a modelling approach using seedbank and soil

8/12/2019 A Modelling Approach Using Seedbank and Soil

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A modelling approach using seedbank and soil

properties to predict the relative weed density in

organic fields of an Italian pre-alpine valley

S OTTO*, M C ZUIN*, G CHISTE` & G ZANIN**National Research Council (CNR), Institute of Agro-environmental and Forest Biology, Legnaro (PD), Italy, and Istituto Agrario San

Michele allAdige (ISMAA), San Michele allAdige (TN), Italy

Received 18 September 2006

Revised version accepted 27 March 2007

Summary

In 1996, a study was conducted on the seedbanks of a

pre-alpine valley in northern Italy which had beenorganically farmed since 1986. The seedbanks were

evaluated using soil cores taken from 16 organic fields

located at various altitudes and seed numbers were

determined using the seedling emergence method.

Thirteen soil properties were also evaluated. In 2003,

the germinable seedbank was assessed in five other fields

chosen at random. Soil properties were evaluated by the

same method as in 1996. Using the data of the first 16

fields as the analysis data set and those of the latter five

as an independent validation data set, a quadratic weed

seedbank-soil properties model was built with partial

least square regression analysis. The model estimates therelative abundance of the various species as the sum of

the contribution of individual soil properties and has a

high predictive capacity. With a novel graphic approach,

it is possible to describe the nonlinear relationship

between each soil property and weed species relativeabundance, giving a rational, quantitative, explanation

as to why some species are widespread (e.g. Chenopo-

dium album, Galinsoga parviflora and Chenopodium

polyspermum), whereas others tend to concentrate in

specific fields (e.g.Spergula arvensis). The approach can,

when some hypotheses hold, give a rational basis for the

explanation of the relative abundance of species in a

weed community and constitutes a useful methodology

for study and research.

Keywords: seedbank, germinable seed, soil proper-

ties, quadratic model, partial least square regression

analysis.

OTTOS, ZUINMC, CHISTE` G & ZANING (2007). A modelling approach using seedbank and soil properties to predict

the relative weed density in organic fields of an Italian pre-alpine valley. Weed Research 47, 311326.

Introduction

The interest in linking soil quality and weed manage-

ment derives from the belief that greater understanding

of key soilweed relationships will lead to the design ofagro-ecosystems with greater capacity and opportunity

to suppress weeds (Gallandt et al., 1999). There is great

interest in predicting the presence and spatial distribu-

tion of species from studies of the soil properties

(Streibig et al., 1984; Andreasen et al., 1991; Milberg

& Hallgren, 2000). Soil properties and weed abundance

are known to vary spatially in agricultural fields and

landscapes. However, the mechanisms giving rise to

spatial heterogeneity of weeds are poorly understood.

Only recently have the weed management implications

of precision agriculture increased interest in this topic,

with the purpose of evaluating whether the abundance

of weeds are consistently associated with a variety of siteproperties (Walter et al., 2002). These authors stated

that the conclusions of other studies must be noted with

caution because of the different methods used, for

example Hausler and Nordmeyer (1995) reported that

the distribution ofPolygonum amphibiumL. was similar

to the distribution of high soil phosphorus concentration

and clay content and low sand content. However, the

distribution ofVeronica hederifoliaL. was similar to that

Correspondence: S Otto, National Research Council (CNR), Institute of Agro-environmental and Forest Biology, Via dellUniversita` , 16 - 35020

Legnaro (PD), Italy. Tel: (+39) 498272884; Fax: (+39) 498272818; E-mail: [email protected]

2007 The Authors

Journal Compilation 2007 European Weed Research Society Weed Research 47, 311326


2/16

of sand content. Nordmeyer and Dunker (1999) found

significant correlations between Viola arvensis Murr.

and pH, phosphorus and magnesium contents. They

also found correlations betweenStellaria mediaL. (Vill.)

and organic matter, phosphorus and magnesium con-

tents, amongst others.

Recent multivariate analysis techniques (Kenkelet al., 2002) allow the qualitative relationships between

abundance of species and soil properties to be identi-

fied, provided that the number of variables does not

exceed the number of observations. A quantitative

study of such relationships, for a data set with many

variables but few objects, is possible using partial least

square (PLS) regression analysis (de Jong, 1993;

Rannar et al., 1994).

The aim of this study was to determine whether weed

seedbank composition could be predicted from soil

properties. For this, a model was constructed that links

weed seedbank composition and soil properties (analysisdata set) using a specific regression analysis. An external

validation was performed with an independent data set

of germinable seedbank composition (validation data

set). A method to represent graphically the quantitative

relationships between soil properties and the relative

abundance of weed species is then suggested.

Materials and methods

Site information

The Val di Gresta is situated in north-eastern Italy. It isa small valley (approx. 3000 ha), 4001300 m a.s.l.,

between Lake Garda and the River Adige. Average

annual rainfall (over a period of 20 years) is 1192 mm.

Average maximum and minimum temperatures are

13.6C and 4.5C, respectively, with high daynight

temperature fluctuations.

The soils lack or have very little gravel, are fairly deep

and easily tilled, with variable organic matter content

(2565 g kg)1) and cation exchange capacity. The pH

also varies (4.68.3), even on a local scale, and the most

uniform values are found at low altitudes, while the

lowest values are found at high altitudes, probably dueto accentuated leaching phenomena.

Since the mid-1980s, horticultural produce has been

grown on an ever-increasing area using organic farming

techniques. The typical crops are autumn-harvested

long-storage vegetables. Organically farmed crops cur-

rently cover more than half the arable land in the valley,

amounting to approximately 150 ha. Weed management

and cultivation practices (harrowing and inter-row

hoeing) currently form the basis of weed control in the

organically farmed fields. Thermal weeders are less

widely used. Important weed suppression methods are

cover crops in combination with one or two stale

seedbed preparations. Rotation is the primary means for

maintaining soil fertility and achieving weed, insect pest

and disease control. The type of tillage used in the

previous year, preceding crop, altitude and coding of the

16 fields are reported in Table 1.

