4727
Views & Citations3727
Likes & Shares
SPAD meter
is a simple, portable diagnostic device that measures the greenness or relative
chlorophyll content of leaves. Compared with the traditional destructive
methods of chlorophyll extraction, the use of this tool device saves time,
space, and resources. The objective of this article is to review the
associations between the photosynthetic pigments content (chlorophylls,
carotenoids) extracted in dimethylsulfoxide or acetone, with the SPAD readings
in leaves for estimating their content non-destructively. To estimate photosynthetic pigment
content in flag leaves of wheat using the chlorophyll meter, linear models were
developed by Kumar and Sharma (2019) from the relationship between the Chlorophyll
meter SPAD readings and photosynthetic pigments, i.e., Chla (Chla=0.0690
× SPAD Value - 1.082), Chlb (Chlb=0.021 × SPAD Value -
0.396), total Chl (Total chlorophyll=0.090 × SPAD Value - 1.477) and total
carotenoids (Total Carotenoids=0.013 × SPAD Value - 0.074). Therefore, it is obvious that the portable chlorophyll
meters, SPAD-502 produced readings are associated with leaf photosynthetic
pigments and thus allow for a quick determination of the concentration of
photosynthetic pigments in the leaves of crop plants with high accuracy and avoiding
the use of chemical reagents and extensive laboratory protocols.
Keywords: Carotenoids, Chlorophyll,
Photosynthetic pigments, SPAD readings
INTRODUCTION
Chlorophylls and carotenoids are the most
abundant pigment molecules occurred in the chloroplast of leaf mesophyll cells
of green plants which trap the solar energy for photosynthetic process. The
amount of leaf photosynthetic pigments is key variables in characterizing
photosynthetic capacity and gross primary production in the biosphere [1-4].
Besides light harvesting, photosynthetic pigments play a central role in
photosystem protection and other growth functions [5,6]. Carotenoids are
composed of carotenes and xanthophylls and represents another key
photosynthetic pigment group. Being essential structural components of the
photosynthetic antenna, carotenoids participate in harvesting light energy for
photosynthesis [7,8]. In addition to the direct contribution in the
photosynthetic process, carotenoids are also involved in the defense mechanism
against oxidative stress [9,10] and play an essential role in the dissipation
of excess light energy and provide protection to reaction centers [11].
In eco-physiological studies amount of
chlorophylls indicates responses of plants to different stresses such as
nitrogen deficit [12,13], water deficit [14,15] and high irradiance [16-19].
Carotenoids play a role in protection of the photosynthetic machinery against
excess energy and their high content in leaf may indicate a photo inhibitory
stress [20-22]. In addition, the amount of chlorophyll content present in
plants, gives an indirect estimation of the nutrient status of the plants
because much of the leaf nitrogen is incorporated in chlorophylls [23].
Furthermore, leaf chlorophyll concentration is strongly associated to plant
stress and senescence [16].
Conventionally, the methods used
to estimate the photosynthetic pigment in laboratory as suggested by Porra et
al. [24] and Wellburn [25], are based on extraction of pigments from the plants
followed by estimation using spectrophotometry. Essentially, these methods are
destructive in nature, time consuming, requiring specific equipment’s and
solvents which are toxic to human health and environments. Moreover, the
methods are not applicable in the conditions, under which researchers want to
retain the whole plant as such.
Therefore, non-invasive,
inexpensive and rapid methods of
WHAT IS SPAD?
The Soil-Plant Analyses Development (SPAD) unit of Minolta Camera Co. built up
the SPAD-502 chlorophyll meter (Minolta Camera Co., Osaka, Japan). It is a portable, hand-held, light weight,
self-calibrating device used to calculate the amount of chlorophyll
non-destructively in plant leaves [35]. It is
widely used for the rapid, accurate and non-destructive measurement of
leaf photosynthetic pigments. It is being employed extensively in research as
well as for agricultural applications with a range of different plant species.
