Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida

Agricultural Water Management 96 (2009) 1828–1836

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Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida S.L. Davis a, M.D. Dukes a,*, G.L. Miller b a b

Agricultural and Biological Engineering Department, University of Florida, P.O. Box 110570, Gainesville, FL 32611-0570, USA Crop Science Department, North Carolina State University, P.O. Box 7620, Raleigh, NC, 27695-7620, USA

A R T I C L E I N F O

A B S T R A C T

Article history: Received 16 December 2008 Received in revised form 6 August 2009 Accepted 7 August 2009 Available online 5 September 2009

Due to high demand for aesthetically pleasing urban landscapes from continually increasing population in Florida, new methods must be explored for outdoor water conservation. Three brands of evapotranspiration (ET) controllers were selected based on positive water savings results in arid climates. ET controllers were evaluated on irrigation application compared to a time clock schedule intended to mimic homeowner irrigation schedules. Three ET controllers were tested: Toro Intelli-sense; ETwater Smart Controller 100; Weathermatic SL1600. Other time-based treatments were TIME, based on the historical net irrigation requirement and RTIME that was 60% of TIME. Each treatment was replicated four times for a total of twenty St. Augustinegrass plots which were irrigated through individual irrigation systems. Treatments were compared to each other and to a time-based schedule without rain sensor (TIME WORS) derived from TIME. The study period, August 2006 through November 2007, was dry compared to 30-year historical average rainfall. The ET controllers averaged 43% water savings compared to a time-based treatment without a rain sensor and were about twice as effective and reducing irrigation compared to a rain sensor alone. There were no differences in turfgrass quality across all treatments over the 15-month study. The controllers adjusted their irrigation schedules to the climatic demand effectively, with maximum savings of 60% during the winter 2006–2007 period and minimum savings of 9% during spring 2007 due to persistent dry conditions. RTIME had similar savings to the ET controllers compared to TIME WORS indicating that proper adjustment of time clocks could result in substantial irrigation savings. However, the ET controllers would offer consistent savings once programmed properly. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Drought Turfgrass Water conservation

1. Introduction Water is a limited resource as evidenced by water shortages seen in areas all over the world despite differences in climate. Water shortages in Florida have become more prevalent in the last few decades. Florida has the second largest withdrawal of groundwater used for public supply in the United States (Solley et al., 1998) and the largest net gain in population with an inflow of approximately 1100 people per day (United States Census Bureau, 2005). New home construction has increased to accommodate the large influx of people and most new homes include in-ground automated irrigation systems. However, homes with these systems have been shown to increase outdoor water use by 47% (Mayer et al., 1999). The need for landscape irrigation will continually grow with increased population and home construction if the demand for the current type of urban landscapes does not change.

* Corresponding author. Tel.: +1 352 392 1864x205; fax: +1 352 392 4092. E-mail address: mddukes@ufl.edu (M.D. Dukes). 0378-3774/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2009.08.005

Evapotranspiration (ET) is defined as the evaporation from the soil surface and the transpiration through plant canopies (Allen et al., 1998). ET is a part of a balanced energy budget that exchanges energy for outgoing water at the surface of the plant. The components of ET are solar radiation, air temperature, relative humidity, and wind speed (ASCE-EWRI, 2005). Reference ET (ETo) is the evapotranspiration from a hypothetical reference crop assumed to be similar to an actively growing, well-watered, dense green grass of uniform height (ASCE-EWRI, 2005). Evapotranspiration-based controllers, also known as ET controllers, are irrigation controllers that use an estimation of ET to schedule irrigation. Each controller works differently depending on manufacturer, but is typically programmed with landscapespecific conditions intended to make them more efficient (Riley, 2005). ET controllers receive ETo information in three general ways, consequently dividing ET controllers into three main types: (1) standalone controllers, (2) signal-based controllers, and (3) historical-based controllers. Standalone controllers receive climatic data from on-site sensors and use calculations to determine ETo whereas signal-based controllers receive ETo calculated off-site

