DEVELOPMENT OF Botrytis cinerea UNDER DIFFERENT CLIMATIC CONDITIONS AT VINES

One of the most important plant disease in viticulture is gray mold caused by
Botrytis cinerea Pers. Fr., the anamorph of an ascomycete fungus (Botryotinia
fuckeliana Whetzel). Gray mold development on grape berries depends on the genetic
structure of the pathogen population but is also driven by some key factors, including
climatic conditions, cluster architecture and berry susceptibility. Numerous treatments
with fungicides are required for management of the gray mold which intensifies the
risk of resistance development since B.cinerea has a high risk of resistance
development. The forecasting model for B. cinerea Pers. Which will be shown here is
only pioneering attempt to prevent development of gray mold. The white grapevine
varieties Smederevka and Zilavka was continuously observed at last three years in
the two experimental fields located at Smilica and Sopot, Kavadarci, Republic of North
Macedonia. The working hypothesis was to follow the development of the disease after
increasing glucose over 11%, until the time of the grape harvest.

5.









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%*+,*-./,0%" $


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6. GOCE DELCEV UNIVERSITY STIP
DEPARTMENT FOR PLANT AND ENVIROMENTAL PROTECTION






DEVELOPMENT OF Botrytis cinerea UNDER DIFFERENT CLIMATIC
CONDITIONS AT VINES

Gligor Bojkov, Emilija Arsov, Sasa Mitrev

7.

Abstract
One of the most important plant disease in viticulture is gray mold caused by
Botrytis cinerea Pers. Fr., the anamorph of an ascomycete fungus (Botryotinia
fuckeliana Whetzel). Gray mold development on grape berries depends on the genetic
structure of the pathogen population but is also driven by some key factors, including
climatic conditions, cluster architecture and berry susceptibility. Numerous treatments
with fungicides are required for management of the gray mold which intensifies the
risk of resistance development since B.cinerea has a high risk of resistance
development. The forecasting model for B. cinerea Pers. Which will be shown here is
only pioneering attempt to prevent development of gray mold. The white grapevine
varieties Smederevka and Zilavka was continuously observed at last three years in
the two experimental fields located at Smilica and Sopot, Kavadarci, Republic of North
Macedonia. The working hypothesis was to follow the development of the disease after
increasing glucose over 11%, until the time of the grape harvest.
Key words: gray mold, forecasting model, pathogen population, climatic conditions,
fungicide treatments.

8.

CONTENTS


PART 1 Introduction -----------------------------------------------------------------------------------4
1.1 Impact of microclimatic conditions on the development of B. cinerea ---------------7
1.1.1 Impact of light -----------------------------------------------------------------------------8
1.1.2 Impact of temperature -----------------------------------------------------------------10
1.1.3 Impact of relative humidity ------------------------------------------------------------12
PART 2 Materials and method --------------------------------------------------------------------13
2.1 Field analysis ---------------------------------------------------------------------------------13
2.2 Working Hypothesis ------------------------------------------------------------------------14
2.3 Variants and Calculations -----------------------------------------------------------------17
PART 3 Results ----------------------------------------------------------------------------------------20
3.1 Results obtained from the observation of the development of B. cinerea at
Sopot locality in 2017-----------------------------------------------------------------------------20
3.2 Results obtained from the observation of the development of B. cinerea at
Smilica locality in 2018 ------------------------------------------------------------------------------ 22
3.3 Results obtained from the observation of the development of B. cinerea at
Smilica locality in 2019 -------------------------------------------------------------------------------29
PART 4 Discussion --- -------------------------------------------------------------------------------31
PART 5 Conclusion ----------------------------------------------------------------------------------43
References ---------------------------------------------------------------------------------------------43

9.

1. Introduction
Grey mould or Botryotinia fuckeliana (de Bary) Whetzel (syn. Sclerotinia
fuckeliana (de Bary) Fuckel) (anamorph B. cinerea Pers.), according to taxonomic
characteristics, belongs to the genus Botryotinia (family Sclerotiniaceae). First
described by Christiaan Hendrik Persoon in 1794. As a pleomorphic species, it is
highly variable and produces conidiophores, conidia, phialoconidia, microconidia,
sclerotia, apothecia, accuses and ascospores. Differences between isolates in terms
of the speed of growth, the formation of conidia and sclerotia, production of enzymes
and pathogenicity, have been noted by several authors (Di Lenna et al., 1981; Grindle,
1979; Leone, 1990; Movahedi & Heale, 1990), who have explored grey
mould.Botryotinia fuckeliania causes significant economic losses in viticulture around
the world.
Members of Botrytis genus are generally necrotrophic pathogens they
induce host cell death and lysis to access cellular nutrients. The grey mold causes
serious losses in more than 200 crop species worldwide.
The severity of this disease
can be seen even after the harvest when the agricultural products that are transported
to distant markets spoil.
Grapes can be successfully protected but in the ripening
phase gray mold can compromise the harvest (Pejchinovski and Mitrev,2007).
Although there are fungicides for its control, many classes of fungicides have failed
due to its genetic plasticity(Williamson et al., 2007). Consequently, additional chemical
treatments must be applied immediately before harvesting the grapes raising concerns
regarding human health and environmental pollution.
Information on B. cinerea
epidemics is publicly available for the range of different crops. Many authors publish
reports of gray rot damage, most commonly including the following crops: kiwi Elmer
et al. (1995), Pears Spotts & Cervantes (2001); Spotts & Serdani (2006), strawberries
Braun & Sutton (1986) and grapes Nair & Nadtotchei (1987); Nair et al. (1988); Elmer
et al. (1994); Nair et al. (1995); Seyb (2004); Elmer & Michailides (2007); Beresford et
al. (2006).
It is estimated that B. cinerea causes a $10 to 100 billion of produce loss
annually worldwide(Weiberg et al., 2013).
According to the mentioned authors, the
pathogen appears in the vineyards in the form of sclerotia (Nair and Nadtotchei, 1987),
conidia (Corbaz, 1972) and mycelium (Northover, 1987).
Nair & Nadtotchei (1987)
report that conidia are the primary inoculum for berries infection and represent a
prominent stage in the disease.
There are various infectious pathways through which
B.cinerea attacks the vines: stigma and style (McClellan and Hewitt, 1973; Nair and

10.

Parker, 1985), pedicel and vascular tissues in the berries (Pezet and Pont, 1986) or
through direct penetration of the grape berries (Nelson, 1951; Coertze and Holz,
1999).
Floral debris bearing mycelia are dispersed by wind and rain and provide a
large saprophytically based inoculum that may stick to plant surfaces when wet (Jarvis,
1980) .
Botrytis cinerea is able to infect a wide range ofliving and dead tissues (vine
trash) within the vineyard (Seyb et al., 2000) and rapidly colonises senescing or
moribund tissue including stamens, loose or adhering calyptra, shed pollen and pistils,
immature aborted berries, dead flowers and miscellaneous leaf, stem and tendril
pieces (Jarvis, 1980; Northover, 1987; Nair et aI., 1988).
Young grape leaves were
highly susceptible and were infected especially at the leaf base, which often remained
asymptomatic (Holz et al., 2003). As the leaves matured, they got increasingly
resistant to infection due to a thicker cuticle layer and the presence of inhibitory
compounds.Grapes can be wounded by several agents such
as hail, frost, sunscald etc. (Jarvis 1980) and it has been shown that such damage
predisposes them to infection by B. cinerea (Du Plessis 1937;Vanderwalle 1937).
Grey
mould caused by Botrytis cinerea is a serious threat to grapevine cultivation, in
particular during wet and rainy weather periods(Kretschmer et al., 2006). Grapes can
be successfully protected but in the ripening phase gray mold can compromise the
harvest.
Sporulation of B. cinerea on the berry surface occurred on average half to 1
day later with non-wounded than with wounded berries (Kretschmer et al., 2006).
Botryotinia fuckeliana is facultative saprophyte which means that in its development
the saprophytic and parasitic phases are present. The control of B. cinerea in grapes
has always been a challenge especially if its adaptability to environmental factors is
taken into account. Hence gray mold is a facultative saprophyte which means that in
its development the saprophyte and parasitic phases are present, which are an
adaptive response of the pathogen to external factors. According to Van Kan (2006)
in the parasitic phase, the gray mold kills the living cells of the plant and the colonized
them, while in the saprophytic phase it draws nutrients from the dead tissue. The
appearance of the saprophytic phase on the flowering elements increases the
potential inoculum for more intensive development of B. cinerea in the ripening phase
of the grapes. It is quite characteristic that during the pathogenesis, in different periods
of time, different tissues of the vine are infected, which serve as a bridge and
eventually the infection covers the mature grapes. The gray mold always runs parallel

