Download Weed seedbanks in arable fields

DOI: 10.1111/j.1365-3180.2011.00872.x
Weed seedbanks in arable fields: effects of
management practices and surrounding landscape
L JOSÉ-MARÍA & F X SANS
Departament de Biologia Vegetal, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
Received 13 January 2011
Revised version accepted 16 May 2011
Subject Editor: Matt Liebman, IA, USA
Summary
Weed seedbanks are a reserve of weed diversity and can
contribute to the prediction of future weed problems in
arable fields. Managing seedbanks should therefore help
in optimising biodiversity and controlling weed infestations. This study assessed the effects of management
system (organic vs. conventional) and landscape complexity on seedbank size and species richness at the edges
and centres of Mediterranean dryland cereal fields and
examines the relationship between specific management
practices and seedbanks. Field edges and organic fields
had more species-rich, denser seedbanks than field
centres and conventional fields, and landscape complexity had a limited effect on arable seedbanks. Accord-
ingly, the promotion of low-intensity farming practices
regardless of landscape complexity, especially at field
edges, would be an effective measure for conservation
purposes in Mediterranean agroecosystems. Nevertheless, the high seed density of organic seedbanks reveals
the need for more effective seedbank management. The
analysis of the effects of specific management practices
highlights the importance of cleaning crop seeds properly to reduce seedbank size and using complex rotations, especially as this tends to conserve species richness
while reducing seed abundance.
Keywords: crop edge, landscape complexity, land-use
intensity, organic farming, seedbank density, species
richness.
JOSÉ-MARÍA L & SANS FX (2011). Weed seedbanks in arable fields: effects of management practices and surrounding
landscape. Weed Research.
Introduction
Agricultural intensification is a process occurring at
various scales that has caused a decrease in biodiversity,
which may in turn negatively affect ecosystem services
(Matson et al., 1997). Intensification at the field scale is
related to the farming practices performed, such as
increasing the amount of external inputs (e.g. fertilisers
and pesticides), simplifying rotational schemes and
improving seed-cleaning procedures. At landscape scale,
agricultural intensification has caused a decrease in
landscape complexity, because of the aggregation of
fields and suppression of non-cultivated areas, which
leads to large, uniformly cropped areas with low spatial
heterogeneity and habitat diversity (Gabriel et al., 2006).
Nowadays, there is widespread interest in understanding the effects of agricultural intensification to
mitigate the loss of biodiversity. A focus on arable weed
communities is especially relevant, because they are
highly sensitive to agricultural intensification and support diversity of higher taxa (Marshall et al., 2003).
Moreover, besides their conservation value, arable
weeds offer many ecological and agronomic services,
such as nutrient recycling (Clergue et al., 2005). There
exists extensive knowledge regarding the effects of landuse intensity and landscape complexity on aboveground
weed flora (Gabriel et al., 2006; Gaba et al., 2010; JoséMarı́a et al., 2010), but fewer studies have addressed
their effects on arable soil seedbanks (though see
Roschewitz et al., 2005; Hawes et al., 2010).
Correspondence: Laura José-Marı́a, Departament de Biologia Vegetal, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 645,
08028 Barcelona, Spain. Tel: (+34) 93 4021471; Fax: (+34) 93 4112842; E-mail: [email protected]
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2 L José-Marı́a & F X Sans
The weed seedbank is a key component of arable
farming systems, because it plays many functional roles
in such systems (Franke et al., 2009). The arable
seedbank is a reserve of weed diversity and the primary
source of weeds in cultivated soils. Thus, it determines
the nature and extent of weed problems in future crops,
while it reflects the effects of past management practices
on weed population dynamics (Roberts & Chancellor,
1986; Légère et al., 2011). Seedbanks are therefore
considered a better indicator of medium and long-term
influences of management than aboveground vegetation
(Hawes et al., 2010), as the latter is more affected by
environmental stochasticity and management of a given
year and competition with the actual crop (Albrecht &
Pilgram, 1997).
