TWO DECADES OF SMART IRRIGATION CONTROLLERS
IN U.S. LANDSCAPE IRRIGATION
M. D. Dukes
Collection
Review
HIGHLIGHTS
 Savings numbers in new studies across multiple soil types and climates are similar to those summarized in 2011 and are
summarized here as 51% in research plot studies and 30% in single-family homes.
 Studies of the human factors have begun showing how important the users are to success of the technology.
 Education in implementation remains important to achieve potential water conservation.
ABSTRACT. Smart irrigation controllers, such as evapotranspiration (ET) and soil moisture sensor (SMS) controllers, have
become commonly available from virtually all irrigation controller manufacturers. This review summarizes the literature
since the Fifth Decennial National Irrigation Symposium (NIS) concerning these controllers in research studies and pilot
implementations. Studies have expanded to multiple climates throughout the U.S. on a variety of soils and plant types. When
these devices are implemented properly on sites that have potential irrigation savings (i.e., excess irrigation), they are able
to reduce irrigation while maintaining plant quality. The level of reduction depends on many factors, including the amount
of excess irrigation, climate, plant type, and human interaction with the technology. When studies report positive savings,
the levels documented here range from 40% to 61% (51% avg.) in plot studies and from 28% to 32% (30% avg.) in residential studies. Of 17 identified studies in the past decade, five reported negative savings, and in most cases those results were
due to ET controllers installed on sites with little excess irrigation or controller programming that was not optimized for
savings. New trends in the industry include Wi-Fi signal-based ET controllers with smartphone app capability, an upcoming
standard for SMS controllers, as well as smart controllers becoming mandatory in areas of the U.S. As identified in the
Fifth Decennial NIS, it remains important to implement controllers on sites with the potential for irrigation reduction as
well as proper implementation with the best current information. Finally, there is a need to understand human interaction
with these devices because improper programming can make the difference between a water-saving device and ineffective
technology with a dissatisfied customer.
Keywords. ET controller, Landscape irrigation, Smart controller, SMS, Soil moisture sensor, Soil water sensor.
T
urfgrass and thus lawns and landscapes are estimated to cover more than three times the area of
any irrigated agricultural crop (Milesi et al., 2005)
and account for as much as 23% of the total urban
area (Robbins and Birkenholtz, 2003). Low-density housing
accounts for 26% of total U.S. land cover (Brown et al.,
2005), and urban areas are projected to continue to grow,
doubling by 2050 worldwide (Angel et al., 2011). Although
how many of these lawns are irrigated is not known, irrigation
systems and their associated water use are a common feature
of development in the last 20 years. Accordingly, water supplies have become strained in some areas of the U.S.; in areas
such as Florida, reducing water demand of irrigated land-
Submitted for review in January 2020 as manuscript number NRES
13930; approved for publication as an Invited Review and as part of the
National Irrigation Symposium 2020 Collection by the Natural Resources
& Environmental Systems Community of ASABE in April 2020.
The author is Michael D. Dukes, Professor, Department of Agricultural
and Biological Engineering, and Director, Center for Land Use Efficiency,
University of Florida, P.O. Box 110570, Gainesville, FL 32611; phone:
352-294-6270; e-mail: mddukes@ufl.edu.
scapes has been projected as the single most effective method
of reducing potable water demand (Carr and Zwick, 2016).
In the decade of 2000 to 2009, significant progress was
made in indoor water conservation due to high-efficiency appliances and low-flow fixtures and toilets. DeOreo and
Mayer (2012) summarized this trend, reporting that indoor
water use dropped from 708 L d-1 in 1997 to 405 L d-1 for
homes built to EPA WaterSense specifications, a 43% reduction. However, they also reported that, during the same time
period, outdoor water use did not change on a per home basis.
In the classic study of residential end uses of water,
Mayer et al. (1999) determined not only water use trends of
single-family homes but also disaggregated total use into end
uses for individual indoor appliances. Much detail was provided about indoor end uses down to details such as flushing
characteristics of toilets, length of showers, and water use
versus time of day. Although outdoor use was a byproduct
of the study, it was not a main objective. However, outdoor
use across 12 North American cities averaged 59% of total
potable water use. Some other key discoveries were that
homes with irrigation systems used 35% more water than
homes without, and that homes with a traditional automatic
Transactions of the ASABE
Vol. 63(5): 1593-1601
© 2020 American Society of Agricultural and Biological Engineers ISSN 2151-0032 https://doi.org/10.13031/trans.13930
1593
timer used 47% more water than those without. Thus, the
convenience of an irrigation timer leads to higher irrigation
use, and often the amount used is in excess of landscape
plant needs (e.g., Haley et al., 2007).
