Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
1–10 of 13 datasets
FDRE - Ministry of Agriculture (MoA)
Predicted USDA 2014 suborder classes (as integers) predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE')
FDRE - Ministry of Agriculture (MoA)
Predicted WRB 2006 subgroup classes (as integers) predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE')
FDRE - Ministry of Agriculture (MoA)
Predicted probability in percent per class predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: probability. Legend: 1: "Haplic Acrisols", 2: "Haplic Acrisols (Alumic)", 3: "Haplic Acrisols (Ferric)", 4: "Haplic Acrisols (Humic)", 5: "Plinthic Acrisols", 6: "Vetic Acrisols", 7: "Haplic Albeluvisols", 8: "Histic Albeluvisols", 9: "Umbric Albeluvisols", 10: "Cutanic Alisols", 11: "Haplic Alisols", 12: "Aluandic Andosols", 13: "Haplic Andosols", 14: "Vitric Andosols", 15: "Albic Arenosols", 16: "Ferralic Arenosols", 17: "Haplic Arenosols", 18: "Haplic Arenosols (Calcaric)", 19: "Hypoluvic Arenosols", 20: "Protic Arenosols", 21: "Haplic Calcisols", 22: "Haplic Calcisols (Sodic)", 23: "Luvic Calcisols", 24: "Petric Calcisols", 25: "Endogleyic Cambisols", 26: "Ferralic Cambisols", 27: "Haplic Cambisols", 28: "Haplic Cambisols (Calcaric)", 29: "Haplic Cambisols (Chromic)", 30: "Haplic Cambisols (Dystric)", 31: "Haplic Cambisols (Eutric)", 32: "Haplic Cambisols (Humic)", 33: "Haplic Cambisols (Sodic)", 34: "Leptic Cambisols", 35: "Vertic Cambisols", 36: "Calcic Chernozems", 37: "Haplic Chernozems", 38: "Luvic Chernozems", 39: "Haplic Cryosols", 40: "Turbic Cryosols", 41: "Vitric Cryosols", 42: "Petric Durisols", 43: "Acric Ferralsols", 44: "Haplic Ferralsols", 45: "Haplic Ferralsols (Rhodic)", 46: "Haplic Ferralsols (Xanthic)", 47: "Umbric Ferralsols", 48: "Haplic Fluvisols", 49: "Haplic Fluvisols (Arenic)", 50: "Haplic Fluvisols (Calcaric)", 51: "Haplic Fluvisols (Dystric)", 52: "Haplic Fluvisols (Eutric)", 53: "Calcic Gleysols", 54: "Haplic Gleysols", 55: "Haplic Gleysols (Dystric)", 56: "Haplic Gleysols (Eutric)", 57: "Mollic Gleysols", 58: "Umbric Gleysols", 59: "Calcic Gypsisols", 60: "Haplic Gypsisols", 61: "Calcic Histosols", 62: "Cryic Histosols", 63: "Fibric Histosols", 64: "Hemic Histosols", 65: "Sapric Histosols", 66: "Calcic Kastanozems", 67: "Haplic Kastanozems", 68: "Haplic Leptosols", 69: "Haplic Leptosols (Eutric)", 70: "Lithic Leptosols", 71: "Mollic Leptosols", 72: "Rendzic Leptosols", 73: "Haplic Lixisols", 74: "Haplic Lixisols (Chromic)", 75: "Haplic Lixisols (Ferric)", 76: "Albic Luvisols", 77: "Calcic Luvisols", 78: "Gleyic Luvisols", 79: "Haplic Luvisols", 80: "Haplic Luvisols (Chromic)", 81: "Haplic Luvisols (Ferric)", 82: "Leptic Luvisols", 83: "Stagnic Luvisols", 84: "Vertic Luvisols", 85: "Alic Nitisols", 86: "Haplic Nitisols (Rhodic)", 87: "Haplic Phaeozems", 88: "Leptic Phaeozems", 89: "Luvic Phaeozems", 90: "Endogleyic Planosols", 91: "Haplic Planosols (Dystric)", 92: "Haplic Planosols (Eutric)", 93: "Luvic Planosols", 94: "Solodic Planosols", 95: "Acric Plinthosols", 96: "Lixic Plinthosols", 97: "Gleyic Podzols", 98: "Haplic Podzols", 99: "Aric Regosols", 100: "Calcaric Regosols", 101: "Haplic Regosols (Dystric)", 102: "Haplic Regosols (Eutric)", 103: "Haplic Regosols (Sodic)", 104: "Leptic Regosols", 105: "Gypsic Solonchaks", 106: "Haplic Solonchaks", 107: "Haplic Solonchaks (Sodic)", 108: "Calcic Solonetz", 109: "Gleyic Solonetz", 110: "Haplic Solonetz", 111: "Mollic Solonetz", 112: "Luvic Stagnosols", 113: "Haplic Umbrisols", 114: "Leptic Umbrisols", 115: "Calcic Vertisols", 116: "Haplic Vertisols", 117: "Haplic Vertisols (Eutric)", 118: "Mollic Vertisols"
FDRE - Ministry of Agriculture (MoA)
