{"type": "FeatureCollection", "features": [{"id": "10.3389/fmicb.2016.01446", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:07Z", "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": "10.3389/fpls.2017.01892", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:08Z", "type": "Journal Article", "created": "2017-11-06", "title": "Variability in Susceptibility to Anthracnose in the World Collection of Olive Cultivars of Cordoba (Spain)", "description": "Anthracnose of olive (Olea europaea ssp. europaea L.), caused by Colletotrichum species, is a serious disease causing fruit rot and branch dieback, whose epidemics are highly dependent on cultivar susceptibility and environmental conditions. Over a period of 10 years, there have been three severe epidemics in Andalusia (southern Spain) that allowed us to complete the assessment of the World Olive Germplasm Bank of C\u00f3rdoba, one of the most important cultivar collections worldwide.A total of 308 cultivars from 21 countries were evaluated, mainly Spain (174 cvs.), Syria (29 cvs.), Italy (20 cvs.), Turkey (15 cvs.), and Greece (16 cvs.). Disease assessments were performed using a 0-10 rating scale, specifically developed to estimate the incidence of symptomatic fruit in the tree canopy. Also, the susceptibility of five reference cultivars was confirmed by artificial inoculation. Because of the direct relationship between the maturity of the fruit and their susceptibility to the pathogen, evaluations were performed at the end of fruit ripening, which forced coupling assessments according to the maturity state of the trees. By applying the cluster analysis to the 308 cultivars, these were classified as follows: 66 cvs. highly susceptible (21.4%), 83 cvs. susceptible (26.9%), 66 cvs. moderately susceptible (21.4%), 61 cvs. resistant (19.8%), and 32 cvs. highly resistant (10.4%). Representative cultivars of these five categories are 'Ocal,' 'Lech\u00edn de Sevilla,' 'Arbequina,' 'Picual,' and 'Frantoio,' respectively. With some exceptions, such as cvs. Arbosana, Empeltre and Picual, most of the Spanish cultivars, such as 'Arbequina,' 'Cornicabra,' 'Hojiblanca,' 'Manzanilla de Sevilla,' 'Morisca,' 'Picudo,' 'Farga,' and 'Verdial de Hu\u00e9var' are included in the categories of moderately susceptible, susceptible or highly susceptible. The phenotypic evaluation of anthracnose reaction is a limiting factor for the selection of olive cultivars by farmers, technicians, and breeders.", "keywords": ["0301 basic medicine", "2. Zero hunger", "anthracnose", "0303 health sciences", "Plant culture", "Plant Science", "15. Life on land", "16. Peace & justice", "olive", "diseases", "SB1-1110", "03 medical and health sciences", "fruit rot", "Colletotrichum"], "contacts": [{"organization": "J. R. Viruega, Luis F. Roca, Carlos Xavi\u00e9r, Juan Caballero, Antonio Trapero, Juan Moral, Juan Moral,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3389/fpls.2017.01892"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2017.01892", "name": "item", "description": "10.3389/fpls.2017.01892", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2017.01892"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-06T00:00:00Z"}}, {"id": "10.3389/fpls.2019.00191", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:08Z", "type": "Journal Article", "created": "2019-02-22", "title": "Interannual and Seasonal Dynamics of Volatile Organic Compound Fluxes From the Boreal Forest Floor", "description": "In the northern hemisphere, boreal forests are a major source of biogenic volatile organic compounds (BVOCs), which drive atmospheric processes and lead to cloud formation and changes in the Earth's radiation budget. Although forest vegetation is known to be a significant source of BVOCs, the role of soil and the forest floor, and especially interannual variations in fluxes, remains largely unknown due to a lack of long-term measurements. Our aim was to determine the interannual, seasonal and diurnal dynamics of boreal forest floor volatile organic compound (VOC) fluxes and to estimate how much they contribute to ecosystem VOC fluxes. We present here an 8-year data set of forest floor VOC fluxes, measured with three automated chambers connected to the quadrupole proton transfer reaction mass spectrometer (quadrupole PTR-MS). The exceptionally long data set shows that forest floor fluxes were dominated by monoterpenes and methanol, with relatively comparable emission rates between the years. Weekly mean monoterpene fluxes from the forest floor were highest in spring and in autumn (maximum 59 and 86 \u03bcg m-2 h-1, respectively), whereas the oxygenated VOC fluxes such as methanol had highest weekly mean fluxes in spring and summer (maximum 24 and 79 \u03bcg m-2 h-1, respectively). Although the chamber locations differed from each other in emission rates, the inter-annual dynamics were very similar and systematic. Accounting for this chamber location dependent variability, temperature and relative humidity, a mixed effects linear model was able to explain 79-88% of monoterpene, methanol, acetone, and acetaldehyde fluxes from the boreal forest floor. The boreal forest floor was a significant contributor in the forest stand fluxes, but its importance varies between seasons, being most important in autumn. The forest floor emitted 2-93% of monoterpene fluxes in spring and autumn and 1-72% of methanol fluxes in spring and early summer. The forest floor covered only a few percent of the forest stand fluxes in summer.", "keywords": ["VOC EMISSIONS", "Plant Science", "ATMOSPHERIC OH", "01 natural sciences", "forest floor", "SB1-1110", "MONOTERPENE EMISSIONS", "vegetation", "biogenic volatile organic compound", "11. Sustainability", "SCOTS PINE", "EXCHANGE", "0105 earth and related environmental sciences", "decomposition", "CLIMATE-CHANGE", "seasonality", "temperature", "Plant culture", "Forestry", "15. Life on land", "SOIL", "MODEL", "Environmental sciences", "flux", "13. Climate action", "PTR-TOF", "METHANOL"]}, "links": [{"href": "https://doi.org/10.3389/fpls.2019.00191"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2019.00191", "name": "item", "description": "10.3389/fpls.2019.00191", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2019.00191"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-22T00:00:00Z"}}, {"id": "10.3390/rs13061133", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:21Z", "type": "Journal Article", "created": "2021-03-16", "title": "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002\u20132003, 2005\u20132006 and 2008\u20132009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers\u2019 socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.</p></article>", "keywords": ["0106 biological sciences", "2. Zero hunger", "FAO-56 soil water balance", "550", "[SDE.MCG]Environmental Sciences/Global Changes", "Science", "water", "Q", "evapotranspiration", "balance", "15. Life on land", "01 natural sciences", "630", "irrigation", "6. Clean water", "[SDE.MCG] Environmental Sciences/Global Changes", "remote sensing", "evapotranspiration; irrigation; water; remote sensing; FAO-56 soil water balance; NDVI time series", "FAO-56 soil water", "NDVI time series"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://doi.org/10.3390/rs13061133"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13061133", "name": "item", "description": "10.3390/rs13061133", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13061133"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "10.4141/p02-188", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-08-25T16:19:32Z", "type": "Journal Article", "created": "2011-04-24", "description": "<p> In the Brown soil zone of western Canada summerfallowing (F) is traditionally used to reduce the water deficit associated with cereal production, but frequent use of this practice results in soil degradation and reduces the N-supplying capacity of soils. Some scientists suggest that an annual legume green manure crop (LGM) could be used as a partial-fallow replacement to protect the soil against erosion and increase its N fertility, particularly when combined with a snow-trapping technique to replenish soil water used by the legume. We assessed this possibility by comparing yields, N economy, water use efficiency, and economic returns for hard red spring wheat (W) (Triticum aestivum L.) grown in rotation with Indianhead black lentil (Lens culinaris Medikus) green manure (i.e., LGM-W-W) vs. that obtained in a traditional F-W-W system. Further, we assessed whether a change in manage ment of the LGM crop (i.e., moving to earlier seeding and earlier turn-down) was advantageous to the overall performance of this practice. The study was conducted over 12 yr (1988\uffe2\uff80\uff9399) on a medium-textured Orthic Brown Chernozem at Swift Current, Saskatchewan. Wheat stubble was left tall to trap snow, tillage was kept to a minimum, and the wheat was fertilized based on NO3 soil tests. When we examined results after 6 yr, we concluded that by waiting until full bloom to turn down the legume (usually late July or early August) so as to maximize N2 fixation, soil water was being depleted to the detriment of yields of the following wheat crop. The change in management of the LGM crop since 1993 resulted in wheat yields following LGM equaling those after fallow (due to improved water use efficiency), a gradual and significant increase over time in grain protein and in N yield of aboveground plant biomass of wheat in the LGM-W-W compared to the F-W-W system, plus a gradual decrease in fertilizer N requirements of wheat in the LGM system accompanying an improvement in the N supplying power of the soil. These savings in N fertilizer, together with savings in tillage and herbicide costs for weed control on partial-fallow vs. conventional-fallow areas, and higher revenues from the enhanced grain protein, more than offset the added costs for seed and management of the LGM crop. Thus, our results imply that, if producers seed the LGM in April and turn it down in early July, an annual LGM-cereal rotation is a viable option in the semiarid Canadian prairies; however, one negative consequence of adopting this management strategy is the possibility of enhancing NO3 leaching. Key words: Nitrogen yields, grain protein, green fallow, summerfallow substitute, economic returns, NO3 leaching </p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water"], "contacts": [{"organization": "F. Selles, Yantai Gan, Robert P. Zentner, V. O. Biederbeck, Paul G. Jefferson, Reynald Lemke, C. A. Campbell,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.4141/p02-188"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Canadian%20Journal%20of%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4141/p02-188", "name": "item", "description": "10.4141/p02-188", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4141/p02-188"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-01-01T00:00:00Z"}}, {"id": "10.5061/dryad.3sm0340", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:19:36Z", "type": "Dataset", "title": "Data from: Vegetation type controls root turnover in global grasslands", "description": "unspecifiedRoot turnover in  grasslands", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Wang, Jinsong, Sun, Jian, Yu, Zhen, Li, Yong, Tian, Dashuan, Wang, Bingxue, Li, Zhaolei, Niu, Shuli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3sm0340"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3sm0340", "name": "item", "description": "10.5061/dryad.3sm0340", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3sm0340"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-15T00:00:00Z"}}, {"id": "10.5061/dryad.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:42Z", "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": "10.5194/bg-15-1933-2018", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:46Z", "type": "Journal Article", "created": "2017-11-21", "title": "Straw incorporation increases crop yield and soil organic carbon sequestration but varies under different natural conditions and farming practices in China: a system analysis", "description": "<p>Abstract. Loss of soil organic carbon (SOC) from agricultural soils is a key indicator of soil degradation associated with reductions in net primary productivity in crop production systems worldwide. Simple technical and locally appropriate solutions are required for farmers to increase SOC and to improve cropland management. In the last 30 years, straw incorporation has gradually been implemented across China in the context of agricultural intensification and rural livelihood improvement. A meta-analysis of data published before the end of 2016 was undertaken to investigate the effects of straw incorporation on crop production and SOC sequestration. The results of 68 experimental studies throughout China in different edaphic, climate regions and under different farming regimes were analyzed. Compared with straw removal, straw incorporation significantly sequestered SOC (0\uffe2\uff80\uff9320\uffe2\uff80\uff89cm depth) at the rate of 0.35 (range 0.31\uffe2\uff80\uff930.40)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921, increased crop grain yield by 13.4\uffe2\uff80\uff89% (range 9.3\uffe2\uff80\uff89%\uffe2\uff80\uff9318.4\uffe2\uff80\uff89%) and had a conversion efficiency of the applied straw-C as 16\uffe2\uff80\uff89%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff892\uffe2\uff80\uff89% across the whole of China. The combined straw incorporation at the rate of 3\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 with mineral fertilizer of 200\uffe2\uff80\uff93400\uffe2\uff80\uff89kg N\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921 was demonstrated to be the best combination for farmers to use with crop yield increased by 32.7\uffe2\uff80\uff89% (range 17.9\uffe2\uff80\uff89%\uffe2\uff80\uff9356.4\uffe2\uff80\uff89%) and SOC sequestrated by the rate of 0.85 (range 0.54\uffe2\uff80\uff931.15)\uffe2\uff80\uff89Mg C\uffe2\uff80\uff89ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921. Straw incorporation achieved higher SOC sequestration rate and crop yield increment when applied to clay soils, under high cropping intensities, and in areas like Northeast China where the soil is being degraded. SOC responses were the greatest in the initial starting phase of straw incorporation and then declined and finally were negligible after 28\uffe2\uff80\uff9362 years, however, crop yield responses were initially low and then increased reaching their highest level at 11\uffe2\uff80\uff9315 years after straw incorporation. Overall, our study confirmed that straw incorporation did create a positive feedback loop of SOC enhancement together with increased crop production, and this is of great practical significance to straw management as agricultural intensifies in China and other regions in the world with different climate conditions.                         </p>", "keywords": ["2. Zero hunger", "QE1-996.5", "info:eu-repo/classification/ddc/550", "Ecology", "Life", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "QH540-549.5"]}, "links": [{"href": "https://doi.org/10.5194/bg-15-1933-2018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-15-1933-2018", "name": "item", "description": "10.5194/bg-15-1933-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-15-1933-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-21T00:00:00Z"}}, {"id": "10.5194/bg-19-2487-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:47Z", "type": "Journal Article", "created": "2022-05-13", "title": "Climatic variation drives loss and restructuring of carbon and nitrogen in boreal forest wildfire", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The boreal forest landscape covers approximately 10\u2009% of the earth's land area and accounts for almost 30\u2009% of the global annual terrestrial sink of carbon\u00a0(C). Increased emissions due to climate-change-amplified fire frequency, size, and intensity threaten to remove elements such as C and nitrogen\u00a0(N) from forest soil and vegetation at rates faster than they accumulate. This may result in large areas within the region becoming a net source of greenhouse gases, creating a positive feedback loop with a changing climate. Meter-scale estimates of area-normalized fire emissions are limited in Eurasian boreal forests, and knowledge of their relation to climate and ecosystem properties is sparse. This study sampled 50 separate Swedish wildfires, which occurred during an extreme fire season in 2018, providing quantitative estimates of C and N loss due to fire along a climate gradient. Mean annual precipitation had strong positive effects on total fuel, which was the strongest driver for increasing C and N losses. Mean annual temperature\u00a0(MAT) influenced both pre- and postfire organic layer soil bulk density and C\u2009:\u2009N ratio, which had mixed effects on C and N losses. Significant fire-induced loss of C estimated in the 50 plots was comparable to estimates in similar Eurasian forests but approximately a quarter of those found in typically more intense North American boreal wildfires. N loss was insignificant, though a large amount of fire-affected fuel was converted to a low C\u2009:\u2009N surface layer of char in proportion to increased MAT. These results reveal large quantitative differences in C and N losses between global regions and their linkage to the broad range of climate conditions within Fennoscandia. A need exists to better incorporate these factors into models to improve estimates of global emissions of C and N due to fire in future climate scenarios. Additionally, this study demonstrated a linkage between climate and the extent of charring of soil fuel and discusses its potential for altering C and N dynamics in postfire recovery.