Some extraordinary changes have occurred across the globe over the past decades regarding human habitation. Globally, between 1975 and 2015 built-up areas increased by approximately 250 %, while population increased by a factor of 1.8. The most considerable changes occurred in Africa where it has nearly quadrupled. The extent of built-up area poses a number of challenges to global sustainable development. As urban clusters expand, productive land and soil is sealed, and natural ecosystems are replaced by land use to support urban centres. This layer highlights the areas of concern for built-up related issues derived from the convergence of global evidence of human-environment interactions that can have land degradation consequences.

Shapefile showing the location of protected areas in Africa as national parcs and natural reserves.

This dataset represents a geographic clip to Africa of the World Database on Protected Areas (WDPA), which is the authoritative and most complete global dataset on terrestrial and marine protected areas. The parent database is a joint initiative between the UN Environment Programme (UNEP) and the International Union for Conservation of Nature (IUCN), managed by the UNEP World Conservation Monitoring Centre (UNEP-WCMC). Information is submitted and verified by international secretariats, national and regional governments, NGOs, communities, and landowners.

This spatial dataset has been specifically filtered and clipped to include only protected areas located within the terrestrial and marine boundaries of the African continent and its associated island nations.

The dataset comprises both spatial data (GIS boundaries) and attribute data (descriptive information) for protected sites across Africa. Key attributes include site name, designation type (national, regional, or international), governance model, IUCN management category, marine/terrestrial status, and legal establishment date.

The WDPA is the primary global mechanism used to track progress toward international area-based conservation targets. This African subset is widely used across various sectors for:

• Conservation & Science: Ecological research, protected area monitoring, and biodiversity assessments specific to African biomes.
• Corporate & Financial Risk: Environmental impact assessments and supply chain risk analyses for infrastructure and commercial projects operating in Africa.
• Policy & Reporting: Informing national and international decision-making and tracking regional progress toward the UN Sustainable Development Goals and the Kunming-Montreal Global Biodiversity Framework.

The global WDPA dataset is updated and released on a monthly basis through the Protected Planet platform. This clip was extracted on May 2026.

Aridity represents the ‘dryness’ of the climate. Dry areas have a higher potential for land degradation. This layer displays the areas of concern for aridity related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation. It highlights dryland areas, where the Aridity Index (ratio of precipitation to evapotranspiration) is inferior to 0.65.

This layer is part of the World Altas of Desertification

Biomes are distinct biological communities (collection of plants and animals) that have formed in response to a shared physical climate. The 11 major terrestrial biomes found in Africa (out of 14 worldwide) are partially covered by Protected Areas. This map shows the percentage of areas protected in each of the terrestrial biomes. For example: in 2020, 12,3% of montane grasslands and shrublands in Africa were covered by Protected Areas.

The Copernicus Global Land Service - Burnt Area products depict burn scars, surfaces which have been sufficiently affected by fire to display significant changes in the vegetation cover (destruction of dry material, reduction or loss of green material) and in the ground surface (temporarily darker because of ash). The monthly products are available at global scale in the spatial resolution of 300 m and cover the period from January 2019 to April 2022. The pixel bears the value of the day of the year in which the burn scars were visible while, if selected for mapping in this platform, the dataset shows the burn scars detected in the selected month.

Changes in vegetation biomass are critical in assessing land degradation. Climate variations, alone or in combination with human-induced land use and land change, can affect biomass productivity and may trigger changes in vegetation type and structure. Depending on their severity and duration, precipitation anomalies can trigger or aggravate existing land pressures. This layer displays the areas of concern for climate-vegetation trends derived from the convergence of global evidence of human-environment interactions that can have consequences on land degradation. It highlights areas with declining plant productivity in response to climate fluctuations (drought conditions in particular).

Côte d'Ivoire and Ghana are the main largest producers of cocoa in the world. However, the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa farms are essential for the implementation of sustainable cocoa practices and the periodic and effective monitoring of forests. This map, generated using Random Forest image classification, shows the 2019 distribution of cocoa farms in both countries. The estimated area for cocoa is 4.8Mha for Cote d'Ivoire and 2.3Mha for Ghana.

Percentage of cohort of young people three to five years older than the intended age for the last grade of upper secondary level of education who have completed that level of education. This indicator measures the potential impact of electricity on youth education, that represents a crucial pillar for the development of a country.

