Agricultural
and
Forest
Meteorology
151 (2011) 1163–
1172
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Agricultural
and
Forest
Meteorology
jou
rn
al
h
om
epa
g
e:
www.elsevier.com/locate/agrformet
Multi
metric
evaluation
of
leaf
wetness
models
for
large-area
application
of
plant
disease
models
Simone
Bregaglio
a,b,∗
,
Marcello
Donatelli
b,c
,
Roberto
Confalonieri
a
,
Marco
Acutis
a
,
Simone
Orlandini
d
a
University
of
Milan,
Department
of
Plant
Production,
via
Celoria
2,
I-20133
Milan,
Italy
b
European
Commission
Directorate
General
Joint
Research
Centre,
Institute
for
Environment
and
Sustainability,
MARS
Unit,
AGRI4CAST
Action,
via
E.
Fermi
2749-TP
483,
I-21027
Ispra
(VA),
Italy
c
Agriculture
Research
Council,
Research
Center
for
Industrial
Crops,
Bologna,
Italy
d
University
of
Florence,
Department
of
Plant,
Soil
and
Environmental
Science,
Piazzale
delle
Cascine,
18,
50144
Florence,
Italy
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
31
December
2009
Received
in
revised
form
3
March
2011
Accepted
10
April
2011
Keywords:
Fuzzy
logic
Composite
metrics
Disease
potential
infection
Hourly
values
Weather
variables
a
b
s
t
r
a
c
t
Leaf
wetness
(LW)
is
one
of
the
most
important
input
variables
of
disease
simulation
models
because
of
its
fundamental
role
in
the
development
of
the
infection
process
of
many
fungal
pathogens.
The
low
reliability
of
LW
sensors
and/or
their
rare
use
in
standard
weather
stations
has
led
to
an
increasing
demand
for
reliable
models
that
are
able
to
estimate
LW
from
other
meteorological
variables.
When
working
on
large
databases
in
which
data
are
interpolated
in
grids
starting
from
weather
stations,
LW
estimation
is
often
penalized
by
the
lack
of
hourly
inputs
(e.g.,
air
relative
humidity
and
air
temperature),
leading
researchers
to
generate
such
variables
from
the
daily
values
of
the
available
weather
data.
Although
it
is
possible
to
find
several
papers
about
models
for
the
estimation
of
LW,
the
behavior
and
reliability
of
these
models
were
never
assessed
by
running
them
with
inputs
at
different
time
resolutions
aiming
at
large-area
applications.
Furthermore,
only
a
limited
number
of
papers
have
assessed
the
suit-
ability
of
different
LW
models
when
used
to
provide
inputs
to
simulate
the
development
of
the
infection
process
of
fungal
pathogens.
In
this
paper,
six
LW
models
were
compared
using
data
collected
at
12
sites
across
the
U.S.
and
Italy
between
2002
and
2008
using
an
integrated,
multi
metric
and
fuzzy-based
expert
system
developed
ad
hoc.
The
models
were
evaluated
for
their
capability
to
estimate
LW
and
for
their
impact
on
the
simulation
of
the
infection
process
for
three
pathogens
through
the
use
of
a
potential
infection
model.
This
study
indicated
that
some
empirical
LW
models
performed
better
than
physically
based
LW
models.
The
classification
and
regression
tree
(CART)
model
performed
better
than
the
other
models
in
most
of
the
conditions
tested.
Finally,
the
estimate
of
LW
using
hourly
inputs
from
daily
data
led
to
a
decline
of
the
LW
models
performances,
which
should
still
be
considered
acceptable.
However,
this
estimate
may
require
further
work
in
data
collection
and
model
evaluation
for
applications
at
finer
spatial
resolutions
aimed
at
decision
support
systems.
© 2011 Elsevier B.V. All rights reserved.
1.
Introduction
Among
the
inputs
required
by
fungal
disease
simulation
mod-
els,
leaf
wetness
(LW;
as
yes/no
state)
is
widely
recognized
as
a
crucial
input
(Huber
and
Gillespie,
1992;
Gleason
et
al.,
1994;
Kim
et
al.,
2002).
In
particular,
the
time
free
water
remains
on
the
sur-
face
of
plant
tissues,
named
leaf
wetness
duration
(LWD;
h
day
−1
),
is
one
of
the
most
important
driving
variables
for
the
forecast-
ing
of
plant
disease
epidemics
because
of
its
considerable
impact
on
processes,
such
as
the
start
of
the
fungal
pathogens
active
life
cycle,
their
penetration
into
the
leaves,
primary
infection
occur-
rence
and
secondary
infection
occurrence.
Carrying
out
reliable
∗
Corresponding
author
at:
University
of
Milan,
Department
of
Plant
Production,
via
Celoria
2,
I-20133
Milan,
Italy.
Tel.:
+39
02
50316578.
E-mail
address:
simone.bregaglio@unimi.it
(S.
Bregaglio).
measurements
of
LWD
is
often
challenging
because
of
the
phys-
ical
complexity
of
the
processes
involved,
i.e.,
its
relationship
with
the
structural
and
optical
properties
of
the
tissue
surface
and
with
micrometeorological
aspects
(Sentelhas
et
al.,
2004).
No
standard
for
its
measurement
has
yet
been
accepted
(Dalla
Marta
et
al.,
2005).
The
sensors
also
require
crop-specific
calibrations
(Giesler
et
al.,
1996)
and
frequent
maintenance,
and
the
sensors
need
to
be
posi-
tioned
on
each
individual
farm
(Dalla
Marta
et
al.,
2005).
For
these
reasons,
the
simulation
of
LWD
is
widely
suggested
as
a
viable
alter-
native
to
direct
measurements
(Pedro
and
Gillespie,
1982;
Huber
and
Gillespie,
1992;
Hoppmann
and
Wittich,
1997),
especially
for
large
area
applications.
The
existing
approaches
for
the
genera-
tion
of
LW
can
be
classified
into
two
categories
as
follows:
fully
empirical
(e.g.,
Gleason
et
al.,
1994;
Rao
et
al.,
1998;
Wichink
Kruit
et
al.,
2004)
and
process-based
(e.g.,
Pedro
and
Gillespie,
1982;
Luo
and
Goudriaan,
2000;
Magarey
et
al.,
2006;
Sentelhas
et
al.,
2006).
Empirical
models
simulate
LW
using
simple
relationships
between
0168-1923/$
–
see
front
matter ©
2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.agrformet.2011.04.003