</p></article>", "keywords": ["QE1-996.5", "Ecology", "Life", "13. Climate action", "QH501-531", "Geology", "15. Life on land", "01 natural sciences", "QH540-549.5", "Climate Science", "Klimatvetenskap", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5194/bg-19-2487-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-19-2487-2022", "name": "item", "description": "10.5194/bg-19-2487-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-19-2487-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-13T00:00:00Z"}}, {"id": "10.5194/bg-7-315-2010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:48Z", "type": "Journal Article", "created": "2010-04-29", "description": "<p>Abstract. Global climate change in the real world always exhibits simultaneous changes in multiple factors. Prediction of ecosystem responses to multi-factor global changes in a future world strongly relies on our understanding of their interactions. However, it is still unclear how nitrogen (N) deposition and elevated atmospheric carbon dioxide concentration [CO2] would interactively influence forest floor soil respiration in subtropical China. We assessed the main and interactive effects of elevated [CO2] and N addition on soil respiration by growing tree seedlings in ten large open-top chambers under CO2 (ambient CO2 and 700 \uffce\uffbcmol mol\uffe2\uff88\uff921) and nitrogen (ambient and 100 kg N ha\uffe2\uff88\uff921 yr\uffe2\uff88\uff921) treatments. Soil respiration, soil temperature and soil moisture were measured for 30 months, as well as above-ground biomass, root biomass and soil organic matter (SOM). Results showed that soil respiration displayed strong seasonal patterns with higher values observed in the wet season (April\uffe2\uff80\uff93September) and lower values in the dry season (October\uffe2\uff80\uff93March) in all treatments. Significant exponential relationships between soil respiration rates and soil temperatures, as well as significant linear relationships between soil respiration rates and soil moistures (below 15%) were found. Both CO2 and N treatments significantly affected soil respiration, and there was significant interaction between elevated [CO2] and N addition (p&lt;0.001, p=0.003, and p=0.006, respectively). We also observed that the stimulatory effect of individual elevated [CO2] (about 29% increased) was maintained throughout the experimental period. The positive effect of N addition was found only in 2006 (8.17% increased), and then had been weakened over time. Their combined effect on soil respiration (about 50% increased) was greater than the impact of either one alone. Mean value of annual soil respiration was 5.32 \uffc2\uffb1 0.08, 4.54 \uffc2\uffb1 0.10, 3.56 \uffc2\uffb1 0.03 and 3.53 \uffc2\uffb1 0.03 kg CO2 m\uffe2\uff88\uff922 yr\uffe2\uff88\uff921 in the chambers exposed to elevated [CO2] and high N deposition (CN), elevated [CO2] and ambient N deposition (CC), ambient [CO2] and high N deposition (NN), and ambient [CO2] and ambient N deposition (CK as a control), respectively. Greater above-ground biomass and root biomass was obtained in the CN, CC and NN treatments, and higher soil organic matter was observed only in the CN treatment. In conclusion, the combined effect of elevated [CO2] and N addition on soil respiration was apparent interaction. They should be evaluated in combination in subtropical forest ecosystems in China where the atmospheric CO2 and N deposition have been increasing simultaneously and remarkably.                     </p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "QE1-996.5", "Ecology", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "Life", "13. Climate action", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "QH540-549.5", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qi Deng, Shuangquan Liu, Honglang Duan, Guoyi Zhou, Juxiu Liu, Dainan Zhang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5194/bg-7-315-2010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-7-315-2010", "name": "item", "description": "10.5194/bg-7-315-2010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-7-315-2010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-08-24T00:00:00Z"}}, {"id": "10.5194/egusphere-egu24-3127", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:51Z", "type": "Report", "created": "2024-03-08", "title": "Cracking Ground Truth Barriers: Harnessing the Power of Transfer Learning for Crop Mapping", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Transfer learning (TL) is rapidly gaining popularity in recent years in various research disciplines due to its practicality, temperate need for resources, and often quite promising results. The same principle applies for Earth observation, especially for tasks such as crop mapping where TL already showed a certain potential. Our focus in this research was on temporal transfer learning for a single agricultural region. In our study we built an initial CNN-1D crop mapping model for Vojvodina province, Serbia, using SAR satellite imagery and ground truth (GT) data collected for 2017-2020. We did it using a leave-one-year-out approach where each year served only once as a validation dataset. The top-performing model was further employed for transfer learning analysis, utilising a limited set of target season ground truth data. The aim was to diminish reliance on labour-intensive and time-consuming large-scale ground truth data collection, typically carried out through hands-on field inspections. Instead of collecting it all over Vojvodina for the 2021 season, we tried to focus on a limited area around the departure point. Three options were analysed, i.e., approximately 20, 25, and 30 km radius around the departure point for which the province capital Novi Sad was taken. From the practical standpoint, labels of these parcels are easier to record than those more distant (distributed), so it seems reasonable to visit only these locations to reduce the costs of ground truth collection. Visited parcels that fell within these radiuses served for retraining the model, and the remaining parcels (those outside 30 km radius) served for testing and accuracy assessment. For each parcel 50 randomly selected pixels were used for the analysis. After 5 retraining cycles, the average F1 score for transfer learning approach of the CNN-1D model for 20, 25 and 30 km buffer zones was 74%, 79% and 83%, respectively. Training the same CNN-1D model from scratch reached 69%, 73% and 78% respectively, i.e., approximately 5% lower score on average. Inferencing using the pre-trained model as such (without adaptations) achieved F1 score of 78%, which set 20 km radius case to the irrational use of TL, while the use of other two buffer areas were justified as they achieved comparably better results. Also, three buffer cases achieved between 3% and 9% lower F1 score than their respective pairs when the same number of parcels used for retraining were randomly distributed all over the test area. This was likely in a relationship with the restricted sampling region characteristics (uniform soil type, management practice, weather conditions) and distribution of classes in that region which may not have properly represented the entire test area. In addition, the comparison of these two approaches showed that adding more samples for retraining scaled down the difference. The viability of the presented approach was confirmed within the experiment and, therefore, practitioners are encouraged to weigh the trade-off between practicality and accuracy in their future work.</p></article>", "keywords": ["2. Zero hunger", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu24-3127"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu24-3127", "name": "item", "description": "10.5194/egusphere-egu24-3127", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu24-3127"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-20T00:00:00Z"}}, {"id": "10.5194/soil-2020-96", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:19:55Z", "type": "Report", "created": "2021-02-06", "title": "Controls on heterotrophic soil respiration and carbon cycling in geochemically distinct African tropical forest soils", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Heterotrophic soil respiration is an important component of the global terrestrial carbon (C) cycle, driven by environmental factors acting from local to continental scales. For tropical Africa, these factors and their interactions remain largely unknown. Here, using samples collected along strong topographic and geochemical gradients in the East African Rift Valley, we study how soil chemistry and soil fertility, derived from the geochemical composition of soil parent material, can drive soil respiration even after many millennia of weathering and soil development. To address the drivers of soil respiration, we incubated soils from three regions with contrasting geochemistry (mafic, felsic, and mixed sedimentary) sampled along slope gradients. For three soil depths, we measured the potential maximum heterotrophic respiration under stable environmental conditions as well as the radiocarbon content (\u039414C) of the bulk soil and respired CO2. We found that soil microbial communities were able to mineralize C from fossil as well as other poor quality C sources under laboratory conditions representative of tropical topsoils. Furthermore, despite similarities in terms of climate, vegetation, and the size of soil C stocks, soil respiration showed distinct patterns with soil depth and parent material geochemistry. The topographic origin of our samples was not a main determinant of the observed respiration rates and \u039414C. In situ, however, soil hydrological conditions likely influence soil C stability by inhibiting decomposition in valley subsoils. Our study shows that soil fertility conditions are the main determinant of C stability in tropical forest soils. Further, in the presence of organic carbon sources of poor quality or the presence of strong mineral related C stabilization, microorganisms tend to discriminate against these sources in favor of more accessible forms of soil organic matter as energy sources, resulting in a slower rate of C cycling. Our results demonstrate that even in deeply weathered tropical soils, parent material has a long-lasting effect on soil chemistry that can influence and control microbial activity, the size of subsoil C stocks, and the turnover of C in soil. Soil parent material and its lasting control on soil chemistry need to be taken into account to understand and predict C stabilization and rates of C cycling in tropical forest soils.                         </p></article>", "keywords": ["2. Zero hunger", "13. Climate action", "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/10.5194/soil-2020-96"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/soil-2020-96", "name": "item", "description": "10.5194/soil-2020-96", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/soil-2020-96"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10060810", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:19:57Z", "type": "Dataset", "title": "SoilCompDB: Global soil compressive properties database. Version 1.0", "description": "Data collection and processing Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. \u00a0A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. \u00a0After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. \u00a0We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries. Time and place The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.\u00a0\u00a0 Instruments The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamand\u00e9 et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006). Resources Web of Science, Scopus \u2013 literature search Endnote Web \u2013 removal of duplicates Rayyan software \u2013 initial paper selection based on title and abstract WebPlotDigitizer \u2013 data extraction from figures Microsoft Access \u2013 database platform Description of the collected data (column, unit, and description) Sample ID-\u00a0\u00a0\u00a0 A unique identification number assigned to each individual sample within the database\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Study ID- Identification number assigned to each research study in the database Reference - Research paper reference Year - Year of research paper publication \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Language - Language of the research paper \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers Soil classification (original in paper) - Soil classification described in research paper\u00a0 Soil classification (convertion to Soil Taxonomy orders) -\u00a0 Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Location - Study location country\u00a0\u00a0\u00a0 Texture classification (USDA) -\u00a0Soil textural classification according USDA Texture\u00a0 classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)\u00a0 Sand (USDA) - % - Soil sand content (weight based)\u00a0 - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available Clay - g kg-1 - Soil clay content - original in the paper Clay class upper boundary - \u00b5m - The clay class upper boundary informed in source publication Silt - g kg-1 - Silt clay content - original in the paper Silt class upper boundary - \u00b5m - The silt class upper boundary informed in source publication Sand - Soil sand content - original in the paper Sand class upper boundary - \u00b5m - The sand class upper boundary informed in source publication Particle size data flag - =0 if no issues; =1 if there are issues (summing) Sum particle size- g kg-1 - Sum of clay, silt, and sand content Soil depth FROM \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil depth TO \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Depth \u2013 cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations Particle density - Mg m-3 - Soil particle density\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial matric potential \u2013 hPa - Soil water matric potential before loading log Initial matric potential - Soil water matric potential expressed by log\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Wetness (based on initial matric potential) - \u00a01=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported Initial water content data source -\u00a0Graph or table from where the data was collected, or explanation on calculation used Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial bulk density - Mg m-3 - Soil bulk density before loading\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density Precompression stress \u2013 kPa - Precompression stress \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress (SD) \u2013 kPa - Standard deviation for precompression stress values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used Compression index - Compression index \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index (SD) - Standard deviation of compression index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index data source - Graph or table from where the data was collected, or explanation on calculation used Swelling index - Swelling index\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index (SD) - Standard deviation of swelling index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used N -\u00a0Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported Land use (paper) -\u00a0Land use described in the research paper Land use (categories) -\u00a0Land use categorized Land use standardized -\u00a0Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna Land use (number code) -\u00a0Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation Tillage system -\u00a0Tillage system Tillage system (arable soils) - Tillage system for arable soils classified as 'conventional' and 'conservation' Coordinates -\u00a0\u00a0Geographical coordinates\u00a0 of study location Climate -\u00a0Climatic region classification: temperate, tropical, subtropical Climatecod -\u00a0\u00a0\u00a0 Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical Sampling position (paper) -\u00a0Field position where soil samples were collected with details described in the paper Sampling position -\u00a0Field position where soil samples were collected standardized Treatment -\u00a0Experimental treatment type where the soil samples were collected Stress rate - \u00a0kPa - Stress applied in compressive tests\u00a0 Minimum stress \u2013\u00a0kPa - Minimum stress applied in compressive tests Maximum stress \u2013\u00a0kPa - Maximum stress applied in compressive tests Number of stress rate steps -\u00a0Number of steps in stepwise stress application procedure Stess application type -\u00a01=Stepwise stress 2=one sample per stress 3=Strain controlled Stess application type \u2013\u00a0min - Time for stress application in each step in stepwise stress application procedure Degree of deformation at the end of loading -\u00a0% - Degree of deformation at the end of compressive test Sample diameter \u2013\u00a0cm - Diameter of the soil samples Sample height \u2013\u00a0cm - Height of the soil samples Ratio sample diameter and height\u00a0-\u00a0Ratio between diameter and height of the soil samples Sample volume -\u00a0cm3 -Sample volume when the sample diameter and height are nor presented Precompression stress calculation method -\u00a0Calculation method of precompression stress Precompression stress calculation method (number code) -\u00a0Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamand\u00e9 et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006) Description of precompression stress calculation -\u00a0Brief explanation of precompression stress calculation Soil compressive curve components -\u00a0Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain.