The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

This layer represents the spatial distribution of agriculture research stations, as of 2003, in Namibia, within the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the GIS Database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Population database compiled on a 30x30” lat/long grid, for the years: 1998, 2000 and 2007. within the Okavango Basin. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other information.Source: Oak Ridge National Laboratory (ORNL). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

The soil degradation index is based on the Global Assessment of Human-induced Soil Degradation (GLASOD) (Oldeman et al., 1991). The UNEP-funded GLASOD project produced a world map of human-induced soil degradation. Data were compiled in cooperation with a large number of soil scientists throughout the world, using uniform guidelines and international correlation. The status of soil degradation was mapped within physiographic units, based on expert judgments, indicating the type, extent, degree, rate and main causes of the degradation process. To compare the impact of land degradation between different sites we created a land degradation index that attributes the following weights to area shares of the soil degradation classes 'light'= 1, 'moderate'=2, 'severeâ' = 3 and 'very severe' = 4. Next, we scaled the index between a range of 0-1. Combining classes and area shares in a single land degradation index is common practice in many other peer reviewed studies (e.g. Leiwen et al., 2005; McCoubrey, 1998; Pace at al., 2008; Safriel, 1999; Sonneveld and Dent, 2009), which gives us, sufficient confidence to apply the index for our analysis. This dataset has been used to complement the survey data and included in the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Bulk density of the whole soil including coarse fragments, air dried (kg/dm³). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.

Relative humidity at 15h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Location of boreholes, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The “financial development index” symbolizes the degree of financial development of a country in 2010. Well-developed financial systems may reduce climate change impact because it underlying the diffusion of services and tertiary economic activity that reduce the dependence to agriculture income of a certain population. The index results from the first cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies three dominant variables, namely “investment per capita”, “global commerce volume per capita” and “gross national saving per capita”, assigning weights of 0.35, 0.35 and 0.3, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. Country based data for “investment per capita” (expressed as a ratio of total investment in current local currency and GDP in current local currency. Investment or gross capital formation is measured by the total value of the gross fixed capital formation and changes in inventories and acquisitions less disposals of valuables for a unit or sector), “global commerce volume per capita” (expressed as a ratio of commerce volume in current local currency and GDP in current local currency. Commerce volume is the sum of exports and imports of goods and services) and “gross national saving per capita” (expressed as a ratio of gross national savings in current local currency and GDP in current local currency. Gross national saving is gross disposable income less final consumption expenditure after taking account of an adjustment for pension funds) were collected jointly from International Monetary Fund and World Bank (for global commerce volumes) and records the average of the period 2008-2012. The variables represent the share of GDP, thus they were multiplied by total GDPppp in order to have absolute value in international dollars and then divided by population to calculate the per capita values of each variable. The tabular data were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Investment and global commerce per capita are proxy of economic transition out of agriculture, while national gross saving represents the financial resources buffer that can facilitate the implementation of climate change adaptation strategies. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.