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Journal of Plant Nutrition
ISSN: 0190-4167 (Print) 1532-4087 (Online) Journal homepage: http://www.tandfonline.com/loi/lpla20
Relationship between chlorophyll meter readings
and nitrogen in poinsettia leaves
Bruce L. Dunn, Hardeep Singh & Carla Goad
To cite this article: Bruce L. Dunn, Hardeep Singh & Carla Goad (2018) Relationship between
chlorophyll meter readings and nitrogen in poinsettia leaves, Journal of Plant Nutrition, 41:12,
1566-1575, DOI: 10.1080/01904167.2018.1459697
To link to this article: https://doi.org/10.1080/01904167.2018.1459697
Published online: 01 May 2018.
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Relationship between chlorophyll meter readings and nitrogen
in poinsettia leaves
Bruce L. Dunn
a
, Hardeep Singh
a
, and Carla Goad
b
a
Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater, OK, USA;
b
Department
of Statistics, Oklahoma State University, Stillwater, OK, USA
ARTICLE HISTORY
Received 29 June 2017
Accepted 28 November 2017
ABSTRACT
Chlorophyll meters are used for non-destructive estimation of leaf nitrogen
(N). The objective of this study was to evaluate the readings with several
chlorophyll meters (SPAD-502, atLEAF, and CCM-300), different sampling
sites on leaves, number of leaves used for sampling, and different types of
leaf N sampling on estimation of N in leaves of potted poinsettia (Euphorbia
pulcherrima)“Prestige Red.”Results showed that all meters gave readings
that were correlated for N determination and also were correlated with each
other. SPAD and atLEAF showed interaction between different N treatments
and different sampling sites on the leaves, while CCM readings were affected
by different sampling sites on the leaf. atLEAF readings showed interaction
between number of leaves sampled and different N treatments. Thus, during
sensor-based leaf N estimation, sampling site on leaf, number of leaves
sampled, and stage of plant development should be considered to minimize
error.
KEYWORDS
greenhouse; nutrient status;
sensor
Introduction
Nitrogen (N) is an essential element for plant growth, and needs to be applied in the correct amount
required by a plant. If applied more than needed by a plant, runoff will lead to environment pollution
as it moves to ground and surface water, whereas too little application of N will lead to lower yield
than crop potential and economical losses (Bullock and Anderson 1998). Besides effecting plant qual-
ity, N concentration of a plant affects pest and disease defense and tolerance to various stresses. The
requirement of N fertilizer rate varies among field and production practices (Schmitt and Randall
1994). So, researchers are using various devices like chlorophyll meters to collect reflectance measure-
ments to assess N status of crops without affecting crop yield and plant quality in a destructive deter-
mination (Rorie et al. 2011).
Chlorophyll, is a good indicator of leaf N concentration (Lohry and Schepers 1988). Modern agri-
cultural farming is interested in fast, non-destructive methods for estimating chlorophyll and N con-
tent, whereas old methods of using organic solvents to estimate leaf chlorophyll and N are still in use
but are time consuming and destructive methods (Murdock et al. 1997). There are non-destructive
hand-held devices available such as the soil plant analysis development (SPAD) and atLEAF sensors,
which can be used to measure chlorophyll content of green leaves (Loh, Grabosky, and Bassuk 2002).
The chlorophyll content meter (CCM) is also one of these devices (Ghasemi et al. 2011). Transmission
of red and near-infrared light through leaves is measured by these chlorophyll meters (Scharf, Brouder,
and Hoeft 2006). Since most leaf N is in proteins associated with chlorophyll, these instruments used
CONTACT Bruce L. Dunn bruce.dunn@okstate.edu 358 Ag Hall, Department of Horticulture and Landscape Architecture,
Oklahoma State University, Stillwater OK 74078-6027, USA.
© 2018 Taylor & Francis Group, LLC
JOURNAL OF PLANT NUTRITION
2018, VOL. 41, NO. 12, 1566–1575
https://doi.org/10.1080/01904167.2018.1459697
for estimating chlorophyll content can be used to correlate determinations with N content of a leaf
(Paven et al. 2004).