PRINCIPLE INVOLVES IN SPAD
CHLOROPHYLL METER
Markwell et al. [27] described the physical principles
and equations on which the functioning of the SPAD Chl meter was based. In
brief, this device measures the transmittance of light by leaves at two
different wavelengths: red (650-660 nm) and near-infrared (930-940 nm). Red
light is absorbed by chlorophylls and its absorption is associated with the
chlorophyll content. The peak absorbance areas of chlorophyll are in blue and
red regions. The wavelength ranges chosen to be used for measurements are the
red area where chlorophylls a and b absorbance is high and
unaffected by carotenoids (Chlorophyll Meter SPAD-502Plus; Instruction Manual
2009). Near-infrared absorption is used as a “reference value” for adjusting
the differences in leaf structure. However, the values given by the chlorophyll
meter are in SPAD units which have to be converted into physiological units
(pigment concentration: mg g–1 fresh weight
or dry weight and pigment content: mg m–2). Thus, the calibration curve between the chlorophyll
meter readings and chlorophyll content determined with an extraction method
should be generated before an attempt to assess physiological responses of
plants to environmental factors.
ASSOCIATION BETWEEN THE SPAD READINGS AND THE LEAF
PHOTOSYNTHETIC PIGMENTS
Kumar and
Sharma [36] using 468 contrasting wheat genotypes established the relationship
between the SPAD-502Plus meter readings and the leaf photosynthetic pigments
content estimated using spectrophotometer. Wide range of variability was
recorded in these genotypes for SPAD values and for amount of different
photosynthetic pigments. The SPAD values ranged from 21.0 to 54.7 with mean value of 44.09. The photosynthetic pigments
content (mg g-1 FW) (Chla (varied from 0.0893 to 4.0279
with mean value of 2.119), Chlb (ranged from 0.0144 to 1.3825 with
mean value of 0.573), total chlorophylls (varied from 0.1037 to 4.458 with mean
value of 2.693) and total carotenoids (varied from 0.0204 to 0.9889 with mean
value of 0.499)). The chlorophylls and carotenoids
contents in wheat leaf were found to be significantly correlated with the SPAD
value measured using the Chlorophyll meter SPAD-502Plus, as exhibited in Figures 1-4. The linear regression was
found to be significant in sorghum [37] and Malus domestica Borkh [38]
and some other tree species [39].
SPAD 502Plus readings have been found significantly
associated with laboratory estimated
Chla content in diverse wheat genotypes because in both the
techniques chlorophyll absorption property is used for chlorophyll measurement [27,36,40,41]. These research
workers also reported that SPAD-502 meter gives differing prediction responses
for different plant species, the calibration lines found species specific.
Therefore, calibration models demand individual regression for particular
species.
Kumar and
Sharma [36] depicted the relationship between the chlorophyll readings from
SPAD and the Chla contents in linear model (Chla=0.0690 ×
SPAD Value - 1.082) and an R2 value of 0.302** (n=468) was obtained
for SPAD chlorophyll meter, as depicted in Figure
1. Similarly, measured SPAD values were found to be significantly
associated with Chlb content in diverse wheat genotypes. Other
workers [27,40,41] also reported similar findings. Figure 2 shows the association between the SPAD values noted and
the Chlb content obtained in wheat leaves. The association between
the SPAD values and the estimated contents of Chlb was fit in linear
model (Chlb=0.021 × SPAD Value - 0.396), and an R2 value
of 0.240** (n=468) was obtained for SPAD chlorophyll meter by Kumar and Sharma
[36] as shown in Figure 2.
Earlier
research workers like Brito et al. [40]
and Shah et al. [41] also reported similar findings. The correlation
between the SPAD values and the content of total chlorophyll was fit in linear
model (Total chlorophyll=0.090 × SPAD Value - 1.477) and an R2 value
of 0.332** (n=468) was obtained by Kumar and Sharma [36] (Figure 3).
