{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.4748444", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Dataset", "title": "The extent of woody plant invasion in selected sites of the communally managed Molopo District, North West Province.", "description": "EmbargoWoody plant invasion (bush encroachment) is a problem in the semi arid communal areas of the North West Province which had been affecting the Molopo Area as early as 1960. It affects the livelihoods of the communal farmer because it reduces carrying capacity and is a form of veld degradation. The extent of woody plant encroachment was quantified at selected sites and reference sites in the Molopo District. There was a study site and reference site selected in a commercially managed area. Soil samples from these selected sites were also analysed for chemical and physical properties as well as nutrient content that could have an influence on the proliferation of the woody plants. Social surveys were also conducted to investigate the perceptions and influence of the affected communities towards woody plant invasion. The prominent species identified in the area included Acacia mellifera, Dichrostachys cenerea, Prosopis velutina and Terminalia sericea. All of the study sites, except the benchmark sites, had woody plant densities of more than 2 000 TE/ha that according to Moore &amp; Odendaal (1987), almost totally suppress grass growth. It was clear from the data that the nutrient status of soils of encroached areas was higher than the benchmark sites although some of the differences were statistically insignificant. Organic carbon was higher at most of the encroached sites (71 % of the sites) where 80 % of the enriched sites had significantly higher organic carbon than that of the benchmark sites. There is a need to develop small scale farming practices that are appropriate in terms of sustainable development in the local context.", "keywords": ["2. Zero hunger", "woody plants", "Molopo district", "Prosopis velutina", "Dichrostachys cenerea", "Masters", "15. Life on land", "Acacia mellifera", "North West Province", "Terminalia sericea", "Molopo Area"], "contacts": [{"organization": "Mogodi, Phemelo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4748444"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4748444", "name": "item", "description": "10.5281/zenodo.4748444", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4748444"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4767189", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Dataset", "title": "Data to support the publication The use of Twitter for knowledge exchange on sustainable soil management", "description": "5 farmer semi-structured interviews", "keywords": ["2. Zero hunger", "Social media", "Knowledge exchange", "Twitter", "Sustainable soil management", "15. Life on land", "Twitter", " social media", " sustainable soil management", " knowledge exchange"], "contacts": [{"organization": "Mills, Jane, Skaalsveen, Kamilla,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4767189"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4767189", "name": "item", "description": "10.5281/zenodo.4767189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4767189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "10.5281/zenodo.4884672", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Soil microbial, fungal and bacterial abundance impacted by crop diversification, tillage and fertilizer type", "description": "This data set contains a data-mining performed to assess the impact of crop diversification, tillage and fertilizer type on soil microbial, fungal and bacterial abundance under arable crops worldwide by a further meta-analysis of the data. These data correspond to the open-access article ' <strong>The impact of crop diversification, tillage and fertilization type on soil total microbial, fungal and bacterial abundance: A worldwide meta-analysis of agricultural sites</strong> ' published in Agriculture Ecosystems and Environment. (doi:10.1016/j.agee.2022.107867), funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Morug\u00e1n-Coronado, Alicia, P\u00e9rez-Rodr\u00edguez, Paula, Insolia, Eliana, Soto-G\u00f3mez, Diego, Fern\u00e1ndez-Calvi\u00f1o, David, Zornoza, Ra\u00fal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4884672"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4884672", "name": "item", "description": "10.5281/zenodo.4884672", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4884672"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-31T00:00:00Z"}}, {"id": "10.5281/zenodo.4765528", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Dataset", "title": "Soil physicochemical properties for Diverfarming LT1 case study (diversified vegetable crops in spain)", "description": "Physicochemical soil properties of the long-term case study LT1 of Diverfarming H2020 project for diversified vegetables in southeast Spain. Includes data of the research article 'Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems' ( https://doi.org/10.3390/agriculture11050445)", "keywords": ["2. Zero hunger", "vegetables", "Soil", "Soil organic carbon", "Crop diversification", "soil fertility", "horticulture", "15. Life on land", "soil structure", "soil pesticides"], "contacts": [{"organization": "S\u00e1nchez-Navarro, Virginia, \u00d6zbolat, Onurcan, Mart\u00ednez-Mena, Mar\u00eda, Boix-Fayos, Carolina, D\u00edaz-Pereira, Elvira, Cuartero, Jessica, Pascual, Jose Antonio, Ros, Margarita, Egea-Cortines, Marcos, Belmonte, Ra\u00fal Zornoza,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4765528"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4765528", "name": "item", "description": "10.5281/zenodo.4765528", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4765528"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-16T00:00:00Z"}}, {"id": "10.5281/zenodo.4772795", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Other", "title": "SoildiverAgro brochure Danish", "description": "SoildiverAgro brochure Danish", "keywords": ["brochure", " leaflet", "15. Life on land"], "contacts": [{"organization": "Tamara Rodr\u00edguez Silva", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4772795"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4772795", "name": "item", "description": "10.5281/zenodo.4772795", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4772795"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-19T00:00:00Z"}}, {"id": "10.5281/zenodo.5348287", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment", "description": "Open AccessThis archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support: Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. <em>Science Advances</em> doi.org/10.1126/sciadv.abm8999<em>.</em> An archive of the analysis scripts developed for this project can be found at: https://github.com/chriscra/abandonment_trajectories (https://doi.org/10.5281/zenodo.6383127). Note that the label '_2022_02_07' in many file names refers to the date of the primary analysis. 'dts\u201d or \u201cdt\u201d refer to \u201cdata.tables,' large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, http://r-datatable.com/). \u201cRasters\u201d refer to \u201c.tif\u201d files that were processed using the raster and terra packages in R (Hijmans, 2022; https://rspatial.org/terra/; https://rspatial.org/raster). Data files fall into one of four categories of data derived during our analysis of abandonment: <strong>observed</strong>, <strong>potential</strong>, <strong>maximum</strong>, or <strong>recultivation</strong>. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using \u201cage_dts\u201d for our site in Shaanxi Province, China as an example): <strong>observed</strong> abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv); <strong>potential</strong> abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label \u201c_potential\u201d (e.g., shaanxi_potential_age_2022_02_07.csv); <strong>maximum</strong> age of abandonment over the course of the time series, with the label \u201c_max\u201d (e.g., shaanxi_max_age_2022_02_07.csv); <strong>recultivation </strong>periods, corresponding to the lengths of recultivation periods following abandonment, given the label \u201c_recult\u201d (e.g., shaanxi_recult_age_2022_02_07.csv). <strong>This archive includes multiple .zip files, the contents of which are described below:</strong> <strong>age_dts.zip</strong> - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with 'y' followed by the year, e.g., 'y1987'). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (\u201c_potential\u201d), maximum age (\u201c_max\u201d), and recultivation lengths (\u201c_recult\u201d) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., \u201cmax age\u201d, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions 'cc_filter_abn_dt(),' \u201ccc_calc_max_age(),' \u201ccc_calc_potential_age(),\u201d and \u201ccc_calc_recult_age();\u201d see '_util/_util_functions.R.' <strong>age_rasters.zip</strong> - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (\u201c_potential\u201d), and maximum age (\u201c_max\u201d) rasters for all sites. Maximum age rasters include just one band (\u201clayer\u201d). These rasters match the corresponding .csv files contained in 'age_dts.zip.\u201d <strong>derived_data.zip</strong> - summary datasets created throughout this analysis, listed below. <strong>diff.zip</strong> - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in 'input_data.tables.zip' and 'age_dts.zip.' These files were produced using the custom function 'cc_diff_dt()' (much like the base R function 'diff()'), contained within the custom function 'cc_filter_abn_dt()' (see '_util/_util_functions.R'). Folder contains diff files for observed abandonment, potential abandonment (\u201c_potential\u201d), and recultivation lengths (\u201c_recult\u201d) for all sites. <strong>input_dts.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment </em>(https://doi.org/10.1016/j.rse.2020.111873)<em>. </em>Like \u201cage_dts,\u201d these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., 'y1987'). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a \u201cclean\u201d version produced by applying 5- and 8-year temporal filters to the raw input (see custom function \u201ccc_temporal_filter_lc(),\u201d in \u201c_util/_util_functions.R\u201d and \u201c1_prep_r_to_dt.R\u201d). These files correspond to those in 'input_rasters.zip,' and serve as the primary inputs for the analysis. <strong>input_rasters.zip</strong> - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 <em>Remote Sensing of Environment. </em>Maps are stored as '.tif' files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a \u201cclean\u201d version that has been processed with 5- and 8-year temporal filters (see above). These files match those in 'input_dts.zip.' <strong>length.zip</strong> - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function 'cc_filter_abn_dt()' and \u201ccc_extract_length();\u201d see '_util/_util_functions.R.' <strong>derived_data.zip</strong> contains the following files: '<strong>site_df.csv</strong>' - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below). <strong>Primary derived datasets </strong>for both observed abandonment (\u201carea_dat\u201d) and potential abandonment (\u201cpotential_area_dat\u201d). <strong>area_dat</strong> - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for &gt;=5 years) or no threshold (abandoned for &gt;=1 years). Produced using custom functions 'cc_calc_area_per_lc_abn()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' <strong>persistence_dat</strong> - A .csv containing the area of cropland abandoned (ha) for a given 'cohort' of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called 'year_abn') in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ('initial_area_abn'). The 'age' is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and 'time' is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions 'cc_calc_persistence()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' This serves as the main input for our linear models of recultivation (\u201cdecay\u201d) trajectories. <strong>turnover_dat</strong> - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions 'cc_calc_abn_diff()' via 'cc_summarize_abn_dts()' (see '_util/_util_functions.R'), implemented in 'cluster/2_analyze_abn.R.' This file is only produced for observed abandonment. <strong>Area summary files </strong>(for observed abandonment only) <strong>area_summary_df</strong> - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) 'site,' 2) 'total_site_area_ha_2017' - the total site area (ha) in 2017, 3) 'cropland_area_1987' - the area in cropland in 1987 (ha), 4) 'area_abn_ha_2017' - the area of cropland abandoned as of 2017 (ha), 5) 'area_ever_abn_ha' - the total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017), 6) 'total_crop_extent_ha' - the total