Spatiotemporal analyses of rhizopus rot progress in peach fruit inoculated with Rhizopus stolonifer


Baggio, J. S.; Hau, B.; Amorim, L.

Plant Pathology 66(9): 1452-1462

2017


Rhizopus rot, caused by Rhizopus stolonifer, is one of the main postharvest diseases in stone fruits, but there is little known about the processes of disease development during transport and postharvest storage. The objective of this study was to characterize temporal progress and spatial distribution of the disease in peach fruit. Rhizopus rot development was evaluated using two different fruit arrangements. Only one fruit of each arrangement was inoculated with a R.stolonifer spore suspension. Disease incidence and severity were assessed daily for all the fruit. Nonlinear models were fitted to the quantity of fruit and to the area of fruit that became infected over time and distance in relation to the source of inoculum. Disease-free fruit placed next to the artificially inoculated peaches showed disease symptoms due to pathogen dissemination by mycelial stolons. The disease incidence and severity progress rates varied from 0.33 to 0.53day(-1) and from 0.30 to 0.49day(-1), respectively. The spatial spread of the disease followed a dispersive wave pattern with increasing speed over time, but decreasing speed with disease severity. For disease severity y = 0.5, the velocity at day 3 varied from 0.14 to 0.32 fruit diameter day(-1), while it ranged from 0.38 to 1.46 fruit diameter day(-1) at day 12.

Plant
Pathology
(2017)
66,
1452-1462
Doi:
10.1111/ppa.12691
Spatiotemporal
analyses
of
rhizopus
rot
progress
in
peach
fruit
inoculated
with
Rhizopus
stolonifer
J.
S.
Baggio
a
,
B.
Hau
b
and
L.
Amorim
a
*
a
Departamento
de
Fitopatologia
e
Nematologia,
Escola
Superior
de
Agricultura
'Luiz
de
Queiroz,
Universidade
de
Sao
Paulo,
Piracicaba,
SP
13418-900,
Brazil;
and
°
Institut
fur
Gartenbauliche
Produktionssysteme,
Abtl.
Phytomedizin,
Leibniz
Universitat
Hannover,
30419
Hannover,
Germany
Rhizopus
rot,
caused
by
Rhizopus
stolonifer,
is
one
of
the
main
postharvest
diseases
in
stone
fruits,
but
there
is little
known
about
the
processes
of
disease
development
during
transport
and
postharvest
storage.
The
objective
of
this
study
was
to
characterize
temporal
progress
and
spatial
distribution
of
the
disease
in
peach
fruit.
Rhizopus
rot
development
was
evaluated
using
two
different
fruit
arrangements.
Only
one
fruit
of
each
arrangement
was
inoculated
with
a
R.
stolonifer
spore
suspension.
Disease
incidence
and
severity
were
assessed
daily
for
all
the
fruit.
Nonlinear
models
were
fitted
to
the
quantity
of
fruit
and
to
the
area
of
fruit
that
became
infected
over
time
and
distance
in
relation
to
the
source
of
inoculum.
Disease-free
fruit
placed
next
to
the
artificially
inoculated
peaches
showed
disease
symptoms
due
to
pathogen
dissemination
by
mycelial
stolons.
The
disease
incidence
and
severity
progress
rates
varied
from
0.33
to
0.53
day
-1
and
from
0.30
to
0.49
day
-1
,
respectively.
The
spatial
spread
of
the
disease
followed
a
dispersive
wave
pattern
with
increasing
speed
over
time,
but
decreasing
speed
with
disease
severity.
For
disease
severity
y
=
0.5,
the
velocity
at
day
3
varied
from
0.14
to
0.32
fruit
diameter
day
-1
,
while
it
ranged
from
0.38
to
1.46
fruit
diameter
day
-1
at
day
12.
Keywords:
distribution,
epidemiology,
Prunus,
Rhizopus
stolonifer
Introduction
Stone
fruits
are
affected
by
high
levels
of
postharvest
dam-
age
that
can
occur
in
up
to
50%
of
fruit
during
storage
in
wholesale
markets
in
Brazil
(Martins
et
al.,
2006).
In
most
cases,
peaches
are
transported
or
stored
under
room
temperature
conditions,
and
are
packed
either
in
boxes
or
single-layer
trays
(Martins
et
al.,
2006).
In
Brazil's
whole-
sale
markets,
postharvest
diseases
are
the
main
cause
of
peach
rejection
and
quality
degradation
(Amorim
et
al.,
2008;
Lima
et
al.,
2009).
The
presence
of
decayed
fruits
during
transportation
and
postharvest
storage
jeopardizes
fruit
quality
and
sale
price,
even
if
the
rotted
fruit
is
dis-
carded
and
there
are
no
apparent
symptoms
(Lima
et
al.,
2009).
For
example,
a
1%
increase
in
the
incidence
of
fruit
rot
causes
0.91%
and
1.24%
reduction
of
sale
price
in
wholesale
and
retail
markets,
respectively
(Lima
et
al.,
2009).
In
Brazil,
Rhizopus,
Monilinia
and
Cladosporium
are
the
most
common
pathogens
causing
postharvest
dis-
eases
on
peaches
(Martins
et
al.,
2006).
Rhizopus
rot,
caused
mainly
by
Rhizopus
stolonifer,
is
one
of
the
most
serious
postharvest
diseases
in
stone
fruits
and
causes
losses
during
fruit
transportation
and
storage
(Ogawa,
1995).
Infected
fruit
become
soft
and
*E-mail:
lilian.amorim@usp.br
Published
online
17
March
2017
1452
watery
2-3
days
after
pathogen
infection,
and,
approxi-
mately
1
day
after
symptom
emergence,
an
abundant
mycelial
mass
grows
on
the
fruit
surface
and
the
fungus
produces
long
mycelial
stolons
(Snowdon,
1990;
Ogawa,
1995).
These
mycelial
stolons
of
R.
stolonifer
can
cause
extensive
'nesting'
by
invading
adjacent
healthy
fruits
located
next
to
diseased
ones
(Snowdon,
1990;
Ogawa,
1995).
In
Brazil,
treatment
with
the
postharvest
fungicide
dicloran
is
recommended
to
control
rhizopus
rot
(Agro-
fit,
2016).
Biological
control
with
yeasts
has
also
been
shown
to
be
effective
against
postharvest
development
of
R.
stolonifer
in
China
(Xu
et
al.,
2013),
but
this
has
not
been
used
by
Brazilian
growers.
It
is
well
established
that
R.
stolonifer
penetrates
its
host
tissues
exclusively
through
injuries
(Maas,
1988;
Davis,
1991;
Ogawa
English,
1991;
Tavares
8c
Silva,
2006).
However,
recent
research
has
shown
that
esterase
enzymes,
especially
cuti-
nase,
are
produced
by
R.
stolonifer,
and
can
contribute
to
the
direct
penetration
of
unwounded
peaches
and
nec-
tarines
(Baggio
et
al.,
2016).
Little
is
known
about
the
spread
of
rhizopus
rot
and
the
spatial
and
temporal
progress
during
storage.
