Africa GIS data

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on June 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for April-June 2015 * southernafrica201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for October-December 2014 * southernafrica201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * WA201304_ML1 Most likely food security outcome for October-December 2014 * WA201304_ML2 Most likely food security outcome for January-March 2015 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201307_ML1 Most likely food security outcome for July-September 2013 * southernafrica201307_ML2 Most likely food security outcome for October-December 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on February 10, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for January-March 2015 * southernafrica201304_ML2 Most likely food security outcome for April-June 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * southernafrica201304_ML1 Most likely food security outcome for April-June 2014 * southernafrica201304_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for January-March 2013 * xx201304_ML2 Most likely food security outcome for April-June 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on February 05, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * SA201304_ML1 Most likely food security outcome for October-December 2015 * SA201304_ML2 Most likely food security outcome for January-March 2016 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Southern Africa**. It was last updated on August 19, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * SA201304_ML1 Most likely food security outcome for July-September 2015 * SA201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201401_ML1 Most likely food security outcome for January-March 2014 * xx201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for July-September 2013 * xx201304_ML2 Most likely food security outcome for October-December 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on November 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for October-December 2013 * xx201304_ML2 Most likely food security outcome for January-March 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on June 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * westafrica201304_ML1 Most likely food security outcome for April-June 2015 * westafrica201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on March 25, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * westafrica201304_ML1 Most likely food security outcome for January-March 2015 * westafrica201304_ML2 Most likely food security outcome for April-June 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **West Africa**. It was last updated on September 29, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * WA201304_ML1 Most likely food security outcome for July-September 2014 * WA201304_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on September 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * westafrica201304_ML1 Most likely food security outcome for July-September 2015 * westafrica201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

Shapefile contains admin 1 boundaries for countries in East and southern Africa

Administrative boundaries for West and Central Africa (levels 0–2). Settlements in West and Central Africa, including administrative capitals.

This dataset contains OpenStreetMap data exported from the HOTOSM disaster mapping response to the Ebola outbreak in Democratic Republic of the Congo and Uganda started in May 2026 - Glide number: EP-2026-000071-COD. In May 2026, an epidemic of Ebola disease was reported in the Ituri Province of the Democratic Republic of the Congo (DRC). On 15 May 2026, the Ministry of Public Health, Hygiene and Social Welfare, DRC, officially declared the 17th Ebola Disease outbreak in DRC. Imported cases from Ituri have been reported in the capital city of Kinshasa and its North Kivu Province, as well as in Uganda's capital city of Kampala. The data has been contributed by volunteers through the Humanitarian OpenStreetMap Team's Tasking Manager and represents **buildings, roads, residential areas and settlement points (i.e. place) in the affected area**. It is updated as mapping progresses. Subscribe to this dataset to be notified of any new updates. HOT worked closely with Médecins Sans Frontières (MSF) to define priority areas and features. Tasking Manager Projects can be found here. See the HOTOSM activation page here for more details. As of 5th of August, over 1400 OSM contributors supported this activation.

This dataset contains shapefiles for Guinea, Liberia, and Sierra Leone from the OpenStreetMap (OSM) project. Each country has its individual file. The dataset counts with contributions of hundreds of users. **This dataset is updated daily.** The original dataset can be downloaded from the OSM West Africa Ebola response wiki.

The Global Precipitation Climatology Centre (GPCC) Monthly Precipitation dataset provides gridded gauge-analysis products focusing on land-surface precipitation. This dataset serves as a high-quality observational baseline for climate monitoring across Africa. It is constructed from quality-controlled data from thousands of rain gauges worldwide. At ACMAD, this data often serves as the "truth" or reference layer for calibrating satellite estimates and for calculating long-term indices like SPI. It is critical for retrospective analysis, model verification, and understanding the climatological context of current weather events.

The Soil Moisture Anomaly (SMA) dataset provides a measure of deviation in root-zone soil moisture conditions relative to the long-term climatological mean. Generated typically from hydrological models or satellite assimilation systems, this product highlights deficits or surpluses in soil water availability that are critical for agriculture and pastoralism. Because soil moisture integrates rainfall over time and accounts for evapotranspiration losses, SMA offers a more direct indicator of agricultural drought stress than precipitation deficits alone. This dataset is essential for monitoring the onset of agricultural drought, as soil moisture deficits often precede visible vegetation stress. It is widely used to assess potential yield reductions in rainfed agriculture and to monitor fodder availability in pastoral regions.

