ArticlePDF Available

Abstract and Figures

Studies have shown minimal impact by smart irrigation controllers when installed indiscriminately, but targeting overirrigators increases the water conservation potential. The objective was to evaluate different methodologies defining residential overirrigators. Two independent smart controller studies were conducted by utilities in Hillsborough County [Hillsborough County Water Resource Services (HCWRS)] and Orange County [Orange County Utilities (OCU)], Florida. In HCWRS, the cooperators qualified when irrigation was in the top 50th percentile of potable water users in the county. Additionally, the 21 cooperators were located in three cities determined as having high water use relative to other cities in the same area. In OCU, 132 cooperators received smart controllers when frequently irrigating more than 1.5 times the gross irrigation requirement (GIR). Actual ratios of historical average irrigation to the GIR ranged from 1.45 to 2.37 in HCWRS and 6.04-8.33 in OCU. As a result, cooperators in OCU showed significant reductions in irrigation with a return on investment of 4-14 months compared to HCWRS with a payback period of 17-27 months despite higher water rates and larger irrigated areas in HCWRS. Using the GIR as a benchmark proved to be a better method than using utility-wide percentile ranges of irrigation application to target homeowners for smart controllers to ensure irrigation reductions. Smart controllers are recommended for homeowners who average two times the monthly GIR for at least three months per year over at least three years when implemented in conjunction with relatively well-maintained irrigation systems. Water savings were guaranteed when ratios averaged more than six using the same frequency standards. Additional requirements for successful implementation include site-specific programming and providing basic knowledge to the homeowner. (C) 2014 American Society of Civil Engineers.
Content may be subject to copyright.
Methodologies for Successful Implementation
of Smart Irrigation Controllers
S. L. Davis, Ph.D., M.ASCE1; and M. D. Dukes, Ph.D., P.E.2
Abstract: Studies have shown minimal impact by smart irrigation controllers when installed indiscriminately, but targeting overirrigators
increases the water conservation potential. The objective was to evaluate different methodologies defining residential overirrigators. Two
independent smart controller studies were conducted by utilities in Hillsborough County [Hillsborough County Water Resource Services
(HCWRS)] and Orange County [Orange County Utilities (OCU)], Florida. In HCWRS, the cooperators qualified when irrigation was in the
top 50th percentile of potable water users in the county. Additionally, the 21 cooperators were located in three cities determined as having high
water use relative to other cities in the same area. In OCU, 132 cooperators received smart controllers when frequently irrigating more than
1.5 times the gross irrigation requirement (GIR). Actual ratios of historical average irrigation to the GIR ranged from 1.45 to 2.37 in HCWRS
and 6.048.33 in OCU. As a result, cooperators in OCU showed significant reductions in irrigation with a return on investment of
414 months compared to HCWRS with a payback period of 1727 months despite higher water rates and larger irrigated areas in HCWRS.
Using the GIR as a benchmark proved to be a better method than using utility-wide percentile ranges of irrigation application to target
homeowners for smart controllers to ensure irrigation reductions. Smart controllers are recommended for homeowners who average two
times the monthly GIR for at least three months per year over at least three years when implemented in conjunction with relatively
well-maintained irrigation systems. Water savings were guaranteed when ratios averaged more than six using the same frequency standards.
Additional requirements for successful implementation include site-specific programming and providing basic knowledge to the homeowner.
DOI: 10.1061/(ASCE)IR.1943-4774.0000804.© 2014 American Society of Civil Engineers.
Author keywords: Evapotranspiration; Gross irrigation requirement; Landscape irrigation ratio; Smart controller; Turfgrass; Water
conservation.
Introduction
Research has shown that in many cases half of total household
water use goes toward irrigation when homes have automatic irri-
gation systems. In Nevada, Devitt et al. (2008) found that outdoor
water use was 66% þ=16% of total water used at 27 residential
sites. Similar results were also found in central Florida where 64%
of the total household water use was irrigation (Haley et al. 2007).
When faced with population growth, limited water resources, and
installation of automatic irrigation systems becoming the standard
practice, increasing efficiency of automatic irrigation is a priority.
In general, turfgrass requires the most irrigation in Florida land-
scapes with a University of Florida-Institute of Food and Agricul-
tural Sciences (UF-IFAS) recommendation of 610660 mm per
year (Romero and Dukes 2011) compared to established ornamen-
tals that maintain quality under normal rainfall conditions (Scheiber
et al. 2008;Gilman et al. 2009). There are benefits to maintaining
turfgrass as a functional part of the landscape such as reducing
soil erosion, preventing particulate inhalation from excess dust,
dissipating heat at the soil surface level, and providing a wildlife
habitat (Beard and Green 1994).
Smart irrigation controllers are technologies designed to adjust
or override irrigation based on weather or soil conditions, thus lim-
iting overirrigation (Dukes 2012). Based on the current products
available on the market, smart controllers include weather-based
irrigation controllers also known as evapotranspiration (ET) con-
trollers, and soil moisture sensor controllers (SMS). Though there
are many variations, ET controllers typically use weather informa-
tion, user-selected program settings, and proprietary algorithms to
determine the irrigation schedule instead of relying on manually
selected runtimes. Soil moisture sensor controllers bypass irrigation
events when the measured soil moisture is greater than a threshold,
generally selected based on available water holding capacity.
In Colorado, seven cooperators participated in a three-year
study from 2000 to 2002 to determine the reliability and effective-
ness of ET controllers (Aquacraft 2002,2003). Due to voluntary
participation in the program, five of the seven sites were historical
underirrigators that maintained their historical average during the
study, thus resulting in no actual water savings. However, the ET
controllers captured 88% (2001) and 92% (2002) of potential
water savings based on estimated irrigation requirements.
Mayer (2009) analyzed the performance of 3,112 ET controllers
of varying models at residential and commercial properties in
California. These devices were distributed through rebate and
voucher programs, exchange programs, and direct installation pro-
grams. Overall, ET controllers had significant reductions of 6%
after one year compared to a year of irrigation prior to smart-
controller use. A subset of 384 sites monitored for three years
showed increased reductions over time of 16.4% after the third year
(Mayer 2009). Though reductions were statistically significant,
1Assistant Professor, Louisiana State Univ., Red River Research
Station, 262 Research Station Dr., Bossier City, LA 71112. E-mail:
sdavis@agcenter.lsu.edu
2Professor, Agricultural and Biological Engineering, Director Center for
Landscape Conservation and Ecology, Univ. of Florida, Institute of Food
and Agricultural Sciences, 205 Frazier Rogers Hall, Gainesville, FL 32611
(corresponding author). E-mail: mddukes@ufl.edu
Note. This manuscript was submitted on December 11, 2013; approved
on July 15, 2014; published online on August 18, 2014. Discussion period
open until January 18, 2015; separate discussions must be submitted for
individual papers. This paper is part of the Journal of Irrigation and Drai-
nage Engineering, © ASCE, ISSN 0733-9437/04014055(9)/$25.00.
© ASCE 04014055-1 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
savings were lower than expected due to smart controllers increas-
ing irrigation for historical underirrigators.
A homeowner cooperator study conducted in Pinellas County,
Florida, using Acclima TDT RS500 SMS (Meridian, Idaho) con-
trollers resulted in irrigation reduction of 65% over 26 months com-
pared to neighboring homes with homeowner maintained timers.
Additional treatments of automatic irrigation system with rain sen-
sor and automatic system with rain sensor and educational materi-
als did not have significant reductions. The higher reductions were
achieved by SMS from frequent bypassed irrigation events; the
SMS treatment averaged two irrigation events per month whereas
the other three treatments averaged 4.56 events per month.
Though the SMS treatment averaged irrigation below the GIR, typ-
ically considered to be deficit irrigation practices, turfgrass quality
remained adequate for this treatment indicating that reductions
were not at the expense of landscape quality.
In the communities of Apollo Beach, Riverview, and Valrico
located in southwest Florida, 36 cooperators were selected where
21 cooperators were outfitted with Toro Intelli-Sense (Blooming-
ton, Minnesota) TIS-612 (ET controller treatment) and the remain-
ing 15 were used as comparisons (Davis and Dukes 2014). Results
of the ET controllers were variable across communities compared
to the comparison homes with the highest reduction occurring in
Apollo Beach (24%), whereas irrigation increased in Riverview
(54%) and Valrico (14%). The ET controllers reduced irrigation
by 2341% compared to GIR and by 2334% compared to the
historical average. Davis and Dukes (2014) recommended that
this brand of ET controller would be appropriate for homeowners
with average annual irrigation greater than 696 mm in southwest
Florida.