S.L. Davis et al. / Agricultural Water Management 96 (2009) 1828–1836

from local weather stations. Historical-based controllers rely on historical ETo information to adjust irrigation based on general climate patterns, but are not as efficient as other methods because actual changes in weather are not taken into account. ET controllers have been used frequently over the last five years for studies performed by irrigation districts and other agencies in the western United States. Savings are usually reported in terms of actual or potential. Potential savings is defined by Hunt et al. (2001) as the ‘‘difference between actual outdoor water applied and what should have been applied taking weather into account.’’ Actual savings is determined by comparing current use to some reference use which is usually based on water use history. A study conducted in 2002 in west San Fernando Valley, California by the Los Angeles Department of Water and Power showed 17% actual savings by a WeatherTRAK enabled controller relative to a normalized weather year found through statistical modeling from the pre-retrofit time period and 78% of potential savings (Bamezai, 2004). A residential runoff reduction study was conducted using a modified Sterling irrigation controller to accept a broadcast signal from the WeatherTRAK ET Everywhere service in Irvine California; the ET controller group potentially reduced dry weather runoff 49% and saved 71% compared to the control groups (Diamond, 2003). Aquacraft Inc. performed an ET controller study in Colorado to determine savings compared to ETo for the area and six sites were already irrigating below historical ETo. The first year resulted in 94% of ETo replacement by irrigation with 20% error between sites and achievement of 88% of the potential savings while the second year resulted in 71% of ETo replacement and achievement of 92% of the potential savings (Aquacraft Inc., 2002, 2003). Devitt et al. (2008) found that using signal-based ET controllers in Las Vegas homeowner landscapes reduced water applied by 20% on average compared to sites without an ET-based controller. Results showed that 13 out of 16 ET controller sites reduced water applied compared to 4 of 10 sites without ET controllers. To date, results from ET controller studies generally have not been published in peerreviewed journals. Additionally, these controllers have not been evaluated in a subtropical climate such as Florida. The objective of this study was to evaluate the ability of three brands of ET-based controllers to schedule irrigation by comparing irrigation application to a time clock schedule intended to mimic homeowner irrigation schedules, while maintaining acceptable turfgrass quality. 2. Materials and methods This study was conducted at the University of Florida Gulf Coast Research and Education Center (GCREC) in Wimauma, Florida and at the University of Florida Agricultural and Biological Engineering Department in Gainesville, Florida. There were a total of twenty plots at the GCREC that measured 7.62 m  12.2 m, with 3.05 m buffer zones between adjacent plots. Each plot consisted of 65% St. Augustinegrass (Stenotaphrum secundatum cv. ‘Floratam’) and 35% mixed ornamentals to represent a typical residential landscape in Florida. This research focuses only on the turfgrass. Landscapes were maintained through mowing, pruning, edging, mulching, fertilization, and pest and weed control according to current University of Florida Institute of Food and Agricultural Sciences (UF-IFAS) recommendations (Sartain, 1991; Black and Ruppert, 1998). The controllers set up in Gainesville were connected only to a CR10X data logger (Campbell Scientific, Logan, UT) to record run times to study the variability in water application between ET controllers of the same brand. Weather data available on site included rainfall, solar radiation, wind speed, air temperature and relative humidity at 15 min intervals from a Florida Automated Weather Network (FAWN)

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station. The FAWN station was located within 100 m of the test site. Five treatments were established at the GCREC that were replicated four times for a total of twenty plots in a completely randomized block design. The irrigation treatments were as follows: Weathermatic SL1600 controller with SLW15 weather monitor (Dallas, TX); Toro Intelli-sense (Riverside, CA) utilizing the WeatherTRAK ET Everywhere service (Hydropoint Datasystems Inc., Petaluma, CA); ETwater Smart Controller 100 (Corte Madera, CA); TIME, a time-based treatment determined by UF-IFAS recommendations (Dukes and Haman, 2002); and RTIME, a time-based treatment that was 60% of TIME. All treatments utilized rain sensors to bypass irrigation after 6 mm of rainfall. Individual valve and flow meter combinations were used to supply and monitor irrigation to each zone (separate irrigation zones for turfgrass and ornamentals) of each plot. The flow meters (15.9 mm V100 w/Pulse Output, AMCO Water Metering Systems, Ocala, FL) used to monitor irrigation water application were connected to five Campbell Scientific SDM-SW8A switch closure input modules that in turn were connected to a CR10X data logger. The CR10X data logger monitored switch closures every 18.9 l from the water meters. The meters were also read manually each week. Irrigation sprinklers specified for the turfgrass portions of the plots consisted of Rain Bird (Glendora, CA) 1806 15 cm pop up spray bodies and Rain Bird R13-18 black rotary nozzles. In each plot, there were four sprinklers with a 1808 arc (R13-18H) and a center sprinkler with a 3608 arc (R13-18F). The application rate of the sprinklers was specified by the manufacturer as 15.5 mm/h. Thirty-year historical rainfall averages were calculated from monthly rainfall data collected by the National Oceanic and Atmospheric Administration (NOAA, 2005) from 1975 through 2005. The closest NOAA weather station from the project site with available rainfall data was located approximately 28 km away, in Parrish, FL. There were five periods of data collection: 13 August 2006 through 30 November 2006 as fall 2006; 1 December 2006 through 26 February 2007 as winter 2006–2007; 27 February 2007 through 31 May 2007 as spring 2007; 1 June 2007 through 31 August 2007 as summer 2007; and 1 September 2007 through 30 November 2007 as fall 2007. All five treatments were set up with two days per week watering restrictions during fall 2006 and winter 2006–2007, Wednesday and Saturday, and no watering between 10 am and 4 pm. Also, the ET controller treatments were established based on the site location without accounting for system efficiency (Table 1). The Weathermatic controller was set to apply 100% of the calculated water requirement while the Toro and ETwater controllers were set to the maximum controller efficiency of 95%. The monthly irrigation depth for TIME was 60% of the net irrigation requirement derived from historical ET and effective rainfall specific to south Florida (Dukes and Haman, 2002) and RTIME was a reduced treatment, applying 60% of the irrigation depth calculated from TIME equaling 36% of the net irrigation requirement (Table 2). Spring, summer, and fall 2007 differed from the previous two periods in that the ET controller treatments could irrigate any day of the week and up to everyday instead of two days per week and were updated with a system efficiency of 80% determined from irrigation uniformity testing instead of 100% or 95% as described above (Table 1). TIME was increased to apply irrigation to replace 100% of the net irrigation requirement instead of 60% used during the first three periods (Table 2). Once again, RTIME applied 60% of TIME resulting in the reduced treatment applying 60% of the net irrigation requirement. The first two testing periods were meant to simulate a worst-case scenario of minimal irrigation; whereas, the last three testing periods were intended to simulate typical ET controller settings and a reasonable homeowner time clock schedule.

