Journal
Journal of Applied Horticulture, 19(3): 180-185, 2017
Appl
Effects of soil moisture stress on chlorophyll sensor readings in
Monarda and Veronica
Bruce L. Dunn*, Megha Poudel, Mark Payton1, and Jessica Lilienthal
Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater OK 74078-6027, USA.
1
Department of Statistics, Oklahoma State University, Stillwater OK 74078-6027, USA.
*E-mail:bruce.dunn@okstate.edu
Abstract
Different handheld nondestructive optical sensors have been developed for measuring leaf nitrogen status over time, but environmental
factors like water status may affect readings and correlations with leaf nitrogen. Thus, a greenhouse study was conducted to evaluate the
effect of different handheld optical sensors on determination of chlorophyll level under different irrigation regimes using tensiometers.
In this study, Monarda spicata ‘Sunny Border Blue’ and Veronica didyma ‘Gardenview Scarlet’ were grown at 5, 10, and 15 centibars
controlled irrigation soil moisture levels. Chlorophyll content meter (CCM) and atLEAF sensor measurements were taken from single
mature leaves of 10 plants from each treatment. Measurements were taken from the leaf tip, blade, and base of a single leaf excluding
mid rib. Both optical sensors were used for 14 days consecutively starting 65 days after planting. Similarly, foliar nitrogen analysis
was conducted from a single plant per treatment for 14 consecutive days. For the same 14 days, total daily water output and rate of
transpiration from each irrigation treatment were also determined. Sensor readings varied between the two sensors for both species.
Sensor sampling location was also significant for both atLEAF and CCM. For atLEAF, readings were greater at the leaf blade for both
species; whereas, CCM showed greater readings at the leaf blade in Veronica and at the leaf base and blade in Monarda. Similarly,
water stress × day interaction was significant for atLEAF in both species, whereas no water stress × day interaction was found in
CCM. The correlation between sensor location and leaf N was significant only for atLEAF at the leaf blade (r = 0.57) and base (r
= 0.47) in Monarda. No correlation with leaf N for either species was observed for CCM, thus atLEAF would be recommended for
nondestructive leaf nitrogen estimation. However, readings should be taken from the same location of the leaf to achieve consistent
readings and to minimize error.
Key words: atLEAF, CCM, tensiometer, leaf N
Introduction
Extensive periods of drought leads to water stress in a plant,
which can alter photosynthesis, transpiration rate and respiration,
along with other physiological processes within the plant (Zhao
et al., 2013). Win et al. (2015) reported that all plants that were
subjected to drought conditions had reduced biomass and leaf
area. Brown and Pezeshki (2007) also showed that water stress
caused the breakdown and inhibition of chloroplast activity. In
addition to reduced physiological processes, water stress leads
to the accumulation of harmful compounds in the cell, such as
hydrogen peroxide, ultimately leading to pigment bleaching
(Nikolaeva et al., 2010). The research also reported a 13 to
15% loss of chlorophyll content in wheat (Triticum aestivum L.)
plants that were exposed to drought durations of 3, 5, or 7 days
with plants showing a greater chlorophyll loss in the 5 and 7 day
treatments.
Thus, effective water management is crucial in commercial
agriculture, since under and over irrigation reduces crop yield
and quality. Irrigation efficiency is influenced by the method of
irrigation, type of crops, and irrigation schedule. Thompson et
al. (2007) stated that, inefficient management of drip irrigation
without proper scheduling increases nitrogen (N) leaching and
use of excess water than plants require. Soil volumetric water
or soil matric potential can be measured through use of sensors,
such as a tensiometer, to estimate available water in the soil.
A tensiometer is useful for controlling time and duration of
irrigation (Pardossi et al., 2009). The number of different matric
potential (set-points) in a tensiometer helps in creating different
water stress level according to plant requirement (Montesano et
al., 2015). Sanchez et al. (1983) claimed that increasing water
stress reduces the chlorophyll level in a plant up to 40%. Yet,
water stress induced loss of chlorophyll did not affect leaf N
content and rate of photosynthesis compared to nutrient induced
chlorophyll loss.