Few
studies
on
epidemiology
of
postharvest
diseases
are
found
in
literature.
Most
of
the
research
is
focused
on
environmental
factors
that
influence
disease
development
such
as
relative
humidity,
temperature,
carbon
dioxide
concentration
and
water
content
of
tissues
etc.
(Berger,
1984).
Models
and
simulations
of
disease
progress
are
practically
nonexistent
for
postharvest
disease,
and
were
©
2017
British
Society
for
Plant
Pathology
Q0
.
BSPP
0)
0
O
Epidemiology
of
rhizopus
rot
on
peaches
1453
(a)
(b)
Figure
1
Experimental
layout
of
peach
fruit,
noninoculated
(open
circles)
and
inoculated
(closed
circles)
with
Rhizopus
stolonifer
spore
suspension:
(a)
arrangement
1:
seven
rows
with
11
fruit
per
row
and
central
fruit
inoculation;
(b)
arrangement
2:
seven
rows
with
seven
fruit
per
row
and
inoculation
of
the
fourth
fruit
of
the
first
column.
proposed
mainly
for
foliar
and
soilborne
diseases
(Ber-
ger,
1984).
In
one
of
the
few
epidemiological
studies
on
postharvest
diseases,
Sholberg
&
Ogawa
(1983)
demon-
strated
that
Rhizopus
spp.
spread
quickly
in
three
dimen-
sions
throughout
a
bin
of
freshly
harvested
prunes
if
left
for
48
h
under
normal
harvest
temperature
conditions.
Up
to
25%
of
the
prunes
showed
symptoms
of
decay;
however,
information
and
data
about
temporal
progress
and
spatial
distribution
were
not
provided.
Therefore,
the
objective
of
this
work
was
to
characterize
the
spatiotemporal
progress
of
rhizopus
rot
in
peaches.
Materials
and
methods
The
pathogen
One
isolate
of
R.
stolonifer
was
collected
from
diseased
peaches
obtained
from
a
wholesale
market
in
Brazil
and
identified
by
molecular
techniques
(Baggio
et
al.,
2016).
The
pathogen
was
grown
on
potato
dextrose
agar
(PDA)
medium
(Oxoid)
and
kept
at
25
°C
for
3
days
under
fluorescent
light
to
promote
mycelial
growth
and
sporulation.
Rhizopus
stolonifer
spore
suspensions
were
produced
by
adding
sterile
distilled
water
to
3-day-old
cul-
tures,
and
the
concentrations
were
adjusted
to
10
5
spores
mL
-1
.
Peach
fruit
Peaches
from
cultivars
Dourado
(Sao
Paulo
State)
and
Eragil
(Rio
Grande
do
Sul
State)
were
used.
Two
experiments
were
performed
for
each
cultivar.
For
the
first
Dourado
experiment,
fruit
at
mature
stage
(commercial
maturity)
were
acquired
from
a
Sao
Paulo
State
peach
grower,
and
for
all
the
other
experi-
ments,
peaches
at
physiological
maturity
were
purchased
from
the
Sao
Paulo
wholesale
market.
Fruit
were
surface-disinfected
with
0.5%
sodium
hypochlorite
solution
for
3
min
and
placed
on
paper
towels
to
dry
at
room
temperature.
Spatiotemporal
progress
of
rhizopus
rot
on
inoculated
peaches
Peaches
were
placed
on
shelves
inside
an
acclimatized
room
in
two
different
arrangements:
in
arrangement
1,
the
central
fruit
of
a
rectangular
arrangement
(seven
rows
with
11
fruit
per
row)
was
wounded
with
a
hypodermic
needle
(1
mm
in
diameter
by
3
mm
in
depth)
and
inoculated
with
an
aliquot
of
30
µL
of
R.
stolonifer
spore
suspension
(10
5
spores
mL
-1
;
Fig.
la);
and
in
arrangement
2,
the
central
fruit
of
the
first
column
of
a
square
arrangement
(seven
rows
with
seven
fruit
per
row)
was
wounded
and
inoculated
with
the
pathogen
suspension
(Fig.
lb).
Control
treatments
did
not
receive
pathogen
spores.
All
fruits
were
incubated
for
24
h
at
25
°C
in
a
dark,
humid
chamber.
The
disease
incidence
and
severity
of
fruit
were
assessed
for
14-18
days
after
R.
stolonifer
inoculation.
Experiments
were
conducted
twice
for
each
cultivar,
and
each
experiment
had
three
replications.
Progress
curves
of
disease
incidence
For
each
arrangement,
the
number
of
diseased
fruit
was
counted
and
the
disease
incidence
was
calculated
daily.
The
logistic
and
the
Gompertz
functions
as
described
by
Eqns
(1)
and
(2)
(Camp-
bell
&
Madden,
1990)
were
fitted
to
the
observed
disease
pro-
gress
curve
of
fruit
incidence
by
using
nonlinear
regression
analyses:
y=
1/{1
+
exp[-(a
+
bt)]}
(1)
y
=
exp{-exp[-
(a
+
bt)]}
(2)
in
which
y
is
the
proportion
of
diseased
fruit;
t
is
the
evaluation
time
(days);
a
is
a
parameter
value
obtained
by
In[y
o
/(1
-
y
o
)],
in
which
y
o
is
the
disease
incidence
at
time
t
=
0,
according
to
the
different
fruit
arrangements;
and
b
is
the
disease
progress
rate
(day
-1
)
estimated
by
the
models.
It
was
considered
that
at
t
=
0,
one
fruit
was
already
diseased;
therefore,
yo
=
1/
77
=
0.0130
for
arrangement
1
and
1/49
=
0.0204
for
arrange-
ment
2
and
thus
the
parameter
a
was
fixed
at
-4.33
and
-3.87
for
arrangements
1
and
2,
respectively.
Progress
curves
of
disease
severity
Additionally,
the
proportion
of
the
visible
part
of
each
fruit
that
became
infected
was
estimated
daily
and
averaged,
resulting
in
the
disease
severity.
Again,
the
logistic
and
Gompertz
models
given
by
Eqns
(1)
and
(2),
respectively,
were
fitted
to
the
observed
disease
severity
progress
curves
using
nonlinear
regres-
sion
analyses.
Here,
y
is
the
disease
severity
(proportion
of
the
fruit
diseased
area)
and
a
is
related
to
the
disease
severity
at
time
t
=
0,
estimated
by
the
models.
Spatiotemporal
curves
of
disease
severity
In
order
to
analyse
spatiotemporal
dynamics,
changes
in
dis-
ease
severity
of
each
peach
were
considered
simultaneously
in
Plant
Pathology
(2017)
66,
1452-1462
1454
J.
S.
Baggio
et
al.
time
(days
after
primary
inoculation)
and
space
(distance
from
the
artificially
inoculated
fruit
measured
in
number
of
fruit
diameters,
average
4.5
cm).
