Weed vegetation of poppy (Papaver somniferum) fields in Hungary: effects of management and environmental factors on species composition


Pinke, G.; Pal, R.W.; Toth, K.; Karacsony, P.; Czucz, B.; Botta-Dukat, Z.

Weed Research 51(6): 621-630

2011


Poppy (Papaver somniferum) is a sporadically cultivated crop species, with idiosyncratic life history traits, management systems and highly specific and under-researched weed communities. This study aimed to assess the management and environmental factors determining the weed species composition of poppy fields over a relatively large area (Hungary in central-eastern Europe), one of the focal regions of poppy production worldwide. The abundance of weed flora was measured in 12 poppy fields across Hungary, along with 41 management and environmental factors. The set of explanatory variables was reduced by stepwise backward selection to a minimal adequate model containing 15 terms, which explained 34.3% of the total variation in species data. The net effects of 1 variables on species composition were significant. Sowing season was found to be the most important explanatory variable, showing a clear distinction between the weed flora of autumn-sown food poppy and spring-sown alkaloid poppy fields. Other management factors, such as preceding crop, herbicides mesotrione and isoxaflutole, fertiliser N and row spacing, were also significant. Only four environmental variables (temperature, soil texture, soil Mg and Ca content) were significant, which can be attributed to the narrow ecological tolerance of poppy, resulting in short environmental gradients.

A
WEED
RESEARCH
An
International
Journal
of
Weed
Biology,
Ecology
and
Vegetation
Management
DOI:
10.1111/j.1365
-3180.2011.00885.x
Weed
vegetation
of
poppy
(Papaver
somniferum)
fields
in
Hungary:
effects
of
management
and
environmental
factors
on
species
composition
G
PINKE*,
R
W
PALL,
K
TOTH*,
P
KARACSONY*,
B
CZI'JCZT
&
Z
BOTTA-DUKATT
*Faculty
of
Agricultural
and
Food
Sciences,
University
of
West
Hungary,
Mosonmagyarevar,
Hungary,
t
Faculty
of
Sciences,
University
of
Pecs, Pecs,
Hungary,
and
IInstitute
of
Ecology
and
Botany,
Hungarian
Academy
of
Sciences,
Vacratet,
Hungary
Received
13
May
2011
Revised
version
accepted
26
July
2011
Subject
Editor:
Adam
Davis,
USDA-ARS,
USA
Summary
Poppy
(Papaver
somniferum)
is
a
sporadically
cultivated
crop
species,
with
idiosyncratic
life
history
traits,
management
systems
and
highly
specific
and
under
-
researched
weed
communities.
This
study
aimed
to
assess
the
management
and
environmental
factors
determining
the
weed
species
composition
of
poppy
fi
elds
over
a
relatively
large
area
(Hungary
in
central
-
eastern
Europe),
one
of
the
focal
regions
of
poppy
production
worldwide.
The
abundance
of
weed
fl
ora
was
measured
in
102
poppy
fi
elds
across
Hungary,
along
with
41
management
and
environmental
factors.
The
set
of
explanatory
variables
was
reduced
by
stepwise
backward
selection
to
a
minimal
adequate
model
containing
15
terms,
which
explained
34.3%
of
the
total
variation
in
species
data.
The
net
effects
of
10
variables
on
species
composition
were
significant.
Sowing
season
was
found
to
be
the
most
important
explanatory
variable,
showing
a
clear
distinction
between
the
weed
fl
ora
of
autumn
-sown
food
poppy
and
spring
-sown
alkaloid
poppy
fi
elds.
Other
manage-
ment
factors,
such
as
preceding
crop,
herbicides
mesotrione
and
isoxaflutole,
fertiliser
N
and
row
spacing,
were
also
significant.
Only
four
environmental
variables
(temperature,
soil
texture,
soil
Mg
and
Ca
content)
were
significant,
which
can
be
attributed
to
the
narrow
ecological
tolerance
of
poppy,
resulting
in
short
environmental
gradients.
Keywords:
agroecology,
opium
poppy,
crop
type,
redundancy
analysis,
survey,
weed
fl
ora,
herbicides.
PINKE
G,
PAL
RW,
TOTH
K,
KARACSONY
P,
Czucz
B
&
BOTTA-DUKAT
Z
(2011).
Weed
vegetation
of
poppy
(Papaver
somniferum)
fi
elds
in
Hungary:
effects
of
management
and
environmental
factors
on
species
composition.
Weed
Research
51,
621-630.
Introduction
Neolithic
remains
of
opium
poppy
(Papaver
somniferum
L.)
are
known
throughout
Europe.
The
domestication
that
probably
occurred
around
the
Aegean
Sea
was
followed
by
introductions
to
all
continents
with
suitable
climates
(Sarkany
et
al.,
2001).
Currently,
there
are
only
a
few
countries
worldwide
where
significant
legal
production
of
poppy
takes
place,
including
Australia
(Tasmania),
Hungary,
Turkey,
Spain
and
India
(Meakin,
2007),
and
also
the
Czech
Republic,
France
and
Croatia
(Faostat,
2009).
In
Hungary,
poppy
has
a
long
tradition
as
a
rural
garden
crop,
but
large-scale
cropping
of
poppy
in
arable
fi
elds
only
started
in
the
1930s.
Now,
cultivation
is
concentrated
in
specific
poppy
-growing
districts
of
Hungary,
which
are
close
to
the
special
environmental
demands
of
the
crop.
Poppy
is
either
used
as
(i)
a
pharmaceutical
crop
(alkaloids
are
extracted
from
its
Correspondence:
G
Pinke,
Faculty
of
Agricultural
and
Food
Sciences,
University
of
West
Hungary,
H-9200
Mosonmagyarovar,
Var
2,
Hungary.
Tel:
(+36)
70
573
5590;
Fax:
(+36)
96
566
610;
E-mail:
pinkegy@mtk.nyme.hu
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
622
G
Pinke
et
al.
dry
capsule)
or
(ii)
a
food
crop (poppy
seeds
are
a
traditional
ingredient
in
several
European
cuisines).
The
production
target
strongly
determines
every
aspect
of
the
cultivation
(Sarkany
et
al.,
2001).
At
the
beginning
of
its
vegetation
period,
poppy
develops
slowly.
It
has
only
a
weak
competitive
ability
against
weeds
and
it
is
relatively
susceptible
to
herbi-
cides.
For
this
reason,
the
weed
control
system
for
poppy
crops
is
complex
and
it
demands
a
high
level
of
technological
knowledge
from
farmers.
Depending
on
preceding
crops,
sowing
technologies,
crop
development
stages
and
weed
species
composition,
the
weed
manage-
ment
of
poppy
should
include
multiple
chemical
and
mechanical
treatments
and
herbicide
rotations
(Sarkany
et
al.,
2001).
Weed
communities
in
arable
land
are
simultaneously
governed
by
several
anthropogenic
and
environmental
factors
and
there
have
been
numerous
studies
which
have
assessed
and
ranked
the
influences
of
such
factors
(Lososova
et
al.,
2004;
Fried
et
al.,
2008;
Silc
et
al.,
2009;
Hyvonen
et
al.,
2011).