\u00a0 Soil compressive curve components (number code) -\u00a0Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio Curve components source -\u00a0Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper Compressive curve available -\u00a0Original soil compressive curve available in the paper: 1= No 2=Yes Comments -\u00a0Brief comments on the paper Issues and remarks We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 \u03bcm, utilizing the k-nearest neighbor approach (referred to as 'similarity method' by Nemes et al. (1999). \u00a0 Reference Associa\u00e7\u00e3o Brasileira de Normas T\u00e9cnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990. Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60\u201364.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954\u2013960 10.1139/T2012-054. doi:10.1139/t2012-054 Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562\u2013567. Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139\u2013151. doi:10.1016/0933-3630(95)00015-8 Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012 Lamand\u00e9, M., Schj\u00f8nning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274. Nemes, A., \u00a0W\u00f6sten, J.H.M., Lilly, A., \u00a0Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database.\u00a0Geoderma 90: 187-202. 129\u00a0 O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 \u2013 4):161 \u2013 173.", "keywords": ["2. Zero hunger", "soil compression curve", "precompression stress", "15. Life on land", "soil mechanical properties", "compression index", "soil moisture", "uniaxial compression test", "swelling index"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10060810"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10060810", "name": "item", "description": "10.5281/zenodo.10060810", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10060810"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-06T00:00:00Z"}}, {"id": "10.5281/zenodo.10065971", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:19:57Z", "type": "Dataset", "title": "Database of topsoil  chemical and physical properties in Croatia", "description": "Sources Data for database is collected from four main sources:\u00a0  Data published in book 'Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima, I. Dr\u017eavna uprava za za\u0161titu prirode i okoli\u0161a, Zagreb' labeled as 'martinovic_1997' in the database.  This source consists of 2199 pedological profiles sampled from 1963 to 1996, most of which include depth to bedrock information. Data from project: 'Spatial variability of trace and toxic metals in agricultural soils of Croatia', Ministry of Science and Education and Croatian Waters. Project leader: prof.dr.sc. Marija Romi\u0107 from Faculty of Agriculture, Zagreb, labeled as 'agricultural_2013'.  Data are sampled from 'database of properties and quality of agricultural soils of Croatia' on 8x8 km grid and consists only from top soil samples (0-30 cm). There are 811 samples in this database. Data from the the project: 'Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio', from Ministry of Environmental Protection and Energy, carried on by Croatian Geological Institute (HGI), the Croatian Forestry Institute (H\u0160I) and the Agricultural Land Agency (APZ).  This dataset consists of two subsets:  'azo_2013' - 2519 samples of topsoil (0-25 cm), from 1994 to 2004 for making of Geochemistry Atlas of Croatia 'azo_2016' - 742 locations were revisited during 2015-2016 and new samples are taken and analyzed in horizons 0-10 cm, 10-20 cm, 20-30 cm. Network of piezometers Description of sources\u00a0 Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima (Database of Croatian Soils)\u00a0 The database contains data on soil profiles and covers the total area of the Republic of Croatia. Only data accepted by external control are entered in the Database, as well as those profiles for which there is a minimum data. External control of data reliability was performed by comparing the genetical-morphological characteristics of the soil determined by field research and the data of laboratory soil analyses. The profiles for which the field and laboratory analyses are found to differ are rejected. In addition to data on soil properties, basic data on pedogenetic factors are given. The soil profiles surveyed in the period 1963-1996 are entered in the database. The majority of data come from the Basic Pedological Map of Croatia (Osnovna pedolo\u0161ka karta Hrvatske - OPKH) project. The following are entered in the Database: 1347 profiles in volume I and 851 profiles in volume II, a total of 2198 pedological profiles Spatial variability of trace and toxic metals in agricultural soils of Croatia, Project Leader: Marija Romi\u0107\u00a0 The problem of exposure of agricultural soils to different anthropogenic inputs of toxic metals, but also of other potentially toxic substances, has acquired global dimensions in the last decades. Besides atmospheric deposition, environmental dispersion of chemicals used in agriculture is an important factor directly affecting the natural soil functions, or indirectly endangering the biosphere by bioaccumulation and inclusion into the food chain. Metal concentrations in soil can be generally predicted starting with the element abundance in the parent material. The extent to which pedogenesis affects heavy metals distribution varies according to the prevailing factors affecting soil processes. Because of the toxicity to plants and animals, it is important to determine their content, forms and distribution. Such hypotheses may be tested by total metal content determination, as well as other elements relevant for geochemical valorization of the agricultural soils of Croatia. Thus, the spatial variability and baseline of elements in soils will be determined by means of relevant statistical and geostatistical methods. The maps of toxic metal distribution will be produced and the suitability of soils for agriculture will be assessed. GIS is increasingly used in environmental assessment studies because of its ability to superimpose different spatial information and to combine them with the results of statistical analysis, enabling thus the detection of complex spatial relationships among different parameters. Geostatistics and multivariate statistics has been widely used in geochemical studies to identify pollution sources and to apportion natural vs. anthropogenic contribution, establishing a geochemical background as well. The main objectives of the investigation are: (i) to provide a geochemical database relevant to the agricultural soils in Croatia; (ii) to provide a detailed information about the natural variability of the geochemical background which is pertinent to administrative and legal issues as well as to safety food production and environmental protection; (iii) presenting the influence of human and other environmental activities on the soil quality mainly regarding the toxic and trace metal contents, and (iv) we are going to observe the influence of natural conditions on regional differences which have been widely neglected so far, and have not been taken into account while national regulations and guidelines on soil toxic metal contents have been established. Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio\u00a0 The project is funded by the Fund for Environmental Protection and Energy Efficiency within the Program 'Upgrading and Development of the Environmental Information System and Improving the Monitoring and Reporting System on the State of the Environment in the Republic of Croatia', Component 2: Improving the Monitoring and Reporting System on the State of the Environment Croatia; improving the system of data collection and exchange and developing methodologies for their processing in accordance with the guidelines of the UNFCCC and the Kyoto Protocol defined by the IPCC (Intergovernmental Panel on Climate Change).\u00a0 The project holder is the Ministry of Environmental Protection and Energy, and the executors are the Croatian Geological Institute, the Croatian Forestry Institute and the Agricultural Land Agency. In the period 2014-2017, field and laboratory research of soil conditions was conducted at 725 representative locations. General data on the location of sampling were collected, which contain administrative, locational, geographical and other data (relief, climatic and meteorological data, detailed data on land use and vegetation cover, description of surface soil properties). Field soil sampling for each LULUCF land use category was performed according to a modified methodology described in the EU DG JRC (Joint Research Center) 'Protocol for soil sampling to confirm changes in organic carbon stocks in the EU' by Stolbovoy et al. 2007 (Soil sampling protocol to certify the changes of organic carbon stock in mineral soil of the European Union - EU JRC). The protocol modifications aimed to ensure reporting under the UNFCCC and Kyoto protocols, i.e., to ensure compliance with the IPCC methodology. Soil sampling on forest land (FL) according to the JRC protocol is planned at two depths of 0-10 cm and 10 - 20 cm and an organic layer (list), but due to reporting requirements under the UNFCCC and Kyoto protocol, sampling was carried out at a depth of 20 - 30 cm. Land under crops (CL) was sampled at two depths (0-20 cm and 20-30 cm) and grasslands (GL), wetlands (WL), settlements (SL) and other land (OL) were sampled at three depths 0- 10, 10-20 and 20-30 cm. Geochemical analyzes were performed at depths of 0\u201310 and 20\u201330 cm for forest soils (FL) and for meadows and pastures (GL) while for soils under crops (CL) composite samples of 0\u201330 cm and 0\u201320 cm were analyzed. Network of piezometers To get a more accurate depth to bedrock parameter, positions of 812 piezometers are considered as they have at least 4 meters of depth to bedrock. Description of database Column names and descriptions: Metadata columns: site_key - unique identifier that identifies sample in source database\u00a0source_db - label of source database\u00a0source_sampled - label of organization/team who sampled and analyzed data\u00a0site_obsdate - year of taking sample\u00a0longitude_decimal_degrees - longitude in degrees in WGS84 geographical projection\u00a0latitude_decimal_degrees - latitude in degrees in WGS84 geographical projection\u00a0pedon_completeness_index - quality factor (0-100)\u00a0taxgrtgroup - classification of sample according to WBR 2014/2016 classification\u00a0 Soil properties columns: column name - property - measurement units - descriptionoc - Carbon, Organic - % wt - CMS analyte. Organic carbon is a measure of all organic forms of carbon in the soil, including organic carbon within minerals.\u00a0n_tot_ncs - Nitrogen, Total NCS - % wt - Total nitrogen is a measure of all organic and inorganic nitrogen, including that found in nitrogen minerals.ca_mehlich3 - Calcium, Mehlich3 Extractable \u00a0- mg/kg - The calcium extracted by the Mehlich III solution.\u00a0k_mehlich3 - Potassium, Mehlich3 Extractable - mg/kg - The potassium extracted by the Mehlich III solution.\u00a0mg_mehlich3 - Magnesium, Mehlich3 Extractable - mg/kg - The magnesium extracted by the Mehlich III solution.\u00a0p_mehlich3 - Phosphorus, Mehlich3 Extractable - mg/kg - The phosphorus extracted by the Mehlich III solution.\u00a0cec_sum - Cation Exchange Capacity, Summary \u00a0 cmol(+)/kg - The effective cation exchange capacity is calculated by BASE_SUM+AL_KCL. It is not calculated if soluble salts are present. It is reported as meq per 100 grams on a <2 mm base. CMS derived value default\u00a0ec_satp - Electrical Conductivity , Saturation Extract - dS/m - The electrical conductivity of the saturation extract is used to estimate the concentration of salts in a sample, and provides inferences on cation concentration in solution and osmotic pressure. It is reported as mmhos per centimeter.\u00a0caco3 - Carbonates - % wt - Carbonate in the < 2mm fraction is measured by CO2 evolution after acid treatment. It is reported as gravimetric percent CaCO3 on a <2 mm base, even though carbonates of Mg, Na, K, and Fe may be present and react with the acidph_h2o - pH, 1:1 Soil-Water Suspension - (NA) - The pH, 1:1 soil-water suspension is the pH of a sample measured in distilled water at a 1:1 soil:solution ratio. If wider ratios increase the pH, salts are indicated.\u00a0ph_kcl - The pH, 1:1 soil-KCl suspension - (NA) - The pH, 1:1 soil-KCl suspension is the pH of a sample measured in 1.0N KCl at a 1:1 soil:solution ratio. If the pH in KCl < pH in water, Al+++ is indicated.\u00a0total_clay - Clay, Total - % wt - Total clay is the soil separate with <0.002 mm particle diameter. Clay size carbonate is included. Total clay is reported as a weight percent of the <2 mm fraction.\u00a0total_silt - Silt, Total - % wt - Total silt is the soil separate with 0.002 to 0.05 mm particle size. It is reported as a gravimetric percent on a <2 mm base.\u00a0total_sand - Sand, Total - % wt - Total sand is the soil separate with 0.05 to 2.0 mm particle diameter. It is reported as a gravimetric percent on a <2 mm base.\u00a0wpg2 - Coarse fragments - % wt - The weight fraction of particles with >2 mm diameter is reported as a gravimetric percent on a whole soil base.\u00a0db_od Bulk Density, <2mm Fraction, Ovendry - g/cc - Bulk density, oven dry (105 C) is the weight per unit volume of the <2 mm fraction, with volume measured on oven dry (105 C) natural fabric (clods). It is reported as grams per cubic centimeter on a <2 mm base.\u00a0dbr - Depth to bedrock - cm - Depth to the R horizon or similar", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "16. Peace & justice", "3. Good health"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10065971"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10065971", "name": "item", "description": "10.5281/zenodo.10065971", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10065971"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-02T00:00:00Z"}}, {"id": "10.5281/zenodo.10404481", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:19:59Z", "type": "Report", "title": "D.4.1 \u2013 Coaching and Capacity  Building Report, Round #1", "description": "This deliverable reports on the work related to tasks 4.1 and 4.2, carried out by consortium partners from Department of Agroecology at Aarhus University and ENoLL (European Network of Living Labs), respectively. These partners provide applicants with tools and coaching (T4.1), to ease the application process and guide them through consortium building and to design sustainable and well-thought soil health improving living labs. To provide possible applicants from all over Europe with valuable advice, NATI00NS has found mentors in 18 European\u00a0countries, who can be consulted by possible applicants.  This deliverable is written to report on the implementation and execution of tasks 4.1,\u00a0Coaching Sessions, and 4.2, Capacity Building. The tasks feed into NATI00NS\u2019 main objective,\u00a0that is enhancing the possibilities of more viable and well-planned soil health improving living\u00a0lab applications under the Mission auspices, which hopefully will lead to the establishment of\u00a0well-functioning living labs in the near future. The deliverable will provide both the public and\u00a0the funding body, with knowledge on NATI00NS\u2019 initial progress and results.  In short, the function of T4.1 has been to identify candidate Soil Health Living Lab Mentors in\u00a0all EU member states and associated countries, followed up by a process aligning the\u00a0candidates' perceptions on the meaning of a living lab and understanding the topic description\u00a0in dept by participating in on-line training sessions. This concluded in mentor candidates\u00a0signing the Non-Disclosure Agreement (NDA) agreements to officially become mentors and\u00a0thereby be mandated to coach possible living lab applicants within the NATI00NS framework.  Alongside the coaching of mentors, NATI00NS\u2019 has carried out capacity building, prepared and\u00a0implemented by ENoLL, the European Network of Living Labs, a NATI00NS consortium partner,\u00a0that leads online support to bolster up stakeholders around the Soil Mission (hereafter only\u00a0described as the Mission) and broaden their understanding of what a LL is. ENoLL have for this\u00a0purpose, produced e-learning materials, including factsheets and webinars. The Capacity\u00a0Building in combination with Coaching Session activities, provide information and training,\u00a0that enhance the chances of well-conceived and relevant Soil Health Living Labs being created,\u00a0by making sure living lab applicants are not only trained well by mentors with knowledge on\u00a0living lab concepts; participants will also have capacity building material available to them in\u00a0order to design and create a strong Living Lab consortium. The materials include manuals\u00a0which they can use to design a living lab. The capacity building provided by NATI00NS does, in\u00a0general, provide applicants with hands-on capacities, whether it be factsheets or webinars on\u00a0specific living lab related questions.In supporting the applicants at national level identifying It has been important to associate\u00a0skilled mentors has been of the essence. Therefore, the NATI00NS consortium has mapped\u00a0stakeholders across EU Member States and Associated Countries during most of its first\u00a0\u2018introduction and pilot\u2019 phase, to get in contact with gatekeepers in each country.  