Vegetatio 125: 1-10, 1996. 1

Q 1996 KluwerAcademic Publishers. Printed in Belgium.

The weed seed bank of soils in a landscape segment in southern Bavaria - I. Seed content, species composition and spatial variability

H. A l b r e c h t 1 & E. -M. Fors t e r 2 I Lehrstuhlfiir LandschaftsOkologie II, Technische Universitiit Miinchen-Weihenstephan, 85350 Freising, Germany; 2GSF-Forschungszentrum fiir Umwelt und Gesundheit, P.O. Box 1129, 85758 OberschleiJ3heim, Germany

Received 8 June 1995; accepted in revised form 7 February 1996

Key words: Geostatics, Soil seed bank, Spatial variability, Species composition, Weeds

Abstract

The purpose of the present investigation was to examine the composition and the spatial variability of the arable soil seed bank in one large, coherent landscape segment, where the cultivation practice of neighboring farms and the gradients in soil properties determine the distribution of the weed seeds in soil. To this end, a 50 × 50 m grid of 338 reference points was fixed throughout a 143 ha area in southern Bavaria. The seed bank contained a median density of 4950 seeds per m 2 able to germinate, These values showed remarkable variation throughout the investigated area. Thus, 27% of the sampling area contained less than 2500 seeds per m 2, another 23% were below 5000 seeds per m 2 and 19% ranged among 5000 and 10 000 germinating seeds per m 2. Among the remaining sites, 22% showed seed densities between 10 000 and 20 000 and 9% more than 20 000 seeds per m 2. To visualize the spatial distribution of the weed seed bank over the whole study area, the interpolation method kriging was used. For this purpose the spatial continuity must be described by means of a semivariogram analysis. The goodness of the interpolation results depends on the fit of the semivariogram model which shows the spatial dependency. Two important characteristic parameters of this model are the nugget and the range. The nugget expresses the sampling error and the local variability up to the 50 m sampling distance, which cannot be detected by the semivariogram. Therefore the interpolation cannot describe the seed bank variation on a scale below this threshold. In our investigations the nugget comprises about one third of the total variance. The estimation error increases towards the border of the investigated area. In general, this is due to missing sampling points at the field edges. The range observed in the present investigations was 340 m. Beyond this no more spatial variability exists and the measurements are independent of each other. The species occurring in more than five of the investigated samples were ordinated by Detrended Correspondence Analysis and classified by k-means cluster analysis of the ordination axes. Five weed communities were recognized from this analysis which distinctly differed in their relation to the soil type and to the preceding management.

Introduction

The site properties, the management and the dispersal biology of species are the deciding factors that deter- mine the weed seed content in arable soils. Under prac- tical conditions, these factors cause distinct gradients in the size and in the species composition of the soil seed bank. Detailed investigations into the 'natural' variability of the soil seed bank in experimental plots were presented by Goyeau & Fabler (1982), Barralis

et al. (1986), Benoit et al. (1989) and by Dessaint et al. (1990). These authors found that already under homo- geneous soil and management conditions the soil seed content can vary to a factor of ten and more. The reduc- tion of the weed concurrence for crop plants is one of the main intentions of arable farming. Therefore the modifying influence that the management practice has on the potential weed flora buried in arable soils was the object of numerous scientific publications which were recently reviewed by Roberts (1981 ) and by Cavers &