Nitrogen is a major structural element of the plastid chlorophyll
(Basyouni and Dunn, 2013). Due to this close relation, chlorophyll
meters are regularly used to make N fertilizer recommendations
for crops (Murdock et al., 1997). Determination of plant N
status through measurements of leaf chlorophyll is a common
practice in plant science (Brito et al., 2011). Destructive
methods of determining chlorophyll and N measurement,
through use of organic solvents, are laborious, costly, and time
consuming practices; however, nondestructive methods using
handheld devices are widely used in the field to generate instant
recommendations (Munoz-Huerta et al., 2013). The Soil Plant
Analysis Development (SPAD) chlorophyll meter (Minolta
Camera Company, Tokyo, Japan) was initially used in 1963 for
leaf N estimation in rice (Oryza sativa L.), but has since been used
on horticultural crops too (Wood et al., 1993). SPAD readings
Journal of Applied Horticulture (www.horticultureresearch.net)
Effects of soil moisture stress on chlorophyll sensor readings in Monarda and Veronica
are calculated on the basis of transmission of red light at 650 nm
and infrared light at 940 nm (Xiong et al., 2015). An alternative
to SPAD is atLEAF, which is a newly developed sensor, and
has shown a positive correlation with SPAD in terms of sensor
readings and leaf chlorophyll content analyzed in the laboratory
(Dunn and Goad, 2015; Basyouni et al., 2015; Basyouni et
al., 2017). The atLEAF meter is based on the transmission of
red light at 660 nm and far red light at 940 nm to estimate the
chlorophyll content (Basyouni and Dunn, 2013). Another sensor,
Chlorophyll Content Meter-200 (CCM-200) was developed to
report a chlorophyll content index (CCI) of a leaf (Biber, 2007;
Silla et al., 2010,) and the CCI calculated by CCM-200 was also
strongly correlated with chlorophyll a content in leaves of three
coastal wetland plant species (Biber, 2007). The CCM-200 uses
a slightly varied form of red light (653 nm) and near-infrared
(931 nm) light for its calculation of chlorophyll content (OptiSciences, 2014a).
A number of factors including sensor data collection protocol,
plant growth stage, and environmental factors can affect sensor
readings. Loh et al. (2002) noted that leaf age, sampling time,
nutrient interactions, and complex source-sink relationships can
affect the ability to detect N. Basyouni et al. (2015) noted that
plant water status may also affect sensor readings. The objective
of this study was to evaluate if chlorophyll sensor readings are
affected by soil moisture status.
Materials and methods
Plant material, growth conditions, and irrigation treatments:
On 17 December 2014, rooted plugs of bee balm (Monarda
spicata L. ‘Sunny Border Blue’) and Gypsyweed (Veronica
didyma L. ‘Gardenview Scarlet’) in 72 cell trays were obtained
from Park Seed (Greenwood, SC). Plugs were transplanted into
15.24 cm diameter and 1.35 L volume pots with about 0.35 kg
902 Metro-Mix media (Sun Gro Horticulture, Bellevue, WA)
12 day later. A single plant was placed in each pot and plants
were grown in the Department of Horticulture and Landscape
Architecture research greenhouses in Stillwater, OK under natural
photoperiods. Temperature was set at 21°C/18°C day/night with a
photosynthetic photon flux density (PPFD) range of 300 to 1200
μmol m-2 s-1 at 1200 HR. Plants received 200 mg L-1 20N-10P20K Jack’s Professional® General Purpose acidic fertilizer
