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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #428355
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: Comparing four irrigation scheduling methods on maize growth, yield, and water productivity

Author
item Zhang, Huihui
item DeJonge, Kendall
item Ma, Liwang
item Yemoto, Kevin
item Steward, Ross
item Pokoski, Tyler
item ALTENHOFEN, JON - Northern Water

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/20/2026
Publication Date: 6/24/2026
Citation: Zhang, H., DeJonge, K.C., Ma, L., Yemoto, K.K., Steward, R.J., Pokoski, T.C., Altenhofen, J. 2026. Comparing four irrigation scheduling methods on maize growth, yield, and water productivity. Agricultural Water Management. 333. Article e110566. https://doi.org/10.1016/j.agwat.2026.110566.
DOI: https://doi.org/10.1016/j.agwat.2026.110566

Interpretive Summary: Efficient irrigation scheduling is essential for maximizing crop yields while conserving water, particularly in water-scarce regions. This three-year field study (2022–2024) in Northern Colorado evaluated four irrigation scheduling methods—FAO56 crop coefficient (FAO56), RZWQM2 model-based (RZRS), and two soil water balance methods (SWB1 and SWB2)—under both full (F) and deficit (L) irrigation regimes in maize. Despite uniform irrigation dates, total water applied and frequency varied across treatments. SWB1-F applied the most water, while RZRS-F used 20% less on average and maintained over 91% of the yield, showing high water-use efficiency. Under deficit irrigation, RZRS-L outperformed other methods, producing 45% higher yield with just 17% more water, and RZRS methods always achieved the highest water productivity. Overall, the results demonstrate that the model-based scheduling with RZWQM2 enhances yield and water productivity under both full and limited irrigation.

Technical Abstract: Efficient irrigation scheduling is critical for optimizing water use and sustaining crop productivity, especially under water-limited conditions. This three-year field study (2022–2024) evaluated the performance of four irrigation scheduling methods: a standardized crop coefficient method (FAO56), Root Zone Water Quality Model 2 modeling method (RZRS), and two soil water balance methods (SWB1 and SWB2), under full (F) and deficit (L) irrigation regimes in a maize cropping system in Northern Colorado. Despite irrigation events occurring on fixed dates, treatments differed in both total water applied and irrigation frequency. Full irrigation treatment, SWB1-F, applied the most water across all years, whereas RZRS-F used an average of 20% less water compared to SWB1-F while maintaining about 91.4% yields. Among deficit treatments, RZRS-L consistently achieved the highest grain yield and crop water productivity (WP), outperforming SWB1-L and SWB2-L with just 17% more irrigation water applied on average, resulting in an average of 45% higher yield. The highest WP was found in RZRS-F in 2022 (2.59 kg m-3), and in RZRS-L in both 2023 (2.08 kg m-3) and 2024 (2.17 kg m-3). Differences in leaf area index (LAI) and biomass accumulation indicated that RZRS-L reduced drought stress and supported better crop development than the other deficit strategies. These findings highlight the benefits of adopting an adaptive, RZWQM2 model-based irrigation scheduling method for high-frequency application to maximize yield and improve crop water productivity under both optimal and limited water supply conditions.