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

Location: Soil and Water Management Research

2024 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 3090-13000-016-00D 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. Since that time, a scientist from the unit currently serves as the acting Laboratory Director and Ogallala Aquifer Program Manager while another serves as the acting Research Leader. Two permanent, full-time technician vacancies were filled in FY24. Researchers fully met or substantially met all but one milestone in FY2023 due to a critical vacancy. Significant progress was made in subsidiary projects under the Ogallala Aquifer Program administered by ARS Bushland, Texas. Related papers demonstrated advances in sensor-based irrigation management, alternative cropping systems, and economic analyses. Progress Towards Objective 1. Following repair and calibration of the eddy covariance (EC) systems in 2022, they functioned properly during the 2023 and 2024 seasons. 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. A third year of cotton was 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). The weighing lysimeter fields were fallowed in the summer of 2024 to facilitate field and lysimeter maintenance, including control of persistent weeds in the west fields, in preparation of sowing of alfalfa in the fall. The EC systems were deployed over the fallow ground along with five compact EC systems from ARS Lubbock, Texas, to determine their efficacy for estimating evaporation from bare soil. Watermelons were successfully harvested under the ARS 3-span VRI center pivot system to complete Year 2 of the comparison between mobile drip irrigation (MDI) and low elevation sprinkler application (LESA) irrigation using the ISSCADA system. An early maturity variety of cotton was planted under this center pivot system in May of 2024 to provide a third year of comparisons between the LESA and MDI application methods for a row crop using the ISSCADA system for irrigation scheduling. A long-term dryland cropping systems study continued in the Bench Terraces with sorghum planted after the precipitation in May to assess tillage and rotation effects on soil water storage and soil conservation. Year 3 of the cotton and corn experiment was initiated in May 2024 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. This goal was modified last year to reflect development of a dual smart camera that uses red-green-blue (RGB) and infrared imagers to estimate evapotranspiration (ET) using image processing to identify sunlit and shaded vegetation, soil and residue for use in a multi-source energy balance model. Two unit scientists are continuing the second year of this cooperative effort with an ARS PI in Washington State. Substantial progress has been made in using computer vision algorithms to automatically identify vegetation, residue and soil, and extract thermal values. Progress towards Objective 3. Two additional databases from the large weighing lysimeter fields for cotton and sorghum were published to the Ag Data Commons in FY2024 as were agronomic calendar and crop growth and yield data. 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. This work was published along with a supplemental paper comparing soil temperature simulation of the same models in 2024. 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 under bare soil conditions continued in FY2024. These efforts were part of the 4th year of a Cooperative Research And Development Agreements (CRADA) and much of this work was performed by an ARS awarded postdoc.

Accomplishments
1. Low-cost soil water sensor for irrigation scheduling. Due to declines in the Ogallala aquifer, farmers in the Southern High Plains are forced to apply less than full irrigation, which means there is little latitude for error in irrigation management before yield and quality are hurt. The gold standard for accurate irrigation scheduling costs upwards of $10,000 per sensor and is subject to regulatory concerns and high labor costs, so an alternative is badly needed. Scientists with the USDA ARS and Texas AgriLife at Bushland, Texas, tested two new soil water sensors against the gold standard in field trials under several levels of irrigation. Both sensors were substantially cheaper than the gold standard but only one was shown to be as accurate as the gold standard and useful for irrigation scheduling. This sensor, which was developed by ARS at Bushland, Texas, cooperatively with Acclima, Inc., is the TDR-315 and costs about $300 per unit. This technology will enable producers to more efficiently use limited water resources while maintaining crop yield and quality.