(J.R. Peters Inc., Allentown, PA) at each watering event. Pots
were drip irrigated daily based on a tensiometer (IRROMETER,
Riverside, CA) settings of 5, 10, or 15 centibars (cb) starting on
6 February 2015.
atLEAF, CCM, transpiration, water usage, and leaf N
concentration: Individual plants were scanned from the same 10
pots per treatment using an atLEAF (FT Green LLC, Wilmington,
DE) and CCM-300 (Opti-Sciences, Inc., Hudson, NH) chlorophyll
meters every day (total of 14 rating dates) in the afternoon starting
on 23 February 2015. For each pot, three atLEAF and three CCM
measurements were taken from one mature leaf from the tip,
blade, and base of the leaf not including the midrib in the middle
of the canopy. Foliar analysis consisted of collecting all leaves
from a single plant per treatment for total leaf N per sampling
treatment daily. Leaf samples were analyzed for total N content
(g kg-1 DM) by the Soil, Water and Forage Analytical Laboratory
(SWFAL) at Oklahoma State University, using a LECO TruSpec
181
Carbon and N Analyzer (LECO Corporation, St. Joseph, MI).
Total daily water output from the irrigation was collected from
each tensiometer treatment. A LI-1600 Steady State Porometer
(LI-COR, Inc., Lincoln, NE) was utilized to collect transpiration
rates from each plant. At the end of the study, data was collected
on shoot weight (stems cut at media level) after drying for 2 days
at 52.2°C from the same 10 plants used for sensor readings.
All statistical analyses were conducted with SAS Version 9.4
(SAS Institute, Cary, NC) for each plant species separately.
Analysis of variance methods were used to determine the effects
of treatment. When measurements were taken over different
days, a repeated measures model and planned comparisons of
treatments for a given day were made. Correlation analyses of
atLEAF, CCM, and transpiration readings with leaf N samples
were also computed. In instances where interactions were
insignificant, main effect means were reported and analyzed.
Significance was set at the 0.05 level, except for correlation in
which 0.001 and 0.0001 were also considered.
Results
Leaf senor location: For both V. ‘Gardenview Scarlet’ and M.
‘Sunny Border Blue’, no sensor leaf location interactions were
observed; however, location as a main affect was significant (P
< 0.0001) for both genera. Leaf blade atLEAF readings were
greater than any other treatment for both V. ‘Gardenview Scarlet’
and M. ‘Sunny Border Blue’, followed by leaf base values,
which were also different from leaf tip reading (Table 1). For
V. ‘Gardenview Scarlet’, leaf base CCM values were greatest
(Table 1). CCM readings taken from the leaf tip were the lowest,
while CCM values for leaf blade were intermediate between
leaf base and leaf tip values (Table 1). Leaf base and leaf blade
had greater CCM values than the leaf tip for M. ‘Sunny Border
Blue’ (Table 1).
atLEAF and water stress: For both V. ‘Gardenview Scarlet’ and
M. ‘Sunny Border Blue’, tensiometer × day were significant (P
< 0.0001). At 8 days after start of the water stress treatments, V.
‘Gardenview Scarlet’ plants at 5 or 15 cb had a greater atLEAF
value than plants at 10 cb (Table 2). At 14 days, plants watered
at 10 and 15 cb had greater atLEAF values than 5 cb plants (Table
2). For M. ‘Sunny Border Blue’ at day 1, the 15 cb plants had a
greater atLEAF value than 5 cb plants, but neither treatment was
different from the 10 cb plants (Table 2). At day 2 the 15 cb M.
‘Sunny Border Blue’ plants had a greater atLEAF value than the
5 and 10 cb treatments; whereas, at 12 days plants watered at 5 cb
had a greater atLEAF value than the 10 or 15 cb plants (Table 2).
