Research Project: Advancing Water Management and Conservation in Irrigated Arid Lands

Location: Water Management and Conservation Research

2022 Annual Report

Objectives
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.

Approach
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.

Progress Report
This is the final report for project 2020-13660-008-000D, Advancing Water Management and Conservation in Irrigated Arid Lands, which has been replaced by new project 2020-13660-009-000D, Improving Water Management for Arid Irrigated Agroecosystems. For additional information, please review the annual report for the new project. In support of Sub-objective 1A, three seasons of a cotton irrigation experiment in Maricopa, Arizona, were completed in 2016, 2017, and 2018. 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% of recommended amounts from an irrigation scheduler) and two timings (from squaring to peak bloom and from peak bloom to 90% open boll). Cotton yield and fiber quality were measured for each irrigation treatment. The experiments provided important verification of the performance of irrigation scheduling models and provided data for model improvement. Two additional seasons of a separate cotton experiment were completed in 2019 and 2020, also using the overhead sprinkler irrigation machine. This study tested several methods for computing irrigation requirements, evaluating crop water stress, and applying irrigation. The results demonstrated small but significant agronomic improvements by irrigation on a site-specific basis and identified advantages for thermal imaging from unoccupied aircraft systems to identify crop water stress. In support of Sub-objective 1B, crop coefficients (Kc) were developed for cotton grown under subsurface drip irrigation using data from experiments conducted at Maricopa, Arizona, in 2016, 2017, and 2018. The study also developed calibration procedures to adjust cotton evapotranspiration (ET) for deficit irrigation management. Studies of the perennial natural rubber crop, guayule, were conducted in Maricopa and Eloy, Arizona, to develop irrigation scheduling methods for re-growing guayule following initial two-year harvests that were made in 2020. In addition, a separate guayule study in Eloy was conducted to determine imposed water stress strategies for increasing rubber content with reduced irrigation. Other progress included coding of our soil water balance models for cotton and wheat, which are being developed into irrigation decision-support phone apps for Arizona growers and testing a spatially-based wheat ET model in seven commercial wheat fields in Arizona using vegetation index time-series field maps obtained by high-resolution remote sensing data from two new satellites. Estimated ET using remote sensing was found to agree well in most cases with the daily ground-based observations of actual ET in each field. In support of Sub-objective 1C, three years of nitrogen by water subsurface drip irrigation studies for cotton were completed in 2016, 2017, and 2018 at Maricopa, Arizona, which resulted valuable new information on management guidelines. Recovery efficiency of added liquid nitrogen (N) fertilizer was as high as 91%. Nitrous oxide emissions were extremely low in this system. Proximal sensing-based N management showed N fertilizer savings without a reduction in fiber yield, but in 2018 there was a reduction in seed yield. An application was written in Visual Basic which interprets in-season cotton petiole nitrate data by growth stage and/or growing degree days. The application returns one of three classes: “deficient”, “optimum”, or “excess.” If petiole nitrate points are deficient, then a nitrogen fertilizer rate is recommended. In support of Sub-objectives 2A and 2B, a methodology was developed that combines time-series vegetation indices derived from five-day observations with Sentinel 2 images to create daily evapotranspiration estimates. The estimates were validated for wheat, lettuce, cotton, spinach, and melons using ground-based eddy covariance observations. A second method using satellite data and crop classification maps provide by the U.S. Bureau of Reclamation for the Yuma-Wellton irrigation districts has created a way to revise and improve FAO-56 crop duration values for vegetables, small grains, and alfalfa. Using drone data, we created functions to transform vegetation indices to fractional cover for vegetables. In support of Sub-objective 2C, under collaboration with faculty at Arkansas State, we developed a calibration routine for thermal sensors used on drones for phenomics and evapotranspiration. Also, progress was made in evaluating the benefits of three different plant height measurements at fine spatial scales: Fraunhofer laser scanning, drone-based