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Table 1 Program settings for each brand of ET controller for a warm season turfgrassa on a sandy soilb and a slope of 08. Controller

Fall 2006 and winter 2006–2007 Weathermatic

Toro

ETwater

Weathermatic

Toro

ETwater

Sprinkler typec Root depth Efficiencye Zip codef Microclimate Days allowedg

15.2 mm/h NAd 100% 33598 NA Wed, Sat

15.5 mm/h 152 mm 95% NA Full sun Wed, Sat

15.5 mm/h 152 mm 95% NA Full Sun Wed, Sat

15.2 mm/h NA 80% 33598 NA Any day

15.5 mm/h 152 mm 80% NA Full sun Any day

15.5 mm/h 152 mm 80% NA Full sun Any day

a b c d e f g

Spring, summer, and fall 2007

The plant type setting is used to choose crop coefficients to calculate plant evapotranspiration. The soil type setting is used to determine the depth of available water for the root zone. Sprinkler type is a term commonly used by ET controllers to specify the application rate of an irrigation zone. NA applies to controller settings that were not applicable to a particular controller. Scheduling efficiency is used to calculate gross irrigation once net irrigation is determined. Zip code is used to find the latitude to determine the monthly solar radiation for ET calculations. Days allowed refers to the days irrigation was allowed to occur per week.

Table 2 Runtimes and application amounts per irrigation eventa for the time-based treatment (TIME) operating on a twice-weekly schedule. Month

Fall 2006 and winter 2006–2007b Time (min)

January February March April May June July August September October November December Total potential annual irrigation a b

23 24 35 37 34 31 48 53 31 32 33 29

Spring, summer, and fall 2007

Application amount (mm) 6 6 9 10 9 8 12 14 8 8 8 7

Time (min) 39 41 58 62 56 51 80 88 52 53 55 48

840

Application amount (mm) 10 11 15 16 15 13 21 23 14 14 14 12 1424

Application rate used to calculate runtimes was 15.5 mm/h. Assumed 60% system efficiency and estimated effective rainfall for south Florida with 60% net irrigation replacement.

Results were quantified by comparing all treatments to a time-based treatment without a rain sensor (TIME WORS). The time-based treatment without a rain sensor was calculated from TIME by including water application from irrigation events that were bypassed due to rain and was not an actual treatment. Irrigation runtimes for this treatment were adjusted monthly according to Table 2. It is important to note that the TIME WORS comparison was scheduled to apply 1424 mm/yr (Table 2); however, studies have shown that homeowners in Central Florida apply as much as 1788 mm/yr (Haley et al., 2007). Thus, this comparison schedule may be conservative relative to water use in some areas. Turfgrass quality was measured monthly using the National Turfgrass Evaluation Program (NTEP) standards (Shearman and Morris, 1998). The turfgrass was rated on a scale from 1 to 9 where 1 represented dead turfgrass or bare ground, 9 represented an ideal turfgrass, and 5 was considered minimally acceptable quality for a residential setting. Each rating was determined monthly by the same person examining aspects of color, density, uniformity, texture, and disease or environmental stress. Water application was summed into weekly totals for statistical comparisons between treatments using weeks as repeated measures. The SAS statistical software (SAS Institute Inc., Cary, NC) was used for all statistical analyses, utilizing the General Linear Model (GLM) procedure and the mixed procedure with a 95% confidence level. Means separation was conducted using Duncan’s multiple range test and least squares means separation was conducted using the Tukey–Kramer test for pairwise comparisons.

3. Results All months received less rain than the historical average except for April 2007, 69% higher than average, and October 2007, 104% higher than average (Fig. 1). Overall, both years were drier than the historical average with a total of 1326 mm of rainfall for the approximately 16-month study period occurring from August 2006 through November 2007. This rainfall was 33% less than the historical total from the local NOAA weather station. There were 145 rain events over 472 days; 69% of the study period contained dry days. Irrigation water application data were collected from the three replications of each brand of ET controller at the Gainesville turfgrass plots (Table 3) to assess variability between different units of controllers within a brand. It was determined through an ANOVA that there were no differences between the Weathermatic replications (P = 0.926), the Toro replications (P = 0.999), or the ET Water replications (P = 0.999). 3.1. Fall 2006 All plots in fall 2006 suffered from an infestation of chinch bugs (Blissus insularis ‘Barber’) and a fungal disease known as Curvularia. Chinch bugs are small pests that inhabit areas of thatch in St. Augustinegrass and live off plant fluids causing the turfgrass to die (Buss, 1993). Curvularia is caused by a pathogen that typically attacks stressed plant material (Wong et al., 2005). Damaged turfgrass was replaced with new sod during the week following 26

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Fig. 1. Comparison of rainfall for the 2006–2007 study period and average historical rainfall on a monthly and cumulative basis for southwest Florida.