In a follow-up to the Mayer et al. (1999) report, the same
investigators conducted the most recent comprehensive
study of residential end uses of water (DeOreo et al., 2016).
Given that outdoor water use, and irrigation specifically, is a
large portion of residential demand and that since the 1990s
new plumbing codes have mandated efficient indoor fixtures, more focus was given to outdoor water use in the new
study. The investigators studied irrigation efficiency by
comparing measured irrigation applied with the theoretical
requirements of individual home sites (i.e., gross irrigation
requirement, GIR). This study included a group of homes
where irrigation was examined in detail. Indoor household
use dropped from 670 L d-1 in the 1999 study to 522 L d-1 in
2016, with the decreases due primarily to more efficient toilets and clothes washers. The irrigation study group (838 single-family homes) had 50% of their total use for irrigation.
Of that group, 72% irrigated less than the GIR, 16% were
approximately on target (70% to 130% of GIR), and 13%
irrigated excessively. Thus, a relatively small proportion of
the overall customer base across nine utilities in North
America had excess irrigation use. However, their excess use
accounted for the majority of the excess for the entire population of irrigating customers. In our studies of utility data to
target irrigation customers, we have found similar trends
(e.g., Davis and Dukes, 2015a), i.e., a relatively small number of customers account for the majority of excessive irrigation. Thus, in the coming decades, increasing the efficiency of landscape irrigation and reducing this discretionary demand will become even more important for urban water systems and for agriculture where those two demands are
in conflict.
For the Fifth Decennial National Irrigation Symposium
(NIS), Dukes (2012) summarized the studies up to then that
investigated smart irrigation controllers for landscapes.
Smart controllers had been commercially available for just
over ten years and were not typically used in landscape irrigation. Their use was limited to research studies, pilot studies, and limited utility incentive programs. In addition, in
2011, the EPA WaterSense program had just finished releasing a specification for weather-based irrigation controllers
(WBICs), also known as evapotranspiration (ET) based controllers (USEPA, 2011). At that time, there were a handful
of commercially available controllers on the market. Today
there are more than 700 controller models with the WaterSense label (USEPA, 2020). Thus, there is little doubt that
the WaterSense program contributed to the development and
use of these devices. These controllers are one category of
smart irrigation controllers, with the other being soil moisture sensor (SMS) based controllers. Although it is technically correct to refer to SMS as soil water sensors, the SMS
terminology will be used in this article as that is the common
term used in the industry. As this manuscript is being written, an ASABE committee (X633: Testing protocol for landscape soil moisture-based control technologies) is finalizing
a standard that outlines a test procedure for SMS controllers.
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It is likely that this standard will result in a WaterSense specification for these controllers in the future. The available ET
controllers have evolved in the last ten years to meet demand
and to adapt to technological development, while additional
SMS controllers have also been developed. The objective of
this review is to summarize current research on smart controllers and discuss current and future industry trends.
EVOLUTION OF SMART CONTROLLERS
The definition of smart controllers has not changed in the
past decade. These controllers determine an irrigation schedule or alter a preset schedule based on measured variables.
For ET controllers, the measured variables consist of
weather parameters such as temperature, relative humidity,
wind speed, and solar radiation, i.e., the classic variables
needed to estimate reference ET (ETo, Allen et al., 1998).
These variables may be measured on-site with sensors, or the
measured parameters may be provided to a controller (i.e.,
signal-based). All or some of these variables may be used.
Few controllers ever appeared on the market with measured wind speed and relative humidity. In the past decade,
most controllers using on-site sensors have a reduced sensor
set for simplicity, reduced cost, and robustness. Twenty
years ago, the industry favored on-site sensors, but in the
past ten years there has been a shift to signal-based controllers. This shift coincides with the ubiquitous nature of publicly available weather data from sources such as Weather
Underground (2020) and WeatherBug (2020). New sources
such as DarkSky (2020) and ETo forecasts from the National
Weather Service (NWS, 2020) promise new prediction and
visualization tools, which will likely be part of future smart
controllers.
