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

Location: Soil and Water Management Research

2025 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
Progress towards Objective 1: Algorithms to determine the water balance and evapotranspiration (ET) from large weighing lysimeter data were written in MATLAB and compared to the water balance calculated by spreadsheet. The spreadsheet algorithms for determining ET were refined and spreadsheet reorganized for efficient use. Water balance data from the lysimeters included sprinkler and drip irrigation, and crops included two seasons of corn and one season of soybean. Fallow periods before and after the crop seasons were also included. A two-source energy balance model that calculated ET was further refined, and ET results were compared to those determined by large weighing lysimeters using the MATLAB algorithm. The Irrigation System Supervisory Control and Data Acquisition (ISSCADA) System was previously developed and patented by ARS scientists. The ISSCADA system was tested and compared to manual irrigation scheduling methods using a field-calibrated neutron probe for cotton irrigated by a three-span center pivot. Cotton yield and quality data were obtained for the ISSCADA and manual scheduling method treatments at the conclusion of the 2024 season. Additional treatments included irrigation rates of 50, 75, and 100 percent of full evapotranspiration. Additional data included soil water and canopy temperature that will be used to test and refine the two-source energy balance model. Cotton yield and quality will be compared for low elevation spray irrigation using a three-span center pivot in the 2025 season. Both irrigation application methods will be scheduled using ISSCADA and the manual field-calibrated neutron probe at irrigation rates of 50, 75, and 100 percent of full ET. A rainfed treatment will also be included to calculate irrigation crop water productivity. Cotton was planted and successfully established, and field sensors and the ISSCADA system are presently being deployed. A second season of a Cooperative Research and Development Agreement (CRADA) was completed in 2024 to test a satellite-based nitrogen management algorithm for cotton. Reflectance band images were acquired from high resolution commercial satellites and input to a proprietary algorithm that determined nitrogen needs of cotton that was irrigated by a six-span center pivot. Treatments included full and limited nitrogen applications for irrigation rates of 75% of full ET. Cotton yield and quality data were obtained and will be analyzed for the different nitrogen treatments over two seasons. A field experiment was designed and initiated to determine crop water productivity of alfalfa under subsurface drip irrigation. The experiment included four varieties (two Roundup-ready and two non-Roundup ready) and irrigation rates of 50, 75, and 100 percent of full ET. Four alfalfa varieties were planted on October 11, 2024, and the experiment is anticipated to be conducted over four calendar years. Alfalfa was established, and the first cutting was completed on June 12, 2025. Comparisons of ET data from weighing lysimeters and eddy covariance systems deployed in sprinkler (NW) and subsurface drip (NE) irrigated fields planted to cotton in 2023 were completed. Five additional compact eddy covariance systems were deployed in the NE lysimeter field over fallow conditions in 2024. Comparisons of evaporation estimates with weighing lysimeter data have begun. A third conventional EC system was added in 2025 and deployed over forage sorghum in the SE lysimeter field. Comparisons of ET estimations from both conventional and compact eddy covariance systems with lysimeter ET are planned. Development of regional crop coefficients for upland cotton using both sprinkler and subsurface drip irrigation was completed using three years of data from the weighing lysimeter fields. Different irrigation scheduling strategies were required to manage the indeterminate nature of cotton to optimize crop water productivity for each irrigation system. Progress towards Objective 2. A dual smart camera was tested for a second year over irrigated cotton during the 2024 season. The dual smart camera is being developed and tested in collaboration with an ARS PI in Pullman, Washington, and through a CRADA. The dual smart camera uses red-green-blue (RGB) and thermal infrared imagers input to a multiple-source energy balance model to estimate crop evapotranspiration. The image processing component of the firmware underwent refinements to improve identification of sunlit and shaded soil and canopy pixels and extract their surface temperatures. The energy balance model also underwent refinement to improve calculation of crop evapotranspiration. A new version of software called ARSPivot is being developed by ARS and collaborators at the University of Nevada. The ARSPivot software is the control and feedback component of the ISSCADA system. The new version of ARSPivot is being incorporated into the ISSCADA system and tested on a three-span center pivot with irrigated cotton in the 2025 season. The ISSCADA system will also include a prototype plant canopy height sensor that uses lidar. The lidar-based height sensor is being developed by an ARS Pathways student at Bushland, Texas, in collaboration with scientists at the University of Nevada. The height sensor was successfully bench tested and will be deployed on the three-span center pivot in the irrigated cotton experiment. An experimental high accuracy, inexpensive GPS system for center pivot lateral positioning developed by University of Nevada cooperators is being deployed and tested on the three-span center pivot. Progress towards Objective 3. The Bushland weighing lysimeter sorghum and cotton data were uploaded to the USDA ARS National Agricultural Library Ag Data Commons and published there. Both the sorghum and cotton dataset collections will be finalized when the 5-minute data analysis is completed, which is in progress. The spreadsheet for 5-minute data analysis was improved with new algorithms and organization to speed execution, and it was shared with the public on the Ag Data Commons. Infrared thermometer data for select years and crops was also shared on the Ag Data Commons. Bushland winter wheat data (3 seasons) were shared with the public on the Harvard Dataverse. Comparison of trained models with cotton water use was completed in 2024 and published, as were results of comparison of infrared thermometry and soil water derived stress indices, completing this subobjective. Four journal articles utilizing the Bushland corn and winter wheat data collections to evaluate, intercompare and improve more than 33 crop models were completed and published in cooperation with researchers at universities and institutes nationally and internationally.