SPAD estimates chlorophyll content of a leaf by measuring transmittance of light through a leaf at
wavelengths of red (650 nm) and near-infrared (940 nm). According to Hawkins et al. (2007), the low-
est limit of wavelength used by SPAD, 650 nm, coincides with the spectral region associated with maxi-
mum chlorophyll activity, whereas the upper limit, 940 nm, compensates for factors such as leaf
thickness and water content. The SPAD sensor has been reported to be correlated with leaf N content
in various field and ornamental crops such as wheat (Triticum aestivum L.) (Jia et al. 2007), corn (Zea
mays L.) (Blackmer and Schepers 1995; Fox, Piekielek, and Macneal 2001), silver birch (Betula pendula
Roth) (Uddling et al. 2007), geranium (Pelargonium sp. L.) (Dunn, Shrestha, and Goad 2015), and
dianthus (Dianthus chinensis L.) (Basyouni, Dunn, and Goad 2016).
The atLEAF sensor is a recently developed inexpensive alternative for SPAD and measures trans-
mittance of light at wavelength of red (660 nm) and near-infrared (940 nm) (Zhu, Tremblay, and Liang
2012). SPAD and atLEAF report unitless values (Basyouni and Dunn 2013). Basyouni, Dunn, and
Goad (2015,2016) reported the use of atLEAF for estimating N status in poinsettia and dianthus and
concluded that atLEAF was more correlated to leaf N than normalized difference vegetation index
(NDVI). Zhu et al. (2012) reported positive correlations between SPAD and atLEAF for canola (Bras-
sica napus L.), wheat, barley (Hordeum vulgare L.), potato (Solanum tuberosum L.), and corn.
The CCM sensor measures transmittance of light through a leaf at wavelengths of red (635 nm) and
near-infrared (935 nm) (Richardson, Duigan, and Berlyn 2002). The sensor measures a chlorophyll
content index (CCI), which is correlated with chlorophyll content in leaves (Biber 2007). Ghasemi
et al. (2011) reported the use of CCM for estimation of N content and chlorophyll content in Asian
pears (Pyrus serotina Rehd.). Cate and Perkins (2003) reported use of CCM for chlorophyll estimation
in sugar maple (Acer saccharum Marshall). All three devices operate in a similar manner by fitting
over the top and bottom of a portion of the leaf to be sampled and have the ability to store, recall, and
average values. Some studies reported no correlation between chlorophyll meters and leaf N content.
For example, non-significant correlation was reported between SPAD and leaf N for red maple (Acer
rubrum L.) by Sibley et al. (1996). Lack of correlation may be due to a number of factors such as time
of sampling and varying locations on a single leaf (Linder 1980; Dunn and Goad 2015), leaf age (Loh
et al. 2002), water stress (Basyouni, Dunn, and Goad 2015), and leaf thickness (Peng et al. 1992). So,
the objectives of this study were to determine 1) the effect of SPAD, atLEAF, and CCM on leaf sam-
pling positions and number of leaves on the ability to estimate leaf N, 2) the effect of different leaf sam-
pling methods on estimating leaf N, and 3) if SPAD, atLEAF, and CCM are correlated with each other
and leaf N in potted poinsettia.
Materials and methods
Plant material and growth conditions
On 20 August 2014, rooted cuttings of poinsettia “Prestige Red”were obtained from SHS Griffin (Lisle,
IL). Cuttings were transplanted into standard (15.2-cm diameter and 1.35-L volume) pots with about
0.35 kg 902 a peat-based medium (Metro Mix, Sun Gro Horticulture, Bellevue, WA) 5 days later. A sin-
gle plant was placed in each pot, and plants were grown in the Department of Horticulture and Land-
scape Architecture Research Greenhouses at Stillwater, OK, under natural photoperiods. Temperature
was set at 21/14C day/night with a photosynthetic photon flux density (PPFD) range of 600 to
1200 mmol m
¡2
s
¡1
at 1200 HR.