Table 3 Average daily irrigation water applicationa (mm) for the three replications of ET controllers located in Gainesville from 22 May 2007 through 30 November 2007. Replication

ET controller brand Weathermatic

Toro

ETwater

1 2 3

1.1 a 1.2 a 1.1 a

1.5 a 1.5 a 1.5 a

1.2 a 1.2 a 1.2 a

Averageb

1.1 B

1.5 A

1.2 B

a

Numbers with different lower case letters indicate differences across replicates of the same brand (columns) at the 95% confidence level using Duncan’s Multiple Range Test. b Statistical analysis was performed on controller brands (row) and where different upper case letters indicate differences across controllers.

September 2006; no more than 25% of any plot was re-sodded and most of the damage was located along the edges of the plots. TIME irrigated the most by applying 230 mm whereas RTIME irrigated the least, applying 144 mm (Fig. 2). Cumulatively, the Weathermatic and Toro controllers applied similar depths over the period totaling 197 mm and 193 mm, respectively. The ETwater

controller was not functional during this period due to circuitry problems. All treatments irrigated less than the TIME WORS treatment (totaling 317 mm). The ET controller treatments applied less irrigation than the TIME WORS treatment except for the month of October as can be seen in the steeper slopes of the lines (Fig. 2). October 2006 had less time-based irrigation because the schedule derived from Dukes and Haman (2002) contained an error for October in south Florida. Irrigation application for the time-based treatments should have resembled September since October had less rainfall and no more than a 4% difference in ET, totaling 119 mm in September and 115 mm in October. Rainfall that occurred within 24 h of a scheduled irrigation event caused many of the scheduled events to be bypassed by all treatments (Table 4). The Weathermatic controller irrigated 19 times and bypassed more events than any other treatment, averaging 1.3 events per week. The increased bypassing of events was due to the mandatory 48-h bypass period initiated for each rainfall event greater than 6 mm in the early part of the period. Since the controller was only allowed to irrigate two days per week to follow watering restrictions, there were limited opportunities for this controller to allow irrigation to occur. However, the Weathermatic controller scheduled larger irrigation depths per event when allowed to irrigate in the latter part of the period resulting in similar cumulative irrigation as the Toro controller. The Toro controller irrigated the same average number of events Table 4 Weekly water application and the average number of irrigation events that occurred per week for each study period. Treatment

Fall 2006

Weekly water applicationa (mm) Weathermatic 12 bc Toro 12 bc ETwater NF TIME 15 b RTIME 9c TIME WORSc 20 a

Winter 2006

Spring 2007

Summer 2007

Fall 2007

7c 6c NF 11 b 7c 14 a

32 ab 30 b NF 29 b 17 d 35 a

NFb 26 bc 24 c 30 b 16 d 44 a

20 15 18 31 18 36

NF 4.2 6.5 1.4 1.3 2.0

5.2 1.7 4.6 1.7 1.7 2.0

Average number of irrigation events per week Weathermatic 1.3 1.3 6.2 Toro 1.5 0.9 4.5 ETwater NF NF NF TIME 1.5 1.6 1.7 RTIME 1.5 1.6 1.7 TIME WORS 2.0 2.0 2.0

Fig. 2. Fall 2006 cumulative water application and daily rainfall (13 August–30 November).

c d cd b cd a

a Numbers with different letters in columns indicate differences at the 95% confidence level using Duncan’s multiple range test. b NF indicates nonfunctional treatments that were over the specified time period. c TIME WORS is a time-based treatment without bypassed rain events in TIME.

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Table 5 Savings compared to TIME WORSa and turfgrass qualityb for each study period. Treatment

Fall 2006

Winter 2006

Savingsc compared to TIME WORS (%) Weathermatic 38 50 Toro 39 60 ETwater NF NF TIME 28 20 RTIME 55 49

Spring 2007

Summer 2007

Fall 2007

9 15 NF 18 50

NFd 41 45 31 63

43 59 50 15 50

NF 6.1 6.1 6.1 5.8

6.4 7.1 7.0 6.6 6.5

All treatments resulted in savings compared to the TIME WORS treatment (Table 5). RTIME showed the most savings at 55% due to the extremely low water application in October. TIME had 28% savings due in part to the low watering schedule in October. Savings from the ET controllers, Weathermatic and Toro, fell between the other treatments by saving 38% and 39%, respectively. 3.2. Winter 2006–2007

per week as TIME and RTIME, each averaging 1.5 events. Weekly water application by the Toro controller compared to the TIME and RTIME treatments was not statistically different. There were differences among treatments (P < 0.0001), but not among replications (P = 0.807) for fall 2006 weekly water application (Table 4). The TIME (15 mm/wk) schedule applied 6 mm/wk more irrigation (P = 0.0002) than RTIME (9 mm/wk). The ET controllers, Weathermatic and Toro, were not different from each other, both averaging 12 mm/wk. There were also no differences between TIME and both ET controllers. The ET controllers were not different (P = 0.063 and P = 0.106, respectively) compared to RTIME. All treatments applied less irrigation than the TIME WORS treatment with an average of 20 mm/wk (P  0.0002). Average turfgrass quality ratings were below the minimally acceptable value of 5.0 for all treatments due to pest and disease problems as described above (Table 5). Water application amount was not correlated with turfgrass quality (P = 0.4503).