The model

We developed a model of weed seedbank composition

(Yi, dependent variables or response) and soil properties

(Xj, independent variables or predictors) using a PLS

polynomial regression, a technique that can explain one

or more Y given one or more X, even with a small

number of objects (observations, that are the rows of the

matrices):

Y1; Y2; . . . YnfX1;X2. . .Xm Error 1

where Ys are weed species relative abundance in a certainfield (percentage of total weed number), and together

constitute the Y-matrix. The Xs are the soil properties in

the fields and together constitute the X-matrix. The Ys and

Xs constitute the analysis set.

The PLS algorithm, originally proposed by Wold

et al. (1984), can analyse data with strongly collinear,

noisy and numerous Xs (Wei et al., 2007) and works by

breaking down the X-matrix as the product of two

smaller matrices, much like principal component analy-

sis (PCA): (i) the loading matrix, which contains a few

vectors (the so-called latent variables, LVs) obtained as

linear combinations of the original Xs; (ii) the scorematrix, which contains information on the objects,

described in terms of LVs, instead of the original

variables. The main difference is that PCA obtains the

principal components that represent at best the structure

of theX-matrix, whereas PLS obtains the LVs under two

constraints: (i) they must represent the structure of the

X-matrix and Y-matrix; (ii) they must maximise

the fitting between the Xs and Ys. More precisely, with

the LVs, the original Xs are transformed to a set of

x-scores (as in PCA). Similarly, the Ys are used to define

another set of components known as the y-scores. The

x-scores are then used to predict the y-scores, which inturn are used to predict the response variables with a

multistage process. An alternative approach could be

univariate multiple regression, but in this case the focus

is for the best fit of each Yi independently, or the

multiple linear regression. However, this technique

works well when the number of objects is much larger

than the number of variables and when the Xs are

independent and uncorrelated.

In this study a quadratic function f is used, which is

flexible and can approximate linear, exponential

and asymptotic relationships. Therefore, given m soil

312 S Ottoet al.

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properties, the relative abundance (Yi) of thei-th species

is calculated as:

Yi I a11 X1a12 X21 a21 X2a22 X

22

. . . am1 Xm am2 X2m Error

2

whereIis the intercept andaijare regression coefficients; in

our model we set I= 0. The algorithm used in our study

assumes that the Xs and Ys have been normalised,

separately for the analysis and the validation set, and was

not constrained so that the sum of all Yvariables must be

less than or equal to 100%. For each species of then species

in the analysis set an R2 value is calculable:

R2i 1

Pni1

Yi Yi2

Pn

i1Yi Y

2

2664

3775

3

where Yi=observed abundance, Yiis the calculated abun-

dance and Y is the observed mean abundance. The average

value ofR2 is then:

Average R2Y X13i1

R2i=n 4

When too many LVs are included in the model the

averageR2 (Y) will increase, but a serious overfit will result

and the model will have little or no predictive capacity for

an independent data set of observations (validation set). It is

then necessary to test the predictive capacity of the model

taking different numbers of LVs into account. For this the

Predicted Sum of Squares Statistic (PRESS) is used. The

value of PRESS for each species iin the validation set is:

PRESSi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXvi1

YiN YiN2=v1

s 5

whereYiNand Y iNare the observed and calculated relative

abundance in normalised units, and v the number of objects

in the validation set. Then the overall PRESS of a set ofn

species is:

PRESSXni1

PRESSi=n 6

The PLS regression with a second-degree polynomial

model was performed with STATISTICA (StatSoft Inc.,

2005). For each n weed species, the PLS regression

calculates two regression coefficients (of first order, C1,

and quadratic, C2) for each of m 13 soil properties, for atotal of (n*m*2) coefficients. Because of the quadratic

model between predictors (Xs) and responses (Ys) (Eqn 8),

immediate interpretation of the effect of the regression

coefficients could be difficult. The relationships between

each soil property and the relative density of each weed

species included in the model become clear when proper

double normalised graphs are used. Obviously the validity

of relationships is restricted to the range of the data set used.

In such a graph, the normalised relative density of a species

(y axis) is plotted against the normalised values of the

various soil properties (x axis) of the analysis set.

Table 1 Sampling year, preceding crop and tillage, altitude, field code in the 16 fields of the analysis set (A) and five fields of the validation

set (V). Fields sorted by ascending altitude

Sampling

year Preceding crop Preceding tillage

Altitude

(m a.s.l.)

Field

code Set

1996 Carrot Ploughing + roto-tillage 530 A0530 A

1996 Cabbage Ploughing 580 A0580 A

1996 Carrot Ploughing 610 A0610 A1996 Leek No tillage 660 A0660 A

1996 Savoy cabbage leek No tillage 800 A0800 A

1996 Potato Ploughing + roto-tillage 830 A0830 A

1996 Field pumpkin Roto-tillage 840 A0840 A

1996 Carrot No tillage 850 A0850 A

1996 Chicory No tillage 1000 A1000 A

1996 Carrot Ploughing + roto-tillage 1010 A1010 A

1996 Potato No till + manure 1030 A1030 A

1996 Carrot Ploughing 1060 A1060 A

1996 Celery No tillage 1180 A1180 A

1996 Potato No tillage 1200 A1200 A

1996 Cauliflower potatoleek No tillage 1220 A1220 A

1996 Cabbage Roto-tillage 1260 A1260 A

2003 Field pumpkin Roto-tillage 750 V0750 V


2003 Carrot Roto-tillage 900 V0900 V


2003 Cabbage Roto-tillage 1160 V1160 V

A weed seedbanksoil properties model 313

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The normalised values (XN) are calculated from the original

values (X) as:

XN XMso=Sso 7

whereMsois the mean andSsothe standard deviation of the

soil properties in the analysis set.

