Comparing Smart Irrigation Controllers for Turfgrass Landscapes

in HortTechnology
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Shane R. EvansMETER Group Inc., 2365 Northeast Hopkins Court, Pullman, WA 99163-5601

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Kelly KoppDepartment of Plants, Soils, and Climate, Utah State University, 4820 Old Main Hill Logan, UT 84322-4820

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Paul G. JohnsonDepartment of Plants, Soils, and Climate, Utah State University, 4820 Old Main Hill Logan, UT 84322-4820

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Bryan G. HopkinsDepartment of Plant and Wildlife Sciences, Brigham Young University, 5117 Life Science Building, Provo, UT 84602-0002

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Xin DaiUtah Agricultural Experiment Station, Utah State University, 4810 Old Main Hill Logan, UT 84322-4810

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Candace SchaibleUtah State University Cooperative Extension-Iron County, 585 North Main Street, #4, Cedar City, UT 84721-6144

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Recent advances in irrigation technologies have led many states to incentivize homeowners to purchase United States Environmental Protection Agency WaterSense-labeled, smart irrigation controllers. However, previous research of smart controllers has shown that their use may still result in excess water application when compared with controllers manually programmed to replace actual water loss. This study compared kentucky bluegrass (Poa pratensis) irrigation applications using three smart irrigation controllers, a conventional irrigation controller programmed according to Cooperative Extension recommendations, and the average irrigation rate of area homeowners in Utah during 2018 and 2019. Of all the controllers tested, the manually programmed controller applied water at amounts closest to the actual evapotranspiration rates; however, smart controllers applied from 30% to 63% less water than area homeowners, depending on the controller and year of the study. Kentucky bluegrass health and quality indicators—percent green cover and normalized difference vegetation indices—varied between years of the study and were lower than acceptable levels on several occasions in 2019 for three of the four controllers tested. Compared with the results of similar studies, these findings suggest that the effects of smart irrigation controllers on turfgrass health and quality may vary by location and over time.

Abstract

Recent advances in irrigation technologies have led many states to incentivize homeowners to purchase United States Environmental Protection Agency WaterSense-labeled, smart irrigation controllers. However, previous research of smart controllers has shown that their use may still result in excess water application when compared with controllers manually programmed to replace actual water loss. This study compared kentucky bluegrass (Poa pratensis) irrigation applications using three smart irrigation controllers, a conventional irrigation controller programmed according to Cooperative Extension recommendations, and the average irrigation rate of area homeowners in Utah during 2018 and 2019. Of all the controllers tested, the manually programmed controller applied water at amounts closest to the actual evapotranspiration rates; however, smart controllers applied from 30% to 63% less water than area homeowners, depending on the controller and year of the study. Kentucky bluegrass health and quality indicators—percent green cover and normalized difference vegetation indices—varied between years of the study and were lower than acceptable levels on several occasions in 2019 for three of the four controllers tested. Compared with the results of similar studies, these findings suggest that the effects of smart irrigation controllers on turfgrass health and quality may vary by location and over time.

Public water utilities, particularly those in the western United States, experience pressure to meet the increasing water demands with diminishing or uncertain supplies. For example, with an average annual precipitation rate of 13 inches, Utah is the second driest state in the United States, and it is subject to periodic drought (Kenny et al., 2009). In addition, similar to other states in the intermountain western United States, Utah is highly urbanized, and the population is expected to more than double by 2050 (Endter-Wada et al., 2008). Many communities along the highly populated Wasatch Front of the state could encounter serious water shortages during this period of growth; therefore, the implementation of a variety of water conservation strategies is necessary to assure adequate future water supplies (Utah Division of Water Resources, 2007).

Providing communities with a reliable public water supply is a priority of federal and local governments (U.S. Environmental Protection Agency, 2016), and improving water use efficiency can help fulfill this goal. Therefore, landscape watering (both residential and commercial) is one of the largest sources of potential conservation in the urban setting (Endter-Wada et al., 2008). Up to 60% of residential water use may be applied to ornamental landscapes in the United States, and it is estimated that as much as 50% of that water may be wasted because of evaporation, wind drift, runoff, and inefficient irrigation methods and systems (U.S. Environmental Protection Agency, 2017). These findings have prompted agencies in western states to increase efforts to improve the efficiency of outdoor irrigation practices (Kopp et al., 2017). To reduce landscape water use, for example, the Utah Division of Water Resources launched a state-wide financial rebate program in 2018 that offered up to $75 toward the purchase of a U.S. Environmental Protection Agency WaterSense-labeled smart irrigation controller.

The U.S. Environmental Protection Agency’s WaterSense program tests and labels water-efficient products to help consumers save water. Products earning the WaterSense label have been certified to use at least 20% less water, save energy, and perform as well as or better than regular models (U.S. Environmental Protection Agency, 2018). WaterSense-labeled smart irrigation controllers adjust irrigation scheduling based on climate data and/or feedback from a soil moisture sensor, and evaluations of smart irrigation controller rebate programs have shown that the devices reduce outdoor water use (Mayer et al., 2009). However, additional studies indicated that smart controllers may apply excess water compared to manually programmed controllers (Devitt et al., 2009; Grabow et al., 2013; Pittenger et al., 2004).

In cooperation with the Weber Basin Water Conservancy District and the Utah Division of Water Resources, this study was conducted to determine if smart irrigation controllers could reduce water applications compared to manually programmed irrigation controllers and to evaluate the average irrigation application rates of area homeowners. We provide an evaluation of three smart irrigation controllers. Additionally, turfgrass health and quality indicators were measured over the course of the study. The research project was conducted for 14 weeks during the 2018 and 2019 growing seasons at Utah Agricultural Experiment Station Greenville Research Farm, North Logan, UT. The three smart controllers chosen for the project were U.S. Environmental Protection Agency WaterSense-labeled as of 2018, thus making them eligible for the rebate program of the Utah Division of Water Resources.