2. Best practices determined for cooler climate cotton production with less water and better profit. Declines in the Ogallala aquifer cause reduced well yields and increased risk for growers. Cotton can be grown with less irrigation than corn and provides an opportunity to reduce water use and maintain profitability in the Southern High Plains, particularly if irrigated with subsurface drip irrigation (SDI) rather than sprinklers. However, the cooler climate in the northern Texas High Plains and southwest Kansas presents unique challenges for cotton producers. ARS scientists from Bushland and Lubbock, Texas, evaluated water use and lint yield of four cotton varieties under three irrigation levels using SDI. They found that irrigation could be terminated earlier than previously thought without reducing lint yields, and that selecting early maturing varieties typically produced greater yields in the cooler climate.

3. Climate-informed management of irrigated cotton can reduce groundwater withdrawals. Due to declines in the Ogallala aquifer, about 30% reduction in pumping is required for sustainable agricultural production in Western Kansas. But that leads to deficit irrigation, greater reliance on precipitation, and a shift to producing drought-tolerant crops like cotton that retain profitability. One adaptation is to apply higher irrigation rates on only portions of a center pivot field by splitting the field between irrigated and dryland production. Scientists from USDA ARS at Bushland, Texas, and Kansas State University used the GOSSYM computer simulation model to find that splitting could reduce producer irrigation by half while retaining approximately 90% of the cotton yield. The model was calibrated with data from ARS at Bushland, Texas. Southwestern Kansas producers can reduce water use in all years by splitting fields and those that use La Niña-El Niño phase climate-informed cotton management may minimize risk using splitting during more severe yield-limiting drought years that are frequent in the La Niña Phase.

4. Accurate prediction of cotton and sorghum water use and yield change with cropping system adaptations to reduce water use. In the face of water limitations, many cropping system adaptations are proposed to enable sustained economic crop production on the Southern High Plains; too many to study in field research. Cropping system computer models are used to predict outcomes from proposed adaptations in crop type, management, irrigation method and level, and other agronomic methods. However, these models are only as good as the crop growth, water use, and yield data used by model developers to improve, test, and calibrate the models; and there was a distinct lack of accurate data for this region. Scientists and engineers from the USDA ARS at Bushland, Texas, published accurate and complete crop growth, water use, and yield data based on research at Bushland (10 years of cotton, 14 years of sorghum). Crop data provided were for cotton and sorghum under sprinkler and subsurface drip irrigation. The data were supplemented with soil water content and quality-controlled weather, both of which are needed by modelers to improve, test, and calibrate the models. Data are available at the USDA ARS National Agricultural Library Ag Data Commons and are already being used in model improvement and calibration.