Accomplishments
1. Best planting dates and densities found to optimize crop water productivity of industrial hemp. Industrial hemp is a fiber crop that can reduce water use from the Ogallala aquifer. Texas Tech University Researchers working in an ARS Ogallala Aquifer Program project showed the critical importance of planting dates and seeding densities in influencing the overall crop productivity, including biomass, fiber, and seed production. They also determined the relationships between root morphology, soil water depletion, and water use of industrial hemp. Early planting facilitated longer vegetative growth, improved plant height, stem diameter, and biomass and fiber accumulation, and also demonstrated better root development. Later planting date led to early transition to reproductive phase, consequently lowering the final biomass and fiber production and their water productivity (WP). But, due to early flowering initiation, late planting resulted in higher seed yield. Seeding densities affected growth, with higher densities resulting in denser canopies, better light absorption, and eventually higher final production. These results collectively provide farmers with optimal cultivation methods, offering insightful information for increasing crop yield while advancing sustainable agricultural practices in the hemp sector.

2. Strategic tillage controls weeds while retaining increased crop water productivity in long-term no tillage operations. Adoption of no-tillage (NT) farming has increased crop water productivity and allowed cropping intensification in semi-arid environments like western Kansas and the Texas and Oklahoma Panhandles. But, maintaining continuous NT has become increasingly challenging because of the lack of herbicides that work against herbicide-resistant (HR) weeds. Kansas State University scientist working in an ARS Ogallala Aquifer Program project investigated the effectiveness of occasional or strategic tillage (ST) to manage weeds, redistribute soil acidity and nutrients, and affect soil water storage, and crop yields. They found that properly implemented ST had no negative effect on soil properties or crop yields, and could be a mitigation option to control herbicide resistant weeds and increase profitability of dryland crop yield under otherwise long-term NT production. Proper implementation was found to involve using sweeps while keeping tillage shallow (1-2 inches) to control weeds while not burying crop residues, and to work best when done during a dry period when no rain is forecast for several days. This finding gives farmers a valid option for weed control in otherwise no-till systems with improved crop water productivity.