Materials and methods

The experiment was conducted during weeks 26 to 39 of 2018 (28 June to 29 Sept.) and weeks 26 to 39 of 2019 (24 June to 30 Sept.) at the Utah Agricultural Experiment Station Greenville Research Farm (lat. 41°45′N, long. 111°48′W; 4636 ft above sea level) to determine the effects of smart irrigation controllers implementing wireless technology on turfgrass health responses and quality. The total water application was compared among the controllers and average residential irrigation amounts in the area. To optimize plant health uniformity across treatments, the same watering schedule for all treatments was used for 4 weeks before the initiation of the experiment each year.

The soil at the site is a Millville silt loam (coarse-silty, carbonatic, mesic Typic Haploxeroll) and is considered well-drained. Soil pH at the site, sampled in June 2018, was an average of 7.7, and the average soil organic matter content was 2.9%. Onsite weather data were collected during the study by an automated station (ET 106; Campbell Scientific, Logan, UT) located ≈650 ft from the experimental plots. Incoming shortwave radiation, wind speed, relative humidity, temperature, dew point, precipitation, soil temperature, and soil moisture data were collected. Then, these data were used to calculate cool-season turfgrass reference evapotranspiration using the Penman-Monteith equation (Allen et al., 2005).

The research site was originally established in 2009, on a total area of 10,000 ft2 divided into 20 plots, with each measuring 300 ft2. After irrigation installation in 2009, plots were planted with 225 ft2 of locally grown kentucky bluegrass (Poa pratensis) sod and 75 ft2 of additional ornamental plants, including burning bush (Euonymus alatus), blue fescue (Festuca glauca), littleleaf boxwood (Buxus microphylla), common garden peony (Paeonia lactiflora), and mulch (Kopp et al., 2017). Plots were designed to be representative of local ornamental landscapes where kentucky bluegrass is the predominant turfgrass species.

Kentucky bluegrass plots were maintained at a mowing height of 3 inches (clippings recycled) with one application of ammonium sulfate fertilizer (21N–0P–0K; Frontier Fertilizer and Chemical Co., Johnstown, CO) applied each spring (1 lb/1000 ft2) and one application of ammonium sulfate fertilizer applied each fall (1.5 lb/1000 ft2) during both years of the study. Turfgrass areas of the plots were irrigated using overhead sprinklers (MP Rotator 2000; Hunter Industries, San Marcos, CA). To reduce weed pressure, a foliar herbicide application of 2,4-dichlorophenoxyacetic acid was applied at the recommended label rates each year in addition to periodic hand weeding. In June 2018, plots were also treated with imidacloprid (Mallet 0.5G; Nufarm, Alsip, IL) at the recommended label rates after the discovery of bluegrass billbug (Sphenophorus parvulus) larvae in the root zone of the turfgrass areas.

The experiment was arranged as a randomized complete block design with five blocks. Four treatments were randomly assigned to plots within each block. Experimental treatments included three commercially available smart irrigation controllers implementing wireless technology and one standard manually programmed irrigation controller.

To determine the actual amount of water used by kentucky bluegrass at the experiment location, four weighing lysimeters were constructed, installed near the site, and hung from precision scales to directly measure the actual evapotranspiration (ETA). Data were collected using a datalogger (CR 1000; Campbell Scientific) that recorded the amount of water lost each day. The following day, the amount of water lost from each lysimeter was replaced using drip irrigation emitters.

Irrigation controllers

The smart controllers implementing wireless technology used in the study were the B-Hyve Smart Sprinkler System (XR Model; Orbit Irrigation Products, North Salt Lake, UT), Rachio Smart Sprinkler Controller (Rachio 2; Rachio, Denver, CO), and Halo Smart Sprinkler System (Skydrop, American Fork, UT); hereafter, these are referred to as B-Hyve, Rachio, and Skydrop, respectively. The standard manually programmed irrigation controller used was a conventional irrigation controller (XC-400, Hunter Industries); hereafter, this is referred to as the control. The control served as the control for the experiment and was chosen based on local availability and irrigation product distributor recommendations. The three commercially available smart controllers implementing wireless technology were selected after consultation with the Weber Basin Water Conservancy District and Utah Division of Water Resources.

The base programming used for all four controllers was the same. However, the site-specific environmental settings programmed into the smart controllers differed depending on the allowed inputs (Table 1). These inputs were made based on questions asked during the initial setup of each irrigation zone programmed through each controller’s smart phone application. Although the soil at the research site is a silt loam, the closest programmable option for soil choice was loam for all controllers tested (Table 1).

Table 1.

Irrigation controller programming questions and required inputs for B-Hyve (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT), Rachio (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO), and Skydrop (Halo Smart Sprinkler System; Skydrop, American Fork, UT) irrigation controllers.

Table 1.

The Rachio controller, in addition to requiring input for individual irrigation zones, required the user to choose an irrigation schedule; the options were “fixed,” “flex monthly,” and “flex daily.” The schedules ranged from “most predictable” to “most water savings,” where “fixed” was the most predictable water application and “flex daily” was the most water-saving. For this experiment, the “flex daily” option was chosen to maximize water savings.