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Benoit (1989). These papers show show that the cul- tivation technique, the use of fertilizers and the appli- cation of pesticides may change the amount and the composition of the weed seed bank significantly with- in only a few years. To minimize the estimation error caused by the site variation such trials were usually carried out in small experimental plots. In contrast, a large landscape scale was chosen to illustrate the mod- ification of the seed contents under different site con- ditions. Such investigations clearly express the impor- tance the soil properties have on the seed bank. Thus, the densities found in samples from calcareous soils ranged between 2700 and 15 000 viable seeds per m 2 (Kropac 1966, Barralis & Chadoeuf 1987, Albrecht & Bachthaler 1990). These values are comparative- ly low. Mean levels of seed contents were observed in soils of moderate acid reaction with a loamy or a sandy texture (Hurle et al. 1988, Albrecht & Bachthaler 1990). The highest concentration of seeds was found in fields with acid sand and/or seasonal moisture in the top soil (Pawlowski 1963, Jensen 1969, Wesolowski 1979, Albrecht & Bachthaler 1990). The values found ranged between 33 600 and 47 600 viable seeds/m 2. Discussing these results Albrecht & Bachthaler (1990) assumed, that the soil conditions exert a significant influence on the weed species composition growing at that site. Thus, the seed production characteristic of these species results in a seed bank specific to the site.

The disadvantage of those large scaled investiga- tions is that the influence of the management is shown rather undifferentiatedly. On the other hand, the effect of the site is only badly revealed on the scale of exper- imental plots. Therefore the present paper is based on the idea of analyzing the soil seed bank variability on a scale where considerable differences in the manage- ment practice as well as distinct gradients in the site properties determine the weed flora. To realize this intention the soil seed bank of an 110 ha sized area in the Terti~irhtigelland in southern Bavaria was investi- gated. The present paper is the first publication out of two which comprise the results of this research work. It deals with the spatial variability and the species com- position of the weed seed bank. In the second paper Albrecht & Pilgram (in prep.) treat the relations of the soil seed bank to environmental factors and to the soil surface vegetation.

Materials and methods

The investigated area

The seed bank analyses were carried out as a part of the investigations made in the Munich Research Associa- tion on Agricultural Ecosystems (FAM). This project investigates changes in agroecosystems induced by their management (Hantschel & Lenz 1993). For this purpose, 110 ha of farmland situated in the Ter- ti~irhtigelland in southern Bavaria, were leased.

Before FAM took over, most of the investigated area was managed by the Scheyern Benedictine Abbey agricultural administration (Figure 1). Their main con- cerns were arable farming and pig production. Cash crops such as wheat, barley and oil seed rape were the most frequent crop species. In addition, several hop fields were cultivated. The field crop harvests surpassed the average yields in this region. The investigated area also includes some adjacent fields rented from farmers in the neighbourhood. The intensity of the cultivation of these fields varied considerably. Thus, 'Bergfeld' and 'Wiesental' were managed on an extensive level, whilst 'am Hopfengarten' was intensively used to grow maize for feeding beef cattle.

The characteristic features of this landscape are a marked difference in relief and considerable gradients in the soil properties. The highest and the lowest point of the investigated area differ up to 50 meters in height, and the slope gradient in the fields can exceed 20%. A soil parameter which has a deciding influence on the growth of plants by determining their water and nutri- ent supply is the soil particle size. Thus, the variation of the geometric mean diameter of soil particles between 0.01 and 1.58 mm illustrates the great variability of soil properties throughout the experimental area. More detailed information on the spatial variability of soil parameters in the investigated landscape segment is given by Sinowski (1995).

Analysis of the soil seed bank

To describe the spatial variability of the soil seed bank, a 50 × 50 m grid of reference points was fixed through- out the experimental farm (Figure 1). At each of the 338 grid points in arable fields, a 10 x 10 m sized area was defined to take soil samples.

To evaluate the weed seed bank in the soil, 20- 30 soil cores were taken at each 10 × 10 m sample area during the vegetative rest in January and February 1991. The samples were collected by using soil bor-

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ers with core diameters between 17 and 20 mm from the whole plough layer, which varied between 11 and 40 cm in thickness. Such a high number of small sam- pies was taken to improve the precision of the seed content estimation (Benoit et al. 1989). To reduce the expenditure of labour the soil cores were composed into one mixed sample of 1 kg fresh weight. The anal- ysis of the seed content was carried out by the seedling emergence method. The experiment began immediate- ly after sampling and the seeds were given two years to germinate. For this purpose the soil was filled in polystyrene seed trays and exposed to open air con- ditions in a wire cage. To stimulate germination, the samples were mixed through and watered regularly. To improve the microclimate for seed germination and seedling establishment and to avoid soil erosion by splashing rain drops, the seed trays stood under wood- en frames covered with light-transmissive curtaining material. The seed content per square meter was cal- culated using the formula: Seed content (n/m 2) = bulk density (kg/m 3) x plough layer depth (m)/dry weight (kg) x number of seedlings counted (n).