Recent research has shown that smart controllers are most
effective at increasing efficient irrigation practices when imple-
mented by historical overirrigators. However, most government
or utility programs that focus on water conservation, such as rebate
or trade-in programs, make smart controllers available to everyone
indiscriminately. The objective of this work was to evaluate meth-
odologies for identifying single family home utility customers
capable of achieving significant benefits from implementing smart
controllers.
Materials and Methods
Two independent irrigation studies were implemented in Hillsbor-
ough and Orange Counties, Florida, to determine the water conser-
vation potential of smart controllers. The Hillsborough County
study occurred from February 2009 to January 2011 in the com-
munities of Apollo Beach, Riverview, and Valrico located within
the Hillsborough County Water Resource Services (HCWRS)
service area. The Orange County study was conducted across the
Orange County Utilities (OCU) service area from November 2011
to October 2012.
HCWRS Study
Thirty-six cooperators voluntarily participated in the study if they
resided in one of the three selected HCWRS communities, were in
the top 50th percentile of county water users as determined by
Romero and Dukes (2010), and had irrigation systems with adequate
performance determined from distribution uniformity testing. The
three communities were selected based on five years of monthly
billing data that indicated a large number of the water users in
the top 50th percentile resided in these communities compared to
the other communities analyzed within the county (Romero and
Dukes 2010). Within this top 50th-percentile group, participants
were limited to within the 25th75th percentile to be eligible for
participation, equivalent to the top 62.587.5 percentile. Letters
were mailed to 1,500 potential participants, 500 letters per commu-
nity, asking for responses to a short questionnaire on a webpage as
the method for volunteering. There were 66 responses to the ques-
tionnaire request.
All 66 landscapes were evaluated prior to selection for the study
to ensure minimum irrigation system performance to adequately
maintain the landscape. During the evaluation, irrigation systems
were checked for functionality and distribution uniformity. The
lower quarter distribution uniformity (DUlq) and lower half distri-
bution uniformity (DUlh) were calculated in one zone based on
catch-can data (IA 2005). The 36 participants were chosen from
the pool of 66 respondents based on the condition of the landscapes
and irrigation systems. Of the 36 chosen participants, the DUlh
results ranged from 0.60 to 0.87, which approximates the efficiency
of the irrigation systems (Mecham 2001).
Participants were separated into two treatment groups: 21 coop-
erators were outfitted with Toro Intelli-Sense TIS-612 (Riverside,
California) ET controllers (ET controller treatment) that used
WeatherTRAK ET Everywhere signal service (Hydropoint
DataSystems, Petaluma, California), and the remaining 15 main-
tained their current irrigation practices (comparison treatment).
The ET Everywhere signal service provided reference evapotran-
spiration (ETO) and rainfall data to the Intelli-Sense controller via a
wireless signal that required an annual subscription. The Intelli-
Sense (Toro Company, Riverside, California) was chosen as the
ET controller based on adequate results from a field plot study also
conducted in southwest Florida (Davis et al. 2009;Davis and
Dukes 2012).
The ET controllers were programmed by UF-IFAS using default
values based on plant type, soil type, and microclimate. The only
exceptions to default values were customized application rates by
zone and irrigation system efficiencies estimated from DUlh values.
Cooperators that received the ET controller also received exemp-
tions from day-of-the-week watering restrictions.
Automatic meter reading (AMRs) devices (FIREFLY, Datamatic,
Plano, Texas) were installed on household water meters in January
2009 and collected subhourly water meter data through January
2011. The AMRs were equipped with an optical sensor that recorded
every sweep of the dial, signifying 0.038 m3of water consumption.
Irrigation water use for each cooperator was determined from the
total household water use by removing any water accumulated
for a single subhourly time period that was less than the minimum
volume capable of the irrigation system based on the calculated
application rates determined during the initial irrigation system
evaluation. The average per capita indoor water use for the cooper-
ators ranged from 0.054 to 0.530 m3d1, averaging 0.260 m3d1,
identical to the average that was found in a US study covering 12
cities (Mayer et al. 1999).
OCU Study
Historical monthly water billing records were provided by Orange
County Utilities for all single-family residential accounts in their
service area over a seven-year period. Irrigation was estimated
by assuming indoor water use based on 0.256 m3d1per person
and 2.2 persons per account (16.9m3=mo). Estimated irrigation
was evaluated against monthly GIR, thus creating monthly ratios
(estimated irrigation/GIR). Ratios greater than one indicate overir-
rigation practices whereas ratios less than one indicate underirriga-
tion practices. Accounts with ratios greater than 1.5 at least 3
months/year for three consecutive years were considered consistent
overirrigators. Orange County Utilities mailed a letter to the 7,408
© ASCE 04014055-2 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
accounts that met these criteria to request participation in the study.
A total of 843 customers responded to the letter by answering a
questionnaire on the UF-IFAS webpage.
Irrigation system evaluations were performed in locations where
there were a large number of respondents within a 10 km2range to
increase the likelihood of meeting the statistical requirements for
treatment replications while minimizing spatial variability. Irriga-
tion systems were checked for functionality and problems were
reported to the homeowner. Due to the large number of evaluations
required for this study, visual inspections of irrigation system effi-
ciency were substituted for catch-can distribution uniformity mea-
surements. Observed problems such as leaks and correctable
misapplication of water were corrected.
There were 139 participants located across seven locations in
Orange County. Each location had five treatments replicated four
times except for one location where one treatment had three rep-
lications (19 cooperators). Two treatments consisted of ESP-SMT
(Rain Bird Corporation, Azusa, California) ET controllers and two
treatments consisted of WaterTec S100 (Baseline, Boise, Idaho)
SMSs. Two treatments, one for each technology, were installed
using methods determined solely by the installing contractor
without UF-IFAS intervention. The remaining two technology
treatments included UF-IFAS training for the contractor prior to
installations, site-specific programming of the smart technology,
and cooperator education of the technology installed at their home.
Education consisted of a five-minute tutorial while on-site with the
cooperator to encourage familiarity with the new technology and
questions. Additionally, smart controller brochures created by
UF-IFAS were provided as a resource concerning the information
discussed during the tutorial and provided contact information for
future correspondence. All participants that received a technology
also received a variance for day-of-the-week water restrictions. The
final treatment was a comparison treatment that did not receive
intervention to their normal irrigation practices.
All 139 participants received E-Coder R900i (Neptune Technol-
ogy Group, Tallassee, Alabama) flow meters installed on dedicated
irrigation lines. These flow meters had AMR capability built into
the meter that recorded irrigation volumes per hour throughout the
study. The minimum volume recognized by the meter was
379 cm3. Irrigation volumes were converted to irrigation depth
using irrigated areas measured during the initial irrigation evaluation.
Weather Data
Weather data were collected from three weather stations installed in
the communities associated with each study during their respective
study periods. The weather stations were equipped to provide tem-
perature, relative humidity, solar radiation, and wind speed at
15-min intervals. Additionally, weather stations were equipped
with rain gauges to determine rainfall totals.
In HCWRS, weather station distances from the cooperating
homes varied but were not greater than 4 km. Two additional rain
gauges were added to ensure rainfall measurements were within
500 m of cooperating homes. Historical weather data were col-
lected from two Florida Automated Weather Network (FAWN)
stations located in the communities of Balm and Dover within
Hillsborough County, Florida. When data were available for both
stations, weather and rainfall values were averaged.
Because there were seven locations and only three weather sta-
tions in OCU, maximum distance between the station and cooper-
ating homes was 10.8 km. Two rain gauges monitored by UF-IFAS
and one rain gauge monitored by Orange County Utilities were
used to better approximate rainfall in three locations. These three
rain gauges were less than 6 km from any cooperating home.
Historical weather data for all OCU locations was collected from
the FAWN station located at the Mid-Florida Research and Educa-
tion Center in Apopka, Florida.
Historical Irrigation Application
Tampa Bay Water (Clearwater, Florida) and Orange County Util-
ities provided historical billing records for the HCWRS study from
2001 to 2009 and OCU study from 2003 to 2009, respectively.
Irrigation was estimated from combined indoor and outdoor water
use using the equation (Dziegielewski and Keifer 2010)
OUMa¼TUMaIUMað1Þ
For each account, OUM (m3d1) is the outdoor water use, TUM
(m3d1) is the total water use, and IUM (m3d1) is the
indoor water use. When actual IUM cannot be measured, it is gen-
erally estimated as an average applied throughout the year.
Friedman et al. (2013) used the average monthly measured
indoor water use from dual-metered billing records as the estimated
indoor water use for single-metered accounts of known irrigators
within the same utility. Their results showed that the average annual
irrigation from the measured accounts (565 mm; 1,294 accounts)
was similar to the average annual irrigation of the estimated
accounts (491 mm; 6,305 accounts), indicating that the method pro-
vided a good representation of average irrigation across all accounts.
