This MapTool contains a collection of layers that are produced and/or hosted by AfSIS. To download a subset of available AfSIS layers, click:
DOWNLOAD SUBSET.
Bounding Box:
Pan Map
Select Region
E-mail:
How to subset and download AfSIS map layers:
  1. Enter the coordinates above or use the "Select Region" (Select Region) tool to draw a bounding box around region of interest for available layer(s)
  2. Fill in E-mail address in form field above
  3. Click "Select Layer (Select Layer) above and choose a layer from the window that appears. Because of possibly large file sizes, this service is limited to providing one layer at a time, but several layers can be downloaded consecutively using the same selection criteria. After each selection, click "Download Data"; when done or to make an new selection, click "Close"
  4. For each layer requested you will recieve an email with the image, metadata and processing information compressed together in a tar.gz format. Note: Depending on the size of the bounding box the email could take a few minutes to several minutes to generate a downloadable link
  5. Check email and download the compressed file
Keyboard navigation:
  1. Use arrow keys to pan
  2. Use +/- keys to zoom

About

The AfSIS MapTool contains a collection of layers produced by AfSIS and other organizations.

Click on the 'Layer Info' tab for more information on data sources and methodology for the layers produced by other organizations. For a list and overview of AfSIS produced layers, go to http://www.africasoils.net/data/datasets

AfSIS Web Map Service

The Africa Soil Information Service (AfSIS) Web Map Service supports the Open Geospatial Consortium (OGC) OpenGIS Web Map Service (WMS) Implementation Specifications and dynamically produces maps of georeferenced data. Support of this international standard opens the AfSIS map collection to users who can access its contents via machine-to-machine interaction.

GetCapabilities Request

The AfSIS WMS uses version 1.1.0. The full GetCapabilities document, including additional layers from the Center for International Earth Science Information Network (CIESIN), is available here:
http://ciesin.columbia.edu/geoserver/ows?service=wms&version=1.1&request...

If you would like the GetCapabilities document for AfSIS layers only (Digital Elevation Model, Specific Catchment Area, and Topographic Wetness Index), it is available at http://www.africasoils.net/afsis_files/ows.

Sample GetMap Requests

Specific Catchment Area
http://ciesin.columbia.edu/geoserver/wms?service=WMS&version=1.1.0&reque...

Topographic Wetness Index
http://ciesin.columbia.edu/geoserver/wms?service=WMS&version=1.1.0&reque...

Digital Elevation Model
http://ciesin.columbia.edu/geoserver/wms?service=WMS&version=1.1.0&reque...

As these links illustrate, the WMS GetMap request: (http://www.ciesin.columbia.edu/geoserver/wms?service=WMS&version=1.1.0&r...) is followed by a list of variables (layers, styles, bbox, width, height, srs, format). The values for each of these can be specified by the user.

Additional information on open map standards can be found here:

-Web Map Service (Wikipedia)
-OGC OpenGIS Web Map Server Cookbook

Documentation on AfSIS-supported Web Coverage Services coming soon.

How to Open and View Layers

The layers in the Map Tool are available for download as compressed archives, each containing a selected layer in GeoTIFF format along with its metadata catalog record and readme documentation. The following are steps to extract and view the GeoTIFF layer using an archiver and GIS package.

Step 1. Decompress the '.tar.gz' File

The layers are provided in 'tar.gz' format. A '.tar.gz' file is a compressed archive, similar to a zip file or a RAR file.

For Windows Users:

7-Zip is a free file archiver for Windows that can be used to open '.tar.gz' files.
Download URL: http://www.7-zip.org/download.html

  1. Install 7-Zip.
  2. Navigate to the directory of the downloaded 'tar.gz' file and open.
  3. Choose to extract the file. Close the application when complete.