A homogenized, global set of 1,125 soil profiles is presented. These profiles have been extracted from the database developed at ISRIC for a project on "World Inventory of Soil Emission Potentials" (WISE), as a contribution to the activities of the Global Soils Data Task Group of IGBP-DIS. The subset consists of a selection of 665 profiles originating from digital data files released by the Natural Resources Conservation Service (NRCS, Lincoln), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen). All profiles are georeferenced and classified in the FAO-Unesco Legend whereby they can be linked to the edited and digital version of the FAO-Unesco Soil Map of the World. This data set is being released in the public domain for use by global modellers and other interested scientists. It is envisaged that the data set will be expanded by ISRIC when new, uniform soil profile data become available. Note: a) A more recent version (some 10,000 profiles) of WISE profiles is available at: http://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c (2009) b) For a larger compilation see the WoSIS database: http://isric.org/explore/wosis (2017)
FDRE - Ministry of Agriculture (MoA)
Version 3.1 of the ISRIC-WISE database (WISE3) was compiled from a wide range of soil profile data collected by many soil professionals worldwide. All profiles have been harmonized with respect to the original Legend (1974) and Revised Legend (1988) of FAO-Unesco. Thereby, the primary soil data ─ and any secondary data derived from them ─ can be linked using GIS to the spatial units of the digitized Soil Map of the World as well as more recent digital Soil and Terrain (SOTER) databases through the soil legend code. WISE3 holds selected attribute data for some 10,250 soil profiles, with some 47,800 horizons, from 149 countries. Individual profiles have been sampled, described, and analyzed according to methods and standards in use in the originating countries. There is no uniform set of properties for which all profiles have analytical data, generally because only selected measurements were planned during the original surveys. Methods used for laboratory determinations of specific soil properties vary between laboratories and over time; sometimes, results for the same property cannot be compared directly. WISE3 will inevitably include gaps, being a compilation of legacy soil data derived from traditional soil survey, which can be of a taxonomic, geographic, and soil analytical nature. As a result, the amount of data available for modelling is sometimes much less than expected. Adroit use of the data, however, will permit a wide range of agricultural and environmental applications at a global and continental scale (1:500 000 and broader). Preferred citation: Batjes NH 2009. Harmonized soil profile data for applications at global and continental scales: updates to the WISE database. Soil Use and Management 5:124–127, http://dx.doi.org/10.1111/j.1475-2743.2009.00202.x
FDRE - Ministry of Agriculture (MoA)
This global data set shows the spatial distribution of generalized soil classes as defined for IPCC Tier-I level national greenhouse gas inventory assessments. The database was derived from the Harmonized World Soil Data Base (HWSD ver. 1.1, at scale 1:1-1:5 M) and a series of taxotransfer procedures to convert FAO soil classifications (1974, 1985 and 1990 Legend) to the seven default IPCC soil classes: high activity clay (HAC), low activity clay (LAC), Sandy (SAN), Spodic (POD), Volcanic (VOL), wetlands (WET) and Organic (ORG). The resulting GIS database may be used for exploratory assessments at national and broader scale, for regions that lack more detailed soil information; inherent limitations of the data are discussed in the documentation. This dataset has been compiled in the framework of the GEF co-funded 'Carbon Benefits Project: Measuring, modelling and monitoring', Component A ( http://carbonbenefitsproject-compa.colostate.edu/index.htm). March 2021 (version 1.2): Minor updates were applied for the 'SAN' class; for details see below and download file.
