Project Number: 2020-13660-008-005-I
Project Type: Interagency Reimbursable Agreement
Start Date: Feb 7, 2017
End Date: Sep 30, 2020
In this project, we will develop an integrated modeling platform using Earth Observations to simulate large-scale hydrological processes and food production in support of agricultural practices and policy. The decision tool will provide timely and relevant information to assess (1) the condition of the water resource, (2) the effectiveness of the water management and (3) the impacts of climate projections on food production. The primary outcomes will be potential best agricultural water management practices to improve water use efficiency, maintain high crop yield and prevent excessive losses of water and nitrogen to the environment. Understanding the context and water resource constraints that stakeholders and smallholder farmers face in climate sensitive regions are critical to target and understand the potential impact of water use management. We will focus on a finite set of regional pilot cases in Argentina, South Africa, Tanzania, and the United States that are representative of a range of agricultural systems, water issues and climate impacts. The application will be integrated in the GEOGLAM Crop Assessment Tool, an existing web-based platform which will aid synthesizing the project outcomes and expert domain knowledge to provide timely and actionable water resources and crop condition reports to assist water management decisions.
Research conducted at the USDA Agricultural Research Service U.S. Arid Land Agricultural Research Service will address objectives 1 & 2 by measuring evapotranspiration at irrigated farm sites in Yuma, Arizona, and using the results to validate and improve the Agricultural Policy/Environmental eXtender Model (APEX) watershed model that is to be implemented for globally distributed sites. Measurements will be based on eddy covariance, soil moisture, and irrigation deliveries. Crop cover types within the Yuma irrigation district will be locally checked and used to verify remote sensing classifications.