In the HCWRS and OCU studies, the average monthly indoor
water use was estimated for each participant during the study period
using AMR technologies. These averages were then applied to the
corresponding historical account billing records so that the monthly
irrigation estimations were as accurate as possible.
Gross Irrigation Requirement
The gross irrigation requirement (GIR), an estimate of theoretical
irrigation needs calculated using a soil water balance, was used as a
comparison to irrigation by the treatments in each study. The GIR
was calculated by multiplying the net irrigation water requirement
(IWRnet) by a scheduling multiplier (SM). The IWRnet is defined as
the amount of irrigation required to increase soil water storage to
field capacity (FC), or the maximum water level that can be stored
before gravitational drainage (IA 2005). The IWRnet was deter-
mined from mass conservation of soil water content (IA 2005)
IWRnet ¼PWR Reð2Þ
The PWR is the plant water requirement (mm) and Reis effec-
tive rainfall (mm). The IWRnet was accumulated daily, but was ap-
plied only on days when the soil water level fell below management
allowable depletion (MAD), calculated as 50% of the difference
between FC and permanent wilting point (PWP) where PWP is
the water level where plants can no longer extract water from the
root zone (IA 2005). The PWP and FC were selected as 9.2 and
51.9 mm for the locations classified as flatwoods soils and 9.2
and 33.2 mm for the locations classified as sandy soils (USDA
1989a,b). Both PWP and FC were calculated based on a root zone
depth of 305 mm for turfgrass. Once IWRnet was applied, the soil
water level increased to field capacity and IWRnet was reset to zero.
Deep percolation and surface runoff, also typically part of the soil
water balance, were considered negligible since deep percolation
and runoff can be avoided with proper design and management
of the irrigation system.
The PWR is the amount of water necessary to maintain healthy
plant material (IA 2005) and was calculated as the plant-specific
© ASCE 04014055-3 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
evapotranspiration (ETC) using the following equation (Allen et al.
1998)
ETC¼KC×ETOð3Þ
Reference evapotranspiration (ETO) is the estimated evapotran-
spiration of a short reference crop assumed to be a dense, well-
watered, cool-season turfgrass maintained at a 0.12 m height. The
ETOwas calculated by the American Society of Civil Engineers
Environmental and Water Resources Institute (ASCE-EWRI)
standardized ET equation (ASCE-EWRI 2005) using the collected
weather station data. The crop coefficient (KC) values are ratios of
average crop-specific evapotranspiration to average reference
evapotranspiration. These values incorporate distinguishing charac-
teristics of the specific crop to the reference crop such as crop
height, crop-soil surface resistance, and albedo of the crop-soil sur-
face (Allen 2000). The KCvalues selected for these studies were
updated monthly for turfgrass with values of 0.45 (December
February), 0.60 (November), 0.65 (March), 0.70 (July, August,
October), 0.75 (June, September), 0.80 (April), and 0.90 (May)
(Jia et al. 2009).
Effective rainfall was limited to the portion of total daily rainfall
that caused the soil water level to reach the maximum soil storage
capacity after PWR was taken into account. Rainfall that exceeded
the soil storage capacity was considered lost due to surface runoff
or deep percolation.
A scheduling multiplier (SM) based on the average uniformity
of the irrigation system was used to convert IWRnet to GIR. The
SM was determined from the DUlq using the following equation (IA
2013):
SM ¼1=ð0.386 þ0.614 ×DUlqÞð4Þ
The calculated DUlq values for each participant were used for
the HCWRS study, whereas an estimated average of 0.675 was
used for the participants in OCU. The GIR was calculated by multi-
plying IWRnet by SM (IA 2013).
Ratios
Ratios were used to evaluate underirrigation or overirrigation by the
cooperators in each study. Ratios were calculated for each month
(m) using the following equation:
Ratiom¼IAPm=IBASEmð5Þ
The IAP (mm) is the irrigation applied during the specified
month and IBASE (mm) is the irrigation being used as a baseline
comparison.
Three ratios were calculated using Eq. (5). The treatment
(TMT)-GIR ratio was calculated using monthly irrigation measured
during the study periods as IAP and the monthly GIR calculated for
the respective months of the study period as IBASE. Similarly, the
Hist-GIR ratio was calculated using monthly irrigation estimated
from historical billing records as IAP and the monthly GIR calcu-
lated for the respective months of the billing records as IBASE. The
final ratio, called the TMT-Hist ratio, was calculated using monthly
irrigation measured during the study periods as IAP and the histori-
cal irrigation averaged by month as IBASE.
Based on Eq. (5), ratio of one indicated that the irrigation
applied, IAP, matched the baseline comparison, IBASE. For the
TMT-GIR ratios and Hist-GIR ratios, a ratio equal to one would
mean that irrigation application equaled the baseline comparison
specified as the theoretical irrigation needs. Similarly for the
TMT-Hist ratios, a ratio of one would indicate that irrigation appli-
cation equaled average historical irrigation used as the baseline
comparison. Ratios that are less than or greater than one indicate
that irrigation application was less than or greater than the baseline,
respectively.
To determine the effect of the study for each treatment, the dif-
ference between the TMT-GIR ratio (posttreatment installation)
and Hist-GIR ratio (pretreatment installation) was calculated to
determine the impact of the treatments that were normalized for
weather conditions. The differences between TMT-GIR ratio and
Hist-GIR ratio was interpreted as: zero represents no change in
irrigation application, positive values represent an overall increase
in irrigation after treatment installation, and negative values re-
present an overall decrease in irrigation after treatment installation.
Statistical analyses were performed using Statistical Analytical
Systems (SAS) software (Cary, North Carolina). The ratios were
analyzed using the glimmix procedure and comparisons were made
using the least mean square differences by treatment. The confi-
dence bounds for the means were determined using a two-sided
t-statistic and the standard deviation. Significance was determined
at a 95% confidence level.
Results
Ratios
The Hist-GIR ratios calculated for HCWRS, ranging from 1.45 to
2.37, were much lower than the Hist-GIR ratios calculated for
OCU, ranging from 6.04 to 8.33 (Table 1), due to higher amounts
of irrigation applied in OCU compared to HCWRS. There were
Table 1. Hist-GIR Ratios Were Calculated Monthly as the Ratio of the Historical Average Irrigation Determined from a Minimum of Five Years to the GIR
Estimated Using the Soil Water Balance
Study Location Treatment Number of homes Hist-GIR ratioaLower boundbUpper boundc
HCWRS AB Comparison 6 2.37a2.17 2.56
HCWRS AB ET+Edu 7 1.92b1.75 2.09
HCWRS R Comparison 3 1.45c1.25 1.66
HCWRS R ET+Edu 5 1.53c1.33 1.73
HCWRS V Comparison 6 2.03a,b 1.77 2.30
HCWRS V ET+Edu 9 2.08a,b 1.87 2.29
OCU All Comparison 28 6.88b6.40 7.36
OCU All ET 27 6.04c5.69 6.40
OCU All ET+Edu 28 8.33a7.64 9.02
OCU All SMS 28 6.41c6.02 6.81
OCU All SMS+Edu 28 7.27b6.82 7.72
aLowercase letters in the ratio column within each study indicate that means are different according to the glimmix procedure at the 95% confidence level.
bLower bound of a 95% confidence interval.
cUpper bound of a 95% confidence interval.
© ASCE 04014055-4 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
significant differences in the Hist-GIR ratios across the treatments
prior to the start of both studies, indicating that not all treatments
were similar in their historical irrigation habits.
When comparing the treatment period to the historical average,
the TMT-Hist Ratio ranged from 0.27 to 1.14 for HCWRS (Table 2).
There was no change in average irrigation application for the ET
treatments in Apollo Beach and Riverview based on the bounds of
the 95% confidence intervals for HCWRS. Additionally, the upper
bound for the 95% confidence interval for the ET treatment in
Valrico was very close to 1 (0.97), indicating that the decrease
in irrigation was small. All three comparison groups irrigated less
than the historical average indicating a systematic decrease prior to
or during the study period. Possible reasons for this decrease were
discussed in detail by Davis and Dukes (2014).
The TMT-Hist Ratio ranged from 0.51 to 0.93 for OCU
(Table 2). The ET treatment had not decreased irrigation applica-
tion from the historical average whereas the other four treatments
averaged below one. The upper bound for the 95% confidence
interval for comparison group was very close to 1 (0.97) indicating
that the decrease during the study period was small. The most sig-
nificant decreases occurred for the SMS and SMS+Edu groups that
applied 63 and 51% of their historical average, respectively.
The TMT-GIR ratio was calculated to determine how well the
treatments maintained landscape water needs during the study
period. In HCWRS, the comparison groups in Apollo Beach
and Valrico maintained well-watered conditions, averaging 1.13
and 0.91, respectively (Table 3). Additionally, the ET treatment
in Valrico also maintained well-watered conditions, averaging
0.91. The confidence intervals for these three groups include the
value of 1, thus indicating good irrigation scheduling techniques.