In most countries, the National Contact Point (NCP) structure, and its responsible officers\u00a0appointed either for the Mission or the Food, Bioeconomy, Natural Resources, Agriculture and\u00a0Environment area, were primary contact points, since it is already an integrated part of their\u00a0job description, to support the Soil Health Mission calls. Consequently, many NCPs have taken\u00a0on the role of mentors themselves while others have tried assisting NATIOONS in finding\u00a0suitable mentor candidates, interested in acting as mentors within the NATI00NS framework.  NCPs are national structures associated to the framework programme. NCPs give personalised support on the spot and in applicants' own languages.  After reaching out to possible mentors, AU AGRO has continuously answered questions about\u00a0the scope of the mentoring work \u2013 such as the mentors\u2019 expected workload, responsibilities,\u00a0and for how long they are expected to commit to mentoring duties. In parallel, NATIOONS has\u00a0planned and implemented two training of trainers webinars that offered training to candidate\u00a0mentors, so they all could be aligned in terms of living lab concepts, practical circumstances\u00a0regarding the application process and confidentiality measures, after which they were able to\u00a0take an informed decision about becoming NATI00NS Mentors or not.All webinar participants, whether they joined for reasons of curiosity or already knew they\u00a0would commit to mentoring, were then briefed on, how it is necessary for them to read and\u00a0sign NDA-documents to officially become NATI00NS appointed Soil Health Living Lab mentors,\u00a0and thus appear on the NATIOONS website with name and contact details. NATIOONS have\u00a0since then continuously collected signed NDA documents and updated the website\u00a0accordingly, thereby expanding the number of mentors available to possible applicants.To carry out the work related to recruiting soil health living lab mentors and training them in\u00a0living lab-affiliated concepts, a number of Aarhus University\u2019s soil and farming systems\u00a0scientists and research support advisers, planned a training programme for mentors.  They have also been responsible for all communication and mapping of possible mentors, organising of the training of trainers event (I.e., training the mentors that will eventually offer\u00a0training to living lab applicants) webinars and gathering and handling Non-disclosure\u00a0Agreement (NDA) documents and FAQ by mentors and applicants. Content for webinars on\u00a0soil health and living labs, have been created and presented by the NATI00NS partners who\u00a0also produced the slides for the National Engagement Events \u2013 another NATIOONS activity\u00a0belonging to another work package, which will be described in its own deliverable.  Additionally, a senior officer from the Aarhus University\u2019s Research Support Office, with great\u00a0experience in providing support for framework programme applicants, provided webinar\u00a0attendants with important guidance on application practices.  The Capacity Building (CB) efforts plays a pivotal role in the NATI00NS project, to ensure the\u00a0success of the Mission. Its main objective is to guarantee the submission of high-quality\u00a0applications for the first two sets of topics aimed at establishing Living Labs (LLs) in 2023 and\u00a02024. These efforts are led by the European Network of Living Labs (ENoLL) as part of Work\u00a0Package 4, 'Supporting Proposal Applicants.'\u00a0NATI00NS\u2019 Capacity Building brings together a comprehensive range of essential training and\u00a0guidance activities tailored specifically for applicants interested in the LL topics related to the\u00a0Mission. At its core, Capacity Building provides online support materials for stakeholders\u00a0involved in the Mission. These materials include a series of e-learning resources, such as\u00a0Factsheets and recorded webinars, offering information about the criteria governing Soil\u00a0Health LLs and the objectives of the Missions within the context of various land use types. Thisinitiative sets the stage for prospective LL applicants in the future.", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "15. Life on land", "16. Peace & justice"], "contacts": [{"organization": "Krabbe, Kasper, Couture, Isabelle, Cavallo, Dolinda,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10404481"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10404481", "name": "item", "description": "10.5281/zenodo.10404481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10404481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-19T00:00:00Z"}}, {"id": "10.5281/zenodo.13374006", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:20:14Z", "type": "Dataset", "title": "Microbial biomass and water-extractable carbon on Mt. Kilimanjaro", "description": "This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project.  Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) \u2013 as sensitive and important parameters for soil fertility and C turnover \u2013 are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro \u2013 altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material \u2013 to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50\u00a0cm, we compared MBC and WOC contents of 2 natural (Ocotea\u00a0and\u00a0Podocarpus forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850\u00a0m a.s.l.  The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.", "keywords": ["land-use change", "microbial carbon dynamics", "tropical ecosystem", "andosol", "elevation gradient", "water-extractable carbon"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13374006"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13374006", "name": "item", "description": "10.5281/zenodo.13374006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13374006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-26T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:20:20Z", "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.14185189", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:20:23Z", "type": "Dataset", "title": "Dataset: experiments on volatile organic compounds uptake by the active layer soils of Greenlandic permafrost areas", "description": "This dataset is associated with a publication currently under peer review (DOI and link to the publication will be updated upon its publication).  Permafrost serves as a significant carbon reservoir, storing up to 1700 petagrams of carbon accumulated over millennia. As global warming accelerates permafrost thaw, this carbon can be mobilized, with a fraction being transformed into volatile organic compounds (VOCs). These VOCs can influence atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols.  In this study, active layer soils\u2014the seasonally unfrozen layer above the permafrost\u2014were collected from two contrasting Greenlandic permafrost locations (Disko Island, and Kangerlussuaq) and incubated to investigate their role in soil-atmosphere VOC exchange. Laboratory incubations were conducted under controlled conditions, where a VOC mixture gas was continuously purged through jars containing the soil samples. Gas concentrations were monitored at the inlet and outlet using a PTR-ToF-MS, allowing for the estimation of VOC uptake rates based on the differences in VOC concentrations.  The results demonstrated that these soils actively function as VOC sinks, despite variations in their physicochemical properties. Soils from upper active layers showed relatively higher uptake capacities, with soil moisture, organic matter, and microbial carbon content identified as key factors influencing uptake rates. Additionally, uptake coefficients for several major VOC species were calculated, providing valuable data for future model development. Correlation analysis and varying uptake coefficients suggest that the sink is likely biotic, with selective preferences for different VOCs.\u00a0The findings indicate that the development of a deeper active layer under climate change could enhance the soil\u2019s sink capacity and mitigate net VOC emissions from permafrost thaw.  Detailed methods and interpretations of the results can be found in the associated publication.", "keywords": ["volatile organic compounds", "Disko Island", "active layer", "sink", "Kangerlussuaq", "permafrost"], "contacts": [{"organization": "Jiao, Yi, Kramsh\u00f8j, Magnus, Davie-Martin, Cleo, Elberling, Bo, Rinnan, Riikka,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14185189"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14185189", "name": "item", "description": "10.5281/zenodo.14185189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14185189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-19T00:00:00Z"}}, {"id": "10.5281/zenodo.14761001", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:20:31Z", "type": "Software", "title": "PyretoClustR (executable version)", "description": "PyretoClustR (executable version)  Distilling the Pareto Optimal Front into Actionable Insights  ##Highlights:    Open-access tool to cluster and visualize complex multi-dimensional Pareto solutions  Bridging the gap between decision and objective space through intuitive visualizations  Implements k-means and k-medoids clustering without expert knowledge  Uses frequency maps to display hotspot locations for spatial optimization problems  Increasing stakeholder accessibility to Pareto solutions   Program languages: Python, RSoftware availability (source code version): https://github.com/SydneyEWhite/Pareto_ClusteringSoftware availability (executable version, with python and libraries implemented): Zenodo (10.5281/zenodo.14761001)  ## Overview    This framework performs k-means and k-medoids clustering on a set of Pareto optimal solutions derived from a multi-objective optimization algorithm.  (Optional) A correlation matrix of the input variables is returned (the goal is to help users reduce the input variable count).  Before clustering occurs, the data is cast onto principal component axes and extreme solutions are handled (if desired).  The code iterates through different possible inputs for the number of clusters, the number of principal components, and the variables that define 'extreme solutions'.  After these iterations, the best solution, as defined by silhouette score, is visualized in several ways:    Representative solutions are plotted on up to 4 dimensions  Distributions of values within the clusters are plotted in a violin plot  (optional) Maps with the frequency of a trait can be plotted, if locational data (*.shp file) is provided     ## Background: executable version  Unlike the version of PyretoClustR available on GitHub (https://github.com/SydneyEWhite/PyretoClustR), this version on Zenodo represents a standalone version of PyretoClustR not requiring a Python installation. The python_files folder contains three executables and one folder called _internal. The executables can be run by clicking on them. The _internal folder is crucial for the standalone operation of the executables and should not be modified or deleted.  ## Example Data  The data provided in the input folder (pareto_and_scen_solutions.csv and the shapefiles) are part of the BiodivERsA project TALE (\u2018Towards multifunctional agricultural landscapes in Europe\u2019). This project optimized four objectives, Agricultural gross margin (AY), Breeding habitat (BH), Low flow (LF), Nitrate load (NL) and four different land use scenarios (SQ, BAU, EXT, INT) were distinguished in the decision space.\u00a0The config.ini has been adapted to run with this dataset. To run it with your own data please alter the respective input values as outlined in config.ini.  ## Directory Structure  project_root/||\u2500\u2500 README.md||\u2500\u2500 input/|\u00a0 |\u2500\u2500 config.ini|\u00a0 |\u2500\u2500 [your_input_data].csv| \u2514\u2500\u2500 [shape_files]||\u2500\u2500 python_files/|\u00a0 |\u2500\u2500 _internal|\u00a0 |\u2500\u2500 correlation_matrix.exe|\u00a0 |\u2500\u2500 kmeans.exe| \u2514\u2500\u2500 kmedoid.exe||\u2500\u2500 r_files/| \u2514\u2500\u2500 plot_frequency_maps.R|\u2514\u2500\u2500 output/ (populated once code is run)\u00a0\u00a0 |\u2500\u2500 correlation_matrix.csv (if run)\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions.csv (created with kmeans.exe when Extreme Solutions are not handled)\u00a0\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions_outliers.csv (created with kmeans.exe when Extreme Solutions are handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions.csv (created with kmedoid.exe when Extreme Solutions are not handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions_outliers.csv (created with kmedoid.exe when Extreme Solutions are handled)\u00a0 \u2514\u2500\u2500 freq_map_cluster_X.png (if run)", "keywords": ["Multi-objective optimization", "Pareto pruning", "Land management", "Pareto optimal data", "visualization", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14761001"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14761001", "name": "item", "description": "10.5281/zenodo.14761001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14761001"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-29T00:00:00Z"}}, {"id": "10.5281/zenodo.15017546", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:20:36Z", "type": "Dataset", "title": "Global Rice Land Suitability and Adaptation Strategies Under Climate Change", "description": "This study evaluated the land suitability of irrigated and rainfed rice cultivation under current and projected climate change scenarios for the year 2050 on a global scale. , the climate and soil factors utilized for this purpose were obtained from the WorldClim website (https://www.worldclim.org/) and represent the historical monthly weather data for the period between 2001 and 2021 (https://worldclim.org/data/monthlywth.html). Moreover, future climate projections were sourced from the same source for the period 2041 to 2060 (centered around 2050).The projections are based on mean value of ten Global Climate Models (GCMs) from the CMIP6 dataset and two scenarios (SSP245 and SSP585), including models such as ACCESS-CM2, CanESM5-CanOE, EC-Earth3-Veg, FIO-ESM-2-0, GISS-E2-1-H, HadGEM3-GC31-LL, INM-CM4-8, IPSL-CM6A-LR, MRI-ESM2-0, and UKESM1-0-LL (https://worldclim.org/data/cmip6/cmip6_clim2.5m.html). The future climate scenarios were developed using a new set of integrated assessment models (IAMs) that incorporate the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), as outlined by O'Neill et al. (2016). Two integrated scenarios were considered in this study. SSP245 (a combination of SSP2 with RCP4.5) and SSP585 (a combination of SSP5 with RCP8.5). SSP2 represents a scenario characterized by a continuation of existing social, economic and technological trends, with minimal deviation from historical patterns. In contrast, SSP5 depicts a pathway of fossil-fuelled development, characterized by rapid technological advancement and human capital growth. With regard to radiative forcing, RCP4.5 represents a medium scenario (4.5 W m\u207b\u00b2 by 2100), whereas RCP8.5 represents a high-emission scenario (8.5 W m\u207b\u00b2 by 2100). To ensure the highest possible spatial accuracy, 2.5 minutes (~21 km2 at the equator) spatial resolution was applied to all maps. Furthermore, maps of rice cultivation area was obtained from the MAP SPAM2020 (Spatial Production Allocation Model) website (https://www.mapspam.info), which provides detailed spatial data on global rice production.Soil parameters were then determined for each point using soil information obtained from the FAO soil map. The FAO soil map provided vital parameters such as organic carbon (OC), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), CaCO3, pH, EC which extracted from the Harmonized World Soil Database (HWSD) at a scale of 1:5,000,000, sourced from the FAO Soil Portal website. (Supplementary Figure 2). The slope map was created using the digital elevation model (for more details, see Guerra et al., 2020). The SPAM2020 for rainfed and irrigated rice was used to intersect the climate zones and soil layers, focusing specifically on the areas dedicated to this area.