FDRE - Ministry of Agriculture (MoA)
The aim of the World Soil Information Service (WoSIS) is to serve quality-assessed, geo-referenced soil data (point, polygon, and grid) to the international community upon their standardisation and harmonisation. So far, the focus has been on developing procedures for legacy point data with special attention to the selection of soil analytical and physical properties considered in the GlobalSoilMap specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments ( greater than 2 mm), cation exchange capacity, electrical conductivity, bulk density, and water holding capacity). Profile data managed in WoSIS were contributed by a wide range of soil data providers; the data have been described, sampled, and analysed according to methods and standards in use in the originating countries. Hence, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values, and soil analytical method descriptions. At the time of writing, the full WoSIS database contained some 118 400 unique shared soil profiles, of which some 96 000 are geo-referenced within defined limits. In total, this corresponds with over 31 million soil records, of which some 20 % have so far been quality-assessed and standardised using the sequential procedure discussed in this paper. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Overall, the data lineage strongly determined which data could be standardised with acceptable confidence in accord with WoSIS procedures, corresponding to over 4 million records for 94 441 profiles. The downloadable ZIP file has the data in TSV (tab separated values). It contains the following files: - ReadmeFirst_WoSIS_2016.pdf (148.1 KB) - wosis_201607_attributes.txt (4.1 KB) - wosis_201607_layers.txt (679.1 MB) - wosis_201607_profiles.txt (8.8 MB) Citation: Batjes NH, Ribeiro E, van Oostrum A, Leenaars J, and Mendes de Jesus J 2016. Standardised soil profile data for the world (WoSIS, July 2016 snapshot), doi:10.17027/isric-wdcsoils.20160003. The dataset accompanies the following data paper: Batjes NH, Ribeiro E, van Oostrum A, Leenaars J, Hengl T, and Mendes de Jesus J 2017. WoSIS: Providing standardised soil profile data for the world, Earth System Science Data 9, 1-14, doi:10.5194/essd-9-1-2017.
FDRE - Ministry of Agriculture (MoA)
This harmonized, gridded global data set of soil parameter estimates includes files listing: (1) soil parameter estimates for the component soil units of each terrestrial grid cell, in un-binned format, and (2) soil parameter estimates aggregated or binned into a number of predefined classes. The spatial data, with a resolution of ½ by ½ degree, was derived from the ISRIC-WISE soil database. The land surface between longitudes -180o W and +180o E and latitudes +90o N and -90o S has been characterized using 45948 unique map units; each of these can comprise from one to ten soil units, characterized according to the original legend of the 1:5 million scale Soil Map of the World (FAO-Unesco 1974). Soil parameter estimates for each of these units were derived from analyses of some 9600 profiles held in a working copy of WISE (ver. 2.0). Twenty-two soil variables, identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil gaseous emissions and analyses of global environmental change, were considered. Parameter estimates for the topsoil (0-30 cm) and the subsoil (30-100 cm) are presented for the following variables: content of organic carbon, total nitrogen, the C/N ratio, pH(H2O), CECsoil, CECclay, base saturation, total exchangeable bases, aluminum saturation, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), calcium carbonate content, gypsum content, content of sand, silt and clay, content of fragments less than 2 mm, bulk density, total porosity. For soil drainage class, effective soil depth, and available water capacity (-10 to -1500 kPa), however, parameter estimates are presented on a profile basis. The parameter estimates - median values - presented here should be seen as best estimates; possible types and sources of uncertainty are discussed in the report. The data are considered appropriate for exploratory studies at global scale (greater than 1:5 000 000). Note: A more recent assessment, at a resolution of 30arcsec (WISE30sec), is available at: http://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/dc7b283a-8f19-45e1-aaed-e9bd515119bc
FDRE - Ministry of Agriculture (MoA)