Review Publications
Unger, P.W., Schwartz, R.C., Baumhardt, R.L., Xue, Q. 2023. Soil water conservation for dryland farming. In: Lal, R., editor. Soil and Drought: Basic Processes. 1st edition. Boca Raton, Florida: CRC Press. p. 1-28. https://doi.org/10.1201/b22954.
Klopp, H.W., Blanco-Canqui, H., Creech, C.F., Easterly, A.C. 2024. Identifying the best tillage system to maintain soil properties and crop yields after Conservation Reserve Program grassland conversion. Soil & Tillage Research. 239. Article 106060. https://doi.org/10.1016/j.still.2024.106060.
Li, B., Tan, L., Zhang, X., Qi, J., Marek, G.W., Li, Y., Dong, X., Zhao, W., Chen, T., Feng, P., Liu, D., Srinivasan, R., Chen, Y. 2023. Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects. Journal of Hydrology: Regional Studies. 50. Article 101547. https://doi.org/10.1016/j.ejrh.2023.101547.
Marek, G.W., Evett, S.R., Marek, T.H., Porter, D.O., Schwartz, R.C. 2023. Field evaluation of conventional and downhole TDR soil water sensors for irrigation scheduling in a clay loam soil. Applied Engineering in Agriculture. 39(5):495-507. https://doi.org/10.13031/aea.15574.
Colaizzi, P.D., O'Shaughnessy, S.A., Evett, S.R., Marek, G.W., Brauer, D.K., Copeland, K.S., Ruthardt, B.B. 2023. Data quality control for stationary infrared thermometers viewing crops. Applied Engineering in Agriculture. 39(4):427-438. https://doi.org/10.13031/aea.15642.
Schwartz, R.C., Witt, T.W., Ulloa, M., Colaizzi, P.D., Baumhardt, R.L. 2024. Irrigation response, water use, and lint yield of upland cotton cultivars. Journal of the ASABE. 67(2):421-437. https://doi.org/10.13031/ja.15868.
Mosqueda, H.M., Blaser, B.C., O'Shaughnessy, S.A., Rhoades, M.B. 2023. Intercropping forage sorghum with sunnhemp at different seeding rates to improve forage production. Agronomy. 13(12). Article 3048. https://doi.org/10.3390/agronomy13123048.
Kiraga, S., Peters, R.T., Molaei, B., Evett, S.R., Marek, G.W. 2023. Reference evapotranspiration estimation using genetic algorithm-optimized machine learning models and standardized Penman-Monteith Equation in a highly advective environment. Water. 16(1). Article 12. https://doi.org/10.3390/w16010012.
Baumhardt, R.L., Haag, L.A., Schwartz, R.C., Marek, G.W. 2024. Climate-informed management of irrigated cotton in Western Kansas to reduce groundwater withdrawals. Agronomy. 14(6). Article 1303. https://doi.org/10.3390/agronomy14061303.
Howell, N., Bhattacharia, S., Aria, S., Garcia, O., Bednarz, C., Guerrero, B. 2024. Utilization of cotton gin waste biochars for agronomic benefits in soils. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-024-05545-x.
Naher, A., Almas, L., Guerrero, B., Shaheen, S. 2023. Spatiotemporal economic analysis of corn and wheat production in the Texas High Plains. Water. 15(20). Article 3553. https://doi.org/10.3390/w15203553.
Quadros, D.G., Kerth, C.R., Miller, R., Tolleson, D.R., Redden, R.R., Xu, W. 2023. Intake, growth performance, carcass traits, and meat quality of feedlot lambs fed novel anthocyanin-rich corn cobs. Translational Animal Science. 7(1). Article txac171. https://doi.org/10.1093/tas/txac171.
Schipanski, M.E., Sanderson, M.R., Mendez-Barrientos, L., Kremen, A., Gowda, P.H., Porter, D.O., Wagner, K., West, C., Rice, C.W., Marsalis, M., Guerrero, B., Haacker, E., Dobrowolski, J., Ray, C., Auvermann, B. 2023. Moving from measurement to governance of shared groundwater resources. Nature Water. 1:30-36. https://doi.org/10.1038/s44221-022-00008-x.
Simon, L.M., Obour, A.K., Holman, J.D., Johnson, S.K., Roozeboom, K.L. 2024. Cover crop grazing effects on soil properties in no-tillage dryland cropping systems in the central Great Plains. Agriculture, Ecosystems and Environment. 374. Article 109140. https://doi.org/10.1016/j.agee.2024.109140.
Singh, A., Deb, S.K., Slaughter, L.C., Singh, S., Ritchie, G.L., Guo, W., Saini, R. 2023. Simulation of root zone soil water dynamics under cotton-silverleaf nightshade interactions in drip-irrigated cotton. Agricultural Water Management. 288. Article 108479. https://doi.org/10.1016/j.agwat.2023.108479.
Zhang, Y., Han, Y., Wen, N., Qi, J., Zhang, X., Marek, G.W., Srinivasan, R., Feng, P., Liu, D., Hu, K., Chen, Y. 2024. Assessing the response mechanisms of elevated CO2 concentration on various forms of nitrogen losses in the Golden Corn Belt. Water Resources Research. 60. Article e2024WR037226. https://doi.org/10.1029/2024WR037226.
Tan, L., Zhang, X., Qi, J., Sun, D., Marek, G.W., Feng, P., Li, B., Liu, D., Li, B., Srinivasan, R., Chen, Y. 2023. Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change. Science of the Total Environment. 899. Article 165619. https://doi.org/10.1016/j.scitotenv.2023.165619.