The
spatiotemporal
dynamics
were
described
by
logistic
functions
(Eqns
3
and
4)
and
Gompertz
functions
(Eqns
5
and
6)
with
3
and
4
parameters,
respec-
tively:
y
=
1/{1
+
exp[-(a
-
cx
+
bt)]}
y=
1/{1
+
exp[-
(a
-
cx
+
bt
+
dxt)]
}
y
=
exp{-exp[-(a
-
cx
+
bt)]}
y
=
exp{-exp[-(a
-
cx
+
bt
+
dxt)]}
In
these
equations,
y
is
the
disease
severity
(proportion
of
the
fruit
diseased
area),
t
is
the
evaluation
time
(days),
x
is
the
dis-
tance
from
the
inoculated
fruit
(fruit
diameters),
a
is
a
parame-
ter
related
to
the
disease
severity
at
time
t
=
0
and
distance
x
=
0,
b
is
the
disease
progress
rate
(day
-1
),
c
is
the
slope
of
the
gradient
(fruit
diameter
-1
),
and
d
is
a
parameter
related
to
the
interaction
between
time
and
distance
(fruit
diameter
-1
day
1
).
The
unit
'fruit
diameter'
is
related
to
the
distance
between
each
fruit
and
the
equatorial
region
of
the
inoculated
fruit.
For
instance,
the
distance
of
fruit
that
are
immediately
adjacent
to
the
inoculated
fruit
in
all
arrangements
is
0.5
fruit
diameter.
If,
in
these
functions,
a
fixed
distance
x
is
considered,
the
disease
severity
increases
with
time
t,
resulting
in
a
disease
progress
curve
(Madden
et
al.,
2007).
However,
if
the
time
t
is
fixed,
the
severity
decreases
with
the
distance
x
from
the
primary
inocu-
lum
source,
resulting
in
a
disease
gradient
(Madden
et
al.,
2007).
In
the
present
analyses,
gradients
of
disease
severity
at
different
times
were
considered,
and
the
four
functions
were
fit-
ted
to
the
observed
gradient
dynamics
using
nonlinear
regression
analyses.
The
speed
with
which
the
disease
spreads
away
from
the
pri-
mary
inoculum
source
can
be
derived
from
the
gradient
dynam-
ics.
For
the
three-parameter
Eqns
3
and
5,
this
velocity,
ax/at,
is
constant
and
independent
from
the
disease
severity
(Eqn.
7)
(Madden
et
al.,
2007).
axlat=bIc
(7)
in
which
t
is
time
(days),
x
is
distance
(fruit
diameters),
b
is
the
disease
progress
rate
(day
1
),
and
c
is
the
gradient
slope
(fruit
diameter
-1
).
In
this
case,
the
gradients
remain
constant
in
shape
over
time
and
the
outward
velocity
is
also
constant
in
time
so
that
the
disease
is
moving
away
from
the
focus
in
a
travelling
wave.
In
the
four-parameter
models
(Eqns
4
and
6),
an
interac-
tion
between
time
and
distance
is
relevant
and
can
interfere
with
the
rate
of
spatial
spread
of
the
disease
over
time.
Then,
the
velocity,
ax/at,
is
not
constant
but
varies
with
time
and
distance
according
to
Eqn.
(8)
ax/at
=
(b
+
dx)/(c
-
dt)
(8)
in
which
t
is
time
(days),
x
is
distance
(fruit
diameter),
b
is
the
disease
progress
rate
(day
1
),
c
is
the
gradient
slope
(fruit
diameter
-1
),
and
d
is
the
parameter
of
the
interaction
between
time
and
distance
(fruit
diameter
-1
day
1
).
For
d
0,
the
gra-
dients
change
their
shape
over
time
and
the
outward
velocity
also
changes
with
time.
The
velocity
can
also
be
expressed
in
relation
to
the
severity
y
and
time
t,
for
the
logistic
model
(Eqn.
4):
ax/at
=
[bc
+
da
-
dlogit(y)]/(c
-
dt)
2
(9)
For
d
>
0,
the
denominator
decreases
with
time
t
resulting
in
increasing
speed
ax/at.
On
the
other
hand,
as
logit(y)
is
getting
larger
with
y,
the
numerator
diminishes
with
progressing
y
for
d
>
0.
Thus
the
speed
increases
with
t
but
decreases
with
y
if
d
>
0.
The
formula
becomes
simpler
if
the
severity
is
fixed
at
y
=
0.5
as
logit(0.5)
=
0.
For
the
Gompertz
model
(Eqn.
6),
the
logit-transformation
in
Eqn.
9
must
be
replaced
by
the
transfor-
mation
-ln[-ln(y)].
The
models
fitted
to
the
incidence
and
severity
data
were
analysed
by
the
coefficients
of
determination
(R
2
)
and
the
values
of
the
standard
error
obtained
from
the
nonlinear
regression
analysis
of
each
arrangement.
The
F-value
of
the
regression
analysis
considers
not
only
the
goodness
of
fit
but
also
the
quantity
of
model
parameters
used
to
determine
whether
the
four-parameter
model
gave
a
significantly
better
fit
than
the
three-parameter
model.
Data
from
repeated
experiments,
experi-
ments
with
same
arrangements
and/or
same
peach
cultivar
were
compared
using
the
dummy
model
test
(Tedihou
et
al.,
2012).
The
analyses
were
performed
with
sTATIsucA
v.
7.0
software
(Statsoft).
Latent
period
as
function
of
peach
maturity
To
determine
the
relation
between
latent
period
of
rhizopus
rot
and
peach
maturity,
three
fruits
(replications)
were
inoculated
every
2
days,
for
13-16
days,
with
R.
stolonifer
spore
suspen-
sion.
Wounds
and
fungal
inoculations
were
made
as
previously
described.
Fruit
were
incubated
for
24
h
at
25
°C
in
a
dark,
humid chamber,
and
disease
incidence
and
severity
were
assessed
for
4-5
days.
The
physicochemical
parameters,
firmness
and
soluble
solids
of
peaches
were
also
determined
every
2
days
for
13-16
days,
using
three
fruit
(replications)
per
evaluation
date
for
each
culti-
var
and
experiment.
Firmness,
expressed
in
kilogram-force
(1
kgf
=
9.81
Newton),
was
determined
with
a
penetrometer,
model
PTR
100
(Instrutherm),
with
a
cylindrical
metal
rod
of
7.9
mm
in
diameter,
by
pressing
it
into
the
cortex
of
the
fruit
without
peel.
The
soluble
solids
content,
expressed
in
°Brix,
was
determined
with
a
digital
refractometer,
model
PAL-1
(Atago).
Data
were
compared
using
linear
and
nonlinear
regression
analyses
with
sTATETKA
v.
7.0.
Results
In
all
experiments,
wounded
peaches
inoculated
with
R.
stolonifer
spore
suspension
became
infected,
and
dis-
ease
spread
from
the
initial
focus
by
mycelial
stolons
to
the
neighbouring
fruits.
Rhizopus
stolonifer
dissemina-
tion
occurred
from
fruit
to
fruit until
almost
all
the
fruit
became
infected
(Figs
2
&
Progress
curves
of
disease
incidence
The
logistic
and
the
Gompertz
functions
adequately
described
the
observed
disease
incidence
progress
curves
of
rhizopus
rot
for
the
peaches.