The base programming for the control was set according to local Cooperative Extension recommendations (Kopp et al., 2013), which are based on a historic (previous 30 years) reference evapotranspiration average and a recommended irrigation depth per application of 0.5 inches. During the months of June and July, irrigation was applied every 3 d. In August, irrigation was applied every 4 d; in September, irrigation was applied every 6 d (Kopp et al., 2013). These monthly irrigation frequencies were adjusted to replace 100% of evapotranspiration, as determined from the previous 30-year average reference evapotranspiration.

The length of time for each irrigation was determined using the catch cup test described by the Irrigation Association (2009). During these tests, catch cups were placed in the turf area of each plot, and the sprinkler system was used for 20 min. Then, irrigation depth measurements were obtained using the cups and distribution uniformity was calculated; thereafter, the precipitation rate of the sprinklers was also calculated. After sprinkler adjustments, an average distribution uniformity of 75% was calculated for the 20 research plots. Based on the average sprinkler precipitation rates, it was determined that a period of 76 min was required to apply the recommended irrigation depth of 13 mm to each plot.

For comparison purposes, an average irrigation water application rate was determined for single-family residences in the area based on data collected from residential irrigation audits conducted during the long-running (2005–present) Utah State University Extension Water Check Program, which found that homeowners in the state apply an average of 1200 mm of water during the growing season timeframe considered during this study.

The percentage differences in irrigation water applications among experimental treatments, ETA, and area homeowner irrigation rates were determined by dividing the total ETA and area homeowner irrigation rates by the total irrigation application of the controllers evaluated during both years of the study.

Data collection

The experiment was conducted for 14 weeks in 2018 (30 June to 30 Sept.) and 14 weeks in 2019 (28 June to 30 Sept.); this timeframe was specifically chosen to include the warmest and driest period of each growing season. The total water application to all plots was measured using low-flow water meters (iPerl; Sensus, Boise, ID) that recorded the water application on an hourly basis, and data were downloaded monthly using associated support software (UniPro version 2.6.2, Sensus). Measurements of the soil volumetric water content (SWC), normalized difference vegetation index (NDVI), and canopy temperature were obtained twice weekly between 1100 and 1300 hr.

Soil volumetric water content

The SWC measurements were obtained using a handheld soil moisture meter (HS2P, Campbell Scientific) incorporating two rods measuring 20 cm in length and using time-domain reflectometry to measure and integrate the SWC along the length of the rods. The SWC data from the meter were downloaded using associated support software (Hydrosense II, Campbell Scientific). To account for in-plot variability, five measurements were obtained twice weekly at random locations in each plot and then averaged by week for the duration of the experiment each year.

Normalized difference vegetation index

The NDVI was measured using a handheld color meter (FieldScout TCM 500; Spectrum Technologies, Aurora, IL). As with the SWC, five measurements were obtained twice weekly at random locations in each plot and then averaged by week for the duration of the experiment each year. Data from the instrument were downloaded weekly using associated support software (Spectrum Technologies).

Percent green cover

Measurements of the percent green cover were obtained weekly for the duration of the experiment according to the methods described by Karcher and Richardson (2013). Digital photographs of each plot were obtained using a light box (21 inches in width, 29 inches in length, and 23 inches in height) and a digital camera (PowerShot; Canon, Tokyo, Japan) during both years of the study. After photographs were obtained, they were analyzed using Turf Analyzer 1.01 software (Green Research Services, LLC, Fayetteville, AR) Karcher et al. (2017). For the software analyses, inputs of hue, saturation, and brightness were required and ranged from 76 to 170 (hue), 10 to 100 (saturation), and 0 to 100 (brightness).

Turfgrass canopy temperature

Turfgrass canopy temperature was measured twice weekly using an infrared camera (FLIR E5; Teledyne Technologies, Thousand Oaks, CA) and averaged by week using images obtained from a height of 13 ft above each plot. The images were downloaded using associated support software (FLIR Tools App; Teledyne Technologies); then, the canopy temperature measurements were recorded manually.

Statistical methods

The effects of the irrigation controller, SWC, NDVI, percent green cover, and canopy temperature were analyzed using a linear mixed model with repeated measures for a mixed model. The time of observation was the repeated measure. The fixed effects for the experiment were the controller and the controller × week interaction. The random effects were the block and the block × controller interaction. Data were log-transformed, and raw values were subtracted from a constant to meet the assumptions for the analysis of variance. The GLIMMIX procedure was performed for all data analyses (SAS version 9.4; SAS Institute, Cary, NC); when appropriate, the means were separated using the Tukey-Kramer method for multiplicity at P ≤ 0.05.

Results and discussion

Weather was similar during the months of June through August for both years of the study, but they differed significantly in September, when much more rain fell in 2019 than 2018. Average maximum daily air temperatures were 29.6 °C and 27.3 °C in 2018 and 2019, respectively (Figs. 1 and 2), and the warmest month of each year was July, with average maximum daily air temperatures of 32.5 °C in 2018 and 31.0 °C in 2019. Precipitation events rarely occurred during either year of the study, except for Sept. 2019, when 110 mm of rain fell (Fig. 2). Total natural precipitation rates during the study were 9.9 mm in 2018 and 123.2 mm in 2019.

Fig. 1.
Fig. 1.

Maximum daily air temperature and precipitation at the Utah State University Greenville Research Farm (North Logan, UT) from 30 June to 30 Sept. 2018. (1.8 × °C) + 32 = °F, 1 mm = 0.0394 inch.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

Fig. 2.
Fig. 2.