Thus, one seedling found in a soil sample repre- sented a seed content between 189 and 678 per m 2 in the field. The nomenclature of species follows Ehren- dorfer (1973).

Statistical analysis

As the background populations from which the soil seed samples were drawn cannot be assumed to be nor- mally distributed (Goyeau & Fablet 1982, Barralis et al. 1986, Benoit et al. 1989), the median, the 95% con- fidence interval and the Mann-Whitney U test, which are nonparametric methods, were used to describe the seed content in the investigated fields.

In order to describe the species composition in the soil seed bank, the seed numbers of a species with n_>5 were ordinated by Detrended Correspondence Analysis. The DCA is a divisive ordination technique which is based on reciprocal averaging. It is particular- ly suitable for analyzing complex sample-by-species data arrays (Gauch 1982). The investigated sampling points were ordinated in four axes using the CANOCO computer program (Ter Braak 1987-1992). To classi- fy groups of species, the first three axes of the DCA ordination procedure were used as variables in a k- means cluster analysis. The k-means cluster analysis is a non-hierarchical agglomeration method which is more efficient at classifying large data sets (Gauch 1980).

Geostatistical methods were used for the descrip- tion of the spatial distribution of seeds. These statis- tical methods are based on the theory that sampling points which are close to each other are more likely to be similar than those further away. The method used to interpolate the values measured at certain points is called kriging. It works by weightening local averages (Journel & Huijbregts 1978). A fundamental require- ment for kriging is the semivariogram analysis (Math- eron 1971), which measures the spatial dependence for properties like the number of seeds in soil.

Semivariogram values, which result from the squared difference between point z(x) and z(x + h), divided by the number of pairs, are calculated for the distances between the investigation points. With an increasing distance between those points the corre- sponding semivariogram values increase, too. In most cases, a threshold (sill) is reached at a certain distance (range), beyond which the measurements are spatially independent. Then a theoretical model of spatial con- tinuity must be fitted. The discontinuity at the starting point is called the 'nugget' and expresses the local vari- ability of measurements at the sampling points. These parameters of the model are used in the kriging equa- tions to calculate the weight, which is attached to the surrounding points.

Ordinary Kriging is said to be the best unbiased linear estimator, assuming intrinsic stationarity is giv- en (Journel & Huijbregts 1978). It is linear because its estimates are weighted linear combinations of the data and unbiased because the mean residual or error is equal to zero. The difference of kriging to other meth- ods is its aim of minimizing the error variance (best estimator). In practical situations we never know the true errors. Therefore geostatisticians build a model of the data (the theoretical semivariogram) and work with the average error variance for the model. They choose weights for the nearby samples that ensure that the average error for the model is exactly zero and that the modeled error variance is minimized.

The software used for geostatistical calculations are GeoEas (EPA 1988) and Variography (Pannatier 1993). To visualize the results the data were transferred to the Geographical Information System Arc/Info V.6.1 (ESRI 1992).

Results and discussion

Size of the weed seed bank

The investigations of the seed bank in 338 soil samples yielded a median density of 4950 germinable seeds per m 2. The 95% confidence interval ranged from 4230 to 6500 seeds per m 2. These values are low compared to the results obtained in other analyses from the same landscape. Thus, Albrecht & Bachthaler (1990) found a median content of 12 800 seeds per m 2 and Otte (1992) counted average densities between 13 000 and 28 000 seeds per m 2 in the moderately acid soils of the Tertiarhiigelland in southern Bavaria. These dif- ferences may be caused by the fact that most of the present samples come from a farm which had been managed by a professional and well educated admin- istrator for several decades. Therefore the chemical weed control was introduced earlier and applied more efficiently than on the other fields in the investigated landscape, which had been predominantly managed by smallholding family farmers.