3. Cropping systems intensification in the semi-arid Great Plains can be successfully accomplished using annual forages with reduced water use. The traditional dryland (rainfed) cropping system in the Southern High Plains is winter wheat-grain sorghum-fallow rotation, which while typically reliable could be intensified for greater profitability by inclusion of forages in the rotation. Annual forages use less water than either sorghum or corn grain crops. Kansas State University researchers, working in an ARS Ogallala Aquifer Program project, found that double cropping forage sorghum after winter wheat in the traditional rotation improved water utilization and profitability overall. A more intensified double-cropped rotation [winter wheat followed by double-cropped forage sorghum rotating to forage sorghum in the second year followed by fallow (W/FS-FS-FL)] increased overall forage productivity by 46%. Importantly, averaged-across-years, growing forages in place of fallow tended to improve cropping system profitability even when increased cropping intensity reduced grain yields. Replacing fallow with forage oats increased overall forage yield by 56%. This finding means that farmers may opt to grow forages rather than grain crops with reasonable expectation of improved profitability and reduced water use, extending the life of the Ogallala aquifer.

4. Grazing or haying is required for positive economic outcomes if cover crops are planted after winter wheat harvest in wheat-sorghum-fallow no-till rotations. Cover crops planted in dryland wheat-sorghum-fallow rotations can reduce water availability and grain yield. Kansas State University researchers, working in an ARS Ogallala Aquifer Program project, showed that grazing or haying of cover crops can provide forage for livestock, increase crop residue cover and soil aggregate stability, maintain grain crop yields, and increase net returns in wheat-sorghum-fallow no tillage (NT) systems. Combining the two, haying in wet years and grazing in dry years, could be a means to balance forage availability and forage demand across years. If strategic tillage is necessary in a long-term NT system, for example to correct root-limiting compaction or to control herbicide-resistant weeds, then plant available water, crop yields, net returns, and soil properties are generally unaffected compared to strict NT. This finding gives farmers a valid option for use of cover crops to control erosion while improving net returns on investment.

5. More accurate crop models assess effects of changing weather. If accurate, crop simulation models can be useful in forecasting effects of changing weather on crop production and nitrogen use. However, commonly used crop models need changes to improve accuracy of predictions of soil temperature, water use, crop yield, and nitrogen losses. ARS scientists teamed with Texas A&M University, China Agricultural University, and the University of New South Wales to improve the commonly used SWAT model to more accurately predict carbon dioxide (CO2) uptake and nitrogen use and loss. Results indicated that fertilizer applications could be reduced by 10-20% under elevated CO2 levels, saving money while reducing nitrogen losses. Future yields for irrigated cotton and sunflower and dryland cotton were predicted to increase with increasing CO2, while yields of irrigated sorghum and dryland soybean were expected to decrease. Water use was expected to decrease except for irrigated cotton. These results provide guide posts for farmers to deal with changing conditions with better probability of success.

6. Comparison of 33 crop models shows path to more accurate predictions. Crop models are commonly used to predict water use and yield in response to weather and agronomic practices, including irrigation. Predicted soil temperature is related to predictions of germination, decomposition, evaporation, losses of nitrogen, and carbon sequestration, but models may not accurately predict soil temperature. ARS scientists teamed with 30 universities and research institutions to compare soil temperature predictions of 33 crop models in comparison with measured soil temperature at Bushland, Texas, and Mead, Nebraska. Results showed that five of the six best soil temperature predictions resulted from numerical iterative solutions to a mechanistic model of soil temperature and water content changes in several layers of soil. Incorporation of this kind of solution into other models would improve prediction of soil temperature and related processes, and make crop models more useful to farmers, consultants, water managers, and other users.

7. Water saving policy in Kansas shown to effectively reduce aquifer depletion but not profitability. The State of Kansas has passed legislation promoting the creation of Local Enhanced Management Areas (LEMAs), which provide a voluntary structure for farmers to come together to set and implement tailored water-use restrictions within an area. However, the effectiveness of LEMAs is debated. ARS scientists teamed with Kansas State University and Northwest Kansas Groundwater Management District #4 to examine ten years of satellite and other data to determine water use before and after the implementation of a LEMA. They found that stakeholder-led restrictions led to 28% reduced withdrawals from the aquifer, while corn yields remained nearly the same across the ten years and cash flow was increased by 4.3%. These results show that the LEMA structure is useful in reducing aquifer depletion while improving profitability.