Maximum daily air temperature and precipitation at the Utah State University Greenville Research Farm (North Logan, UT) from 28 June to 30 Sept. 2019. (1.8 × °C) + 32 = °F, 1 mm = 0.0394 inch.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

Although the same local weather data source was used by each smart controller, significant differences in the amount of water applied by the controllers were observed. These differences may be attributed to the internal algorithms used by each controller to determine irrigation scheduling in relation to the weather data. However, these algorithms are not accessible to users, and specific differences among them are not known. In addition to the differences observed in weather from year to year, these algorithms may explain why differences in the amount of irrigation applied each year were observed.

Total irrigation water application

The weekly depth of irrigation water applied varied significantly across experimental treatments and was affected by the week, controller, and controller × week interactions (Tables 2 and 3). In 2018, weekly amounts of water applied ranged from 28.3 to 89.9 mm (B-Hyve), 18.1 to 57.7 mm (Control), 26.5 to 105 mm (Rachio), and 17.7 to 65.0 mm (Skydrop) (Table 4). In 2019, weekly amounts of water applied ranged from 28.4 to 86.0 mm (B-Hyve), 19.0 to 56.7 mm (Control), 30.0 to 69.2 mm (Rachio), and 16.9 to 60.1 mm (Skydrop) (Table 5). When comparing water application across weeks in 2018, significant differences were observed among the treatments during 7 of the 14 weeks studied (Table 4). In 2019, however, significant differences in the water application among treatments were observed during 12 of the 14 weeks studied (Table 5). Cardenas et al. (2020) also observed differences in the total water application by different smart irrigation controllers, although the weather conditions and specific controllers evaluated were different from those evaluated during this study.

Table 2.

Analysis of variance summary of repeated-measures analyses of water application, soil volumetric water content, normalized difference vegetation index, canopy temperature, and percent green cover of kentucky bluegrass in response to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018.

Table 2.
Table 3.

Analysis of variance summary of repeated-measures analyses of water application, soil volumetric water content, normalized difference vegetation index, canopy temperature, and percent green cover of kentucky bluegrass in response to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019.

Table 3.
Table 4.

Depth of irrigation application by week and actual evapotranspiration (ETA) of kentucky bluegrass irrigated using B-Hyve (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT), Hunter (XC-400; Hunter Industries, San Marcos, CA), Rachio (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO), and Skydrop (Halo Smart Sprinkler System; Skydrop, American Fork, UT) irrigation controllers across 14 weeks (30 June to 30 Sept.) in 2018.

Table 4.
Table 5.

Depth of irrigation application by week and actual evapotranspiration (ETA) of kentucky bluegrass irrigated using B-Hyve (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT), Hunter (XC-400; Hunter Industries, San Marcos, CA), Rachio (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO), and Skydrop (Halo Smart Sprinkler System; Skydrop, American Fork, UT) irrigation controllers across 14 weeks (28 June to 30 Sept.) in 2019.

Table 5.

Daily ETA measurements were summed to determine the weekly ETA for replacement in nearby weighing lysimeters. Then, these applications were compared with the irrigation depths applied by each controller on a weekly basis (Tables 4 and 5). Total growing season ETA values were 539 mm and 570 mm in 2018 and 2019, respectively. In 2018, the B-Hyve and Rachio treatments applied 54% and 56% more water, respectively, than ETA, whereas the control and Skydrop treatment applied 9% and 17% less water, respectively, than ETA. In 2019, the B-Hyve and Rachio treatments applied 30% and 34% more water, respectively, than ETA, whereas the control applied 5% less water than ETA, and the Skydrop treatment applied the same amount of water as ETA. The wide-ranging differences in water application by the smart controllers likely reflect differences in the algorithms used by each controller for irrigation scheduling and may also reflect more or less conservative approaches to irrigation scheduling by the various manufacturers.

Cooperative Extension irrigation recommendations, which guided the programming of the control in the study, were based on a historic 30-year average of local climate data. Although the B-Hyve and Rachio treatments applied more water than the control during the experiment, they still applied 34% less water, on average, than typical area homeowners. Of the four controllers evaluated, the Skydrop applied the least water overall, averaging 58% less irrigation than typical area homeowners. Typical area homeowner irrigation rates were determined from irrigation audits conducted during the long-running Utah State University Extension Water Check Program, which found that area homeowners apply an average of 1200 mm of water per growing season.

Soil volumetric water content

The SWC measured during the study was significantly affected by the week, controller, and the interaction of week × controller (Tables 2 and 3). In 2018, the average SWC was variable across treatments (31% for B-Hyve; 26% for control; 30% for Rachio; 23% for Skydrop). In 2019, the average SWC was also variable across treatments (34% for B-Hyve; 30% for control; 32% for Rachio; 31% for Skydrop) (Figs. 3 and 4). In 2018, significant differences in the SWC among the treatments were observed beginning in week 27 of the experiment, but these observations were not consistent until week 30 (Fig. 3), when the SWC of the B-Hyve and Rachio treatments were often higher than that of the control and Skydrop treatments because of higher applications of irrigation water. In 2019, significant differences in the SWC were observed through week 33 (Fig. 4), but the differences were not observed during weeks 36 to 38, when high amounts of natural precipitation occurred (Fig. 2).

Fig. 3.
Fig. 3.

Soil volumetric water content response of a kentucky bluegrass landscape to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences in the soil volumetric water content among controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

Fig. 4.
Fig. 4.

The soil volumetric water content response of a kentucky bluegrass landscape to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences in the soil volumetric water content among controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when which the week × controller interaction was not significant.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

During both years of the experiment, the soil profiles of the treatments did not exceed 34% or decrease to less than 18% of the SWC (Figs. 3 and 4). For comparison, the field capacity of the Millville silt loam at the study location is 35%, and the permanent wilting point is 13%. Therefore, and throughout the experiment and across treatments, SWCs remained high enough that moisture was readily available to the turfgrass. This finding indicates that the reduced amounts of irrigation water application allowed by the controllers in the study did not reduce the SWC to levels detrimental to the turfgrass.

