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ARS Home Pacific West Area Maricopa, Arizona U.S. Arid Land Agricultural Research Center Water Management and Conservation Research Research





- Bautista, Eduardo
- Hunsaker, Douglas - Doug
- 2023
- 2022
- Publications
- Water Availability and Watershed Management (211)
- Sustainable Agricultural Systems Research (216)
- Plant Genetic Resources, Genomics and Genetic Improvement (301)

Research Project: Improving Water Management for Arid Irrigated Agroecosystems **Location:
                            Water Management and Conservation Research** 2022 Annual Report **Objectives**
                                

In Arizona and the Southwest U.S., irrigation is essential for field crop production. However, long-term drought and increasing urban development have decreased water availability. Historically, surface irrigation has been the main irrigation practice.  However, with current water limitations, development of modern irrigation systems, sensing technologies, irrigation management tools, and crop water use estimates are now critical. The overall goals of this project are to improve knowledge of water, nutrient, and crop growth processes in arid agroecosystems and to develop sensing, computing, and decision support technologies that improve water and nutrient use efficiency for crop production. Objective 1 details the main field component with field experiments, primarily in cotton and guayule, that test different models for irrigation scheduling with feedback from soil water content sensors and imagery from small unmanned aircraft systems (sUAS). Objective 2 focuses on use of satellite remote sensing to monitor and forecast regional crop evapotranspiration (ET). Objective 3 continues the development of tools for improved management of surface irrigation with a special focus on modeling field-scale infiltration processes. The over-arching philosophy of the research is to develop knowledge and tools based on the integration of proximal and remote observations with physical process-based and artificial intelligence-based models. Objective 1: Integrate sensor data and simulation models to improve irrigation and fertilization decision support for irrigated cropping systems. Sub-objective 1A: Develop remote, proximal, and in-situ sensing technologies for estimating crop, water, and nutrient status of irrigated agroecosystems. Sub-objective 1B: Develop and evaluate simulation models, machine learning algorithms, and data integration strategies that better inform crop management decisions. Sub-objective 1C: Develop and field-test decision support tools that integrate data and models for improving in-season crop management. Sub-objective 1D: Develop irrigation guidelines, tools, and models for direct-seeded guayule. Objective 2: Create and evaluate suites of satellite-based hydrology models that enable accurate monitoring and forecasting of evapotranspiration and other soil water balance components over irrigated agriculture, leading to improved irrigation scheduling. Sub-objective 2A: Develop and test crop coefficient models driven by remote sensing data. Sub-objective 2B: Develop and test algorithms that use remote sensing to track water budgets across multiple cropping seasons. Objective 3: Design, test and/or improve sensors and technologies for optimizing surface irrigation systems. Sub-objective 3A. Evaluate and improve infiltration modeling approaches for irrigation design and management, tied to the Natural Resources Conservation Service (NRCS) soils database. Sub-objective 3B. Develop design and management strategies that account for the spatial and temporal variability of conditions, including infiltration, hydraulic resistance, and flow rate. **Approach**
                                

Objective 1 
Goal 1A: Develop novel sensing approaches and data pipelines for timely collection and processing of in-season agroecosystem data that can be immediately used for crop and soil management. Tools to prepare sensor data for immediate integration with irrigation scheduling algorithms are necessary because the data will be incorporated with decision models (Sub-objective 1.B) and used to inform irrigation management decisions for field studies (Sub-objective 1.C). Objective 1 Goal 1B: Develop simulation models or machine learning algorithms as tools to synthesize in-season field data and provide reliable recommendations for real-time or near-term crop management. Data collected during previous field studies will be used to evaluate model responses to experimental conditions. Objective 1 Goal 1C: Field-test decision support tools and methods for irrigation management with focus on identifying approaches that improve crop yield and water use efficiency. Irrigation management experiments will be continuously conducted for summer cotton crops and winter cover crops or small grains for the duration of the project. Objective 1 Goal 1D: Determine irrigation scheduling and timed water stress strategies for optimum rubber yields and water use efficiencies. Develop crop coefficient models and determine remote sensing indices for real-time Kcb and plant growth estimation. Develop a customized soil water balance (SWB) irrigation model that provides decision support for growers in the region. Objective 2 Goal 2A: Develop crop coefficients for all economically significant crops grown in Central Arizona.  Priority crops will be cotton, alfalfa, potato, sorghum, barley, and corn. Years of evaluation are 2016 to current. 

