EGU24-21136, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Enabling long-lead forecasting of agriculture production shocks with soil moisture monitoring and forecasting products to support food insecurity early warning

Shraddhanand Shukla1, Frank Davenport1, Eric Yoon1, Barnali Das1, Weston Anderson2,3, Abheera Hazara2,3, Kim Slinski2,3, and Amy L. McNally3
Shraddhanand Shukla et al.
  • 1Climate Hazards Center, University of California-Santa Barbara, Santa Barbara, California, United States
  • 2Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, United States
  • 3NASA Goddard Space Flight Center, Greenbelt, Maryland, United States

As per USAID’s Famine Early Warning System Network Team (FEWS NET) 110-120 million people are projected to need emergency food assistance across all FEWS NET-monitored countries. Climate shocks such as droughts contribute to acute food insecurity. Better identification and earlier warning of anomalous conditions leading to food insecurity are critical to support decision-making to mitigate the impacts of food insecurity on lives and livelihoods. Agricultural production outlooks are one of the critical components of the famine early warning scenario generation process. Thus far these outlooks have mainly been based on estimates of seasonal rainfall or remotely sensed indicators of vegetation greenness whereas soil moisture estimates (remotely sensed or modeled) have been used as drought indicators but not directly used for crop yield forecasting to assess production shocks, particularly in operational settings. Our past research, which focused on crop yield forecasting in southern Africa, revealed a promising level of skill when soil moisture monitoring products or forecasts were used as predictors of crop yield, relative to traditional predictors such as December to February ENSO. Additionally, a separate study focused on East Africa revealed when and where soil moisture can be the best predictor of crop yield relative to other earth observations. Building upon this initial research, here we investigate the applicability of soil moisture monitoring and forecasting products in crop yield forecasting in up to 20 FEWS NET monitored countries for which processed crop yield data are available at sub-national scale. We first use soil moisture monitoring products, both remotely sensed (such as ESA-CCI) and modeled (such as FEWS NET Land Data Assimilation System) to implement and validate machine learning based within-season crop yield forecasting. We then use seasonal-scale soil moisture forecasts (up to 6 months in future) to enhance the lead-time of crop yield forecasting and implement and validate pre-season (before the start of a crop growing season) long-lead crop yield forecasting, as earlier estimates of food insecurity can provide additional critical time needed for launching famine prevention responses by governments and donor agencies.

How to cite: Shukla, S., Davenport, F., Yoon, E., Das, B., Anderson, W., Hazara, A., Slinski, K., and McNally, A. L.: Enabling long-lead forecasting of agriculture production shocks with soil moisture monitoring and forecasting products to support food insecurity early warning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21136,, 2024.