REDUCING WATER USE, RUNOFF VOLUME, AND NUTRIENT MOVEMENT FOR
CONTAINER NURSERY PRODUCTION BY SCHEDULING IRRIGATION BASED ON
PLANT DAILY WATER USE
By
Nicholas Andrew Pershey

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Horticulture – Master of Science
2014

ABSTRACT
REDUCING WATER USE, RUNOFF VOLUME, AND NUTRIENT MOVEMENT FOR
CONTAINER NURSERY PRODUCTION BY SCHEDULING IRRIGATION BASED ON
PLANT DAILY WATER USE
By
Nicholas Andrew Pershey
Daily water use (DWU) was used in two irrigation studies over two seasons to
schedule irrigation. Plant DWU was determined from the difference in substrate
volumetric water content over a 24 h period as measured using soil moisture sensors.
Each year, irrigation was applied daily to container-grown shrubs in four treatments: 1)
control application of 19 mm.ha-1.d-1, 2) irrigation applied to replace 100% DWU
(100DWU) each day, 3) applications alternating 100% DWU with 75% DWU in a 2-day
cycle (100-75), and 4) a 3-day application cycle replacing 100% DWU the first day and
75% DWU on the second and third days (100-75-75). The objectives between the two
studies were to 1) determine whether irrigating with DWU can save water compared to a
control (time-based application) rate without affecting growth of ornamental shrubs and
2) classify each taxon into irrigation functional groups (IFG). Most taxa in DWU
treatments received less water than the control, yet only growth index of one taxa was
negatively affected by deficit irrigation applications. Additionally, an objective of the
second study was to quantify runoff volume and nutrient content from production areas
irrigated under the same irrigation treatments. Effluent volumes and NO3- -N and PO43-P loads from DWU treatments were reduced compared to the control. Consequently,
irrigating based on DWU also reduced NO3- -N and PO43--P losses and improved %P
and %K foliar concentrations compared to the control.

ACKNOWLEDGMENTS

My major professor, Tom Fernandez and my committee members, Bert Cregg
and Jeff Andresen, have served as tremendous role models during my tenure as a
research assistant at Michigan State University. Each, in his own way, has also made a
permanent impression on my own teaching career. The MSU horticulture office staff,
Horticulture Teaching and Research Center staff, and second floor ornamental
horticulture “neighbors” have all contributed in particular to making MSU a wonderful
place to work and study. The Horticulture Organization of Graduate Students (or
“HOGS”) provided the welcomed camaraderie of my fellow graduate students. Special
gratitude goes to Aaron Warsaw, my research predecessor, for showing me the ropes
on my research project and setting such a high bar for my own achievement at MSU.
My undergraduate student worker, Max Braun was extremely helpful with data collection
and made the work day much more enjoyable. I also have numerous family members
and friends in Illinois who have encouraged me in my formative years and remained
steadfast in their support as I struggled to allocate time toward finishing my degree.
Finally, more than anyone, I have to thank my mother, who took over farm duties at
home so I could live out-of-state for three summers. Without her hard work and endless
support, graduate school would have never been possible for me.
This research was funded by the USDA National Institute of Food and
Agriculture, hatch project number MICL02010 and Michigan State University Project
GREEEN. In-kind donations were received from Spring Meadow Nursery, Inc.,
Renewed Earth LLC, and Harrell's LLC.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ vi
LIST OF FIGURES ........................................................................................................ viii
LITERATURE REVIEW ................................................................................................... 1
Introduction ........................................................................................................... 2
Fundamental Importance of Water in Plants ........................................................ 3
Evapotranspiration and the Diurnal Cycle .................................................. 4
Water Use Efficiency.................................................................................. 6
Special Concerns Pertaining to Container-grown Plants ...................................... 7
Irrigation Scheduling ............................................................................................. 8
Traditional Approach .......................................................................................... 10
Approaches Involving Modeling of Evapotranspiration ....................................... 13
Relation of Evapotranspiration to Crop Yield ........................................... 14
Energy Balance Method Using Crop Coefficients .................................... 15
Soil Water Depletion Approach Using Sensors .................................................. 17
Sensor-based Automated Irrigation Systems........................................... 21
Regulatory Policies on Agricultural Water Quality and Nutrient Content ........... 23
Irrigation Management to Reduce Nursery Runoff ............................................. 25
LITERATURE CITED ......................................................................................... 27
CHAPTER ONE
GROWTH, SEASONAL CROP COEFFICIENTS, AND NUTRITION OF CONTAINERGROWN SHRUBS UNDER FULL AND DEFICIT DAILY WATER USE IRRIGATION
TREATMENTS .............................................................................................................. 36
Abstract .............................................................................................................. 37
Introduction ......................................................................................................... 38
Materials and Methods ....................................................................................... 39
Site........................................................................................................... 39
Irrigation System ...................................................................................... 40
Plant Material ........................................................................................... 41
Experimental Design ................................................................................ 42
Daily Water Use and Irrigation Scheduling, 2009 .................................... 42
Daily Water Use and Irrigation Scheduling, 2010 .................................... 43
Plant Performance ................................................................................... 46
Statistical Analysis ................................................................................... 47
Results and Discussion ...................................................................................... 48
Irrigation Volume, Daily Water Use, and Plant Growth ............................ 48
Crop Coefficients ..................................................................................... 62
Water Use Efficiency................................................................................ 70
Leachate Electrical Conductivity and pH.................................................. 74
iv

Foliar Nutrient Analysis ............................................................................ 82
Conclusions ........................................................................................................ 87
APPENDIX ......................................................................................................... 90
LITERATURE CITED ......................................................................................... 97
CHAPTER TWO
IRRIGATING BASED ON DAILY WATER USE REDUCES NURSERY EFFLUENT
VOLUME AND NUTRIENT LOAD WITHOUT REDUCING GROWTH OF FOUR
CONIFERS ............................................................................................................... 101
Abstract ............................................................................................................ 102
Introduction ....................................................................................................... 103
Materials and Methods ..................................................................................... 105
Site......................................................................................................... 105
Irrigation System .................................................................................... 106
Plant Material ......................................................................................... 107
Experimental Design .............................................................................. 108
Daily Water Use and Irrigation Scheduling, 2009 .................................. 109
Daily Water Use and Irrigation Scheduling, 2010 .................................. 109
Plant Performance ................................................................................. 111
Runoff Collection and Analysis .............................................................. 112
Statistical Analysis ................................................................................. 113
Results and Discussion .................................................................................... 113
Irrigation Volume .................................................................................... 113
Plant Response to Irrigation Treatments................................................ 122
Runoff Volume ....................................................................................... 126
Nitrates and Phosphates........................................................................ 128
Conclusions ...................................................................................................... 135
LITERATURE CITED ....................................................................................... 137

CHAPTER THREE
CONCLUSIONS AND FUTURE RESEARCH PERTAINING TO IRRIGATION
MANAGEMENT USING SOIL MOISTURE SENSORS TO LOWER WATER USE AND
REDUCE RUNOFF AND NUTRIENT LOSSES IN EFFLUENT .................................. 141
Research Summary .......................................................................................... 142
Future Research ............................................................................................... 147
LITERATURE CITED ....................................................................................... 150

v

LIST OF TABLES

Table 1.1
Total irrigation application (L.container-1) to 4 irrigation treatments from 11
June (Day 1) to 14 October 2009 (Day 126) and 23 June (Day 1) to 31 October 2010
(Day 131). ................................................................................................................. 52
Table 1.2

Season average daily irrigation application (mm.container-1.d-1) and

average daily water use (mm.container-1.d-1) of eight shrubs grown in 10.2 L containers
under four irrigation treatments administered 11 June (Day 1) to 14 October 2009 (Day
126) and 23 June (Day 1) to 31 October 2010 (Day 131). ............................................ 53
Table 1.3
Water use efficiency as growth index (GI) increase : sum of DWU for
season (through final day of growth index measurement 2009: Day 124, 2010: Day 117)
for eight shrubs grown in 10.2 L containers under four irrigation treatments beginning 11
June 2009 and 23 June 2010. ....................................................................................... 72
Table 1.4
Top dry weight and dry weight water use efficiency (WUEd w) (season
change in top dry weight in grams : sum of DWU plus precipitation in kg) of three taxa
collected at the termination of the study in 2010. Irrigation treatments were imposed for
131 d from 23 June to 31 October 2010. ....................................................................... 74
Table 1.5
Foliar nutrient content (% dry wt.) sampled on days 63 and 90 of five taxa
grown in 10.2 L containers and subject to four irrigation treatments from 11 June (Day
1) to 14 October 2009 (Day 126). .................................................................................. 83
Table 1.6
Foliar nutrient content (% dry wt.) sampled on days 36 and 64 of six taxa
grown in 10.2 L containers and subject to four irrigation treatments from 23 June (Day
1) to 31 October 2010 (Day 131). .................................................................................. 85
Table A-1
Monthly crop coefficient (KC) from 11 June (Day 1) to 14 October 2009
(Day 126) for 8 shrub taxa grown in 10.2 L containers. ................................................. 91
Table A-2
Monthly crop coefficient (KC) from 23 June (Day 1) to 31 October 2010
(Day 131) for 8 shrub taxa grown in 10.2 L containers. ................................................. 93
Table 2.1

Daily irrigation application (L.container-1.d-1) (± SE) and total irrigation

applied (L.container-1) to 4 irrigation treatments from 25 June through 16 October 2009
and 7 June through 31 October 2010. ......................................................................... 114
Table 2.2
Seasonal crop coefficients (L.container-1.d-1) of four conifers grown in
10.2 L containers under four irrigation treatments administered 25 June through 16
October 2009 and 7 June through 31 October 2010. Seasonal KC calculated as
DWU:ET0.

............................................................................................................... 121
vi

Table 2.3
Growth index increase (GII) (cm) of 4 conifers subjected to 4 irrigation
treatments between 16 October 2009 to 7 June 2010................................................. 125

vii

LIST OF FIGURES

Figure 1.1 Illustration of daily irrigation program operation on CR3000 logger
(Campbell Scientific, Inc.; Logan, UT) using 15 min substrate volumetric water content
(SVWC) scans for the calculation of daily water use (DWU). Example shown is for
Hydrangea arborescens ‘Abetwo’ in the 100DWU treatment from 0000 HR on 23 August
to 0000 HR 25 August 2010. Program operation corresponding to each letter
designation described in text. ............................................................................................
Figure 1.2 Daily (bars) and cumulative (line) precipitation from 11 June to 14 October
(Day 126) 2009 and 23 June to 31 October 2010 (Day 131). Data obtained from the
Enviro-weather Automated Weather Station Network. .................................................. 49
Figure 1.3 Daily total solar flux density (bars) and daily average temperature (line)
from 11 June to 14 October (Day 126) 2009 and 23 June to 31 October 2010 (Day 131).
Data obtained from the Enviro-weather Automated Weather Station Network. ............. 50
Figure 1.4 Plant Growth Index (GI), Daily Crop Coefficient (KC), and Daily Water Use
(DWU) from 10 June (Day 0) to 14 October 2009 (Day 126) for A) Aronia arbutifolia
‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C) Hydrangea paniculata ‘Limelight’, D) Itea
virginica ‘Morton’, E) Physocarpus opulifolius ‘Seward’, F) Spiraea media ‘Darsnorm’, G)
Thuja plicata ‘Grovepli’, and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers.
Left y-axis indicates PGI (lines). Control = 19 mm.application-1; 100DWU = 100% daily
water use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU
and 75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement the first day
then 2 days 75% DWU replacement. Each day was analyzed separately (Tukey’s test,
α = 0.05). Means followed by the same letters are not different. NS = not significant.
n=18. Right y-axis indicates DWU where the entire bars represent DWU (mm)
averaged from all treatments (n=72); shaded portions of bars represent daily K c (DWU :
ET0, n=72). Overhead irrigation scheduling based on lowest DWU of the 8 taxa during
each measurement period; remaining water requirement supplied by hand each day as
necessary. Dotted horizontal line indicates control treatment of 19mm .application-1.
Daily ET0 values for calculation of Kc obtained from the Enviro-weather Automated
Weather Station Network. ............................................................................................. 55
Figure 1.5 Plant Growth Index (GI), Daily Crop Coefficient (KC), and Daily Water Use
(DWU) from 23 June (Day 1) to 31 October 2010 (Day 131) for A) Hydrangea
arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’, C) Rhus aromatica ‘GroLow’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri ‘Palibin’, F) Syringa
xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers. Left y-axis indicates PGI (lines). Control
= 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement each day;
100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75 = 3 day
viii

45

cycle with 100% DWU replacement the first day then 2 days 75% DWU replacement.
Each day was analyzed separately (Tukey’s test, α = 0.05). Means followed by the
same letters are not different. NS = not significant. n=18. Right y-axis indicates DWU
where the entire bars represent DWU (mm) averaged from all treatments (n=12);
shaded portions of bars represent daily Kc (DWU : ET0, n=12). Overhead irrigation
scheduling based on highest DWU of the 8 taxa in each treatment replicate each day.
Dotted horizontal line indicates control treatment of 19mm.application-1. Daily ET0
values for calculation of Kc obtained from the Enviro-weather Automated Weather
Station Network. ............................................................................................................ 58
Figure 1.6 Monthly Crop Coefficient (KC) from 11 June (Day 1) to 14 October 2009
(Day 126) for A) Aronia arbutifolia ‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C)
Hydrangea paniculata ‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus opulifolius
‘Seward’, F) Spiraea media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers. Control = 19 mm .application-1; 100DWU
= 100% daily water use (DWU) replacement each day; 100-75 = 2 day cycle alternating
100% DWU and 75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement
the first day then 2 days 75% DWU replacement. Each month was analyzed separately
(Tukey’s test, α = 0.05). Means followed by the same letters are not different. NS = not
significant. Jun, Jul, Sep, and Oct: n=18, Aug: n=36. Overhead irrigation scheduling
based on lowest DWU of the 8 taxa during each measurement period; remaining water
requirement supplied by hand each day as necessary. Daily ET0 values for calculation
of KC obtained from the Enviro-weather Automated Weather Station Network. ............ 63
Figure 1.7 Monthly Crop Coefficient (KC) from 23 June (Day 1) to 31 October 2010
(Day 131) for A) Hydrangea arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’,
C) Rhus aromatica ‘Gro-Low’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri
‘Palibin’, F) Syringa xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’,
and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers. Control = 19
mm.application-1; 100DWU = 100% daily water use (DWU) replacement each day; 10075 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75 = 3 day cycle
with 100% DWU replacement the first day then 2 days 75% DWU replacement. Each
month was analyzed separately (Tukey’s test, α = 0.05). Means followed by the same
letters are not different. NS = not significant. Jun: n=24 (except 100-75-75 treatment,
n=16). Jul, Aug, and Oct: n= 93 (except 100-75-75, n= 62). Sep: n= 90 (except 100-7575, n=60). Overhead irrigation scheduling based on highest DWU of the 8 taxa in each
treatment replicate each day. Daily ET0 values for calculation of Kc obtained from the
Enviro-weather Automated Weather Station Network. .................................................. 66
Figure 1.8 PourThru leachate electrical conductivity (EC) (dS.m-1) for A) Aronia
arbutifolia ‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C) Hydrangea paniculata
‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus opulifolius ‘Seward’, F) Spiraea
media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H) Weigela florida ‘Alexandra’ grown
in 10.2 L containers from 11 June (Day 1) to 14 October 2009 (Day 126). Control = 19
ix

mm.application-1; 100DWU = 100% daily water use (DWU) replacement each day; 10075 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75 = 3 day cycle
with 100% DWU replacement the first day then 2 days 75% DWU replacement. Each
day was analyzed separately (Tukey’s test, α = 0.05). Means followed by the same
letters are not different. NS = not significant. n=3. Overhead irrigation scheduling
based on lowest DWU of the 8 taxa during each measurement period with remaining
water requirement supplied by hand each day in DWU treatments as necessary. ........ 75
Figure 1.9 PourThru leachate electrical conductivity (EC) (dS.m-1) for A) Hydrangea
arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’, C) Rhus aromatica ‘GroLow’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri ‘Palibin’, F) Syringa
xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers from 23 June (Day 1) to 31 October (Day
131). Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Each day was analyzed separately (Tukey’s test, α = 0.05).
Means followed by the same letters are not different. NS = not significant. n=3.
Overhead irrigation scheduling based on highest DWU of the 8 taxa in each treatment
replicate each day. ........................................................................................................ 77
Figure 1.10 Leachate pH for A) Aronia arbutifolia ‘Brilliantissima’, B) Cornus sericea
‘Farrow’, C) Hydrangea paniculata ‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus
opulifolius ‘Seward’, F) Spiraea media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H)
Weigela florida ‘Alexandra’ grown in 10.2 L containers from 11 June (Day 1) to 14
October 2009 (Day 126). Control = 19 mm.application-1; 100DWU = 100% daily water
use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU and
75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then
2 days 75% DWU replacement. Each day was analyzed separately (Tukey’s test, α =
0.05). Means followed by the same letters are not different. NS = not significant. n=3.
Overhead irrigation scheduling based on lowest DWU of the 8 taxa during each
measurement period with remaining water requirement supplied by hand each day in
DWU treatments as necessary. ..................................................................................... 79
Figure 1.11 Leachate pH for A) Hydrangea arborescens ‘Abetwo’, B) Hydrangea
paniculata ‘Limelight’, C) Rhus aromatica ‘Gro-Low’, D) Spiraea fritschiana ‘Wilma’, E)
Syringa meyeri ‘Palibin’, F) Syringa xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum
‘Ralph Senior’, and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers from 23
June (Day 1) to 31 October 2010 (Day 131). Control = 19 mm.application-1; 100DWU =
100% daily water use (DWU) replacement each day; 100-75 = 2 day cycle alternating
100% DWU and 75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement
the first day then 2 days 75% DWU replacement. Each day was analyzed separately
(Tukey’s test, α = 0.05). Means followed by the same letters are not different. NS = not
significant. n=3. Overhead irrigation scheduling based on highest DWU of the 8 taxa in
each treatment replicate each day. ............................................................................... 81

x

Figure A-1 Substrate volumetric water content (SVWC) (vol : vol) from time
capacitance sensor measurements taken every 15 min from 23 Jun (Day 1) to 1
November (Day 131). Representative plant chosen is Hydrangea arborescens ‘Abetwo’
growing in a 10.2 L container from the 100DWU treatment. Peaks represent daily
irrigation events ............................................................................................................. 96
Figure 2.1 Daily (bars) and cumulative (line) precipitation from 25 June to 16 October
(Day 114) 2009 and 7 June (day 348) to 31 October 2010 (Day 494). Data obtained
from the Enviro-weather Automated Weather Station Network. .................................. 116
Figure 2.2 Daily total solar flux density (bars) and daily average temperature (line)
from 25 June to 16 October (Day 114) 2009 and 7 June (day 348) to 31 October 2010
(Day 494). Data obtained from the Enviro-weather Automated Weather Station
Network.
............................................................................................................... 117
Figure 2.3 Daily Water Use (DWU) from 25 June to 16 October 2009 and 7 June to
31 October 2010 of A) Chamaecyparis obtusa ‘Filicoides’, B) Chamaecyparis pisifera
‘Sungold’, C) Thuja occidentalis ‘Holmstrup’, and D) Thuja plicata ‘Zebrina’ grown in
10.2 L containers. The shaded portions of bars represent daily ET0 from 25 June to 16
October 2009 and 7 June to 31 October 2010. White portions of each bar represent
DWU. Negative values indicate precipitation in excess of DWU. Dotted horizontal line
indicates control treatment of 19mm.application-1. Dashed vertical line separates 2009
and 2010 seasons. Daily ET0 values obtained from the Enviro-weather Automated
Weather Station Network. ........................................................................................... 119
Figure 2.4 Growth index of a) Chamaecyparis obtusa ‘Filicoides’, b) Chamaecyparis
pisifera ’Sungold’, c) Thuja occidentalis ‘Holmstrup’, and d) Thuja plicata ‘Zebrina’
grown in 10.2 L containers and subjected to 4 irrigation treatments from 25 June (Day 0)
to 16 October 2009 and 7 June (Day 348) to 31 October 2010. Control = 19
mm.application-1; 100DWU = 100% daily water use (DWU) replacement each day; 10075 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75 = 3 day cycle
with 100% DWU replacement the first day then 2 days 75% DWU replacement.
Irrigation scheduling based on: 2009) highest DWU of the 4 taxa recorded on each day
of sampling; 2010) highest DWU of the 4 taxa in each treatment replicate each day.
Dashed vertical line separates 2009 and 2010 seasons. Each day was analyzed
separately (Tukey’s test, α = 0.05). Means followed by the same letters are not
different. ns = not significant. n=18. .......................................................................... 123
Figure 2.5 Applied irrigation and recovered runoff from 3m x 6m production areas
(projected to L ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to
31 October 2010 (day 494) for 4 conifers growing in 10.2 L containers. On days with 2
bars, all DWU treatments received irrigation at 100% DWU replacement. On days with
3 bars, 100-75 and 100-75-75 treatments were scheduled at 75% DWU replacement.
Control treatment equivalent of 190 x 103 L.ha-1 applied daily. Means separation for
each day performed using Tukey’s Test (α = 0.05, ns= not significant, n=3). ............. 127
xi

Figure 2.6 NO3--N concentration in 2009 (A) and 2010 (B) and NO3--N quantity in
2009 (C) and 2010 (D) for runoff collected from 3m x 6m production areas (projected to
g ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to 31 October
2010 (day 494) for 4 conifers growing in 10.2 L containers. Fertilizer was applied on 22
June 2009 and 6 June 2010. On days with 2 bars, irrigation was scheduled for all DWU
treatments at 100% DWU replacement. On days with 3 bars, 100-75 and 100-75-75
treatments were scheduled for 75% DWU replacement. Control irrigation volume = 190
x 103 L.ha-1 applied daily. Uppercase letters indicate means separation between
measurement days within each treatment (α = 0.05, 2009 Control and 100% DWU: n=6;
75% DWU: n=3, 2010: Control and 100% DWU: n=8; 75% DWU: n=4). Lowercase
letters indicate means separation between treatments within each measurement day.
Means separation performed using Tukey’s Test (α = 0.05, ns= not significant, n=3). 129
Figure 2.7 PO43--P concentration in 2009 (A) and 2010 (B) and PO43--P quantity in
2009 (C) and 2010 (D) for runoff collected from 3m x 6m production areas (projected to
g ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to 31 October
2010 for 4 conifers growing in 10.2 L containers. Fertilizer was applied on 22 June
2009 and 6 June 2010. On days with 2 bars, irrigation was scheduled for all DWU
treatments at 100% DWU replacement. On days with 3 bars, 100-75 and 100-75-75
treatments were scheduled for 75% DWU replacement. Control irrigation volume = 190
x 103 L.ha-1 applied daily. Uppercase letters indicate means separation between
measurement days within each treatment (α = 0.05, 2009 Control and 100% DWU: n=6;
75% DWU: n=3, 2010: Control and 100% DWU: n=8; 75% DWU: n=4). Lowercase
letters indicate means separation between treatments within each measurement day.
Means separation performed using Tukey’s Test (α = 0.05, ns= not significant, n=3). 132

xii

LITERATURE REVIEW

1

Introduction
Agriculture is the largest consumer of our planet’s water resources (O’Neil and
Dobrowolski, 2011), and ornamental plant nurseries use some of the highest water
inputs on an area basis of any agricultural sector. Container-grown crops now comprise
over half of all ornamental plants sold today (Hodges et al, 2008), and they require
frequent, if not daily irrigation due to the limited volume for water storage in the rooting
substrate (Warren and Bilderback, 2004). Since most nurseries are located on the
periphery of population centers, the conflicting interests between agriculture and
urbanization often means nurseries have reduced access to water resources (Beeson et
al, 2004).
One way to reduce nursery irrigation water inputs is to improve the efficiency of
irrigation scheduling. Inefficient or uninformed irrigation scheduling can increase both
water withdrawals and nutrient losses via runoff, which can move offsite to contaminate
surrounding water sources. Improvements in irrigation scheduling can help improve
water conservation at nurseries (Tyler, et al., 1996), timing and availability of water to
plants for optimum growth (Warren and Bilderback, 2004), and nutrient and pesticide
management to reduce loading in effluent (Warsaw, et al., 2009b).

2

Fundamental Importance of Water in Plants
Water is essential for plant growth and maintenance. In actively-growing tissues,
water comprises 80-90% of fresh weight (Kramer and Boyer, 1995). Water is the
universal solvent through which gases, salts, and dissolved nutrients and other
compounds move throughout the plant (Kramer and Boyer, 1995). It is also responsible
for maintaining cell turgor (Kozlowski and Pallardy, 2002), promoting cell enlargement
(Kramer and Boyer, 1995), and regulation of the stomatal mechanism (Chaves et al.,
1991). Water is the reagent in the photosynthetic reaction responsible for donating
electrons for the synthesis of ATP (Pallardy, 2008). Consequently, a water deficit within
plants negatively impacts the ability to photosynthesize and produce new biomass
(Hsiao, 1993).
Water moves in plants along gradients from high potential energy to low
(Pallardy, 2008). This gradient is responsible for the movement of water from the soil,
where water exists at a higher potential energy state than the roots (when soil moisture
is not limiting), into the roots and through stems to the leaves. In the leaves, water is
either used for carbon assimilation during photosynthesis or transpired to maintain the
water column (Kramer and Boyer, 1995). The cohesion-tension theory explains how
this continuous water column is maintained in plants. As transpired water evaporates
from stomata, a reduction in the water potential at the stomata draws water from within
the leaf to replace that which has just evaporated. Because of the high cohesive forces
of water, this action lowers the water potential within the xylem creating enough tension
on the system to move the entire water column in the direction of transpiration (Kramer
and Boyer, 1995). This process is sometimes referred to as the soil-plant-atmosphere-

3

continuum (SPAC), which refers to the unification of forces exerted on water throughout
the system (Pallardy, 2008).