Slight underirrigation occurred by the ET treatment in Apollo
Beach (0.79).
Underirrigation occurred for both treatments in Riverview, with
ratios of 0.30 for the comparison group and 0.68 for the ET group
(Table 3). Cooperators in Riverview were already the lowest his-
torical irrigators in HCWRS with Hist-GIR ratios of 1.45 and
1.53 (Table 1) and the comparison group applied only 27% of their
historical average during the study period (Table 2). Thus, the
decline in irrigation by the comparison group compared to the GIR
was affected by an outside influence likely unrelated to the study
such as aggressive water conservation marketing during the
drought periods (Davis and Dukes 2014). This would not have
largely affected ET+Edu due to most cooperators submitting to
the automation inherent in the technology.
The TMT-GIR ratios in OCU ranged from 2.00 by the SMS
+Edu group to 4.30 by the comparison group (Table 3). These
ratios indicate that all treatments are irrigating significantly more
than what is required to maintain plant health. However, all four
technology treatments had ratios that were significantly less than
the comparison group and the TMT-GIR ratio for SMS+Edu
was significantly less than the other three technology treatments.
Overirrigation prior to the study was so prominent that even though
overirrigation was still occurring, TMT-GIR ratios were only
2754% of the Hist-GIR ratios, thus producing water savings.
There were negative values for the difference between TMT-
GIR ratios and Hist-GIR ratios for all treatments in both studies
Table 2. TMT-Hist Ratios Were Calculated Monthly as the Ratio of Average Treatment Irrigation to the Historical Average Irrigation Determined from a
Minimum of Five Years
Study Location Treatment Number of homes TMT-Hist ratioaLower boundbUpper boundc
HCWRS AB Comparison 6 0.79b0.66 0.93
HCWRS AB ET+Edu 7 1.14a0.84 1.45
HCWRS R Comparison 3 0.27c0.19 0.35
HCWRS R ET+Edu 5 1.12a0.88 1.36
HCWRS V Comparison 6 0.63b0.51 0.75
HCWRS V ET+Edu 9 0.87b0.78 0.97
OCU All Comparison 28 0.88a0.80 0.97
OCU All ET 27 0.93a0.80 1.06
OCU All ET+Edu 28 0.71b0.57 0.84
OCU All SMS 28 0.63b0.57 0.69
OCU All SMS+Edu 28 0.51b0.41 0.61
aLowercase letters in the ratio column within each study indicate that means are different according to the glimmix procedure at the 95% confidence level.
bLower bound of a 95% confidence interval.
cUpper bound of a 95% confidence interval.
Table 3. TMT-GIR Ratios Were Calculated Monthly as the Ratio of Average Treatment Irrigation to the GIR Estimated Using the Soil Water Balance
Study Location Treatment Number of homes TMT-GIR ratioaLower boundbUpper boundc
HCWRS AB Comparison 6 1.13a0.92 1.35
HCWRS AB ET+Edu 7 0.79b0.63 0.94
HCWRS R Comparison 3 0.30c0.20 0.39
HCWRS R ET+Edu 5 0.68b0.58 0.79
HCWRS V Comparison 6 0.91a,b 0.60 1.23
HCWRS V ET+Edu 9 0.91a,b 0.79 1.03
OCU All Comparison 28 4.30a3.70 4.89
OCU All ET 27 3.30b2.90 3.71
OCU All ET+Edu 28 2.75b2.44 3.05
OCU All SMS 28 2.85b2.42 3.28
OCU All SMS+Edu 28 2.00c1.64 2.35
aLowercase letters in the ratio column within each study indicate that means are different according to the glimmix procedure at the 95% confidence level.
bLower bound of a 95% confidence interval.
cUpper bound of a 95% confidence interval.
© ASCE 04014055-5 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
(Figs. 1and 2), indicating that there was a decline in irrigation
application for all treatments regardless of receiving a technology.
For HCWRS, there was no difference in reductions for Apollo
Beach between treatments, averaging 1.22 for comparison and
1.27 for ET+Edu, but the comparison treatments in both River-
view (1.23) and Valrico (1.49) were significantly more negative
than their respective ET treatments, 0.86 and 0.98 for each
respective community. In OCU, the education treatments for both
technologies were the most negative (5.58 for ET+Edu and 5.67
for SMS+Edu), indicating that there was a significant reduction in
irrigation application from site-specific program settings and home-
owner interaction. The SMS treatment (4.07), was also signifi-
cantly more negative than the comparison treatment (3.00), but
there was little additional impact by implementing an ET controller
(3.18) that had no significant difference from the comparison
ratio difference.
Payback Period
The technology treatments for both studies were evaluated against
their historical annual average to determine the return on invest-
ment from implementing a smart controller. It was assumed that
the purchase and installation of a SMS or ET controller was
$400 and $600, respectively, based on communications with con-
tractors across Florida. Additionally, the average irrigated land-
scape area measured at cooperating homes in both studies were
used, calculated as 864 m2for HCWRS and 446 m2for OCU.
Current rate structures associated with irrigation for each utility
were used to calculate savings (Table 4).
In HCWRS, average annual irrigation during the study period
ranged from 376 mm (Riverview) to 451 mm (Valrico) and histori-
cal annual average irrigation ranged from 572 mm (Riverview) to
694 mm (Apollo Beach) (Table 5). These averages resulted in
an annual return of $270.11, $346.66, and $436.29 in Riverview,
Valrico, and Apollo Beach, respectively, from implementing an ET
controller. Based on these totals, it would take 1727 months to
realize a monetary savings from the ET controller. However, similar
reductions in water use also occurred by the comparison homes that
may suggest savings would have been recouped regardless of the
technology.
Average annual irrigation during the study period in OCU
ranged from 778 mm (SMS+Edu) to 1,217 mm (ET) and the his-
torical annual average ranged from 1,789 mm (ET) to 2,277 mm
(ET+Edu) (Table 5). Annual savings were much higher than the
HCWRS study, ranging from $517.36 to $1,499.97, resulting in
quicker return on investment with payback periods ranging from
4 to 14 months. The most significant savings occurred for the treat-
ments that received the educational training indicating that educa-
tion and professional programming should be completed for all
smart technologies to achieve maximum benefit.
Annual water savings and payback period were not largely dif-
ferent between the utilities. However, annual savings in HCWRS
were primarily driven by higher water rates and larger irrigated
areas. If the rate structure and average irrigated area for OCU
had been used for HCWRS, the payback period would have in-
creased to 13 years from 27 months. Additionally, annual savings
by the education treatments were three times the savings of the
comparison treatment in OCU indicating that savings occurred
in addition to what the cooperators would have done on their own.
Discussion
Based on the ratio results, smart controllers were most effective
when implemented at homes of significant overirrigators (i.e. those
with potential savings). In HCWRS, the timer schedules of the co-
operators prior to the study were already conservative, thus the ET
controllers were not able to significantly decrease irrigation from
historical averages despite any reductions compared to the GIR.
It is likely that the achieved reductions by using ET controllers
occurred during rainy periods at homes with previously faulty
or absent rain sensors. Thus, annual savings were not high. Though
OCU cooperators are still considered overirrigators despite the
smart technology, the cooperators were such high overirrigators
-7
-6
-5
-4
-3
-2
-1
0
Comparison ET ET+Edu SMS SMS+Edu
Difference in Ratios
(Post-Ratio - Pre-Ratio)
Treatment
Fig. 1. Mean difference of TMT-GIR ratio (postratio) and Hist-GIR
ratio (preratio) for the OCU study where the difference represents
the treatment impact and a more negative result suggests an increase
in captured potential water savings; error bars were determined using
95% confidence interval
-7
-6
-5
-4
-3
-2
-1
0
Apollo Beach Riverview Valrico
Difference in Ratios
(Post-Ratio - Pre-Ratio)
Treatment
Comparison
ET+Edu
Fig. 2. Mean difference of TMT-GIR ratio (postratio) and Hist-GIR
ratio (preratio) for the HCWRS study where the difference represents
the treatment impact and a more negative result suggests an increase in
captured potential water savings; error bars were determined using
95% confidence interval
Table 4. Rate Structure for Billing Irrigation That Was Currently in Place
for Each Study Location
Tier
HCWRS OCU
Range (m3) Cost ($) Range (m3) Cost ($)
1019 3.61 013 1.04
21957 4.82 1340 1.43
357114 6.09 4078 2.84
4>114 7.66 78116 5.68
5NA
aNAa>116 11.35
aTier was not applicable to the rate structure.
© ASCE 04014055-6 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
prior to the study that a significant portion of potential water
savings were captured.