Treatment conditions
Plants were divided into treatments of either 150 mg L
¡1
or 250 mg L
¡1
20N-4.37P-16.6K (Jack’s Pro-
fessional General Purpose acidic fertilizer, J.R. Peters Inc., Allentown, PA) added with irrigation water.
Pots were drip irrigated at a rate that allowed media saturation and »20% leaching as needed. Plants
JOURNAL OF PLANT NUTRITION 1567
were pinched at a seven internodes height on 8 September 2014, and fungicide (Banrot, Everris NA
Inc., Dublin, OH) was applied. Insecticide (TriStar, Cleary Chemical Corporation, Dayton, NJ) was
used to control whiteflies.
SPAD, atLEAF, CCM, plant growth, and leaf N correlation determination
Individual plants were scanned from the same ten pots per treatment using a SPAD-502 chlorophyll
meter (SPAD-502, Konica Minolta, Japan), atLEAF chlorophyll meter (FT Green LLC, Wilmington,
DE), and CCM-300 chlorophyll meter (Opti-Sciences, Inc., Hudson, NH) every week (total of six rat-
ing dates) in the morning starting 46 days after planting (DAP). For each pot, SPAD, atLEAF, and
CCM measurements were taken from one, three, or five mature leaves from the middle to upper level
of the plant either at the leaf tip, leaf blade not including the midrib, or leaf base not including the mid-
rib. Leaf foliar analysis consisted of collecting either ten fully developed leaves with petioles from a sin-
gle plant per treatment, ten fully developed leaves without petioles from a single plant per treatment,
ten leaves with petioles from three different plants per treatment and bulked, ten leaves without
petioles from three different plants per treatment and bulked, ten leaves from five different plants per
treatment and bulked using only the tip portion (top »1.5 cm), ten leaves from five different plants
per treatments using only the blade portion (middle »2.0–2.5 cm), ten leaves from five different plants
per treatments using only the base portion with petioles (bottom »2.0 cm), and ten leaves from five
different plants per treatments using only the base portion without petioles (bottom »2.0 cm) for total
leaf N per sampling treatment weekly. 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 Car-
bon and Nitrogen Analyzer (TruSpec LECO Corporation, St. Joseph, MI). At the end of the study, data
was collected on plant height (from the top of the pot to the highest point) and width (average of two
perpendicular measurements), and shoot weight (stems cut at media level) then dried for 2 days at
52.2C from the same ten plants weekly.
Statistics
Pots were arranged in a completely randomized design (CRD) with ten single pot replications. SPAD,
atLEAF, and CCM variables were analyzed using linear mixed models for repeated measures. Fixed
effects in the analysis included the fertilizer treatment, sampling location on the leaf for the sensor
position, the number of leaves sampled per plant, and DAP. Post hoc analyses of the means were con-
ducted using least significant difference (LSD) pairwise comparisons. Correlation analyses of SPAD,
atLEAF, and CCM readings at the three sampling locations with the eight different leaf N sampling
methods were also computed for each fertilizer treatment level. All tests of significance were performed
at the 0.05 level except correlations and main effects which were at the 0.05, 0.01, and 0.001 level. Data
analysis was generated using SAS/STAT software, version 9.4 (SAS Inc., Cary NC).
Results
Leaf sensor locations and sampling dates
For either SPAD or atLEAF, a significant interaction occurred between number of weeks for sampling
and sampling location within the leaves (tips, middle, and base), but not for CCM (Table 1). In all three
leaf locations, SPAD and atLEAF readings increased with increasing DAP, but atLEAF reading at 81
DAP was lower as compared to reading on 74 DAP for all sites on leaf. However, SPAD showed the
greatest readings if leaves were sampled at 81 DAP, a result that was different than all other sampling
dates (Table 2). atLEAF readings for leaves sampled after 74 DAP, except 81 DAP, were greatest, and
were different than atLEAF readings at other sampling dates and were lowest when leaves were sam-
pled after 46 DAP for either SPAD or atLEAF (Table 2). For every sampling date and sampling loca-
tion, atLEAF readings were greater than SPAD readings (Table 2).