Because
of
large
climatic
gradients
and
diverse
soil
types
in
the
study
area,
environmental
factors
are of
greater
importance
than
management
factors
in
shaping
the
weed
communities
of
maize,
sunflower
and
stubble
fi
elds
in
Hungary
(Pinke
et
al.,
2011b).
In
the
case
of
poppy,
the
dominance
of
environmental
over
management
factors
was
expected
to
be
less
important,
because
of
the
relatively
uniform
climatic
and
soil
characteristics
of
the
Hungarian
poppy
-growing
districts,
as
well
as
the
required
strict
agro-technical
protocols.
In
this
study,
we
aimed
to
assess
and
rank
the
role
of
several
management
and
environmental
factors
in
determining
the
weed
species
composition
of
poppy
fi
elds
across
Hungary.
The
knowledge
of
these
variables
might
provide
new
infor-
mation
about
the
assembly
rules
of
weed
communities
and
could
be
used
to
optimise
weed
control
strategies.
Materials
and
methods
Data
collection
As
opium
poppy
is
an
infrequent
crop
even
in
Hungary,
we
could
not
effectively
design
a
geographically
stratified
sampling
procedure,
based
on
random
selection
proce-
dures.
Two
Hungarian
poppy
trading
companies
and
several
contract
providers
were
asked
to
identify
poppy
-
growing
farmers.
Each
farmer
was
mailed
and
telephoned
to
ask
whether
they
would
permit
access
into
their
fi
elds
and
consent
to
being
interviewed
about
management
factors.
We
generally
surveyed
only
one
poppy
fi
eld
from
each
farmer
willing
to
co-operate.
Two
fi
elds
from
the
same
farmer
were
investigated
only
if
they
differed
in
major
management
factors
(sowing
season,
herbicides
or
mechanical
weed
control
vari-
ables).
This
resulted
in
a
set
of
102
arable
fi
elds
across
the
poppy
-growing
districts
of
Hungary
(Fig.
1).
Weed
vegetation
was
sampled
in
the
fi
elds
in
four
randomly
selected
50-m
2
plots
between
30
May
and
14
June
2010.
One
plot
was
located
on
the
fi
eld
edge
(inside
the
outermost
seed
drill
line),
whereas
the
remaining
three
plots
were
located
inside
the
fi
elds
at
different
distances
(between
10
and
300
m)
from
the
edge.
Percentage
ground
cover
of
plant
species
in
the
plots
was
estimated
visually.
In
total,
408
plots
were
sampled
(4
plots
in
102
fi
elds).
A
soil
sample
of
c.
1000
cm
3
was
taken
from
each
fi
eld
as
a
mixture
of
four
subsamples
from
the
surveyed
plots
taken
from
the
upper
10
-cm
soil
layer
after
removing
the
surface
litter.
Soil
analyses
were
carried
out
in
a
laboratory
belonging
to
UIS
Ungarn
GmbH
and
accredited
by
DAP
(German
Accreditation
System
for
Testing).
Crop
management
information
was
obtained
directly
from
the
farmers.
As
the
target
crop
strictly
determines
cultivar
groups
and
sowing
season
(alkaloid
varieties
are
always
sown
in
spring,
whereas
food
cultivars
are
sown
in
autumn).
all
these
characteristics
were
coded
as
a
single
binary
categorical
variable,
called
'Sowing
season'.
The
number
of
investigated
alkaloid
poppy
fi
elds
was
77,
and
that
of
food
poppy
fi
elds
was
25.
The
crop
history
of
the
fi
elds
was
represented
by
a
further
categorical
variable
called
'preceding
crop',
which
could
be
any
of
the
following:
wheat
(Triticum
aestivum
L.),
barley
(Hordeum
vulgare
L.),
poppy,
oilseed
rape
(Brassica
napus
L.),
maize
(Zea
mays
L.),
onion
(Allium
cepa
L.),
sugar
beet
(Beta
vulgaris
L.),
pea
(Pisum
sativum
L.),
sunflower
(Helianthus
annuus
L.),
carrot
(Daucus
carota
L.)
and
white
mustard
(Sinapis
alba
L.).
To
reduce
the
number
of
categories
and
to
avoid
rare
levels
of
categorical
variables,
cereal
species
were
assembled
into
a
single
'cereal'
category
and
categories
occurring
less
than
fi
ve
times
(carrot,
onion,
pea,
sugar
beet,
sunflower
and
white
mustard)
were
considered
to
be
'miscellaneous'.
Herbicides
were
represented
as
con-
tinuous
variables
with
the
quantities
of
their
active
ingredients
applied
during
the
entire
growing
cycle.
The
following
active
ingredients
were
used:
chlortoluron
(Lentipur
500
SC,
500
g
a.i.
L
-1
;
NuFarm),
cyprosulf-
amid
(Merlin
Flexx,
240
g
a.i.
L
-1
;
Bayer),
diquat
(Reglone,
200
g
a.i.
L
-1
;
Syngenta)
fl
uazifop-P-butyl
(Fusilade
Forte,
150
g
a.i.
L
-1
;
Syngenta),
isoxadifen
ethyl
(Laudis,
22
g
a.i.
L
-1
;
Bayer),
isoxaflutole
(Merlin
SC,
480
g
a.i.
L
-1
,
Merlin
Flexx,
240
g
a.i.
L
-1
;
Bayer),
mesotrione
(Callisto
4SC,
480
g
a.i.
L
-1
;
Syngenta),
quizalofop-P-tefuryl
(Pantera
40
EC,
40
g
a.i.
L
-1
;
Chemark),
tembotrione
(Laudis,
44
g
a.i.
L
-1
;
Bayer),
fl
uroxypyr-methylheptyl-ester
(Starane
250
EC,
36%
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
Weed
species
composition
of
poppy
fi
elds
623
48°30'N-
48°O'N-
47°30'N-
47°O'N
46°30'N-
46°O'N-.
45°30'N
0
a
a
Legend
Food
Alkaloid
a
a.
A•
a
a
••
0
25
50
oi•
2
a
e
100 150
200
Kilometers
16°30'E
17
°
0'E
17°30'E
18
°
0'E
18°3▪
0'E
19°0'E
19°30'E
20°0'E
20°30'E
21°0'E
21°30'E
22°0'E
22°30'E
23°0'E
Fig.
1
The
distribution
of
the
102
surveyed
poppy
fi
elds
across
Hungary.
At
this
scale,
individual
points
may
represent
a
number
of
fi
elds.
a.i.;
Dow
AgroSciences)
and
quizalofop-P-ethyl
(Targa
Super,
5% a.i.;
Nissan;
Leopard
5
EC,
5%
a.i.;
Agan).
The
number
of
mechanical
weed
control
treatments,
the
amount
of
organic
manure
and
fertilisers
applied
were
also
used
in
the
analysis,
as
well
as
crop
cover,
crop
row
spacing,
fi
eld
size
and
tillage
depth.
In
addition,
the
type
of
the
neighbouring
habitat
(ditch,
hedge,
meadow,
road
margin)
was
also
included
to
the
set
of
management
variables.