Furthermore, the calculated relative density of eachspecies can be obtained as a sum of the partial relative

density of this species as established by the contribution

of the single soil properties. Let X be a value of a soil

properties and XN the normalised value, let C1 and C2

be the regression coefficients of first and second order

for a species: then the abundance of the species (in

normalised units, YN) as a consequence of the soil

properties is given by the following quadratic equation:

YNC1XNC2 X2

N11=u 8

whereu is the number of objects in the analysis set, and the

term 1u is included in the PLS algorithm to take into

account the size of the analysis set (Wold et al., 1989). This

relative density can be interpreted as the contribution of the

soil properties to the total relative density, that is then given

by the sum of all partial abundance referable to all soil

properties. The total relative density in original units is then

calculated as:

Y YN Ssp Msp 9

whereMspis the mean andSspthe standard deviation of the

relative density of the species in the analysis set.

Soil sampling and analysis

At the end of winter 1996, 25 soil samples were taken

from each of the selected fields with a core sampler 7 cm

in diameter by 25 cm depth, this number being consid-

ered sufficient for estimating the semi-quantitative com-

position of the seedbank (Dessaint et al., 1996). Fields

consisted of uniform plots 300-900 m2. In 1996 and

2003, another 10 soil samples were taken from each field,

bulked and analysed using the Italian standard methods

for soil properties (Anonymous, 1999).

Evaluation of seedbank and germinable seedbank

The methods used to evaluate the flora differed in 1996

and 2003: the seedbank was evaluated in 1996, the

germinable seedbank in 2003.

For the 1996 soil samples, seedbank evaluation was

done according to the seedling emergence method (Zanin

et al., 1989). The individual cores were arranged singly on

plastic trays. Seedlings were identified weekly and coun-

ted by species. The experiment, conducted in a tempera-

ture-controlled greenhouse, lasted for 18 months.

The seedlings from each core were summed and the

seedbank was expressed as number of seeds m)2. The

1996 data set, e.g. soil seedbank in 16 fields estimated with

the seedling emergence method, has been used as analysis

setin the PLS regression model.

In 2003, the germinable seedbank was monitored in

five sites within three permanently marked small plots of1.0 m2 each, where, once or twice a week from March to

October, weed seedlings were counted and removed. The

method is analogous to that used by Zhang et al.(1998)

in a similar study. Mickelson and Stougaard (2003)

showed that in demographic research the use of perma-

nently marked small plots is advantageous, because

increasing the proportion of total area sampled

improves precision. The sampled surface in 2003 was

0.331.00% of the total field area. These same percent-

ages could be obtained taking 195 soil cores with a 7 cm

diameter core sampler. Under the assumption that

germinable seedbank composition is a direct conse-quence of the soil seedbank, the 2003 data set,

e.g. number of seedlings counted in the permanently

marked small plots in five fields, was used as validation

set in the PLS regression model.

On the basis of information and direct observations

on crop management, we assume that the time lag

between the two data sets (7 years) did not affect the

link, if it exists, between seedbank and germinable

seedbank, because (i) herbicides are not used in the

selected fields; (ii) there is no irrigation; (iii) crop

rotations are very varied; and (iv) general crop practices

are unchanged. The selection pressure of crop manage-ment in those fields had been generally low and similar

for all weed species. Finally, we hypothesised that in

fields with this type of crop rotation and management,

the seedbank is the result of various seed rains after

different crop species(Beuret, 1984; Trresen & Skuterud,

2002), so there is no strong effect due to the last crop

preceding the soil sampling. All the conclusions we draw

are valid, if these hypotheses are true.

The importance of each weed species within the

seedbank community can be expressed by its relative

abundance index (RAI), which is computed as follows: let

C the counts of fields where a species has been found,Nthe total number of fields (16), then CNis the absolute

frequency (AF). Given that 95 species are found, then 95

AF are calculable, and their sum is the total absolute

frequency (TAF). The relative frequency (RF) is given by

AF TAF. The relative density (RD) is the number of

seedlings of a species as a fraction of total seedlings

number, then RAI: (RD+RF) 2. The RAI accounts for

both species density and pattern, thus limiting problems

arising from weed patchiness (Derksen et al., 1993). The

weed species nomenclature presented in the results

follows Flora Europea (Tutin et al., 19641983).

314 S Ottoet al.

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Results

Soil characteristics

The main physico-chemical characteristics of the 21

sampled fields were very variable (Table 2). 73.8 percent

of values of the validation set fall within the range of theanalysis set.

Flora characteristics

Seedbank (Analysis set)

A total of 95 weed species were counted in the 16

sampled fields of the analysis set. The number of species

per field ranged from 25 to 41, with an average of

32.9 5.11 (standard deviation). Among these spe-

cies, eight were found in at least 14 fields and three

[Chenopodium album, S. media, Capsella bursa-pastoris

(L.) Medik.] were common to all fields, whereas 14 wererestricted to only one field (rare species).

The total number of seeds ranged from 3473 to 19760

seeds m)2, with an average of 9370 4878. There was

wide variability, both within and between altitudes: for

example, 3473 seeds m)2 were found in field A0850 and

almost three times that (9921 seeds m)2) in A0840. The

highest seedbank was 19760 seeds m)2 (A1060), equal to

six times the poorest field (A0850). Neither altitude nortillage effects were remarkable.