During both years of the study, the Rachio treatment applied the most water, but higher SWC readings were observed with the B-Hyve treatment, which had the highest SWC during several weeks of the study. One possible reason could be the time at which irrigation was applied. To maintain adequate water pressure for the irrigation systems, the timing of application had to be staggered across the treatments and plots. When applied, irrigation began at 0000 HR, with one irrigation controller scheduled to begin irrigating every 20 min thereafter. Using this schedule, a maximum of four irrigation controllers could irrigate concurrently and still maintain adequate system pressure. Irrigation times for the Rachio treatment occurred between 0140 and 0300 hr, whereas irrigation times for the B-Hyve treatment occurred between 0500 and 0620 hr. This time-of-day difference in water application may account for the differences observed in the SWC because the SWC and plant health measurements were obtained between 1100 and 1300 hr. Additionally, there was the occasional potential for wind to heavily effect the uniformity of irrigation application at the study site, which may have affected the SWC in the plots.

Normalized difference vegetation index

Measurements of the NDVI were significantly affected by the week, controller, and interaction of week × controller (Tables 2 and 3) in the study. In 2018, average NDVI values were 0.70 (B-Hyve), 0.66 (control), 0.70 (Rachio), and 0.67 (Skydrop) (Fig. 5). In 2019, the average NDVI values were 0.67 (B-Hyve) and 0.64 for all other treatments (Fig. 6).

Fig. 5.
Fig. 5.

Normalized difference vegetation index response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

Fig. 6.
Fig. 6.

Normalized difference vegetation index response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

During both years of the study, significant differences in the NDVI among the treatments were observed. In 2018, for example, the B-Hyve and Rachio treatments never differed significantly from one another, but both were significantly different from the control and Skydrop treatments during weeks 31 to 39 (Fig. 5). In 2019, the NDVI for the B-Hyve treatment trended higher than the other treatments and was significantly different from all other treatments on four occasions; three of these occasions were in July, which was the warmest month of the 2019 growing season (Figs. 2 and 6). Because of the similarity in irrigation applications between the B-Hyve and Rachio treatments in 2019, this finding was unexpected and may have been the result of the timing of irrigation applications, which occurred between 0140 and 0300 hr for the Rachio treatment and between 0500 and 0620 hr for the B-Hyve treatment.

Percent green cover

The percent green cover was significantly affected by the week, controller, and the interaction of week × controller in both years of the study (Tables 2 and 3). The average percentages of green cover for each treatment in 2018 were 86% (B-Hyve), 75% (control), 83% (Rachio), and 77% (Skydrop) (Fig. 7). In 2019, the average values for each treatment were 84% (B-Hyve), 74% (control), 75% (Rachio), and 76% (Skydrop) (Fig. 8). Powlen et al. (2019) described a threshold of 70% green cover as acceptable. Using this threshold, the percent green cover was unacceptable only once in 2018. In 2019, however, the percent green cover dropped below acceptable levels on multiple occasions, depending on the week and controller (Fig. 8), and only the B-Hyve treatment maintained acceptable percentages of green cover throughout the entire study period despite similar irrigation application rates as the Rachio treatment.

Fig. 7.
Fig. 7.

The percent green cover response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

Fig. 8.
Fig. 8.

The percent green cover response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant. The percent green cover was not measured during week 35.

Citation: HortTechnology 32, 5; 10.21273/HORTTECH04985-21

In 2018, significant differences in percent green cover were not observed among treatments until week 32 of the experiment; thereafter, significant differences continued through most of the remainder of the growing season (Fig. 7), with the B-Hyve and Rachio treatments having significantly higher percent green cover than the control and Skydrop treatments. In 2019, significant differences in percent green cover were observed among experimental treatments during every week except week 39, which is the last week when measurements were obtained (Fig. 8); these were likely the result of natural precipitation events that occurred during the last month of the study period.

Measurements of the percent green cover and NDVI in the study were compared with one another and were highly correlated (R = 0.82), similar to the results of previous studies in which the correlation coefficients of the percent green cover and NDVI have ranged from 0.59 to 0.88 (Jing et al., 2019; Leinauer et al., 2014). The high coefficients of correlation between measurements of the percent green cover and NDVI in this experiment and others suggest that only one of these measurements is adequate for similar future studies.

Turfgrass canopy temperature

Canopy temperature measurements were obtained over the course of the study, but no significant differences were observed (Tables 2 and 3). Although measurements were obtained between 1100 and 1300 hr each day, sporadic cloud cover may have affected results.

Conclusions

Of the controllers tested, the Skydrop treatment applied the least amount of water over the 2 years of the study (1015 mm), followed by the control (1031 mm), B-Hyve (1573 mm), and Rachio (1603 mm) treatments. In addition, every controller tested, including the control, applied less water than typical area homeowners did (2400 mm) during the same time period. The lower water application of the control in the experiment indicates that water savings comparable to the best-performing smart controller tested are achievable using manually programmed irrigation controllers. In fact, if homeowners were to implement program-recommended irrigation schedules, then as much as 59% less water could be applied to turfgrass landscapes in the region, based on the results of this study. However, manually programming irrigation controllers presents challenges for some homeowners and landscape managers because of the programming changes required at regular intervals over the course of a growing season. In contrast, smart irrigation controllers require landscape-specific inputs when installed, and the information is saved and used in conjunction with local weather information for irrigation scheduling thereafter. Then, changes to the irrigation schedule occur automatically without the need for regular manual programming changes. When an irrigation zone and schedule are created, the internal algorithms of these controllers run continuously and irrigate according to real-time local weather conditions.

