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.
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.
This is the first progress report for project 2036-61000-019-000D, which replaced project 2036-61000-018-000D, titled “Sustaining Irrigated Agriculture in an Era of Increasing Water Scarcity and Reduced Water Quality”. Progress has been made on all three objectives, which fall under NP211. In support of Sub-objective 1.A and in continuance of Sub-objective 4.A of project 2036-61000-018-000D, a new field-based approach to quantifying plant salt tolerance approach was evaluated using “mined” data from previous research studies dating back to as early as 1999. The data mining approach was adopted in part because of recent delays starting new field work. Traditional controlled salt tolerance studies require extensive resources and time to establish, particularly for perennial crops. An alternative field-based approach was evaluated for three crops: mustard oilseed (2014 data set), cotton (1999 data set), and sorghum (2007 and 2008 data sets). The alternative approach used soil electrical conductivity sensor-directed soil sampling and boundary line analysis to determine plant salt tolerance parameters (i.e., salinity threshold and percent yield decrement slope). Considerable effort was taken using geostatistical analysis and quality control protocols to develop reliable geospatial data sets to evaluate the alternative salt tolerance approach. Geospatial analysis has been completed for cotton and site-specific salt tolerance parameters have been determined for the west side of the San Joaquin Valley using boundary line analysis. Comparison to traditional salt tolerance parameters for cotton show a higher level of salt tolerance in the San Joaquin Valley when using the alternative approach. Furthermore, the site-specific salt tolerance parameters from the alternative approach requiring fewer resources to determine. In support of Sub-objective 1.B, new laboratory instruments were procured, and measurement protocols established for characterizing hydraulic properties and other physical parameters of soils. Analyses of an initial batch of new soil samples is underway, as is the design of the planned database product. Under Sub-objective 1.C, planning is underway for a plot-scale wastewater reuse irrigation study. In support of Sub-objective 1.D, work began on an updated ESAP code for creating efficient geospatial sampling designs. Also, we very recently had some preliminary discussions with USDA, Natural Resource Conservation Service (NRCS) about whether some of the modeling work planned under Sub-objective 1D might also benefit their projects. These discussions build on the fruitful collaboration reported in the final report for project 2036-61000-018-000D concerning the development and deployment of an updated Rosetta pedotransfer function model and related software. In support of Objective 2, operation of two eddy covariance towers continued in citrus orchards in collaboration with researchers from Parlier, California. Tower instrumentation was altered following severe pruning of one orchard site. Preliminary flux variance partitioning was implemented at these sites to compare soil evaporation and plant transpiration observations to independent approaches, such as water vapor isotopes and hydrologic modeling, implemented by collaborating university researchers from Riverside, California. In support of Sub-objective 2.B, arable monitoring stations were installed in multiple agricultural fields in Southern California. Work by a research associate in Riverside, California, on an externally funded project (2036-61000-019-006-R) was evaluated for potential transferability to domains covered by this project plan. In support of Sub-objective 3.A, researchers in Riverside, California, began collaborative research on the impacts of regenerative agricultural practices on wine grape resilience to abiotic stresses. Farmer-directed management treatment plans (i.e., conventional, regenerative, and accelerated regenerative approaches) were established in sections of three vineyards. Initial soil and microbial samples were collected, and three monitoring stations (meteorology and soil moisture) were installed. Soil samples are currently being processed in preparation for further analyses related to soil chemical, physical and biological health factors impacting soil resilience. This includes quantification of total soil carbon, nitrogen, sulfur, active carbon fractions, cation exchange capacity, plant available nutrients, soil potential of hydrogen (pH) and microbial activity. Subsequent data analyses will then be performed to determine the influence of management thus far on overall soil health in these vineyards. Future sampling will be done near the end of fiscal year (FY) 2022 or the beginning of FY 2023 to follow the influence of management throughout several growing seasons. The potential impact of this work will be developing a better understanding of management practices that accumulate organic matter in soils, which increases the resilience of arid and semi-arid vineyard soils through greater soil nutrient retention, water retention, hydraulic conductivity, and resistance to sodicity.
Acharya, B.R., Sandhu, D., Dueñas, C., Dueñas, M., Pudussery, M.V., Kaundal, A., Ferreira, J.F., Suarez, D.L., Skaggs, T.H. 2022. Morphological, physiological, biochemical, and transcriptome studies reveal the importance of transporters and stress signaling pathways during salinity stress in Prunus. Scientific Reports. 12. Article 1274. https://doi.org/10.1038/s41598-022-05202-1.