When ET-controllers were under development by the industry, the early models borrowed heavily from the agricultural model of irrigation scheduling. That model consisted of
a local weather station that was used to provide weather variables such as relative humidity, temperature, wind speed,
and solar radiation, the necessary inputs to compute ETo via
an equation such as the FAO-56 Penman Monteith method
(Allen et al., 1998) or the ASCE-EWRI Standardized Reference ET method (ASCE, 2005). Early controllers developed
in the late 1990s and early 2000s contained an on-site
weather station with all or most of these measurement capabilities. In addition, most of these controllers had programming requirements for specific inputs that followed the crop
coefficient (Kc) approach (Allen et al., 1998). That approach
is typically modified for landscapes to account for vegetation
type, vegetation density, microclimate, and a stress factor
(Allen et al., 2011, 2020):
KL  Kv Kd Kmc Ksm
(1)
where
KL = landscape coefficient
Kv = vegetation species factor
Kd = vegetation density factor
Kmc = microclimate factor
Ksm = managed stress factor.
TRANSACTIONS OF THE ASABE
The values of the factors and Kc in equation 1 range from
0 to 1 and are dimensionless. Recently, ASABE Standard
S623 was developed to provide guidelines on the minimum
water demands for plants in mixed-species landscapes while
maintaining acceptable aesthetic quality (ASABE, 2017).
In the past ten years, manufacturers have generally simplified the user input requirements from the full soil water
budget approach. Typically inputs now consist of application rate based on irrigation equipment type (e.g., sprinkler,
drip, etc.), vegetation type (Kv), soil type (water holding capacity), exposure (Kmc), and slope (runoff adjustment for cycle soak). Additionally, there will sometimes be an option
for deficit irrigation (Ksm). Previously, users had to also set
values such as irrigation efficiency, application rate, cycle
soak parameters, root zone depth, etc. Although the required
inputs for soil water budget based controllers have been
greatly simplified in the last decade, there has been a trend
for the use of percentage adjustment of a base schedule using
ET. This approach uses ET estimates to make a percentage
change in a user-defined base schedule. For example, if ET
dictates that the conditions are more demanding than the
base schedule, the controller will increase the runtimes accordingly. Conversely, runtimes will be decreased under
lower ET conditions.
Official sales numbers are not available for smart controllers, but industry sources indicate that smart controller sales
may be as high as 20% of total controller sales. Despite this
substantial fraction, sales are still dominated by traditional
timers. Although much research and demonstration has occurred in the past decade, it seems there is still a need to incentivize or mandate use of these technologies. The state of
California has required smart controllers on all new landscapes as part of the Model Water Efficient Landscape Ordinance (MWELO; California, 2020). Small municipalities,
such as Fort Collins, Colorado, are beginning to require
smart controllers as well (Brent Mecham, Irrigation Association, personal communication, 17 Jan. 2020). Alachua
County, Florida, recently mandated smart irrigation controllers in all new installations (Alachua County, 2019). New ET
controllers that receive a signal via Wi-Fi and use a
smartphone as the primary control interface have become
popular in recent years. The Irrigation Association’s Smart
Water Application Technologies (SWAT) program has summarized a list of smartphone friendly controllers (SWAT,
2019).
As mentioned previously, the WaterSense program published a specification for ET-based controllers in 2011
(USEPA, 2011). The test consists of a 30 d period in which
a controller is tested virtually with control signals logged. In
the 30 d period, ETo must equal or exceed approximately 65
mm and rainfall must equal or exceed 10 mm for at least four
2.5 mm events to be considered a valid test. The virtual test
is based on the SWAT test protocol (SWAT, 2008), which
uses measures of irrigation adequacy (under-irrigation) and
scheduling efficiency (over-irrigation) to quantify controller
effectiveness. This test has been evaluated under field conditions, and recently Davis Conger and Dukes (2020) concluded that it was not possible to link the SWAT test scores
with water conservation potential of controllers. McCready
63(5): 1593-1601
and Dukes (2011) analyzed the scheduling efficiency and irrigation adequacy of irrigation controllers in field plots. The
in-field values of irrigation adequacy and scheduling efficiency varied as much as 68 percentile points. They concluded that these measures did not fully represent real-world
conditions.
CONTROLLER LITERATURE SINCE 2011
As of the Fifth Decennial NIS in 2010, much of the research on smart controllers had been done in Florida under
region-specific conditions (Dukes, 2012). Due to the location of this research, which was based on local and regional
needs, the conditions were limited to sandy soils (e.g., >95%
sand-sized particles), warm-season turfgrasses, and a subtropical climate with an average annual rainfall of 1120 to
1500 mm (USDA-NRCS, 2006). Since then, additional research has occurred in varying climates, soils, and to a limited degree plant material. Extensive work on smart controller evaluation and implementation and demonstration also
continued in Florida. Table 1 summarizes the smart controller literature with water use and savings studies since the
summary by Dukes (2012).