Germinable seedbank (Validation set)

A total of 49 weed species were counted in the five fields of

the validation set. The number of species per field ranged

from 20 to 30 averaging 26.6 4.22. Among these

species, 27 were found in at least three fields and 10

(C. album, Chenopodium polyspermum L., Galinsoga

parvifloraCav.,Amaranthus spp. and others) were com-

mon to all fields, whereas 17 were restricted to only one

field [rarespecies, e.g.Digitaria sanguinalis(L.) Scop.].

The total number of weeds ranged from 476 to 1759seeds m)2, with an average of 1210 612. Half of the

Table 2 Soil properties (X-matrix) in the 16 fields of the analysis set (A) and 5 fields of the validation set (V)

Set Field SA* SI CL OM pH CaCO3 P2O5 K2O CaO MgO B CEC CaCO3act

A A0530 500 260 240 34.7 8.00 139 72 468 12.32 760 0.385 17.0 79

A A0580 520 260 220 48.3 7.85 1008 37 117 13.44 480 0.265 19.5 81

A A0610 440 280 280 56.7 7.80 304 101 420 12.04 500 0.395 20.7 78

A A0660 480 280 240 46.2 7.50 64 89 245 11.48 540 0.400 19.7 54

A A0800 320 300 380 54.8 7.55 105 119 396 15.68 1320 0.620 26.8 80

A A0830 440 300 260 44.9 7.35 40 129 240 9.86 450 0.320 20.1 35

A A0840 460 270 270 55.3 7.70 368 204 480 16.80 340 0.445 23.0 94

A A0850 500 260 240 43.6 7.95 544 49 162 11.48 230 0.310 19.1 75

A A1000 220 420 360 46.5 7.90 480 116 195 12.94 260 0.350 25.0 74

A A1010 420 340 240 45.7 7.67 136 117 207 12.88 290 0.375 19.6 72

A A1030 460 340 200 51.2 7.80 1444 142 178 12.94 220 0.485 22.4 66

A A1060 400 340 260 53.1 7.65 240 104 207 12.99 280 0.810 24.9 71

A A1180 400 340 260 48.0 5.70 60 34 390 4.40 270 0.300 20.9 42

A A1200 500 290 210 59.7 7.15 232 116 152 14.84 260 0.250 21.9 79

A A1220 380 380 240 50.8 4.85 31 32 65 2.52 150 0.340 20.0 30

A A1260 440 330 230 53.2 6.00 60 180 162 3.58 230 0.300 21.0 37

A Mean 430 312 258 49.5 7.28 328 102 255 11.26 411 0.397 21.4 65

A SD 77 47 49 6.1 0.93 392 50 131 4.21 289 0.143 2.5 20

A Max 520 420 380 59.7 8.00 1444 204 480 16.80 1320 0.810 26.8 94

A Min 220 260 200 34.7 4.85 31 32 65 2.52 150 0.250 17.0 30

V V0750 470 370 160()) 31.0()) 7.89 384 192 208 7.06 225 0.300 20.3 40

V V0840 600(+) 320 80()) 40.0 7.14 34 24()) 540(+) 11.12 972 0.450 51.7(+) 30V V0900 270 490(+) 240 49.0 7.44 116 7()) 532(+) 7.74 381 0.600 31.9(+) 32

V V0940 520 400 80()) 55.0 7.73 200 30()) 150 6.72 237 0.420 26.3 25())

V V1160 430 490(+) 80()) 45.0 7.68 46 124 438 2.86 544 0.370 20.0 7())

V Mean 458 414 128 44.0 7.58 156 75 374 7.10 472 0.428 30.0 27

V SD 123 75 72 9.1 0.29 144 80 183 2.95 308 0.112 13.1 12

V Max 600 490 240 55.0 7.89 384 192 540 11.12 972 0.600 51.7 40

V Min 270 320 80 31.0 7.14 34 7 150 2.86 225 0.300 20.0 7

*Code = soil properties (units); SA = sand (g kg)1); SI = silt (g kg)1); CL = clay (g kg)1); OM = organic matter (g kg)1); pH =

pH (H2O); CaCO3 = total calcium carbonate content (g kg)1); P2O5 = available phosphorus (mg kg

)1); K2O = available potassium

(mg kg)1); CaO = available calcium (g kg)1); MgO = available magnesium (mg kg)1); B = water-soluble boron (mg kg)1);

CEC = cation exchange capacity (cmol+ kg)1); CaCO3act = active calcium carbonate content (g kg)1).

Values of soil properties of the validation set which fall outside the maximum (+) or minimum ( )) of the analysis set range are indicated.


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seedbank species were found in the germinable seed-

bank, because of the lack of rare species. Referring to

the RAI ranking, taking into account the first ten species

of the analysis set (75% of total seedbank), eight were in

the first 10 species of the validation set (again, 75% of

the total germinable seedbank). It means that a given

correlation between seedbank and the germinable frac-tion exists only in terms of rank for the species with the

highest relative density and relative frequency. Other-

wise, relative densities of the species in the two data sets

were almost uncorrelated. Significant correlations were

found in only 6 of 156 cases, i.e. between C. album and

Sonchus oleraceus L. in the analysis set, G. parviflora

and Galinsoga ciliata (Raf.) Blake in the analysis and

validation sets, D. sanguinalis and Amaranthus spp.,

Spergula arvensis L. and G. parviflora and S. arvensis

and G. ciliata in the validation set. In this study, the

correlation between seedbank and germinable seedbank

cannot be calculated, but is most probably very low orabsent, given the additional effect of the low rate of

emergence of viable seed content. For example, Zhang

et al. (1998) found positive correlations, even if only

37% of the germinable seedbank was capable of

producing seedlings in the field. Barralis et al. (1996)

found an average rate of emergence of 5.54%, with high

variability between species. Therefore, simple correla-

tion analysis may not be the right approach for

constructing a weed flora composition using a set of

available surveys.

