Dynamic simulation of grape downy mildew primary infections - Assam

Dynamic simulation of grape downy mildew primary infections
Vittorio Rossi, Riccardo Bugiani, Tito Caffi, Simona Giosuè
Istituto di Entomologia e Patologia vegetale, Università Cattolica del Sacro Cuore, via E. Parmense 84, 29100
Piacenza, Italy (e-mail:vittorio.rossi@unicatt.it; fax: 0039 0523 599256).
___________________________________________________________________________
Plasmopara viticola (Berk et Curt.) Berlese et de
Toni is the causal agent of grapevine downy mildew, an
important disease in all grape-growing areas
characterized by temperate climate and frequent rain
during spring and summer.
Oospores represent the sexual stage of the fungus and
are a key component in its life cycle: they are the only
source of inoculum for primary infections. Oospores
overwinter in the leaf litter above the vineyard soil;
during this period they complete their morphological
maturation and overcome dormancy. Dormancy is a
physiological stage that does not allow oospores to
germinate in winter even if climatic conditions are
favorable. This is an ecological adjustment that prevents
germination when there is not a susceptible host tissue to
infect. As a further ecological advantage, oospores
overcome dormancy gradually, at a rate which depends
on meteorological conditions occurring during winter
and early spring. Afterwards, oospores germinate in a
macrosporangium that releases zoospores which are
responsible for primary infections on grape leaves and
clusters in spring.
The dynamics of oospores germination is a key factor
for disease epidemics and, consequently, for scheduling
fungicide sprays to control downy mildew. In order to
increase the efficiency of the existing crop protection
strategies, epidemiological models have been introduced
to estimate disease risk and produce warnings for
fungicide applications. Several models have been
elaborated to describe the dynamics of downy mildew
infection, with emphasis on primary infections. Despite
this, none of these models has been demonstrated to be
accurate or robust enough to be used satisfactorily in
scheduling fungicide applications.
A new dynamic model based on ‘systems analysis’
was then elaborated for the sexual stage of P. viticola.
Model structure. The model simulates, with a time
step of one hour, the entire process from oospore
maturation and germination, to zoospore ejection and
dispersal, and finally to infection establishment and
disease symptom onset. This process was separated in
different stages: the pathogen changes from one stage to
another at different rates, depending on environmental
conditions, as shown in the relational diagram of Fig. 1.
Fungal stages are considered as state variables and their
changes are regulated by rate variables; weather
conditions influence rates acting as parameters or
intermediate variables (Tab. 1). Equations linking the
meteorological parameters to the rate variables were
elaborated from both literature and data from specific
studies.
The first state variable is represented by the oospores
109
produced in the leaf residues above the ground (OLL),
the second is represented by the morphologically mature
oospores (MMO). The rate of morphological maturation
(MMR) depends on time elapsed after oospore formation
(t).
The following state variables are the physiologically
mature oospores (PMO), which are oospores that had
overcome the dormancy, and the germinated oospores
(GEO),
Tab. 1. List of variables used in the model.
State variables
OLL Oospores in the leaf litter
MMO Morphologically mature oospores
PMO Physiologically mature oospores
GEO Germinated oospores
ZLL Zoospores on the leaf litter
ZGL Zoospores on the grape leaves
ZCI Zoospores causing infection
ISS Infection sites showing symptoms
Rate variables
MMR Progress of oospore maturation
DOR Progress of dormancy overcoming
GER Progress of oospore germination
SUR Survival
INF Progress of infection establishment
INC Progress of incubation
Auxillary variables and parameters
LLM Leaf litter moisture (%)
VPD Vapour pressure deficit (hPa)
t Time (days)
T Air temperature (°C)
RH Relative humidity (%)
LW Leaf wetness (yes or not)

Fig. 1. Relational diagram of the model simulating primary P.
viticola infection on grape. See Tab. 1 for acronym explanation.
RH
T
LW
LW
S
U
R
OLL
MMO
PMO
GEO
ZLL
ZGL
ZCI
ISS
MMR
DOR
GER
INF
INC
R
LW
T
R
t
T
T
LW
T
RH
T
LLM
VPD
RH
which are oospores that have completed the germination
process and have produced a macrosporangium on the
leaf litter surface. Oospores change from MMO to PMO
at the end of dormancy, and from PMO to GEO when the
germination process is completed; the corresponding
rates (DOR and GER, respectively) both depend on air
temperature (T) when leaf litter moisture (LLM) is not a
limiting factor, but rainfall (R) is necessary to moisten
the leaf litter and trigger germination. LLM depends on
the balance between water absorption from and
desorption to the atmosphere, measured by means of the
vapour pressure deficit (VPD).