Williams et al. (2014) assembled information on smart
controller irrigation savings from the literature, finding a few
new sources not listed by Dukes (2012). The literature reviewed was a combination of research studies and case studies, although the research studies dominated the literature
sources. Average unweighted irrigation reductions ranged
from 15% for ET controllers to 38% for SMS controllers.
Smart controller research has expanded to the Carolinas,
Midwest, and Southwest in the past ten years. SMS-based
irrigation control reduced irrigation by 31% to 70% on tall
fescue grown in a silty clay loam in Kansas compared to a
calendar-based irrigation schedule (Chabon et al., 2017).
The larger savings number was due to a relatively wetter
year. Vick et al. (2017) studied an SMS controller and two
ET controllers (one with a weather signal and one with an
on-site weather sensor). Their study was conducted on single-family homes using lakes as the irrigation water source.
They compared the smart controllers to neighboring homes
with traditional irrigation timers. Compared to the homes
without a controller intervention, all of the smart controller
homes applied similar irrigation or greater in the case of the
SMS controllers. However, the groups had different baseline
irrigation applications prior to the study. Thus, the SMS controller homes had the greatest reduction from the baseline
(18%). All of the smart controller homes reduced irrigation
from 60% more than the estimated GIR to 10% more than
the GIR. The irrigation systems were poorly designed, likely
necessitating overirrigation to meet plant needs during highdemand periods. In addition, the homeowners frequently
overrode the smart controllers and used manual mode.
Five ET controller models were evaluated in an arid environment on a cool-season grass (tall fescue) and a clay
loam soil (Al-Ajlouni et al., 2012). Two of the five controllers reduced irrigation by 53% and 34% relative to a timer
programmed to apply 80% of historical ETo. The other three
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Table 1. Summary of smart irrigation controller studies and irrigation savings beyond the summary provided by Dukes (2012).
Savings
Irrigation Savings
Study
Technology
Conditions and Location
(%)
Comparison
Comments
Al-Ajlouni et al.,
ET
Cool-season turfgrass plots, clay
-259 to 53
Time-based
Two of five ET controllers saved signifi2012
loam, Las Cruces, N.M.
schedule
cant water, while two increased irrigation and one had no change.
Cardenas et al.,
ET and
Warm-season turfgrass plots,
28 to 66 (ET)
Time-based
Compared ET and SMS control to
2020
SMS
sand, Gainesville, Fla.
51 to 63 (SMS)
schedule
smartphone app, which saved similar irrigation but required continuous user interaction.
Cardenas and Dukes,
SMS
Warm-season turfgrass plots,
45 to 68
Time-based
Reclaimed water at 0.75 dS m-1 did not
2016a
sand, Gainesville, Fla.
schedule
impact four brands’ ability to reduce excess irrigation.
Cardenas and Dukes,
SMS
Residential landscapes, sand,
44
Non-intervention
Reclaimed water was supplied. Non-in2016b
Palm Harbor, Fla.
residential
tervention homes applied 2,184 mm
landscapes
year-1.
42 to 72
Time-based
Savings reported for conditions where
Cardenas-Lailhacar
SMS
Warm-season turfgrass plots,
schedule
turf quality remained good.
and Dukes, 2012
sand, throughout Florida
Cardenas-Lailhacar
SMS
Warm-season turfgrass plots,
40 to 88
Time-based
Savings were percentage of scheduled irand Dukes, 2010
sand, Gainesville, Fla.
schedule
rigation cycles bypassed
31 to 70
Time-based
Larger savings in a wetter year.
Chabon et al.,
SMS
Cool-season turfgrass plots, silty
schedule
2017
clay loam, Manhattan, Kan.
Davis and Dukes,
ET and
Residential landscapes, sand,
21 to 31 (ET)
Non-intervention
SMS controllers tended to have higher
2015b
SMS
Orange and Hillsborough
22 to 53 (SMS)
residential
irrigation efficiency under test condiCounties, Fla.
landscapes
tions. Proper programming important to
maximize water savings.
Davis and Dukes,
ET
Residential landscapes, sand,
-54 to 24
Non-intervention
ET controller and non-intervention
2014
Hillsborough County, Fla.
residential
homes reduced irrigation. Study emphalandscapes
sized the need to screen high irrigation
users for ET controller implementation.
