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ARS Home » Pacific West Area » Riverside, California » Agricultural Water Efficiency and Salinity Research Unit » Research » Research Project #436837

Research Project: Improved Irrigation Water Management for Water-Scarce Regions and Salt-Affected Lands

Location: Agricultural Water Efficiency and Salinity Research Unit

Project Number: 2036-61000-019-003-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jul 1, 2019
End Date: Dec 31, 2023

The overall goal of the project is to develop digital agriculture (DA) tools that help farmers increase profits, conserve soil and water resources, and improve water and salinity management. The specific objective is to develop procedures for using proximal and remote sensors in combination with advanced computational approaches to characterize root-zone soil properties and determine field and sub-field water requirements.

Digital Agriculture (i.e., the transdisciplinary use of high-performance computing and hyperdimensional data in agriculture) tools and knowledge will be developed and improved in this project. Existing DA and hydrologic modeling and decision support technologies developed at USSL and UCR include detailed representations of many root-zone processes affecting the function and performance of irrigated agricultural production systems, but they were developed for relatively small scales, focused on single aspects of agricultural systems, and/or utilized relatively simple representations of crop growth in the modeling frameworks. The objectives of this project will be accomplished by: (a.) combining proximal soil sensing and remote vegetation sensing with multi-scale, high-resolution geodata analyses to create a large-scale spatiotemporal soil salinity prediction model; (b.) evaluating modeling frameworks for soil property estimations (e.g., pedotransfer functions, digital soil mapping) across scales (e.g., laboratory analyses, field-scale, farm-scale, regional-scale) using hyperdimensional sensor data (e.g., electromagnetic induction, gamma-ray spectrometry); (c.) developing a transient-state model to generate irrigation recommendations and crop-water production functions for a variety of different irrigation strategies (e.g., variable rate vs uniform) using information on: (i.) average salinity in the soil profile; (ii.) soil hydraulic parameters; (iii.) crop type data; (iv.) average local ETo for previous years; and (v.) irrigation water salinity.