Table 1. Effect of optical sensor sampling location within a leaf of
Monarda ‘Gardenview Scarlet’ and Veronica ‘Sunny Border Blue’
Genus
Monarda
Veronica
Sensor leaf
location
atLEAF value
(unitless)
CCM-300 value
(mg m-2)
Tip
48.2c
445.9cz
Blade
51.0a
472.3b
Base
49.0b
477.4a
Tip
54.6c
482.4b
Blade
56.2a
529.4a
Base
55.6b
524.3a
Means (n = 10) within columns for each sensor reading followed by the
same letter are not significantly different P < 0.05.
z
Journal of Applied Horticulture (www.horticultureresearch.net)
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Effects of soil moisture stress on chlorophyll sensor readings in Monarda and Veronica
Table 2. Optical sensor and transpirations rate readings for Monarda ‘Gardenview
Scarlet’ and Veronica ‘Sunny Border Blue’ at different tensiometer settings over a 14
day period
Day Tensiometer
atLEAF value
CCM-300 value
Transpiration rate
setting
(unitless)
(mg m-2)
(μg cm-2 s-1)
(centibars) Monarda Veronica Monarda Veronica Monarda Veronica
1
5
461.0az
3.3ay
47.7az
52.8bz
443.0bz
2.5ay
2
3
4
5
6
7
8
9
10
11
12
13
14
10
47.5a
55.2ab
389.1c
491.2a
3.8a
3.0a
15
50.0a
56.4a
425.0b
468.9ab
2.4a
2.9a
5
52.2a
57.4b
464.1a
487.8a
2.9a
4.3a
10
52.4a
57.2b
411.2b
414.8b
4.3a
3.0b
15
52.4a
60.3a
443.6a
416.5b
3.6a
3.2b
5
52.6a
57.1a
439.4a
491.8a
5.5b
6.0a
10
53.4a
57.9a
454.9a
493.3a
7.8a
6.1a
15
51.8a
55.9a
447.2a
486.4a
4.9b
6.0a
5
50.6a
55.1a
484.5ab
515.7a
4.5a
4.6a
10
52.4a
56.1a
494.6a
515.7a
3.9a
4.1a
15
51.9a
55.1a
468.6b
514.8a
4.2a
4.5a
5
50.1a
58.3a
523.6a
564.7b
1.5b
2.3a
10
50.8a
57.8a
497.6b
591.6ab
6.3a
2.6a
15
49.6a
58.1a
506.1ab
612.5a
1.4b
2.0a
5
49.8a
56.2a
479.2a
528.8a
2.9a
3.3a
10
50.3a
56.9a
460.2a
549.4a
1.8a
2.3a
15
50.0a
56.1a
467.9a
510.7a
1.5a
2.8a
5
49.6a
56.0a
460.2a
553.9a
4.8a
5.4a
10
51.1a
56.4a
469.3a
533.3ab
3.8a
3.2b
15
51.9a
54.6a
481.1a
510.5b
3.0a
2.9b
5
51.5a
56.6a
505.6a
552.6a
3.5a
4.5a
10
48.3b
55.5a
475.6b
527.3a
4.4a
3.1b
15
49.9ab
55.7a
489.0ab
543.6a
2.1a
3.7ab
5
47.9a
54.8a
450.8b
526.1a
4.0a
5.1a
10
48.3a
56.9a
453.7b
508.4a
5.2a
4.2a
15
47.7a
54.1a
498.4a
531.0a
4.2a
4.5a
5
46.0a
53.2a
548.4a
554.1a
2.2a
3.0a
10
46.9a
54.7a
513.3b
561.0a
2.3a
2.5a
15
47.3a
55.1a
527.5b
581.6a
1.5a
2.6a
5
48.2a
54.1a
450.6a
473.0b
3.5a
3.5a
10
48.5a
55.7a
469.2a
528.4a
2.1a
2.1b
15
47.3a
53.4a
444.4a
508.0a
2.8a
2.8ab
5
47.6a
56.1a
451.4a
495.7a
5.3a
4.1a
10
48.3a
53.6b
434.1a
502.5a
4.3a
4.2a
15
48.5a
52.6b
453.6a
503.0a
1.8b
2.6b
5
47.0a
52.0a
411.9a
457.8a
3.4a
4.4a
10
47.3a
54.0a
418.0a
471.5a
3.7a
4.9a
15
48.8a
52.3a
424.8a
448.7a
2.9a
4.2a
5
44.8b
52.3a
455.3b
499.6a
3.5a
3.5a
10
48.1a
54.3a
455.5b
513.3a
2.5a
4.1a
15
47.8a
54.3a
479.1a
523.1a
2.0a
3.0a
Means (n = 30) within columns for each date followed by the same letter are not
significantly different P < 0.05.
