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    This data was collected to develop baselines for three Land Degradation Neutrality (LDN) indicators: land use and land cover change (LUC) for the period 2001-2017, soil organic carbon (SOC) stocks for 2017 and bush density for 2017 as a baseline for bush encroachment in Omusati region, Namibia.

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    Soil information, from the global to the local scale, has often been the one missing biophysical information layer, the absence of which has added to the uncertainties of predicting potentials and constraints for food and fiber production. The lack of reliable and harmonized soil data has considerably hampered land degradation assessments, environmental impact studies and adapted sustainable land management interventions. Recognizing the urgent need for improved soil information worldwide, particularly in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and the immediate requirement for the FAO/IIASA Global Agro-ecological Assessment study (GAEZ v3.0), the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) took the initiativeof combining the recently collected vast volumes of regional and national updates of soil information with the information already contained within the 1:5,000,000 scale FAOUNESCO Digital Soil Map of the World, into a new comprehensive Harmonized World Soil Database (HWSD). This database was achieved in partnership with: • ISRIC-World Soil Information together with FAO, which were responsible for the development of regional soil and terrain databases and the WISE soil profile database; • the European Soil Bureau Network, which had recently completed a major update of soil information for Europe and northern Eurasia, and • the Institute of Soil Science, Chinese Academy of Sciences which provided the recent 1:1,000,000 scale Soil Map of China.

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    This harmonized set of soil parameter estimates for the Upper Tana river catchment, Kenya. The data set was derived from the 1:250 000 scale Soil and Terrain Database for the Upper Tana (SOTER_UT, ver. 1.1; Dijkshoorn et al. 2011) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of the Upper Tana, Kenya, covering some 18,900 km2, has been mapped in SOTER using 191 unique SOTER units. Each map unit may comprise of up to three different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts. Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_UT have been characterized using 146 profiles consisting of 109 real and 37 so-called synthetic profiles. The latter were used to represent some 18% per cent of the study area. Comprehensive sets of measured attribute data are seldom available for most profiles (109) collated in SOTER_UT, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database. The current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names (43) identical to those mapped for the Upper Tana in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 5745). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range.

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    This harmonized set of soil parameter estimates for Kenya (KENSOTER), at scale 1:1M, compiled by the Kenya Soil Survey. The land surface of the Republic of Kenya - excluding lakes and towns - has been characterized using 397 unique SOTER units corresponding with 623 soil components. The major soils have been described using 495 profiles, which include 178 synthetic profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports and expert knowledge. Gaps in the measured soil profile data have been filled using a step-wise procedure which includes three main stages: (1) collate additional measured soil analytical data where available; (2) fill gaps using expert knowledge and common sense; (3) fill the remaining gaps using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity. These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data. Results are presented as summary files and can be linked to the 1:1M scale SOTER map for Kenya in a GIS, through the unique SOTER-unit code. The secondary data are considered appropriate for studies at the national scale (1:1M). Correlation of soil analytical data, however, should be done more rigorously when more detailed scientific work is considered.

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    ISRIC World Soil Information is compiling legacy soil profile data of Sub Saharan Africa, as a project activity of the AfSIS project (Globally integrated Africa Soil Information Service). http://africasoils.net/services/data/soil-databases/ Africa Soil Profiles database, version. 1.0 (April 2012) identifies less than 15700 unique soil profiles inventoried from a wide variety of data sources. From the less than 14600 profiles that are geo-referenced, soil layer attribute data are available for less than 12500 and soil analytical data for less than 10000 profiles. The database includes, but is not limited, to the soil attributes specified by GlobalSoilMap.net. Soil attribute values are standardized according to e-SOTER conventions and validated according to routine rules. Odd values are flagged. The degree of validation, and associated reliability of the data, varies because reference soil profile data, that are previously and thoroughly validated, are compiled together with non-reference soil profile data of lesser inherent representativeness. Updated milestone versions of the dataset have been posted online and made available to the project serving as input to the soil property maps generated by AfSIS. The continuously growing dataset will also be made available through the World Soil Information Service upon continuation of the project activity. The version is released here is version 1.0., the latest version is 1.1.

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    This harmonized set of soil parameter estimates for Tunisia. It has been derived from the 1:1 million scale Soil and Terrain Database for the country (SOTER_TN, ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of Tunisia, covering some 164,150 km2, has been characterized in SOTER_TN using 250 unique SOTER units. Each map unit consists of up to four different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts (see Dijkshoorn et al. 2008). Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_TN have been characterized using 100 profiles of which 44 are synthetic. The latter represent some 59 per cent of the territory. Comprehensive sets of measured attribute data are not available for most of the measured profiles (56) collated in SOTER_TN, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database ― the current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names identical to those mapped for Tunisia in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 3566). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range. The derived data presented here may be used for exploratory assessments at national scale or broader (greater than 1:1 000 000). They should be seen as best estimates based on the current, still limited, selection of soil profiles in SOTER_TN and data clustering procedure ― the type of taxotransfer rules used to fill gaps in the measured data has been flagged to provide an indication of confidence in the derived data

