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

Location: Water Management and Conservation Research

2020 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
Under Sub-objective 1B, substantial progress was made on the web-based irrigation scheduling tool, including new programming that allows spatially variable inputs and outputs. The tool, hosted by the University of Arizona partner in Tucson, was applied and evaluated against measurements in field studies (2018-2020) in Maricopa and Eloy for direct-seeded guayule crops. 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 evapotranspiration (ET) model in 7 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. Under Sub-objective 1C, an application was written in Visual Basic which interprets in-season cotton petiole nitrate data by growth stage and/or growing degree days. The app returns one of three classes “deficient”, “optimum”, or “excess”. If petiole nitrate points are deficient, then a Nitrogen fertilizer rate is recommended. Under Sub-objective 2A, substantial progress was made in developing evapotranspiration models for irrigated wheat within Maricopa and Yuma Arizona irrigation districts. The vegetation models emphasize use of satellite-based reflectance indices from the Sentinel 2 satellite. Evapotranspiration models incorporate crop coefficient concepts used by the Sentinel 2 satellite but operate independently of ‘Sen2Agri’, a global evapotranspiration mapping package produced from institutions affiliated with the European Space Agency. In support of Sub-objective 2B, progress utilizing drone-based image data was made by developing a camera calibration routine for thermal surveys. Preliminary tests were then made over cotton and broccoli crops in Yuma, Arizona, showing that biomass temperatures agreed within 2 C of ground-based observations and indicating that the camera will be helpful for validation of satellite-based observations. Under Sub-objective 2C, progress was made evaluating the benefits of three different plant height measurements at fine spatial scales: Fraunhofer laser scanning, drone-based Light Detection and Ranging (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. Results showed that root mean squared errors of height estimates from the drone-based LIDAR and from the conventional drone were usually within 3 cm of the Fraunhofer heights. These results mean that crop height estimates from drones are nearly as good as reference values and can be obtained more flexibly and at much lower cost than from a gantry platform. In support of Objective 3, six evapotranspiration (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. The combinations of the three potential ET and two soil water evaporation approaches led to six possible ET simulation options in the model. 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. The default ET method in the model was the worst performing ET method, which is a notable result for model users. 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 the Bushland, Texas, the results were able to identify differences in model performance that depended on choices for ET method. Under Sub-objective 4A, new procedures for the design of runoff recovery systems for surface irrigation were developed and implemented in a spreadsheet application. The application allows tracking the change of storage as a function of the recovered runoff and the flow pumped back into the field at pre-defined time intervals, instead of considering only the changes at the end of each irrigation set as in existing procedures. In contrast with existing procedures, which assume a known constant runoff fraction, the analysis shows that the relationship between inflow rate and runoff and its potential variability needs to be accounted for in the design process and in developing management recommendations. For Sub-objective 4C, progress was made in the development of a water-depth sensor system for evaluation of surface irrigation systems. Units that were tested in the field in 2018 failed to perform adequately due to problems with components and with programming errors. Coding errors have been corrected and new software routines added that check the correct operation of different components as they are being deployed. The circuit board was redesigned to facilitate the replacement of faulty components. The housing is being redesigned to facilitate transportation and storage. Additionally under Sub-objective 4C, furrow infiltration data and irrigation performance were evaluated from irrigation evaluation data originally collected by ARS scientists in Kimberly, Idaho. 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. Effects of sprinkler irrigation rate and timing on Arizona cotton production. Arizona agriculture faces several problems related to water: on-going regional drought, depletion of water in reservoirs, competition from cities, and climate uncertainty. Improving efficiencies of agricultural water use is imperative for the sustainability of Arizona row-cropping. ARS researchers in Maricopa, Arizona, conducted a three-year field study on cotton irrigation practices, which revealed impacts of early-season and late-season irrigation levels on cotton yield, water use, and fiber quality. With improved scientific tools, irrigation could be reduced by 10% while maintaining acceptable cotton yield and fiber quality. The study provides valuable guidance for producers and water management planners in the region, specifically on irrigation practices for cotton using overhead sprinkler irrigation systems.

2. High guayule rubber yield using subsurface drip irrigation (SDI). Guayule, a perennial desert shrub, produces high-quality natural rubber that is suitable for use in commercial-grade tires and major tire companies in the United States have revitalized interest in developing large domestic U.S. supplies of natural rubber to alleviate dependency on imported and synthetic rubbers. Guayule is acclimated to hot, arid environments, but to attain high rubber yields in the desert requires significant amounts of water when using traditional flood irrigation methods. ARS researchers in Maricopa, Arizona, conducted irrigation studies for two and a-half years to compare rubber yield and water use using both flood irrigation and the more efficient, subsurface drip irrigation (SDI) method. Key results were that rubber yields were nearly doubled using SDI over flood irrigation and that water savings can be greatly increased with SDI. This study provides irrigation technology that will help future guayule growers to attain high rubber yields while minimizing water use.