area of those pixels that were classified as cropland at least once during the time series, 7) 'total_area_abn_remaining_2017' - duplicate of 'area_abn_ha_2017,' the area abandoned as of 2017 (ha), taken from 'area_recult_threshold,' 8) 'total_initial_area_abn' - the sum of the initial area of each cohort of abandonment when it is first classified as 'abandoned,' i.e., at the 5 year mark (note that this is cumulative, and because it counts those pixels that were abandoned more than once, it is therefore larger than 'area_ever_abn_ha'), taken from 'area_recult_threshold' 9) 'total_area_abn_recultivated_2017' - the area of abandoned land that was recultivated as of 2017 (cumulatively, i.e., 'total_initial_area_abn' - 'area_abn_ha_2017'), taken from 'area_recult_threshold,' 10) 'proportion_recultivated' - the proportion of all abandoned cropland (including multiple periods per pixel) that was recultivated by 2017, taken from 'area_recult_threshold,' 11) 'area_2017_as_prop_site' - area abandoned as of 2017 as a proportion of the total site area, 12) 'area_2017_as_prop_total_crop' - area abandoned as of 2017 as a proportion of the total crop extent, 13) 'area_2017_as_prop_crop87' - area abandoned as of 2017 as a proportion of cropland area in 1987, 14) 'area_ever_abn_as_prop_site' - area ever abandoned as a proportion of the total site area, 15) 'area_ever_abn_as_prop_total_crop' - area ever abandoned as a proportion of the total crop extent, 16) 'area_ever_abn_as_prop_crop87' - area ever abandoned as a proportion of cropland area in 1987. See script '1_summary_stats.Rmd.' <strong>area_recult_threshold</strong> - Contains data on the proportion of observed abandoned cropland area that is recultivated by the end of our time series. This includes the area of abandoned cropland as of 2017 ('total_area_abn_remaining_2017') and the sum of the initial area of each cohort of abandonment when it is first classified as abandoned (at year 5; 'total_initial_area_abn'). This 'total_initial_area_abn' is cumulative, and allows for pixels that were abandoned multiple times during the time series to be counted multiple times. The difference between these two columns yields the 'total_area_abn_recultivated_2017,' which in turn is used to calculate the 'proportion_recultivated,' and the (ascending) 'order' of sites based on this proportion. This file includes recultivation stats for each site for three abandonment definitions: 5, 7, and 10 years. See script '1_summary_stats.Rmd.' <strong>abn_lc_area_2017</strong> - Contains the number of pixels and corresponding area (in ha) of abandoned cropland in the year 2017 at each site, according to the land cover class (either woody vegetation [2], or herbaceous vegetation [4]) and the age in 2017 (5 to 30 years). See script 'cluster/6_lc_of_abn.R.' <strong>abn_prop_lc_2017 </strong>- Contains the number of pixels and corresponding area (ha) of cropland abandoned in the year 2017 in each land cover type (woody vegetation [2], or herbaceous vegetation [4]). It also shows this area as a proportion of the total area abandoned at each site (i.e., in either land cover class: 2 or 4). See script 'cluster/6_lc_of_abn.R.' <strong>Carbon</strong> <strong>carbon_df </strong>\u2013 contains the observed and potential carbon accumulation in abandoned croplands in each site in each year (in Mg C), for two abandonment thresholds: 5 years (our default abandonment definition) and 1 year (i.e., no threshold). Each data point corresponds to one of two scenarios (\u201ctype\u201d column), either \u201cobserved\u201d or \u201cpotential.\u201d Carbon accumulation figures are for both the sum of forest and soil carbon at each site in a given year. Carbon accumulation is listed in three columns: 1) \u201cC_up_to_20\u201d contains the total carbon accumulated in those abandoned croplands with abandonment durations between 5 and 20 years. 2) \u201cC_21_30\u201d contains the total carbon accumulation in croplands with durations between 21 and 30 years, which are differentiated in order to account for non-linear carbon accumulation rates in soils over time, and 3) \u201ctotal_C_Mg\u201d contains the sum of the previous two columns, representing the total carbon accumulated across all abandoned croplands in each year. <strong>soc_mean</strong> \u2013 contains mean soil organic carbon accumulation rates for years 1-20 and years 21-80, derived from Sanderman et al. 2020 (in Mg C; https://doi.org/10.7910/DVN/HA17D3). These values correspond to accumulation rates in croplands upon abandonment and regeneration to natural vegetation (Sanderman et al. 2020\u2019s \u201crewilding\u201d scenario). These mean values are calculated across those pixels identified as cropland by Sanderman et al. 2020 at each site. Mean values in year 20 and 80 are contained in columns \u201cmean_soc_20\u201d and \u201cmean_soc_80\u201d respectively, and the annualized rate over the first 20 years and the subsequent years 21 through 80 are contained in columns \u201cmean_annual_soc_1_20\u201d and \u201cmean_annual_soc_21_80\u201d respectively. <strong>Decay model data</strong> \u2013 two R data files containing data products for our linear models of abandonment recultivation trajectories. <strong>decay_endpoints_files</strong> \u2013 an R data file (.rds) containing seven data products produced as part of our common endpoint analysis, which calculated mean trajectories for each site across a range of common endpoints, ensuring that means were based on coefficient estimates derived from a consistent number of observations for each cohort. These files are: <strong>common_endpoint_dat \u2013 </strong>a .csv containing subsets of \u201cpersistence_dat\u201d for each \u201cendpoint\u201d (7 through 29). <strong>endpoint_n \u2013 </strong>a .csv describing, for each endpoint, the corresponding number of observations per cohort (\u201cn_obs\u201d), the number of cohorts (\u201cn_cohorts\u201d), the total number of observations across cohorts included (\u201ctotal_obs\u201d), and the cohorts that meet the endpoint threshold (\u201ccohorts\u201d). <strong>coef_l3_endpoints \u2013 </strong>corresponding model coefficients for our primary model (\u201cl3\u201d) parameterized by the range of subsets across endpoints. <strong>augment_endpoints \u2013 </strong>fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets. <strong>fitted_endpoints \u2013 </strong>a simplified .csv containing the mean linear and log coefficients for each site at each endpoint, and the corresponding predicted proportion remaining abandoned through time (based on the \u201cage,\u201d or duration, of abandonment). <strong>time_to_endpoints \u2013 </strong>a .csv containing, for mean trajectories for each endpoint at each site, the estimated time required for a given amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%). <strong>endpoint_half_lives \u2013 </strong>a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site. <strong>decay_mod_archive</strong> - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ('decay'): <strong>lm_mega_lin_log_lin_l</strong> \u2013 the primary linear model produced in our analysis. This model is referred to as \u201clin_log_lin\u201d (or \u201cl3\u201d) because the model predicts linear persistence (\u201clin\u201d) as a function of a log term of time (\u201clog\u201d) and a linear term of time (\u201clin\u201d). \u201cmega\u201d refers to the fact that this model is run for the full dataset, pooled acro", "keywords": ["2. Zero hunger", "Carbon sequestration", "Cropland abandonment", "13. Climate action", "Agricultural abandonment", "Agriculture", "15. Life on land", "Land-cover mapping", "Farmland abandonment", "Biodiversity conservation", "Secondary succession"], "contacts": [{"organization": "Crawford, Christopher L., Yin, He, Radeloff, Volker C., Wilcove, David S.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5348287"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5348287", "name": "item", "description": "10.5281/zenodo.5348287", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5348287"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-26T00:00:00Z"}}, {"id": "10.5281/zenodo.4772759", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Other", "title": "SoildiverAgro brochure Czech", "description": "SoildiverAgro brochure Czech", "keywords": ["brochure", " leaflet", "15. Life on land"], "contacts": [{"organization": "Tamara Rodr\u00edguez Silva", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4772759"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4772759", "name": "item", "description": "10.5281/zenodo.4772759", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4772759"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-19T00:00:00Z"}}, {"id": "10.5445/ir/1000160199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:17Z", "type": "Journal Article", "created": "2023-06-14", "title": "A new process-based and scale-aware desert dust emission scheme for global climate models \u2013 Part I: Description and evaluation against inverse modeling emissions", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass and produces numerous important impacts on the Earth system. However, current global climate models (GCMs) and land-surface models (LSMs) struggle to accurately represent key dust emission processes, in part because of inadequate representations of soil particle sizes that affect the dust emission threshold, surface roughness elements that absorb wind momentum, and boundary-layer characteristics that control wind fluctuations. Furthermore, because dust emission is driven by small-scale (\u223c\u20091\u2009km or smaller) processes, simulating the global cycle of desert dust in GCMs with coarse horizontal resolutions (\u223c\u2009100\u2009km) presents a fundamental challenge. This representation problem is exacerbated by dust emission fluxes scaling nonlinearly with wind speed above a threshold wind speed that is sensitive to land-surface characteristics. Here, we address these fundamental problems underlying the simulation of dust emissions in GCMs and LSMs by developing improved descriptions of (1)\u00a0the effect of soil texture on the dust emission threshold, (2)\u00a0the effects of nonerodible roughness elements (both rocks and green vegetation) on the surface wind stress, and (3)\u00a0the effects of boundary-layer turbulence on driving intermittent dust emissions. We then use the resulting revised dust emission parameterization to simulate global dust emissions in a standalone model forced by reanalysis meteorology and land-surface fields. We further propose (4)\u00a0a simple methodology to rescale lower-resolution dust emission simulations to match the spatial variability of higher-resolution emission simulations in GCMs. The resulting dust emission simulation shows substantially improved agreement against regional dust emissions observationally constrained by inverse modeling. We thus find that our revised dust emission parameterization can substantially improve dust emission simulations in GCMs and\u00a0LSMs.</p></article>", "keywords": ["Atmospheric sciences", "info:eu-repo/classification/ddc/550", "550", "Climate change science", "ddc:550", "Physics", "QC1-999", "15. Life on land", "Atmospheric Sciences", "Climate Action", "[SDU] Sciences of the Universe [physics]", "Earth sciences", "Chemistry", "13. Climate action", "[SDU]Sciences of the Universe [physics]", "Earth Sciences", "Meteorology & Atmospheric Sciences", "QD1-999", "Astronomical and Space Sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/23/6487/2023/acp-23-6487-2023.pdf"}, {"href": "https://escholarship.org/content/qt2fk4w0h1/qt2fk4w0h1.pdf"}, {"href": "https://doi.org/10.5445/ir/1000160199"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5445/ir/1000160199", "name": "item", "description": "10.5445/ir/1000160199", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5445/ir/1000160199"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-14T00:00:00Z"}}, {"id": "10.5281/zenodo.4787631", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale", "description": "Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale produced by GEOZS, Slovenia. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and INSPIRE Lithology and Geochronologic Era URI codelists. Layers include:    EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;  dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;   Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.  Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.", "keywords": ["EGDI", "13. Climate action", "surface geology", "15. Life on land", "Geo-harmonizer"], "contacts": [{"organization": "Isik, Serkan, Minarik, Robert, Hengl, T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4787631"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4787631", "name": "item", "description": "10.5281/zenodo.4787631", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4787631"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-30T00:00:00Z"}}, {"id": "10.5281/zenodo.4884673", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Soil microbial, fungal and bacterial abundance impacted by crop diversification, tillage and fertilizer type", "description": "This data set contains a data-mining performed to assess the impact of crop diversification, tillage and fertilizer type on soil microbial, fungal and bacterial abundance under arable crops worldwide by a further meta-analysis of the data. These data correspond to the open-access article ' <strong>The impact of crop diversification, tillage and fertilization type on soil total microbial, fungal and bacterial abundance: A worldwide meta-analysis of agricultural sites</strong> ' published in Agriculture Ecosystems and Environment. (doi:10.1016/j.agee.2022.107867), funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Morug\u00e1n-Coronado, Alicia, P\u00e9rez-Rodr\u00edguez, Paula, Insolia, Eliana, Soto-G\u00f3mez, Diego, Fern\u00e1ndez-Calvi\u00f1o, David, Zornoza, Ra\u00fal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4884673"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4884673", "name": "item", "description": "10.5281/zenodo.4884673", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4884673"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-31T00:00:00Z"}}, {"id": "10.5281/zenodo.5168031", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Radiocarbon in Bulk and Respired CO2 from the Cowlitz River Chronosequence, Washington, USA", "description": "This is part of a study that measured radiocarbon in bulk soil carbon and CO2 respired in incubations from a soil chronosequence on andesite. The study sites are the same ones that are reported in Lawrence et al. 2015 (reference below). However, the soils reported in Lawrence et al. 2015 were sampled in 2010. The samples reported here (previously unpublished) were collected. in 2009. The incubations were performed at the University of California Irvine; the bulk soil samples were collected at the same time but splits were analyzed separately. Corey R. Lawrence, Jennifer W. Harden, Xiaomei Xu, Marjorie S. Schulz, Susan E. Trumbore, (2015) Long-term controls on soil organic carbon with depth and time: A case study from the Cowlitz River Chronosequence, WA USA. Geoderma, V 247-248: p. 73-87, DOI: 10.1016/j.geoderma.2015.02.005 The associated ISRaD Template Information File provides the names and descriptions of all fields.", "keywords": ["15. Life on land"], "contacts": [{"organization": "Trumbore, Susan, Lawrence, Corey, Khomo, Lesego,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5168031"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5168031", "name": "item", "description": "10.5281/zenodo.5168031", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5168031"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-06T00:00:00Z"}}, {"id": "10.5281/zenodo.4954979", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Dataset: Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil", "description": "Open Access<pre>The files stored in this repository contain data and additional information for the study 'Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil' by Tino Peplau, Julia Schroeder, Edward Gregorich and Christopher Poeplau. climate-data-takhini.txt: Contains a dataset with climate data used for Figure 1a and b. The data was downloaded from https://climatedata.ca/download/ as single variables and later on put into this single file. degree_days.xlsx: Contains soil temperature data with according calculation of cumulative degree days. temperature.xlsx: Contains raw data of soil temperature teabags_HS.xlsx: Contains information about all 24 buried teabags. The table contains 6 columns: 1)'sample' gives the individual name of the sample. 2) 'rep' is the replication at each plot 3) 'plot' is the plot, according to the soil warming intensity 4) 'depth' is the depth at which the teabag was buried 5) 'weight_start' is the weight of tea before at start of the experiment 6) 'weight_end' ist the weight of the tea after one year of burial HS_data_final.xlsx: Contains all data of the soil samples. It is divided into two sheets: 'sample_data': Provides information about every single soil sample, including chemical data, bulk density, organic and inorganic carbon, nitrogen and fractions. 'plot_data': Provides a summary of the data for every soil core (repetition) and plot, including mass corrected SOC and N stocks of the whole profile, topsoil and subsoil.</pre>", "keywords": ["2. Zero hunger", "Soil organic matter", "Canada", "Whole-profile", "13. Climate action", "Soil warming", "Teabags", "Fractionation", "15. Life on land", "Takhini hot springs", "6. Clean water", "Thermosequence"], "contacts": [{"organization": "Peplau, Tino, Schroeder, Julia, Gregorich, Edward, Poeplau, Christopher,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4954979"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4954979", "name": "item", "description": "10.5281/zenodo.4954979", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4954979"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.5040380", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping", "description": "Open AccessThis work was supported by the National Key Research and Development Program of China (2017YFA0603002).", "keywords": ["2. Zero hunger", "Digital soil mapping", "Soil organic carbon", "Process-based SOC model", "15. Life on land", "Long-time series", "Space-for-time substitution"], "contacts": [{"organization": "Zhao, Yongcun, Xie, Enze, Zhang, Xiu, Peng, Yuxuan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5040380"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5040380", "name": "item", "description": "10.5281/zenodo.5040380", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5040380"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.5150647", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Soil microarthropods, ground-dwelling arthropods and soil properties in mown and grazed grasslands in the Veluwe region", "description": "Open AccessIn order to find out which factors limit the restoration of soil life and their ecosystem services under grasslands on sandy soils, we studied 40 grasslands of which 20 had agricultural and 20 nature land use, all after an agricultural history. <strong>Site selection</strong> Within the Veluwe region (The Netherlands), we selected 40 grasslands: 20 agricultural grasslands and 20 nature grasslands which were managed as new nature reserves since last tillage. Within each of these two land-use types, two types of grassland management were selected: mowing and grazing. Within each of the four combinations of land use and management we selected ten grasslands over a broad age range since last tillage. All grasslands were located on sandy soils (Typic Haploquod and Plaggeptic Haploquod; Soil Survey Staff 1999) with a deep water table to rule out dispersal of soil fauna during waterlogging (Siepel 1996; Jabbour &amp; Barbercheck 2008). <strong>Vegetation and insect surveys</strong> Within each grassland a 5\ufffd\ufffd5 meter monitoring plot was laid-out for plant cover surveys, insect and soil-microarthropod sampling and soil analyses. The vegetation surveys were carried out in 2019 at the end of May and in early June, using the Braun-Blanquet method (Braun-Blanquet 1932). In June 2019 soil-surface dwelling insects were sampled with a pitfall trap (Wiggers et al. 2015). Three pitfall traps (8 cm diameter, ca. 20 cm deep) were placed in each plot. Traps were half filled with a solution of water and glycol (3:1) and 3 % Extran soap. A plexiglass cover 20 cm above the trap prevented rainfall diluting the liquid. Traps were removed and emptied after seven days. Insects were identified and grouped at the order level, however, predator groups (carabid and staphylinid beetles, ants and spiders) were identified to the species level in order to group those by their feeding guild. Before analyzing the pitfall trap catches we first removed certain groups from the counts because pitfall traps are not well-suited to catch them systematically: Acari, Collembola, Psocoptera, Thysanoptera, Trichoptera, Lepidoptera, Siphonaptera, Diptera, Symphyta, Apocrita, and Parasitica. The remaining 62.0% of the caught individuals were surface-dwelling animals, and their totals (of three pitfall traps per site) were analyzed with negative-binomial generalized linear models. We also analyzed the subset of predators (73.6% of the surface dwellers). <strong>Soil chemical and pesticide sampling and analysis</strong> On 8, 9 and 16 October 2019, a bulk soil sample of 50 soil cores (0 - 10 cm) was collected from each 5\ufffd\ufffd5 meter monitoring plot. After homogenization a sub-sample was analyzed for soil chemical analysis. Prior to chemical analysis, samples were oven-dried at 40 \ufffd\ufffdC. Soil acidity of the oven-dried samples was measured in 1 M KCl (pH-KCl). Soil Organic Matter (SOM) was determined by loss-on-ignition (Ball 1964). Ammonium-lactate-extractable P (PAL) was determined according to the standard method (Bronswijk et al. 2003). Total potassium (K) in solution was determined using flame photometry after extraction of soil with HCl (0.1 M) and oxalic acid (0.5 M) in a 1:10 M:V ratio and filtration (Bronswijk et al. 2003). Clay (&lt;2 \ufffd\ufffdm diameter) content was determined through density fractionation (NEN 5753, 2018). Another soil sub-sample was sent to Eurofins Zeeuws-Vlaanderen for pesticide/residue analysis. Samples were freeze-dried and homogenized prior to analysis. Homogenized samples were extracted with acetone, petroleum ether and dichloro-methane using an optimized mini-Luke method. In total 664 pesticides and pesticide residues were analyzed with gas chromatography (Agilent) and liquid chromatography (LC-chromatograph (Agilent) and MSMS (Sciex)). Glyphosate, its residue AMPA and gluphosinate were analyzed using single residue analysis. The detection limit (LOD) was 0,1 mg per kg sample. <strong>Soil microarthropods sampling and determination</strong> Grasslands were sampled for microarthropods on 8, 9 and 16 October 2019, taking three cores per monitoring plot of 5\ufffd\ufffd5 m. Cores were 5 cm \ufffd\ufffd and 5 cm deep mineral soil plus upper litter. Cores were taken in the middle of the monitoring plots, 1 m apart from each other. Cores were extracted on a Tullgren funnel for 7 days. During that period temperature was increased from 35 to 45 <sup>0</sup>C. Ethanol 70% was used as conservation fluid and microarthropods obtained were put into lactic acid 30% for clarification and identification (Siepel &amp; van de Bund 1988). Identification for the main groups is according to Weigmann (2006) for Oribatida, Karg (1993) for Gamasina and Karg (1989) for Uropodina. Nomenclature is according to Siepel et al. (2009) (Oribatida), Siepel et al. (2016) (Astigmatina) and Siepel et al. (2018) (Mesostigmata). <strong>Litter decomposition</strong> To determine the potential decomposition of soil organic matter on each grassland the Tea Bag Index (TBI) was used (Keuskamp et al. 2013). In each grassland four green tea and four rooibos tea bags were buried at 8 cm deep in May 2019 in the 5\ufffd\ufffd5 meter monitoring plots. After 90 days tea bags were collected and stored at 4 \ufffd\ufffd\ufffdC prior to drying at 70 \ufffd\ufffd\ufffdC for 48 hours. After drying, remaining sand and (fine) plant roots were carefully removed and the teabags were weighted to determine weight loss. The decomposition rate (<em>k</em>) and the litter stabilization factor (<em>S</em>) of the tea was calculated using the Tea Bag Index (Keuskamp et al. 2013). <strong>Data files</strong> <em><strong>siteData.csv</strong></em> site: grassland ID landuse: agricultural or nature land use treat: mowing or grazing management yearsManaged: number of years since last tillage fertilization: kg available nitrogen applied per hectare nGrazingDaysPerHa: livestock days per hectare per year N: mg nitrogen per 100 g PAl: mg P<sub>2</sub>0<sub>5</sub> per 100 g organicMatter: soil organic matter percentage clay: soil clay percentage nPlantSpecies: number of plant species nForbSpecies: number of forb species nMitesSpringtails: total number of individuals of mites and springtails in three core samples nMitesSpringtailsSpecies: number of mite and springtail species in three core samples shannonMitesSpringtails: Shannon diversity index for microarthropods (mites and springtails) nHerboFungivorousGrazerMitesSpringtails: total number of individuals of mites and springtails that are (herbo-)fungivorous grazers, in three core samples nInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals in pitfall traps nPredatorInsectsSpidersPitfall: number of ground-dwelling insect and spider individuals that are predators, in pitfall traps decompositionRate: decomposition