Dobbs et al.,
ET on-site
Warm-season turfgrass plots,
66 to 70 (ET)
Time-based
ET controller daily soil water budget.
2014
and SMS
gravelly loam, Homestead, Fla. 64 to 75 (SMS)
schedule
Generous time-based comparison.
Grabow et al.,
ET and
Cool-season turfgrass plots, clay,
-11 (ET)
Time-based
Multiple events per day with SMS con2013
SMS
Raleigh, N.C.
24-39 (SMS)
schedule
trollers enhanced water savings.
Haley and Dukes,
SMS
Residential landscapes, sand,
65
Non-intervention Educational group had initial impact that
2012
Palm Harbor, Fla.
residential
did not persist into the second year.
landscapes
Mayer and DeOreo,
ET
Residential and commercial land6
Weather adjusted
Savings include sites with both reduc2010
scapes, soil unknown, throughout
pre/post
tions and increased use.
California
Nautiyal et al.,
ET on-site Residential landscapes, soil un22 (ET)
Non-intervention
ET controller percentage adjust base
2015
and SMS
known, Wake County, N.C.
42 (SMS)
residential
schedule.
landscapes
Rutland and Dukes,
ET
Warm-season turfgrass plots,
25 to 41
Time-based
Adding a rain sensor or enabling rain de2012
sand, Wimauma, Fla.
schedule
lay features increased water savings.
Shober et al.,
ET
Mixed ornamentals in plots, sand,
-14 to 31
Time-based
No negative effect on plant quality or
2009
Wimauma, Fla.
schedule
growth.
Non-intervention
Savings largely negative but SMS reVick et al.,
ET and
Residential landscapes, soil un- -18 to 5 (ET)
-31 (SMS)
residential
duced application compared to pre-study
2017
SMS
known, Catawba-Wateree River
basin, N.C. and S.C.
landscapes
baseline.
controllers overirrigated the turfgrass. An SMS and ET controller were evaluated against a timer with historical based
runtimes and a non-intervention timer group on residential
sites in North Carolina (Nautiyal et al., 2015). The SMS
homes reduced irrigation by 42% compared to the non-intervention homes. The ET controller included on-site weather
parameter measurement and percentage adjustment of the irrigation schedule and resulted in a 22% reduction in irrigation. The historical based runtime homes had 14% less irrigation than the non-intervention homes. Grabow et al.
(2013) assessed irrigation adequacy and scheduling efficiency of ET and SMS controllers at different day of the
week frequencies and multiple within-day frequencies for
SMS. The SMS controllers resulted in 39% water savings for
a twice a day schedule and 24% for once a day compared to
a standard timer-based schedule. The ET controller treatments applied 11% more water than the timer schedule.
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Florida work on ET controllers continued by focusing on
evaluation and refinement of best practices to install and set
up controllers as well as implementing technologies on single-family homes in pilot studies. In the Fifth Decennial NIS,
Dukes (2012) concluded that smart controllers had substantial water conservation potential by adjusting irrigation according to weather or soil water conditions; however, at the
time, the potential water conservation was often not realized
due to improper implementation. An early signal-based ET
controller was evaluated on single-family homes in southwest Florida. Homes were selected as being in the top half of
utility potable use customers. The ET controller homes reduced irrigation relative to the GIR; however, most of the
homes with ET controllers had higher irrigation than comparison non-intervention homes. The authors recommended to
select homes for ET controllers as exceeding the GIR and
with at least 696 mm of annual irrigation to maximize the potential for water conservation (Davis and Dukes, 2014).
TRANSACTIONS OF THE ASABE
As a follow-on study, Davis and Dukes (2015a) compared implementation of ET controllers in two Florida regions. In one region, the upper 50% of potable users were
identified as potential customers that could reduce irrigation.
In the other (later) study, over-irrigation was determined
based on estimated irrigation at least 1.5 times the historical
GIR. Using the GIR method with estimated irrigation was
more successful at identifying over-irrigating customers and
led to a more successful conservation implementation. Smart
controller payback ranged from 4 to 27 months, with quicker
payback associated with higher excess irrigation. Davis and
Dukes (2015b) reported on a study in central Florida in
which both ET and SMS based irrigation controllers were
installed on single-family homes exceeding the GIR at least
by a factor of 1.5 in at least three spring (peak irrigation season) months for at least three years (Davis and Dukes,
2015a). Compared to a non-intervention group of homes, the
smart controllers reduced irrigation on average by 20% to
31% for ET controllers and by 22% to 53% for SMS controllers, with the analysis detailing differences across seasons and soils. The researchers concluded that, under the
conditions tested, the SMS controllers tended to be the most
efficient, and programming and education related to the ET
controllers was essential to maximizing water conservation.