To test the effects of land-use intensity on different
diversity components of the agroecosystem, most studies
have focused on the comparison of organic and conventional fields, and the majority show positive effects of
organic farming on diversity (Hole et al., 2005). These
comparisons assume that agricultural practices, and
consequently land-use intensity, differ clearly between
management systems (MS), being more intense under
conventional farming, and are quite homogeneous within each system. However, recent studies highlight that
there may be considerable heterogeneity in agricultural
practices and thus in land-use intensity within MS
(Armengot, 2010). Accordingly, organic farming can
also be very intense, for instance when rotations are
poorly implemented, weed control relies on many
mechanical operations and ⁄ or high amounts of fertilisers
are used. Hence, information on specific farming practices should be considered for a more accurate assessment of the effects of land-use intensity.
Regarding the effects of landscape complexity on
plant diversity, i.e. the area and spatial arrangement of
the surrounding non-crop habitats, previous research
has found that an increasing amount of natural habitats
surrounding the fields enhances weed diversity, because
they may act as a source of propagules for colonising
fields (e.g. Roschewitz et al., 2005). However, recent
studies have highlighted the fact that there is no effect of
surrounding landscape on aboveground weed species
richness in field centres (Marshall, 2009; Gaba et al.,
2010; José-Marı́a et al., 2010), which could be attributed
to the limited dispersal range of seeds from adjacent
habitats (Devlaeminck et al., 2005) and the higher
impact of management practices in the centre of the
fields, where farming practices (e.g. tillage, fertilisation
and weed control) are performed more effectively than
at the edges (Romero et al., 2008). Because seedbanks
are relatively buffered to the effects of specific management practices occurring during the sampling year, the
study of seedbanks at both edges and centres of arable
fields could help our understanding of the relationships
between landscape complexity and weed diversity.
We focused on seedbank species richness and density
of dryland Mediterranean cereal fields (i) to assess the
effects of within-field position (centre vs. edge), MS
(conventional vs. organic) and landscape complexity on
weed seedbanks and (ii) to gain in-depth knowledge
about the relationships between specific management
practices and the seedbank. To this end, seedbanks from
paired organic and conventional winter cereal fields
located in agricultural areas of Catalonia (NE of Spain),
differing in landscape complexity, were analysed. Furthermore, a thorough knowledge of agricultural practices of each selected field was obtained from interviews
with farmers. We hypothesised that wherever the intensification increases (i.e. in the crop centre, in conventional fields, in simple landscapes or when more intense
management practices are carried out regardless of MS),
the species richness and density of the seedbank would
be reduced.
To our knowledge, this is the first study that focuses
on weed seedbanks at a regional scale in a Mediterranean region, where the climate, characterised by low
rainfall and high year-to-year variation in water availability, affects cereal crop yields and competitive
interactions among plants (Liancourt et al., 2005).
Moreover, the study of seedbanks from a wide range
of commercial arable fields should provide valuable
information about their biodiversity and weediness. The
analyses of the importance of management factors
regardless of MS should disentangle the mechanisms
determining distribution patterns of diversity and plant
abundance in arable fields and thus provide crucial
knowledge to propose appropriate management that
will balance biodiversity conservation and weed control
purposes.
Materials and methods
Study area
Fifteen locations were selected in a dryland cereal
region situated in Central Catalonia, the NE Iberian
Peninsula (4124¢–4205¢N; 105¢–205¢E), with Mediterranean climate [mean annual precipitation range:
400–850 mm; temperatures: 11–14C (Ninyerola et al.,
2005)] and loamy and clayish soils. In each location, an
organic field and a conventional field were selected,
which were cropped with winter cereals (wheat: Triticum
aestivum L., or barley: Hordeum distichon L.) during
the period 2007–2008. Fields were chosen to minimise
differences in their area, perimeter and shape between
locations and within paired fields (José-Marı́a et al.,
2010).
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Weed seedbanks and agricultural intensity 3
The landscape complexity around each field was
characterised within a circular sector of 1-km radius
using Catalan Habitats Cartography (Carreras & Diego,
2004) produced at a 1:25 000 scale. We added the
proportions of arable fields, vineyards, almond and olive
groves and the associated human settlements, to compute the percentage cover of intensive land-use (PIL)
(José-Marı́a et al., 2010). The natural habitats surrounding the fields were: (i) woodlands, mainly pines, as well
as evergreen oaks and deciduous oaks, (ii) shrublands,
including stands of resprouting oaks and young pines,
(iii) perennial-dominated grasslands and (iv) riverine
vegetation. PIL was used as a surrogate of landscape
simplification because of its relationship with other
landscape descriptors. PIL is the complementary of the
percentage of natural habitats and displays, among
other things, a strong negative correlation with the
diversity of habitats and total patch density and a
positive correlation with intensive land-use patch aggregation (A. Romero, unpubl. obs.). PIL values of the
selected fields ranged from 19 % (the most complex
landscape) to 100 % (the simplest one).
converted to seedlings per m2 field surface to assess
seedbank density.