The lowest seed content in the investigated area fell below the minimal detectable value of 200, the high- est density surpassed 104000 seeds per m 2. Table 1 shows a comparison between the seed contents of fields formerly cultivated by the Scheyern Abbey, of the parcels owned by private farmers and of former hop fields. Both the privately owned area and the former hop fields contained significantly more seeds per m 2 than the arable fields possessed by the Abbey. Another comparison between the soil seed bank of grid points positioned near the field edges and those laying in the centre of the fields resulted in significantly higher seed contents for the marginal area. These results suggest that the former management as well as the proximity to the field margin had a decisive influence on the amount of seeds found in the investigated soil samples. There- fore the relation of the soil seed bank to environmental factors is a topic dealt with in the second part of the present investigations (Albrecht & Pilgram, in prep.).

Spatial distribution

The distribution of the seed bank data in the investi- gated area was extremely skewed. Thus, 27% of the sampling area contained less than 2500 seeds per m 2, another 23% were below 5000 seeds per m 2 and 19% ranged between 5000 and 10 000 germinating seeds per m 2. Among the remaining sites, 22% showed seed densities between 10 000 and 20 000 and 9% more than

20 000 seeds per m 2. As the distribution is extremely skewed these values were transformed to logarithms to make sure that the spatial structure is not masked by extreme values or outliers. To obtain the model parameters (nugget, sill and range) which are required for kriging, the semivariogram was calculated. Each semivariogram value is supported by at least 1000 pairs including all directions. The reliability of a fitted model is crucial to the interpolation results.

Therefore the indicative goodness of fit (IGF) was used to indicate how well the model fits to the empiri- cal semivariogram. The IGF is calculated each time the model changes. It is based on the squared difference between the experimental and the modeled semivari- ogram values and it is weighted by the number of pairs and the relative mean distance per lag. A high reliabil- ity of the fitted model is reached when the IGF comes close to zero. In the present investigations the IGF val- ue was 1.8e-03. In order to ensure the fitted model, further the data were cross validated. Cross validation involves estimating values at each sampled location by kriging with the neighboring sample values, excluding the value of the point being estimated. These estimates are compared to the original observations in order to test if the semivariogram model will accurately repro- duce the spatial variability. Therefore the mean of the differences between estimated and observed values and the ratio of the difference and the kriging standard devi- ation were calculated. For the model to be acceptable, the mean should be close to zero and the ratio close to one. In our case the mean was 0.003 and the ration was 1.011. Thus, the model used represents a reliable estimation for the spatial structure of the soil seed bank.

Finally a spherical model with a nugget of 0.544 seeds (ln) was fitted. The nugget expresses the local variability up to the 50 m sampling distance, which cannot be detected by the semivariogram. Therefore the interpolation based on these data cannot describe variations on a smaller scale. In our investigations the nugget comprises about one third of the total variance of 1.4 seeds (ln) (Figure 2). The range found in the present investigations was 340 m. Beyond this distance no more spatial variability exists and the measurements are independent of each other.

The result of the interpolation is shown in the map (Figure 3). The logarithms were transformed back using the formula proposed by Webster et al. (1991). Extreme values over 50 000 seeds could not be estimat- ed well because of a high nugget which is due to the missing support (grid distance of 50 m). For the whole investigated area the kriging standard deviation varies

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Table 1. Seed content per m 2 in soils from the 338 sampling points in the whole FAM research area and from selected subunits of this area (x = median, 7 = arithmetic mean). Different letters (a, b, c)indicate significant differences between the subunits tested by the Mann-Whitney U test (P < 0.001).

