Research Project: Dryland and Irrigated Crop Management Under Limited Water Availability and Drought

Location: Soil and Water Management Research

2023 Annual Report

Objectives
1. Develop tools for evapotranspiration (ET) yield and crop water productivity determinations, and management in irrigated, dryland and mixed precipitation dependent/irrigated cropping systems. Sub-objective 1A: Improved determinations of ET. Sub-objective 1B: Development and Application of Crop Coefficients. Sub-objective 1C: Managing crop water productivity using MDI. Sub-objective 1D: Develop management practices to improve marginally irrigated and dryland cropping systems. Sub-objective 1.E: Develop dryland cropping practices that are resilient and improve performance. 2. Develop sensors, technologies, and models that facilitate site-specific irrigation management. Sub-objective 2A: Develop new plant sensors to facilitate site-specific irrigation. Sub-objective 2B. Develop and evaluate energy and SW balance models. 3. Develop water management decision support tools and databases to facilitate better water allocation and irrigation scheduling decisions under limited irrigation. Sub-objective 3A: Provide long-term high-quality weather, ET, management, and crop development data. Sub-objective 3B: Conduct Sensitivity Analyses on ET Related Models and Decision Support Systems. Sub-objective 3C: Develop and Evaluate Crop and Hydrologic Models for Water Management Decision Support Systems.

Approach
To meet the nutritional, fiber and energy needs of a growing world population, global agricultural productivity needs to increase. While American agriculture has been a key contributor to feeding the world, further increases in agricultural production from much of the Great Plains region may not be able to keep up with anticipated increases in demand because of an inability to meet the water needs of future crops. Mean annual precipitation provides 40% to 80% of crop water demand. The balance of crop water demand is usually supplied by irrigation from the Ogallala Aquifer (OA); unfortunately, groundwater depletion has occurred in much of the aquifer. Over 80% of the newly permitted wells on the Texas High Plains have pumping rates that are insufficient to irrigate a 50 ha-pivot of corn. Because of the severity of aquifer depletion, water management strategies such as shifting to less water-intensive crops, allocating water among sectors within a pivot, conversion to dryland, etc. are being evaluated for their economic feasibility and effectiveness in prolonging the life of irrigated agriculture on the Southern High Plains. This research project seeks knowledge and technologies to decrease the impact of aquifer depletion on crop production by better matching irrigation water supply to targeted yields that tend to be less than maximum. An additional factor challenging crop production on the Southern High Plains is that the severity of multi-year droughts has increased in the past 120 years, which can threaten both irrigated and dryland crop production. Thus, this project also seeks management practices that increase the resilience and sustainability of dryland crop production.