For Linux Users:

Most Linux distributions support gzip compression.
To extract the archive filename.tar.gz into the current directory, type the following command:

tar xzf filename.tar.gz
If the above command fails, the version of tar may not support gzip compression. In this case, you can use the traditional two-stage command:
gzip -dc filename.tar.gz | tar xf -

For Mac Users:

iZip is a free file archiver for Macintosh that can be used to open '.tar.gz' files.
Download URL: http://www.izip.com/

  1. Install iZip.
  2. Navigate to the directory of the downloaded 'tar.gz' file and open.
  3. Choose to extract the file. Close the application when complete.

Step 2. Open .tif file using a GIS package

The extracted file generates a folder containing a Geotiff ('.tif') file, metadata catalog, and readme. In order open the 'tif' layer, GIS software such as QGIS (http://www.qgis.org/en/site/forusers/download.html) is required.

For ArcGIS users:

On average, file sizes larger than 2 GB degrades the performance of ArcMap. To increase performance to make ArcMap start and run faster, refer to:
http://resources.arcgis.com/en/home/

Layer Info

The following details the methods and sources for the layers present within the MapTool.

Projection

Geographic Latitude Longitude Projection

The layers within the Map Tool have been reprojected to Geographic Latitude Longitude projection. The original MODIS time series data used to construct the long term averages were originally in Sinusoidal projection. The original MODIS data can be downloaded at http://e4ftl01.cr.usgs.gov/

Lambert Azimuthal Equal Area Projection

Some of the layers shown in the Map Tool have also been reprojected from their original projections to Lambert Azimuthal Equal Area (LAEA) for use in AfSIS digital soil mapping. To download these Africa continent-wide maps, go to the AfSIS FTP at ftp://africagrids.net/


Layers

Annual Mean Temperature

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

http://www.worldclim.org/current

Anthropogenic Biomes v1

The Anthropogenic Biomes of the World Version 2 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa the year 2000. Potential natural vegetation, biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. The data, as part of a time series provide global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution. This data set is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

http://sedac.ciesin.columbia.edu/data/set/anthromes-anthropogenic-biomes-world-v1

Area Equipped for Irrigation (%)

The map shows the amount of area equipped for irrigation around the year 2005 in percentage of the total area on a raster with a resolution of 5 minutes. Additional map layers show the percentage of the area equipped for irrigation that was actually used for irrigation and the percentages of the area equipped for irrigation that was irrigated with groundwater, surface water or non-conventional sources of water.

Stefan Siebert, Verena Henrich, Karen Frenken and Jacob Burke (2013). Global Map of Irrigation Areas version 5. Rheinische Friedrich-Wilhelms-University, Bonn, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy.

http://www.fao.org/nr/water/aquastat/irrigationmap/index10.stm

Cereal Yield (kg per hectare)

Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.

http://data.worldbank.org/indicator/AG.YLD.CREL.KG?display=default

Croplands, 2000

The Global Agricultural Lands in the Year 2000 data set represents the proportion of land area used as cropland (land used for the cultivation of food) and pasture (land used for grazing) in the year 2000. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l?Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set.

The maps show the extent and intensity of agricultural land use on earth. The data were compiled by Navin Ramankutty et al. (2008). This Web site provides access to the spatial data sets described in Ramankutty’s paper in the journal Global Biogeochemical Cycles. Users may download the data as one global grid or as a grid for each of the six populated continents. The data are available in raster GeoTiff and GRID formats.

Cultivated Irrigated Land

Six geographic datasets were used for the compilation of an inventory of seven major land cover/land use categories at 5’ resolution. The datasets used are:

  1. GLC2000 land cover database at 30 arc-sec (http://www-gvm.jrc.it/glc2000), using regional and global legends;
  2. an IFPRI global land cover categorization providing 17 land cover classes at 30 arc-sec. (IFPRI, 2002), based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000);
  3. FAO’s Global Forest Resources Assessment 2000 (FAO, 2001) at 30 arc-sec. resolution;
  4. digital Global Map of Irrigated Areas (GMIA) version 4.0 of (FAO/University of Frankfurt) at 5’ by 5’ latitude/longitude resolution, providing by grid-cell the percentage land area equipped with irrigation infrastructure;
  5. IUCN-WCMC protected areas inventory at 30-arc-seconds (http://www.unep-wcmc.org/wdpa/index.htm), and
  6. a spatial population density inventory (30-arc seconds) for year 2000 developed by FAO-SDRN, based on spatial data of LANDSCAN 2003, with calibration to UN 2000 population figures.