The
relationship between the SPAD values and the contents of total carotenoids was
also found fit in linear model (Total Carotenoids=0.013 × SPAD Value - 0.074),
and an R2 value of 0.147** (n=468) was obtained for SPAD chlorophyll
meter by Kumar and Sharma [36] as depicted in Figure 4. Comparatively lesser R2 value with carotenoids
indicated indirect association of carotenoids with SPAD values. Such association
findings were also reported earlier in cotton [41] and wheat [42]. It gives the impressions that indirect carotenoids
quantification could be obtained with the chlorophyll meter due to the
significant linear relationship between total chlorophyll and carotenoids
content determined spectrophotometrically.
Relationships
between SPAD-502 readings, the total Chl:Car ratio and the Chl a/b ratio
have been developed in Gossypim hirsutum leaves by Brito et al. [41] and in Sycamore (Acer pseudoplatanus), English Oak (Quercus robur)
and European Beech (Fagus sylvatica) by Percival et al. [13].
Association of SPAD readings with other leaf traits
Usually, non-invasive
techniques for estimating chlorophyll content of foliage have significant
importance to agricultural management operations, predominantly in the area of
precision farming. The scientific interest was verified by Kaufman et al. [42]
showing that chlorophyll content is key parameter with the highest frequency
within investigations of hyper spectral studies carried out in agriculture.
The SPAD meter
was initially developed in Japan to diagnose leaf N status and determine N
fertilizer requirements in crops. Since then, this device was broadly tested in
rice [43,44], wheat [45,46], maize [47-49], cotton [50], tomato [51],
sorghum [37], groundnut [52], tall fescue [53] and
others grown in variable seasons. There have been over 100 publications dealing
with the application of the SPAD meter in predicting foliar chlorophyll or N
status in the last few years alone. With a few exceptions most of the
publications report good utility of the SPAD meter for predicting foliar N
concentrations.
Few studies
indicated that specific leaf area (SLA) and SPAD readings, which are easy to
measure, are also associated with transpiration efficiency [54]. Moreover, both
traits have considerable genetic variation in groundnut [55-58].
CONCLUSION
In
general, portable chlorophyll meters, SPAD-502 produced results showed the
association with photosynthetic pigments in their empirical models with ease.
Thus, it may be concluded that SPAD readings allow for a quick determination of
the concentration of photosynthetic pigments in the leaves of crop plants, with
high accuracy and without disintegrated the plant material and avoiding the use
of chemical reagents and extensive laboratory protocols.
ACKNOWLEDGEMENT
The work was supported
by funding from ICAR-IARI, New Delhi in-house project (Grant No.
CRSCIARISIL20144047279).
1. Blackburn
GA (2007) Hyper spectral remote sensing of plant pigments. J Exp Bot 58:
855-867.
2. Feret,
JB, François C, Asner GP, Gitelson AA Martin RE, et al. (2008) Prospect-4 and
5: Advances in the leaf optical properties model separating photosynthetic
pigments. Remote Sens Environ 112: 3030-3043.
3. Huang
CJ, Wei G, Jie YC, Xu JJ, Zhao SY, et al (2015) Responses of gas exchange,
chlorophyll synthesis and Ros-scavenging systems to salinity stress in two
ramie (Boehmeria nivea L.) cultivars.
Photosynthetica 53: 455-463.
4. Cannella
D, Möllers KB, Frigaard NU, Jensen PE, Bjerrum MJ, et al. (2016) Light-driven
oxidation of polysaccharides by photosynthetic pigments and a metalloenzyme.
Nat Commun 7: 11134.
5. Zhao
D, Reddy KR, Kakani V, Read J, Carter G (2003) Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and
leaf hyper spectral reflectance properties as affected by nitrogen supply.
Plant Soil 257: 205-218.
6. Abramavicius
D, Valkunas L (2016) Role of coherent vibrations in energy transfer and
conversion in photosynthetic pigment-protein complexes. Photosynth Res 127:
33-47.
7. Holt
NE, Zigmantas D, Valkunas L, Li XP, Niyogi KK, et al. (2005) Carotenoid cation
formation and the regulation of photosynthetic light harvesting. Science 307:
433-436.
