The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections, providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes its supply and demand outlook.

More than one billion people worldwide receive cash or in-kind transfers from social protection programs.

Spatially explicit global cropping system data products, which provide critical information on harvested areas, crop yields, other management variables, are imperative to tackle current grand challenges such as global food security and climate cha

تعرض هذه المذكرة المتعلقة بالسياسات مثالين عن كيفية استخدام بيانات الاستشعار

This policy note showcases two examples on how remote sensing data can be used for monitoring agricultural production and economic activities.

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming.

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

Based on experiments to bring about comprehensive crop insurance coverage over the last 50 years, the Indian government introduced a new crop insurance program, called Pradhan Mantri Fasal Bima Yojana (PMFBY), in April 2016.

Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change.

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally.

This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods.

We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, o

This paper discusses the feasibility of applying a near-surface remote sensing approach in the index insurance component of the R4 Rural Resilience Initiative in Ethiopia.

Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environme

Policy-making processes in developing countries often continue to operate devoid of evidence.

Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security.

HarvestChoice began in 2006, when detailed and readily accessible data on agriculture, human welfare, and the environment were scarce for Africa South of the Sahara (SSA).