Evapotranspiration and the Diurnal Cycle
Evapotranspiration (ET) involves both the evaporation of water from soil or the
rooting substrate and transpiration of water through stomata or other porous plant
organs (Pallardy, 2008). ET can be determined for a single plant, or with more intense
investigation, an entire plant system (Pallardy, 2008). While evaporation is a function of
the physical environment surrounding a plant, transpiration is both regulated by
biological processes within the plant and its interaction with the external environment.
The rate of transpiration depends on a variety of environmental factors including net
radiation, soil temperature, soil water status, vapor pressure deficit between the leaf
interior and the free atmosphere, the density of the air, temperature, and wind speed
(Pallardy, 2008). Physiological and morphological traits also contribute to transpiration
including total leaf area, canopy architecture, leaf exposure to sunlight, regulation of the
stomatal aperture, and the water absorption capacity of roots (Pallardy, 2008).
To achieve growth, plants must undergo a minimum (though not absolute)
amount of transpiration, and some flexibility likely exists to varying degrees in the exact
quantities of water that must be transpired by each species to achieve maximum growth
(Beeson, 2006). During periods of darkness, the stomatal aperture generally only
remains partially open. Under well watered conditions, stomata open progressively
further from sunrise until reaching maximum evaporative demand for that day (Hsiao,
1993). Cohen et al. (1985) showed that after a brief delay following sunrise, the

4

transpiration rate in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco.) increased with
solar irradiance, then remained at a maximum rate for several hours into the afternoon
before declining into the evening and nighttime hours. However, as soil moisture
became depleted during prolonged drought of container-grown Acer rubrum L., stomatal
closure reduced transpiration rates and ultimately growth (Bauerle et al., 2002). Midday
transpiration rates and the proportion of leaves to roots was reduced in droughtstressed Fraxinus velutina Torr., Koelreutaria paniculata Laxm., Quercus macrocarpa
Michx., and Q. muehlenbergii Englem. compared to unstressed trees (Balok and St.
Hilaire 2002).
In addition, reductions in photosynthesis often accompany midday water stress.
During late morning and afternoon hours, net photosynthesis and conductance was
reduced midday in Vitis vinifera L. irrigated at 50% ETA, compared to 100% ETA
(Williams and Araujo, 2002). Generally, as drought progresses, cumulative stress
results in the inhibition of photosynthesis progressively earlier each day (Hsiao, 1993).
Consequently, internal plant water deficits often lead to reduced carbon fixation and
overall productivity. For example, production of bedding plants at continuously low
substrate volumetric water content (SVWC) set points (0.09m3.m3 and 0.15 m3.m3)
decreased gas exchange and leaf photosynthetic rate in bedding plants compared to
higher set points (Nemali and van Iersel, 2008). Container-grown plants are especially
vulnerable to daily moisture deficits, and irrigating during midday or afternoon hours,
when transpiration is highest, yielded increased dry weight and CO2 assimilation of
Cotoneaster dammeri ‘Skogholm’ compared to pre-dawn irrigations (Warren and
Bilderback, 2004). However, after imposing minor irrigation deficits, corresponding

5

reductions in growth do not necessarily occur. Among 24 shrub taxa growing in
containers, growth was only reduced in two species in treatments that alternated a 75%
ET replacement with 100% every other day (Warsaw et al., 2009a). Consequently, at
mild substrate moisture deficits, a substantial buffer against growth reductions exists for
temperate, humid-climate shrubs.

Water Use Efficiency
One method to relate productivity through carbon assimilation to water use is by
evaluating the water use efficiency (WUE) of a species over a temporal period of
interest. The following equation has been used by Jarrell et al. (1983), Knox (1989),
and Warsaw et al. (2009a and 2009b) to calculate WUE over the course of an entire
production cycle for container-grown ornamental shrubs:
WUE = increase in plant size / volume of water applied.
In the above studies, irrigation applications were based on ET so that major changes in
ET resulted in modified irrigation application volumes. Generally, Knox (1989) and
Warsaw et al. (2009a and b) showed that as irrigation volume is decreased, WUE
improves. While drought-tolerant species may once have been thought to have higher
WUEs, Knox (1989) showed that Juniperus horizontalis Moench ‘Wiltonii’ had the lowest
WUE compared to four flowering shrub species. The author suggested that the
Juniperus species used water less efficiently than the flowering shrubs during periods of
non-limiting soil water. Warsaw et al. (2009b) reported both growth index increase (GII)
and WUE with irrigation treatments based on DWU was higher compared to
conventional, rate-based control of 19 mm.container.day-1. Species in their study with
6

lower GII also showed lower WUE compared to species with higher GII. However,
irrigation was applied only to meet the needs of the highest water users, which means
that the species having a lower WUE were likely over-irrigated. Their findings reveal
that when emphasis is placed on managing irrigation inputs to optimize WUE during
crop production, water applications can be reduced without sacrificing plant growth.

Special Concerns Pertaining to Container-grown Plants
Over half of all ornamental plants sold by nurseries today are produced in
containers (Hodges et al., 2008). By confining roots to a limited volume of soil, the
container itself substantially reduces quantities of water that can be stored for plant
needs between irrigations compared to field grown production systems. Growers favor
container production because it offers both producers and consumers many advantages
such as easier handling and shipping of individual plants, flexibility of production
schedules, ease of display of the finished product, and increased consumer
convenience (Ingram et al., 1993; Chen et al., 2001). In addition, container substrates
provide flexibility by allowing growers greater relative ease of manipulation of water and
fertility levels than with most mineral soils (Chen et al., 2001). Many horticultural
substrates in use today were intentionally designed with a high proportion of
macropores relative to field soils, resulting in higher air-filled porosity (AFP) (Drzal et al.,
1999) to ensure that they drain quickly in the event of frequent or heavy rainfall in
(Knox, 1989). However, in bark-based substrates, a high AFP limits plant available
water (PAW) as compared to peat (Lea-Cox et al., 2011) or mineral soils (Drzal et al.,
1999). Additionally, black or other dark-colored containers function as heat sinks

7

(Ruter, 1999) resulting in warmer rooting zones than traditional soils (Neal, 2010).
Reductions in growth of Syringa vulgaris L. ‘Monge’ produced in containers relative to
field soils were attributed to increased substrate temperatures (Neal, 2010), which have
been shown to elevate plant water use (Garcia y Garcia et al., 2004). Consequently,
producers of container-grown plants must irrigate frequently during the growing season,
often multiple times daily. Knox (1989) suggests that some growers may take
advantage of the forgiving nature of today’s rapidly-draining container substrates by
over-irrigating to safeguard against possible reductions in growth or quality. This
approach could have negative impacts on crop quality and the environment. Therefore
irrigation scheduling must be approached more quantitatively.

Irrigation Scheduling
Increasing competition for water resources between urban population centers,
industry, and other agricultural sectors has led to water withdrawal policies in states
such as California, Delaware, Florida, Maryland, Michigan, North Carolina, Oregon, and
Texas (Fernandez et al., 2009). However, even if a region has ample water supplies,
the quality of that water may not be acceptable for plant production (Beeson et al.,
2004). Consequently, 44% of nurseries currently incorporate some sort of water
conservation measures into their irrigation scheduling practices, while an additional 12%
are interested in implementing new sustainable water management practices in the
future (Dennis et al., 2010).
Irrigation scheduling is a decision-making process which requires the person
making the decision to assess the volume of water lost across an entire crop, determine
the volume of water to be replaced by irrigation, and activate irrigation to the crop for an
8

appropriate amount of time so as to replace the desired volume. The goal of a typical
irrigation cycle is to provide a plant with enough water to reach container capacity and
meet its total water requirement until the next irrigation period while not providing so
much that excessive water and nutrient leaching from the substrate occurs (Majsztrik et
al., 2011). Additionally, irrigators may impose specific practices in order to meet certain
goals such as cyclic irrigation to minimize mid-day water stress or management allowed
deficit that ensures VWC never falls below a desired threshold. Ideally, irrigation blocks
would consist of plants of similar water requirement.
Overhead irrigation systems involve the distribution of water through sprinkler
emitters that cover a large area containing many container-grown plants using relatively
few emitters. Overhead irrigation is almost exclusively used for 26.5 L (#7 trade-size)
containers and smaller (Beeson et al., 2004). Generally, overhead systems require
fewer labor hours for inspection than microirrigation systems because problems can be
quickly identified and corrected (Beeson et al., 2004). These systems are desirable
because they accommodate some variation in land topography while volume of water
and its distribution can be easily regulated (Goodwin et al., 2003). Irrigation application
efficiency (IAE) is the amount of water retained in the container substrate divided by the
total water applied (Beeson and Knox, 1991). Since the longer an overhead system
applies water, the longer water is being applied off target, an increase in container
spacing results in lower IAE, particularly when small containers are spaced the same
distance apart as larger containers (Beeson and Knox, 1991). One of the best ways to
improve irrigation efficiency (IAE) with overhead systems is to irrigate only for the
duration needed to supply adequate water to plants without causing a reduction in

9

growth. Hence, the timing and volume of irrigation application is especially important
when overhead irrigation systems are employed. For efficient irrigation of containergrown plants, need-based irrigation applications can be based on either a specific
leaching fraction, estimation of crop ET using climactic models, or direct measurement
of substrate moisture with a variety of sensors.

Traditional Approach
Historically, the primary technique employed in determining plant needs for
scheduling irrigation of container plants simply involves decision-making based on a
grower’s experience (Warren and Bilderback, 2004). This process may involve visual or
tactile assessments of plant canopies for losses in turgor, lifting a few representative
containers to estimate weight, or the use of a “feel” test to gauge the overall dampness
of rooting substrates. While these methods of irrigation scheduling may indicate that
plants need water, they do not quantify the precise amount. It is probable that errors
occur in the estimation of plant water needs as well. Additionally, many growers apply
more water than is necessary as a safeguard against under- watering plants (Bauerle
and Post, 2002). While some growers may feel that they are erring on the side of
caution, numerous studies show that permitting moderate moisture deficit has little to no
effect on growth (Warsaw et al., 2009 a and b ; Beeson, 2006; Groves et al., 1998; Tyler
et al., 1996; Welsh and Zajicek, 1993; and Fitzpatrick, 1983). Furthermore, grower
“intuition” or fixed-rate irrigation scheduling may increase crop losses due to disease
(Chappell et al., 2013) and reduce growth of ornamental shrubs compared to
quantitative approaches (Belayneh et al., 2013; Warsaw et al., 2009a; Million et al.,

10

2007; Welsh and Zajicek, 1993). Higher irrigation volumes associated with fixed-rate
scheduling also contribute to leaching of nutrients from containers making them
unavailable for plant growth (Warsaw et al., 2009b).
Fain et al. (2000) surveyed 24 Alabama container nurseries about their irrigation
management practices. The results showed that only 50%, 57%, and 33% of small
(0.4-4.1 ha), medium (4.5-16.6 ha), and large (40+ ha) growers, respectively, test
irrigation system efficiency. When asked how much water was normally applied,
respondents indicated that, on average, irrigation was run for 1 h to apply 2.5 cm (1 in.)
(Fare et al., 1992). However, the study determined that the actual amount applied by
Alabama nurseries was only roughly 1.6 cm during each 1 h irrigation event- 40% less
the growers intended. Additionally, Garber et al. (2002) reported that with each
irrigation event, Georgia nurseries applied 2.5 cm to containers ranging from 3.8L to
11L in volume. Since the Georgia application rates were based on responses to
surveys completed by nursery managers themselves, the application volumes
determined from Georgia are best considered generalizations (Garber et al., 2002).
Leaching fraction (LF) is often suggested as a method to schedule irrigation and can be
determined with common nursery supplies using the following formula:
LF = volume of leachate water / volume of applied water

(Tyler et al., 1996)

Irrigators are often advised to use the above to determine the required volume of water
to apply to container-grown plants.
Warren and Bilderback (2005) indicate that irrigating to a LF of 0.0 would be ideal
to replace the exact volume required necessary to restore the substrate to container
capacity without any leaching from the container. Niemiera and Leda (1993) examined

11

the effects of LF on total N leaching from PVC columns filled with pine bark substrate,
without plants. A total of 140 mg N was applied from CRF, and at a leaching fraction of
0.4, 50.0 mg was recovered in leachate. Irrigating by low LF can reduce water
applications and nutrient losses due to leaching. At a LF of 0.4, approximately 99%
more total N applied to container substrates was leached than at a LF of 0.0 (Niemiera
and Leda, 1993). These results strengthen the case Warren and Bilderback (2005)
make for setting the target LF at 0.0 when scheduling irrigation. Irrigating at low LF can
lead to buildup of salts in substrates, particularly with long-term crops such as woody
ornamentals. Electrical conductivity (EC) is most commonly used to express the
concentration of total soluble salts in a substrate or soil. Published guidelines for ideal
EC ranges for medium to high-nutrient use nursery crops include 0.8-1.5 dS.m-1 and
1.0-2.0 dS.m-1 [Yeager et al., (2003), and Smith and Logos, (2008)]. A correlation was
shown between EC and NO3--N concentrations as they fluctuate over a growing season
in Ilex cornuta Lindl. & Paxt. ‘Burfordii’ (Ruter, 1992). Because this correlation exists,
EC is commonly used as an indicator of substrate nutrient status. Generally, greater
quantities of nutrients are available at a higher EC than at a low EC under a normal
fertilization regime. Therefore, EC should be routinely checked during production of
container plants, particularly in salt sensitive plants, and leached when recommended
maximum levels are reached to avoid the incidence of salt injury.

12

Approaches Involving Modeling of Evapotranspiration
Many approaches to irrigation scheduling involve making an estimate or direct
measurement of ET to determine water needs of a particular crop in order to conserve
water. Estimates of ET are usually based on the equation developed by Thornthwaite in
1944:
ETA = ET0 x KC.

where ETA represents actual evapotranspiration of the crop, ET 0 represents the
potential evapotranspiration of a reference crop, and KC is a crop-specific coefficient
derived for the crop. ETA can be derived in any one of several ways by: 1) conducting
an energy-balance analysis from published weather history and KC values; 2) relation of
crop yield to ETA; 3) direct measurement followed by a water-balance method and 4)
directly measuring water use of the crop using a lysimeter or with a variety of soil
moisture sensors available today (Burt et al., 1997). The ET 0 is the estimated
transpiration specifically derived for a healthy, actively-growing, unshaded, and wellwatered reference crop maintained at a height of 8 - 15 cm, usually turfgrass
(Doorenbos and Pruit, 1975). The equation considers solar radiation, temperature,
relative humidity, and wind speed using a derivative of the Penman-Monteith equation
(Monteith, 1964; Penman, 1948). Finally, KC is determined from experimental
observation of the crop’s total ETA usually over an extended period, which is then
divided by the total ET0 from the same period.

13

Relation of Evapotranspiration to Crop Yield
In ornamental plants, yield is most often expressed as a growth parameter such
as growth index, canopy volume, wet or dry shoot or root weight, or canopy closure.
Water use has been positively correlated with potential evapotranspiration and growth
index when estimated using either the Thornthwaite equation (R2=0.49 - 0.81) or pan
evaporation (R2=0.78 - 0.88) (Knox, 1989). In another study, both canopy dry mass and
final growth index were linearly related to ETA and highly correlated in Viburnum
odoratissimum Ker.-Gawl., Ligustrum japonicum Thunb., and Rhaphiolepis indica Lindl
(Beeson, 2006). Beeson (2010) has since developed a simpler equation requiring only
the input of container spacing once per crop rotation, canopy width every 3 weeks to
determine percent canopy closure (a growth parameter), and daily ET 0 values to
determine a water needs index (WNI). Percent canopy closure was exponentially
correlated to WNI (average weekly ETA : average weekly ET0) with an r2 of 0.868 (P =
0.001). Early in production, a high WNI was attributed to high evaporation rates
resulting from minimal canopy closure (6-12%). But as closure increased, transpiration
accounted for an increasing percentage of measured ETA, and the model began
responding more directly to changes in ET0. Since most nurseries already perform
regular growth measurements to track crop progress, the percent closure model
presents a means to relate these simple growth parameters to ET 0 for precision
irrigation scheduling (Beeson, 2010).

14

Energy Balance Method Using Crop Coefficients
Since KC’s were initially developed for use with field-grown agronomic crops
whose unrestricted roots are assumed to extend to the edge of each plant’s canopy,
adaptation to container-grown nursery stock should consider the small surface area of
substrate (relative to the plant canopy) within the container exposed to the atmosphere.
Burger et al (1987) used the following equation to calculate KC for container plants:
ETA(cm) = volume of water used (cm3) / container surface area (cm2).

Overall, the number of KC values developed for container-grown nursery crops is quite
limited considering the thousands of species and cultivars produced in ornamental
horticulture (Irmak, 2005). Where field-grown crops are generally assumed to have a
soil water reservoir extending to the edge of their canopies, canopies of container crops
may extend several times beyond container surface area, which thereby limits rooting
volume and evaporative surface area within the container (Burger et al., 1987).
Therefore, where field crops rarely exceed KC values of 1.0, container-grown
ornamentals may exceed 5.0 (Burger et al, 1987; Warsaw et al, 2009a). Beeson (2005)
and Irmak (2005) note that the development of KC values for nursery crops is a timeintensive undertaking considering the thousands of woody ornamental cultivars in
production. Furthermore, Schuch and Burger (1997) reported that their K C values
developed for woody shrubs in southern California were less useful in winter than
summer and varied by site. Also, KC values in five of the twelve taxa did not adequately

15

approximate ETA for the reliable scheduling of irrigation; instead they suggested that
high water use taxa would benefit from more frequent adjustments of K C values based
on physiological development throughout the season or microclimate.
One study reported highly positive correlations between KC values developed for
Viburnum odoratissimum and weeks after transplant, plant growth index, cumulative
ET0, and fraction of thermal units (R2 ≥ 0.93) (Irmak, 2005). While these variables are
all less intensive to measure than ETA directly, models relying upon plant growth rate
may require the development of separate KC values for different seasons (fall growth
patterns were linear rather than exponential as in summer), phenological development,
or container spacing. As an alternative to using growth rates, normalization of ET A by
canopy area has shown a strongly negative correlation with KC (R2 = 0.951) (Beeson,
2004). During validation, this model demonstrated a 410 mm reduction in irrigation
inputs compared to plants irrigated with manually-activated irrigation that was manually
scheduled based on observation of weather trends (Beeson and Brooks, 2008). Plants
irrigated using the model also reached saleable size three weeks sooner than those
irrigated by irrigator intuition (Beeson and Brooks, 2008). Since KC values were related
to canopy closure, an additional advantage of this modeling approach is that,
theoretically, the model is not dependent on container size and spacing or season
provided that canopy closure remains constant between meteorological seasons
(Beeson, 2005). Overall, several elements associated with using models to predict ETA

16

of woody ornamentals are still not fully understood such as the frequency with which
measurements of plant growth should be collected or the effects of canopy shedding in
relation to canopy closure (Beeson, 2005). Additionally, models will probably (at a
minimum) need to be developed for representative species in each of the irrigation
functional groups (IFG) proposed by Burger et al (1987) and then selectively applied to
the 1000’s of remaining taxa produced in ornamental horticulture today for which ETA
has not been intensively studied (Beeson, 2005).
Error commonly occurs in the use of ETA- based models due to inaccurate yield
data, variations between varieties of a particular species, or variation in ET 0 between
the modeled year and subsequent years. At nurseries, plants growing in microclimates
due to varying topography, crop canopies, surface albedos, or surrounding vegetation
may perform differently than modeled plants. Additionally, faulty or poorly-calibrated
instrumentation or improper use of equations to derive ET can also reduce model
efficacy (Burt et al, 1997).

Soil Water Depletion Approach Using Sensors
This approach involves taking direct measurements of substrate water depletion
in containers, which eliminates some of the assumptions made when using modeling to
determine ETA. Methods in use include measuring gravimetric change, matric potential,
and estimation of substrate volumetric water content (SVWC) from the dielectric
constants of water in relation to those of substrate solids (van Iersel et al., 2013). In
practice of measuring soil water depletion, a measurement is taken shortly after an

17

irrigation event to establish a baseline moisture content to which the irrigator wishes to
return with the next irrigation. Then, just before the next irrigation occurs, another
measurement must be taken, so the differential between these two measurements is
considered ETA for that period of time. Alternatively, a pre-determined moisture level
can be targeted as a set point for activation of irrigation to maintain substrate moisture
above a certain level (Kim et al., 2011). Many researchers have used weighing
lysimeters to gravimetrically determine SVWC in ornamental shrub taxa since they
provide a direct measure of water lost to ETA (Burger et al., 1987). Welsh et al (1991)
used lysimeters to determine volumes that replaced 50%, 75%, and 100% of ET A in
Photinia xfraseri Dress. on either 3.5 d or 7 d intervals. Measurements of substrate
matric potential can also be used to estimate plant water needs; much like SVWC
measurement, users identify critical water potential set points based on water
availability to plants. The target range for irrigation scheduling using horticultural
growing substrates should be from 0 to -10 kPa (Lea-Cox et al, 2011). Within this range
lie two important parameters: EAW (easily-available water; ≈-5.25 kPa) and WBC (water
buffering capacity; ≈-10.25 kPa). The bulk of water available for plant growth lies in the
EAW range whereas WBC functions to signal that irrigation is required in order to avoid
compromising growth (Lea-Cox, 2011). For 80% pine bark : 20% peat moss (vol:vol)
substrate, Lea-Cox et al (2011) reported that 40.0% of total water at CC is EAW while
the WBC consists of just 7.0% total water. Rose et al (1999) grew Acer xfreemanii E.
Murray ‘Jeffersred’ and Malus xzumi (Rehd.) ‘Calocarpa’ at 5 kPa and 18 kPa tension
setpoints. Plants subjected to the high moisture tension treatment showed reduced

18

whole-plant dry weight, shoot length, and leaf area for both taxa compared to the low
tension treatment.
Dielectric sensors are frequently used when estimating moisture in horticultural
substrates and are based on readings of the dielectric permittivity of the materials in the
substrate. The basis of this technology relies on the differentials between the dielectric
constants of the different media components into which an electrical current is
discharged. The higher the dielectric constant, the better the substance’s ability to
conduct the charge. Water, with a dielectric constant of ≈80 (at 20oC), is distinctly
higher than air (≈1), mineral soil particles (≈4) (Campbell et al, 2007), or organic
substrate constituents such as peat (2.31) and pine bark (2.66) (Naasz et al., 2005).
Since the bulk of the signature will originate from substrate water, it can be related to
SVWC.
Dielectric sensors use two basic technologies. First to be introduced was the
time domain reflectometer (TDR). This type of sensor consists of two or more metal
rods that determine permittivity by analyzing the duration needed for an electromagnetic
wave to pass out of one rod, through the substrate, and back through another rod into
the receiving unit (Bittelli, 2011). TDR’s are especially useful because the rods can be
cut to a wide range of lengths to suit a variety of applications.

Permittivity is averaged

over the entire length of the rod. Thus, the longer the rod, the greater is the depth over
which permittivity is integrated at the expense of resolution within the sampled profile.
However, increasing levels of EC in the soil sample reduce the accuracy of TDR
(Campbell et al, 2007). A frequency domain reflectometer (FDR), also known as a
capacitance sensor, differs from TDR in that it produces a voltage that passes through

19

the substrate (which acts as a resistor) and continues oscillating that voltage until the
strongest resonating frequency is found. This reflects the dielectric constant, and, in
turn, the substrate water content (Greenwood et al, 2009). Capacitance sensors are
more economical than TDR, which makes them ideal for deployment in large numbers,
but they are slightly less accurate (Campbell et al, 2007). High EC can also skew
capacitance readings, but sensors are available today that are minimally affected by
changes in EC up to 10 dS.m-1 (Campbell et al, 2007).
Many moisture sensors are available today that can be used to estimate irrigation
needs with reasonable precision in container nursery substrates. Dataloggers can be
equipped to read hundreds of moisture sensors and even use relays for automated
switching of irrigation control valves to create a fully-automated irrigation system driven
by plant demand (Cornejo et al, 2005; Nemali and van Iersel, 2006; Burnett and van
Iersel, 2008). In these systems, SVWC is usually permitted to fall to a certain level
(though above a level that would induce stress), before irrigation restores the substrate
to CC.
One of the most extensive studies using soil moisture sensors to determine
water use in container-grown ornamentals was conducted by Warsaw et al (2009a).
The researchers used a single hand-held TDR probe to collect SVWC data then
calculated DWU and scheduled irrigation to replace only that water lost to DWU. When
subjected to an irrigation control of 19 mm.ha-1.d-1, three species were smaller in their
respective controls at the end of the production season than those irrigated to replace
100% of their DWU. Among the 24 shrub taxa sampled, water savings in DWU-based
treatments ranged from 6% to 75% compared to their control (Warsaw et al., 2009a).

20

Manually measuring SVWC across many plants to determine DWU was a drawback of
this system, and as such, it was not practical to take DWU measurements daily
throughout the study. Consequently, DWU was calculated and irrigation volumes
adjusted at 10-14 d intervals (Warsaw et al., 2009a). Although the authors took new
DWU measurements anytime they perceived major changes in weather patterns that
may affect DWU, applying one DWU quantity over a protracted period still carries the
risk of over- or under-estimating irrigation needs on individual days when environmental
conditions and plant growth are invariably changing. Nevertheless, plants in DWU
treatments still performed as well as their control.

