1a. Objectives (from AD-416):
The long-term objectives of this project are to develop decision support tools and sensing and computing technologies to support improved crop water use efficiency for irrigated agriculture in arid lands. Objective 1: Develop and integrate models, tools, and strategies to optimize water and nutrient use efficiencies under sufficient, reduced, and variable-rate irrigation strategies in arid environments. Subobjective 1A: Quantify cotton physiological development, fiber yield, fiber quality, and water use responses to variable irrigation rate and timing. Subobjective 1B: Develop end-user irrigation scheduling models for cotton and other crops. Subobjective 1C: Develop nitrogen fertilizer scheduling strategies and tools for cotton. Objective 2: Use remote and proximal sensing at regional and field scales for crop and water management and use proximal sensing for high throughput phenotyping for heat and drought tolerant cultivars. Subobjective 2A: Develop and evaluate airborne and satellite-based remote sensing methods to estimate crop evapotranspiration, ETc, at irrigation district scale. Subobjective 2B: Develop and evaluate airborne and drone-based remote sensing methods to estimate crop evapotranspiration, ETc, at field scale. Subobjective 2C: Develop and evaluate ground-based proximal sensing methods that identify crop heat and drought stress at field scale. Objective 3: Develop and evaluate crop simulation models as tools to synthesize “big” data from agricultural field studies and analyze alternative strategies for crop and water management. Subobjective 3A: Evaluate and improve Cotton2K and DSSAT-CSM models for simulation of cotton physiology, water use, and nutrient use in response to water and nutrient deficit. Subobjective 3B: Develop crop simulation modeling methodologies to analyze potential water savings and production impacts of variable-rate and deficit irrigation practices. Subobjective 3C: Develop methodologies to guide crop simulation and irrigation scheduling models using “big data” from remote and proximal sensing and crop and soil mapping equipment. Objective 4: Develop concepts, technologies, and software tools for the hydraulic analysis of surface irrigation systems. Subobjective 4A: Develop software for the hydraulic analysis of irrigation systems. Subobjective 4B: Model irrigation-induced soil erosion. Subobjective 4C: Develop field technologies for improved surface irrigation management.
1b. Approach (from AD-416):
Objective 1 Goal 1A: Conduct cotton field experiments using a new VRI system on a lateral move overhead sprinkler to deliver precise irrigation treatments to cotton. Goal 1B: Develop improved irrigation scheduling models and software that account for spatial water application and crop water use in irrigation management and provide guayule growers with new irrigation scheduling tools. Goal 1C: Develop improved N management scheduling models and software that will help optimize the N fertilizer application rate guidelines for cotton under lateral move overhead sprinkler and subsurface drip irrigation. Objective 2 Goal 2A: Reduce ETc estimation uncertainty and bias at irrigation district scales by integrating sensing technologies with weather-based approaches. Goal 2B: Develop a field-scale decision support system for mapping ETc using drone platforms. Goal 2C: Demonstrate that field-based high-throughput plant phenotyping with proximal sensors could be an effective approach for crop breeding. Objective 3 Goal 3A: Conduct evaluations of the Cotton2K and DSSAT-CSM cotton simulation models and identify options for model improvement. Goal 3B: Conduct simulation analyses to assess effects of variable-rate irrigation management practices on crop production and water use. Goal 3C: Develop mathematical approaches for integrating remote and proximal sensing data with irrigation scheduling models and crop simulation models. Objective 4 Goal 4A: Enhance the functionality of the WinSRFR software package by improving the design procedures to account for flow-depth dependent infiltration, developing procedures for furrow systems with return flow, and developing procedures for predicting the transverse distribution of infiltrated water in a furrow cross section based on soil textural properties. Goal 4B: Development and testing of a process-based model of sediment transport coupled to surface irrigation flow. Goal 4C: Develop a process for evaluating the field-level seasonal performance of an irrigation system and developing field technologies for acquiring irrigation evaluation data reliably and inexpensively.
