted from agricultural inputs limited to fertilizer N and biologically fixed N (Davidson, E. A. 2009 Nat. Geosci . 2 , 659\uffe2\uff80\uff93662.). However, by also including soil organic N mineralized following land-use change and NO x deposited from the atmosphere in our estimates of the reactive N entering the agricultural cycle, we have now obtained a good fit between the observed atmospheric N 2 O concentrations from 1860 to 2000 and those calculated on the basis of a 4 per cent EF for the reactive N.

", "keywords": ["2. Zero hunger", "Air Pollutants", "330", "Climate", "Nitrous Oxide", "Agriculture", "15. Life on land", "Nitrification", "01 natural sciences", "7. Clean energy", "630", "Soil", "13. Climate action", "Biofuels", "Denitrification", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1098/rstb.2011.0313"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Philosophical%20Transactions%20of%20the%20Royal%20Society%20B%3A%20Biological%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rstb.2011.0313", "name": "item", "description": "10.1098/rstb.2011.0313", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rstb.2011.0313"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-05-05T00:00:00Z"}}, {"id": "10.1111/gcb.12819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:18:00Z", "type": "Journal Article", "created": "2014-12-05", "title": "Soil Warming And Co2 Enrichment Induce Biomass Shifts In Alpine Tree Line Vegetation", "description": "Abstract

Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9\uffc2\uffa0years of free air CO2 enrichment (+200\uffc2\uffa0ppm; 2001\uffe2\uff80\uff932009) and 6\uffc2\uffa0years of soil warming (+4\uffc2\uffa0\uffc2\uffb0C; 2007\uffe2\uff80\uff932012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40\uffc2\uffa0years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above\uffe2\uff80\uff90ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7\uffc2\uffa0kg\uffc2\uffa0m\uffe2\uff88\uff922) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above\uffe2\uff80\uff90ground mass was not altered by soil warming or elevated CO2. However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (\uffe2\uff88\uff9240% for all roots <2\uffc2\uffa0mm in diameter at 0\uffe2\uff80\uff9320\uffc2\uffa0cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "Models", " Statistical", "Temperature", "Larix", "Carbon Dioxide", "15. Life on land", "Pinus", "Global Warming", "01 natural sciences", "Soil", "Species Specificity", "13. Climate action", "Biomass", "Tundra", "Switzerland"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12819", "name": "item", "description": "10.1111/gcb.12819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-30T00:00:00Z"}}, {"id": "10.1111/j.1365-2486.2009.02121.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:18:09Z", "type": "Journal Article", "created": "2009-12-22", "title": "Functional Changes In The Control Of Carbon Fluxes After 3 Years Of Increased Drought In A Mediterranean Evergreen Forest?", "description": "Abstract

Our objective was to test how a long\uffe2\uff80\uff90term increased water limitation affects structural and functional properties of a Mediterranean ecosystem, and how these changes modify the response of the main carbon fluxes to climatic controls. In 2003, a 27% throughfall exclusion experiment was installed in a Quercus ilex L. forest in France. Gross primary production (GPP), ecosystem respiration (RECO) and net ecosystem exchange (NEE) were estimated in a control and a dry treatment. Decreasing throughfall decreased GPP by 14% and had a smaller effect on RECO (\uffe2\uff88\uff9212%), especially soil respiration RS (\uffe2\uff88\uff9211%). Interannual variability of GPP (29%) was higher than for RECO (12%). Error propagation was used to estimates uncertainties in the NEE fluxes, which ranged from 3% to 10% in the control treatment but up to 167% for NEE in the dry treatment because more steps and data types were involved in the scaling. After 3 years of throughfall exclusion, we found no acclimation of RS to climatic drivers. Functional properties of the response of RS to soil water, temperature and rain pulse remained similar in the control and the dry treatments. A diurnal clockwise hysteresis in RS was probably controlled by canopy photosynthesis with a 3\uffe2\uff80\uff83h lag. The proportion of diurnal variation of respiration due to photosynthesis was similar in all treatments (4\uffe2\uff80\uff935%). Because of the characteristic of rain in Mediterranean climates, a continuous decrease of water input in these environments have an effect on topsoil water and consequently on RS only during short periods when rainfall is characterized by infrequent and small events that does not allow the topsoil to reach field capacity and does not allow to dry completely. However, in the longer term, we expect a stronger decrease in RS in the dry treatment driven by the decrease in GPP.

", "keywords": ["0106 biological sciences", "550", "15. Life on land", "gross primary production", "soil respiration", "01 natural sciences", "630", "6. Clean water", "Quercus ilex", "throughfall exclusion", "13. Climate action", "rain pulse", "eddy-covariance", "Q(10)", "error propagation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2486.2009.02121.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2009.02121.x", "name": "item", "description": "10.1111/j.1365-2486.2009.02121.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2009.02121.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-08-01T00:00:00Z"}}, {"id": "10.1111/sum.12198", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:18:20Z", "type": "Journal Article", "created": "2015-07-31", "title": "Long-Term Effects Of Tillage, Nutrient Application And Crop Rotation On Soil Organic Matter Quality Assessed By Nmr Spectroscopy", "description": "Abstract

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

", "keywords": ["Fertilizer application", "2. Zero hunger", "Crop rotation", " fertilizer application", " soil organic carbon (SOC)", " soil organic matter (SOM)", " tillage", " NMR spectroscopy.", "NMR spectroscopy", "Crop rotation", "Soil organic matter (SOM)", "13. Climate action", "Soil organic carbon (SOC)", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Tillage"]}, "links": [{"href": "https://doi.org/10.1111/sum.12198"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Use%20and%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/sum.12198", "name": "item", "description": "10.1111/sum.12198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/sum.12198"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-07-31T00:00:00Z"}}, {"id": "10.1590/s0100-06832009000100016", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:18:47Z", "type": "Journal Article", "created": "2009-03-11", "title": "Atributos F\u00edsicos, Qu\u00edmicos E Biol\u00f3gicos De Solo De Cerrado Sob Diferentes Sistemas De Uso E Manejo", "description": "

\uffc3\uff80 medida que o conhecimento do sistema plantio direto se amplia, verifica-se que o uso de indicadores qu\uffc3\uffadmicos isolados n\uffc3\uffa3o permite melhor caracteriza\uffc3\uffa7\uffc3\uffa3o dos solos, sendo necess\uffc3\uffa1rio utilizar um conjunto de indicadores da qualidade do solo com a entrada de outros atributos, entre eles os f\uffc3\uffadsicos e os biol\uffc3\uffb3gicos. Objetivou-se avaliar os efeitos de sistemas de manejo e uso do solo nos atributos f\uffc3\uffadsicos, qu\uffc3\uffadmicos e biol\uffc3\uffb3gicos de um Latossolo Vermelho distr\uffc3\uffb3fico e um Neossolo Quartzar\uffc3\uffaanico \uffc3\uffb3rtico sob Cerrado, no entorno do Parque Nacional das Emas. Os aspectos avaliados no Latossolo foram: Cerrado nativo, pastagem, milheto em preparo convencional, nabo forrageiro em plantio direto e sorgo em plantio direto. No Neossolo: Cerrado nativo, pastagem nativa, integra\uffc3\uffa7\uffc3\uffa3o agricultura-pecu\uffc3\uffa1ria, pastagem cultivada, plantio direto com soja no ver\uffc3\uffa3o e plantio direto com milho no ver\uffc3\uffa3o. As amostras de solo foram coletadas na profundidade de 0 a 10 cm. O delineamento experimental foi o inteiramente casualizado, com cinco parcelas de 150 m\uffc2\uffb2, sendo coletadas 10 subamostras aleat\uffc3\uffb3rias. As an\uffc3\uffa1lises qu\uffc3\uffadmicas, f\uffc3\uffadsicas e biol\uffc3\uffb3gicas foram realizadas no Laborat\uffc3\uffb3rio de Solos da UFG/CJ. Os manejos promoveram altera\uffc3\uffa7\uffc3\uffb5es na densidade do solo, volume total de poros, macroporos e resist\uffc3\uffaancia do solo \uffc3\uffa0 penetra\uffc3\uffa7\uffc3\uffa3o no Neossolo e no Latossolo, excetuando-se neste o volume total de poros. Houve pequena varia\uffc3\uffa7\uffc3\uffa3o nos atributos qu\uffc3\uffadmicos nos dois solos, com o Cerrado apresentando maior acidez potencial e menor teor de c\uffc3\uffa1tions troc\uffc3\uffa1veis e P. Os atributos biol\uffc3\uffb3gicos do solo foram alterados pelos sistemas de manejo, sendo mais prejudicados em sistemas com maior revolvimento do solo. A an\uffc3\uffa1lise can\uffc3\uffb4nica dos dados demonstrou que os atributos f\uffc3\uffadsicos foram os de menor import\uffc3\uffa2ncia por apresentar maior coeficiente de pondera\uffc3\uffa7\uffc3\uffa3o nas vari\uffc3\uffa1veis can\uffc3\uffb4nicas. Os atributos do solo, isoladamente, pouco contribu\uffc3\uffadram para a avalia\uffc3\uffa7\uffc3\uffa3o da qualidade do solo: no entanto, quando se usou a an\uffc3\uffa1lise multivariada, subsidiaram a constata\uffc3\uffa7\uffc3\uffa3o dos manejos do solo mais sustent\uffc3\uffa1veis.

