  In the agricultural sector, the COVID-19 threatens to lead to a severe food
security crisis in the region, with disruptions in the food supply chain and
agricultural production expected to contract between 2.6% and 7%. From the food
crop production side, the travel bans and border closures, the late reception
and the use of agricultural inputs such as imported seeds, fertilizers, and
pesticides could lead to poor food crop production performances. Another layer
of disruption introduced by the mobility restriction measures is the scarcity
of agricultural workers, mainly seasonal workers. The lockdown measures and
border closures limit seasonal workers' availability to get to the farm on time
for planting and harvesting activities. Moreover, most of the imported
agricultural inputs travel by air, which the pandemic has heavily impacted.
Such transportation disruptions can also negatively affect the food crop
production system.
  This chapter assesses food crop production levels in 2020 -- before the
harvesting period -- in all African regions and four staples such as maize,
cassava, rice, and wheat. The production levels are predicted using the
combination of biogeophysical remote sensing data retrieved from satellite
images and machine learning artificial neural networks (ANNs) technique. The
remote sensing products are used as input variables and the ANNs as the
predictive modeling framework. The input remote sensing products are the
Normalized Difference Vegetation Index (NDVI), the daytime Land Surface
Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration
(ET). The output maps and data are made publicly available on a web-based
platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access
to such information to policymakers, deciders, and other stakeholders.
