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Irrigation scheduling performance by evapotranspiration-based controllers

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  • Davis, S.L.
  • Dukes, M.D.

Abstract

Evapotranspiration-based irrigation controllers, also known as ET controllers, use ET information or estimation to schedule irrigation. Previous research has shown that ET controllers could reduce irrigation as much as 42% when compared to a time-based irrigation schedule. The objective of this study was to determine the capability of three brands of ET-based irrigation controllers to schedule irrigation compared to a theoretically derived soil water balance model based on the Irrigation Association Smart Water Application Technologies (SWAT) protocol to determine the effectiveness of irrigation scheduling. Five treatments were established, T1-T5, replicated four times for a total of twenty field plots in a completely randomized block design. The irrigation treatments were as follows: T1, Weathermatic SL1600 with SLW15 weather monitor; T2, Toro Intelli-sense; T3, ETwater Smart Controller 100; T4, a time-based treatment determined by local recommendations; and T5, a reduced time-based treatment 60% of T4. All treatments utilized rain sensors set at a 6Â mm threshold. A daily soil water balance model was used to calculate the theoretical irrigation requirements for comparison with actual irrigation water applied. Calculated in 30-day running totals, irrigation adequacy and scheduling efficiency were used to quantify under- and over-irrigation, respectively. The study period, 25 May 2006 through 27 November 2007, was drier than the historical average with a total of 1326Â mm of rainfall compared to 1979Â mm for the same historical period. It was found that all treatments applied less irrigation than required for all seasons. Additionally, the ET controllers applied only half of the irrigation calculated for the theoretical requirement for each irrigation event, on average. Irrigation adequacy decreased when the ET controllers were allowed to irrigate any day of the week. All treatments had decreased scheduling efficiency averages in the rainy season with the largest decrease of 29 percentile points with a timer and rain sensor (T4) and an average decrease of 20 percentile points for the ET controllers, indicating that site specific rainfall has a significant effect on scheduling efficiency results. Rainfall did not drastically impact the average irrigation adequacy results. For this study, there were two controller program settings that impacted the results. The first setting was the crop coefficients where specific values were chosen for the location of the study when calculating the theoretical requirement whereas the controllers used default values. The second setting was the soil type that defines the soil water holding capacity of the soil. The ET controllers were able to regularly adjust to real-time weather, unlike the conventional irrigation timers. However, the incorporation of site specific rainfall measurements is extremely important to their success at managing landscape water needs and at a minimum a rain sensor should be used.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:agiwat:v:98:y:2010:i:1:p:19-28
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    References listed on IDEAS

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    1. 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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    1. Li, Dazhi & Hendricks Franssen, Harrie-Jan & Han, Xujun & Jiménez-Bello, Miguel Angel & Martínez Alzamora, Fernando & Vereecken, Harry, 2018. "Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain," Agricultural Water Management, Elsevier, vol. 208(C), pages 465-477.
    2. Shi, Jianchu & Wu, Xun & Wang, Xiaoyu & Zhang, Mo & Han, Le & Zhang, Wenjing & Liu, Wen & Zuo, Qiang & Wu, Xiaoguang & Zhang, Hongfei & Ben-Gal, Alon, 2020. "Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation," Agricultural Water Management, Elsevier, vol. 230(C).
    3. Kang, Jian & Hao, Xinmei & Zhou, Huiping & Ding, Risheng, 2021. "An integrated strategy for improving water use efficiency by understanding physiological mechanisms of crops responding to water deficit: Present and prospect," Agricultural Water Management, Elsevier, vol. 255(C).
    4. Nandan, Rohit & Woo, Dong K. & Kumar, Praveen & Adinarayana, J., 2021. "Impact of irrigation scheduling methods on corn yield under climate change," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Li, Shengping & Tan, Deshui & Wu, Xueping & Degré, Aurore & Long, Huaiyu & Zhang, Shuxiang & Lu, Jinjing & Gao, Lili & Zheng, Fengjun & Liu, Xiaotong & Liang, Guopeng, 2021. "Negative pressure irrigation increases vegetable water productivity and nitrogen use efficiency by improving soil water and NO3–-N distributions," Agricultural Water Management, Elsevier, vol. 251(C).
    6. Marjan Aziz & Madeeha Khan & Naveeda Anjum & Muhammad Sultan & Redmond R. Shamshiri & Sobhy M. Ibrahim & Siva K. Balasundram & Muhammad Aleem, 2022. "Scientific Irrigation Scheduling for Sustainable Production in Olive Groves," Agriculture, MDPI, vol. 12(4), pages 1-14, April.
    7. 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).
    8. Wu, Bingfang & Jiang, Liping & Yan, Nana & Perry, Chris & Zeng, Hongwei, 2014. "Basin-wide evapotranspiration management: Concept and practical application in Hai Basin, China," Agricultural Water Management, Elsevier, vol. 145(C), pages 145-153.
    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.
    10. Wanjiru, Evan M. & Xia, Xiaohua, 2015. "Energy-water optimization model incorporating rooftop water harvesting for lawn irrigation," Applied Energy, Elsevier, vol. 160(C), pages 521-531.
    11. Mbabazi, Deanroy & Migliaccio, Kati W. & Crane, Jonathan H. & Fraisse, Clyde & Zotarelli, Lincoln & Morgan, Kelly T. & Kiggundu, Nicholas, 2017. "An irrigation schedule testing model for optimization of the Smartirrigation avocado app," Agricultural Water Management, Elsevier, vol. 179(C), pages 390-400.

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