Sensor-based Automated Irrigation Systems
Several systems have used dataloggers to control capacitance sensors and
activate irrigation for entire production seasons in greenhouse crops based on
predetermined SVWC set points (Kim et al., 2011; van Iersel et al., 2010; Burnett and
van Iersel, 2008). Using one of these systems, stem length increased with increasing
SVWC set point in Gaura lindheimeri Engelm. & Gray ‘Siskiyou Pink’ (Burnett and van
Iersel, 2008) and Petunia xhybrida (Kim et al., 2011). Where overhead irrigation is used
at container nurseries, set point systems may not be easily integrated into their
production. Under this scenario, set points could activate irrigation at any time
throughout the workday, thereby surprising unsuspecting workers and forcing them to
relocate outside the active irrigation zone until irrigation ceases. Unpredictable irrigation
events could also inadvertently wash away pesticides before achieving their desired

21

effects or cause increased foliar diseases resulting from extended periods of foliage
wetness.
Lea-Cox et al. (2013) described a wireless sensor network that includes a control
node capable of reading up to five capacitance sensors and provides control function for
one irrigation solenoid. This offered growers a rapidly-deployable system with wireless
nodes that can transmit data over distances greater than 1 km enabling growers to
connect all parts of a nursery to the same network without wires. The system also had
the ability to monitor substrate temperature, EC, rainfall, irrigation water applications, air
temperature, relative humidity, and photosynthetically active radiation at 15 min
resolution. A user-friendly interface has now been developed integrating real-time data
views, configurable charts, irrigation control, online access, alerts, and plant models
(Kohanbash et al., 2013). A system consisting of 40 sensors, eight nodes, computer,
base station, and software was assembled in 2011 for $10,310 (USD) (Lichtenberg et
al., 2013). The system nearly halved production time of Gardenia augusta ‘MADGA 1’,
reduced losses due to foliar diseases, and increased overall profitability 119%. Using
similar hardware, Belayneh et al. (2013) reduced water applications to medium and high
water use landscape tree species by 63% and 34% in a pot-in-pot operation compared
to a control irrigation schedule based on actual grower practice.
Given the expense, infrastructure modifications, and time required for nurseries
to adopt wireless sensor networks, nurseries will not likely be able to directly monitor
each of their hundreds of ornamental crops for irrigation scheduling (Lea-Cox et al.,
2013). Until more water use information is developed, representative, or “indicator”
crops, can form the basis for grouping plants of into “irrigation functional groups” (IFG)

22

(as by KC; Warsaw et al., 2009a) to appropriately assign them to irrigation blocks
(Yeager et al., 2003). It should also be noted that although many manufacturers claim
that their systems are “plug-and-play”, all dielectric sensors benefit from calibration to
the specific substrate (van Iersel et al., 2013). Logically, incorrectly-calibrated sensors
could result in gross under- or over-approximation of plant water needs. Also, during
sampling, a disturbance of sensor-soil contact could result in inaccurate readings (Burt
et al, 1997).

Regulatory Policies on Agricultural Water Quality and Nutrient Content
In addition to being high users of water for irrigation, nurseries can also be
sources of non-point pollution if NO3--N and PO43--P are allowed to escape from
production areas into the surrounding environment (Sharma, et al., 2008). The
environmental impacts of both of these nutrients are coming under more intensive
legislative scrutiny, which could become a major issue for container nurseries (Sharma
et al., 2008). The U.S. legal NO3--N threshold for safe drinking water is 10 mg.L-1
(USEPA, 1986). Sharma and Bolques (2007) sampled runoff water from two container
production nurseries to analyze NO3--N and PO43--P concentrations. While levels were
much higher in containment areas onsite, outflow concentrations into natural surface
waters were 8 mg.L-1 NO3-- N and 5 mg.L-1 PO43--P. These levels are below the
USEPA threshold for drinking water safety but the Michigan Department of
Environmental Quality (MDEQ) reports that just 0.3 mg.L-1 PO43--P may promote
cyanobacteria blooms in surface waters (MDEQ, 2008).
23

In Section 132.2 of “Final Water Quality Guidance for the Great Lakes System”,
the USEPA defines a TMDL as “the sum of the individual wasteload allocations for point
sources and load allocations for nonpoint sources” (USEPA, 1995). The purpose of a
TMDL is to set a maximum tolerable rate at which pollutants can be added to a water
body on a case-specific basis (USEPA, 1995).

Although the severity of TMDL

regulation varies by watershed, the Chesapeake Bay is one of the most noteworthy
cases because its legislation affects six states. Current NO3--N and PO43--P deposition
rates for the bay are approximately 60% and 50% above the guidelines mandated by
the EPA (CBF, 2010). Since acreage of agricultural production has been shown to
increase total nitrogen and NO3--N levels in surface waters (Jordan et al, 1997), the
state of Maryland required almost all agricultural operations to draft their own
management plans for nitrogen and phosphorus by the end of 2002 (Lea-Cox et al,
2001) in hopes that doing so would encourage growers to take steps that would reduce
nutrient losses into the bay and other surface waters. In Florida, agriculture accounts
for 98% of all phosphorus imports into the Lake Okeechobee watershed where the
phosphorus TMDL of 40 ug.L-1 places agricultural nonpoint sources under intense
scrutiny (FDEP, 2001). Another example of TMDL regulation is the state of Michigan’s
restrictions on phosphorus levels in 12 watersheds throughout the state (Anonymous,
2011). Relatively few Michigan nurseries occur in close proximity to the areas regulated
and no statewide regulations are in place for the concentration of PO43--P from nonpoint
pollution sources at this time (PPAC, 2007). However, with several states restricting
nutrient concentration (Beeson et al., 2004) or quantity (as with TMDL’s) leaving

24

agricultural operations, many nurseries may have to reevaluate and modify current
irrigation and fertilization practices in order to become compliant with forthcoming
legislation.

Irrigation Management to Reduce Nursery Runoff
One of the most effective ways to reduce nutrient losses from nurseries is to
reduce leaching from container substrates by means of proper irrigation scheduling
(Majsztrik et al., 2011). Volume of water applied to Cotoneaster dammeri Schneid.
‘Skogholm’ growing in 3.8 L containers was reduced 44% under a low LF of 0.0 to 0.2
compared to a high LF of 0.4 to 0.6 while volume of effluent recovered from containers
was reduced 63% (Tyler et al., 1996). A corresponding decrease of 66%, 62%, and
57% was observed in total NO3--N, NH4+-N, and P2O quantities in effluent,
respectively. Furthermore, excessive LF’s may deplete available nutrients prematurely
in the growing season, forcing either a second CRF application or sacrificed late-season
growth (Mohammed et al. 2009).
Basing irrigation on ETA of shrubs growing in 2.4 L containers versus a standard
fixed rate of 10 mm.d-1 has been shown to reduce runoff volumes by 42% while total N
and P losses were reduced 19% and 27% (Million et al., 2010). Warsaw et al. (2009b)
showed runoff volume reductions of 66% and 79% under 100% and 75% DWU
applications compared to their control application of 19 mm.d-1 for shrubs growing in
10.2 L containers. In addition, total quantities of NO3--N lost in the runoff were 38% and
59% lower than the control under 100% DWU and 75% DWU applications. Similarly,

25

quantities of PO43--P lost in the runoff under 100% DWU and 75% DWU application
volumes were 46% and 74% lower than the control volume (Warsaw et al., 2009b). The
above research indicates that plant-based irrigation scheduling shows great promise in
reducing nutrient losses from containers and subsequent nutrient loading from nurseries
into the environment that contributes to non-point source contamination of surface
waters.

26

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27

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35

CHAPTER ONE
GROWTH, SEASONAL CROP COEFFICIENTS, AND NUTRITION OF CONTAINERGROWN SHRUBS UNDER FULL AND DEFICIT DAILY WATER USE IRRIGATION
TREATMENTS

36

Abstract
The study objectives were to: 1) determine whether irrigating with daily water use
(DWU) can save water compared to a control, time-based, application rate in 14 shrub
taxa without affecting growth 2) determine whether DWU irrigation reduces nutrient
losses, and 3) classify shrubs into irrigation functional groups (IFG). During the
experimental period of each year, irrigation was applied daily to 8 shrub taxa in four
treatments: 1) control application of 19 mm.ha-1.d-1, 2) irrigation applied to replace
100% DWU (100DWU) each day, 3) applications alternating 100% DWU with 75%
DWU in a 2-day cycle (100-75), and 4) a 3-day application cycle replacing 100% DWU
the first day and 75% DWU on the second and third days (100-75-75). In 2009,
average DWU was highest for Hydrangea paniculata ‘Limelight’ in the 100DWU
treatment at 17.3 mm.container.day-1 and lowest for Aronia arbutifolia ‘Brilliantissima’
and Thuja plicata ‘Grovepli’ in the 100DWU treatment at 8.8 and 9.0 mm.container.day1

. Most taxa received less water than the control, yet only growth index (GI) of

Hydrangea paniculata ‘Limelight’ was affected with 100DWU plants finishing larger than
the control. In 2010, average DWU for the season was highest in the 100-75 treatment
for Hydrangea arborescens ‘Abetwo’ at 20.5 mm.container.day-1 and Hydrangea
paniculata ‘Limelight’ at 22.0 mm.container.day-1. Syringa xhyacinthiflora ‘Evangeline’
required the least water of any taxa in the 100-75-75 treatment at 2.1 mm.container.day1

. In every case, GI of DWU plants was equal to control plants except Hydrangea

37

paniculata ‘Limelight’ in the 100-75-75 treatment, which was smaller than the other
treatments. Our results suggest that nurseries can use substrate moisture sensors to
precisely schedule irrigation according to changing plant need and consequently reduce
irrigation volume without affecting growth of most species.

Introduction
Irrigation efficiency can be improved with proper irrigation scheduling that also
reduces water, nutrient and labor inputs. Because producers must irrigate containergrown nursery plants frequently, often multiple times daily, large water withdrawals
made by nurseries often places them in competition for water resources with population
centers, industry, and other agricultural sectors (Beeson et al., 2004). Besides
increasing water withdrawals, inefficient irrigation scheduling also increases nutrient
losses into runoff, which can move offsite to contaminate surrounding water sources.
Irrigation should be scheduled to provide sufficient water to raise substrate moisture to
container capacity while minimizing leaching to only that which is necessary (Majsztrik
et al., 2011).
Belayneh et al. (2013) showed that sensor-controlled irrigation reduced water
applications to medium and high water use landscape tree species by 63% and 34% in
a pot-in-pot operation compared to a control irrigation schedule based on grower
intuition. Additionally, Warsaw et al. (2009a and 2009b) demonstrated that irrigating
according to plant daily water use (DWU) reduces water inputs while maintaining similar
or improved growth versus a standard, time-based nursery irrigation schedule.
Furthermore, when used to schedule irrigation, soil moisture sensors have recently

38

been shown to reduce crop losses due to disease and increase profit for ornamental
plant producers mainly by shortening plant production time (Lichtenberg et al., 2013).
Sensor-based estimation of DWU has also resulted in decreased total quantities of
NO3--N in effluent 38% and 59% in 100% DWU and 75% DWU applications compared
to a standard irrigation rate (Warsaw et al., 2009b).
In this study, DWU was determined with both intermittent time domain
reflectometry (TDR) samples and time capacitance sensors in real time for the
application of three irrigation treatments. The objectives of this project were to 1)
determine whether irrigating with daily water use (DWU) can save water compared to a
control rate of 19 mm.ha-1.d-1 in 14 shrub taxa without affecting growth 2) determine
whether DWU irrigation reduces nutrient losses, and 3) classify shrubs into irrigation
functional groups (IFG). Plant growth index (GI) and dry weight, DWU, KC, water use
efficiency (WUE), leachate electrical conductivity (EC) and pH, and foliar nutrient
concentrations were measured to compare effects of three DWU-based irrigation
treatments to the control.

Materials and Methods
Site
The Michigan State University Horticulture Teaching and Research Center
(HTRC), Holt, Mich., is located at latitude 42.67 degrees north, longitude -84.48 degrees
west, and elevation of 264 m. Plants were grown on a site representative of a typical
production nursery. Containers were placed on a level surface of limestone gravel with

39

landscape fabric underlain at a 15 cm depth to reduce weed emergence. Production
areas were 4.9 m x 7.3 m and spaced 0.3 m apart. A Michigan Enviro-weather station
is located at the HTRC to monitor environmental conditions (MSU, 2011).

Irrigation System
Irrigation was activated in each production bed by a 1.9 cm diameter 24 V
alternating current solenoid valve (various manufacturers). Irrigation nozzles (ProSpray®, Hunter Industries Incorporated; San Marcos, CA) were mounted on 1.3 cm
diameter by 0.66 m high risers. The nozzles were spaced 2.44 m apart along the
perimeter of each production area with all water directed inward. Four- 90o nozzles
were positioned on the corners of the production area, two- 180o nozzles were
positioned between the corner nozzles on each E-W perimeter, and one- 180o nozzle
was placed on the N-S production area. Additionally, two- 360o nozzles were positioned
in the center. Each nozzle had a 2.44 m radius of throw to provide 100% nozzle- tonozzle overlap.
Irrigation system distribution uniformity (DU) and output in each treatment
replicate was tested in 2009 using 16 rain gauges randomly interspersed throughout the
production area and allowed to collect water for 20 min. The average application rate
was 0.79 mm.min-1 with a distribution uniformity of 74.8%. Distribution uniformity and
output in each treatment replicate was again tested in 2010. The average application
rate was 0.77 mm.min-1 and the DU was 76.8%.

40

Plant Material
Rooted cuttings of Aronia arbutifolia Persh. ‘Brilliantissima’, Cornus sericea L.
‘Farrow’, Hydrangea paniculata Sieb. ‘Limelight’, Itea virginica L. ‘Morton’, Physocarpus
opulifolius Maxim. ‘Seward’, Spiraea media F. Schmidt ‘Darsnorm’, Thuja plicata
Donn.‘Grovepli’, and Weigela florida A. DC. ‘Alexandra’ were obtained from a
commercial nursery in 5.7 x 5.7 cm plug containers on 1 August 2008. They were
planted in 10.2 L containers with an 85 pine bark : 15 peat moss (vol:vol) substrate
between 2 – 9 Sept. 2008 for use in the 2009 study. Plants were overwintered from
2008 to 2009 in a minimally-heated (-2.2o C) quonset house covered with 4 mil
overwintering film permitting 30% light transmission. In spring 2009, the film was
removed and plants uniformly irrigated as needed until beginning the treatments. From
the same nursery, cuttings of Hydrangea arborescens L. ‘Abetwo’, Hydrangea
paniculata ‘Limelight’, Rhus aromatica Aiton ‘Gro-Low’, Spiraea fritschiana C.K.
Schneid. ‘Wilma’, Syringa meyeri C.K. Schneid. ‘Palibin’, Syringa xhyacinthiflora Rehd.
‘Evangeline’, Viburnum dentatum L. ‘Ralph Senior’, and Weigela florida ‘Alexandra’
were potted as described above between 15 - 18 Sept. 2009 for use in the 2010 study.
All cultural practices in both years except irrigation were identical for all treatments. On
9 June 2009 and 22 June 2010, 54 g of 19-2.6-10 (%N-P-K) controlled release fertilizer
with micronutrients (Harrell’s Inc., Lakeland, FL) was topdressed to each container.
Total quantities of NO3--N and PO43--P applied in both 2009 and 2010 were equivalent
to 285 kg.ha-1 and 39 kg.ha-1, assuming identical container spacing to this study and
100% land utilization. Predicted release duration at substrate temperatures of 21.1o C

41

and 26.7o C ranged from 6 to 5 months. Weeds were removed by hand pulling as
necessary.

Experimental Design
Four irrigation treatments were replicated three times and randomly assigned to
12 production areas in a completely randomized design. The treatments were: 1)
control application of 19 mm.ha-1.d-1, 2) irrigation applied to replace 100% daily water
use (100DWU), 3) applications alternating 100% DWU with 75% DWU in a 2-day cycle
(100-75), and 4) a 3-day application cycle replacing 100% DWU on the first day and
75% DWU on the second and third days (100-75-75). Each treatment replicate
contained six subreplicates of each of the eight shrub species for a total of 96
experimental plants in each production treatment replicate. Experimental plants were
randomized in six rows of eight at the center of the production area and inset at least
1.2 m from the edge with guard plants surrounding the perimeter to reduce edge effects.
Guard plants consisted of several species having similar growth rates to the
experimental plants and were arranged in the same sequence in each treatment
replicate.

Daily Water Use and Irrigation Scheduling, 2009
Substrate volumetric water content (SVWC) was measured in 2009 for every
plant using a time domain reflectometry (TDR) soil moisture sensor (ThetaProbe Type
ML2x, Delta-T Devices Ltd., Cambridge, U.K.) and digital reader (ThetaMeter HandHeld Readout Unit Type HH1, Delta-T Devices Ltd.). At initiation of the study, irrigation

42

was applied until the substrate for all plants exceeded container capacity. Gravimetric
water was allowed to drain for 30 min. Then an initial SVWC measurement was taken
for each plant, and a final measurement was taken after 24 h. The Theta probe was
inserted vertically into the substrate to a depth of 6 cm in three locations 120 o apart
halfway between the center and the outer edge of the container. The measurements
were converted to SVMC using a substrate-specific equation developed by Warsaw et
al. (2009a) for the same substrate. The SVWC differential was multiplied by the
average container substrate volume (9.7 L.container-1) to determine daily water use
(DWU). Irrigation was then applied daily beginning at 0700 HR through a uniform
overhead system to meet the needs of the lowest water user(s). Any taxa requiring
more than 50 mL (0.89 mm) in addition to the base irrigation level received the balance
by hand watering. Control and DWU-based irrigation run times were rounded up to the
nearest whole min and programmed into a irrigation controller (Rain Bird ESP-12LX
Plus, Rain Bird Corporation; Azusa, CA). Irrigation began at 0700 HR daily. New DWU
were obtained approximately every 21 d, and irrigation was adjusted accordingly.

Daily Water Use and Irrigation Scheduling, 2010
In 2010, time capacitance soil moisture sensors (Model 10HS, Decagon Devices,
Inc.; Pullman, WA) replaced the ThetaProbe to provide continuous real-time SVWC
sampling and irrigation control. A total of 96 sensors, one per species in each replicate,
were connected in single-ended configuration to an multiplexer (AM16/32B, Campbell
Scientific, Inc.; Logan, UT), which was operated by a micrologger (CR3000, Campbell
Scientific, Inc.). The micrologger recorded SVWC for each probe at 15 min intervals

43

from 23 June to 31 October 2010. A relay controller (SDM-CD16AC, Campbell
Scientific, Inc.) connected to the micrologger controlled the irrigation solenoid valves
located at each treatment replicate. A common wire carrying 24 V alternating current
power from the “Valve Test” terminal on the irrigation controller was connected to the
relay controller for irrigation solenoid valve actuation.
On 20 June 2010, each of the 96 sensors was individually calibrated to the
substrate in situ. Irrigation was applied to bring the substrate to container capacity, and
each container was weighed after drainage ceased using a electric balance (PM 30,
Mettler-Toledo, Inc.; Columbus, OH). Five subsequent weights were taken during a 2 d
dry-down period and recorded with coinciding sensor output. These data were plotted
using Microsoft Excel (2007) and the trend line feature was used to obtain a single bestfit quadratic equation. Calibrations were verified using the PROC REG function in SAS
(SAS Institute; Cary NC) before inclusion in the irrigation control program.
Beginning on 23 June 2010 at 0700 HR, DWU was calculated in the first irrigation
zone as (see Figure 1.1):
DWU = AI - BI * substrate volume in container
where AI is the SVWC value 1 h from the conclusion of the current irrigation event, BI is
the SVWC value immediately preceding irrigation on the following day approximately 23
h later, and the substrate volume in the containers was 9690 ml. A run time was then
calculated for each taxon in the irrigation zone by:
Run Time = DWU / 0.673
where 0.673 is the irrigation system application rate in mL.s.container-1. The highest

44

Figure 1.1. Illustration of daily irrigation program operation on CR3000 logger
(Campbell Scientific, Inc.; Logan, UT) using 15 min substrate volumetric water content
(SVWC) scans for the calculation of daily water use (DWU). Example shown is for
Hydrangea arborescens ‘Abetwo’ in the 100DWU treatment from 0000 HR on 23 August
to 0000 HR 25 August 2010. Program operation corresponding to each letter described
in text.
Hydrangea arborescens 'Abetwo'- Days 62 - 64
0.45

SVWC (vol : vol)

0.40
0.35
AI

0.30

*

*

0.25

BI

0.20
0.15
0:00 4:00 8:00 12:00 16:00 20:00 0:00 4:00 8:00 12:00 16:00 20:00 0:00

run time from the 8 plant taxa was selected, the treatment fraction applied (1.0 or 0.75,
depending on treatment cycle), and irrigation initiated for the required duration. The
micrologger repeated this process in all treatment replicates each day for the duration of
the study. However, with no initial SVWC to reference from previous days, substrate
moisture had to be raised to container capacity [(CC= 0.323 SVWC (vol : vol)] to obtain
the first AI value on day 0 prior to the first calculation of DWU in the experiment. From
0300 HR to 0600 HR on 22 Jun, 23.61 mm of steady rainfall occurred, which added 1.32
L to each container. This was considered sufficient to bring the substrate to container
capacity for treatment commencement on 23 Jun.

45

Plant Performance
To standardize for environmental conditions between the two seasons,
comparisons of water needs between the different species grown can be made using
crop coefficients (KC), which were determined using the formula KC = ETA / ET0 (Allen
et al., 1998). These calculations were made for each taxon in both years using data
obtained from the on-site weather station (www.enviroweather.msu.edu). Plants were
classified in this study as low (KC < 2), moderate (2 ≤ KC < 3), or heavy (KC ≥3) water
users as described by Warsaw et al. (2009a).
Monthly growth index (GI) was calculated to assess plant performance under the
four irrigation treatments. Prior to the first measurement, all taxa were pruned to a
uniform size. Then plant height (H) measurements were taken from the container rim.
Plant widths were measured along the north – south (WNS) and the east – west axis
(W EW) axis, and GI was calculated as [(H + W NS + W EW) / 3]. A permanently affixed
container label was used to maintain orientation of the plants. For Rhus aromatica ‘GroLow’, Viburnum dentatum ‘Ralph Senior’, and Weigela florida ‘Alexandra’, plant shoot
dry weight was measured at the end of the season in 2010 using three plants taken
from each treatment replicate. The stem was cut at the substrate surface, and the
entire top was bagged, oven dried, and weighed.
Using the PourThru leachate extraction procedure (Wright, 1986) electrical
conductivity (EC) and pH were determined using an EC meter and a pH meter (Horiba
Cardy Twin EC Meter; Horiba Cardy Twin pH Meter, Spectrum Technologies, Inc.,

46

Plainfield, IL). Testing was performed 30 to 60 min after irrigation for each taxa using
the same test plant throughout the season.
Both mid- and late-season foliar samples were collected from five taxa in 2009
and six taxa in 2010. Sixty recently fully-expanded leaves were collected between the
subreplicates of individual taxa within every treatment replicate. Samples were
maintained in a cooler at 3oC overnight then taken to the Michigan State University Soil
and Plant Nutrient Laboratory (East Lansing, MI) the day after sampling. Percent (dry
weight) N, P, K, Ca, Mg, Na, S, and concentration Fe, Zn, Mn, Cu, B, and Al were
analyzed. The laboratory used the Dumas combustion procedure (AOAC 968.06;
Horwitz, 2005) to determine foliar N. Minerals were dissolved from organic material
using open vessel microwave digestion (SW846-3050B) with mineral analysis
determined using inductively coupled argon plasma (ICAP analysis (AOAC 985.01;
Horwitz, 2005).

Statistical analysis
Data for each species were analyzed individually for irrigation volume, DWU, KC,
GI, GII, dry weight, water use efficiency (WUE), dry weight water use efficiency
(WUEdw), substrate EC and pH, and foliar nutrient content. Data normality were
checked using the PROC UNIVARIATE procedure in SAS (SAS Version 9.1; SAS
Institute, Cary, NC). DWU data showed several outliers due to a variety of factors
ranging from probes becoming dislodged in the substrate to rodent damage.
Consequently, any DWU values in excess of 3000 ml were removed excluded from the

47

analyses for DWU, KC, and WUE. Data were tested with analysis of variance using the
PROC GLM procedure of SAS. When significant (α = 0.05), Tukey’s Honestly
Significant test was used to separate means. After the experiment was left unattended
for 4 d when air temperatures and ET0 averaged 31.4oC and 4.56 mm, irreversible wilt
and leaf scorch was discovered on H. paniculata ‘Limelight’ on 31 August 2010 (day
70) in the last 100-75-75 treatment replicate to run each day. The fault occurred
because the farm irrigation well pump was not scheduled to run long enough during this
high period of ET0. Therefore, data from that treatment replicate was excluded from
analysis of WUE throughout the entire season and DWU, KC, GI, EC, pH, and foliar
nutrient concentration excluded from day 70 onward.

Results and Discussion
Irrigation Volume, Daily Water Use, and Plant Growth
Cumulative and average daily ET0 for the 2009 treatment period equaled 414
mm and 3.23 mm and in 2010 were 431 mm and 3.29 mm. A total of 310 mm and 233
mm of rainfall (Figure 1.2) occurred during the 2009 and 2010 treatment periods. In
2009, irrigation was not applied on three days when rainfall events exceeded 19 mm.
During the treatment period, total irrigation applied to the control was 2483 mm (134.1
L.container-1; Table 1) in 2009 and 2594 mm (140.1 L.container-1) in 2010.

48

Figure 1.2. Daily (bars) and cumulative (line) precipitation from 11 June to 14 October
(Day 126) 2009 and 23 June to 31 October 2010 (Day 131). Data obtained from the
Enviro-weather Automated Weather Station Network.
50
350
2009

40

Precipitation
Cumulative Precipitation

300

30

200

20

150

Daily Precipitation (mm)

100
10
50

0
Day 0
Month
50

40

0
20
Jul.

40

60
Aug.

80
Sept.

100

120
350

2010
Precipitation
Cumulative Precipitation

300
250

30

200
150

20

100
10
50
0
Day 0
Month

0
20
Jul.