11.


Figure 1. B. cinerea colonises senescing immature aborted berries and flowers elements on
green grape berry at variety Smederevka (photo of the author 11.07.2018)
to the ripeness ofthe grapes and the autumn rainy season before harvest. This
pathogen manifests its destructive influence from the veraison phase (onset of
ripening) to grape harvest.
During the process of grape berry infection by B. cinerea,
various biochemical interactions take place.
These interactions have been
investigated thoroughly with respect to host resistance to the fungus and involve both
constitutive factors and induced ones following stress or infection (Van Baarlen et.al.,
2004).
In case of grape berry infection with gray mold is missing hypersensitive
response, oxidative burst may be detoxified by the superoxide dismutase
and catalase, respectively, which are secreted by B. cinerea.
Further, B. cinerea is
known to produce laccase and other enzymes which can degrade or detoxify
phytoalexins(Prins et al., 2000).In varieties with very compact bunch, during
maturation, more pressure is created on the grape berries and as result at this growth
it happens that one berry suppresses the other and at place of junction with peduncle
a small cleavage or separation of the grape berries occurs. A drop of grape juice flows
through this small opening, which is at the same time a nutrient base for the
development of B. cinerea. Then the gray mold spores germinate in the drop of the
grape juice and easily penetrate through the small opening and infect the grape
berries. Rainy weather during grape ripening only further stimulates infection.
This type of infection that occurs from the inside of the bunch is very difficult to

12.

control. In case of favorable weather conditions for the development of gray
mold, mycelial structures develop on the surface of the grains, and then the infection
quickly spreads to the whole bunch. In our country, during the vegetation, exclusively
the conidial stage of B. cinerea development, and its conidia
spread with the air and thus a dispersion of the infection occurs in the space. In
the vineyards appearance of apothecium development is rare and the biological
cycle goes in the direction of creating conidial stage and sclerotium.Sclerotia can
survive adverse environmental conditions and have a considerable capacity for
producing successive crops of conidia(Coley-Smith, 1980).
Botrytis outbreaks have
been shown repeatedly to be associated with the grape berry
moth, Lobesia botrana.
The first generation of the pest attacks flowers, the second
feeds on immature berries and the third damages the mature grape berries.
1.1 Impact of microclimatic conditions on the development of B. cinerea
B. cinerea survives during the overwinter as sclerotia on branches.
When the
weather warms up in spring and summer with optimum temperature and humidity
conditions the germination of conidia take place smoothly by spreading the infection.

The survival of conidia largely depends on extreme temperatures, moisture
availability and sunlight exposure
It is widely accepted that the most critical stages for
B. cinerea infection are flowering and the period between berry ripening and harvest
(Bulit et al., 1988).Measures such as limitation of nitrogen fertiliser to avoid excessive
vigour (Leroux, 1995 ; Chambers et al., 1993), removal of leaves around bunches
(Leroux, 1995), effective control of downy mildew (Plasmopara viticola) and powdery
mildew (Uncinula necator) by using compounds that have a secondary effect on B.
cinerea (Leroux & Clerjean, 1985; Leroux, 1995), insects (Leroux, 1995) and weed
control (De Kock & Holz, 1991) should be applied to assist B. cinerea control.
Leaf
reduced
epidemics thereby improving Botrytis control in grapes in European (Zoecklein et al.,
1992). Increased wind speed after leaf plucking (English et al., 1989) increased the
evaporative potential on the berry surface, thereby significantly reducing B. cinerea
infection and development (Thomas et al., 1988; English et al., 1993). As a green
measure, the defoliation of the leaves around bunches is performed in order to
prevent favorable conditions for the development of gray mold.
The goal is to canopy

13.

modifications to optimize microclimate conditions to abolish gray mold development.
Leaf removal enhanced the drying condition in the fruit zone sufficiently to reduce
bunch rot development significantly (English et al., 1993). Gray mold need to respond
to the behavior of their host that grows to expose bunches towards light, and the
pathogen may therefore be directly exposed to sunlight.
The grapes are exposed to
increased temperature and light, which changes the microclimatic conditions for the
development of B. cinerea, primarily the presence of relative humidity inside the
habitat of the plant.
Hence the influence of temperature, moisture and light also has
a role in the development of the infection on bunches. Sunlight is an important
environmental factor because it is a source of both energy and physiological stress.
1.1.1 Impact of light
Light is an important environmental factor in almost all ecosystems
by being a source of energy, information as well as stress (Schumacher,2017).
When
B. cinerea conidia were exposed to direct sunlight at midday in an Israeli summer,
survival was only minutes but conidial survival was considerable longer when conidia
were protected from direct sunlight by host tissues
(Rotem & Aust, 1991). According
to research by Seyb (2003) the percentages of conidia germinating after exposure to
4 h sunlight ranged between 81% and 91% and between 49 and 50 % after 8 h of
sunlight exposure. Upon re-exposure on the second day, just 10 min of exposure to
sunlight caused germination to drop between 26 and 27 % for all isolates tested (Seyb,
2003). The UV spectrum of sunlight appeared to be the most important environmental
factor influencing mortality of conidia (Rot em & Aust, 1991; Seyb, 2003)
All organisms
have to protect themselves against the harmful effects of light such as the damage
inflicted on macromolecules by the UV fraction and by emerging singlet oxygen and
other reactive oxygen species (ROS), (Schumacher ,2017).
Light is almost always
associated with higher temperatures, leading to desiccation and osmotic stress.
Light
regulates carotenogenesis, conidiation, protoperithecia formation and the circadian
clock in N. crassa (Linden et al., 1997), and secondary metabolism and the balance
between sexual (cleistothecia formation) and asexual development (conidiation) in A.
Nidulans (Bayram et al., 2010). Gray mold has a network of light-sensitive proteins or
photoreceptors and signal transduction pathways that allow it to adapt to the new
defoliation situation and to continue its development if there is a sufficient amount of
relative humidity in the air.
However, little is known about the influence of light on the

14.

development of its anamorph and teleomorph stages.Photoreceptors possessed by
B. cinerea consist of apoproteins and phytochromophores that respond by
isomerization or reduction of light by absorbing photons that contain a certain amount
of energy.
However, photoreceptors essentially do not allowreactive oxygen species
(ROS) to form that would disrupt the metabolic process, thereby killing gray mold cells.

ROS are well-known signaling molecules, which are produced specifically or arise as
by-products of metabolic pathways and under stress conditions (Finkel, 2011).