Though the 95% confidence intervals for the Hist-GIR ratios in
HCWRS were above 1 (representing the perfect irrigation sched-
ule), the cooperators were not good candidates for smart control-
lers. The most likely reason for elevated ratio averages occurs when
the rain sensor was faulty or absent, allowing automatic irrigation
during months of frequent rainfall when the GIR was extremely
low. This scenario was observed previously where only 6% of
homes evaluated in Pinellas County had functioning rain sensors
(Dukes and Haley 2009). Additionally, Whitcomb (2005) found
that 50% of homes across the Southwest Florida Water Manage-
ment District did not use rain sensors. As was seen in HCWRS,
it is possible to maintain acceptable turfgrass quality of landscapes
when averaging a TMT-GIR ratio of less than one. In an unrelated
study, a ratio of 0.56 was calculated for an SMS treatment from
monthly averages of irrigation applied (Iactual ) and the GIR
(Igross) while maintaining acceptable turfgrass quality (Haley and
Dukes 2012). In OCU, the TMT-GIR ratio results were much
higher than one, indicating that there is still the potential for de-
creasing irrigation application while maintaining good landscape
quality. There was a significant decline in the average ratio differ-
ence between TMT-GIR ratio and Hist-GIR ratio for the technology
with education treatments compared to the technology only treat-
ments (Fig. 1). This suggests that the initial technology adjustments
and homeowner education had a positive impact on the success of
the technology. Thus, providing additional opportunities for home-
owner education and smart controller adjustments after initial in-
stallation may be important to capturing more of the potential
savings.
When the potential water savings are marginal, such as with the
cooperators in HCWRS, the SMS combined with a deficit irrigation
schedule is preferable to an ET controller. The ET controller is gen-
erally designed to apply as close to GIR as possible thus maintain-
ing well-watered landscapes. However, short periods of deficit
conditions may not harm the quality of the landscape. Replacing
a timer programmed as a deficit irrigation schedule with an ET
controller may increase irrigation application. The SMS acts as a
bypass device that skips irrigation events when soil moisture is suf-
ficient by capturing rainfall. As a result, an SMS cannot increase
irrigation application, making the device an ideal addition to
already conservative irrigation practices.
Identifying the reasons for excessive irrigation practices is
extremely important to the successful implementation of a smart
controller. A smart controller performs best when implemented
on an efficient irrigation system when overirrigation occurs due
to poor timer programs. Inefficiencies such as poor system design,
high volume leaks, and multiple low volume leaks are also
common reasons for high outdoor water use. These problems cause
hot spots or dead areas that increase susceptibility to pests and
diseases while creating an undesirable appearance. Overirrigation
occurs to compensate, but mostly contributes to increasing runoff
and deep drainage. The installation of a smart controller does not
fix these problems and cannot substitute for regular maintenance. A
better solution would be to redesign the irrigation system for better
overall efficiency and checking each zone for leaks on a regular
basis. All of the cooperators were visually evaluated for good irri-
gation system efficiency prior to the start of the studies.
The indiscriminate installation of smart controllers through
rebate programs or research activities has proven to produce poor
overall results. Rebate programs typically attract volunteers who
are already fiscally or environmentally conservative, thus not
attracting excessive overirrigators. In the City of Tampa Water
Department, located next to HCWRS, the average estimated irriga-
tion per household was estimated as 54 mm=month (Romero and
Dukes 2013) resulting in annual irrigation of 648 mm=year, well
within the UF-IFAS recommendation of 610660 mm (Romero and
Dukes 2011). Additionally, the UF-IFAS recommendation does not
take into account irrigation system efficiency and could be consid-
ered 762826 mm when considering an average efficiency of 80%
thus resulting in deficit annual irrigation by HCWRS. In contrast,
the entire OCU service area averaged 104 mm=month (Romero
and Dukes 2013) resulting in almost twice the UF-IFAS recommen-
dation and 1.5 times the recommendation considering efficiency.
Additionally, the cooperators selected for the OCU study were cal-
culated as 68 times the GIR thus applying much more
(1,7782,286 mm=year) than the area average (1,245 mm=year)
which contributed to the success of the study. Mayer et al.
(2009) found that 3,112 homes with newly installed ET controllers
without education decreased irrigation by only 6% where 42% of
the homes were already underirrigators. Thus, determining the
water conservation potential of the area as well as the individuals
receiving the technology is extremely important to achieving
significant water savings.
It was more important to program the ET controllers with site-
specific settings to avoid inadvertently increasing irrigation by
using default values. Though the SMS with site-specific settings
performed better than without, both SMS treatments produced sig-
nificant reductions in irrigation application. There were very little
reductions in irrigation in HCWRS where monthly Hist-GIR ratios
averaged 1.532.08, thus ratios should be higher when selecting
homes. Based on the results in OCU, reductions in irrigation
application were guaranteed when smart controllers were pro-
vided for homeowners that frequently average six times the
GIR by month with efficient, well-maintained irrigation systems.
Additional requirements for successful implementation include
site-specific programming and providing basic knowledge to
the homeowner.
Table 5. Payback Period of Implementing a Smart Controller Was Calculated Based on Average Historical Irrigation, the Utility Rate Structure, Average
Irrigated Area
Study Location Treatment
Number of
homes
Treatment annual
average (mm)
Historical annual
average (mm)
Annual
savings ($)
Payback period
(months)
HCWRS AB ET+Edu 7 403 694 436.29 17
HCWRS R ET+Edu 5 376 572 270.11 27
HCWRS V ET+Edu 9 451 691 346.66 21
OCU All ET 27 1,217 1,789 517.36 14
OCU All ET+Edu 28 970 2,277 1,499.97 5
OCU All SMS 28 1,027 1,890 906.78 6
OCU All SMS+Edu 28 778 2,041 1,440.32 4
Note: Assumptions included average irrigated area of 4,800 ft2for OCU and 9,300 ft2for HCWRS based on study measurements, and purchase and
installation prices of $400 and $600 for SMS and ET controllers, respectively.
© ASCE 04014055-7 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
Conclusions
The water conservation potential of a smart controller was much
higher in OCU than in HCWRS according to the ratio of historical
averages to GIR. As a result, irrigation was significantly less than
the historical average in three of the four technology treatments in
OCU compared to no difference from historical irrigation for any of
the ET controllers treatments in HCWRS. However, smart control-
lers in HCWRS were able to match estimated landscape needs by
meeting the GIR whereas cooperators in OCU were still overirri-
gating with TMT-GIR ratios ranging from 2.00 to 3.30. All treat-
ments in both studies significantly decreased irrigation compared to
their respective historical average.
Based on these results, smart controllers are recommended for
homeowners that frequently average more than two times the GIR
by month with efficient, well-maintained irrigation systems.
Additionally, savings were guaranteed when ratios averaged more
than 6. It is likely that reductions in water use would occur at lower
thresholds but were not guaranteed, as was seen in the HCWRS
study. Additional requirements for successful implementation
include site-specific programming and providing basic knowledge
to the homeowner. Treatments that had significant reductions in
irrigation had payback periods less than six months.
Partitioning utility customers based on percentile distribution,
such as selecting the 25th75th percentile within the top 50th per-
centile of utility billing records as was done in HCWRS, did not
produce overirrigators because most of the excessive irrigation oc-
curs by customers in a higher percentile range. However, not all
customers identified as excessive irrigators would benefit from a
smart controller when overirrigation occurs because of irrigation
system inefficiencies and leaks. Ultimately, the gross irrigation
requirement combined with an irrigation evaluation proved to be a
better method than using utility-wide percentile distribution of bill-
ing records to target homeowners as candidates when focused on
reducing the overall potable water demand.
Acknowledgments
The authors would like to thank the Water Research Foundation,
Orange County Utilities, Hillsborough County Water Resource
Services, St. Johns River Water Management District, South
Florida Water Management District, Florida Agricultural Experi-
ment Station, and Tampa Bay Water for their generous support
of this research. We would also like to acknowledge Maria Carver,
Camille Reynolds, and Michael Gutierrez for their hard work and
dedication to maintaining the quality of the research throughout
their involvement in respective studies.
References
Allen, R. G. (2000). Using the FAO-56 dual crop coefficient method over
an irrigated region as part of an evapotranspiration intercomparison
study.J. Hydrol., 229(12), 2741.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapo-
transpiration: Guidelines for computing crop requirements.Irrig.
Drain. Paper No. 56, FAO, Rome, Italy.
ASCE, and Environmental and Water Resources Institute (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,
Reston, VA.
Aquacraft. (2002). Performance evaluation of WeatherTRAK irrigation
controllers in Colorado.http://www.aquacraft.com(Oct. 21, 2005).