3. Nitrogen (N) management practices for subsurface drip irrigated (SDI) cotton. Declining water availability in the American Southwest continues to generate interest in efficient subsurface drip irrigation (SDI) for cotton production. Fertigating urea ammonium nitrate at low rates with high frequency is an important advantage of SDI, but nitrogen (N) fertilizer management guidelines, specific to SDI cotton are lacking. ARS researchers in Maricopa, Arizona, conducted a 3-yr study to test a pre-plant soil profile NO3 algorithm and a canopy reflectance approach to manage in-season N fertilizer for SDI cotton. Nitrogen recovery efficiency of added N was high with 24 fertigations during the 6 weeks between first square and mid bloom, ranging from 58 to 93%. The key result of this study is that reflectance-based N management saved 17 to 112 kg N ha-1 without reducing lint yields, compared to the soil test-based N treatment This technology can save cotton growers money on fertilizer in all regions that utilize SDI, which will partially offset the expense of installing a new SDI system.

4. Satellite-based remote sensing of evapotranspiration. Efficient irrigation management relies on timely information about crop water requirements and a practical and widely used method to estimate crop water requirements, “FAO56”, estimates water use of a crop by multiplying a crop coefficient by reference water use value determined from weather station data. However, coefficients change over the season and are difficult to estimate every day. ARS researchers in Maricopa, Arizona, determined the actual daily crop coefficient and water use of wheat crops using remote sensing information from satellites. The approach accurately estimates the daily measured water use, particularly during periods when the need for irrigation was the greatest. ARS is collaborating with private growers atnthe University of Arizona, and a private technology firm, to implement these results into accessible, low-cost tools for water management. These results will lead to future development of this remote sensing technology for providing reliable guidance for efficient irrigation management.

5. Long-term simulations of site-specific irrigation management for Arizona cotton production. Engineering technologies are currently available for applying different irrigation rates at different locations in the field. However, further studies must identify cases where these technologies improve crop yield or save water. A comprehensive analysis of temporal weather patterns and spatial soil patterns was conducted by ARS researchers in Maricopa, Arizona. Assessments of irrigation requirements for cotton production among the different weather and soil patterns were performed. The results demonstrated little benefit for technologies that apply different rates of water at different spatial locations, because no improvements in crop yield or savings of water were shown as compared to spatially uniform irrigation management. Soils could hold enough water for weekly uniform irrigation management to sustain crop production at full potential. Field investigations are currently underway to verify the results of this simulation study and identify technologies that are most helpful for improving irrigation management. The research is particularly useful for growers who are considering options for technologies to improve water management on their farms and for researchers in the area of irrigation science.

6. Update to software for designing long-throated flumes. WinFlume is a software package for the design of long-throated flumes, devices that measure flow rate in open channels, and are essential for allocating water supplies to different water users and for managing on-farm water supplies. Long-throated flumes are widely used in the United States and worldwide due to their accuracy and low cost. ARS researchers in Maricopa, Arizona, developed many of the hydraulic relationships used for their design. At the request of US Bureau of Reclaimation, WinFlume1 was reprogrammed to address compatibility problems caused by the evolution in operating systems, and to provide with new functionalities. The new software, WinFlume 2, provides greater design guidance through a more intuitive user interface and an enhanced manual, and faulty procedures in the original software were identified and corrected. Users of WinFlume 2 include US Bureau of Reclamation and the USDA Natural Resource Conservation Service. Benefits will be better measurements of irrigation water flows, which in turn translates to water conservation.