rate based on the Tea Bag Index litterStabilisationFactor: litter stabilization factor based on the Tea Bag Index nPesticides: number of detected pesticides avicidesTotalConcentration: microgram antraquinon per kg dry soil fungicidesTotalConcentration: total microgram of fungicides per kg dry soil insecticidesTotalConcentration: total microgram of insecticides per kg dry soil herbicidesTotalConcentration: total microgram of herbicides per kg dry soil pesticidesTotalConcentration: total microgram of pesticides (avicides+fungicides+herbicides+insecticides) per kg dry soil nPredatorCarabids: number of predator carabid beetles in pitfall traps nPredatorStaphylinids: number of predator staphylinid beetles in pitfall traps distanceToNearestHighway: shortest distance (in meters) to the nearest highway (A-road) distanceToNearestNroad: shortest distance (in meters) to the nearest national road (N-road) <em><strong>mitesSpringtails.csv</strong></em> core: core ID, consisting of the site ID (number) and core-within-site ID (letter) species: soil mite or springtail taxon encountered in a soil core guild: feeding guild of the soil mite or springtail taxon: b: bacterivorous fb: fungivorous browser fg: fungivorous grazer gp: general predator hb: herbivorous browser hfg: (herbo-)fungivorous grazer hg: herbivorous grazer o: omnivore ohf: opportunistic herbo-fungivore droughtSens: drought strategy of soil mite and springtail taxa 1: drought avoiders 2: drought sensitive 3: drought mesotolerant 4: drought tolerant microart: number of individuals of a taxon found in a soil core <em><strong>insecticideData.csv</strong></em> <em><strong>fungicideData.csv</strong></em> <em><strong>herbicideData.csv</strong></em> site: grassland ID other variables: microgram of a certain pesticide per kg dry soil", "keywords": ["2. Zero hunger", "tillage; nature restoration; regenerative agriculture; disturbance effects; food web interactions; pesticide residues", "food web interactions", "pesticide residues", "tillage", "disturbance effects", "15. Life on land", "regenerative agriculture", "nature restoration", "6. Clean water"], "contacts": [{"organization": "van Eekeren, Nick, Jongejans, Eelke, van Agtmaal, Maaike, Guo, Yuxi, van der Velden, Merit, Versteeg, Carmen, Siepel, Henk,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5150647"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5150647", "name": "item", "description": "10.5281/zenodo.5150647", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5150647"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-31T00:00:00Z"}}, {"id": "10.5281/zenodo.5171830", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Cryoturbation leads to iron-organic carbon associations along a permafrost soil chronosequence in northern Alaska", "description": "In permafrost soils, substantial amounts of organic carbon (OC) are potentially protected from microbial degradation and transformation into greenhouse gases by association with reactive iron (Fe) minerals. As permafrost environments respond to climate change, increased drainage of thaw lakes in permafrost regions is predicted. Soils will subsequently develop on these drained thaw lakes, but the role of Fe-OC associations in future OC stabilization during this predicted soil development is unknown. To fill this knowledge gap, we have examined Fe-OC associations in organic, cryoturbated and mineral horizons along a 5500-year chronosequence of drained thaw lake basins in Utqia\u0121vik, Alaska. By applying chemical extractions, we found that ~17 % of the total OC content in cryoturbated horizons is associated with reactive Fe minerals, compared to ~10 % in organic or mineral horizons. As soil development advances, the total stocks of Fe-associated OC more than double within the first 50 years after thaw lake drainage, because of increased storage of Fe-associated OC in cryoturbated horizons (from 8 to 75 % of the total Fe-associated OC stock). Spatially-resolved nanoscale secondary ion mass spectrometry showed that OC is primarily associated with Fe(III) (oxyhydr)oxides which were identified by <sup>57</sup>Fe M\u00f6ssbauer spectroscopy as ferrihydrite. High OC:Fe mass ratios (&gt;0.22) indicate that Fe-OC associations are formed via co-precipitation, chelation and aggregation. These results demonstrate that, given the proposed enhanced drainage of thaw lakes under climate change, OC is increasingly incorporated and stabilized by the association with reactive Fe minerals as a result of soil formation and increased cryoturbation.", "keywords": ["carbon", " iron", " thermokarst", " cryoturbation", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Joss, Hanna, Patzner, Monique S., Maisch, Markus, Mueller, Carsten W., Kappler, Andreas, Bryce, Casey,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5171830"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5171830", "name": "item", "description": "10.5281/zenodo.5171830", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5171830"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-09T00:00:00Z"}}, {"id": "10.5281/zenodo.5205401", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "SOILCARE_WP_7_D7.2_Adoption factors and policy actions", "description": "Dataset accompanying D7.2: \u201cReport on the selection of good policy alternatives at EU and study site level\u201d. The file provides data collected from stakeholders at the European level as well as at national, regional and local level within the 16 SoilCare study site countries. The data set provides stakeholder views on factors enabling and hampering the uptake of Soil Improving Cropping Systems as well as actions to facilitate their adoption. The methodology for collecting the data is detailed in D7.2 available at https://www.soilcare-project.eu/resources/deliverables.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "sustainable agricultural practices", "sustainable soil management", " adoption factors", " adoption barriers", " policy", " stakeholders"], "contacts": [{"organization": "McNeill, Alicia, Muro, Melanie, Tugran, Tugce, Lucakova, Zuzana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5205401"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5205401", "name": "item", "description": "10.5281/zenodo.5205401", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5205401"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5226666", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "SOILCARE_database1_WP2_SICS_aspects", "description": "Open Access{'references': ['Oenema, O., M. Heinen, R. Rietra, and R. Hessel. 2017. A review of soil-improving cropping systems (full report). SoilCare Scientific Report 07, Deliverable D2.1, SoilCare Project, Wageningen Environmental Research, the Netherlands. Available at: https://soilcare-project.eu/downloads/soilcare-reports-and-deliverables']}", "keywords": ["2. Zero hunger", "Soil improving cropping systems", "Literature review of published meta-analysis studies", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Heinen, Marius, Rietra, Ren\ufffd\ufffd, Oenema, Oene,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5226666"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5226666", "name": "item", "description": "10.5281/zenodo.5226666", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5226666"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5148787", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Global process-based characterization factors of soil carbon depletion for life cycle impact assessment", "description": "Supporting information files (rasters) of the paper: Teixeira, R.F.M., Morais, T.G., Domingos, T. 2021. Global process-based characterization factors of soil carbon depletion for life cycle impact assessment.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Teixeira, R.F.M., Morais, T.G., Domingos, T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5148787"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5148787", "name": "item", "description": "10.5281/zenodo.5148787", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5148787"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-04T00:00:00Z"}}, {"id": "10.5281/zenodo.5205400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "SOILCARE_WP_7_D7.2_Adoption factors and policy actions", "description": "Dataset accompanying D7.2: \u201cReport on the selection of good policy alternatives at EU and study site level\u201d. The file provides data collected from stakeholders at the European level as well as at national, regional and local level within the 16 SoilCare study site countries. The data set provides stakeholder views on factors enabling and hampering the uptake of Soil Improving Cropping Systems as well as actions to facilitate their adoption. The methodology for collecting the data is detailed in D7.2 available at https://www.soilcare-project.eu/resources/deliverables.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "sustainable agricultural practices", "sustainable soil management", " adoption factors", " adoption barriers", " policy", " stakeholders"], "contacts": [{"organization": "McNeill, Alicia, Muro, Melanie, Tugran, Tugce, Lucakova, Zuzana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5205400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5205400", "name": "item", "description": "10.5281/zenodo.5205400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5205400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5226665", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "SOILCARE_database1_WP2_SICS_aspects", "description": "Open Access{'references': ['Oenema, O., M. Heinen, R. Rietra, and R. Hessel. 2017. A review of soil-improving cropping systems (full report). SoilCare Scientific Report 07, Deliverable D2.1, SoilCare Project, Wageningen Environmental Research, the Netherlands. Available at: https://soilcare-project.eu/downloads/soilcare-reports-and-deliverables']}", "keywords": ["2. Zero hunger", "Soil improving cropping systems", "Literature review of published meta-analysis studies", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Heinen, Marius, Rietra, Ren\ufffd\ufffd, Oenema, Oene,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5226665"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5226665", "name": "item", "description": "10.5281/zenodo.5226665", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5226665"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5235030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Software", "title": "A Colab-Python script code to identify palaeo-landscape features", "description": "Open Access{'references': ['1. Python Software Foundation. Python Language Reference. 2020. Available: http://www.python.org', '2. Wu Q. geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software. 2020;5: 2305', '3. Bisong E. Google Colaboratory. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: u00a0 u00a0  u00a0A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. pp. 59 u201364', '4. Project Jupyter. Jupyter Notebook. 2020. Available: https://jupyter.org/', '5. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2019. u00a0  u00a0  u00a0Available: https://www.qgis.org/en/site/index.html', '6. Gillies S et al. Rasterio: geospatial raster I/O for Python programmers. Mapbox; 2013. Available: https://github.com/mapbox/rasterio', '7. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9: 90 u201395.']}", "keywords": ["Remote Sensing", "Multispectral analysis", "Landscape Archaeology", "Spectral decomposition", "15. Life on land", "Sentinel-2", "Riverscape", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Python"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5235030"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5235030", "name": "item", "description": "10.5281/zenodo.5235030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5235030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.5509226", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Exploiting The Multifunctional Potential Of Belowground Biodiversity In Organic Farming: A Chance For Improving Horticultural Productions", "description": "Poster presented at World Organic Congress, 6-10 September 2021, explaining the aims and activities of Excalibur. This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 817946.