Location: Water Management and Conservation Research
2017 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 first report for this new project which began on January 16, 2017 and continues research from the previous project, 2020-13660-007-00D, “Enhancing Water Conservation and Crop Productivity in Irrigated Agriculture". Please see the report for the previous project for additional information.
Under Objective 1, data from cotton irrigation studies in the previous project plan were used to develop an Excel-based soil water balance model to determine appropriate irrigation rates for cotton. This year, work was initiated to further improve the algorithms and store information in a database. The ultimate goal is to provide a user-friendly tool for determining spatially-variable cotton water requirements. Guayule crop coefficients developed in the 2012-2015 studies were incorporated in a simple crop coefficient irrigation scheduling model. The guayule irrigation scheduling model is currently being tested in Maricopa, Arizona. Nitrogen by water subsurface drip irrigation studies for cotton are progressing and yielding valuable new information on management guidelines. Also under objective 1, the first season of a cotton irrigation experiment using a linear move irrigation machine was completed. 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% 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 provide data for model improvement.
Under Objective 2, Landsat 8 and Sentinel 2 satellite data are now collected about every 8 days, over the Maricopa/Stanfield and Central Arizona Irrigation Districts, and the process of using the final release of the Sen2Agri software package for crop mapping was begun. The drone acquisition program became very active in 2017 with close to weekly multispectral flights over 5 different experimental fields at the Maricopa Agricultural Research Center. Tractor and cart-based light detection and ranging data acquisitions, initiated under our prior project plan, have continued to be collected over cotton phenotyping trials in the summer and fall of 2017.
Under Objective 3, a significant effort has been made to get two cotton simulation models to run on the new ARS cluster computing system located in Ames, Iowa. This has permitted great increases in the number of runs that can be conducted over a period of time. The effort has enabled a large global sensitivity analysis of the cotton models. Millions of runs are now being conducted to assess parameter sensitivity of both cotton models, likely a more thorough analysis than has ever been conducted before. The results are expected to provide authoritative guidance on proper techniques for calibrating the cotton models. Furthermore, the aim is to evaluate evapotranspiration methods within the cotton models using data from lysimeter fields in Bushland, Texas.
Under Objective 4, testing and developing procedures began that will be used to generate a large database of furrow infiltration simulation results, and associated moment analysis calculations. The moment analysis technique allows us to predict and describe the subsurface distribution of infiltrated water in terms of mean and standard deviation of the flow in two dimensions. This approach may facilitate two-dimensional infiltration calculations for irrigation modeling purposes. An initial set of results has been developed. Development of an advance/recession timer and water depth sensor with radio transmission capabilities was initiated. A prototype will be available soon.
Accomplishments
Review Publications
Hunsaker, D.J., Elshikha, D.E. 2017. Surface irrigation management for guayule rubber production in the US desert southwest. Agricultural Water Management. 185:43-57.
Zerihun, D., Sanchez, C.A., Subramanian, J., Badaruddin, M., Bronson, K.F. 2017. Fertigation uniformity under sprinkler irrigation: evaluation and analysis. Journal of Irrigation and Drainage Engineering. 6:1-13.
Bronson, K.F., White, J.W., Conley, M.M., Hunsaker, D.J., Thorp, K.R., French, A.N., Mackey, B.E., Holland, K.H. 2017. Active optical sensors in irrigated durum wheat: Nitrogen and water effects. Agronomy Journal. 109:1060-1071.
Sarah, D.C., Kuzmick, E.R., Niechayev, N., Hunsaker, D.J. 2017. Productivity and water use efficiency of Agave americana in the first field trial as bioenergy feedstock on arid lands. Global Change Biology Bioenergy. 9:314-325.
Thorp, K.R., Wang, G., Bronson, K.F., Badaruddin, M., Mon, J. 2017. Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and yield. Computers and Electronics in Agriculture. 136:1-12.
Jacob, F., Audrey, L., Olioso, A., Weiss, M., Caillault, K., Jacquemoud, S., Nerry, F., French, A.N., Schmugge, T., Briottet, X., Lagouarde, J. 2017. Reassessment of the temperature-emissivity separation from multispectral thermal infrared data: Introducing the impact of vegetation canopy by simulating the cavity effect with the SAIL-Thermique model. Remote Sensing of Environment. 198:160-172.
Spater, M., Kim, S., Kucera, L., Fisher, J., Lee, C., French, A.N. 2017. Linking managed and natural ecosystems through evapotranspiration and NASAs upcoming ECOSTRESS mission. Earthzine. https://earthzine.org/2017/06/14/linking-managed-and-natural-ecosystems-through-evapotranspiration-and-nasa-upcoming-ecostress-mission/.
Eranki, P.L., Elshika, D., Hunsaker, D.J., Bronson, K.F., Landis, A.E. 2017. A comparative life cycle assessment of flood and drip irrigation for guayule rubber production using experimental field data. Industrial Crops and Products. 99:97-108.