Preceding Former Location n ,~ ~ 95% Conf, Limits Min. Max. Significant

Crops Management of ~ Differences

all all all 338 4950 8870 4230-6500 <200 104200

Arable crops Abbey all 257 4040 7680 3300-4470 <200 104200 a

Arable crops Private Farmers all 48 10790 11480 7310-14430 330 32890 b

Hop Fields Abbey all 29 13930 15280 11780-16930 3080 35070 c

all all Field Centre 261 4260 7190 3520-4950 <200 44990 a

all all Field Edges 77 10600 14660 8040-13470 360 104200 b

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Figure 2. Omnidirectional semivariogram for the number of weed seeds in arable soil. The spherical model 0.544 + 0.949 * sph 341.85 (h) fitted to show the spatial dependency of the measurements. The nugget of 0.544 indicates a high local variability within the sampling distance of 50 m, which could not be detected by the semivariogram. Spatial continuity exists up to 340 m. Beyond that distance the total variance is reached and the values observed are independent to each other.

between 0.34 and 0.56 (Figure 4). The mean error of 0.37 is only 4.35% of the mean logarithm number of 8.494 seeds. This is quite a small relative estimation error and it is mainly due to the nugget variance. In most cases it lies within the 25% quartile. The kriging equations make it impossible to transform the estima- tion variance back. Therefore it cannot be expressed in terms of the original data. Thus, the error estimates can be interpreted more easily if they are strongly tied to the local variability. Mapping shows that only at the border and in a small part along the 'Pappelallee' the error exceed 0.354 (Figure 3).

Species composition

Ninety higher plant species were found in the seed bank of the experimental area. With the sample size used in our case, one kg of soil, only the dominant taxa present at the individual sites could be detected (Lambelet- Haueter 1986; Dessaint et al. 1990; Albrecht 1995a). Thus the number of species per grid point ranged from 0 to 19 with a median value of 5. The most frequent species was Matricaria chamomilla, which occurred in 66% of all samples. In these samples the wild chamomile was easy to detect because of its common occurrence in the soil surface vegetation (Albrecht & Mattheis 1996) and its ability to produce a high number of seeds (Hanf 1984).

To classify the species occurring in more than five of the investigated samples, a DCA ordination pro- cedure was carried out. The eigenvalues for the ordi- nation, which give some indication of the amount of community variation associated with each axis, were 0.58, 0.42, 0.34 and 0.29 for axes 1-4 respectively. These values suggest that axes 1 and 2 explained most of the between species variation and therefore figure 5 shows an axes 1 by 2 ordination plot. The splitting into species groups resulted from a k-means cluster analy- sis of the first three DCA ordination axes values. With this analysis the following five different groups in Fig- ure 5 were obtained.

Group h The first cluster contains the taxa Juncus bufonius, Plantago major subsp intermedia, Sagi- na procumbens, Gnaphalium uliginosum, Poa triv- ialis, Cerastium glomeratum, Agrostis stolonifera, Chenopodium polyspermum and Veronica serpyllifo- lia. In Central Europe, all these species are well known to characterize (temporarily) wet soils (Hofmeister &

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Garve 1986). In the investigated area, these species predominantly occurred at sites that seasonally tend to log water. Only Stellaria media, which was situated close to other groups in the ordination plot, occurred on a much wider range of soil types. Thus, cluster 1 is well distinguished from the other groups by the similar habitat requirements of the species included.

Group 2: Species that were widely distributed through- out the investigated area and that h a d - with the excep- tion of some small areas they avoided - no main dis- tribution centre: Matricaria chamomilla, Polygonum lapathifolium, Geranium dissectum, Galium aparine, Veronica persica, Cerastium holosteoides, Lamium purpureum, Aphanes arvensis, Veronica arvensis, Polygonum hydropiper, Myosotis arvensis and Fallopia convolvulus. In the Central European vegetation these species are ubiquitous weeds, the occurrence of which is focused on cereals and on soils with a good nutrient supply (Hofmeister & Garve 1986).

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remarkable content of coarse-grained particles in the topsoil of their stands.

