DOI: 10.1111/j.1365-3180.2011.00860.x

Modelling rotations: can crop sequences explain arableweed seedbank abundance?

D A BOHAN*�, S J POWERS*, G CHAMPION�, A J HAUGHTON*, C HAWES§,G SQUIRE§, J CUSSANS* & S K MERTENS**Rothamsted Research, Harpenden, Hertfordshire, UK, �INRA, UMR 1210 Biologie et Gestion des Adventices, Dijon CEDEX, France,

�Broom�s Barn Research Station, Bury St Edmunds, Suffolk, UK, and §Scottish Crop Research Institute, Invergowrie, Dundee, UK

Received 15 July 2010

Revised version accepted 21 February 2011

Subject Editor: Jose Gonzalez-Andujar, CSIC, Spain

Summary

We investigated the effects of crop sequences on

monocotyledon, dicotyledon and total weed seedbank

abundance. Using seedbank data sampled from the

conventionally cropped part of the GB farm-scale

evaluations of genetically modified, herbicide-tolerant

(GMHT) crops, we asked whether it is possible to

identify crop sequence effects, to identify their duration

and to simplify crop sequences into crop management

classes with similar effects on weed seedbanks. This

work showed that it is possible to detect historical effects

of past crops, sown in sequence, on weed seedbanks for

up to 3 years and that crop sequences may be simplified

to crop management classes describing the season of

sowing, crop type and weed target for herbicide appli-

cation. Model estimates for the seedbanks were vali-

dated against an independent, follow-up seedbank data

set. The analysis provided abundance estimates that

ranged over 3 and 1.7 orders of magnitude for the

monocotyledon and dicotyledon weed seedbanks for

different crop sequences. This work yields a methodol-

ogy for estimating seedbank abundance in current crop

sequences, potentially allowing sequences to be identi-

fied that better reconcile the competing needs for weed

control to maintain crop productivity and the demand

for increased farmland biodiversity.

Keywords: crop sequence, model, rotation, sowing sea-

son, crop type, herbicide management, seedbank.

BOHAN DA, POWERS SJ, CHAMPION G, HAUGHTON AJ, HAWES C, SQUIRE G, CUSSANS J & MERTENS SK (2011).

Modelling rotations: can crop sequences explain arable weed seedbank abundance? Weed Research 51, 422–432.

Introduction

Analyses of national-scale arable farmland data sets

across Great Britain (GB) have shown that the

community composition of standing weed species

and weed functional types is strongly related to the

crop being grown, with the different crops tested

having statistically different weed compositions (Smith

et al., 2008). Indeed, the relatively low within-crop

variation observed for weed compositions, when com-

pared with the variation between crops, suggests that

all crops might have distinct weed compositions and

that this in-field composition would change through a

cropping sequence (Smith et al., 2008). One implica-

tion of these findings would be that the contribution

of seed to the weed seedbank is partly determined by

the sequence of crops grown. If so, it might sensibly

be asked: is the weed seedbank the result of a

historical accumulation of seeds produced by sequ-

ences of crops, each with their own associated

standing weed composition, and what is the duration

of effect of the crop sequence? It might also be asked

whether sequences that contain crops of similar type,

such as cereals, produce weed seedbanks that are more

Correspondence: David A. Bohan, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK.

Tel: (+44) 1582 763133; Fax: (+44) 1582 760981; E-mail: david.bohan@bbsrc.ac.uk; weed.dynamics@gmail.com

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Weed Research � 2011 European Weed Research Society Weed Research 51, 422–432

similar than the seedbanks produced from sequences

of other types of crop.

The cropping history of a particular field will

typically include a variety of crops. Strict rotations, of

prescribed sequences of crops, have been used for

ecological and hygiene reasons, such as weed and

disease control, or to improve soil fertility to support

economic production. For example, alternating years of

maize and soyabean is a typical �rotation� in central

areas of North America. Even within rotations, though,

crops may be varied for economic reasons, to take

advantage of a particular market or the availability of

subsidies. Modern agricultural management, such as

tillage and agro-chemical inputs, has reduced the

reliance on rotations for pest control and fertility and

led to more flexibility in the crops that may be grown in

sequence. In GB and across some of northern Europe,

cropping patterns may be especially flexible, with

farmers often making crop choices using information

on likely future commodity prices. For this paper, we

adopt the term �crop sequence� to cover both fixed

rotations and flexible sequences of crops.

Crop-specific weed compositions may arise from a

combination of effects of crop plants and their associ-

ated crop management practice. Direct effects occur

when the crop is a superior competitor for resources

such as light, water and nutrients (Harper, 1977). In

such cases, weed plant fitness will be negatively affected

through reduced recruitment from the seedbank, higher

plant mortality and lower fecundity (Christensen, 1995;

Mohler, 1996). Indirect effects of the crop on weed

plants are mediated through weed control strategies and

tillage practices implemented by farmers. Herbicide

management typically targets, and consequently selects

against, some weed species or groups whilst favouring

others (Zimdahl, 1999). At the most general level, a

division can be made between cereal (monocotyledon-

ous) and broad-leaved (dicotyledonous) crops. Because

of their differences in biochemistry, dicotyledon weeds

might be targeted in cereal crops, while, in broad-leaved

crops, monocotyledon weeds might be targeted. One

could hypothesise that cereal crops would tend to have

higher populations of grass weeds, while vegetables and

oilseed crops would have a higher proportion of broad-

leaved weeds. As a consequence of selective pressures of

crop competition and weed management and the ability

of weed seeds to survive (remain dormant) for a number

of years (Roberts & Feast, 1973), the abundance of weed

seeds and the species composition of the weed seedbank

may be expected to reflect past cropping history and

diversity (Ulber et al., 2009; Smith et al., 2010).