An iterative calculation procedure has been implemented to estimate land cover class weights, consistent with aggregate FAO land statistics and spatial land cover patterns obtained from (the above mentioned) remotely sensed data, allowing the quantification of major land use/land cover shares in individual 5’ by 5’ latitude/longitude grid cells. The estimated class weights define for each land cover class the presence of respectively cultivated land and forest. Starting values of class weights used in the iterative procedure were obtained by cross-country regression of statistical data of cultivated and forest land against land cover class distributions obtained from GIS, aggregated to national level. The percentage of urban/built-up land in a grid-cell was estimated based on presence of respective land cover classes as well as regression equations relating built-up land with number of people and population density. Remaining areas were allocated to:

  1. grassland and other vegetated areas (excluding cultivated land and forest);
  2. barren or very sparsely vegetated areas, and
  3. water bodies

according to indicated land cover classes. Barren or very sparsely vegetated areas (class (ii) above) were delineated from (i) using the respective land cover information in GLC 2000 and a minimum bio-productivity threshold.

The resulting seven land use land cover categories shares are:

  1. Rain-fed cultivated land;
  2. Irrigated cultivated land;
  3. Forest;
  4. Pastures and other vegetated land;
  5. Barren and very sparsely vegetated land;
  6. Water; and
  7. Urban land and land required for housing and infrastructure.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/CULTIR_2000.html

Cultivated Rain-fed Land (%)

Six geographic datasets were used for the compilation of an inventory of seven major land cover/land use categories at 5’ resolution. The datasets used are:

  1. GLC2000 land cover database at 30 arc-sec (http://www-gvm.jrc.it/glc2000), using regional and global legends;
  2. an IFPRI global land cover categorization providing 17 land cover classes at 30 arc-sec. (IFPRI, 2002), based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000);
  3. FAO’s Global Forest Resources Assessment 2000 (FAO, 2001) at 30 arc-sec. resolution;
  4. digital Global Map of Irrigated Areas (GMIA) version 4.0 of (FAO/University of Frankfurt) at 5’ by 5’ latitude/longitude resolution, providing by grid-cell the percentage land area equipped with irrigation infrastructure;
  5. IUCN-WCMC protected areas inventory at 30-arc-seconds (http://www.unep-wcmc.org/wdpa/index.htm), and
  6. a spatial population density inventory (30-arc seconds) for year 2000 developed by FAO-SDRN, based on spatial data of LANDSCAN 2003, with calibration to UN 2000 population figures.

An iterative calculation procedure has been implemented to estimate land cover class weights, consistent with aggregate FAO land statistics and spatial land cover patterns obtained from (the above mentioned) remotely sensed data, allowing the quantification of major land use/land cover shares in individual 5’ by 5’ latitude/longitude grid cells. The estimated class weights define for each land cover class the presence of respectively cultivated land and forest. Starting values of class weights used in the iterative procedure were obtained by cross-country regression of statistical data of cultivated and forest land against land cover class distributions obtained from GIS, aggregated to national level. The percentage of urban/built-up land in a grid-cell was estimated based on presence of respective land cover classes as well as regression equations relating built-up land with number of people and population density. Remaining areas were allocated to:

  1. grassland and other vegetated areas (excluding cultivated land and forest);
  2. barren or very sparsely vegetated areas, and
  3. water bodies

according to indicated land cover classes. Barren or very sparsely vegetated areas (class (ii) above) were delineated from (i) using the respective land cover information in GLC 2000 and a minimum bio-productivity threshold.