Aquacraft. (2003). Report on performance of ET based irrigation
controller: Analysis of operation of WeatherTRAK controller in field
conditions during 2002.http://www.aquacraft.com(Oct. 21, 2005).
Beard, J. B., and Green, R. L. (1994). The role of turfgrasses in environ-
mental protection and their benefits to humans.J. Environ. Qual.,
23(3), 452460.
Davis, S. L., and Dukes, M. D. (2012). Landscape irrigation with evapo-
transpiration controllers in a humid climate.Trans. ASABE, 55(2),
571580.
Davis, S. L., and Dukes, M. D. (2014). Irrigation of residential landscapes
using the Toro Intelli-Sense in southwest Florida.J. Irrig. Drain. Eng.,
10.1061/(ASCE)IR.1943-4774.0000694, 04013020.
Davis, S. L., Dukes, M. D., and Miller, G. L. (2009). Landscape irrigation
by evapotranspiration-based irrigation controllers under dry conditions
in Southwest Florida.Agric. Water Manage., 96(12), 18281836.
Devitt, D. A., Carstensen, K., and Morris, R. L. (2008). Residential water
savings associated with satellite-based ET irrigation controllers.
J. Irrig. Drain. Eng.,10.1061/(ASCE)0733-9437(2008)134:1(74),
7482.
Dukes, M. D. (2012). Water conservation potential of landscape irrigation
smart controllers.Trans. ASABE, 55(2), 563569.
Dukes, M. D., and Haley, M. B. (2009). Evaluation of soil-moisture based
on-demand residential irrigation controllers, Phase II.Southwest Florida
Water Management District, Brooksville, FL. http://www.swfwmd.state.fl
.us/files/database/site_file_sets/13/SMS_Phase_II_Final_Report_12-17-09
.pdf(Jun. 11, 1011).
Dziegielewski, B., and Keifer, J. C. (2010). Appropriate design and evalu-
ation of water use and conservation metrics and benchmarks.J. Am.
Water Works Assn., 102(6), 115.
Friedman, K., Heaney, J. P., Morales, M., and Palenchar, J. E. (2013).
Predicting and managing residential potable irrigation using parcel-
level databases.J. Am. Water Works Assn., 105(7), E372E386.
Gilman, E. F., et al. (2009). Effects of irrigation volume and frequency
on shrub establishment in Florida.J. Environ. Hort., 27(3),
149154.
Haley, M. B., and Dukes, M. D. (2012). Validation of landscape irrigation
reduction with soil moisture sensor controllers.J. Irrig. Drain. Eng.,
10.1061/(ASCE)IR.1943-4774.0000391, 135144.
Haley, M. B., Dukes, M. D., and Miller, G. L. (2007). Residential irriga-
tion water use in central Florida.J. Irrig. Drain. Eng.,10.1061/(ASCE)
0733-9437(2007)133:5(427), 427434.
Irrigation Association (IA). (2005). Landscape irrigation scheduling
and water management.Irrigation Association Water Management
Committee, Falls Church, VA.
Irrigation Association (IA). (2013). Landscape irrigation auditor.3rd Ed.,
Irrigation Foundation, Falls Church, VA.
Jia, X., Dukes, M. D., and Jacobs, J. M. (2009). Bahiagrass crop coeffi-
cients from eddy correlation measurements in central Florida.Irrig.
Sci., 28(1), 515.
Mayer, P., et al. (2009). Evaluation of California weather-based smart
irrigation controller programs.http://www.aquacraft.com/Download
_Reports/Evaluation_of_California_Smart_Controller_Programs_-_Final
_Report.pdf(Jul. 14, 2010).
Mayer, P. W., et al. (1999). Residential end uses of water.AW WA
Research Foundation, Denver.
Mecham, B. Q. (2001). Distribution uniformity results comparing catch-
can tests and soil moisture sensor measurements in turfgrass irrigation.
Proc., Irrigation Associations 2001 Int. Irrigation Show, Irrigation
Association (IA), Falls Church, VA, 133139.
Romero, C. C., and Dukes, M. D. (2010). Are landscapes over-irrigated in
central and southwest Florida? A spatial-temporal analysis of observed
data.5th National Decennial Irrigation Conf., Phoenix Convention
Center, Phoenix, AZ.
Romero, C. C., and Dukes, M. D. (2011). Net irrigation requirements for
Florida turfgrass lawns: Part 3Theoretical irrigation requirements.
AE482, Institute of Food and Agricultural Sciences, Univ. of Florida,
Gainesville, FL.
Romero, C. C., and Dukes, M. D. (2013). Estimation and analysis of
irrigation in single family homes in central Florida.J. Irrig. Drain.
Eng., 04013011.
© ASCE 04014055-8 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
Scheiber, S. M., Gilman, E. F., Sandrock, D. R., Paz, M., Wiese, C., and
Brennan, M. M. (2008). Post establishment landscape performance
of Florida native and exotic shrubs under irrigated and nonirrigated
conditions.HortTechnology, 18(1), 5967.
USDA. (1989a). Soil survey of Hillsborough County, Florida.http://
soils.usda.gov/survey/online_surveys/florida/(Feb. 9, 2013).
USDA. (1989b). Soil survey of Orange County, Florida.http://soils.usda
.gov/survey/online_surveys/florida/(Feb. 9, 2013).
Whitcomb, J. B. (2005). Florida water rates evaluation of single-family
homes.Southwest Florida Water Management District, http://www
.swfwmd.state.fl.us/documents/report /water_rate_report.pdf(Aug. 20,
2008).
© ASCE 04014055-9 J. Irrig. Drain Eng.
J. Irrig. Drain Eng.
Downloaded from ascelibrary.org by LOUISIANA STATE UNIV on 10/17/14. Copyright ASCE. For personal use only; all rights reserved.
... The specifications of a portal of a farm, from the external stakeholders point of view, revealed that the history of the farm, information about the producers in the form of curriculum vitae, farm location, climatic and soil conditions and, last but not least, farming practices, is the information that the consumers would like to see in it. The consortium believes that further information and communication (ICT) developments in agriculture will include the development of agricultural robotics (Cruz et al. 2020; Davis and Dukes 2015) in collaboration with advanced FMIS systems. ...
... The specifications of a portal of a farm, from the external stakeholders point of view, revealed that the history of the farm, information about the producers in the form of curriculum vitae, farm location, climatic and soil conditions and, last but not least, farming practices, is the information that the consumers would like to see in it. The consortium believes that further information and communication (ICT) developments in agriculture will include the development of agricultural robotics (Cruz et al. 2020; Davis and Dukes 2015) in collaboration with advanced FMIS systems. ...
Chapter
The aim of this work is to assess the effectiveness of ozone and sanodyna® treatments in olive washing stage to reduce microbial load, water consumptions, and environmental impacts, safeguarding the extra virgin olive oil characteristics. With respect to ozonization treatments, 12 samples of olives were tested (six treated insufflating ozone and 6 (not treated) used as control). Three of the treated samples were processed to produce olive oil within 24 h, while the others three were processed after seven days. The same apply for the six untreated samples. In this case, the effect on olives microbiological load and on the quality of extra virgin olive oil were assessed. Sanodyna® was sprayed directly to olives (at 0.05%) and was also tested at three different concentrations (4, 8, 12%) in the olives washing water. Notably, the effects on the microbial load, on washing water characteristics, and on the obtained oil were assessed. The ozonization treatment did not reduce the olive’s microbial load. In addition, ozone had detrimental effects on the total phenolic content of the olive oil, highlighting its unsuitability to this application. The direct treatment with sanodyna® did not decrease the microbial load. On the other hand, the application of higher dosages in the washing stage showed promising results. In particular, sanodyna® treatments reduced the microbial load and extended the duration of washing water. In conclusion, despite the effects on the olive oils need to be evaluated, the latter application seems to be effective in reducing microbiological load and environmental impacts.
... However, implementing efficient irrigation practices with many individual users and diverse landscapes is challenging [25]. One approach is an educational solution; many water conservation programs and university extension programs seek to communicate optimum watering practices to water users, including proper timing and depth of irrigation as well as fertilizer application [26][27][28][29][30]. Another option is a technical solution; irrigation systems with automated soil moisture sensors may help reduce water use as the systems only irrigate when plants require water, reducing over-irrigation [31][32][33][34][35]. A third approach is a policy solution designed to influence water use [36]; tiered rates-that is, water fees where the unit price for water increases with volume [37]-have been a key ingredient to successful water conservation programs in Tucson [38], Los Angeles [39], Irvine Ranch [40], Charlotte [41], Southwest Florida [42], and elsewhere, because of the price signals associated with excessive consumption. ...