An intact forest landscape (IFL) is a seamless mosaic of forest and naturally treeless ecosystems with no remotely detected signs of human activity and a minimum area of 500 km2. (Potapov et al.2017) Intact forests are complex and diverse ecosystems that if lost, are irreplaceable. Research shows that designating intact forest landscapes as protected areas has proven effective at limiting their fragmentation. Since 2000, around 100 conserved and protected areas were created in intact forests areas in ACP countries, increasing the percentage of IFL protected from 11% in 2000 to 26% in 2020.

Differences in countries in terms of IFL area reduction: Cuba has not experienced any reduction in IFLs and nowadays its intact forests are fully protected by PAs (544sqkm). At the contrary Angola still not has any kind of protection for its IFLs and experienced a reduction of 39,83% of its IFL (1160 sqkm) in country.The reduction of IFL area in ACP countries was higher outside PAs (19%) than within PAs (5%).Madagascar is the country where the reduction of IFL areas was very high inside protected areas (2420 sqkm). The IFL loss inside PAs has been more than 40% between 2000 and 2020. Central African Republic experienced 23% of reduction inside PAs (938,13 sqkm). This layer shows the percentage of IFLs protected by country

Analysis performed by Simona Lippi

This dataset is an index that estimates the relative value provided by coral reefs that protect coastlines through reduction of wave height and wave energy. The value as of 2014 is modelled as a function of exposed populations and infrastructure that received some level of protection from coastal and barrier reefs, and is described in relative terms, classified by decile (i.e., grouped into the most valuable ten percent of reefs for protection, the second-most valuable tenth of reefs, etc.). It was calculated at 1 kilometre (km) resolution globally, encompassing all countries and territories containing coral reefs.

Africa is very rich in biodiversity and is the last place on Earth with a significant assemblage of large mammals. This natural richness, accumulated over millions of years, coupled with the wealth of indigenous and local knowledge on the continent, is central to the pursuit of sustainable development in the region. Yet the decline and loss of biodiversity is reducing nature’s contributions to people in Africa, affecting daily lives and hampering the sustainable social and economic targets set by African countries. This map shows the number of threatened mammal species by country, assessed by the International Union for the Conservation of Nature (IUCN) and documented in the IUCN Red List of Threatened Species. Established in 1964, the IUCN Red List is a critical indicator of the health of the world’s biodiversity that helps inform necessary conservation decisions.

Healthy coral reefs provide a home for millions of aquatic species and numerous ecosystemic services. Yet they are severely threatened. When stressed, corals expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch daily global 5km satellite coral Bleaching Alert Area (7-day maximum) is a composite product that summarizes the current Degree Heating Week (a cumulative measurement of both intensity and duration of heat stress) and Coral Bleaching HotSpot (occurrence and magnitude of instantaneous heat stress) values. At a glance, this layer outlines the current locations, coverage, and potential risk level of coral bleaching heat stress.

Humans need increasingly more biomass for food, fodder, fibre and energy. Meeting these demands changes global ecosystems. Tracking changes in total biomass production or land productivity is an essential part of monitoring land transformations that are typically associated with land degradation. Land productivity dynamics (LPD) are used as an indicator of change or stability of the land’s capacity to sustain primary production. This layer displays the areas of concern for land productivity related issues, derived from the convergence of global evidence of human-environment interactions that can have consequences on land degradation.

This layer provides the Effective Leaf Area Index (LAIe), a critical parameter for modeling evapotranspiration and carbon fluxes between the biosphere and the atmosphere. Monitoring the distribution and seasonal evolution of LAI is essential for assessing vegetation health and ecosystem dynamics across Africa.

The Effective Leaf Area Index is a quantitative measure of the amount of live green leaf material present per unit ground surface. Unlike "True LAI," which represents the total physical leaf area, the Effective LAI is derived from canopy gap fraction measurements and assumes a random distribution of foliage. It is a non-dimensional quantity, typically expressed in units of m²/m².

This dataset is widely utilized in agro-meteorology, biogeochemical modeling, and General Circulation Models (GCMs) to parameterize vegetation cover and its complex interactions with the atmosphere.

This layer is part of the Earth Land Information System (ELIS).