\u00a0  The final land suitability map was generated by overlaying the suitability output with raster files representing both irrigated and rainfed harvested areas, thus creating a composite map of land suitability . This stage ensured the precise delineation of areas suitable for rice cultivation. Following the creation of land suitability maps for a variety of scenarios and adaptation strategies, the results were compared in order to identify the optimal strategy. The final map identifies the most efficacious adaptation strategy for enhancing land suitability in each region in the context of projected climate change. Also, The overview of the status of rainfed and irrigated rice worldwide.", "keywords": ["Adaptation strategy", "Climate chnage", "Rice", "land suitablity"], "contacts": [{"organization": "Dadrasi, Amir, Va\u010dk\u00e1\u0159ov\u00e1, Davina, Fatemeh, Salmani, Nguyen, Trong Can, Weinzettel, Jan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15017546"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15017546", "name": "item", "description": "10.5281/zenodo.15017546", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15017546"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:20:38Z", "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.15328215", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:20:41Z", "type": "Dataset", "title": "1000 Soils Pilot Dataset, version 8, May 2025", "description": "This record hosts data generated by the 1000 Soils Pilot. Data will be updated as more become available. Please see the most recent data upload for current data.  A beta visualization tool is available for some data types at\u00a0https://shinyproxy.emsl.pnnl.gov/app/1000soils. Please submit any suggestions or comments through the 'contact' tab. We are actively working to improve visualizations and value all feedback.  Data completed include:    Geochemistry, texture, respiration, and enzyme activities  FTICR-MS organic matter chemistry  Microbial biomass C and N  TOC/TDN of water-extractable OM  X-ray computed tomography (derived metrics available here, raw data available upon request)  Metagenomes; a variety of data formats are available upon request  Soil hydraulic properties   Data in progress:    LC-MS/MS in development, timeline TBD, inquire for status   1000S_processed_BGC_summary.csv contains all available biogeochemical data; microbial biomass C and N; and TOC/TDN of water-extractable OM; and\u00a0  1000S_Tomography.xslx contains a summary of data generated via X-ray computed tomography.  icr_v2_corems2.csv contains FTICR-MS data processed by CoreMS version 2. These data are merged by formula across instrument runs to enable cross-sample comparisons. Technical replicates are merged by retaining peaks present in 2 out of 3 replicates.  1000Soils_Metadata_Site_Mastersheet_v1.csv contains site information.  Soil Hydraulics_corrected_02042025.xlsx contains soil hydraulics information.  Readme File_v4.xlsx is the readme file.  Please contact the MONet project (monet.emsl@pnnl.gov) or Emily Graham (emily.graham@pnnl.gov) with questions.  The following file and all raw data are\u00a0available upon request:  icr_by_mass_for_single_sample_analysis_only.csv\u00a0contains FTICR-MS data processed by CoreMS and is intended for usage in the calculation of biochemical transformations within samples only. These data are not acceptable for cross-sample comparison of masses because they are from multiple instrument runs.  For more information, please see: https://www.emsl.pnnl.gov/monet and https://sc-data.emsl.pnnl.gov/monet  Acknowledgment:\u00a0  Soil data were provided by the Molecular Observation Network (MONet) at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), a DOE Office of Science user facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. The work (proposal: 10.46936/10.25585/60008970) conducted by the U.S. Department of Energy, Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science user facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.\u00a0  The Molecular Observation Network (MONet) database is an open, FAIR, and publicly available compilation of the molecular and microstructural properties of soil. Data in the MONet open science database can be found at\u00a0https://sc-data.emsl.pnnl.gov/.", "keywords": ["2. Zero hunger", "decomposition", "13. Climate action", "FTICR-MS", "biogeochemistry", "carbon", "molecular", "15. Life on land", "6. Clean water", "soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15328215"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15328215", "name": "item", "description": "10.5281/zenodo.15328215", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15328215"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-02T00:00:00Z"}}, {"id": "10.5281/zenodo.16894966", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:20:53Z", "type": "Report", "created": "2023-02-22", "title": "Management of alternative water resources for variable rate irrigation - a Hungarian case study", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Most of the climate scenarios predict increased water scarcity in arid areas, such as Hungary. However, the irrigated area in Hungary covers 2% of agricultural land, mostly with outdated irrigation technology. The aim of the research was to develop the basis of a variable rate irrigation for water-saving precision sprinkler irrigation system on an arable area (85 ha) which is located in the reference area of the Tisza Riven Basin. There is limited available water resources at the site, therefore alternative water sources utilization system was set up for irrigation to adapt to climate change and reduce fertilizers. The basis of the alternative water resources are excess water, treated wastewater, biogas fermentation sludge which is collected in a water reservoir with 114000 m3 capacity. For proper irrigation scheduling, heterogeneity of topography, hydrological, soil and crop conditions has to be explored and monitored. Therefore physically-based modelling of the water balance and remote sensing-based surplus water and &amp;#160;vegetation status surveying are tested to use for accurate irrigation scheduling.Shallow groundwater and/or soil compaction can also contribute to excess inland water. This may occur even if there are drought periods in a year (e.g. in the Pannonian region), resulting in spots with a low crop yield. A LiDAR-based digital elevation model was found to provide appropriate data to identify sites affected by excess inland water. The spots identified can be used as spatial input data to compile a variable rate irrigation prescription map for imposing reduced (or zero) irrigation at areas more vulnerable to the occurrence of excess inland water. The water balance was also assessed for sites with physically-based models. Hydrus was used to model soil moisture changes at the Hungarian case study site.A model concept for crop evapotranspiration estimation was also developed based on vegetation indices calculated from satellite imagery. Several combinations of sensors and remote sensing products were tested to use in ETc modelling potentially. This approach was tested both at the Hungarian case study sites. Remote sensing-based analysis of crop evapotranspiration, combined with physically-based modelling, appears to be a promising method in water balance modelling of maize fields, especially if these fields are in summer when the crop is fully developed. However, the remotely sensed information verification is essential for the proper utilization of the remote sensing data in ETc modelling and predicting the spatio-temporal dynamics of crop yield, evapotranspiration, and irrigation demands.There is a need further benchmark scenarios to improve both physically-based models and satellite-based crop evapotranspiration models to achieve more accurate and valid simulations.The abstract was funded by European Union&amp;#8217;s Horizon 2020 &amp;#8220;WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production&amp;#8221; research and innovation programme under Grant Agreement No. 858375. This research was supported by the J&amp;#225;nos Bolyai Research Scholarship of the Hungarian Academy of Sciences.</p></article>", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16894966"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16894966", "name": "item", "description": "10.5281/zenodo.16894966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16894966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4287780", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:02Z", "type": "Dataset", "title": "Forest carbon prospecting for climate change mitigation: Version 1.0", "description": "This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44\u00b0N and 23.44\u00b0S produced from the study: 1) investible forest carbon (in tCO<sub>2</sub>e ha<sup>-1</sup>y<sup>-1</sup>) and 2) forest carbon return-on-investment (Net Present Value in USD ha<sup>-1</sup>y<sup>-1</sup>) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. <em><strong>Investible Forest Carbon</strong>: </em>The investible forest carbon map was produced based on the total volume of CO<sub>2</sub>e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics. <em>Aboveground carbon.</em> A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Belowground carbon</em>. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are: Belowground biomass = 0.489\u00d7aboveground biomass^0.89; and Belowground biomass = 0.26\u00d7aboveground biomass A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Soil Organic Carbon</em>. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Applying VCS criteria</em>. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO<sub>2</sub>e associated with tropical forests as estimated above, deriving the volume of CO<sub>2</sub>e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects. <strong><em>Return</em>-<em>on-Investment</em></strong>. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. <em>Cost of project establishment</em>. The cost of project establishment was estimated to be at $25 ha<sup>-1</sup>. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. <em>Cost for annual maintenance</em>. The cost for annual maintenance was estimated to be $10 ha<sup>-1</sup>, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration. <em>Carbon price</em>. A constant carbon price of $5.8 t<sup>-1</sup>CO\u00ad<sub>2</sub>e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends\u2019 Ecosystem Marketplace (i.e. for the period 2006 \u2013 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years. <em>Discount rate</em>. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (zengyiwen@nus.edu.sg).", "keywords": ["Carbon stocks", "Climate change mitigation", "13. Climate action", "Carbon finance", "15. Life on land"], "contacts": [{"organization": "Koh, Lian Pin, Zeng, Yiwen, Sarira, Tasya Vadya, Siman, Kelly,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4287780"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4287780", "name": "item", "description": "10.5281/zenodo.4287780", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4287780"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-25T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-08-25T16:21:00Z", "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 &gt;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.4487144", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-08-25T16:21:03Z", "type": "Dataset", "title": "Eddy Covariance data from ICOS-associated station IT-NIV \u2013 August-November 2019", "description": "RestrictedData stored here refer to Eddy Covariance (EC) data measured in 2019 between August and November at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO<sub>2</sub> flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO<sub>2</sub>, H<sub>2</sub>0, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO<sub>2</sub> fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.", "keywords": ["13. Climate action", "alpine grassland", "15. Life on land", "Gran Paradiso National Park", "Mountain", "EO_Data", "Eddy Covariance", "Net Ecosystem Exchange", "ecosystem-atmosphere carbon exchange"], "contacts": [{"organization": "Vivaldo, Gianna, Raco, Brunella, Baneschi, Ilaria, Giamberini, Maria Silvia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4487144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4487144", "name": "item", "description": "10.5281/zenodo.4487144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4487144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:05Z", "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": "<p>         &amp;lt;p&amp;gt;Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (&amp;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.&amp;lt;/p&amp;gt;         </p>", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.6202061", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:06Z", "type": "Dataset", "title": "Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies", "description": "Open AccessBias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for: -period 1981-2099/2100, -6 RCM, -3 scenarios (RCPs 2.6, 4.5 and 8.5), -7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and -18 domains and 23 locations within these domains. Bias correction and further downscaling to 0.1\ufffd\ufffd was done using ERA5-Land reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging. <strong>Organization of the data</strong> The name of the files are <em>domain</em>-<em>type</em>.zip, where <em>type</em> is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: <em>experiment</em>/<em>modelNumber</em>/<em>variable</em>.nc for gridded and <em>experiment</em>/<em>modelNumber</em>/<em>variable-pilotFieldNumber</em>.txt for point data, where <em>experiment </em>is rcp26, rcp45 or rcp85. <em>domain and pilotFieldNumber</em> <strong>domain</strong> <strong>domain </strong><strong>location (min and max. Longitude, min and max latitude</strong><strong>)</strong> <strong>pilotFieldNumber</strong> <strong>pilot field </strong><strong>location (longitude, latitude)</strong> <strong>case study</strong><strong> number</strong> <strong>country</strong> <strong>Name (OPTAIN case study)</strong> 01 50.95 51.45 14.55 15.05 1 DEU Schoeps 02 46.35 47.05 6.55 7.15 2 46.816667 6.95 2 CHE Petite Glane 02_1 46.75 47.25 7.25 7.75 1 46.983333 7.466667 02_34 47.35 47.85 8.35 3 4 47.433333 8.516667 47.683333 8.616667 02_5 46.15 46.65 5.95 6.45 5 46.4 6.233333 03a 46.65 47.15 17.45 17.95 1 2 3 4 46.92649 17.68246 46.9166 17.68976 46.91283 17.69754 46.91283 17.69723 3a HUN Csorsza 03b 46.45 46.95 16.65 17.15 3b HUN Felso Valicka 04 52.35 52.85 18.45 18.95 1 52.597469 18.728617 4 POL Upper Zglowiaczka 05 46.35 46.85 15.35 15.85 5 SVN Pesnica 06 46.45 46.95 16.15 16.65 6 HUN/SVN Kebele/Kobiljski 07 49.85 50.35 4.75 5.25 7 BEL La Wimbe 08 55.15 55.75 23.55 24.05 1 2 55.522057 23.799235 55.42233194 23.82580339 8 LTU Dotnuvele 09 45.45 45.95 9.65 10.15 9 ITA Cherio 10 59.45 59.95 10.75 11.25 1 2 3 4 5 6 7 8 59.71949 10.83576 59.6833306 10.8833298 59.6833306 10.8833298 59.665 10.9475 59.665 10.9475 59.841012 10.903597 59.757631 11.072031 59.539623 10.856447 10 NOR Krogstad 11 46.45 46.95 17.55 18.05 1 2 46.658333 17.75583 46.656944 17.75833 11 HUN Tetves 12 49.35 49.85 14.75 15.25 1 49.616837 15.078266 12 CZE Cechticky 13 55.85 56.35 25.85 26.45 13 LVA Dviete 14 59.75 60.25 17.55 18.05 14 SWE Ingvastaan Lehstaan <em>modelNumber</em> <strong>modelNumber</strong> <strong>Driving Model (GCM)</strong> <strong>Ensemble</strong> <strong>RCM </strong> <strong>End date</strong> 1 EC-EARTH r12i1p1 CCLM4-8-17 31.12.2100 2 EC-EARTH r3i1p1 HIRHAM5 31.12.2100 3 HadGEM2-ES r1i1p1 HIRHAM5 30.12.2099 4 HadGEM2-ES r1i1p1 RACMO22E 30.12.2099 5 HadGEM2-ES r1i1p1 RCA4 30.12.2099 6 MPI-ESM-LR r2i1p1 REMO2009 31.12.2100 <em>variable</em> <strong>variable</strong> <strong>description</strong> <strong>Unit</strong> Tmean Mean temperature \ufffd\ufffdC Tmin Min temperature \ufffd\ufffdC Tmax Max temperature \ufffd\ufffdC prec Precipitation mm solarRad Solar radiation MJ/m2 windSpeed Wind speed at 2m m/s relHum Relative humidity % <strong>Methodolody</strong> Bias correction was done using non-parametric empirical quantile mapping with modified method from R package qmap. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010. The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages automap and gstat with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model Shuttle Radar Topography Mission was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z. <strong>Funding</strong> This project has received funding from the European Union\ufffd\ufffd\ufffds Horizon 2020 research and innovation programme under grant agreement No 862756.", "keywords": ["CORDEX", "13. Climate action", "RCM", "ERA5-Land", "OPTAIN", "EURO-CORDEX", "bias correction"], "contacts": [{"organization": "Honzak, Luka", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6202061"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6202061", "name": "item", "description": "10.5281/zenodo.6202061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6202061"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-21T00:00:00Z"}}, {"id": "10.5281/zenodo.6320617", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:07Z", "type": "Dataset", "title": "MOSSO_SoilChemistry_AllSites_Monthly_2016-2020", "description": "<strong>Abstract</strong> The dataset provides information about the soil chemical properties at eight permanent LTER sites (named site 1, 2, 3, 6, 7, 8, 9, and 10, according to the LTER site numerations), located between 2686 (site 10) and 2854 m a.s.l. (site 6). The investigated period is 2016-2020. Details: Site 1 (coordinates: 45\ufffd\ufffd52'22.43'N, 7\ufffd\ufffd52'25.84'E; elevation: 2840 m a.s.l.), Site 2 (coordinates: 45\ufffd\ufffd52'22.17'N, 7\ufffd\ufffd52'38.07'E; elevation: 2800 m a.s.l.), Site 3 (coordinates: 45\ufffd\ufffd52'13.52'N, 7\ufffd\ufffd52'35.01'E; elevation: 2770 m a.s.l.), Site 6 (coordinates: 45\ufffd\ufffd52'32.21'N, 7\ufffd\ufffd52'31.87'E; elevation: 2854 m a.s.l.), Site 7 (coordinates: 45\ufffd\ufffd52'29.13'N, 7\ufffd\ufffd52'44.71'E; elevation: 2813 m a.s.l.), Site 8 (coordinates: 45\ufffd\ufffd52'27.74'N, 7\ufffd\ufffd52'56.86'E; elevation: 2749 m a.s.l.), Site 9 (coordinates: 45\ufffd\ufffd52'23.80'N, 7\ufffd\ufffd53'3.96'E; elevation: 2720 m a.s.l.), and Site 10 (coordinates: 45\ufffd\ufffd52'21.76'N, 7\ufffd\ufffd53'9.32'E; elevation: 2686 m a.s.l.). The bedrock is primarily micaschists, with some inclusions of amphibolites and calcschists. The vegetation of the sites is included in the \ufffd\ufffd\ufffdSiliceous alpine and boreal grasslands\ufffd\ufffd\ufffd (habitat 6150, according to the EU Habitat Directive). At each site, consisting of paired plots for soil and vegetation survey, three 9 m<sup>2 </sup>plots are established, where three topsoil samples (A horizon, 0\ufffd\ufffd\ufffd10 cm depth) are collected each month during the snow-free season. On soil samples the following analysis are performed: N-NH4, N-NO3, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), dissolved organic nitrogen (DON), microbial carbon (Cmicr), and microbial nitrogen (Nmicr). <strong>Method Description</strong> Each soil sample consists of three subsamples that are homogenised by sieving at 2 mm. An aliquot of 20 g of fresh soil is extracted with 100 mL K2SO4 0.5 M, while 10 g are fumigated using chloroform for 18 h before extraction with 50 mL K2SO4 0.5 M. The concentration of DOC in not fumigated soil extracts (extractable DOC) is determined with a TOC analyzer (Elementar, Vario TOC, Hanau, Germany) after filtration with 0.45 \ufffd\ufffdm nylon membrane filters. The microbial carbon (Cmicr) is estimated as the difference in extractable DOC between fumigated and non-fumigated samples, corrected using a recovery factor of 0.45 (Brookes et al. 1985, https://doi.org/10.1016/0038-0717(85)90144-0). Extractable N-NH4 concentration in soil extracts is measured spectrophotometrically (U-2000, Hitachi, Tokyo, Japan) using a modified Berthelot method based on the reaction with salicylate in the presence of alkaline sodium dichloroisocyanurate (Crooke and Simpson 1971, https://doi.org/10.1002/jsfa.2740220104). Extractable N-NO3 concentration in soil extracts is measured spectrophotometrically (U-2000, Hitachi, Tokyo, Japan) using the Greiss reaction (Mulvaney 1996, ISBN-10: \ufffd\ufffd\ufffd 0891188258; ISBN-13: \ufffd\ufffd\ufffd 978-0891188254) modified according to Cucu et al. (2014, https://doi.org/10.1007/s00374-013-0893-4). Extractable TDN is measured as reported for DOC. Extractable DON is determined as the difference between extractable TDN and inorganic nitrogen (extractable N-NH4 + N-NO3) in the extracts. Nmicr is estimated from the difference in extractable TDN between fumigated and non-fumigated samples corrected using a recovery factor of 0.54 (Brookes et al. 1985, https://doi.org/10.1016/0038-0717(85)90144-0). <strong>Instrumentation</strong> Spectrophotometer U-2000, Hitachi, Tokyo, Japan (N-NH4 and N-NO3) Elementar, Vario TOC, Hanau, Germany (DOC and TDN)", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Freppaz, Michele, Colombo, Nicola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6320617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6320617", "name": "item", "description": "10.5281/zenodo.6320617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6320617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7307449", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:14Z", "type": "Dataset", "title": "Components of the complete budget for SAFE intensive carbon plots", "description": "<strong>Description: </strong> Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.<br> <br> These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.<br> <br> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf. <strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong> <strong>Funding: </strong>These data were collected as part of research funded by: Sime Darby Foundation (Grant, SAFE Core data) European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131) NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1) NERC standard grant: The multi-year impacts of the 2015/2016 El Ni\u00f1o on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1) HSBC Malaysia (Grant) The University of Zurich (Grant) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. <strong>Permits: </strong>These data were collected under permit from the following authorities: Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76)) <strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available here <strong>Files: </strong>This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx <strong>SAFE_CarbonBalanceComponents.xlsx</strong> This file contains dataset metadata and 1 data tables: <strong>Carbon balance components data</strong> (described in worksheet Data) Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project Number of fields: 64 Number of data rows: 11 Fields: <strong>ForestType</strong>: Old-growth or Logged (Field type: categorical) <strong>SAFEPlotName</strong>: SAFE plot name, as in the SAFE Gazetteer (Field type: location) <strong>PlotName</strong>: Plot name (used in field work) (Field type: id) <strong>ForestPlotsCode</strong>: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id) <strong>WoodyNPP_Stem</strong>: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric) <strong>WoodyNPP_CoarseRoot</strong>: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric) <strong>WoodyNPP_BranchTurnover</strong>: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric) <strong>WoodyNPP_Total</strong>: Total woody net primary producivity (Field type: numeric) <strong>CanopyNPP_Leaf</strong>: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric) <strong>CanopyNPP_Twig</strong>: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric) <strong>CanopyNPP_Reproductive</strong>: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric) <strong>CanopyNPP_Miscellaneous</strong>: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric) <strong>CanopyNPP_Herbivory</strong>: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric) <strong>CanopyNPP_Total</strong>: Total canopy net primary producivty (Field type: numeric) <strong>FineRootNPP</strong>: Fine root productivity (Field type: numeric) <strong>TotalNPP_WithoutMycorrhiza</strong>: Total net primary productivity without mycorrhiza (Field type: numeric) <strong>TotalNPP_WithMycorrhiza</strong>: Total net primary productivity including mycorrhiza (Field type: numeric) <strong>GPP_WithoutMycorrhiza</strong>: Gross primary productivity without mycorrhiza (Field type: numeric) <strong>GPP_WithMycorrhiza</strong>: Gross primary productivity including mycorrhiza (Field type: numeric) <strong>R_Stem</strong>: Respiration from woody stems (Field type: numeric) <strong>R_Leaf</strong>: Leaf Respiration (Field type: numeric) <strong>R_FineRoots</strong>: Respiration from fine roots (Field type: numeric) <strong>R_CoarseRoots</strong>: Respiration from coarse roots (Field type: numeric) <strong>R_SOM</strong>: Respiration from soil organic matter (Field type: numeric) <strong>R_Mycorrhiza</strong>: Respiration from mycorrhiza (Field type: numeric) <strong>R_Litter</strong>: Respiration from litter layer (Field type: numeric) <strong>R_Deadwood</strong>: Deadwood respiration (Field type: numeric) <strong>R_auto</strong>: Total autotrophic respiration (Field type: numeric) <strong>R_het</strong>: Total heterotrophic respiration (Field type: numeric) <strong>R_eco</strong>: Total ecosystem respiration (Field type: numeric) <strong>NEP_WithoutMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) <strong>NEP_WithMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) <strong>AbovegroundBiomassCarbonStock</strong>: Plot above-ground biomass carbon stock (Field type: numeric) <strong>CoarseRootBiomassCarbonStock</strong>: Biomass carbon stock of coarse roots (Field type: numeric) <strong>SE_WoodyNPP_Stem</strong>: Standard error of woody stem productivity (Field type: numeric) <strong>SE_WoodyNPP_CoarseRoot</strong>: Standard error of coarse root productivity (Field type: numeric) <strong>SE_WoodyNPP_BranchTurnover</strong>: Standard error of branch turnover productivity (Field type: numeric) <strong>SE_WoodyNPP_Total</strong>: Standard error of total woody net primary producivity (Field type: numeric) <strong>SE_CanopyNPP_Leaf</strong>: Standard error of leaf productivity (Field type: numeric) <strong>SE_CanopyNPP_Twig</strong>: Standard error of twig productivity (Field type: numeric) <strong>SE_CanopyNPP_Reproductive</strong>: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric) <strong>SE_CanopyNPP_Miscellaneous</strong>: Standard error of unidentified canopy debris (Field type: numeric) <strong>SE_CanopyNPP_Herbivory</strong>: Standard error of leaf productivity lost to herbivory (Field type: numeric) <strong>SE_CanopyNPP_Total</strong>: Standard error of total canopy net primary producivty (Field type: numeric) <strong>SE_FineRootNPP</strong>: Standard error of fine root productivity (Field type: numeric) <strong>SE_TotalNPP_WithoutMycorrhiza</strong>: Standard error of total net primary productivity without mycorrhiza (Field type: numeric) <strong>SE_TotalNPP_WithMycorrhiza</strong>: Standard error of total net primary productivity including mycorrhiza (Field type: numeric) <strong>SE_GPP_WithoutMycorrhiza</strong>: Standard error of gross primary productivity without mycorrhiza (Field type: numeric) <strong>SE_GPP_WithMycorrhiza</strong>: Standard error of gross primary productivity including mycorrhiza (Field type: numeric) <strong>SE_R_Stem</strong>: Standard error of respiration from woody stems (Field type: numeric) <strong>SE_R_Leaf</strong>: Standard error of leaf Respiration (Field type: numeric) <strong>SE_R_FineRoots</strong>: Standard error of respiration from fine roots (Field type: numeric) <strong>SE_R_CoarseRoots</strong>: Standard error of respiration from coarse roots (Field type: numeric) <strong>SE_R_SOM</strong>: Standard error of respiration from soil organic matter (Field type: numeric) <strong>SE_R_Mycorrhiza</strong>: Standard error of respiration from mycorrhiza (Field type: numeric) <strong>SE_R_Litter</strong>: Standard error of litter layer respiration (Field type: numeric) <strong>SE_R_Deadwood</strong>: Standard error of deadwood respiration (Field type: numeric) <strong>SE_R_auto</strong>: Standard error of total autotrophic respiration (Field type: numeric) <strong>SE_R_het</strong>: Standard error of total heterotrophic respiration (Field type: numeric) <strong>SE_R_eco</strong>: Standard error of total ecosystem respiration (Field type: numeric) <strong>SE_NEP_WithoutMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric) <strong>SE_NEP_WithMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric) <strong>SE_AbovegroundBiomassCarbonStock</strong>: Standard error of plot above-ground biomass carbon stock (Field type: numeric) <strong>SE_CoarseRootBiomassCarbonStock</strong>: Standard error of biomass carbon stock of coarse roots (Field type: numeric) <strong>Date range: </strong>2011-08-25 to 2018-07-17 <strong>Latitudinal extent: </strong>4.1830 to 5.0700 <strong>Longitudinal extent: </strong>114.0190 to 117.8200", "keywords": ["2. Zero hunger", "Soil carbon cycle", "Soil organic matter", "Flux", "Respiration", "15. Life on land", "Carbon balance", "Autotrophic respiration", "6. Clean water", "SAFE core data", "13. Climate action", "SAFE project", "Heterotropchic respiration", "Litter", "Carbon plot", "Carbon flux", "Productivity"], "contacts": [{"organization": "Riutta, Terhi, Ewers, Robert M, Malhi, Yadvinder, Majalap, Noreen, Khoon, Kho Lip, Mills, Maria,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307449"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307449", "name": "item", "description": "10.5281/zenodo.7307449", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307449"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}, {"id": "10.5281/zenodo.7353721", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:14Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are &lt;0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. <strong>References</strong> FAO, Food, and Agricultural Organization. 1990. <em>Guidelines for Soil Profile Description.</em> 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d <em>Geoderma</em> 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d <em>Soil Science Society of America Journal</em> 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. <em>Soil survey manual, U.S. Dept. Agriculture Handbook No. 18.</em> Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353721"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353721", "name": "item", "description": "10.5281/zenodo.7353721", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353721"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7353722", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:14Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are &lt;0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. <strong>References</strong> FAO, Food, and Agricultural Organization. 1990. <em>Guidelines for Soil Profile Description.</em> 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d <em>Geoderma</em> 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d <em>Soil Science Society of America Journal</em> 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. <em>Soil survey manual, U.S. Dept. Agriculture Handbook No. 18.</em> Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353722", "name": "item", "description": "10.5281/zenodo.7353722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:15Z", "type": "Dataset", "title": "Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis", "description": "Open Access<strong><em>Data description</em></strong> 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<sup>-1</sup>) 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 <em><strong>Data collection method</strong></em> 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. <em><strong>Acknowledgements</strong></em> 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.7695641", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:16Z", "type": "Report", "title": "Soil and land management ontology reference document", "description": "The Soil Mission Support (SMS) project supports the European Commission and the Mission Board of the Horizon Europe<br> Mission in the area of Soil Health and Food in delivering its objectives and related targets. It is assumed that the<br> Soil Mission and its related objectives and specific targets can only be achieved through healthy soils and for that,<br> stakeholder engagement is needed. Healthy soils are defined as soils that are in good chemical, biological and physical<br> condition and thus are able to continuously provide as many ecosystem services as possible (EC, 2021a). Stakeholders<br> are defined as those who are affected in their interest or concern by changes in soil and land management (Brils et al.,<br> 2022).<br> With multi-stakeholder processes, language and use of language is very important. The capability to understand each<br> other is critical. Communication difficulties originate to a large extent from the \u2018jargon\u2019 used in the different communities.<br> A common language facilitates \u2018learning together\u2019 which helps to build trust, develop a common view on the issues<br> at stake, resolve conflicts and arrive at joint solutions that are technically sound and that can be implemented in<br> practice. Ontology defines a common vocabulary for those who, for example, need to converse about a common issue<br> or share information in a specific domain.<br> In first instance the shared domain of discourse was defined and then at different levels of hierarchy:<br> \u00b7 Primary objects of relevance for the domain of discourse were selected;<br> \u00b7 The inter-relational links between these objects was conceptualized (conceptual model); and<br> \u00b7 These objects were defined in a representational vocabulary (a common language).<br> The domain of discourse covers soil and land management aimed to achieve the first six (of the eight) Soil Mission<br> objectives, which are: 1. reduce desertification, 2. conserve soil organic carbon stocks, 3. stop soil sealing and increase<br> re-use of urban soils, 4. reduce soil pollution and enhance restoration, 5. prevent erosion, and 6. improve soil structure<br> to enhance soil biodiversity.<br> The first level of hierarchy covers soil and land and its use. At this level the following objects have been selected, interrelated<br> in a conceptual model (i.e. visual of soil and land-use) and defined in a common language: soil, land, landuse<br> and land-use types (including: urban, industrial, agriculture, forest, nature and protected land).<br> The second level of hierarchy covers soil management. At his level the following objects have been selected, interrelated<br> in a conceptual soil management model and defined in a common language: soil management (including: soil<br> management strategy, measures, program of measures), soil ecosystems (including: ecosystem services, pressures,<br> healthy soil ecosystems), users (stakeholders) and information.