For
each
investigated
fi
eld,
a
set
of
environmental
variables
was
also
compiled,
including
(i)
soil
proper-
ties,
(ii)
climatic
conditions
represented
by
mean
annual
temperature
obtained
from
the
WorldClim
database
(Hijmans
et
al.,
2005)
and
mean
annual
precipitation
obtained
from
the
Hungarian
Meteoro-
logical
Service
(HMS,
2001)
and
(iii)
altitude
(measured
by
a
GPS
receiver).
Altogether,
25
manage-
ment
and
16
environmental
variables
were
included
in
the
analysis
(Table
1).
Data
analysis
As
a
fi
rst
step,
species
richness
of
fi
eld
edges
and
cores
were
compared
using
a
paired
Wilcoxon
rank
sum
test.
The
species
richness
of
a
fi
eld
core
was
interpreted
as
the
mean
richness
of
three
plots
from
the
same
fi
eld.
Next,
we
performed
a
multivariate
analysis
to
deter-
mine
the
average
community
composition
of
the
indi-
vidual
fi
elds.
For
each
fi
eld,
we
averaged
the
cover
values
of
the
weed
species
across
all
the
four
plots.
Mean
cover
values
were
then
subjected
to
a
Hellinger
trans-
formation
(Legendre
&
Gallagher,
2001)
and
were
examined
in
a
redundancy
analysis
(RDA),
together
with
the
management
and
environmental
data.
Accord-
ing
to
Legendre
and
Gallagher
(2001),
this
procedure
is
able
to
relate
species
data
to
explanatory
variables
more
accurately
than
the
commonly
applied
canonical
corre-
spondence
analysis
(CCA),
even
if
the
species
response
curves
are
unimodal
(owing
to,
e.g.
long
gradients).
The
number
of
explanatory
variables
was
reduced
by
step-
wise
backward
selection
using
a
P
<
0.05
threshold
for
type
I
error,
which
led
to
a
minimal
adequate
model
containing
15
terms.
As
a
next
step
of
the
multivariate
analysis,
we
assessed
gross
and
net
effects
of
each
explanatory
variable
of
the
reduced
model,
according
to
the
methodology
of
Lososova
et
al.
(2004).
The
gross
effect
of
a
variable
was
defined
as
the
variation
explained
by
a
`univariate'
RDA
containing
the
studied
predictor
as
the
only
explanatory
variable.
The
net
effect,
on
the
other
hand,
was
assessed
as
the
signif-
icance
of
a
similar
partial
RDA
(pRDA)
with
the
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
624
G
Pinke
et
al.
Table
1
Units
and
ranges
of
continuous
variables
and
values
of
categorical
variables
used
in
the
analysed
data
set
Variable
(unit)
Range/Values
Sowing
season
Preceding
crop
Autumn,
spring
Cereal,
poppy,
oilseed
rape,
maize,
miscellaneous
Herbicides
(g
ha
-1
)
Chlortoluron
0-1500
Cyprosulfamid
0-96
Diquat
0-400
Fluazifop-P-butyl
0-150
Isoxadifen
ethyl
0-49.5
Isoxaflutole
0-96
Mesotrione
0-288
Quizalofop-P-tefuryl
0-100
Tembotrione
0-99
Herbicides
(L
ha
-1
)
Fluroxypyr-methylheptyl-ester
0-0.252
Quizalofop-P-ethyl
0-0.06
Mechanical
weed
control
(times)
0-3
Organic
manure
(t
ha
-1
)
0-60
Amount
of
fertiliser
(kg
ha
-1
)
N
0-177
P205
0-130
K
2
0
0-180
MgO
0-25
CaO
0-35
Crop
cover
(%)
5-95
Crop
row
spacing
(cm)
10-50
Field size
(ha)
1-70
Tillage
depth
(cm)
15-60
Neighbouring
habitat
Ditch,
hedge,
Altitude
(m)
Mean
annual
precipitation
(mm)
Mean
annual
temperature
(°C)
Soi
l
pH
(KCI)
Soi
l
texture
(KA)
Soi
l
properties
(m
m%
-1
)
Humus
CaCO
3
Soi
l
properties
(mg
kg
-1
)
P705
K
2
0
Na
Mg
NO
2
-NO
3
-N
SO
4
Cu
Zn
Mn
meadow,
road
margin
83-205
478-657
9.67-11.23
5.26-7.68
20-54
0.72-4.8
0.03-27.2
67.9-2220
79.3-1460
10.6-127
38.3-827
3.83-87.8
9-77.8
0.73-24.4
0.45-10.5
7.25-480
studied
predictor
still
being
the
only
constraining
variable,
but
all
the
other
variables
of
the
reduced
model
were
also
involved
as
conditioning
variables
(`co
-variables'),
the
effect
of
which
was
`partialled
out'
(i.e.
removed
before
the
actual
RDA).
In
the
case
of
the
net
effects,
model
significances
were
assessed
as
type
I
error
rates
obtained
by
permutation
tests.
There
was
only
one
constrained
axis
in
the
partial
RDAs,
except
the
analysis of
preceding
crop,
where
there
were
four
constrained
axes
(number of
categories
1),
and
all
axes
were
tested
separately
(Lepg
&
Smilauer,
2003).
Based
on
these
results,
a
common
rank
of
'importance'
was
established
among
all
explanatory
variables
(i.e.
both
management
and
environmental
variables)
according
to
the
R
2
adj
-values
of
the
net
effects
of
the
pRDA
models.
To
demonstrate
the
responses
of
the
weed
species
to
the
individual
significant
manage-
ment
and
environmental
factors,
in
each
case,
we
identified
those
10
species
(with
>
9
occurrences)
that
expressed
the
highest
explained
variation
by
the
con-
strained
axis
in
the
partial
RDA.
To
check
for
the
presence
of
spatial
autocorrelation
or
spatially
structured
species
—environment
relationship,
we
applied
spatial
partitioning
of
the
ordination
results
(Wagner,
2004).
Having
found
no
significant
spatial
effects,
we
followed
the
analysis
without
considering
spatial
position
of
fi
elds.
Intercorrelations
of
model
terms
were
checked
prior
to
the
analysis
by
calculating
variance
inflation
factors
(Fox
&
Monette,
1992).
Significant
variables
showed
only
slight
intercorrela-
tions,
which
should
not
bias
the
analysis
(the
highest
GVIF
score
adjusted
by
degree
of
freedom
was
2.88).
In
the
RDA
ordination
diagrams
of
the
reduced
model,
co-ordinates
of
continuous
variables
were
calculated
from
their
linear
constraints,
while
categorical
variables
were
transformed
to
'dummy'
indicator
variables
and
these
dummies
were
placed
in
the
ordination
space
by
weighted
averaging.
The
entire
statistical
analysis
was
performed
in
the
R
Environment
(R
Development
Core
Team,
version
2.11.1)
using
the
Vegan
add-on
package
(vegan
1.17-2).
Results
Altogether,
173
weed
species
were
recorded.
Papaver
rhoe
as
L.
(common
poppy)
was
the
most
abundant
weed
species,
both
in
alkaloid
and
food
poppy
fi
elds
(Fig.
2).
Field
edges
were
significantly
more
species
rich
than
fi
eld
cores
(mean
richness
are
16.44
and
9.01
species,
respec-
tively;
P
<
0.01%).
The
full
RDA
model
explained
54.8%
of
the
variance,
while
the
reduced
model
(comprising
15
explanatory
variables)
still
explained
34.3%
of
the
total
variation
in
species
data.