Progress Report
Research project 3090-13000-016-000D entitled “Dryland and Irrigated Crop Management Under Limited Water Availability and Drought” was started in January 2022 after successfully completing the peer review process. A headquarter funded postdoc was hired midway through fiscal year (FY) 2022 and left the unit on July 28,2023 to accept a permanent employment offer. The Unit’s Research Leader/ Laboratory Director retired in June of 2023. The drought that began on the Texas High Plains (THP) during the summer of 2021 persisted through the winter of 2022 and the early spring of 2023. May of 2023 was characterized by cool temperatures and a period of frequent and intense precipitation events. These conditions delayed planting of some crops, affected germination in cotton, and presented considerable weed control issues. It is uncertain if this rainy period signals a return to more normal rainfall or just a brief interruption to a prolonged drought. The NOAA Climate Prediction Center has projected a strong El Niño for the summer to extend into the winter in the Northern Hemisphere. However, July was characterized by high temperatures and less rainfall than experienced in the spring. An unexpected microburst overturned the Unit’s 6-span Variable Rate Irrigation (VRI) center pivot sprinkler on the south section, resulting in a delay in the Cooperative Research and Development Agreement (CRADA) related project. Despite challenges from the weather, researchers fully met or substantially met all milestones in FY2023. Significant progress was made in subsidiary projects under the Ogallala Aquifer Program administered by the Soil and Water Research Unit. Two papers demonstrated advances in cotton crop and irrigation management, and four papers demonstrated advances in cotton breeding for drought and heat tolerance that could lead to large increases in cotton lint yield under both water stressed and non-stressed growing conditions. Progress towards Objective 1. Problems with the integrated gas analyzers were observed prior to deployment of the eddy covariance (EC) systems in 2022. The systems were sent to the manufacturer for repair and re-calibration, but an extended turnaround precluded deployment in 2022. The EC systems were successfully cross calibrated over native rangeland prior to deployment in the large weighing lysimeter fields planted to cotton in 2023. Data from the lysimeter from prior years continues to be complied and processed for quality and prepared for publication on the National Agricultural Library's Ag Data Commons. After growing corn in 2022, a third year of cotton is being grown in the lysimeter fields in 2023 to complement data from previous crops grown in 2020 and 2021. The resulting data will be used to develop crop coefficients for upland cotton grown under low-elevation sprinkler irrigation and subsurface drip irrigation (SDI). The data will also be used for further refinement of the two-source energy balance model (TSEB). In the early part of FY23 watermelon fruits and cotton lint yields were sampled to complete Year 1 and Year 2, respectively, of studies comparing irrigation application methods between mobile drip irrigation (MDI) technology and low elevation spray application (LESA). Watermelon yields were determined to be similar between irrigation application methods. However, irrigation water productivity (IWP) was significantly greater for watermelons grown under MDI, indicating that less water was needed to produce the same amount of watermelon fruit using this technology. This study also compared irrigation methods between the ARS-patented Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system and the benchmark method of using weekly neutron probe readings to replace crop water use. There was no significant difference in yield or IWP between irrigation methods, however, the ISSCADA system is automated and requires less labor to determine irrigation amounts. In the cotton study, overall lint yield was greater in plots under MDI as compared with LESA, but IWP was not significantly different. In June of 2023, watermelons were successfully planted under the 3-span VRI center pivot system to initiate Year 2 of the comparison between MDI and LESA and the irrigation scheduling method using the ISSCADA system. Long-term dryland cropping systems continued in the Bench Terraces with dryland sorghum planted after the precipitation in May to assess tillage and rotation effects on soil water storage and soil conservation. Year 2 of the cotton and corn experiment was initiated in June 2023 in which a set amount of water allocation is spread between the two crops to determine optimal yield and returns in time and acreage. Progress towards Objective 2. In FY23, the scientist leading this goal moved into the dual roles of Acting Laboratory Director and Ogallala Aquifer Program Manager. In FY22, a key cooperator at the Beltsville Agricultural Research Center left ARS. These changes have meant that there was virtually no progress on the original goal in FY23. Because of this, plans for a CRADA were tabled. However, two other unit scientists began a cooperative effort with an ARS collaborator in Washington State on a similar goal involving development of a thermal infrared temperature camera coupled with a visual camera and a prototype of that instrument has been installed at the unit’s NE lysimeter where it is undergoing testing. This similar goal is now substituted for the original goal as a permanent change in the plan going forward. Given that change, we consider the goal substantially met. Progress towards Objective 3. Two additional databases from the large weighing lysimeter fields for soybean and sunflower were published to the Ag Data Commons in FY2023 as was a soil water content database spanning the years 1989-2021. Previously published corn data were used for the Agricultural Model Intercomparison and Improvement Project (AgMIP) to evaluate several models including the Decision Support System for Agrotechnology Transfer (DSSAT) model. Additional datasets are being organized and processed for distribution to the public. Experiments under controlled conditions to better understand soil water flow between layers in the Pullman clay continued in FY2023. These efforts were part of the 4th year of a CRADA and much of this work was performed by an ARS awarded postdoc. One year’s work in calibration of the TDR-315N soil moisture sensors over a range of media properties has been completed. We can now predict media properties (specific surface area) using the waveforms acquired from the sensors.

Accomplishments
1. Irrigation decision support system showcased for crop production in Nebraska. Precision irrigation scheduling methods could help sustain the Ogallala Aquifer and rural communities. However, some precision irrigation methods are complicated to implement and require a steep learning curve to understand. Under a collaborative effort between ARS scientists from Bushland, Texas, and University of Nebraska scientists, the ARS-patented Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system was outfitted onto a center pivot sprinkler in Nebraska to test its feasibility for crop production of corn and soybean and to compare scheduling results side-by-side with the Spatial EvapoTranspiration (ET) Modeling Interface (SETMI). The ISSCADA uses canopy temperature sensors mounted on a center pivot and in the field coupled with data from a nearby weather station to automatically build prescription maps that guide the irrigation system. The SETMI system requires satellite information at regular intervals and ET modeling to estimate spatially variable crop water use. Both precision irrigation scheduling methods were compared with the irrigation method commonly used by farmers in Nebraska. Irrigation amounts, grain yield and crop water productivity were similar between precision irrigation methods in both years and prescribed less water than the method commonly used by farmers. Providing producers with technology that performs well, saves water, and is easy to use, helps to facilitate adoption. Making collaborators aware of this distinction helps transfer irrigation scheduling technology in other regions of the United States (U.S.).

2. Decision support system coupled with artificial intelligence predicts crop water stress. As water resources become more limited, the efficiency of the conversion of water to crops needs to improve. Crop water productivity can be improved by using sensors to precisely monitor where within a field and when crops are water stressed so that irrigation is applied where needed and not elsewhere. However, at times, data from sensors are lost or unavailable. ARS scientists at Bushland, Texas, and scientists at the University of Nevada at Reno developed an AI algorithm using artificial neural networks (ANN) trained on historical canopy temperature and weather data, irrigation treatment level, and last irrigation date to predict canopy temperature and the level of crop water stress. The AI algorithm is important as it adds redundancy to the ARS-patented Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system and could be used in other decision support systems that use canopy temperature to characterize crop water stress levels.