The effects of crop sequences on weed populations

have been studied theoretically, using population

dynamic models to evaluate �what-if� scenarios at

varying levels of complexity, from simple deterministic

models (Pino et al., 1998; Mertens et al., 2002; van den

Berg et al., 2010) through to more complex simulation

approaches (Colbach & Debaeke, 1998; Madsen et al.,

1999). Empirical evaluations of the effects of crop

sequences have been conducted using field experiments.

Typically, these are designed to examine the effects of

defined sequences of crops that reflect rotations used in

local farming situations (Bellinder et al., 2004; Teasdale

et al., 2004), often in combination with effects of tillage

methods, weed control intensity and nutrient inputs

(Blackshaw et al., 1994; Kegode et al., 1999; Legere &

Samson, 1999). These theoretical and empirical studies

have been useful for understanding particular crop

sequences and associated management practices, often

at the within- and between-field scales.

Making national-scale predictions for the effects of

crop sequences on weed populations and appropriate

simplification of crops, to groups or types with similar

effects on weeds, could have great value for scientific and

policy-making purposes. Simplification from the many

crops that might be used across arable agriculture to a

parsimonious number of amalgamated crop types shar-

ing similar effects on weeds would greatly reduce the

apparent complexity of the agro-ecosystem and ease

comparison of effects across cropping systems. Consis-

tent differences in crop sequence effects, across crop

types and at the national scale, would allow for the

identification of changes to cropping sequences that

achieve particular policy objectives for weeds.

Here, we develop a statistical model for the effect of

cropping sequence, in order to analyse a comprehensive

data set on cropping history and weed seedbank

abundance from 257 fields at the national scale, across

GB. For this model, we assume that the seedbank at the

start of the current year, prior to a crop being sown, is

dependent upon the crop grown the previous year, with

its associated management. It may also be dependent

upon the crop grown 2 years prior to the current year,

3 years prior to it and so on. Most importantly, though,

previous crops have less influence on the current

seedbank than the most recent crop. There is a clear

time-line running through this model that we wish to

test against data. We also consider whether similar crop

types, with similar weed managements, might have

similar effects on the weed seedbank, so that such types

may be amalgamated, by way of shared properties of

crop type and management, in the model. This means

that we approach the statistical fitting with an a priori

model structure, where previous crops are not fitted as

independent factors but in a nested fashion to allow

comparison of the full effect of complete 2- or 3- or

4-year, etc. crop sequences. We ask: (i) how many

cropping years, in sequence, are required before the

Crop sequence effects on seedbanks 423

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effect of an additional cropping year explains a non-

significant component of variation in the weed seedbank

and (ii) can this crop-specific model be simplified, by

amalgamation of specific crops, to factors of crop type

and management properties with no significant change

in the variation explained? We then test whether the

amalgamated model predictions are valid using a second

weed seedbank data set.

Materials and methods

Data

The data for the abundance of arable weed seedbanks

comes from the farm-scale evaluations (FSE) of genet-

ically modified, herbicide-tolerant (GMHT) crops

(Champion et al., 2003; Heard et al., 2003; Bohan et al.,

2005). This study sampled the seedbank in GMHT and

conventionally managed (current best practice for eco-

nomic weed control) halves of 66 spring-sown beet, 59

spring maize, 67 spring oilseed rape and 65 winter

oilseed rape field sites. Each field was sampled for one

cropping year (Firbank et al., 2003b), and the experi-

ment ran between 2000 and 2004 with c. one-third of the

fields being sampled each year. The fields were spread

across the geographical regions and conditions under

which these crops are commercially grown in the United

Kingdom (Champion et al., 2003; Bohan et al., 2005).

Seedbank samples were taken immediately prior to

sowing in the FSE experimental cropping year, t, and

immediately prior to sowing in the following cropping

year, t + 1, which hereafter are termed the �initial� and�follow-up� seedbank respectively. In each case, seed-

bank abundance was estimated by taking soil cores at

eight standardised locations in the GMHT and in the

conventional halves of each field. Approximately 2 L of

soil was removed, to a depth of 15 cm, at 2- and 32-m

sampling points along four transects running into the

crop around each half-field (Heard et al., 2003). The soil

was sieved to remove stones larger than 10 mm, and

about 1.2 L of the sample was placed in a plastic tray to

a depth of 40 mm. Identification to species and counting

of the seeds in the seedbank samples was carried out by

allowing the seeds to germinate from the soil samples

in an unheated glasshouse, for up to 18 weeks after

sampling, using the method described by Heard et al.

(2003). The volume (c. 10 L per half-field), depth of soil

and conditions of emergence were similar to those of

past studies (Heard et al., 2003). The seedbank counts

were then pooled to give an estimate of the seedbank for

each weed species in each half-field.