Model variables

All 13 soil properties were taken as predictor variables,

each entered twice in the regression model (at first and

second degree). So the X-matrix had 13 original varia-

bles (Table 2) but the model had 26 predictors. Even if

PLS regression analysis can run with a number of

response variables greater than that of predictor varia-

bles, for a model with a reasonable predictive capacity a

choice has to be made between the 102 theoretical

response variables, e.g. the complete weed species list,.

In this study, the selected response variables were the

main nine species in the 21 fields of the entire data set:C. album, G. parviflora, C. polyspermum, Portulaca

oleracea L., Amaranthus spp., S. media, S. arvensis,

Chaenorrhinum minus (L.) Lange, D. sanguinalis, and

four species not dominant in any field but with overall

importance, i.e. C. bursa-pastoris (RAI rank = 6), S.

oleraceus (RAI rank = 7), Veronica persica Poir. (RAI

rank = 9) and G. ciliata (RAI rank = 10). As a result,

the Y-matrix had 13 variables (Table 3). These species

are annuals, with no dispersal adaptations and a

persistence of >1 year, except for C. minus (persistence


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Table3

Relativedensity(%

oftotaldensity)ofthe13selectedweedsspecies(Y-matrix)inthe16fieldsoftheanalysisset(A)an

dfivefieldsofthevalidationset(V);relative

densityofotherspecies

andtotalseednumber

1

2

3

4

5

9

6

7

10

11

17

19

28

Other

species

Totseed

(n)

RAIrank

Field

Chenop.

album

Galinso.

parviflo.

Che

nop.

poly

sper.

Amarant.

spp.

Stellaria

media

Veronica

persica

Capsella

bursa-p.

Sonchus

olerace.

Galinso.

ciliata

Portula.

olerac.

Spergula

arvensis

Chaenor.

minus

Digitaria

sanguin.

A0530

8.0

52

.6

0.2

5.9

0.9

1.1

0.6

0.3

7.6

1.

4

0.0

0.2

5.2

16

.0

6739

A0580

5.0

12

.7

0.0

7.7

20

.1

0.0

2.6

0.0

0.3

30.

7

0.0

0.0

7.7

13

.2

3931

A0610

56

.0

8.5

0.0

8.9

7.9

0.5

0.1

0.1

0.0

1.

6

0.0

0.2

0.0

16

.1

8528

A0660

11

.9

8.0

0.0

40

.5

0.9

0.8

15

.4

0.5

0.1

7.

7

0.0

0.1

0.3

13

.8

9516

A0800

5.5

0.7

5.1

0.5

11

.3

9.6

10

.0

0.9

0.9

0.

0

0.0

20

.8

1.7

32

.8

7790

A0830

12

.4

3.2

2.4

1.1

6.6

11

.1

5.8

0.3

0.5

0.

0

0.0

0.3

0.0

56

.5

3942

A0840

7.3

0.1

0.1

0.1

62

.7

9.2

5.8

1.0

0.0

0.

0

0.0

0.0

0.0

13

.6

9921

A0850

2.1

5.4

0.0

1.2

12

.3

6.3

4.2

0.6

0.0

42.

8

0.0

0.0

0.0

25

.2

3473

A1000

15

.6

35

.4

0.0

0.0

3.3

4.8

7.2

0.5

8.0

0.

0

0.0

0.0

0.0

25

.3

8226

A1010

14

.8

28

.7

8.6

23

.8

2.0

0.6

1.9

0.4

1.9

1.

6

0.0

0.1

0.0

15

.7

16401

A1030

3.5

46

.5

2.0

20

.0

0.5

2.5

4.7

0.5

15

.5

0.

0

0.0

0.0

0.0

4.3

16557

A1060

14

.9

0.4

50.7

1.1

0.6

0.3

4.0

25

.3

0.1

0.

1

0.0

0.2

0.0

2.4

19760

A1180

23

.9

0.0

13.3

0.0

3.5

12

.2

11

.2

5.9

0.5

2.

1

0.0

1.3

0.0

26

.1

6490

A1200

14

.2

1.4

14.8

0.0

4.1

0.5

2.3

0.3

0.8

0.

1

20

.7

0.0

0.0

40

.9

9693

A1220

45

.7

0.1

7.7

0.1

0.2

1.1

15

.7

0.4

0.0

0.

0

1.1

0.0

0.0

28

.1

13593

A1260

0.4

0.0

0.0

0.0

0.2

0.0

5.2

0.0

0.0

0.

0

27

.1

0.0

5.8

61

.2

5366

Mean

15

.1

12

.7

6.5

6.9

8.6

3.8

6.0

2.3

2.3

5.

5

3.1

1.4

1.3

24

.5

9370

SD

15

.3

17

.8

12.8

11

.6

15

.5

4.4

4.8

6.3

4.4

12.

5

8.2

5.2

2.5

16

.7

4878

Max

56

.0

52

.6

50.7

40

.5

62

.7

12

.2

15

.7

25

.3

15

.5

42.

8

27

.1

20

.8

7.7

61

.2

19760

Min

0.4

0.0

0.0

0.0

0.2

0.0

0.1

0.0

0.0

0.

0

0.0

0.0

0.0

2.4

3473

V0750*

8.6

5.2

0.2

9.1

2.2

0.2

0.6

2.3

0.0

0.

0

0.0

0.0

27

.1(+)

44

.7

628

V0840

25

.8

0.5

2.0

3.2

1.0

8.2

3.5

4.1

0.6

0.

4

0.0

0.2

0.0

50

.7

476

V0900

34

.0

13

.5

7.8

2.4

2.8

3.7

2.0

3.4

1.5

0.

0

0.0

0.0

0.0

28

.9

1485

V0940

54

.3

1.0

2.0

5.6

1.3

3.2

2.1

3.5

0.0

0.