8. Zakar
T, Laczko-Dobos H, Toth TN, Gombos Z (2016) Carotenoids assist in
cyanobacterial photosystem II assembly and function. Front Plant Sci 7: 295.
9. Bouvier
F, Isner JC, Dogbo O, Camara B (2005) Oxidative tailoring of carotenoids: A
prospect towards novel functions in plants. Trends Plant Sci 10: 187-194.
10. Campos
MD, Nogales A, Cardoso HG Campos C Grzebelus D, et al. (2016) Carrot plastid
terminal oxidase gene (dcptox) responds early to chilling and harbors intronic
pre-miRNAs related to plant disease defense. Plant Gene 7: 21-25.
11. Nagy
L, Kiss V, Brumfeld V, Osvay K, Börzsönyi Á, et al. (2015) Thermal effects and
structural changes of photosynthetic reaction centers characterized by wide
frequency band hydrophone: Effects of carotenoids and terbutryn. Photochem
Photobiol 91: 1368-1375.
12. Zhao
DL, Reddy KR, Kakani VG, Reddy VR (2005) Nitrogen deficiency effects on plant
growth, leaf photosynthesis and hyper spectral reflectance properties of
sorghum. Eur J Agron 22: 391-403.
13. Percival
GC, Keary IP, Noviss K (2008) The potential of a chlorophyll content SPAD meter
to quantify nutrient stress in foliar tissue of sycamore (Acer pseudoplatanus), English oak (Quercus robur) and European beech (Fagus sylvatica). Arboriculture and Urban Forestry 34: 89-100.
14. Anderson
PD, Tomlinson PT (1998) Ontogeny affects response of northern red oak seedlings
to elevated CO2 and water stress. I. Carbon assimilation and biomass
production. New Phytol 140: 477-491.
15. Schlemmer
MR, Francis DD, Shanahan JF, Schepers JS (2005) Remotely measuring chlorophyll
content in corn leaves with differing nitrogen levels and relative water
content. Agronomy J 97: 106-112.
16. Merzlyak
MN, Gitelson AA (1995) Why and what for the leaves are yellow in autumn? On the
interpretation of optical spectra of senescing leaves (Acer platanoides L.). J Plant Physiol 145: 315-320.
17. Valladares
F, Chico JM, Aranda I, Balaguer L, Dizengremel EM, et al. (2002) The greater
seedling high-light tolerance of Quercus
robur over Fagus sylvatica is
linked to a greater physiological plasticity. Trees 16: 395-403.
18. Adams
WW, Zarter CR, Ebbert V, Demmig-Adams B (2004) Photo protective strategies of
overwintering evergreens. BioScience 54: 41-49.
19. Main
R, Cho MA, Mathieu R, O’Kennedy M, Ramoelo A, et al. (2011) An investigation into
robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogramm
Remote Sens 66: 751-761.
20. Demmig-Adams
B, Winter K, Krüger A, Czygan FC (1989) Light response of CO2
assimilation, dissipation of excess excitation energy and zeaxanthin content of
sun and shade leaves. Plant Physiol 90: 881-886.
21. Adams
WW, Demmig-Adams B (1994) Carotenoid composition and down regulation of
photosystem II in three conifer species during the winter. Physiol Plant 92:
451-458.
22. Niyogi
KK (1999) Photoprotection revisited: Genetic and molecular approaches. Annu Rev
Plant Physiol Plant Mol Biol 50: 333-359.
23. Filella
I, Serrano L, Serra J, Peñuelas J (1995) Evaluating wheat nitrogen status with
canopy reflectance indices and discriminant analysis. Crop Sci 35: 1400-1405.
24. Porra
RJ, Thompson WA, Kriedemann PE (1989) Determination of accurate extinction
coefficients and simultaneous equations for assaying chlorophylls a and b
extracted with four different solvents: Verification of the concentration of
chlorophyll standards by atomic absorption spectroscopy. BBA Bioenergetics 975:
384-394.