LIDAR, and conventional red-green-blue drone mapping of canopy heights. The techniques were tested over a wheat crop for normal and water-stressed conditions under the TERRA-Ref gantry system. For Sub-objective 3A, six ET methodologies were evaluated and compared within a popular crop growth simulation model using agronomic and ET data from cotton field studies at Bushland, Texas. Three options were tested for estimating potential ET demand, and two methods were tested for calculation of soil water evaporation. An intensive computational strategy was developed to select among model parameterization options and ensure that modeler bias did not influence ET method comparisons. Among 23 measurements that included ET, various cotton growth variables, and soil water content in multiple soil layers, one soil water evaporation method performed statistically better than the other one. Notably, the default ET method in the model was the worst performing ET method. When the better performing soil water evaporation method was paired with standardized ET computation algorithms, the model performed statistically equivalent to or better than the other five ET options for all but 1 of 23 crop system measurements. Based on three years of cotton data from Bushland, Texas, the results were able to identify differences in model performance that depended on choices for ET method. In support of Sub-objective 3B, extensive simulations with a cotton simulation model were conducted on the ARS SciNet cluster computer. A comprehensive analysis of temporal weather patterns and spatial soil patterns on cotton production was conducted at the Maricopa Agricultural Center (MAC) in Arizona. Assessments of irrigation requirements for cotton production among the different weather and soil patterns were performed. The assessments were designed to quantify the potential for site-specific irrigation management to save water and/or improve cotton yield in central Arizona. Most soils at the MAC have enough available water holding capacity to sustain cotton production at full potential with weekly conventional uniform irrigation management, and advantages of site-specific irrigation management were not consistently demonstrated by the simulations. In support of Sub-objective 3C, a computational approach was developed to model daily cotton canopy cover from weekly field imagery collected from a small unoccupied aircraft system (sUAS). During the 2019 and 2020 cotton field seasons, the sUAS was flown weekly to image a 3 ha area under cotton production. Cotton canopy coverage was estimated in 6 m x 6 m zones using image processing to separate plant and soil areas in the images. As additional coverage estimates were obtained on a weekly basis, a mathematical model was fit to the data, which enabled canopy coverage estimation both between imaging dates and into the future. A smoothing technique was developed to obtain best canopy coverage estimates from measured and modeled data. The canopy coverage estimation technique was used for estimating and predicting crop water use, which has potential value for improving irrigation management decisions while considering both spatial and temporal variation in crop growth. In support of Sub-objectives 4A and 4B, research was conducted to characterize the transverse distribution of infiltrated water in furrow irrigation systems as a function of soil texture and flow variables. The study consisted of a large number of simulations conducted with a two-dimensional porous-media flow model and a soil hydraulic model. Using data from various soils, relationships were developed for a typical furrow geometry and initial and boundary conditions that predict the standard deviation of the water distribution as a function of sand-silt-clay content and the volume of infiltrated water. This relationship can be used to determine when interference can be expected for a given furrow spacing. Overall, results suggest that non-uniformity of infiltration in furrow systems may be only of concern when irrigating alternate furrows, which is a practice used by some irrigators. In support of Sub-objective 4C, furrow infiltration data and irrigation performance were evaluated from irrigation evaluation data. The analysis was conducted in support of a study that examined the impact of poly-acrylamide applications on infiltration. Despite substantial seasonal and yearly infiltration variability, results suggest slight poly-acrylamide benefits on infiltration and irrigation uniformity. Results revealed substantial differences in the seasonal pattern of infiltration variation between years. This year-to-year variation currently cannot be explained and is a future researchable item. Variations in water source and its quality seems a possible factor.