This layer presents the Effective Leaf Area Index (LAIe) Anomalies, representing the deviation of current vegetation density from the historical average. Monitoring the change of LAI is essential for assessing the evolution of the vegetation over Africa.

LAI anomalies are calculated relative to the average values between 2003 and 2010. The dataset captures LAI anomalies every 10 days, reflecting the high variability and rapid changes in African vegetation cover. Increases in temperature and precipitation deficits are the primary drivers for negative anomalies (reduced foliage density). Beyond climatic factors, human activities or animal grazing may also locally impact the state and density of leaves.

This layer is part of the a href="https://fapar.jrc.ec.europa.eu/_www/index.php">Earth Land Information System (ELIS).

Displays areas where the geographic range of two or more endemic bird species overlaps. While many bird species are widespread, over 2,500 are endemic and restricted to an area smaller than 5 million hectares (restricted-range species). BirdLife International has mapped every restricted-range species using geo-referenced locality records. Through this process, they identified regions of the world—known as “Endemic Bird Areas” (EBAs)—where the distributions of two or more of these species overlap. Half of all restricted-range species are globally threatened or near-threatened, and the other half remain vulnerable to loss or degradation of habitat. The majority of EBAs are also important for the conservation of restricted-range species from other animal and plant groups. The unique landscapes where these bird species occur, amounting to just 4.5% of the earth's land surface, are high priorities for broad-scale ecosystem conservation. Geographically, EBAs are often islands or mountain ranges, and vary considerably in size, from a few hundred hectares to more than 10,000,000 hectares. EBAs also vary in the number of restricted-range species that they support (from two to 80). EBAs are found around the world, but most (77%) of them are located in the tropics and subtropics.

Fire is a natural part of all ecosystems. Wildfires have been burning vegetation and shaping landscapes far longer than people have been on Earth. However, changes in fire frequency and timing can result in degradation if the vegetation is not adapted to the new fire regimes. This can cause long-term damage to land biomass components affecting soil structure, nutrients and water cycling. This layer displays the areas of concern for fires related issues derived from the convergence of global evidence of human-environment interactions that can have consequences on land degradation.

The application of fertiliser is a key component in increasing agricultural production. However, there are thresholds beyond which the cost of inputs fails to lead to corresponding increases in yield. Beyond economic inefficiency, overuse of commercial inorganic fertiliser can also result in a decline in soil condition and structure, including reduced soil carbon content, water-holding capacity and porosity, and to environmental pollution. This layer displays the areas of concern for high-input agriculture related issues derived from the convergence of global evidence of human-environment interactions that can have land degradation consequences.

This layer is part of the World Altas of Desertification

The IUCN IMMA layer identifies specific habitat areas crucial for one or more marine mammal species, which may be suitable for conservation through delineation and management. IMMAs represent regions that could benefit from targeted protection and/or monitoring. They serve as a marine mammal data layer highlighting important biodiversity, and potentially ecosystem health, which can be prioritized for protection and management by governments, intergovernmental organizations, conservation groups, and the public.

Smallholder farmers and pastoralists have restricted access to capital and may not have the capacity to invest in management practices that mitigate land degradation. This layer displays the areas of concern for the issues related to income level derived from the convergence of global evidence of human-environment interactions that can lead to land degradation.

Irrigation enables farmers to increase crop production by reducing their dependence on natural rainfall. It is considered a vital part of ensuring food security in the future. Yet it also causes extensive environmental damage and undermines human resilience to water scarcity. Irrigation is responsible for 70 % of all freshwater withdrawals in the globe. Human induced salinisation is a widespread problem as around 30 % of irrigated land are affected and becoming commercially unproductive. This layer displays the areas of concern for irrigation related issues, derived from the convergence of global evidence of human-environment interactions that can have land degradation consequences.

This layer is part of the World Altas of Desertification

The EC JRC global map of forest cover provides a spatially explicit representation of forest presence and absence for the year 2020 at 10m spatial resolution.

The year 2020 corresponds to the cut-off date of the Regulation from the European Union "on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation" (EUDR, Regulation (EU) 2023/1115). In the context of the EUDR, the global forest cover map can be used as a non-mandatory, non-exclusive, and not legally binding source of information. Further information about the map and its use can be found on the EU Observatory on Deforestation and Forest Degradation (EUFO) in the section on Frequently Asked Questions.

