Research Project: Improving Water Management for Arid Irrigated Agroecosystems

Location: Water Management and Conservation Research

Project Number: 2020-13660-009-000-D
Project Type: In-House Appropriated

Start Date: Jan 16, 2022
End Date: Jan 15, 2027

Objective:
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.