Review Publications
Jimenez, J., Leiva, L., Cardoso, J.A., French, A.N., Thorp, K.R. 2020. Proximal sensing of Urochloa grasses increases selection accuracy. Crop and Pasture Science. 71(4):401-409. https://doi.org/10.1071/CP19324.
Ale, S., Omani, N., Himanshu, S.K., Bordovsky, J.P., Thorp, K.R., Barnes, E.M. 2020. Determining optimum irrigation termination periods for cotton production in the Texas High Plains. Transactions of the ASABE. 63(1):105-115. https://doi.org/10.13031/trans.13483.
Thorp, K.R., Marek, G.W., DeJonge, K.C., Evett, S.R., Lascano, R.J. 2019. Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model. Journal of Environmental Modeling and Software. 199:214-227. https://doi.org/10.1016/j.envsoft.2019.06.007.
Huang, J., Scherer, L., Lan, K., Chen, F., Thorp, K.R. 2019. Advancing the application of a model-independent open-source geospatial tool for large spatiotemporal simulations. Environmental Modelling & Software. 119:374-378. https://doi.org/10.1016/j.envsoft.2019.07.003.
Bronson, K.F., Hunsaker, D.J., Meisinger, J.J., Rockholt, S.M., Thorp, K.R., Conley, M.M., Williams, C.F., Norton, E.R., Barnes, E.M. 2019. Improving nitrogen fertilizer use efficiency in subsurface drip-irrigated cotton in the desert southwest. Soil Science Society of America Journal. 83(6):1712-1721. https://doi.org/10.2136/sssaj2019.07.0210.
Thorp, K.R. 2019. Long-term simulations of site-specific irrigation management for Arizona cotton production. Irrigation Science. 38(1):49-64. https://doi.org/10.1007/s00271-019-00650-6.
DeJonge, K.C., Thorp, K.R., Marek, G.W. 2020. The apples and oranges of reference and potential evapotranspiration: Implications for agroecosystem models. Agricultural and Environmental Letters. 5(1). https://doi.org/10.1002/ael2.20011.
Bronson, K.F., Conley, M.M., Hunsaker, D.J., Thorp, K.R., French, A.N., Barnes, E.M. 2020. Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton? Agronomy Journal. 112(3):2205-2218. https://doi.org/10.1002/agj2.20120.
Nelson, A.D., Ponciano, G.P., McMahan, C.M., Ilut, D.C., Pugh, N.A., Elshikha, D.E., Hunsaker, D.J., Pauli, D. 2019. Transcriptomic and evolutionary analysis of the mechanisms by which P. argentatum, a rubber producing perennial, responds to drought. Biomed Central (BMC) Plant Biology. 19:494. https://doi.org/10.1186/s12870-019-2106-2.
Puppo, L., Garcia, C., Bautista, E., Hunsaker, D.J., Beretta, A., Girona, J. 2019. Seasonal basal crop coefficient pattern of young non-bearing olive trees grown in drainage lysimeters in a temperate sub-humid climate. Agricultural Water Management. 226. https://doi.org/10.1016/j.agwat.2019.105732.
Chen, X., Thorp, K.R., van Oel, P., Xu, Z., Zhou, B., Li, Y. 2019. Environmental impact assessment of water-saving irrigation systems across 60 irrigation construction projects in northern China. Journal of Cleaner Production. 245. https://doi.org/10.1016/j.jclepro.2019.118883.
Huang, J., Ridoutt, B.G., Sun, Z., Lan, K., Thorp, K.R., Wang, X., Yin, X., Huang, J., Chen, F., Scherer, L. 2020. Balancing food production within the planetary water boundary. Journal of Cleaner Production. 253. https://doi.org/10.1016/j.jclepro.2019.119900.
Joshi, V.R., Thorp, K.R., Coulter, J.A., Johnson, G.A., Porter, P.M., Strock, J.S., Garcia Y Garcia, A. 2019. Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model. Agronomy. 9(11). https://doi.org/10.3390/agronomy9110719.
Thorp, K.R., Thompson, A.L., Bronson, K.F. 2020. Irrigation rate and timing effects on Arizona cotton yield, water productivity, and fiber quality. Agricultural Water Management. 234. https://doi.org/10.1016/j.agwat.2020.106146.
Zerihun, Z., Sanchez, C.A., Thorp, K.R., Hagler, M.J. 2019. Hydraulics of linear-move sprinkler irrigation systems, III: Model evaluation. Journal of Irrigation and Drainage Engineering. 8(2):1-11.
Huang, J., Ridoutt, B.G., Thorp, K.R., Wang, X., Lan, K., Liao, J., Tao, X., Wu, C., Huang, J., Chen, F., Scherer, L. 2019. Water-scarcity footprints and water productivities indicate unsustainable wheat production in China. Environmental Science and Technology. 224. https://doi.org/10.1016/j.agwat.2019.105744.
Luo, Z., Thorp, K.R., Abdel-Haleem, H.A. 2019. A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy. Plant Methods. 15. https://doi.org/10.1186/s13007-019-0544-3.
French, A.N., Hunsaker, D.J., Sanchez, C.A., Saber, M., Gonzalez, J.R., Anderson, R.G. 2020. Satellite-based NDVI crop coefficients and evapotranspiration with Eddy Covariance Validation for multiple durum wheat fields in the US Southwest. Agricultural Water Management. 239. https://doi.org/10.1016/j.agwat.2020.106266.
Wang, D.R., Venturas, M.D., Mackay, S.D., Hunsaker, D.J., Thorp, K.R., Gore, M.A., Pauli, D. 2020. Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought. New Phytologist. 228(3):898-909. https://doi.org/10.1111/nph.16751.