Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with crops/rangeland). Data are scaled between 1 and 200 (50 = 25%, 100 = 50%, 150 = 75%, 200 = 100%): image values V = 0-200, scaling 0.5 - > physical value 0-100%. These layers were generated for ASAP, combining existing data sets.

Other SDGs

The dataset contains warnings about low or delayed vegetation performance at sub-national level for crops . The warning classification scheme is applied globally and is based on rainfall estimates (RFE) and NDVI anomalies. The results are a reliable warning of hydrological stress for agricultural production and the warning level ranges from 1 to 4. The historical frequency of ASAP warnings reports the percentage of dekads with a warning for crop out of the total number of active dekads in the period 2004-2019.

Other SDGs

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 reflect 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.

Other SDGs