Article
Full-text available
To understand how landscape irrigation can be better managed, we selected two urban irrigation systems in northern Utah, USA, and performed a statistical analysis of relationships among water use, irrigated area, plant health (based on the Normalized Difference Vegetation Index), and water rate structures across thousands of parcels. Our approach combined remote sensing with 4-band imagery and on-site measurements from water meters. We present five key findings that can lead to more efficient irrigation practices. First, tiered water rates result in less water use when compared to flat water rates for comparable plant health. Second, plant health does not strictly increase with water application but has an optimum point beyond which further watering is not beneficial. Third, many water users irrigate beyond this optimum point, suggesting that there is water conservation potential without loss of aesthetics. Fourth, irrigation is not the only contributor to plant health, and other factors need more attention in research and in water conservation programs. Fifth, smaller irrigated areas correlate with higher water application rates, an observation that may inform future land use decisions. These findings are especially pertinent in responding to the current drought in the western United States.
... Recently, some techniques have been used to monitor and control irrigation systems [25]. Davis and Dukes have related the achievement of water use efficiency to the coupling smart designed irrigation systems and the use of smart controllers [26]. ...
Article
Full-text available
The scarcity of water available for landscape irrigation is a great challenge facing urban greenery and landscaping initiatives in arid and semi-arid regions. The Imam Abdulrahman bin Faisal University (IAU) has established a new campus on 3,2000,000 M² of land extended to the eastern coast of the Arabian Gulf. Since moving to the Eastern Campus in 2014, the IAU has been working hard to increase the green spaces to minimize the environmental impact of harsh climate, hard surfaces, and building masses. In its endeavor to achieve this goal, the management of landscape plant materials and irrigation systems has not kept pace with the development of the campus. Almost 800 M³/day of irrigation water has been pumped into the system however it fails to maintain good quality landscape plants. As a result, green spaces suffer great degradation especially during hot months due to insufficient irrigation management and other factors. This study is an attempt to explore the relationship between the expansion of green spaces on the IAU’s Eastern Campus and their irrigation requirements. It applied the investigative analytical method to study and analyze the green spaces elements and their daily water budget, which led to the proposal of a synchronous management approach balancing the amount of water available for irrigation purposes and the qualities and expansion of landscape plant materials. The study found that applying the required water budget to plants since their establishment on their permanent locations is a waste of available resources, but it must be gradually increased based on the plant's size, its growth rate, and seasonal changes. However, a synchronous management approach is required to ensure that good quality landscapes are maintained while using limited available resources.
... Plant evapotranspiration is considered as an alternative parameter to determine crop irrigation. System-based evapotranspiration has developed and enabled water conservation on time-based irrigation [13]. Different types of IoT-based soil moisture sensors (i.e., ESP8266 NodeMCU Module and DHT11 Sensor) have been reported to be used in the agricultural system for monitoring the soil moisture, which cut down the excess or less amount of water through irrigation. ...
... Proximal measurement of canopy temperature by IR sensors (e.g., Jones et al. [102]) and field IR cameras integrated with an IoT system are also adopted [98] to the purpose. • Water availability in soil, based on direct "soil moisture" observation, then use the lower and upper thresholds criteria as in the previous method, to get advice on a possible water stress condition [103]. • Water availability by "water budget", based on the estimate of water loss of a canopy (Evapotranspiration-ET, [104]), from observed temperature, relative humidity, wind speed, and solar radiation. ...
Article
Full-text available
In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.
... Technologies, such as soil moisture sensors that prevent irrigation from running if there is adequate moisture in the soil, have been shown to reduce more than 50% of irrigation water over a range of irrigation schedules and applications (Cabrera et al. 2018;Haley et al. 2007). These technologies require adequate knowledge, and high water users and residents with inefficient lawn and landscape watering habits are most likely to benefit (Davis and Dukes 2015;Tsai et al. 2011). Ahmad and Prashar (2010) reported that retrofitting homes with indoor and outdoor low-volume technologies had the greatest potential to reduce water usage in South Florida, when compared to pricing and low-water plant materials. ...
Article
Using the water source as a factor, a typology of residential irrigation water users was developed by segmenting 3,310 Floridians who used irrigation by their irrigation water source (i.e., well water, reclaimed water, and city water). Based on three years of survey data, there was a moderate association between living in a homeowners’ association and using city or reclaimed water for irrigation. There was an association between the water source and engagement in nine conservation behaviors. Well water users were less likely to use recycled water, use a rain sensor, calibrate their sprinklers, and use smart irrigation controls. Reclaimed water users were more likely to use recycled water and use a rain sensor and also less likely to have retrofitted a portion of the landscape so that it is not irrigated and have turned off zones or capped irrigation heads for established plants. City water users had the strongest personal and social norms surrounding water conservation, although this did not translate into conservation as the theory indicates it should have. The findings reveal that identifying the irrigation water source can provide meaningful insights into outdoor water use and should be integrated into residential water conservation interventions.
Article
Competition for water sources in urban areas of Florida has increased due to increased population and human activities. High water users have been identified as a specific group on which Extension should focus water conservation education due to their low awareness of water issues and active landscape water use. In order to ensure the effectiveness of Extension programs targeting high water users statewide, this study sought to explore regional differences in water conservation behavior engagement within Florida high water users. An online survey was conducted to capture responses of high water users (N = 932) in three distinct regions for this comparative study. Respondents were asked to indicate their current engagement in water use behavior, application of water conservation strategies, and likelihood of engaging in water conservation and related societal behaviors. Regional differences were found in all four examined constructs. The findings imply Extension educators should tailor educational programs to regional audiences’ behavior patterns instead of designing statewide programs to ensure program effectiveness.
Chapter
Full-text available
Agriculture is the backbone of any developing country for their sustainable development. So, it is our responsibility to educate the society regarding the sustainable development of agriculture. In the last 10-15 years, technology has been developing at a rapid speed. Various researchers are giving more emphasis to applying technology to agriculture. This is called smart farming. Smart farming uses computer technology and communication for greater yield and production of crops. This chapter studies the various technological developments in the field of smart farming. A few of them are related to internet of things (IOT), wireless communication, irrigation system, and agriculture automation. This chapter helps the new researchers in the field of smart farming to understand the current technological developments.
Article
Full-text available
Although new and innovative measures to reduce landscape water consumption are being sought, traditional methods of water restrictions and plant selection prevail. Species native to North America are often promoted as drought tolerant with little information to support or refute such claims. Furthermore, species performance is unknown in maintained environments such as commercial and residential landscapes. Thus, 10 native and 10 exotic species, commonly used in landscapes, were evaluated independently for postestablishment growth and aesthetics under irrigated and nonirrigated landscape conditions. Growth indices were recorded monthly, with dieback and plant density evaluated at termination of the experiment. At termination of the experiment, canopy size of eight native [beautyberry (Callicarpa americana), fringe tree (Chionanthus virginicus), yaupon holly (Ilex vomitoria 'Nana'), Virginia sweetspire (Itea virginica), wax myrtle (Myrica cerifera), chickasaw plum (Prunus angustifolia), saw palmetto (Serenoa repens), and coontie (Zamia floridana)] and eight exotic [golden dewdrop (Duranta erecta), cape jasmine (Gardenia augusta), crape myrtle (Lagerstroemia indica), oleander (Nerium oleander), Japanese pittosporum (Pittosporum tobira), indian hawthorn (Rhaphiolepis indica), sweet viburnum (Viburnum odoratissimum), and sandankwa viburnum (V. suspensum)] species were similar for irrigated and nonirrigated treatments. Irrigation resulted in larger canopy sizes for two native [walter's viburnum (V. obovatum) and inkberry (I. glabra)] and two nonnative [Japanese privet (Ligustrum japonicum) and fringe flower (Loropetalum chinensis)] species. Among the native species with larger canopy sizes under irrigated conditions, all are indigenous to swamps and streams. With the exception of virginia sweetspire, plant density and dieback were similar for irrigated and nonirrigated plants of all taxa examined. Irrigated Virginia sweetspire plants had higher plant density and dieback ratings than nonirrigated plants. Results indicate that, aesthetically, irrigated and nonirrigated plants were similar. Data emphasize the importance of selecting plant material adapted to existing environmental landscape conditions.
Article
Full-text available
The objective of the research reported in this paper was to determine the potential water-savings effectiveness of Toro Intelli-Sense evapotranspiration (ET)-based irrigation controllers in single-family homes. Of the 36 cooperators selected, 21 cooperators were outfitted with Toro Intelli-Sense TIS-612 (the ET group) and the remaining 15 were used as comparisons. The ET group reduced irrigation compared to the gross irrigation requirement by 23–41% and the 9-year historical average by 23–34%. Results varied based on the relationship between historical irrigation and the gross irrigation requirement, with water savings of 24%, no difference, and an increase of 54% when the historical value exceeded, approximated, and was less than the gross irrigation requirement, respectively. Turfgrass quality was maintained above an acceptable threshold for both treatments. In southwest Florida, a properly programmed Intelli-Sense controller is recommended when irrigation exceeds the gross irrigation requirement and at least 696 mm of irrigation is applied annually. Other smart-irrigation technologies would be more appropriate in locations for which the irrigation rate is less than the gross irrigation requirement.