Regarding measures of turfgrass health and quality, there were significant differences between the two years of the study. In 2018, for example, the NDVI and percent green cover of the Rachio and Skydrop treatments were often significantly higher than that of the control and Skydrop treatments, likely in response to higher irrigation water applications. In 2019, however, the NDVI and percent green cover of the Rachio treatment decreased, despite similar irrigation water applications as the B-Hyve treatment, and tracked more closely to those of the control and Skydrop treatments. This finding is in contrast to that of several other studies that found that turfgrass quality remained acceptable with the implementation of smart irrigation controllers (Cardenas et al., 2020; Davis et al., 2009; Serena et al., 2019) and suggests that the effects of smart irrigation controllers on turfgrass health and quality may vary by location and over time.

Units

TU1

Literature cited

  • Allen, R.G., Walter, I.A., Elliott, R.L., Howell, T.A., Itenfisu, D., Jensen, M.E. & Snyder, R.L. 2005 The ASCE standardized reference evapotranspiration equation Am. Soc. Civil Eng. Reston, VA

    • Search Google Scholar
    • Export Citation
  • Cardenas, B., Migliaccio, K.W., Dukes, M.D., Hahus, I. & Kruse, J.K. 2020 Irrigation savings from smart irrigation technologies and a smartphone app on turfgrass Trans. ASABE 63 1697 1709

    • Search Google Scholar
    • Export Citation
  • Davis, S.L., Dukes, M.D. & Miller, G.L. 2009 Landscape irrigation by evapotranspiration-based controllers under dry conditions in southwest Florida Agric. Water Man. 96 1828 1836

    • Search Google Scholar
    • Export Citation
  • Devitt, D.A., Carstensen, K. & Morris, R.L. 2009 Residential water savings associated with satellite-based ET irrigation controllers J. Irrig. Drain. Eng. 134 74 82

    • Search Google Scholar
    • Export Citation
  • Endter-Wada, J., Kurtzman, J., Keenan, S.P., Kjelgren, R.K. & Neale, C.M. 2008 Situational waste in landscape watering: Residential and business water use in an urban Utah community J. Am. Water Res. Assoc. 44 902 920

    • Search Google Scholar
    • Export Citation
  • Grabow, G.L., Ghali, I.E., Huffman, R.L., Miller, G.L., Bowman, D. & Vasanth, V. 2013 Water application efficiency and adequacy of ET-based and soil moisture–based irrigation controllers for turfgrass irrigation J. Irrig. Drain. Eng. 139 113 123

    • Search Google Scholar
    • Export Citation
  • Irrigation Association 2009 Recommended audit guidelines 15 May 2020. <https://www.irrigation.org/IA/FileUploads/IA/Resources/CLIA-CGIA_AuditGuidelines.pdf>

    • Search Google Scholar
    • Export Citation
  • Jing, Z., Virk, S., Porter, W., Kenworthy, K., Sullivan, D. & Schwartz, B. 2019 Applications of unmanned aerial vehicle based imagery in turfgrass field trials Front. Plant Sci. 10 279

    • Search Google Scholar
    • Export Citation
  • Karcher, D.E. & Richardson, M.D. 2013 Digital image analysis in turfgrass research 1133 1149 Stier, J.C., Horgan, B.P. & Bonos, S.A. Agron. Monogr. 56. Turfgrass. Biology, use, and management. Am. Soc. Agron.,Crop Sci. Soc. Am., Soil Sci. Soc. Am. Madison, WI

    • Search Google Scholar
    • Export Citation
  • Karcher, D.E., Purcell, C.J., Richardson, M.D., Purcell, L.C. & Hignight, K.W. 2017 A new Java program to rapidly quantify several turfgrass parameters from digital images Am. Soc. Agron., Crop Sci. Soc. Am., Soil Sci. Soc. Am. Abstr. 108681. 4 Mar. 2020. <https://scisoc.confex.com/crops/2017am/webprogram/Paper109313.html>

    • Search Google Scholar
    • Export Citation
  • Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., Lovelace, J.K. & Maupin, M.A. 2009 Estimated use of water in the United States in 2005 U.S. Geol. Surv. Circ. 1344. 12 Apr. 2020. <https://pubs.usgs.gov/circ/1344/>

    • Search Google Scholar
    • Export Citation
  • Kopp, K., Allen, N. & Beddes, T. 2013 Simple sprinkler performance testing for Cache County Paper 328. 12 Apr. 2020. <https://digitalcommons.usu.edu/extension_curall/328>

    • Search Google Scholar
    • Export Citation
  • Kopp, K., Kjelgren, R., Urzagaste, P. & Dai, X. 2017 Physiological and quality responses of turfgrass and ornamental plants to weather-based irrigation control Int. Turfgrass Soc. Res. J. 13 537 546

    • Search Google Scholar
    • Export Citation
  • Leinauer, B., VanLeeuwen, D.M., Serena, M., Schiavon, M. & Sevostianova, E. 2014 Digital image analysis and spectral reflectance to determine turfgrass quality Agron. J. 106 1787 1794 https://doi.org/10.2134/agronj14.0088

    • Search Google Scholar
    • Export Citation
  • Mayer, P.W., DeOreo, W.B., Hayden, M. & Davis, R. 2009 Evaluation of California weather-based “smart” irrigation controller programs Report to California Department of Water Resources by the Metropolitan Water District of Southern California and the East Bay Municipal Utility District. 12 Apr. 2020. <https://ucanr.edu/sites/UrbanHort/files/99641.pdf>

