Remote sensing and machine learning for food crop production data in Africa post-COVID-19
The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2).
The world is experiencing an unprecedented health crisis during the spread of COVID-19 (SARS-CoV-2, or Severe Acute Respiratory Syndrome Coronavirus 2).
Since Amartya Sen's famous work on Poverty and Famines, economists have understood that the impacts of food market shocks on the poor depend much more on their impacts on households’ incomes and access to food than on overall food availability, an
Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020.
Since Amartya Sen’s famous work on Poverty and Famines, economists have understood that policy responses to food market shocks should be guided by changes in households’ incomes and access to food, rather than by overall food availability.
Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure.
Consumption-based household surveys are used to look at the impact of weather variabilities on welfare, food consumption, and poverty, finding disproportionate welfare impacts from floods.
Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development stra