Forest means land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land that is predominantly under agricultural or urban land use. Agricultural use means the use of land for the purpose of agriculture, including for agricultural plantations (i.e. tree stands in agricultural production systems such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems) and set- aside agricultural areas, and for rearing livestock. All plantations of relevant commodities other than wood, that is cattle, cocoa, coffee, oil palm, rubber, soya, are excluded from the forest definition.

The global map of forest cover was created by combining available global datasets (wall-to-wall or global in their scope) on tree cover, tree height, land cover and land use into a single harmonized globally-consistent representation of where forests existed in 2020.

The workflow consisted in first mapping the global maximum extent of tree cover circa the year 2020 from the combination of ESA World Cover 2020 and 2021, WRI Tropical Tree Cover 2020, UMD Global land cover and land use 2019, Global Mangrove Watch 2020, and JRC Tropical Moist Forest 2020 datasets. In the second step, a series of overlays and decision rules were applied to reduce this maximum extent of tree cover and align it with the Forest definition using datasets covering cropland and commodity expansion (ESA World Cereal, UMD Global land cover and land use 2019, UMD Global Cropland Expansion, High-resolution global map of smallholder and industrial oil palm plantations, and WRI Spatial Database of Planted Trees), land use change (UMD global forest cover loss, JRC Tropical Moist Forest, IIASA Global Forest Management), built-up (JRC Global Human Settlement), and water (JRC Global Surface Water).

The detailed mapping approach will be described in a separate technical report expected to be released by March 2024. The accuracy of this map has not been yet assessed but will be reported as soon as available.

Please also refer to the list of known issues and to the JRC Data Catalogue entry.

Key Biodiversity Areas (KBAs) are the most important places in the world for species and their habitats. Faced with a global environmental crisis we need to focus our collective efforts on conserving the places that matter most. The KBA Programme supports the identification, mapping, monitoring and conservation of KBAs to help safeguard the most critical sites for nature on our planet – from rainforests to reefs, mountains to marshes, deserts to grasslands and to the deepest parts of the oceans. By providing the precise location of places that contribute significantly to the global persistence of biodiversity, KBAs can accelerate efforts to reverse the loss of nature, by ensuring conservation efforts are focussed in the places that matter most, and by enabling entities that may have negative impacts on nature to avoid or reduce those impacts in the places they would be most damaging. This layer shows the location of the KBAs, identified and mapped by the KBA partnership.

Some areas in Africa represent spectacular, still viable examples of Africa’s wildlife and wild places. They are of such outstanding importance and value that they should be conserved at all costs and in principle forever. Those areas are referred to as Key Landscapes for Conservation or KLCs. A suitable network of KLCs has the potential to protect the well-known wildlife species within natural ecosystems and to stimulate rural economic growth.

<p>This spatial dataset contains the geographic boundaries and ecological evaluations of 45 Key Landscapes for Conservation and Development (KLCDs) across Sub-Saharan Africa. Building on the <a href="https://africa-knowledge-platform.ec.europa.eu/dataset/key-landscapes-c…; Key Landscapes for Conservation (KLCs) </a> identified in the EU report <a href="https://op.europa.eu/sl/publication-detail/-/publication/d76ac7eb-bc4a-…;“Larger than elephants”</a>, the NaturAfrica initiative is rolled out across these key biodiversity and development landscapes.</p>

<p>NaturAfrica's efforts are concentrated on ‘mega-landscapes’—biogeographical regions identified as crucial for both conservation and development. These regions encompass:</p>
<ul>
<li>The forest ecosystems of the Congo Basin;</li>
<li>Landscapes of transhumance pastoralists in North Cameroon, the Central African Republic, and Chad;</li>
<li>Guinean forests of West Africa;</li>
<li>Savannahs in the Sudano-Sahelian zone of West Africa;</li>
<li>Savannahs and watersheds of the East Africa rift;</li>
<li>Transfrontier conservation areas of Southern Africa.</li>
</ul>