Seedbank sampling
Statistical analyses
Soil samples of the 30 selected fields were collected in
autumn 2008, after tillage operations for seedbed
preparation. In each field, two areas (hereafter positions) were delimited: the field edge, the first cultivated
metre adjacent to field boundary and the field centre,
20 m away from the edge. Four blocks were established
along the field perimeter, which consisted of two
1 · 10 m plots parallel to the boundary and placed in
each position. In each of these plots, eight soil cores
(2.9 cm diameter by 15 cm depth) were randomly taken
and were mixed to obtain a composite sample per plot
(2 positions · 4 blocks · 30 fields = 240 samples).
Samples were stored in a dark, cool room at 4C for
2 weeks to stimulate germination of weeds requiring
chilling. Afterwards, samples were put into
30 · 20 · 4 cm aluminium trays with 0.5 L of inert
substrate (perlite and vermiculite) to ease drainage.
Trays were placed on benches covered with a mosquito
net to prevent contamination of seeds in an unheated
glasshouse, where they were kept well watered and
under natural light for 13 months. Seedling censuses
were undertaken weekly, counting and removing identified species and transplanting seedlings for later
identification when necessary. Position of the trays
was randomised every 2 weeks, and soil samples were
mixed monthly to favour the germination of seeds.
Information of all sampling dates was considered to
obtain the number of weed species per sample, as well as
the number of seedlings emerged. The latter was
The variability in species number and seedling density
per sample (n = 240) was analysed using mixed models,
which account for non-independent errors that may
occur because of hierarchically nested designs, including
location and field nested within location as random
factors. To meet the assumptions of the models,
numbers of species were square-root transformed and
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Characterisation of farming practices
Farmers were interviewed in person to obtain information about their procedures during the last 5 years
(Table 1). From all information available, only those
variables differing among farmers which may affect weed
populations were considered for further analyses
(McLaughlin & Mineau, 1995; Hole et al., 2005; Gabriel
et al., 2006). These variables were: use of herbicides (H),
weed harrowing with long-flex spring tines (WH), mean
annual inputs of exogenous nitrogen (N, calculated by
means of local tables of nitrogen content), seed origin
(SO, purchase of commercially available seeds or reuse
of own seeds), crop species diversity (CD, number of
different plant-families cropped), farm type according to
its production orientation (FT, crop-specialised farms
solely involved with growing crops vs. mixed farms, also
including livestock), grazing after crop harvest (G) and
previous crop (PC, during the period 2006–2007).
Table 1 Farming characteristics of the selected fields (15 organic
and 15 conventional ones)
Conventional
Weed control
Herbicide use (H)
Weed harrowing (WH)
Farm type (FT)
Mixed farm
Crop-specialised farm
Nitrogen inputs (N)
Crop diversity (CD)
Seed origin (SO)
Purchase of seeds
Reuse of own seeds
Previous crop (PC):
cereal
Grazing after crop
harvest (G)
15 ⁄ 15
0 ⁄ 15
Organic
0 ⁄ 15
5 ⁄ 15
1 ⁄ 15
14 ⁄ 15
143.67 ± 20.809
1.37 ± 0.210
5 ⁄ 15
10 ⁄ 15
37.34 ± 10.804
3.27 ± 0.296
10 ⁄ 15
5 ⁄ 15
2 ⁄ 15
13 ⁄ 15
12 ⁄ 15
5 ⁄ 15
4 ⁄ 15
8 ⁄ 15
Mean ± SE of N (mean annual inputs of exogenous nitrogen in
kg ha)1) and CD (number of different plant-families cropped in
5 years). Weed control; FT, SO, PC and G are assessed by the
proportion of farmers using the stated practices.
4 L José-Marı́a & F X Sans
seedling densities were log transformed. After transformation, models had normally distributed residuals
(according to Shapiro–Wilk test of normality), they
were homoscedastic (checked by plotting residuals
against fitted values) and displayed good predictive
power (observed against fitted values).