", "keywords": ["C fra\u00e7\u00e3o leve", "multivariate analysis", "an\u00e1lise multivariada", "plantio direto", "light carbon fraction", "0401 agriculture", " forestry", " and fisheries", "soil quality", "04 agricultural and veterinary sciences"]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832009000100016"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832009000100016", "name": "item", "description": "10.1590/s0100-06832009000100016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832009000100016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-02-01T00:00:00Z"}}, {"id": "10.2139/ssrn.4556085", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:19:17Z", "type": "Journal Article", "created": "2023-08-29", "title": "A Laser Diffractometry Technique for Determining the Soil Water Stable Aggregates Index", "description": "Open AccessPeer reviewed", "keywords": ["Water stable aggregates index", "Laser diffractometry", "Wet sieving", "Soil aggregates"]}, "links": [{"href": "https://doi.org/10.2139/ssrn.4556085"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2139/ssrn.4556085", "name": "item", "description": "10.2139/ssrn.4556085", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.4556085"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10261/359343", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:22:19Z", "type": "Dataset", "title": "Plant affinity to extreme soils and foliar sulphur mediate species-specific responses to sheep grazing in gypsum systems [Dataset V2]", "description": "Open AccessPeer reviewed", "keywords": ["Semiarid systems", "Gypsophiles", "Elemental composition", "Gypsum soils", "Herbivory", "Functional traits"], "contacts": [{"organization": "Cera, Andreu, Montserrat-Mart\u00ed, Gabriel, Luzuriaga, Arantzazu L., Pueyo, Yolanda, Palacio, Sara,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/359343"}, {"rel": "self", "type": "application/geo+json", "title": "10261/359343", "name": "item", "description": "10261/359343", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/359343"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.3389/fmicb.2016.01446", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:19:31Z", "type": "Journal Article", "created": "2016-09-14", "description": "Soil management is fundamental to all agricultural systems and fertilization practices have contributed substantially to the impressive increases in food production. Despite the pivotal role of soil microorganisms in agro-ecosystems, we still have a limited understanding of the complex response of the soil microbiota to organic and mineral fertilization in the very long-term. Here, we report the effects of different fertilization regimes (mineral, organic and combined mineral and organic fertilization), carried out for more than a century, on the structure and activity of the soil microbiome. Organic matter content, nutrient concentrations, and microbial biomass carbon were significantly increased by mineral, and even more strongly by organic fertilization. Pyrosequencing revealed significant differences between the structures of bacterial and fungal soil communities associated to each fertilization regime. Organic fertilization increased bacterial diversity, and stimulated microbial groups (Firmicutes, Proteobacteria, and Zygomycota) that are known to prefer nutrient-rich environments, and that are involved in the degradation of complex organic compounds. In contrast, soils not receiving manure harbored distinct microbial communities enriched in oligotrophic organisms adapted to nutrient-limited environments, as Acidobacteria. The fertilization regime also affected the relative abundances of plant beneficial and detrimental microbial taxa, which may influence productivity and stability of the agroecosystem. As expected, the activity of microbial exoenzymes involved in carbon, nitrogen, and phosphorous mineralization were enhanced by both types of fertilization. However, in contrast to comparable studies, the highest chitinase and phosphatase activities were observed in the solely mineral fertilized soil. Interestingly, these two enzymes showed also a particular high biomass-specific activities and a strong negative relation with soil pH. As many soil parameters are known to change slowly, the particularity of unchanged fertilization treatments since 1902 allows a profound assessment of linkages between management and abiotic as well as biotic soil parameters. Our study revealed that pH and TOC were the majors, while nitrogen and phosphorous pools were minors, drivers for structure and activity of the soil microbial community. Due to the long-term treatments studied, our findings likely represent permanent and stable, rather than transient, responses of soil microbial communities to fertilization.", "keywords": ["Soil nutrients", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "long-term fertilization", "microbial biomass", "15. Life on land", "microbial activity", "Microbiology", "QR1-502", "03 medical and health sciences", "13. Climate action", "soil microbial communities", "soil nutrients", "454 pyrosequencing"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2016.01446"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2016.01446", "name": "item", "description": "10.3389/fmicb.2016.01446", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2016.01446"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-14T00:00:00Z"}}, {"id": "10.5061/dryad.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:05Z", "type": "Dataset", "title": "Data from: Interactions among roots, mycorrhizae and free-living microbial communities differentially impact soil carbon processes", "description": "unspecifiedPlant roots, their associated microbial community and free-living soil microbes interact to regulate the movement of carbon from the soil to the atmosphere, one of the most important and least understood fluxes of terrestrial carbon. Our inadequate understanding of how plant\u2013microbial interactions alter soil carbon decomposition may lead to poor model predictions of terrestrial carbon feedbacks to the atmosphere. Roots, mycorrhizal fungi and free-living soil microbes can alter soil carbon decomposition through exudation of carbon into soil. Exudates of simple carbon compounds can increase microbial activity because microbes are typically carbon limited. When both roots and mycorrhizal fungi are present in the soil, they may additively increase carbon decomposition. However, when mycorrhizas are isolated from roots, they may limit soil carbon decomposition by competing with free-living decomposers for resources. We manipulated the access of roots and mycorrhizal fungi to soil in situ in a temperate mixed deciduous forest. We added 13C-labelled substrate to trace metabolized carbon in respiration and measured carbon-degrading microbial extracellular enzyme activity and soil carbon pools. We used our data in a mechanistic soil carbon decomposition model to simulate and compare the effects of root and mycorrhizal fungal presence on soil carbon dynamics over longer time periods. Contrary to what we predicted, root and mycorrhizal biomass did not interact to additively increase microbial activity and soil carbon degradation. The metabolism of 13C-labelled starch was highest when root biomass was high and mycorrhizal biomass was low. These results suggest that mycorrhizas may negatively interact with the free-living microbial community to influence soil carbon dynamics, a hypothesis supported by our enzyme results. Our steady-state model simulations suggested that root presence increased mineral-associated and particulate organic carbon pools, while mycorrhizal fungal presence had a greater influence on particulate than mineral-associated organic carbon pools. Synthesis. Our results suggest that the activity of enzymes involved in organic matter decomposition was contingent upon root\u2013mycorrhizal\u2013microbial interactions. Using our experimental data in a decomposition simulation model, we show that root\u2013mycorrhizal\u2013microbial interactions may have longer-term legacy effects on soil carbon sequestration. Overall, our study suggests that roots stimulate microbial activity in the short term, but contribute to soil carbon storage over longer periods of time.", "keywords": ["2. Zero hunger", "roots", "13. Climate action", "simulation model", "carbon dynamics", "Rhizosphere", "stable isotope", "plant-soil (belowground) interactions", "15. Life on land", "extra-cellular enzyme activity", "mycorrhizae"], "contacts": [{"organization": "Moore, Jessica A. M., Jiang, Jiang, Patterson, Courtney M., Wang, Gangsheng, Mayes, Melanie A., Classen, Aim\u00e9e T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.pb271"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.pb271", "name": "item", "description": "10.5061/dryad.pb271", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.pb271"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-14T00:00:00Z"}}, {"id": "10.5281/zenodo.10959077", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:32Z", "type": "Dataset", "created": "2023-10-30", "title": "Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies", "description": "The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the 'Soil management effect assessment' in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ('Knowledge gap in tab'-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other", "keywords": ["Water management", "EJP SOIL", "Climate change mitigation", "Nitrogen leaching", "CH4", "Conservation agriculture", "Cropping systems", "SOMMIT", "N2O", "Organic matter inputs", "Tillage"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10959077"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10959077", "name": "item", "description": "10.5281/zenodo.10959077", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10959077"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.13952097", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:42Z", "type": "Dataset", "created": "2024-01-01", "title": "SERENA EJPSoil Soil erosion control in Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. One of the\u00a0objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the methodology of the SERENA Soil erosion and soil erosion control cookbook.\u00a0 Soil loss was used as an indicator for soil erosion (ST). The map of soil mass not eroded was based on the RUSLE model. Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass retained by vegetation, Mg/ha/y). For Italy, the cookbook was applied in the Tuscany region. \u00a0 To create the soil loss map we used:\u00a0 for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990\u20132022, Lamma Consortium)\u00a0 and a local linear equation between R and mean annual precipitation (P); for C -factor, Regional Land use map 1:10.000 (2018, freely available at:\u00a0https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html) and ESDAC method (https://doi.org/10.1016/j.landusepol.2015.05.021) ; \u00a0 for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO\u2019s methodology in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function; for LS-factor, DEM 10 m of Tuscany, (freely available at\u00a0https://www502.regione.toscana.it/geoscopio/cartoteca.html99) and Desmet & Govers (1996) SAGA tool (applied at 10 m and upscaled); for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor of\u00a0 0.5\u00a0 was considered, based on expert evaluation) for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor\u00a0of\u00a0 0.5\u00a0 was considered, based on expert evaluation)\u00a0 Maps was delivered in the GeoTIFF format in the resolution of 100m. \u00a0 Delivered data will be validated by stakeholders from Italy (scientist) in October, 2024.", "keywords": ["Italy", "Tuscany", "SERENA project", "Soil-based ecosystem service", "RUSLE", "soil erosion control", "Grant n. 862695", "EJP-Soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13952097"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13952097", "name": "item", "description": "10.5281/zenodo.13952097", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13952097"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:45Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. \u00a0\u00a0The following data were used:\u00a0 Large-scale Reference Database (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector)\u00a0 Medium-scale annual winter aerial images of Flanders (15 or 25 cm raster resolution) This dataset is originally hosted at Geopunt (www.geopunt.be). For the most up-to-date version of the dataset, please access the data from the Geopunt repository.", "keywords": ["soil sealing", "remote sensing", "BELGIUM (FLANDERS)", "aerial images", "SERENA", "EJP-Soil", "photointerpretation"], "contacts": [{"organization": "Cockx, Kasper, Oorts, Katrien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14044657"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14044657", "name": "item", "description": "10.5281/zenodo.14044657", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14044657"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.14833053", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:56Z", "type": "Dataset", "created": "2025-02-06", "title": "Surface soil moisture for Europe 2014-2024 at 1 km annual and quarterly aggregates", "description": "Copernicus Land Monitoring Services provides Surface Soil Moisture 2014-present (raster 1 km), Europe, daily \u2013 version 1. Each day covers only 5 to 10% of European land mask and shows lines of scenes (obvious artifacts). This is the long-term aggregates of daily images of soil moisture (0\u2013100%) based on two types of aggregation: Long-term quarterly (qr.1 - winter, qr.2 - spring, qr.3 - summer and qr.4 - autumn), Annual quantiles P.05, P.50 and P.95, The soil moisture rasters are based on Sentinel 1 and described in detail in:\u00a0 Bauer-Marschallinger, B. ; Freeman, V. ; Cao, S. ; Paulik, C. ; Schaufler, S. ; Stachl, T. ; Modanesi, S. ; Massari, C. ; Ciabatta, L. ; Brocca, L. ; Wagner, W.\u00a0Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles.\u00a0IEEE Transactions on Geoscience and Remote Sensing\u00a02019, 1 - 20. DOI\u00a010.1109/TGRS.2018.2858004 You can access and download the original data as .nc files from: https://globalland.vito.be/download/manifest/ssm_1km_v1_daily_netcdf/. The files with pattern 'soil.moisture_s1.clms.qr.*.p0.*.gf_m_1km_20140101_20241231_eu_epsg4326_v20250211.tif' are the gap-filled soil moisture quarterly estimates. For gap filling I build a model using cca 250k random training points and relationship with CHELSA climate bioclimatic variables, ESA CCI snow cover probability, ESA CCI forest and bare areas percent cover and Global Water Pack long-term surface water fraction. The gap-filling model had an R-square of 0.96 and RMSE of 6.5% of soil moisture. Aggregation has been generated using the terra package in R in combination with the matrixStats::rowQuantiles function. Tiling system and land mask for pan-EU is also available. library(terra) library(matrixStats) g1 = terra::vect('/mnt/inca/EU_landmask/tilling_filter/eu_ard2_final_status.gpkg') ## 1254 tiles tile = g1[534] nc.lst = list.files('/mnt/landmark/SM1km/ssm_1km_v1_daily_netcdf/', pattern = glob2rx('*.nc$'), full.names=TRUE) ## 3726 ## test it #r = terra::rast(nc.lst[100:210]) agg_tile = function(r, tile, pv=c(0.05,0.5,0.95), out.year='2015.annual'){ bb = paste(as.vector(ext(tile)), collapse = '.') out.tif = paste0('./eu_tmp/', out.year, '/sm1km_', pv, '_', out.year, '_', bb, '.tif') if(any(!file.exists(out.tif))){ r.t = terra::crop(r, ext(tile)) r.t = as.data.frame(r.t, xy=TRUE, na.rm=FALSE) sel.c = grep(glob2rx('ssm$'), colnames(r.t)) t1s = cbind(data.frame(matrixStats::rowQuantiles(as.matrix(r.t[,sel.c]), probs = pv, na.rm=TRUE)), data.frame(x=r.t$x, y=r.t$y)) ## write to GeoTIFFs r.o = terra::rast(t1s[,c('x','y','X5.','X50.','X95.')], type='xyz', crs='+proj=longlat +datum=WGS84 +no_defs') for(k in 1:length(pv)){ terra::writeRaster(r.o[[k]], filename=out.tif[k], gdal=c('COMPRESS=DEFLATE'), datatype='INT2U', NAflag=32768, overwrite=FALSE) } rm(r.t); gc() tmpFiles(remove=TRUE) } } ## quarterly values: lA = data.frame(filename=nc.lst) library(lubridate) lA$Date = ymd(sapply(lA$filename, function(i){substr(strsplit(basename(i), '_')[[1]][4], 1, 8)})) #summary(is.na(lA$Date)) #hist(lA$Date, breaks=60) lA$quarter = quarter(lA$Date, fiscal_start = 11) summary(as.factor(lA$quarter)) for(qr in 1:4){ #qr=1 pth = paste0('A.q', qr) rs = terra::rast(lA$filename[lA$quarter==qr]) x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(rs, tile=g1[i], out.year=pth) )}, mc.cores=20) for(type in c(0.05,0.5,0.95)){ x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE) out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt') vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt') cat(x, sep=' n', file=out.tmp) system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp)) system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.qr.', qr, '.p', type, '_m_1km_20140101_20241231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27')) } } ## per year ---- for(year in 2015:2023){ l.lst = nc.lst[grep(year, basename(nc.lst))] r = terra::rast(l.lst) pth = paste0(year, '.annual') x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(r, tile=g1[i], out.year=pth) )}, mc.cores=40) ## Mosaics: for(type in c(0.05,0.5,0.95)){ x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE) out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt') vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt') cat(x, sep=' n', file=out.tmp) system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp)) system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.annual.', type, '_m_1km_', year, '0101_', year, '1231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27')) } }", "keywords": ["Soil", "Soil moisture"], "contacts": [{"organization": "Hengl, Tomislav", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14833053"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14833053", "name": "item", "description": "10.5281/zenodo.14833053", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14833053"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-07T00:00:00Z"}}, {"id": "10.5281/zenodo.14252610", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:51Z", "type": "Dataset", "title": "Data from: Turfgrass pedogenesis under low maintenance: an experimental analysis with Festuca rubra subspecies at different fertilization levels", "description": "unspecifiedThatchMatThickeness.csv (Frc = Festuca rubra commutata, Frt = Festuca rubra trichophylla, Frr = Festuca rubra rubra) spec = subspecies var = variety rep = replicate number Nfert = N fertilization (kg N ha-1) thatch1 = thatch thickness at 18-5-2018 (cm) thatch2 = thatch thickness at 26-10-2018 (cm) thatch3 = thatch thickness at 17-5-2019 (cm) thatch4 =\u00a0 thatch thickness at 29-10-2019 (cm) thatch5 = thatch thickness at 10-6-2020 (cm) thatch6 = thatch tcicknesss at 16-6-2021 (cm) mat1 = mat thickness at 18-5-2018 (cm) mat2 = mat thickness at 26-10-2018 (cm) mat3 = mat thickness at 17-5-2019 (cm) mat4 =\u00a0 mat thickness at 29-10-2019 (cm) mat5 = mat thickness at 10-6-2020 (cm) mat6 = mat tcicknesss at 16-6-2021 (cm)", "keywords": ["Festuca rubra", "festuca rubra", "fertilization", "carbon", "soil layers", "pedogenesis", "turfgrass", "microbes", "Turfgrass", "nitrogen", "Carbon", "organic matter"], "contacts": [{"organization": "Evers, Maurice, De Caluwe, Hannie, Visser, Eric J.W., De Kroon, Hans,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14252610"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14252610", "name": "item", "description": "10.5281/zenodo.14252610", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14252610"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14936177", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:20:59Z", "type": "Dataset", "title": "Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository", "description": "Overview Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper 'LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping' by Schmidinger et al. (2025). You may also use our R\u00a0and Python package likewise called LimeSoDa. \u00a0 Citation Upon usage of datasets from LimeSoDa, please cite our associated paper: Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping.", "keywords": ["Environmental sciences", "Soil Organic Carbon", "Pedometrics", "pH", "Soil Organic Matter", "Clay", "Remote sensing", "Digital Soil Mapping"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14936177"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14936177", "name": "item", "description": "10.5281/zenodo.14936177", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14936177"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:01Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers. The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions. For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes. The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables. The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location. The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions. Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15772619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:11Z", "type": "Dataset", "title": "Dataset to: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Near-Infrared (NIR) Austrian Soil Spectral Library (ASSL, 680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL \u2018Towards climate-smart sustainable management of agricultural soils\u2019 (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "Spectroscopy", " Near-Infrared", "data"], "contacts": [{"organization": "Fohrafellner, Julia", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15772619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15772619", "name": "item", "description": "10.5281/zenodo.15772619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15772619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-07T00:00:00Z"}}, {"id": "10.5281/zenodo.15797289", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:12Z", "type": "Dataset", "title": "Database of soil characteristics under specific pesticide management", "description": "Database of soil characteristics under specific pesticide management Contributors: Mark\u00e9ta Mayerov\u00e1 and Veronika \u0158ez\u00e1\u010dov\u00e1 Affiliation: Czech Agrifood Research Center, Drnovsk\u00e1 507/73, CZ-160 00 Prague 6, Czech Republic Database of soil characteristics will contribute to the realization of the project\u00b4s goal to identify appropriate and inappropriate pesticides from the point of the view of their impact on the non-target organisms and soil characteristics. Field I. The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The experiment took place at the site of the experiment that had been running there since 2013 and included 5 different herbicide treatments in 4 replications (Mayerov\u00e1 et al. 2018) The new trial area was split into 20 randomised plots with 2 different herbicide treatments in 8 replications and control without herbicides in 4 replications. Herbicide treatments differed in the mode of action (Table 1) Table 1. Summary of the herbicides and active ingredients used in the trial. Classification Group by Herbicide Resistance Action Committee (HRAC). herbicide dose formulation active ingredient content of a.i. HRAC group target weeds Agritox 50 SL 1.5 l/ha EC MCPA 500 g/l O dicot Glean 75 PX 15 g/ha WG chlorsulfuron 750 g/l B dicot + annual grasses \u00a0 The area of each plot was 100 m2 and the 10 by 10 m plots were separated from field boundaries and from each other by 2 m on all sides to eliminate interaction between plots. Herbicides were applied post emergency in spring (April 26, 2024) from the tillering crop stage to the beginning of stem elongation (BBCH 21\u201331) by the Agrio-Napa 12 sprayer. Winter wheat was grown in the experimental field in 2024. At the beginning of March, it was mineral fertilized with LAD (ammonium nitrate with dolomite - NH4NO3\u00a0+\u00a0CaMg (CO3)2; 27 % N) at a dose 100 kg/ha. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer in each replication before herbicide application (April 24, 2024), 14 days after herbicide application (May 9, 2024) and 7 weeks after herbicide application (June 14, 2024). \u00a0A total of 20 soil samples were collected at each sampling. The soil samples were subsequently dried and sieved through a 2 mm sieve, thus simultaneously homogenised. The following soil properties were determined: pH (H2O), electric conductivity, available P and K, concentration NH4 and NO3, soil organic carbon, and total organic nitrogen content. Available P and K were assessed according to the Mehlich III method (Mehlich, 1984) on an Agilant ICP-OES 5110 VDV instrument. NO3 and NH4 were determined using calcium chloride solution as extractant according to ISO 14255:1998 on automated chemistry analyser SKALAR. Soil organic carbon and soil organic matter content were determined by sulfochromic oxidation according to ISO 14235:1998. Field II The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The total area of the experiment is about 11 ha including the protective area around the entire experiment. The experimental area is divided into two halves, 120m wide and 300m long.\u00a0 One half was treated on June 17, 2024, with insecticide Decis forte (active ingredient deltamethrin) at a dose 62.5ml/ha, the other half was without insecticide treatment. Both areas are further divided into other halves. One half was treated on May 15, 2024, with herbicide Agritox (active ingredient MCPA) at a dose 1.5l/ha, the other was treated with hoeing only. We thus obtained 4 strips 60m wide with following treatment combinations: (A) herbicide + insecticide; (B) hoeing + insecticide; (C) hoeing; (D) herbicide. Spring wheat was grown in the experimental field in 2024. It was fertilized with mineral nitrogen at a dose of 150 kg N/ha before sowing and with 39 kg N/ha (DAM 390 - ammonium nitrate with urea) in the tillering phenophase. In the middle of each strip (i.e. treatment), 8 sampling sites were marked in a row, 20 m apart from each other. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer at each sampling site 14 days after herbicide application and 14 days after insecticide application. A total of 32 soil samples were collected at each sampling. Further sample processing was the same as for Field I. The database will be gradually supplemented in the following years. Funding: Development for this work is funded primarily by the Technology Agency of the Czech Republic, project SS07020100: \u201cThe impact of plant protection products on non-target biodiversity: soil microorganisms, invertebrates and wild plants\u201d, and the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0425. The database was approved on September 2, 2025, by the Ministry of Agriculture of the Czech Republic. References: Mayerov\u00e1 M., Mikulka J., Soukup J. (2018): Effects of selective herbicide treatment on weed community in cereal crop rotation. Plant Soil Environ., 64: 413\u2013420. https://doi.org/10.17221/289/2018-PSE \u00a0Mehlich A. (1984): Mehlich 3 Soil Test Extractant. A Modification of the Mehlich 2 Extractant. Commun. Soil Sci. Plant Anal. 15, 1409-1416. http://dx.doi.org/10.1080/00103628409367568.", "keywords": ["field trial", " herbicides", " insecticides", " soil properties"], "contacts": [{"organization": "Mayerov\u00e1, Mark\u00e9ta, \u0158ez\u00e1\u010dov\u00e1, Veronika,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15797289"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15797289", "name": "item", "description": "10.5281/zenodo.15797289", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15797289"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.16017208", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:13Z", "type": "Dataset", "title": "Cashew orchard soil properties, Dodamarg, Northern Western Ghats, India", "description": "Soil properties of cashew orchards of the Northern Western Ghats, India This project contains chemical properties of soil collected from cashew orchards of Dodamarg, Northern Western Ghats, for a study investigating the factors influencing the effects of forest cover, flower abundance, temperature and (potentially) soil composition on cashew pollinators. Taxonomic Coverage:\u00a0Not applicable Geographic Coverage: Dodamarg, Sindhudurg District, Maharashtra, India Temporal Coverage: March 2025 \u00a0 Description of field and lab methods Soil collection: Soil samples were collected from 30 cashew orchards, using soil core sampler. The diameter of the core sampler was measured before soil collection. All soil samples were collected from 10 cm depth after removing all the leaf litter from the ground. From each orchard, 10 soil columns were collected for analysis of chemical properties. Chemical Properties: We estimated thirteen soil chemical properties for all soil samples collected. The following parameters were analyzed by Zuari Farmhubs Laboratory: pH, electrical conductivity (E.C.) at 25\u00b0C, organic carbon (O.C.), available phosphorus (P\u2082O\u2085), available potassium (K\u2082O), available calcium (Ca), magnesium (Mg), sulfur (S), boron (B), zinc (Zn), iron (Fe), copper (Cu), and manganese (Mn). More details about the data can be obtained from Aditya Satish (adityasatish@ncf-india.org) and Rohit Naniwadekar (rohit@ncf-india.org) from the Nature Conservation Foundation (www.ncf-india.org). File Descriptions: Data file: Dodamarg_2025_Cashew_Soil_Properties.csv We have also included a ReadMe.txt file that explains the data file, akin to the description in the metadata. Description of the columns of the data file: Sl no: Serial number Site: Site ID Code: Site code (General location) Latitude: latitude co-ordinate of the plot (in decimal degrees, \u00b0N) Longitude: longitude co-ordinate of the plot (in decimal degrees, \u00b0E) pH: pH of the soil E.C.: Electrical Conductivity at 25\u00b0C (in dS/m) O.C.: Organic Carbon (in %) P\u2082O\u2085: Available P\u2082O\u2085 (in Kg /acre) K\u2082O: Available Potassium (in Kg /acre) Ca: Available Calcium (in mg/Kg) Mg: Available Magnesium (in mg/Kg) S: Available Sulphur (in mg/Kg) B: Available Boron (in mg/Kg) Zn: Available Zinc (in mg/Kg) Fe: Available Iron (in mg/Kg) Cu: Available Copper (in mg/Kg) Mn: Available Manganese (in mg/Kg) Funding:\u00a0 Godrej Consumer Products Limited Arvind Datar Rohini Nilekani Philanthropies", "keywords": ["Soil chemical properties", "Cashew orchards", "Ecology", "FOS: Biological sciences", "Northern Western Ghats"], "contacts": [{"organization": "Sadekar, Vishal, Satish, Aditya, Naniwadekar, Rohit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16017208"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16017208", "name": "item", "description": "10.5281/zenodo.16017208", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16017208"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16261617", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:13Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16261617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16261617", "name": "item", "description": "10.5281/zenodo.16261617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16261617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.17475742", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:16Z", "type": "Dataset", "title": "In-situ and UAV dataset with crop and soil parameters obtained from winter wheat fields", "description": "unspecifiedThe study area was located in the Danubian Plain, Bulgaria, near the town of Knezha (43.495\u00b0N, 24.081\u00b0E). Five field campaigns (November, March, April, May, and June) were conducted during the 2016-2017 agricultural season and two (November and April) during the 2017-2018 agricultural season. Each campaign took four-five days to complete. A hierarchical sampling strategy, consisting of three levels, was adopted: Unit (a single commercial agricultural field), Elementary Sampling Unit (ESU; a 20 \u00d7 20 m plot), and Elementary Sub-Sampling Unit (ESSU; a particular location in the ESU where measurement is taken). Six Units were selected for sampling during each agricultural season. Different number of ESUs were sampled in each Unit depending of its size and diversity of soils. The location of the ESUs was kept unchanged during the season except in case relocation was needed due to waterlogging. The geographic coordinates were measured each time an ESU was visited, irrespective of whether it was relocated or not and a unique ID assigned to the ESU. Thus, data from each visit are presented as a separate independent record (row) in the dataset. In each ESU there were 3 ESSUs, one at the center and two at opposite corners of the ESU. The biological yield was measured only in the central ESSU. The dataset contains only data averaged at ESU level. Two unique identifiers, Unit_ID and ESU_ID, were used across the dataset's tables, helping identify data records and connect tables. The Unit_ID consists of the season and the Unit number, for instance \u201cS16-17_U1\u201d, where \u201cS16-17\u201d is the agricultural season 2016-2017, and \u201cU1\u201d is Unit 1. The ESU_ID consists of the month and year of the field campaign, the Unit number, and the ESU number, for instance \u201capr2017_1_2\u201d, where \u201capr2017\u201d is the April 2017 field campaign, \u201c1\u201d is the Unit number, and \u201c2\u201d is the ESU number. The LAI, fIPAR, FAPAR, and fCover were measured using AccuPAR LP-80 ceptometer. The CC was measured using CCM-300. The AGB and plant nitrogen samples were collected from a 1 m x 1 m plot at the central ESSU and 50 cm x 50 cm plots in the other two ESSUs. The nitrogen (N) content of the above-ground organs was determined by the Kjeldahl method and expressed as per-cent of the dry weight. N uptake (g m-2) was determined as the product of N content and the dry AGB. Soil moisture content (W) was measured gravimetrically at the surface (0-5 cm) and, in some ESUs, at three additional depths (0-30 cm, 30-60 cm, and 60-100 cm) in 10 cm increments. The data were expressed in percent by mass. Reflectance data was collected using ASD FieldSpec 4 HiRes Field Spectrometer (ASD HH FS4 HiRes). Crop phenology was reported after Zadoks et al. (1974). The damages and weeds status were assessed using five-point rating scales. The data from the four soil profiles included morphological description of soil horizons, texture (the particle-size distribution was determined by sieving and the pipette method in accordance with ISO 11277-2020), concentration of organic carbon, pH, bulk density, water retention at suctions from 0.25 kPa (pF 0.4) to 33 kPa (pF 2.7), total porosity. Soil water retention curves (pF curves) were obtained during the drainage of the samples by using the suction plate method, pressure membrane apparatus, and desiccators methods, as described in (Rousseva et al., 2017). The field capacity (FC) was estimated by the water content retained at a suction of 10 kPa (pF = 2.0). The wilting point was estimated at a suction 1500 kPa (pF = 4.2). The water retention experimental data at different suctions were approximated with the van Genuchten equation (Van Genuchten, 1980). The crop calendar and management data included information for the studied winter wheat fields, including soil type, predecessor, pre-sowing preparation, variety, sowing date and norm, dates and type of fertilization, plant protection measures, dates of the main development stages of winter wheat, harvesting date and yield. \u00a0\u00a0 Multispectral image mosaics of the studied fields were generated using the fixed-wing drone eBee Ag furnished with Sequoia camera. The camera has Multi-channel sensor with green (550 nm), red (660 nm), red edge (735 nm), and NIR (790 nm) bands. Meteorological data were collected by an automatic telemetric agro-meteorological station ADCON addVantage Pro6.5 installed in the vicinity of the town of Knezha. The station operated between 30.11.2016 and 22.03.2017 on its initial location (Location 1: 43.521917\u00b0N, 24.098848\u00b0E) and was later moved to another location, 1.3 km southwards (Location 2: 43.511391\u00b0N, 24.092252\u00b0E), where it collected data between 26.04.2017 and 3.12.2018. The station was equipped with sensors for air temperature, relative humidity, solar radiation, wind speed, wind direction, rain, PAR, soil moisture and temperature (12 depths between 5 cm and 115 cm at 10 cm intervals). Data from all sensors were recorded at 30 min intervals. References: Zadoks, J.C., T.T. Chang, C.F. Konzak. 1974. A decimal code of the growth stages of cereals. Weed Research, 14, 415-421. Rousseva, S., Kercheva, M., Shishkov, T., Lair, G.J., Nikolaidis, N.P., Moraetis, D., Kr\u00e1m, P., Bernasconi, S.M., Blum, W.E.H., Menon, M., et al. 2017. Soil Water Characteristics of European SoilTrEC Critical Zone Observatories. In Advances in Agronomy; Academic Press: Cambridge, MA, USA, Volume 142, pp. 29\u201372. van Genuchten, M. T., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892\u2013898.", "keywords": ["Winter wheat", "Chlorophyll", "Phenology", "UAV", "Yield (agricultural)", "Soil profile", "Biomass", "Remote sensing", "Spectroscopy", "LAI"], "contacts": [{"organization": "Dimitrov, Petar, Roumenina, Eugenia, Jelev, Georgi, Filchev, Lachezar, Gikov, Alexander, Kamenova, Ilina, Ilieva, Iliana, Ganeva, Dessislava, Kercheva, Milena, Banov, Martin, Krasteva, Veneta, Kolchakov, Viktor, Dimitrov, Emil, Miteva, Nevena,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17475742"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17475742", "name": "item", "description": "10.5281/zenodo.17475742", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17475742"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-31T00:00:00Z"}}, {"id": "10.5281/zenodo.17941270", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:18Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0 meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and measured NIR soil spectra, also for the standards. Project description This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries. References Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17941270"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17941270", "name": "item", "description": "10.5281/zenodo.17941270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17941270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.3247592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:19Z", "type": "Dataset", "title": "Daily cycles in soil carbon flux", "description": "Description: Measurements of 24 hour cycles in soil CO2 flux taken from soil collars in the Belian Carbon plot at Maliau. Measurements were taken from 12 subplots over four days at 5-hourly intervals, ensuring good coverage of the complete 24 hour cycle. Air and soil temperatures, soil moisture content and CO2 flux were taken from each plot at each visit. 9 subplots only have a single total soil respiration collar, but 3 subplots also have soil flux partitioning treatments to separate contributions to total respiration from soil organic matter, mycorrhizae and roots.

