Csajbók, József

Lelesz, Judit Éva

Nagy, Éva

Acta Agraria Debreceniensis

University of Debrecen Electronic Archive

AGRáRTUDOMáNyI KöZLEMéNyEK, 2016/70.
57
The marigold (Calendula officinalis L.) drug essential oil agents change under
different fertilization settings in small plot trial 
1Judit Éva Lelesz – 2Éva Nagy – 1József Csajbók
University of Debrecen Faculty of Agricultural and Food Sciences and Environmental Management,
1Institute of Crop Sciences, Debrecen, Hungary
2Laboratory of Animal Genetics, Debrecen, Hungary
iltsi18@gmail.com
SUMMARY
During our research we investigated the marigold's (Calendula officinalis L.) nutrient requirements with different fertilization setting
in small-plot trial. We measured SPME (Solid phase microextraction) and GC-MS (gas chromatograph-mass spectrometer) we examined the
effects of the different fertilization settings for the herb's main active ingredients of essential oil's percentage.
Based on the results, it was concluded, the essential oil agents' percentage breakdowns significantly depending on the cropping
technologies. Besides that it is possible, based on Pearson's correlation test the marigold essential oil agents relationship can also be a
major factor.
Keywords: herb, nutrient supply, marigold, essential oil, active agent
ÖSSZEFOGLALÁS
Kutatásunk során a körömvirág (Calendula officinalis L.) tápanyagigényét vizsgáltuk különböző tápanyag-utánpótlási beállításokkal
kisparcellás kísérletben. SPME (Szilárd fázisú mikroextrakció) és GC-MS (gázkromatográf-tömegspektrométer) alkalmazásával a különböző
tápanyag-beállítások hatásait vizsgáltuk a növény főbb illóolaj hatóanyagainak százalékos megoszlására. 
Az eredmények alapján megállapítható volt, hogy az illóolaj hatóanyagok százalékos megoszlásában a termesztéstechnológia jelentős be-
folyással bír. Ezen kívül a Pearson-féle korrelációs vizsgálat alapján feltételezhető, hogy a körömvirág illóolaj-hatóanyagok kapcsolata egymás-
sal szintén jelentős befolyásoló tényező lehet.
Kulcsszavak: gyógynövény, tápanyagellátás, körömvirág, illóolaj, hatóanyag
INTRODUCTION
Phytotherapy is getting more and more emphasis in
medicine (Nagy, 1994). The use and the cultivation of
herbs in the XXI. Century is a re-discovered research
field. There is an increasing need to develop modern,
species and variety specific methods of nutrient supply
that ensure profitable yields. In the same time we must
comply with the directives of the European Union in
terms of the requirements of quality assurance and
environmental protection as well (Zámboriné et al., 2010).
There are many uncertainties that give reason to doubt
how to determine the specific nutrient requirements of
herbs (Valkovszki, 2011). The statement that herbs are
undemanding is incorrect (Zámboriné, 2010).
Marigold (Calendula officinalis L.) is West Asian
origin, mediterranean medicinal plant (Rápóti and
Romváry, 1987). As a drug, its flower is gathered with
or without the speal (Calendulae flos cum calycibus
and Calendulae flos sine calycibus) (Dános, 2006). The
essential oil components content of marigold is 0.1%.
One main agent is the Alpha-cadinol (Bernáth, 2000).
It is slightly laxative and spasmolytic, but due to its
high E vitamin content it is used for healing of the skin
firstly. It is one of the most effective herbs that we can
apply to treat lacerations, torn skin wounds and surgical
scars and to alleviate itch occurring during wounds
healing (Varró, 2011). A stable LGP (lamellar gel phase)
emulsion is under development with using marigold,
which can be an alternative to facilitate the healing of
wounds (Okuma et al., 2015).
In our research we analyse the nutrient requirements
and fertilizer reaction of marigold according to the change
in the essential oil components and their distribution, as
an effect of the different nutrient dosages.
The tested essetial oil active agents are terpenes
include. Their common properties are the periodically
recurring contracts and terminal methyl groups. The
terpenes are volatile compounds, the major cause are the