y
Means (n = 5) within columns for each date followed by the same letter are not
significantly different P < 0.05.
z
CCM and water stress: For both V. ‘Gardenview
Scarlet’ and M. ‘Sunny Border Blue’ no CCM leaf
sensor location interactions occurred; however,
water stress × day was significant (P < 0.0001).
CCM values were greater for 5 cb V. ‘Gardenview
Scarlet’ plants at 1 day. At 2 days, both 5 and 15 cb
plants had greater CCM values than 10 cb plants.
At 4 days, 10 cb V. ‘Gardenview Scarlet’ plants had
a greater CCM value than 15 cb plants, but neither
was different from 5 cb plants (Table 2). At 5 and
8 days, 5 cb V. ‘Gardenview Scarlet’ plants had a
greater CCM value than 10 cb plants, but neither
treatment was different from 15 cb plants (Table 2).
CCM value was greatest at 15 cb for V. ‘Gardenview
Scarlet’ plants at 9 and 14 days, while plants in the 5
and 10 cb treatments did not differ (Table 2). At 10
days, the 5 cb treatment was greater than both the 10
and 15 cb treatments, which were not different from
each other (Table 2). For M. ‘Sunny Border Blue’
at 1 day, the 10 cb plants had a greater CCM value
than plants grown at 5 cb though neither treatment
was different from 15 cb plants. CCM was greatest
for 5 cb M. ‘Sunny Border Blue’ plants at 2 days.
At 5 days, M. ‘Sunny Border Blue’ plants grown at
15 cb had a greater CCM value than plants grown
at 5 and 10 cb, but did not differ from the other two
treatments. M. ‘Sunny Border Blue’ plants grown at
5 cb on 7 days had greater CCM values than plants
grown at 15 cb, but neither treatment differed from
the 10 cb treatments. Both 10 and 15 cb M. ‘Sunny
Border Blue’ plants had greater CCM values than
the 5 cb treatment at 11 days.
Transpiration: No water stress × day interaction
was found for V. ‘Gardenview Scarlet’ though both
main effects were significant (P = 0.006) and (P <
0.0001), respectively. Transpiration rate was greater
for 10 cb V. ‘Gardenview Scarlet’ plants at 3 and 5
days, while at 12 days plants grown at 5 and 10 cb
had greater transpiration rates than 15 cb plants. For
M. ‘Sunny Border Blue’ a significant (P = 0.019)
water stress × day interaction was observed. M.
‘Sunny Border Blue’ plants had greater transpiration
rates for 5 cb plants at 2 and 7 days, while on 8 and
11 days the 5 cb plants had a greater transpiration
rate than 10 cb plants, but neither treatment differed
from plants grown at 15 cb. For M. ‘Sunny Border
Blue’, transpiration rates were greatest and did not
differ for both 5 and 10 cb and were greater than 15
cb on day 12 (Table 2).