    • Search Google Scholar
    • Export Citation
  • Pittenger, D.R., Shaw, D.A. & Richie, W.E. 2004 Evaluation of weather-sensing landscape irrigation controllers Report to Office of Water Use Efficiency, California Department of Water Resources. 15 May 2020. <https://ucanr.edu/sites/UrbanHort/files/800 78.pdf>

    • Search Google Scholar
    • Export Citation
  • Powlen, J., Bigelow, C., Patton, A.J., Jiang, Y. & Fraser, M. 2019 Irrigation needs of drought susceptible and tolerant tall fescue and kentucky bluegrass cultivars at two mowing heights Am. Soc. Agron., Crop Sci. Soc. Am., Soil Sci. Soc. Am. Abstr. 119472. 4 Mar. 2020. <https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/119472>

    • Search Google Scholar
    • Export Citation
  • Serena, M., Velasco-Cruz, C., Friell, J., Schiavon, M., Sevostianova, E., Beck, L., Sallenave, R. & Leinauer, B. 2019 Irrigation scheduling technologies reduce water use and maintain turfgrass quality Agron. J. 112 3456 3469 https://doi.org/10.1002/agj2.20246

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2016 Best practices to consider when evaluating water conservation and efficiency as an alternative for water supply expansion (EPA-810-B-16-005) 4 Mar. 2020. <https://www.epa.gov/sustainable- water-infrastructure/best-practices-water- conservation-and-efficiency-alternative-water>

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2017 Outdoor water use in the United States 4 Mar. 2020. <https://19january2017snapshot.epa.gov/www3/watersense/pubs/outdoor.html>

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2018 About WaterSense 4 Mar. 2020. <https://www.epa.gov/watersense/about-watersense>

  • Utah Division of Water Resources 2007 Drought in Utah: Learning from the past- preparing for the future 11 Apr. 2020. <https://water.utah.gov/wp-content/uploads/2020/06/Drought-Report-Final-VersionBinder2.pdf>

    • Search Google Scholar
    • Export Citation

Contributor Notes

We extend appreciation to the Weber Basin Water Conservancy District, the Utah Division of Water Resources, and Utah State University’s Center for Water Efficient Landscaping for funding support provided for this research. We also thank the reviewers and editors of this paper for their constructive comments and suggestions.

K.K. is the corresponding author: E-mail: kelly.kopp@usu.edu.

  • Collapse
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  • View in gallery
    Fig. 1.

    Maximum daily air temperature and precipitation at the Utah State University Greenville Research Farm (North Logan, UT) from 30 June to 30 Sept. 2018. (1.8 × °C) + 32 = °F, 1 mm = 0.0394 inch.

  • View in gallery
    Fig. 2.

    Maximum daily air temperature and precipitation at the Utah State University Greenville Research Farm (North Logan, UT) from 28 June to 30 Sept. 2019. (1.8 × °C) + 32 = °F, 1 mm = 0.0394 inch.

  • View in gallery
    Fig. 3.

    Soil volumetric water content response of a kentucky bluegrass landscape to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences in the soil volumetric water content among controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

  • View in gallery
    Fig. 4.

    The soil volumetric water content response of a kentucky bluegrass landscape to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences in the soil volumetric water content among controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when which the week × controller interaction was not significant.

  • View in gallery
    Fig. 5.

    Normalized difference vegetation index response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

  • View in gallery
    Fig. 6.

    Normalized difference vegetation index response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by different letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

  • View in gallery
    Fig. 7.

    The percent green cover response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (30 June to 30 Sept.) in 2018. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant.

  • View in gallery
    Fig. 8.

    The percent green cover response of kentucky bluegrass to four irrigation controller treatments across 14 weeks (28 June to 30 Sept.) in 2019. Significant differences between controllers were determined using the Tukey-Kramer method for multiplicity at P ≤ 0.05 and are noted by letters. The top letter represents the B-Hyve controller (B-Hyve Smart Sprinkler System XR Model; Orbit Irrigation Products, North Salt Lake, UT). The second letter represents the Hunter (control; XC-400; Hunter Industries, San Marcos, CA). The third letter represents the Rachio controller (Rachio Smart Sprinkler Controller Rachio 2; Rachio, Denver, CO). The bottom letter represents the Skydrop controller (Halo Smart Sprinkler System; Skydrop, American Fork, UT). Mean separations are not presented for weeks when the week × controller interaction was not significant. The percent green cover was not measured during week 35.

  • Allen, R.G., Walter, I.A., Elliott, R.L., Howell, T.A., Itenfisu, D., Jensen, M.E. & Snyder, R.L. 2005 The ASCE standardized reference evapotranspiration equation Am. Soc. Civil Eng. Reston, VA

    • Search Google Scholar
    • Export Citation
  • Cardenas, B., Migliaccio, K.W., Dukes, M.D., Hahus, I. & Kruse, J.K. 2020 Irrigation savings from smart irrigation technologies and a smartphone app on turfgrass Trans. ASABE 63 1697 1709

    • Search Google Scholar
    • Export Citation
  • Davis, S.L., Dukes, M.D. & Miller, G.L. 2009 Landscape irrigation by evapotranspiration-based controllers under dry conditions in southwest Florida Agric. Water Man. 96 1828 1836

    • Search Google Scholar
    • Export Citation
  • Devitt, D.A., Carstensen, K. & Morris, R.L. 2009 Residential water savings associated with satellite-based ET irrigation controllers J. Irrig. Drain. Eng. 134 74 82