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Malus\u00e0, Eligio, Canfora, Loredana, Mocali, Stefano,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5509226"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5509226", "name": "item", "description": "10.5281/zenodo.5509226", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509226"}, {"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": "10754/685569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:20Z", "type": "Journal Article", "created": "2022-11-03", "title": "Environmental micro\u2010niche filtering shapes bacterial pioneer communities during primary colonization of a Himalayas' glacier forefield", "description": "Abstract<p>The pedogenesis from the mineral substrate released upon glacier melting has been explained with the succession of consortia of pioneer microorganisms, whose structure and functionality are determined by the environmental conditions developing in the moraine. However, the microbiome variability that can be expected in the environmentally heterogeneous niches occurring in a moraine at a given successional stage is poorly investigated. In a 50\uffe2\uff80\uff89m2 area in the forefield of the Lobuche glacier (Himalayas, 5050\uffe2\uff80\uff89m above sea level), we studied six sites of primary colonization presenting different topographical features (orientation, elevation and slope) and harbouring greyish/dark biological soil crusts (BSCs). The spatial vicinity of the sites opposed to their topographical differences, allowed us to examine the effect of environmental conditions independently from the time of deglaciation. The bacterial microbiome diversity and their co\uffe2\uff80\uff90occurrence network, the bacterial metabolisms predicted from 16S rRNA gene high\uffe2\uff80\uff90throughput sequencing, and the microbiome intact polar lipids were investigated in the BSCs and the underlying sediment deep layers (DLs). Different bacterial microbiomes inhabited the BSCs and the DLs, and their composition varied among sites, indicating a niche\uffe2\uff80\uff90specific role of the micro\uffe2\uff80\uff90environmental conditions in the bacterial communities' assembly. In the heterogeneous sediments of glacier moraines, physico\uffe2\uff80\uff90chemical and micro\uffe2\uff80\uff90climatic variations at the site\uffe2\uff80\uff90spatial scale are crucial in shaping the microbiome microvariability and structuring the pioneer bacterial communities during pedogenesis.</p", "keywords": ["0301 basic medicine", "Pedogenesis", "0303 health sciences", "Glacier Foreland Succession", "Bacteria", "Biological soil crust", "15. Life on land", "Primary Colonization", "Soil", "03 medical and health sciences", "13. Climate action", "RNA", " Ribosomal", " 16S", "Glacier Moraines", "Cold Deserts", "Pioneer Bacterial Communities", "Ice Cover", "Soil moisture", "Research Articles", "Soil Microbiology"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/949070/2/Rolli%20et%20al%202022%20Environmental%20micro%e2%80%90niche%20filtering%20shapes%20bacterial%20pioneer%20communities.pdf"}, {"href": "https://eprints.ncl.ac.uk/fulltext.aspx?url=302678/40A25368-9064-4886-B8E6-E7942511FA71.pdf&pub_id=302678"}, {"href": "https://doi.org/10754/685569"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10754/685569", "name": "item", "description": "10754/685569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10754/685569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-18T00:00:00Z"}}, {"id": "10.5281/zenodo.5509889", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Journal Article", "created": "2021-08-24", "title": "Reviewing the Potential of Sentinel-2 in Assessing the Drought", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth\u2019s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.</p></article>", "keywords": ["land use and land cover analysis", "vegetation response", "Sentinel-2; drought; soil moisture; evapotranspiration; vegetation response; surface water and wetland analysis; land use and land cover analysis", "Science", "Q", "evapotranspiration", "0207 environmental engineering", "drought", "02 engineering and technology", "15. Life on land", "01 natural sciences", "6. Clean water", "surface water and wetland analysis", "13. Climate action", "Sentinel-2; drought", "Sentinel-2", "soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/13/17/3355/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5509889"}, {"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": "10.5281/zenodo.5509889", "name": "item", "description": "10.5281/zenodo.5509889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-24T00:00:00Z"}}, {"id": "10.5281/zenodo.5511764", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Application of organic fertilizers alter the physical and biogeochemical properties of agricultural topsoil and subsoil", "description": "Open AccessvEGU21: Gather Online | 19\u201330 April 2021", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "7. Clean energy", "6. Clean water", "12. Responsible consumption", "Organic amendments", " Organic carbon stocks", " subsoil ", " Vis-NIR"], "contacts": [{"organization": "Neumeier, Anke, Guigue, Julien, Ostovari, Yaser, Muskolus, Andreas, Holmer, Anna, Martens, Henk, Me\u0161inovi\u0107, Emina, K\u00f6gel-Knabner, Ingrid, Creamer, Rachel, Van Groenigen, Jan Willem, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5511764"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5511764", "name": "item", "description": "10.5281/zenodo.5511764", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5511764"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5509227", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Exploiting The Multifunctional Potential Of Belowground Biodiversity In Organic Farming: A Chance For Improving Horticultural Productions", "description": "Poster presented at World Organic Congress, 6-10 September 2021, explaining the aims and activities of Excalibur. This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 817946.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Malus\u00e0, Eligio, Canfora, Loredana, Mocali, Stefano,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5509227"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5509227", "name": "item", "description": "10.5281/zenodo.5509227", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509227"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-06T00:00:00Z"}}, {"id": "10.5281/zenodo.5511746", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Effects of mineral versus organic fertilizers on soil fertility and organic carbon stocks in agricultural topsoil and subsoil", "description": "Open AccessThe main goals of sustainable agricultural practices are to rebuild soil organic carbon (SOC) stocks and to sustain soil fertility. The use of organic amendments such as manure, slurry and biogas digestate, as sources of carbon and nutrients, is one of the levers to achieve these goals, as an alternative to the use of mineral fertilizers. However, the effects of organic amendments compared with traditional mineral fertilizers on topsoil and subsoil SOC stocks and soil fertility are still uncertain. Hence, we aimed at investigating the effects of mineral and organic fertilizers (i.e., manure, pig slurry and biogas digestate) on topsoil and subsoil biogeochemistry, and soil structure, after seven years of application. To this end, we sampled soil cores down to 1 m depth in a randomized field experiment in North Germany, running since 2011. We quantified the SOC and nitrogen stocks, as well as some nutrient contents (e.g., nitrate, available phosphorus). Selected samples were further analysed for aggregate size distribution, as well as organic carbon and nitrogen contents within these aggregates. A hyperspectral camera in the range of Vis-NIR was used to scan undisturbed core-samples in order to reveal hotspots of carbon storage along the soil profile. Soil carbon distribution was predicted as a function of spectral response coupled with a machine learning ensemble. Overall, the mean SOC stocks were low (53 t ha<sup>-1</sup>), reflecting the sandy loam texture of the Northeast German soils under permanent cropping. The application of organic fertilizers (whatever their nature) resulted in higher SOC contents in the first 10 cm (+26 %) and from 20-40 cm (+30%), as compared to the mineral fertilizer treatments. The application of mineral fertilizer or digestate, as compared to the control, resulted in higher relative amount of microaggregates (versus macroaggregates) (+ 19-40 %) in the soil down to 80 cm. These results will provide essential information to develop management strategies that could increase nutrient recycling as well as SOC stocks.", "keywords": ["2. Zero hunger", "13. Climate action", "Organic amendments", " Organic carbon stocks", " subsoil", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "YASER OSTOVARI, Guigue, Julien, Neumeier, Anke, Overtuf, Emily, Muskolus, Andreas, Martens, Henk, Me\u0161inovi\u0107, Emina, Knabner, Ingrid K\u00f6gel, Creamer, Rachel, Van Groenigen, Jan Willem, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5511746"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5511746", "name": "item", "description": "10.5281/zenodo.5511746", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5511746"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-23T00:00:00Z"}}, {"id": "10.5281/zenodo.5541296", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Other", "title": "SoilCare database 3: schema (empty database) and Report 34 (D5.1): Database with monitoring data", "description": "The Deliverable 5.1 reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested\u00a0cropping systems and/or\u00a0field agricultural experiments in the 16 Study sites.\u00a0\u00a0   To properly monitor a cropping system and/or a field agricultural experiment a lot of information is required to capture all the possible interactions. The SoilCare WP5 devised a common data management system for all the study-sites. One important objective is to collect complete and comparable data for an analysis across study sites and data that allows any user to get all the required information when analysing a cropping system.The data model structure created based on the entity-relationship diagram and designed\u00a0to capture all the possible dependencies and complex interactions in a cropping system.   All information is grouped in different pools: i. (experiments\u2019) Basic information such as institution and person metadata ii. (experimental) Field information like climate, inherent soil properties and spatial arrangement iii. The experimental setup which includes the details for the different treatments and the factors iv. Management data that includes all the detailed information for each group of management categories v. Results which include the measured data and metadata for the measurements/observations.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "", "15. Life on land"], "contacts": [{"organization": "Panagea Ioanna, Dangol Anuja, Olijslagers Marc, Wyseure Guido,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5541296"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5541296", "name": "item", "description": "10.5281/zenodo.5541296", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5541296"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-05T00:00:00Z"}}, {"id": "10.5281/zenodo.5562482", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model", "description": "Open Access{'references': ['Sulman, B. N., E. R. Brzostek, C. Medici, E. Shevliakova, D. N. L. Menge, and R. P. Phillips. 2017. Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association. Ecology Letters 20:1043-1053.']