We tested, as fixed factors, the effect of field position
(pos, field centre (C) vs. edge (E)) and, within each
position, the effect of management system (MSC and
MSE), landscape complexity (PILC and PILE) and their
interaction (MS · PILC and MS · PILE). This model
was implemented because the effects of MS (conventional vs. organic) and landscape complexity (PIL) are
not homogenous at the field edges and centres. After
fitting models by restricted maximum likelihood
(REML), the significance of the explanatory variables
was estimated using Markov Chain Monte Carlo
(MCMC) sampling from the posterior distribution of
parameters (Baayen et al., 2008). MCMC is a Bayesian
approach that computes confidence intervals for estimated model parameters and allows evaluating the fitted
models with respect to the stability of their parameters.
This approach takes the uncertainty in both fixed- and
random-effect parameters into account, capitalises on
the computational efficiency of frequentist approaches
and avoids the difficulties of estimating degrees of
freedom in mixed-effects models (Bolker et al., 2009).
We also evaluated the relationships between the
number of species and seedling density with all the
management variables: H, WH, N, SO, CD, FT, G and
PC. For this purpose, we used the methods described by
Burnham and Anderson (2002), which account for
potential problems of variable colinearity and avoid
the loss of explanatory power caused by dropped
variables (Graham, 2003). For each data set, 255 models
fitted by maximum likelihood (ML) with all possible
combinations of the explanatory variables were compared by AkaikeÕs information criterion corrected for
small sample sizes (AICc), which is a measure of relative
model fit. We calculated the size of information loss for
the various models, compared with the best model
estimated (Di = AICci ) AICcmin) and an Akaike
weight for each model (wi, the probability that a certain
model is the best model of those considered). Instead of
relying solely on the estimates of the best model, we
followed multimodel inference to analyse the effect of
each variable. From the smallest subset of AICc-ranked
models for which the sum of wi reached 0.95, we
averaged parameters and their standard errors weighted
by wi. We also computed the relative importance of each
variable by summing wi of those models containing that
variable. The sum of wi accounts for the probability that
this variable would be in the best approximating model
if we had collected the data again under identical
circumstances. Because poor predictors are not expected
to have selection probabilities close to zero, we also
computed the 95 % confidence intervals of the variables
to evaluate the significance of their contributions.
Statistical analysis was carried out using R 2.8.1 (R
Development Core Team, 2008) with packages lme4
(Bates et al., 2008) and languageR (Baayen, 2008) for
mixed models. All variables were coded to test the
effects of increasing levels of agricultural intensity;
levels of categorical variables were compared by
orthogonal contrasts, and continuous variables were
standardised to have a mean of zero and a standard
deviation of one.
Results
Seedbank communities: overview
In total, we counted 12 886 seedlings, corresponding to
173 species. In organic fields, we found 142 species, 125
at the edges and 83 in the centres, whereas in conventional fields, we found 123, 110 at the edges and 62 in the
centres.
The most frequent species in conventional fields,
recorded in more than 30 % of the samples, were
Papaver rhoeas L., Polygonum aviculare L., Lolium
rigidum Gaudin, Chenopodium vulvaria L. and Diplotaxis erucoides (L.) DC. These species were also among
the more common ones in organic fields, accompanied
by Chenopodium album L., Capsella bursa-pastoris (L.)
Medic., Veronica hederifolia L. and Kickxia spuria (L.)
Dumort. Regarding seedling density (Appendix 1),
P. rhoeas was the most abundant species in conventional
fields, followed by Euphorbia prostrata Ait., L. rigidum,
P. aviculare, Galium parisiense L. and D. erucoides
(627.7, 626.1, 583.5, 473.1, 416.3 and 395.8 seedlings m)2 respectively). In organic fields, P. rhoeas was
by far the most abundant species (5497.6 seedlings m)2),
followed by C. vulvaria, L. rigidum, C. bursa-pastoris,
P. aviculare and D. erucoides (824.8, 758.6, 758.6, 708.1
and 361.1 seedlings m)2 respectively).
