Location: Agricultural Water Efficiency and Salinity Research Unit
2023 Annual Report
Objectives
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.
Progress Report
This report documents the progress achieved in fiscal year (FY) 2023 for project 2036-61000-019-000D, titled, “Water Management for Crop Production in Arid and Semi-Arid Regions and the Safe Use of Alternative Water Resources”.
The goal of Sub-objective 1A is to develop and evaluate an integrated system of sensors for site-specific salinity and irrigation management. In FY23, soil salinity surveys of 35 orchards were completed by ARS researchers in Riverside, California, using soil sampling protocols developed specifically for drip-irrigated tree crops. The surveys were done in collaboration with University of California Cooperative Extension. FY23 surveys included nine pistachio fields located south of Los Banos, California, in the Panoche Water District and 11 citrus fields near Visalia, California. In total, more than 500 soils samples were collected and analyzed for salinity and basicity. Data from the FY23 fields are being merged with data from 15 earlier tree crop field surveys to create a database that will be used to evaluate and improve a regional-scale San Joaquin Valley salinity model developed previously using a fusion of proximal and satellite sensor data.
The focus of Sub-objective 1B is the development of databases and predictive models for characterizing the hydraulic properties of soils. In FY23, soil physical measurements (texture, bulk density) were completed on about 500 samples while soil hydraulic measurements have been completed on about 75. A database is being compiled that will be used in the development and testing of machine learning models for predicting soil hydraulic properties from readily available soil characterization data. In support of Sub-objective 1D, significant progress was made coding a new website, including apps for analyzing plant salt tolerance response data and analyzing soil hydraulic properties.
In support of Sub-objective 1C, three halophyte (salt-tolerant) species were identified and cultivated in outdoor field plots in Vietnam with high-salinity recycled aquaculture water. Samples of these crops were sent to Riverside, California, to be analyzed for mineral nutrition, accumulation of salts, and antioxidant capacity. This experiment was done in collaboration with Seawater Solutions (Scotland) and The Plant Resource Center (Vietnam). Deliverables in this project were: (1.) A chemical and mineral report of the water, soil, and plants used in the Vietnamese field trials; (2.) A halophyte cultivation manual; and (3.) A cookbook with recipes on different ways to consume halophyte crops used in the project. Items 2 and 3 were delivered in both Vietnamese and English.
In support of Objective 2, operation of two eddy covariance towers in citrus orchards continued with ARS colleagues from Parlier, California, and collaborators from the University of California. In FY23, work began on comparing eddy covariance and satellite remote sensing estimates of evapotranspiration in the monitored fields. Preliminary flux variance partitioning was implemented to compare soil evaporation and plant transpiration observations to independent approaches (e.g., water vapor isotopes and hydrologic modeling). Also, in support of Objective 2 (and in conjunction with project 2036-61000-019-006R), work continued on enhanced satellite remote sensing of crop water use in specialty crops, optimization of irrigation timing through precise management of allowed soil moisture depletion, and detection of early season irrigation through the use of daily, commercial, multi-spectral imagery.
In support of Sub-objective 3A, research continued on the impacts of regenerative agricultural on wine grape resilience to abiotic stresses. Farmer-directed experimental treatment plans were continued in commercial vineyards and three monitoring stations (meteorology and soil moisture) were maintained. In FY23, a comprehensive set of soil chemical and organic matter properties were characterized for the conventional, regenerative, and initial accelerated regenerative vineyard soils. The results showed that soils maintained under regenerative practices had significantly higher total carbon, nitrogen and active carbon relative to conventionally managed soils. Furthermore, regenerative soils had higher cation exchange capacity and greater exchangeable ammonium relative to conventional soils. Soil sampling at the same sites was conducted to follow the influence of management over several growing seasons.