The resulting seven land use land cover categories shares are:

  1. Rain-fed cultivated land;
  2. Irrigated cultivated land;
  3. Forest;
  4. Pastures and other vegetated land;
  5. Barren and very sparsely vegetated land;
  6. Water; and
  7. Urban land and land required for housing and infrastructure.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/CULTRF_2000.html

Dams

The Global Reservoir and Dam Database, Version 1 (Revision 01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines at high spatial resolution. Dams have multiple attributes, such as name of the dam and impounded river, primary use, nearest city, height, area and volume of reservoir, and year of construction (or commissioning). While the main focus was to include all dams associated with reservoirs that have a storage capacity of more than 0.1 cubic kilometers, many smaller dams and reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

Lehner, B., Reidy Liermann, C., Revenga, C., Vorosmarty, C., Fekete, B., Crouzet, P., Doll P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., Wisser, D.

FAO Soil groups

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.fao.org/nr/water/art/2008/soil_map2.html

Global Carbon Biomass

Ruesch, Aaron, and Holly K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ornl.gov], Oak Ridge National Laboratory, Oak Ridge, Tennessee.

http://cdiac.ornl.gov/epubs/ndp/global_carbon/carbon_documentation.html

Hydrologically Corrected / Adjusted Shuttle Radar Topography Mission Digital Elevation Model (AfrHySRTM)

AfrHySRTM is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (fiĀlled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flow paths can also be generated. This dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya, for the Africa Soil Information Service.

Metadata for the AfHySRTM dataset are available here.

Human Influence Index Version 2

The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) and is available in the Geographic Coordinate system.

http://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic

Land Cover-MERIS

Land cover maps are categorical-type maps, commonly derived using semi-automated methods and remote sensing images as the main input. There are at least four global land cover mapping projects in the world where such data can be found (they differ in legends, resolution, temporal coverage etc). A Global Land Cover map for the year 2000 (GLC2000) at 1 km resolution is distributed by the Joint Research Centre in Italy (Bartholome et al., 2002). A slightly outdated (1998) global map of land cover is provided by the AVHRR Global Land Cover Classification, provided at resolutions of 1 and 8 km (Hansen et al. 2000). International Steering Committee for Global Mapping provides access to the Global Land Cover by National Mapping Organizations (GLCNMO) map, produced using MODIS data observed in 2003. European Space Agency has recently released the GlobCover Land Cover version V2 dataset, produced using the ENVISAT MERIS images. So far, this is the highest resolution (300 meters) Global Land Cover product in the world. The fourth important source of land cover data is the MODIS12C1 Land Cover Type Yearly L3 Global product (available in resolution from 500 m to 0.05 arcdegrees). The advantage of using the MODIS Land cover maps (17 land cover classes defined by the International Geosphere Biosphere Programme - IGBP) is that this is a temporal dataset so that one can also derive various change indices and quantify the land cover dynamics (Friedl et al. 2002).

Maximum Temperature Warmest Month

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

http://www.worldclim.org/current

Moderate Resolution Imaging Spectroradiometer (MODIS) Data Sets

The Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) Day and Night, Red Reflectance, Blue Reflectance, Near-Infrared Reflectance, Mid-Infrared Reflectance, Albedo, Fraction of Photosynthetically Active Radiation, and Leaf Area layers were constructed using UNIX shell scripts to automate the download and processing of MODIS monthly and long-term average layers. For more information, go to http://www.africasoils.net/data/datasets

National Boundaries

National Boundaries for Africa were derived from the Gridded Population of the World, Version 3 (GPWv3) Subnational Administrative Boundaries produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

http://sedac.ciesin.columbia.edu/data/set/gpw-v3-national-admin-boundaries

Nitrogen Fertilizer Application

Data values derived by fusing global maps of harvest areas for 175 crops with national information on fertilizer use for each crop.