Most species recorded (133 of 174) were present in
very few samples (£5 %); 15 of them were found
exclusively in the centres and 78 at the edges. Among
the latter, we recorded mainly species thriving in natural
habitats surrounding the fields (e.g. Plantago lanceolata
L., Alyssum alyssoides (L.) L., Rubus sp. and Poa
pratensis L.), but also some characteristic arable weed
species (e.g. Legousia hybrida (L.) Delarbre, Lathyrus
aphaca L., Lithospermum arvense L., Aphanes arvensis
L., Euphorbia falcata L. and Ranunculus arvensis L.),
whose populations have been extremely reduced owing
to an increase in agricultural intensification (Romero
et al., 2008; José-Marı́a et al., 2010).
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Weed seedbanks and agricultural intensity 5
Effects of field position, management system and
landscape complexity
Agricultural intensification affected the seedbank,
although its effects depended on the scale at which
intensification is considered and the response variable
analysed. Species richness and seedling density were
significantly higher at the field edges (where farming
practices are less intensively performed) than in the
centres (Fig. 1, Table 2). In each position, seedbanks
A
contained higher numbers of seeds and species in the
organic fields, which were less intensively managed than
conventional ones (see Table 1). Moreover, the effect of
MS for both response variables was more important in
the core of the fields than at the edges, as shown by their
greater estimates in the centre than at the edge (species
richness: MSC = )0.317, MSE = )0.188; seedling density: MSC = )0.628, MSE = )0.179). Landscape complexity appeared to be important only for species
richness at the edges (PILE = )0.199), favouring the
occurrence of more species in complex landscapes (less
intense) than in simple ones (more intense). We could
not detect any difference on the studied seedbanks
because of the interaction MS · PIL.
Importance of management practices for weed
seedbank
B
Fig. 1 Mean values ± SE per position in organic and conventional
samples of number of species (A) and seedling density (B).
Table 2 Estimate, P-value and 95 %
confidence interval (CI) of predictor
variables and direction of their effect for
species richness (A) and seedling density
(B) of soil seedbank
The best model for both species richness and seedling
density included the variables H and SO (Table 3), but
little differences in the AICc values with the competing
models encouraged us to use the multimodel approach
not to drop variables nor to lose information
(Table 4).
Species richness (Table 4A) was highly related both
to H and SO, as shown by their relative importance
values (over 0.7), and their similar and relatively large
effect sizes (based on model-averaged estimates, )0.215
and )0.195 respectively and confidence intervals, which
did not include 0). Thus, species richness was strongly
reduced when herbicides were used and when commercially available seeds were sown. FT, G and WH
were also important, although their smaller relative
Estimate
P-value
CI
Direction of effect
(A) Species richness
pos
)0.237
MSC
)0.317
MSE
)0.188
PILC
)0.079
PILE
)0.199
MS · PILC
0.051
MS · PILE
)0.088
0.000
0.000
0.005
0.409
0.044
0.416
0.182
)0.305,
)0.440,
)0.308,
)0.260,
)0.393,
)0.076,
)0.210,
)0.165
)0.193
)0.063
0.111
)0.022
0.178
0.044
C<E
con < org
con < org
(B) Seedling density
pos
)0.118
MSC
)0.628
MSE
)0.179
PILC
0.061
PILE
)0.127
MS · PILC
0.091
MS · PILE
)0.106
0.010
0.000
0.032
0.617
0.302
0.285
0.243
)0.210,
)0.806,
)0.353,
)0.172,
)0.377,
)0.086,
)0.275,
)0.033
)0.470
)0.015
0.310
0.115
0.267
0.075
C<E
con < org
con < org
Simple < complex
pos, position (C, centre; E, edge); MS, management system (con, conventional; org,
organic); PIL, percentage cover of intensive land-use (as proxy for landscape complexity).
Subscripts indicate the position within which the factors are evaluated.