25. Wellburn
AR (1994) The spectral determination of chlorophylls a and b, as well as total
carotenoids, using various solvents with spectrophotometers of different
resolution. J Plant Physiol 144: 307-313.
26. Gratani
L (1993) A non-destructive method to determine chlorophyll content of leaves.
Photosynthetica 26: 469-473.
27. Markwell
J, Osterman JC, Mitchell JL (1995) Calibration of the Minolta SPAD-502 leaf
chlorophyll meter. Photosynth Res 46: 467-472.
28. Gáborčik
N (2003) Relationship between contents of chlorophyll (a+b) (SPAD values) and
nitrogen of some temperate grasses. Photosynthetica 41: 285-287.
29. Pinkard
EA, Patel V, Mohammed C (2006) Chlorophyll and nitrogen determination for
plantation-grown Eucalyptus nitens
and E. globulus using a
non-destructive meter. Forest Ecol Manag 223: 211-217.
30. Uddling
J, Gelang-Alfredsson J, Piikki K, Pleijel H (2007) Evaluating the relationship
between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings.
Photosynth Res 91: 37-46.
31. Marenco
RA, Antezana-Vera SA, Nascimento HCS (2009) Relationship between specific leaf
area, leaf thickness, leaf water content and SPAD502 readings in six Amazonian
tree species. Photosynthetica 47: 184-190.
32. Bauerle
WL, Weston DJ, Bowden JD, Dudley JB, Toler JE (2004) Leaf absorptance of
photosynthetically active radiation in relation to chlorophyll meter estimates
among woody plant species. Sci Horticult 101: 169-178.
33. Hawkins
TS, Gardiner ES, Comer GS (2009) Modeling the relationship between extractable
chlorophyll and SPAD-502 readings for endangered plant species research. J Nat
Conserv 17: 123-127.
34. Coste
S, Baraloto C, Leroy C, Marcon E, Renaud A, et al. (2010) Assessing foliar
chlorophyll contents with the SPAD-502 chlorophyll meter: A calibration test
with thirteen tree species of tropical rainforest in French Guiana. Ann Forest
Sci 67: 607-611.
35. Minolta
Camera Co. Ltd. (1989) Chlorophyll meter SPAD-502 Instructional Manual.
Minolta, Osaka, Japan, p: 22.
36. Kumar
P, Sharma RK (2019) Development of SPAD value-based linear models for
non-destructive estimation of photosynthetic pigments in wheat (Triticum aestivum L.). Indian J Genet
79: 96-99.
37. Yamamoto
A, Nakamura T, Adu-Gyamfi JJ, Saigusa M (2002) Relationship between chlorophyll
content in leaves of sorghum and pigeon pea determined by extraction method and
by chlorophyll meter (SPAD-502). J Plant Nutr 25: 2295-2301.
38. Campbell
RJ, Mobley KN, Marini RP, Pfeiffer DG (1990) Growing conditions alter the
relationship between SPAD-501 values and apple leaf chlorophyll. Horticult Sci
25: 330-331.
39. Samsone
I, Andersone U, Vikmane M, Ievina B, Pakarna G, et al. (2007) Non-destructive
methods in plant biology: An accurate measurement of chlorophyll content by a
chlorophyll meter. Acta Univ Latv 723: 145-154.
40. Brito
GG, Sofiatti V, Brandão ZN, Silva VB, Silva FM, et al. (2011) Non-destructive
analysis of photosynthetic pigments in cotton plants. Acta Sci Agron 33:
671-678.
41. Shah
SH, Houborg R, McCabe MF (2017) Response of chlorophyll, carotenoid and
SPAD-502 measurement to salinity and nutrient stress in wheat (Triticum aestivum L.). Agronomy 7: 61.
42. Kaufmann
H, Segl K, Itzerott S, Bach H, Wagner A, et al. (2010) Hyper spectral
algorithms: Report in the frame of EnMAP preparation activities (Scientific
Technical Report; 10/08), Potsdam: Deutsches GeoForschungsZentrum GFZ, p: 268.