double bonds. Essential oils are non-uniform materials,
their typical components are the terpenes. From the
essential oil agents the terpenes can be monoterpenes
and sesquiterpenes with opened or closed-chain. Their
derivatives may also can be alcohols (Banai, 2005).
MATERIAL AND METHODS
Our experiment for the marigold research took
place in the experiment site of the University of Deb -
re cen, Institute of Crop Sciences. The experimental
place’s soil is chernozem. It is characterized by the
accumulation of humus and easy tillage. There is not
whitewash in the topsoil, it is prone to cracking in dry
periods. The soil has got medium nutrient content, with
good nutrient dynamics. The uniformly humus layer
has a thickness of 50–70 cm. The water table is situated
at depths of 7–9 m.
Plot size was 8 m2 and plots were arranged in 4
replicates in randomized blocks, with 6 different
fertilizer treatment levels, in 4 rows with 40 cm row
space and 1 cm depth sowing. The sowing took place
on the spot on 7th April 2015.
LeleszJÉ et al.:Layout 1  10/24/16  3:24 PM  Page 1
The fertilizer doses (N%, P2O5%, K2O%) were
come out like this:
− N0P0K0 (control),
− N15P20K30,
− N30P40K60,
− N45P60K90,
− N60P80K120,
− N75P100K150.
In the previous year, before our research could be
planned, the regular annual nutrient dosages were
spread on the land. First on 5 March 2014 48 kg ha-1
nitrogen, 66 kg ha-1 phosphorous (P2O5) and 88 kg ha
-1
potassium (K2O) were spread. The second nutrient
supply took place on 28 October 2014 in the form of
38 kg ha-1 nitrogen, 31 kg ha-1 phosphorous (P2O5), and
37 kg ha-1 potassium (K2O). The nutrient supply
necessarily affected the yield. The fertilizer dosages
of the experiment were spread manually.
The first emerged plants appeared on 20th April.
The first flowers were observed on 5th June.
The rainfall on the experimental area from 1st January
to 30th September was considerably less (286.2 mm)
than the 30-year average (445.8 mm). From January till
the end of September the average temperature of each
month were higher than the 30-year average (except
April).
Gathering was done 6 times manually between 6th
July and 18th August 2015. 
The analysis of the essential oil components was
carried out in the NanoFood Laboratory by applying
SPME (solid phase microextraction), then GC-MS
(gas chromatograph-mass spectrometer). The sample
preparation were manually. We used polyacrylate fibers
with 85 µm size. The extraction time was one hour, on
50 °C temperature. The desorption temperature was
200 °C in the gas chromatograph injector. The time of
desorption was 30 sec. We measured the samples for the
SPME sampling in lockable pots with septum, which
are qualifed for 20 ml airspace analysis. We made the
extraction through the septum from the airspace.
During the GC-MS analysis the colonna were HP-5
stationary phase, 25 m×0.25 mm×0.25 μm capillary
column. The carrier gas was helium with 1 ml min-1
flow rate, in 40 °C temperature, under constant injection
pressure. The temperature of the analysis started from
50 °C two minutes, then the temperature was raised to
150 °C with 20 °C min-1 speed. After this we raised the
temperature to 240 °C with 15 °C min-1 speed, which
we held for 10 minutes.
The total analysis time was 23 minutes. The temperature
of the injector was 200 °C, the injector liner was silanised
liner without charge. The temperature of the transfer
line was 280 °C. The ionization occurred on 70 eV, the
mass range was 10–500 AMU (atomic mass unit). We
used HP (Hewlett-Packard) 5890 Series II type gas
chromatograph and 5971A type mass spectrometer.
Components were identified by applying mass spectrums
and Nist98 and Wiley databases. Active agents of the
samples taken from each plot were analysed. During
processing of the gained data, variance analysis and
Pearson’s correlation analysis were applied by using
MS Excel 2010 and IBM SPSS 22.0 programmes.
RESULTS AND DISCUSSION
Figure 1 shows the values of Alpha-cadinol, one of the
important essential oil components. The N75P100K150