Article
Full-text available
The objective of this article is to present summary findings of multiple research studies concerning evapotranspiration (ET) controllers. Each study provided unique information concerning the performance and implementation techniques necessary to ensure successful integration with irrigation systems to optimize scheduling for water conservation. Based on these studies, ET controllers have the potential for irrigation savings of as much as 63%, without sacrificing landscape quality, when implemented in moderate to high water use scenarios and programmed correctly. Only homes that irrigated more than 450 mm per year had irrigation savings with an ET controller in southwest Florida. The ET controllers that underwent Irrigation Association Smart Water Application Technologies (SWAT) testing experienced oscillations in irrigation adequacy and scheduling efficiency dependent on rainfall. Assuming acceptable levels for irrigation adequacy and scheduling efficiency of 80% and 95%, respectively, there were only a few periods during the Florida SWAT test when both scores were above these thresholds. A maximum of 10% of scores were passing in any of the three evaluation periods with frequent rainfall, indicating that properly accounting for rainfall is a challenge for many of these controllers. The SWAT scores are indicators of water savings only if there is a potential for savings due to excess irrigation prior to implementation of the ET controller. © 2012 American Society of Agricultural and Biological Engineers.
Article
Irrigation frequency and volume effects were evaluated on recently installed #3 container grown shrubs of three taxa, Ilex cornuta Lindl. & Paxt. ‘Burfordii Nana’, Pittosporum tobira Thunb. ‘Variegata’, and Viburnum odorotissimum Ker Gawl. Irrigation frequency and volume had no effect on Pittosporum at any time for any measured root or shoot parameter. Irrigation frequency and volume had no effect on Ilex and Viburnum canopy biomass, root biomass, root dry weight:canopy dry weight ratio, and stem water potential at any time after planting. Canopy growth was affected by irrigation treatment only for Viburnum plants installed in May 2004, and growth response to more frequent irrigation only occurred while plants were irrigated, with no lasting impact on growth once irrigation ceased. Root spread and root spread:canopy spread ratio for only one species, Ilex, were influenced by irrigation treatment. Applying excessive irrigation volume (in this case 9L) reduced root dry weight: shoot dry weight ratio for Ilex and could increase the time needed for plants to grow enough roots to survive without irrigation. Our study found only slight influences on shrub growth from the tested values of irrigation frequency and volume regardless of the time of year when data was collected. This indicates that these shrubs can be established with 3 liters irrigation applied every 4 days until roots reach the edge of the canopy under the mostly above normal rainfall conditions of this study. Applying more volume or irrigating more frequently did not increase survival or growth. Canopy growth and plant quality data combined with past research suggest that establishment of these shrub species may be more influenced by environmental conditions such as rainfall than by the irrigation frequency and volume used in this test.
Article
A laboratory investigation was conducted to measure wetted radii and drop sizes and to estimate the energy characteristics of a rotating spray-plate sprinkler. Maximum wetted radii were positively related to increasing sprinkler elevation above an irrigated surface and increasing nozzle pressure. Nozzle diameter had a minimal effect on drop size, but nozzle pressure had a significant inverse influence. Energy parameters were calculated for sprinkler operational scenarios. Average kinetic energies over sprinkler-wetted areas were inversely related to nozzle pressure and the square of nozzle pressure. Rapidly and slowly rotating spray plate sprinklers had similar time-averaged specific power distributions. However, the rapidly rotating sprinklers had continuous rotational distribution patterns in space with relatively low peak specific power values that corresponded to natural rainfall intensities of about 20 mm/h. Slowly rotating sprinklers had discontinuous spatial distribution patterns with very high peak values that corresponded to natural rainfall intensities of about 200 mm/h.
Article
The objective of this project was to determine if automatic residential irrigation systems with soil moisture sensor irrigation controllers could reduce irrigation water application while maintaining acceptable turfgrass quality as successfully in homes as in plot studies. Research was conducted on-site at cooperating homes (n = 58) in southwest Florida. Experimental treatments were (1) automatic timer with integration of bypass soil moisture sensor control system, (2) automatic timer with rain sensor and educational materials, (3) automatic timer with rain sensor, and (4) automatic timer only (typical for the region). Irrigation application, frequency, quarterly turf quality ratings, and weather data were collected over 26 months. Homes with soil moisture sensor irrigation controllers bypassed unneeded events during both rainy and dry periods, averaging 2 irrigation events per month; all other treatments averaged 4.5-6 events per month. Reduction in number of irrigation events by soil moisture sensor control systems resulted in significant savings, with 65% cumulative reduction compared to homes with typical timer irrigation control. Observed on-site savings were comparable to previous plot research, indicating that plot savings could be scaled up so long as soil moisture control systems are installed and set properly.
Article
In the past ten years, smart irrigation controllers have been developed by a number of manufacturers and have been promoted by water purveyors in an attempt to reduce excessive irrigation. Legislation has been introduced in California and Texas and passed in Florida mandating or incentivizing the use of these controllers. As a result of the interest in smart controllers, their use is increasing in new installations and retrofits of residential and light commercial irrigation systems. A number of controlled research studies using formal experimental design and statistical analyses indicate substantial water savings of anywhere from 40% to more than 70% when using these devices; however, real-world savings in larger pilot-scale projects indicate savings of typically less than 10%. Reasons for the divergence between the apparent potential savings and the realized savings in pilot projects are related to the lack of: targeting of high irrigation users (on either a relative or absolute scale), education for contractors and end users, and timely follow-up to assess water savings. In addition, much of the scientific research on smart controllers has been conducted in humid regions where higher potential savings are likely due to irrigation needed only to supplement rainfall. Future pilot projects should include comprehensive educational components aimed at irrigation sites with potential irrigation savings based on estimated landscape irrigation demand from climatic variables (i.e., high irrigation users). © 2012 American Society of Agricultural and Biological Engineers.
Article
There are several studies that have estimated how much water is used for residential irrigation, principally at the national level, with outputs varying from 30 to 64% of total household potable water use. A methodology to estimate irrigation from potable use data in central Florida is presented in this paper. Monthly potable water billing records of single-family homes for the City of Tampa Water Department (TWD) and Orange County Utilities (OCU), Florida, were available from 2003 to 2007. Basic indoor water use at the household scale was estimated using two methods: the minimum month method and the per capita method. A range of impervious surfaces values (5, 15, and 20% from total green area) was considered to estimate the irrigable area. Irrigation was estimated on a monthly basis as the difference of total water use minus the estimated indoor water use, divided by the irrigable area. The estimated irrigation values were compared to a monthly theoretical irrigation requirement calculated by a daily soil water balance. The average total potable water use was 29.2 and 53.3m3/month in TWD and OCU, respectively. The basic indoor water use ranged from 15.7 to 35.9m3/month in TWD and OCU, respectively, based on the minimum month method, and from 16.9 to 18.3m3/month when the per capita method was used. Some inaccuracy in the minimum month method was detected, at least for OCU, and the per capita method would be better for estimating indoor water use. Our results showed that 57-62% and 45-64%% of homeowners overirrigated in OCU, with an estimated irrigation amount of 104-62mm/month when the per capita method and the minimum month method were used, respectively. In TWD, 31-36 to 22-27% of homeowners overirrigated when the per capita method and the minimum month method were used, with averages of 54 and 31mm/month, respectively. The minimum month method showed the lowest estimated values on irrigation compared to the per capita method. Further work is needed to determine which indoor use method is most accurate.
Conference Paper
In the past 10 years, “Smart” Irrigation Controllers have been developed by a number of manufacturers and have been promoted by water purveyors in an attempt to reduce over-irrigation. Legislation has been introduced in California and Texas and passed in Florida mandating or incentivizing the use of these controllers. As a result of the interest in these controllers, use is increasing on new installations and retrofits of residential and light commercial irrigation systems. A number of controlled research studies indicate substantial water savings anywhere from 40% to as high as 70% using these devices; however, “real world” savings in larger pilot scale projects indicate savings typically less than 10%. Reasons for the divergence between the apparent potential savings and realized savings in pilot projects are related to the following: lack of targeting high irrigation users (either a relative or absolute scale) in pilot projects, lack of education for contractors and end users, lack of timely follow-up to assess water savings. In addition, much of the scientific research on smart controllers has been conducted in humid regions where higher potential savings is likely due to irrigation needed only to supplement rainfall. Future pilot projects should include comprehensive educational components aimed at users and base potential irrigation savings on estimated landscape irrigation demand from climatic variables.