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., Chl_a (Chl_a=0.0690 × SPAD Value - 1.082), Chl_b (Chl_b=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 measuring chlorophyll have been developed based on optical leaf properties. For this purpose a portable chlorophyll meter (SPAD-502Plus, Minolta Camera Co., Osaka, Japan) has been often used in plant physiology, agriculture and less frequently in forestry research and practice [26-31]. This handy device allows a convenient estimation of total chlorophyll content in leaves, especially with repeated number of measurements on the same leaf [32-34].

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) (Chl_a (varied from 0.0893 to 4.0279 with mean value of 2.119), Chl_b (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 Chl_a 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 Chl_a contents in linear model (Chl_a=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 Chl_b 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 Chl_b content obtained in wheat leaves. The association between the SPAD values and the estimated contents of Chl_b was fit in linear model (Chl_b=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.