Accomplishments
1. On-farm approach to quantifying crop salt tolerance. Irrigation efficiency and salinity control are foremost on the minds of producers in the southwest United States, where drought and climate-change are negatively impacting irrigated agriculture. Quantifying the tolerance of different types of crops to soil salinity is an essential step in enabling economic water and salinity management. Standard crop salt tolerance studies conducted in greenhouses and other controlled environments have been criticized for generating data that are not representative of field conditions. An on-farm alternative approach to quantifying plant salt tolerance has been developed by an ARS scientist in Riverside, California, and validated for onion, mustard oilseed, and cottonseed. Compared with standard methods, the on-farm approach requires relatively few resources, and will be a valuable tool for researchers and farm managers seeking location-specific data for optimal water and salinity management.
2. New model improves crop water use estimation in winter vegetable crops. Vegetable crops are a major consumer of irrigation water in the Lower Colorado River Basin, which is under increased water stress. However, these crops have phenological characteristics that make it challenging to accurately assess water use and they are particularly susceptible to over- or under-irrigation. ARS researchers at Riverside, California, and Maricopa, Arizona, have implemented a new satellite-based evapotranspiration model in the Lower Colorado River Basin and tested it in lettuce fields near Yuma, Arizona. The new model estimated seasonal evapotranspiration to within 3% of the measured value. This model provides specialty vegetable growers a potential new tool to improve irrigation scheduling and efficiency.
Review Publications
Dhungel, R., Anderson, R.G., French, A.N., Skaggs, T.H., Saber, M., Sanchez, C.A., Scudiero, E. 2023. Remote sensing-based energy balance for lettuce in an arid environment: Influence of management scenarios on irrigation and evapotranspiration modeling. Irrigation Science. 41(2):197-214. https://doi.org/10.1007/s00271-023-00848-9.
Volk, J.M., Huntington, J.L., Melton, F., Minor, B., Wang, T., Anapalli, S.S., Anderson, R.G., Evett, S.R., French, A.N., Jasoni, R., Bambach, N., Kustas, W.P., Alfieri, J.G., Prueger, J.H., Hipps, L., McKee, L.G., Castro, S.J., Alsina, M.M., McElrone, A.J., Reba, M.L., Runkle, B., Saber, M., Sanchez, C., Tajfar, E., Allen, R., Anderson, M.C. 2023. Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset. Data in Brief. 48. Article 109274. https://doi.org/10.1016/j.dib.2023.109274.
Fu, Y., Zhang, X., Anderson, R.G., Shi, R., Wu, D., Ge, Q. 2022. Spatiotemporal distribution of drought based on the standardized precipitation index and cloud models in the Haihe Plain, China. Water. 14(11). Article 1672. https://doi.org/10.3390/w14111672.
Dhungel, R., Anderson, R.G., French, A.N., Saber, M., Sanchez, C.A., Scudiero, E. 2023. Assessing evapotranspiration in a lettuce crop with a two-source energy balance model. Irrigation Science, 41(2):183-196. https://doi.org/10.1007/s00271-022-00814-x.
Bughici, T., Skaggs, T.H., Corwin, D.L., Scudiero, E. 2022. Ensemble HYDRUS-2D modeling to improve apparent electrical conductivity sensing of soil salinity under drip irrigation. Agricultural Water Management. 272. Article 107813. https://doi.org/10.1016/j.agwat.2022.107813.
Zhang, Y., Xu, Y., Skaggs, T.H., Ferreira, J.F., Chen, X., Sandhu, D. 2023. Plant phase extraction: A method for enhanced discovery of the RNA-binding proteome and its dynamics in plants. The Plant Cell. 35(8):2750-2772. https://doi.org/10.1093/plcell/koad124.
Araújo, A.F., Cavalcante, E.S., Lacerda, C.F., Albuquerque, F.A., Sales, J.R., Lopes, F.B., Ferreira, J.F., Costa, R.N., Lima, S.C., Bezerra, M.A., Gheyi, H.R. 2022. Fiber quality, yield, and profitability of cotton in response to supplemental irrigation with treated wastewater and NPK fertilization. Agronomy. 12(10). Article 2527. https://doi.org/10.3390/agronomy12102527.
