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 Agricultural Water Efficiency and Salinity Research Unit: Riverside, CA
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ARS Home Pacific West Area Riverside, California Agricultural Water Efficiency and Salinity Research Unit Research





- Skaggs, Todd
- Anderson, Raymond - Ray
- Corwin, Dennis
- Schmidt, Michael - Mike
- Ferreira, Jorge
- 2023
- 2022
- Publications
- Water Availability and Watershed Management (211)
- Soil and Air (212)
- Sustainable Agricultural Systems Research (216)

Research Project: **Location:
                            Agricultural Water Efficiency and Salinity Research Unit** Project Number: 2036-61000-019-000-D
                        
                        Project Type: In-House Appropriated Start Date: Feb 6, 2022
                        
                        End Date: Feb 5, 2027 **Objective:**
                        
                        Drought, climate change, and competition for resources are reducing the availability of irrigation water and farmland in arid and semi-arid regions, including the western United States. One strategy for maintaining or enhancing productivity in the face of diminished resource availability is to make greater use of marginal lands and alternative water sources. Sustainable use of impaired waters requires soil, water, and crop management practices that optimize crop production while minimizing the degradation of natural resources by salts and other contaminants. Advanced models and decision-support tools are needed to evaluate alternative management practices and to assist growers and water managers in satisfying increasingly stringent regulations.

Objective 1: Develop and deploy digital technologies, models, and best management practices for the management of saline and sodic soils and the safe use of alternative water resources for irrigation.
Sub-objective 1.A: Develop and evaluate an integrated system of sensors for site-specific irrigation management to control soil salinity and related adverse conditions when using degraded waters.
Sub-objective 1.B: Develop databases and machine learning models for rapid estimation of soil-hydraulic and related parameters needed in water quality models and decision support tools.
Sub-objective 1.C: Investigate wastewater reuse and water quality impacts on soil properties and contaminant loading to underlying and downstream water resources.
Sub-objective 1.D: Expand user-friendly, web-based informatics and modeling platform for the diagnosis and management of saline and sodic soils.

Objective 2: Develop comprehensive datasets for agricultural water use, crop productivity, and carbon balance in salt-affected, semi-arid regions for a range of crops using various management practices.
Sub-objective 2.A: Observe water use and crop productivity in contrasting mature citrus varietals to determine potential time periods for applying deficit irrigation for water conservation.
Sub-objective 2.B: Extend artificial intelligence tools for water, nutrient, and salinity management to perennial specialty crops in Southern California.

Objective 3: Determine the G x E x M interactions related to crop salt tolerance and drought resistance.
Sub-objective 3.A: Evaluate the impact of regenerative agricultural practices in wine grapes on productivity, water use, and resilience to abiotic stress. **Approach:**
                        
                        This project uses a combination of field, plot, and modeling studies to develop knowledge and technologies needed to enable optimal use of fresh, degraded, and recycled waters for irrigation. 

Under Objective 1, it is hypothesized that for saline soils a multi-sensor platform consisting of gamma-ray spectrometry and electromagnetic induction (EMI) instrumentation combined with Landsat 7 spectral imagery will improve the spatial delineation of salinity and matric and osmotic stress patterns at field scale. To test the hypothesis, the spatial distribution of salinity and texture using EMI alone, EMI and gamma-ray spectrometry in combination, and EMI and gamma-ray in combination with spectral imagery will be compared to ground-truth measurements. Three field sites in the southwestern U.S. containing a range of soil textures, salinities, and parent materials will be evaluated.

The robustness of the U.S. Salinity Laboratory (USSL) regional-scale salinity assessment model will be enhanced by: (i.) incorporating orchards and vineyards into the model; (ii.) modifying and validating ECa-directed soil sampling protocols for fields under drip irrigation; (iii.) evaluating the reliability and credibility of the USSL regional-scale model through validation with a separate data set; and (iv.) establishing the temporal stability of the USSL regional-scale salinity model.

Databases and machine learning models for rapid estimation of soil-hydraulic and related parameters will be developed. Soil hydraulic properties will be measured in the laboratory using evaporation and dew point methods. A new standardized database of training data for developing and testing pedotransfer functions will be produced. A web-based platform will be developed for disseminating information, tools, and recommendations for evaluating and managing saline irrigation waters.

Plot scale studies will be conducted at the USSL in Riverside, California. A vegetable crop will be grown in rows irrigated periodically with either synthetic or collected tertiary treated wastewater by surface drip lines. Waters will contain a baseline concentration of inorganic and prominent antibiotic contaminants adjusted to a range of salinity levels. A cross section of contaminant distribution and speciation across the wetting zone in relation to soil chemistry and mineralogy will be determined.

Under Objective 2, water use and crop productivity in contrasting mature citrus varietals will be monitored to determine possible time periods for applying deficit irrigation for water conservation. Uncertainties and variances between different monitoring techniques (eddy covariance, surface renewal, and simplified surface renewal) will be evaluated.

Under Objective 3, the Agricultural Input Management tool with Artificial Intelligence (AIM-AI) will be extended. AIM-AI is an artificial intelligence tool for water, nutrient, and salinity management currently being developed for Imperial, Coachella, San Jacinto, Salinas, and San Joaquin Valleys. The current project expands the reach of AIM-AI to specialty perennial crops in Central and Southern California, including citrus, dates, wine grapes, and avocados.


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