<p>The layer was developed to support the prioritization of intervention areas for the second phase of this initiative, which combines conservation with sustainable job creation. It provides a spatially explicit characterization of ecological value across these landscapes, evaluated and attributed based on five core ecological and environmental dimensions:</p>
<ul>
<li><span class="label">Threatened Species Richness:</span> Concentration and diversity of threatened species.</li>
<li><span class="label">Species Endemicity:</span> Presence of endemic species unique to the specific geographic region.</li>
<li><span class="label">Ecosystem Integrity:</span> The integrity of protected and conserved areas, accounting for degrees of human modification and habitat fragmentation.</li>
<li><span class="label">Ecological Connectivity:</span> The degree of connectivity and spatial linkages between protected and conserved areas.</li>
<li><span class="label">Ecosystem Services:</span> Provisioning and regulating services, with a specific focus on carbon storage and water services.</li>
</ul>

<strong>Purpose</strong>
<p>The layer was created to inform the EC Directorate General for International Partnerships (DG INTPA) and policymakers in selecting and prioritizing funding intervention areas based on specific targets (e.g., species conservation, connectivity, or water security) within different biogeographical regions. It serves as a spatial decision-support tool to align with the European Green Deal and the EU Biodiversity Strategy for 2030.</p>

<strong>Geographic Extent</strong>
<ul>
<li><span class="label">Region:</span> Sub-Saharan Africa</li>
<li><span class="label">Coverage:</span> 45 distinct Key Landscapes for Conservation and Development (KLCDs)</li>
</ul>

<strong>Keywords</strong>
<ul>
<li><span class="label">Thematic:</span> Biodiversity Conservation, NaturAfrica, Ecosystem Services, Protected Areas, Habitat Connectivity, Species Endemicity, European Green Deal, Ecosystem Integrity, Transfrontier Conservation, Mega-landscapes.</li>
<li><span class="label">Spatial:</span> Sub-Saharan Africa, Congo Basin, West Africa, East Africa, Southern Africa, Sudano-Sahelian zone.</li>
</ul>

<strong>Lineage / Data Source</strong>
<p>This dataset is derived from the <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC145021"&gt; technical assessment conducted for the European Commission Knowledge Centre for Biodiversity (KCBD) </a>.</p>

Given the massive scale of livestock production systems, it is unlikely that any other single human activity has a larger environmental impact on the terrestrial land mass of the planet. As the world’s largest user of land, livestock production has a huge footprint, affecting many components of the global environment. In many developing countries, the per-capita consumption of livestock foodstuffs is projected to continue to rise. This layer displays the areas of concern for livestock density related issues, derived from the convergence of global evidence of human-environment interactions. The density of livestock is related to environmental pressures from livestock related land use change, grazing lands and fodder production, and greenhouse gas emissions.

Smallholder farmers have limited access to capital and are reluctant to trade their low-risk system (low input and low yield) to a high-risk system (high input and potentially higher yields). But Insufficient application of fertilizer on agricultural land may lead to soil nutrients depletion, lower yields, and eventually land abandonment. This layer displays the areas of concern for low-input agriculture related issues derived from the convergence of global evidence of human-environment interactions that can have land degradation consequences.

Critical natural assets are defined as the natural and semi-natural terrestrial and aquatic ecosystems required to maintain 12 of nature’s ‘local’ contributions to people (local NCP) in the ocean (blue). 12 Local NCP for key benefits like security in food, water, hazards, material and culture. as follows: for food, pollinator habitat sufficiency ofr pollination dependent crop production, fodder production for livestock, wild riverine and marine fish catch ; for water, water quality regulation, via sediment retention and nutrient retention; for natural hazards, flood risk reduction and coastal risk reduction. For materials, timber production, fuelwood production and access to nature. For cultural benefits, coral reef tourism and access to nature for recreation or other uses. Criticality of the natural assets was defined on the basis of the highest value areas across all NCPs, the magnitude of benefits and the number of beneficiaries. Cropland, urban areas, bare areas and permanent snow and ice are excluded from the analysis.

The Marine Ecoregions of the World is a global system to classify the oceans, helping to plan and prioritise marine conservation measures. How much are marine areas are protected at ecoregion level? For each African marine ecoregion, this map shows the percentage covered by protected areas.

The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population change related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation. It reflects the dynamics of increasing number of people in a certain area.