Potter, P., N. Ramankutty, E. M. Bennett and S. D. Donner. 2010. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interactions 14(002):1-22. Data distributed by the Socioeconomic Data and Applications Center (SEDAC): http://sedac.ciesin.columbia.edu/data/collection/fertilizer-and-manure.html. [2011, July 13]

Pastures, 2000

The Global Pastures dataset represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty, et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Population Count Future Estimates 2010

Gridded Population of the World: Future Estimates (GPWFE) consists of estimates of human population for the years 2005, 2010, 2015 by 2.5 arc-minute grid cells.The data products include a population grid (raw counts). These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). Spatial reference metadata refers to global extent. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. Additional global grids are created from the 2.5 arc-minute grid at 1/4, 1/2, and 1 degree resolutions. (Spatial reference metadata refers to global extent, 2.5 arc-minute resolution). GPWFE is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the United Nations Food and Agriculture Programme (FAO) and the Centro Internacional de Agricultura Tropical (CIAT).

Population Density Future Estimates 2015

Gridded Population of the World: Future Estimates (GPWFE) consists of estimates of human population for the years 2005, 2010, 2015 by 2.5 arc-minute grid cells. Population density estimates were also created using the GPWv3 land area grid for the year 2000 (also 2.5 arc-minute resolution). The data products include a population grid (raw counts), and a population density grid (per square km). These products vary in GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). Spatial reference metadata refers to global extent. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. Additional global grids are created from the 2.5 arc-minute grid at 1/4, 1/2, and 1 degree resolutions. (Spatial reference metadata refers to global extent, 2.5 arc-minute resoulution). GPWFE is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the United Nations Food and Agriculture Programme (FAO) and the Centro Internacional de Agricultura Tropical (CIAT).

Satellite (NASA Blue Marble)

This spectacular “blue marble” image is the most detailed true-color image of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. These images are freely available to educators, scientists, museums, and the public.

Much of the information contained in this image came from a single remote-sensing device-NASA’sModerate Resolution Imaging Spectroradiometer, or MODIS. Flying over 700 km above the Earth onboard the Terra satellite, MODIS provides an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of these images are based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the sensor’s view of the surface on any single day. Two different types of ocean data were used in these images: shallow water true color data, and global ocean color (or chlorophyll) data. Topographic shading is based on the GTOPO 30 elevation dataset compiled by the U.S. Geological Survey’s EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration’s AVHRR sensor—the Advanced Very High Resolution Radiometer. The cloud image is a composite of two days of imagery collected in visible light wavelengths and a third day of thermal infra-red imagery over the poles. Global city lights, derived from 9 months of observations from the Defense Meteorological Satellite Program, are superimposed on a darkened land surface map.

http://visibleearth.nasa.gov/view_cat.php?categoryID=1484

Soil Nutrient Availability

Soil texture, soil organic carbon, soil pH, total exchangeable bases.

This soil quality is decisive for successful low level input farming and to some extent also for intermediate input levels. Diagnostics related to nutrient availability are manifold. Important soil characteristics of the topsoil (0-30 cm) are: Texture/Structure, Organic Carbon (OC), pH and Total Exchangeable Bases (TEB). For the subsoil (30-100 cm), the most important characteristics considered are: Texture/Structure, pH and TEB.

The soil characteristics relevant to soil nutrient availability are to some extent correlated. For this reason, the most limiting soil characteristic is combined in the evaluation with the average of the remaining less limiting soil characteristics to represent soil quality SQ1.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ1.html

Soil Nutrient Retention Capability

Soil Organic carbon, Soil texture, base saturation, cation exchange capacity of soil and of clay fraction.

Nutrient retention capacity is of particular importance for the effectiveness of fertilizer applications and is therefore of special relevance for intermediate and high input level cropping conditions.