Winter water application was less than that of any other period due to the reduced climatic demand. Irrigation (Fig. 3) ranged from 84 mm for the Toro controller to 169 mm for TIME. The ETwater controller did not function during this period due to the fact that continued circuitry problems and results were not reported. It is important to note that the problems with the ETwater controller were isolated to this individual unit. The same model controller on other sites functioned reliably. Rainfall totaled 167 mm over the 88-day period. Irrigation events were less frequent for the ET controllers; the Toro controller irrigated 12 times and the Weathermatic controller irrigated 16 times out of a possible 25 irrigation days compared to 20 events for the time-based treatments, TIME and RTIME (Table 4). Water savings occurred for all treatments compared to the TIME WORS treatment, ranging from 20% to 60% (Table 5). There were differences among treatments (P < 0.0001), but not among replications (P = 0.948) for weekly water application (Table 4). The Weathermatic controller, Toro controller, and RTIME had similar weekly application rates of 6–7 mm/wk. However, these three treatments irrigated less than TIME (P < 0.0001), at 11 mm/wk. Finally, TIME WORS (P  0.0004) irrigated more than any of the other irrigation treatments; water application averaged 14 mm/wk for this period. The time-based treatments, TIME and RTIME, irrigated the most events per week, on average as 1.6 events. The ET controllers irrigated similar depths of irrigation on a weekly basis, but applied water less often. The Weathermatic controller averaged 1.3 events per week and the Toro controller averaged 0.9 events per week. Turfgrass quality ratings ranged from 5.7 to 6.0 and were not different across treatments (Table 5). There was no correlation between water application and turfgrass quality (P = 0.082).

Fig. 3. Winter 2006–2007 cumulative and daily water applied and daily rainfall (1 December–26 February).

Fig. 4. Spring 2007 cumulative and daily water applied and daily rainfall (27 February–31 May).

Turfgrass qualitye Weathermatic Toro ETwater TIME RTIME

4.8 4.9 NF 4.7 4.8

a a a a

5.7 5.9 NF 6.0 5.7

a a a a

6.2 6.4 NF 6.2 6.1

a a a a

a a a a

a a a a a

a

TIME WORS is a time-based treatment without bypassed rain events in TIME. Turfgrass quality ratings used a 1–9 scale where 1 was of lowest quality, 9 was of highest quality, and 5 was minimally acceptable. c Savings were calculated using cumulative period totals. d NF indicates nonfunctional treatments that were over the specified time period. e Numbers with different letters in columns indicate differences at the 95% confidence level using Duncan’s multiple range test. b

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Both ET controllers applied less water than RTIME unlike any other time of year. The ET controller treatments showed the potential to save over 50% of water applied during the cooler months of the year. These results suggest that the time schedule can be greatly reduced in this part of the year. 3.3. Spring 2007 In spring 2007, irrigation application ranged from 244 mm by RTIME to 445 mm by the Weathermatic controller (Fig. 4). Since the ET controllers were given the option of irrigating any day of the week, these devices irrigated a smaller amount per event, but more frequently than the time-based treatments (Table 4). The Weathermatic controller irrigated almost every day, averaging 6.2 events per week, and the Toro controller irrigated almost three times as often, averaging 4.5 events per week. Weekly water applications were not necessarily less by the ET controllers relative to TIME. Weekly water applications were similar by the Weathermatic controller, averaging 32 mm/wk, the Toro controller, averaging 30 mm/wk, and TIME, averaging 29 mm/wk. Each of these treatments applied more weekly irrigation than RTIME that averaged 17 mm/wk (P < 0.0001). The ET controllers had similar weekly application rates and were higher than RTIME (P < 0.0001). Irrigation events occurred every day for the Weathermatic controller except when a daily event was bypassed by the rain sensor integral to that controller (Table 4). All treatments maintained similar turfgrass quality ratings well above the minimally acceptable level; averages ranging from 6.1 to 6.4, were not statistically different (Table 5). Turfgrass quality was not correlated with irrigation application (P = 0.745). Despite the reduced watering by RTIME, the reduced time-based schedule still had a turfgrass quality rating similar to other treatments (6.1). Rainfall totaled 109 mm over this period. The time-based schedules, TIME and RTIME, applied irrigation during every scheduled event for the months of March and May (Fig. 4). Each rain event occurring in March was not substantial enough to trigger the rain sensor to bypass irrigation and there was no rainfall in May. Irrigation savings by the ET controller treatments were based purely on their ability to match irrigation application with environmental demand and not affected by the variability of the rain sensor during these two months. Water savings by all treatments compared to the TIME WORS treatment ranged from 9% by the Weathermatic controller to 50% by RTIME (Table 5). Average weekly water application for the TIME WORS treatment was higher than the Toro controller (P = 0.0009), but was not different from the Weathermatic controller at 35 mm/ wk versus 32 mm/wk (P = 0.165; Table 4). The time-based treatments, TIME and RTIME had lower weekly application rates (P < 0.0001) than TIME WORS. 3.4. Summer 2007 Rainfall was more frequent during the summer of 2007, totaling 446 mm, but was still below the historical average. Irrigation ranged from 228 mm by RTIME to 425 mm by TIME (Fig. 5). The ETwater controller irrigated every day because it was programmed with a 25% allowable depletion instead of 50% originally programmed causing the controller to irrigate when 25% of the water was calculated to have left the root zone. This controller also would not recognize a rain sensor despite repeated attempts with customer service to repair. A power outage occurring on 8 June 2007 caused the equipment associated with the Weathermatic controller to discontinue taking measurements to calculate ETo. Since the Weathermatic controller did not operate based on an ET schedule during this time, data for this controller were removed for this period.