0

0.0

0.2

0.0

26

.8

1704

V1160

21

.3

57

.4(+)

1.2

3.5

0.6

3.7

4.4

1.4

4.0

0.

0

0.0

0.0

0.0

2.5

1759

Mean

28

.8

15

.5

2.6

4.7

1.6

3.8

2.5

2.9

1.2

0.

1

0.0

0.1

5.4

30

.7

1210

SD

17

.0

24

.0

3.0

2.7

0.9

2.9

1.5

1.1

1.7

0.

2

0.0

0.1

12

.1

18

.8

612

Max

54

.3

57

.4

7.8

9.1

2.8

8.2

4.4

4.1

4.0

0.

4

0.0

0.2

27

.1

50

.7

1759

Min

8.6

0.5

0.2

2.4

0.6

0.2

0.6

1.4

0.0

0.

0

0.0

0.0

0.0

2.5

476

RAI,relativeabundantindex.

*Valuesofrelativedensityofthevalidationsetwhichfalloutsidethemaximum

(+

)orminimum

())oftheanalysissetrange.


2007 The Authors



8/16

dominate in fields with low sand content, because the effect

of other soil properties can mask that of sand, indeed

C. album abundance is also favoured by a high silt content

and average clay content. The contribution of the silt contentis given by:

C: album norm: units 0:110 0:685

0:055 0:6852 11=16 0:10111

where 0.110 and )0.055 are the regression coefficients.

Also the silt content pushes the abundance of C. album

below the overall mean, and Fig. 1 shows that in this field

silt is not enoughto strongly increaseC. albumabundance.

With a high silt content, the steep slope of the line Silt

indeed suggests a high abundance ofC. album.

The same calculation approach applies to all other

soil properties. The final relative abundance ofC. album

calculated for field A0610 is therefore the result of the

single contribution of all soil properties, some of them

decreasing, others increasing the abundance with respect

to the mean calculated for the whole analysis set. This

same calculation applies to all species for all fields.

Taking into account that a soil cannot have high sand,

high silt and high clay contents all at the same time, it istherefore possible to rationalise why some species are

widespread, while others tend to concentrate in specific

fields.

Chenopodium album

Chenopodium album was the species with the highest

relative density in fields A0610, A0830, A1180, A1220 of

the analysis set and also characterised fields V0840,

V0900 and V0940 of the validation set. In this group of

fields, tillage differed widely, in agreement with the

results of Trresen and Skuterud (2002) concerningthe indifference of this species to changing tillage. The

species seemed to be favoured by high silt and average

clay contents; its abundance was always inversely

proportional to the sand content. It was also favoured

by low pH, in agreement with the result of Basset and

Crompton (1978). The relationship with organic matter

content suggests that it was favoured by quite high or

quite low (extreme) organic matter contents, even if not

so penalised when this content is average. It appears to

be penalised by high P2O5 and MgO contents and

favoured by high K2O contents.

Table 4 Fitting model parameters with increasing number of

latent variables (LVs)

LVs (n)

Average

R2 (Y) PRESS

1 0.139 0.975

2 0.211 0.965

3 0.350 0.9514 0.437 0.937

5 0.494 0.915

6 0.552 0.907

7 0.637 0.831

8 0.682 0.822

9 0.776 0.920

10 0.817 0.929

11 0.856 0.971

12 0.900 1.180

13 0.941 1.793

14 0.972 1.945

15 1.000 2.226

PRESS, predicted sum of squares statistic.

Table 5 Fitting parameters of the selected species in the model

with eight latent variables

Species R2 PRESS

Chenopodium album 0.383 0.841

Galinsoga parviflora 0.943 1.025

Chenopodium polyspermum 0.910 0.381

Amaranthus spp. 0.311 0.801

Stellaria media 0.951 0.811

Veronica persica 0.250 0.833

Capsella bursa-pastoris 0.519 0.709

Sonchus oleraceus 0.954 0.864

Galinsoga ciliata 0.980 1.129

Spergula arvensis 0.539 0.579

Portulaca oleracea 0.847 0.882

Chaenorrhinum minus 0.983 1.024

Digitaria sanguinalis 0.298 0.808

Average (Y) 0.682 0.822

PRESS, predicted sum of squares statistic.

C.album

relativedensity(norm.val.)

3 2 1 0

Soil properties (norm. val.)

0.3

0.1

0.1

0.3

0.5

1 2 3

Fig. 1 The double normalised graph of the relationships

between relative density of Chenopodium album and soil texture( sand; silt; clay).

318 S Ottoet al.

2007 The Authors



9/16

3 2 1 0 1 2 30.3

0.1

0.1

0.3

0.5C. album

0.4

0.0

0.4

0.8

1.2G. parviflora

0.6

0.2

0.2

0.6

1.0G. ciliata

0.7

0.5

0.3

0.1

0.1C. polyspermum

0.7

0.5

0.3

0.1

0.1

0.3S. oleraceus

0.4

0.2

0.0

0.2Amaranthus spp.

0.4

0.2

0.0

0.2

0.4 S. media

0.4

0.2

0.0

0.2

0.4

0.6V. persica

0.2

0.0

0.2

0.4

C. bursa-pastoris

0.8

0.4

0.0

0.4

P. oleracea

1.0

0.6

0.2

0.2

0.6

1.0C. minus

0.5

0.3

0.1

0.1

D. sanguinalis

0.4

0.3

0.2

0.1

0.1

0.2 S. arvensis

3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3

Fig. 2 Relationships between soil texture (x-axis, normalised values) and the relative density (y-axis, normalised values) of species included

in the model ( sand; silt; clay).