Total daily water, leaf N, EC, and plant dry
weight: No interactions were seen for total daily
water use, leaf N, EC, or plant dry weight. Day
main effects were significant for total daily water (P
= 0.0136) and V. ‘Gardenview Scarlet’ leaf N (P =
0.0003). Total daily water use was greatest on 1 day,
but did not differ from 3 d, which did not differ from
6, 8, 9, 10, 11, 12, 13, and 14 days (Table 3). Plants
had the least amount of water at 4 d, but total water
Journal of Applied Horticulture (www.horticultureresearch.net)
Effects of soil moisture stress on chlorophyll sensor readings in Monarda and Veronica
Table 3. Amount of water applied and leaf nitrogen (N) concentration collected
for Monarda ‘Gardenview Scarlet’ and Veronica ‘Sunny Border Blue’ plants
watered using a tensiometer
Day
Total daily water
Monarda leaf N
Veronica leaf N
(mL)
(g kg-1 DM)z
(g kg-1 DM)z
1
2
3
4
5
6
7
8
9
10
11
12
13
14
90.3a
7.0cd
57.7ab
1.3d
7.7cd
27.7bcd
14.0cd
44.0bc
24.0bcd
25.7bcd
45.0bc
30.3bcd
28.7bcd
47.3bc
0.76ay
------x
0.71a
-----0.69ab
-----0.60cd
-----0.63bc
-----0.59cd
-----0.55d
------
0.64a
-----0.61a
-----0.63a
-----0.62a
-----0.61a
-----0.63a
-----0.59a
------
Sample included taking all leaves from a single plant.
Means (n = 3) within genera followed by the same letter are not significantly
different P < 0.05. xData not taken.
z
y
Table 4. Pearson’s correlation coefficient matrix on measured leaf nitrogen
(N), transpiration, and optical sensor parameters for Monarda and Veronica
watered using a tensiometer
Transpiration rate
Monarda leaf N
(g kg-1 DM)z
Veronica leaf N
(g kg-1 DM)z
0.109
-0.197
0.360
-0.222
atLEAF blade
0.574**y
0.056
atLEAF base
0.475*
0.083
CCM tip
-0.157
0.021
CCM blade
-0.165
0.066
CCM base
-0.045
0.288
atLEAF tip
Sample included taking all leaves from a single plant. For transpiration rate
and all sensors (n = 21) and (n = 105), respectively.
y
P ≤ 0.05 (*), P ≤ 0.001 (**), or P ≤ 0.0001 (***).
z
Table 5. Pearson’s correlation coefficient matrix on measured optical sensor
parameters for Monarda ‘Gardenview Scarlet’ and Veronica ‘Sunny
Border Blue’ watered using a tensiometer. (n = 105)
atLEAF
blade
atLEAF
base
CCM
tip
CCM
blade
CCM
base
0.698***
0.130
0.094
0.107
0.706***
0.113
0.132
0.141
0.528
0.0819
0.106
0.825***
0.737***
Monarda
atLEAF tip
0.636***
z
atLEAF blade
atLEAF base
CCM tip
0.850***
CCM blade
Veronica
atLEAF tip
atLEAF blade
atLEAF base
0.558***
0.322**
0.431***
0.465***
0.333**
0.417***
0.420***
0.465***
0.333**
0.217*
0.245*
0.260**
0.767***
0.628***
CCM tip
0.695***
CCM blade
P ≤ 0.05 (*), P ≤ 0.001 (**), or P ≤ 0.0001 (***).
z
183
collected did not differ from 2, 5, 6, 7, 9, 10, 12, and 13 days
(Table 3). For V. ‘Gardenview Scarlet’ leaf N was greatest at
1, 3, and 5 days, but 5 days did not differ from 9 days. The
lowest leaf N concentration was found at 13 days, but did not
differ from 7 and 11 days. No differences were observed for
EC, which ranged from 0.4 mS cm-1 at 11 days to 3.1 mS cm-1
at 7 days for V. ‘Gardenview Scarlet’ and 0.9 mS cm-1 at 10
days to 2.68 mS cm-1 at 5 days for M. ‘Sunny Border Blue’
(data not shown). Similarly, no differences were observed in
shoot dry weight of V. ‘Gardenview Scarlet’, which ranged
from 5.1 g to 5.9 g in different soil moisture levels and 3.8 g
to 4.6 g for M. ‘Sunny Border Blue’ (data not shown).
Correlations: atLEAF readings from the leaf blade and base
were correlated with leaf N for V. ‘Gardenview Scarlet’;
however, no correlations were seen for transpiration rate and
sensors with leaf N for M. ‘Sunny Border Blue’ (Table 4).