According
to
the
RDA
and
pRDA
models,
the
most
important
predictor
was
sowing
season,
which
was
followed
by
preceding
crop,
soil
texture,
soil
Mg
content,
mesotrione,
temperature,
isoxaflutole,
soil
CaCO
3
content,
fertiliser
N
and
row
spacing
(Table
2).
Although
neighbouring
habitat,
fertiliser
K,
precipitation,
altitude
and
mechanical
weed
control
remained
in
the
model
in
the
course
of
backward
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
Weed
species
composition
of
poppy
fi
elds
625
s.
Mean
cover
(/o)
6.00
5.00
4.00
3.00
2.00
1.00
0.00
O
ry
M
I-
er,
Alkaloid
00
O
poppy
00
00
,
0
O
p
d
C
nrinn
4c
4
\
N
x
-
b'
\
\`,
‘b
,
4
00"
4
4
'
e
b
d,
,R
,
6c
`'
e
b
4
,,
c
o
,b
e
-e
s,„?"
-e
o
'S
c
ie
,
c)
G`9"
,s9
<"
,
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
en
O
O
d
Food
poppy
O
O
e,'z''
''
'.\"zw
`.,
c5
"
.c.,
.S'"
e.,
e.,
e
e
,
..
,
,.
..
<'w
-'w
,
,
,
,,
o
0
S
,
e65z
..:,... .,
--
.4
..$
0
,
$
c
p
-
..
.
,
c
,
eb
,
'"
l
'
4
,
s.
-§'"
e
.4•
,\
,
'
.s.
. .
„,.
,
c
„,..''
,
,
,
s
,e
,,,s
-
,,,,,..
05c
,
ow
,,
,
c
,
e
.,
,
b
,
,
,
0
,
,b,
co
\
.6,0
.
.,
,0
<z.
oz
,
,,sc.
.
..,,
Fig.
2
The
ten
most
abundant
weed
species
of
the
surveyed
fi
elds.
Table
2
Gross
and
net
effects
of
the
explanatory
variables
on
the
weed
species
composition,
identified
using
(p
-)redundancy
analysis
with
single
explanatory
variables
Factors
d.f.
Gross
effect
Net
effect
Explained
variation
(%)
R2
adj
Explained
variation
(%)
R2adj
F
P
-value
Sowing
season
Preceding
crop
Soil
texture
Soil
Mg
content
Mesotrione
Temperature
Isoxaflutole
Soil
CaCO
3
content
Fertiliser
N
Row
spacing
Neighbouring
habitat
Fertiliser
K
Precipitation
Altitude
Mechanical
weed
control
1
6.556
4
4.631
1
1.842
1
3.105
1
2.289
1
2.536
1
2.734
1
2.368
1
2.166
1
4.024
3
3.627
1
1.294
1
4.761
1
4.816
1
1.959
0.05622
0.00699
0.00860
0.02136
0.01312
0.01561
0.01761
0.01391
0.01187
0.03065
0.00677
0.00307
0.03809
0.03864
0.00979
4.242
4.468
1.774
1.646
1.604
1.553
1.343
1.334
1.290
1.283
2.994
1.126
1.119
0.0111
1.013
0.04226
0.01453
0.01186
0.01028
0.00977
0.00913
0.00655
0.00644
0.00590
0.00581
0.00674
0.00388
0.00379
0.00369
0.00248
5.2297
1.377
2.1875
2.0291
1.978
1.9141
1.6553
1.6451
1.5907
1.5814
1.2304
1.3886
1.3797
1.3696
1.2485
0.005
0.01
0.005
0.005
0.005
0.005
0.016
0.013
0.005
0.02
NS
NS
NS
NS
NS
NS,
not
significant.
selection,
they
did
not
explain
significant
amounts
of
variation
in
species
composition.
The
responses
of
the
weed
species
with
the
highest
fi
t
are
listed
in
Tables
3
and
4.
In
the
reduced
RDA
ordination,
the
fi
rst
axis
can
be
related
to
the
explanatory
variables
sowing
date
and
row
spacing,
while
the
second
axis
is
strongly
correlated
with
soil
Ca
content
and
the
quantity
of
isoxaflutole
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
626
G
Pinke
et
al.
Table
3
Names,
fi
t
and
score
values
of
the
ten
species
giving
the
highest
fi
t
along
the
fi
rst
constrained
axis
in
the
partial
redundancy
analysis
models
of
the
significant
management
variables
specified
in
Table
2
Ax
1
score
Fit
Ax
1
score
Fit
Sowing
season
(+
spring;
-
autumn)
Preceding
crop
(+
cereal,
poppy
or
rape;
-
maize)
Chenopodium
album
0.235 0.138
Polygonum
aviculare
0.179
0.115
Echinochloa
crus-galli
0.178
0.109
Chenopodium
album
0.157
0.070
Polygonum
aviculare
0.148 0.056
Lolium
perenne
0.057
0.078
Sonchus
asper
0.111
0.050
Microrrhinum
minus
0.038
0.095
Conyza
canadensis
-0.038
0.084
Avena
fatua
0.004
0.091
Stellaria
media
-0.055
0.065
Amaranthus
retroflexus
-0.005
0.074
Tripleurospermum
inodorum
-0.094
0.055
Portulaca
oleracea
-0.01
1
0.200
Consolida
regalis
-0.133
0.156
Sorghum
halepense
-0.075
0.074
Descurainia
sophia
-0.136
0.099
Setaria
viridis
-0.106
0.084
Papaver
rhoeas
-0.290
0.106
Echinochloa
crus-galli
-0.133
0.079
Mesotrione
(+
high;
-
low)
Isoxaflutole
(+
high;
-
low)
Ambrosia
artemisiifolia
0.128
0.050
Fallopia
convolvulus
0.198
0.109
Lolium
perenne
0.086
0.084
Mercurialis
annua
0.081
0.062
Setaria
pumila
0.081
0.043
Lolium
perenne
0.056
0.036
Solanum
nigrum
-0.023
0.028
Veronica
polita
0.053
0.029
Conyza
canadensis
-0.039
0.093
Datura
stramonium
0.042
0.027
Artemisia
vulgaris
-0.045
0.065
Atriplex
patula
0.021
0.022
Bromus
sterilis
-0.053
0.048
Solanum
nigrum
-0.028
0.041
Chenopodium
hybridum
-0.085
0.082
Euphorbia
falcata
-0.041
0.023
Capsella
bursa-pastoris
-0.106
0.105
Descurainia
sophia
-0.067
0.024
Chenopodium
album
-0.129
0.042
Chenopodium
album
-0.109
0.030
Ferti
liser
N
(+
high;
-
low)
Row
spacing
(+
high;
-
low)
Elymus
repens
0.090
0.021
Chenopodium
album
0.095
0.022
Viola
arvensis
0.059 0.039
Echinochloa
crus-galli
0.078
0.021
Veronica
persica
0.049
0.008
Panicum
miliaceum
0.071
0.039
Apera
spica-venti
0.046 0.015
Xanthium
italicum
-0.034
0.041
Euphorbia
helioscopia
-0.035
0.011
Veronica
persica
-0.038
0.027
Stellaria
media
-0.039
0.041
Capsella
bursa-pastoris
-0.050
0.024
Consolida
orientalis
-0.052
0.027
Helianthus
annuus
-0.054
0.021
Mercurialis
annua
-0.054
0.024
Tripleurospermum
inodorum
-0.060
0.022
Setaria
pumila
-0.060
0.026
Convolvulus
arvensis
-0.073
0.026
Ambrosia
artemisiifolia
-0.125
0.044
Papaver
rhoeas
-0.188
0.044
herbicides
applied,
and
some
levels
of
the
variable
`preceding
crop'
(Fig.