Fig. 5. Summer 2007 cumulative and daily water applied and daily rainfall (1 June– 31 August).

The ET controllers, Toro and ETwater, irrigated less depth per event, but applied irrigation more frequently as can be seen by the reduced slopes (compared to TIME WORS) for these treatments (Fig. 5); however, average weekly irrigation applied by the ET controllers was not statistically different (P = 0.802), 26 mm/wk by Toro and 24 mm/wk by ETwater, but was greater than RTIME (16 mm/wk; P < 0.0001; Table 4). The TIME schedule resulted in nearly twice the weekly irrigation rate compared to RTIME (P < 0.0001), 25% higher irrigation rate than the ETwater controller (P = 0.001), and a 15% greater rate than the Toro controller (P = 0.046). Turfgrass quality ratings were not different across treatments (P = 0.933) and remained above the minimally acceptable levels. Also, turfgrass quality was not correlated with water application (P = 0.591). Water savings by all treatments compared to the TIME WORS treatment (Table 5) ranged from 31% for TIME to 63% for RTIME. Savings for the ET controller treatments fell between the other treatments by saving 41% and 45%, respectively. The average weekly water application for TIME WORS was 44 mm/wk and was higher than all treatments (P < 0.0001). 3.5. Fall 2007 Irrigation ranged from 209 mm for the Toro controller to 427 mm for TIME in fall 2007 (Fig. 6). Rainfall during this period totaled 264 mm with only a few small events later in the season. Average weekly water application (Table 4) for the ETwater controller was 18 mm/wk and was not different compared to the other ET controller treatments, Weathermatic (20 mm/wk; P = 0.552) and Toro (15 mm/wk; P = 0.149), as well as RTIME (18 mm/wk; P = 1.000). The Toro controller had a lower weekly irrigation rate compared to the Weathermatic controller (P = 0.001). The Toro applied less events per week than the other ET controllers, averaging 1.7 events compared to 5.2 events for the Weathermatic and 4.6 for the ETwater. Irrigation scheduling by the Toro controller was similar to the RTIME schedule; both treatments applied similar average depths for 1.7 events per week. All treatments had lower weekly application rates than TIME (31 mm/wk; P < 0.0001). Turfgrass quality was similar across all treatments and higher than the minimally acceptable value of 5,

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Fig. 6. Fall 2007 cumulative and daily water applied and daily rainfall (1 September–30 November).

ranging from 6.4 to 7.1 (Table 5); quality was not different between treatments (P = 0.170). Similar to all other experimental periods, turfgrass quality was not correlated with irrigation depth (P = 0.178). The weather monitor for the Weathermatic controller measures temperature and senses rainfall using an expanding disk rain sensor. This monitor was found inverted and hanging from its mounted location on 9 October 2007. The length of time prior to this date that the monitor was damaged was unknown. Irrigation occurred despite rain events due to the misalignment of the weather monitor. The Weathermatic controller saved 43% compared to TIME WORS while the Toro and ETwater controllers saved 59% and 50%, respectively (Table 5). Both TIME and RTIME also showed water savings of 15 and 50%, respectively. Savings from the Weathermatic controller would have been slightly higher had the weather monitor been aligned appropriately for at least seven rain events that exceeded the 6 mm threshold of the sensor. 4. Discussion All treatments applied less water compared to cumulative irrigation for TIME WORS (Table 5). Average potential water savings using a rain sensor at a 6 mm threshold was 21% over the entire study period. Rainfall was less than the historical average resulting in dry conditions (Fig. 1). These savings occurred despite dry conditions due to a schedule of only two irrigation events per week. There was a high probability of rainfall events greater than 6 mm occurring within each period to cause at least one of the irrigation events to bypass during random weeks, resulting in water savings. RTIME averaged 53% savings for the study period due to the reduced runtimes and bypassing by the rain sensor. When operating properly, all ET controller treatments exhibited considerable savings according to statistical differences compared to TIME WORS for every time period except spring 2007 (Table 5). Differences were not significant in spring 2007 because the timebased treatments were developed considering historical effective rainfall. However, the spring 2007 period experienced only half of historical normal rainfall, increasing irrigation demand. Even though more irrigation occurred compared to the time-based