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    The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the first ‘WoSIS snapshot’, in July 2016, many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed in WoSIS were contributed by a wide range of data providers, therefore special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement), and soil analytical method descriptions. We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. Further, for each profile, we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data as well as a first approximation for the uncertainty associated with the operationally defined analytical methods are presented, for possible consideration in digital soil mapping and subsequent earth system modelling. The present snapshot, referred to as ‘WoSIS snapshot - September 2019’, comprises 196,498 geo-referenced profiles originating from 173 countries. They represent over 832 thousand soil layers (or horizons), and over 6 million records. The actual number of observations for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The downloadable ZIP file has the data in TSV (tab separated values) and GeoPackage format. It contains the following files: - ReadmeFirst_WoSIS_2019dec04.pdf (546.7 KB) - wosis_201909.gpkg (2.2 GB, same data as in the tsv) - wosis_201909_attributes.tsv (8.7 KB) - wosis_201909_layers_chemical.tsv (893.5 MB) - wosis_201909_layers_physical.tsv (890.7 MB) - wosis_201909_profiles.tsv (18.8 MB) To read the data in R, please, uncompress the ZIP file and specify the uncompressed folder. Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time). setwd("/YourFolder/WoSIS_2019_September/") attributes = readr::read_tsv('wosis_201909_attributes.tsv', col_types='cccciicd') profiles = readr::read_tsv('wosis_201909_profiles.tsv', col_types='icccdddiicccciccccicccc') chemical = readr::read_tsv('wosis_201909_layers_chemical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') physical = readr::read_tsv('wosis_201909_layers_physical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') For more detailed instructions on how to read the data with R, please visit https://www.isric.org/accessing-wosis-using-r. Citation: Batjes N.H, Ribeiro E, and van Oostrum A.J.M, 2019. Standardised soil profile data for the world (WoSIS snapshot - September 2019), https://doi.org/10.17027/isric-wdcsoils.20190901. The dataset accompanies the following data paper: Batjes N.H., Ribeiro E., and van Oostrum A.J.M., 2019. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot - 2019). Earth System Science Data, https://doi.org/10.5194/essd-12-299-2020.

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    This harmonized set of soil parameter estimates for the Indo-Gangetic Plains (IGP) of India, at scale 1:1 000 000, has been derived from soil and terrain data collated in SOTER format by staff of the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP) at Nagpur, India. The data set has been prepared for use in the project on "Assessment of soil organic carbon stocks and change at ... national scale" (GEFSOC), which has IGP-India as one of its four case study areas (see http://www.nrel.colostate.edu/projects/gefsoc-uk/). The land surface of IGP-India has been characterized using 36 unique SOTER units, corresponding with 497 polygons. The major soils of these units have been described using 36 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. Gaps in the measured soil profile data have been filled using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity(-33 to-1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure; taxotransfer rules have been flagged to provide an indication of the confidence in the derived data. Results are presented as summary files and can be linked to the 1:1M scale SOTER map in a GIS, through the unique SOTER-unit code. The secondary SOTER data set for IGP-India is considered appropriate for exploratory studies at regional scale (greater than1:1M); correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.

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    The Free Brazilian Repository for Open Soil Data – febr, www.ufsm.br/febr – is a centralized repository targeted at storing open soil data and serving it in a standardized and harmonized format. The repository infrastructure was built using open source and/or free (of cost) software, and was primarily designed for the individual management of datasets. A dataset-driven structure helps datasets authors to be properly acknowledged. Moreover, it gives the flexibility to accommodate many types of data of any soil variable. This is accomplished by storing each dataset using a collection of spreadsheets accessible through an online application. Spreadsheets are familiar to any soil scientist, the reason why it is easier to enter, manipulate and visualize soil data in febr. They also facilitate the participation of soil survey experts in the recovery and quality assessment of legacy data. Soil scientists can help in the definition of standards and data management choices through a public discussion forum, febr-forum@googlegroups.com. A comprehensive documentation is available to guide febr maintainers and data contributors. A detailed catalog gives access to the 14 477 soil observations – 42% of them from south and southeastern Brazil – from 232 datasets contained in febr. Global and dataset-specific visualization and search tools and multiple download facilities are available. The latter includes standard file formats and connections with R and QGIS through the febr package. Various products can be derived from data in febr: specialized databases, pedotransfer functions, fertilizer recommendation guides, classification systems, and detailed soil maps. By sharing data through a centralized soil data storing and sharing facility, soil scientists from different fields have the opportunity to increase collaboration and the much needed soil knowledge.

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    The ICRAF-ISRIC Soil VNIR Spectral Library contains visible near infrared spectra of 4,438 soils selected from the Soil Information System (ISIS) of the International Soil Reference and Information Centre (ISRIC). The samples consist of all physically archived samples at ISRIC in 2004 for which soil attribute data was available. The spectra were measured at the World Agroforestry Center's (ICRAF) Soil and Plant Spectral Diagnostic Laboratory. The samples are from 58 countries spanning Africa, Asia, Europe, North America, and South America. Associated attribute data, such as geographical coordinates, horizon (depth), and physical and chemical properties, are provided in a single relational database. The purpose of the library is to provide a resource for research and applications for sensing soil quality both in the laboratory and from space.