3A).
Consequently,
samples
from
autumn
-sown
food
poppy
fi
elds
with
narrower
row
spacing,
which
are
also
typically
characterised
with
the
presence
of
the
Papaver
rhoeas,
generally
exhibit
low
values
on
the
fi
rst
RDA
axis
(Fig.
3A
and
B).
On
the
other
hand,
sites
with
spring
-sown
alkaloid
poppy
fi
elds
can
be
characterised
with
high
axis
1
values
and
the
frequent
presence
of
Echinochloa
crus-galli
(L.)
P.
Beauv
(barnyard
grass).
Negative
values
along
the
second
axis
are
characteristic
of
former
oilseed
rape
fi
elds
with
Ca
-
rich
soils,
and
high
doses
of
isoxaflutole
herbicides
applied,
as
well
as
the
presence
of
Polygonum
aviculare
L.
(knotgrass)
and
Fallopia
convolvulus
(L.)
A.
Love
(black
-bindweed).
High
axis
2
values,
on
the
other
hand,
refer
to
former
maize
fi
elds
with
relatively
acid
soils
and
no
isoxaflutole
applications,
and
generally
with
Descu-
rainia
sophia
L.
(flixweed)
in
the
weed
fl
ora
(Fig.
3A
and
B).
In
the
partial
RDA
of
'preceding
crop'
only
the
fi
rst
axis
was
significant,
along
which
maize
was
separated
from
cereal,
poppy
and
oilseed
rape.
If
maize
was
omitted,
there
were
no
significant
differences
between
the
remaining
factor
levels
(F
=
71.0961,
P
=
0.28).
Discussion
Management
variables
In
our
study,
sowing
season
and
preceding
crop
was
found
to
be
the
most
important
factors
affecting
the
weed
fl
ora.
However,
sowing
season
was
strictly
deter-
mined
by
'cultivation
target',
because
alkaloid
poppy
cultivars
were
sown
always
in
spring,
while
those
of
food
poppy
always
in
autumn.
Our
results
are
consistent
with
other
recent
investigations
in
Europe
that
indicate
that
the
most
important
driver
in
determining
weed
species
composition
in
annual
arable
fi
elds
is
the
sowing
time
(autumn
or
spring)
of
the
crop
species.
In
particular,
divergent
soil
cultivation
and
sowing
dates
induce
the
development
of
distinct
weed
communities
(Fried
et
al.,
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
Weed
species
composition
of
poppy
fi
elds
627
A
71
:
B
N
o
-
0
o
0
0
0
‘.o
0
Autumn-sownS
Maize•
(Food
poppy)
Temperature
Species
richness
Oilseed
rape
Fertilizer
N
Soil
Ca
Isoxaflutole
Row
spacing
Soil
texture
Soil
Mg
Mesotrione
Poppy
El•re
0
Spring
-sown
(Alkaloid
poppy)
Ceal
1 1 1
l
1
—0.6
—0.4
—0.2
0.0
0.2
Axis
1
(10.7%)
0.4
0.6
Descurainia
sophia
Amaranthus
retroflexus•
Consolida
regalis
Camelina
microcarpa•
Hibiscus
trionum
•Sonchus
asper
Echin
ochloa
crus-galli
Amaranthus
powelii
4apaver
rhoeas
Viola
.
arvensis
Fallopia
convolvulus
Mercurialis
annua
Polygonum
aviculare
—0.6
—0.4
—0.2
0.0
0.2
Axis
1
(10.7%)
0.4
Fig.
3
Ordination
diagrams
of
the
reduced
redundancy
analysis
(RDA)
model
containing
the
10
significant
explanatory
variables
for
(A)
variables
and
(B)
the
species.
Only
the
species
with
the
highest
weight
on
the
fi
rst
two
RDA
axes
are
presented.
Circle
=
sowing
season
(poppy
crop
type);
square
=
preceding
crop.
Species
richness
was
passively
projected
into
plot
A.
2008).
Our
results
suggest
that
summer
annuals,
such
as
Chenopodium
album
L.
(fat
-hen)
and
E.
crus-galli
were
the
most
strongly
associated
with
spring
-sown
alkaloid
poppy,
while
winter
annuals,
such
as
P.
rhoeas
and
D.
sophia
preferred
autumn
-sown
food
poppy
cultiva-
tion
(Table
3).
Preceding
crop
was
the
second
most
important
factor,
which
is
in
accordance
with
the
results
of
Fried
et
al.
(2008)
in
France,
who
found
that
current
and
preceding
crop
type
were
the
two
most
important
variables.
In
our
analysis,
two
weed
assemblages
could
be
distinguished
statistically
with
respect
to
this
variable:
(i)
weed
communities
after
maize
and
(ii)
communities
succeeding
other
crop
species.
Table
3
shows
that
E.
crus-galli
and
Setaria
viridis
(L.)
Beauv.
(green
bristle
grass)
were
the
most
strongly
associated
with
preceding
crop
maize,
while
P.
aviculare
and
C.
album
preferred
other
preceding
crops.
According
to
Mas
et
al.
(2010),
maize
as
a
previous
crop
also
affected
weed
community
structure
in
soyabean.
This
was
because
the
two
crops
differed
considerably
in
the
herbicides
used
and
herbi-
cides
are
particularly
important
fi
lters
determining
weed
community
composition.
According
to
farmers'
obser-
vations,
poppy
emerges
best
after
maize
in
the
crop
rotation.
This
is
most
likely
due
to
the
fact
that
because
of
rough
stubble
residues
of
maize,
soil
needs
to
be
thoroughly
cultivated,
which
results
in
a
very
fi
ne
seedbed.
In
winter
wheat,
Ulber
et
al.
(2009)
reported
that
crop
rotation
accounted
for
the
largest
part
of
the
explained
variation
in
weed
species
composition.
Two
of
the
eleven
active
ingredients
of
herbicides
have
proven
to
be
significant:
mesotrione
and
isoxaflu-
tole.
Mesotrione
is
applied
usually
once
early
post
-
emergence
and
it
is
very
effective
against
dicotyledonous
weeds
in
poppy.
It
is
widely
used
among
Hungarian
poppy
growers
and
its
application
is
often
later
followed
by
a
spraying
with
a
combination
of
tembotrione
&
isoxadifen
ethyl.
The
application
of
mesotrione
gave
positive
results
also
in
Polish
poppy
cultivars
(Wojto-
wicz
&
Wojtowicz,
2009).
Our
investigation
shows
that
C.
album
and
Capsella
bursa-pastoris
(L.)
Medik.
(shepherd's
purse)
were
the
species
most
sensitive
to
mesotrione,
while
Ambrosia
artemisiifolia
L.