treatments, the ET controllers were reacting to the plant water needs based on real-time conditions and not historical needs. Despite this fact, it is clear that the ET controllers could have been programmed to apply less water (e.g. some fraction of ET) and turf quality would not have suffered as evidenced by application of half as much water by RTIME with damaging turf quality. The ET controllers schedule irrigation so that the plant material being irrigated is always well-watered. However, it is acceptable for turfgrass to have some stress from deficit irrigation strategies and still be able to recover quickly without loss of quality. Deficit irrigation occurred during the spring 2007 period for the timebased treatments due to the lack of rainfall compared to the historical average (Fig. 4). The ET controllers responded to the increased climatic demand, keeping the turfgrass healthy under well-watered conditions, while still producing some water savings over the same period. Programming these controllers to allow some deficit conditions could potentially result in both acceptable turfgrass quality and higher water savings assuming good irrigation distribution uniformity. Overall, water savings by these controllers were relatively similar despite the different approaches to irrigation scheduling by the manufacturers. The Weathermatic controller averaged 35% savings for the entire study period compared to the TIME WORS treatment. Average savings during 2 d/wk irrigation, fall 2006 and winter 2006–2007, were 44%. This could be attributed to less cumulative irrigation application over the winter months due to more accurate estimation of water need for the period (Fig. 3). Savings for 2007 periods averaged 26% and was lower than that of other time periods due to dry spring conditions. Savings from the Weathermatic controller would have been slightly higher had the weather monitor been aligned appropriately for at least seven rain events that exceed the 6 mm threshold of the sensor. The Toro controller showed considerable savings during both years averaging 50% for the fall 2006 and winter 2006–2007 periods and 38% for the 2007 periods, averaging 43% overall. Average savings were similar to the Weathermatic controller, rarely statistically different, and savings were less for the 2007 periods due to increased water demand for spring. The ETwater controller resulted in 47% savings for the last two periods, also similar to other ET controller savings. Water savings using ET controllers was not at the expense of turfgrass quality (Table 4). The ET controller treatments consistently applied less than TIME for the first two periods with only the exception of the incorrect time-based irrigation schedule in October. The ET controller treatments always applied less than TIME over the last three periods except for the following three months: 44% more for April (Weathermatic), 7% more for May (Weathermatic), and 5% more for July (Toro). The Weathermatic and Toro controllers resulted in more water savings for fall 2007 compared to fall 2006 (Table 5). It was likely that water savings resulted from these treatments because watering restrictions were removed and the controllers were more efficient at scheduling irrigation and accounting for rainfall given more opportunities to irrigate each week. More savings were also possible for fall 2007 due to increasing the net irrigation requirement replacement for the time-based schedules from 60% to 100% after winter 2006–2007. Haley et al. (2007) found that homes in Central Florida used an average of 149 mm/month when their time clocks were not adjusted over the year. Compared to this benchmark, fall 2006 and winter 2006–2007 savings for the ET controller treatments were 60% and 71% while the time-based treatments, TIME, RTIME, and TIME WORS, saved 47%, 63%, and 29%, respectively. During the last three periods, all treatments irrigated less than 149 mm/month except for TIME WORS (29% increase). Savings ranged from 6% by TIME to 46% by RTIME. ET controller savings were 20%, 26%, and

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30% for the Weathermatic, Toro, and ETwater controllers, respectively. The 2006 periods, fall and winter, had time-based treatments that were very conservative as well as ET controllers that were scheduled with almost 100% efficiency. Despite scheduling conservatively, the ET controllers still exhibited water savings, with maximum savings during winter 2006–2007, and above acceptable turfgrass quality (Table 5). Fall 2006 had more rainfall distributed over the treatment period than fall 2007 causing more rainfall to be effectively stored in the root zone and less irrigation to be required. This difference produced similar savings between the periods despite the alternative settings. There are financial benefits to using ET controllers compared to TIME WORS. Assuming an average irrigated landscape size of 500 m2 and a conservative flat rate of $ 4.00 per 3786 l of water used, the cost for TIME WORS was $ 1144 over the entire study period. Comparatively, the average cost for the ET controller treatments was $ 697 over the study period. The potential water savings by the ET controllers could save $ 28 per month, on average, for a typical homeowner. If the average cost of implementing one of the ET controller technologies is $ 500, then the payback period would be about 18 months. All treatments maintained acceptable turfgrass quality despite conservative settings, but it is unlikely that all treatments could have maintained above acceptable quality during the rest of the study. The time-based treatments had lower weekly water application and higher water savings during spring 2007 even after adjusting these schedules to apply 40% more water than the previous periods. It is possible that allowing the conservative schedule to continue through spring 2007 would have decreased turfgrass quality from prolonged water stress. Though ET controllers could be programmed incorrectly by homeowners, such as using 100% efficiency, the controllers would continually respond to changes in climatic demand whereas the time-based treatments could not adjust. 5. Conclusions The TIME treatment developed from 100% replacement of the net irrigation requirement, consistently applied more cumulative irrigation compared to the ET controller treatments. The RTIME schedule applied the least amount of water in all periods except winter 2006–2007 and fall 2007. Turfgrass quality remained above the minimally acceptable level for both of these time-based treatments and there were no statistical differences between the ratings among treatments. As a result, 60% replacement of net irrigation requirements is appropriate for effective water application with good irrigation distribution uniformity and weather similar to the historical average. The ET controllers averaged 43% water savings compared to a time-based treatment without a rain sensor and were about twice as effective at reducing irrigation compared to a rain sensor alone. Turfgrass quality remained above minimally acceptable despite water savings and the dry conditions compared to the historical average. The controllers adjusted their irrigation schedules to the climatic demand effectively, with maximum savings of 60% during the winter 2006–2007 period and minimum savings of 9% during spring 2007 when demand was highest. The RTIME treatment resulted in similar savings as ET controllers. Thus, as has been shown in previous research in Florida, changing time clock settings throughout the year can result in substantial irrigation savings. Fall 2006 and winter 2006– 2007 were scheduled for only 36% replacement of net irrigation requirement for the reduced time-based treatment, but still irrigated more in the winter compared to the ET controller treatments. This result indicates that the ET controllers were