2007 The Authors



10/16

0.2

0.0

0.2

0.4

0.6 C. album

0.4

0.0

0.4

0.8

0.4

0.0

0.4

0.8

0.6

0.2

0.2

0.6

0.4

0.8

0.0

0.4

0.8

1.2

1.6

2.0

0.4

0.0

0.4

0.8

1.2

1.6G. parviflora G. ciliata

0.3

0.1

0.1

0.3

C. polyspermum

0.3

0.1

0.1

0.3

0.3

0.1

0.1

0.3

0.5

0.3

0.5

0.1

0.1

0.3

S. oleraceus Amaranthusspp.

1.0

0.6

0.2

0.2

0.6S. media V. persica C. bursa-pastoris

1.2

0.8

0.4

0.0

0.4P. oleracea S. arvensis C. minus

0.4

0.2

0.0

0.2

D. sanguinalis

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3 3 2 1 0 1 2 3

3 2 1 0 1 2 3

3 2 1 0 1 2 3

3 2 1 0 1 2 3 3 2 1 0 1 2 3

Fig. 3 Relationships between soil chemical properties (x-axis, normalised values) and the relative density (y-axis, normalised values) of

species included in the model ( organic matter; pH; cation exchange capacity; CaCO3act).

320 S Ottoet al.

2007 The Authors



11/16

3 2 1 00.8

0.4

0.0

0.4

0.6

0.2

0.2

0.6

0.6

0.2

0.2

0.6C. album G. parviflora G. ciliata

1.5

0.5

0.5

1.5

2.5

1.5

0.5

0.5

1.5

2.5

3.5C. polyspermum S. oleraceus Amaranthus spp.

0.6

0.2

0.2

0.6

1.0

1.4

0.4

0.0

0.4

0.8

0.4

0.0

0.4

0.8

0.4

0.0

0.4

0.8

1.2

1.6

2.0

S. media V. persica C. bursa-pastoris

0.8

0.4

0.0

0.4

0.8

1.2

0.8

0.4

0.0

0.4

0.5

0.3

0.1

0.1

0.3

P. oleracea S. arvensis C. minus

0.8

0.4

0.0

0.4

0.8D. sanguinalis

1 2 3 4 3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4

3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4

3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4

3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4

3 2 1 0 1 2 3 4

Fig. 4 Relationships between soil chemical properties (x-axis, normalised values) and the relative density (y-axis, normalised values) of

species included in the model ( P2O5; K2O; CaO; MgO; B).


2007 The Authors



12/16


13/16

penalised by low pH values. Veronica persica was

favoured by intermediate organic matter content, was

relatively abundant with low P, K and CaO contents,

and is considered a typical species of organic farming or

of soils with a low chemical input (Ba` rberiet al., 1998).

Veronica hederifolia and Veronica verna L., two

Veronicaspecies of minor importance, were also foundin Val di Gresta.Veronica hederifoliawas found in seven

fields, V. verna only in two. Both species were always

found together with V. persica, i.e. the fields with

V. verna were a subset of the fields with V. hederifolia,

which were a subset of those with V. persica.

Notably, the two fields where V. verna was present

(A0800 and A0840) were characterised by average or

low CaCO3 content. The characteristic calcifuge beha-

viour of V. verna is in agreement with Jauzein (1995),

however a clear preference for specific soil texture was

not evident. It is interesting to analyse which soil

properties are changed or lost passing from the set ofV. persica soils to the V. hederifolia subset. Veronica

persicawas found in 14 of 16 fields, so the V. persicaset

includes a wide range of soil properties. The narrowing

of the V. hederifolia set was due to the fact that this

species had a small set of preferences, e.g. V. hederifolia

was not found where pH was very low. Similarly, the

reduced presence ofV. vernacould be linked to a further

narrowing of the set of preferences, and V. verna was

found only where the clay, organic matter, K2O, CaO,

B and CEC contents were high or very high.

Capsella bursa-pastoris

This species was found in all fields of the analysis set,

although it was never the dominant species. It was also

found in all fields of the validation set, with a maximum

relative density of 4.35%. Therefore, C bursa-pastoris

was a widely-distributed species, like V. persica, and

indeed their relationships with soil texture and the main

chemical properties were very similar, with C. bursa-

pastoris less sensitive to organic matter content and

CEC, but more sensitive to pH.

Portulaca oleracea

This species was the main species in fields A0580 and

A0850 of the analysis set, whereas it was very scarce or

absent in those of the validation set. Relationships

between P. oleracea abundance and soil texture were very

clear: this species was favoured by soil rich in sand and

poor in silt and the clay content was of minor importance.

It was very abundant in fields A0850 and A0580, which

were very rich in sand (A0580 had the overall highest sand

content) but scarce in silt (A0580 had the overall lowest

silt content). The organic matter content must be inter-

mediate andpH andCaCO3 contentsmust not below.It is

worth noting that the relationship with organic matter

(i.e. the parabolas shape) was opposite to that found for

C. album. The pH range (between 7.85 and 7.95) and the

high CaCO3 content in the fields where P. oleracea was

very abundant, are partially in agreement with Miyanishi

and Cavers (1980).

Spergula arvensis

This species was typical of field A1260 and was also

very abundant in field A1200 of the analysis set.

However, it was virtually absent from fields of the

validation set. Spergula arvensis was very abundant

where the clay content was low and there was equilib-

rium between sand and silt. It was favoured by low but

not extreme values of pH, as reported by Jauzein

(1995), and average conditions of CEC. The favourable

conditions of soil texture agreed with that reported byColuma (1983), and, in part, by Hallgren (1990) who

suggested that the abundance of S. arvensis was

proportional to the sand content. The inverse relation-

ship with CaCO3act and B contents was of note, the

latter being very marked. In Val di Gresta, S. arvensis

was only found at about 1200 m a.s.l., because of the

compatibility of its biological cycle with lower temper-

atures (Radics et al., 2000).