}", "keywords": ["2. Zero hunger", "Biogeochemical bioenergy model", "mechanistic tillage simulation", "biofuel sustainability", "15. Life on land", "7. Clean energy", "soil carbon protection"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5562482"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5562482", "name": "item", "description": "10.5281/zenodo.5562482", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5562482"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5597222", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Branislav Pejak, Milos Pandzic, Predrag Lugonja, Oskar Marko,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597222"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597222", "name": "item", "description": "10.5281/zenodo.5597222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5793126", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Data to support the publication \"Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe\",  https://doi.org/10.3390/land10121362", "description": "Open Access{'references': ['https://doi.org/10.3390/land10121362']}", "keywords": ["2. Zero hunger", "13. Climate action", "SOC", " SICS", "15. Life on land"], "contacts": [{"organization": "Panagea, Ioanna, Wyseure, Guido,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5793126"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5793126", "name": "item", "description": "10.5281/zenodo.5793126", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5793126"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5592751", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "Simulated terrestrial biosphere variables across Termination V (iLOVECLIM model)", "description": "The following files contain output data from the 32-kyr iLOVECLIM simulation covering Termination V: both sequences start at 436 kyr BP and end at 404 kyr BP with a yearly time step. Simulated_carbon_stock.nc: the average simulated carbon stock over latitudinal bands for each of the four carbon components (green biomass, structural biomas, slow Soil Organic Matter (SOM) and fat SOM) and the total carbon stock (sum of the four components). Simulated_tree_fraction.nc: the global simulated tree fraction (in %).", "keywords": ["Deglaciation", "Vegetation", "13. Climate action", "MIS 11", "Carbon cycle", "15. Life on land", "Climate model", "Termination V"], "contacts": [{"organization": ", Gabriel, , Nathaelle,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5592751"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5592751", "name": "item", "description": "10.5281/zenodo.5592751", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5592751"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-29T00:00:00Z"}}, {"id": "10.5281/zenodo.5597223", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Pejak, Branislav, Pandzic, Milos, Lugonja, Predrag, Marko, Oskar,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597223"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597223", "name": "item", "description": "10.5281/zenodo.5597223", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597223"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5597232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Integration of proximal sensor data with satellite images through signal processing on graph", "description": "Understanding the causation of vegetative components variation using sensing technology is quite promising in the agriculture domain. Different types of sensor platforms have been rapidly developing over the last decade with the aim to provide instantaneous and worthful information to the grower. Regarding plant growth conditions evaluation, remote and proximal sensing are the most common techniques that provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture. Differences in working principles of both sensing platforms provide different output data for mapping in terms of spatial resolution and measurement noise. For a proper fusion of information coming from remote and proximal sensors for the evaluation of the crop condition, an inevitable step is the reduction of present noise in the measurements and data alignment. In this study, we address the problem of integration of two types of measurements coming from optical satellite Sentinel 2A and multiband optical sensing device Plant-O-Meter (POM) for remote and proximal sensing of the crop respectively. Presenting both measurements as signals on graphs, we utilize two procedures on the graph: filtration and clusterization in order to achieve noise removal and registration of data with different spatial resolutions. This result indicates that properly preprocessed POM measurements exhibit strong potential for accurately assessment of plant canopy condition.", "keywords": ["2. Zero hunger", "Proximal sensing", " remote sensing", " interpolation", " data filtration", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597232", "name": "item", "description": "10.5281/zenodo.5597232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.5615357", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "Supplementary Table for Earth observation data-driven cropland soil monitoring: A review", "description": "Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.", "keywords": ["soil organic carbon", "hyperspectral", "spectral signatures", "carbon farming", "deep learning", "earth observation", "food security", "15. Life on land", "common agricultural policy"], "contacts": [{"organization": "Tziolas, Nikolaos, Tsakiridis, Nikolaos, Chabrillat, Sabine, Dematt\u00ea, Jos\u00e9 A.M., Ben-Dor, Eyal, Gholizadeh, Asa, Zalidis, George, Van Wesemael, Bas,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5615357"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5615357", "name": "item", "description": "10.5281/zenodo.5615357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5615357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.5653246", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Data from: Diversity and functionality of soil fauna", "description": "The goal of this study was to assess the recovery of soil micro-arthropods in different periods, in heathlands. <br> Study area and sampling strategy <br> All studied plots were located in the Veluwe area, a large nature area central in the Netherlands, consisting of extended forests and heathlands. The area has a humid Atlantic climate with an average temperature of 3.1 \ufffd\ufffdC in January and 17.9 \ufffd\ufffdC in July and an annual precipitation of 950 mm more or less spread evenly over the year (data from a 30-year period). For our research we needed detailed management information, which was obtained from two nature management organizations: National Park De Hoge Veluwe (52\ufffd\ufffd05\ufffd\ufffd\ufffdN, 5\ufffd\ufffd50\ufffd\ufffd\ufffdE) and the municipality of Nunspeet (52\ufffd\ufffd23\ufffd\ufffd\ufffdN, 5\ufffd\ufffd47\ufffd\ufffd\ufffdE). Both organizations keep detailed records on heathland management making it possible to select plots that have been sod-cut only once in a given year, without additional management ever since. <br> At the Hoge Veluwe area we selected 5 plots (uncut and cut 40, 7and 2 years before sampling) and at the Nunspeet area we selected 10 plots (uncut and cut 30, 29, 28, 27, 26, 25, 18, 16, 12 years before sampling. Soil type on all plots is a spodic dystrudept (Soil Survey Staff, 1999), developed on the gentle slope of push moraine ridges with some cover sand on top. The parent material is coarse sandy with 13-18% of loam in the deeper soil layers. Topsoil has been extensively leached, creating a more spodic tophorizon, resembling the one of an orthod. Humus form is originally moderlike, but contains also larger proportions of amorphous organic compounds leaching into the B-horizon as seen in orthods. Organic matter in the topsoil (0-10 cm.) is 5.5 \ufffd\ufffd 2.5%, pH[NaCl] is 3.6 \ufffd\ufffd 0.7 on average over all plots. Soil micro-arthropod and soil chemical sampling<br> All plots were sampled on 8 March 2019, taking four cores per plot. Cores were 5 cm \ufffd\ufffd and 5 cm deep mineral soil plus upper litter. Cores were taken in the middle of the plots, 1 m apart of each other. Cores were extracted on a Tullgren funnel for 7 days. During that period temperature was increased from 35 to 45 \ufffd\ufffdC. Ethanol 70% was used as preservative and micro-arthropods obtained were put into lactic acid 40% for clarification and identification (Siepel and van de Bund 1988). All micro-arthropods from the Tullgren funnel were identified at the species level using appropriate keys. Nomenclature and identification for the main groups is according to Weigmann (2006) for Oribatida, Karg (1993) for Gamasina and Karg (1989) for Uropodina.<br> The soil core samples were taken to measure environmental variables after extraction of the soil micro-arthropods. These air-dried soil samples were sieved to 1 mm. Soil chemistry variables used in our analysis included soil organic matter, soil total nitrogen, moisture, pH [NaCl], available phosphorus (P-Olsen). The pH of the solution was measured immediately using a combined pH electrode after mixing fresh soil with NaCl solution. The soil phosphorus (P-Olsen) was determined using extraction with sodium bicarbonate (Olsen et al., 1954). <br> We have two data files: <br> sod cutting environment factor.csv<br> sod cutting microarthropods.csv Explanation of the variables in the datasets:<br> Area: Nunspeet, Hoge Veluwe<br> year: sod cutting happened in which year<br> Feeding: B, FB, FG, HG, OHF, H, HB, HG,O, P B: browsers<br> FB:Fungivorous browsers<br> FG:Fungivorous grazers<br> HG:Herbofungivorous grazers<br> OHF: Opportunistic Herbofungivorous<br> H: herbofungivores<br> HB: Herbivorous browsers<br> HG: Herbivorous grazers<br> O: Omnivores<br> P: Predators pH: The pH of the solution was measured immediately using a combined pH electrode after mixing fresh soil with NaCl solution. <br> moisture %: percentage moisture of the soil sample <br> org matter %: percentage organic matter of the soil sample<br> N (mg/g): total nitrogen concentration of the soil sample<br> C (mg/g): total carbon concentration of the soil sample<br> mol P per kgram dry soil: The soil phosphorus (P-Olsen) was determined using extraction with sodium bicarbonate (Olsen et al., 1954).<br> C/N ratio: ratio of total carbon to total nitrogen in the soil sample", "keywords": ["sod cutting; soil micro-arthropods; recovery; life history strategy; feeding guilds; GLMM", "recovery", "life history strategy", "15. Life on land", "sod cutting", "soil micro-arthropods", "sod cuttingsoil micro-arthropodsrecoverylife history strategyfeeding guildsglmm", "GLMM", "feeding guilds"], "contacts": [{"organization": "Guo, Y., Guo, Y., Jongejans, E., Siepel, H.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5653246"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5653246", "name": "item", "description": "10.5281/zenodo.5653246", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5653246"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5597233", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Report", "title": "Integration of proximal sensor data with satellite images through signal processing on graph", "description": "Understanding the causation of vegetative components variation using sensing technology is quite promising in the agriculture domain. Different types of sensor platforms have been rapidly developing over the last decade with the aim to provide instantaneous and worthful information to the grower. Regarding plant growth conditions evaluation, remote and proximal sensing are the most common techniques that provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture. Differences in working principles of both sensing platforms provide different output data for mapping in terms of spatial resolution and measurement noise. For a proper fusion of information coming from remote and proximal sensors for the evaluation of the crop condition, an inevitable step is the reduction of present noise in the measurements and data alignment. In this study, we address the problem of integration of two types of measurements coming from optical satellite Sentinel 2A and multiband optical sensing device Plant-O-Meter (POM) for remote and proximal sensing of the crop respectively. Presenting both measurements as signals on graphs, we utilize two procedures on the graph: filtration and clusterization in order to achieve noise removal and registration of data with different spatial resolutions. This result indicates that properly preprocessed POM measurements exhibit strong potential for accurately assessment of plant canopy condition.", "keywords": ["2. Zero hunger", "Proximal sensing", " remote sensing", " interpolation", " data filtration", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597233"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597233", "name": "item", "description": "10.5281/zenodo.5597233", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597233"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.5652048", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Transport of Particulate Organic Carbon in The Huanghe: Insights from Lateral And Vertical Heterogeneity in a River Cross-section", "description": "The Huanghe (Yellow River), one of the largest turbid river systems in the world, has long been recognized as a major contributor of suspended particulate matter (SPM) to the ocean. However, over the last few decades, the SPM export flux of the Huanghe has decreased over 90% due to the high management, impacting the global export of particulate organic carbon (POC). To better constrain sources and modes of transport of POC beyond the previously investigated transportation of POC near the channel surface, SPM samples were for the first time collected over a whole channel cross-section in the lower Huanghe. Riverine SPM samples were analyzed for particle size and major element contents, as well as for POC content and dual carbon isotopes (<sup>13</sup>C and <sup>14</sup>C). The results show clear vertical and lateral heterogeneity of SPM physical and chemical characteristics within the river cross section, with for example finer SPM carrying more POC with higher <sup>14</sup>C activity near the surface and the right bank. Notably, we discuss how bank erosion in the alluvial plain is likely to generate lateral heterogeneity in POC composition. The Huanghe POC is millennial-aged (4,020 \u00b1 500 radiocarbon years), dominated by organic carbon (OC) from the biosphere, while the lithospheric fraction reaches up to ca. 33%. The mobilization of aged