Only seedbank data from the conventionally man-

aged halves were used in the analyses presented in this

paper. The counts of weed species were summarised into

broad-leaved (dicotyledon), grass (monocotyledon) and

total weed classes.

Management information for each field site was

routinely gathered (Champion et al., 2003; Firbank

et al., 2003b; Bohan et al., 2005). Farmers were asked

to provide information on the crops grown within the

field, in sequence, for up to nine cropping years prior

to the commencement of the FSEs. Because of missing

cropping information, 36 of the 257 fields were not

included in the analysis. Each field site was assigned a

level of a factor SowingYear, denoting the FSE exper-

imental cropping year, t (2000 or 2001 or 2002). Each

site was also assigned a level of a factor (Zone) for one

of the six Environmental Zones of the Institute of

Terrestrial Ecology Land Classification of GB (Firbank

et al., 2003a) to describe the fundamental environmental

and geographical properties of each site. Four zones,

defined as the more southerly and easterly lowlands of

England and Wales (Zone 1), the more northerly and

westerly lowlands of England and Wales (Zone 2), the

uplands of England and Wales (Zone 3) and the

lowlands of Scotland (Zone 4), were represented.

Crop and management properties

We hypothesise that there are similarities between crops

and their management that can be used to categorise

specific crops and simplify (amalgamate) cropping

sequences. For example, grain-producing crops can be

described as cereals, and, owing to herbicide selectivity,

broad-leaved weeds are more likely to be affected by

weed control in these crops. This approach of catego-

rising crops with characteristic agronomic and manage-

ment properties was used in earlier analyses of FSE data

(see, for example Bohan et al., 2005 and Hawes et al.,

2009).

We use three factors to categorise crops and man-

agement, so that each crop is assigned a Type [Vegetable

(including roots), Cereal, Ley, Oilseed, Set-Aside or

Various], a Season of sowing (Spring, Winter or

Miscellaneous) and a predominant weed Target in that

crop (Grass, Broad-leaved, Both or None) for control

with herbicide (see Appendix S1). Thus, winter wheat

would be characterised as a �Cereal�, sown in the �Winter�with herbicide applications that would be predominantly

targeted at controlling �Broad-leaved� weeds. We chose

to use these factors, Season, Target and Type, because,

through discussion with agronomists and weed scien-

tists, these classifications were most often mentioned as

being a comprehensive yet simple classification of crops

and their management. It should be noted that the

Target weeds are determined by the crop Type, for

example, oilseeds are defined as always having grass

herbicides. However, Target weeds are shared between

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crop types, so that oilseed and vegetable crops both have

grass herbicides, thus allowing further useful amalgam-

ation.

Statistical analysis

The model is developed in three distinct stages. In the

first, we fit models to estimate the likely duration of

effect of crop sequences on each of the initial weed

(dicotyledon, monocotyledon and total) seedbanks.

Then, the resulting �full model� is simplified using the

crop and management factors to amalgamate crop

sequences. Finally, for particular amalgamated crop

sequences, the model-estimated initial seedbank is com-

pared with the model-estimated follow-up seedbank for

validation.

Stage 1: The duration of crop sequence effects

A linear, mixed modelling approach was used to

investigate the effects of crop sequence on the initial

weed seedbank. The seedbank data were log10-trans-

formed, with a constant of 0.5 added to adjust for

cases of zero weeds. The random terms consisted of the

factors Zone and SowingYear. The fixed terms consisted

of factors with levels being the crops grown in previous

years, represented as Crop(t ) x), where x is the number

of years before the start of the experiment in each field at

time t. These factors were fitted in chronological order

using a nested structure. The additional effect of

Crop(t ) 2), for example, was assessed by adding the

interaction term of Crop(t ) 2) with Crop(t ) 1). Thus,

we assume that recent crops and their associated

management have greater influence on current seedbank

numbers than crops grown further in the past. In

addition, the nesting gives a model structure for which

the effects of the crops are combined across the crop

sequence to give a single effect on seedbank abundance.

Hence, although we do not separate out the Crop(t ) x)

main effects, these effects are included in year interaction

terms when added in after the Crop(t ) 1) effect. The

form of the �full model� is as follows:

yðtÞij...qr ¼ aþ Zonei þ SowingYearj þ Cropðt � 1Þkþ ½Cropðt � 1Þ � Cropðt � 2Þ�kl þ . . .

þ ½Cropðt � 1Þ . . .Cropðt � xÞ�k...q þ eijk...qr

where the variate y(t) is the log10-transformed initial

seedbank count found in Zone i (i = 1, …, 4), for the jth

SowingYear (j = 1, 2, 3), for the kth Crop sown in year

t ) 1 (k = 1, …, 38) and so on to the qth crop sown in year

t ) x, and in the rth site (r = 1, …, 221), with � as residual

and a as a regression constant. Model fitting was carried out

using the method of residual maximum likelihood (REML)

(Patterson & Thompson, 1971) implemented in GenStat

(GenStat� (2004) version 7.2; Lawes Agricultural Trust,

Rothamsted Research, UK). The significance of previous

cropping years was assessed using a Wald test, implemented

as part of the REML modelling routine in GenStat (Welham

& Thompson, 1997). It provided Wald statistics and

corresponding P-values from the asymptotic Chi-squared

distribution on the degrees of freedom used from adding

in extra years and thus allowed an assessment of how many

years, x, of previous crops were required, with the addition

of a further year being non-significant. The residuals of

the selected model were then checked for the conformation

to the Normal distribution and for any systematic variation.