Phytochromophores are bound by highly conserved bonds with certain protein
domains that determine the wavelength and type of light they absorb.
The B. cinerea
spores that are the product of asexual reproduction so called conidia form in the
presence of light, while sclerotia forms exclusively in dark conditions, even a small
amount of light interrupts their formation.
The fungus reaction of light will depend of
the physiological stage in which is.
However, light becomes less efficient when the
mycelium is already formed. Conidiation is a biological process in which filamentous
fungi reproduce asexually from spores and according to Brandhoff et al., (2017)
overexpression differentially affects conidiation in light and darkness.
Whereas light
only shifts the balance from cleistothecia (sexual development) towards conidia
(asexual development) in A. Nidulans (Bayram et al., 2010; Rodriguez-Romero et al.,
2010) light, or its absence, triggers absolute responses in B. cinerea (Schumacher,
2017).
In this fungus the conidia are exclusively formed in the light and sclerotia
exclusively in constant darkness (Schumacher,2017). During the vegetative stage of
the mycelium even very small doses of light interrupt the process of sclerotial
development.
The sclerotia are primarily asexual survival structures, which germinate
by producing mycelia and conidia.
When, however, microconidia possessing the
opposite mating type are available, the sclerotia are fertilized and bear later, under
illumination, the sexual structures (apothecia)
(Faretra et al., 1988). More conidia are
produced by cultures that are cultivated in light-dark cycles (LD) than in constant light
(LL), indicating that transient darkness is as important as light for proper conidiation

(Tan and Epton, 1973). Which suggests that transient darkness is just as important as
transient light.
The multicellular reproductive organs of the pathogen do not always
grow by default towards the light source, but require a certain photoinduction reaction
when they are at a very early stage of development.
Once the photoinduction signal is
given, the growth of the germ tubes begins, which in the initial stage of development
they not need so much light.
Germ tubes emerge on the unilluminated sides of the

15.

conidia and grow away from daylight (negative phototropism) whereas they emerge
and grow randomly in darkness(Jaffe & Etzold, 1962).
After penetrating the plant
tissue, the fungus is protected from direct sunlight and can proliferate even during the
day (Schumacher, 2017).
1.1.2 Impact of temperature
In the vegetation seasons when we have a rush of the so-called temperature
shocks, ie a period of increased temperatures during the vegetation, defoliation in our
country has proved to be a disastrous measure (Figure 2), regardless of our previous
decades of experience in viticulture. Rising temperatures as a result of climate change
and simultaneous defoliation only increase the heat stress in the vines.

Figure 2. Harmful consequences of the vines on which the defoliation was carried out after
heat stroke at variety Smederevka Sopot locality (photo of the author 18.08.2017)
As can be seen from the photo, the damage from the high temperatures occurred in
the part of the habitat of the vine where the defoliation was performed, i.e. around
bunches, while the vines on which this ampelotechnical measure was not performed
did not suffer such major damage.
Defoliation also has an effect on the application of
pesticides, the reduced leaf mass allows more direct contact of bunches with the
pesticide drops which at increased temperature cause a phytotoxic reaction on the
surface of the epidermal tissue of the grape berries (Figure 3).

16.


Figure 3. Phytotoxic reaction on grape berries with chemical treatment which is the result of
increased temperature and defoliation Smilica locality (photo of the author 2019)
On the other hand, the general impression is that the dense leaf mass causes better
conditions for the development of B. cinerea. Laboratory findings and analyzes are
also important to understand the effect of temperature on the development of B.
cinerea. !"#
(Dik and Wubben, 2004).
Cardinal temperatures are 35.5$C (maximum), 15-25$C
(optimum), and near 0$C (minimum) (Martinez et all., 2009).
The studied effects of
temperature on the conidia germination in the laboratory have been observed by a
number of scientists including Schneider - Orelli (1912) and Brooks and Cooley (1917)

who observed germination at a temperature of 0$C on corn agar for 31 days. According
to the studied numerous populations of B. cinerea Doran (1922) concluded that the
maximum temperature for the conidia to germinate in the laboratory was 26$C and the
minimum was 7$C. The author Brown (1922c) noticed the conidia to germinate at a
temperature of 5$C.
The data on the growth of mycelium which were followed in
conditions of favorable relative humidity are also interesting.
Schneider-Orelli (1912)
studies showed that B. cinerea mycelium was capable of growing on a gelatinous
substrate at 0$C for 35 days.
The optimum temperature for its development is from 20
to 22$C, while above 25$C the growth of mycelium decreases significantly.
The initial
temperature for sporulation according to Jarvis (1980) is 15$C.
Sporulation as a
biological process is much more sensitive to temperature amplitude changes than

17.

mycelial growth and development needs.Mycelium as a whole is much more resistant
to temperature changes if there is adequate relative humidity. According Townsend
(1952) and Vanev (1962) temperature factor promoting mycelium biogenesis acts
negatively on the development of sclerotia and vice versa.
Particularly striking are the
data of Morotchkovski and Vitas (1939), who claim that a temperature of 11 to 13$C is
optimal for sclerotia biog%&$# % '
($# )Since this species is very heterogeneous a
number of studies sometimes provide different data on the development of sclerotia,
because this pathogen species is planetary spread in different climatic zones and
according to its adaptability, the data on the temperature at which certain stages of its
development develop are different.
1.1.3 Impact of relative humidity
There have been many studies on the effect of relative humidity on the
germination of fungal spores. The results, however, must be viewed with
extreme caution, especially in atmospheres of relative humidities greater than
90%, because the usual limits of control in this type of experiment can easily
permit the temperature to fall below the dew point, so that spores come to
lie in condensate (Schein 1964). ir movement are clearly important not only
dislodging spores of gray mold from the host plant but also are dispersed throughout
the space spreading the infection to other vine areas.The fungus moves in vineyards
as conidia in air currents (Jarvis, 1962a)and to a lesser degree, through rain droplets
(Jarvis, 1962b).Hardly any of the B.cinerea conidia become wet enough to enter a
rain droplet and are rather carried on thedroplet surface (Jarvis, 1962b).In air currents,
conidia were only transported over a shortrange (Fitt et al., 1985).
On the other hand,
in conditions where we have the occurrence of condensation of water vapor Rippel
(1933b) observed 100% germination of conidia B. cinerea, also he noticed the same
such a high percentage of germination of conidia at temperatures of 20$%&*$
5$+ +
the air with 100% water vapor.
There was complete germination of the Botrytis sp. at
95% RH, 80-85% germination at 90% RH, and none at 85% RH. (Jarvis, 1980).
At
95% RH, 80% of conidia of B. cinerea &*$#*$#
!$#, -0% RH, 85% of conidia germinated at 20$C, and none at lower
temperatures or lower humidities (Jarvis,1980).Snow (1949) concluded that conidia

18.

of B. cinerea require high levels of moisture for germination 93-100% RH. Yarwood
(1950) found that the water content of conidia of B. cinerea was only 17% of their fresh
weight but that a high proportion was 'hygroscopic water' when the conidia were on
glass and in equilibrium with the laboratory air 42-51% RH.
Research by Thomas et
al., (1988) has shown that surface mycelium develops most rapidly at relative humidity
of 94% and temperatures up to 21$C.
The appearance of mycelium indicates an
increased infectious potential at favorable relative humidity above 90%. The amount
of B. cinerea on berry surfaces is low throughout the season, and B. cinerea occurs
as single colony-forming units (Coertze & Holz, 2002).
The pathogen does not live for
extended periods on immature and mature berry surfaces because needed drop water
or high relative humidity.
2. MATERIALS AND METHODS
2.1 Field analysis
The research was completed in a vineyard located at Smilica and Sopot near
Kavadarci, Republic of North Macedonia, on white grape varieties
Smederevka and Zilavka (Table 1).
A double Guyot pruning system was applied in the
vineyard.The research lasted for three consecutive years (2017; 2018; 2019).
The
main criterion for the selection of this localities was have not conditions for
development grape berry moth, Lobesia (Polichrosis) botrana (Tortricidae)
to be able
to more accurately determine the factors for the development of B.cinerea.