Nutrient retention capacity refers to the capacity of the soil to retain added nutrients against losses caused by leaching. Plant nutrients are held in the soil on the exchange sites provided by the clay fraction, organic matter and the clay-humus complex. Losses vary with the intensity of leaching which is determined by the rate of drainage of soil moisture through the soil profile. Soil texture affects nutrient retention capacity in two ways, through its effects on available exchange sites on the clay minerals and by soil permeability.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

Soil Rooting Conditions

Soil textures, bulk density, coarse fragments, vertical soil properties and soil phases affecting root penetration and soil depth and soil volume.

Rooting conditions include effective soil depth (cm) and effective soil volume (vol. %) related to presence of gravel and stoniness. Rooting conditions may be affected by the presence of a soil phase either limiting the effective rooting depth or decreasing the effective volume accessible for root penetration. Rooting conditions address various relations between soil conditions of the rooting zone and crop growth. The following factors are considered in the evaluation:

  1. Adequacy of foothold, i.e., sufficient soil depth for the crop for anchoring;
  2. available soil volume and penetrability of the soil for roots to extract nutrients;
  3. space for root and tuber crops for expansion and economic yield in the soil; and
  4. absence of shrinking and swelling properties (vertical) affecting root and tuber crops.

Soil depth/volume limitations affect root penetration and may constrain yield formation (roots and tubers). Relevant soil properties considered are: soil depth, soil texture/structure, vertic properties, gelic properties, petric properties and presence of coarse fragments. This soil quality is estimated by multiplying of the soil depth limitation with the most limiting soil or soil phase property

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

Soil Salinity

Soil salinity, soil sodicity and soil phases influencing salt conditions.

Accumulation of salts may cause salinity. Excess of free salts referred to as soil salinity is measured as Electric Conductivity (EC in dS/m) or as saturation of the exchange complex with sodium ions, which is referred to as sodicity or sodium alkalinity and is measured as Exchangeable Sodium Percentage (ESP).

Salinity affects crops through inhibiting the uptake of water. Moderate salinity affects growth and reduces yields; high salinity levels may kill the crop. Sodicity causes sodium toxicity and affects soil structure leading to massive or coarse columnar structure with low permeability. Apart from soil salinity and sodicity, conditions indicated by saline (salic) and sodic soil phases may affect crop growth and yields.

In case of simultaneous occurrence of saline (salic) and sodic soils the limitations are combined. The most limiting of the combined soil salinity and/or sodicity conditions and occurrence of saline (salic) and/or sodic soil phase is selected.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ5.html

FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.

Soil Toxicity

Calcium carbonate and gypsum.

Low pH leads to acidity related toxicities, e.g., aluminum, iron, manganese toxicities, and to various deficiencies, e.g., of phosphorus and molybdenum. Calcareous soils exhibit generally micronutrient deficiencies, for instance of iron, manganese, and zinc and in some cases toxicity of molybdenum. Gypsum strongly limits available soil moisture. Tolerance of crops to calcium carbonate and gypsum varies widely (FAO, 1990; Sys, 1993).

Low pH and high calcium carbonate and gypsum are mutually exclusive. Acidity related toxicities such as aluminum toxicities and micro-nutrient deficiencies are accounted for respectively in SQ1, nutrient availability, and in SQ2, nutrient retention capacity. This soil quality SQ6 is therefore only including calcium carbonate and gypsum related toxicities. The most limiting of the combination of excess calcium carbonate and gypsum in the soil, and occurrence of petrocalcic and petrogypsic soil phases is selected for the quantification of SQ6.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ6.html

Soil Workability

Soil texture, effective soil depth/volume, and soil phases constraining soil management (soil depth, rock outcrop, stoniness, gravel/concretions and hardpans)

