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.

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

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.

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.

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable.

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.

Predicting high-magnitude, low-frequency crop losses in the first half of the growing season can help identify need for early national and local interventions.

Using crowdsourced near-surface remote sensing imagery to monitor winter wheat phenology and identify damage events in NW India.

Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level.

CGIAR is addressing the urgent need to create a data sharing culture and enabling environment for Open Access and Open Data (OA/OD) that includes projects planning for OA/OD and allocating funds to support it, in parallel with the technical infras