treatment was the most effective. According to the
calculations, there is a medium correlation (r=0.52,
P=5%) between presence of the Alpha-cadinol in
percentage and the increase of nutrient supply. 
Figure 1: Presence of the Alpha-cadinol in the drug of marigold
depending on the nutrient supply
Figure 2 shows how the values of the Alpha-thujon
changed with the different nutrient settings of the
experiment. It is well demonstrated that after decreasing
the N15P20K30 treatment, presence of the active agent
in percentage is continuously increasing. The highest
value was measured with N75P100K150 nutrient level,
such as in case of the Alpha-cadinol.
Between presence of the Alpha-cadinol and the
Alpha-thujon in percentage a very strong correlation
(r=0.93, P=5%) was detected. Presence of the Alpha-
thujon – as distinct from the Alpha-cadinol – has only a
very weak correlation (r=0.25) with the nutrient settings.
Figure 2: Presence of the Alpha-thujon in the drug of marigold
depending on the nutrient supply
Figure 3 shows, how the values of Alpha-pinen
changed with the different nutrient settings of the
experiment. The N15P20K30 fertilizer treatment reached
the lowest measured values. The highest percentage
value of presence was produced with the N75P100K150
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AGRáRTUDOMáNyI KöZLEMéNyEK, 2016/70.
nutrient setting. The presence of the active agent in
percentage from the N30P40K60 is increasing.
Between the Alpha-pinen and the increasing nutrient
dosages a very weak correlation (r=0.35) was detected.
Between presence of the Alpha-cadinol and the Alpha-
pinen in percentage a very strong correlation (r=0.94,
P=1%) was detected.
Figure 3: Presence of the Alpha-pinen in the drug of marigold
depending on the nutrient supply
It is clearly visible that on Figure 4, presence of the
Alpha-cariophyllene active agent reached the highest
value with the N75P100K150 nutrient setting. The presence
of the active agent is fluctuate from the control to the
N60P80K120 nutrient dosage.
Between the Alpha-cariophyllene and the increasing
nutrient dosages a weak correlation (r=0.266) was
observed. There was a very strong correlation (r=0.907,
P=5%) between the presence of the Alpha-cariophyllene
and the Alpha-cadinol in percentage in the dry drug of
marigold.
Figure 4: Presence of the Alpha-cariophyllene in the drug of
marigold depending on the nutrient supply 
Figure 5 shows the values of the Ledol, which in
itself is essentially a toxic compound. The Control
group has the highest measured values. The lowest
measured values was in the N15P20K30 treatment. Then
while the treatments increasing, the presence of the
agent increasing also, but does not reach the level of
the control group.
There is a weak correlation (r=0.33) between
presence of the Ledol in percentage and the increase
of nutrient supply. Between presence of the Alpha-
cadinol and the Ledol in percentage a strong correlation
(r=0.868) was detected.
Figure 5: Presence of the Ledol in the drug of marigold de-
pending on the nutrient supply 
It is clearly visible that presence of the T-cadinol
reached the highest value with the N75P100K150 nutrient
setting. The presence of the active agent is fluctuate
from the control to the N60P80K120 nutrient dosage
(Figure 6).
Between the T-cadinol and the increasing nutrient
dosages a weak correlation (r=0.408) was measured.
There was a very strong correlation (r=0.971, P=1%)
between the presence of the Alpha-cadinol and the T-
cadinol in the dry drug of marigold.
Figure 6: Presence of the T-cadinol in the drug of marigold
depending on the nutrient supply
Figure 7 shows, the plots with N30P40K60 had the
most favourable nutrient setting, followed by the results
of the plots with N45P60K90, then that of the control
groups, considering the quantity of the raw crop. The
lowest nutrient level (N15P20K30) had the weakest effect
on the quantity of the raw drug. The mass data of the
dry drug yield in proportions are similar to those of the
raw drug. It can be observed too, the difference between