Chaenorrhinum minus

This species was characteristic of field A0800 of theanalysis set, and was virtually absent from those of the

validation set. Chaenorrhinum minus abundance was

favoured by soil with high clay content and poor in silt,

and was insensitive to the sand content. The abundance

ofC. minus was average when sand, silt and clay were

average, and these conditions are in agreement with

Columa (1983). The correspondenceaverage abundance

in average texture was also in agreement with Jauzein

(1995). Soil pH appeared to be of little importance,

whereas abundance was favoured by a high CEC (field

A0800 has the highest CEC). A clear direct proportion-

ality exists with MgO content, the other elements beingof little importance. In this study, C. minus did not

appear to be a fertility indicator, in contrast to the

results of Caputa (1984).

Digitaria sanguinalis

This species is thinly spread throughout the valley and

was ranked 20 in the flora list of the analysis set in terms

of relative density. However, it characterised field V0750

of the validation set. Digitaria sanguinalis was favoured

by average sand and silt contents and in this respect it


2007 The Authors



14/16

was very similar to Amaranthus spp., while clay content

seemed to have little importance. The abundance of this

species appears to be independent of pH, but propor-

tional to organic matter content and CEC value. It was

also more abundant where MgO content was high and

B content low.

Discussion

The seedbank in the 16 fields was fairly consistent, with

a range of between 34 and 200 million seeds ha)1,

values not unlike those found in other research on

arable soils. Annual weeds predominated, in some

fields reaching almost 100%. The most distinctive

result was observed in the seed population structure.

The seedbank was characterised by a large number of

species (32.7, range 2541), in agreement with many

other authors who have found that organic farming or

relaxed management usually amplifies populations ofrare species (Hebden et al., 1998). The results indicated

that the variability of the seedbank was also very high

in fields with the same management and at the same

altitude. This indicates that relationships between weed

species density and soil properties are field-specific, or

even site-specific within a field, because of the different

preferences of the weed species for the various soil

types (Dessaint, 2000; Walter et al., 2002). This is the

case even if some weed species are sometimes ambigu-

ous indicators, i.e. they are not only found in their

preferred habitat (Jauzein, 1995).

The germinable seedbank was characterised by fewerspecies, and in some cases by a high density of one or

two, suggesting that weed flora composition can also be

dramatically simplified in organic farming. A strong

presence at high density and frequency levels of

continuously fruiting annuals (e.g. G. ciliata, G. parvi-

flora, P. oleracea and S. media) and true monocarpic

annual weeds (e.g. C. album, C. polyspermum and

Amaranthusspp.) was also noted in the valley.

The relative density of weeds in the germinable

seedbank can be predicted using seedbank composition

and soil properties. Hypothesising a quadratic relation-

ship between weed abundance and soil properties in aPLS regression analysis, the overall predictive capacity

of the resulting model was quite good and for many

species very high. Part of the remaining unexplained

variability was probably caused by tillage effect and

sampling error, e.g. due to a validation set that does not

span the range for the analysis set. Furthermore, the

elementary relationships between soil properties and

weed species abundance can be plotted in a simple and

informative way in a two-dimensional graph, which can

help in understanding the behaviour and spread of some

species. The use of quadratic relationships increases the

analysis complexity, whereas a model with only first-

order terms would be simple.

When the final abundance of a species is seen as a

result of the single soil property contribution, it is

possible to rationalise why some species are widespread,

whereas others tend to concentrate in specific fields. For

example, when a species is slightly favoured by somesoil properties and slightly penalised by others, it can

reach high relative abundance in fields with quite

different characteristics (i.e. widespread in various

types of soils) (e.g. C. album, G. parviflora and

C. polyspermum). When, instead, the abundance of a

species has a strong proportionality with certain soil

properties, and a field with extreme values (e.g. very

high or very low) of those properties exists, then the

species will have very high or very low relative

abundance in that field (e.g. S. arvensis for organic

matter and boron, P. oleracea for silt). In contrast, in

some other cases relative abundance could be higherwhen a certain soil condition value is average (e.g.

P. oleracea for organic matter).

In Val di Gresta, S. arvensis was found in abun-

dance in two fields. Therefore, it appears that some

arable weeds, now considered uncommon, could return

to having detectable populations with appropriate

management systems (Beveridge & Naylor, 1999). This

in particular in soil with low clay content and

equilibrium between sand and silt, sub-acid pH, and

low B content. Portulaca oleraceaabundance seemed to

be favoured by soil rich in sand and poor in silt and

with average organic matter content. The two Cheno-podium species were not always abundant in the same

field; the texture preferences highlighted by the model

indicate it would be unlikely to find them both with

high relative density. Soil pH was not a discriminating

property, whereas C. polyspermum seems to reach

higher relative abundance with increasing levels of

organic matter and cation exchange capacity. Amaran-

thus spp. was also abundant when organic matter and

cation exchange capacity were low, provided that pH

was not.

The soil properties can explain the presence and

relative abundance of weed species in Val di Gresta.Although climatic factors generally influence the dis-

tribution of a species on a large scale, soil factors are a

major cause of local distribution patterns, even if the

effect of seed dispersal pattern is taken into account.

Crop management inevitably adapts environmental

conditions and the flora variability on a landscape

scale. However, on a smaller scale, and when the

assumptions on crop management described in the

Materials and methods section hold, it is still possible

to identify the links between soil properties and species

abundance.

324 S Ottoet al.

2007 The Authors



15/16

Acknowledgements

The research was financed by the Italian CNR and in

part by the INRM in the program Miglioramento delle

tecniche di controllo delle malerbe in un sistema di

economia corta: il caso della Val di Gresta in Trentino.

The authors are particularly grateful to Michela Luise,

Massimo Bonetti and Alberto Cappelletti, the Consor-

zio Ortofrutticolo Val di Gresta and to all the farmers

they met in the valley.

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