Modelling was performed separately for each of the broad-

leaved, grass and total counts.

Stage 2: Simplifying cropping sequences

We then wished to investigate whether crop types and

their associated management could be used to simplify

the full model, so that a model composed of fewer

parameters (factor levels) might be developed without

compromising explanatory power.

The crop and management categories (Appendix S1)

were used to form amalgamations for each crop in

sequence up to the duration of significant effect.

Amalgamations of different categories were constructed

so that, for example, the amalgamation of crops by

Season and Type might be tested against the amalgam-

ation by Season, Type and Target. The most complex

potential form of the amalgamation model is then:

yðtÞijkr ¼ aþ Zonei þ SowingYearj

þ SeasonTypeTargetk þ eijkr

where y(t)ijkr is again the log10-transformed initial seedbank

density in year t, a, Zonei (i = 1, …, 4) and SowingYearj(j = 1, 2, 3) are also as above and where SeasonTypeTargetkis the kth combination of the Season, Type and Target factor

levels.

The factors formed by the amalgamations were tested

in the models using the Wald test (Welham & Thomp-

son, 1997). For each amalgamation, the significance of

the change in deviance relative to that of the full

crop-specific model was assessed from the asymptotic

Chi-squared distribution on the change in degrees of

freedom. A non-significant change in deviance would

indicate that the model with amalgamation was not

statistically worse than the full model. Model checking,

as above, was again carried out.

Stage 3: Validation

The predictive value of the amalgamated model was

assessed by the comparison of the estimated initial

seedbank to the follow-up seedbank for levels of the

amalgamation factor (e.g. SeasonTypeTarget) pertaining

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to both initial and follow-up seedbanks. Hence, the

amalgamated model was fitted to the initial seedbank

numbers, y(t)ijk, and the follow-up seedbank counts,

y(t + 1)ijk, and 95% confidence intervals for corre-

sponding estimates were calculated. Plots of these fitted

y(t)ijk and y(t + 1)ijk values were then used for valida-

tion. A crop sequence estimate was considered valid if

the 95% confidence intervals of the corresponding initial

and follow-up seedbank values overlapped a line plotted

to reflect a 1:1 ratio between initial and follow-up

seedbank counts.

Results

The duration of crop sequence effects

Of the random effects, Zone was significant for the

dicotyledon (P = 0.016) and total (P = 0.032) initial

weed seedbank (Table 1). These seedbanks were esti-

mated to be higher in lowland Scotland (Zone 4) than

in easterly lowland England and Wales (Zone 1), with

intermediate values being found in westerly lowland

England and Wales (Zone 2) and upland England and

Wales (Zone 3). No significant zonal difference was

found for the monocotyledon (P = 0.140) initial weed

seedbank (Table 1), but Zone was retained in the

monocotyledon model for consistency with the other

weed groups. The SowingYear did not have an impor-

tant effect (P > 0.05) for any weed group analysed and

so was omitted from the models.

The fixed effects factors, relating to cropping history,

were found to have a significant effect for the

three previous crops, with the model terms Crop

(t ) 1), Crop(t ) 1) � Crop(t ) 2) and Crop(t ) 1) �

Crop(t ) 2) � Crop(t ) 3) being significant in one or all

weed groups. The respectiveP-values of these three terms

for the differentweed species groupswere 0.116, 0.034 and

0.044 formonocotyledons,<0.001, 0.119 and<0.001 for

dicotyledons and 0.039, 0.135 and 0.033 for total weeds.

Addition of the fourth crop year [i.e. the term Crop

(t ) 1) � Crop(t ) 2) � Crop(t ) 3) � Crop(t ) 4)] was

not significant in any of the three models (P = 0.14,

P = 0.66 and P = 0.49) for the monocotyledons, dicot-

yledons and total weeds respectively. The best crop-

specific model (i.e. �full model�) for all three weed groups

was therefore as follows:

yðtÞiklmn ¼ aþ Zonei þ Cropðt � 1Þk þ ½Cropðt � 1Þ� Cropðt � 2Þ�kl þ ½Cropðt � 1Þ � Cropðt � 2Þ� Cropðt � 3Þ�klm þ eiklmn

where the variate y(t) is the log-transformed initial

seedbank density found in Zone i, for the kth Crop sown

in year t ) 1 and so on, and in the nth field site, with �

residual variation, and where a is a regression constant. The

residuals from the models indicated good fits with estimates

of variation explained of 83%, 88% and 85% for the

monocotyledon, dicotyledon and total weeds respectively.

The number of parameters (factor levels) in the models

owing to the crop factors was 172, i.e. there were 172

different crop sequences found in the data set over 3 years

prior to the FSE experimental year t.