8. Mobile drip irrigation produces greater watermelon yields with less water than sprinklers. Center pivot irrigation systems using spray application are the predominant irrigation application method in the Southern High Plains, and when low-elevation spray application (LESA) is utilized these systems are recognized as being efficient. However, wetting the soil surface means that some water is lost to evaporation rather than reaching the crop, and alternative water application systems may be more effective. ARS and Texas A&M AgriLife scientists at Bushland, Texas, employed a new irrigation application system called mobile drip irrigation (MDI) to apply water to a watermelon crop while comparing it to LESA. The MDI system uses drip irrigation lines that are connected to the center pivot irrigation system but that drag on the ground and apply water directly to a small area where it enters the soil and replenishes water needed by the crop without wetting the entire soil surface. The MDI system produced 35% greater yields than did LESA, in part because there were more fruits per plant and larger plants. Fruit quality was not affected, and yield per unit of water used was much greater, indicating that MDI would be a profitable system for watermelon production on the High Plains while reducing withdrawals from the declining Ogallala aquifer.

9. Sensor data for irrigation scheduling improved by quality control algorithm. Infrared thermometers can measure crop canopy temperatures, and have been useful for irrigation scheduling, yield prediction, and early disease detection, all of which have been shown to improve crop water productivity. The recent availability of wireless infrared thermometers has resulted in large datasets of crop canopy temperature data becoming available. These large datasets are too big for manual correction and so require a computerized quality control algorithm to detect spurious or poor-quality data and apply corrections where required. Scientists and engineers at USDA ARS Bushland, Texas, developed a quality control algorithm and tested the algorithm for six calendar years; the years included three seasons of corn, two seasons of cotton, one season of soybean, and fallow periods before and after the crop seasons. The complete quality-controlled dataset was published on the USDA ARS National Agricultural Library Ag Data Commons. The dataset will be useful to calibrate and test models designed for irrigation scheduling, yield prediction, and crop disease detection.

10. Soil water sensors installed by robots had improved accuracy with new calibration procedure. Soil water sensors are useful for irrigation scheduling, but manual installation is costly. Robots have been designed to measure soil moisture by inserting sensor rods into the soil. However, the force required for insertion is oftentimes greater than that generated by these small robots. Incomplete insertion leads to air gaps which can result in large underestimation errors in soil moisture. A new calibration methodology was developed by scientists from ARS-Bushland, Texas, and Utah State University to account for incomplete insertion during automated measurements. The new method enables the estimation of accurate soil moisture contents when rods are incompletely inserted into the soil, thus making automated soil moisture measurements with robotic technologies more reliable, saving time and money.

11. Pathways to sustainable agricultural water use revealed. Irrigation uses more than 50% of freshwater supplies in the U.S. and between 70 and 80% of freshwater supplies worldwide. Economically strategic agricultural production regions of the U.S. depend on water withdrawn from aquifers but at rates greater than the rate of natural recharge. This can result in aquifer declines and shrinking irrigated acreages in regions over the Ogallala (High Plains) aquifer in the Great Plains, aquifers underlying the Mississippi delta regions of Arkansas, Mississippi and Louisiana, and aquifers in the productive Central Valley of California. Strategies and technologies for sustaining agricultural water use are thus of utmost importance. Scientists with ARS at Bushland, Texas, partnered with Colorado State University, Kansas State University, Technion—Israel Institute of Technology in Israel, Guangdong Academy of Sciences in China, and the Indian Institute of Technology Kanpur in India to assess effective policy frameworks that combined with technological advances can lead to sustainable agricultural water management. Strategies included crop switching for optimized production, soil management, modern irrigation technologies, artificial intelligence and big data, water treatment and reuse, reallocation of water use to more direct production of human food stuffs, and minimizing food loss and waste. The key to achieving a sustainable agricultural water system was found to lie in policies that effectively incentivize use of these strategies. Technology without policy will have limited effect.