Table 1. Grapevine varieties that were the target of the research
Varieties ha Locality Coordinates Years of
research
Smederevka
1,7

Smilica

.&/.0'&).001%
/!0&!)'*002


2019
Zilavka
0,5
2018
Smederevka
1

Sopot

41/49'21.9''N
22/03'53.7''E


2017
Zilavka 0,5

19.

2.2 Working Hypothesis
The working hypothesis or the period of monitoring the B.cinerea started
when the grapes reached a sugar content of 11% until the technological maturity of
the grapes.Working hypothesis was to follow development of the disease after
increasing glucose over 11% until the time of the grape harvest (Table 2), and
microclimate was monitored at the same time. At each variety had two variants, treated
and untreated grapes. In both white varieties Smederevka and Zilavka on the control
treatments to distinguish between the variants that were
conventionally treated against B. cinerea. Except for the control (no treatments against
grey mould), which was represented by only one row, treatments against downy
mildew and powdery mildew were regularly performed, but no active substances were
used which could have a side effect against grey mould. From each variant, five plants
were marked, and from each plant, six bunches were selected, which were marked on
the rachis (handle) of the bunches with red tape. In the control, the disease was
monitored in three plants. For that time, temperature and humidity were measured
using a digital thermohydrometer in the vines habitat. From each variant, a sample of
30 bunches was taken. The aim of the research was to determine how the
microclimate change impacts upon development of the B. cinerea and consequently
to create forecasting model for gray mold.
The forecasting model for B. cinerea based
on relationship between temperature and humidity in the vines canopies. The aim of
the research is prevent development of B. cinerea and consequently reduce the
number of chemical treatments.

20.

Table 2. View of the working hypothesis with dates for chemical treatments

The setting of the alternative and null hypothesis was based on the percentage of
grape infection and reduction of the last fungicidal treatments against B. cinerea in
order to preserve the health and ecological component of the grapes, ie the criteria
prescribed by the European Comm ission were taken into account.
(https://ec.europa.eu/food/plant/pesticides/eu
pesticidesdatabase/public/?event=activesubstance.selection&language=EN) for the
permitted amount of residues of the active substance Boscalid [(2-Chloro-N-.-
chlorobiphenyl-2- yl)-nicotinamide, EF: C
18H12Cl2N2O, CAS:188425-85-6] which can
be found in grapes after IV chemical treatment just before harvest.
According to the
World Health Organization (WHO) acceptable daily intake (ADI) of intake of products
treated with this active substance is 0.04 mg / kg body weight.
If we take into account
the WHO recommendation that the average weight of a person is 75 kg then the
permissible intake of this substance is 3 mg or 0.003 gr ( ) directive
(08/44/EC). Such recommendations can be followed only if the prescribed dose is
observed and period of decomposition of active substance.
The working or alternative
hypothesis (H1) was set according to our field experience that it is possible to tolerate
the disease up to 9.6% grape infection just before the grape harvest without agro-
economic implications. While null hypothesis (H0) claims that just before the grape
harvest the maximum control of the grapes that can be controlled is 2.5% and with
exceeding this percentage fungicides must be used because the development of the
disease can be accelerated by factors that are not known, or


The statistics obtained from infection
index according to Mc-3 4567 %!!'%
8 4567 %!!'% kind
of bivariate values whose sum gives the significance of the statistical study.
Varieties Number of Chemical
Treatments
Working Hypothesis Years of
Research
I II III IV started ended
Smederevka 25.05 12.06 08.07 / 14.08 18.09 2019
Zilavka
Smederevka 18.05 07.06 11.07 27.08 16.08 18.09 2018
Zilavka 11.08 15.09
Smederevka 05.06 01.07 / / 16.08 15.09 2017

21.

Table 3. Chemical treatments during the research
















locality years of
research
Number of
chemical
treatments
a.m.against
P. viticola
a.m.against E.
necator
a.m.against P.
botrana
a.m.against B.
cinerea
2019 I Ametoctadrin
Dimethomor
p
h
Propiconazol
Sul
p
hur
Chlorpyrifos-
meth
y
l
Pyrimethanil
II Cyazofamid
Folpet
Metrafenon
Mepthyldinocap
Hlorantraniliprol Piraclostrobin
Boscalid
III Metiram
Copper
ox
y
chloride
Epoksikonazol
Pyraklostrobin
Chlorpyrifos
Cypermethrin
Cyprodinil
Fludioxonil
2018 I Dimethomorph
Mancozeb
Fenbukonazol
Sul
p
hur
Alpha-
c
yp
ermethrin
Pyrimethanil
II Ametoctradin
Metiram
Miklobutanil
Quinox
y
fen
Metaflumizon Fenhexamid
III Metiram
Copper
ox
y
chloride
Penconazol
Mepthyldinocap
Emamectin-
benzoate
Cyprodinil
Fludioxonil
IV / / Hlorantranili
p
rol Boscalid
2017 I Cymoxanil
Mancozeb
Penconazol
Me
p
th
y
ldinoca
p

/ /
II Fosetyl-Al
Mancozeb
Boscalid
Kresoxim-
Meth
y
l
/ /

22.

2.3 Variants and Calculations
The essence of the initial observation is to understand the trend of the
disease.For this purpose, in the first part of the field analysis, when the incubation
period and the appearance of the first symptoms should be determined, a
mathematical-statistical method was used. This method involves daily measurement
of temperature and relative humidity in the habitat of the vine while the working
hypothesis is in progress. The aim of the research was to determine how the climate
change impacts upon development of the B. cinerea and consequently to
create forecasting model for gray mold. The temperatures we take into account
in the calculation are those that range from 1$C to 30$C because in this interval
we have the development of the pathogen. According to (Pejcinovski & Mitrev, 2007)
conidia germinate at a temperature of 1$C to 30$C, and most massively at 18$C.
Temperatures !/# )9
the following formula:
Tm = (Tda : Tmin) / (Tmax : Tmin)

Tm;temperature development factor for B. cinerea;
Tmin;minimum temperature;
Tmax;maximum temperature;
Tda;daily average temperature.

The next parameter to be determined is humidity point (Hp). Where is the
length of retention the dew on the plant organs of the vine expressed in hours.
The
length of moisture retention was measured in hours. Humidity of less than half an hour
was not taken into account. Also not considered are very low they intensity rains up to
0.2 mm/h, when we have long lasting rains of more than half an hour.
Then we
measured the length of the rain and the period of water drop retention on the vines.
The forecasting model for B. cinerea based on relationship between temperature and
humidity in the vines canopies. The aim of the research is prevent development
of B. cinerea and consequently reduce the number of chemical treatments.
The next parameter to be determined is humidity point (Hp). Where is the
length of retention the dew on the plant organs of the vine expressed in hours.

23.