However,
the
logistic
model
showed
higher
values
for
the
coefficient
of
Plant
Pathology
(2017)
66,
1452-1462
(b).
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on
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Figure
2
Arrangement
1,
with
inoculation
of
the
central
peach
fruit
(arrow)
with
a
spore
suspension
of
Rhizopus
stolonifer
in
water.
Pathogen
spread
to
the
neighbouring
fruits
is
shown
2
(a),
3
(b),
4
(c),
5
(d),
6
(e),
7
(f),
8
(g),
9
(h),
10
(i),
11
(j),
12
(k),
13
(l),
14
(m),
15
(n),
16
(o),
17
(p)
and
18
(q)
days
after
inoculation.
[Colour
figure
can
be
viewed
at
wileyonlinelibrary.com
]
determination
(R
2
)
and
lower
values
for
the
standard
error
of
the
regression
than
the
Gompertz
model,
and
therefore,
only
the
results
of
the
logistic
model
are
presented.
For
both
arrangements,
the
disease
incidence
progress
rates
(parameter
b)
were
significantly
different
between
cultivars
Dourado
and
Eragil.
For
cultivar
Dourado,
the
Plant
Pathology
(2017)
66,
1452-1462
1456
J.
S.
Baggio
et
al.
Table
1
Estimated
parameter
values
of
the
logistic
model
y=
1/
(1
+
exp[-(a
+
bt)]}
fitted
to
progress
data
of
rhizopus
rot
incidence
of
peaches
in
two
different
arrangements
and
two
different
peach
cultivars
Cultivar
Arrangement
R
ea
a"
b
°
Error
b
d
RSE
a
Dourado
1
0.96
-4.33
0.53
Ba
0.012
0.08
Dourado
2
0.96
-3.87
0.40
Aa
0.007
0.06
Eragil
1
0.99
-4.33
0.41
Ab
0.003
0.03
Eragil
2
f
0.99
-3.87
0.33
Ab
0.001
0.04
Arrangement
1
represents
the
central
fruit
inoculated
with
Rhizopus
stolonifer
spore
suspension;
and
in
arrangement
2
the
central
fruit
of
the first
column
was
inoculated
with
the
fungus.
Curves
of
the
logistic
model
were
fitted
to
the
data
of
two
experiments
for
each
cultivar
and
arrangement.
Values
followed
by
the
same
lower
case
letters
for
the
same
arrangement
and
by
the
same
upper
case
letter
for
the
same
cultivars,
in
the
column,
do
not
differ
among
them
according
to
the
dummy
model
test.
a
Coefficient
of
determination.
b
The
parameter
value
of
a
was
fixed
as
In[Yo/(
1
-
Yo)]
,
in
which
y
o
is
the
disease
incidence
at
time
t=
0,
according
to
the
different
fruit
arrangements.
°
b
is
the
disease
incidence
progress
rate
(per
day).
d
Error
of
the
parameter
value
of
b.
a
Regression
standard
error,
i.e.
the
standard
error
of
the
nonlinear
model
fitted
to
the
data
of
two
experiments
for
each
variety
and
arrangement.
Nonlinear
model
fitted
to
the
data
of
one
experiment.
values
of
parameter
b
were
different
between
arrange-
ments;
however,
they
were
the
same
for
the
different
arrangements
of
cultivar
Eragil
(Table
1).
The
value
of
parameter
a
was
fixed
according
to
each
arrangement
because
it
was
considered
that
one
fruit
was
already
dis-
eased
at
time
t
=
0.
The
observed
disease
progress
curves
could
be
well
described
by
the
logistic
functions
(Table
1;
Figs
3
8c
4).
No
differences
were
observed
among
the
three
replica-
tions
of
each
experiment
and
between
the
two
repeated
experiments,
so
data
were
grouped
and
only
one
disease
incidence
progress
curve
was
fitted
to
the
data
of
each
arrangement
1
(Fig.
3a)
and
2
(Fig.
3b)
for
Dourado
and
arrangement
1
(Fig.
4a)
for
Eragil.
For
arrangement
2
for
Eragil,
the
logistic
model
was
fitted
to
the
combined
data
of
three
replications
of
only
one
experiment
(Fig.
4b).
After
R.
stolonifer
had
penetrated
into
the
inoculated
fruit,
the
spread
of
the
pathogen
to
other
fruit
occurred
at
different
progress
rates,
which
were
0.53
and
0.41
day
-1
for
arrangement
1,
and
0.40
to
0.33
day
-1
for
arrangement
2,
for
Dourado
and
Eragil,
respectively
(Table
1).
Progress
curves
of
disease
severity
As
for
disease
incidence,
disease
severity
was
better
described
by
the
logistic
model
than
the
Gompertz
model.
For
both
arrangements,
the
disease
severity
pro-
gress
rates
(parameter
b)
and
disease
severity
at
time
t
=
0
(parameter
a)
were
significantly
different
for
culti-
vars
Dourado
and
Eragil.
For
Dourado,
the
values
of
parameter
b
were
different
between
arrangements;
how-
ever,
they
were
the
same
for
Eragil.
The
parameter
a
was
not
significantly
different
between
arrangements
for
either
cultivar
(Table
2).
Disease
severity
varied
over
time
and
the
spread
of
the
pathogen
to
the
noninoculated
fruit
occurred
at
dif-
ferent
progress
rates,
which
were
0.49
and
0.30
day
1
for
arrangement
1,
and
0.38
to
0.30
day
1
for
arrange-
ment
2,
for
Dourado
and
Eragil,
respectively
(Table
2).
Differences
among
the
three
replications
of
each
experi-
ment
and
between
the
two
experiments
were
not
observed,
so
data
were
grouped
and
only
one
disease
severity
progress
curve
was
fitted
to
the
data
of
arrangements
1
(Fig.
5a)
and
2
(Fig.
5b)
for
Dourado
and
arrangement
1
(Fig.
6a)
for
Eragil.
A
logistic
curve
was
fitted
to
the
combined
data
of
three
replica-
tions
of
one
experiment
for
arrangement
2
for
Eragil
(Fig.
6b).
Spatiotemporal
curves
of
disease
severity
To
describe
the
gradient
dynamics,
the
spatiotemporal
models
(3)
to
(6)
were
fitted
to
disease
severity
(y)
taking
into
consideration
the
fruit
distance
(x),
in
fruit
diame-
ters,
and
the
time
(t),
in
days.
The
dynamics
of
the
gradi-
ents
are
shown
as
graphs
in
Figures
7
and
8.
The
severity
gradients
are
given
for
fixed
times
with
a
con-
stant
time
interval
between
gradients
(2
days
for
Dour-
ado
and
3
days
for
Eragil).