Correlations among leaf sampling location within a senor
was significant for both plants (Table 5). atLEAF and CCM
sensors were not correlated for V. ‘Gardenview Scarlet’, but
were correlated for M. ‘Sunny Border Blue’ (Table 5).
Discussion
Different readings were observed among the two optical
sensors. Variation in readings occurred because the
sensors work at different wavelengths, sampling areas, and
compensation algorithms. atLEAF uses 660 nm and 940 nm
wavelength of light to measure the transmittance of red and
near infrared light through leaves (Basyouni and Dunn, 2013),
while CCM operates in wavelength of 700 to 710 nm and 730
to 740 nm for red and near infrared wavelength, respectively
(Opti-Sciences, 2014b). In both sensors, readings from the
leaf tip, leaf blade, and leaf base were different for both V.
‘Gardenview Scarlet’ and M. ‘Sunny Border Blue’ (Table 1).
atLEAF showed greater readings at the leaf blade for both
species, whereas CCM showed greater readings at the leaf
base in V. ‘Gardenview Scarlet’ and at the leaf blade and
base in M. ‘Sunny Border Blue’. Bolhar-Nordenkampf and
Grunweis (1987) suggested that the difference in chlorophyll
content in different leaf locations has an anatomical relation.
The additional layers of palisade and spongy parenchyma in
veins and blade of leaves increases photosynthetic pigment
chlorophyll in these areas. In contrast, the tip and base of a leaf
has a lower chlorophyll concentration (Bolhar-Nordenkampf
and Grunweis, 1987; Dunn and Goad, 2015; Ticha, 1985).
Greater differences between readings were observed with
the CCM meter in different water stress conditions than with
the atLEAF sensor. Similar to this study where we observed
no difference between water stress treatments for most days,
others have reported no difference in atLEAF readings under
different environmental conditions (Novichonok et al., 2016;
Zhu et al., 2012). For most days, the readings of both sensors
were different for both species in different water stress
conditions, which could be because of the drought tolerant
nature of V. ‘Gardenview Scarlet’ and M. ‘Sunny Border
Blue’ (Gilman and Delvalle, 2014; Kessler, 2008), resulting
in greater readings even if water-stressed. Upadhyaya (2005)
also reported greater SPAD readings in peanut (Arachis
hypogaea L.) in post-rainy season than during the rainy season.
Journal of Applied Horticulture (www.horticultureresearch.net)
184
Effects of soil moisture stress on chlorophyll sensor readings in Monarda and Veronica
In contrast, Yuan et al. (2016) reported decreasing chlorophyll
content in tomato (Solanum lycopersicum L. cv. Jinfen 2) leaves
with increasing water stress. In addition, differences between
different water stress conditions were not observed in that study
for chlorophyll sensor readings. Another reason for this might
be the malfunction of a tensiometer because of cavitation, which
will not allow the desired water stress level to be maintained
(Tarantino and Mongiovi, 2001). Also, sensor-based leaf
chlorophyll and leaf N estimation is highly influenced by
environmental conditions including light, temperature, nutrients,
and leaf characteristics like age of the leaf and location of the leaf
(Minotta and Pinzauti, 1996; Xiong et al., 2015).
In this study, there was a significant difference in rate of
transpiration among the three tensiometer set points of 5, 10 and
15 cb. With increasing water stress, both V. ‘Gardenview Scarlet’
and M. ‘Sunny Border Blue’ showed a lower rate of transpiration.
Both genera reduced the transpiration loss during the waterstressed condition. Increasing water stress forces plants to close
stomata and reduce leaf area, which reduces transpiration (Akinci
and Losel, 2012; Arve et al., 2011; Waister and Hudson, 1970).
In addition, Hsiao (1973) claimed that besides water availability,
temperature, light, humidity, and wind also effect transpiration.