1568 B. L. DUNN ET AL.
atLEAF, number of leaves sampled, and fertilizer treatment
For atLEAF, a significant interaction occurred between treatments of fertilizer and number of leaves
sampled (Table 1). For fertilizer treatment of 150 mg L
¡1
, atLEAF readings were greatest if three
mature leaves were sampled, a value that was not significantly different than atLEAF readings from
one or five mature leaves of a plant (Table 3). For fertilizer treatment of 250 mg L
¡1
, atLEAF readings
were greatest if one or five leaves were sampled; however, there was no difference between three and
five leaves (Table 3).
CCM, sampling dates, and fertilizer treatment
For CCM, a significant interaction occurred between treatments of fertilizer and sampling date
(Table 1). For fertilizer treatment of 150 mg L
¡1
, the CCM readings were greatest for leaves sampled
81 DAP, a value that was different than CCM readings at all other dates of sampling, whereas for fertil-
izer treatment of 250 mg l
¡1
, the CCM readings were greatest for leaves sampled at 74 and 81 DAP, for
which readings were different from CCM reading at all other sampling dates (Table 4). Lowest CCM
readings occurred for leaves sampled at 53 DAP for both fertilizer treatments (Table 4).
Leaf sensors, leaf locations, and fertilizer treatments
CCM was affected by location of leaf sampling, and SPAD was affected by treatment of fertilizer
(Table 1). CCM readings were greater if leaves were sampled from the middle of leaf blade not
Table 1. Test of results using three different optical sensors, two different fertilizer treatments, three leaf sampling methods, over a
six-week period, using different sampling locations within a leaf on poinsettia “Prestige Red”in 2014.
SPAD-502 atLEAF CCM-300
Treatment (TRT)
z
NS
Leaf (LF)
y
NS NS NS
Week (WK)
x
Sampling location (SL)
w
TRT £LF NS
NS
TRT £WK NS NS
WK £SL
NS
z
Treatments of either 150 mg L
¡1
or 250 mg L
¡1
20N-4.37P-16.6K (Jack’s ProfessionalÒGeneral Purpose acidic fertilizer, J.R. Peters Inc.,
Allentown, PA).
y
Means significant or nonsignificant (NS) at
P<0.05,
P<0.01, and
P<0.0001. Sensor readings were taken from one, three, or
five mature leaves from the middle to upper level of the plant.
x
Sensor readings were taken every week (total of six rating dates) in the morning starting 46 days after planting.
w
Sensor readings were taken either at the leaf tip, from the middle of the leaf not including the midrib, or toward the base of the leaf
not including the midrib.
Table 2. Effects of SPAD and atLEAF sensor readings on leaf sampling location over six weekly sampling days after planting (DAP)
using poinsettia “Prestige Red.”
Sampling location
z
46 DAP 53 DAP 60 DAP 67 DAP 74 DAP 81 DAP
SPAD (unitless)
Leaf tip 45.3h
y
46.5gh 49.3f 50.1def 50.7de 52.1bc
Leaf blade 46.3gh 49.4ef 50.7d 50.9cd 52.5b 53.8a
Leaf base 46.2gh 47.4g 49.8def 51.0cd 52.4b 53.9a
atLEAF (unitless)
Leaf tip 49.7i 51.8g 53.3ef 54.3de 55.5bc 55.3bc
Leaf blade 50.1i 52.8f 52.9f 54.9cd 56.8a 56.0ab
Leaf base 50.5hi 51.5gh 52.9f 54.6cd 56.3ab 56.0ab
z
Sensor readings were taken either at the leaf tip, blade not including the midrib, or toward the base of the leaf not including the
midrib.
y
Means within a sensor followed by same letter are not significantly different by LSD.
JOURNAL OF PLANT NUTRITION 1569
including the midrib and were significantly different from leaf tip and base readings (Table 5). SPAD
readings were greater in the 250 mg L
¡1
fertilizer treatment and were significantly different than those
in the 150 mg L
¡1
treatment (Table 5).