40

60
80
100
Sept.
Aug.
Day of Treatment

49

120
Oct.

Cumulative Precipitation (mm)

250

Figure 1.3. Daily total solar flux density (bars) and daily average temperature (line) from
11 June to 14 October (Day 126) 2009 and 23 June to 31 October 2010 (Day 131).
Data obtained from the Enviro-weather Automated Weather Station Network.

50

30

2009

25

40

20

30
15
10
10

5

0
Day 0
Month Jun
50

0
20

40
Jul

60
Aug

80

100
Sep

120
30

2010

25

40

20
30

15
20
10
10

5

0

0
0

20

40
Jul

60
80
Aug
Sep
Day of Experiment

50

100

120
Oct

Average Daily Temperature (oC)

Total Solar Flux Density (MJ.m2.d-1)

20

In 2009, I. virginica ‘Morton’ only received the base irrigation rate, never any
hand watering. All other taxa required hand water applications for at least one interval
between DWU measurements (Table 1.1). Water reductions compared to the control
for H. paniculata ‘Limelight’ were 7%, 19%, and 23% in the 100DWU, 100-75, and 10075-75 treatments, respectively. Water applications were reduced 48%, 55%, and 57%
for I. virginica ‘Morton’ in the 100DWU, 100-75, and 100-75-75 treatments, respectively.
Table 1.1 does not report average water application to each taxon grown in 2010
because all taxa within each treatment replicate received uniform irrigation applications
based on the highest water use plant each day. Compared to the control, total irrigation
applications in 2010 were 19% and 50% greater in the 100DWU and 100-75 treatments
while the 100-75-75 treatment received 18% less water.
Average DWU was highest in 2009 for H. paniculata ‘Limelight’ in the 100DWU
treatment at 17.3 mm.day-1 and lowest for A. arbutifolia ‘Brilliantissima’ and T. plicata
‘Grovepli’ in the 100DWU treatment at 8.8 and 9.0 mm.container.day-1 (Table 1.2).
Water use of both C. sericea ‘Farrow’ and I. virginica ‘Morton’ was lower in the 100DWU
treatment than the control, but did not differ for the deficit treatments. No other
differences existed between treatments for individual taxa. Compared to 2009 in which
DWU was measured intermittently, more differences were observed when DWU was
calculated daily in 2010. Highest DWU was observed in H. paniculata ‘Limelight’ than
any other species, except in the control and 100-75 treatment in which it did not differ
from H. arborescens ‘Abetwo’. Rhus aromatica ‘Gro-low’ and H. arborescens ‘Abetwo’
had similar DWU in the control and 100-75-75 treatments. All three of these high wateruse taxa exceeded 75 cm at the end of the season, which is also larger than the other

51

Table 1.1. Total irrigation application (L.container-1) to 4 irrigation treatments from 11
June (Day 1) to 14 October 2009 (Day 126) and 23 June (Day 1) to 31 October 2010
(Day 131).
Taxa

Controlz

100DWU

100-75

100-75-75

Aronia arbutifolia 'Brilliantissima'

134.1

78.2

68.6

65.4

Cornus sericea 'Farrow'

134.1

100.5

88.3

84.3

Hydrangea paniculata
'Limelight'

134.1

124.7

108.8

103.3

Itea virginica 'Morton'

134.1

69.1

60.5

57.6

Physocarpus opulifolius
'Seward'

134.1

100.7

88.1

84.1

Spiraea media 'Darsnorm'

134.1

78.8

69.3

66.1

Thuja plicata 'Grovepli'

134.1

76.9

67.3

64.1

Weigela florida 'Alexandra'

134.1

113.5

99.3

94.8

140.1 Cy

167.3 B

209.8 A

115 D

2009

2010
Average water application
(mm.d-1) to all taxa
z

Control = 19 mm.application-1 (1.07 L.container-1.day-1); 100DWU = 100% daily water
use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU and
75% DWU; and 100-75-75 = 3 day cycle 100% DWU replacement the first day then 2
days 75% DWU replacement. Overhead irrigation scheduling based on 2009) lowest
DWU of the 8 taxa during each measurement period with remaining water requirement
supplied by hand each day as necessary; 2010) highest DWU of the 8 taxa in each
treatment replicate each day.
y

Means separation performed with Tukey’s Test (α = 0.05), n = 3.

taxa in the study. The two Hydrangea taxa exhibited higher DWU in the 100-75
treatment than either the 100DWU treatment or the 100-75-75 treatment, both of which
received less water than the 100-75 treatment. Lowest water use taxa included
52

Table 1.2. Season average daily irrigation application (mm .container-1.d-1) and average
daily water use (mm.container-1.d-1) of eight shrubs grown in 10.2 L containers under
four irrigation treatments administered 11 June (Day 1) to 14 October 2009 (Day 126)
and 23 June (Day 1) to 31 October 2010 (Day 131).
Controlz

100DWU

100-75

100-75-75

19.0 A

9.8 B

8.6 C

8.2 C

10.7 Aa

8.8 Ac

9.4 Ac

10.5 Ac

Cornus sericea 'Farrow'

14.9 Aa

10.0 Bbc

13.2 ABab

13.4 ABbc

Hydrangea paniculata
'Limelight'

14.0 Aa

17.3 Aa

15.9 Aa

17.1 Aa

Itea virginica 'Morton'

12.9 Ba

10.6 ABbc

10.6 ABc

Physocarpus opulifolius
'Seward'

13.1 Aa

13.0 Aab

14.6 Aab

Spiraea media 'Darsnorm'

10.7 Aa

9.7 Abc

12.4 Abc

11.5 Abc

Thuja plicata 'Grovepli'

11.0 Aa

9.0 Ac

9.0 Ac

11.7 Abc

Weigela florida 'Alexandra'

14.9 Aa

11.9 Abc

13.5 Cab

13.9 Aabc

19.1 C

22.8 B

2009
Average base water
application
(mm.d-1)
Taxa
Aronia arbutifolia
'Brilliantissima'

9.6 Bbc
13.2 Ab

2010
Average water application
(mm.d-1)
Taxa
Hydrangea arborescens
'Abetwo'
Hydrangea paniculata
'Limelight'

15.0 Bab

9.4 Ccd

28.6 A

15.4 D

20.9 Aa

6.4 Cc
15.1 Ba

18.4 Ba

18.5 Ba

22.5 Aa

Rhus aromatica 'Gro-Low'

13.0 Abc

12.8 Ab

14.0 Ab

6.9 Bbc

Spiraea fritschiana 'Wilma'

8.5 Bc

11.1 Abc

8.6 Bc

5.6 Cc

Syringa meyeri 'Palibin'

3.7 Be

10.0 Abc

9.0 Ac

4.8 Bc

53

Table 1.2 (cont’d)
2010
Syringa xhyacinthiflora
'Evangeline'
Viburnum dentatum 'Ralph
Senior'
Weigela florida 'Alexandra'

Controlz

100DWU

10.8 Acd

100-75

100-75-75

2.3 Cf

3.5 BCd

4.6 Bc

4.3 Bd

7.0 Ade

4.2 Bd

6.5 Ac

10.8 Ac

5.1 Bef

5.7 Bcd

10.1 Ab

z

Control = 19 mm.application-1 (1.02 L.day-1); 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Overhead irrigation scheduling based on 2009) lowest DWU of
the 8 taxa during each measurement period with remaining water requirement supplied
by hand each day in DWU treatments as necessary; 2010) highest DWU of the 8 taxa in
each treatment replicate each day.
y

Separation of means performed with Tukey’s Test (α = 0.05). Average daily irrigation
application: 2009) n = 126, 2010) n = 393. Average DWU: 2009) n= 108, 2010) n= 393.
Means followed by the same letters within rows are not different.
x

Comparisons between means in each column are marked with lowercase letters.
Means of the same letters are not different; Tukey’s Test (α = 0.05), 2009: n = 108,
2010: n=393.

S. meyeri ‘Palibin’ at 3.7 mm in the control, S. xhyacinthiflora ‘Evangeline’ at 2.3 mm
and 4.6 mm in the 100DWU and 100-75-75 treatments, and V. dentatum ‘Ralph Senior’
at 4.2 mm in the 100-75 DWU treatment. Additionally, R. aromatica ‘Gro-low’ and S.
fritschiana ‘Wilma’ showed lowest DWU in the 100-75-75 treatment.
Figures 1.4 and 1.5 show that DWU tends to steadily increase from June to midAugust with plant growth, hold steady in late summer as growth plateaus, decline in
September, and again plateau late September through October. In 2009 (Fig. 1.5),
DWU peaked on day 57 for all taxa when DWU also exceeded the control rate of

54

Figure 1.4. Plant Growth Index (GI), Daily Crop Coefficient (KC), and Daily Water Use
(DWU) from 10 June (Day 0) to 14 October 2009 (Day 126) for A) Aronia arbutifolia
‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C) Hydrangea paniculata ‘Limelight’, D) Itea
virginica ‘Morton’, E) Physocarpus opulifolius ‘Seward’, F) Spiraea media ‘Darsnorm’, G)
Thuja plicata ‘Grovepli’, and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers.
60.0

A)

80.0

60.0
40.0

NS

NS

20.0

40.0

NS

NS

20.0

NS

0.0

0.0
60.0
NS

Plant Growth Index (cm)

60.0

40.0

NS
40.0

NS
NS

NS

20.0

20.0
0.0
C)

80.0
60.0
40.0

B
A
AB
AB

B
A
B
A

B
A
B
A

NS
NS

20.0
0.0

60.0
40.0

20.0

NS

D)

80.0

0.0

NS

0.0
60.0
50.0

NS

60.0
40.0

40.0
30.0

NS

NS

20.0

20.0
0.0
Day 0
Month

10.0
0.0
Jun
DWU

20

Jul
Kcc

40

60
Aug
Control

80

Sep

100 DWU

55

100

120
Oct

100-75

140
100-75-75

Kc and Daily Water Use (mm)

B)

80.0

Figure 1.4 (cont’d)
100.0
E)
80.0

60.0

60.0
40.0

NS

NS

NS

40.0

NS

NS

20.0

20.0
0.0
60.0

F)

80.0

Plant Growth Index (cm)

60.0
40.0

40.0
NS

NS

NS

NS

NS
20.0

20.0

0.0

0.0
60.0

G)

80.0
60.0
40.0

NS

NS

NS

40.0

NS

NS

20.0

20.0
0.0
80.0
60.0
40.0

H)
AB
A
AB
B

0.0
60.0
50.0

NS

NS

40.0

NS

30.0
20.0

NS

20.0

10.0

0.0
Day 0
Month

0.0
Jun
DWU

Kc and Daily Water Use (mm)

0.0

20

Jul
Kcc

40

60
Aug
Control

80

Sep

100 DWU

100

120
Oct

100-75

140
100-75-75

Left y-axis indicates PGI (lines). Control = 19 mm.application-1; 100DWU = 100% daily
water use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU
and 75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement the first day
then 2 days 75% DWU replacement. Each day was analyzed separately (Tukey’s test,
α = 0.05). Means followed by the same letters are not different. NS = not significant.
n=18. Right y-axis indicates DWU where the entire bars represent DWU (mm)

56

Figure 1.4 (cont’d)
averaged from all treatments (n=72); shaded portions of bars represent daily Kc (DWU :
ET0, n=72). Overhead irrigation scheduling based on lowest DWU of the 8 taxa during
each measurement period; remaining water requirement supplied by hand each day as
necessary. Dotted horizontal line indicates control treatment of 19mm.application-1.
Daily ET0 values for calculation of Kc obtained from the Enviro-weather Automated
Weather Station Network.
19 mm.container-1 for C. sericea ‘Farrow’ (20.4 mm), H. paniculata ‘Limelight’ (28 mm),
P. opulifolius ‘Seward’ (24.8 mm), and W. florida ‘Alexandra’ (20 mm). No other taxa
peaked above the control on any other sampling day except W. florida ‘Alexandra’ on
day 75 (DWU = 19.2mm). In 2010, DWU peaked on Day 55 (ET0=4.92 mm) for H.
arborescens ‘Abetwo’ (36 mm), H. paniculata ‘Limelight’ (43 mm), R. aromatica ‘Grolow’ (32 mm), and W. florida ‘Alexandra’ (23 mm). For Hydrangea arborescens
‘Abetwo’, H. paniculata ‘Limelight’, and R. aromatica ‘Gro-low’, DWU peaked above the
control irrigation rate for 19, 52, and 27 days, respectively. In contrast, many days
preceding or following peak days had DWU far below the control. Thus, the data reveal
that when intermittent sampling techniques are employed, under- or overestimation of
DWU could result in applying too little or too much water between irrigation rate
adjustments. Additionally, in October when temperatures and insolation fell to their
lowest for the study (Figure 1.3), DWU did not peak above the control application rate
for any taxa except H. paniculata ‘Limelight’, so the control rate was excessive in the
majority of cases for the month.
Since DWU was based on the highest water user each day in 2010, lower- use
taxa such as both Syringa species and V. dentatum ‘Ralph Senior’ were over-irrigated
to varying degrees. In addition, these taxa never required more water than the control
57

100

A)

NS

80

NS

NS

NS

NS

NS

A
A
A
B

NS

60

NS

40 NS
20
0
100

B)

Plant Growth Index (cm)

80

NS

NS

60

NS

40 NS
20

0
100

C)
NS

80

NS

NS
NS
Day of Treatment

NS

60

NS

40 NS
20
0
100

D)

80

NS

NS

60
Aug
Control

80

NS

NS

60
40 NS

NS

NS

20
0
Day 0
Month
Kcc

20
Jul
DWU

40

Sep
100 DWU

58

100

70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0

Kc and Daily Water Use (mm)

Figure 1.5. Plant Growth Index (GI), Daily Crop Coefficient (KC), and Daily Water Use
(DWU) from 23 June (Day 1) to 31 October 2010 (Day 131) for A) Hydrangea
arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’, C) Rhus aromatica ‘GroLow’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri ‘Palibin’, F) Syringa
xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers.

120
Oct
100-75
100-75-75

100

E)

80
60
40

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

20

0
100

F)

Plant Growth Index (cm)

80
60
40 NS
20

0
100

NS

G)

80
60
40 NS
20

0
100

NS

NS

H)

80
60
40 NS

NS

NS

NS

NS

20

0
Day 0
Month
Kcc

20
Jul
DWU

40

60

80
100
Aug
Sep
Control
100 DWU
100-75
Accumulated Days of Treatment

70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0
70
60
50
40
30
20
10
0

Kc and Daily Water Use (mm)

Figure 1.5 (cont’d)

120
Oct
100-75-75

Left y-axis indicates PGI (lines). Control = 19 mm.application-1; 100DWU = 100% daily
water use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU
and 75% DWU; and 100-75-75 = 3 day cycle with 100% DWU replacement the first day
then 2 days 75% DWU replacement. Each day was analyzed separately (Tukey’s test,
α = 0.05). Means followed by the same letters are not different. NS = not significant.

59

Figure 1.5 (cont’d)
n=18. Right y-axis indicates DWU where the entire bars represent DWU (mm)
averaged from all treatments (n=12); shaded portions of bars represent daily Kc (DWU :
ET0, n=12). Overhead irrigation scheduling based on highest DWU of the 8 taxa in
each treatment replicate each day. Dotted horizontal line indicates control treatment of
19mm.application-1. Daily ET0 values for calculation of Kc obtained from the Enviroweather Automated Weather Station Network.

throughout the season. Had irrigation been individually applied to all taxa based on
their respective DWU, water savings could have been 88%, 92%, and 89% for S.
xhyacinthiflora ‘Evangeline’ in the 100DWU, 100-75, and 100-75-75 treatments
(considering both fraction and day cycle), respectively. A moderate water use species
such as S. media ‘Wilma’ could have saved 73%, 79%, and 55% in its respective
100DWU, 100-75, and 100-75-75 treatments where in the same treatments an even
heavier user such as R. aromatica ‘Gro-low’ could save 33%, 30%, and 74.5%,
respectively. Conversely, H. paniculata ‘Limelight’ would have received 3.2% more
water in the 100-75 treatment than the control. Thus, in addition to saving water for low
and moderate-use taxa, irrigation scheduling based on DWU accommodates the
demands of high water users to ensure they are not under-watered.
In both 2009 and 2010, plant GI steadily increased in June and July, tapered between
August and September, and reached a plateau in September to October. In 2009, H.
paniculata ‘Limelight’ control plants were smaller than the 100DWU and 100-75-75
treatment plants on days 46 and 79. From day 52 to 74, plants in the 100DWU and
100-75-75 treatments received 34.5 L and 28.8 L compared to the control, which
received 23.5 L. At the conclusion of the study, GI for H. paniculata ‘Limelight’ was still
lower in the control than 100DWU treatment even though more water was applied to the

60

control over the season at 134.1 L versus the 100DWU at 124.7 L (Table 1.1). This
higher growth in the 100DWU treatment may have resulted from better timing of water
application volumes during the plant’s growth or physiological development compared to
the steady control rate. When DWU scheduling was conducted in real time, no growth
differences were observed on any measurement days throughout 2010 except the final
day. On day 108, H. paniculata ‘Limelight’, the largest-growing taxa overall, showed
reduced growth in the 100-75-75 treatment compared to other DWU treatments or the
control. Under production conditions, all plants would have been considered
marketable. Contrasting the two scheduling methods used in 2009 and 2010,
intermittent DWU quantification risks over- or under-estimation of crop water needs for
the interval between measurements whereas real-time estimation compensates for
changes in DWU at daily resolution. Still, substantial water reductions were realized
using intermittent measurements without reductions in growth in this study or by
Warsaw et al. (2009a). Others have demonstrated substantial reductions in water
applications when irrigating based on ETA without sacrificing growth. Growing in 2.4 L
containers, Viburnum odoratissimum grew to the same size using 39% less water when
irrigated based on ETA-based compared to a fixed irrigation rate of 1 cm.d-1 (Million et
al., 2010). In Ligustrum japonica growing in 11.4 L containers, 1690 mm of irrigation
was applied based on ETA and produced marketable plants 3 weeks faster than a
manual total application volume of 2100 mm (Beeson and Brooks, 2008). Real-time
irrigation scheduling using DWU shortened the production of Gardenia jasminoides by
as much 64% at one production nursery in Georgia by increasing its rate of growth

61

(Chappell et al., 2013). Other incentives for sensor adoption include shortened crop
production windows that free up space for new crops sooner, reduced losses from
disease, and increased overall crop profitability (Lichtenberg et al., 2013).
In a study of 24 ornamental shrub taxa, Warsaw et al. (2009a) showed only one
taxon in a deficit DWU treatment that grew less than the plants receiving 100% of their
DWU. Their findings and those from the current study strongly suggest that a buffer
exists in many ornamental plant species against light to moderate moisture deficits
before growth reductions occur. Although tension curves were not developed for the 85
pine bark : 15 peat moss (vol : vol) substrate used in this study, SVWC typically ranged
from 0.32 (vol : vol) at CC to a minimum of around 0.15 before irrigation occurred.
Since only H. paniculata ‘Limelight’ showed a reduction in growth in the greatest deficit
(100-75-75) treatment, this would indicate that substrate moisture usually remained
within the range of easily available water (EAW) (Lea-Cox, et al., 2011), while perhaps
falling into the water buffering range on occasion.

Crop coefficients
For all taxa in 2009, DWU and KC peaked mid-summer on 6 Aug. (ET0=4.27
mm) and 28 Aug. (ET0=1.03 mm) (Figure 1.4, shaded portion of bars). Season average
KC in 2009 ranged from 3.2 and 3.6 for T. plicata ‘Grovepli’ and I. virginica ‘Morton’ to
5.0 and 5.3 for W. florida ‘Alexandra’ and H. paniculata ‘Limelight’. Under the
classification system used by Warsaw et al. (2009a), all taxa were high water users in

62

Figure 1.6. Monthly Crop Coefficient (KC) from 11 June (Day 1) to 14 October 2009
(Day 126) for A) Aronia arbutifolia ‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C)
Hydrangea paniculata ‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus opulifolius
‘Seward’, F) Spiraea media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers.
10
8

A)

6
NS
4

NS

NS

NS

NS

2
0
10

Crop Coefficient (DWU : ET0)

8

B)

NS

NS

6

NS

4

NS

A

B

AB AB

2

0
10
8

A

C)

NS
AB
B

B

6
A A

4

NS

NS

B AB

2
0
10

8

D)

A
NS

6
4

AB B

A AB

AB AB

NS
B

NS

2

0
Month

Jun

Jul
Control

Aug
100DWU
100-75

63

Sep
100-75-75

Oct

Figure 1.6 (cont’d)
10
8

E)

NS

NS
NS

6
4

NS

NS

2
0
10
8

F)

Crop Coefficient (DWU : ET0)

NS

NS

6

NS

4

NS

A AB
AB B

2

0
10
8

G)

6
4

AB

B

NS

NS

A
AB

NS

NS
B

2

0
10
8

A

H)

6
4

NS

AB

2
0
Month

B AB

Jun

A

B

AB A
AB

B

AB
AB

A AB AB
B

Jul
Control

Aug
100DWU
100-75

Sep
100-75-75

Oct

Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each month was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. Jun, Jul, Sep, and
Oct: n=18, Aug: n=36. Overhead irrigation scheduling based on lowest DWU of the 8
taxa during each measurement period; remaining water requirement supplied by hand
64

Figure 1.6 (cont’d)
each day as necessary. Daily ET0 values for calculation of KC obtained from the
Enviro-weather Automated Weather Station Network.

2009 (KC >3.0). Similarly, in 2010, DWU peaked mid-summer on 16 Aug. (ET0=4.92
mm), while highest KC of 11.3 was observed on 11 Sep. (ET0= 0.71 mm) for both H.
arborescens ‘Abetwo’ and H. paniculata ‘Limelight’. Highest KC was also recorded on 8
Sep. for R. aromatica ‘Gro-Low’ at 9.9 (ET0=2.0 mm) (Figure 1.5). These three taxa
were all high water users in 2010 with season average KC of 4.3, 5.4, and 4.1,
respectively. Spiraea fritschiana ‘Wilma’, V. dentatum ‘Ralph Senior’, and W. florida
‘Alexandra’ were moderate users with KC values of 2.8, 2.0, and 2.6, respectively. Low
water users included S. xhyacinthiflora ‘Evangeline’ and S. meyeri ‘Palibin’ with average
KC of 1.8 and 1.9.
Because KC values normalize for environmental variables (Allen et al., 1998),
cross-season comparisons can be for the identical taxa. Hydrangea paniculata
‘Limelight’ was a high user in both 2009 and 2010 whereas W. florida ‘Alexandra’ was
classified as a high water user in 2009, but only a moderate user in 2010. Using the
same production site as the current study, Warsaw et al. (2009a) rated C. sericea
‘Farrow’ a high user at KC of 3.4 in 2006 whereas it was rated KC of 4.4 in 2009 by the
current study (cumulative ET0= 393.7 mm in 2006 VS 413.7 in 2009). In 2007, Warsaw
et al., (2009a) rated both S. fritschiana ‘Wilma’ and W. florida ‘Alexandra’ high users at
65

Figure 1.7. Monthly Crop Coefficient (KC) from 23 June (Day 1) to 31 October 2010
(Day 131) for A) Hydrangea arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’,
C) Rhus aromatica ‘Gro-Low’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri
‘Palibin’, F) Syringa xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’,
and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers.
10
A
A
8 A)
A
B
B
6
A
BC
BC
B B
B
C
4
C
C
AB A
C
C
AB
2
B
0
10

Crop Coefficient (DWU : ET0)

8

A

B)
A
B

B

6

BC
C

AB

A

AB

A
B

B

4
2

AB

A A

AB AB
B

A
A

0
10
8

C)

6
4
2

A

A

AB

A

B

A

AB AB A
B

B

A AB AB

C

A A
AB

B

B

0
10

8

D)

6

A
A A

4
2

A A AB

0
Month

Jun

A

B B

B

B

A
B

B

B

A A AB

B

Jul
Control

Aug
100DWU
100-75

66

Sep
100-75-75

Oct

B

Figure 1.7 (cont’d)
10
8

E)

6
4

2

NS

A
AB A B AB

A A
B

AB
C

A
B

BC B

B

AB

0
10
8

F)

Crop Coefficient (DWU : ET0)

6
A

4
2

A

B B B

A

B B B

B B B

A

A

B B B

B B B

0
10
8

G)

6
4
NS

2

NS

AB
A
B
AB

A

NS
B

B

B

0
10
8

H)

6

A

A

4

A

2

NS

0
Month

Jun

AB
BC
C

Jul
Control

B B

Aug
100DWU
100-75

A

AB
BC C

Sep
100-75-75

A

A
B B

Oct

Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each month was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. Jun: n=24 (except
100-75-75 treatment, n=16). Jul, Aug, and Oct: n= 93 (except 100-75-75, n= 62). Sep:
n= 90 (except 100-75-75, n=60). Overhead irrigation scheduling based on highest
67

Figure 1.7 (cont’d)
DWU of the 8 taxa in each treatment replicate each day. Daily ET0 values for
calculation of Kc obtained from the Enviro-weather Automated Weather Station
Network.

