Project period: 6 17 24-6 16 25PROJECT title: advancing community-engaged climate-change health research: data infrastructure and researchwomen and children in the poorest countries of the world…

Project period: 6 17 24-6 16 25PROJECT title: advancing community-engaged climate-change health research: data infrastructure and researchwomen and children in the poorest countries of the world face some of the greatest adverse health risks associated with global food insecurity and climate change. These risks result from a combination of climate extremes, social and market instabilities, farming and raising animals in more marginal lands, and a variety of other factors. Improving the efficiency of humanitarian aid to reach the neediest people on the planet, at the right time, with the right amount of relief requires innovative strategies and advances. In this project build on the longstanding data science and user focused approach to humanitarian engagement and develop a series of data products, model outputs and user support that reflect the needs of humanitarian and scientific agencies and the lived experiences of people on the ground. The proposal reflects two different approaches to these issues. First, propose the development of user focused data products that engage field work with spatial quantitative data including precipitation, temperature, and livelihood zones with spatially referenced health datato help identify communities and individuals facing the greatest risks of food insecurity in africa. This portion of the project will result in the development of computer code for researchers and policy-makers interested in developing the own climate-health data sets. The project will also support the development of online training materials (blog posts) and scientific publications that highlight how to link health, environmental, climate, and qualitative data. Second, seek to quantitatively understand improvements in crop yield forecasts when surface variables are used (soil moisture and surface temperature) compared to atmospheric variables (precipitation and air temperature) in simulating preseason crop yield forecast models. As such, will generate a dataset with preseason crop yield forecast models. This then feeds back to first question of improved data products to help the vulnerable population from the direct impact of food production losses. The overall outcomes of this research is a roadmap for how best to use climate, environmental and associated forecast data to produce quantitative data, code, and modeling frameworks designed for researchers and policy-makers in a range of disciplines supporting humanitarian agencies as they respond to food insecurity related to human health and agricultural yields.