In the presence of a film of water (LW)
macrosporangia release zoospores (ZLL); otherwise they
can survive for a few days and then die: the survival rate
(SUR) depends on T and relative humidity (RH).
These zoospores, swimming in the film of water
covering the leaf litter, reach the grape leaves (ZGL) by
splashes and aerosols triggered by rainfall. If the litter
surface dries up before rainfall they do not survive;
therefore the SUR of these zoospores depends on LW.
Zoospores in the ZGL stage go to the next stage of
zoospores causing infection (ZCI) according to an
infection rate (INF) which depends on LW and T during
the wet period. During this period zoospores swim in the
direction of stomata, form a cyst and produce germ tubes
that penetrate the stomatal rimae. If the leaf surface dries
before penetration, the zoospores dry out; therefore their
survival depends on a combination of LW and T.
110
At the end of incubation, the infection sites become
visible as disease symptoms (ISS); incubation progress
(INC) is influenced by T and RH.
Model running. Considering that oospores usually
reach the MMO stage in autumn, it is assumed that the
population of oospores formed at the end of a downy
mildew epidemic is all in the MMO stage on the 1 st of
January of the following year.
Since it is well known that oospore germination is a
gradual process over the grape-growing season, the
model considers that the oospore population of a
vineyard overcomes dormancy in many subsequent
groups (cohorts), and that the density of each cohort
follows a normal distribution. Therefore, the model
calculates the time when the first cohort of oospores
enter the PMO stage at the end of its dormancy; further
cohorts enter this stage progressively (Fig. 2). A
measurable rainfall moistening the leaf litter is the event
triggering germination of the oospore cohorts in the
PMO stage at that time. Times required for completing
both dormancy and germination are calculated by the
variables DOR and GER, respectively, using two
counters that increase according to T when LLM is not a
limiting factor. When these counters reach a fixed
threshold the model assumes that both dormancy and
germination of an oospore cohort are finished.
Afterwards, the model calculates the time when the
macrosporangia produced by the germinated cohort of
oospores survive, the possibility that they release
zoospores, that these oospores survive, reach the grape
leaves and successfully infect the leaf tissue (Fig. 3).
Finally the model calculates the incubation period and
defines a time period when the downy mildew symptoms
should appear on the affected grape organs.
Model outputs. The model provides tables showing
the hourly progress of the main infection stages (Tab. 2),
and graphs showing the state of the infection cycle on
each day during the primary inoculum season (Fig. 3).
Tab. 2. Example of the tabular output of the model in a day with
a successful infection.
Hours of
the day
Oospore
germination
Sporangium
survival
Zoospore
release
Zoospore
dispersal
Infection
progress
Incubation
progress
2 0.99
3 0.99
4 0.99
5 1.00 0.01
6 0.01
7 0.02
8 0.03 yes
9 yes 0.30
10 0.77
11 1.00
12 0.01
13 0.02

Fig. 2. Example of model simulations. A: progress of DOR over
time after 1 st January and thresholds for the first and last cohorts
of oospores overcoming dormancy. B: progress of GER for the
cohorts of oospores able to start germination at each rainfall and
density distribution of these cohorts.
B)
A)
rainfall
Primary inoculum season
January February March April May June July
111
Fig. 3. Example of model outputs: bars represent the daily
rainfall; lines show the progress of germination in different
cohorts of P. viticola oospores; dots show the occurrence of the
different stages of the infection cycle, from zoospore ejection to
leaf infection (dots are black when the infection cycle is
successfully completed); the box shows the period of the
expected appearance of downy mildew symptoms.