Group 3: Brassica napus, Apera spica-venti, Lapsana com- munis, Capsella bursa-pastoris, Viola arvensis and Anthemis arvensis are the taxa combined in this group. Seeds of these species predominantly germinated from soils sampled in fields where oil seed rape was the crop preceding the seed bank analysis. Brassica napus is well known for shatting lots of seeds at harvest which cause severe weed infestation problems in subsequent crops (Diercks & Heitefuss 1990). Germination tem- perature tests with seeds from the investigated area have shown that the other taxa of this group are - like Brassica napus - able to germinate at low tempera- tures (Toetz, personal communication). This ability may have been beneficial for them to emerge during the early stages of winter rape development. Capsella bursa-pastoris may have profited in addition from its close phylogenetic relation to the cultivated crop. This attribute makes it difficult to control Capsella in oil seed rape.

Group 4: This group comprises only four species: Ara- bidopsis thaliana, Lamium amplexicaule, Polygonum aviculare and Anagallis arvensis. While Arabidopsis thaliana in Germany predominantly occurs on soils with a sandy texture, the other three taxa commonly grow on all types of soil (Hofmeister & Garve 1986). In the investigated area all four species were - in con- trast to their large scale behaviour- associated with a

Group 5 contains the taxa Solanum nigrum, Chenopodium ficifolium, Rumex obtusifolius, Trifoli- um repens, Urtica dioica, Poa annua, Chenopodium album, Galinsoga ciliata and Thlaspi arvense. All the species of this group are more or less closely associ- ated with the cultivation of summer annual and root crops. Solanum nigrum and Chenopodium ficifolium are weeds which occur predominantly in maize and which have tremendously spread in Germany with the expansion of the maize cultivation area during the last decades (Albrecht 1995b). On the Scheyern experi- mental farm both species were restricted to fields which were rented from private farmers and which had a high share of maize in the crop rotation. Rumex obtusi- folius, Trifolium repens and Urtica dioica generally have their main habitats in pastures. In the investigat- ed area these species were also recorded in the soil seed bank of fields which belong to privately owned livestock farms. From the fact that these fields were frequently fertilized with organic manure it must be assumed that those populations originated from the manure applications. Poa annua was - together with Stellaria media - the predominant taxon in former hop fields. Both species have a very short reproduction cycle and therefore they are well adapted to the fre- quent hoeing which is practiced in hop fields. The remaining three species Chenopodium album, Galin- soga ciliata and Thlaspi arvense mainly occurred in the seed bank of fields with a high percentage of root crops in their former crop rotation. An adaptation to

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Figure 5. Axis 1 by axis 2 plot of the CANOCO detrended correspondence analysis ordination of the weed species seed bank in soil. Polygons enclose space containing the species within each of the groups derived from a k-means clustering of the ordination scores. The full species names are given in the text.

specific crops or management measures however could not be observed.

Conclus ions

The present investigations have shown that despite the restricted area of 110 ha there were considerable gra- dients in the soil seed content. At 27% of the samples containing less than 2500 seeds per m 2 and 9% show- ing densities of more than 20 000 seeds per m 2 great parts of the research area differed at least by the factor ten. A cluster analysis assumes these differences may be caused by the modification of the species composi- tion found at the individual grid points. Geostatistics which, up to now, have mainly been used in earth sci- ences (e.g. Journel & Huijbregts 1978; Dahiya et al. 1985) turned out to be a suitable instrument to describe the spatial variability of the seed content in the top soil, too. On the one hand, this method permits a statistical- ly supported transfer of data sampled in parcels to the whole area. On the other hand, geostatistics facilitate the analysis of which distance between two sampling

points is necessary until there is no more significant relation between them. In the present case, this distance amounted to 340 m. As such information contributes to optimize grids of measurement points for blanket cov- ering and labour-intensive vegetation analyses, it may be of great methodical interest. In the present example, the low kriging error in the central part of the inves- tigated area shows that the reduction of measurement points would not have led to a noticeable loss in pre- cision. In contrast, in the field margin area an increase in the sampling frequency would have been appropri- ate. Thus, these investigations clearly illustrate that the geostatistics are a method suitable for the use in vege- tation science. Nevertheless, further research is needed to test the varied fields for its application.