Leaf N, fertilizer treatment, sampling method, and sampling date
Leaf N was affected by treatment of fertilizer, leaf N sampling method, and sampling date
(Table 6). Leaf N was greater in plants treated with 150 mg L
¡1
of fertilizer than in plants
treated with 250 mg L
¡1
of fertilizer. Among different sampling methods of leaf N, leaf N was
greatest with samples collected from the leaf tip and leaf blade, but did not differ from samples
collected from a single plant with petioles, three plants with no petioles, or three plants with
petioles. For different sampling dates, leaf N was greatest if samples were collected 74 DAP and
was different from all other sampling dates (Table 6). Lowest leaf N was in leaves collected 46
DAP.
Correlation (r) for sampling methods and sensors at different leaf locations
For treatment of 150 mg L
¡1
of fertilizer, N concentration of leaves from five plants using only the leaf
base without petioles was correlated with SPAD readings from the leaf tip (0.915) and leaf blade
Table 3. Effects of two Jack’s fertilizer (20N-4.37P-16.6K) rates and number of leaves sampled for atLEAF optical sensor using poinset-
tia “Prestige Red.”
No. of leaves
z
atLEAF (unitless) atLEAF (unitless)
150 mg L
¡1
250 mg L
¡1
1 52.7b
y
54.8a
3 53.4ab 53.7bc
5 53.0b 54.2ab
z
Sensor readings were taken from one, three, or five mature leaves from the middle to upper level of the plant.
y
Means within a column followed by same letter are not significantly different by LSD.
Table 4. CCM-300 sensor values among two Jack’s fertilizer (20N-4.37P-16.6K) rates over six weekly sampling days after planting (DAP)
using poinsettia “Prestige Red.”
–Relative chlorophyll content (mg m
¡2
)—-
Fertilizer rate 46 DAP 53 DAP 60 DAP 67 DAP 74 DAP 81 DAP
150 (mg L
¡1
) 434.9g
z
322.3h 558.6cde 554.0e 574.3bcd 593.8a
250 (mg L
¡1
) 467.3f 332.4h 556.9de 556.4de 576.2abc 588.5ab
z
Means within a row followed by same letter are not significantly different by LSD.
Table 5. Main effects of leaf sampling location for CCM-300 sensor given as relative chlorophyll content and two Jack’s fertilizer (20N-
4.37P-16.6K) rates for SPAD using poinsettia “Prestige Red.”
Leaf location and fertilizer rate Sensor value
(mg m
¡2
)
Leaf tip
z
505.3b
y
Leaf blade
z
514.8a
Leaf base
z
508.8b
SPAD
150 (mg L
¡1
) 49.2b
y
250 (mg L
¡1
) 50.7a
z
Sensor readings were taken either at the leaf tip, from the middle of the leaf not including the midrib, or toward the base of the leaf
not including the midrib.
y
Means within a column followed by same letter are not significantly different by LSD.
1570 B. L. DUNN ET AL.
(0.954), while atLEAF readings from the leaf tip (0.874) and leaf base (0.874) were correlated (Table 7).
No correlation of any sampling method was found with CCM at any leaf location. For treatment of
250 mg L
¡1
of fertilizer, N concentration of leaves without petioles from a single plant was correlated
with atLEAF reading from the leaf blade with petioles and atLEAF readings from the leaf tip (Table 7).
N concentration of leaves from five plants using only the leaf blade was correlated with atLEAF read-
ings from the leaf tip and blade (Table 7). All three sensors were correlated with each other for all sam-
pling locations for either fertilizer treatments. In fertilizer treatment of 150 mg L
¡1
,rvalue ranged
from 0.564 to 0.997, whereas for fertilizer treatment of 250 mg L
¡1
,rvalue ranged from 0.570 to 0.998
(Table 8).
Table 6. Main effects of Jack’s fertilizer (20N-4.37P-16.6K), leaf nitrogen sampling method, and sampling date on poinsettia ‘Prestige
Red’.