Total daily water input had a linear downward slope, which
means rate of daily water input was decreasing with increasing
plant age. Briggs and Shantz (1913) reported similar findings
from the Von Seelhorst’s (1910) experiment on water requirement
of lupin (Lupinus angustifolius L.). Climatic conditions highly
influences the water requirement in plants. For example, a
decrease in nutrient content in the substrate reduces the rate of
dry matter accumulation with increasing plant age, which further
increases water demand to maintain the balance, but would not be
the case if additional nutrients are supplied. Water requirement is
inversely proportional to rate of dry matter accumulation (Briggs
and Shantz, 1913). Therefore, no conclusion is given for age of
plant and water requirements.
Leaf N concentration also decreased with increasing age in V.
‘Gardenview Scarlet’. The newly formed leaves contain high
N content and with no additional N, leaf N decreases with
increasing plant age (Hikosaka et al., 1994; Yang et al., 2014). In
contrast, spinach (Spinacia oleracea L.) grown with high amount
of N had shown greater leaf N and chlorophyll content with
increasing plant age (Bottrill and Possingham, 1969). Correlation
between atLEAF base and blade with V. ‘Gardenview Scarlet’
leaf N was observed. Dunn and Goad (2015) also observed a
greater correlation between atLEAF tip and blade with leaf N in
ornamental cabbage (Brassica oleracea var. capitata L.). None
of the sensors showed correlation with leaf N for M. ‘Sunny
Border Blue’. Use of SPAD in a study of 12 different cultivars of
red maple (Acer rubrum L.) reported nonsignificant correlations
between sensor readings and leaf N content (Sibley et al.,
1996). Similarly, study of three different cultivars of peace lily
(Spathiphyllum Schott) also found that SPAD readings and leaf
N were not correlated (Wang et al., 2004). Xiong et al. (2015)
states that the sensor readings are affected by environmental factor
and leaf characteristics. Thus, cultivar specific calibration of
sensors is essential for proper functioning of sensors. Although
CCM did not show a correlation with leaf N in both plant species
in our study, other studies found a positive correlation between
CCM and leaf N (Cate and Perkins, 2003; Ghasemi et al., 2011)
Based on the study, leaf sampling location affected the
sensor readings. The readings were also affected by the leaf
characteristics and type of sensor used. atLEAF and CCM values
were not correlated. CCM readings were not correlated with leaf
N, thus would not be recommended for use in determining leaf
N, however, atLEAF showed correlation with leaf N even under
various soil moisture conditions. No clear pattern was observed
for greater or lower sensor values in different tensiometer settings;
however, plant water status could affect sensor readings. This is
similar with findings from Yuan et al. (2016), who reported that
soil moisture status can affect leaf chlorophyll status of tomato.
Differences may be more influenced by some outside factor
like number of samples, time of sampling, or different water
stress level. Zhang et al. (2011) reported a midday depression
phenomenon that occurred in water-stressed oriental lily (Lilium
L. cv. Sorbonne), but not in the control group, that reduced gas
exchange around the plant. Plant water status can be difficult to
determine when plants require more water because of limited
water holding capacity of soilless mix (Fonteno et al., 1981;
Karlovich and Fonteno, 1986). This problem can be solved
through the use of a tensiometer by directly measuring the water
availability in the substrate, but proper care and maintenance
should be done for accurate functioning of the tensiometer.
Although research is limited on conducting automated water
management with a tensiometer, different set points help in
maintaining different water regime according to plant needs
(Montesano et al., 2015). Future research should look at tracking
plants over the course of production, different species and even
cultivars, increasing number of samples, optimum tensiometer set
points for potted plants, and observing how environmental factors
affect or interact with plant water status and sensor readings.
Acknowledgements
This work was supported by the USDA National Institute of Food
and Agriculture, Hatch project and the Division of Agricultural
Sciences and Natural Resources at Oklahoma State University.
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Received: December, 2016; Revised: January, 2017; Accepted: March, 2017
Journal of Applied Horticulture (www.horticultureresearch.net)