Following the description of the composition and the spatial variability in the soil seed content the sec- ond part of the present investigations will elucidate the reasons for this variation (Albrecht & Pilgram, in prep.). In addition, the soil seed bank composition and its relation to environmental factors will be compared to the soil surface vegetation.

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Acknowledgements

The scientific activities of the research network 'Forschungsverbund Agrar6kosysteme MiJnchen' (FAM) are financially supported by the Federal Min- istry of Research and Technology (BMFr 0339370). Rent and operating expenses are paid by the Bavarian State Ministry for Education and Culture, Science and Art.

References

Albrecht, H. 1995a. Modelluntersuchung und Literaturauswertung zum Diasporenvorrat geffihrdeter Wildkr~iuter in Ackerb6den. Schriftenreihe der Stiftung gef~ihrdeter Pflanzen 5 (Interna- tionales Symposium 'Flora und Fauna der A.cker und Weinberge' ; 17-20 June 1992, Kommern/Eifel): 123-140.

Albrecht, H. 1995b. Changes in the arable weed flora of Germany during the last five decades. Proc. 9th EWRS Symposium 'Chal- lenges for Weed Science in a Changing Europe'; 10-12 July 1995; Budapest: 41-48.

Albrecht, H. & Bachthaler, G. 1990. Unkrautsamengehalte und Bodeneigenschaften von Ackerfl~ichen in Bayern. Weed Research 30: 101-108.

Albrecht, H. & Mattheis, A. 1996. Die Entwicklung der Ackerwild- krautflora nach Umstellung von konventionellem auf integrierten bzw. 6kologoschen Landbau. Zeitschrift fiir Pflanzenkrankheiten und Pflanzenschutz, Sonderheft XV: 211-224.

Albrecht, H. & Pilgram, M. The weed seed bank of soils in a land- scape segment in southern Bavaria - I I . Relation to environmental factors and to the soil surface vegetation. Vegetatio (in prep.).

Barralis, G., Chadoeuf, R. & Gouet, J. P., 1986. Essai de d6termination de la taille de l'6chantillon pour l'&ude du poten- tiel semencier d'un sol. Weed Research 26: 291-297.

Barralis, G. & Chadoeuf, R, 1987. Potential semencier des terres arables. Weed Research 27: 417-424.

Benoit, D. L., Kenkel N. C. & Cavers P. B. 1989. Factors influencing the precision of soil seed bank estimates. Canadian Journal of Botany 67: 2833-2840.

Cavers, P. B. & Benoit, D. L. 1989. Seed banks in arable soils. In: Leck, M. A., Parker, V. T. & Simpson, R. L. (eds): Ecology of soil seed banks, pp. 309-328. Academic Press, San Diego.

Dahiya, I. S., Anlauf, R., Kersebaum, K. C. & Richter, J. 1985: Spatial variability of some nutrient constituents of an Alfisol from loess. 1I. Geostatistical analysis. Zeitschrift ftir Pflanzen- ernahrung und Bodenkunde 148: 268-177.

Dessaint, E, Barralis, G., Beuret, E., Caixinhas, M. L., Post, B. J., Zanin, G. 1990. Etude coop6rative EWRS: la d6termination du potentiel semencier: I. Recherche d'une relation entre la moyenne et la variance d'6chantillonnage. Weed Research 30: 421-429.

Diercks, R. & Heitefuss, R. (Eds.) 1990. Integrierter Landbau. Ver- lagsunion Agrar, Miinchen.

Ehrendorfer, E 1973. Liste der Gefiil3pflanzen Mitteleuropas. 2 ed. Fischer, Stuttgart.

EPA (ed.), 1988. GeoEas (Geostatistical Environmental Assessment Software), Users Guide. Las Vegas.