<br> Lastly, the third level of hierarchy covers the achievement of the first six Soil Mission objectives. At this level the<br> most relevant objects related to each of these objectives are selected and interrelated to their position in the DPSIR<br> (Drivers-Pressures-State-Impact-Response) framework which is at this 3rd level superimposed on the soil management<br> model as used for level 2.<br> The remaining two Soil Mission objectives, i.e. 7. reduce the EU global footprint on soils and 8. improve soil literacy in<br> society, do not directly relate to the actual management of soil and land. However, also for these mission objectives<br> some important objects have been selected and defined in a common language.<br> Experts in the SMS project \u2013 jointly covering the fields of expertise related to all the 8 Soil Mission objectives \u2013 developed<br> this ontology. This ontology should now be used in soil policy and management practice, such as Living Labs. In<br> such settings, the ontology can be improved through interaction with stakeholders from different backgrounds, further<br> increasing its value.<br> The key-recommendations are:<br> \u00b7 use this ontology in soil policy and management practice (e.g. Living Labs)<br> \u00b7 soil policy makers and managers should promote its use in such practice<br> \u00b7 use the feedback from stakeholders to further improve the ontology<br> In support of the dissemination of this document a policy brief is prepared and attached as annex in this document.<br> Both documents are made publicly available via de SMS website: https://www.soilmissionsupport.eu/outputs", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Nougues, Laura, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695641"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695641", "name": "item", "description": "10.5281/zenodo.7695641", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695641"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.7856487", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:17Z", "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": "2026-08-25T16:21:18Z", "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": {"license": "Open Access", "updated": "2026-08-25T16:21:20Z", "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": "10.5281/zenodo.8320433", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:21Z", "type": "Dataset", "title": "Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type", "description": "These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868\u20131880 Bright, R. M., &amp; O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990. Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990\u20132020. (US Dept. Ag. For. Service, Madison, WI; https://doi.org/10.2737/FS-RU-382). Erb, Li, Sun, Paynter, Wang, &amp; Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320. Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.", "keywords": ["climate change", "forest carbon", "13. Climate action", "15. Life on land", "Landsat", "albedo"], "contacts": [{"organization": "Healey, Sean, Yang, Zhiqiang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8320433"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8320433", "name": "item", "description": "10.5281/zenodo.8320433", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8320433"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-06T00:00:00Z"}}, {"id": "10.5846/stxb201201140085", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-25T16:21:41Z", "type": "Journal Article", "created": "2013-05-23", "description": "Typical steppe is a type of grassland which forms in inland temperate semi-arid climatic conditions,where xerophytic perennial bush grasses,especially Stipa spp.,dominate.Grassland ecosystems are one of the largest terrestrial ecosystems affected by human activities.It is important to study the impact of different types of disturbance on the biodiversity and biomass of grasslands ecosystems during restoration and reconstruction work.Exclosures,grazing,fertilization and burning are four major types of anthropogenic disturbance occurring in grassland ecosystems,and have very important impacts on them.To provide a scientific basis for the protection of grassland biodiversity and the sustainable use of grasslands,the influences of different disturbances on the characteristics of above-ground vegetation were studied in a typical steppe in the loess region of China based on the field investigation and monitoring.We established plots representing four different types of treatments: exclosure(E),exclosure plus fertilization(EF),exclosure plus burning(EB) and grazing(G).All plots had the same soil type,topography and hydrological conditions.During the entire experiment,we determined total coverage,vegetation height,individual density,aboveground biomass and diversity of the plant community in each plot.Our results show the dominant vegetation community type of G plots is Agropyron cristatum+Potentilla acaulis+Artemisia sacrorum,a community which is the result of long-term grazing.The dominant vegetation community type of the E plots is Stipa grandis+Poa sphondylodes+Medicago ruthenica,a result of fencing.The dominant vegetation community type of the EF plots is Carex duriuscula+Stipa bungeana+Stipa grandis.The dominant vegetation community type of the EB plots is Artemisia sacrorum+Stipa bungeana+Stipa grandis.The different disturbance regimes strongly influenced community diversity and biomass.To be more specific,total coverage,above-ground biomass and the dominance indices were all at the highest level in the EF treatments in four types of treatments.This may attributed to grass species which play a dominant role in this community.Also,with low evenness and diversity indices,this result was consistent with niche theory which states differences among species in their environmental tolerances,niche requirements,and competitive abilities determine coexistence.Total coverage,height and aboveground biomass were all lowest for the G treatment,while community richness and diversity indices were higher for G than other three treatments.This result supports the intermediate disturbance hypothesis which states that biodiversity will be greatest in communities subjected to moderate levels of disturbance.Only the individual density and evenness index was higher with the E treatment than with the other treatments.Thus we can document the trend for biomass as EF EB E G.These demonstrate long-term exclosure is a severe type of disturbance in grassland ecosystems.Also,the community richness indices(R and Ma) showed a certain tendency within these four treatments: G EF EB E and community evenness indices(Jsw and Ea) could be sequenced in the order of E G EB EF.Meanwhile,the order of diversity indices(H\u2032 and D) was as follows: G E EB EF,while the dominance index was opposite the order of diversity index.Finally,there was negative correlation between community productivity and Shannon-Wiener and Simpson indices,which can be explained by interactions between root and shoot competition.", "keywords": ["2. Zero hunger", "0106 biological sciences", "13. Climate action", "15. Life on land", "01 natural sciences"]}, "links": [{"href": "https://doi.org/10.5846/stxb201201140085"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Ecologica%20Sinica", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5846/stxb201201140085", "name": "item", "description": "10.5846/stxb201201140085", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5846/stxb201201140085"}, {"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": "10.6084/m9.figshare.16650170", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:44Z", "type": "Report", "created": "2021-09-21", "title": "Additional file 2 of Trophic level drives the host microbiome of soil invertebrates at a continental scale", "description": "Additional file 1: Supplementary Text. Characterization of microbial community in the soil food web; Table S1. The information of used primers of the DNA barcoding; Table S2. Comparison of soil fauna microbiome composition using PERMANOVA (Adonis test); Table S3. Effects of the removal of Unknown OTUs from the network on network metrics; Figure S1. The potential relationship between the loss of microbial species living within the microbiome of soil fauna and soil faunal extinction; Figure S2. The distribution of sample sites across China based on different climatic zones (A suffix of 1 in the location indicates farmland, while 2 indicates forested land); Figure S3. The relative abundance of 18 most abundant bacterial families (a) and 16 most abundant bacterial species (b) in all samples, classified by sample types; Figure S4. The alpha diversity of soil and fauna microbial communities at a sequencing depth of 13551; Figure S5. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the weighted unifrac distance in each sampling site; Figure S6. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the unweighted unifrac distance in each sampling site; Figure S7. Shared OTUs between soil and soil fauna; Figure S8. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the weighted unifrac distance in each soil faunal group; Figure S9. Principal coordinates analysis (PCoA) revealing the distribution of soil faunal bacterial communities using the unweighted unifrac distance in each soil faunal group; Figure S10. The PERMANOVA analysis revealing the relative contribution of landuse, sampling site and soil faunal species to the variation of each soil faunal group microbiome; Figure S11. Enterotyping (clustering) of each soil faunal group, which was clustered using the Jensen\u2013Shannon distance and partitioning around medoids method based on the relative abundance of bacterial genera; Figure S12. Analysis of microbial network taxa interconnectedness from the soil food web microbiome; Figure S13. Network analysis revealing microbial taxa interconnectedness at the Genus level; Figure S14. Boxplots presenting the difference of betweenness, closeness, and degree centrality values of nodes between the three network types (Original-Without Unknown, Original-Bootstrap and Without Unknown-Bootstrap) at different taxonomic levels; Figure S15. Fit of neutral model for collembola (a), nematodes (b), potworms (c), earthworms (d), oribatid mites (e) and predatory mites (g); Figure S16. Boxplot revealing natural 15N fractionation (\u03b4 15N value) of different soil fauna (centre line, median; box limits, first and third quartiles; whiskers, 1.5 \u00d7 interquartile range); Figure S17. FEAST estimations of source contribution to the sink (predatory mite) in each sampling site; Figure S18. Venn diagram revealing the shared OTUs number between different trophic level of soil fauna; Figure S19. Change in relative read abundance of 26 dominant bacterial taxa along an increase in trophic level (\u03b4 15N value) of the soil fauna; Figure S20. Random forest classification of soil fauna microbial communities (including 58 bacterial taxa which were found in more than 70% of all soil fauna samples) based on different animal types (a and b) and trophic levels (c and d); Figure S21. Changes in relative read abundance of 23 unique bacterial taxa across increased trophic level as measured by \u03b4 15N value (-0.25-16.79) in the soil fauna; Figure S22. Linear fitting diagrams revealing unique bacterial taxa that enriched with increasing \u03b4 15N value (-0.25-16.79) in the soil fauna (P &lt; 0.001); Figure S23. Scatter diagrams revealing changes in relative read abundance of 23 unique bacterial taxa across increased trophic level as measured by \u03b4 15N value (-0.25-16.79) in the soil fauna; Figure S24. Boxplots presenting the difference of betweenness, closeness, and degree centrality values of nodes between the three network types (Original-Without Unknown, Original-Bootstrap and Without Unknown-Bootstrap) at the Genus level, which reflected effects of Unknown taxa on different trophic level soil faunal network metrics.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Zhu, Dong, Delgado-Baquerizo, Manuel, Ding, Jing, Gillings, Michael R., Zhu, Yong-Guan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.16650170"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.16650170", "name": "item", "description": "10.6084/m9.figshare.16650170", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.16650170"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.19498606", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:21:45Z", "type": "Report", "created": "2022-04-02", "title": "Additional file 1 of Rhizosheath\u2013root system changes exopolysaccharide content but stabilizes bacterial community across contrasting seasons in a desert environment", "description": "Additional file 1. Result S1. Analysis of rhizosheath sand composition. Result S2. Analysis of cultivable bacteria. Table S1. Climatic conditions throughout the year in Ksar Ghilane, Sahara Desert (Tunisia); data elaborated from https://www.worldweatheronline.com ; data as reported as monthly average measurement from January 2019 to now. Table S2. List of FISH probes used and conditions applied; probe sequence (5\u2019-3\u2019), attached fluorochrome (fluor.), bacterial target group, percentage of formaldehyde (FA) used during treatment, and references are also reported. Table S3. Number of KTUs and sequences used for each sample. RH: rhizosphere, RS: rhizosheath, RT: root tissue, BS: bulk sand. Table S4. Humidity (RH%) and temperature (T\u00b0C) measured in summer (June, 2016) and winter (November, 2016) for bulk sand and belowground speargrasses (i.e., plant rhizosheath\u2013root system, RS). Table S5. Physico-chemical analyses conducted on bulk sand (BS) and rhizosheath matrix (RS) collected in summer (S) and winter (W). Table S6. PERMANOVA pair-wise comparison test of physico-chemical conditions (Table S5) in rhizosheath (RS) and bulk sand (BS) collected during summer (S) and winter (W) seasons. Table S7. Multivariate test (pairwise comparison) of beta-diversity associated with root tissue, rhizosheath, rhizosphere, and bulk sand. Table S8. Mean and standard error of multivariate dispersions from centroid calculated for each compartment (within-betadiversity). Table S9. Relative importance of different ecological processes in the assembly of bacterial community associated with the rhizosheath-root system compartments and bulk sand in two contrasting seasons. Table S10. Generalized linear model univariate test indicates the KTUs contributing to the difference in bulk soil among summer and winter. Table S11. Tukey\u2019s honest significance difference (TukeyHSD) pairwise comparison tests for the degree, betweenness and keystone species detected across the four co-occurrence networks, namely bulk sand winter, bulk sand winter summer, rhizosheath\u2013root system winter and rhizosheath\u2013root system summer. Table S12. List of bacterial isolates, PGP activity, and abiotic resistance tested in vitro. Figure S1. Stipagrostis pungens rhizosheath-root system. Figure S2. Rarefaction curves of bacterial reads obtained by pair-ends MiSeq Illumina sequencing in bulk sand, rhizosheath, rhizosphere, and root tissues. Figure S3. Visualization of Stipagrostis pungens rhizosheath with scanning electron microscopy (SEM). Figure S4. SEM images and electron micrographs of the sand grains and root tissue within the rhizosheath of Stipagrostis pungens using environmental scanning electron microscopy (ESEM) to reveal chemical composition. Figure S5. Localization of bacteria in rhizosheath-root system by confocal laser-scanning microscopy (CLSM) and fluorescence in situ hybridization (FISH). Figure S6. Bacterial KTUs distribution across samples. Figure S7. Venn diagram shows the distribution of bacterial KTUs across compartment categories. Figure S8. Alpha diversity expressed as richness (number of KTUs) and Shannon diversity across the compartment categories. Figure S9. Quantification of betadiversity components in bulk sand bacterial communities across seasons (summer and winter). Figure S10. Alpha diversity expressed as richness (number of KTUs) and Shannon diversity across the compartments in summer and winter. Figure S11. Venn diagrams showing the number of KTUs present in summer and/or in winter in each compartment category. Figure S12. Analysis of 2-fold change was performed to evaluate the KTUs that had a significantly (p &lt; 0.01) different relative abundance (2-fold change) over summer and winter. Figure S13. Taxonomy of co-occurrence network degrees in bulk sand and rhizosheath matrix across seasons. Figure S14. Microcosms to evaluate sand wettability in vitro. Evaluation of sand weight at 48 h and along the entire incubation (0, 24 and 48 h).", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Marasco, Ramona, Fusi, Marco, Mosqueira, Maria, Booth, Jenny Marie, Rossi, Federico, Cardinale, Massimiliano, Michoud, Gr\u00e9goire, Rolli, Eleonora, Mugnai, Gianmarco, Vergani, Lorenzo, Borin, Sara, De Philippis, Roberto, Cherif, Ameur, Daffonchio, Daniele,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.19498606"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.19498606", "name": "item", "description": "10.6084/m9.figshare.19498606", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.19498606"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/GVNJAB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:52Z", "type": "Dataset", "created": "2019-06-24", "title": "Physical topsoil  properties in Murugusi, Western Kenya", "description": "Open Access&lt;b&gt;General:&lt;/b&gt; Lab determined topsoil bulk density, contents of sand, clay and organic carbon in Murugusi, W. Kenya, together with spatial coordinates of where the soil samples were taken (rounded to the closest center point of a 250 m \u00d7 250 m raster). All lab analyses were carried out at the ILRI/CIAT lab in Nairob, Kenya.  &lt;br&gt;  &lt;b&gt;Soil sampling:&lt;/b&gt; At each sample location, one composite topsoil sample was taken; three cores of 7 cm in diameter taken within an area of one square meter. The soil was taken from 0-0.2 m depth below any organic (O) horizon.   &lt;br&gt;  &lt;b&gt;Determination of soil properties:&lt;/b&gt; The bulk density of the soil was determined by taking two undisturbed soil samples (0-10 cm and 10-20 cm depth) of known volume (100 cm2) and weighting them after air drying. Soil fractions of clay (&lt;0.002 mm) and sand (0.05-2 mm) were determined by the hydrometer method (Estefan et al., 2014), using 10% sodium hexametaphosphate as the dispersing agent. Soil pH was determined potentiometrically on a soil suspension of 1:2 (soil: water). Total carbon was measured after dry combustion using an elemental analyser (Elementar Vario max cube; ISO 10694, first edition 1995-03-01)  &lt;br&gt;  &lt;b&gt;Reference: &lt;/b&gt;Estefan G., Sommer R., Ryan J. (2014) Analytical Methods for Soil-Plant and Water in Dry Areas. A Manual of Relevance to the West Asia and North Africa Region. 3rd Edition, International Center for Agricultural Research in the Dry Areas, Aleppo, 255 pp. Available online at: http://repo.mel.cgiar.org:8080/handle/20.500.11766/7512?show=full. Verified: October 9, 2018.  &lt;br&gt;  &lt;b&gt;Acknowledgements: &lt;/b&gt; We are deeply thankful for the good services provided by John Mukulama (soil sampling), John Yumbya Mutua (soil sampling) and Francis Mungthu Njenga (lab analyses) The project was carried out within the CGIAR Research Program on Water, Land and Ecosystems (WLE).", "keywords": ["Soil organic matter", "Agricultural Sciences", "Soil organic carbon", "sand", "Kenya", "Carbon", "Latin America and the Caribbean", "soil", "Soil", "Soil bulk density", "Sand", "soil organic matter", "Earth and Environmental Sciences", "Soil texture", "Murugusi", "Africa", "Clay", "Texture", "Western Kenya", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Piikki, Kristin, S\u00f6derstr\u00f6m, Mats, Sommer, Rolf, Da Silva, Mayesse,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/GVNJAB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/GVNJAB", "name": "item", "description": "10.7910/DVN/GVNJAB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/GVNJAB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/HXAH87", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:21:52Z", "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.&lt;br&gt;&lt;br&gt;", "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": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:22:13Z", "type": "Journal Article", "created": "2024-05-01", "title": "Arabinosylation of cell wall extensin is required for the directional response to salinity in roots", "description": "Abstract                   <p>Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity.</p", "keywords": ["580", "0301 basic medicine", "2. Zero hunger", "570", "Salinity", "0303 health sciences", "Glycosylation", "Arabidopsis Proteins", "Arabidopsis", "Breakthrough Report", "Sodium Chloride", "15. Life on land", "Arabinose", "Plant Roots", "Gravitropism", "03 medical and health sciences", "Cell Wall", "Gene Expression Regulation", " Plant", "Life Science", "Glycoproteins", "Plant Proteins"]}, "links": [{"href": "https://academic.oup.com/plcell/article-pdf/36/9/3328/59006321/koae135.pdf"}, {"href": "https://doi.org/11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "name": "item", "description": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-01T00:00:00Z"}}, {"id": "11369/372709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:14Z", "type": "Journal Article", "created": "2018-09-07", "title": "Soil resources and element stocks in drylands to face global issues", "description": "Abstract<p>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.</p", "keywords": ["2. Zero hunger", "0301 basic medicine", "Conservation of Natural Resources", "0303 health sciences", "Multidisciplinary", "Nitrogen", "Climate", "Climate Change", "Phosphorus", "15. Life on land", "Article", "Carbon", "Food Supply", "Soil", "03 medical and health sciences", "element cycles", "13. Climate action", "carbon cycle", "Life Science", "Humans", "Desert Climate", "Ecosystem", "geochemistry"]}, "links": [{"href": "https://iris.univr.it/bitstream/11562/1001390/1/Soil%20resources%20and%20element%20stocks%20in%20drylands%20to%20face%20global%20issues.pdf"}, {"href": "https://www.nature.com/articles/s41598-018-32229-0.pdf"}, {"href": "https://doi.org/11369/372709"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11369/372709", "name": "item", "description": "11369/372709", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11369/372709"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-13T00:00:00Z"}}, {"id": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "type": "Feature", "geometry": null, "properties": {"license": "http://dcat-ap.ch/vocabulary/licenses/terms_by", "updated": "2020-08-20T13:49:14Z", "type": "Dataset", "language": "en", "title": "Anthropogenic change and soil net N mineralization", "description": "This dataset contains all data on which the following publication below is based.  Paper Citation:  Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Sch\u00fctz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology  Please cite this paper together with the citation for the datafile.  We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth\u2019s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).", "formats": [{"name": "XLS"}], "keywords": ["ammonification", "ch", "climate", "fertilization", "global-change", "grassland", "herbivore", "mineralization", "nitrification", "nitrogen", "nutrient-network", "soil"], "contacts": [{"organization": "Anita C. Risch", "roles": ["creator"]}, {"organization": "https://envidat.ch/#/about", "roles": ["publisher"]}]}, "links": [{"href": "https://www.envidat.ch/#/metadata/anthropogenic-change-and-net-n-mineralization"}, {"href": "https://www.envidat.ch/dataset/anthropogenic-change-and-net-n-mineralization/resource/13089b78-5a54-47a5-abe2-243a1e32772d"}, {"href": "http://data.europa.eu/88u/dataset/14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat"}, {"rel": "self", "type": "application/geo+json", "title": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "name": "item", "description": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "1854/LU-8732814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:24Z", "type": "Journal Article", "created": "2021-11-09", "title": "Litter quality, mycorrhizal association, and soil properties regulate effects of tree species on the soil fauna community", "description": "Abstract   Forest management, including selection of appropriate tree species to mitigate climate change and sustain biodiversity, requires a better understanding of factors that affect the composition of soil fauna communities. These communities are an integral part of the soil ecosystem and play an essential role in forest ecosystem functioning related to carbon and nitrogen cycling. Here, by performing a field study across six common gardens in Denmark, we evaluated the effects of tree species identity and mycorrhizal association (i.e., arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM)) on soil fauna (meso- and macrofauna) taxonomic and functional community composition by using diversity, abundance, and biomass as proxies. We found that (1) tree species identity and mycorrhizal association both showed significant effects on soil fauna communities, but the separation between community characteristics in AM and ECM tree species was not entirely consistent; (2) total soil fauna abundance, biomass, as well as taxonomic and functional diversity were generally significantly higher under AM tree species, as well as lime, with higher litter quality (high N and base cation and low lignin:N ratio); (3) tree species significantly influenced the properties of litter, forest floor, and soil, among which litter and/or forest floor N, P, Ca, and Mg concentrations, soil pH, and soil moisture predominantly affected soil fauna abundance, biomass, and taxonomic and functional diversity. Our results from this multisite common garden experiment provide strong and consistent evidence of positive effects of tree species with higher litter quality on soil fauna communities in general, which helps to better understand the effects of tree species selection on soil biodiversity and its functions related to forest soil carbon sequestration.", "keywords": ["DECOMPOSITION", "EARTHWORMS", "Diversity", "PH", "FOREST FLOOR", "Common garden experiment", "Soil meso- and macrofauna", "DIVERSITY", "Biology and Life Sciences", "04 agricultural and veterinary sciences", "15. Life on land", "NITROGEN", "CARBON", "Taxonomic group", "FUNCTIONAL TRAITS", "Abundance", "13. Climate action", "Earth and Environmental Sciences", "Functional group", "0401 agriculture", " forestry", " and fisheries", "BIODIVERSITY", "ABUNDANCE", "Biomass"]}, "links": [{"href": "https://doi.org/1854/LU-8732814"}, {"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": "1854/LU-8732814", "name": "item", "description": "1854/LU-8732814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8732814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-25T16:22:25Z", "type": "Journal Article", "created": "2022-11-21", "title": "Global biomass burning fuel consumption and emissions at 500\u2009m spatial resolution based on the Global Fire Emissions Database (GFED)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500\u2009m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e.\u00a0fire-related forest loss, was modelled based on the overlap between 30\u2009m forest loss data and MODIS burned area and active fire detections. Using this new 500\u2009m model, we calculated global average carbon emissions from fire of 2.1\u00b10.2 (\u00b11\u03c3 interannual variability, IAV)\u2009Pg\u2009C\u2009yr\u22121 during 2002\u20132020. Fire-related forest loss accounted for 2.6\u00b10.7\u2009% (uncertainty range =1.9\u2009%\u20133.3\u2009%) of global burned area and 24\u00b16\u2009% (uncertainty range =16\u2009%\u201331\u2009%) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13\u00b14\u2009% of global emissions. Our global fire emissions estimate was higher than the 1.5\u2009Pg\u2009C\u2009yr\u22121 from GFED4 and similar to 2.1\u2009Pg\u2009C\u2009yr\u22121 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500\u2009m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30\u2009m and finer show a substantial amount of burned area that remains undetected with 500\u2009m sensors, suggesting that global carbon emissions from fire are likely higher than our 500\u2009m estimates. The ability to model fire emissions at 500\u2009m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.</p></article>", "keywords": ["QE1-996.5", "13. Climate action", "11. Sustainability", "Geology", "15. Life on land", "7. Clean energy", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "name": "item", "description": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-30T00:00:00Z"}}, {"id": "1959.7/uws:72836", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:22:27Z", "type": "Journal Article", "created": "2023-04-24", "title": "Different Cerrado Ecotypes Show Contrasting Soil Microbial Properties, Functioning Rates, and Sensitivity to Changing Water Regimes", "description": "Abstract<p>Soil moisture is among the most important factors regulating soil biodiversity and functioning. Models forecast changes in the precipitation regime in many areas of the planet, but how these changes will influence soil functioning, and how biotic drivers modulate such effects, is far from being understood. We evaluated the responses of C and N fluxes, and soil microbial properties to different soil water regimes in soils from the main three ecotypes of the world's largest and most diverse tropical savanna. Further, we explored the direct and indirect effects of changes in the ecotype and soil water regimes on these key soil processes. Soils from the woodland savanna showed a better nutritional status than the other ecotypes, as well as higher potential N cycling rates, N2O emissions, and soil bacterial abundance but lower bacterial richness, whereas potential CO2 emissions and CH4 uptake peaked in the intermediate savanna. The ecotype also modulated the effects of changes in the soil water regime on nutrient cycling, greenhouse gas fluxes, and soil bacterial properties, with more intense responses in the intermediate savanna. Further, we highlight the existence of multiple contrasting direct and indirect (via soil microbes and abiotic properties) effects of an intensification of the precipitation regime on soil C- and N-related processes. Our results confirm that ecotype is a fundamental driver of soil properties and functioning in the Cerrado and that it can determine the responses of key soil processes to changes in the soil water regime.</p", "keywords": ["2. Zero hunger", "Ecotype", "0301 basic medicine", "Take urgent action to combat climate change and its impacts", "Naturgeografi", "ecotype", "Cerrado", "greenhouse gases.", "04 agricultural and veterinary sciences", "15. Life on land", "precipitation regime", "Precipitation regime", "cerrado", "03 medical and health sciences", "Greenhouse gases", "Physical Geography", "13. Climate action", "N cycle", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "C cycle", "http://metadata.un.org/sdg/13", "cerrado; ecotype; precipitation regime; C cycle; N cycle; greenhouse gases"]}, "links": [{"href": "https://doi.org/1959.7/uws:72836"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:72836", "name": "item", "description": "1959.7/uws:72836", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:72836"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-24T00:00:00Z"}}, {"id": "1959.7/uws:75008", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-25T16:22:27Z", "type": "Journal Article", "created": "2023-10-04", "title": "Plant footprint decreases the functional diversity of molecules in topsoil organic matter after millions of years of ecosystem development", "description": "AbstractAim<p>Theory suggests that the diversity of molecules in soil organic matter (SOM functional diversity) provides key insights on multiple ecosystem services. We aimed to investigate how and why SOM functional diversity and composition change as topsoils develop, and its implications for key soil functions (e.g., from nutrient pool to water regulation).</p>Location<p>We reported data on 16 soil chronosequences globally distributed in nine countries from six continents.</p>Time Period<p>2016\uffe2\uff80\uff932017.</p>Major Taxa Studied<p>Soil microbes (bacteria and fungi) and vascular plants.</p>Methods<p>SOM functional diversity and composition without mineral interference were measured using diffuse reflectance mid\uffe2\uff80\uff90infrared Fourier transform spectroscopy (DRIFT). We aimed to characterize the main environmental factors related to SOM functional diversity and composition. Also, we calculated the links among SOM functional diversity and key soil functions.</p>Results<p>We found that SOM functional diversity declines after millions of years of soil formation (pedogenesis). We further showed that increases in plant cover and productivity led to a higher ratio of reduced (e.g., alkanes) over oxidized carbon forms (i.e., C: O\uffe2\uff80\uff90functional groups ratio), which was positively correlated to SOM functional diversity as soils age. Our findings indicated that the plant footprint (i.e., the accumulation of plant\uffe2\uff80\uff90derived material promoting the C: O\uffe2\uff80\uff90functional group ratio) would explain the reduction of SOM functional diversity as ecosystems develop. Moreover, the dissimilarity in SOM composition consistently increased with soil age, with the soil development stage emerging as the main predictor of SOM dissimilarity across contrasting biomes.</p>Main Conclusions<p>Our global survey contextualized the natural history of SOM functional diversity and composition during long\uffe2\uff80\uff90term soil development. Together, we showed how plant footprint drives the losses of SOM functional diversity with increasing age, which might provide a novel mechanism to explain typically reported losses in ecosystem functions during ecosystem retrogression.</p", "keywords": ["2. Zero hunger", "0301 basic medicine", "03 medical and health sciences", "13. Climate action", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/1959.7/uws:75008"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Ecology%20and%20Biogeography", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:75008", "name": "item", "description": "1959.7/uws:75008", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:75008"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-03T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=50&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=50&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=0", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=100", "hreflang": "en-US"}], "numberMatched": 15601, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-08-25T16:49:23.499131Z"}