FFD = Tm x Hp
FDD;Factor for development disease;
EFDD = [ 0,2
x Tm (1-Tm) ] x Hp
EFDD;External factor for development disease.
1; Dispersion rate of conidia
0,2; P^ the probability obtained according to Panitrur et al (2017) where:
Table 4. Classification of wine grape cultivars according to their susceptibility to B. cinerea
No. of
susceptibility
Categories of susceptibility %
1 Highly Resistant 0-3,5
2 Resistant 3,51-10
3 Intermediate 10,1-25
4 Susceptible 25,1-50
5 Highly Susceptible 50,1-100

According to table 4 we have 5 conditions that can occur in case of favorable external
conditions for the development of gray mold so the probability (P ^) will be:





The model assumes that, on any day, there are favourable conditions for conidia to
disperse and settle on host plant surfaces so that dispersion rate of conidia is equal to
1.This assumption is consistent with and is in context with the biological cycle of
development of B.cinerea.
The disease parameters are described in Tab 5.

24.

Table 5. Formulas for determining the disease parameters

For each of the variants,(
treated and untreated grapes) the following were calculated:
average number of grape berries in the bunch, average number of healthy grape
berries in the bunch, average number of diseased grape berries in the bunch
infection
index according to Mc-3 formula (Pejchinovski & Mitrev, 2007), and efficiency
8 formula (Pejchinovski & Mitrev, 2007) to determine
the category of the diseased bunches (Table 6).
Table 6. Categorisation of the diseased bunches.

Numerical
category
of
infection


Terminological category
of infection

Expressed in %
0

1

2

3

4

without of infection

rare infection

significant infection

strong infection

whole infection
0 : 0,9

1 : 5

6 : 30

31 : 70

71 : 100

To increase the accuracy of the assessment of the infection of the bunches, BRAT
(Bunch Rot Assessment Trainer) software was used ( http://bunchrot.co.nz/ ), which
Category of infection of
the
bunch
Calculating the index of
infection according to
Mc-Kynney`s formula
Calculating the
efficiency of
fungicide according to
Abott`s formula
0 : without of infection
1 : rare infection
2 : significant infection
3 : strong infection
4 : whole infection
+
<= +&!0
N x K
I : index of infection
n : no. of bunches by
category
N : simple size (30
bunches)
K : no. of categories
: sum
It
2= +&!!
Ik
E : efficiency of fungicide
It : index of infection in
treated
plants
Ik- index of infection in
untreated
plants
(control)

25.

measures the percentage of the total area of the bunch that is affected by the disease.
The results obtained using this software were compared with
statistical analysis of diseased and healthy grape berries. The statistical difference
was minimal >&?) healthy bunches were
categorised as described in Tab 6.
3. RESULTS
3.1 Results obtained from the observation of the development of B. cinerea at
Sopot locality in 2017
The observation started on 16.08.2017 and ended with the grape harvest on
15.09.2017. It was quite characteristic that during the whole vegetation season only
two chemical treatments were performed (Table 3).
During the observation the
temperatures were unusually high, while the relative humidity was very low as can be
seen from the graph 1.
Due to such weather conditions, no development of B.cinerea
was observed because measurements of microclimatic conditions in the vine canopy
indicated that there would be no development of gray mold.
During the working
hypothesis, the vineyard was exposed to the constant influence of the warm wind,
which led to the rapid drying of the drops of condensed water. Condensed water was
present for less than half an hour.
Temperatures were above 30$C and relative
humidity was at a level outside the range of influence to cause the development of B.
cinerea (Figure 4).

Figure 4. Damage to the Smederevka variety as a result of high temperatures, Sopot locality
(photo of the author, 30.08.2017)

26.


Graph 1. Illustration of temperature v ariations and relative humidity in the vines habitat during the working hypothesis (Sopot 2017)

27.

3.2 Results obtained from the observation of the development of B. cinerea at
Smilica locality in 2018

This analysis aimed to create a model where the infectious characteristics
of the grey mould would be elaborated. In developing this model, the infectious
properties of grey rot depended on external factors. Base on the way in which the B.
cinerea infection occurs, the working hypothesis can be roughly divided into two
parts: 1) the aim was to determine the incubation period and to predict the appearance
of the first symptoms based on the measurements of microclimatic and
phillospheric conditions inside of the vines canopies; 2) with the appearance of the
first symptoms to determine the dynamics of development of B. cinerea by calculating:
average number of grape berries in the bunch, average number of healthy berries in
the bunch, average number of diseased berries in the bunch.The determination of the
incubation period started for each of the varieties from the moment when 11% sugar
content was measured. At the Smederevka variety, this percentage of sugar was
measured on 16.08.2018 and results were taken on a daily basis and statistically were
processed (Table 7) to create a graph (Graph 2) that will provide insight into the
situation on the field. The incubation period of the gray mold in
the Zilavka variety was quite characteristic and unusual, and it started on
11.08.2018 when 11% sugar content was measured. Statistical data processing for
Zilavka variety is shown in Tab 8. from which it arises Graph 3. According to the
observations and calculations, the length of the incubation in this case lasted until
24.08.2018 but the first symptoms of B. cinerea appeared on 27.08.2018. while
latent period was registered on August 25 and 26, 2018. The curve determined
the incubation period and the onset of symptoms. After the first symptoms of
the control appeared on August 27, 2018 at the Smederevka (Graph 2) and Zilavka
(Graph 3) varieties, with visual observation starting to follow the dynamics of the
disease development, while in the treated grapes there was still no symptoms of the
disease. The appearance of symptoms of B. cinerea in the treated grapes was noticed
on September 11, 2018, i.e. 16 days after the last chemical treatment. Between the
last chemical spray on 27.08 (Table 2) at the treated grapes and the appearance
of the first symptoms on 11.09 was followed and calculated with Mc-3
8 Table 9). In this way, the relevance of the research study
was proved by analyzing the treated and untreated grapes at the same time while

28.

monitoring the development of the disease.The B.cinerea impact of the detrimental
effect on the yield is visible at the control at both varieties (chemical untreated grapes)
(Table 10).
Table 7. Determination of incubation and occurrence of the first symptoms of B. cinerea in
the Smederevka variety in August 2018
Limit value







Working
Hypothesis

Daily Average Temperature
(Tda)


Hp in
hours
!"#$-!%&!$'-!%&
FDD=Hp !
EFDD=m(1-! Hp






Dates 0.7

13

19

Tda Hp Tm EFDD FDD Mi
16.08.2018 13 25 19 19 4 0,5 0,32 2 1,16
17.08.2018 13 26 20 20 4 0,53 0,18 2,1 1,15
19.08.2018 11 22 19 17,3 4 0,57 0,18 2,2 1,23
21.08.2018 19 29 20 22,6 6 0,36 0,26 2,1 1,21
23.08.2018 14 25 19 19,3 4 0,48 0,18 1,9 1,05
24.08.2018 12 27 20 19,6 4 0,50 0,2 2 1,1
27.08.2018 21 29 24 24,6 8 0,45 0,39 3,6 1,99
28.08.2018 19 25 28 24 4 0,55 0,19 2,2 1,19
29.08.2018 19 29 20 22,6 6 0,36 0,26 2,16 1,21
30.08.2018 21 29 19 23 6 0,4 0,28 2,4 1,34
31.08.2018 24 30 29 27,6 3 0,6 0,14 1,8 0,97
02.09.2018 21 30 29 29,3 7 0,39 0,28 2,73 1,5
03.09.2018 21 29 28 26 6 0,62 0,24 3,72 1,98
05.09.2018 23 30 30 27,6 2 0,65 0,08 1,3 0,69
07.09.2018 19 28 24 23,6 2 0,51 0,09 1,02 0,55
1,24
Limit
Value

29.