ESRI (ed.) 1992. Arc/Info ( ~ Users Guide, V. 6.0. Redlands. Gauch, H. G. 1980. Rapid initial clustering for large data sets. Veg-

etatio 42:103-111.

Gauch, H. G. 1982. Multivariate analysis in community ecology. Cambridge Studies in Ecology. Cambridge University Press.

Goyeau, H. & Fablet, G. 1982. Etude du stock de semences de manvaises herbes dans le sol: le probl~me de 1'6chantillonage. Agronomie 2: 545- 552.

Hantschel, R. E. & Lenz, R. J. M. 1993. Management induced changes in agroecosystems - Aims and research approach of the Munich Research Network on Agroecosystems. In: Eijsackers, H. J. E & Hamers, T. (eds): Integrated soil and sediment research: A basis for proper protection. Soil and environment (Kluwer), 142-144.

Hanf, M. 1984. Ackerunkr~iuter Europas mit ihren Keimlingen und Samen. - Verlagsunion Agrar, Miinchen/Frankfurt/Mtinster- Hiltrup/Wien/Bern.

Hofmeister, H. & Garve, E. 1986. Lebensraum Acker. Parey, Ham- burg/Berlin.

Hurle, K., Maier, J., Amann, A., Weishaar, T., Mozer, B., Pulcher-H~iussling, M. 1988: Auswirkungen unterlassener Pflanzenschutz- und DiingungsmaBnahmen auf die Unkrautflora - Erste Ergebnisse aus einem mehrj~ihrigen Versuchsprogramm. Zeitschrift fiir Pflanzenkrankheiten und Pflanzenschutz, Sonder- heft XI: 175-187.

Jensen, H. A. 1969. Content of buried seeds in arable soil in Denmark and its relation to the weed population. Dansk botanisk Arkiv 27: 1 - 5 6 .

Journel, A. G. & Huijbregts, Ch. J. 1978: Mining Geostatistics. Academic Press, London/New York/San Francisco.

Kropac, Z. 1966: Estimation of weed seeds in arable soils. Pedobi- ologia 6: 105-128.

Matheron, G. 1971. The theory of regionalized variables and its applications. Cahiers du Centre de Morphologie Mathematique 5, Fontainbleau.

Lambelet-Haueter, C. /986. Analyse de la flore potentielle, en rela- tion avec la flore r6elle, en grandes cultures de l a r6gion genevoise. Candollea 41: 299-323.

Otte, A. 1992: Entwicklung im Samenpotential von Ackerb6den nach dem Aussetzen von UnkrautregulierungsmaBnahmen. Land- wirtschaftliches Jahrbuch 69: 837-860.

Pannatier, Y. 1993. MS Windows programs for exploratory variog- raphy and variogram modeling in 2D. International workshop on statistics of spatial processes - theory and applications. Bary.

Pawlowski, E 1963. Content and specific composition of weed seeds in more important soils of the Lublin district. Annales Universi- tatis Mariae Curie, Sectio E., 18: 125-154.

Roberts, H. A. 1981. Seed banks in soil. Advances in Applied Biol- ogy 6: 1-55.

Sinowski, W. 1995. Die dreidimensionale Variabilit~it von Boden - eigenschaften - Ausmal~, Ursachen und Interpolation, - Disser- tation TU MiJnchen - Weihenstephan.

Ter Braak, C. J. E 1987-1992. CANOCO - a FOR- TRAN program for canonical community ordination by [par- tial][detrended][canonical] correspondence analysis, principal components analysis and redundancy analysis. Microcomputer Power, Ithaca/New York.

Webster, R., Campbell, G. W. & Irwin, J. G. 1991. Spatial analy- sis and mapping the annual mean concentrations of acidity and major ions in precipitation over the United Kingdom in 1986. Environmental Monitoring and Assessment 16:1-17.

Wesolowski, M. 1979. Type composition and number of weed seeds in the soils of the South-East Poland. a. Soils of the lowland region. Annales Universitatis Mariae Curie, Sectio E, 34: 23-36.