For both controllers, installing and programming using research-based best practices tended to increase irrigation efficiency and water conservation.
In a climate where irrigation supplements rain, understanding the input of rainfall in an ET controller schedule can
define the efficiency level of a controller. Rutland and Dukes
(2012) tested the rain delay features of a signal-based ET controller. They found that using the controller rain pause and an
on-site rain sensor both reduced irrigation during a rainy period. The controller rain pause reduced irrigation by 25%,
while the use of rain pause and a rain sensor reduced irrigation by 41%. Rutland and Dukes (2014) tested the accuracy
of ETo estimates of two irrigation controllers in Florida. One
controller used on-site sensors, while the other received a signal. The on-site controller consistently over-estimated ETo by
10% to 30%. The signal-based controller estimated ETo accurately (1% to 3%) when accessing an on-site weather station but overestimated ETo by up to 10% when using publicly
available weather data. Dobbs et al. (2014) evaluated an SMS
and ET controller compared to a timer irrigation schedule
with and without a rain sensor in bahiagrass field research in
Homestead, Florida, with tropical weather conditions and a
rocky/loamy soil. Compared to a typical timer schedule, the
SMS reduced irrigation by 64% to 75%, and the ET controller
reduced irrigation by 66% to 70%. The ET controller was a
Rain Bird ESP-SMT that used on-site weather data and a
daily soil water budget to schedule irrigation.
Most research studies on smart controllers have focused
on turfgrass plots or turfgrass-dominated landscapes for the
simple reason that this category of plant material dominates
urban landscapes (e.g., Robbins and Birkenholtz, 2003; Milesi et al., 2005). Thus, there is a lack of studies on landscape
plants. Shober et al. (2009) studied the growth and quality of
three common Florida landscape plants irrigated with ET
controllers compared to a traditional time-based schedule.
63(5): 1593-1601
Growth and quality were not diminished despite the ET controllers reducing irrigation by up to 31% for a signal-based
controller and increasing irrigation by 14% for the onsite
sensor-based controller.
Work also continued in Florida on SMS controllers similar to ET controllers with a goal of understanding the best
method to implement in typical conditions. CardenasLailhacar and Dukes (2010) analyzed the precision of four
brands of SMS controllers. Precision is a key metric of performance for an SMS controller because a controller’s ability to react the same way (i.e., irrigation allowed or bypassed) under the same conditions (i.e., same moisture content) determines whether the controller will minimize excess
irrigation. The four brands tested had variable precision
ranging from the narrowest soil water content between
which irrigation was always allowed or always bypassed of
1.4% to a high of 7.8% in a soil having ~6% water holding
capacity by volume. Despite this wide range in precision, all
of these controllers have been shown to significantly reduce
excess irrigation; however, the more precise SMSs are more
reliable and tend to reduce excess to a greater degree (Cardenas-Lailhacar et al., 2008).
Cardenas-Lailhacar and Dukes (2016a) evaluated four
SMS controller brands on turfgrass plots under reclaimed irrigation water. Because reclaimed water often contains substantial dissolved solids, this study investigated whether this
lower quality water interfered with the ability of the controllers to reduce excess irrigation. Savings ranged from 46% to
78% with potable water and from 45% to 68% with reclaimed water having a mild salinity of 0.75 dS m-1. There
was no evidence that this level of salinity impacted the performance of these different brands. In contrast, CardenasLailhacar and Dukes (2015) found that two of three brands
of SMS controllers were sensitive to salinity as high as 5 dS
m-1 in terms of absolute soil moisture accuracy. However,
they determined that these controllers still had adequate precision to reduce excess irrigation in practical application.
Cardenas and Dukes (2016b) identified the most promising
brand of SMS controller evaluated in an earlier study (Cardenas and Dukes, 2016a) and used in residential landscapes.
Only the SMS controller homes reduced irrigation (44%) relative to the non-intervention homes over the 2.5 year study.
The addition of a rain sensor and giving homeowners educational materials about proper timer settings depending on
season of the year did not result in significant irrigation reduction. The non-intervention homes irrigated 2,184 mm
year-1; the SMS controllers reduced irrigation to 1,248 mm
year-1, which was still excessive relative to turfgrass net irrigation requirements of approximately 700 mm year-1 for the
region (Romero and Dukes, 2013).