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6 L José-Marı́a & F X Sans
H
SO
N
WH
PC
CD
G
(A) Species richness
x
x
x
x
x
x
x
x
x
x
x
x
x
(B) Seedling density
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
FT
x
x
x
x
x
x
x
x
AICc
Di
wi
479.66
481.02
481.16
481.18
481.39
481.66
0
1.36
1.49
1.52
1.73
2.00
0.064
0.033
0.031
0.030
0.027
0.024
586.20
587.02
587.28
587.47
587.49
587.73
587.85
587.96
587.96
0
0.81
1.07
1.26
1.28
1.52
1.64
1.75
1.75
0.055
0.037
0.032
0.029
0.029
0.026
0.024
0.023
0.023
Table 3 Models for which Di £ 2 for
species richness (A) and seedling density
(B) of soil seedbank
Results from information-theoretic-based model selection, x indicates variable inclusion in
each individual model: H, herbicide use; SO, seed origin; N, nitrogen inputs; WH, weed
harrowing; PC, previous crop; CD, crop diversity; G, grazing after crop harvest; FT, farm
type. AICc, AkaikeÕs information criterion corrected for small sample size; Di, the AICc
differences compared with the most parsimonious model; wi, Akaike weights (more details
in text).
Estimate
RI
CI
Direction of effect
(A) Species richness
Herbicide use (H)
Seed origin (SO)
Crop diversity (CD)
Farm type (FT)
Grazing (G)
Nitrogen inputs (N)
Weed harrowing (WH)
Previous crop (PC)
)0.215
)0.195
0.047
)0.072
)0.064
)0.045
)0.126
0.002
0.787
0.743
0.307
0.303
0.301
0.289
0.288
0.250
)0.367,
)0.330,
)0.029,
)0.139,
)0.112,
)0.094,
)0.236,
)0.032,
)0.064
)0.059
0.124
)0.006
)0.015
0.004
)0.015
0.036
Sprayed < unsprayed
Purchased < reused
(B) Seedling density
Seed origin
Herbicide use
Nitrogen inputs
Previous crop
Crop diversity
Weed harrowing
Farm type
Grazing
)0.317
)0.318
)0.154
0.104
0.115
)0.181
0.003
0.057
0.870
0.819
0.468
0.404
0.339
0.292
0.267
0.265
)0.522,
)0.551,
)0.257,
0.036,
0.015,
)0.328,
)0.077,
0.003,
)0.112
)0.086
)0.051
0.171
0.215
)0.033
0.082
0.112
Purchased < reused
Sprayed < unsprayed
High < low
Cereal > others
Low > high
Weeded < non-weeded
Table 4 Model-averaged estimate, relative
importance (RI) and 95 % confidence
interval (CI) of management variables and
direction of their effect for species richness
(A) and seedling density (B) of soil
seedbank
Crop-specialised < mixed
Non-grazed < grazed
Weeded < non-weeded
Non-grazed > grazed
Variables were coded to test the effects of increasing intensity in management practices. See
text for details.
importance values (c. 0.3) and estimates ()0.072, )0.064
and )0.126 respectively) indicated a weaker effect.
Accordingly, species richness was slightly reduced in
crop-specialised farms and non-grazed fields and by
mechanical weed control. The confidence intervals of
N, CD and PC, which include 0, prevent us from
making inference based on these effects.
Seedling density (Table 4B) was also highly reduced
when commercially available seeds were used and when
herbicides were applied (SO and H, relative importance
over 0.8; estimates: )0.317 and )0.318 respectively). N,
PC, CD and WH were also important, although to a
lesser extent (smaller relative importances and estimates). Fertilisation and WH negatively affected the
number of seedlings recorded (estimates: N = )0.154,
WH = )0.181), whereas a tendency to monoculture
increased the number of seedlings (PC = 0.104,
CD = 0.115). FT and G had very small regression
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Weed seedbanks and agricultural intensity 7
coefficients compared with the other variables, and
therefore, we do not consider them relevant for seedbank density.
Discussion
Limited effect of landscape on weed seedbank
Landscape complexity was an important factor shaping
seedbank species richness at field edges (Table 2). This
may be a result of the higher diversity of adjacent
habitats in complex areas (M. Bassa, unpubl. obs., JoséMarı́a et al., 2010), which would act as a source of
propagules for colonising field edges. In the centres,
landscape complexity did not enhance seedbank species
richness, as has been previously described for aboveground weed flora (Marshall, 2009; Gaba et al., 2010;
José-Marı́a et al., 2010). These results reflect that the
entrance of margin species towards the field centre is
very limited, because they have a short dispersal range
(Wilson & Aebischer, 1995; Devlaeminck et al., 2005)
and are not adapted to agricultural practices. Therefore,
and contrary to most arable weeds, most margin species
produce few seeds with short longevity, and hence, their
contribution to arable seedbank size is negligible, even at
the edges. Similarly, De la Fuente et al. (2010) found
that weeds were not enhanced in soyabean fields with
unsprayed field margins, where species growing in
uncultivated land are favoured but not arable weeds.