KC =3.6 while these two taxa were rated moderate users at KC = 2.8 and 2.6 in the
current study during 2010 (cumulative ET0= 490.1 mm in 2007 VS 430.8 in 2010). The
values reported by Warsaw et al. (2009a) for identical taxa are likely higher than those
reported in this study because cumulative ET0 was higher in 2007 than 2010.
Therefore, when making comparisons of KC, the effect of both season duration and
cumulative ET0 should also be considered.
Irrigation treatments had an effect on KC. When both irrigation applications and
GI were higher in the 100DWU treatment than the control, H. paniculata ‘Limelight’, the
plant with highest 2009 season average KC, exhibited higher KC in the 100DWU than
control and 100-75 treatments in August. In July through October 2010, the high water
use taxa, H. arborescens ‘Abetwo’, H. paniculata ‘Limelight’, and R. aromatica ‘GroLow’ had highest KC in the 100-75 treatment (Figure 1.8), which also had the highest
total water applications (Table 1.1). In contrast, low and moderate use species tended
to show lowest KC at higher irrigation rates. In June 2009, the lowest water users of the
season, I. virginica ‘Morton’ and T. plicata ‘Grovepli’, each had lower KC in the 100DWU
than at least one other deficit treatment. In August 2010, the 100-75 treatment, which
received the most water, had KC values of 1.0, 1.2, and 1.7 for S. meyeri Palibin’, S.
68

xhyacinthiflora ‘Evangeline’, and V. dentatum ‘Ralph Senior’. Because irrigation
applications were based on the highest user in each treatment replicate in 2010, these
taxa were over-watered most days throughout 2010, particularly mid-season when KC
peaked for high users.
In annual field crops, KC begins increasing early in the season as leaf area index
increases (USDA, 1993). Water use continues to increase through the rapid growth
stage followed by a water use ceiling at full canopy cover. Late in the season, water
use decreases in annual crops as they near harvest. During the current study, K C
began low and increased until plants reached their growth plateaus in mid-summer
(Figures 1.7 and 1.8). At this point, KC remained fairly stable for the rest of the season.
Others have shown similar responses in ornamental shrubs with low K C increasing as
plants grow and develop, then stabilizing for the rest of the season (Niu et al., 2006).
Calendar month serves as a convenient interval to reevaluate KC for seasonal
changes in plant growth or environmental conditions (Schuch and Burger, 1997). In
both 2009 and 2010, most taxa tended to show lowest KC in June or July (Tables A-1
and A-2) when GI was also low (Figures 1.4 and 1.5). Crop coefficients of high water
use plants varied by as much as 3.7 between months and were often highest as plants
neared maximum seasonal growth in August to October. However, while KC of high
water users tended to increase between June and August, KC was more static in low
water use taxa. Similarly, Schuch and Burger (1997) found that KC’s of low water use

69

ornamental shrubs grown in containers are relatively consistent across warm and cool
phases of a production cycle and thus serve as effective estimators of actual plant water
use. While DWU fell in all plants from late August to October, KC remained high during
these times. Schuch and Burger (1997) showed a similar relationship between water
use and KC. The current study reveals that higher water use plants exhibit greater
increases in KC during active growth midseason than do lower water users, which
remain relatively constant. Therefore, greatest benefit may be realized by grouping
plants into IFG’s having similar KC values for periods of establishment, full canopy
cover, and quiescence rather than just a single, season-wide average.

Water Use Efficiency
The equation used by Jarrell et al. (1983), Knox (1989), and Warsaw et al.
(2009a and 2009b) to calculate WUE considers volume of water applied over the course
of an entire production cycle for container-grown ornamental shrubs. However,
because plants in the 2010 season were over-irrigated in the current study, WUE could
be grossly underestimated in lower-use or smaller plants. Consequently, we used:
WUE = GII (cm) / [taxon specific DWU (L.container-1)].
where GII= growth index increase from beginning of the season to the final
measurement. This represents a sort of “potential” WUE as it shows how efficient
plants would have been if only their required DWU had been applied as irrigation.

70

In 2009, the low water use taxa A. arbutifolia ‘Brilliantissima’, I. virginica ‘Morton’,
and T. plicata ‘Grovepli’ all exhibited higher WUE in the 100DWU treatment than the
control (Table 1.3). These taxa also generally had a lower GII compared to higher
water use taxa. Additionally, taxa can be ranked according to WUE by comparing
differences between taxa within each treatment. Compared to the other taxa,
Physocarpus opulifolius ‘Seward’ had high WUE across all treatments and was more
water efficient in the 100-75-75 than both C. sericea ‘Farrow’ and H. paniculata
‘Limelight’. It also had the highest WUE of any taxa in the 100-75 treatment. The three
high water use taxa H. paniculata ‘Limelight’, P. opulifolius ‘Seward’, and W. florida
‘Alexandra’ did not differ in WUE for the control or 100 DWU treatments. Cornus sericea
‘Farrow’ also showed high WUE in the 100DWU treatment. In 2010, no differences in
WUE between treatments occurred, and only one difference occurred between species.
Weigela florida ‘Alexandra’ had greater WUE in the 100DWU treatment compared to S.
meyeri ‘Palibin’. Only V. dentatum ‘Ralph Senior’ showed a difference in WUEDW
where the 100-75 treatment was lower than all other treatments (Table 1.4).
Taxa with greater overall GI also tended to have higher WUE than those with
lower GI. Similarly, Knox (1989) showed WUE as high as 1.67 for Photinia xfraseri
Dress., a larger-growing taxa and as low as 0.58 for the smaller coniferous taxa,
Juniperus horizontalis Moench ‘Wiltonii’. In Texas, WUE of shrubs generally increased
in species showing larger or denser canopies (Still and Davies, 1993). In contrast,

71

Table 1.3. Water use efficiency as growth index (GI) increase : sum of DWU for season
(through final day of growth index measurement 2009: Day 124, 2010: Day 117) for
eight shrubs grown in 10.2 L containers under four irrigation treatments beginning 11
June 2009 and 23 June 2010.
Controlz

100DWU

100-75

100-75-75

2009
Aronia arbutifolia
'Brilliantissima'

0.491 Bybcx

0.676 Aa

0.610 ABab

0.562 ABab

Cornus sericea 'Farrow'

0.373 Acd

0.891 Aa

0.467 Abc

0.508 Ab

Hydrangea paniculata
'Limelight'

0.631 Aab

0.486 Aab

0.512 Abc

0.498 Ab

Itea virginica 'Morton'

0.338 Bcde

0.512 Aab

0.445 ABc

0.466 ABb

Physocarpus opulifolius
'Seward'

0.762 Aa

0.853 Aa

0.764 Aa

0.714 Aa

Spiraea media 'Darsnorm'

0.203 ABde

0.259 Ab

0.183 Bd

0.201 ABc

Thuja plicata 'Grovepli'

0.140 Be

0.242 Ab

0.207 ABd

0.175 ABc

Weigela florida 'Alexandra'

0.489 Abc

0.580 Aab

0.536 Abc

0.529 Aab

0.621 Aa

0.782 Aab

0.411 Aa

0.916 Aa

0.742 Aa

0.630 Aab

0.374 Aa

0.997 Aa

Rhus aromatica 'Gro-Low'

0.983 Aa

0.770 Aab

0.623 Aa

1.009 Aa

Spiraea fritschiana 'Wilma'

0.495 Aa

0.368 Aab

0.599 Aa

0.805 Aa

Syringa meyeri 'Palibin'

0.563 Aa

0.289 Ab

0.792 Aa

0.550 Aa

Syringa xhyacinthiflora
'Evangeline'

0.157 Aa

0.373 Aab

0.316 Aa

0.370 Aa

Viburnum dentatum 'Ralph
Senior'

0.821 Aa

0.750 Aab

0.999 Aa

0.617 Aa

Weigela florida 'Alexandra'

0.640 Aa

1.154 Aa

1.016 Aa

0.653 Aa

Taxa

2010
Hydrangea arborescens
'Abetwo'
Hydrangea paniculata
'Limelight'

72

Table 1.3 (cont’d)
z

Control = 19 mm.application-1 (1.02 L.day-1); 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Overhead irrigation scheduling based on 2009) lowest DWU of
the 8 taxa during each measurement period with remaining water requirement supplied
by hand each day in DWU treatments as necessary; 2010) highest DWU of the 8 taxa in
each treatment replicate each day.
y

Comparisons between means in each row are marked with capital letters. Means of
the same letters are not different; Tukey’s Test (α = 0.05), 2009: n = 18, 2010: n=3.
x

Comparisons between means in each column are marked with lowercase letters.
Means of the same letters are not different; Tukey’s Test (α = 0.05), 2009: n = 18, 2010:
n=3.

Warsaw et al. (2009a), using GI, attributed most differences in WUE to varying irrigation
volumes. Warsaw, et al. (2009b) observed that three of four species had higher WUE in
their most water limiting 100-75-75 treatment than the control, very similar to the
findings of the current study. Roberts and Schnipke (1987) reported that among 5
species of Acer, those having the fastest growth rates also had the highest relative
water demand (which was calculated the same way as WUE in the current study).
These findings support the current study in which DWU irrigation or other plant-based
metrics generally improved WUE compared to irrigation regimes not based on plant
water demand.

73

Table 1.4. Top dry weight and dry weight water use efficiency (WUEdw) (season
change in top dry weight in grams : sum of DWU plus precipitation in kg) of three taxa
collected at the termination of the study in 2010. Irrigation treatments were imposed for
131 d from 23 June to 31 October 2010.
Treatment
Controlz

100DWU

100-75

100-75-75

146.7 Ay

138.3 A

128.0 A

127.9 A

1.78 A

1.31 A

1.31 A

2.55 A

Viburnum dentatum 'Ralph Senior'
Shoot Dry Weight (g)
57.4 A

71.4 A

71.9 A

59.2 A

1.62 A

1.75 A

2.26 B

1.21 A

156.4 A

143.8 A

149.0 A

165.3 A

2.31 A

4.07 A

3.88 A

2.63 A

Rhus aromatica 'Gro-Low'
Shoot Dry Weight (g)
WUEdw (g/kg)

WUEdw (g/kg)
Weigela florida 'Alexandra'
Shoot Dry Weight (g)
WUEdw (g/kg)
z

Control = 19 mm.application-1 (1.02 L.day-1); 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Overhead irrigation scheduling based on highest DWU of the
8 taxa in each treatment replicate each day.
y

Comparisons between means in each row are marked with capital letters. Means of
the same letters are not different; Tukey’s Test (α = 0.05), n=9.

Leachate Electrical Conductivity and pH
When using controlled-release fertilizer (CRF) on container-grown nursery crops,
desirable substrate leachate EC levels range from 0.8 to 1.5 dS.m-1 during periods of
active growth (Yeager, 2003). No differences were found in EC between treatments in
any taxa except T. plicata ‘Grovepli’ on day 16 when leachate EC was higher in the

74

Figure 1.8. PourThru leachate electrical conductivity (EC) (dS.m-1) for A) Aronia
arbutifolia ‘Brilliantissima’, B) Cornus sericea ‘Farrow’, C) Hydrangea paniculata
‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus opulifolius ‘Seward’, F) Spiraea
media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H) Weigela florida ‘Alexandra’ grown
in 10.2 L containers from 11 June (Day 1) to 14 October 2009 (Day 126).

2.5
10
2

A)

1.58

E)

B)
NS

16

0.54

Control
100-75
NS

NS

NS

NS

NS

NS

NS

100DWU
100-75-75
NS

NS

NS

2.5
02
10
20

C)
F)

EC (ds m-1)
Crop Coefficient (DWU : ET0)

1.58
NS
6
1
4
NS
0.5
2
2.5
0
0
10
2 E) NS
8 G)
1.5
6
1
A
4
AB AB
0.5
B
2
2.5
0
0
10
2.0 G)
8 H)
A
1.5
6 AB
1.0
B
4
AB B AB A
0.5
B
2
0.0
0
16
Month Jun

D)
D)

NS

NS
NS

NS

NS
NS

NS
NS

A AB
AB B

F)
F

NS

NS

NS

NS

NS

NS

NS
NS

NS
B

H)
H)

A

NS
NS NS
A AB
B

62

Jul
Control

NS
AB

125

16
Day
Aug
100DWU
100-75

75

B

AB A
AB
NS

62
Sep
100-75-75

AB
B NSAB

125
Oct

Figure 1.8 (cont’d)
Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on lowest DWU of the 8 taxa during each measurement
period with remaining water requirement supplied by hand each day in DWU treatments
as necessary.

100-75-75 treatment than the other DWU treatments (Figure 1.8). EC remained above
the minimum recommended threshold of 0.8 dS.m-1 for all taxa throughout the
experiment except I. virginica ‘Morton’ and S. media ‘Darsnorm’ on day 125. None
exceeded the upper limits during the season. In 2010, EC was almost double the
recommended levels on day 2 for all taxa except the Syringa species (Figure 1.9). By
day 34, EC had fallen below the upper threshold of 1.5 in most taxa. The only
difference in EC occurred on day 59 in V. dentatum ‘Ralph Senior’ when the control and
100-75-75 treatments were higher than the 100-75, which received more water than any
other treatment. Of 24 shrubs studied, Warsaw et al. (2009a) reported few differences
in EC except in the highest water treatment (control), which was higher than DWUbased treatments. Million et al. (2007) demonstrated that an EC of <0.5 dS.m-1 caused
reductions in growth of Viburnum odoratissimum L. (high nutrient and water user);
however, no treatment differences in GI or EC support this in the current study. Another
source suggests that EC of 0.5 dS.m-1 is adequate late in the growing season
(Bilderback et al., 1999), which is close to the levels observed for 2010 in the current
study. Overall, end-of-season EC levels were likely lower in 2010 than 2009 because

76

Figure 1.9. PourThru leachate electrical conductivity (EC) (dS.m-1) for A) Hydrangea
arborescens ‘Abetwo’, B) Hydrangea paniculata ‘Limelight’, C) Rhus aromatica ‘GroLow’, D) Spiraea fritschiana ‘Wilma’, E) Syringa meyeri ‘Palibin’, F) Syringa
xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum ‘Ralph Senior’, and H) Weigela
florida ‘Alexandra’ grown in 10.2 L containers from 23 June (Day 1) to 31 October (Day
131).
6.0
5.0
10

A)

4.08
3.0
6
2.0
4
1.0
6.02
0.0

E)
NS

0
10
5.0

C)
F)
NS

4.08

EC (ds m-1)
Crop Coefficient (DWU : ET0)

3.06
2.0
4
1.0
2
6.0
0.0
0
10
5.0
4.08
3.06
2.0
4
1.0
2
6.0
0.0
0
10
5.0
4.08
3.06

B)
NS
NS

NS
NS

NS

NSNS NS

NS

NS NS
NS

NS

NS
NS D) NS

NS NS

NS
NS

ANS
ABABNS
B

NS

NS

NS
NS

NS

NS NS

NS

NS

E)
G)

F)

NS
NS A
AB AB NS
B

G)
H)
NS

NS
NS

NSNS NS
B

NS

NS

NS

NS

NS

NS

NS

NS

H)
NS

A

NS
NS

NS

NS

AB A
AB
B
NS
NS

A A
2.04
AAB
AB AB A
AB
AB NS
NS
B
B B
1.0
2
0.0
0
2
17
34
59
79 111
2
17
34
59
Day
Jul
Aug
Sep
Month Jun
Control
100DWU
100-75
100-75-75

77

AB
B AB

NS

NS

79

111
Oct

Figure 1.9 (cont’d)
Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on highest DWU of the 8 taxa in each treatment replicate
each day.

all plants were irrigated at the same level as the highest user which resulted in leaching
from the lower users. Excess irrigation leaches both salts and nutrients from substrates
and can lead to plant nutrient deficiencies (Bilderback, et al., 1999) and increased
nutrient loading in effluent, which leads to water quality concerns (Warsaw et al.,
2009b). Although not observed in this study or by Warsaw et al. (2009a), EC can reach
potentially harmful levels in plants subjected to low-leaching irrigation regimes;
therefore, EC should monitored regularly under these conditions.
Substrate leachate pH remained fairly consistent in 2009, but several differences
occurred between treatments (Figure 1.10). The control exceeded pH of the 100-75
treatment on day 62 for C. sericea ‘Farrow’. On days 62 and 125, the control had
higher pH than the 100-75-75 treatment for I. virginica ‘Morton’. The control and
100DWU were greater than the 100-75-75 treatment on day 125 for I. virginica ‘Morton’
and on day 62 for W. florida ‘Alexandra’. For every observed difference in pH, the
control was higher than at least one DWU treatment. In 2010, similar results were
observed when Syringa meyeri ‘Palibin’ showed higher pH in the 100DWU treatment
than the control on day 79 and higher pH in the 100DWU and 100-75 treatments than
100-75-75 treatment on day 111. Both the 100DWU and 100-75-75 treatments
received more water than those with low pH. In addition, pH increased throughout the

78

Figure 1.10. Leachate pH for A) Aronia arbutifolia ‘Brilliantissima’, B) Cornus sericea
‘Farrow’, C) Hydrangea paniculata ‘Limelight’, D) Itea virginica ‘Morton’, E) Physocarpus
opulifolius ‘Seward’, F) Spiraea media ‘Darsnorm’, G) Thuja plicata ‘Grovepli’, and H)
Weigela florida ‘Alexandra’ grown in 10.2 L containers from 11 June (Day 1) to 14
October 2009 (Day 126).
8.5
8.0
10

A)

7.58
7.0
6
6.5
4
6.0
8.52
5.5

E) NS

0
10
8.0

C)
F)

7.06
6.5
4
6.0
2
8.5
5.5
0
10
8.0
Crop Coefficient (DWU : ET0)

pH

7.58

7.58
7.06
6.5
4
6.0
2
8.5
5.5
0
10
8.0
7.58

B)
NS
NS

NS

NS

A
NS
AB
B

NS

AB

NS
NS

NS

NS
D)

NS

NS

NS

AB

NS
NS

E)
G)

F)

NS
NS

NS
NS

A
AB

B

AB

AA
AB
NS
B

NS BC
C

A AB
AB B

NS

A

NS
NS
NS

NS

NS
B

G)
H)

H)
NS

NS

7.06
6.54
AB AB A
B
6.0
2
5.5
0
16
Month Jun

NS
A AB

62

NS
B

Jul
Control

NS

AA
AB AB
A
AB
B
B

NS A
AB
B AB

AB

125

16
Day
Aug
100DWU
100-75

79

62
Sep
100-75-75

125
Oct

Figure 1.10 (cont’d)
Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on lowest DWU of the 8 taxa during each measurement
period with remaining water requirement supplied by hand each day in DWU treatments
as necessary.

2010 season. The last known irrigation water test at the MSU HTRC was taken in 2007
by Warsaw et al. (2009a). They reported mildly basic pH of 7.7, which was close to the
final leachate pH observed in nearly every treatment-taxa combination by the end of the
study. The basicity of the irrigation water, combined with known high alkalinity in the
area, likely contributed to this increase in substrate pH. Burnett and van Iersel (2008)
reported similar increase in pH when using basic, alkaline water during an irrigation
study with herbaceous perennials.

80

9.0
8.5
10 A)
B)
NS
NS NS
8.0
NS NS
E)
NS
A
7.58
NS
NS
AB
NS
7.06
BC
NS C
6.5 NS
4
NS
NS
6.0
2
9.0
5.5
8.5
0 C)
10
D)
NS
NS
NS
8.0
NS
NS
NS
8 F)
A
7.5
AB
NS
NS
7.06
NS
BB
NS
NS
6.54
NS
A AB
AB B
6.0
2
9.0
5.5
8.5
0 E)
10
F)
A
8.0
A AB A
8 G)
NS NS
AB
NS NS
NS
7.5
AB B
NS
B
NS
7.06
NS
NS
A
NS
6.54
AB AB
NS
B
6.0
B
2
9.0
5.5
0
10
8.5
H)
G)
NS
8.08 H)
AB
NS NS
A NS NS
A
AB
A
7.5
B
NS ABAB A
6
NS
B
7.0
NS
B
AB
A
6.54
NS
A AB AB
AB AB
B
B
6.02
5.5
0
2
17
34
59
79 111
2
17
34
59
Day
Jul
Aug
Sep
Month Jun
Control
100DWU
100-75
100-75-75
Crop Coefficient (DWU : ET0)

pH

Figure 1.11. Leachate pH for A) Hydrangea arborescens ‘Abetwo’, B) Hydrangea
paniculata ‘Limelight’, C) Rhus aromatica ‘Gro-Low’, D) Spiraea fritschiana ‘Wilma’, E)
Syringa meyeri ‘Palibin’, F) Syringa xhyacinthiflora ‘Evangeline’, G) Viburnum dentatum
‘Ralph Senior’, and H) Weigela florida ‘Alexandra’ grown in 10.2 L containers from 23
June (Day 1) to 31 October 2010 (Day 131).

81

NS
NS
NS

NS

NS
NS

NS
NS
NS

NS
A

NSAB
B AB

79

111
Oct

Figure 1.11 (cont’d)
Control = 19 mm.application-1; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on highest DWU of the 8 taxa in each treatment replicate
each day.

Foliar Nutrient Analysis
Analysis of foliar nutrient levels revealed higher %P and / or %K in at least one of
the DWU treatments compared to the control for every taxa on the second sampling
date (Table 1.5) in 2009. Additionally, I. virginica ‘Morton’ showed higher %P in the
100-75-75 treatment than the control on day 63. Since the control received the most
water of any irrigation treatment in 2009, this difference could be attributable to either
high irrigation levels leaching nutrients from the substrate or nutrient dilution within the
plant, or a combination of both. Although no taxa fell below the minimum recommended
foliar %N level (Plank, 2008) during 2009, %K was low in the control on day 90 in every
taxa except S. media ‘Darsnorm’. Additionally, %P was low in the largest growing taxa,
H. paniculata ‘Limelight’ on day 90. However, no visual nutrient deficiency symptoms
were observed, and plant quality appeared acceptable in every taxa. In 2010, only one
difference occurred where foliar %N was lower in the 100-75 than 100DWU treatment
for H. paniculata ‘Limelight’.

Since the 100-75 treatment received highest irrigation

82

Table 1.5. Foliar nutrient content (% dry wt.) sampled on days 63 and 90 of five taxa
grown in 10.2 L containers and subject to four irrigation treatments from 11 June (Day
1) to 14 October 2009 (Day 126).
Foliar Nutrient Content
Controlz

Recommended

100DWU
100-75
100-75-75
Hydrangea paniculata 'Limelight'

Rangey

Day 63
N (%)
P (%)
K (%)
Day 90
N (%)
P (%)
K (%)
Day 63
N (%)
P (%)
K (%)
Day 90
N (%)
P (%)
K (%)
Day 63
N (%)
P (%)
K (%)
Day 90
N (%)
P (%)
K (%)
Day 63
N (%)
P (%)
K (%)
Day 90
N (%)
P (%)
K (%)

2.87 Ay
0.24 A
1.65 A

2.88 A
0.29 A
2.23 A

2.24 A
0.14 B
0.41 B

2.35 A
2.38 A
2.31 A
0.17 AB
0.18 A
0.17 AB
0.65 A
0.61 AB
0.67 A
Itea virginica 'Morton'

2.50 A
0.22 A
0.65 A

2.69 A
0.22 A
0.55 A

2.37 A
0.16 B
0.48 A

2.99 A
0.30 A
2.07 A

2.46 A
0.22 A
0.58 A

2.96 A
0.29 A
2.07 A

2.65 A
0.24 A
0.66 A

2 - 4.5
0.2 - 0.6
1.5 - 3.5

2 - 4.5
0.2 - 0.6
1.5 - 3.5

2.74 A
2.59 A
2.55 A
0.20 AB
0.20 AB
0.21 A
0.53 A
0.54 A
0.55 A
Physocarpus opulifolius 'Seward'

3.19 A
0.31 B
1.09 B

3.19 A
0.37 A
1.46 A

3.19 A
0.37 A
1.59 A

2.15 A
0.21 B
0.38 B

2.20 A
2.28 A
2.28 A
0.23 AB
0.25 A
0.24 A
0.41 A
0.45 A
0.42 A
Spiraea media 'Darsnorm'

2.27 A
0.63 A
1.26 A

2.38 A
0.67 A
1.63 A

2.23 A
0.66 A
1.66 A

2.42 A
0.66 A
1.64 A

2.50 A
0.72 B
1.14 B

2.70 A
0.81 AB
1.39 AB

2.63 A
0.87 A
1.52 A

2.74 A
0.81 AB
1.32 AB

83

3.33 A
0.39 A
1.66 A

2 - 4.5
0.2 - 0.6
1.5 - 3.5

2 - 4.5
0.2 - 0.6
1.5 - 3.5

Table 1.5 (cont’d)
Foliar Nutrient Content (continued)
Control

100DWU
100-75
100-75-75
Weigela florida 'Alexandra'

Recommended
Rangey

Day 63
2 - 4.5
N (%)
2.05 A
2.12 A
2.20 A
2.21 A
0.2 - 0.6
P (%)
0.34 A
0.37 A
0.38 A
0.39 A
1.5 - 3.5
K (%)
1.91 B
2.38 A
2.31 AB
2.55 A
Day 90
N (%)
2.18 A
2.02 A
2.06 A
2.05 A
P (%)
0.30 A
0.35 A
0.38 A
0.40 A
K (%)
0.98 A
1.18 A
1.11 A
1.21 A
z
.
-1
Control = 19 mm application ; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on lowest DWU of the 8 taxa during each measurement
period with remaining water requirement supplied by hand each day in DWU treatments
as necessary.
y

Nutrient recommendations for woody ornamental plants obtained from Plank (2008).

applications in 2010, this also likely caused the %N reduction in this treatment. Many
nutrients were below recommended levels on day 64, even in the 100-75-75 treatment,
which received less water than any other treatment. Since cumulative precipitation was
lower in 2010 than 2009 (Figure 1.3), this cannot likely be attributed to leaching from
rainfall. However, average daily temperature was 19.82oC between days 1 and 64 in
2010 compared to 17.94oC the same calendar dates in 2009. Consequently, the
release rate of nutrients from the CRF was likely higher, contributing to this relatively
early seasonal depletion of foliar nutrients.

