Research Project: Improving Water Management for Arid Irrigated Agroecosystems

Location: Water Management and Conservation Research

2024 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 began in January 2022. In support of Sub-objective 1C, a cotton field experiment was conducted between April and November 2023 to examine the use of the gravity drip irrigation method, a non-pressurized drip system, which in theory could significantly reduce cotton irrigation water use compared with flood irrigation. The experiment included an irrigation treatment for full crop evapotranspiration (ET) replacement and 2 treatments with deficit irrigation that replaced 80 and 60% of ET for gravity drip and included 100 and 80% ET treatments for the flood method. In FY24, study results were analyzed and developed into a conference proceedings paper that was delivered in July 2024. Results indicated cotton growth rate for full irrigation treatments were higher for gravity drip than furrow, though this resulted in about 15% more irrigation applied than for furrow. However, the final lint yield was about 11% greater for gravity drip under full irrigation. Somewhat higher water use efficiencies (yield/water applied) were obtained for the 80% deficit treatments in both methods. While the gravity drip method in this experiment for cotton showed potential to increase yield, more evaluation studies are needed to refine this method to obtain higher water use efficiency. In support of Sub-objective 3B, limited progress was made toward adding the proposed sensitivity analysis component to WinSRFR. Progress was hampered by a retirement and the resulting lack of programming support. Development of the user-interface component needed to specify inputs was nearly completed. However, problems were identified with code modifications made the previous year, which were intended to support the sensitivity analysis computations, which have not been completely resolved. Development of the framework for capturing, processing, and displaying the outputs was not started. Because the programmer retired due to health problems, documentation of the current development is lacking. Thus, substantial effort has been dedicated to archiving and documenting the current code, which is essential for future development.

Accomplishments
1. Analysis of lettuce crop water use and updating of crop coefficients. Lettuce is a key crop for Yuma, Arizona, that contributes billions of dollars to the agricultural economy. Proper water management is essential for sustainability, quality of crops, minimization of water use, and optimization of soil salt content. However, proper management has become difficult because knowledge of water requirements is inaccurate and out-of-date. To provide accurate water management data, ARS researchers in Maricopa, Arizona, conducted studies to measure lettuce water use and to develop new crop coefficients. The studies, conducted between 2016 and 2020, used eddy covariance and remote sensing technologies to quantify current water requirements and to update predictive tools for long-term water management. The tools integrate crop coefficients, heat units, and soil salinity data that improve sustainability of the Yuma lettuce industry. Producers, water districts, and government agencies responsible for water resources along the Lower Colorado River will find these results to have high practical value.

2. Combining sensors and models for improved irrigation scheduling. Irrigation scheduling models and soil water sensors have been separately developed as technologies to assist irrigation management decisions. However, the two technologies should ideally be integrated, because they are complementary and can work together to provide improved recommendations. An ARS researcher in Maricopa, Arizona, compared cotton yield and water use outcomes when managed by three irrigation scheduling models and when those models were assisted by field measurements of soil water status. The results showed that adding soil water measurements could reduce irrigation requirements by 9-21% while often maintaining cotton fiber yield. Producers and commercial industries will benefit from this research, including supporting agricultural irrigation, U.S. cotton production, and the development of soil water sensing equipment.

3. Development of guidelines for maximizing the productivity of ratooned guayule. Guayule is a rubber- and resin-producing shrub that grows in the arid conditions of the Southwestern United States. The crop can be harvested after a two-year growth cycle, after which it can be regrown from the stub (ratooned). Water management practices for guayule so far focused on the initial two-year growth cycle. ARS researchers in Maricopa, Arizona, and researchers at the University of Arizona, Tucson, Arizona, conducted studies to characterize the response of ratooned guayule to water management practices. The study compared different levels of irrigation deficit with two water application methods, subsurface drip and furrow. Results show that dry biomass production increases with water application, but that rubber and resin productivity are maximized when imposing some level of water deficit with subsurface drip irrigation. This practice will help offset the cost of initial crop planting, which is relatively high, and thus making the crop more economically viable as an alternative in arid agricultural production systems. Producers and companies involved in guayule production and processing should find this research of considerable value.