and refractory OC from deeper soil horizons of the loess-paleosol sequence through erosion in the Chinese Loess Plateau is an important mechanism contributing to fluvial POC in the Huanghe drainage basin. The involvement of this OC fraction has significance for the regional and global carbon cycles, especially regarding its final fate in the estuary. Altogether, this study sheds light on the mechanism of fluvial transfer of POC and corresponding impacts on the carbon cycle in large river systems strongly perturbed by anthropogenic activities.", "keywords": ["particulate organic carbon", "13. Climate action", "Huanghe", "bank erosion", "radiocarbon", "depth profile sampling", "14. Life underwater", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Yutian, Ke, Calmels Damien, Bouchez Julien, Massault Marc, Chetelat Benjamin, Noret Aur\u00e9lie, Hongming, Cai, Jiubin, Chen, Gaillardet J\u00e9r\u00f4me, Quantin C\u00e9cile,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5652048"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5652048", "name": "item", "description": "10.5281/zenodo.5652048", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5652048"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-06T00:00:00Z"}}, {"id": "10.5281/zenodo.5736535", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Supporting data for manuscript: Beyond Bulk", "description": "Open AccessWe used soil density fraction data from The International Soil Radiocarbon Database (ISRaD v. 1.1.2 Lawrence et al., 2020; www.soilradiocarbon.org). ISRaD is an online repository for environmental radiocarbon data with a specific emphasis on soils and soil fractions. We utilized a subset of ISRaD data comprising measurements of radiocarbon (persistence), organic C concentration (abundance), or the proportion of organic C in the mineral-associated fraction (distribution) made on soil density fractions for the current analysis. Radiocarbon data are reported in units of \ufffd\ufffd<sup>14</sup>C (\ufffd\ufffd\ufffd) normalized to account for the year of sampling (Shi et al., 2020) (see below). In studies that employed sequential density separation (isolation of multiple free light, occluded light, and heavy fractions for the same sample), the multiple fractions were combined by taking a mass-weighted average for C abundance and C-weighted average for \ufffd\ufffd<sup>14</sup>C values. C distribution among density fractions was normalized to sum to 100%. Overall, our meta-analysis included data from 52 studies. In addition to C measurements, ISRaD compiles ancillary data regarding site and sample characteristics that were either provided directly in the associated published works or provided as supplementary information from manuscript authors. When variables of interest were not available directly through ISRaD, these variables were populated through utilization of geolocated databases (see supplemental materials in associated published manuscript).", "keywords": ["soil fractions", " radiocarbon", " persistence", " soil organic matter", " soil carbon", " climate change", " terrestrial carbon cycle", "15. Life on land"], "contacts": [{"organization": "Heckman, Katherine A, Pries, Caitlin EH, Lawrence, Corey R, Rasmussen, Craig, Crow, Susan E, Hoyt, Alison M, von Fromm, Sophie F, Shi, Zheng, Stoner, Shane, McGrath, Casey, Beem-Miller, Jeffrey, Berhe, Asmeret A, Blankinship, Joseph C, Keiluweit, Marco, Mar\u00edn-Spiotta, Erika, Monroe, J Grey, Plante, Alain F, Sierra, Carlos A, Thompson, Aaron, Wagai, Rota,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5736535"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5736535", "name": "item", "description": "10.5281/zenodo.5736535", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5736535"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-29T00:00:00Z"}}, {"id": "10.5281/zenodo.5770286", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Journal Article", "created": "2021-08-18", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.</p></article>", "keywords": ["<i>Map of the Natural Vegetation of Europe</i>", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5770286"}, {"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": "10.5281/zenodo.5770286", "name": "item", "description": "10.5281/zenodo.5770286", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5770286"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "10.5281/zenodo.5675793", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India", "description": "Raw data to the manuscript entitled 'Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India' by Severin-Luca Bell\ufffd\ufffd, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven. Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip). Files ending with var_names are the README files.", "keywords": ["soil organic carbon", "forest", "15. Life on land", "soil", "tropics"], "contacts": [{"organization": "Bell\ufffd\ufffd, Severin-Luca, Abiven, Samuel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5675793"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5675793", "name": "item", "description": "10.5281/zenodo.5675793", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5675793"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-11T00:00:00Z"}}, {"id": "10.5281/zenodo.5767789", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Initial soil conditions outweigh management in a cool-season dairy farm's carbon sequestration potential", "description": "Data used in the manuscript 'Initial soil conditions outweigh management in a cool-season dairy farm\ufffd\ufffd\ufffds carbon sequestration potential' (10.1016/j.scitotenv.2021.152195) Soil samples, gas fluxes, and biomass samples measured at the Organic Dairy Research Farm at the University of New Hampshire. Soil samples were in two sets, a spatially explicit set from 0 - 15 cm depth, and less spatially explicit samples taken at 10 cm increments. Soils were sampled for soil carbon and nitrogen content. Gas fluxes were measured using the chamber method with carbon dioxide and nitrous oxide gases measured on gas chromatographs with the change over time used to measure the gas flux rates. Forage biomass was measured by collecting biomass in 1 m2 plots. Please see the manuscript for more details on sampling.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Arndt, Kyle, Campbell, Eleanor, Dorich, Chris, Grandy, Stuart, Griffin, Tim, Ingraham, Peter, Perry, Apryl, Varner, Ruth, Contosta, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5767789"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5767789", "name": "item", "description": "10.5281/zenodo.5767789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5767789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-08T00:00:00Z"}}, {"id": "10.5281/zenodo.5793125", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Data to support the publication \"Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe\", https://doi.org/10.3390/land10121362", "description": "Open Access{'references': ['https://doi.org/10.3390/land10121362']}", "keywords": ["2. Zero hunger", "13. Climate action", "SOC", " SICS", "15. Life on land"], "contacts": [{"organization": "Panagea, Ioanna, Wyseure, Guido,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5793125"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5793125", "name": "item", "description": "10.5281/zenodo.5793125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5793125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5795200", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Report", "title": "Factors affecting farmers' adoption decision of innovative circular farming practices and solutions in EU.", "description": "<strong>Abstract: </strong>The agricultural and livestock sectors are facing several challenges to achieve the current EU environmental objectives. Reducing Greenhouse Gas (GHG) emissions and ensuring that a great share of nitrogen, phosphorus, and potassium coming from renewable sources is one of the major policy goals. In this context, farmers are continuously looking to adopt innovative technologies and solutions that may ensure sustainable food production systems. The adoption process of innovations at the farm level based on the circular economy concept may improve resource efficiency, allow the reuse and recovery of nutrients, and reduce the negative effect of emissions on soils, water, and air. The farmers\ufffd\ufffd\ufffd decision to accept and adopt the innovative solutions depends mainly on the initial investments and return, benefits and costs, farm structure, farmers\ufffd\ufffd\ufffd socio-economic characteristics, farmers\ufffd\ufffd\ufffd attitudes, opinions and behavior and, external markets conditions among other determinant reasons. This study aims at identifying the determinant factors and barriers that affect farmers\ufffd\ufffd\ufffd adoption of several Circular Agronomy Solutions using a semi-structured questionnaire on an exploratory sample of farmers in 5 EU countries. Preliminary results showed that the level of acceptance of the proposed innovations is highly related to farmers\ufffd\ufffd\ufffd economic motivations and objectives. The cost of the investment, the return rate, the institutional support, risk, and environmental attitude play a relevant role in the adoption decision. Results of the adoption preferences may assist policy-makers in designing more specific and efficient measures and tools that may help farmers to face the current environmental challenges and social needs.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "innovation; adoption; farmers decision; circular economy", "15. Life on land", "7. Clean energy", "12. Responsible consumption"], "contacts": [{"organization": "Ornelas, Selene, Kallas, Zein, Serebrennikov, Dmytro, Fern\ufffd\ufffdndez, Bel\ufffd\ufffdn, Mantovi, Paolo, Grassauer, Florian, Holba, Mark, McCarthy, Sinead, Riau, Victor,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5795200"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5795200", "name": "item", "description": "10.5281/zenodo.5795200", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5795200"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-09T00:00:00Z"}}, {"id": "10.5281/zenodo.580814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Journal Article", "title": "Mapping the abstractions of forest landscape patterns", "description": "The evaluation of landscape patterns is necessary to explain the relationships between ecological processes and spatial patterns. For decades, landscape metrics have been used for measuring and abstracting landscape patterns. Since the emergence of FRAGTATS in 1993 the measures and methods incorporated in this software are very widely used and they have become a de facto standard tool for calculating landscape metrics. There are no special metrics for forest landscapes. The selection of metrics rather depends on the purpose of the study than on the land use type. However, there are some metrics that are more used for forest habitats. Forest landscape patterns are changing fast due to natural and human disturbances. Remote sensing offers rapid method of acquiring up-to-date information over a large geographical area and is therefore widely used as a source of data needed for pattern assessment.  