The
coefficients
of
determination
(R
2
)
from
the
logistic
and
Gompertz
models
with
three
parameters
varied
from
Disease
inc
idence
1.0
1.0-
(a)
5
I
(b)
0.8
-
0.8
-
0
0.6
-
0.6
-
0.4
0.4
0.2
0.2
0.0
0.0
0
10
12
14
0
2
6
8
10
12
14
Time
(days)
Figure
3
Observed
progress
data
of
incidence
of
rhizopus
rot
in
arrangements
1
(a)
and
2
(b)
for
cultivar
Dourado
and
fitted
curves
using
the
logistic
model
y=
1/
(1
+
exp[-(a
+
bt)]}.
Closed
and
open
circles
represent
the
average
incidence
of
the
first
and
second
experiments,
respectively.
Curves
were
fitted
to
the
combined
data
of
two
experiments
for
both
arrangements.
Bars
represent
mean
errors
of
three
replications.
Plant
Pathology
(2017)
66,
1452-1462
1.0
(a)
8
0.8
-
c
a)
i5
0.2
0.0
1.0
0.8
0.6
0.4
-
0.2
-
0.0
(b)
iz
Epidemiology
of
rhizopus
rot
on
peaches
1457
0
2
4
6
8
10
12
14
16
18
0
2
4
6
8
10
12
14
16 18
Time
(days)
Figure
4
Observed
progress
data
of
incidence
of
rhizopus
rot
in
arrangements
1
(a)
and
2
(b)
for
peach
cultivar
Eragil
and
fitted
curves
using
the
logistic
model
y=
1/(1
+
exp[-(a
+
bt)]}.
Closed
and
open
circles
represent
the
average
incidence
of
the
first
and
second
experiments,
respectively.
Curves
were
fitted
to
the
data
of
two
experiments
for
arrangement
1
(a)
and
one
experiment
for
arrangement
2
(b).
Bars
represent
mean
errors
of
three
replications.
Table
2
Estimated
parameter
values
of
the
logistic
model
y
=
1/(1
+
exp[-(a
+
bt)]}
fitted
to
progress
data
of
rhizopus
rot
severity
of
peaches
in
two
different
arrangements
and
two
different
peach
cultivars
Cultivar
Arrangement
Rea
a
b
Error
a
°
b
b
Error
b
°
RSE
d
Dourado
1
0.99
-4.43
Aa
0.23
0.49
Aa
0.01
0.05
Dourado
2
0.98
-3.99
Aa
0.17
0.38
Ba
0.02
0.04
Eragil
1
0.99
-3.39
Ab
0.07
0.30
Ab
0.01
0.02
Eragil
2
a
0.98
-3.45
Ab
0.21
0.30
Ab
0.02
0.04
Arrangement
1
represents
the
central
fruit
inoculated
with
Rhizopus
stolonifer
spore
suspension;
and
in
arrangement
2
the
central
fruit
of
the
first
column
was
inoculated
with
the
fungus.
Curves
of
the
logistic
model
were
fitted
to
the
data
of
two
experiments
for
each
cultivar
and
arrangement.
Values
followed
by
the
same
lower
case
letters
for
the
same
arrangement
and
by
the
same
upper
case
letter
for
the
same
cultivars,
in
the
column,
do
not
differ
among
them
according
to
the
dummy
model
test.
a
Coefficient
of
determination.
b
a
is
related
to
the
disease
severity
at
time
t=
0 and
b
is
the
disease
progress
rate
(per
day).
°
Errors
obtained
for
each
estimated
parameter
value.
d
Regression
standard
error,
i.e.
the
standard
error
of
the
nonlinear
model
fitted
to
the
data.
Nonlinear
model
fitted
to
the
data
of
one
experiment.
to
-
(a)
o
a
0.4
0.2
0.0
Figure
5
Observed
progress
data
of
8
0.8
-
rhizopus
rot
severity
in
arrangements
1
(a)
and
2
(b)
for
peach
cultivar
Dourado
and
(/)
0.6
-
fitted
curves
using
the
logistic
model
y=
(1
exp[-(a
+
bt)]}.
Closed
and
open
circles
represent
the
average
severity
of
the
5
0.2
first
and
second
experiments,
respectively.
Curves
were
fitted
to
the
combined
data
of
0.0
two
experiments
for
both
arrangements.
Bars
represent
mean
errors
of
three
replications.
as
0.4
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
Time
(days)
0.881
to
0.953
and
from
0.877
to
0.955,
respectively
(data
not
shown).
For
the
models
with
four
parameters,
the
coefficient
of
determination
ranged
from
0.950
to
0.973
for
the
logistic
model
(Table
3)
and
from
0.947
to
0.972
for
the
Gompertz
model
(data
not
shown).
The
standard
error
of
the
nonlinear
regressions
from
the
logistic
and
Gompertz
models
with
three
parameters
var-
ied
from
0.10
to
0.16
and
from
0.09
to
0.15,
respec-
tively
(data
not
shown).
For
the
models
with
four
parameters,
the
standard
error
of
the
nonlinear
regres-
sions
ranged
from
0.07
to
0.09
for
the
logistic
model
(Table
3)
and
from
0.08
to
0.10
for
the
Gompertz
model
(data
not
shown).
As
expected,
the
models
with
four
parameters
gave
a
better
fit
to
rhizopus
rot
severity
on
peaches.
In
addition,
the
F-values
of
the
logistic
model
with
four
parameters
varied
from
1227.83
to
5532.67
(Table
3)
and
were
higher
than
the
F-values
of
the
corresponding
Plant
Pathology
(2017)
66,
1452-1462
1.o
-
(
a)
0.8
-
0.6
-
0.4
-
0.2
-
0.0
I
0
2
4
6 6
10
12
14
16
18
0
2
Time
(days)
4
6
8
10
12
14
16
18
Disease
sever
ity
1.0-
0.8
-
0.6
-
0.4
-
0.2
-
(b)
1.0
°
0.8
0.6
0.4
1
,0
.
0.2
--•
2
days
(d)
---
4
days
-
6
days
8
days
10
days
--
12
days
1.0
0.8
0.2
2
days
(C)
4
days
5
days
days
10
days
--
12
days
0.0 0.0
0
1
2
3
4
5
6
7
0
1
Distance
(fruit
diameter)
3
4
5
1458
J.
S.
Baggio
et
al.
Figure
6
Observed
progress
data
of
rhizopus
rot
severity
in
arrangements
1
(a)
and
2
(b)
for
peach
cultivar
Eragil
and
fitted
curves
using
the
logistic
model
y=
1/(1
+
exp[-(a
+
bt)]}.
Closed
and
open
circles
represent
the
average
incidence
of
the
first
and
second
experiments,
respectively.
Curves
were
fitted
to
the
data
of
two
experiments
for
arrangement
1
(a)
and
one
experiment
for
arrangement
2
(b).
Bars
represent
mean
errors
of
three
replications.
1.0
a.,
2
day,
(a)
4
days
-
5
days
2
days
(b)
4
days
-
8
days
\
8
days
10
days
0.8
\
8
days
10
days
\
-
--
12
days
--
12
days
0.6
0.4
\
,01
0.2
0.0
0
2
3
4
5
6
7
1.0
0.8
0.6
0.4
0.2
0.0
0
Figure
7
Observed
gradient
data
of
rhizopus
rot
severity
in
arrangements
1
(a,
b)
and
2
(c,
d)
for
peach
cultivar
Dourado
at
six
different
times
after
primary
inoculation
and
fitted
curves
using
the
logistic
model
y=
1/
(1
+
exp[-(a
-
cx
+
bt
+
dxt)]}.