12. Pathways to correction revealed for corn and wheat crop model underestimation of water use. Crop models are increasingly used in irrigation and other water management decision support systems, but accuracy of crop water use estimates is key to their successful use. ARS scientists at Bushland, Texas, teamed with researchers from several states and international partners to intercompare more than 40 winter wheat and corn crop models. They tested the models’ water use estimates against mass balance direct measurements of grain corn and winter wheat water use made in the semi-arid southern Great Plains environment using highly accurate facilities at Bushland, and also against water use measurements made in semi-arid parts of Europe. Crop models routinely underestimated water use of both crops, but reasons for this were discovered, leading to pathways for model improvement. Detailed weather and other environmental data observed at Bushland were key to understanding why models were incorrect. Models that relied on satellite remote sensing were less accurate than those depending on daily on-site weather observations, leading to cautionary conclusions about use of satellite remote sensing-based water use estimates for irrigation scheduling. Models tended to underestimate water use during the height of the irrigation season and overestimate water use as the crops matured. Evaluation of several multi-model averaging approaches led to discovery that averaging several models can lead to more accurate water use estimates than using a single model.

13. Improved simulation of soil freeze-thaw cycles leads to more accurate estimates of nitrate leaching. Nitrate leaching in intensively managed agricultural production areas such as the Upper Mississippi River Basin (UMRB) can be affected by freeze-thaw cycles. However, limited research has addressed nitrate leaching at deeper soil depths as influenced by the timing and magnitude of freeze-thaw cycles and how future climate change may affect these processes. Researchers from USDA-ARS Bushland and university partners from the U.S., Australia, and China simulated the effects freeze-thaw cycles on nitrate leaching in the UMRB using and improved Soil and Water Assessment Tool (SWAT-FT) model with projected climate data. Fluctuations in winter surface soil temperatures were greater for the SWAT-FT than those of the native model SWAT. Although nitrate leaching was most pronounced at shallow soil depths during May, leaching at deeper soil depths was observed for transition months of March, April, and November with cumulative values representing nearly 70 percent of annual total values for future scenarios. These findings highlight the need for targeted management strategies to mitigate both short- and long-term nitrate leaching in the UMRB.

14. Economically viable alternatives to alfalfa found to reduce water use in the southern Ogallala Aquifer region. Due to aquifer declines, it is important to develop cropping systems for efficient management of precipitation and irrigation to optimize profits and crop production in the southern Ogallala Aquifer region. Working in the ARS Ogallala Aquifer Program, Kansas State University researchers found that summer annual grasses would have higher forage yields and productivity than summer annual legumes, but that summer annual legumes would have higher crude protein concentration and greater digestibility. Researchers identified summer annual legume species that can serve as viable alternatives to alfalfa in the region. Under irrigation, forage soybean, cowpea, and lablab all have potential as alternatives to alfalfa due to their forage yield, nutritive value, and economic profit. In dryland environments, lablab and cowpea showed the most promise, and greater potential than dryland alfalfa. They did not recommend growing either forage soybean or sunn hemp in dryland environments across the semi-arid Great Plains due to both establishment challenges and low economic profitability. They also cautioned against growing sunn hemp under irrigation for forage purposes because it had negative net economic returns even under irrigation. The study provided strong evidence that further investigation is warranted into summer annual legume forage, particularly for the species cowpea and lablab. These species could provide more profit, greater yield, more cropping flexibility, and greater yield stability than dryland alfalfa. Further areas of investigation for these two species include 1) tracking the impact of forage nutritive value and forage yield based on growing degree days as opposed to physiological growth stage, and 2) exploring the impact that using early- vs. late-maturing genotypes have on forage yield, nutritive value, water use and profit. These results mean that dryland annual legumes could be grown to meet the high crude protein forage demand of regional animal industries.

15. AI shown to provide best maps of irrigated area on the Texas High Plains. Underground water conservation districts and state and regional water planning agencies need accurate yearly maps of irrigated areas within their management areas. Texas A&M scientists working in an ARS Ogallala Aquifer Program project developed a mapping tool with 97% accuracy by utilizing artificial intelligence (AI) based on machine learning to process satellite imagery. Comparison with other mapping techniques showed that the AI based method had superior accuracy. The new tool was used to provide high resolution maps from 2014 through 2024, filling an information need of Ogallala aquifer water managers.