Rain (mm)
(mm)
20
10
0
1/4
Zoospore
release
Oospore
germination
8/4
Infection Infection
Zoospore
Zoospore
dispersal dispersal dispersal dispersal dispersal dispersal
15/4
22/4
29/4
6/5
13/5
20/5
27/5
3/6
10/6
End of incubation
17/6
24/6

Validation of a simulation model for Plasmopara viticola primary infections
in different vine-growing areas across Italy
Tito Caffi, Vittorio Rossi, Riccardo Bugiani, Federico Spanna, Lucio Flamini, Antonello Cossu,
Camilla Nigro
Istituto di Entomologia e Patologia vegetale, Università Cattolica del Sacro Cuore, via E. Parmense 84, 29100
Piacenza, Italy (e-mail: tito.caffi@unicatt.it; fax: 0039 0523 599256)
___________________________________________________________________________
Downy mildew of grape, caused by Plasmopara
viticola (Berk et Curt.) Berlese et de Toni, is a disease of
major importance in grape-growing areas with a
temperate climate. It is a potentially destructive disease
that requires repeated fungicide application during the
growing season.
Some epidemiological models have been elaborated
to support decisions about disease control but none are
accurate or robust enough to be used for scheduling
fungicide application. Consequently, warning systems
are still based on the empirical rule called “three tens”
even if it is frequently unreliable.
A new model has recently been elaborated, which
can simulate the dynamics of primary inoculum and
infection during the season. This model uses
meteorological data (air temperature, relative humidity,
rainfall, leaf wetness) to simulate, with a time step of one
hour, the infection chain from oospore germination to the
onset of disease symptoms, including the germination
progress, survival of sporangia, zoospore ejection and
survival, zoospore dispersal, infection and incubation.
The model performs several simulation runs per season,
considering that the overwintering oospore population
overcomes dormancy gradually. In particular, the
oospore population of a vineyard is composed of
different cohorts that become able to germinate
according to a normal distribution. When a measurable
rainfall wets the leaf litter containing these oospores a
simulation run starts with the beginning of oospore
germination. This simulation run can be interrupted at
any stage of the infection chain if the environmental
conditions do not favour the fungus, or can complete the
infection chain until the appearance of the disease. The
model provides both tables showing the hourly progress
of each simulation run and graphs showing the state of
the infection cycle for each day during the primary
inoculum season (Fig. 1).
Validations were performed in 77 commercial
vineyards throughout five regions of Italy, between 1995
and 2005 (Fig .1). Some data were provided by historical
series available at the local services delegate to
producing disease warnings for vine-growers in the
different areas. Other, more recent data, were specifically
collected for validation. In both cases, vineyards can be
considered representative of the different vine-growing
areas, for soil type, varieties, training systems and
cropping regimes. They also contained a representative
dose of overwintering inoculum because a regular
fungicide schedule was applied the previous season.
During winter, a plot which included several rows of
vines was set apart in each vineyard and not sprayed with
112
fungicides against downy mildew till the time of first
disease onset. Starting from bud burst, plots were
carefully inspected at least once per week, to detect the
time of appearance of the first disease symptoms such as
“oil spots” on leaves.
Data collection was coordinated by the team working
on this paper, from the regional phytosanitary services of
Emila-Romagna (R. Bugiani) and Piedmont (F. Spanna),
SAR (regional agrometeorological service) of Sardinia
(A. Cossu), Assam in Marche (L. Flamini), and Alsia in
Basilicata (C. Nigro). In Oltrepò Pavese (Lombardy) data
were collected by the first author of this work.
Rain (mm)
(mm)
20
10
0
Oospore
germination
1/4
8/4
Zoospore
release
Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection
Infection
Zoospore
Zoospore
dispersal
dispersal
15/4
22/4
29/4
6/5
13/5
20/5
27/5
3/6
10/6
End of incubation
17/6
24/6
Fig. 1. Model output showing 10 simulation runs triggered by
rainfall (bars). Lines show the germination course for different
P. viticola oospore cohorts; dots show progress over time of the
infection process, stage by stage: they are white when the
infection chain aborts and black when it is successfully
completed; the box shows the period of expected downy mildew
appearance.
1998-2002
5 vineyards
1999-2004
19 vineyards
1999-2004
6 vineyards
2004-2005
2 vineyards
1995-2004
38 vineyards
2004-2005
7 vineyards
Fig. 2. Distribution of the vineyards used in model validation.

Hourly meteorological data of air temperature,
relative humidity, rainfall, and leaf wetness were
collected to be used as model inputs. In Piedmont and
Oltrepò Pavese, data were measured by automatic and
mechanic weather stations, respectively, installed in the
vineyards. In the other regions data were supplied by the
regional networks for the nearest automatic station. In
Emilia-Romagna, after the year 2000, the meteorological
data were extracted from a data-base containing data
interpolated on a grid of 5x5 km.