(common
ragweed)
and
Lolium
perenne
L.
(perennial
ryegrass)
seem
to
have
tolerated
even
its
high
concentrations
(Table
3).
According
to
Pannacci
and
Covarelli
(2009),
C.
album
and
other
broad
leaved
annuals
could
be
satisfactorily
controlled
with
this
herbicide,
but
it
did
not
prove
effective
against
Portulaca
oleracea
L.
(com-
mon
purslane),
even
at
maximum
labelled
dose
in
maize.
Nurse
et
al.
(2010)
found
that
mesotrione
provided
effective
control
of
A.
artemisiifolia
at
lower
doses,
while
according
to
Whaley
et
al.
(2006)
it
provided
less
than
40%
control
of
this
weed,
even
at
a
higher
dose.
Although
A.
artemisiifolia
is
the
most
noxious
weed
species
in
Hungary
(Novak
et
al.,
2009;
Pinke
et
al.,
2011a)
and
despite
its
likely
tolerance
against
this
herbicide,
it
did
not
cause
any
unmanageable
weed
problem
in
the
surveyed
poppy
fi
elds.
Isoxaflutole
is
used
pre
-emergence
and
it
keeps
the
poppy
fi
elds
weed
-
free
during
the
early
critical
growing
stage
of
the
crop.
Chenopodium
album
and
D.
sophia
were
highly
suscep-
tible
to
this
herbicide,
while
it
was
tolerated
most
by
F.
convolvulus
and
Mercurialis
annua
L.
(annual
mercury;
Table
3).
These
results
correspond
with
the
investigation
of
Jursik
et
al.
(2008),
who
found
that
isoxaflutole
gave
good
control
of
C.
album,
while
M.
annua
was
not
totally
controlled
even
at
very
high
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
628
G
Pinke
et
al.
doses.
According
to
both
published
literature
(Sarkany
et
al.,
2001)
and
the
results
of
this
study,
the
most
abundant
weed
species
is
Papaver
rhoeas,
both
in
food
and
alkaloid
poppy.
The
fact
that
weed
and
crop
species
belong
to
the
same
plant
family,
result
in
a
very
low
efficiency
of
chemical
control.
Although
mechanical
weed
control
was
shown
not
to
be
a
significant
control
method
in
the
present
study,
several
investigations
from
poppy
-growing
countries
throughout
the
world
indicate
that
mechanical
weed
control
can
also
be
of
use
in
poppy
fi
elds
(Baldwin,
1977;
Sarkany
et
al.,
2001;
Kubni
&
Tiwari,
2004;
WOjtowicz
&
WOjtowicz,
2009).
Nitrogen
fertiliser
was
also
significant
in
our
inves-
tigation.
It
had
positive
effects,
e.g.
on
Elymus
repens
(L.)
Gould
(common
couch)
and
Viola
arvensis
Murr.
(field
pansy),
while
it
negatively
affected
the
abundance
of
other
species,
e.g.
A.
artemisiifolia
and
Setaria
pumila
(Poir.)
Schult.
(yellow
bristle
grass;
Table
3).
Both
high
and
low
N
inputs
were
preferred
by
species
previously
considered
nitrophilous
(Table
3).
This
might
be
attrib-
uted
to
the
fact
that,
although
N
is
important
for
plant
growth,
its
concentration
varies
during
the
growing
season,
and
even
nitrophilous
species
are
not
able
to
utilise
nitrogen
equally
from
all
N
compounds.
The
effect
of
fertiliser
N
on
weed
growth
is
also
dependent
on
the
timing
of
its
application
and
environmental
condi-
tions
(Sweeney
et
al.,
2008).
It
is
widely
accepted
that
fertilisers,
particularly
nitrogen,
have
large
impact
on
weed
species
composition,
they
can
influence
weed
crop
competition,
alter
weed
seedbank
density
and
even
affect
the
herbicide
susceptibility
of
certain
weed
species
(Cathcart
et
al.,
2004;
De
Cauwer
et
al.,
2010).
Crop
row
spacing
was
also
significant,
with
wider
spacing
favouring
C.
album
and
E.
crus-galli.
In
con-
trast,
other
species,
such
as
P.
rhoeas
and
Convolvulus
arvensis
L.
(field
bindweed)
were
likely
to
be
negatively
correlated
with
row
widths
(Table
3).
The
fi
rst
two
species
are
light
-demanding
summer
annuals,
favoured
by
abundant
space
to
thrive,
while
the
latter
species
are
seemingly
competitive
in
denser
crop
stands
as
well.
Wider
row
spacing
allows
the
use
of
cultivators
for
mechanical
weed
control
(Sarkany
et
al.,
2001),
but
narrow
row
spacing,
in
general,
can
increase
crop
competitiveness
and
lead
to
decreased
weed
growth,
as
recently
reported
in
organic
winter
wheat
(Drews
et
al.,
2009)
and
in
aerobic
rice
(Chauhan
&
Johnson,
2010).
In
accordance
with
recent
studies
of
Fried
et
al.
(2009)
and
Jose
-Maria
and
Sans
(2011),
our
research
also
showed
that
fi
eld
edges
were
more
species
-rich
than
fi
eld
centres.
This
is
most
likely
due
to
the
fact
that
edges
are
generally
less
affected
by
certain
management
factors,
like
herbicide
and
fertiliser
applications,
and
the
light
conditions
may
also
be
more
favourable
than
in
the
inner
parts
of
the
fi
elds
dominated
by
the
crop.
Soil
properties
and
climatic
conditions
We
found
soil
texture
to
be
the
third
most
important
variable
in
terms
of
net
effects
determining
the
compo-
sition
of
the
weed
communities
of
poppy
fi
elds.
Heavier
soils
were
typically
characterised
by
C.
album
and
E.
crus-galli,
whereas
P.
rhoeas
and
Consolida
regalis
S.
F.
Gray
(forking
larkspur)
usually
indicated
looser
soils
(Table
4).
Nevertheless,
these
species
are
not
typical
clay
and
sand
indicators,
which
might
be
a
consequence
of
the
fact
that
the
cropping
of
poppy
is
usually
concentrated
on
loamy
soils
and
it
is
not
cultivated
in
extremely
sandy
and
clay
soils.
Poppy
definitely
does
not
tolerate
heavy
soils
and
very
light
soils
are
not
ideal
either
(Meakin,
2007).
In
our
previous
investigations
of
cereal,
maize
and
sunflower
weed
fl
ora,
soil
texture
also
proved
to
be
very
important
(Pinke
et
al.,
2011b).
Even
though
several
former
studies
(Fried
et
al.,
2008;
Cimalova
&
Lososova,
2009;
Pinke
et
al.,
2010)
showed
soil
pH
to
be
also
highly
significant,
it
was
not
significant
in
this
study,
probably
because
of
the
narrow
pH
tolerance
of
poppy
(Meakin,
2007).
Our
results
indicated
that
soil
magnesium
content
was
also
significantly
associated
with
differences
in
the
weed
fl
ora
of
poppy
fi
elds.
In
this
case,
F.
convolvulus
and
Avena
fatua
L.
(wild
oat)
responded
more
strongly
to
high
Mg,
while
C.
album
and
P.
aviculare
were
associated
with
low
concentrations
(Table
4).