4. Calibration of a model for simulating the soil water balance and water use with irrigated guayule. Irrigation scheduling models based on the soil water balance concept have been under development for years. However, their use has been hampered by an incomplete understanding of the relationship between the water use of a reference crop and that of a specific crop under particular microclimatic conditions. This relationship has been studied mostly for traditional crops. Guayule is a rubber-producing plant that is being promoted as an alternative crop in cropping systems of the desert Southwest U.S. Unlike traditional crops, it is grown in a two-year cycle and its water use is a function of plant and root growth that is not well known. ARS researchers in Maricopa, Arizona, collaborated with researchers at the University of Arizona, Tucson, Arizona, on studies that aimed to parameterize a model for predicting the soil water balance and managing irrigation with fully irrigated guayule. The study characterized the plant and root development during the two-year growing cycle and the corresponding crop coefficients that relate guayule to reference evapotranspiration. The study showed that the calibrated model can track water soil water content in various soil layers with reasonably accuracy. This information should be of interest to producers and to companies involved in guayule production and processing.

Review Publications
Bautista, E., Lazarovitch, N. 2024. Accounting for interference effects in furrow infiltration with moment analysis. Journal of Irrigation and Drainage Engineering. 150(4). Article 04024012. https://doi.org/10.1061/JIDEDH.IRENG-10043.
Katterman, M., Waller, P., Eldin Elshikha, D., Wall, G.W., Hunsaker, D.J., Loeffler, R.S., Ogden, K. 2023. WINDS model simulation of guayule irrigation . Water. 15(19). Article 3500. https://doi.org/10.3390/w15193500.
Costanzo, C., Costabile, P., Gangi, F., Argiro, G., Bautista, E., Gandolfi, C., Masseroni, D. 2024. Promoting precision surface irrigation through hydrodynamic modelling and microtopographic survey. Agricultural Water Management. 301. Article 108950. https://doi.org/10.1016/j.agwat.2024.108950.
Yost, J.L., Smith, D.R., Adhikari, K., Arnold, J.G., Collins, H.P., Flynn, K.C., Hajda, C.B., Menefee, D.S., Mohanty, B.P., Schantz, M.C., Thorp, K.R., White, M.J. 2024. The LTAR cropland common experiment at the Texas Gulf. Journal of Environmental Quality. https://doi.org/10.1002/jeq2.20592.
Schantz, M.C., Smith, D.R., Harmel, R.D., Goodwin, D.J., Tolleson, D.R., Osorio Leyton, J.M., Flynn, K.C., Krecker-Yost, J.L., Thorp, K.R., Arnold, J.G., White, M.J., Adhikari, K., Hajda, C.B. 2024. The LTAR-integrated grazing land common experiment at the Texas Gulf. Journal of Environmental Quality. https://doi.org/10.1002/jeq2.20573.
Thorp, K.R. 2023. Combining soil water content data with computer simulation models for improved irrigation scheduling. Journal of the ASABE. 66(5):1265-1279. https://doi.org/10.13031/ja.15591.
French, A.N., Sanchez, C.A., Hunsaker, D.J., Anderson, R.G., Saber, M.N., Czyzowska-Wisniew, E.H. 2024. Lettuce evapotranspiration and crop coefficients using eddy covariance and remote sensing observations. Irrigation Science. https://doi.org/10.1007/s00271-024-00921-x.
Elshikha, D.M., Waller, P.M., Hunsaker, D.J., Thorp, K.R., Wang, G., Dierig, D., Cruz, V.M., Attalah, S., Bautista, E., Katterman, M.E., Williams, C.F., Ray, D.T., Norton, R., Orr, E., Wall, G.W., Ogden, K. 2023. Water use, growth, and yield of ratooned guayule under sub-surface drip and furrow irrigation in the US Southwest Desert. Water. 15(19). Article 3412. https://doi.org/10.3390/w15193412.