3. Progress Report:
Progress was made on all four objectives and their sub-objectives, all of which fall under National Program 211, Water Availability and Watershed Management. Sub-objective 1A: The second season of a cotton irrigation experiment was completed in 2017, irrigated by a linear move irrigation machine. These experiments were conducted to evaluate cotton yield responses to variable irrigation rates and timings during the growing season. Sixteen experimental treatments were established based on four irrigation rates, 60, 80, 100, and 120 percent of recommended amounts from an irrigation scheduler, and two timings, from squaring to peak bloom and from peak bloom to 90 percent open boll. Soil moisture in each plot was measured weekly. On a biweekly basis, plants in each treatment were sampled to assess crop development and measure weights of plant parts. Cotton yield and fiber quality were measured for each irrigation treatment. The experiments provide important verification of the performance of irrigation scheduling models and data for model improvement. Sub-objective 1B: Improvements were made to the Excel-based soil water balance model that determines irrigation scheduling and irrigation rates for spatially-variable crop water requirements. Work was completed on converting the Excel soil water balance algorithms for cotton into a user-friendly irrigation decision support tool, programmed in Python script. A web-based interface is being developed to make the irrigation tool available to end-users over the internet and will be hosted by a University of Arizona collaborator in Tucson, Arizona. Progress was made in advancement of the guayule irrigation scheduling tool. This includes incorporating newly developed crop coefficients for guayule grown under subsurface irrigation into the irrigation tool. In April 2018, new guayule irrigation experiments were initiated at the University of Arizona, Maricopa Agriculture Center (MAC), in Maricopa, Arizona, and at Bridgestone, Inc., in Eloy, Arizona. The guayule irrigation scheduling tool is currently being tested at these sites under both subsurface drip and flood irrigation. In addition, initial progress is being made for developing drone and proximal remote sensing data over guayule to provide supplemental information for guayule irrigation management. Sub-objective 1C: Two of three years of Nitrogen (N) by water subsurface drip irrigation studies for cotton are completed and yielding valuable new information on management guidelines. Recovery efficiency of added liquid N fertilizer is very high, more than 90 percent. Nitrous oxide emissions are extremely low in this system. Proximal sensing-based N management is showing N fertilizer savings without a reduction in lint yields. Objective 2: Data from the recently launched microsatellite ‘Venus’ are being collected approximately every 2 days at 5 meters (m) resolution. Crop water use estimates, derived from vegetation indices from Venus, Landsat 8, and Sentinel 2 satellites, are being computed and compared with field-level water delivery data at the Ak-Chin Indian Reservation, Maricopa, Arizona. The drone acquisition program continued at Maricopa in 2018 with frequent multispectral flights over 4 different experimental fields at the MAC in Maricopa, Arizona. Tractor, drone, cart-based light detection and ranging (LIDAR) data acquisitions to observe evolution of plant heights and biomass of cotton phenotyping trials were conducted in the summer and fall of 2018. Objective 3: Extensive simulations with the Cotton2K model have been conducted on the ARS SciNet cluster computer to iteratively test different parameter sets for soil water holding characteristics and variety parameters. Model outcomes were also compared to measured plant growth, soil moisture, and lysimeter evapotranspiration (ET) data for three irrigation treatments over three growing seasons at Bushland, Texas. Results will be used for global sensitivity analysis and model calibration. Three ET algorithms were tested during the simulation study: 1) the original ET approach based on daily weather data, 2) a simple update to incorporate hourly weather data into the original ET approach, and 3) a full incorporation of the American Society of Civil Engineers (ASCE) standardized reference ET method with dual crop coefficient based on hourly data. Intercomparison of the ET method demonstrated that none of the three could outperform both other two methods when considering 22 different agroecosystem measurements. Sub-objective 4A: Development of the WinSRFR 5.1 software package, a hydraulic analysis tool for surface irrigation systems, has continued and will be completed by the end of August 2018. A few technical problems have been identified and corrected over the last year. The manual required extensive revisions and is being upgraded. Release to the public is expected by the end of September. The current version of the software has been downloaded over 200 times since the beginning of the year. Work continued on the development of moment analysis procedures for describing the distribution of infiltrated water in furrow systems. Problems were identified and corrected in the WinSRFR code that was used to conduct initial computations. Sub-objective 4B: Per a request from the U.S. Bureau of Reclamation (USBR), a project was started in the summer of 2017 to reprogram the WinFlume program. The program is used by the USBR and U.S. irrigation districts to design long-throated flumes for flow measurement. It was originally developed by ARS in collaboration with the Institute of Land Reclamation and Improvement (ILRI), Wageningen, The Netherlands, and later redeveloped as a Windows application by the USBR. Six beta versions have been provided to USBR for review. The work is expected to be completed by the end of September 2018. Sub-objective 4C: Development of sensors that can be used for field evaluation of surface irrigation systems is continuing. Two sensor systems are under development, one an advance/recession timer, like the unit developed in the past by an ARS researcher at Maricopa, Arizona, but based on modern electronic components. This will make the proposed devices easier to maintain. The second is a depth sensor equipped with self-contained data-logging and radio-transmission capabilities. Two units of the first type and four of the second type have been built and are currently being tested in the field.