Multiple Florida studies on SMS controllers were summarized as part of the Fifth Decennial NIS (CardenasLailhacar and Dukes, 2012). When installed and programmed properly, these devices nearly always eliminated
excessive irrigation under controlled research conditions as
well as single-family homes. Generally, approximately 20
mm week-1 of irrigation was needed under dry conditions to
maintain adequate turfgrass quality, while no irrigation was
required under wet conditions to maintain quality. This summary highlighted the fact that a medium threshold setting
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(~80% of field capacity) resulted in good turf quality, but
lower settings resulted in degraded quality in dry weather.
Over all the studies in which turf quality was maintained, the
irrigation reduction ranged from 42% to 72%. Under dry
conditions, the reduction ranged from -1% to 64%, with
some degradation in turf quality at the higher irrigation reduction levels.
In the largest evaluation of utility implementation of
smart controllers, Mayer and DeOreo (2010) analyzed 2,294
sites that had ET-based smart controllers installed across
California as part of various utility incentive programs. They
found a 6% reduction in irrigation across all sites based on a
weather-adjusted pre/post analysis. Sites that irrigated less
than the theoretical requirement based on 80% of ETo minus
effective rainfall (i.e., GIR) were likely to increase irrigation
with a smart controller. They found that sites with an average
irrigation to GIR ratio of 1.31 had increased irrigation with
an ET controller, indicating that these sites had irrigation
ranging from adequate to deficit. In contrast, sites with irrigation reductions had average irrigation to GIR ratios of
1.82. Accordingly, the researchers concluded that the preinstallation irrigation amount was the most significant factor
in determining whether water savings would occur across a
range of factors. Similarly, Davis and Dukes (2015a) concluded that a GIR ratio of at least 2 led to a likely chance of
reducing irrigation. In addition, Mayer and DeOreo (2010)
pointed out that proper controller programming is key to
achieving potential savings.
HUMAN FACTOR
Research on smart controllers has mainly focused on the
technology and the testing of that technology for irrigation
scheduling, water conservation, and landscape plant performance. In the first decade of smart controllers, there were no
intentional studies on the human factor (Dukes, 2012), i.e.,
what effect does the interaction of the end user and installer
have on the effectiveness of the technology? During the last
decade, a number of studies have assessed consumer preferences and satisfaction with smart controllers. However, we
know from studies on rebate programs, such as those detailed by Mayer and DeOreo (2010), that differences in how
programs are implemented (e.g., giveaway, rebate, etc.) influence how utility customers interact with smart controllers
and accordingly how effective controllers are at increasing
irrigation efficiency. Since then, several studies have examined customer preferences and education with regard to
smart controllers.
Smartphone applications to make irrigation scheduling
easier based on local weather data have been developed
(Migliaccio et al., 2015), and these apps can result in similar
irrigation savings as smart controller technologies (Cardenas
et al., 2020); however, studies have shown that trying to alter
behavior alone can have a limited effect over time. Haley et
al. (2007) showed that manually adjusting timer irrigation
schedules led to 30% reduction in irrigation with no negative
effect on turfgrass quality. Taking that work a step farther,
those researchers hypothesized that educating homeowners
on irrigation scheduling based on historical irrigation need
1598
would result in water conservation. They found that when
educating homeowners with a runtime irrigation schedule
that only required four adjustments per year (e.g., seasonally), the homeowners did not adjust their timers after the
first year of a two-year study, resulting in no significant water savings (Haley et al., 2012). Similarly, Morera et al.
(2017) found that homeowners with education and training
about their smart controllers did not have any significant difference in knowledge when compared to a non-intervention
group when surveyed more than two years after being educated about their smart controller. Thus, while education can
have benefits, ongoing efforts are required to maintain a usable knowledge base with which homeowners can make decisions. In addition, education of irrigation contractors is
critical because they come into contact with homeowners on
a regular basis.
Khachatryan et al. (2019) surveyed homeowners from the
most populated landscape irrigation states to assess smart
controller preferences. Homeowners with an interest in potential water bill savings preferred smart controllers to conventional systems. They were most interested in sensorbased technologies as opposed to those with remote data acquisition. In addition, user-friendly features such as wireless
operation and alert notifications were preferred and would
likely increase adoption. It is interesting that consumers preferred sensor-based technologies as opposed to remote data
acquisition when Wi-Fi based ET controllers are currently
one of the most popular market segments.