The Combined Drought Indicator (CDI) dataset developed at ACMAD is designed as an operational drought-monitoring product that synthesizes multiple biophysical signals into a single, coherent assessment of drought conditions across Africa. Rather than relying on a single indicator, the ACMAD CDI combines information from rainfall-based drought indices and satellite-derived vegetation condition metrics, applying rule-based logic to identify areas where these independent indicators consistently point to drought stress. This convergence approach reduces the risk of false alarms that can arise from short-term rainfall variability or vegetation anomalies unrelated to drought. The data exist in two forms one based on GPCC while another based on Era5 precipitation. The dataset is particularly useful in the African context, where ground observations are sparse and climatic gradients are strong across regions. By using harmonized, gridded datasets, the CDI provides a comparable drought signal across national boundaries, supporting regional coordination and continental-scale analysis. Its categorical outputs are intentionally simple and policy-ready, making them suitable for drought bulletins, early-warning communications, and high-level decision support. Importantly, the CDI is not intended to replace detailed drought diagnostics or impact assessments. Instead, it serves as a convergence and confirmation tool that highlights areas requiring closer analysis and expert interpretation. In this role, the CDI acts as a bridge between complex climate data and practical drought-risk management, enabling timely, consistent, and defensible drought monitoring at regional and continental scales.

For every pixel, the number of rainy days in a month is reported. This dataset is created by using daily rainfall data from WaPOR precipitation data. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: days/month **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The land use (LU) map based on Water Accounting Plus land use classification is produced based on WaPOR land cover class and protected areas from the World Database on Protected Areas. Please read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Data lineage:** Read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The evapotranspiration from precipitation (ETRAIN) is the portion of the actual evapotranspiration and interception (ETIa) that is satified from precipitation. It is computed from precipitation, actual evapotranspiration and soil moisture and calculated by spliting ETIa to rainfall and incremental ET using pixel based Soil Moisture Water Balance Model. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The incremental evapotranspiration is part of the actual evapotranspiration and Interception (ETIa) that is not satified from precipitation but from supply or runoff from other cells or from groundwater. It is calculated by spliting ETIa to rainfall and incremental ET using pixel based soil moisture water balance model. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Precipitation minus actual evapotranspiration (P-ET) calculated over the Niger River Basin. The P-ET is the difference between WaPOR precipitation (based on CHIRPS) and the actual evapotranspiration and interception. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Number of rainy days calculated in the Nile basin area. For every pixel, the number of rainy days in a month is reported. This dataset is created by using daily rainfall data from WaPOR precipitation data. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The land use (LU) map based on Water Accounting Plus land use classification is produced based on WaPOR land cover class and protected areas from the World Database on Protected Areas. Please read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Resource Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Data lineage:** Read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The evapotranspiration from precipitation (ETRAIN) is the portion of the actual evapotranspiration and Interception (ETIa) that is satified from precipitation. It is computed from precipitation, actual evapotranspiration and soil moisture and calculated by spliting ETIa to rainfall and incremental ET using pixel based Soil Moisture Water Balance Model (SMWBM). **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The incremental evapotranspiration is part of the actual evapotranspiration and Interception (ETIa) that is not satified from precipitation but from supply or runoff from other cells or from groundwater. It is calculated by spliting ETIa to rainfall and incremental ET using pixel based soil moisture water balance model. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Precipitation minus actual evapotranspiration (P-ET) calculated over the Nile River Basin. The P-ET is the difference between WaPOR precipitation (based on CHIRPS) and the actual evapotranspiration and interception. **Data publication:** 2020-06-01 **Supplemental Information:** No data value: -9999 Unit: mm **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Extractable Sodium Copper (Cu) of the soil fine earth fraction in mg/100kg (pp100m) as measured according to the soil analytical procedure of Mehlich 3 and spatially predicted for 0-30 cm depth interval at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.

The Africa Soil Profiles Database, Version 1.2, is compiled by ISRIC - World Soil Information (World Data Center for Soils) as a project activity for the Globally integrated- Africa Soil Information Service (AfSIS) project. It replaces version 1.1. The Africa Soil Profiles Database is a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa. Version 1.2 (November 2014) identifies 18,532 unique soil profiles inventoried from a wide variety of data sources and includes profile site and layer attribute data. Soil analytical data are available for 15,564 profiles of which 14,197 are georeferenced, including the attributes as specified by the Global Soil Map project. Soil attribute values are standardized according to SOTER conventions and are 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.

This harmonized set of soil parameter estimates for Southern Africa has been derived from the 1:2M scale Soil and Terrain Database for Southern Africa (SOTERSAF ver. 1.0) and ISRIC-WISE soil profile database. The land surface of Southern Africa has been characterized using 4022 unique SOTER units, corresponding with 6099 polygons. The major soils have been described using 941 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. These sources seldom hold all the physical and chemical attributes ideally required by SOTER. Gaps in the measured soil profile data have been filled using a step-wise procedure that uses taxonomy-based pedotransfer rules, based on some 9600 soil profiles held in the WISE2 database. 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, aluminium 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 (less than 2 mm), 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 possible confidence in the derived data. Results are presented as summary files and have been linked to the 1:2M scale SOTERSAF map in a GIS, through the unique SOTER-unit code.