Hence, non-ruderal adjacent habitats would not act as a
problematic source of weeds (Marshall, 1989; Devlaeminck et al., 2005) and do not need to be managed in
such terms. These findings contrast with most farmersÕ
point of view, which are concerned about the negative
effects of weeds on crop yields and perceive margins as
sources of potential weeds and pests.
Seed distribution in conventional and organic
cereal fields
Organic farming in Mediterranean areas, which generally
displays lower levels of land-use intensity than conventional farming (Armengot, 2010), favours seedbank
species richness and density, as described previously in
other climatic areas (Roschewitz et al., 2005; Koocheki
et al., 2009; Hawes et al., 2010; Ryan et al., 2010).
Seedbank diversity and density were also enhanced at
field edges, where farming practices are less effectively
performed. Hence, seedbanks at the crop edges become a
refuge for a wide range of plant species, including
characteristic arable weeds whose conservation is a
growing concern (Romero et al., 2008; Fried et al., 2009).
Moreover, the positive effect of organic farming on
both seedbank diversity and density was larger in the
2011 The Authors
Weed Research 2011 European Weed Research Society Weed Research
centre than at the edges of the fields (Table 2). These
findings support the idea that organic fields have a more
homogenous distribution of weeds in relation to the
distance to the edge than conventional ones (Romero
et al., 2008).
In view of these results, we can state that the practices
associated with conventional production systems (especially herbicide use, Table 1) limit weed species diversity
and abundance more than the practices associated with
organic production (Ryan et al., 2010). In fact, herbicides were found to be among the main factors controlling arable seedbanks (see Table 4) and have strong
effects on reducing the weed pool of conventional soils,
because they limit both weed growth and seed production. Accordingly, limiting the use of herbicides seems
crucial for biodiversity conservation purposes in Mediterranean agroecosystems, particularly at crop edges,
where most weed species have their optimum (Marshall,
1989; Wilson & Aebischer, 1995).
Appropriate practices for weed seedbank
management
Seedbank management should balance the benefits of
greater biodiversity levels and the risks that weeds may
cause to crop yield, thus keeping seedbank densities
under appropriate thresholds to limit crop–weed competition but maintaining plant diversity. Therefore,
understanding the effects of different agricultural practices on seedbank size and species richness may help to
develop successful weed management strategies.
Seedbanks in organic fields not only had greater
values of diversity but also had a very high density of
seeds (Table 2, Fig. 1), especially among some problematic weeds such as Papaver rhoeas and Lolium rigidum
(Appendix 1). The high seedling densities reported
reflect the lack of appropriate weed management among
organic farmers, the majority of whom did not carry out
any mechanical weed control during the cropping period
(see Table 1). Moreover, weed harrowing with long-flex
spring tines had only a moderate negative effect on both
seedbank species richness and size (Table 4), which
supports the idea that mechanical weed control is less
effective than herbicides, particularly if fields are only
harrowed once (Ulber et al., 2009). Therefore, weed
harrowing was not a sufficient measure per se for
controlling weediness in organic fields, at least with the
frequency of current use.
Together with herbicide use, the main factor determining seedbank size was seed origin (Table 4B). Fields
sowed with commercially available seeds had much
lower seed densities than those reusing their own seeds,
which reflected a significant entry of weed seeds in the
seedbank when seeds used for sowing were not properly
8 L José-Marı́a & F X Sans
cleaned. Hence, farmers reusing their own seeds should
focus on improving their seed-cleaning procedures.
Mixed farms and field grazing were positively related
to species diversity without having an important effect
on seed densities, whereas complex crop rotations (as
determined by crop diversity and previous crop) did help
to reduce seed density without affecting species richness
(Table 4). Accordingly, these low-intensity practices,
which often co-occur, can be appropriate measures for
sustainable weed seedbank management, as they have
positive or neutral effects on seedbank species richness
and neutral or negative effects on seed density. In this
sense, previous studies had already identified the importance of crop rotations within low-input cropping
systems (Koocheki et al., 2009), because different agronomic practices under different crops (e.g. tillage and
harvesting regimes) may reduce opportunities for weed
growth and regeneration. Thus, complex crop rotations
help to control weed infestations, preventing any single
species from dominating (McLaughlin & Mineau, 1995).