Review Publications
Tan, L., Qi, J., Marek, G.W., Zhang, X., Ge, J., Sun, D., Li, B., Feng, P., Liu, D., Li, B., Srinivasan, R., Chen, Y. 2025. Assessing the impacts of extreme precipitation projections on Haihe Basin hydrology using an enhanced SWAT model. Journal of Hydrology: Regional Studies. 58. Article 102235. https://doi.org/10.1016/j.ejrh.2025.102235.
Dakshinamurthy, H., Jones, S.B., Schwartz, R.C., Young, S. 2025. Waveform analysis for short time domain reflectometry (TDR) probes to obtain calibrated moisture measurements from partial vertical sensor insertions. Computers and Electronics in Agriculture. 235. Article 110233. https://doi.org/10.1016/j.compag.2025.110233.
Schwartz, R.C., Colaizzi, P.D., Dominguez, A., Baumhardt, R.L., Ulloa, M. 2024. Comparison of infrared thermometry and soil water derived stress indices and crop ET in cotton. Applied Engineering in Agriculture. 40(5):537-551. https://doi.org/10.13031/aea.16104.
Evett, S.R., Marek, G.W., Colaizzi, P.D., Copeland, K.S., Ruthardt, B.B., Howell, T.A. 2025. The Bushland, Texas, maize evapotranspiration, growth, and yield dataset collection. Scientific Data - Nature. 12. Article 209. https://doi.org/10.1038/s41597-025-04539-2.
Ding, B., Li, Y., Marek, G.W., Ge, J., Han, Y., Hu, K., Yan, T., Ale, S., Zhang, G., Srinivasan, R., Chen, Y. 2024. Impacts of land use changes on water conservation in the Songhuajiang River basin in Northeast China using the SWAT model. Journal of Hydrology. 306. Article 109185. https://doi.org/10.1016/j.agwat.2024.109185.
Dhungel, R., Aiken, R., Lin, X., Kenyon, S., Colaizzi, P.D., O'Brien, D., Baumhardt, R.L., Kutikoff, S. 2025. Water savings policy in western Kansas: a decade-long satellite-based examination. Irrigation Science. https://doi.org/10.1007/s00271-025-01024-x.
Holman, J.D., Obour, A.K., Assefa, Y. 2023. Forage sorghum grown in a conventional wheat-grain sorghum-fallow rotation increased cropping system productivity and profitability. Canadian Journal of Plant Science. 103(1):61-72. https://doi.org/10.1139/cjps-2022-0171.
Bajwa, P., Saini, R., Singh, S., Makkar, J., Trostle, C., Singh, H. 2025. Effect of early and late post emergence herbicides on weed suppression, crop injury, and biomass yield of industrial hemp in semiarid conditions. Agrosystems, Geosciences & Environment. 8(1). Article e70078. https://doi.org/10.1002/agg2.70078.
Bajwa, P., Singh, S., Kafle, A., Saini, R., Trostle, C. 2025. Effect of planting dates and seeding densities on growth, physiology, and yield of industrial hemp. Crop Science. 65(2). Article e70017. https://doi.org/10.1002/csc2.70017.
Zhang, Y., Zhang, X., Ding, B., Qi, J., Marek, G.W., Feng, P., Liu, D., Srinivasan, R., Chen, Y. 2025. Simulating vertical soil nitrate migration induced by freeze-thaw cycles. Journal of Hydrology. 660. Article 133451. https://doi.org/10.1016/j.jhydrol.2025.133451.
Yuan, C., Li, X., Wu, Y., Marek, G.W., Ale, S., Srinivasan, R., Chen, Y. 2025. Impacts of change in multiple cropping index of rice on hydrological components and grain production in the Zishui River Basin, Southern China. Agricultural Water Management. 316. Article 109572.
Malik, H.T., Zvulonov, Y., Kinnebrew, E., Gates, T., Evett, S.R., Vanderroest, J.P., Radian, A., Chi, J., Abhijith, G.R., Mueller, N.D., Ostfeld, A., Fang, L., Borch, T. 2025. Advancing sustainable water use across the agricultural lifecycle in the United States. Nature Water. 3:655-667. https://doi.org/10.1038/s44221-025-00450-7.
