FEWS NET Agroclimatology Training and Learning Management System to Build Capable Users of EO Data Products
Abstract
The Famine Early Warning Systems Network (FEWS NET) monitors food insecurity across some of the most-vulnerable and food insecure regions of the world to identify shocks and provide decision-making support to relief efforts. This work requires a well-developed team of agroclimatic scientists, nutrition experts, markets and trade specialists, and food security analysts in the United States and elsewhere to identify and communicate concerning conditions over wide areas.
To best monitor these large regions, which often have limited infrastructure, Earth Observation (EO) data are typically employed. Data analysis and monitoring look for consensus among precipitation estimates, remotely sensed vegetation conditions, outputs from hydrologic models, crop models, and a variety of other products to identify and quantify abnormal conditions. To build a team of users that can appropriately interpret and consolidate information from a variety of EO systems, FEWS NET scientists developed agroclimatic training material. This training material establishes a foundation—shared by FEWS NET analysts around the world—regarding the variety of different products that are available, how to interpret and properly use these products, and when in the seasonal timing it is appropriate to rely on different products to identify potential food production shocks. To deliver this material to team members, FEWS NET leverages a Learning Management System platform (LMS) allowing for on-demand access to the training materials (which include course eModules, videos, slide decks, exercises, and knowledge checks) by participants who are spread out across time zones. The on-demand material can be followed by small-group live trainings with a more specific focus, delivered by subject matter experts who can share use cases and facilitate questions and discussion. This method results in consistent messaging, flexible updating, and managed version control helping to ensure that the participants are receiving and understanding the proper messages. The LMS will allow team members across the globe to benefit from a common foundation of how to use EO data in their work.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMSY12B0390H