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effective at reducing irrigation application during low climatic demand. Time-based treatments were developed from the net irrigation requirement for the area resulting in less water applied than if scheduled without using historical ET and effective rainfall. However, time-based schedules do not fluctuate with changing weather conditions and many homeowners do not adjust time clock irrigation schedules on a regular basis. Thus, the ET controllers tested here have shown that they can adjust irrigation in response to climatic demand in Florida. Actual water conservation potential of these controllers in landscapes will depend on irrigator habits and preferences. These controllers need to be evaluated under ‘‘real world’’ conditions to verify water savings. Acknowledgments The authors would like to acknowledge the following funding agencies for their support of this research: Hillsborough County Water Resource Services, Florida Department of Agricultural and Consumer Services, Florida Nursery Growers and Landscape Association, and Florida Agricultural Experiment Station. The authors would also like to thank the following individuals for their efforts in making this project possible: David Crockett, Larry Miller, Daniel Preston, Sudeep Vyapari, Sydney Park Brown, Amy Shober, Melissa Baum Haley, Mary Shedd McCready, and Gitta Shurberg. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy, 300 pp. ASCE-EWRI, 2005. The ASCE standardized reference evapotranspiration equation. Technical Committee Report to the Environmental and Water Resources Institute of the American Society of Civil Engineers from the Task Committee on Standardization of Reference Evapotranspiration. ASCE-EWRI, 1801 Alexander Bell Drive, Reston, VA 20191-4400, 173 pp. Aquacraft Inc., 2002. Performance evaluation of WeatherTRAK irrigation controllers in Colorado. Available at: http://www.aquacraft.com/Download_Reports/ WeatherTRAK_2001_Study_Report.pdf (Accessed 1 July 2009) 22 pp. Aquacraft Inc., 2003. Report on Performance of ET Based Irrigation Controller: Analysis of operation of WeatherTRAK controller in field conditions during 2002. Available at: http://www.aquacraft.com/Download_Reports/ WthrTrk_2002_Study_Report.pdf (Accessed 1 July 2009) 31 pp. Bamezai, A., 2004. LADWP weather-based irrigation controller pilot study. Available at: http://www.cuwcc.org/uploads/product/LADWP-Irrigation-ControllerPilot-Study.pdf (Accessed 31 October 2005) 40 pp. Black, R.J., Ruppert, K., 1998. Your Florida Landscape: A Complete Guide to Planting and Maintenance. UF-IFAS publication. University of Florida Press, Gainesville, FL, p. 241. Buss, E.A., 1993. Southern chinch bug management on St. Augustinegrass. ENY-325, Institute of Food and Agricultural Sciences. University of Florida, Gainesville, FL. Available at: http://edis.ifas.ufl.edu/LH036 (Accessed 1 July 2009) 5 pp. Devitt, D.A., Carstensen, K., Morris, R.L., 2008. Residential water savings associated with satellite-based ET irrigation controllers. Journal of Irrigation and Drainage Engineering. 134, 74–82. Diamond, R.A., 2003. Project review of Irvine ET controller residential runoff reduction study. Irvine Ranch Water District. Available at: http://www.irrigation.org/swat/images/irvine_runoff_reduction.pdf (Accessed 1 November 2005) 4 pp. Dukes, M.D., Haman, D.Z., 2002. Operation of residential irrigation controllers. CIR1421, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. Available at: http://edis.ifas.ufl.edu/AE220 (Accessed 1 July 2009) 10 pp. Haley, M.B., Dukes, M.D., Miller, G.L., 2007. Residential irrigation water use in Central Florida. Journal of Irrigation and Drainage Engineering 133 (5), 427– 434. Hunt, T., Lessick, D., Berg, J., Wiedmann, J., 2001. Residential weather-based irrigation scheduling: Evidence from the Irvine ‘‘ET Controller’’ study. Available at: http://www.irrigation.org/swat/images/irvine.pdf (Accessed 30 October 2005) 53 pp. Mayer, P.W., DeOreo, W.B., Opitz, E.M., Kiefer, J.C., Davis, W.Y., Dziegielewski, B., Nelson, J.O., 1999. Residential End Uses of Water. American Water Works Association Research Foundation, Denver, CO, 310 pp. NOAA (National Oceanic and Atmospheric Administration), 2005. Monthly precipitation 1975–2005 for Parrish, FL. Available at: http://cdo.ncdc.noaa.gov/pls/ plclimprod/poemain.cdobystn?dataset=DS3220&StnList=086880NNNNN (Accessed 1 November 2006).

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Solley, W.B., Pierce, R.R., Perlman, H.A., 1998. Estimated use of water in the United States in 1995. United States Geological Survey Circular 1200, p. 78. United States Census Bureau, 2005. Population estimates. Washington, DC. Available at: http://www.census.gov/popest/estimates.php (Accessed 19 January 2006). Wong, F., Harivandi, M.A., Hartin, J., 2005. UC IPM Pest Management Guidelines: Turfgrass. University of California Agriculture and Natural Resources Publication 3365-T, Davis, CA Available at: http://www.ipm.ucdavis.edu/PMG/ r785100311.html (Accessed 15 March 2008) p. 2.