The “natural resources sensitivity” symbolizes the ecosystem vitality and degree of conservation in 2010. Deforestation and loss of water resources quality may render certain areas more sensitive to climate stressors on account of the loss of normal vegetation cover, the depletion of biodiversity, the reduction in ecosystem services and significant loss of beneficial assets. The index results from the first cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify five dominant variables, namely “water availability per capita”, “net primary production”, “forest accessibility”, “vegetation continuity” and “climatic resources availability”, assigning respectively the weights of 0.19, 0.21, 0.165, 0.21 and 0.225. Before to perform the analysis the variables “water availability per capita”, “forest accessibility” and “vegetation continuity” were log transformed to shorten the extreme variation and then together with the other two variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method) in order to be comparable. The 6 arc-minute grid “water availability per capita” of 2005 was computed by sum of the run-off and discharge grids produced by World Water Development Report II and then sampled at 0.5 arc-minutes. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of major rivers as a resources for local people. To calculate the available water per capita it was then divided by the population. The 5 arc-minute grid “net primary production” of 2000 was gathered from FAO GeoNetwork and sampled at 0.5 arc-minute. Also in this case a focal statistic ran with a radius of 22 cells (about 20 Km) in order to represents the extend effect of primary production as natural resources for local people. The 0.5 arc-minute grid “forest accessibility” was build using the grid of travel distance in minutes to large cities (which one with population greater than 50,000 people), produced by the European Commission and the World Bank to represent the connectivity in 2000, and the grid of forest occurrence, extracted from the FAO Global Land Cover-SHARE dataset of 2014. The result measures the distance in minutes between forest and cities, thus is a proxy for remoteness and naturalness of forest. The 0.125 arc-minute grid “vegetation continuity” of 2010 were collected from University of Maryland and NASA and sampled at 0.5 arc-minute. A focal statistic ran with a radius of 55 cells (about 50 Km). This had a smoothing effect and represents some of the extend influence of vegetation concentration as a resources for local people. Finally the 0.5 arc-minute grid “climatic resources availability” was produced within the ClimAfrica project. The “water availability per capita” represents the potential water available per people in a certain area. We can consider the area with small values more sensitive to climatic stress, because lack a buffer of water resources, precious in a prevalently rain-fed agricultural system like in Africa. The “net primary production” and the “vegetation continuity” are proxies of the potential vegetal productivity available in a certain area. Moreover “vegetation continuity” is an indicator of abundance of natural ecosystem services that can reduce the sensitivity of human-environment systems. The “forest accessibility” assessing the distance between human and natural system measure the anthropogenic degree of a forest. A forest recording a high anthropogenic degree (thus near in terms of minute from a city) may potentially be threaded by human activity and thus represent a fragile ecosystem. Finally the “climatic resources availability” is an indicator of the climatic potential for biomass production. It is based on the climatically determined biomass productivity index that is a proxy for the atmospheric energy available for biomass production, as expressed by accumulated temperature, adjusted for drought stress. 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.

The 1km raster dataset represents top location score areas filtered by exclusive criteria: access to finance, distance to major roads and access to IT. The layer is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20 km (0.18 degree) buffer radius. • Major roads - approx. 2 km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Toxicities (SQ.6) 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. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

The datasets represent monthly Sensible and Latent Heat Flux at 0.5 degree spatial resolution from 1982 - 2010 over the African continent. For a detailed description of the data sets see: Jung, M. et all. (2011) Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research - Biogeosciences, 116, doi:10.1029/2010JG001566. request by email. This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aimed to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

On the basis of soil parameters provided by the Harmonized World Soil Database (HWSD) seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Excess salts (SQ5) 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. Note that the classes used in the Soil Quality evaluation are: 1: No or slight limitations 2: Moderate limitations 3: Sever limitations 4: Very severe limitations 5: Mainly non-soil 6: Permafrost area 7: Water bodies Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40%.

Vulnerable population identified by the nutritional status of women (BMI) as indicator for food security, in sample of households in East Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of 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.