This data was collected by the 2019 cohort of the Tropical Forest Ecology MRes at Imperial College London. Project: This dataset was collected as part of the following SAFE research project: MRes Tropical Forest Ecology Field Course XML metadata: GEMINI compliant metadata for this dataset is available here Files: This dataset consists of 2 files: Carbon_corrected_slopes.xlsx, raw_egm.zip Carbon_corrected_slopes.xlsx This file contains dataset metadata and 2 data tables: 24 hour observations of CO2 flux patterns (described in worksheet Carbon_flux_data) Description: Estimated CO2 flux values, soil moisture and air and soil temperatures from Carbon subplots Number of fields: 14 Number of data rows: 296 Fields: record_no: EGM 'Plot' value - record number on EGM machine for this collar (Field type: ID) plot: Carbon subplot number (Field type: Location) date: Calendar date that measurements taken (Field type: Date) time: Time that measurements taken (Field type: Time) soil_wmc: Soil water moisture content (Field type: Numeric) soil_temp: Soil temperature (Field type: Numeric) air_temp: Air temperature (Field type: Numeric) treatment: Exclusion treatments for partitioning soil respiration components (Field type: Categorical) field_flux: CO2 flux reported in the field by EGM (Field type: Numeric) Source: EGM dat file of source data used for corrected fluxes where available (Field type: File) corrected_flux: Corrected flux measurements using by eye exclusion of raw flux data (Field type: Numeric) n_points: Number of points in EGM record (Field type: Numeric) n_used: Number of points used for corrected slope estimation (Field type: Numeric) flux: Final flux values, using corrected values where available (Field type: Numeric) EGM raw data (described in worksheet EGM_raw_data) Description: Duplicates key information from raw EGM files and indicates points excluded in calculation of corrected flux values Number of fields: 7 Number of data rows: 7114 Fields: Plot: EGM recorder 'plot' code, actually just the record sequence number. (Field type: ID) RecNo: EGM record number - time points of gas measurement at a single plot (Field type: ID) Datetime: Time of gas concentration measurement (Field type: Datetime) CO2.Ref: Measured CO2 (Field type: Numeric) Input.E: EGM internal variable used in slope estimation (Field type: Numeric) Source: Original EGM dat file containing the flux data (Field type: File) ignore: Indicates where points from raw data excluded from corrected slope calculations (Field type: Categorical) raw_egm.zip Description: Zipfile of raw EGM dat files Date range: 2019-02-18 to 2019-02-21 Latitudinal extent: 4.7467 to 4.7480 Longitudinal extent: 116.9693 to 116.9704", "keywords": ["2. Zero hunger", "550", "soil CO2 flux", "24 hour cycle", "partitioning", "15. Life on land", "6. Clean water", "630"], "contacts": [{"organization": "Orme, C David L, Riutta, Terhi, Fraser, Adam, Mackintosh, Emma, Vollans, Maisie, Graves, Kristina, Roxby, Gabrielle, Baynham, Georgina, Noble, Ciar, Adzhar, Rahayu, MacLean, Catherine, Martin, Stephanie, McKinlay, Amelia, Poznansky, Frederica, Prairie, Aaron, Brasington, Ella, Wiederkehr, Fabienne,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3247592"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3247592", "name": "item", "description": "10.5281/zenodo.3247592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3247592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-17T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:21Z", "type": "Dataset", "title": "Organic matter content (om) soil maps of the Upper Colorado River Basin", "description": "UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma", "keywords": ["2. Zero hunger", "13. Climate action", "soil organic matter", "digital soil mapping", "15. Life on land", "6. Clean water", "predictive soil mapping", "soil property mapping"], "contacts": [{"organization": "Nauman, Travis", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3591992"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3591992", "name": "item", "description": "10.5281/zenodo.3591992", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3591992"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-28T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:25Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": "