1. Comparison of evapotranspiration methods in Cotton2K. Improved irrigation management is needed to conserve limited water resources in the western U.S. One option is to enhance agroecosystem models, which calculate daily plant growth and crop water use based on local soil and weather conditions. Accurate simulation of evapotranspiration (ET) in these models is necessary to realize improved irrigation recommendations. Using data from three cotton growing seasons in Bushland, Texas, ARS researchers at Maricopa, Arizona, Fort Collins, Colorado, and Bushland, Texas, developed a computational approach to intercompare three evapotranspiration methods in the Cotton2K agroecosystem model. Three ET methods led to differences in simulation accuracy for ET, soil water content, and several plant growth metrics. However, no ET method could consistently outperform both of the other two ET methods when collectively considering 22 measured metrics of the agroecosystem. Improvements to the Cotton2K simulation methodologies for soil water flux near the soil surface and for linking water use with crop growth are the first steps to make the model better simulate cotton production in the western U.S.
2. Irrigation scheduling for guayule. In recent years, U.S. tire companies have invested significantly in development and commercialization of guayule in the arid U.S. Southwest to supplement natural rubber supplies from Asia. Some of the critical issues in making guayule both profitable and sustainable in the area are improving the irrigation management and water use efficiency for this native crop. ARS researchers in Maricopa, Arizona, developed irrigation scheduling guidelines for achieving economic guayule rubber yields with reduced water use. This guayule irrigation research modernizes limited information that was provided over 30 years ago and will significantly help improve the domestic guayule production efforts of the U.S. tire industry and other rubber industry partners.
3. Nitrogen (N) management practices for surface irrigation (SI) and overhead sprinkler-irrigated (OSI). Nitrogen management recommendations for cotton in central Arizona have not been updated for more than 20 years, and there are no specific guidelines for OSI. Nitrogen management practices for SI and OSI were evaluated and improved in a four-year study by ARS scientists in Maricopa, Arizona. The guidelines developed include the use of soil testing and canopy reflectance measurements to guide in-season N management. When implemented by growers, substantial savings of N fertilizer can be achieved without hurting yields.
4. Nitrous oxide emissions from irrigated cotton. Nitrogen (N) fertilizer applied to row crops like cotton is an important source of the potent greenhouse gas nitrous oxide (N2O). ARS researchers in Maricopa, Arizona, completed a comprehensive six-year field study assessing N2O emissions from various N fertilizer management approaches for the three main irrigation systems used in North America, surface irrigation (SI), overhead sprinkler irrigation (OSI), and subsurface drip-irrigation (SDI). N2O emission factors (% applied N Fertilizer emitted as N2O) ranged from a negligible 0.1 % with SDI, 0.5% with SI and 1.1 % in OSI. The dramatically low N2O emission factors in SDI are encouraging as this irrigation system also has negligible leaching of irrigation and nitrates. Subsurface drip irrigation is a climate-friendly system with minimal N export to the environment.
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