Graph 2. Viewof the Smederevka variety control.Data above the limit value (1.24) indicate a possible onset of symptoms, except for the
incubation period.Where the blue line intersects the incuba tion limit value (1,2) indicates the fav orable conditions for i ncubation period
development

30.
Table 8. Determination of incubation and occurrence of the first symptoms of B. cinerea in
the Zilavka variety in August 2018
Limit value




Working
Hypothesis
Daily Average Temperature
(Tda)
Hp in
hours
!"#$-!%&!$'-!%&
FDD=Hp !
EFDD=m(1-! Hp


dates 07 13 19 Tda Hp EFFD FDD Mi
11.08.2018 14 22 18 18 6 0,5 0,3 3 1,65
12.08.2018 12 20 17 16,3 5 0,54 0,28 2,7 1,49
13.08.2018 13 25 21 19,6 5 0,55 0,25 2,75 1,5
14.08.2018 10 27 24 20,3 4 0,6 0,19 2,4 1,3
15.08.2018 15 26 21 20,6 6 0,5 0,3 3 1,65
16.08.2018 13 25 19 19 4 0,5 0,32 2 1,16
17.08.2018 13 26 20 20 4 0,53 0,18 2,12 1,15
19.08.2018 11 22 19 17,3 4 0,57 0,18 2,28 1,23
21.08.2018 19 29 20 22,6 6 0,36 0,26 2,16 1,21
23.08.2018 14 25 19 19,3 4 0,48 0,18 1,9 1,05
24.08.2018 12 27 20 19,6 4 0,50 0,2 2 1,1
27.08.2018 21 29 24 24,6 8 0,45 0,39 3,6 1,99
28.08.2018 19 25 28 24 4 0,55 0,19 2,2 1,19
29.08.2018 19 29 20 22,6 6 0,36 0,26 2,16 1,21
30.08.2018 21 29 19 23 6 0,4 0,28 2,4 1,34
1,35
Limit
Value

31.


Graph 3.View of the Zilavka variety control. Data above the limit va lue (1.35) indicate a possible onset of symptoms, except for the incubation
period

32.

Table 9. Overview of variants and the calculated results





variants Average
number
of grape
berry in
the
bunch

%
Average
number
of
healthy
grape
berry in
the
bunch

%
Average
number of
diseased
grape
berry in
the bunch

%
Infection
index
according
to formula
of Mc-
Kinney (%)
Efficiency
of
fungicide
according
to formula
of Abott
(%)
Allowed a
level of
significance
of
5% (p < 0,05)
Smederevka
(chemically
treated)
156 100 149 95,5 7 4,5 5 94,1 99,1
Smedervka
control
(untreated)
149 100 30 20,1 119 79,9 84,2 / /
Zilavka
(chemically
treated)
113 100 104 92 9 8 7,7 90,2 97,9
Zilavka
control(untreated)
121 100 18 14,9 103 85,1 78,3 / /

33.

Calculation according to Tab 9.
Smederevka (chemically treated) (Mc Kinney)


=




Smederevka control (untreated) (Mc Kinney)






Smederevka (Abott)


=



Zilavka (chemically treated) (Mc Kinney)







Zilavka control (untreated) (Mc Kinney)







Zilavka (Abott)


=

34.

3.3 Results obtained from the observation of the development of B. cinerea at
Smilica locality in 2019

During the working hypothesis, the development of B.cinerea was not
observed, ie during the control the percentage of diseased bunches was extremely
low, ranging from 1 to 1.5%.
Symptoms of B. cinerea appeared seven days before
grape harvest in controls (untreated grapes) with very slow pathogenesis. On the
vineyard where the chemical treatments were performed, the disease did not appear
at all.
Temperature and relative humidity were measured throughout the working
hypothesis. The retention of water droplets in the morning, on average, lasted about
an hour for the entire duration of the working hypothesis while the maximum daily
!$#%0@)
which would cause more serious damage to the grapes and yield (Graph 4).
As there
was no development of B. cinerea in the range that would have an impact on yield
further statistical operations were not carried out.

35.


Graph 4. View of temperature variations and relative humidit y in the vines habitat (Smilica locality,2019)

36.

4. DISCUSSION

Most studies on host resistance, timing of fungicide applications, biological
control, control by cultural practices and disease prediction models of B. cinerea on
grapevines were based on assumptions and conclusions made on mature berries
(Avissar & Pesis, 1991; Broome et al., 1995; Chardonet et al., 1997; De Kock & Holz,
1991; Marios et al., 1986b; Nair & Nadtotchei, 1987; Nair et al., 1988).
For these
reasons the working hypothesis was set when the bunches reaches a sugar content
of more than 11% (onset of ripening or color change of grape berries from green to
yellow ) until the grape harvest and microclimate conditions were monitored at the
same time.
Hence,the goal is to implement model who relies on the rational
assessment in the vine canopy microclimatic conditions.
The forecating model for
botrytis is essentially based on the relationship between relative humidity and
temperature in the vine canopy and the aim is to create a graph where the curve will
represent the tendency of B. cinerea to develop.
The results of the measurement of relative humidity and temperature during the
working hypothesis in the vine canopy for the location Sopot in 2017, showed that
% !$#
winds, had an inhibitory effect on the development of B. cinerea.
Measurements of
relative humidity and temperature showed that the conditions for the development of
B.cinerea were not met and chemical treatments were reduced to only two sprays of
the vineyard during the vegetation (Figure 5).

Figure 5.Overview between chemical treatments and grape harvest time
Due to the evident low level of relative humidity which on average ranged between 30
and 40 percent and temperatures that ranged above 30$C, the appearance of
symptoms of the disease during the working hypothesis was not expected (Graph 1).

During the ripening of the grapes, the appearance of B. cinerea was at the level of

37.

permissible economic threshold of 2%, due to the presence of wasps and other
representatives of the order Hymenoptera, which physically damaged the grapes,
resulting in an extremely weak infections of the grape berries.
In the observed bunches
where it was noticed B. cinerea infection due to the strong sunlight, the pathogenic
process was stopped.
In the 2018 the forecasting gray mold model describes two key stages of the B. cinerea
life cycle in vineyards: 1) infection of mature berries by conidia;
2) development of
mycelium when it occurs berry to berry infection. In order to simplify the biological
cycle of development to explain the infectious features of grey mold, the following
phases are emphasised: attachment of conidia to the surface of the grape berry,
germination of conidia, differentiation of infection structures on the host surface,
penetration of the host surface and excretion of a spectrum of phytotoxic compounds
(necrotrophic activity).
Spores are dispersed in the wind. Once deposited on the
surface of the plant, the spores adhere to the surface tissue of the grape berries.
This phase represents the interaction of the spore with the cuticle surface.
Initially,
hydration of conidia occurs, which typically involves weak adhesive
forces, resulting from hydrophobic interactions between the host and conidial
surfaces ( Doss et al., 1995).
A few hours after inoculation, the conidia germination
germ tubes are covered with a fibrillar-like extracellular matrix material ( Doss et al.,
1995). These adhesive structures are excreted by the spore and consist of
carbohydrates and proteins ( Doss,1999).
The spores are attached to the surface of
the cuticle by the fibrillar extracellular matrix material, which also protects it from
dehydration and the various defence mechanisms of the plant cells.
Several factors
influence the germination of a conidium. Free surface water or high relative humidity
(>93%) is essential to germinate and penetrate the host epidermis (Williamson et
al.,1995).
After germination of the spore in a drop of water, the germ tube grows and
extends. When the tip of the germ tube touches the surface of the plant tissue, the
wax of the cuticle degrades forming a recess or hole for appressorium formation. The
invasion of the plant tissue by B. cinerea can involve active penetration or penetration
over natural openings and plant wounds of the tissue. The wounds of the tissue can
be caused by an abiotic or biotic agent.
Exclusively physical damage or brutal
mechanical penetration through the cuticle by B. cinerea has not been seen ( Cole et
al.,1996).
Most often, penetration is followed by enzyme activity by the pathogen. The

38.

infectious forecasting model for botrytis is essentially based on the relationship
between relative humidity and temperature inside of the canopies of vines and the goal
was to create a graph based on the parameters listed earlier (FDD, EFDD).
A
phenomenon can be understood only in the phase of the tendency of its development.