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

", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.7041394", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:30Z", "type": "Other", "title": "QBS-ar and QBS-ar_BF index toolbox for biodiversity assessment of microarthropods community in soil", "description": "This toolbox contains a proposal and detailed instructions for the standardization of the QBS-ar index procedure. The QBS-ar index (Parisi, 2001; Parisi et al., 2005) is an index aimed at assessing soil-dwelling microarthropod communities in relation to their soil adaptation. The core of QBS-ar index principle is: the higher is soil quality, the higher will be the number of microarthropod groups well adapted to soil habitats living there. Soil quality here stands for good stability, high organic matter content, and good biodiversity level. Worth of notice, QBS-ar is not comprehensive of the whole soil biodiversity or whole soil quality. For each sample to be assessed with the QBS-ar method, three different samplings are performed 5-10 meters apart (Menta et al., 2018), obtaining three subsamples that are considered representative of an area homogeneous for slope and vegetation. 1-page guidelines for sampling and extraction were detailed in file \u20181.QBS-ar sampling and extraction guidelines.pdf\u2019. The functional characteristics for soil adaptation of microarthropods are the reduction of visual structures as microphthalmia or even anophthalmia, the reduction or loss of pigmentation and dehydration adaptations (thinner cuticle, shorter setas or chaeta loss), appendage reduction (as shorter and/or smaller antennae, legs, furca), miniaturization, streamlined body form. Starting from these principles, soil organisms could be divided into a discrete set of Biological Forms (BFs, eco-morpho-types) according to their morphological adaptation to soil (Menta et al., 2018); in the EXCALIBUR and EJP-Minotaur projects, CREA Research Centre for Agriculture and Environment, in collaboration with the National QBS-ar working group (D\u2019Avino et al., 2021), recognized no less than 54 different BFs, 16 of which were classified as less frequent. Each BF is shortly described and associated with a score, the so-called Eco-Morphological Index (EMI), which ranges from 1 to 20 in proportion to the increasing degree of the soil adaptation of microarthropods. As reported by Parisi et al. (2005), some taxa show only one single EMI value because all species belonging to these taxa report the same adaptation level to the soil. Other groups show a range of EMIs in relation to the different adaptation levels of species to the soil. In general, eu-edaphic forms get EMI = 20, hemi-edaphic get an EMI rating proportionate to their degree of soil adaptation, while epedaphic (epigean) forms get EMI score = 1 (Menta et al., 2018). In QBS-ar whenever two eco-morphological forms are present in the same group, the final score is determined by the higher EMI. In other words, the most highly adapted microarthropods belonging to a group determine the overall EMI score for that group (Parisi et al 2005). This statement is not valid for the QBS-ar_BF proposed here, for which every biological form concurs to the calculation of QBS-ar_BF index, regardless of whether or not it belongs to the same group (i.e. class or order). Moreover, to assess variability between subsamples an index based on spectral analysis (D\u2019Avino, 2019) is proposed. The file \u20182.Sheet for QBS-ar record and calculation_v2.xlsx\u2019 provides the selection of 54 most common BFs in European soils and should be used as a template for data registration during the microarthropods identification and count at the stereomicroscope. As outlined in the \u201cREADME\u201d sheet of the excel file, the user should register the abundance of each BF and other sampling-related information, and the template will automatically perform a series of calculations and will format several output sheets. In particular it calculates: mean abundance of microarthropods and relative class of abundance, QBS-ar, QBS-ar_BF, variability index community based on spectral analysis. One example of the compilated sheet is reported as file \u20182a.Filled_template_example_v2.pdf\u2019. The sampling was carried out during MINOTAUR project, the example was implemented starting from file \u20182. Sheet for QBS-ar record and calculation.xlsx\u2019. The collection, merging and organization of data from the precompiled sheet could be a cumbersome task, especially for surveys with many different samples. The user would need to copy-paste each row in the \u201cExport_Sample\u201d or \u201cExport_Subsample\u201d sheets (from file 2.) to a new file, to perform overall analysis; an error-prone operation. To ease this process, we have compiled a short R script that can be found in this submission as the third file \u20183.QBS-ar data merging_v2.R\u2019. By using the script, the user would automatically obtain three files (csv and/or xlsx, that will be stored in a \u201cResults\u201d folder) resulting from the merging of any number of QBS precompiled templates collected in a folder: 1. The result of the merge of \u201cExport_Sample\u201d sheets 2. The result of the merge of \u201cExport_Subsample\u201d sheets 3. The result of the merge of 'Export_MINOTAUR' sheets Cited reference D\u2019Avino, L., Menta, C., Jacomini, C., Cassi, F., L\u2019Abate, G., La Terza, A., Staffilani, F., Pocaterra, F., Piazzi, M., Parisi, V. (2021). The Italian skill network of Soil Biological Quality assessed by microarthropods\u2019 community, in: FAO. 2021. Keep soil alive, protect soil biodiversity \u2013Global Symposium on Soil Biodiversity 19\u201322 April 2021. Proceedings. Rome. ISBN [978-92-5-135218-2] pages 182-188. Available at https://doi.org/10.4060/cb7374en (accessed 01/09/2022) D\u2019Avino 2019. Soil mesofauna QBS-ar index in: Malusa et al. Common guidelines for analytical methods. https://cordis.europa.eu/project/id/817946/results/it (Accessed 28/03/2023) Menta, C., Conti, F. D., Pinto, S., Bodini, A. (2018). Soil Biological Quality index (QBS-ar): 15 years of application at global scale. Ecological Indicators, 85, 773-780. Parisi V. (2001). La qualit\u00e0 biologica del suolo. Un metodo basato sui microartropodi [In Italian] Acta Naturalia de L\u2019Ateneo Parmense 37, 97-106. Parisi, V., Menta, C., Gardi, C., Jacomini, C., Mozzanica, E. (2005). Microarthropod communities as a tool to assess soil quality and biodiversity: a new approach in Italy. Agriculture, Ecosystems & Environment, 105 (12), 323-333.", "keywords": ["2. Zero hunger", "Data manipulation", "Mesofauna", "15. Life on land", "QBS data template", "Soil biodiversity", "Soil quality"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7041394"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7041394", "name": "item", "description": "10.5281/zenodo.7041394", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7041394"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-05T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:34Z", "type": "Dataset", "title": "Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis", "description": "Open AccessData description This dataset includes detailed information about five different types of land use change reported in \u201cThe effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; https://doi.org/10.1016/j.agee.2023.108402)\u201d. Lists of five different types of land use change 1) conversion of primary forest to cropland 2) conversion of primary forest to grassland 3) conversion of cropland to forest 4) conversion of grassland to forest 5) conversion of grassland to cropland Lists of detailed information Land use change (pre-LUC, post-LUC) Country, Location, Geographic position (Longitude, Latitude) Altitude (m) Climate zone Weather [rainfall (mm yr-1) and temperature (\u00b0C)] Reported time of change (years) Vegetation type (pre-LUC, post-LUC) Fertilizer (pre-LUC, post-LUC: type, application; change) Soil sampling depth (cm) Soil type [units, pre-LUC, post-LUC, change rate (%)] Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)] Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)] Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)] Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)] Soil C:N [units, pre-LUC, post-LUC, change rate (%)] Soil C:P [units, pre-LUC, post-LUC, change rate (%)] Soil N:P [units, pre-LUC, post-LUC, change rate (%)] Reference Data collection method We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland. We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases. While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation. When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents. Acknowledgements We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand\u2019s Ministry of Business, Innovation and Employment.", "keywords": ["2. Zero hunger", "13. Climate action", "land-use change", " greenhouse gas emissions", " soil", " carbon", " nitrogen", " phosphorus", " stoichiometry", " time", " temperature", " rainfall", " forest type", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7656722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7656722", "name": "item", "description": "10.5281/zenodo.7656722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7656722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7856487", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:36Z", "type": "Dataset", "title": "HiLSS Project", "description": "This\u00a0repository is periodically updated. Historic Landscape and Soil Sustainability (MSCA-IF-2019 - Individual Fellowships) The HiLSS Project aims to investigate the relationships between sustainability and landscape heritage with particular reference to soil loss and degradation over the long term. The project will take a multidisciplinary approach that combines archaeology, Historical Landscape Characterisation (HLC), geosciences, and computer-based geospatial analysis (GIS - Geographical Information Systems) and modelling (RUSLE - Revisited Universal Soil Loss Equation). The research objectives of the HiLSS project are to quantify the impact of human activities during the Late Holocene in order to create spatial models which can inform the development of sustainable conservation strategies for rural landscape heritage. This project will focus on two mountainous regions that present historical and cultural similarities but located in different climatic zones of Europe (1- Tuscan-Emilian Apennines, Italy; 2- Northern-mid Galicia, Spain). In previous HLC studies, land-use has been evaluated from the perspective of cultural heritage, whereas RUSLE have used it as a proxy for the land-cover of an area and its effect on soil erosion. The HiLSS project will propose an innovative methodology that combines both the historic/cultural values and the environmental values of land-use to inform development of a model for the sustainable conservation. By considering the different agricultural land-use HLC types in GIS-RUSLE modelling, it will be possible to quantify the effect on soil loss for each HLC type and consequently to devise more environmentally sustainable management for each type. Environmental sustainability and historic landscape conservation are typically treated as two separate fields, but the HiLSS project will develop a transformative model for interdisciplinary research, proposing a new way to embrace both cultural and natural values as components of the same landscape management plans. HLC_RUSLE.zip The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: 'Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)'. List of files included in HLC_RUSLE.zip: R_script_code named 'HLC_RUSLE'\u00a0in .rmd format Output folder: Figures folder: .png products of the R script code Rasters\u00a0folder: .png products of the R script code Tables\u00a0folder: .pdf\u00a0products of the R script code GeoTiff folder (.TIFF file format): Regional RUSLE\u00a0Data GPKG:\u00a0HLC dataset\u00a0and\u00a0Region Of Interest file in .gpkg format Spatial statistics to reveal patterns and connections in the historic landscape The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: '\u00a0F.\u00a0Brandolini & S.\u00a0Turner\u00a0(2022)\u00a0Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy),\u00a0Journal of Maps,\u00a0DOI:\u00a010.1080/17445647.2022.2088305.\u00a0'. It is available at:\u00a0https://doi.org/10.5281/zenodo.5907229 Supplementary material_Land _SI_Historic Landscape Evolution.zip Supplementary Materials to accompaing\u00a0the paper:\u00a0The evolution of historic agroforestry landscape in the Northern Apennines (Italy) and its consequences for slope geomorphic processes, submitted to\u00a0Land,\u00a0Special Issue\u00a0Historic Landscape Transformation. Project_Publications.zip List of .pdf file included in the folder:\u00a0 1) Brandolini F, Domingo-Ribas G, Zerboni A and Turner S. A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features [version 2; peer review: 2 approved, 1 approved with reservations]. Open Res Europe 2021,\u00a01:22\u00a0(https://doi.org/10.12688/openreseurope.13135.2) 2) Brandolini F., Turner S.\u00a0 2022 - Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy), \u00a0Journal of Maps,\u00a0 (https://doi.org/10.1080/17445647.2022.2088305) 3) Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. 2023 -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023), (https://doi.org/10.1038/s41598-023-31334-z) 4)\u00a0Brandolini, F., Compostella, C., Pelfini, M., and Turner, S. 2023 - 'The Evolution of Historic Agroforestry Landscape in the Northern Apennines (Italy) and Its Consequences for Slope Geomorphic Processes' Land 12, no. 5: 1054. (https://doi.org/10.3390/land12051054)", "keywords": ["2. Zero hunger", "13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "USPED", "15. Life on land", "Historic Landscape Characterisation", "Soil Sustainability", "Soil Erosion Modelling", "12. Responsible consumption"], "contacts": [{"organization": "Brandolini Filippo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7856487"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7856487", "name": "item", "description": "10.5281/zenodo.7856487", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7856487"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-10T00:00:00Z"}}, {"id": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:37Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8089699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8109600", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:38Z", "type": "Dataset", "title": "Data on soil compounds, respiration and incorporation of 13C-labeled substrate", "description": "Open AccessSee Readme.pdf", "keywords": ["2. Zero hunger", "microdialysis", "respiration rates", "compound concentration in soil solution", "PLFA and NLFA", "13C isotopic labeling", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Wiesenbauer, Julia, Kaiser, Christina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8109600"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8109600", "name": "item", "description": "10.5281/zenodo.8109600", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8109600"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-18T00:00:00Z"}}, {"id": "10773/25427", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:22:26Z", "type": "Journal Article", "created": "2018-01-07", "title": "Toxicokinetics of Zn and Cd in the earthworm Eisenia andrei exposed to metal-contaminated soils under different combinations of air temperature and soil moisture content", "description": "This study evaluated how different combinations of air temperature (20\u202f\u00b0C and 25\u202f\u00b0C) and soil moisture content (50% and 30% of the soil water holding capacity, WHC), reflecting realistic climate change scenarios, affect the bioaccumulation kinetics of Zn and Cd in the earthworm Eisenia andrei. Earthworms were exposed for 21\u202fd to two metal-contaminated soils (uptake phase), followed by 21\u202fd incubation in non-contaminated soil (elimination phase). Body Zn and Cd concentrations were checked in time and metal uptake (k1) and elimination (k2) rate constants determined; metal bioaccumulation factor (BAF) was calculated as k1/k2. Earthworms showed extremely fast uptake and elimination of Zn, regardless of the exposure level. Climate conditions had no major impacts on the bioaccumulation kinetics of Zn, although a tendency towards lower k1 and k2 values was observed at 25\u00a0\u00b0C\u00a0+\u00a030% WHC. Earthworm Cd concentrations gradually increased with time upon exposure to metal-contaminated soils, especially at 50% WHC, and remained constant or slowly decreased following transfer to non-contaminated soil. Different combinations of air temperature and soil moisture content changed the bioaccumulation kinetics of Cd, leading to higher k1 and k2 values for earthworms incubated at 25\u00a0\u00b0C\u00a0+\u00a050% WHC and slower Cd kinetics at 25\u00a0\u00b0C\u00a0+\u00a030% WHC. This resulted in greater BAFs for Cd at warmer and drier environments which could imply higher toxicity risks but also of transfer of Cd within the food chain under the current global warming perspective.", "keywords": ["Soil invertebrates", "Bioavailability", "Climate Change", "0211 other engineering and technologies", "02 engineering and technology", "Global Warming", "01 natural sciences", "Soil", "Metals", " Heavy", "SDG 13 - Climate Action", "Climate change", "Animals", "Soil Pollutants", "Oligochaeta", "0105 earth and related environmental sciences", "2. Zero hunger", "Triazines", "Temperature", "Water", "Bioaccumulation", "Mining wastes", "Toxicokinetics", "Zinc", "Heavy metals", "Metals", "13. Climate action", "Environmental Pollution", "Cadmium"]}, "links": [{"href": "https://doi.org/10773/25427"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Chemosphere", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10773/25427", "name": "item", "description": "10773/25427", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10773/25427"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-01T00:00:00Z"}}, {"id": "10.7910/DVN/HXAH87", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:22:10Z", "type": "Dataset", "title": "Arbuscular and ectomycorrhizal fungi diversity in the Indian subcontinent", "description": "Mycorrhizal fungi (MF) are below-ground organisms playing a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Arbuscular and ectomycorrhizal fungi are the two mycorrhizal types most relevant to worldwide ecosystems, but areas like the Indian sub-continent remain under-represented in global maps. The dataset presented here reports the available information regarding arbuscular and ectomycorrhizal fungi diversity in cultivated and natural ecosystems of the Indian subcontinent. We have selected studies published in English in ISI Web of Science during the years 2005 - 2020 that provided a taxonomic classification of MF and their associated abundance in terms of percentage of root colonization or number of spores per quantity of soil. From the screening of 74 studies, we have recorded: i. the scientific or common name of the plant or the generic habitat sampled for MF identification; ii the MF genus and species; iii. the location of the study with associated altitude and geographic coordinates; iv. main soil physico-chemical properties (soil pH, texture, organic Carbon, Total Nitrogen, available Phosphorus); climatic variables such as mean annual precipitation and temperature.<br><br>", "keywords": ["ecosystem management", "Asia", "Agricultural Sciences", "CGIAR Research Program on Water", " Land and Ecosystems", "Multifunctional Landscapes", "gesti\u00f3n de ecosistemas", "soil biology", "MYCORRHIZAE", "CGIAR Research Program", "Earth and Environmental Sciences", "SOIL BIOLOGY", "BIODIVERSITY", "mycorrhizae", "biolog\u00eda del suelo"], "contacts": [{"organization": "Beggi, Francesca, Dasgupta, Debarshi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HXAH87"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HXAH87", "name": "item", "description": "10.7910/DVN/HXAH87", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HXAH87"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "11369/372709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:22:30Z", "type": "Journal Article", "created": "2018-09-07", "title": "Soil resources and element stocks in drylands to face global issues", "description": "Abstract

Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth\uffe2\uff80\uff99s land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in the face of future challenges including increased population, food security, desertification, and climate change. Aridity limits plant production and results in poorly developed soils, with coarse texture, low C:N and C:P, scarce organic matter, and high vulnerability to erosion. Dryland soils store 646 Pg of organic C to 2\uffe2\uff80\uff89m, the equivalent of 32% of the global soil organic C pool. The magnitude of the historic loss of C from dryland soils due to human land use and cover change and their typically low C:N and C:P suggest high potential to build up soil organic matter, but coarse soil textures may limit protection and stabilization processes. Restoring, preserving, and increasing soil organic matter in drylands may help slow down rising levels of atmospheric carbon dioxide by sequestering C, and is strongly needed to enhance food security and reduce the risk of land degradation and desertification. The ecotoxicological effects of microplastics in soil ecosystems are complex, particularly in areas of intensive agriculture and livestock production, where plant protection products and veterinary drugs commonly coexist with plastic residues. In this study, we investigated the impact, under laboratory conditions, of 3 MP types (non-biodegradable low-density polyethylene (LDPE) and biodegradable polybutylene adipate terephthalate-based (PBAT-based) and a starch-based polymer) on the soil-dwelling species Folsomia candida (Willem, 1902) in soils contaminated with the anthelmintic albendazole and the fungicide pyraclostrobin. These organic pollutants (OPs) are frequently found in areas of intensive agriculture and livestock production. F. candida individuals were exposed for 28\uffc2\uffa0days to soils contaminated by the OPs at 0.0001 w/w% (1\uffc2\uffa0mg/kg), with and without MPs at 0.01 and 0.1 w/w% concentrations (100 and 1000\uffc2\uffa0mg/kg respectively), under laboratory conditions (21\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 C\uffc2\uffb0, 80%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 RH). Adults\uffe2\uff80\uff99 survival, egg production, and juveniles\uffe2\uff80\uff99 occurrence were recorded as endpoints. Our findings indicate that microplastics alone did not significantly affect the survival and reproductive outcomes of F. candida . However, in soils contaminated with albendazole and pyraclostrobin, the presence of biodegradable MPs resulted in significant effects compared to the control and the treatment with only microplastics. Specifically, PBAT-based MPs significantly impacted adult survival, juvenile occurrence, and egg counts, while starch-based MPs primarily affected egg counts. On the contrary, co-exposure to OPs and LDPE MPs did not show significant effects. These results suggest that different MPs influence the bioavailability and toxicity of co-occurring fungicides and veterinary drug in soil ecosystems in different ways, with implications for assessing the ecological risks of biodegradable and non-biodegradable plastics in contaminated soils. The potential of MPs to influence the spatial distribution and bioavailability of organic pollutants for soil mesofauna needs further investigation. Nitrogen (N) is the primary growth\uffe2\uff80\uff90limiting nutrient in many terrestrial ecosystems, and therefore plant production per unit N taken up (i.e., N use efficiency, NUE) is a fundamentally important component of ecosystem function. Nitrogen use efficiency comprises two components: N productivity (AN, plant production per peak biomass N content) and the mean residence time of N in plant biomass (MRTN). We utilized a five\uffe2\uff80\uff90year fertilization experiment to examine the manner in which increases in N and phosphorus (P) availability affected plant NUE at multiple biological scales (i.e., from leaf to community level). We fertilized a natural gradient of nutrient\uffe2\uff80\uff90limited peatland ecosystems in the Upper Peninsula of Michigan, USA, with 6 g N\uffc2\uffb7m\uffe2\uff88\uff922\uffc2\uffb7yr\uffe2\uff88\uff921, 2 g P\uffc2\uffb7m\uffe2\uff88\uff922\uffc2\uffb7yr\uffe2\uff88\uff921, or a combination of N and P. Our objectives were to determine how changes in carbon and N allocation within a plant to leaf and woody tissue and changes in species composition within a community, both above\uffe2\uff80\uff90 and belowground, would affect (1) NUE; (2) the adaptive trade\uffe2\uff80\uff90off between the components of NUE; (3) the efficiency with which plants acquired N from the soil (N uptake efficiency); and (4) plant community production per unit soil N availability (N response efficiency, NRE). As expected, N and P addition generally increased aboveground production and N uptake. In particular, P availability strongly affected the way in which plants took up and used N. Nitrogen use efficiency response to nutrient addition was not straightforward. Nitrogen use efficiency differed between leaf and woody tissue, among species, and across the ombrotrophic\uffe2\uff80\uff93minerotrophic gradient because plants and communities were adapted to maximize eitherANor MRTN, but not both concurrently. Increased N availability strongly decreased plant and community N uptake efficiency, while increased P availability increased N uptake efficiency, particularly in a nitrogen\uffe2\uff80\uff90fixing shrub. Nitrogen uptake efficiency was more important in controlling overall plant community response to soil N availability than was NUE, and above\uffe2\uff80\uff90 and belowground community N uptake efficiencies responded to nutrient addition in a similar manner. Our results demonstrate that plants respond to nutrient availability at multiple biological scales, and we suggest that N uptake efficiency may be a more representative measurement of plant responses to nutrient availability gradients than plant NUE.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "Michigan", "Nitrogen", "Phosphorus", "04 agricultural and veterinary sciences", "Plants", "15. Life on land", "01 natural sciences", "6. Clean water", "Soil", "13. Climate action", "Wetlands", "0401 agriculture", " forestry", " and fisheries", "Biomass", "Fertilizers"]}, "links": [{"href": "https://doi.org/10.1890/09-0064.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1890/09-0064.1", "name": "item", "description": "10.1890/09-0064.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1890/09-0064.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7687513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:21:34Z", "type": "Report", "title": "Effects of a fungal invasion on soil bacteria", "description": "Presentation by F.Pinzari at The World Congress of Soil Science 2022, which took place in Glasgow from 31st July - 5th August 2022 Abstract: Effects of a fungal invasion on soil bacteria Pinzari F.1,2, Clark M.D.1, Misra R. 3, Chooneea D.3, Xu X.-M.4, Jungblut A.D.1 1Life Sciences Department, Natural History Museum, Cromwell Road, SW7 5BD London, UK 2Institute for Biological Systems, Council of National Research of Italy (CNR), Monterotondo (RM), Italy 3Core Research Laboratories, Molecular Biology, Natural History Museum, London, United Kingdom 4National Institute of Agricultural Botany, East Malling Research Station (EMR), East Malling, UK Fungal bioinoculants have a vast potential in agriculture because they can help increase crop yields and quality and reduce the application of chemicals. Their effectiveness has been widely tested (Malus\u00e0 et al., 2016). However, little is known about the effect of bioinoculants on microbial assemblages in non-rhizospheric soil. A sudden artificial introduction of a fungal species in soil could theoretically impact the biodiversity of local microbial communities and lead to changes in nutrient availability (van Elsas et al., 2012). We assessed the impact of a competitive fungal inoculum, the globally-used biofertiliser Trichoderma afroharzianum T22, on soil microcosms to understand 1) to what extent the native microbial community richness and relative abundance are influenced by a fungal strain introduced to soil; 2) whether microbial taxa are resilient to the disturbance caused by the fungus; 3) how far the bioinoculant impacts the soil microorganisms functions. We used bacterial 16S rRNA gene amplicon sequencing (Illumina) and a shotgun metagenomic analysis (Oxford Nanopore Sequencing) to analyse the microbial communities in bioreactors after seven weeks of incubation with and without the fungus. The presence of the fungus had a negative impact on the abundance of some groups of bacteria, such as the genus Pseudomonas, and it stimulated the presence of species metabolically linked to the fungus, including chitin degrading Chitinophagaceae. In conclusion, the results suggest that more than an impact on bacteria's overall biodiversity, the fungus has favoured some groups at the expense of others, even creating new food webs and trophic niches. References Malus\u00e0 E, Pinzari F, Canfora L (2016) Efficacy of Biofertilizers: Challenges to Improve Crop Production. In: D.P. Singh et al. (eds.), Microbial Inoculants in Sustainable Agricultural Productivity: Microbial Inoculants in Sustainable Agricultural Productivity, pp.17-40 Springer India doi.org/10.1007/978-81-322-2644-4_2 van Elsas JD, Chiurazzi M, Mallon CA, Elhottova D, Kristufek V, Salles JF. (2012) Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci U S A. 24;109(4):1159-64. doi: 10.1073/pnas.1109326109.", "keywords": ["2. Zero hunger", "soil", " Trichoderma", " invasion", " microbial community", " bioinoculants", " T22", "13. Climate action", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Pinzari, Flavia, Jungblut, Anne D., Clark, M.D., Misra, R., Xu, X.-M., Chooneea, D.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7687513"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7687513", "name": "item", "description": "10.5281/zenodo.7687513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7687513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-01T00:00:00Z"}}, {"id": "10.1016/j.firesaf.2017.03.052", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:16:06Z", "type": "Journal Article", "created": "2017-04-14", "title": "Self-ignition of natural fuels: Can wildfires of carbon-rich soil start by self-heating?", "description": "Abstract Carbon-rich soils, like histosols or gelisols, cover more than 3% of the Earth's land surface, and store roughly three times more carbon than the Earth's forests. Carbon-rich soils are reactive porous materials, prone to smouldering combustion if the inert and moisture contents are low enough. An example of soil combustion happens in peatlands, where smouldering wildfires are common in both boreal and tropical regions. This work focuses on understanding soil ignition by self-heating, which is due to spontaneous exothermic reactions in the presence of oxygen under certain thermal conditions. We investigate the effect of soil inorganic content by creating under controlled conditions soil samples with inorganic content (IC) ranging from 3% to 86% of dry weight: we use sand as a surrogate of inorganic matter and peat as a surrogate of organic matter. This range is very wide and covers all IC values of known carbon-rich soils on Earth. The experimental results show that self-heating ignition in different soil types is possible, even with the 86% inorganic content, but the tendency to ignite decreases quickly with increasing IC. We report a clear increase in ambient temperature required for ignition as the IC increases. Combining results from 39 thermostatically-controlled oven experiments, totalling 401\u00a0h of heating time, with the Frank-Kamenetskii theory of ignition, the lumped chemical kinetic and thermal parameters are determined. We then use these parameters to upscale the laboratory experiments to soil layers of different thicknesses for a range of ambient temperatures ranging from 0\u00a0\u00b0C to 40\u00a0\u00b0C. The analysis predicts the critical soil layer thicknesses in nature for self-ignition at various possible environmental temperatures. For example, at 40\u00a0\u00b0C a soil layer of 3% inorganic content can be ignited through self-heating if it is thicker than 8.8\u00a0m, but at 86% IC the layer has to be 1.8\u00a0km thick, which is impossible to find in nature. We estimate that the critical IC for a ambient temperature of 40\u00a0\u00b0C and soil thickness of 50\u00a0m is 68%. Because those are extreme values of temperature and thickness, no self-heating ignition of soil can be expected above the 68% threshold of inorganic content. This is the first in-depth experimental quantification of soil self-heating and shows that indeed it is possible that wildfires are initiated by self-heating in some soil types and conditions.", "keywords": ["Technology", "Engineering", " Civil", "550", "Materials Science", "PEAT", "0904 Chemical Engineering", "Materials Science", " Multidisciplinary", "MOISTURE", "Civil Engineering", "7. Clean energy", "Wildfires", "Soil", "COAL", "Engineering", "Smouldering", "FIRES", "Multidisciplinary", "Science & Technology", "Civil", "624", "15. Life on land", "Ignition", "13. Climate action", "SPREAD", "BEHAVIOR", "SMOLDERING COMBUSTION"]}, "links": [{"href": "https://doi.org/10.1016/j.firesaf.2017.03.052"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Fire%20Safety%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.firesaf.2017.03.052", "name": "item", "description": "10.1016/j.firesaf.2017.03.052", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.firesaf.2017.03.052"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-01T00:00:00Z"}}, {"id": "10.1016/j.apsoil.2014.12.005", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:15:42Z", "type": "Journal Article", "created": "2015-01-16", "title": "Short-Term Mesofauna Responses To Soil Additions Of Corn Stover Biochar And The Role Of Microbial Biomass", "description": "Biochar additions have been suggested to influence soil microbial communities that, through a cascade effect, may also impact soil fauna. In turn, any direct biochar effects on fauna can influence microbial communities through grazing, physical fragmentation of organic debris (and biochar) and modifying soil structure. If biochar creates a favorable environment for soil microorganisms, it is also plausible for fauna to be attracted to such microbially enriched habitats. However, how soil fauna respond to biochar addition to soil and what are the main factors that drive their behavior has rarely been experimentally addressed. Therefore, the behavior of two mesofauna species was assessed as a result of corn stover biochar (slow pyrolysis at 600 \u00b0C) additions to a loamy temperate soil, after preincubation for 2, 17, 31 and 61 d, and related to variations in microbial biomass and activity. Microbial biomass increased by 5-56% and activity by 6-156% with increasing biochar rates for the different preincubation times. Over the incubation time, microbial biomass did not change or increased at most 15% with the different biochar rates, while in turn microbial activity decreased steadily (around 70-80% at day 61). Enchytraeids generally did not show avoidance or preference to biochar when provided with an alternative unamended soil, while collembolans often showed avoidance responses. However, collembolan avoidance to biochar decreased or disappeared in biochar mixtures with higher microbial biomass and water extractable NH4-N content, agreeing with the plausible role of microorganisms to potentially attract soil fauna after biochar applications. Avoidance response was mainly explained by environmental preferences of the test species and not by any toxic effect of the biochar in this study. However, avoidance after the application of biochar may still need to be considered due to the potential negative impacts of individuals' migration on soil ecosystem functioning.", "keywords": ["2. Zero hunger", "Biochar", "Behavior", "Avoidance", "Microbial biomass", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Ammonium", "Soil fauna"]}, "links": [{"href": "https://doi.org/10.1016/j.apsoil.2014.12.005"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Soil%20Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.apsoil.2014.12.005", "name": "item", "description": "10.1016/j.apsoil.2014.12.005", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.apsoil.2014.12.005"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-01T00:00:00Z"}}, {"id": "10.1016/j.envpol.2021.116827", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:16:02Z", "type": "Journal Article", "created": "2021-03-09", "title": "Cocktails of pesticide residues in conventional and organic farming systems in Europe \u2013 Legacy of the past and turning point for the future", "description": "