Fertilizer rate (mg L
¡1
)
z
Leaf N Leaf N sampling method
y
Leaf N Days after planting
Leaf N
150 6.5a
x
Leaf tip from five plants 6.7a 46 6.1d
250 6.3b Leaf blade from five plants 6.7a 53 6.3bc
Leaf base no petiole from five plants 6.2c 60 6.4bc
Leaf base with petiole from five plants 5.9d 67 6.1cd
Single plant no petiole 6.4bc 74 7.0a
Single plant with petiole 6.4abc 81 6.5b
Three plants no petiole 6.5abc
Three plants with petiole 6.4abc
z
Main effects significant at p0.001 (
).
y
Sampling method consisted of collecting either ten fully developed leaves with petioles from a single plant per treatment, ten fully
developed leaves without petioles from a single plant per treatment, ten leaves with petioles from three different plants per treat-
ment and bulked, ten leaves without petioles from three different plants per treatment and bulked, ten leaves from five different
plants per treatment and bulked using only the leaf tip (top »1.5 cm), ten leaves from five different plants per treatments using only
the leaf blade (middle »2.0–2.5 cm), ten leaves from five different plants per treatments using only the leaf base with petioles (bot-
tom »2.0 cm), and ten leaves from five different plants per treatments using only the leaf base without petioles (bottom »2.0 cm) for
total leaf N per sampling treatment weekly.
x
Means (nD10) within a column followed by the same letter are not significantly different by LSD.
Table 7. Pearson correlation (r) matrix for measured sensor parameters and leaf nitrogen sampling methods for poinsettia “Prestige
Red”across six sampling dates. (nD6).
150 mg L
¡1
20-10-20 Jack’s fertilizer
Leaf nitrogen
sampling methods
SPAD
tip
z
SPAD
blade
SPAD
base
atLEAF
tip
z
atLEAF
blade
z
atLEAF
base
z
CCM
tip
CCM
blade
CCM
base
Single plant no petioles 0.550
y
0.653 0.447 0.746 0.596 0.723 0.513 0.514 0.504
Single plant with petioles 0.456 0.578 0.182 0.531 0.343 0.493 0.069 0.128 0.142
Three plants no petioles 0.777 0.810 0.546 0.730 0.556 0.711 0.279 0.351 0.374
Three plants with petioles 0.617 0.630 0.396 0.606 0.121 0.646 0.571 0.633 0.668
Leaf tip from five plants 0.544 0.616 0.242 0.555 0.199 0.551 0.238 0.316 0.344
Leaf blade from five plants 0.730 0.781 0.428 0.667 0.341 0.662 0.256 0.346 0.385
Leaf base no petioles from five plants 0.915
0.954
0.704 0.872
0.571 0.874
0.514 0.581 0.618
Leaf base with petioles from five plants 0.159 0.289 0.176 0.370 0.078 0.389 0.518 0.480 0.491
250 mg L
¡1
20-10-20 Jack’s fertilizer
Single plant no petioles 0.125 0.633 0.418 0.742 0.895
0.792 0.578 0.570 0.525
Single plant with petioles 0.672 0.757 0.574 0.841
0.702 0.646 0.649 0.645 0.654
Three plants no petioles ¡0.229 0.326 ¡0.038 0.515 0.690 0.403 0.180 0.153 0.105
Three plants with petioles ¡0.210 0.267 ¡0.195 0.402 0.550 0.120 ¡0.138 ¡0.180 ¡0.213
Leaf tip from five plants 0.018 0.517 0.111 0.683 0.785 0.521 0.331 0.301 0.266
Leaf blade from five plants 0.320 0.801 0.288 0.841
0.864
0.806 0.688 0.648 0.625
Leaf base no petioles from five plants 0.019 0.541 0.059 0.651 0.727 0.630 0.514 0.477 0.441
Leaf base with petioles from five plants 0.239 0.236 ¡0.007 0.340 0.227 ¡0.004 ¡0.054 ¡0.074 ¡0.056
z
Sensor readings taken from the leaf tip, leaf blade excluding the midrib, and leaf base.
y
Pearson correlation (r) significant at p0.05 (
), p0.01 (
), or p0.001 (
).