In developing this predictive infectious model for gray mold, only the microclimatic
parameters representing the biological range for B. cinerea development, which are
characteristic of the geographical area where the research was conducted, were taken
into account.
The incubation period of B. cinerea at the Smederevka variety according
to this analysis is considered to have lasted a total of 8 days from 16.08.2018 to
24.08.2018 (Graph 2).
The first symptoms appeared on 27.08.2018. The days just
before the onset of the first symptoms on 25.08.2018 and 26.08.2018 are considered
as latent period, taking into account the measured microclimatic parameters inside of
the canopies of vine.
Gray mold shows a degree of adaptability just before the onset
of the first symptoms in the control variant that was not treated with botricides.
Recent
microscopic, histological-chemical researches and gene function analysis indicate that
these structures act as functional appressoria, useful for attaching the pathogen to the
host surface before penetration of the tissue, due to a fibrillar-like extracellular matrix
material covering, which retains water while the polysaccharide component is
extremely hygroscopic, allowing the pathogen to adapt to external factors.
According
to Coley-Smith (1980) latency, once described as an enigmatic aspect of Botrytis
ecology, has been the focus of many research studies in order to define
epidemiological role and relationship to crop loss.
The incubation period of the gray
mold at Zilavka variety was quite characteristic and was accompanied by
discontinuities as a result of variable microclimatic conditions in the canopies of the
vines (Graph 3).
In general, the incubation period can be divided into two parts: 1) the
period when there were favorable conditions for the process of installing the infection
starting from: 11.08.2018, 12.08.2018, 13.08.2018, and 15.08.2018, with the
exception on 14.08.2018 there was a slight decline in the incubation
process due to the variation of external factors and thus short discontinuity;
2)
latency period lasting from 16.08.2018 to 24.08.2018.
In addition to the unfavorable
microclimatic conditions that affect the occurrence of latency, immature
grape berries also play a role. First symptoms of gray mold at Zilavka variety at
same time appeared as in the Smederevka variety on 27.08.2018 indicating the fact
that the occurrence of the disease depends on favorable microclimatic factors.

39.
Table 10. Statistical analysis of botrytis disease model at Smederevka variety




!"#!
$! !% &
'# (&$#
)*



+ ##&$
#!
&






!"

!"
,$ -
%.//
%,/0 11,0,
#$

%%

%%

2 2 07.09.2018
05.09.2018
31.08.2018
2 2 23.08.2018
2 2 16.08.2018
2 2 24.08.2018
17.08.2018
21.08.2018
2 2 29.08.2018
2 2 19.08.2018
28.08.2018
30.08.2018
02.09.2018
2 2 27.08.2018
2 2 03.09.2018

40.
Graph 5.
LEGEND: FDD- factor disease development;

16.08.2018-onset of
incubation;
17.08.2018, 19.08.2018, 21.08.2018- favorable conditions for
incubation; 23.08.2018, 24.08.2018-latency period; 27.08.2018-first symptoms on control (untreated grapes); 28.08.2018, 29.08.2 018,30.08.2018-reduction of
infection; 31.08.2018-latency period of infection;02.09.2018-ons et of second infection;03.09.2018-second infection;05.09.2018, 07.09.2018-reduction of second
infection.

.//33

.//3 3

.//3 3
3

.//3 3
.//33

.//33

.//33

.//33

.//3 3

.//33

.//3 3

.//33

3
.//33









FDD
EFDD
OVERVIEW OF REGRESSION ANALYSIS FOR DISEASE FORECATING MODEL AT
SMEDEREVKA VARIETY

41.
Table 11. Statistical analysis of botrytis disease model at Zilavka variety




!"#!
$! !% &
'# (&$#
)*



+ ##&$
#!
&






!"

!"
,$ -
%../
%,/0 11,0,
#$

%%

%%






2 2


2 2
2 2
2 2


2 2
2 2

42.


Graph 6.
LEGEND: FDD- factor disease development; 11.08.2018-onset of
incubation;
12.08.2018,13.08.2018- favorable conditions for incubation; from
14.08.2018 to 24.08.2018-latency period; 27.08.2018-first symptoms on control (untreated grapes);28.08.2018-duration of first infection;
29.08.2018,30.08.2018-reduction of infection;

.//33

.//3 3

.//33

.//33

.//33

.//33

.//3 3

.//3 3

.//33

.//3 3

.//33

.//33

.//33

.//33

.//3 3









FDD
EFFD
OVERVIEW OF REGRESSION ANALYSIS FOR DISEASE FORECASTING MODEL AT
ZILAVKA VARIETY

43.

Linear regression is a statistical method to find the relationship between one
dependent and one or more independent variables.The graph for linear regression
analysis links the interrelationships of two or more phenomena, ie this graph gives the
answer to the interdependence of the factor for disease development (FDD) and the
external factors for the development of botrytis (EFDD). To explain the forecasting
disease model for gray mold we determine the phenomenon which represent
dependent variable and in this case that is FDD. While external factors for the
development of B. cinerea (EFDD) represent the second phenomenon that is
independent variable and that affects the dependent variable (FDD). The values of the
independent variable EFDD allow us to explain the variations of the dependent
variable FDD. The key benefit of regression analysis is determining how changes in
the independent variables are associated with shifts in the dependent variable.
In
linear regression, coefficients are the values that multiply the predictor values. The
sign of each coefficient indicates the direction of the relationship between a predictor
variable and the response variable. A positive sign indicates that as the predictor
variable increases, the response variable also increases and vice versa. The
correlation between these values is strong which can be seen from the calculated
Pearson coefficient (Multiple R) according to Tab.10 at Smederevka variety which is
r= 0.736855, while at Zilavka variety is r= 0,710804 Tab.11 The results showed that in
every case (at both varieties) there was a high correlation between FDD and EFDD.