Initial work by Morera et al. (2015) determined that
homeowners with smart controllers were satisfied with the
technologies if there were perceived (but not necessarily actual) water savings. In addition, if the homeowners experienced challenges with the technology, their satisfaction degraded. Later follow-up with these homeowners with longterm (>5 years) use determined that the likelihood of continued use was 12 times higher if they were satisfied with the
controller (Morera et al., 2019). These results point to the
need for education of installers and end users to minimize
preventable poor performance of the technology due to incorrect installation, setup, or management as well as lack of
education about how the technology works.
Consumers are more likely to purchase a smart controller
if they have increased knowledge of irrigation systems and
residential landscaping (Suh et al., 2017). Purchase likelihood was higher for SMS controllers than for ET controllers.
Purchase of smart irrigation technologies was positively influenced if the consumers believed that water conservation
affected water supply. If they believed that smart controllers
were lower priced, easier to use, or more reliable, they were
also more likely to purchase.
Finally, there may be additional benefits of smart irrigation controllers, such as reducing utility peak demand. Some
utility infrastructure may be stressed on watering days when
numerous customers irrigate simultaneously. Reducing this
strain by shifting demand to alternate days or times can save
the utility costly infrastructure upgrades. Mayer et al. (2018),
working with a water utility, showed that the utility could
successfully reduce peak demand by installing Wi-Fi ET
controllers on customer irrigation systems. They estimated
TRANSACTIONS OF THE ASABE
that 500 to 1,700 controllers would result in demand reduction of approximately 4,500 m3 d-1. This benefit would be in
addition to any water savings realized with the controllers.
CONCLUSIONS
Since 2011, extensive research and development has occurred for smart irrigation controllers. In particular, ET controllers have advanced with a trend toward Wi-Fi controllers
that receive remote weather information for ETo calculation
and/or plant water requirement determination. These controllers offer the convenience of smartphone access and do
not need specialized communication networks or costly subscription services. The industry has moved toward these devices and away from on-site sensing and subscription
weather data feeds. A disadvantage of these Wi-Fi based
controllers is that the controller is not smart without Wi-Fi,
for example before Wi-Fi is available in a new home. The
advantages of convenience, customization, and continuous
software updates outweigh this disadvantage. Conversely,
SMS controllers lack a test specification and label from the
WaterSense program, although a specification is currently in
development. This specification will act as an incentive and
will likely result in considerable market growth for SMS
controllers.
The body of evidence presented here indicates that smart
controllers are versatile and robust in their ability to reduce
excess irrigation for a variety of climates, soil types, and applications. Thus, there is little doubt that they can potentially
save irrigation water. However, when these controllers are
used on sites with deficit irrigation, it has been shown that
ET controllers may increase irrigation. In this summary of
the past decade of studies that reported water savings for
smart controllers, research studies averaged 51% savings
and case studies for home and commercial landscapes averaged 30% savings. Five of 17 studies reported negative savings (e.g., irrigation increase), which was mostly associated
with the installation of ET controllers on deficit irrigation
sites and/or improper programming.
Finally, as with many technologies, the human factor has
just begun to be studied. Initial evidence suggests that if users perceive that the controller is saving water, they are
likely to be more satisfied. However, they are quickly dissatisfied if there are issues with the controller, which are often
associated with understanding the technology. Users are
drawn to the newer smartphone-capable technologies for
convenience.
Work should continue to validate savings from smart controller installations. Many questions remain, such as:
 How will educational programs for end users and irrigation professionals evolve to optimize smart controller performance and customer satisfaction?
 What are the water savings with respect to GIR and
pre-installation irrigation?
 Do the savings persist over time?
 What is the life span of various technologies, e.g.,
sensor based and signal based?
 What are other benefits of smart controllers?
63(5): 1593-1601
ACKNOWLEDGEMENTS
The author thanks the many funding agencies that has
made our work possible: Southwest Florida Water Management District, Orange County Utilities, Water Research
Foundation, Hillsborough County Water Resource Services,
Florida Department of Agriculture and Consumer Services,
Florida Nursery Growers and Landscape Association, Florida Turfgrass Association, U.S. Environmental Protection
Agency, Florida Agricultural Experiment Station, and Florida Cooperative Extension Service. Thank you to the many
individuals of the University of Florida’s Department of Agricultural and Biological Engineering and the Institute of
Food and Agricultural Sciences staff who made this work
possible.
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