Title: Africa Regional Centres of Excellence - ArcX: Agroecology Component.
Main Objective: Strengthen Regional Centres of Excellence (RCoEs) in agroecology across sub-Saharan Africa to accelerate the transition towards productive, resilient, and sustainable food systems by integrating agroecological principles with scientific and local knowledge.
Specific Objectives:
Strengthen the scientific, technological, and institutional capacities of sub-regional organisations (SROs) and RMRNs/RCoEs in agroecology;
Enhance the contribution of RMRNs/RCoEs to transformative, high-quality agroecological research;
Improve cross-sectoral and inter-regional coordination to tackle science, technology, and innovation challenges in Africa’s green transition.
Data collection & sharing: Document and disseminate agroecological practices through open-source platforms;
Decision-support: Produce scientific and policy analyses for informed strategic planning;
Capacity development: Deliver targeted training and strengthen professional networks;
Innovation support: Develop and scale up innovative agroecological solutions;
Global engagement: Integrate African agroecology into international frameworks and global fora.
Target Groups: Scientific community (economists, agronomists, food-system experts, etc.), data and information managers, international organisations, private sector, universities, local communities, regional economic communities, continental bodies, African farmers, and citizens.
Component Coordinator: Forum for Agricultural Research in Africa (FARA) and Regional Universities Forum for Capacity Building in Agriculture (RUFORUM).
Leading Regional Centre of Excellence (RCoE): Centre for Coordination of Agricultural Research and Development for Southern Africa (CCARDESA), International Centre of Insect Physiology and Ecology (ICIPE), West and Central African Council for Agricultural Research and Development (CORAF)
Scientific and Technical Support from EC - DG JRC:DG-JRC Directorate D (Sustainable Resources) Unit D5 (Food Security) and Unit D4 (Economics of the Food System).
The dataset is the result of a crop simulation and machine learning exercise to evaluate the irrigation potentials in sub-Saharan Africa. Samples of points (representative for each Agro-ecological zone in Africa) have been selected from a cropland map (from Tubliello et al., 2023) and, by using the DSSAT crop model fed with soil information from the ISRIC project (https://isric.org/projects/soil-information-system-for-africa-soils4africa/) and 10 years of daily climate data (from NASAPOWER), simulations have been conducted in each location under rainfed conditions and optimal irrigation. This last has been achieved y letting the crop model add irrigation water each time a water stress was perceived. Other management practices have been set as realistic as possible, particularly the amount of provided fertilization has been kept low to represent current conditions. Yields have been finally averaged along the different years of simulations to generate expected yields. Finally, by using the XGboost algorithm fed with the same soil and climatic variables used for the crop model, yields under both rainfed and irrigation conditions have been imputed to all remaining cropland locations in Sub-Saharan Africa. What is presented here are the yield increases, as percentages over rainfed yields, obtainable in each location for the four simulated cereals: maize, millet, rice and sorghum.
All known formal and informal settlements of forcibly displaced and stateless people in Africa from UNHCR’s GIS database as of November 2025. Population in the settlement. It may include several categories: Refugees, Returnees, IDP, Asylum-seeker, Stateless and Mixed.Formal Settlement: Formal settlements are places where official land is allocated for a group of asylum seekers, refugees, or IDPs. They are accommodated in purpose-built settlements with access to facilities and services. An official management entity is assigned. Camps are a type of formal settlement.
Informal Settlement: In an informal settlement, a group of asylum-seekers, refugees, or IDPs choose to settle in self-identified spontaneous sites. Self-settled settlements are often located on state-owned, private, or communal land, with or without negotiations with the local population or private landowners.
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.
Building upon the Joint Research Centre’s frameworks for utilizing Remote Sensing (RS) and Geographic Information System (GIS) resources to evaluate agroecological performance, this spatial dataset provides critical baseline intelligence on cropping systems to support resilience assessment and food security planning for a selected region in Kenya,.
The dataset comprises two layers:
Number of Crop Types (Crop Richness):
The absolute count of distinct cultivated crop species within a given spatial resolution. Crop richness serves as a fundamental baseline indicator of agricultural variety. It allows analysts to rapidly distinguish between highly vulnerable monoculture landscapes and more traditional, diverse multi-cropping or intercropping systems present in the Kenyan study area.
Crop Diversity (Shannon Diversity Index):
Going beyond a simple count, the Shannon Diversity Index evaluates both the abundance (the number of different crop types) and the evenness (the proportional spatial distribution of those crops). A higher index value indicates a highly diverse and balanced cropping ecosystem. Because evenly diverse systems are typically more resilient to climate volatility, pests, and diseases, this layer is a vital proxy for assessing local agroecological health.
Reference evapotranspiration (Global - Monthly - ~10 km) August 2025- AQUASTAT (FAO) AgERA5 derived.
Reference evapotranspiration per month with a spatial resolution of 0.1 degree. Unit: mm month-1. The dataset contains monthly values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.
https://data.apps.fao.org/catalog/dataset/c91430cc-681e-4ca8-a5f4-8c97dc92c547/resource/ff49908e-2e07-48b3-8deb-0c69af411859
Water Footprint in Africa, considered as the sum of both the green and blue WF and defined as the ratio between evapotranspiration (in m3 per hectare) and crop yield (in ton per hectare). The values are expressed in m3/ton.
This dataset provides the global geographic distribution of key livestock species—cattle, sheep, and goats—for the year 2010, sourced from the Gridded Livestock of the World (GLW 3) database. Expressed as the total number of animals per pixel at a spatial resolution of 5 minutes of arc, these layers are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessments, and epidemiology.
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.
Humans need increasingly more biomass for food, fodder, fiber and energy. In Africa, circa 22% of the vegetated land surface showed a decline or unstable land productivity between 1999 and 2013. Persistent reduction of land productivity points to long-term alteration of the health and productive capacity of the land, which are characteristic of land degradation. It has impact on ecosystem services and benefits, thus on the sustainable livelihoods of human communities. This map shows the dynamics of (vegetated) land productivity over a time period, in other terms the trajectories of above-ground biomass. It reflects changes in ecosystem functioning e.g. vegetation growth cycles due to natural variation and/or human intervention, and can be associated with processes of land degradation or recovery. The 5 classes depict two levels of persistent productivity decline, one level of instability or stress in capacity, one level of stable productivity and one level of increased productivity.
By detecting areas where agricultural production deficits might occur, it is possible to prevent food security crises and anticipate response planning. To do this, we need accurate and reliable information on agricultural land cover. This layer shows the extent of cropland in Africa. Each pixel represents the fraction of the area covered by cropland (i.e. the percentage of the pixel with crops).
In most people's mind, soil would not figure highly in a list of the natural resources of Africa. However, healthy and fertile soils are the cornerstones of food security, key environmental services, social cohesion and the economies of most African countries. Unfortunately, soil in Africa tends to reach public awareness only when it fails – often with catastrophic consequences as seen by the famine episodes of the Sahel in the 1980s and more recently in Niger and the Horn of Africa. In the context of major global environmental challenges such as food security, climate change, fresh water scarcity and biodiversity loss, the protection and the sustainable management of soil resources in Africa are of paramount importance. This layer presents the diversity of soil types across Africa. This map was produced by the Joint Research Centre of the European Commission for the Soil Atlas of Africa.
