Title: The importance of soil moisture in predicting agricultural yields: from regional processes toward a global scale analysis
Abstract: Understanding the response of agriculture to environmental stressors is essential to adapt food systems to climate change. Although evidence of crop yield loss with extreme temperature is abundant, quantifying the response of yield to water availability has proven challenging. This is largely due to limited soil moisture data and the tight coupling between soil moisture and temperature at the land surface. In this seminar, I will discuss my work developing a statistical model that resolves the yield response to both soil moisture and atmospheric demand using remotely sensed observations. When the model is applied across the Midwestern United States for maize, results suggest that yield damages are overestimated by approximately double if rising atmospheric demand is considered without the moderating effect of changing soil moisture. Then, I will demonstrate how this model can be applied in regions with poorly resolved yield data by highlighting a case study in Kenya. In this application, I leverage satellite-based observations of solar-induced fluorescence to downscale nationally reported yields prior to determining the climate dependence. Preliminary results indicate that an analogous downscaling procedure can be applied to many countries across the globe. To conclude, I will outline a roadmap for determining the global hydrologic vulnerability of agriculture to climate change by coupling observational datasets (remotely sensed soil moisture and solar-induced fluorescence, crop distribution maps, and weather station measurements), statistical models, and climate model simulations.