Simplifying cropping sequences

Models based on the amalgamation of the crop factors

into Season of sowing, and ⁄or crop Type, and ⁄or weedTarget were fitted for each weed group. The structure of

Table 1 Estimated initial seedbank [y(t)] by environmental zone (Zone). The number of fields (n) in each zone is presented alongside the

estimated seedbank on the log10 scale, with standard errors, and back-transformed seedbank counts per square metre

Seedbank classes by environmental zone

Number

of field

sites (n)

Estimated

seedbank,

y(t)

Standard error

of estimate

Back-transformed

seedbank

estimate m)2

Monocotyledon

Easterly lowland England and Wales (Zone 1) 135 1.44 0.05 502.69

Westerly lowland England and Wales (Zone 2) 98 1.60 0.06 733.86

Upland England and Wales (Zone 3) 2 1.62 0.20 777.38

Lowland Scotland (Zone 4) 16 1.80 0.12 1172.25

Dicotyledon

Easterly lowland England and Wales (Zone 1) 135 1.67 0.03 867.75

Westerly lowland England and Wales (Zone 2) 98 1.73 0.04 1003.31

Upland England and Wales (Zone 3) 2 1.72 0.13 966.00

Lowland Scotland (Zone 4) 16 1.90 0.08 1488.56

Total

Easterly lowland England and Wales (Zone 1) 135 1.95 0.03 1666.88

Westerly lowland England and Wales (Zone 2) 98 2.07 0.04 2206.88

Upland England and Wales (Zone 3) 2 2.06 0.15 2141.25

Lowland Scotland (Zone 4) 16 2.28 0.08 3560.63

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the best amalgamation model was different for the

dicotyledon seedbank from those of the monocotyledon

and total weed seedbank. The models for the monocot-

yledon and total weeds only required the factors Season

and Type (Table 2, Appendix S2) and recovered 66

degrees of freedom compared with the crop-specific

(full) model. The general form of the model for the

monocotyledon and total weed types is given as follows:

yðtÞijk ¼ aþ Zonei þ SeasonTypej þ eijk

where a is a constant, i = 1, …, 4 for the four environ-

mental zones in the FSEs, j = 1, …, 106 for the combina-

tions of Season by Type factors and where k indicates the

variable number of observations present for each ij combi-

nation. The Wald statistics for the monocotyledon and total

weed amalgamation models compared to the corresponding

crop-specific (full) models were not significantly different

(P = 0.098 and P = 0.217 respectively) indicating that

parsimony had been attained. The estimates of variation

accounted for by the amalgamation models were 55% and

62%, for the monocotyledon and total weeds respectively.

For the dicotyledon weed seedbank, the best model

used an amalgamation of all three factors: Season, Type

and Target, giving:

yðtÞijk ¼ aþ Zonei þ SeasonTypeTargetj þ eijk

where a is a constant, i = 1, …, 4 for the four environ-

mental zones, j = 1, …, 106 combinations of Season by

Type by Target factors and where k indicates the variable

number of observations present for the ij combinations. This

amalgamation did lose some explanatory power in compar-

ison with the crop-specific (full) model, with the Wald

statistic being significant on the 66 degrees of freedom

recovered (P = 0.010). The fit of the dicotyledon amal-

gamation model was otherwise good with 64% variation

being accounted for. As the model fitted the data well, we

retained it for examination alongside the amalgamation

models for the monocotyledon and total weed groups

(Table 2, Appendix S2). The residuals from all three models

revealed that the Normal distribution could be assumed for

the log10-transformed data with no evidence of systematic

variation.

Total, monocotyledon and dicotyledon weed seed-

bank abundances ranged over 1.6, 3 and 1.7 orders of

magnitude respectively (Table 2, Appendix S2). Crop

sequences including vegetable crops appeared to be

associated with low-to-medium monocotyledon seed-

banks. Spring- and winter-sown cereals tended to

produce low dicotyledon seedbanks and low-to-med-

ium-sized monocotyledon seedbanks. High monocoty-

ledon and dicotyledon seedbanks appeared to be

associated with spring-sown cropping, leys and oilseed

rape.

Validation

For those 3-year crop sequences that occurred in both

the initial and final seedbanks, the estimated numbers

of seeds were generally found to be not significantly

different, i.e. had overlapping 95% confidence intervals

(Fig. 1, Appendix S3). Of the 79 comparisons, compris-

ing 26 for the monocotyledon and total, and 27 for the

dicotyledon weed seedbanks, the 95% confidence inter-

vals suggested that only eight initial and final seedbank

estimates were significantly different. In all eight cases,

these differences were for comparisons estimated from

only one observation.

Discussion

Appraisal of the modelling

Our statistical model for the effects of crop sequences is

consistent with GB, national-scale patterns in arable

seedbank abundance. We found that the sequence of

crops grown in fields in the previous 3 years can explain

over 80% of the variation in weed seedbanks. The

addition of a fourth crop in the sequence did not explain

significant further variation (P > 0.05). Although the

ability to detect the effects of further years (i.e. five or

more) was limited by the relatively small amounts of

residual variability and few degrees of freedom remain-

ing following the fitting of models with 4 years of crops

(172 distinct crop sequences across 221 field sites being

used), the non-significance of the addition of a fourth

year provided a clear stopping point for all three weed

group models.

The analysis suggested that crop sequences could be

simplified into broad crop and management classes.