Accomplishments
1. Agronomic outcomes of precision irrigation management technologies. A variety of information technologies have been developed to assist irrigation management decisions, particularly related to managing irrigation in unique spatial zones. However, results are mixed on whether these technologies lead to meaningful improvements in agronomic outcomes like yield and water use. ARS researchers at Maricopa, Arizona, tested several methods for computing irrigation requirements, evaluating crop water stress, and applying irrigation to a cotton field trial. The results demonstrated small but significant agronomic improvements by irrigating on a site-specific basis and identified advantages for thermal imaging to identify crop water stress. In addition to producers, several commercial industries will benefit from this research, including industries supporting agricultural irrigation, U.S. cotton production, and the development of unoccupied aircraft systems (drones).

2. Irrigation management for direct-seeded guayule. Tire companies in the United States have accelerated efforts to commercialize guayule for developing a national supply of natural rubber. A crucial breakthrough for these efforts is that guayule crops can now be established in fields by direct-seeding in soil rather than by transplanting, a method which has proven to be cost-ineffective for growers. The challenge was that information about the irrigation management of direct-seeded guayule is unavailable. Research studies conducted by ARS scientists in Maricopa, Arizona, determined crop water use (WU) requirements and irrigation management criteria for direct-seeded guayule. Prediction tools for estimating guayule WU were developed to assist growers with irrigation management. This research provides valuable information on direct-seeded guayule that will be of interest to guayule growers, irrigation consultants, the U.S. rubber industry, and other guayule researchers.

3. New design procedures for furrow irrigation tailwater recovery systems developed. Tailwater recovery systems are still promoted by the Natural Resources Conservation Service (NRCS) and U.S. university agricultural extension services as a water conservation technique in furrow irrigation. However, their design is still based on concepts developed in the 1970’s, which assume that all pertinent inputs are known with certainty. Management of those systems is extremely complicated if actual conditions differ from those assumed in the design. New design concepts for tailwater recovery systems have been developed by ARS researchers in Maricopa, Arizona. The new concepts attempt to incorporate the uncertainty of inputs, and system flexibilities, into the design process. This technology is useful for landowners and consultants considering the use of tailwater recovery systems.

Review Publications
Melandri, G., Thorp, K.R., Broeckling, C., Thompson, A.L., Hinze, L.L., Pauli, D. 2021. Assessing drought and heat stress-induced changes in the cotton leaf metabolome and their relationship with hyperspectral reflectance. Frontiers in Plant Science. 12. Article 751868. https://doi.org/10.3389/fpls.2021.751868.
Pugh, N.A., Thorp, K.R., Gonzalez, E.M., Elshikha, D.E., Pauli, D. 2021. Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems. The Plant Phenome Journal. 4(1). Article e20026. https://doi.org/10.1002/ppj2.20026.
Wang, G., Elshikha, D.M., Katterman, M.E., Sullivan, T., Dittmar, S., Cruz, V.M., Hunsaker, D.J., Waller, P.M., Ray, D.T., Dierig, D.A. 2021. Irrigation effects on seasonal growth and rubber production of direct-seeded guayule. Industrial Crops and Products. 177. Article 114442. https://doi.org/10.1016/j.indcrop.2021.114442.
Elshikha, D.M., Hunsaker, D.J., Waller, P.M., Thorp, K.R., Dierig, D., Wang, G., Cruz, V.M., Katterman, M.E., Bronson, K., Wall, G.W., Thompson, A.L. 2022. Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data. Agricultural Water Management. 265. Article 107540. https://doi.org/10.1016/j.agwat.2022.107540.
Thorp, K.R., Calleja, S., Pauli, D., Thompson, A.L., Elshikha, D. 2022. Agronomic outcomes of precision irrigation management technologies with varying complexity. Transactions of the ASABE. 65(1):135-150. https://doi.org/10.13031/ja.14950.
Thorp, K.R., Drajat, D. 2021. Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia. Remote Sensing of Environment. 265. Article 112679. https://doi.org/10.1016/j.rse.2021.112679.
Bautista, E., Schlegel, J.L., French, A.N. 2022. Design of tailwater recovery systems accounting for irrigation system operation and performance. Journal of Irrigation and Drainage Engineering. 148(9). Article 04022029. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001703.