South Africa

The map is based on Copernicus Global Land Cover data which shows actual land cover (what physically covers land across the globe—forests, grasslands, croplands, lakes, wetlands, built-up area, etc) at 100m x 100m resolution. The land cover data for Africa was overlaid on (high resolution) satellite imagery of Africa, which is then used to determine the land use (by visual interpretation) associated with the various land cover patterns on the Copernicus Global Land Cover map.Copernicus land cover data offers several advantages: it is of high quality, has a high resolution (100m x 100m), and offers time series satellite imagery. More importantly, the Copernicus Global Land Service is a continuous process and its datasets are updated annually. This means that the Soils4Africa map of agricultural land can be updated every time the land cover data for the new year becomes available (the map is currently based on 2019 data).The Copernicus dataset already includes the category ‘cropland,’ which by definition is part of agricultural land. The Soils4Africa map broadens the scope of that dataset by infering information on agricultural use and including other kinds of agricultural land use such as grazing pastures and plantations.When land is mapped as other than ‘cropland'-- like ‘shrubland’ for example-- it is more difficult to interpret and determine whether it is under agricultural use. The Copernicus dataset includes information about ‘fractional cover’-- or the percentage of a particular pixel under a particular kind of land cover (for example, 30% of a 100m x 100m pixel could be forest and 20% could be shrubland). The Soils4Africa map takes into account how fractional cover varies over an area to establish rules for interpreting its land cover data to determine whether it is under agricultural use and for what purpose. These rules were validated by comparing it with ground level information on land use (or land use pattern) for specific areas drawn from the interpretation of satellite imagery from Google Earth.For example, ground-level observation shows that forest cover upwards of 30% in a given area when matched by shrubland cover of over 30%, is characterized by woody vegetation with a smooth canopy. Therefore, such area is more likely to be under plantations rather than natural forest. Thus, such an area should be counted as agricultural land, even if less than 15% of it is under crops.

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.

Food crisis response planning can save lives if put in place in a timely manner. To do this, decision makers must be warned of climate extreme events impacting agricultural production. The Anomaly hotSpot of Agricultural Production tool (ASAP) is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning anticipation. This map shows the frequency of ASAP anomaly warnings for rangeland growth for 2004-2018. It highlights the high sensitivity of the main agricultural areas in Northern Africa, the Horn of Africa and the Southern African Development Community to drought conditions.

Food crisis response planning can save lives if put in place in a timely manner. To do this, decision makers must be warned of climate extreme events impacting agricultural production. The Anomaly hotSpot of Agricultural Production tool (ASAP) is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning anticipation. This map shows the frequency of ASAP anomaly warnings for crop growth for 2004-2018. It highlights the high sensitivity of the main agricultural areas in Northern Africa, the Horn of Africa and the Southern African Development Community to drought conditions.

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 rangeland in Africa. Each pixel represents the fraction of the area covered by rangeland (i.e. the percentage of the pixel with rangeland).

Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates, which are hampered or biased by a lack of accurate information on environmental impacts. This dataset presents a global crop map. It covers areas where oil palm plantations were detected at global scale, and includes industrial and smallholder mature oil palm plantations.

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.

Soil organic carbon (SOC) is the carbon that remains in the soil after partial decomposition of any material produced by living organisms. It constitutes a key element of the global carbon cycle through atmosphere, vegetation, soil, rivers and the ocean. It is a crucial contributor to food production, mitigation and adaption to climate change. Soils represent the largest terrestrial organic carbon reservoir. Depending on local geology, climatic conditions and land use and management (amongst other environmental factors), soils hold different amounts of SOC. This map shows the amount of carbon stored in the soil (from 0 to 30 cm depth), expressed in Mg (megagrams or tonnes) per km2.

Roots are a long term and stable carbon sink, accounting for about 0.4 of the above ground biomass across biogeographical regions. Well established and developed root systems provide various ecosystem services related to improved soil quality (higher cation exchange capacity and nutrient turnaround) and characteristics (improved soil porosity and aeration). Spatially explicit data and assessments of forest biomass and carbon are paramount to design and implement effective sustainable forest management options and forest related policies. The belowground biomass carbon index (BBCI) presented in this dataset is expressed in Mg (Megagrams or Tonnes) of carbon per km2. It represents an estimation of the carbon stored in the roots of all living trees. Together with the above-ground carbon index (AGCI) and the soil organic content index (SOCI), it provides a complete overview of the total carbon stored in forest areas (trees and soil).

The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons).

The large-scale expansion of built infrastructure is profoundly reshaping the geographies of Africa, generating lock-in patterns of development for future generations. Understanding the impact of these massive investments can allow development opportunities to be maximised and therefore be critical for attaining the United Nations Sustainable Development Goals and Africa Union Agenda 2063. However, until now information on the types, scope, and timing of investments, how they have evolved, and their spatial-temporal impact was dispersed amongst various agencies. We developed the first comprehensive database of 79 ongoing and planned investment corridors across Africa, synthesizing data from multiple sources covering 184 projects on railways, wet and dry ports, pipelines, airports, techno-cities, and industrial parks. The georeferenced interlinked tabular and spatial database includes 22 attributes with sources provided for each observation. We expect this database will improve coordination, efficiency, monitoring, oversight, strategic planning, transparency, vulnerability risk, and impact assessments, among other uses for inter alia investment banks, governments, impact assessment practitioners, communities, conservationists, economists, and regional economic bodies.