84

Table 1.6. Foliar nutrient content (% dry wt.) sampled on days 36 and 64 of six taxa
grown in 10.2 L containers and subject to four irrigation treatments from 23 June (Day
1) to 31 October 2010 (Day 131).
Foliar Nutrient Content
Controlz

Recommended

100DWU
100-75
100-75-75
Hydrangea arborescens 'Abetwo'

Rangey

Day 36
N (%)
P (%)
K (%)
Day 64
N (%)
P (%)
K (%)
Day 36
N (%)
P (%)
K (%)
Day 64
N (%)
P (%)
K (%)
Day 36
N (%)
P (%)
K (%)
Day 64
N (%)
P (%)
K (%)
Day 36
N (%)
P (%)
K (%)
Day 64
N (%)
P (%)
K (%)

2.69 Ay
0.26 A
1.09 A
1.72 A
0.18 A
1.09 A

2.73 A
0.32 A
1.47 A

2.72 A
0.32 A
1.46 A

2.60 A
0.32 A
1.44 A

2 - 4.5
0.2 - 0.6
1.5 - 3.5

1.58 A
1.59 A
1.33 A
0.21 A
0.20 A
0.23 A
1.47 A
1.46 A
1.44 A
Hydrangea paniculata 'Limelight'

2.60 AB
0.25 A
1.56 A

2.79 A
0.27 A
1.68 A

2.40 B
0.26 A
1.57 A

1.72 A
0.17 A
0.88 A

1.58 A
1.59 A
1.33 A
0.16 A
0.15 A
0.18 A
0.92 A
0.74 A
0.95 A
Rhus aromatica 'Gro-low'

1.95 A
0.29 A
1.84 A

2.02 A
0.30 A
2.02 A

1.76 A
0.20 A
0.52 A

1.65 A
1.79 A
1.55 A
0.20 A
0.20 A
0.19 A
0.72 A
0.60 A
0.54 A
Spiraea fritschiana 'Wilma'

3.17 A
0.47 A
1.26 A

3.17 A
0.41 A
1.12 A

3.00 A
0.51 A
1.09 A

3.08 A
0.43 A
1.23 A

2.81 A
0.34 A
1.04 A

2.66 A
0.33 A
1.01 A

2.61 A
0.34 A
0.88 A

2.51 A
0.34 A
1.09 A

2.05 A
0.30 A
2.05 A

85

2.52 AB
0.29 A
1.81 A

1.84 A
0.29 A
1.84 A

2 - 4.5
0.2 - 0.6
1.5 - 3.5

2 - 4.5
0.2 - 0.6
1.5 - 3.5

2 - 4.5
0.2 - 0.6
1.5 - 3.5

Table 1.6 (cont’d)
Foliar Nutrient Content (continued)
Controlz
Day 36
N (%)
P (%)
K (%)
Day 64
N (%)
P (%)
K (%)

100DWU
100-75
100-75-75
Viburnum dentatum 'Ralph Senior'

2.18 A
0.31 A
1.61 A

2.23 A
0.31 A
1.85 A

2.13 A
0.32 A
1.81 A

2.11 A
0.31 A
1.93 A

2.37 A
0.31 A
1.58 A

2.32 A
1.80 B
2.31 A
0.26 A
0.25 A
0.29 A
1.45 A
1.39 A
1.34 A
Weigela florida 'Alexandra'

Recommended
Rangey

2 - 4.5
0.2 - 0.6
1.5 - 3.5

Day 36
2 - 4.5
N (%)
2.12 A
1.98 A
1.82 A
1.94 A
0.2 - 0.6
P (%)
0.37 A
0.38 A
0.33 A
0.39 A
1.5 - 3.5
K (%)
1.71 A
1.91 A
1.67 A
1.81 A
Day 64
N (%)
1.89 A
1.70 A
1.70 A
1.69 A
P (%)
0.29 A
0.29 A
0.26 A
0.29 A
K (%)
1.13 A
1.37 A
1.19 A
1.26 A
z
.
-1
Control = 19 mm application ; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Each day was analyzed separately (Tukey’s test, α = 0.05). Means
followed by the same letters are not different. NS = not significant. n=3. Overhead
irrigation scheduling based on highest DWU of the 8 taxa in each treatment replicate
each day.
y

Nutrient recommendations for woody ornamental plants obtained from Plank (2008).

Similar to the current study, reductions in foliar nutrient concentrations occurred
at a higher irrigation leaching fraction (LF) of 0.4 compared to 0.1 in Ligustrum texanum,
which was ascribed to both nutrient dilution within plants at higher LF’s and lower K
quantities leaching from the substrate (Jarrell et al., 1983). Tyler et al. (1996) also
showed that a high LF of 0.4 - 0.6 increased N utilization in plant shoots. Additionally, P
86

content in tops of Cotoneaster dammeri C.K. Schneid. ‘Skogholm’ fertilized with CRF’s
decreased with increasing irrigation volume (Groves et al., 1998), which supports the
findings in the current study that higher irrigation rates tend to lower foliar nutrient
contents.

Conclusions
Less water was applied to plants in DWU water-based treatments in 2009, yet
growth was not negatively impacted compared to the control, nursery standard irrigation
rate of 19 mm.container.day-1. Hydrangea paniculata ‘Limelight’, a high water user,
grew larger in the 100DWU treatment than the control. When sensors were employed
for real-time calculation of DWU in 2010, the system determined the highest water user
of eight taxa each day to apply that DWU to the entire irrigation treatment replicate.
This resulted in over-watering of the other seven taxa, and consequently, more water
was applied in the 100DWU and 100-75 treatments than either the control or 100-75-75.
In 2010, the highest overall water user, H. paniculata ‘Limelight’ was 10% smaller than
the control in the 100-75-75, though it was the same size as the control in the milder
deficit of the 100-75 treatment. However, all plants maintained acceptable size,
appearance, and quality so that an average consumer would likely not perceive the
growth difference. Plants in low-volume treatments, particularly those that are smaller
or slower-growing, used water more efficiently than the higher volume treatments.
Additionally, substrate leachate pH generally increased over the course of production
seasons and was higher in the control irrigation rate in several instances. Foliar nutrient
%P and %K was higher in DWU-based treatments than the control in many cases

87

indicating that DWU-based irrigation regimes conserve nutrients. Reduced NO3--N and
PO43--P loading in effluent has also been reported in irrigation treatments (Chapter 3;
Warsaw et al., 2009b). Consequently, producers can reduce water inputs and manage
nutrition more effectively with DWU-based irrigation regimes.
Adding to the convenience of scheduling irrigation in real-time, valuable DWU
and KC data obtained by the system showed marked fluctuations from day to day for
container-grown shrubs and monthly KC changes. Seasonal KC for 10 new taxa add to
the growing number of published KC values for container-grown woody ornamentals
(Warsaw et al. 2009a and b; Schuch and Burger, 1997; Burger et al.,1987) further
helping to establish indicator species for low, moderate, and high water use
classifications that prove useful when grouping plants into irrigation blocks with similar
water needs. Additionally, KC increases during establishment of young plants and
generally climaxes midsummer through autumn for temperate, cold-hardy ornamental
shrubs produced in containers. Therefore, newly-published KC information should
include separate values for ornamentals during establishment, maximum seasonal
growth or full canopy cover, and quiescence to match KC during each period, not just
over the course of the entire season.
Grower-friendly systems are now being tested on commercial operations that
permit multiple sensor inputs in a multi-hop, self-configuring network capable of
transmitting data wirelessly managed through intuitive, yet powerful software (Lea-Cox

88

et al., 2008; Lea-cox et al., 2013). Used alone, these systems provide growers with
additional data to make more informed scheduling decisions on their own (Chappell et
al., 2013). However, they also permit full automation of the irrigation scheduling
process and have shown to reduce irrigation while increasing growth of ornamental
trees in pot-in-pot production compared to “experience”-based irrigation (Belayneh et
al., 2013). Although widespread adoption of these systems may take several years,
monitoring of every ornamental crop produced at most nurseries will not likely be
economically feasible. Therefore, knowledge of relative water use classifications,
derived by KC or other means, will be an essential part of effective sensor integration
into existing irrigation infrastructure to help growers maximize water use savings and
achieve greater crop profitability.

89

APPENDIX

90

Table A-1. Monthly crop coefficient (KC) from 11 June (Day 1) to 14 October 2009 (Day
126) for 8 shrub taxa grown in 10.2 L containers.
Crop Coefficient
Month

Controlz

June
July
August
September
October
Season

2.40 Ab
2.42 Ab
4.17 Aa
3.78 Aab
5.22 Aa
3.60 A

June
July
August
September
October
Season

2.58 Ab
2.84 Ab
5.88 Aa
5.28 Aab
7.35 Aa
4.80 A

June
July
August
September
October
Season

2.12 Ac
3.55 Abc
5.72 Bab
5.92 Aa
6.75 Aa
4.92 B

June
July
August
September
October
Season

2.00 ABb
2.57 Ab
4.42 Aab
2.61 Ab
6.02 Aa
3.71 A

June
July
August
September
October
Season

2.43 Ac
2.95 Abc
5.87 Aa
4.59 Aab
5.74 Aa
4.56 A

100DWU
100-75
100-75-75
Aronia arbutifolia 'Brilliantissima'
2.80 Aab
3.14 Aa
2.16 Ab
2.23 Ab
2.02 Aa
2.32 Ab
4.05 Aa
3.76 Aa
3.56 Ab
3.09 Aab
3.39 Aa
2.76 Ab
3.74 Aab
3.50 Aa
5.13 Aa
3.35 A
3.22 A
3.18 A
Cornus sericea 'Farrow'
2.32 Abc
2.34 Ac
2.12 Ac
1.96 Bc
2.52 ABc
2.60 ABc
4.82 Aa
6.52 Aa
4.56 Ab
4.64 Aa
3.95 Abc
5.71 Aab
4.19 Aab
5.87 Aab
6.62 Aa
3.71 A
4.09 A
4.12 A
Hydrangea paniculata 'Limelight'
2.02 Ac
2.42 Ac
2.11 Ac
3.48 Ac
2.53 Bb
3.11 ABc
8.03 Aa
7.24 ABa
6.08 Bb
5.83 Ab
5.84 Aa
5.78 Ab
8.35 Aa
7.25 Aa
8.96 Aa
6.07 A
5.35 AB
5.31 AB
Itea virginica 'Morton'
1.42 Bc
3.05 Aab
2.69 ABa
2.40 Abc
2.21 Ab
2.52 Aa
4.93 Aa
3.56 Aab
3.47 Aa
3.65 Aab
4.23 Aa
3.15 Aa
3.05 Babc
4.22 ABa
4.13 ABa
3.55 A
3.45 A
3.21 A
Physocarpus opulifolius 'Seward'
2.79 Ab
2.25 Ab
2.24 Ab
2.44 Ab
2.33 Ab
2.34 Ab
6.59 Aa
5.82 Aa
5.53 Aa
5.20 Aa
4.44 Aa
6.35 Aa
6.25 Aa
5.75 Aa
7.13 Aa
5.06 A
4.29 A
4.77 A

91

Table A-1 (cont’d)
Crop Coefficient (continued)
Month

Controlz

100DWU
100-75
100-75-75
Spiraea media 'Darsnorm'
June
2.14 Ab
2.10 Ab
3.01 Abc
2.88 Abc
July
2.85 Ab
2.19 ABb
2.30 ABc
1.97 Bc
August
5.05 Aa
4.39 Aa
4.89 Aab
3.49 Abc
September 2.88 Ab
4.12 Aa
3.31 Abc
4.03 Ab
October
5.92 Aa
3.67 Aab
5.47 Aa
6.45 Aa
Season
3.94 A
3.51 A
3.92 A
3.78 A
Thuja plicata 'Grovepli'
June
2.33 ABb
2.14 Ba
2.68 ABab
3.74 Ab
July
2.72 Aab
2.59 Aa
2.25 Ab
2.79 Ab
August
4.42 Aab
4.06 Aa
3.30 Aab
3.26 Ab
September 5.16 Aa
4.08 Aa
2.74 Aab
3.22 Ab
October
4.99 Aa
3.25 Aa
3.87 Aa
5.62 Aa
Season
3.97 A
3.39 AB
3.00 B
3.97 A
Weigela florida 'Alexandra'
June
2.40 ABc
1.64 Bb
2.27 ABb
2.88 Acd
July
2.91 Abc
2.44 ABb
2.18 Bb
2.65 ABd
August
4.43 Aab
4.89 Aa
3.97 Aab
4.37 Abc
September 3.48 Babc
5.24 ABa
4.37 ABa
5.38 Ab
October
4.73 Ba
5.92 ABa
5.31 ABa
7.48 Aa
Season
3.69 B
4.30 AB
3.65 B
4.70 A
z
.
-1
Control = 19 mm application ; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Overhead irrigation scheduling based on lowest DWU of the 8 taxa during
each measurement period; remaining water requirement supplied by hand each day as
necessary. Daily ET0 values for calculation of Kc obtained from the Enviro-weather
Automated Weather Station Network.
y

Separation of means performed with Tukey’s Test (α = 0.05). Means followed by the
same letters within rows are not different. Jun, Jul, Sep, and Oct: n=18, Aug: n=36.
Season: n=108.
x

Comparisons between means in each column are marked with lowercase letters.
Means of the same letters are not different; Tukey’s Test (α = 0.05). Jun, Jul, Sep, and
Oct: n=18, Aug: n=36.

92

Table A-2. Monthly crop coefficient (KC) from 23 June (Day 1) to 31 October 2010 (Day
131) for 8 shrub taxa grown in 10.2 L containers.
Crop Coefficient
Month

Controlz

June
July
August
September
October
Season

1.56 ABb
3.69 Aa
5.07 Ba
4.82 Ba
4.09 Ba
4.24 B

June
July
August
September
October
Season

1.91 ABb
3.94 Ba
4.79 Ba
4.99 ABa
4.71 ABa
4.41 C

June
July
August
September
October
Season

2.26 Ac
4.67 Aab
5.67 Aa
4.50 ABab
2.27 ABc
4.21 A

June
July
August
September
October
Season

1.94 Ab
3.83 Aa
2.45 Bab
3.00 Bab
2.24 Ab
2.76 B

June
July
August
September
October
Season

1.11 Aab
1.77 Aa
0.58 Cb
1.95 Aa
1.69 Bab
1.45 BC

100DWU
100-75
100-75-75
Hydrangea arborescens'Abetwo'
1.55 ABb
1.96 Ac
0.79 Bb
2.26 Bb
4.73 Ab
1.92 Bab
3.83 BCa
7.83 Aa
2.92 Ca
4.00 BCa
6.90 Aa
2.94 Ca
4.25 Ba
6.24 Aab
2.39 Cab
3.51 C
6.09 A
2.51 D
Hydrangea paniculata 'Limelight'
2.08 ABb
2.70 Ab
1.27 Bb
5.13 ABa
6.67 Aa
3.81 Ba
6.82 Aa
7.92 Aa
5.03 Ba
6.14 ABa
6.89 Aa
4.58 Ba
4.90 ABa
6.21 Aa
3.51 Bab
5.46 B
6.64 A
4.04 C
Rhus aromatica 'Gro-low'
1.96 ABb
1.99 ABc
0.95 Bb
3.66 ABa
4.90 Aab
2.38 Bab
4.67 ABa
5.66 Aa
3.76 Ba
4.43 ABa
4.85 Aab
3.24 Ba
3.69 Aa
3.82 Ab
2.49 Bab
3.98 A
4.63 A
2.87 B
Spiraea fritschiana 'Wilma'
1.84 Ac
1.42 ABb
0.58 Bc
3.92 Aab
3.19 Aa
1.82 Bab
4.87 Aa
3.58 Ba
2.38 Ba
4.46 Aa
2.44 Bab
1.80 Bab
2.44 Abc
1.85 ABb
1.21 Bbc
3.74 A
2.70 B
1.71 C
Syringa meyeri 'Palibin'
1.60 Ac
0.89 Ab
1.06 Aa
1.72 Abc
0.98 Bb
1.39 ABa
3.02 Aab
1.05 BCb
1.65 Ba
3.15 Aa
1.97 Aa
2.08 Aa
3.02 Aab
1.54 Bab
2.34 ABa
2.67 A
1.31 C
1.83 B

93

Table A-2 (cont’d)
Crop Coefficient (continued)
Month

Controlz

100DWU
100-75
100-75-75
Syringa xhyacinthiflora 'Evangeline'
June
2.02 Ab
0.81 Ba
1.06 Ba
1.01 Ba
July
3.43 Aab
0.82 Ba
1.25 Ba
1.20 Ba
August
3.53 Aab
0.86 Ba
1.24 Ba
0.92 Ba
September 3.91 Aab
0.92 Ba
1.39 Ba
0.82 Ba
October
4.50 Aa
1.07 Ba
1.49 Ba
1.19 Ba
Season
3.76 A
0.92 B
1.33 B
1.03 B
Viburnum dentatum 'Ralph Senior'
June
0.98 Ab
1.43 Ab
0.74 Ac
1.56 Aa
July
1.41 Bb
1.74 Bb
1.24 Bbc
2.39 Aa
August
2.20 Aa
2.23 Aab
1.73 Aab
2.00 Aa
September 2.10 Aa
2.72 Aab
2.16 Aa
2.40 Aa
October
2.23 Ba
3.52 Aa
1.87 Bab
2.44 Ba
Season
1.96 BC
2.50 A
1.69 C
2.26 AB
Weigela florida 'Alexandra'
June
1.45 Ac
0.83 Ac
1.09 Ab
1.47 Ab
July
2.75 Abc
1.33 Bbc
1.78 Bab
2.78 Aab
August
4.53 Aa
2.33 Bab
2.18 Ba
4.62 Aa
September 4.14 Aab
2.72 BCa
2.35 Ca
3.90 ABa
October
3.83 Aab
1.91 Babc
2.21 Ba
3.38 Aab
Season
3.69 A
1.99 B
2.08 B
3.57 A
z
.
-1
Control = 19 mm application ; 100DWU = 100% daily water use (DWU) replacement
each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75
= 3 day cycle with 100% DWU replacement the first day then 2 days 75% DWU
replacement. Overhead irrigation scheduling based on highest DWU of the 8 taxa in
each treatment replicate each day. Daily ET0 values for calculation of Kc obtained from
the Enviro-weather Automated Weather Station Network.
y

Separation of means performed with Tukey’s Test (α = 0.05). Means followed by the
same letters within rows are not different. Jun: n=24 (except 100-75-75 treatment,
n=16). Jul, Aug, and Oct: n= 93 (except 100-75-75, n= 62). Sep: n= 90 (except 100-7575, n=60). Season: n=393.
x

Comparisons between means in each column are marked with lowercase letters.
Means of the same letters are not different; Tukey’s Test (α = 0.05). Jun: n=24 (except
100-75-75 treatment, n=16). Jul, Aug, and Oct: n= 93 (except 100-75-75, n= 62). Sep:
n= 90 (except 100-75-75, n=60).

94

Figure A-1. Substrate volumetric water content (SVWC) (vol : vol) from time capacitance sensor measurements taken
every 15 min from 23 Jun (Day 1) to 1 November (Day 131). Representative plant chosen is Hydrangea arborescens
‘Abetwo’ growing in a 10.2 L container from the 100DWU treatment. Peaks represent daily irrigation events.

Hydrangea arborescens 'Abetwo'

0.60
0.55

SVWC (vol : vol)

0.50
0.45

0.40
0.35
0.30
0.25

0.20
0

20

40

60

80
Day

96

100
99

120
119

LITERATURE CITED

97

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100

CHAPTER 2
IRRIGATING BASED ON DAILY WATER USE REDUCES NURSERY EFFLUENT
VOLUME AND NUTRIENT LOAD WITHOUT REDUCING GROWTH OF FOUR
CONIFERS

101

Abstract
The objectives of this study were to quantify irrigation volume, runoff volume and
nutrient content, and plant growth of container-grown conifers when subjected to a daily
water use (DWU)- based irrigation regime in contrast to a standard irrigation rate. Four
conifer taxa were grown in 10.2-L (#3) containers subjected to four irrigation treatments
from 23 June through 16 October 2009 and 6 June through 31 October 2010, based
either on daily water use (DWU) or a control (nursery standard) application rate. The
plants studied were: 1) Chamaecyparis obtusa Sieb. & Zucc. ‘Filicoides’, 2)
Chamaecyparis pisifera (Sieb. & Zucc.) Endl. ‘Sungold’, 3) Thuja occidentalis L.
‘Holmstrup’, and 4) Thuja plicata D. Donn ‘Zebrina’. The four irrigation treatments
applied were: 1) control application of 19 mm.ha-1.d-1, 2) irrigation applied to replace
100% DWU (100DWU) each day, 3) applications alternating 100% DWU with 75%
DWU in a 2-day cycle (100-75), and 4) a 3-day application cycle replacing 100% DWU
the first day and 75% DWU on the second and third days (100-75-75). Irrigation
treatments did not affect plant growth index {GI= [(H + W NS + W EW) / 3]} in 2009. In
2010, 100DWU increased growth index of C. obtusa ‘Filicoides’ relative to control
plants. Season (114 d) total water applied for 100DWU, 100-75, and 100-75-75
treatments was 22%, 32%, and 56% less, respectively, than the control of 117
L.container-1 in 2009 and 24%, 18%, and 24% less than the control of 165 L .container-1
in 2010 (147 d). Scheduling irrigation based on DWU reduced effluent volumes
collected from growing areas and NO3- -N and PO43--P load when compared to the
control. Consequently, irrigating based on DWU reduced water applications and NO3- -

102

N and PO43--P losses while also producing plants of equal or greater size than control
plants.

Introduction
Producers must irrigate container-grown nursery plants frequently, often multiple
times daily, because of container volume limitations and substrate design. This
increases water extraction demands and places nurseries in competition for water
resources with population centers, industry, and other agricultural sectors (Beeson et
al., 2004). Since 1992, water applications to container nursery stock in Florida (mean
rainfall= 1,100 mm.y-1) had been capped at 2,290 mm.y-1 until 2003 when this amount
was further limited to 1,830 mm.y-1 for nurseries in direct competition with municipalities
for potable water (Beeson, 2004). Nursery water withdrawal policies are also in place in
California, Oregon, North Carolina, and Texas (Beeson et al., 2004).
One method to aid nurseries in conserving water is to schedule irrigation in
response to plant water use. Warsaw et al. (2009a) showed that irrigating based on
daily water use (DWU) reduced water applications from 6% to 75% compared to a 19
mm.ha-1.d-1 control without negatively impacting growth of over 20 ornamental shrubs.
From a management perspective, scheduling irrigation based on plant water use is
much more practical when the process is automated. Nemali and van Iersel (2006)
designed an automated irrigation system that successfully maintained substrate
volumetric water content (SVWC) near one of four set points for 43 d. The system
produced larger annual bedding plants when grown at a fixed SVWC of 0.22 m 3m-3

103

compared to 0.32 m3m-3. Using a similar irrigation system that monitored SVWC using
TDR sensors, Cornejo et al. (2005) improved irrigation application efficiency 22%
compared to a timer-controlled irrigation system. In order for commercial producers to
achieve such efficiency gains, refined automation protocols will be required before
widespread adoption of use-based irrigation systems by nurseries is realized.
In addition to concerns about the quantity of water consumed in plant
production, effluent leaving nurseries may carry NO3--N and PO43--P at concentrations
as high as 8 mg.L-1 and 5 mg.L-1 (Sharma and Bolques, 2007). Although those NO3--N
levels do not exceed the USEPA safe drinking water threshold (10 mg.L-1), the
Michigan Department of Environmental Quality (MDEQ) reported that where NO3--N is
already present, just 0.3 mg.L-1 of PO43--P may promote cyanobacteria blooms that
can damage sensitive ecosystems (Anonymous, 2008). In addition to regulating
concentration, total maximum daily load (TMDL) standards have been developed on a
case-specific basis to set a maximum rate at which nutrients can enter a water body
(EPA, 2011). The six states in the Chesapeake Bay watershed contribute 60% NO3--N
and 50% PO43--P in excess of EPA limits to the bay (CBF, 2010). As a result, since
2002 the state of Maryland requires agricultural operations to draft management plans
for nitrogen and phosphorus (Lea-Cox et al., 2001). In Florida, agriculture accounts for
98% of all phosphorus imports into the Lake Okeechobee watershed where a
phosphorus TMDL of 40 ug.L-1 there places agricultural nonpoint sources under intense

104

scrutiny (FDEP, 2001). Nutrient TMDLs have been implemented in Michigan as well for
phosphorus levels in 12 watersheds, although few nurseries are affected in this instance
(Anonymous, 2011). Research has shown decreased nutrient loading with precision
irrigation management (Million et al., 2010; Warsaw et al., 2009b). The former reported
reduced total N and P loading into effluent by 19% and 27% compared to a single
application of 1 cm.ha-1. Similarly, the Warsaw et al. (2009b) system reduced total
quantities of NO3--N in effluent 38% and 59% in 100% DWU and 75% DWU
applications compared to their control.
The objectives of this project were to study the effects of an automated, DWUbased irrigation system on plant performance and runoff water volume and quality.
Plant growth index (GI) and dry weight, DWU, water use efficiency (WUE), runoff water
volume, and NO3 and PO4 concentrations and loads were measured to compare the
irrigation system and treatments.

Materials and Methods
Site
The Michigan State University Horticulture Teaching and Research Center
(HTRC), Holt, Mich., is located at latitude 42.67 degrees north, longitude -84.48 degrees
west, and elevation of 264 m. Plants were grown on 3 x 6 m nursery production beds
covered with 6-mil impermeable polypropylene plastic and overlain by permeable
landscape fabric. The beds were positioned east to west along the long axis and sloped
to the center and westward, thereby channeling runoff water into a collection basin

105

positioned below grade on the west end of each bed. Rectangular basin frames were
made of wood and spanned the width of the production beds. An impermeable
polypropylene pond liner was inserted and affixed to each frame (see Warsaw et al.,
2012 for schematic). Production beds were spaced 3.7 m apart to minimize irrigation
drift from neighboring beds. A Michigan Enviro-weather station is located at the HTRC
to monitor environmental conditions (MSU, 2011).