The seedbank variation accounted for by the amal-

gamated models fell to between 55% and 64%. Except

for the dicotyledon model, these reductions in

explained variance, of between 24% and 28%, were

not significant (P > 0.098) compared with the full

models. The simplified models are parsimonious

descriptions of a large proportion of the variance in

the weed seedbank. For the monocotyledon and total

seedbank, the full model could be amalgamated to a

3-year sequence of sowing season and crop type

management classes. For the dicotyledon seedbank,

this simple classification was not sufficient, and the

inclusion of a management class for the weed group

target for herbicide application in each crop was

necessary. Even so, the simplified description of the

crop sequence for dicotyledon weeds was statistically

(P = 0.010) poorer than the full model. However, the

amalgamated dicotyledon model was maintained for

consistency, because the description explained 64% of

Crop sequence effects on seedbanks 427

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Table 2 Estimated initial seedbank [y(t)], by weed group, for selected 3-year crop sequences encountered within the data set. The 3-year

triplet for each amalgamated management classification (Season: Spring, Winter or Miscellaneous; Type: Vegetable, Cereal, Ley,

Oilseed, Set-Aside or Various; Target: Grass, Broad-leaved, Both or None) should be read left to right, from year t ) 1 to year t ) 3,

with year t ) 1 being the most recent crop prior to the seedbank being modelled. The seedbanks are presented on the log10 scale, with

standard errors, and as back-transformed counts per square metre

Seedbank

Season

t ) 1, t ) 2, t ) 3

Type

t ) 1, t ) 2, t ) 3

Target

t ) 1, t ) 2, t ) 3

Number of

observations

(n)

Estimated

seedbank,

y(t)

Standard

error of

estimate

Back-transformed

seedbank estimate

per metre

Monocotyledon Spring, Spring,

Spring

Vegetable, Cereal,

Vegetable

2 1.09 0.43 221.30

Spring, Spring,

Spring

Cereal, Oilseed,

Cereal

7 1.13 0.23 243.56

Spring, Winter,

Misc

Vegetable, Cereal,

Set-Aside

2 1.15 0.43 255.48

Spring, Spring,

Spring

Cereal, Cereal,

Cereal

8 1.34 0.22 400.83

Winter, Winter,

Winter

Cereal, Cereal,

Cereal

20 1.57 0.14 687.25

Misc, Misc, Misc Ley, Ley, Ley 3 2.00 0.35 1865.63

Spring, Winter,

Spring

Oilseed, Cereal,

Vegetable

2 2.10 0.43 2351.11

Spring, Spring,

Spring

Oilseed, Oilseed,

Oilseed

2 2.26 0.43 3402.56

Dicotyledon Winter, Winter,

Spring

Cereal, Cereal,

Vegetable

Broad-leaved,

Broad-leaved,

Grass

11 1.38 0.11 440.41

Winter, Winter,

Winter

Cereal, Cereal,

Cereal

Broad-leaved,

Broad-leaved,

Broad-leaved

20 1.58 0.08 700.20

Spring, Winter,

Spring

Vegetable, Cereal,

Vegetable

Grass,

Broad-leaved,

Grass

4 1.58 0.18 701.84

Spring, Spring,

Spring

Cereal, Cereal,

Cereal

Broad-leaved,

Broad-leaved,

Broad-leaved

8 1.72 0.13 965.62

Spring, Spring,

Spring

Vegetable, Cereal,

Vegetable

Grass,

Broad-leaved,

Grass

2 1.73 0.25 995.24

Spring, Spring,

Spring

Cereal, Oilseed,

Cereal

Broad-leaved,

Grass,

Broad-leaved

7 1.87 0.13 1374.20

Misc, Misc, Misc Ley, Ley, Ley None, None,

None

3 1.89 0.20 1459.56

Spring, Spring,

Spring

Oilseed, Oilseed,

Oilseed

Grass, Grass,

Grass

2 2.26 0.25 3426.21

Total Winter, Winter,

Spring

Cereal, Cereal,

Vegetable

11 1.80 0.12 1173.67

Spring, Spring,

Spring

Cereal, Cereal,

Cereal

8 1.90 0.13 1479.99

Spring, Winter,

Winter

Vegetable, Cereal,

Cereal

4 1.92 0.18 1550.18

Winter, Winter,

Winter

Cereal, Cereal,

Cereal

20 1.95 0.09 1661.72

Spring, Spring,

Spring

Cereal, Oilseed,

Cereal

7 1.97 0.14 1740.48

Misc, Spring,

Winter

Set-Aside,

Vegetable, Cereal

2 2.26 0.25 3402.56

Misc, Misc, Misc Ley, Ley, Ley 3 2.28 0.21 3563.36

Spring, Spring,

Spring

Oilseed, Oilseed,

Oilseed

2 2.59 0.25 7285.22

428 D A Bohan et al.

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seedbank variability and because it was agriculturally

and ecologically easy to interpret.