    • Search Google Scholar
    • Export Citation
  • Endter-Wada, J., Kurtzman, J., Keenan, S.P., Kjelgren, R.K. & Neale, C.M. 2008 Situational waste in landscape watering: Residential and business water use in an urban Utah community J. Am. Water Res. Assoc. 44 902 920

    • Search Google Scholar
    • Export Citation
  • Grabow, G.L., Ghali, I.E., Huffman, R.L., Miller, G.L., Bowman, D. & Vasanth, V. 2013 Water application efficiency and adequacy of ET-based and soil moisture–based irrigation controllers for turfgrass irrigation J. Irrig. Drain. Eng. 139 113 123

    • Search Google Scholar
    • Export Citation
  • Irrigation Association 2009 Recommended audit guidelines 15 May 2020. <https://www.irrigation.org/IA/FileUploads/IA/Resources/CLIA-CGIA_AuditGuidelines.pdf>

    • Search Google Scholar
    • Export Citation
  • Jing, Z., Virk, S., Porter, W., Kenworthy, K., Sullivan, D. & Schwartz, B. 2019 Applications of unmanned aerial vehicle based imagery in turfgrass field trials Front. Plant Sci. 10 279

    • Search Google Scholar
    • Export Citation
  • Karcher, D.E. & Richardson, M.D. 2013 Digital image analysis in turfgrass research 1133 1149 Stier, J.C., Horgan, B.P. & Bonos, S.A. Agron. Monogr. 56. Turfgrass. Biology, use, and management. Am. Soc. Agron.,Crop Sci. Soc. Am., Soil Sci. Soc. Am. Madison, WI

    • Search Google Scholar
    • Export Citation
  • Karcher, D.E., Purcell, C.J., Richardson, M.D., Purcell, L.C. & Hignight, K.W. 2017 A new Java program to rapidly quantify several turfgrass parameters from digital images Am. Soc. Agron., Crop Sci. Soc. Am., Soil Sci. Soc. Am. Abstr. 108681. 4 Mar. 2020. <https://scisoc.confex.com/crops/2017am/webprogram/Paper109313.html>

    • Search Google Scholar
    • Export Citation
  • Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., Lovelace, J.K. & Maupin, M.A. 2009 Estimated use of water in the United States in 2005 U.S. Geol. Surv. Circ. 1344. 12 Apr. 2020. <https://pubs.usgs.gov/circ/1344/>

    • Search Google Scholar
    • Export Citation
  • Kopp, K., Allen, N. & Beddes, T. 2013 Simple sprinkler performance testing for Cache County Paper 328. 12 Apr. 2020. <https://digitalcommons.usu.edu/extension_curall/328>

    • Search Google Scholar
    • Export Citation
  • Kopp, K., Kjelgren, R., Urzagaste, P. & Dai, X. 2017 Physiological and quality responses of turfgrass and ornamental plants to weather-based irrigation control Int. Turfgrass Soc. Res. J. 13 537 546

    • Search Google Scholar
    • Export Citation
  • Leinauer, B., VanLeeuwen, D.M., Serena, M., Schiavon, M. & Sevostianova, E. 2014 Digital image analysis and spectral reflectance to determine turfgrass quality Agron. J. 106 1787 1794 https://doi.org/10.2134/agronj14.0088

    • Search Google Scholar
    • Export Citation
  • Mayer, P.W., DeOreo, W.B., Hayden, M. & Davis, R. 2009 Evaluation of California weather-based “smart” irrigation controller programs Report to California Department of Water Resources by the Metropolitan Water District of Southern California and the East Bay Municipal Utility District. 12 Apr. 2020. <https://ucanr.edu/sites/UrbanHort/files/99641.pdf>

    • Search Google Scholar
    • Export Citation
  • Pittenger, D.R., Shaw, D.A. & Richie, W.E. 2004 Evaluation of weather-sensing landscape irrigation controllers Report to Office of Water Use Efficiency, California Department of Water Resources. 15 May 2020. <https://ucanr.edu/sites/UrbanHort/files/800 78.pdf>

    • Search Google Scholar
    • Export Citation
  • Powlen, J., Bigelow, C., Patton, A.J., Jiang, Y. & Fraser, M. 2019 Irrigation needs of drought susceptible and tolerant tall fescue and kentucky bluegrass cultivars at two mowing heights Am. Soc. Agron., Crop Sci. Soc. Am., Soil Sci. Soc. Am. Abstr. 119472. 4 Mar. 2020. <https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/119472>

    • Search Google Scholar
    • Export Citation
  • Serena, M., Velasco-Cruz, C., Friell, J., Schiavon, M., Sevostianova, E., Beck, L., Sallenave, R. & Leinauer, B. 2019 Irrigation scheduling technologies reduce water use and maintain turfgrass quality Agron. J. 112 3456 3469 https://doi.org/10.1002/agj2.20246

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2016 Best practices to consider when evaluating water conservation and efficiency as an alternative for water supply expansion (EPA-810-B-16-005) 4 Mar. 2020. <https://www.epa.gov/sustainable- water-infrastructure/best-practices-water- conservation-and-efficiency-alternative-water>

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2017 Outdoor water use in the United States 4 Mar. 2020. <https://19january2017snapshot.epa.gov/www3/watersense/pubs/outdoor.html>

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency 2018 About WaterSense 4 Mar. 2020. <https://www.epa.gov/watersense/about-watersense>

  • Utah Division of Water Resources 2007 Drought in Utah: Learning from the past- preparing for the future 11 Apr. 2020. <https://water.utah.gov/wp-content/uploads/2020/06/Drought-Report-Final-VersionBinder2.pdf>

    • Search Google Scholar
    • Export Citation
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