The model was used to simulate, for each vineyard,
the infection process in each cohort of oospores between
the end of dormancy and the time of first disease onset in
the vineyard. Total simulations were first classified as
aborted or successful. A simulation was considered
aborted when the infection process stopped at any stage
before infection, while it was considered successful when
all the stages progressed until infection was established.
Both aborted and successful simulations were then
classified as: i) accurate, when the model produced a
successful simulated infection that actually produced
symptom appearance in the vineyard, or when an aborted
simulated infection did not correspond to an actual
symptom onset; ii) overestimated, when the model
produced a successful simulated infection but the disease
did not appear; iii) underestimated, when the model did
not simulate an infection that actually occurred. A
possible criticism of the above mentioned classification
is that there is no proof that an aborted simulation
process actually occurred in the vineyard; nevertheless,
from a practical point of view, the model produces
accurate information in such a case, because it indicates
that there is no risk of infection occurring.
The model produced 736 simulations in total: the 2
test showed a significant correlation between model
simulations and actual observations (Tab. 1). The model
was very accurate in simulating successful infections,
because all the observed disease appearances in the
vineyards were correctly simulated by the model; this
happened 122 times (16.6% of total simulations). In 543
simulations (73.8%) the model rightly interrupted the
simulation runs because the environmental conditions did
not permit the infection process to proceed; no disease
symptoms were actually observed in the vineyards as a
consequence of these possible infection events. In these
cases, the infection process was aborted because
sporangia died before zoospore release (14% of cases),
zoospores died before dispersal (75%) or before infection
establishment (8%) (data not shown). Considering both
successful and aborted infections, there were 665
accurate simulations out of 736 (90.4%) (Tab. 1). In 71
simulation runs the model simulated an infection event
that did not actually occur (9.6% of cases overestimated).
On the contrary, the model never failed to simulate actual
infections.
Model outputs were validated in different
epidemiological conditions (areas x vineyards x years)
(Tab. 2). In Piedmont the model was validated in 19
different situations; it provided 153 simulations with only
4% of cases overestimated. In Oltrepò Pavese, the model
was validated for a 5-year period in the same vineyard
providing 26 simulations; only in one case (4%) infection
was overestimated. In Emilia-Romagna, the model was
validated in 38 cases that provided 365 simulations in
113
aggregate; 7% of them were overestimated while the
remaining 93% were accurate. In the Marche region the
model provided 104 simulations, under 7 different
epidemiological conditions; it produced 88 accurate
simulations, with 15% overestimation. In Basilicata the
model produced 34 simulations in two different vinegrowing
areas: in 91% of cases the model correctly
simulated the primary infection process, with 3 (9%)
unjustified alarms. In Sardinia the model was validated in
the same vineyard over a six-year period; it provided 54
simulations. Also in this area the model never failed to
signal an actual infection, but 19 simulations (35%)
signalled an infection that actually did not occur.
In conclusion, this dynamic model for primary P.
viticola infections was accurate and robust: in more than
90% of the simulations performed the model correctly
simulated either aborted or successful infections, under
different epidemiological conditions and over a wide
seasonal period, from early to late disease onsets (Fig. 3).
In the remaining cases, the model overestimated the
actual situation because it simulated an infection that did
not actually produce disease symptoms; these cases
frequently occurred in early spring, at the beginning of
the primary inoculum season (data not shown).
Moreover, the model never failed to simulate actual
infections.
Inaccuracies produced by the model were always due
to false positive infections, which have a low negative
impact on model performances because it simply
produced unjustified warnings. Nevertheless, the model
can be improved to reduce false positive prognoses.
Considering that this model runs using only
meteorological data and does not include information
about the growth stage of the host at the time of a
possible infection, there are good reasons to believe that
unjustified alarm could be avoided by ignoring infections
which occur before the beginning of host susceptibility.
Tab. 1. Comparison between model simulations and observed
occurrence of P. viticola primary infections ( 2 = 411.4
calculated using the Yate’s correction, significant at P

100
Basilicata 34 79.4 11.8 8.8
Sardinia 54 53.7 11.1 35.2
120
140 160
Days after 1st 140 160
Days after 1 January
st January
180
200
Fig. 3. Box plot representing distribution over time of the downy
mildew appearance for the 122 infections observed in 77
vineyards.
114