The coefficient of determination (R
2
) or R Square for the two different cases were R
2
=
0,542955 at Smederevka Tab.10 and R
2
= 0,505242 at Zilavka variety Tab.11 This
value (R
2
) is an indication of how much changes in one variable (EFDD) cause
changes in the other variable (FDD)
and the convection is expressed in percentage,
respectively R
2
=0,542955 x 100 =54% at Smederevka and R
2
=0,505242 x 100 =
50,5% .
This means that the other 46% of Smederevka and the remaining 49.5% at
Zilavka variety belong to the category of unknown factors.
Adjusted R Square typically
always lower than the R Square in both variety, respectively Adjusted R Square
=0,542955 at Smederevka Tab.10 and Adjusted R Square =0,467184 at Zilavka
variety Tab.11 Frequently R-squared values range from 0 to 1 and are commonly
stated as percentages from 0% to 100%. In essence Adjusted R-squared is a modified
version of R-A
than expected. It was indicated that the development of the B. cinerea largely depends
on the influence of microclimatic conditions which creates the possibility of its

44.

prognosis. B B2
predictions are using the units of the dependent variable (FDD) and in this case at
Smederevka variety is SE= 0,501084 while at Zilavka variety is SE= 0,346823.
The
determination of linear regression model i.e. its significance we consider the data for
F-statistic along with the corresponding p-value.
Hence F-statistic given in the ANOVA
tables (Table 10 and Table11) as well as the p-value which is labeled as Significance
F.
F-statistic:
15,44361; Tab.10
F-statistic:
13,27548 Tab.11
Technical note: The F-statistic is calculated as MS regression divided by MS residual.
MS regression / MS residual =3,877664/0,251085 = 15,44361 Tab.10
MS regression / MS residual = 1,596855/0,120286 = 13,27548 Tab.11
The Significance F in fact is p value for the regression model.
The null hypothesis is
the formal basis for testing statistical significance(Chaudhury and Banerjee , 2009) .
The alternative hypothesis cannot be tested directly; it is accepted by exclusion if the
test of statistical significance rejects the null hypothesis. In this case null hypothesis
suggests that no linear relationship between the EFDD and FDD vs alternative
hypothesis which assumes linear relationship between the EFDD and FDD.
A good
hypothesis must be based on a good research question. It should be simple, specific
and stated in advance
(Hulley et al., 2001)The null hypothesis is rejected in favor of
the alternative hypothesis if the P value is less than alpha ( type I error), the
predetermined level of statistical significance
(Daniel, 2000).Nonsignificant results are
those with P value greater than alpha( type I error).In this case alpha value is 0.05
this means that it is rejected null hypothesis and accepted alternative hypothesis if the
p- A4C!)!*)As you can see in both tables (table 10 and
table 11) the p-value for this forecasting model was considerably lower than alpha
value of 0.05 .It can be concluded that the linear regression model is significant.
The
intercept is the point where the function crosses the y-axis.
With intercept coefficient
(Y
i ) are shows the point where the line of the best fit or regression line crosses y axis
when the value x is zero ,
respectively Y i = 0,848502 (Table 10 ) and Y i = 1,104838
(Table 11 ). The second value is coefficient on EFDD as a result of the slope. For a
simple linear regression model the most basic version of the equation is:

45.







A need arises again to interpret this p -value only with little more detail because of our
hypotheses.
In this case the null hypothesis is that the intercept or slope is zero (b=0),
while alternative hypothesis is that the intercept or slope is not zero (b), as you can
see the both values are less than alpha ( type I error), respectively p-value (intercept)
= 0,039639; p-value (EFDD) = 0,001726 Tab.10 and p-value (intercept) =0,011959 ;
p-value (EFDD) =0,002974 Tab.11 This means the EFFD is a significant variable that
impact FDD.
From each observation from data that was entered into regression test
we get a predicted value of FDD (table 10 and table 11) based on the regression
model.
Technical note: if I put this value into the regression equation along with the slope and
intercept values I get predicted FDD value. For example:
Y= 6,26987 x 0,32 +0,
848502 =2,85486 (Predicted FDD) Tab.10
Technical note: Residuals is simply the distance between the actual data point and the
line of best fit. For example:
Actual Data Point - Predicted FDD =2 -2,85486 = -0,85486
Tab
It can be concluded the predicted values come from residuals.
From the moment of the appearance of the first symptoms of the disease on
27.08.2018 to 11.09.2018, there was a great trend of increasing the infection rate
in the control bunches and during that time, the infection increased with
geometric progression (


); gray mold infection at Smederevka variety in
control variant expressed in % (2.1; 7.5; 18.9; 36.1; 79.9); gray mold infection at
Zilavka variety in control variant expressed in % (3.4; 9.3; 21.9; 43.9; 85.1) from
27.08.2018 to 11.09.2018; Tab.12

46.
Table 12.Development of B.cinerea disease from the moment of the appearance of the first
symptoms of the disease of the control (untreated grapes) to grape harvest

The last fungicide spray in the treated grape variant was executed on 27.08.2018 at
the same time when the first symptoms appeared in control grape variant.
The first
symptoms in treated grapes appear 16 days after last fungicide spray in the treated
grape variant more precisely on 11.09.2018. Then from 12.09.2018 until that time of
grape harvesting there was certain stabilization of gray mold development, this slow
development of the disease was partly caused by the unusually high temperatures
which were above 30$C and the low relative humidity.
Considering the state of
the research fields and the complexity of variable factors that influenced the
development of B. cinerea the forecasting model for gray mold proved to be functional.

Because it clearly foresaw the need for the latest chemical treatment, testing this
infectious model for gray mold on the other hand was comparable to the
control variants Tab.13
Variants
Period of disease observation
27.08.2018 30.08.2019 03.09.2018 07.09.2018 11.09.2018 15.09.2018 19.09.2018
Expressed in %
Smederevka
(chemical
treated)
0 0 0 0 4,5 5,8 7,1
Smederevka
control
(untreated)
2,1 7,5 18,9 36,1 79,9 82,5 87,6
Zilavka
(chemical
treated)
0 0 0 0 8 11,3 13.2
Zilavka
control
(untreated)
3,4 9,3 21,9 43,9 85,1 87,8 89,2

47.

Table 13. View of yield loss of the control in relation to treated grapes (Smilica, 2018)

The analysis of infection index according to Mc Kinney`s formula and Efficacy of
fungicide according to Abbott's formula was performed on September 11, 2018 when
the first symptoms appeared in the treated and was accepted alternative hypothesis
%*DE&C-%F?G!%!* B he
percentage of diseased grape berries was 4.5% and the sum of the parameters of the
formulas of Mc Kinney and Abott was 99.1 [5 (Mc Kinney) + 94.1 (Abott) = 99.1 (p
<0.05)] while the percentage of diseased grape berries on the treated grapes of the
Zilavka variety was 8% and the statistical significance was 97.9 [7.7 (Mc Kinney) +
90.2 (Abott) = 97.2 (p <0 , 05)] (table 9).
During the working hypothesis in 2019, the development of gray mold was not
observed, i.e. during the control the percentage of diseased bunches was extremely
low, ranging from 1 to 1.5%.
Symptoms of B. cinerea appeared seven days before
grape harvest at untreated grapes (control) with very slow pathogenesis.
In the
vineyard where the chemical treatments were performed, the disease did not appear
at all.
Temperature and relative humidity were measured throughout the working
hypothesis.
The retention of water droplets in the morning, on average, lasted about
an hour and a half for the entire duration of the working hypothesis while the maximum
daily temperatures were above 30$C, which conditioned to there wasn't development
of the disease.




varieties variants average
yield in kg
per vine
average
yield in kg
per ha
yield loss in
kg / ha at
control
loss of
control
yield
in% Smederevka chemically
treated
4,75 19000
Zilavka 4,45 17800
Smederevka
control
untreated 1,81 7240 -11760 62%
Zilavka
control
1,65 6600 -11200 63 %

48.

5.Conclusion
As a result of monitoring the microclimatic conditions for the development of
B.cinerea, it can be concluded that they are an essential denominator for the
development of the disease. If the incubation phase of the pathogen was completed,
the infection will depend on the moment when favorable conditions occur, regardless
of the interruptions in the incubation process that occur as a result of
unfavorable microclimatic conditions. Deteriorated external conditions during
incubation in B. cinerea can cause a resistance reaction which sometimes leads
us to the erroneous conclusion that there are no conditions for the development
of the disease. The insight in determining the incubation period of B. cinerea is
the basis for reducing the last chemical treatments just before the grape harvest.

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