Irrigation System
Irrigation was activated in each production bed by a 1.9 cm diameter 24 V
alternating current solenoid valve (various manufacturers). Six U8 Series nozzles (Rain
Bird Corporation; Azusa, CA) were mounted on 1.3 cm diameter by 0.66 m high risers.
The sprinklers were spaced 2.44 m apart along the long edge of each production bed
with all water directed inward. Four 90o nozzles were positioned on the corners of the
production area, and one- 180o nozzle was positioned between the corner nozzles on
each long axis for a total of 6 nozzles per production area, each with a 2.44 m radius of
throw to provide 100% nozzle- to-nozzle overlap.
Irrigation system distribution uniformity (DU) and output in each treatment
replicate was tested in 2009 using 16 rain gauges randomly interspersed throughout the
irrigation zone and allowed to collect water for 20 min. The average application rate
was 0.51 mm.min-1 (30.6 mm.hr-1) with a distribution uniformity of 69.3%. Distribution
uniformity and output in each treatment replicate was again tested in 2010. The
average application rate was 0.50 mm.min-1 (30 mm.hr-1) and the DU was 87.8%. The

106

improvement in DU occurred when the system operating pressure was increased in
order to maintain a similar application rate compared to 2009.

Plant Material
Rooted cuttings of Chamaecyparis obtusa Sieb. and Zucc. ‘Filicoides’,
Chamaecyparis pisifera Sieb. and Zucc. ‘Sungold’, Thuja occidentalis L. ‘Holmstrup’,
and Thuja plicata Donn ‘Zebrina’ were obtained from a commercial nursery in 5.7 x 5.7
cm plug containers on 1 August 2008. They were planted in 10.2 L containers with an
85 pine bark : 15 peat moss (vol:vol) substrate between 2 – 9 Sept. 2008. All cultural
practices except irrigation were identical for all treatments. On 22 June 2009 and 6
June 2010, 54 g of 19-2.6-10 (%N-P-K) fertilizer with micronutrients and Polyon®
Reactive Layers Coating controlled release technology (Harrell’s Inc., Lakeland, FL)
was topdressed to each container. Total quantities of NO3--N and PO43--P applied in
both 2009 and 2010 were equivalent to 285 kg.ha-1 and 39 kg.ha-1, assuming identical
container spacing to this study and 100% land utilization. Predicted release duration at
substrate temperatures of 21.1o C and 26.7o C ranged from 6 to 5 months. Weeds
were removed by hand pulling as necessary. Plants were overwintered from 2009 to
2010 in a minimally-heated (-2.2o C) quonset house covered with 4 mil overwintering
film permitting 30% light transmission. After over-wintering, plants were placed on the
production beds in their original configuration on 11 May 2010.

107

Experimental Design
Four irrigation treatments were replicated three times and randomly assigned to
12 production beds in a completely randomized design. The treatments were: 1) control
application of 19 mm.ha-1.d-1, 2) irrigation applied to replace 100% daily water use
(100DWU), 3) applications alternating 100% DWU with 75% DWU in a 2-day cycle
(100-75), and 4) a 3-day application cycle replacing 100% DWU on the first day and
75% DWU on the second and third days (100-75-75). Each treatment replicate
contained six subreplicates of each of the four species for a total of 24 experimental
plants in each production bed. Experimental plants were randomized in three rows of
eight at the center of the production bed with guard plants surrounding the perimeter to
reduce edge effects. Guard plants consisted of many species having similar growth
rates to the experimental plants and were arranged in the same sequence in each
treatment replicate. Guard plants were assigned a position in each treatment replicate
in 2009 and were placed in the same position in 2010. Irrigation treatments were
applied approximately every 24 h beginning at 0700 HR from 23 June 2009 to 16
October 2009 (treatment duration = 114 d) and 7 June 2010 to 31 October 2010
(treatment duration = 147 d). In both seasons and within each treatment, DWU
irrigation applications were always made based on the species with the highest DWU
within each treatment. In the cool months between growing seasons, all plants were
uniformly irrigated to container capacity via an overhead system as necessary.

108

Daily Water Use and Irrigation Scheduling, 2009
Substrate volumetric water content (SVWC) was measured in 2009 for every
plant using a ThetaProbe Type ML2x TDR soil moisture sensor (Delta-T Devices Ltd.,
Cambridge, U.K.) read with a ThetaMeter Hand-Held Readout Unit Type HH1 (Delta-T
Devices Ltd.). At initiation of the study, irrigation was applied until the substrate for all
plants exceeded container capacity. Gravimetric water was allowed to drain for 30 min.
Then an initial SVWC measurement was taken for each plant, and a final measurement
was taken after 24 h. The Theta probe was inserted vertically into the substrate to a
depth of 6 cm in three locations 120o apart halfway between the center and the outer
edge of the container. The measurements were converted to SVMC using a substratespecific equation developed by Warsaw et al. (2009a) for the same substrate. The
SVWC differential was multiplied by the average container substrate volume (9.7
L.container-1) to determine daily water use (DWU). Control and DWU-based treatments
were programmed into a Rain Bird ESP-12LX Plus controller (Rain Bird Corporation;
Azusa, CA). Irrigation began at 0700 HR daily. New DWU were obtained approximately
every 21 d, and irrigation was adjusted accordingly.

Daily Water Use and Irrigation Scheduling, 2010
In 2010, 10HS time capacitance soil moisture sensors (Decagon Devices, Inc.;
Pullman, WA) replaced the ThetaProbe to provide continuous real-time SVWC sampling
and irrigation control. A total of 48 sensors, one per species in each replicate, were
connected in single-ended configuration to an AM16/32B multiplexer (Campbell
Scientific, Inc.; Logan, UT), which was operated by a CR1000 micrologger (Campbell

109

Scientific, Inc.). An SDM-CD16AC relay controller (Campbell Scientific, Inc.) connected
to the micrologger controlled the irrigation solenoid valves located at each treatment
replicate. The 24 V alternating current power required by the solenoid valves was
provided via a common wire from the “Master Valve” terminal on an ICC-800PL
irrigation controller (Hunter Industries, Inc., San Marcos, CA) to each relay on the
controller.
The micrologger recorded SVWC for each probe at 15 min intervals from 7 June
to 31 October 2010. Between 7 June and 19 June, several out-of-range SVWC
readings occurred, mostly from the same sensors. To correct for this, each of the 48
sensors was individually calibrated to the substrate in situ from 19 June to 20 June.
Irrigation was applied to bring the substrate to container capacity, and each container
was weighed after drainage ceased using a PM 30 electric balance (Mettler-Toledo,
Inc.; Columbus, OH). Five subsequent weights were taken during the 24 hour dry-down
period and recorded with coinciding sensor output. These data were plotted using
Microsoft Excel (2007) and the trend line feature was used to obtain a single best-fit
quadratic equation. Calibrations were verified using the PROC REG function in SAS
(SAS Institute; Cary NC) before inclusion in the CR1000 program. The calibration
equations were then incorporated in the CR1000 programming using Version 2.7.0.16
of CRBasic Editor (Campbell Scientific, Inc.; 2006).
From 7 June to 31 October, DWU was calculated as:
DWU = SVWCinitial - SVWCfinal
where: SVWCinitial = SVWC one hour after irrigation
SVWCfinal = SVWC immediately before next irrigation cycle
110

The micrologger program began the irrigation regimen each day at 0700 HR.
Beginning June 6, with no initial SVWC to reference from previous days, substrate
moisture had to be raised to container capacity before recording the first initial SVWC.
From 2000 HR on 5 June to 0600 HR on 6 June, 31.23 mm of steady rainfall occurred,
which added 1.75 L to each container. This was considered sufficient to bring the
substrate to container capacity for the calculation of DWU on Day 1 of the experiment.
The program determined the highest DWU in the treatment replicate, which was then
multiplied by the irrigation system application rate to determine a run time in seconds.
Last, the appropriate treatment proportion of 100% or 75% was then multiplied by the
run time before activating the relay controller for the necessary duration. The initial
SVWC measurement for calculation of DWU for the following day was logged 1 h after
irrigation termination to allow for gravimetric water drainage. The following day just
before initiating irrigation in each zone, final SVWC was logged and the new DWU
calculation made. The micrologger repeated this process in all treatment replicates
each day for the duration of the study.

Plant Performance
Comparisons of DWU between different species grown over the two seasons
were made to standardize for environmental conditions by comparing crop coefficients
(KC), which were determined using the formula KC = ETA / ET0 (Allen et al., 1998).
These calculations were made for each taxon in both 2009 and 2010 using data
obtained from the on-site weather station (www.enviroweather.msu.edu). Plants were

111

classified in this study as low (KC < 2), moderate (2 ≤ KC < 3), or heavy (KC ≥3) water
users as described by Warsaw et al. (2009a).
Monthly growth index (GI) was calculated to assess plant performance under the
four irrigation treatments. Plant height (H) measurements were taken from the
container rim. Plant widths were measured along the north – south (WNS) and the east
– west axis (W EW) axis, and GI was calculated as [(H + WNS + WEW) / 3]. A
permanently affixed container label was used to maintain orientation of the plants. For
one species, plant shoot dry weight was measured using three C. obtusa ‘Filicoides’
taken from each treatment replicate. The stem was cut at the substrate surface, and
the entire top was bagged, oven dried, and weighed.

Runoff Collection and Analysis
Runoff water from the production beds was collected for two consecutive days
each month. Effluent collection occurred on a day when treatments received 100%
DWU and another day when 75% DWU was applied. Water was allowed to drain off the
production beds for 0.5 h after irrigation. A small pump and vacuum were used to
transfer water from the collection basin into a container to measure volume of total
effluent recovered. Water samples were obtained from each treatment replicate to
measure NO3--N and PO43--P concentrations in runoff water. Samples were
maintained at 3o C until being submitted to the Michigan State University Soil Testing
Laboratory for NO3--N and PO43--P analysis. The cadmium reduction method was used

112

for NO3–-N analysis and the Bray and Kurtz P-1 Test for PO43–-P analysis (Frank et al.,
1998). To determine net nutrient load, bulk quantities of nutrients in the effluent were
calculated by multiplying nutrient concentrations by the volume of effluent collected and
are expressed as g.ha-1d-1. The proportion of NO3-N and PO4-P recovered were
calculated as the ratio of nutrient quantities recovered : applied.

Statistical analysis
Data for each species were analyzed individually for irrigation volume, DWU, KC,
GI, GII, WUE, WUEp, dry weight, effluent recovery volume, nutrient concentration,
nutrient load, and percent nutrient recovery. Data were found to be normal using the
PROC UNIVARIATE procedure in SAS (SAS Version 9.1; SAS Institute, Cary, NC).
Data was tested with analysis of variance using the PROC GLM procedure of SAS.
When significant (α = 0.05), Tukey’s Honestly Significant test was used to separate
means.

Results and Discussion
Irrigation Volume
Cumulative and average daily ET0 for the 2009 treatment period equaled 369
mm and 3.15 mm and in 2010 were 491 mm and 3.34 mm. A total of 226 mm and 275
mm of rainfall (Figure 2.1) occurred during the 2009 and 2010 treatment periods
contributing 13 L.container-1 and 16 L.container-1. In 2009, irrigation was not applied
on three days when rainfall events exceeded 19 mm. Between the time the plants were

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Table 2.1. Daily irrigation application (L.container-1.d-1) (± SE) and total irrigation
applied (L.container-1) to 4 irrigation treatments from 25 June through 16 October 2009
and 7 June through 31 October 2010.
Treatment

Irrigation application (L.container1. -1

d )

Total irrigation applied
(L.container-1)

2009
Controlz

1.07 ± 0.00y A

116.96 A

100DWU

0.80 ± 0.02 B

91.29 B

100-75

0.70 ± 0.03 C

79.46 C

100-75-75

0.67 ± 0.03 D

75.81 D

Control

1.07 ± 0.00 A

157.29 A

100DWU

0.84 ± 0.02 B

124.89 A

100-75

0.91 ± 0.03 B

135.11 A

100-75-75

0.85 ± 0.03 B

125.55 A

2010

z

Control = 19 mm.application-1 (1.07 L.container-1.day-1); 100DWU = 100% daily water
use (DWU) replacement each day; 100-75 = 2 day cycle alternating 100% DWU and
75% DWU; and 100-75-75 = 3 day cycle 100% DWU replacement the first day then 2
days 75% DWU replacement. Irrigation scheduling based on: 2009) highest DWU of the
4 taxa recorded on each day of sampling; 2010) highest DWU of the 4 taxa in each
treatment replicate each day.
y

Means separation performed with Tukey’s Test (α = 0.05), n = 115 d (2009) and n =
147 d (2010). Means followed by the same letter(s) are not different.

114

moved outdoors on 11 May 2010 and the beginning of treatments on 7 June, 10 mm of
irrigation was applied daily to all treatments. An additional 133 mm of precipitation fell
during this period for a total of 403 mm of water received by all treatments. During the
treatment period, total irrigation applied to the control was 2166 mm (117 L.container-1;
Table 2.1) in 2009 and 2795 mm (157 L.container-1) in 2010. Compared to the control,
daily irrigation applications to the 100DWU, 100-75, and 100-75-75 treatments were
reduced by 22%, 32%, and 35% in 2009 and 22%, 15%, and 21% in 2010, respectively
(Table 2.1). Across each two or three-day cycle, irrigation applications in the 100-75
and 100-75-75 treatments were 87.5% and 83.33% of DWU.
In 2009, seasonal DWU of T. plicata ‘Zebrina’ and C. obtusa ‘Filicoides’ in the
100-75-75 treatment was 0.849 L.container-1.d-1 and 0.889 L.container-1.d-1, both
higher than respective control plants at 0.644 L.container-1.d-1 and 0.675 L.container1. -1

d . In addition, C. pisifera ‘Sungold’ in the 100-75-75 treatment exhibited higher

DWU at 0.859 L.container-1.d-1 than in the 100-75 treatment of 0.700 L.container-1.d-1.
In 2010, DWU of C. obtusa ‘Filicoides’ in the 100-75 and 100-75-75 treatments was
0.675 L.container-1.d-1 and 0.643 L.container-1.d-1, both higher than the 100DWU of
0.619 L.container-1.d-1. Similarly, T. occidentalis ‘Holmstrup’ had higher DWU in the
100-75-75 treatment at 0.622 L.container-1.d-1 than either the 100DWU (0.381
L.container-1.d-1) or control treatments (0.533 L.container-1.d-1).

115

Figure 2.1. Daily (bars) and cumulative (line) precipitation from 25 June to 16 October
(Day 114) 2009 and 7 June (day 348) to 31 October 2010 (Day 494). Data obtained
from the Enviro-weather Automated Weather Station Network.

116

Figure 2.2. Daily total solar flux density (bars) and daily average temperature (line) from
25 June to 16 October (Day 114) 2009 and 7 June (day 348) to 31 October 2010 (Day
494). Data obtained from the Enviro-weather Automated Weather Station Network.

117

Daily fluctuations in DWU very closely tracked fluctuations in ET0 (Figure 2.3).
Maximum DWU in 2009 occurred on day 78 (10 Sep.). In the first half of 2010, DWU
was generally higher before showing a marked decline in early September (Figure 2.3).
This decline corresponded to increasing frequency of precipitation (Figure 2.1) and
decreasing temperatures and solar flux (Figure 2.2). Maximum DWU occurred on
different days for each species, with the highest recorded DWU of all taxa occurring in
Chamaecyparis obtusa ‘Filicoides’ on day 399 (28 July 2010). Moreover, DWU only
exceeded the control on three days in 2010, and in only two of the four taxa (Figure
2.3). Precipitation resulted in calculation of a negative DWU on 10 days throughout
2010 when irrigation was not applied. DWU only exceeded control irrigation
applications on four days for C. obtusa ‘Filicoides’, three days for T. plicata ‘Zebrina’,
two for T. occidentalis ‘Holmstrup’, and one day for C. pisifera ‘Sungold’. Consequently,
irrigation was applied to the control in excess of plant needs on the majority of days
throughout this study, particularly later in the season as temperatures fell and
precipitation was frequent.
Under the classification system adopted from Warsaw et al. (2009a), all taxa in
2009 were high water users and most were high users in 2010 (KC>3; Table 2.2).
However, seasonal KC of T. plicata ‘Zebrina’ in 2010 was <3 for all but the 100 DWU
treatment, which classifies it in the remaining treatments as a moderate water user.
Among all taxa, T. plicata ‘Zebrina’ had both the lowest crop coefficient at 2.1 in the
2010 season from the 100-75 treatment and the highest at 5.8 in the 100-75-75

118

Daily Water Use (mm)

Figure 2.3. Daily Water Use (DWU) from 25 June to 16 October 2009 and 7 June to 31 October 2010 of A)
Chamaecyparis obtusa ‘Filicoides’, B) Chamaecyparis pisifera ‘Sungold’, C) Thuja occidentalis ‘Holmstrup’, and D) Thuja
plicata ‘Zebrina’ grown in 10.2 L containers.
25
20
15
10
5
0
-5
25
20
15
10
5
0
-5
25
20
15
10
5
0
-5
25
20
15
10
5
0
-5

A)

B)

C)

D)

0 21 41 78 110 340 350
Jun. Aug. Oct.
Jun.
2009

360

370

380
Jul.

390

119

400 410
Aug.
2010

420

430

440
Sept.

450

460

470
Day
Oct. Month
Year

Figure 2.3 (cont’d)
The shaded portions of bars represent daily ET0 from 25 June to 16 October 2009 and
7 June to 31 October 2010. White portions of each bar represent DWU. Negative
values indicate precipitation in excess of DWU. Dotted horizontal line indicates control
treatment of 19mm.application-1. Dashed vertical line separates 2009 and 2010
seasons. Daily ET0 values obtained from the Enviro-weather Automated Weather
Station Network.

treatment for 2009 (Table 2.2). When comparing treatments in 2010, C. obtusa
‘Filicoides’ and T. occidentalis ‘Holmstrup’ both had KC <3 in the 100DWU treatment
making them moderate water users in that treatment only. For the other taxa, lowest KC
occurred in the 100-75 or 100-75-75 treatments, also making these moderate users.
Using the same classification methods as the current study, Thuja plicata Donn
‘Atrovirens’ was rated a low user in Michigan in 2006 with a KC of 1.7, and T.
occidentalis L. ‘Techny’ grown in 2007 was rated a moderate user with a KC of 2.6
(Warsaw et al., 2009a). Overall KC in flowering shrubs reached as high as 6.8 (Warsaw
et al., 2009a). Others reported maximum KC = 4.7 (Schuch and Burger, 1997) and 5.1
(Burger et al., 1987). From the DWU data obtained using the capacitance sensors in
the current study, Figure 3 also illustrates the day-to-day fluctuations in the 2010 KC,
expressed as DWU:ET0. Calculation of KC at this resolution requires sensor
monitoring, which is not widely adopted by nurseries at this time. For more immediate
implementation, Schuch and Burger (1997) presented an approach involving the

120

Table 2.2. Seasonal crop coefficients (L.container-1.d-1) of four conifers grown in 10.2 L
containers under four irrigation treatments administered 25 June through 16 October
2009 and 7 June through 31 October 2010. Seasonal KC calculated as DWU:ET0.
Taxa

Crop Coefficient

Controlz
2009
100DWU
100-75
100-75-75
Chamaecyparis obtusa
3.41 Bay
'Filicoides'
4.46 ABa
4.16 ABa
5.36 Aa
Chamaecyparis pisifera
'Sungold'
4.53 Aa
4.73 Aa
4.10 Aa
5.65 Aa
Thuja occidentalis
'Holmstrup'
4.39 Aa
4.19 Aa
4.68 Aa
5.32 Aa
Thuja plicata
'Zebrina'
3.74 Ba
3.99 Ba
4.05 Ba
5.79 Aa
2010
Chamaecyparis obtusa
3.84 Aax
'Filicoides'
2.86 Bc
4.15 Aa
3.95 Aa
Chamaecyparis pisifera
'Sungold'
3.70 Aab
4.15 Aa
4.27 Aa
2.46 Bb
Thuja occidentalis
'Holmstrup'
3.20 Ab
2.11 Bd
3.31 Ab
3.57 Aa
Thuja plicata
'Zebrina'
2.27 Cc
3.43 Ab
2.10 Cc
2.81 Bb
z
.
-1
.
-1
Control = 19 mm application (1.07 L day ); 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Irrigation scheduling based on: 2009) highest DWU of the 4
taxa recorded on each day of sampling; 2010) highest DWU of the 4 taxa in each
treatment replicate each day.
y

Separation of means performed with Tukey’s Test (α = 0.05), 2009 n = 18, 2010 n = 3.
Means followed by the same letters (capital letters in rows, lower case letters in
columns) are not different within respective years.

121

reassessment of KC every few weeks. Additionally, Niu et al. (2006) showed that KC
differed by calendar month for container-grown shrubs in Texas. Such intervals are
similar to those between DWU measurements made in 2009 from the current study. At
the very least, KC are likely best reassessed seasonally with changing weather patterns
since KC made a few abrupt transitions, particularly with the marked decrease in early
September of 2010 (Figure 2.3).

Plant Response to Irrigation Treatments
Growth index did not differ among any taxa in 2009 (Figure 2.4). In 2010, GI for
C. obtusa ‘Filicoides’ in the 100DWU treatment was larger than the control plants on all
sampling dates. In 2010, C. obtusa ‘Filicoides’ also had a higher GII in the 100DWU
treatment than the control (Table 2.3). During the treatment period, T. plicata ‘Zebrina’
grew more than any taxa in the DWU treatments (9.7 cm, 8.8 cm, and 8.6 cm in the
100DWU, 100-75, and 100-75-75 treatments, respectively). An exception was that T.
plicata ‘Zebrina’ in the 100DWU treatment did not differ from T. occidentalis ‘Holmstrup’.
Similar to GI, shoot dry weight of C. obtusa ‘Filicoides’ was 163.6 g in the 100DWU
treatment and 134.9 g in the 100-75-75 treatment, both higher than the control of 51.7
g. It is noteworthy that while the DWU treatments received less water than the control,
growth was not lower for any taxa at the conclusion of the study.
The ability to successfully produce plants under water conserving irrigation
regimes without impacting growth has been documented in several cases. In a deficit

122

Figure 2.4. Growth index of a) Chamaecyparis obtusa ‘Filicoides’, b) Chamaecyparis
pisifera ’Sungold’, c) Thuja occidentalis ‘Holmstrup’, and d) Thuja plicata ‘Zebrina’
grown in 10.2 L containers and subjected to 4 irrigation treatments from 25 June (Day 0)
to 16 October 2009 and 7 June (Day 348) to 31 October 2010. Control = 19
mm.application-1; 100DWU = 100% daily water use (DWU) replacement each day; 10075 = 2 day cycle alternating 100% DWU and 75% DWU; and 100-75-75 = 3 day cycle
with 100% DWU replacement the first day then 2 days 75% DWU replacement.
Irrigation scheduling based on: 2009) highest DWU of the 4 taxa recorded on each day
of sampling; 2010) highest DWU of the 4 taxa in each treatment replicate each day.
Dashed vertical line separates 2009 and 2010 seasons. Each day was analyzed
separately (Tukey’s test, α = 0.05). Means followed by the same letters are not
different. ns = not significant. n=18.

123

Figure 2.4 (cont’d)

irrigation study, 90% of shoot growth could still be produced at irrigation volumes of 1.0x
available water compared to 1.5x available water, a ≈40% reduction in irrigation volume
(Groves et al., 1998). Similarly, a low LF of 0.0 to 0.2 conserved 44% of irrigation water
versus a LF of 0.4 to 0.6, although the former resulted in an 8% reduction in total plant
dry weight (Tyler et al., 1996). In both studies, precise irrigation applications saved
water, yet growth was not seriously affected. In the current study, plants in the 100-7575 treatment incurred minor substrate moisture deficits during two-thirds of the study yet
there were no cases of reduced growth from this treatment in any of the four taxa.
124

Table 2.3. Growth index increase (GII) (cm) of 4 conifers subjected to 4 irrigation
treatments between 16 October 2009 to 7 June 2010.
Taxa
Treatment
z

Control

2009
GII
C. o.
C. p.
T. o.
T. p.
2010
GII
C. o.
C. p.
T. o.
T. p.

y

y x

100DWU

'Filicoides'
'Sungold'
'Holmstrup'
'Zebrina'

1.45 B b
1.70 Aab
2.63 Aab
4.22 Aa

5.22 ABa
1.89 Ab
3.26 Aab
5.30 Aa

'Filicoides'
'Sungold'
'Holmstrup'
'Zebrina'

7.50 Ba
4.78 Aa
4.48 Aa
7.28 Aa

12.71 Aa
4.96 Ac
5.76 Abc
8.92 Ab

y

100-75
4.48 ABab
2.37 Ab
3.57 Ab
6.43 Aa

9.50 ABa
5.04 Ab
5.31 Ab
9.02 Aa

100-75-75
y

7.69 Aa
1.39 Ac
2.49 Ac
5.04 Ab

y

9.32 ABa
5.09 Ac
5.90 Abc
8.64 Aa

z

Control = 19 mm.application-1 (1.07 L.day-1); 100DWU = 100% daily water use (DWU)
replacement each day; 100-75 = 2 day cycle alternating 100% DWU and 75% DWU;
and 100-75-75 = 3 day cycle with 100% DWU replacement the first day then 2 days
75% DWU replacement. Irrigation scheduling based on highest DWU of the 4 taxa
recorded on each day of sampling.
y

Comparisons between means in each row are marked with capital letters. Means of
the same letters are not different; Tukey’s Test (α = 0.05), 2009: n = 18, 2010: n=3.
x

Comparisons between means in each column are marked with lowercase letters.
Means of the same letters are not different; Tukey’s Test (α = 0.05), 2009: n = 18, 2010:
n=3.

Warsaw et al. (2009a) reported that whenever plants in DWU treatments were larger
than control plants, they had received less water than those in the control. However,
the majority of plants studied (20 of 24) were unaffected by irrigation treatment (Warsaw
et al., 2009a), as was the case in the current study; only one of four species was
affected by treatment.