&lt;p&gt;Considering that pesticides have been used in Europe for over 70 years, a system for monitoring pesticide residues in EU soils and their effects on soil health is long overdue. In an attempt to address this problem, we tested 340 EU agricultural topsoil samples for multiple pesticide residues. These samples originated from 4 representative EU case study sites (CSS), which covered 3 countries and four of the main EU crops: vegetable and orange production in Spain (S-V and S-O, respectively), grape production in Portugal (P-G), and potato production in the Netherlands (N-P). Soil samples were collected between 2015 and 2018 after harvest or before the start of the growing season, depending on the CSS. Conventional and organic farming results were compared in S-V, S-O and N-P. Soils from conventional farms presented mostly mixtures of pesticide residues, with a maximum of 16 residues/sample. Soils from organic farms had significantly fewer residues, with a maximum of 5 residues/sample. The residues with the highest frequency of detection and the highest content in soil were herbicides: glyphosate and its main metabolite AMPA (P-G, N-P, S-O), and pendimethalin (S-V). Total residue content in soil reached values of 0.8 mg kg-1 for S-V, 2 mg kg-1 for S-O and N-P, and 12 mg kg-1 for P-G. Organic soils presented 70-90% lower residue concentrations than the corresponding conventional soils. There is a severe knowledge gap concerning the effects of the accumulated and complex mixtures of pesticide residues found in soil on soil biota and soil health. Safety benchmarks should be defined and introduced into (soil) legislation as soon as possible. Soil remediation techniques should be developed to keep the levels of pesticide residues below such benchmarks. Furthermore, the process of transitioning to organic farming should take into consideration the residue mixtures and their residence time in soil. &amp;#160;&lt;/p&gt;

