Simulation and seasonal prediction of irrigated cereal crop yields in data-scarce region using a hydrologic-groundwater-crop (HGC) modeling framework
Abstract
Irrigation is a common practice for growing cereal crops during dry seasons and in water-limited regions. In some of the Least Developed Countries (LDCs) such as Ethiopia where irrigation infrastructures are outdated and socioeconomic development is slow, farmers often irrigate their crops without proper regulation and recording. Irrigation in these regions are poorly documented and irrigation data is scarce, which poses a major challenge for crop yield simulation and prediction. To overcome such challenges, here we make sure of three renowned process-based hydrologic, groundwater and crop models (CREST, MODFLOW and DSSAT respectively) to develop a distributed hydrologic-groundwater-crop (HGC) modeling framework using soil moisture as the primary link, and apply it to the simulation of major cereal crops in dry season in Lake Tana basin, Ethiopia. Based on the HGC modeling framework, we then perform both real time simulation and analog approach for seasonal prediction of crop yields. Results from this study will help illustrate how dry season hydrologic cycle may influence crop growth and provide guidance for local farmers and decision makers for water and crop management.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.B52A..05Y
- Keywords:
-
- 0402 Agricultural systems;
- BIOGEOSCIENCES;
- 0466 Modeling;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1922 Forecasting;
- INFORMATICS