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Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California

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  • Haghverdi, Amir
  • Singh, Amninder
  • Sapkota, Anish
  • Reiter, Maggie
  • Ghodsi, Somayeh

Abstract

A three-year (2017–2019) irrigation research trial was conducted to evaluate the response of hybrid bermudagrass to a wide range of irrigation scenarios and assess the efficacy of Weathermatic Evapotranspiration-based (ET-based) smart controller for autonomous landscape irrigation management during dry seasons in inland southern California. The irrigation levels applied throughout the experiment ranged between 39% and 103% reference ET (ETo) and the irrigation frequency restrictions imposed were 3, 5 and 7 d/wk. Normalized difference vegetation index (NDVI) data were continuously collected to evaluate the response of hybrid bermudagrass [‘Tifgreen’ Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] to irrigation treatments. Plots were also visually assessed and scaled from 1 (dead plot) to 9 (ideal turfgrass) following the National Turfgrass Evaluation Program (NTEP) standards. Turfgrass water response function (TWRF) was introduced as a statistical regression model to estimate hybrid bermudagrass quality response (NDVI values) to irrigation levels over time. In the years 2018 (p < 0.01) and 2019 (p < 0.001), the irrigation levels showed a significant effect on NDVI values. The irrigation frequency restrictions showed no significant impact on NDVI in any of the years. We observed a high correlation (r = 0.84) between visual rating (VR) and NDVI data. The TWRF shows a high accuracy (RMSE = 0.047, no units), and estimated NDVI values were highly correlated (r = 0.89) with measured NDVI values. A comparison between the California irrigation management information system (CIMIS) reference evapotranspiration (ETo) versus temperature-based ETo estimations by the controller revealed the smart controller on average over-irrigated by 12%, 2% and 3% throughout the experimental periods in 2017, 2018 and 2019, respectively. A long term (34 years) analysis using CIMIS ETo data and TWRF model revealed 75% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in the inland southern California semiarid climate for months with high irrigation demand (i.e., May to November). The results also showed that hybrid bermudagrass could withstand more severe deficit irrigation treatments for shorter periods depending on ETo demand.

Suggested Citation

  • Haghverdi, Amir & Singh, Amninder & Sapkota, Anish & Reiter, Maggie & Ghodsi, Somayeh, 2021. "Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California," Agricultural Water Management, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321338
    DOI: 10.1016/j.agwat.2020.106586
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    References listed on IDEAS

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    1. Davis, S.L. & Dukes, M.D., 2010. "Irrigation scheduling performance by evapotranspiration-based controllers," Agricultural Water Management, Elsevier, vol. 98(1), pages 19-28, December.
    2. Wherley, B. & Dukes, M.D. & Cathey, S. & Miller, G. & Sinclair, T., 2015. "Consumptive water use and crop coefficients for warm-season turfgrass species in the Southeastern United States," Agricultural Water Management, Elsevier, vol. 156(C), pages 10-18.
    3. Davis, S.L. & Dukes, M.D. & Miller, G.L., 2009. "Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida," Agricultural Water Management, Elsevier, vol. 96(12), pages 1828-1836, December.
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    Cited by:

    1. Dong, Juan & Xing, Liwen & Cui, Ningbo & Guo, Li & Liang, Chuan & Zhao, Lu & Wang, Zhihui & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China," Agricultural Water Management, Elsevier, vol. 291(C).
    2. Ahmadi, Arman & Kazemi, Mohammad Hossein & Daccache, Andre & Snyder, Richard L., 2024. "SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation," Agricultural Water Management, Elsevier, vol. 295(C).
    3. Zeng, Yuan-Fu & Chen, Ching-Tien & Lin, Gwo-Fong, 2023. "Practical application of an intelligent irrigation system to rice paddies in Taiwan," Agricultural Water Management, Elsevier, vol. 280(C).

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