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Water conservation potential of smart irrigation controllers on St. Augustinegrass

Author

Listed:
  • McCready, M.S.
  • Dukes, M.D.
  • Miller, G.L.

Abstract

A variety of technologies for reducing residential irrigation water use are available to homeowners. These "Smart Irrigation" technologies include evapotranspiration (ET)-based controllers and soil moisture sensor (SMS) controllers. The purpose of this research was to evaluate the effectiveness of these technologies, along with rain sensors, based on irrigation applied and turfgrass quality measurements on St. Augustinegrass (Stenotaphrum secundatum (Walter) Kuntze). Testing was performed on two types of SMS controllers (LawnLogic LL1004 and Acclima Digital TDT RS500) at three soil moisture threshold settings. Mini-Clik rain sensors (RS) comprised six treatments at two rainfall thresholds (3mm and 6mm) and three different irrigation frequencies (1, 2, and 7d/wk). Two ET controllers were also tested, the Toro Intelli-Sense controller and the Rain Bird ET Manager. A time-based treatment with 2 days of irrigation per week without any type of sensor (WOS) to bypass irrigation was established as a comparison. All irrigation controller programming represented settings that might be used in residential/commercial landscapes. Even though three of the four testing periods were relatively dry, all of the technologies tested managed to reduce water application compared to the WOS treatment, with most treatments also producing acceptable turf quality. Reductions in irrigation applied were as follows: 7-30% for RS-based treatments, 0-74% for SMS-based treatments, and 25-62% for ET-based treatments. The SMS treatments at low threshold settings resulted in high water savings, but reduced turf quality to unacceptable levels. The medium threshold setting (approximately field capacity) SMS-based treatment produced good turfgrass quality while reducing irrigation water use compared to WOS by 11-53%. ET controllers with comparable settings and good turf quality had -20% to 59% savings. Reducing the irrigation schedule (treatment DWRS) by 40% and using a rain sensor produced water savings between 36% and 53% similar to smart controllers. Proper installation and programming of each of the technologies was essential element to balancing water conservation and acceptable turf quality. Water savings with the SMS controllers could have been increased with a reduced time-based irrigation schedule. Efficiency settings of 100% (DWRS) and 95% (TORO) did not reduce turf quality below acceptable limits and resulted in substantial irrigation savings, indicating that efficiency values need not be low in well designed and maintained irrigation systems. For most conditions in Florida, the DWRS schedule (60% of schedule used for SMS treatments) can be used with either rain sensors or soil moisture sensors in bypass control mode as long as the irrigation system has good coverage and is in good repair.

Suggested Citation

  • McCready, M.S. & Dukes, M.D. & Miller, G.L., 2009. "Water conservation potential of smart irrigation controllers on St. Augustinegrass," Agricultural Water Management, Elsevier, vol. 96(11), pages 1623-1632, November.
    • Handle: RePEc:eee:agiwat:v:96:y:2009:i:11:p:1623-1632
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    Citations

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    Cited by:

    1. Migliaccio, Kati W. & Schaffer, Bruce & Crane, Jonathan H. & Davies, Frederick S., 2010. "Plant response to evapotranspiration and soil water sensor irrigation scheduling methods for papaya production in south Florida," Agricultural Water Management, Elsevier, vol. 97(10), pages 1452-1460, October.
    2. Snyder, R.L. & Pedras, C. & Montazar, A. & Henry, J.M. & Ackley, D., 2015. "Advances in ET-based landscape irrigation management," Agricultural Water Management, Elsevier, vol. 147(C), pages 187-197.
    3. Chen, Xiaoping & Qi, Zhiming & Gui, Dongwei & Sima, Matthew W. & Zeng, Fanjiang & Li, Lanhai & Li, Xiangyi & Gu, Zhe, 2020. "Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate," Agricultural Water Management, Elsevier, vol. 234(C).
    4. McCready, M.S. & Dukes, M.D., 2011. "Landscape irrigation scheduling efficiency and adequacy by various control technologies," Agricultural Water Management, Elsevier, vol. 98(4), pages 697-704, February.
    5. Silva, Marcos Dornelas Freitas Machado e & Calijuri, Maria Lúcia & Sales, Francisco José Ferreira de & Souza, Mauro Henrique Batalha de & Lopes, Lucas Sampaio, 2014. "Integration of technologies and alternative sources of water and energy to promote the sustainability of urban landscapes," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 71-81.
    6. Khachatryan, Hayk & Suh, Dong Hee & Xu, Wan & Useche, Pilar & Dukes, Michael D., 2019. "Towards sustainable water management: Preferences and willingness to pay for smart landscape irrigation technologies," Land Use Policy, Elsevier, vol. 85(C), pages 33-41.
    7. M. Safdar Munir & Imran Sarwar Bajwa & M. Asif Naeem & Bushra Ramzan, 2018. "Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming," Energies, MDPI, vol. 11(12), pages 1-18, December.
    8. Cardenas-Lailhacar, B. & Dukes, M.D., 2010. "Precision of soil moisture sensor irrigation controllers under field conditions," Agricultural Water Management, Elsevier, vol. 97(5), pages 666-672, May.
    9. Sara Komenda & Martha C. Monroe, 2023. "Clues in the Data: The Role of Education in Adopting Technology That Enhances Sustainable Lifestyle Choices," Sustainability, MDPI, vol. 15(11), pages 1-15, May.

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