Different
curves
and
symbols
represent
the
disease
severity
in
2-day
intervals,
starting
at
day
2
and
ending
at
day
12.
Curves
were
fitted
to
the
combined
data
of
three
replications
for
experiments
1
(a,
c)
and
2
(b,
d).
(!1)
--
-
-
1
days
(b)
3
days
:.
-4.
1
.0
-
..
a
-.
'''
,
7t
-4--
-
u
a
.....
8
5
dal
,.
----..,
\ -
9
days
6
-
-
12
days
\
1.0
°
8
days
9
days
12
days
0.8
1
P
'?,
u•
a
\
-
-
15
days
--
18
days
0.8
1
15
days
18
days
Figure
8
Observed
gradient
data
of
rhizopus
rot
severity
in
arrangements
1
(a)
0.6
0.6
1
and
2
(b)
for
peach
cultivar
Eragil
at
six
1
1
e
different
times
after
primary
inoculation
and
0-4
0.4
1
1
fitted
curves
using
the
logistic
model
y=
1/
0.2
0
°
\
0.2
1
1
(1
+
exp[-(a
-
cx
+
bt
+
dxt)]}.
Different
curves
and
symbols
represent
the
disease
severity
in
3-day
intervals,
starting
at
day
3
.
\
and
ending
at
day
18.
Curves
were
fitted
to
0.0
2
3
4
0.0
5
6
7
0
1
2
3
4
5
6
7
the
combined
data
of
two
experiments
for
arrangement
1
(a)
and
one
experiment
for
Distance
(fruit
diameter)
arrangement
2
(b).
Disease
sever
ity
3-parameter
model,
which
varied
from
660.86
to
3086.60
(data
not
shown).
As
the
goodness
of
fit
of
the
logistic
and
the
Gompertz
models
(Eqns
4
and
6)
were
rather
similar,
only
the
results
of
the
analyses
with
the
four-parameter
logistic
model
are
presented
here.
Plant
Pathology
(2017)
66,
1452-1462
Epidemiology
of
rhizopus
rot
on
peaches
1459
Table
3
Estimated
parameter
values
of
the
logistic
model
y=
1/(1
+
exp[-(a
-
cx
+
bt
+
dxt)]}
fitted
to
spatiotemporal
data
of
rhizopus
rot
severity
of
peaches
in
two
different
arrangements
and
two
different
peach
cultivars
Cultivar
Arrangement
Experiment
R
ea
a
(error)
b
b
(error)
b
c
(error)
b
d
(error)
b
RSE
°
F
Dourado
1
1
0.973
-1.48
A
(0.21)
0.78
A
(0.05)
5.23
A
(0.31)
0.30
A
(0.02)
0.07
3336.57
Dourado
1
2
0.973
-2.63
A
(0.25)
0.97
A
(0.06)
3.64
B
(0.22)
0.17
A
(0.01)
0.08
3600.20
Dourado
2
1
0.950
-1.04
A
(0.26)
0.72
A
(0.06)
5.99
A
(0.47)
0.30
A
(0.03)
0.09
1227.83
Dourado
2 2
0.967
-2.34
A
(0.24)
0.81
A
(0.05)
4.22
A
(0.26)
0.18
A
(0.02)
0.08
2288.93
Eragil
1
1,
2
0.962
-2.38
A
(0.16)
0.79
A
(0.13)
2.99
B
(0.03)
0.06
B
(0.01)
0.09
5532.67
Eragil
2
1
0.959
-1.27
B
(0.21)
0.64
B
(0.04)
5.59
A
(0.35)
0.22
A
(0.02)
0.09
2213.77
Arrangement
1
represents
the
central
fruit
inoculated
with
Rhizopus
stolonifer
spore
suspension;
and
in
arrangement
2
the
central
fruit
of
the
first
column
was
inoculated
with
the
fungus.
Values
followed
by
the
same
letters
for
the
same
cultivars
and
experiments,
in
the
column,
do
not
differ
among
them
according
to
the
dummy
model
test.
a
Coefficient
of
determination.
b
a
is
a
parameter
related
to
the
disease
severity
at
time
t
=
0
and
distance
x
=
0,
b
is
the
disease
progress
rate
(per
day),
c
is
the
slope
of
the
gra-
dient
(per
fruit
diameter)
and
d
is
a
parameter
related
to
the
interaction
between
time
and
distance
(per
fruit
diameter
per
day).
Values
in
parenthe-
ses
represent
errors
obtained
for
each
estimated
parameter
value.
°
Regression
standard
error,
i.e.
the
standard
error
of
the
nonlinear
model
fitted
to
the
data.
Differences
between
the
two
experiments
were
observed
for
arrangements
1
(Fig.
7a,b)
and
2
(Fig.
7c,d)
for
Dourado.
However,
differences
between
experiments
were
not
observed
for
Eragil
and
data
were
grouped
for
arrangement
1
(Fig.
8a).
In
addition,
logistic
curves
were
fitted
to
the
combined
data
of
three
replications
of
one
experiment
for
arrangement
2
for
Eragil
(Fig.
8b).
For
Dourado,
the
disease
progress
rate
values
(parame-
ter
b)
varied
from
0.78
to
0.97
day
1
and
0.72
to
0.81
day
-1
for
arrangements
1
and
2,
respectively,
and
the
values
of
parameter
c
ranged
from
3.64
to
5.23
fruit
diameter
-1
and
4.22
to
5.99
fruit
diameter
-1
,
respec-
tively.
The
values
of
parameter
d
varied
from
0.17
to
0.30
fruit
diameter
-1
day
1
and
0.18
to
0.30
fruit
diam-
eter
-1
day
1
for
arrangements
1
and
2,
respectively.
Although
the
values
of
parameters
b,
c
and
d
were
not
significantly
different
for
arrangements
1
and
2,
they
were
found
to
differ
between
experiments
1
and
2
for
Dourado
(Table
3).
For
Eragil
the
disease
progress
rate
values
(parameter
b)
were
0.64
and
0.79
day
1
for
arrangements
1
and
2,
respectively.
The
values
of
parameter
c
were
2.99
and
5.59
fruit
diameter
-1
and
the
values
of
parameter
d
were
0.06
and
0.22
fruit
diameter
-1
day
1
for
arrangements
1
and
2,
respectively.
The
values
of
parameters
b,
c
and
d
were
significantly
different
between
the
arrangements
1
and
2
(Table
3).
The
value
of
parameter
d
of
Eragil
in
arrangement
1
was
the
lowest
(d
=
0.06)
(Fig.
8a)
among
all
the
experi-
ments,
where
distances
between
the
gradients
in
the
other
experiments
clearly
increased
(Figs
7a-d
&
8b).
For
instance,
the
velocity
of
the
disease
level
y
=
0.5
(Eqn.