This layer is part of the World Altas of Desertification

According to UN estimates, the global population will increase by 2.4 billion between 2015 and 2050. Of this, an overwhelming 50 % will be concentrated in Africa (1.3 billion). There could be up to four times as many people in sub-Saharan Africa by the end of the century. The human imprint on the planet has a major impact on the functioning of the Earth system. Because the impact on the environment is closely intertwined with population dynamics, it is important to monitor and include these in the evaluation of land degradation. This layer displays the areas of concern for population density related issues derived from the convergence of global evidence of human-environment interactions that can lead to land degradation.

Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.

Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.

Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.

Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.

Substantial crop losses occur at various stages along the postharvest value chain. Losses result from poor handling and storage practices combined with limited awareness, infrastructure, and knowledge. The African Postharvest Losses Information System (APHLIS) (www.aphlis.net) is the foremost international effort to collect, analyse and disseminate data on postharvest losses of cereal grains in sub-Saharan Africa. The cumulative % loss in weight incurred during harvesting, drying, threshing/shelling, winnowing, household-level storage, transport and market-level storage for the selected crop, location, and year is presented. Complimentary data sets are collected and used to convert this % loss into absolute loss values in tonnes, US$ and nutrients, along with the nutritional and financial impacts of these losses by province and country. Understanding the magnitude of postharvest loss, the points in the value chain where losses occur, and the causes and impacts of loss helps decision-makers formulate effective policies and invest in successful postharvest loss programmes.

Droughts affect millions of people in the world each year and have long-lasting socioeconomic impacts. They can occur over most parts of the world, even in wet and humid regions, and can profoundly impact agriculture, basic household welfare, tourism, ecosystems and the services they provide. The Risk of Drought Impact for Agriculture (RDrI-Agri) is a categorized risk index, indicating the probability of having impacts from a drought, with particular focus on vegetation. Higher risk (in red) means that the areas affected will be the most likely to report impacts due to droughts. It is updated every ten days.

The availability and quantity of observational species occurrence records have greatly increased due to technological advancements and the rise of online portals, such as the Global Biodiversity Information Facility (GBIF), coalescing occurrence records from multiple datasets. Funded by the world's governments, this international network and data infrastructure is aimed at providing anyone, anywhere, open access to data about all types of life on Earth. This map displays all the observations reported to the GBIF from 1600 to present. This knowledge derives from many sources, including everything from museum specimens collected in the 18th and 19th century to geotagged smartphone photos shared by amateur naturalists in recent days and weeks. Individual records are available at https://www.gbif.org

Critical natural assets are defined as the natural and semi-natural terrestrial and aquatic ecosystems required to maintain 12 of nature’s ‘local’ contributions to people (local NCP) on land. 12 Local NCP for key benefits like security in food, water, hazards, material and culture. as follows: for food, pollinator habitat sufficiency ofr pollination dependent crop production, fodder production for livestock, wild riverine and marine fish catch ; for water, water quality regulation, via sediment retention and nutrient retention; for natural hazards, flood risk reduction and coastal risk reduction. For materials, timber production, fuelwood production and access to nature. For cultural benefits, coral reef tourism and access to nature for recreation or other uses. Criticality of the natural assets was defined on the basis of the highest value areas across all NCPs, the magnitude of benefits and the number of beneficiaries. Cropland, urban areas, bare areas and permanent snow and ice are excluded from the analysis.

Forests are the most biologically diverse land ecosystems and are critical for sustaining local and global livelihoods. Deforestation can be considered a type of land degradation when forest ecosystems, with all of their important provisioning, regulating and cultural services, are exchanged for another land use, such as crop agriculture, with a narrow provisioning service focus. Damages to the land resource include the immediate reduction or loss of biomass productivity with a linked loss in habitat, biodiversity, and carbon stock. Clearance of natural forests accelerates soil erosion and the alteration of soil functioning. This layer displays the areas of concern for tree loss issues, derived from the convergence of global evidence of human-environment interactions that can have consequences on land degradation.

Africa is projected to have the fastest urban growth rate in the world — by 2050, Africa’s cities will be home to an additional 950 million people. Urban planning and management are essential development challenges. Understanding urbanisation, its drivers, dynamics and impacts, is key to designing targeted, inclusive and foward-looking policies at the local, national and continental levels. Africapolis data and evidence supports cities and governments to make urban areas more inclusive, productive and sustainable. This map of urban population covers 7 500 agglomerations in 50 countries for the base year 2015.