In
their
recent
study,
Andreasen
and
Skovgaard
(2009)
also
showed
that
these
soil
properties
influence
the
occur-
rence
of
some
species.
The
associations
of
weed
fl
ora
with
soil
Mg
are
likely
to
be
driven
by
complex
soil
chemical
interactions
with
plant
functions,
or
even
might
be
an
artefact
of
a
correlation
with
other
soil
properties,
which
require
further
study.
The
effect
of
soil
calcium
(CaCO
3
)
content
was
also
significant.
The
weed
species
that
most
strongly
responded
to
this
variable
were
P.
aviculare
and
M.
annua
preferring
high,
while
Hibiscus
trionum
L.
(flower
-of
-an
-hour)
and
P.
oleracea
preferring
low
con-
centrations
(Table
4).
This
element
is
beneficial
to
the
soil
structure
and
fertility
and
soil
pH
is
also
influenced
by
calcium
content.
Of
all
the
studied
climatic
factors,
it
was
only
mean
annual
temperature
that
exerted
significant
influence
on
weed
composition
in
our
study,
being
the
sixth
most
important
variable.
The
species
most
associated
with
higher
temperature
values
were
Sorghum
halepense
(L.)
Pers.
(Johnson
grass)
and
S.
viridis,
whereas
Tripleuro-
spermum
inodorum
(L.)
Sch.
Bip.
(scentless
mayweed)
and
M.
annua
favoured
lower
temperatures
(Table
4).
In
our
previous
investigations
of
the
late
summer
weed
fl
ora
of
Hungary,
temperature
was
the
second
and
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
Weed
species
composition
of
poppy
fi
elds
629
Table
4
Names,
fi
t
and
score
values
of
the
ten
species
giving
the
highest
fi
t
along
the
fi
rst
constrained
axis
in
the
partial
redundancy
analysis
models
of
the
significant
environmental
variables
specified
in
Table
2
Ax
1
score
Fit
Ax
1
score
Fit
Soil
texture
(+
heavy;
-
loose)
Soil
Mg
content
(+
high;
-
low)
Chenopodium
album
0.143
0.051
Fallopia
convolvulus
0.121
0.040
Echinochloa
crus-galli
0.107
0.039
Avena
fatua
0.094
0.077
Amaranthus
retroflexus
0.075
0.043
Cirsium
arvense
0.049
0.034
Euphorbia
falcata
0.063
0.053
Chenopodium
hybridum
0.045
0.023
Stachys
annua
0.052
0.050
Fumaria
schleicheri
-0.015
0.023
Conium
maculatum
0.044
0.037
Stachys
annua
-0.039
0.028
Persicaria
lapathifolia
0.033
0.032
Euphorbia
falcata
-0.057
0.044
Silene
alba
-0.041
0.044
Amaranthus
retroflexus
-0.062
0.029
Consolida
regalis
-0.089
0.070
Polygonum
aviculare
-0.104
0.028
Papaver
rhoeas
-0.178
0.040
Chenopodium
album
-0.219
0.120
Soil
CaCO
3
content
(+
high;
-
low)
Temperature
(+
high;
-
low)
Polygonum
aviculare
0.141
0.051
Sorghum
halepense
0.115
0.107
Mercurialis
annua
0.082
0.064
Setaria
viridis
0.087
0.044
Euphorbia
helioscopia
0.066
0.094
Bromus
sterilis
0.051
0.045
Euphorbia
falcata
0.048
0.031
Cynodon
dactylon
0.042
0.033
Artemisia
vulgaris
0.036
0.042
Lathyrus
tuberosus
0.026
0.042
Solanum
nigrum
0.026
0.035
Atriplex
patula
-0.027
0.037
Persicaria
lapathifolia
-0.039
0.046
Viola
arvensis
-0.053
0.039
Amaranthus
retroflexus
-0.060
0.028
Veronica
persica
-0.056
0.058
Portulaca
oleracea
-0.064
0.057
Mercurialis
annua
-0.139
0.182
Hibiscus
trionum
-0.078
0.045
Tripleurospermum
inodorum
-0.141
0.125
precipitation
the
fourth
most
important
explanatory
variable
(Pinke
et
al.,
2011b).
The
reason
for
the
relatively
low
importance
of
climatic
variables
during
this
study
is
most
likely
due
to
the
fact
that
poppy
is
cultivated
in
Hungary
only
in
regions
of
rather
similar
climatic
conditions.
Hence,
regions
with
more
precipi-
tation
and
cooler
temperatures
were
not
involved
in
this
study
and
shorter
climatic
gradients
generally
result
in
reduced
influence
of
the
respective
climatic
variables
(Cimalova
&
Lososova,
2009).
Conclusions
Among
the
ten
most
important
variables
determining
the
species
composition
of
the
surveyed
weed
vegetation
in
poppy
crops,
there
were
six
management
(sowing
season,
preceding
crop,
two
herbicides,
fertilizer
N,
row
spacing)
and
only
four
environmental
(temperature,
soil
texture,
soil
Mg
and
Ca
content)
ones.
This
latter
fact
can
be
attributed
to
the
constrained
but
complex
agro-
technical
treatments
and
to
the
narrow
ecological
tolerance
of
poppy,
resulting
in
short
environmental
gradients
in
ordinations.
Acknowledgements
This
work
was
supported
by
project
FVM
12.932/1/2009.
We
thank
Victoria
and
Malcolm
Kehoe
for
revising
our
English.
References
ANDREASEN
C
&
SKOVGAARD
IM
(2009)
Crop
and
soil
factors
of
importance
for
the
distribution
of
plant
species
on
arable
fi
elds
in
Denmark.
Agriculture,
Ecosystems
and
Environment
133,
61-67.
BALDWIN
BJ
(1977)
Chemical
weed
control
in
oil
-seed
poppy
(Papaver
somniferum).
Australian
Journal
of
Experimental
Agriculture
17,
837-841.
CATHCART
RJ.
CHANDLER
K
&
SWANTON
CJ
(2004)
Fertilizer
nitrogen
rate
and
the
response
of
weeds
to
herbicides.
Weed
Science
52,
291-296.
CHAUHAN
BS
&
JOHNSON
DE
(2010)
Implications
of
narrow
crop
row
spacing
and
delayed
Echinochloa
colona
and
Echinochloa
crus-galli
emergence
for
weed
growth
and
crop
yield
loss
in
aerobic
rice.
Field
Crops
Research
117,
177-182.
CIMALOVA
S
&
LososovA
Z
(2009)
Arable
weed
vegetation
of
the
northeastern
part
of
the
Czech
Republic:
effects
of
environmental
factors
on
species
composition.
Plant
Ecology
203,
45-57.
DE
CAUWER
B,
VAN
DEN
BERGE
K,
COUGNON
M,
BULCKE
R
&
REHEUL
D
(2010)
Weed
seedbank
responses
to
12
years
of
applications
of
composts,
animal
slurries
or
mineral
fertilisers.
Weed
Research
50,
425-435.
DREWS
S,
NEUHOFF
D
&
KOPKE
U
(2009)
Weed
suppression
ability
of
three
winter
wheat
varieties
at
different
row
spacing
under
organic
farming
conditions.