9)
for
Eragil
in
arrangement
1
increased
from
0.28
fruit
diameters
day
-1
at
day
3
to
0.44
at
day
12
(Fig.
8a),
while
in
arrangement
2
the
velocity
increased
from
0.14
to
0.38
(Fig.
8b).
For
Dourado
in
arrangement
1,
the
velocity
increased
from
0.20
and
0.32
fruit
diame-
ters
day
1
at
day
3
to
1.46
and
1.27
at
day
12
for
exper-
iments
1
and
2,
respectively
(Fig.
7a,b),
while
in
arrangement
2,
the
speed
increased
from
0.15
and
0.22
fruit
diameters
day
-1
at
day
3
to
0.67
and
0.68
at
day
12
(Fig.
7c,d).
Considering
the
gradient
dynamics
and
the
description
by
the
four-parameter
model,
it
can
be
observed
that
the
speed
with
which
the
disease
gradients
are
moving
away
from
the
primary
focus
increases
with
time.
Over
all
cases,
the
speed
(for
y
=
0.5)
varied
from
0.14
to
0.32
fruit
diameters
day
1
on
day
3,
but
ranged
from
0.38
to
1.46
on
day
12.
The
shape
of
the
gradients
changes,
but
this
may
not
be
so
clearly
visible.
However,
the
speed
decreases
with
higher
disease
severity
at
a
given
fixed
time,
according
to
Eqn.
9.
For
example,
considering
the
gradient
of
arrangement
1
for
Dourado
on
day
12,
the
speed
was
1.59
and
1.34
fruit
diameter
day
1
for
y
=
0.25,
1.46
and
1.27
for
y
=
0.5
and
1.33
and
1.19
for
y
=
0.75,
for
experiments
1
and
2,
respectively.
For
arrangement
1
for
Eragil
on
day
12,
the
speed
was
0.46
fruit
diameter
day
1
for
y
=
0.25,
0.44
for
y
=
0.5
and
0.43
for
y
=
0.75.
Thus,
the
speed
of
R.
stolonifer
spatial
spread
was
not
constant:
the
velocity
increased
with
increasing
time,
but
decreased
with
increasing
severity,
resulting
in
changing
disease
gradients
over
time.
Therefore,
the
spread
of
the
disease
occurs
according
to
a
dispersive
wave
pattern.
Latent
period
as
function
of
peach
maturity
Soluble
solids
data
were
compared
by
linear
regressions
and
the
slope
(parameter
b)
was
not
different
from
0.
Therefore,
soluble
solids
content
remained
constant
over
time
(Fig.
S2).
On
the
other
hand,
firmness
data
were
compared
by
nonlinear
regression
with
a
decreasing
exponential
function
and,
except
for
Dourado
in
experi-
ment
1,
the
gradient
slope
(parameter
b)
was
different
from
0
and
reduction
of
fruit
firmness
was
observed
(Fig.
S3).
Although
no
regression
model
could
be
fitted
to
the
latent
period,
it
was
observed
that,
except
for
Dourado
in
experiment
1,
the
latent
period
of
rhizopus
rot
was
approximately
2
days
at
the
beginning
of
the
experiment
but,
towards
the
end,
was
only
1
day
Plant
Pathology
(2017)
66,
1452-1462
1460
J.
S.
Baggio
et
al.
(Fig.
S4).
Reduction
of
fruit
firmness
throughout
the
course
of
the
experiments
was
related
to
fruit
ripening,
and,
as
the
disease
latent
period
also
decreased,
this
sug-
gests
there
was
an
increase
in
susceptibility
of
the
fruit
to
R.
stolonifer.
Discussion
Rhizopus
stolonifer
was
able
to
penetrate
healthy
unin-
jured
fruit
located
next
to
infected
fruit
by
producing
stolons,
leading
to
symptoms
known
as
nested
infection
(Snowdon,
1990).
Previous
research
has
demonstrated
that
prunes
with
rhizopus
rot
used
as
primary
inoculum,
spread
the
disease
to
noninoculated
fruit
(Sholberg
8c
Ogawa,
1983).
In
addition,
the
production
of
esterase
enzymes
that
enable
the
mycelium
of
R.
stolonifer
to
penetrate
unwounded
fruit
directly
has
been
reported
(Baggio
et
al.,
2016).
For
postharvest
diseases,
it
is
assumed
that
there
are
two
types
of
disease
progress.
In
the
first,
there
is
no
spread
to
neighbouring
fruit
and
the
infected
fruit
are
randomly
dis-
tributed
within
the
population;
alternatively,
there
is
spread
to
neighbouring
fruit
and,
although
the
initial
appearance
is
random,
the
subsequent
spread
could
occur
three
dimensionally
in
a
more
or
less
well-defined
focus
(Berger,
1984).
In
the
present
study,
the
spread
of
the
dis-
ease
to
neighbouring
fruit
has
been
documented
only
in
two
dimensions.
The
increase
of
disease
from
the
initial
focus
occurred,
most
probably,
via
infection
by
mycelium.
Studies
with
peach
brown
rot,
a
quiescent
disease
caused
by
Monilinia
spp.,
showed
that
a
monomolecular
model
fitted
properly
to
the
incidence
data
for
diseased
fruit
over
time
at
the
beginning
of
the
epidemic.
How-
ever,
at
the
end
of
the
harvest
period,
the
progress
curves
showed
a
new
wave
of
disease
increase,
most
probably
due
to
a
secondary
spread
of
the
disease
produced
in
ripened
fruit
(Amorim
et
al.,
2007).
Surface
wounds
on
peaches
could
also
modify
the
brown
rot
development
pattern
(Amorim
et
al.,
2007),
but
this
has
not
been
found
for
rhizopus
rot.
In
the
present
investigation,
the
speed
of
R.
stolonifer
spatial
spread
was
not
constant:
the
velocity
increased
with
increasing
time,
but
decreased
with
increasing
sever-
ity,
resulting
in
changing
disease
gradients
over
time.
The
shape
of
rhizopus
rot
gradients
followed
a
dispersive
wave
pattern.
Dispersive
waves
have
been
demonstrated
by
Ferrandino
(1993)
using
a
simulation
model,
in
which
it
was
assumed
that
the
velocity
of
radial
expansion
increases
indefinitely
with
time
and
distance.
However,
in
contrast
to
the
results
of
the
present
study,
Ferrandino
(1993)
reported
that
the
velocity
increased
despite
the
level
of
disease
severity.
Other
authors
support
the
hypothesis
that
the
spread
of
epidemics
occurs
with
con-
stant
frontal
velocity
as
travelling
waves,
and
a
focal
epi-
demic,
once
it
gains
force,
expands
at
a
constant
rate
(van
den
Bosch
et
al.,
1999).
Zadoks
(2001)
reported
that
the
two
types
of
epidemic
waves,
travelling
and
dispersive,
are
supported
by
empir-
ical
data.
For
a
potato
late
blight
epidemic,
there
was
empirical
evidence
for
an
acceleration
of
the
spread
of
disease
over
time
and
space
and
travelling
waves
were
inadequate
for
describing
the
epidemic
spread
on
differ-
ent
temporal
and
spatial
scales
(Scherm,
1996);
this
was
also
observed
for
rhizopus
rot
in
the
present
study.
