Irrigation scheduling technologies reduce water use and maintain turfgrass quality

Abstract

Smart irrigation controllers have demonstrated potential for turfgrass water conservation in humid and temperate environments but have not been comprehensively tested in arid environments. The objective of this study was to determine the accuracy of a wireless capacitance sensor over a wide soil moisture range and to ascertain if smart irrigation controllers resulted in water savings without reducing quality of tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.] and bermudagrass (Cynodon dactylon L.). A two-yr study was conducted to compare turfgrass quality, root morphology, and water use of plots irrigated with a constant run time to plots for which irrigation was scheduled using soil moisture sensors (SMS), evapotranspiration (ET) [Climate Logic (CL)] controllers, or 80% of historic ET (ET80) for tall fescue and 60% (ET60) for bermudagrass. Sensors accurately tracked soil moisture up to salinity levels of 4 dS m −1. Turf performance and root morphology were not affected by irrigation treatments for either grass. Compared to tall fescue plots irrigated with constant run time, plots irrigated using ET80 and CL required 38% less water, and SMS plots used 44% less than tall fescue. Scheduling bermudagrass irrigation by ET60, CL, and SMS resulted in a 29, 42, and 39% reduction in water applied compared to constant run time. The majority of water savings was in spring and fall. Water requirement for bermudagrass during the summer did not differ between the scheduling treatments. Our study confirms that smart irrigation controllers can be used as an effective measure to conserve water in an arid environment.

Evaluation of irrigation scheduling approaches within sand‐capped turfgrass systems

“…When comparing effects of irrigation treatments on maintenance of visual quality in our study, all treatments supported acceptable quality, with no differences between weather-based or SMS-based approaches. Our observations are consistent with those of Serena et al (2020), who reported acceptable 'Princess 77' bermudagrass summer turf performance when using ET-based, calendarbased, and SMS-based scheduling approaches in a New Mexico study.…”

“…Differences in K c among studies may be attributed to factors such as fertility, mowing height, or differences in climatic conditions under which studies were conducted. A year‐round K c value of 0.6 was proposed by Meyer and Gibeault (1987) and has been demonstrated to be adequate for maintaining summer bermudagrass quality in multiple studies (Hejl et al., 2016; Serena et al., 2020).…”

Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

“…In [ 35 ], soil moisture sensors and evapotranspiration prediction were used to create a better irrigation scheduling, achieving a 39% reduction in water consumption. In [ 36 ] presents a solution capable of reducing 23% of water in irrigation using LoRa nodes that gather information about air temperature and humidity and soil moisture, that were analysed using fog computing.…”

Turf solutions for the future, the 7th European turfgrass society research conference: Agronomy Journal special issue

“…Findings on turfgrass performance under different irrigation strategies are reported by Serena et al. (2020) and Dyer et al. (2020).…”

“…Topics also include the effect of fertilization on establishment (Lindsey, Thoms, & Christians, 2020;Øgaard & Aamlid, 2020) and maintenance strategies for disease control (Hempfling, Murphy, & Clarke, 2020;Mattox, Kowalewski, McDonald, Lambrinos, & Pscheidt, 2020). Findings on turfgrass performance under different irrigation strategies are reported by Serena et al (2020) and Dyer et al (2020). We would like to thank Dr. John Stier, University of Tennessee, and Dr. Mike Richardson, Agronomy Journal.…”