USDA-ARS Mapping and Monitoring Soil Moisture and Salinity Orchard Crops Using Non-Invas

Organization

U.S. Department of Agriculture (USDA)

USDA-ARS-PWA-

10/23/2026 3:00:00 PM Eastern Time Zone

• Applications are reviewed on a rolling-basis.

A research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located in Davis, California.

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief scientific in-house research agency with a mission to find solutions to agricultural problems that affect Americans every day from field to table. ARS will deliver cutting‑edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well‑being of all people; sustain our nation’s agroecosystems and natural resources;

and ensure the economic competitiveness and excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence.

Research Project:
Cover crops are used to improve soil health in orchard crops, e.g., by improving nutrient/moisture retention in soils, reducing soil compaction, attracting pollinators, etc., but the overall impact of cover crops on rootzone soil moisture availability and deep percolation is not well understood. This creates large uncertainties when modelling orchard water and nutrient fluxes as well as deep percolation below the root zone, critical topics in semi‑arid, agriculturally productive regions such as California’s Central Valley.

In this educational research project, we want to quantify spatial variations in root zone soil moisture variations in orchard crops as a function of cover cropping. In order to map moisture content, we will use laterally extensive time‑lapse electrical resistivity tomography (ERT) surveys as well as co‑located borehole nuclear magnetic resonance (bNMR) logging to quantify water content. Additionally, we intend to measure root water uptake using sap flow meters.

The data will be integrated using recently developed physics‑informed neural networks in order to translate apparent resistivity data to spatial maps of water content.

All research will be conducted at the Sustainable Agricultural Water Systems (SAWS) unit, a part of the Agricultural Research Service (ARS), the U.S. Department of Agriculture's chief scientific in-house research agency with a mission to find solutions to agricultural problems that affect Americans every day from field to table. The mission of the SAWS unit is to increase the efficiency and sustainability of irrigated agriculture in California under changing environmental conditions, with a special focus on vine and tree orchard crops in the Central Valley.

This is accomplished by: (i) improving irrigation efficiency; (ii) optimizing on‑farm strategies for managed aquifer recharge; and (iii) assessing the long‑term impacts of irrigated agriculture on crop production, soil health, and groundwater quantity and quality under changing environmental conditions.

The SAWS unit, located in Davis, CA, hosts researchers studying water, carbon and nutrient fluxes in agricultural lands, as well as groundwater geophysicists, hydrologists and economists. Participation with the SAWS unit will grant access to USDA physical and computational resources, including geophysical instrumentation, soil and plant sensors, laboratory and greenhouse space, as well as cloud and supercomputing resources. We also engage closely with researchers at University of California campuses, especially those in Davis.

• Learn electrical geophysics and bNMR survey design.
• Understand the deployment and maintenance of buried ERT arrays and bNMR access tubes for time‑lapse data acquisition.
• Gain experience in installation and maintenance of sensors for sapflow, soil moisture/salinity, etc.
• Build experience in the integration of diverse data streams, including field measurements and remote sensing data products, in a unified data processing workflow to recover models of subsurface water content variation.
• Learn about the…