", "keywords": ["2. Zero hunger", "Organic Agriculture", "Portugal", "Pesticide Residues", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "EU agricultural soils", "Europe", "Soil", "Mixtures of pesticide residues", "Spain", "13. Climate action", "Mixtures of pesticide residues; EU agricultural soils Organic", " conventional farming", "Soil Pollutants", "0401 agriculture", " forestry", " and fisheries", "Organic and conventional farming", "Netherlands"]}, "links": [{"href": "https://doi.org/10.1016/j.envpol.2021.116827"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envpol.2021.116827", "name": "item", "description": "10.1016/j.envpol.2021.116827", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envpol.2021.116827"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-03T00:00:00Z"}}, {"id": "3211739244", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:23:28Z", "type": "Journal Article", "created": "2021-11-08", "title": "Quantifying Cereal Productivity on Sandy Soil in Response to Some Soil-Improving Cropping Systems", "description": "

Little information is available on the effect of soil-improving cropping systems (SICS) on crop productivity on low fertility sandy soils although they are increasingly being used in agriculture in many regions of the world due to the growing demand for food. The study aimed at quantifying the effect of four soil-improving cropping systems applied on sandy soil on cereal productivity (yield of grain and straw and plant height) in a 4-year field experiment conducted in Poland with spring cereal crops: oat (2017), wheat (2018), wheat (2019), and oat (2020). The experiment included the control (C) and the following SICS: liming (L), leguminous catch crops for green manure (LU), farmyard manure (M), and farmyard manure + liming + leguminous catch crops for green manure together (M + L + LU). To quantify the effect of the SICS, classic statistics and the Bland\u2013Altman method were used. It was shown that all yield trait components significantly increased in the last study year (2020) under SICS with M and M + L + LU. All yield trait components were significantly lower in the dry years (2018\u20132019) than in the wet years (2017 and 2020). The relatively large rainfall quantity in May during intensive growth at shooting and the scarce precipitation during later growth in the dry year 2019 resulted in a significantly greater straw yield compared to the other dry year 2018. The values of Bland\u2013Altman bias (mean difference between the particular SICS and the control) varied (in kg m\u22122) from \u22120.002 for LU in 2019 to 0.128 for M and 0.132 for M + L + LU in 2020. The highest limits of agreement (LoA) were in general noted for all yield trait components (the least even yield) in the most productive SICS including M and M + L + LU in the wet year 2020. The Bland\u2013Altman ratio (BAR) values indicate that quantification of the effects of all soil-improving practices was most uncertain in the dry year 2018 for the grain yield and in the wet year 2020 for the straw yield and much less uncertain for the plant height in all SICS and study years. The results of this study provide helpful information about the effect of the SICS on the different yield trait components depending on the period of their application and weather conditions prevailing during the growing season.

", "keywords": ["2. Zero hunger", "Bland\u2013Altman statistics", "S", "soil improving practices", "0401 agriculture", " forestry", " and fisheries", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Podzol soil", "crop response", "weather conditions"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/10/11/1199/pdf"}, {"href": "https://doi.org/3211739244"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3211739244", "name": "item", "description": "3211739244", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3211739244"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-05T00:00:00Z"}}, {"id": "10.1016/j.agee.2006.09.012", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:15:25Z", "type": "Journal Article", "created": "2006-11-14", "title": "Influence Of No-Tillage On The Distribution And Lability Of Phosphorus In Finnish Clay Soils", "description": "Abstract No-tillage (NT) is a method adopted to reduce erosion and particulate phosphorus (P) load from arable land to watercourses. However, it has been found to increase the loss of dissolved P with surface runoff, but the reasons for that have rarely been examined in detail. The objective of the present study was to determine the chemical factors explaining this response by investigating the impact of NT on the type and distribution of P reserves as well as on organic carbon (C) in the 0\u201335\u00a0cm topsoil layer of clay soil profiles (Vertic Cambisols). Soil samples were taken from two experimental fields (Jokioinen and Aurajoki) at 0\u20135, 5\u201320 and 20\u201335\u00a0cm depths in conventionally tilled (CT) and non-tilled (for 4\u20135 years) plots. The plots had been cultivated and fertilized according to the common field practices in Finland (15\u201318\u00a0kg\u00a0P and 100\u2013128\u00a0kg\u00a0N\u00a0ha \u22121 \u00a0year \u22121 ). Inorganic and organic P reserves characterized by a modified Chang and Jackson fractionation procedure were not significantly affected by the cultivation methods. However, in the uppermost soil layer (0\u20135\u00a0cm) in NT of the Jokioinen field, the labile P determined by water extraction (P w ) increased significantly, whereas the increase in P extracted with acid ammonium acetate (P AAC ) remained statistically insignificant. The increase in labile P coincided with a significant increase in organic carbon (C), which supports the theory that competition between organic anions and phosphate for the same sorption sites on oxide surfaces will enhance the lability of soil P. In the Aurajoki field with distinct soil cracking, P w and P AAC were not affected by NT in the uppermost soil layer, but they increased in the deepest soil layer (20\u201335\u00a0cm) concomitantly with an increase in Al-bound P and organic C. However, the increases were not statistically significant. In both fields, soil acidification due to the repeated application of N fertilizers at a shallow soil depth as well as the accumulation of organic C lowered pH of the uppermost soil layer in NT compared to the deeper soil layers. The results indicated that even short-term NT can increase the labile P in clay soil. However, further studies are needed to assess the long-term changes in lability of surface soil P and, consequently, the possible need for readjustment of the fertilization level in NT.", "keywords": ["suorakylv\u00f6", "2. Zero hunger", "330", "no-tillage", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "puskurikyky", "6. Clean water", "ploughing", "inorganic phosphorus", "kynt\u00f6", "Suomi", "clay soils", "0401 agriculture", " forestry", " and fisheries", "Ka", "savimaat", "water-soluble phosphorus", "phosphorus", "fosfori", "P buffering capacit", "vesiliukoinen fosfori", "Finland", "ep\u00e4orgaaninen fosfori", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2006.09.012"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2006.09.012", "name": "item", "description": "10.1016/j.agee.2006.09.012", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2006.09.012"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-05-01T00:00:00Z"}}, {"id": "10.1111/ejss.70088", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:17:59Z", "type": "Journal Article", "created": "2025-03-17", "title": "Pedotransfer Functions Versus Model Structure: What Drives Variance in Agro\u2010Hydrological Model Results?", "description": "ABSTRACT

Pedotransfer functions (PTFs) are widely used empirical relationships to estimate soil hydraulic parameters. PTFs are usually derived from point soil samples analysed in the field or laboratory; thus, they contain uncertainties at different levels (i.e., from sampling and measuring techniques, as well as empirical approaches chosen to quantify relationships). When PTFs are used to parametrize agro\uffe2\uff80\uff90hydrological models, both the choice of PTF and the choice of the model may influence the simulation results. Both sources of variance (PTF choice and model structural differences) were found to be relevant in previous studies, but how they relate to each other has rarely been investigated. In this study, we addressed this research gap by conducting a systematic analysis of the variance in selected agro\uffe2\uff80\uff90hydrological model outputs (i.e., seepage water, soil water content, actual evapotranspiration, transpiration, biomass production) based on an ensemble of 18 PTFs applied to four agro\uffe2\uff80\uff90hydrological models, namely: APEX, CANDY, DAISY and SWAP. The models were calibrated for aboveground biomass and phenology of silage maize and evaluated using data of actual evapotranspiration, seepage water and soil water content obtained from a lysimeter facility in Switzerland. ANOVA\uffe2\uff80\uff90based variance partitioning was applied to attribute variance in model outputs to two uncertainty sources (PTF choice, model choice). Overall, we found that agro\uffe2\uff80\uff90hydrological model structural differences had a larger influence on the variance in model outputs than PTF differences. Further analyses undertaken per model showed that the sensitivity of the simulated outputs to the choice of PTF differed between the models; our results showed that the models integrating the Richards equation (SWAP, DAISY) were more sensitive to the choice of PTF than those using a reservoir cascade approach (APEX, CANDY). Our results also showed that simulated outputs using the mean of a PTF ensemble performed better than when using a single PTF, irrespective of the model and output variable. We therefore recommend using PTF ensembles in agro\uffe2\uff80\uff90hydrological modelling studies. The benefit of using large PTF ensembles is, however, likely to be reduced in larger ensembles of agro\uffe2\uff80\uff90hydrological models, as structural model uncertainties will dominate over PTF uncertainties, according to the four\uffe2\uff80\uff90member model ensemble investigated here.