However, in order to obtain meaningful results from landscape metrics calculation, the correct preparation of the data is essential. In this chapter we will give an overview of the various metrics used to measure forest landscapes for different purposes. The chapter will deal with five main issues from the perspective of forest landscape patterns: (1) data preparation for metrics calculation (vector vs raster, scale, classification etc); (2) landscape configuration and composition measured by metrics; (3) interpretation of the results; (4) possible usages of the outcomes; (5) future perspectives (3D landscape metrics).", "keywords": ["0106 biological sciences", "pattern analysis", " configuration", " composition", " landscape metrics", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Uuemaa, Evelyn; Oja, T\u00f5nu", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.580814"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Mapping%20Forest%20Landscape%20Patterns", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.580814", "name": "item", "description": "10.5281/zenodo.580814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.580814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-18T00:00:00Z"}}, {"id": "10.5281/zenodo.5907228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Software", "title": "Spatial statistics to reveal patterns and connections in the historic landscape", "description": "The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: F. Brandolini &amp; S. Turner (2022<em>) Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy),</em> Journal of Maps, DOI: 10.1080/17445647.2022.2088305 <strong>Abstract</strong> In the Northern Apennines, significant modifications to the characteristic historic features of landscapes occurred since the 1950s as agriculture declined in importance and villages were progressively depopulated. Today European and national policies are promoting the repopulation of these regions in order to help preserve the cultural identity of territories and to reduce demographic pressure in urban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of a set of strategies to underpin the preservation of landscape heritage and to foster local development on the basis of the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components. <strong>Methods</strong> This dataset was explored with two spatial statistical tools using the programming language R (R Core Team 2021): Local Indicators for Categorical Data (LICD) and Point Pattern analysis (PPA). The LICD method is based on join-count statistics (JCS), a solid method to measure the correlation between binomial variables and the distance between observations. LICD has been recently employed in landscape archaeological studies for verifying visible patterns and disclosing hidden spatial relationships (article: Carrer et al. 2021, Data: Zenodo Repository) The application of PPA in landscape studies has been widely applied in Ecology and it is growing popular also in Archaeology (Knitter and Nakoinz 2018; Brandolini and Carrer 2020; Costanzo et al. 2021). In this study, PPA was employed to provide a quantitative assessment of the correlations between different components of the Vetto landscape. <strong>List of files included in Brandolini_Turner_tjom_2022.zip:</strong> R_script_code named 'tjom_supplementary' in .rmd format Output folder: png and .txt products of the R script code GeoTiff folder (.TIFF file format): Geomorphons Euclidean distances from Irregular Fields (IF) Euclidean distances from Combined Fields (CF) EsriSHP folder (.shp file format): H_sites folder: historic settlements (h_sites.shp) rural_ruins folder: abandoned rural ruins (rural_ruins.shp) hlc folder: HLC_periods.shp HLC_types.shp roi folder: Region Of Interest (roi.shp) <strong>Contacts</strong> <em>dr. F. Brandolin</em>i: filippo.brandolini@newcastle.ac.uk <strong>Acknowledgements</strong> The authors would like to acknowledge the help of the mayor Mr Fabio Ruffini and all the staff of Vetto d\u2019 Enza, Dr. Alessandra Curotti and Dr. Chiara Cantini and (Unione Montana dei Comuni dell\u2019Appennino Reggiano) and Dott.ssa Annalisa Capurso (Soprintendenza Archeologia Belle Arti e Paesaggio per la citt\u00e0 metropolitana di Bologna e le province di Modena, Reggio Emilia e Ferrara) for their administrative assistance in preparation of the project fieldwork activities. Also, we wish to thank Dr Anna Campeol and Mr Davide Cavecchi (Provincia di Reggio Emilia - Ufficio Topografico) for their help in retrieving and digitising the Nuovo Catasto Terreni cadastral map. The authors also thank the AsRe (Archivio Stato di Reggio Emilia) and AsPr (Archivio Stato di Parma) administration and staff for giving the right to digitise the historical maps and for helping during the consultation at the archives. Finally, we thank Francesco Carrer (Newcastle University, Newcastle upon Tyne, UK) for his comments on the R script code, and Christopher Sevara (Newcastle University, Newcastle upon Tyne, UK) for his suggestions in retrieving historical satellite images.", "keywords": ["Landscape Heritage", "Landscape Management", "Landscape Archaeology", "Spatial Statistics", "11. Sustainability", "Spatial Humanities", "Digital Geoarchaeology", "15. Life on land", "Historic Landscape Characterisation", "Point Pattern Analysis", "Local Indicator for Categorical Data"], "contacts": [{"organization": "Brandolini, Filippo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5907228"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5907228", "name": "item", "description": "10.5281/zenodo.5907228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5907228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-26T00:00:00Z"}}, {"id": "10.5281/zenodo.6024053", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Data supporting \"Crop yield after 5 decades of contrasting residue management\"", "description": "Data supporting the pubblication 'Crop yield after 5 decades of contrasting residue management' by Piccoli et al. (2020). Nutr Cycl Agroecosyst (2020) 117:231\ufffd\ufffd\ufffd241. https://doi.org/10.1007/s10705-020-10067-9(0123456789().,-volV() 0123458697().,-volV)", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Piccoli, Ilaria, Sartori, Felice, Polese, Riccardo, Berti, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6024053"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6024053", "name": "item", "description": "10.5281/zenodo.6024053", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6024053"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-09T00:00:00Z"}}, {"id": "10.5281/zenodo.5841415", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Raw data for \"Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring\"", "description": "This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Poeplau, Christopher", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5841415"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5841415", "name": "item", "description": "10.5281/zenodo.5841415", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5841415"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "10.5281/zenodo.5907229", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Software", "title": "Spatial statistics to reveal patterns and connections in the historic landscape", "description": "The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: F. Brandolini &amp; S. Turner (2022<em>) Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy),</em> Journal of Maps, DOI: 10.1080/17445647.2022.2088305 <strong>Abstract</strong> In the Northern Apennines, significant modifications to the characteristic historic features of landscapes occurred since the 1950s as agriculture declined in importance and villages were progressively depopulated. Today European and national policies are promoting the repopulation of these regions in order to help preserve the cultural identity of territories and to reduce demographic pressure in urban areas. Such initiatives increase the need for cultural and natural landscape management to be better integrated using interdisciplinary approaches. Sustainable landscape management is a dynamic process involving the formulation of a set of strategies to underpin the preservation of landscape heritage and to foster local development on the basis of the values and opportunities provided by landscapes themselves. This study uses landscape archaeology and spatial statistics to provide insights into which parts of the historic landscape retain the greatest time-depth and which parts reflect more recent radical change, enabling an understanding which goes beyond the basic spatial relationships between landscape components. <strong>Methods</strong> This dataset was explored with two spatial statistical tools using the programming language R (R Core Team 2021): Local Indicators for Categorical Data (LICD) and Point Pattern analysis (PPA). The LICD method is based on join-count statistics (JCS), a solid method to measure the correlation between binomial variables and the distance between observations. LICD has been recently employed in landscape archaeological studies for verifying visible patterns and disclosing hidden spatial relationships (article: Carrer et al. 2021, Data: Zenodo Repository) The application of PPA in landscape studies has been widely applied in Ecology and it is growing popular also in Archaeology (Knitter and Nakoinz 2018; Brandolini and Carrer 2020; Costanzo et al. 2021). In this study, PPA was employed to provide a quantitative assessment of the correlations between different components of the Vetto landscape. <strong>List of files included in Brandolini_Turner_tjom_2022.zip:</strong> R_script_code named 'tjom_supplementary' in .rmd format Output folder: png and .txt products of the R script code GeoTiff folder (.TIFF file format): Geomorphons Euclidean distances from Irregular Fields (IF) Euclidean distances from Combined Fields (CF) EsriSHP folder (.shp file format): H_sites folder: historic settlements (h_sites.shp) rural_ruins folder: abandoned rural ruins (rural_ruins.shp) hlc folder: HLC_periods.shp HLC_types.shp roi folder: Region Of Interest (roi.shp) <strong>Contacts</strong> <em>dr. F. Brandolin</em>i: filippo.brandolini@newcastle.ac.uk <strong>Acknowledgements</strong> The authors would like to acknowledge the help of the mayor Mr Fabio Ruffini and all the staff of Vetto d\u2019 Enza, Dr. Alessandra Curotti and Dr. Chiara Cantini and (Unione Montana dei Comuni dell\u2019Appennino Reggiano) and Dott.ssa Annalisa Capurso (Soprintendenza Archeologia Belle Arti e Paesaggio per la citt\u00e0 metropolitana di Bologna e le province di Modena, Reggio Emilia e Ferrara) for their administrative assistance in preparation of the project fieldwork activities. Also, we wish to thank Dr Anna Campeol and Mr Davide Cavecchi (Provincia di Reggio Emilia - Ufficio Topografico) for their help in retrieving and digitising the Nuovo Catasto Terreni cadastral map. The authors also thank the AsRe (Archivio Stato di Reggio Emilia) and AsPr (Archivio Stato di Parma) administration and staff for giving the right to digitise the historical maps and for helping during the consultation at the archives. Finally, we thank Francesco Carrer (Newcastle University, Newcastle upon Tyne, UK) for his comments on the R script code, and Christopher Sevara (Newcastle University, Newcastle upon Tyne, UK) for his suggestions in retrieving historical satellite images.", "keywords": ["Landscape Heritage", "Landscape Management", "Landscape Archaeology", "Spatial Statistics", "11. Sustainability", "Spatial Humanities", "Digital Geoarchaeology", "15. Life on land", "Historic Landscape Characterisation", "Point Pattern Analysis", "Local Indicator for Categorical Data"], "contacts": [{"organization": "Brandolini, Filippo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5907229"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5907229", "name": "item", "description": "10.5281/zenodo.5907229", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5907229"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-26T00:00:00Z"}}, {"id": "10.5281/zenodo.5982577", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Global Agricultural Land Resources \u2013 A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)", "description": "<strong>Agricultural land resources \u2013 a global suitability evaluation (v3.0)</strong> Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature. <strong>Determining Agricultural Suitability</strong> Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach. <strong>Agricultural Suitability</strong> The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km<sup>2</sup> at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops. <strong>Food, feed, fiber and first-generation bioenergy crops</strong> Barley Potato Sugarbeet Cassava Rapeseed Sugarcane Groundnut Rice Sunflower Maize Rye Summer wheat Millet Sorghum Winter wheat Oilpalm Soybean <strong>Second-generation bioenergy crops</strong> Jatropha Reed canary grass Miscanthus Eucalyptus Switchgrass Willow <strong>Growing Season Adaptation</strong> The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account. <strong>Most Suitable Crop</strong> The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability. <strong>Further information</strong> Detailled information are available in the following publications: Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources \u2013 A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: 10.1371/journal.pone.0107522 Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: 10.1111/gcbb.12697 Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: 10.1038/s41597-022-01632-8 Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas \u2013 a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119\u20131133, 2018. doi: 10.5194/hess-22-1119-201 Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, <em>J. Adv. Model. Earth Syst.</em>, 8, 41\u2013 65, doi: 10.1002/2015MS000526. <strong>Improvements in v3.0</strong> Compared to the previous version (v2.0), this version (v3.0) <em>uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.</em> <strong>Contact</strong> Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department of Geography, LMU M\u00fcnchen (www.geografie.uni-muenchen.de)", "keywords": ["2. Zero hunger", "13. Climate action", "Climate Change", "Agriculture", "Global", "15. Life on land", "Suitability", "7. Clean energy", "6. 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