Objective 2 
Goal 2B: Develop and test remote sensing-based surface energy balance algorithms and incorporate them into an app tool for irrigation decision support. While research under Sub-objective 2.A focuses on answering questions about irrigation management for specific crops grown in Arizona, Sub-objective 2.B addresses research to improve management skill across entire districts and spanning multiple years. Objective 3 Goal 3A: Provide WinSRFR with additional infiltration modeling capabilities, namely an alternative to the NRCS furrow infiltration families, and Green-Ampt based soil models for two soil layers, for one- and two-dimensional infiltration. Two groups of activities will be undertaken as part of this subobjective: 1) new infiltration modeling options will be developed and added to the software and 2) studies will be conducted to further validate procedures for the estimation of GA and WGA infiltration parameters from irrigation evaluation data. Objective 3 Goal 3B: Provide WinSRFR users with additional capabilities for examining the uncertainty of model outputs as a function of uncertainty of variable inputs. The first part of the proposed work involves adding new options for examining the sensitivity of outputs visually. The second part will use data analysis tools to conduct uncertainty and sensitivity studies to develop quantitative measures for various synthetic scenarios. **Progress Report**
                                

This report documents progress for project 2020-13660-009-000D, Improving Water Management for Arid Irrigated Agroecosystems, which started in January 2022 and continues research from project 2020-13660-008-000D, Advancing Water Management and Conservation in Irrigated Arid Lands.

In support of Sub-objective 1A, research continued on the development of methods to incorporate in-situ soil water content data with irrigation scheduling models and agroecosystem models for improving the irrigation management recommendations from these models. The techniques are currently being field-tested in cotton irrigation management trials conducted in the summer of 2022 at Maricopa, Arizona. Results from the previous season demonstrated that use of soil water content feedbacks could reduce applied irrigation water by 100 mm (approximately 10%) compared to stand-alone model recommendations. There were no differences in cotton yield among treatments, thereby improving water productivity; however, these results must be verified for additional growing seasons.

In support of Sub-objective 1B, an ARS scientist at Maricopa, Arizona, continues participation with the international agroecosystems modeling community to conduct intercomparisons of evapotranspiration (ET) simulations for maize, soybean, and wheat. Specifically, the ARS scientist manages all simulations from the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) and delivers simulation results to the project coordinators for intercomparison with other agroecosystem models. The studies typically involve collaboration among 20 or more modeling groups across the globe and represent a state-of-the-art evaluation of ability to accurately simulate evapotranspiration with agroecosystem models.

In support of Sub-objective 1C, cotton field experiments have been initiated to compare agronomic outcomes (yield and water use) by using three modern irrigation scheduling models to make irrigation decisions and by using weekly soil water content measurements to update the recommendations from each model. Multiple years of the field experiment are planned for intercomparison of the three irrigation scheduling tools and to quantify amounts of water saved by using soil water content feedback to adjust model recommendations.

In support of Sub-objective 1D, guayule regrowth studies in Maricopa were completed with final harvests in early spring, 2022. Field data from the studies are presently being evaluated. Guayule deficit irrigation studies in Eloy were completed with final harvest also in early spring 2022. Although the data analyses for the Eloy studies are not yet finalized, preliminary results suggest that reducing irrigation by one-half during the second year of growth does not reduce rubber yields compared to a fully-irrigated treatment.

In support of Sub-objective 2A, significant progress was made developing satellite-based crop coefficient models for lettuce, wheat, broccoli, cotton, and alfalfa. The coefficient models were validated against ground observations obtained by ARS and University of Arizona scientists at Yuma, Arizona.

In support of Sub-objective 2B, ARS scientists, in collaboration with University of Arizona researchers, are developing a smart phone app that will track water and soil salinity over multiple cropping seasons. A pre-release version is being evaluated and tested by Arizona farmers and irrigators.

In support of Sub-objective 3A, progress was made toward the development of modified Natural Resources Conservation Service (NRCS) infiltration families for furrows. The proposed modification is conceptually related to an approximate solution to the two-dimensional Richards equation, and it affects the calculation of the lateral flow. In the original method, in use since 1974, lateral flow is represented empirically, as a non-linear function of time. In the modified approach, lateral flow is linearly dependent on time. It is also a function of soil sorptivity, and therefore specific to a soil texture. Initial tests indicate that differences between infiltration predictions with the original and modified families depend on soil texture and flow conditions.  

In support of Sub-objective 3B, and as part of the continuing development of WinSRFR, a software package for surface irrigation modeling, programming was completed of procedures for running the simulator in multiple threads. This allows the program to run multiple simulations simultaneously. This modification has already been used to modify the Operational Analysis component of the software. Previously, this component developed operational solutions using approximate volume balance equations. Therefore, it could only be used in combination with empirical infiltration equations. The modified Operation Analysis module uses multi-threading to run multiple unsteady flow simulations. Thus, it can be used now to develop solutions with semi-physical infiltration equations (Green-Ampt and Warrick-Green-Ampt). Work has begun to use the multi-threading capabilities to add a sensitivity analysis component to WinSRFR. These new features will be made available to the public in the next release of WinSRFR. **Accomplishments** **Review Publications**
                            *Thompson, A.L., Conley, M.M., Herritt, M.T., Thorp, K.R. 2022. Response of upland cotton (Gossypium hirsutum L.) leaf chlorophyll content to high heat and low-soil water in the Arizona low desert. Photosynthetica. 60(2):280-292.*
                                *Herritt, M.T., Thompson, A.L., Thorp, K.R. 2022. Irrigation management impacts on cotton reproductive development and boll distribution. Crop Science. 62(4):1559-1572. https://doi.org/10.1002/csc2.20749.*


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