For
Puccinia
lagenophorae
on
Senecio
vulgaris,
it
was
also
concluded
that
the
epidemics
caused
by
the
pathogen
expand
as
dispersive
waves
(Frantzen
8c
van
den
Bosch,
2000).
In
contrast,
for
some
authors,
the
shape
of
the
disease
wave
front
did
not
change
over
time
and
velocity
was
constant
(Zadoks
8c
Kampmeijer,
1977);
however,
the
foci
expanded
at
a
constant
rate
after
an
initial
per-
iod
of
build-up
(Zadoks
8c
van
den
Bosch,
1994).
Most
work
with
epidemic
waves
has
dealt
with
poly-
cyclic
fungal
disease
and
pathogens
that
propagate
through
airborne
spores,
and
considers
long
distance
dissemination
(Scherm,
1996;
Aylor,
1999;
van
den
Bosch
et
al.,
1999).
However,
in
the
present
investigation
of
a
postharvest
dis-
ease,
the
spread
occurred
through
stolons
by
contact
or
over
very
short
distances
(e.g.
centimetres).
In
temporal
and
the
spatiotemporal
analyses
of
disease
severity,
differ-
ences
were
observed
between
the
two
fruit
arrangements;
these
may
be
related
to
variation
in
the
environmental
con-
ditions
to
which
the
inoculated
fruit
(primary
source
of
inoculum)
were
exposed
in
the
different
arrangements.
Although
all
experiments
were
carried
out
in
the
same
room
with
same
environmental
conditions,
the
inoculated
fruit
from
arrangement
2
may
have
been
exposed
to
the
border
effect,
whereas
the
primary
source
of
inoculum
in
arrangement
1
was
surrounded
by
several
other
fruit,
lead-
ing
to
differences
between
some
of
the
parameters.
Peaches
are
climacteric
fruit
because
they
show
a
decline
in
firmness
(softening),
and
an
increase
in
ethy-
lene
evolution
during
ripening
after
harvest
(Tonutti
et
al.,
1997).
Similar
results
were
found
in
the
present
experiments,
where
reduction
in
firmness
of
fruits
was
observed
over
time
in
all
cases
except
experiment
1
with
cultivar
Dourado,
which
were
already
mature.
Fruit
from
other
experiments,
acquired
from
wholesale
markets,
were
unripe
at
harvest,
meaning
they
were
physiologi-
cally
mature
before
the
onset
of
climacteric
rise
(Laksh-
minarayana
et
al.,
1970),
and
became
mature
at
postharvest
due
to
climacteric
events.
Thus,
differences
between
the
epidemiological
parameters
of
experiments
1
and
2
of
cultivar
Dourado
might
be
related
to
different
fruit
maturity
at
day
1.
The
decrease
in
firmness
and
softening
of
the
fruit
are
due
to
the
decrease
in
cell
tur-
gor
from
chemical
breakdown
(Jackman
8c
Stanley,
1995).
This
contributes
to
an
increase
in
susceptibility
to
pathogen
infection,
because
the
cell
associations
to
be
broken
are
smaller.
In
Fuji
apples,
it
was
found
that
the
lower
the
firmness,
the
higher
the
incidence
of
rots,
as
the
chances
of
pathogen
penetration
in
softened
fruits
were
higher
(Brackmann
et
al.,
2010).
In
addition
to
firmness,
the
decrease
in
acid
content
and
the
increase
in
soluble
solids
during
growth
and
ripening
of
grape
ber-
ries
enabled
R.
stolonifer
infection
(Lisker
et
al.,
1996).
Moreover,
the
reduction
of
latent
period
of
rhizopus
rot
on
peaches
over
time
in
the
present
study
may
be
a
result
Plant
Pathology
(2017)
66,
1452-1462
Epidemiology
of
rhizopus
rot
on
peaches
1461
of
the
efficiency
of
the
infection
and
colonization
process
of
the
pathogen
and
can
also
be
related
to
fruit
ripening;
ripe
peaches
show
higher
availability
of
nutrient
sub-
stances
that
enable
infection
and
colonization
by
the
pathogen.
The
increase
of
disease
velocity
over
time
may
be
associated
to
the
reduction
of
latent
period
of
rhizo-
pus
rot
over
time.
However,
the
velocity
decreased
with
higher
disease
severities
at
the
end
of
experiment,
which
may
be
explained
by
the
decrease
in
healthy
tissue
avail-
able
for
pathogen
infection.
The
decrease
in
susceptible
host
area
with
increasing
distance
from
the
initial
disease
front
can
cause
the
wave
front
to
travel
at
a
slower
speed
with
increasing
distance
(Aylor,
1999).
Finally,
R.
stolonifer
can
penetrate
healthy
unwounded
peaches
placed
next
to
diseased
fruit
through
mycelium
stolons.
Therefore,
it
is
important
to
emphasize
that
dis-
ease
could
be
controlled
by
reducing
or
eliminating
the
initial
source
of
inoculum,
and
by
separating
unwounded
and
diseased
fruit
to
avoid
dissemination
of
the
pathogen
to
other
fruit.
Acknowledgements
This
work
was
supported
by
contracts
nos.
2011/03034-
8
and
2012/03270-6
from
Sao
Paulo
Research
Founda-
tion
(FAPESP).
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Supporting
Information
Additional
Supporting
Information
may
be
found
in
the
online
version
of
this
article
at
the
publisher's
web-site.
Figure
Sl.
Arrangement
2,
where
the
central
fruit
of
the
first
column
(arrow)
was
inoculated
with
a
spore
suspension
of
Rhizopus
stolonifer
in
Plant
Pathology
(2017)
66,
1452-1462
1462
J.
S.
Baggio
et
al.
water.
Pathogen
spread
to
the
neighbouring
fruits
is
shown
2
(a),
3
(b),
4
(c),
5
(d),
6
(e),
7
(f),
8
(g),
9
(h),
10
(i),
11
(j),
12
(k),
13
(1),
14
(m),
15
(n),
16
(o),
17
(p)
and
18
(q)
days
after
inoculation.
Figure
S2.
Soluble
solids
(°Brix)
content
of
peaches
of
cultivars
Dour-
ado
(a)
and
Eragil
(b)
in
experiments
1
(open
circles)
and
2
(closed
cir-
cles)
over
time.
Data
were
taken
every
2
days.
Figure
S3.
Firmness
(kgf)
of
peaches
of
cultivars
Dourado
(a)
and
Era-
gil
(b)
in
experiments
1
(open
circles)
and
2
(closed
circles)
over
time.
Data
were
taken
every
2
days.
Figure
S4.
Latent
period
(days)
of
rhizopus
rot
on
peaches
of
cultivars
Dourado
(a)
and
Eragil
(b)
in
experiments
1
(white
bars)
and
2
(black
bars)
inoculated
every
2
days
with
Rhizopus
stolonifer
spore
suspension.
Plant
Pathology
(2017)
66,
1452-1462
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