Effects of zones and year

Although for simplicity we have tabulated weed seed-

bank numbers for a theoretical average zone, we found

that the estimates of crop sequence effects on the

seedbank differed between environmental zones. While

there was no significant (P = 0.140) effect on the

monocotyledon seedbank, by zone, all the seedbanks

showed higher seedbank estimates in lowland Scotland

(Zone 4) than in easterly lowland England and Wales

(Zone 1), with westerly and upland England and Wales

(Zones 2 & 3) having intermediate estimates. Seedbank

variability tended to be marginally lower in Zones 1 and

2 than in Zones 3 and 4. There were only two fields

in Zone 3, but Bohan et al. (2005) noted that a higher

proportion of field sites in Zone 4 received pre-

emergence herbicides in response to dicotyledon weed

abundance that could also affect monocotyledons. If this

variation in farmer behaviour is typical, then it might

suggest that lowland Scotland (Zone 4) would have

systematically higher variation in the weed seedbank in

comparison with Zones 1 and 2. The non-significance

(P > 0.05) of SowingYear indicates that it does not

matter which year was the FSE experimental one in

the models. This means that the models are simply

constructed of crop sequences (with environmental zone

effects), and the model estimates are general for all years

of experimentation (2000–2002) in this data set.

Validation

We assessed the validity of the crop sequence model

seedbank estimates, as predictions of seedbank abun-

dances, by comparing the model-estimated initial seed-

banks with model estimates from fitting the follow-up

seedbank data. This could only be done for crop

sequences that occurred in both the initial and follow-

up seedbank data. It was found that the initial and

follow-up seedbank estimates tallied well, with only 8 of

the 79 comparisons proving to have significantly differ-

ent values when comparing the 95% confidence intervals

(Fig. 1, Appendix S3). In all eight cases, these differ-

ences occurred where the follow-up seedbank estimate

was made from a low number of observations, suggest-

ing a lack of information as the reason for the

discrepancy. Where the replication was higher, there

was a better accordance between the initial and follow-

up seedbank model estimates. Therefore, the results of

this comparison would argue for the model estimates

being valid predictions of the seedbank at the end of

particular 3-year crop sequences. However, the results

warn that estimates ⁄predictions made from only a few

field observations should be adopted with some care.

The fits of the models and the validation would

support the expectation of Smith et al. (2008) that effects

on the seedbank would be more similar within a crop

type than between crop types, allowing for considerable

simplification in the way crop effects on the weed

seedbank are considered. So, in place of discussing

wheat and rye, and their perceived differences, both

crops can be as treated �cereals� with some validity. The

importance of season and crop type in both the

monocotyledon and dicotyledon seedbank models

would suggest that these management classes, in partic-

ular, have distinct effects on the seedbanks. The inclu-

sion of the weed target management class only in the

description of the dicotyledon seedbank would indicate

a systematic difference in the management of dicotyle-

don and monocotyledon plants. It might suggest that

Fig. 1 Estimates and 95% confidence intervals for the initial [y(t)] and follow-up [y(t + 1)] seedbank for corresponding 3-year crop

sequences (Appendix S3) using the amalgamated effects of crop history model, for: (A) the monocotyledon seedbank; (B) the dicotyledon

seedbank; and (C) the total seedbank. Closed symbols represent the comparisons where the initial and follow-up seedbank estimates

were not significantly different, and open symbols indicate comparisons where the initial and follow-up seedbank estimates differ

significantly, using the 95% confidence intervals.

Crop sequence effects on seedbanks 429

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Weed Research � 2011 European Weed Research Society Weed Research 51, 422–432

crop type and herbicide management are highly con-

founded for monocotyledons in a way that they are not

for dicotyledons. In essence, specific crops are used to

control monocotyledons, while dicotyledon seed num-

bers may be less influenced by being controlled in

specific crop types. Clearly though, an analysis of the

total seedbank would not well describe the dynamics of

the monocotyledon or dicotyledon weed seedbanks, and

these two need to be considered separately.

We note that the crop and management classes used

here are only three of the many alternative ways that

could have been investigated as amalgamation factors.

For example, the intensity of crop management could

have been considered, although this factor might be

difficult to assign. Our classes, using the factors crop

type, sowing season and target weeds, are comparatively

simple and relatively easy to assign. One caveat is that

the assignment of factor Target is to all fields growing a

particular crop type, when in reality herbicide manage-

ment will be carried out differently in each field.

Implications for weed management

The models produced total, monocotyledon and dicot-

yledon weed seedbank abundances that ranged over 1.6,

3 and 1.7 orders of magnitude, respectively, across all

field sites. We can use the model estimates of monocot-

yledon and dicotyledon seedbank abundances to suggest

the effects of different crop sequences on these seedbanks

(see Table 2 and Appendix S2). For example, we found

that sequences including vegetable crops appeared to

be associated with low-to-medium monocotyledon

seedbanks. Spring- and winter-sown cereals tended to

produce low dicotyledon seedbanks, as expected from

crop effects on the weed seedbank and seed return, but

also produced low-to-medium-sized monocotyledon

seedbanks. High monocotyledon and dicotyledon seed-

banks tended to be associated with spring-sown crop-

ping, and numbers were also particularly high in

sequences including leys and oilseed rape. Other studies

in Britain, using similar crop sequences, have shown that

higher weed densities tend to be associated with oilseed

rape and field beans (Cook et al., 1996; Ogilvy et al.,

1996). Similar trends were also suggested in data from

Canada (Blackshaw et al., 1994) and Switzerland (Streit

et al., 2000).