125

Other authors have provided a rationale for similar differences in growth that also
may explain the growth differences that were observed for C. obtusa ‘Filicoides’. Since
control irrigations were generally above DWU, some of the nutrients supplied by the
slow-release fertilizer may have been lost from the substrate due to higher leaching
compared to DWU treatments. In their two-year study, Warsaw et al. (2009b) explained
that leaching in the control was the probable cause for three of their four species in
DWU-based treatments being larger than their control (also 19 mm.ha-1.application-1).

Runoff Volume
Runoff volumes from the DWU treatments were lower than the control on all
measurement days except day 3 (Figure 2.5). Both C. obtusa ‘Filicoides’ and T. plicata
‘Zebrina’ showed higher DWU in the 100-75-75 treatment than the control, which means
that prior to irrigation, SVWC in the 100-75-75 treatment was frequently lower than the
control. Watering to a deficit likely contributed to reduced effluent recovery in the 10075-75 treatment following irrigation applications. Additionally, higher irrigation rates of
the control and the 100% application rate led to higher volumes of water being applied
off target, which increased total effluent. Highest volumes of irrigation and effluent
recovered occurred on day 81 for the 75% DWU applications, day 82 for the 100%
applications, and highest effluent volume recovery for the control occurred on day 82.
The lowest volumes applied and recovered for these rates occurred on day 39. The
100% and 75% DWU irrigation volumes overall were 34% and 51% less than the
control. Of the volume applied, 49%, 46% and 39% was recovered as effluent in the
control, 100% DWU and 75% DWU applications, respectively. Warsaw et al. (2009b)
126

Figure 2.5. Applied irrigation and recovered runoff from 3m x 6m production areas
(projected to L ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to
31 October 2010 (day 494) for 4 conifers growing in 10.2 L containers.

127

Figure 2.5 (cont’d)
On days with 2 bars, all DWU treatments received irrigation at 100% DWU replacement.
On days with 3 bars, 100-75 and 100-75-75 treatments were scheduled at 75% DWU
replacement. Control treatment equivalent of 190 x 103 L.ha-1 applied daily. Means
separation for each day performed using Tukey’s Test (α = 0.05, ns= not significant,
n=3).

reported similar results with 60%, 37%, and 32% effluent being recovered after their
control, 100% DWU, and 75% DWU irrigation applications. Similarly, studies by Fare et
al. (1994) and Karam and Niemiera (1994) demonstrated that reduced irrigation
volumes in turn reduced leaching.
In contrast to 2009, irrigation application rates in 2010 were not consistent
between runoff collection days since DWU was determined every day rather than only
once and applied for several weeks as in 2009. Whereas irrigation rates were different
between DWU treatments and the control on every day in 2009, irrigation volumes in
2010 DWU treatments were only lower than the control on days 414 and 462 by 46%
and 41%, (Figure 2.5). Recovery volume did not differ on any day in 2010.

3.4. Nitrates and Phosphates
Effluent concentrations of NO3--N tended to be low early and late during the
production periods while peaking in the middle of both seasons (Figure 2.6). In 2009,
this peak occurred on day 39 for the 100% DWU rate at 35.8 mg.L-1 and 39.3 mg.L-1 for
75% DWU while the control peaked on both days 39 and 40 at 17.2 mg.L-1 (Figure 2.6).
Therefore, peak NO3--N concentrations reached nearly four times as high as the EPA

128

Figure 2.6. NO3--N concentration in 2009 (A) and 2010 (B) and NO3--N quantity in
2009 (C) and 2010 (D) for runoff collected from 3m x 6m production areas (projected to
g ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to 31 October
2010 (day 494) for 4 conifers growing in 10.2 L containers. Fertilizer was applied on 22
June 2009 and 6 June 2010.

On days with 2 bars, irrigation was scheduled for all DWU treatments at 100% DWU
replacement. On days with 3 bars, 100-75 and 100-75-75 treatments were scheduled
for 75% DWU replacement. Control irrigation volume = 190 x 103 L.ha-1 applied daily.
Uppercase letters indicate means separation between measurement days within each
treatment (α = 0.05, 2009 Control and 100% DWU: n=6; 75% DWU: n=3, 2010: Control
129

Figure 2.6 (cont’d)
and 100% DWU: n=8; 75% DWU: n=4). Lowercase letters indicate means separation
between treatments within each measurement day. Means separation performed using
Tukey’s Test (α = 0.05, ns= not significant, n=3).

limit for safe drinking water of 10 mg.L-1 (Anonymous, 1986), although effluent leaving
nurseries becomes diluted when joining other water bodies. Concentrations of NO3--N
recovered in the 100% DWU and 75% DWU basins were over twice as high as
concentrations in the control basins on day 39 because irrigation rates applied to the
control were higher than both the 100% DWU and 75% DWU irrigation applications,
(Figures 2.5 and 2.6).
In 2010, peak NO3--N concentrations occurred on day 378 at 19.9 mg.L-1 for the
100% irrigation applications and on day 379 for the 75% applications at 22.9 mg.L-1and
control at 16.6 mg.L-1 (Figure 2.6), which, although less than concentrations observed
in 2009, still represent an approximately twofold increase above the EPA limit
(Anonymous, 1986). The lowest concentrations occurred on days 462 and 463 (Figure
2.6). On day 348, concentration of NO3--N was greater in the control than for the 100%
DWU and greater than the 75% DWU irrigation on day 349 (Figure 2.6). Differences in
NO3--N concentrations did not occur on any other days in 2010. In both seasons, peak
effluent recovery was observed around 30 d after fertilizer applications were made.

130

The effluent NO3--N load was greater for the control than the 100% DWU on
days 4 and 40 in 2009 but only day 348 in 2010 (Figure 2.6). When all three irrigation
treatments were applied on day 39, NO3--N load from the control exceeded the 75%
DWU. Similar to the response seen in 2009, nutrient loading in 2010 peaked
midseason on day 378 (August) for the 100% DWU and day 379 for the other two
irrigation treatments. Peak nutrient loading corresponded to peak season temperatures
in August (Figure 2.2). Birrenkott et al. (2005) documented a similar controlled-release
fertilizer release pattern with daily N release peaking mid-season, which they attributed
to higher temperatures during that period. A season equivalent of 285 kg.ha-1 NO3--N
was applied in both 2009 and 2010. Of total NO3--N applied, day 40 represented the
highest percentages of NO3--N recovered in 2009 in the control and 100% DWU at
0.724% and 0.311%; however recovery in the 75% DWU did not differ between any
days, although 0.06% was recovered on day 39. When only the 100% DWU and
control applications were made on days 4 and 40, NO3--N quantities recovered in
effluent were 50% and 57% lower than the control, which received the greater irrigation
volume. In 2010, highest recovery of total applied NO3--N occurred on day 378 for
100% DWU applications at 0.783% and day 379 for control and 75% DWU applications
at 0.518% and 0.897%. On day 378, NO3--N quantities recovered in the 100% DWU
applications represented 219% of quantities recovered in the control, and on day 379,
the 75% applications were 115% of quantities recovered in the control.
131

Figure 2.7. PO43--P concentration in 2009 (A) and 2010 (B) and PO43--P quantity in
2009 (C) and 2010 (D) for runoff collected from 3m x 6m production areas (projected to
g ha-1) from 25 June (day 0) to 16 October 2009 and 7 June (day 348) to 31 October
2010 for 4 conifers growing in 10.2 L containers. Fertilizer was applied on 22 June
2009 and 6 June 2010.

On days with 2 bars, irrigation was scheduled for all DWU treatments at 100% DWU
replacement. On days with 3 bars, 100-75 and 100-75-75 treatments were scheduled
for 75% DWU replacement. Control irrigation volume = 190 x 103 L.ha-1 applied daily.
Uppercase letters indicate means separation between measurement days within each
132

Figure 2.7 (cont’d)
treatment (α = 0.05, 2009 Control and 100% DWU: n=6; 75% DWU: n=3, 2010: Control
and 100% DWU: n=8; 75% DWU: n=4). Lowercase letters indicate means separation
between treatments within each measurement day. Means separation performed using
Tukey’s Test (α = 0.05, ns= not significant, n=3).
Effluent PO43--P concentrations tended to peak shortly after nutrient applications
were made. Peak PO43--P concentrations in 2009 occurred on day 4 for the control at
3.4 mg.L-1, while concentrations for the 100% DWU and 75% DWU were generally
higher during the first four collection days than the latter two days (Figure 2.7). The only
difference between irrigation treatments observed in 2009 occurred on day 82 where
the PO43--P concentration in the 100DWU was 270% higher than the control, in which
irrigation volume exceeded the 100 DWU. In 2010, PO43--P concentration peaked on
days 348 and 349 for the control and days 349 and 379 for the 75% DWU (Figure 2.7).
Effluent PO43--P concentrations in the 100% DWU were greater on the first three
sampling days than the last three sampling days. Similarly, control and 75% DWU
concentrations were lower late in the season compared to earlier. As with 2009, only
one difference occurred in 2010 between irrigation treatments; the control PO43--P
concentration exceeded the 100% DWU on day 348 (Figure 2.7). Similarly, Warsaw et
al. (2009b) also reported occurrences of effluent nutrient concentrations recovered from
their control being lower than 100% DWU or 75% DWU irrigation application rates.
On days 4 and 40, PO43--P loading of the 100% DWU application was 59% and
67% less than the control, and on day 39, loading in the 75% DWU was 81% lower than
133

the control (Figure 2.7). Peak PO43--P loading occurred on day 4 for the control and
100% DWU irrigation treatment (Figure 2.7). Only one difference in PO43--P loading
occurred in 2010 with the control exceeding the 100% DWU on day 348 (Figure 2.7).
Highest PO43--P loading occurred on days 348 for the control, day 378 for the 100%
DWU, and day 379 for the 75% DWU. Lowest PO43--P loading was observed during
the last collection period (days 462-463) for all application rates (Figure 2.7). On day
348, quantities of PO43--P recovered in the 100% DWU was 63% less than the control;
however, irrigation volumes did not differ.
In contrast to concentration, nutrient loading in effluent tended to increase with
increasing irrigation volume. Fewer differences in NO3--N loading in 2010 compared to
2009 likely resulted from irrigation volumes of the DWU-based application rates and the
control only differing between two of eight measurement days rather than in 2009 when
the control and DWU application rate differed on every measurement day (Figure 2.5).
Warsaw et al. (2009b) found that compared to their control, NO3--N loading in effluent
was reduced, on average, 38% and 59% with their 100% DWU and 75% DWU
application rates. Likewise, PO43--P loading in their 100% DWU and 75% DWU
treatments were 46% and 74% lower than their control. Fare et al. (1994) showed that
at an irrigation volume of 13 mm, 63% of NO3--N applied was recovered in effluent
whereas only 19% was recovered under 6 mm irrigation applications. Similarly,
compared to their high LF treatment of 0.4 - 0.6, Tyler et al. (1996) reported that a low
134

LF of 0.0 - 0.2 treatment reduced NO3--N and NH4+-N quantities recovered in effluent
by 66% and 62%. Total P recovered in the effluent was 57% lower in the low LF than
the high LF. These studies all indicate that increasing irrigation volume generally leads
to increased nutrient quantities in nursery effluent, which supports the increased
quantities of nutrients recovered from the control compared to DWU irrigation
applications in the current study.

Conclusions
Both TDR and capacitance substrate moisture sensors can be incorporated into
real-time automated irrigation systems to further streamline the data acquisition and
control tasks required when irrigating based on DWU. DWU treatments produced the
same growth as the control, except C. obtusa ‘Filicoides’, which showed increased
growth in the 100DWU treatment. The relatively wide KC ranges identified by the
current study and others imply that great variation exists between the many woody
ornamental crops produced by nurseries. In addition, the classification of the four
container-grown taxa by water use group adds to the body of work by Warsaw et al
(2009a and 2009b) and the earlier Burger et al. (1987). Grouping of plants in nursery
irrigation blocks according to KC reduces probability of over- or under-irrigation, thereby
improving the use of water resources. Both Warsaw et al. (2009b) and Karam and
Niemiera (1994) showed that in humid climates, scheduling irrigation to replace 100%
DWU (a LF of 0) can be accomplished without EC exceeding recommended levels

135

(above 1.5 dS.m-1, Owen et al., 2011); however, if a 0 LF is desired, EC should be
frequently monitored and substrates leached whenever necessary.
The ability of the automated system to adapt to changes in DWU with daily
resolution demonstrates great potential as a convenient and accurate scheduling tool.
Grower-friendly systems are being developed that permit multiple sensor inputs,
including soil moisture, EC, soil temperature, air temperature, relative humidity,
precipitation, and photosynthetically-active radiation, in a multi-layer, self-configuring
network capable of transmitting data wirelessly (Lea-Cox et al., 2008 and Lea-Cox et
al., 2011). Ultimately, such accessible and flexible automated systems in the hands of
growers will help them expediently make more informed production decisions (Lea-Cox
et al., 2011). Some of the potential benefits of an automated sensing system were
realized in the current study when irrigation applications averaged over both years to
the 100DWU, 100-75, and 100-75-75 treatments were reduced by 22%, 24%, and 28%,
respectively, compared to the control. Also, the 100% DWU and 75% DWU irrigation
applications reduced effluent NO3--N loading by 36% and 67% and PO43--P loading by
38% and 57% when averaged over all measurement days. Not only does this translate
to less eutrophication potential, but it could also save growers money in the form of
fewer nutrient inputs and potentially lower energy costs for the pumping and distribution
of water. DWU-based irrigation scheduling can serve as a promising solution in
reducing effluent volume and the quantity of nutrients lost from production areas.

136

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137

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140

CHAPTER THREE

CONCLUSIONS AND FUTURE RESEARCH PERTAINING TO IRRIGATION
MANAGEMENT USING SOIL MOISTURE SENSORS TO LOWER WATER USE AND
REDUCE RUNOFF AND NUTRIENT LOSSES IN EFFLUENT

141

Research Summary
Eight ornamental shrub taxa were grown from June to October 2009 to
investigate three irrigation levels based on estimated daily water use (DWU) (“irrigation
study”). All plants were potted the previous September in 10.2 L containers and 85%
pine bark, 15% peat moss (vol : vol) substrate at the Horticultural Teaching and
Research Center (HTRC) in Holt, MI. All treatments received identical cultural practices
except irrigation. From 11 June to 14 October 2010, substrate volumetric water content
(SVWC) was measured approximately every 3 weeks using a ThetaProbe Type ML2x
(Delta-T Devices Ltd.) to calculate DWU. Four irrigation treatments were applied daily:
1) control application of 0.75 acre-inches, 2) irrigation applied to replace 100% DWU, 3)
applications alternating 100% DWU with 75% DWU in a 2-day cycle, and 4) a 3-day
application cycle replacing 100% DWU on the first day and 75% DWU on the second
and third days. Each treatment was replicated 3 times with 6 subreplications of each
taxa per treatment replicate. Irrigation treatments were applied daily via overhead
irrigation to meet the needs of the lowest water user(s) with supplemental DWU
requirements supplied by hand.
In 2010, eight new ornamental shrub taxa were grown from June to October
using the same irrigation treatments, experimental design and methods as 2009 with a
few exceptions. From June 23 to October 31, 2010, substrate volumetric water content
(SVWC) was continuously monitored in each of the eight taxa using Decagon 10 HS soil
moisture sensors. Plants were irrigated daily, and initial SVWC values were recorded
after 1 h New SVWC readings were taken 24 hr later to calculate DWU
(DWU=SVWC1h – SVWC24h) and determine irrigation treatments for the day. One
142

subreplicate of each taxa in each treatment replicate was monitored by a Decagon 10
HS sensor for a total of 96 sensors in the study. A CR3000 Micrologger (Campbell
Scientific, Logan, UT) coordinated the treatment and irrigation scheduling. Daily
irrigation applications were based on the needs of the highest DWU for each treatment
replicate, so all taxa in each replicate, regardless of water need, received the same
amount.
Additionally, from 2009 to 2010, a related study (“runoff study”) was also
conducted at the HTRC to analyze the effects of the above-mentioned treatments on
runoff volume and NO3--N and PO43--P concentration and load in effluent. Four
conifers were grown identically to the above description, except overwintered in plastic
film-covered quonset houses for continued study in 2010. Calculation of DWU was
conducted using the ThetaProbe in 2009 and Decagon 10 HS sensors, also as
described above. Monthly, the runoff basins were emptied, volumes measured, and
water samples collected for nutrient analysis.
Daily water use treatments conserved water in both studies. In 2009, water
reductions in the irrigation study ranged from 7% in the 100DWU treatment to 57% in
the 100-75-75 treatment depending on species. Daily water use of high water users
exceeded the control during two measurement days in August. The remainder of the
plants required less irrigation than the control treatment throughout the study. Plants in
the runoff study received 22%, 32%, and 35% less irrigation in the 100DWU, 100-75,
and 100-75-75 treatments, respectively. Plants in neither study had decreased growth
in DWU deficit treatments relative to the control, however, Hydrangea paniculata
‘Limelight’ grew larger than the control in the 100DWU treatment. The ability of the
143

automated irrigation system to adapt to changes in DWU with daily resolution
demonstrates great potential as a convenient and accurate scheduling tool.
In 2010, the 100DWU and 100-75 treatments actually received more water than
the control because irrigation was scheduled based on the water needs of the highest
user in each treatment replicate. However, the automated system effectively identified
the highest water user each day to meet its water needs; no signs of wilt or lost growth
were recorded throughout the study. However, in the 100-75-75 treatment, Hydrangea
paniculata ‘Limelight’ was smaller than the other treatments, probably because overall
irrigations in that treatment were 52% less than the control.
Daily water use of the runoff plants decreased relative to the first year, possibly
because DWU was determined from daily calculations. However, throughout most of
the season, Chamaecyparis obtusa ‘Filicoides’ was larger in the 100DWU treatment
than the control. These observations further substantiate the results of Warsaw et al.,
(2009a and b) that irrigating according to DWU conserves water, that under moderate
cumulative moisture stress plants possess a buffer against decreases in growth, and
that growth can be increased when scheduling according to DWU relative to time-based
approaches.
Continuous sensor data from 2010 provided insight into daily changes in DWU of
each species. Crop coefficients (KC) were calculated from this data and averaged over
each month of the study. This revealed a consistent pattern with KC beginning
consistently low for each taxa in spring, increasing until mid-season, and leveling off
toward the end of the season. However, KC of individual taxa increased variably so that
144

by mid-summer, KC could be used to rate plants as low, moderate, or high water users.
In the 2006 - 2008 growing seasons, Warsaw et al. (2009 a) classified 24 plants using
KC at the HTRC, so this project adds 17 new KC to their work. Over five years,
“indicator” species have now been identified to help group plants into irrigation blocks at
nurseries enabling irrigation to be applied more precisely based on plant need.
Because these KC were developed over the course of different seasons, there were
some inconsistencies between the same taxa over different years. However, these KC
still provide a useful ranking of species water needs for others to follow. In addition,
monthly values were reported in 2009 and 2010, which help provide an understanding
of how the water needs of container-grown shrubs change over a production season.
Since container plants are often re-spaced during production, KC values for ornamental
shrub taxa could be published by month or stage of growth to further aid in grouping as
they are moved from one production area to another.
Effluent volume was reduced at lower irrigation rates. In 2009, differences in
runoff volume collected were clear between each treatment because the same amount
of water was applied in each treatment replicate, and 75% fractions were based on the
same DWU calculations (averaged over every taxa replicate) as 100% fractions.
However, in 2010, the DWU was based on the highest water user in each treatment
replicate. Since less water was generally applied in the DWU treatments, they also
tended to produce lower runoff volumes. Because of these lower runoff volumes, NO3-N and PO43--P concentrations tended to be higher in DWU treatments. However, bulk
145

nutrient quantities recovered in effluent were generally higher at increasing irrigation
volumes. This would intuitively indicate that CRF fertilizers were being depleted, which
may have contributed to the decreased growth in the control across both studies relative
to the lower-volume DWU treatments.
Foliar nutrient analysis revealed noteworthy differences between treatments. In
every case that a difference occurred, the control had lower %P or %K than plants in
the DWU treatments. Following the depletion of substrate nutrients, dilution of nutrients
may occur in new plant tissues (Tyler et al., 1996). Since the runoff study shows
greater nutrient recovery from production beds irrigated at high volumes, it can be
presumed that fertilizer was also being leached from rooting substrates at higher rates
in the control compared to DWU treatments in the irrigation study as well. Additionally,
pH measurements in the irrigation study increased over time due, likely due to high pH
and alkalinity of the irrigation water. However, higher-volume irrigation treatments often
had higher pH at conclusion of both seasons. Consequently, reductions in water
applications improve nutrient efficacy and can help minimize the effects of the irrigation
water itself on substrate chemistry.
Particular insight was gained from using the two different sensor designs over
two seasons. Whereas the thin metal rods of the ThetaProbe (TDR) easily punctured
the substrate matrix and demonstrated good repeatability when reinserted in the same
position, the wide blades of the Decagon capacitance sensors made insertion difficult at
times. Large particles of pine bark or clumps of peat necessitated probe reinsertion in
different positions within containers for in situ monitoring. Although sensors were
inserted when substrate was moist to minimize the introduction of air pockets, the initial
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data suggests that poor substrate-sensor contact was achieved. Fortunately, the
probes were inserted into the substrate weeks prior to the beginning of both studies
while plants were watered regularly. This apparently helped the substrate settle for a
more uniform contact with the probes as their outputs became more consistent prior to
treatment initiation. Still, several sensors reported out of range values, which prompted
us to perform substrate-specific calibrations in the field. Sensors were left undisturbed
in situ, and a dry-down was performed over two days (see appendix section). The
resulting calibrations responded much better to changes in SVWC and were put into
use for the duration of the two studies.

Future Research
In the last several years, wireless sensor networks (WSN’s) have been in
development between several institutions involved in both software and hardware
engineering and testing (Lea-Cox, et al., 2013). During the development of their
system, we provided our experience in using the sensors to measure DWU in containergrown shrubs. In addition, the authors have begun tests at commercial nursery
operations in which they have received feedback from several enthusiastic growers.
This feedback has led to the development of software that is easily configured to
provide custom data output for data monitoring and irrigation control at the user’s
discretion (Kohanbash et al., 2013). Similar to our system, their approach involves the
use of user-defined irrigation set points. To accommodate changing management and
production objectives (such as pesticide applications, pruning, pulling plants for

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shipping, etc.), the program also features a suspend mode to ensure irrigation will not
activate at undesirable times.
These systems still represent a significant capital investment beyond the means
of smaller operations (Lichtenberg et al., 2013), so it will take years to achieve
widespread adoption by nurseries. In addition, many individual nurseries produce
hundreds of cultivars on relatively large acreages, which likely means that direct
measurement of substrate moisture for every plant grown by nurseries will not be
possible in the foreseeable future (Lea-Cox et al., 2013). This emphasizes the
importance of continuing to classifying plants according to water use groupings. Now, a
few dozen KC exist for temperate ornamental shrub taxa that can serve as “indicator”
species of relative water requirement, but currently no action has been taken to formally
define water use groups. As water use data is continually collected, water use classes
will become better defined as taxa, container size, substrate properties, growth rate,
phenological stage, season, and climate can all affect plant water needs.
Additionally, the development of KC for each of these taxa provides the
opportunity for the development of models to determine actual evapotranspiration (ET A)
using easily-measured parameters at the nursery site such as growth index, or percent
canopy closure (Beeson, 2010). A single model developed for Ligustrum japonicum has
been shown to successfully predict the water needs of Viburnum odoratissimum using
percent canopy closure (Beeson, 2010). Consequently, the construction of various
models to describe the water needs of a particular type or group of plants presents
another technique for grouping plants into irrigation groups. As with all models, these
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will have to be validated in real-world production conditions before achieving
widespread adoption.
Water has become a major topic in media, and agricultural water use is
particularly sensitive to widespread water shortages where the majority of crops are
irrigated. Each agricultural operation can take steps appropriate for their size and crops
to conserve water. Ornamental plant producers now have several options at their
disposal to reduce water consumption, including using sensors to determine DWU,
development of weather-based models, and improving irrigation application equipment.
Reducing water withdrawals will also reduce nutrient movement offsite to protect our
public water bodies. Growers who seek out new technologies and practices will likely
be rewarded as environmentally-conscious customers recognize their commitments to
environmental stewardship and choose to do business with them.

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LITERATURE CITED

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LITERATURE CITED

Beeson, Jr., R.C. 2010. Modeling actual evapotranspiration of Viburnum
odoratissimum during production from rooted cuttings to market size plants in 11.4-L
containers. HortScience 45(8):1260-1264.
Kohanbash, D., G. Kantor, T. Martin, and L. Crawford. 2013. Wireless sensor network
design for monitoring and irrigation control: user-centric hardware and software
development. HortTechnology 23(6):725-734.
Lea-Cox, J.D, W.L. Bauerle, M.W. van Iersel, G.F. Kantor, T.L. Bauerle, E. Lichtenberg,
D.M. King, and L. Crawford. 2013. Advancing wireless sensor networks for irrigation
management of ornamental crops: An overview. HortTechnology 23:717-724.
Lichtenberg, E., J. Majsztrik, and M. Saavoss. 2013. Profitability of sensor-based
irrigation in greenhouse and nursery crops. HortTechnology 23:770-774.
Tyler, H.H., S.L. Warren, and T.E. Bilderback. 1996 b. Reduced leaching fractions
improve irrgation use efficiency and nutrient efficacy. J. Environ. Hort. 14(4):199-204.
Warsaw, A.L., R.T. Fernandez, B.M. Cregg, and J.A. Andresen. 2009 a. Water
Conservation, Growth, and Water Use Efficiency of Container-grown Woody
Ornamentals Irrigated Based on Daily Water Use. HortScience 44(5):1308-1318.
Warsaw, A.L., R.T. Fernandez, B.M. Cregg, and J.A. Andresen. 2009 b. Containergrown Ornamental Plant Growth and Water Runoff Nutrient Content and Volume Under
Four Irrigation Treatments. HortScience 44(6):1573-1580.

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