the data of the raw and the dry yield is significant. The
three nutrient setting – which provided the best results –
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Banai V. (2005): Gyógynövény- és drogismeret. Műszaki Könyv Ki -
adó. Budapest. 258.
Bernáth J. (szerk.) (2000): Gyógy- és aromanövények. Mezőgazda
Kiadó. Budapest.
Dános B. (2006): Farmakobotanika, gyógynövényismeret. Semmelweis
Kiadó. Budapest.
Nagy G. (1994): Gyógynövény-történelem. Recept: független egész -
ség politikai magazin. 5. 10: 22–23.
Okuma, C. H.–Andrade, T. A.–Caetano, G. F.–Finci, L. I.–Maciel, N.
R.–Topan, J. F.–Cefali, L. C.–Polizello, A. C.–Carlo, T.–Rogerio,
A. P.–Spadaro, A. C.–Isaac, V. L.–Frade, M. A.–Rocha-Filho,
P. A. (2015): Development of lamellar gel phase emulsion
containing marigold oil (Calendula officinalis) as a potential
modern wound dressing. European Journal of Pharmaceutical
Sciences. 71: 62–72.
Rápóti J.–Romváry V. (1987): Gyógyító növények. Medicina Könyv -
kiadó. Budapest.
Valkovszki N. J. (2011): Egyéves konyhakömény tápanyag igényé -
nek vizsgálata. Agrofórum. 22. 3: 102–104.
Varró A. B. (2011): Gyógynövények gyógyhatásai – növényi gyógy -
szerek. Hazai gyógynövényeink ismertetése, gyűjtésüknek mód -
ja és felhasználásuk a mindennapi életben az egészség szol gá-
latában. Kinizsi Nyomda. Debrecen.
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pótlása. Agrofórum. 21. 10: 64–69.
Zámboriné N. é.–Rajhárt P.–Szabó K.–Antal T. (2010): A tápanyag-
utánpótlás hatása gyógynövények hozamára és drogminőségére.
Kertgazdaság. 42. 3–4: 128–135.
60
were similarly the N30P40K60, the N45P60K90 and the
control setting. The dry drug yield produced with the
nutrient setting N15P20K30 was higher than that of the
N60P80K120 and N75P100K150 was setting in contrary
to the data of the raw crop.
Figure 7: The marigold's raw and dry drug yield depending on
the nutrient supply
CONCLUSIONS
The nutrient supply has effect on the biomass
production and yield of the crops also in case of herbs.
Marigold’s drug yield depended on the nutrient supply
as well. Even so, we could not prove significant correlation
between the presence of the essential oil active agents
and the different nutrient settings based on the correlation
analyses. We measured the highest percentage value of
essential oil active agents with the N75P100K150 nutrient
setting except of Ledol.
Between the essential oil components in the dry
drug there were strong and very strong correlations in
P=5% and P=1% significance level. It is possible, based
on Pearson's correlation test, that the relationship between
the marigold’s essential oil agents is the major factor.
The connecting network of the active agents maybe has
larger effect than the increasing nutrient doses. The
fluctuation that occurred in the presence of the given
active agents in percentage could also be caused by the
different proportions of nutrients. We need more research
work to do to clear the complex connections between
the essential oil agents and the effect of the nutrient
supply on the volatile oil content of the drug and the
proportions.
As for the raw and the dry drug yield of the marigold,
it seems the N30P40K60 level was the ideal nutrient
setting. In our opinion, one of the main reason for the
fluctuation of the yield besides the different fertilizer
dosages was the very warm and dry weather of the
vegetative period. The variance analysis of the raw and
dry drug mass’ data did not show significant differences
between the plots with different fertilizer treatments.
AGRáRTUDOMáNyI KöZLEMéNyEK, 2016/70.
REFERENCES
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
LeleszJÉ et al.:Layout 1  10/24/16  3:24 PM  Page 4


The marigold (Calendula officinalis L.) drug essential oil agents change under different fertilizatton settingi in small plot trial

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The marigold (Calendula officinalis L.) drug essential oil agents change under different fertilizatton settingi in small plot trial

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