This uniform soil data set for the development of pedotransfer functions was developed at the request of the Global Soil Data Task (GSDT) of the Data and Information System (DIS) of the International Geosphere Biosphere Programme (IGBP). The necessary chemical and physical soil data have been derived from ISRIC's Soil Information System (ISIS) and the soil CD-ROM of the Natural Resources Conservation Service (USDA-NRCS). Analytical data were clustered into functional groups based on soil textural class (FAO) and calculated activity of the clay size minerals. Samples from organic and allophanic soils were flagged as separate categories. The file contain analytical data for 131,472 soil samples, originating from 20,920 profiles. Being based on available data, there are several gaps in the measured data
FDRE - Ministry of Agriculture (MoA)
ABSTRACT: The World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the ‘WoSIS snapshot 2019’ many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement). 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. For each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases. Three measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data. DATA SET DESCRIPTION: The 'WoSIS 2023 snapshot' comprises data for 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files: - Readme_WoSIS_202312_v2.pdf: Provides a short description of the dataset, file structure, column names, units and category values (this file is also available directly under 'online resources'). The pdf includes links to tutorials for downloading the TSV files into R respectively Excel. See also 'HOW TO READ TSV FILES INTO R AND PYTHON' in the next section. - wosis_202312_observations.tsv: This file lists the four to six letter codes for each observation, whether the observation is for a site/profile or layer (horizon), the unit of measurement and the number of profiles respectively layers represented in the snapshot. It also provides an estimate for the inferred accuracy for the laboratory measurements. - wosis_202312_sites.tsv: This file characterizes the site location where profiles were sampled. - wosis_2023112_profiles: Presents the unique profile ID (i.e. primary key), site_id, source of the data, country ISO code and name, positional uncertainty, latitude and longitude (WGS 1984), maximum depth of soil described and sampled, as well as information on the soil classification system and edition. Depending on the soil classification system used, the number of fields will vary . - wosis_202312_layers: This file characterises the layers (or horizons) per profile, and lists their upper and lower depths (cm). - wosis_202312_xxxx.tsv : This type of file presents results for each observation (e.g. “xxxx” = “BDFIOD” ), as defined under “code” in file wosis_202312_observation.tsv. (e.g. wosis_202311_bdfiod.tsv). - wosis_202312.gpkg: Contains the above datafiles in GeoPackage format (which stores the files within an SQLite database). HOW TO READ TSV FILES INTO R AND PYTHON: A) To read the data in R, please uncompress the ZIP file and specify the uncompressed folder. setwd("/YourFolder/WoSIS_2023_December/") ## For example: setwd('D:/WoSIS_2023_December/') 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). observations = readr::read_tsv('wosis_202312_observations.tsv', col_types='cccciid') observations ## show columns and first 10 rows sites = readr::read_tsv('wosis_202312_sites.tsv', col_types='iddcccc') sites profiles = readr::read_tsv('wosis_202312_profiles.tsv', col_types='icciccddcccccciccccicccci') profiles layers = readr::read_tsv('wosis_202312_layers.tsv', col_types='iiciciiilcc') layers Do this for each observation 'XXXX', e.g. file 'Wosis_202312_orgc.tsv': orgc = readr::read_tsv('wosis_202312_orgc.tsv', col_types='iicciilccdccddccccc') orgc Note: One may also use the following R code (example is for file 'observations.tsv'): observations <- read.table("wosis_202312_observations.tsv", sep = "\t", header = TRUE, quote = "", comment.char = "", stringsAsFactors = FALSE ) B) To read the files into python first decompress the files to your selected folder. Then in python: import the required library import pandas as pd Read the observations data observations = pd.read_csv("wosis_202312_observations.tsv", sep="\t") # print the data frame header and some rows observations.head() Read the sites data sites = pd.read_csv("wosis_202312_sites.tsv", sep="\t") Read the profiles data profiles = pd.read_csv("wosis_202312_profiles.tsv", sep="\t") Read the layers data layers = pd.read_csv("wosis_202312_layers.tsv", sep="\t") Read the soil property data, e.g. 'cfvo' (do this for each observation) cfvo = pd.read_csv("wosis_202312_cfvo.tsv", sep="\t") CITATION: Calisto, L., de Sousa, L.M., Batjes, N.H., 2023. Standardised soil profile data for the world (WoSIS snapshot – December 2023), https://doi.org/10.17027/isric-wdcsoils-20231130 Supplement to: Batjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data, https://doi.org/10.5194/essd-16-4735-2024.