Water is a critical natural resource for both natural ecosystems and human subsistence. Some of the most immediate pressures on land that lead to degradation include diversion of surface waters and the removal of groundwater reserves to meet agricultural, industrial and domestic demands. This layer displays the areas of concern for water use related issues derived from the convergence of global evidence of human-environment interactions that can have consequences on land degradation. It highlights areas where the total water withdrawal (by the agriculture, industrial and domestic sectors) exceeds 40% of the total available annual renewable water supply.

Where are the best places to spot the most requested wildlife-watching species? This grid layer presents a richness index of top wildlife watching species/groups, including rhinos, elephants, lions, leopards, buffalos, giraffes, lowland and mountain gorillas, chimpanzees, bonobos, sifakas, ringtails, aye-aye, mouse lemurs and colobus.

Crop conditions monitoring is highly relevant for food security early warning and response planning in food-insecure areas of the world. GEOGLAM (the Group on Earth Observations' Global Agricultural Monitoring Initiative) aims to reinforce the international community's capacity to produce and disseminate relevant, timely, and accurate forecasts of agricultural production at national, regional, and global scales using Earth Observation data.

Copernicus4GEOGLAM, one of the Copernicus Land Monitoring Services managed by the EC Joint Research Centre, aims to produce baseline information that allows countries in Africa to improve their agricultural monitoring systems.

This dataset aggregates crop maps requested by three East African nations, showing the agricultural situation at the end of the long rain season of 2021. The results are made fully and freely accessible, covering the following areas:

• Kenya: 98,687 km²
• Tanzania: 116,190 km²
• Uganda: 89,296 km²

Higher energy demands in Africa has led to a wide expansion of the number of hydropower sites, mainly between the 1960s and 1980s. The construction of dams causes impoundments of rivers and reservoirs in the regions of dam influence, with higher evaporation and water temperatures due to increased water surfaces. This map shows the he surface area (sqkm) of African reservoirs subject to hydropower production for the year 2016. It includes reservoirs of associated hydropower plants with installed capacities above 5MW. Apart from this, only reservoirs with a detected dam-caused impoundment of water surface are considered.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and education using the indicator of percentage of population with secondary education .
The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where electricity access is below 80%, ensuring that the results remain relevant for policy intervention.
The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited electricity access is most strongly associated with higher education, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and gender using the indicator of proportion of women with secondary school. The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where electricity access is below 80%, ensuring that the results remain relevant for policy intervention. The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited electricity access is most strongly associated with lower proportion of women at secondary school, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and health using the indicator of under-5 mortality.

The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where electricity access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited electricity access is most strongly associated with higher mortality among children under five, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to electricity and four nutrition indicators among children under five: stunting, wasting, severe wasting, and underweight.

The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where electricity access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited electricity access is most strongly associated with poorer nutritional outcomes among children under five, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to sanitation infrastructure and education using the indicator of percentage of population with secondary education . The tool highlights locations where the local association between electricity access and the selected nutrition indicator is both negative and statistically significant. The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited sanitation access is most strongly associated with lower education, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to sanitation infrastructure and gender using the indicator of percentage of women with secondary education. The tool highlights locations where the local association between sanitation access and the selected gender indicator is both negative and statistically significant. The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited sanitation access is most strongly associated with lower women education, supporting targeted interventions, programme planning, and place-based development strategies.

Priority areas are identified using local coefficients estimated through Geographically Weighted Regression (GWR), which measures the spatially varying relationship between access to sanitation and four nutrition indicators among children under five: stunting, wasting, severe wasting, and underweight.

The tool highlights locations where the local association between sanitation access and the selected nutrition indicator is both negative and statistically significant. In this application, priority areas are further restricted to locations where sanitation access is below 80%, ensuring that the results remain relevant for policy intervention.

The identified areas are classified as having strong, moderate, or mild associations according to the magnitude of the local GWR coefficient. This classification helps users determine the appropriate intensity of intervention. The spatially varying coefficients therefore allow users to identify areas where limited sanitation access is most strongly associated with poorer child nutrition outcomes, supporting targeted interventions, programme planning and place-based development strategies.