JOURNAL OF PLANT NUTRITION 1571
Discussion
In general, SPAD and atLEAF readings increased with time of plant growth. Basyouni, Dunn, and
Goad (2015), using two cultivars of poinsettias, also noted that sensor readings increased over time,
meaning that chlorophyll or N concentrations increased with stage of growth of the crop. Khoddamza-
deh and Dunn (2016) reported an increase in SPAD and atLEAF readings over time in chrysanthemum
(Chrysanthemum sp. L.), and Swiader and Moore (2002) reported that SPAD readings increased with
sampling dates for pumpkins (Cucurbita pepo L.). Ghosh (1973) and Neilsen et al. (1995) reported that
with increase in age of apple (Malus sp. Mill.) leaves within a growing season, SPAD readings increased
and was not due to an increase in N content but may have been due to an increase in leaf thickness
(Wooge and Barden 1987; Campbell et al. 1990). Results of Minotti, Halseth, and Sieczka (1994) con-
tradicted our findings as a decrease in SPAD readings was reported as potato plants aged.
Chapman and Barreto (1997) reported that at least four leaves per plot are needed, with several
observations per leaf for sensor-based N estimation. Perry and Davenport (2007) also reported that
single-leaf measurement using a chlorophyll meter was not an accurate method for N estimation.
CCM showed greater readings at 81 DAP in fertilizer treatment of 150 mg L
¡1
, while in fertilizer treat-
ment of 250 mg L
¡1
greater readings were at 74 and 81 DAP (Table 4). In both fertilizer treatments,
CCM readings were greatest at the maximum number of DAP, which may be due to effect of different
growth stage on chlorophyll or N accumulation. Ziadi et al. (2010) reported that chlorophyll meter
reading were affected by growth stages, and elongation stage was the best stage for estimating nutri-
tional status in wheat. Some other studies reported correlation of CCM with leaf N concentration
across sampling dates (Cate and Perkins 2003; Ghasemi et al., 2014), but our results showed that CCM
was not correlated with leaf N with any sampling method at all leaf sampling locations.
Dunn and Goad (2015) reported that SPAD readings were affected by leaf sampling location and
were greatest if the leaf tip or middle of leaf was sampled in ornamental cabbage (Brassica oleracea L.).
Yuan et al. (2016) also reported two-third position on the fourth fully expanded leaf of rice (Oryza sat-
iva L.) as the best position for sensor-based estimation of leaf N. Chapman and Barreto (1997) recom-
mended an area lying between about 40% and 70% from the leaf base to the tip for sensor-based N
estimation in maize (Zea mays L.). This result is similar to our results as CCM also was affected by the
Table 8. Pearson correlation (r) matrix for measured sensor parameters for poinsettia “Prestige Red”across six sampling dates and two
different Jack’s fertilizer (20N-10P-20K) rates. (nD18).
150 mg L
¡1
20-10-20 Jack’s fertilizer
SPAD tip
z
SPAD blade SPAD base atLEAF tip
z
atLEAF blade
z
atLEAF base
z
CCM tip CCM blade CCM base
SPAD tip 0.908
y
0.883
0.870
0.800
0.878
0.783
0.792
0.774
SPAD blade 0.876
0.931
0.842
0.946
0.658
0.666
0.665
SPAD base 0.844
0.849
0.892
0.802
0.801
0.785
atLEAF tip 0.905
0.961
0.661
0.661
0.658
atLEAF blade 0.920
0.599
0.590
0.564
atLEAF base 0.669
0.680
0.671
CCM tip 0.997
0.992
CCM blade 0.792
250 mg L
¡1
20-10-20 Jack’s fertilizer
SPAD tip 0.892
0.914
0.884
0.778
0.803
0.736
0.744
0.757
SPAD blade 0.831
0.873
0.860
0.794
0.577
0.581
0.591
SPAD base 0.846
0.804
0.810
0.687
0.707
0.724
atLEAF tip 0.915
0.940
0.718
0.733
0.730
atLEAF blade 0.894
0.570
0.579
0.580
atLEAF base 0.763
0.781
0.781
CCM tip 0.998
0.994
CCM blade 0.998
z
Sensor readings taken from the leaf tip, leaf blade excluding the midrib, and leaf base.
y
Pearson correlation (r) significant at p0.05. Not significant (NS), p0.05 (
), p0.01 (
), or p0.001 (
).