Weed
Research
49,
526-533.
FAOSTAT
(2009)
Food
and
Agriculture
Organization
of
the
United
Nations.
Available
at:
http://faostat.fao.org
(last
accessed
20
January
2011).
Fox
J
&
MONETTE
G
(1992)
Generalized
collinearity
diagnostics.
Journal
of
the
American
Statistical
Association
87,
178-183.
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630
630
G
Pinke
et
al.
FRIED
G,
NORTON
LR
&
REBOUD
X
(2008)
Environmental
and
management
factors
determining
weed
species
composition
and
diversity
in
France.
Agriculture,
Ecosystems
and
Envi-
ronment
128,
68-76.
FRIED
G,
PETIT
5,
DESSAINT
F
&
REBOUD
X
(2009)
Arable
weed
decline
in
Northern
France:
crop
edges
as
refugia
for
weed
conservation?
Biological
Conservation
142,
238-243.
HIJMANS
RJ,
CAMERON
SE,
PARRA
JL,
JONES
PG
&
JARVIS
A
(2005)
Very
high
resolution
interpolated
climate
surfaces
for
global
land
areas.
International
Journal
of
Climatology
25,
1965-1978.
HMS
(2001)
Magyarorszcig
Eghajlati
Atlasza.
Hungarian
Meteorological
Service,
Budapest,
Hungary.
HYVONEN
T,
GLEMNITZ
M,
RADICS
L
&
HOFFMANN
J
(2011)
Impact
of
climate
and
land
use
type
on
the
distribution
of
Finnish
casual
arable
weeds
in
Europe.
Weed
Research
51,
201-208.
JOSE
-MARIA
L
&
SANS
FX
(2011)
Weed
seedbanks
in
arable
fi
elds:
effects
of
management
practices
and
surrounding
landscape.
Weed
Research,
doi:
10.1111/j.1365
-
3180.2011.00872.x.
JURSiK
M,
JANKU
J,
HOLEC
J
&
SOUKUP
J
(2008)
Efficiency
and
selectivity
of
herbicide
Merlin
750
WG
(isoxaflutole)
in
relation
to
dose
and
precipitation
after
application.
Journal
of
Plant
Diseases
and
Protection,
Special
Issue
21,
555-560.
KUBNI
GS
&
TIWARI
PN
(2004)
Weed
management
in
opium
poppy
(Papaver
somniferum
L.).
Indian
Journal
of
Weed
Science
36,
104-107.
LEGENDRE
P
&
GALLAGHER
E
(2001)
Ecologically
meaningful
transformations
for
ordination
of
species
data.
Oecologia
129,
271-280.
LEp§
J
&
SMILAUER
P
(2003)
Multivariate
Analysis
of
Ecological
Data
Using
CANOCO.
Cambridge
University
Press,
Cambridge,
UK.
LososovA
Z,
CHYTRY
M,
CIMALOVA
S
et
al.
(2004)
Weed
vegetation
of
arable
land
in
Central
Europe:
gradients
of
diversity
and
species
composition.
Journal
of
Vegetation
Science
15,
415-422.
MAS
MT,
VERDO
AMC,
KRUK
BC,
DE
ABELLEYRA
D,
GUGLIELMINI
AC
&
SATORRE
EH
(2010)
Weed
communities
of
transgenic
glyphosate-tolerant
soyabean
crops
in
ex
-pasture
land
in
the
southern
Mesopotamic
Pampas
of
Argentina.
Weed
Research
50,
320-330.
MEAKIN
5
(2007)
Crops
for
Industry.
A
Practical
Guide
to
Non
-Food
and
Oilseed
Agriculture.
The
Crowood
Press,
Ramsbury,
UK.
NOVAK
R,
DANCZA
I,
SZENTEY
L
&
KARAMAN
J
(2009)
Arable
Weeds
of
Hungary.
Fifth
National
Weed
Survey
(2007-
2008).
Ministry
of
Agriculture
and
Rural
Development,
Budapest,
Hungary.
NURSE
RE,
HAMILL
AS,
SWANTON
CJ,
TARDIF
FJ
&
SIKKEMA
PH
(2010)
Weed
control
and
yield
response
to
mesotrione
in
maize
(Zea
mays).
Crop
Protection
29,
652-657.
PANNACCI
E
&
COVARELLI
G
(2009)
Efficacy
of
mesotrione
used
at
reduced
doses
for
post
-emergence
weed
control
in
maize
(Zea
mays
L.).
Crop
Protection
28,
57-61.
PINKE
G,
PAL
R
&
BOTTA-DUKAT
Z
(2010)
Effects
of
environmental
factors
on
weed
species
composition
of
cereal
and
stubble
fi
elds
in
western
Hungary.
Central
European
Journal
of
Biology
5,
283-292.
PINKE
G,
KARACSONY
P,
CZOCZ
B
&
BOTTA-DUKAT
Z
(2011a)
Environmental
and
land
-use
variables
determining
the
abundance
of
Ambrosia
artemisiifolia
in
arable
fi
elds
in
Hungary.
Preslia
83,
219-235.
PINKE
G,
KARACSONY
P,
CZOCZ
B,
BOTTA-DUKAT
Z
&
LENGYEL
A
(2011b)
The
influence
of
environment,
manage-
ment
and
site
context
on
species
composition
of
summer
arable
weed
vegetation
in
Hungary.
Applied
Vegetation
Science,
doi:
10.1111/j.1654-109X.2011.01158.x.
SARKANY
5,
BERNATH
J
&
TETENYI
P
(2001)
A
Mak
(Papaver
somniferum
L.).
Magyarorszag
kulturfloraja,
V/22.
Akade-
‘,
miai
Kiado,
Budapest,
Hungary.
‘,
SILC
U,
VRBNICANIN
5,
BOZIC
D,
CARNI
A
&
STEVANOVI
D
(2009)
Weed
vegetation
in
the
north-western
Balkans:
diversity
and
species
composition.
Weed
Research
49,
602-612.
SWEENEY
AE.
RENNER
KA,
LABOSKI
C
&
DAVIS
A
(2008)
Effect
of
fertilizer
nitrogen
on
weed
emergence
and
growth.
Weed
Science
56,
714-721.
ULBER
L,
STEINMANN
HH,
KLIMEK
S
&
ISSELSTEIN
J
(2009)
An
on
-farm
approach
to
investigate
the
impact
of
diversified
crop
rotations
on
weed
species
richness
and
composition
in
winter
wheat.
Weed
Research
49,
534-543.
WAGNER
HH
(2004)
Direct multi
-scale
ordination
with
canonical
correspondence
analysis.
Ecology
85,
342-351.
WHALEY
CM,
ARMEL
GR,
WILSON
HP
&
HINES
TE
(2006)
Comparison
of
mesotrione
combinations
with
standard
weed
control
programs
in
corn.
Weed
Technology
20,
605-611.
Worrowicz
M
&
Worrowicz
A
(2009)
Effectiveness
of
chemical
protection
against
weeds
applied
to
poppy
(Papaver
somniferum
L.).
Journal
of
Plant
Protection
Research
49,
209-215.
©
2011
The
Authors
Weed
Research
©
2011
European
Weed
Research
Society
Weed
Research
51,
621-630