Diagnostic characteristics to indicate soil workability vary by type of management applied. Workability or ease of tillage depends on interrelated soil characteristics such as texture, structure, organic matter content, soil consistence/bulk density, the occurrence of gravel or stones in the profile or at the soil surface, and the presence of continuous hard rock at shallow depth as well as rock outcrops. Some soils are easy to work independent of moisture conditions, other soils are only manageable at an adequate moisture status, in particular for manual cultivation or light machinery. Irregular soil depth, gravel and stones in the profile and rock outcrops, might prevent the use of heavy farm machinery. The soil constraints related to soil texture and soil structure are particularly affecting low and intermediate input farming LUTs, while the constraints related to irregular soil depth and stony and rocky soil conditions are foremost affecting mechanized land preparation and harvesting operations, of high-level input mechanized farming LUTs. Workability constraints are therefore handled differently for low/intermediate and high inputs.

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

http://www.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SQ7.html

Specific Catchment Area (SCA)

The Africa Soil Information Service: Specific Catchment Area (SCA) is a 90m raster dataset showing local flow accumulation and flow direction using the formula:

SCA = A/I

where A is unit contributing area of land upslope of a length of contour I. Unit flow width was calculated from the DEM cell/pixel dimensions and varies depending on whether flow direction in horizontal vertical or diagonal through the cell. The specific catchment area contributing to flow at any given location can be used to determine relative saturation and water runoff and, together with other topographic factors, can be used to model soil erosion, sediment yield, and landslide risk. The digital elevation model used to construct this dataset was AfHydSRTM, based on the CGIAR-SRTM 90m Version 4. This dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya, for the Africa Soil Information Service.

Metadata for the AfSIS SCA dataset are available here.

Tree Cover

Representation of vegetation land cover into a limited number of classes (such as "grassland", "forest", "urban", etc.) does not account for internal variability, nor do distinct classes recognize transition zones between adjacent cover types. Because of this, an alternative paradigm to describing land cover as discrete classes is offered in this 1 Kilometer Tree Cover Continuous Fields product, where vegetation is represented as continuous fields of land cover, resulting in every pixel having a percentage value for tree cover.

DeFries, R., M. Hansen, J.R.G. Townshend, A.C. Janetos, and T.R. Loveland (2000), 1 Kilometer Tree Cover Continuous Fields, 1.0, Department of Geography, University of Maryland, College Park, Maryland, 1992-1993.

Topographic Wetness Index (TWI)

The Africa Soil Information Service: Topographic Wetness Index (TWI) is a 90m raster dataset showing zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low. The topographic wetness index, originally developed by Beven and Kirkby in 1979, provides a measure of wetness conditions at the catchment scale. Local upslope contributing area and slope are combined to determine the wetness index:

WI = ln (A s / tan(b) )

where As is flow accumulation or effective drainage area and b is slope gradient. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. The use of effective drainage area gives a quasi-dynamic index overcomes assumptions of of steady-state (i.e. uniform soil properties). This index predicts zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low and is commonly used in soil landscape modeling and in the analysis of vegetation patterns. The digital elevation model used to construct this dataset was AfHydSRTM, based on the CGIAR-SRTM 90m Version 4. This dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya, for the Africa Soil Information Service.

Metadata for the AfSIS TWI are available here.

Click here for further details on AfSIS SRTM processing (pdf).

Tropical Rainfall Measuring Mission (TRMM)

The Tropical Rainfall Measuring Mission (TRMM) layers were constructed using UNIX shell scripts to automate the download and processing of TRMM data averages, variance, average number of rainy days, and modified Fournier index. For more information, go to http://www.africasoils.net/data/datasets

WorldClim BIO1 Temperature and BIO12 Precipitation

The WorldClim Temperature and Precipitation layers were constructed using UNIX shell scripts to automate the download and processing of global climate data averages, variance, average number of rainy days, and modified Fournier index using raw data developed by Robert J. Hijmans, Susan Cameron, and Juan Parra at http://www.worldclim.org/. For more information, go to http://www.africasoils.net/data/datasets