1572 B. L. DUNN ET AL.
leaf sampling location and showed greatest readings when the leaf blade was sampled. Dunn et al.
(2018) also reported that SPAD readings were affected by phosphorus and potassium concentration
present in the fertilizer. Loh et al. (2002) also reported differences in SPAD readings due to the effect
of nutrients other than N. Results of Loh et al. (2002) supported our results as SPAD readings were
greater for fertilizer treatment of 250 mg L
¡1
, while leaf N was higher in fertilizer treatment of 150 mg
L
¡1
. These results mean SPAD readings were also affected by fertilizer concentrations.
Khoddamzadeh and Dunn (2016) reported that leaf N concentration for two cultivars of chrysan-
themum were greatest at 38 days after treatment (DAT), which corresponds to our results that leaf N
concentration was highest at 74 DAP (35 DAT). It was also reported that leaf N concentration
increased with increasing fertilizer rate, which was in contrast to our results as leaf N was greatest in
the lowest fertilizer rate of 150 mg L
¡1
. Richardson and Hardgrave (1992) noted that higher rates of
fertilizer also produce more rapid growth in plants and as a result have lower plant N levels.
Perry and Davenport (2007) suggested that an individual leaf measurement was not an accurate
method for N estimation in apple. This result corresponds to our results as SPAD and atLEAF readings
at different leaf locations were correlated with leaf N if leaves were collected from five different plants
rather than from one plant. Leaf N was also greater if leaf N was measured in the leaf tip or leaf blade
of ten leaves from five different plants, whereas leaf N was significantly lower when samples were col-
lected from five different plants with petiole and without petiole and samples collected from a single
plant without petiole. Our results contradict Ecke et al. (2004) as SPAD and atLEAF readings were cor-
related with leaf samples not consisting of a petiole, though the recommendation is to send a leaf sam-
ple with petioles for foliar analysis for poinsettias. Leaf collections with or without petioles for leaf N
concentration varies, as Wang et al. (2004) detached leaves from petioles for leaf analysis in peace lily
(Spathiphyllum wallisii Regel).
Basyouni, Dunn, and Goad (2015) reported correlation of SPAD, atLEAF, and NDVI with each
other for two poinsettia cultivars “Prestige Red”and “Freedom Red.”Their results support our results
that SPAD, atLEAF, and CCM readings were correlated with each other for every sampling location on
a leaf and for both fertilizer treatments. In contrast, Khoddamzadeh and Dunn (2016) reported no cor-
relation of atLEAF with SPAD, NDVI, and leaf N for two cultivars of chrysanthemum. Zhu, Tremblay,
and Liang (2012) also reported correlation between SPAD and atLEAF for five different agronomic
crops grown in different environments.
Conclusion
Based on this study, different leaf sampling locations and sampling dates affected sensor readings in the
estimation of chlorophyll or N in leaves. Sensor readings increased with increasing plant age and corre-
sponded to an increase in N content over time. Readings were greatest if leaf N was sampled from the
leaf blade. SPAD was affected by different fertilizer treatments and was greater in 250 mg L
¡1
fertilizer
rate. SPAD, atLEAF, and CCM were correlated with each other for different leaf locations. CCM was
not correlated with leaf N for any sampling method, and thus cannot be recommended for leaf N esti-
mation of poinsettia “Prestige Red.”Leaf N was best correlated with SPAD sensor reading from the
leaf blade if N was sampled using the leaf base of five leaves without petioles. As supported by other
studies (Barreto 1997), the middle of leaf can be recommended as the best place for leaf N estimation.
However, it is important to consider that sensor-based N estimation also is affected by some environ-
mental conditions like light, temperature, as well as leaf thickness, leaf location, cultivar, plant age, and
nutrients other than N (Xiong et al. 2015; Minotta and Pinzauti 1996). Future research should look to
develop crop and cultivar specific linear models for different chlorophyll meters, which should consider
factors affecting sensor readings to minimize error in estimating N on basis of chlorophyll meters.
Funding
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.
JOURNAL OF PLANT NUTRITION 1573
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