Acknowledgements
We thank the farmers who allowed access to their land
and farm data. We are especially grateful to Montse
Bassa for her help with the interviews and field work,
José M. Blanco-Moreno for statistical advice and Laura
Armengot for providing useful discussions. Acknowledgements are also due to the Experimental Field
Services of the Biology Faculty and to Marta Sala for
their help at the glasshouse, and Joan Romanyà for his
help with nitrogen input data calculations. This research
was funded by the Spanish Ministry of Education and
Science (project CGL2009-13497-C02-01 and fellowship
to the first author) and by the Research section of the
Government of Catalonia (project 2009SGR1058).
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Appendix 1 Seedling density (seedlings m)2) by species in seedbank of edges (E) and centres (C) of organic and
conventional cereal fields. Only species recorded in more than 5% of the samples are listed
Organic
E
Amaranthus blitoides
S. Watson
Amaranthus retroflexus
L. subsp. retroflexus
Anagallis arvensis L.
Anacyclus clavatus
(Desf.) Pers.
Arenaria serpyllifolia L.
Avena sterilis L.
Capsella bursa-pastoris
(L.) Medic.
Centaurium pulchellum
(Swartz) Druce
Cerastium glomeratum
Thuill.
Chaenorhinum minus
(L.) Lange subsp. minus
Chenopodium album L.
Chenopodium vulvaria L.
Conyza sp.
Diplotaxis erucoides (L.) DC.
Euphorbia prostrata Ait.
Filago pyramidata L.
Galium parisiense L.
Heliotropium europaeum L.
Hordeum distichon L.
Hypericum perforatum L.
Juncus bufonius L.
Kickxia spuria (L.) Dumort.
Lactuca serriola L.
Lamium amplexicaule
L. subsp. amplexicaule
Lolium rigidum Gaudin
Conventional
C
E
C
6.3
41.0
22.1
22.1
104.1
182.9
138.8
145.1
72.5
82.0
22.1
37.8
141.9
69.4
41.0
–
50.5
22.1
211.3
59.9
44.2
1305.8
3.2
85.2
104.1
3.2
53.6
25.2
41.0
41.0
50.5
25.2
82.0
15.8
9.5
–
59.9
94.6
47.3
44.2
217.6
719.1
110.4
305.9
397.4
346.9
66.2
141.9
34.7
97.8
182.9
160.9
18.9
28.4
239.7
930.5
261.8
416.3
44.2
274.4
37.8
517.3
9.5
–
504.7
268.1
9.5
66.2
97.8
157.7
110.4
201.9
656.0
157.7
719.1
132.5
18.9
91.5
312.3
230.2
15.8
31.5
186.1
217.6
63.1
589.8
596.1
53.6
113.5
545.7
25.2
3.2
66.2
211.3
12.6
25.2
763.3
753.8
684.4
482.6
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Weed Research 2011 European Weed Research Society Weed Research
10 L José-Marı́a & F X Sans
Appendix 1 (Continued )
Organic
Medicago lupulina L.
Medicago polymorpha L.
Papaver hybridum L.
Papaver rhoeas L.
Phleum paniculatum Huds.
Polygonum aviculare L.
Portulaca oleracea L.
Samolus valerandi L.
Sonchus sp.
Veronica arvensis L.
Veronica hederifolia L.
Verbena officinalis L.
Veronica persica Poiret
in Lam.
Veronica polita Fries
Viola tricolor L. subsp.
arvensis (Murray) Gaud.
Conventional
E
C
E
C
50.5
135.6
15.8
4519.8
100.9
564.6
164.0
34.7
72.5
151.4
135.6
173.5
220.8
47.3
201.9
12.6
6475.3
37.8
851.6
230.2
6.3
22.1
18.9
328.0
182.9
126.2
12.6
12.6
15.8
829.5
12.6
343.8
50.5
233.4
167.2
132.5
25.2
154.5
160.9
15.8
–
12.6
425.8
–
602.4
97.8
41.0
37.8
–
3.2
123.0
31.5
72.5
18.9
44.2
31.5
28.4
50.5
12.6
25.2
2011 The Authors
Weed Research 2011 European Weed Research Society Weed Research