The seedbank estimates for the dicotyledons, mono-

cotyledons and totals may be dominated by a few

abundant species, such as Poa annua L. in the monocot-

yledon weed seedbank. The estimates might therefore

reflect the effects of cropping sequences on dominant

species, rather than the less abundant but pernicious

grass weeds commonly associated, for example, with

cereals (Wilson & Phipps, 1985; Cousens & Mortimer,

1995). However, these estimates suggest that different

3-year crop sequences have marked and consistent effects

on the abundance of the seedbank. The results of the

modelling might further imply that if 3 years is adequate

to predict weed seedbank abundance, then long-lived

seed might not make up a high proportion of the

seedbank. Although we model the abundance of mono-

cotyledon, dicotyledon and total seeds in the seedbank,

there is no reason to believe that this statistical method-

ology could not be gainfully applied to the seedbank

variation of individual weed species or functional groups.

It is important to note that the seedbank abundances and

standard errors we present are between-field estimates

for cropping practices across a large number of fields at

the national scale. Given that each crop in the sequence

was managed as the farmer normally would, this

statement would suggest that our seedbank predictions

should hold generally, at the landscape level.

Estimates for crop sequence effects on the weed

seedbank have clear practical application for the man-

agement of these seedbanks. Weeds have two clear roles

within arable farmland. Firstly, they perform an eco-

logically positive role, supporting wildlife at higher

trophic levels. Grass and broad-leaved weeds and weed

seeds provide shelter and food to a wide variety of

animals, including invertebrates, small mammals and

birds, and form the basis of farmland biodiversity

(Marshall et al., 2003). Secondly, they have an econom-

ically negative role. Farmland weed vegetation competes

directly with the crop, reducing crop yield. Farmers

attempt to decrease the negative impact on yield by

controlling weeds using a variety of methods, in partic-

ular the application of herbicides. Changes in crop

management practices, such as the intensification in

weed control using herbicides over the past 40 years

(Krebs et al., 1999; Robinson & Sutherland, 2002), are

now widely believed to have had a considerable impact

on weed germination and return of weed seeds to the

seedbank. These impacts have led to a decline in the

abundance and diversity of weeds in arable fields and to

long-term declines in the invertebrates, small mammals

and birds that rely upon them (Chamberlain et al., 2000;

Robinson & Sutherland, 2002). With the strategic goal

of arable faming in the UK being �food security� andenvironmental protection, our results might be used to

identify crop sequences that better reconcile the com-

peting needs for weed control to maintain or increase

crop productivity and conserve farmland biodiversity.

Acknowledgements

Rothamsted Research receives grant-aided support from

the Biotechnology and Biological Sciences Research

Council (BBSRC).

430 D A Bohan et al.

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Weed Research � 2011 European Weed Research Society Weed Research 51, 422–432

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Supporting information

Additional supporting information may be found in the

online version of this article.

Appendix S1 Farmers reported the cropping sequence in

each field for up to 9 years prior to the FSE experimen-

tal cropping year, t. The crop grown and the number of

times this crop occurred in any year of all crop

sequences (NC) are presented. NC might give a subjective

indication of the importance of a crop in GB cropping

sequences. Where two or more crops were reported, this

was because the field had been split during that year.

The crop and management factors, as used for crop

amalgamation, are also presented. The three factors are

the Season of sowing (Spring, Winter or Miscellaneous),

the Type of crop [Vegetable (including roots), Cereal,

Ley, Oilseed, Set-Aside or Various] and the weed-type

Target for herbicide control (Grass, Broad-leaved, None

or Both). The factor levels reflect what we believe to be

the predominant values appropriate to each crop, or set

of crops, in current, conventional management practice.

The factor level �Miscellaneous� was assigned to crops

grown either where the Season of sowing was not

reported by the farmer (as in the case of �Barley�) or

where the crops might be grown in either season (for

example �Forage Beans and Grass Ley�), or both. The

factor level �Various� was used where more than one

crop was grown, belonging to one or more different

Type. The factor level �Both� was assigned to a crop

where both types of weeds might be targeted, either

because the crop might allow this (such as in �set-aside�)or because more than one crop was sown in the field,

each of which having a different herbicide Target (such

as �Fodder Beet and Maize�).Appendix S2 Estimated initial seedbank [y(t)], by weed

group, for each of the 3-year rotations encountered

using the amalgamated crop sequences model. The

number of observed seedbanks (n), for each rotation,

is presented. The 3-year triplet for each management

classification should be read left to right, from year t ) 1

to year t ) 3, year t ) 1 being the most recent crop prior

to the seedbank being modelled. The seedbanks are

presented on the log10 scale, with standard errors, and as

back-transformed counts per square metre.

Appendix S3 Estimated initial [y(t)] and follow-up

[y(t + 1)] seedbank abundances, by weed group, for

each corresponding 3-year rotation encountered using

the amalgamated crop sequences model. The estimated

seedbanks are presented on the log10 scale, with 95%

confidence intervals along with the number of observed

seedbanks for each rotation. Initial and follow-up

seedbank estimates that differ significantly given the

95% confidence intervals are denoted by an asterisk.

The 3-year triplet for each management classification

should be read left to right, from year t ) 1 to year

t ) 3, year t ) 1 being the most recent crop prior to the

seedbank being modelled.

Please note: Wiley-Blackwell is not responsible for

the content or functionality of any supporting materials

supplied by the authors. Any queries (other than missing

material) should be directed to the corresponding author

for the article.

432 D A Bohan et al.

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Weed Research � 2011 European Weed Research Society Weed Research 51, 422–432