3. Simulation modeling evaluates cropping management strategies in the face of climate change. The impacts of climate change, including increased air temperatures and atmospheric carbon dioxide (CO2) concentrations, are expected to threaten global food security in both irrigated and rainfed crop production regions. Although the impacts of such increases are unknown, crop simulation modeling coupled with climate predictions from global circulation models may provide insight into potential effects on crop production and soil and water resources. ARS researchers from Bushland, Texas, along with university partners from the U.S., Australia, and China simulated the effects of climate change on hydrology and crop yields for regions both in the U.S. (Texas High Plains, Nebraska, and Kansas) and China through the end of the of the 21st century. Researchers adapted the Soil and Water Assessment Tool (SWAT) model to improve CO2 and auto irrigation algorithms. Results varied by region but overall pointed to reductions in yield and evapotranspiration (ET) unless there are successful adaptations, which could include improved plant genetics and management adaptations such as planting date and irrigation scheduling.

4. 30 years of crop water use and yield data drive simulation modeling development and improvement. Computer modeling of crop growth and yield in response to weather, crop choice, irrigation, fertilization, and management is increasingly adopted by small and large agribusinesses for efficient managing of these inputs, as well as by water districts, government planning agencies, and many other users. However, recent studies have shown that many models are ineffective at predicting management outcomes and require improvement and calibration. Accurate field data of crop growth, water use, fertilizer response, and yield are necessary for model improvement and calibration. ARS scientists at Bushland, Texas, have studied these systems for 35 years and have now published complete collections of the field data needed for improvement and calibration of alfalfa, corn, soybean, sunflower, and winter wheat models. The data have recently been used in large studies of more than 50 corn and winter wheat models by the Agricultural Model Improvement and Intercomparison Project (AgMIP), leading to improvement of those models for many environments including the semi-arid conditions of the United States (U.S.) Southern High Plains. The AgMIP winter wheat team is also using these data. The data have also been used by large crop water use prediction systems such as OpenET (a website dedicated to providing easily accessible satellite-based estimation of ET) that aim to provide real-time crop water use data to farmers across the irrigated western U.S. Data for multiple years of cotton and sorghum crops are in preparation and soon will be added to the dataset collections on the USDA ARS National Agricultural Library Ag Data Commons where they are freely available for download.