43. Peng
S, Garcia FV, Laza RC, Cassman KG (1993) Adjustment for specific leaf weight
improves chlorophyll meter’s estimate of rice leaf nitrogen concentration.
Agron J 85: 987-990.
44. Turner
FT, Jund MF (1991) Chlorophyll meter to predict nitrogen top-dress requirement
for semi-dwarf rice. Agron J 83: 926-928.
45. Reeves
DW, Mask PL, Wood CW, Delaney DP (1993) Determination of wheat nitrogen status
with a hand-held chlorophyll meter: Influence of management practices. J Plant
Nutr 16: 781-796.
46. Fox
RH, Piekielek WP, Macneal KM (1994) Using a chlorophyll meter to predict
nitrogen fertilizer needs of winter wheat. Commun Soil Sci Plant Anal 25:
171-181.
47. Chapman
SC, Barreto HJ (1997) Using a chlorophyll meter to estimate specific leaf
nitrogen of tropical maize during vegetative growth. Agron J 89: 557-562.
48. Waskom
RM, Westfall DG, Spellman DE, Soltanpour PN (1996) Monitoring nitrogen status
of corn with a portable chlorophyll meter. Commun Soil Sci Plant Anal 27:
545-560.
49. Piekielek
WP, Fox RH (1992) Use of a chlorophyll meter to predict sidedress nitrogen
requirements for maize. Agron J 84: 59-65.
50. Wu
FB, Wu LH, Xu FH (1998) Chlorophyll meter to predict nitrogen sidedress
requirements for short-season cotton (Gossypium
hirsutum L.). Field Crop Res 56: 309-314.
51. Fontes
PCR, de Araujo C (2006) Use of a chlorophyll meter and plant visual aspect for
nitrogen management in tomato fertigation. J Appl Hort 8: 8-11.
52. Nigam
SN, Aruna R (2008) Stability of soil plant analytical development (SPAD)
chlorophyll meter reading (SCMR) and specific leaf area (SLA) and their
association across varying soil moisture stress conditions in groundnut (Arachis hypogaea L.). Euphytica 160:
111-117.
53. Kantety
RV, Van Santen E, Woods FM, Wood CW (1996) Chlorophyll meter predicts nitrogen
status of tall fescue. J Plant Nutr 19: 881-899.
54. Nageswara
Rao RC, Talwar H S, Wright GC (2001) Rapid assessment of specific leaf area and
leaf nitrogen in peanut (Arachis hypogaea
L.) using a chlorophyll meter. J Agron Crop Sci 186: 175-182.
55. Upadhyaya
HD (2005) Variability for drought resistance related traits in the mini core
collection of peanut. Crop Sci 45: 1432-1440.
56. Lal
C, Hariprasanna K, Rathnakumar AL, Samdur MY (2006) High yielding, water use
efficient Spanish groundnut (Arachis
hypogaea) genotypes for rainfed production system. Indian J Agric Sci 76:
148-150.
57. Sheshshayee
MS, Bindumadhava H, Rachaputi NR, Prasad TG, Udayakumar M, et al. (2006) Leaf
chlorophyll concentration relates to transpiration efficiency in peanut. Ann
Appl Biol 148: 7-15.
QUICK LINKS
- SUBMIT MANUSCRIPT
- RECOMMEND THE JOURNAL
-
SUBSCRIBE FOR ALERTS
RELATED JOURNALS
- Journal of Microbiology and Microbial Infections (ISSN: 2689-7660)
- Journal of Genomic Medicine and Pharmacogenomics (ISSN:2474-4670)
- Journal of Womens Health and Safety Research (ISSN:2577-1388)
- Journal of Agriculture and Forest Meteorology Research (ISSN:2642-0449)
- Advances in Nanomedicine and Nanotechnology Research (ISSN: 2688-5476)
- Journal of Biochemistry and Molecular Medicine (ISSN:2641-6948)
- Food and Nutrition-Current Research (ISSN:2638-1095)