Zhang, L., Bai, G., Evett, S.R., Colaizzi, P.D., Xue, Q., Marek, G.W., Dhungel, R., Zhao, H., Wan, N., Lin, X. 2025. Increased irrigation could mitigate future warming-induced maize yield losses in the Ogallala Aquifer. Communications Earth & Environment. 6. Article 483. https://doi.org/10.1038/s43247-025-02459-y.
Soto, A., Shrestha, R., Xue, Q., Colaizzi, P.D., O'Shaughnessy, S.A., Workneh, F., Adhikari, R., Rush, C.M. 2024. Evaluation of three irrigation application systems for watermelon production in the Texas High Plains. Agronomy Journal. 116(5):2532-2550. https://doi.org/10.1002/agj2.21653.
Wen, N., Marek, G.W., Srinivasan, R., Brauer, D.K., Qi, J., Wang, N., Han, Y., Zhang, X., Feng, P., Liu, D., Chen, Y. 2024. Assessing the impacts of long-term climate change on hydrology and yields of diversified crops in the Texas High Plains. Agricultural Water Management. 302. Article 108985. https://doi.org/10.1016/j.agwat.2024.108985.
Nand, V., Qi, Z., Ma, L., Helmers, M.J., Madramootoo, C.A., Smith, W.N., Zhang, T.Q., Weber, T.K., Pattey, E., Li, Z., Wang, J., Jin, V.L., Jiang, Q., Tenuta, M., Trout, T.J., Chang, H., Harmel, R.D., 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.M., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., Migliorati, M., Dumont, B., Durand, J., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Kim, S., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D.J., Trabucco, A., Webber, H., Willaume, M., Williams, K., Van Der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W. 2025. Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models. Journal of Hydrology. 661(Part B). Article e133631. https://doi.org/10.1016/j.jhydrol.2025.133631.
Wen, N., Han, Y., Qi, J., Marek, G.W., Sun, D., Feng, P., Srinivasan, R., Liu, D., Chen, Y. 2024. Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: implications for water resources. Water Research. 265. Article 122279. https://doi.org/10.1016/j.watres.2024.122279.
Li, B., Tan, L., Zhang, X., Qi, J., Marek, G.W., Feng, P., Liu, D., Luo, X., Srinivasan, R., Chen, Y. 2024. Enhanced freeze-thaw cycle altered the simulations of groundwater dynamics in a heavily irrigated basin in the temporate region of China. Water Resources Research. 60(9). Article e2023WR036151. https://doi.org/10.1029/2023WR036151.
Webber, H., Cooke, D., Wang, C., Asseng, P., Martre, P., Ewert, F., Kimball, B., Hoogenboom, G., Evett, S.R., Chanzy, A., Garrigues, S., Olioso, A., Copeland, K.S., Steiner, J.L., Cammarano, D., Chen, Y., Crépeau, M., Diamantopoulos, E., Ferrise, R., Manceau, L., White, J. 2025. Wheat crop models underestimating drought stress in semi-arid and Mediterranean environments. Field Crops Research. 332. Article 110032. https://doi.org/10.1016/j.fcr.2025.110032.
Simon, L.M., Obour, A.K., Holman, J.D., Roozeboom, K.L. 2022. Long-term cover crop management effects on soil properties in dryland cropping systems. Agriculture, Ecosystems & Environment. 328. Article 107852. https://doi.org/10.1016/j.agee.2022.107852.
Stockle, C.O., Liu, M., Kadam, S.A., Evett, S.R., Marek, G.W., Colaizzi, P.D. 2025. Comparing evapotranspiration estimations using crop model-data fusion and satellite data-based models with lysimetric observations: Implications for irrigation scheduling. Agricultural Water Management. 311. Article 109372. https://doi.org/10.1016/j.agwat.2025.109372.
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