Review Publications
Li, X., Tan, L., Li, Y., Qi, J., Feng, P., Li, B., Liu, D., Zhang, X., Marek, G.W., Zhang, Y., Liu, H., Srinivasan, R., Chen, Y. 2022. Effects of global climate change on the hydrological cycle and crop growth under heavily irrigated management - A comparison between CMIP5 and CMIP6. Computers and Electronics in Agriculture. 202. Article 107408. https://doi.org/10.1016/j.compag.2022.107408.
Evett, S.R., Marek, G.W., Colaizzi, P.D., Copeland, K.S., Ruthardt, B.B. 2022. Methods for downhole soil water sensor calibration - complications of bulk density and water content variations. Vadose Zone Journal. Article e20235. https://doi.org/10.1002/vzj2.20235.
Bhatti, S., Heeren, D.M., Evett, S.R., O'Shaughnessy, S.A., Rudnick, D.R., Franz, T.E., Ge, Y., Neale, C.M. 2022. Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska. Agricultural Water Management. 274. Article 107946. https://doi.org/10.1016/j.agwat.2022.107946.
Bhatti, S., Heeren, D., O'Shaughnessy, S.A., Neale, C., Larue, J., Melvin, S., Wilkening, E., Bai, G. 2023. Toward automated irrigation management with integrated crop water stress index and spatial soil water balance. Precision Agriculture. 24(4). https://doi.org/10.1007/s11119-023-10038-4.
Andrade, M.A., O'Shaughnessy, S.A., Evett, S.R. 2023. Forecasting of crop water stress indicators using machine learning algorithms. Journal of the ASABE. 66(2):297-305. https://doi.org/10.13031/ja.15213.
Ding, B., Liu, H., Li, Y., Zhang, X., Feng, P., Li Liu, D., Marek, G.W., Ale, S., Brauer, D.K., Srinivasan, R., Chen, Y. 2022. Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth. Environmental Modelling & Software. 156. Article 105492. https://doi.org/10.1016/j.envsoft.2022.105492.
Kothari, K., Ale, S., Marek, G.W., Munster, C.L., Singh, V.P., Chen, Y., Marek, T.H., Xue, Q. 2022. Simulating the climate change impacts and evaluating potential adaptation strategies for irrigated corn production in the Northern High Plains of Texas. Climate Risk Management. 37. Article 100446. https://doi.org/10.1016/j.crm.2022.100446.
Zhang, Y., Qi, J., Pan, D., Marek, G.W., Zhang, X., Feng, P., Liu, H., Li, B., Ding, B., Brauer, D.K., Srinivasan, R., Chen, Y. 2022. Development and testing of a dynamic CO2 input method in SWAT for simulating long-term climate change impacts across various climatic locations. Journal of Hydrology. 614(B). Article 128544. https://doi.org/10.1016/j.jhydrol.2022.128544.
Zhang, Y., Ge, J., Qi, J., Liu, H., Zhang, X., Marek, G.W., Ding, B., Feng, P., Liu, D., Srinivasan, R., Chen, Y. 2023. Evaluating the effects of single and integrated extreme climate events on hydrology in the Liao River Basin, China using a modified SWAT-BSR model. Journal of Hydrology. 623. Article 129772. https://doi.org/10.1016/j.jhydrol.2023.129772.
Zhang, Y., Liu, H., Qi, J., Feng, P., Zhang, X., Liu, D., Marek, G.W., Srinivasan, R., Chen, Y. 2022. Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model. Science of the Total Environment. 857(2). Article 159482. https://doi.org/10.1016/j.scitotenv.2022.159482.
Kimball, B.A., Thorp, K.R., Boote, K.J., Stockle, C., Suyker, A.E., Evett, S.R., Brauer, D.K., Coyle, G.G., Copeland, K.S., Marek, G.W., Colaizzi, P.D., Acutis, M., Alimagham, S., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., De Antoni Migliorati, M., Dumont, B., Durand, J., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Jiang, Q., Kim, S., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Qi, Z., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D.J., Trabucco, A., Webber, H., Weber, T., Willaume, M., Williams, K., van der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W. 2023. Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models. Agricultural and Forest Meteorology. 333. Article 109396. https://doi.org/10.1016/j.agrformet.2023.109396.
Volk, J., Huntington, J., Melton, F., Allen, R.G., Anderson, M.C., Fisher, J., Kilic, A., Senay, G.B., Halverson, G., Knipper, K.R., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S.R., French, A.N., Jasoni, R., Kustas, W.P. 2023. Development of a benchmark eddy flux ET dataset for evaluation of remote sensing ET models over the CONUS. Agricultural and Forest Meteorology. 331. Article 109307. https://doi.org/10.1016/j.agrformet.2023.109307.
Volk, J.M., Huntington, J.L., Melton, F., Minor, B., Wang, T., Anapalli, S.S., Anderson, R.G., Evett, S.R., French, A.N., Jasoni, R., Bambach, N., Kustas, W.P., Alfieri, J.G., Prueger, J.H., Hipps, L., McKee, L.G., Castro, S.J., Alsina, M.M., McElrone, A.J., Reba, M.L., Runkle, B., Saber, M., Sanchez, C., Tajfar, E., Allen, R., Anderson, M.C. 2023. Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset. Data in Brief. 48. Article 109274. https://doi.org/10.1016/j.dib.2023.109274.
Klopp, H.W., Jabro, J.D., Allen, B.L., Sainju, U.M., Stevens, W.B., Rana Dangi, S. 2023. Does increasing diversity of small grain cropping systems improve aggregate stability and soil hydraulic properties? Agronomy. 13. Article 1567. https://doi.org/10.3390/agronomy13061567.
Li, B., Marek, G.W., Marek, T.H., Porter, D.O., Ale, S., Moorhead, J.E., Brauer, D.K., Srinivasan, R., Chen, Y. 2023. Impacts of ongoing land-use change on watershed hydrology and crop production using an improved SWAT model. Land. 12(3). Article 591. https://doi.org/10.3390/land12030591.
O'Shaughnessy, S.A., Colaizzi, P.D., Bednarz, C.W. 2023. Sensor feedback system enables automated deficit irrigation scheduling for cotton. Frontiers in Plant Science. 14:1-14. https://doi.org/10.3389/fpls.2023.1149424.
Tan, L., Zhang, Y., Marek, G.W., Ale, S., Brauer, D.K., Chen, Y. 2021. Modeling basin-scale impacts of cultivation practices on cotton yield and water conservation under various hydroclimatic regimes. Agriculture. 12(1). Article 17. https://doi.org/10.3390/agriculture12010017.
Bawa, A., Samanta, S., Himanshu, S.K., Singh, J., Kim, J., Zhang, T., Chang, A., Jung, J., Delaune, P., Bordovsky, J., Barnes, E., Ale, S. 2022. A support vector machine and image processing based approach for counting open cotton bolls and estimating lint yield from UAV imagery. Smart Agricultural Technology. 3. Article 100140. https://doi.org/10.1016/j.atech.2022.100140.