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

Location: Soil and Water Management Research

2023 Annual Report

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.